Author: "Broday, David M." - Searchworks@Jio Institute Digital Library Search Results

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101. CONTRIBUIÇÃO DAS EXPOSIÇÕES DE ÓXIDO DE NITROGÊNIO E DIÓXIDO DE ENXOFRE, EMITIDAS DE USINAS DE ENERGIA, NA PREVALÊNCIA DE DOENÇAS E SINTOMAS RESPIRATÓRIOS.

Author: Amster, Eric D., Haim, Maayan, Dubnov, Jonathan, and Broday, David M.
Abstract: Copyright of InterfacEHS is the property of Revista InterfacEHS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Published: 2014

102. Exploring the applicability of future air quality predictions based on synoptic system forecasts.

Author: Yuval, Broday, David M., and Alpert, Pinhas
Subjects: EMISSION inventories, AIR quality management, AIR pollution forecasting, SYNOPTIC climatology, SPATIO-temporal variation, EMISSIONS (Air pollution)
Abstract: For a given emissions inventory, the general levels of air pollutants and the spatial distribution of their concentrations are determined by the physiochemical state of the atmosphere. Apart from the trivial seasonal and daily cycles, most of the variability is associated with the atmospheric synoptic scale. A simple methodology for assessing future levels of air pollutants'' concentrations based on synoptic forecasts is presented. At short time scales the methodology is comparable and slightly better than persistence and seasonal forecasts at categorical classification of pollution levels. It''s utility is shown for air quality studies at the long time scale of a changing climate scenario, where seasonality and persistence cannot be used. It is demonstrated that the air quality variability due to changes in the pollution emissions can be expected to be much larger than that associated with the effects of climatic changes. [Copyright &y& Elsevier]
Published: 2012
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103. Studying the lime Scale Dependence of Environmental Variables Predictability Using Fractal Analysis.

Author: YUVAL and BRODAY, DAVID M.
Subjects: *AIR quality research, *METEOROLOGICAL research, *AIR pollution, *FRACTALS, *ENVIRONMENTAL exposure, *PREDICTION models, *TEMPORAL databases, *STATISTICAL correlation
Abstract: Prediction of meteorological and air quality variables motivates a lot of research in the atmospheric sciences and exposure assessment communities. An interesting related issue regards the relative predictive power that can be expected at different time scales, and whether it vanishes altogether at certain ranges. An improved understanding of our predictive powers enables better environmental management and more efficient decision making processes. Fractal analysis is commonly used to characterize the self-affinity of time series. This work introduces the Continuous Wavelet Transform (CWT) fractal analysis method as a tool for assessing environmental time series predictability. The high temporal scale resolution of the CWT enables detailed information about the Hurst parameter, a common temporal fractality measure, and thus about time scale variations in predictability. We analyzed a few years records of half-hourly air pollution and meteorological time series from which the trivial seasonal and daily cycles were removed. We encountered a general trend of decreasing Hurst values from about 1.4 (good autocorrelation and predictability), in the sub-daily time scale to 0.5 (which implies complete randomness) in the monthly to seasonal scales. The air pollutants predictability follows that of the meteorological variables in the short time scales but is better at longer scales. [ABSTRACT FROM AUTHOR]
Published: 2010
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104. Revealing source signatures in ambient BTEX concentrations.

Author: Zalel, Amir, Yuval, and Broday, David M.
Subjects: ENVIRONMENTAL protection, ENVIRONMENTAL sciences, ENVIRONMENTAL engineering, LAND use
Abstract: Abstract: Management of ambient concentrations of Volatile Organic Compounds (VOCs) is essential for maintaining low ozone levels in urban areas where its formation is under a VOC-limited regime. The significant decrease in traffic-induced VOC emissions in many developed countries resulted in relatively comparable shares of traffic and non-traffic VOC emissions in urban airsheds. A key step for urban air quality management is allocating ambient VOC concentrations to their pertinent sources. This study presents an approach that can aid in identifying sources that contribute to observed BTEX concentrations in areas characterized by low BTEX concentrations, where traditional source apportionment techniques are not useful. Analysis of seasonal and diurnal variations of ambient BTEX concentrations from two monitoring stations located in distinct areas reveal the possibility to identify source categories. Specifically, the varying oxidation rates of airborne BTEX compounds are used to allocate contributions of traffic emissions and evaporative sources to observed BTEX concentrations. [Copyright &y& Elsevier]
Published: 2008
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105. Allocation of Routinely Monitored Mixing Ratios of Nitrogen Oxides to Their Sources.

Author: Dubowski, Yael and Broday, David M.
Subjects: *NITROGEN oxides & the environment, *ATMOSPHERIC nitrogen oxides, *AIR quality, *AIR pollution, *AIR pollution monitoring, *POLLUTION, *ENVIRONMENTAL degradation, *ENVIRONMENTAL quality, *ENVIRONMENTAL protection
Abstract: In many urban areas throughout the world, the rising mean and peak levels of nitrogen oxides (NOx) are a concern. Road traffic and local industry are usually the major NOx sources in urban environments, but their relative contributions to the spatial distribution of the NOx volumetric mixing ratio is normally unknown. This missing piece of information is required for designing effective abatement measures to reduce ambient NOx levels. A new method for estimating the shares of which traffic and industry contribute to the mean ambient NOx mixing ratios observed in urban environments is proposed in this paper. The estimation is based on data obtained by routine air pollution monitoring, using a few assumptions about the temporal emission patterns of NOx and SO2 in the area of study. A set of equations is formulated for the unknown industry and traffic contributions to the NOx mixing ratios at each monitoring site. These equations are solved using the gradient projection optimization method. The bootstrap technique is used to estimate the errors in the process. Spatial maps of the separate shares of industry and traffic in the total ambient NOx levels can be obtained where a sufficient aerial coverage of stations is available. An application of the method to the allocation of NOxmixing ratios to traffic and industrial sources in the Haifa Bay area, Israel, is demonstrated. The results are expected to be useful for future planning of traffic thoroughfares and industrial development in the area. [ABSTRACT FROM AUTHOR]
Published: 2007
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106. High-resolution spatial patterns of long-term mean concentrations of air pollutants in Haifa Bay area

Author: Yuval and Broday, David M.
Subjects: *EMISSION standards, *AIR quality, *ATMOSPHERIC deposition
Abstract: Abstract: High-resolution maps of the mean concentrations of , NO, , , and in the period 2002–2004 were produced using half-hourly data from the local air quality monitoring network in Haifa Bay area, Israel. The network consists of 20 monitoring stations within an area of , which encompasses a major industrial and power generation centre in the midst of a population of about 500000. The pollutants’ spatial features agree well with their known sources and the expected dispersion by the prevailing meteorology. The ranking of their spatial variations agree with published observations on larger spatial scales. The high-resolution maps capture in a small spatial scale the and cycle relationships expected by theory, and previously observed by analyses of monitoring time series. High correlation was found between the spatial patterns of the , and , whereas the correlation between the spatial features of the and is low. This suggests that the traffic, a major source of , rather than industry, the major source of , is the main contributor to the anthropogenic in the study area. This inference is corroborated by the low sulphur to nitrogen ratio throughout the region, which is typical of traffic-dominated pollution. A general conclusion drawn from this study is that high-resolution monitoring and mapping can significantly contribute to air quality management programmes in terms of both pollution abatement and exposure and risk assessment. [Copyright &y& Elsevier]
Published: 2006
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107. Mapping spatio-temporal variables: The impact of the time-averaging window width on the spatial accuracy

Author: Yuval, Broday, David M., and Carmel, Yohay
Subjects: *MISSING data (Statistics), *INTERPOLATION, *RESEARCH, *NUMERICAL analysis
Abstract: Abstract: Spatial mapping of variables that vary in space and time is a common procedure in many research fields. Very often it is of interest to map the time-average or time-integration of the variable over the whole period of interest. Normally, such a map is produced by spatially interpolating the whole period averages of the observed data. An alternative option is to first spatially interpolate narrow time slice averages of the variable and then sum the resultant maps. This paper discusses the latter option, and the accuracy of the spatio-temporal variable interpolation as a function of the width of the time-averaging window. Theoretically, using a linear and data-value independent operator to interpolate a complete data set (i.e. without missing data), the accuracy is independent of the width of the time-averaging window. However, using a nonlinear or a data-value dependent interpolation operator, and/or in the presence of missing data, the accuracy of the interpolation can vary with the averaging window width. The concept is demonstrated using a set of half-hourly concentrations measured at 20 monitoring stations in Haifa Bay area, Israel, during the years 1996–2002. Cross-validated interpolation accuracy measures calculated for this data set vary significantly with the time-averaging window width, showing a clear minimum at daily averaging. The results and their general implications for the interpolation of spatio-temporal variables are discussed. [Copyright &y& Elsevier]
Published: 2005
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108. Deposition of Ultrafine Particles at Carinal Ridges of the Upper Bronchial Airways.

Author: Broday, David M.
Subjects: *PARTICLES, *BRONCHI, *AIRWAY (Anatomy), *SEDIMENTATION & deposition, *RESPIRATION
Abstract: Bifurcations of the upper bronchial airways are primary hot spots for deposition of inhaled particles and noxious gases. Deposition of coarse particles in the carinal ridges results from inertial impaction, and deposition distal to these sites is attributed to secondary flows. Diffusional deposition of ultrafine particles on carinae surfaces is studied here. Similarity solutions for both the flow and concentration fields at the respective boundary layers that develop near the surface of a wedge are presented, corresponding to a relatively high Re number. The expressions developed for the deposition efficiency compare favorably to those obtained by rigorous computational fluid dynamics simulations. Yet unlike simulation-derived expressions that pertain to the specific geometry and flowconditions studied, our expressions are robust and can account for different branching angles, airflow rates, and particle sizes. The average diffusive flux toward the carina walls is in good agreement with experimental deposition data, as well as with simulation results specifically designed to account for deposition hot spots at airway bifurcations. The expressions obtained can be easily implemented in algebraic inhalation dosimetry models to estimate deposition profiles along the whole respiratory system. [ABSTRACT FROM AUTHOR]
Published: 2004
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109. Analysis of image color and effective bandwidth as a tool for assessing air pollution at urban spatiotemporal scale

Author: Bouman, Charles A., Pollak, Ilya, Wolfe, Patrick J., Etzion, Yael, Broday, David M., and Fishbain, Barak
Published: 2013
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110. Associations between Exposure to Industrial Air Pollution and Prevalence of Asthma and Atopic Diseases in Haifa Bay Area.

Author: Raz, Raanan, Yuval, Lev Bar-Or, Ruth, Kark, Jeremy D., Sinnreich, Ronit, Broday, David M., Harari-Kremer, Ruthie, Bentur, Lea, Gileles-Hillel, Alex, Keinan-Boker, Lital, Lyubarsky, Andrey, Tsur, Dorit, Afek, Arnon, Levin, Noam, Derazne, Estela, and Twig, Gilad
Subjects: INDUSTRIAL pollution, AIR pollution, RECRUITING & enlistment (Armed Forces), ASTHMA, ALLERGIC rhinitis, TEENAGERS
Abstract: Haifa Bay Area (HBA) contains Israel's principal industrial area, and there are substantial public concerns about health effects from its emissions. We aimed to examine associations between exposure to air pollution from HBA industrial area with prevalent asthma and other atopic diseases at age 17. This is a cross-sectional study. The study population included all adolescents born in Israel and whose medical status was evaluated for mandatory military recruitment by the Israeli medical corps during 1967–2017. We analyzed prevalent asthma, allergic rhinitis, atopic dermatitis, and rhinoconjunctivitis. We estimated exposure to industrial air pollution by a kriging interpolation of historical SO2 observations and adjusted the associations to the year of birth, SES, school orientation, and traffic pollution. The study population included n = 2,523,745 adolescents, among which 5.9% had prevalent asthma and 4.6% had allergic rhinitis. Residency in HBA was associated with a higher adjusted risk of asthma, compared with non-HBA residency. Still, this association was limited to the three lowest exposure categories, while the highest exposure group had the lowest adjusted risk. Sensitivity analyses and other atopic diseases presented similar results. These results do not provide support for causal relationships between HBA industry-related emissions and prevalent atopic diseases. [ABSTRACT FROM AUTHOR]
Published: 2021
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111. Predictors of the Indoor-to-Outdoor Ratio of Particle Number Concentrations in Israel.

Author: Zhang, Siyu, Yuval, Broday, David M., and Raz, Raanan
Subjects: PARTICLES, AIR pollution, DEVELOPED countries, INDOOR air pollution, EPIDEMIOLOGISTS
Abstract: Exposure to airborne particles is a risk factor of many short- and long-term health effects. Most epidemiological studies include estimates of exposure to ambient particles, however, people living in developed countries spend most of their time indoors. This work presents an analysis of a field campaign of simultaneous measurements of indoor-to-outdoor particle number concentrations (PNCs) in Israel. Fine and coarse PNCs were continuously measured using Dylos DC1700 devices from October 2016 to October 2017. The median outdoor PNC was always higher than the indoor PNC in all the five sampling locations. Outdoor fine PNCs peak during the night and experience a trough in the afternoon. The median of the fine indoor-to-outdoor PNC ratio (IOR) was 0.83, with an inter quartile range (IQR) of 0.59. The median of the coarse IOR was 0.70, with an IQR of 0.77. Lower IORs were experienced at night than during the day, with a daily peak (IOR > 1) around noon. Information about the IOR in different regions and seasons may help epidemiologists and policy makers understand the true health effects of particulate air pollution, and correct their exposure estimations such that they account for indoor exposure as well. [ABSTRACT FROM AUTHOR]
Published: 2020
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112. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

Author: Forouzanfar, Mohammad H., Afshin, Ashkan, Alexander, Lily T., Anderson, H. Ross, Bhutta, Zulfiqar A., Biryukov, Stan, Brauer, Michael, Burnett, Richard, Cercy, Kelly, Charlson, Fiona J., Cohen, Aaron J., Dandona, Lalit, Estep, Kara, Ferrari, Alize J., Frostad, Joseph J., Fullman, Nancy, Gething, Peter W., Godwin, William W., Griswold, Max, Hay, Simon I., Kinfu, Yohannes, Kyu, Hmwe H., Larson, Heidi J., Liang, Xiaofeng, Lim, Stephen S., Liu, Patrick Y., Lopez, Alan D., Lozano, Rafael, Marczak, Laurie, Mensah, George A., Mokdad, Ali H., Moradi-Lakeh, Maziar, Naghavi, Mohsen, Neal, Bruce, Reitsma, Marissa B., Roth, Gregory A., Salomon, Joshua A., Sur, Patrick J., Vos, Theo, Wagner, Joseph A., Wang, Haidong, Zhao, Yi, Zhou, Maigeng, Aasvang, Gunn Marit, Abajobir, Amanuel Alemu, Abate, Kalkidan Hassen, Abbafati, Cristiana, Abbas, Kaja M., Abd-Allah, Foad, Abdulle, Abdishakur M., Abera, Semaw Ferede, Abraham, Biju, Abu-Raddad, Laith J., Abyu, Gebre Yitayih, Adebiyi, Akindele Olupelumi, Adedeji, Isaac Akinkunmi, Ademi, Zanfina, Adou, Arsène Kouablan, Adsuar, José C., Agardh, Emilie Elisabet, Agarwal, Arnav, Agrawal, Anurag, Kiadaliri, Aliasghar Ahmad, Ajala, Oluremi N., Akinyemiju, Tomi F., Al-Aly, Ziyad, Alam, Khurshid, Alam, Noore K. M., Aldhahri, Saleh Fahed, Aldridge, Robert William, Alemu, Zewdie Aderaw, Ali, Raghib, Alkerwi, Ala A., Alla, François, Allebeck, Peter, Alsharif, Ubai, Altirkawi, Khalid A., Martin, Elena Alvarez, Alvis-Guzman, Nelson, Amare, Azmeraw T., Amberbir, Alemayehu, Amegah, Adeladza Kofi, Amini, Heresh, Ammar, Walid, Amrock, Stephen Marc, Andersen, Hjalte H., Anderson, Benjamin O., Antonio, Carl Abelardo T., Anwari, Palwasha, Ärnlöv, Johan, Artaman, Al, Asayesh, Hamid, Asghar, Rana Jawad, Assadi, Reza, Atique, Suleman, Avokpaho, Euripide Frinel G. Arthur, Awasthi, Ashish, Quintanilla, Beatriz Paulina Ayala, Azzopardi, Peter, Bacha, Umar, Badawi, Alaa, Bahit, Maria C., Balakrishnan, Kalpana, Barac, Aleksandra, Barber, Ryan M., Barker-Collo, Suzanne L., Bärnighausen, Till, Barquera, Simon, Barregard, Lars, Barrero, Lope H., Basu, Sanjay, Batis, Carolina, Bazargan-Hejazi, Shahrzad, Beardsley, Justin, Bedi, Neeraj, Beghi, Ettore, Bell, Brent, Bell, Michelle L., Bello, Aminu K., Bennett, Derrick A., Bensenor, Isabela M., Berhane, Adugnaw, Bernabé, Eduardo, Betsu, Balem Demtsu, Beyene, Addisu Shunu, Bhala, Neeraj, Bhansali, Anil, Bhatt, Samir, Biadgilign, Sibhatu, Bikbov, Boris, Bisanzio, Donal, Bjertness, Espen, Blore, Jed D., Borschmann, Rohan, Boufous, Soufiane, Bourne, Rupert R. A., Brainin, Michael, Brazinova, Alexandra, Breitborde, Nicholas J. K., Brenner, Hermann, Broday, David M., Brugha, Traolach S., Brunekreef, Bert, Butt, Zahid A., Cahill, Leah E., Calabria, Bianca, Campos-Nonato, Ismael Ricardo, Cárdenas, Rosario, Carpenter, David O., Carrero, Juan Jesus, Casey, Daniel C., Castañeda-Orjuela, Carlos A., Rivas, Jacqueline Castillo, Castro, Ruben Estanislao, Catalá-López, Ferrán, Chang, Jung-Chen, Chiang, Peggy Pei-Chia, Chibalabala, Mirriam, Chimed-Ochir, Odgerel, Chisumpa, Vesper Hichilombwe, Chitheer, Abdulaal A., Choi, Jee-Young Jasmine, Christensen, Hanne, Christopher, Devasahayam Jesudas, Ciobanu, Liliana G., Coates, Matthew M., Colquhoun, Samantha M., Manzano, Alejandra G. Contreras, Cooper, Leslie Trumbull, Cooperrider, Kimberly, Cornaby, Leslie, Cortinovis, Monica, Crump, John A., Cuevas-Nasu, Lucia, Damasceno, Albertino, Dandona, Rakhi, Darby, Sarah C., Dargan, Paul I., Das Neves, José, Davis, Adrian C., Davletov, Kairat, Castro, E. Filipa, La Cruz-Góngora, Vanessa, Leo, Diego, Degenhardt, Louisa, Del Gobbo, Liana C., Del Pozo-Cruz, Borja, Dellavalle, Robert P., Deribew, Amare, Jarlais, Don C. Des, Dharmaratne, Samath D., Dhillon, Preet K., Diaz-Torné, Cesar, Dicker, Daniel, Ding, Eric L., Dorsey, E. Ray, Doyle, Kerrie E., Driscoll, Tim R., Duan, Leilei, Dubey, Manisha, Duncan, Bruce Bartholow, Elyazar, Iqbal, Endries, Aman Yesuf, Ermakov, Sergey Petrovich, Erskine, Holly E., Eshrati, Babak, Esteghamati, Alireza, Fahimi, Saman, Faraon, Emerito Jose Aquino, Farid, Talha A., Farinha, Carla Sofia E. Sa, Faro, André, Farvid, Maryam S., Farzadfar, Farshad, Feigin, Valery L., Fereshtehnejad, Seyed-Mohammad, Fernandes, Jefferson G., Fischer, Florian, Fitchett, Joseph R. A., Fleming, Tom, Foigt, Nataliya, Foreman, Kyle, Fowkes, F. Gerry R., Franklin, Richard C., Fürst, Thomas, Futran, Neal D., Gakidou, Emmanuela, Garcia-Basteiro, Alberto L., Gebrehiwot, Tsegaye Tewelde, Gebremedhin, Amanuel Tesfay, Geleijnse, Johanna M., Gessner, Bradford D., Giref, Ababi Zergaw, Giroud, Maurice, Gishu, Melkamu Dedefo, Giussani, Giorgia, Goenka, Shifalika, Gomez-Cabrera, Mari Carmen, Gomez-Dantes, Hector, Gona, Philimon, Goodridge, Amador, Gopalani, Sameer Vali, Gotay, Carolyn C., Goto, Atsushi, Gouda, Hebe N., Gugnani, Harish Chander, Guillemin, Francis, Guo, Yuming, Gupta, Rahul, Gupta, Rajeev, Gutiérrez, Reyna A., Haagsma, Juanita A., Hafezi-Nejad, Nima, Haile, Demewoz, Hailu, Gessessew Bugssa, Halasa, Yara A., Hamadeh, Randah Ribhi, Hamidi, Samer, Handal, Alexis J., Hankey, Graeme J., Hao, Yuantao, Harb, Hilda L., Harikrishnan, Sivadasanpillai, Haro, Josep Maria, Hassanvand, Mohammad Sadegh, Hassen, Tahir Ahmed, Havmoeller, Rasmus, Heredia-Pi, Ileana Beatriz, Hernández-Llanes, Norberto Francisco, Heydarpour, Pouria, Hoek, Hans W., Hoffman, Howard J., Horino, Masako, Horita, Nobuyuki, Hosgood, H. Dean, Hoy, Damian G., Hsairi, Mohamed, Htet, Aung Soe, Hu, Guoqing, Huang, John J., Husseini, Abdullatif, Hutchings, Sally J., Huybrechts, Inge, Iburg, Kim Moesgaard, Idrisov, Bulat T., Ileanu, Bogdan Vasile, Inoue, Manami, Jacobs, Troy A., Jacobsen, Kathryn H., Jahanmehr, Nader, Jakovljevic, Mihajlo B., Jansen, Henrica A. F. M., Jassal, Simerjot K., Javanbakht, Mehdi, Jayaraman, Sudha P., Jayatilleke, Achala Upendra, Jee, Sun Ha, Jeemon, Panniyammakal, Jha, Vivekanand, Jiang, Ying, Jibat, Tariku, Jin, Ye, Johnson, Catherine O., Jonas, Jost B., Kabir, Zubair, Kalkonde, Yogeshwar, Kamal, Ritul, Kan, Haidong, Karch, André, Karema, Corine Kakizi, Karimkhani, Chante, Kasaeian, Amir, Kaul, Anil, Kawakami, Norito, Kazi, Dhruv S., Keiyoro, Peter Njenga, Kemmer, Laura, Kemp, Andrew Haddon, Kengne, Andre Pascal, Keren, Andre, Kesavachandran, Chandrasekharan Nair, Khader, Yousef Saleh, Khan, Abdur Rahman, Khan, Ejaz Ahmad, Khan, Gulfaraz, Khang, Young-Ho, Khatibzadeh, Shahab, Khera, Sahil, Khoja, Tawfik Ahmed Muthafer, Khubchandani, Jagdish, Kieling, Christian, Kim, Cho-Il, Kim, Daniel, Kimokoti, Ruth W., Kissoon, Niranjan, Kivipelto, Miia, Knibbs, Luke D., Kokubo, Yoshihiro, Kopec, Jacek A., Koul, Parvaiz A., Koyanagi, Ai, Kravchenko, Michael, Kromhout, Hans, Krueger, Hans, Ku, Tiffany, Defo, Barthelemy Kuate, Kuchenbecker, Ricardo S., Bicer, Burcu Kucuk, Kuipers, Ernst J., Kumar, G. Anil, Kwan, Gene F., Lal, Dharmesh Kumar, Lalloo, Ratilal, Lallukka, Tea, Lan, Qing, Larsson, Anders, Latif, Asma Abdul, Lawrynowicz, Alicia Elena Beatriz, Leasher, Janet L., Leigh, James, Leung, Janni, Levi, Miriam, Li, Xiaohong, Li, Yichong, Liang, Juan, Liu, Shiwei, Lloyd, Belinda K., Logroscino, Giancarlo, Lotufo, Paulo A., Lunevicius, Raimundas, Macintyre, Michael, Mahdavi, Mahdi, Majdan, Marek, Majeed, Azeem, Malekzadeh, Reza, Malta, Deborah Carvalho, Manamo, Wondimu Ayele Ayele, Mapoma, Chabila C., Marcenes, Wagner, Martin, Randall V., Martinez-Raga, Jose, Masiye, Felix, Matsushita, Kunihiro, Matzopoulos, Richard, Mayosi, Bongani M., Mcgrath, John J., Mckee, Martin, Meaney, Peter A., Medina, Catalina, Mehari, Alem, Mejia-Rodriguez, Fabiola, Mekonnen, Alemayehu B., Melaku, Yohannes Adama, Memish, Ziad A., Mendoza, Walter, Mensink, Gert B. M., Meretoja, Atte, Meretoja, Tuomo J., Mesfin, Yonatan Moges, Mhimbira, Francis Apolinary, Millear, Anoushka, Miller, Ted R., Mills, Edward J., Mirarefin, Mojde, Misganaw, Awoke, Mock, Charles N., Mohammadi, Alireza, Mohammed, Shafiu, Mola, Glen Liddell D., Monasta, Lorenzo, Hernandez, Julio Cesar Montañez, Montico, Marcella, Morawska, Lidia, Mori, Rintaro, Mozaffarian, Dariush, Mueller, Ulrich O., Mullany, Erin, Mumford, John Everett, Murthy, Gudlavalleti Venkata Satyanarayana, Nachega, Jean B., Naheed, Aliya, Nangia, Vinay, Nassiri, Nariman, Newton, John N., Ng, Marie, Nguyen, Quyen Le, Nisar, Muhammad Imran, Pete, Patrick Martial Nkamedjie, Norheim, Ole F., Norman, Rosana E., Norrving, Bo, Nyakarahuka, Luke, Obermeyer, Carla Makhlouf, Ogbo, Felix Akpojene, Oh, In-Hwan, Oladimeji, Olanrewaju, Olivares, Pedro R., Olsen, Helen, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Opio, John Nelson, Oren, Eyal, Orozco, Ricardo, Ortiz, Alberto, Ota, Erika, Pa, Mahesh, Pana, Adrian, Park, Eun-Kee, Parry, Charles D., Parsaeian, Mahboubeh, Patel, Tejas, Caicedo, Angel J. Paternina, Patil, Snehal T., Patten, Scott B., Patton, George C., Pearce, Neil, Pereira, David M., Perico, Norberto, Pesudovs, Konrad, Petzold, Max, Phillips, Michael Robert, Piel, Frédéric B., Pillay, Julian David, Plass, Dietrich, Polinder, Suzanne, Pond, Constance D., Pope, C. Arden, Pope, Daniel, Popova, Svetlana, Poulton, Richie G., Pourmalek, Farshad, Prasad, Noela M., Qorbani, Mostafa, Rabiee, Rynaz H. S., Amir Radfar, Rafay, Anwar, Rahimi-Movaghar, Vafa, Rahman, Mahfuzar, Rahman, Mohammad Hifz Ur, Rahman, Sajjad Ur, Rai, Rajesh Kumar, Rajsic, Sasa, Raju, Murugesan, Ram, Usha, Rana, Saleem M., Ranganathan, Kavitha, Rao, Puja, García, Christian Aspacia Razo, Refaat, Amany H., Rehm, Colin D., Rehm, Jürgen, Reinig, Nikolas, Remuzzi, Giuseppe, Resnikoff, Serge, Ribeiro, Antonio L., Rivera, Juan A., Roba, Hirbo Shore, Rodriguez, Alina, Rodriguez-Ramirez, Sonia, Rojas-Rueda, David, Roman, Yesenia, Ronfani, Luca, Roshandel, Gholamreza, Rothenbacher, Dietrich, Roy, Ambuj, Saleh, Muhammad Muhammad, Sanabria, Juan R., Sanchez-Riera, Lidia, Sanchez-Niño, Maria Dolores, Sánchez-Pimienta, Tania G., Sandar, Logan, Santomauro, Damian F., Santos, Itamar S., Sarmiento-Suarez, Rodrigo, Sartorius, Benn, Satpathy, Maheswar, Savic, Miloje, Sawhney, Monika, Schmidhuber, Josef, Schmidt, Maria Inês, Schneider, Ione J. C., Schöttker, Ben, Schutte, Aletta E., Schwebel, David C., Scott, James G., Seedat, Soraya, Sepanlou, Sadaf G., Servan-Mori, Edson E., Shaddick, Gavin, Shaheen, Amira, Shahraz, Saeid, Shaikh, Masood Ali, Levy, Teresa Shamah, Sharma, Rajesh, She, Jun, Sheikhbahaei, Sara, Shen, Jiabin, Sheth, Kevin N., Shi, Peilin, Shibuya, Kenji, Shigematsu, Mika, Shin, Min-Jeong, Shiri, Rahman, Shishani, Kawkab, Shiue, Ivy, Shrime, Mark G., Sigfusdottir, Inga Dora, Silva, Diego Augusto Santos, Silveira, Dayane Gabriele Alves, Silverberg, Jonathan I., Simard, Edgar P., Sindi, Shireen, Singh, Abhishek, Singh, Jasvinder A., Singh, Prashant Kumar, Slepak, Erica Leigh, Soljak, Michael, Soneji, Samir, Sorensen, Reed J. D., Sposato, Luciano A., Sreeramareddy, Chandrashekhar T., Stathopoulou, Vasiliki, Steckling, Nadine, Steel, Nicholas, Stein, Dan J., Stein, Murray B., Stöckl, Heidi, Stranges, Saverio, Stroumpoulis, Konstantinos, Sunguya, Bruno F., Swaminathan, Soumya, Sykes, Bryan L., Szoeke, Cassandra E. I., Tabarés-Seisdedos, Rafael, Takahashi, Ken, Talongwa, Roberto Tchio, Tandon, Nikhil, Tanne, David, Tavakkoli, Mohammad, Taye, Belaynew Wasie, Taylor, Hugh R., Tedla, Bemnet Amare, Tefera, Worku Mekonnen, Tegegne, Teketo Kassaw, Tekle, Dejen Yemane, Terkawi, Abdullah Sulieman, Thakur, J. S., Thomas, Bernadette A., Thomas, Matthew Lloyd, Thomson, Alan J., Thorne-Lyman, Andrew L., Thrift, Amanda G., Thurston, George D., Tillmann, Taavi, Tobe-Gai, Ruoyan, Tobollik, Myriam, Topor-Madry, Roman, Topouzis, Fotis, Towbin, Jeffrey Allen, Tran, Bach Xuan, Dimbuene, Zacharie Tsala, Tsilimparis, Nikolaos, Tura, Abera Kenay, Tuzcu, Emin Murat, Tyrovolas, Stefanos, Ukwaja, Kingsley N., Undurraga, Eduardo A., Uneke, Chigozie Jesse, Uthman, Olalekan A., Donkelaar, Aaron, Os, Jim, Varakin, Yuri Y., Vasankari, Tommi, Veerman, J. Lennert, Venketasubramanian, Narayanaswamy, Violante, Francesco S., Vollset, Stein Emil, Wagner, Gregory R., Waller, Stephen G., Wang, Jian Li, Wang, Linhong, Wang, Yanping, Weichenthal, Scott, Weiderpass, Elisabete, Weintraub, Robert G., Werdecker, Andrea, Westerman, Ronny, Whiteford, Harvey A., Wijeratne, Tissa, Wiysonge, Charles Shey, Wolfe, Charles D. A., Won, Sungho, Woolf, Anthony D., Wubshet, Mamo, Xavier, Denis, Xu, Gelin, Yadav, Ajit Kumar, Yakob, Bereket, Yalew, Ayalnesh Zemene, Yano, Yuichiro, Yaseri, Mehdi, Ye, Pengpeng, Yip, Paul, Yonemoto, Naohiro, Yoon, Seok-Jun, Younis, Mustafa Z., Yu, Chuanhua, Zaidi, Zoubida, Zaki, Maysaa El Sayed, Zhu, Jun, Zipkin, Ben, Zodpey, Sanjay, Zuhlke, Liesl Joanna, Murray, Christopher J. 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Hassanvand, Mohammad Sadegh, Hassen, Tahir Ahmed, Havmoeller, Rasmu, Heredia-Pi, Ileana Beatriz, Hernández-Llanes, Norberto Francisco, Heydarpour, Pouria, Hoek, Hans W, Hoffman, Howard J, Horino, Masako, Horita, Nobuyuki, Hosgood, H Dean, Hoy, Damian G, Hsairi, Mohamed, Htet, Aung Soe, Hu, Guoqing, Huang, John J, Husseini, Abdullatif, Hutchings, Sally J, Huybrechts, Inge, Iburg, Kim Moesgaard, Idrisov, Bulat T, Ileanu, Bogdan Vasile, Inoue, Manami, Jacobs, Troy A, Jacobsen, Kathryn H, Jahanmehr, Nader, Jakovljevic, Mihajlo B, Jansen, Henrica A F M, Jassal, Simerjot K, Javanbakht, Mehdi, Jayatilleke, Achala Upendra, Jee, Sun Ha, Jeemon, Panniyammakal, Jha, Vivekanand, Jiang, Ying, Jibat, Tariku, Jin, Ye, Johnson, Catherine O, Jonas, Jost B, Kabir, Zubair, Kalkonde, Yogeshwar, Kamal, Ritul, Kan, Haidong, Karch, André, Karema, Corine Kakizi, Karimkhani, Chante, Kasaeian, Amir, Kaul, Anil, Kawakami, Norito, Kazi, Dhruv S, Keiyoro, Peter Njenga, Kemp, Andrew Haddon, Kengne, Andre Pascal, Keren, Andre, Kesavachandran, Chandrasekharan Nair, Khader, Yousef Saleh, Khan, Abdur Rahman, Khan, Ejaz Ahmad, Khan, Gulfaraz, Khang, Young-Ho, Khatibzadeh, Shahab, Khera, Sahil, Khoja, Tawfik Ahmed Muthafer, Khubchandani, Jagdish, Kieling, Christian, Kim, Cho-il, Kim, Daniel, Kimokoti, Ruth W, Kissoon, Niranjan, Kivipelto, Miia, Knibbs, Luke D, Kokubo, Yoshihiro, Kopec, Jacek A, Koul, Parvaiz A, Koyanagi, Ai, Kravchenko, Michael, Kromhout, Han, Krueger, Han, Ku, Tiffany, Defo, Barthelemy Kuate, Kuchenbecker, Ricardo S, Bicer, Burcu Kucuk, Kuipers, Ernst J, Kumar, G Anil, Kwan, Gene F, Lal, Dharmesh Kumar, Lalloo, Ratilal, Lallukka, Tea, Lan, Qing, Larsson, Ander, Latif, Asma Abdul, Lawrynowicz, Alicia Elena Beatriz, Leasher, Janet L, Leigh, Jame, Leung, Janni, Levi, Miriam, Li, Xiaohong, Li, Yichong, Liang, Juan, Liu, Shiwei, Lloyd, Belinda K, Logroscino, Giancarlo, Lotufo, Paulo A, Lunevicius, Raimunda, Macintyre, Michael, Mahdavi, Mahdi, Majdan, Marek, Majeed, Azeem, Malekzadeh, Reza, Malta, Deborah Carvalho, Manamo, Wondimu Ayele Ayele, Mapoma, Chabila C, Marcenes, Wagner, Martin, Randall V, Martinez-Raga, Jose, Masiye, Felix, Matsushita, Kunihiro, Matzopoulos, Richard, Mayosi, Bongani M, Mcgrath, John J, Mckee, Martin, Meaney, Peter A, Medina, Catalina, Mehari, Alem, Mejia-Rodriguez, Fabiola, Mekonnen, Alemayehu B, Melaku, Yohannes Adama, Memish, Ziad A, Mendoza, Walter, Mensink, Gert B M, Meretoja, Atte, Meretoja, Tuomo J, Mesfin, Yonatan Moge, Mhimbira, Francis Apolinary, Miller, Ted R, Mills, Edward J, Mirarefin, Mojde, Misganaw, Awoke, Mock, Charles N, Mohammadi, Alireza, Mohammed, Shafiu, Mola, Glen Liddell D, Monasta, Lorenzo, Hernandez, Julio Cesar Montañez, Montico, Marcella, Morawska, Lidia, Mori, Rintaro, Mozaffarian, Dariush, Mueller, Ulrich O, Mullany, Erin, Mumford, John Everett, Murthy, Gudlavalleti Venkata Satyanarayana, Nachega, Jean B, Naheed, Aliya, Nangia, Vinay, Nassiri, Nariman, Newton, John N, Ng, Marie, Nguyen, Quyen Le, Nisar, Muhammad Imran, Pete, Patrick Martial Nkamedjie, Norheim, Ole F, Norman, Rosana E, Norrving, Bo, Nyakarahuka, Luke, Obermeyer, Carla Makhlouf, Ogbo, Felix Akpojene, Oh, In-Hwan, Oladimeji, Olanrewaju, Olivares, Pedro R, Olsen, Helen, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Opio, John Nelson, Oren, Eyal, Orozco, Ricardo, Ortiz, Alberto, Ota, Erika, Pa, Mahesh, Pana, Adrian, Park, Eun-Kee, Parry, Charles D, Parsaeian, Mahboubeh, Patel, Teja, Caicedo, Angel J Paternina, Patil, Snehal T, Patten, Scott B, Patton, George C, Pearce, Neil, Pereira, David M, Perico, Norberto, Pesudovs, Konrad, Petzold, Max, Phillips, Michael Robert, Piel, Frédéric B, Pillay, Julian David, Plass, Dietrich, Polinder, Suzanne, Pond, Constance D, Pope, C Arden, Pope, Daniel, Popova, Svetlana, Poulton, Richie G, Pourmalek, Farshad, Prasad, Noela M, Qorbani, Mostafa, Rabiee, Rynaz H S, Radfar, Amir, Rafay, Anwar, Rahimi-Movaghar, Vafa, Rahman, Mahfuzar, Rahman, Mohammad Hifz Ur, Rahman, Sajjad Ur, Rai, Rajesh Kumar, Rajsic, Sasa, Raju, Murugesan, Ram, Usha, Rana, Saleem M, Ranganathan, Kavitha, Rao, Puja, García, Christian Aspacia Razo, Refaat, Amany H, Rehm, Colin D, Rehm, Jürgen, Reinig, Nikola, Remuzzi, Giuseppe, Resnikoff, Serge, Ribeiro, Antonio L, Rivera, Juan A, Roba, Hirbo Shore, Rodriguez, Alina, Rodriguez-Ramirez, Sonia, Rojas-Rueda, David, Roman, Yesenia, Ronfani, Luca, Roshandel, Gholamreza, Rothenbacher, Dietrich, Roy, Ambuj, Saleh, Muhammad Muhammad, Sanabria, Juan R, Sanchez-Niño, Maria Dolore, Sánchez-Pimienta, Tania G, Sandar, Logan, Santomauro, Damian F, Santos, Itamar S, Sarmiento-Suarez, Rodrigo, Sartorius, Benn, Satpathy, Maheswar, Savic, Miloje, Sawhney, Monika, Schmidhuber, Josef, Schmidt, Maria Inê, Schneider, Ione J C, Schöttker, Ben, Schutte, Aletta E, Schwebel, David C, Scott, James G, Seedat, Soraya, Sepanlou, Sadaf G, Servan-Mori, Edson E, Shaheen, Amira, Shahraz, Saeid, Shaikh, Masood Ali, Levy, Teresa Shamah, Sharma, Rajesh, She, Jun, Sheikhbahaei, Sara, Shen, Jiabin, Sheth, Kevin N, Shi, Peilin, Shibuya, Kenji, Shigematsu, Mika, Shin, Min-Jeong, Shiri, Rahman, Shishani, Kawkab, Shiue, Ivy, Shrime, Mark G, Sigfusdottir, Inga Dora, Silva, Diego Augusto Santo, Silveira, Dayane Gabriele Alve, Silverberg, Jonathan I, Simard, Edgar P, Sindi, Shireen, Singh, Abhishek, Singh, Jasvinder A, Singh, Prashant Kumar, Slepak, Erica Leigh, Soljak, Michael, Soneji, Samir, Sorensen, Reed J D, Sposato, Luciano A, Sreeramareddy, Chandrashekhar T, Stathopoulou, Vasiliki, Steckling, Nadine, Steel, Nichola, Stein, Dan J, Stein, Murray B, Stöckl, Heidi, Stranges, Saverio, Stroumpoulis, Konstantino, Sunguya, Bruno F, Swaminathan, Soumya, Sykes, Bryan L, Szoeke, Cassandra E I, Tabarés-Seisdedos, Rafael, Takahashi, Ken, Talongwa, Roberto Tchio, Tandon, Nikhil, Tanne, David, Tavakkoli, Mohammad, Taye, Belaynew Wasie, Taylor, Hugh R, Tedla, Bemnet Amare, Tefera, Worku Mekonnen, Tegegne, Teketo Kassaw, Tekle, Dejen Yemane, Terkawi, Abdullah Sulieman, Thakur, J.S., Thomas, Bernadette A, Thomas, Matthew Lloyd, Thomson, Alan J, Thorne-Lyman, Andrew L, Thrift, Amanda G, Thurston, George D, Tillmann, Taavi, Tobe-Gai, Ruoyan, Tobollik, Myriam, Topor-Madry, Roman, Topouzis, Foti, Towbin, Jeffrey Allen, Tran, Bach Xuan, Dimbuene, Zacharie Tsala, Tsilimparis, Nikolao, Tura, Abera Kenay, Tuzcu, Emin Murat, Tyrovolas, Stefano, Ukwaja, Kingsley N, Undurraga, Eduardo A, Uneke, Chigozie Jesse, Uthman, Olalekan A, van Donkelaar, Aaron, van Os, Jim, Varakin, Yuri Y, Vasankari, Tommi, Veerman, J Lennert, Venketasubramanian, Narayanaswamy, Violante, Francesco S, Vollset, Stein Emil, Wagner, Gregory R, Waller, Stephen G, Wang, Jian Li, Wang, Linhong, Wang, Yanping, Weichenthal, Scott, Weiderpass, Elisabete, Weintraub, Robert G, Werdecker, Andrea, Westerman, Ronny, Whiteford, Harvey A, Wijeratne, Tissa, Wiysonge, Charles Shey, Wolfe, Charles D A, Won, Sungho, Woolf, Anthony D, Wubshet, Mamo, Xavier, Deni, Xu, Gelin, Yadav, Ajit Kumar, Yakob, Bereket, Yalew, Ayalnesh Zemene, Yano, Yuichiro, Yaseri, Mehdi, Ye, Pengpeng, Yip, Paul, Yonemoto, Naohiro, Yoon, Seok-Jun, Younis, Mustafa Z, Yu, Chuanhua, Zaidi, Zoubida, Zaki, Maysaa El Sayed, Zhu, Jun, Zipkin, Ben, Zodpey, Sanjay, Zuhlke, Liesl Joanna, Murray, Christopher J L, Public Health, Gastroenterology & Hepatology, Health Services Management & Organisation (HSMO), Psychiatry, Kardiyoloji, RS: MHeNs - R2 - Mental Health, MUMC+: MA Psychiatrie (3), MUMC+: Hersen en Zenuw Centrum (3), and Psychiatrie & Neuropsychologie
Subjects: Blood Glucose, Alcohol Drinking, Global Health, Risk Assessment, Body Mass Index, Middle East, Life Expectancy, Risk-Taking, Africa, Northern, General & Internal Medicine, Quality-Adjusted Life Year, Occupational Exposure, Africa South of the Sahara, Medicine(all), Unsafe Sex, Risk Factor, Medicine (all), Malnutrition, Smoking, Public Health, Global Health, Social Medicine and Epidemiology, Sodium, Dietary, Articles, 11 Medical And Health Sciences, Biomarker, Environmental Exposure, Substance-Related Disorder, Cost of Illne, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, Cholesterol, Air Pollution, Indoor, Hypertension, Disabled Person, Human
Abstract: Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors-the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57.8% (95% CI 56.6-58.8) of global deaths and 41.2% (39.8-42.8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211.8 million [192.7 million to 231.1 million] global DALYs), smoking (148.6 million [134.2 million to 163.1 million]), high fasting plasma glucose (143.1 million [125.1 million to 163.5 million]), high BMI (120.1 million [83.8 million to 158.4 million]), childhood undernutrition (113.3 million [103.9 million to 123.4 million]), ambient particulate matter (103.1 million [90.8 million to 115.1 million]), high total cholesterol (88.7 million [74.6 million to 105.7 million]), household air pollution (85.6 million [66.7 million to 106.1 million]), alcohol use (85.0 million [77.2 million to 93.0 million]), and diets high in sodium (83.0 million [49.3 million to 127.5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Copyright (C) The Author(s). Published by Elsevier Ltd.

113. Abstract 15429: Cancer and Mortality in Relation to Traffic-Related Air Pollution Among Coronary Artery Disease Patients: Using an Ensemble of Exposure Estimates to Identify High-Risk Individuals.

Author: Cohen, Gali, ., Yuval, Levy, Ilan, Chen, Shimon, Kark, Jeremy D, Levin, Noam D, Witberg, Guy, Bental, Tamir, Broday, David M, Steinberg, David M, Kornowski, Ran, and Gerber, Yariv
Published: 2018

114. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

Author: Forouzanfar, Mohammad H., Alexander, Lily, Anderson, H. Ross, Bachman, Victoria F., Biryukov, Stan, Brauer, Michael, Burnett, Richard, Casey, Daniel, Coates, Matthew M., Cohen, Aaron, Delwiche, Kristen, Estep, Kara, Frostad, Joseph J., KC, Astha, Kyu, Hmwe H., Moradi-Lakeh, Maziar, Ng, Marie, Slepak, Erica Leigh, Thomas, Bernadette A., Wagner, Joseph, Aasvang, Gunn Marit, Abbafati, Cristiana, Ozgoren, Ayse Abbasoglu, Abd-Allah, Foad, Abera, Semaw F., Aboyans, Victor, Abraham, Biju, Abraham, Jerry Puthenpurakal, Abubakar, Ibrahim, Abu-Rmeileh, Niveen M. E., Aburto, Tania C., Achoki, Tom, Adelekan, Ademola, Adofo, Koranteng, Adou, Arsène K., Adsuar, José C., Afshin, Ashkan, Agardh, Emilie E., Al Khabouri, Mazin J., Al Lami, Faris H., Alam, Sayed Saidul, Alasfoor, Deena, Albittar, Mohammed I., Alegretti, Miguel A., Aleman, Alicia V., Alemu, Zewdie A., Alfonso-Cristancho, Rafael, Alhabib, Samia, Ali, Raghib, Ali, Mohammed K., Alla, François, Allebeck, Peter, Allen, Peter J., Alsharif, Ubai, Alvarez, Elena, Alvis-Guzman, Nelson, Amankwaa, Adansi A., Amare, Azmeraw T., Ameh, Emmanuel A., Ameli, Omid, Amini, Heresh, Ammar, Walid, Anderson, Benjamin O., Antonio, Carl Abelardo T., Anwari, Palwasha, Cunningham, Solveig Argeseanu, Arnlöv, Johan, Arsenijevic, Valentina S. Arsic, Artaman, Al, Asghar, Rana J., Assadi, Reza, Atkins, Lydia S., Atkinson, Charles, Avila, Marco A., Awuah, Baffour, Badawi, Alaa, Bahit, Maria C., Bakfalouni, Talal, Balakrishnan, Kalpana, Balalla, Shivanthi, Balu, Ravi Kumar, Banerjee, Amitava, Barber, Ryan M., Barker-Collo, Suzanne L., Barquera, Simon, Barregard, Lars, Barrero, Lope H., Barrientos-Gutierrez, Tonatiuh, Basto-Abreu, Ana C., Basu, Arindam, Basu, Sanjay, Basulaiman, Mohammed O., Ruvalcaba, Carolina Batis, Beardsley, Justin, Bedi, Neeraj, Bekele, Tolesa, Bell, Michelle L., Benjet, Corina, Bennett, Derrick A., Benzian, Habib, Bernabé, Eduardo, Beyene, Tariku J., Bhala, Neeraj, Bhalla, Ashish, Bhutta, Zulfiqar A., Bikbov, Boris, Abdulhak, Aref A. Bin, Blore, Jed D., Blyth, Fiona M., Bohensky, Megan A., Başara, Berrak Bora, Borges, Guilherme, Bornstein, Natan M., Bose, Dipan, Boufous, Soufiane, Bourne, Rupert R., Brainin, Michael, Brazinova, Alexandra, Breitborde, Nicholas J., Brenner, Hermann, Briggs, Adam D. 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M., Giroud, Maurice, Giussani, Giorgia, Goenka, Shifalika, Goginashvili, Ketevan, Dantes, Hector Gomez, Gona, Philimon, de Cosio, Teresita Gonzalez, González-Castell, Dinorah, Gotay, Carolyn C., Goto, Atsushi, Gouda, Hebe N., Guerrant, Richard L., Gugnani, Harish C., Guillemin, Francis, Gunnell, David, Gupta, Rahul, Gupta, Rajeev, Gutiérrez, Reyna A., Hafezi-Nejad, Nima, Hagan, Holly, Hagstromer, Maria, Halasa, Yara A., Hamadeh, Randah R., Hammami, Mouhanad, Hankey, Graeme J., Hao, Yuantao, Harb, Hilda L., Haregu, Tilahun Nigatu, Haro, Josep Maria, Havmoeller, Rasmus, Hay, Simon I., Hedayati, Mohammad T., Heredia-Pi, Ileana B., Hernandez, Lucia, Heuton, Kyle R., Heydarpour, Pouria, Hijar, Martha, Hoek, Hans W., Hoffman, Howard J., Hornberger, John C., Hosgood, H. Dean, Hoy, Damian G., Hsairi, Mohamed, Hu, Guoqing, Hu, Howard, Huang, Cheng, Huang, John J., Hubbell, Bryan J., Huiart, Laetitia, Husseini, Abdullatif, Iannarone, Marissa L., Iburg, Kim M., Idrisov, Bulat T., Ikeda, Nayu, Innos, Kaire, Inoue, Manami, Islami, Farhad, Ismayilova, Samaya, Jacobsen, Kathryn H., Jansen, Henrica A., Jarvis, Deborah L., Jassal, Simerjot K., Jauregui, Alejandra, Jayaraman, Sudha, Jeemon, Panniyammakal, Jensen, Paul N., Jha, Vivekanand, Jiang, Fan, Jiang, Guohong, Jiang, Ying, Jonas, Jost B., Juel, Knud, Kan, Haidong, Roseline, Sidibe S Kany, Karam, Nadim E., Karch, André, Karema, Corine K., Karthikeyan, Ganesan, Kaul, Anil, Kawakami, Norito, Kazi, Dhruv S., Kemp, Andrew H., Kengne, Andre P., Keren, Andre, Khader, Yousef S., Khalifa, Shams Eldin Ali Hassan, Khan, Ejaz A., Khang, Young-Ho, Khatibzadeh, Shahab, Khonelidze, Irma, Kieling, Christian, Kim, Daniel, Kim, Sungroul, Kim, Yunjin, Kimokoti, Ruth W, Kinfu, Yohannes, Kinge, Jonas M., Kissela, Brett M., Kivipelto, Miia, Knibbs, Luke D., Knudsen, Ann Kristin, Kokubo, Yoshihiro, Kose, M. Rifat, Kosen, Soewarta, Kraemer, Alexander, Kravchenko, Michael, Krishnaswami, Sanjay, Kromhout, Hans, Ku, Tiffany, Defo, Barthelemy Kuate, Bicer, Burcu Kucuk, Kuipers, Ernst J., Kulkarni, Chanda, Kulkarni, Veena S., Kumar, G. Anil, Kwan, Gene F., Lai, Taavi, Balaji, Arjun Lakshmana, Lalloo, Ratilal, Lallukka, Tea, Lam, Hilton, Lan, Qing, Lansingh, Van C., Larson, Heidi J., Larsson, Anders, Laryea, Dennis O., Lavados, Pablo M., Lawrynowicz, Alicia E., Leasher, Janet L., Lee, Jong-Tae, Leigh, James, Leung, Ricky, Levi, Miriam, Li, Yichong, Li, Yongmei, Liang, Juan, Liang, Xiaofeng, Lim, Stephen S., Lindsay, M. Patrice, Lipshultz, Steven E., Liu, Shiwei, Liu, Yang, Lloyd, Belinda K., Logroscino, Giancarlo, London, Stephanie J., Lopez, Nancy, Lortet-Tieulent, Joannie, Lotufo, Paulo A., Lozano, Rafael, Lunevicius, Raimundas, Ma, Jixiang, Ma, Stefan, Machado, Vasco M. P., MacIntyre, Michael F., Magis-Rodriguez, Carlos, Mahdi, Abbas A., Majdan, Marek, Malekzadeh, Reza, Mangalam, Srikanth, Mapoma, Christopher C., Marape, Marape, Marcenes, Wagner, Margolis, David J, Margono, Christopher, Marks, Guy B., Martin, Randall V., Marzan, Melvin B., Mashal, Mohammad T., Masiye, Felix, Mason-Jones, Amanda J., Matsushita, Kunihiro, Matzopoulos, Richard, Mayosi, Bongani M., Mazorodze, Tasara T., McKay, Abigail C., McKee, Martin, McLain, Abigail, Meaney, Peter A, Medina, Catalina, Mehndiratta, Man Mohan, Mejia-Rodriguez, Fabiola, Mekonnen, Wubegzier, Melaku, Yohannes A., Meltzer, Michele, Memish, Ziad A., Mendoza, Walter, Mensah, George A., Meretoja, Atte, Mhimbira, Francis Apolinary, Micha, Renata, Miller, Ted R., Mills, Edward J., Misganaw, Awoke, Mishra, Santosh, Ibrahim, Norlinah Mohamed, Mohammad, Karzan A., Mokdad, Ali H., Mola, Glen L., Monasta, Lorenzo, Hernandez, Julio C. Montañez, Montico, Marcella, Moore, Ami R, Morawska, Lidia, Mori, Rintaro, Moschandreas, Joanna, Moturi, Wilkister N, Mozaffarian, Dariush, Mueller, Ulrich O., Mukaigawara, Mitsuru, Mullany, Erin C., Murthy, Kinnari S., Naghavi, Mohsen, Nahas, Ziad, Naheed, Aliya, Naidoo, Kovin S., Naldi, Luigi, Nand, Devina, Nangia, Vinay, Narayan, KM Venkat, Nash, Denis, Neal, Bruce, Nejjari, Chakib, Neupane, Sudan P., Newton, Charles R., Ngalesoni, Frida N., de Dieu Ngirabega, Jean, Nguyen, Grant, Nguyen, Nhung T., Nieuwenhuijsen, Mark J., Nisar, Muhammad I., Nogueira, José R., Nolla, Joan M., Nolte, Sandra, Norheim, Ole F., Norman, Rosana E., Norrving, Bo, Nyakarahuka, Luke, Oh, In-Hwan, Ohkubo, Takayoshi, Olusanya, Bolajoko O., Omer, Saad B., Opio, John Nelson, Orozco, Ricardo, Pagcatipunan, Rodolfo S., Pain, Amanda W., Pandian, Jeyaraj D., Panelo, Carlo Irwin A., Papachristou, Christina, Park, Eun-Kee, Parry, Charles D., Caicedo, Angel J. 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Srinath, Refaat, Amany H., Rehm, Jürgen, Remuzzi, Giuseppe, Ribeiro, Antonio L., Riccio, Patricia M., Richardson, Lee, Riederer, Anne, Robinson, Margaret, Roca, Anna, Rodriguez, Alina, Rojas-Rueda, David, Romieu, Isabelle, Ronfani, Luca, Room, Robin, Roy, Nobhojit, Ruhago, George M., Rushton, Lesley, Sabin, Nsanzimana, Sacco, Ralph L, Saha, Sukanta, Sahathevan, Ramesh, Sahraian, Mohammad Ali, Salomon, Joshua A., Salvo, Deborah, Sampson, Uchechukwu K., Sanabria, Juan R., Sanchez, Luz Maria, Sánchez-Pimienta, Tania G., Sanchez-Riera, Lidia, Sandar, Logan, Santos, Itamar S., Sapkota, Amir, Satpathy, Maheswar, Saunders, James E., Sawhney, Monika, Saylan, Mete I., Scarborough, Peter, Schmidt, Jürgen C., Schneider, Ione J. C., Schöttker, Ben, Schwebel, David C., Scott, James G., Seedat, Soraya, Sepanlou, Sadaf G., Serdar, Berrin, Servan-Mori, Edson E., Shaddick, Gavin, Shahraz, Saeid, Levy, Teresa Shamah, Shangguan, Siyi, She, Jun, Sheikhbahaei, Sara, Shibuya, Kenji, Shin, Hwashin H., Shinohara, Yukito, Shiri, Rahman, Shishani, Kawkab, Shiue, Ivy, Sigfusdottir, Inga D., Silberberg, Donald H., Simard, Edgar P., Sindi, Shireen, Singh, Abhishek, Singh, Gitanjali M., Singh, Jasvinder A., Skirbekk, Vegard, Sliwa, Karen, Soljak, Michael, Soneji, Samir, Søreide, Kjetil, Soshnikov, Sergey, Sposato, Luciano A., Sreeramareddy, Chandrashekhar T., Stapelberg, Nicolas J. 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E., Aburto, Tania C., Achoki, Tom, Adelekan, Ademola, Adofo, Koranteng, Adou, Arsène K., Adsuar, José C., Afshin, Ashkan, Agardh, Emilie E., Al Khabouri, Mazin J., Al Lami, Faris H., Alam, Sayed Saidul, Alasfoor, Deena, Albittar, Mohammed I., Alegretti, Miguel A., Aleman, Alicia V., Alemu, Zewdie A., Alfonso-Cristancho, Rafael, Alhabib, Samia, Ali, Raghib, Ali, Mohammed K., Alla, François, Allebeck, Peter, Allen, Peter J., Alsharif, Ubai, Alvarez, Elena, Alvis-Guzman, Nelson, Amankwaa, Adansi A., Amare, Azmeraw T., Ameh, Emmanuel A., Ameli, Omid, Amini, Heresh, Ammar, Walid, Anderson, Benjamin O., Antonio, Carl Abelardo T., Anwari, Palwasha, Cunningham, Solveig Argeseanu, Arnlöv, Johan, Arsenijevic, Valentina S. Arsic, Artaman, Al, Asghar, Rana J., Assadi, Reza, Atkins, Lydia S., Atkinson, Charles, Avila, Marco A., Awuah, Baffour, Badawi, Alaa, Bahit, Maria C., Bakfalouni, Talal, Balakrishnan, Kalpana, Balalla, Shivanthi, Balu, Ravi Kumar, Banerjee, Amitava, Barber, Ryan M., Barker-Collo, Suzanne L., Barquera, Simon, Barregard, Lars, Barrero, Lope H., Barrientos-Gutierrez, Tonatiuh, Basto-Abreu, Ana C., Basu, Arindam, Basu, Sanjay, Basulaiman, Mohammed O., Ruvalcaba, Carolina Batis, Beardsley, Justin, Bedi, Neeraj, Bekele, Tolesa, Bell, Michelle L., Benjet, Corina, Bennett, Derrick A., Benzian, Habib, Bernabé, Eduardo, Beyene, Tariku J., Bhala, Neeraj, Bhalla, Ashish, Bhutta, Zulfiqar A., Bikbov, Boris, Abdulhak, Aref A. Bin, Blore, Jed D., Blyth, Fiona M., Bohensky, Megan A., Başara, Berrak Bora, Borges, Guilherme, Bornstein, Natan M., Bose, Dipan, Boufous, Soufiane, Bourne, Rupert R., Brainin, Michael, Brazinova, Alexandra, Breitborde, Nicholas J., Brenner, Hermann, Briggs, Adam D. M., Broday, David M., Brooks, Peter M., Bruce, Nigel G., Brugha, Traolach S., Brunekreef, Bert, Buchbinder, Rachelle, Bui, Linh N., Bukhman, Gene, Bulloch, Andrew G., Burch, Michael, Burney, Peter G. J., Campos-Nonato, Ismael R., Campuzano, Julio C., Cantoral, Alejandra J., Caravanos, Jack, Cárdenas, Rosario, Cardis, Elisabeth, Carpenter, David O., Caso, Valeria, Castañeda-Orjuela, Carlos A., Castro, Ruben E., Catalá-López, Ferrán, Cavalleri, Fiorella, Çavlin, Alanur, Chadha, Vineet K., Chang, Jung-chen, Charlson, Fiona J., Chen, Honglei, Chen, Wanqing, Chen, Zhengming, Chiang, Peggy P., Chimed-Ochir, Odgerel, Chowdhury, Rajiv, Christophi, Costas A., Chuang, Ting-Wu, Chugh, Sumeet S., Cirillo, Massimo, Claßen, Thomas K. D., Colistro, Valentina, Colomar, Mercedes, Colquhoun, Samantha M., Contreras, Alejandra G., Cooper, Cyrus, Cooperrider, Kimberly, Cooper, Leslie T., Coresh, Josef, Courville, Karen J., Criqui, Michael H., Cuevas-Nasu, Lucia, Damsere-Derry, James, Danawi, Hadi, Dandona, Lalit, Dandona, Rakhi, Dargan, Paul I., Davis, Adrian, Davitoiu, Dragos V., Dayama, Anand, de Castro, E. Filipa, De la Cruz-Góngora, Vanessa, De Leo, Diego, de Lima, Graça, Degenhardt, Louisa, del Pozo-Cruz, Borja, Dellavalle, Robert P., Deribe, Kebede, Derrett, Sarah, Jarlais, Don C. Des, Dessalegn, Muluken, deVeber, Gabrielle A., Devries, Karen M., Dharmaratne, Samath D., Dherani, Mukesh K., Dicker, Daniel, Ding, Eric L, Dokova, Klara, Dorsey, E. 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M., Giroud, Maurice, Giussani, Giorgia, Goenka, Shifalika, Goginashvili, Ketevan, Dantes, Hector Gomez, Gona, Philimon, de Cosio, Teresita Gonzalez, González-Castell, Dinorah, Gotay, Carolyn C., Goto, Atsushi, Gouda, Hebe N., Guerrant, Richard L., Gugnani, Harish C., Guillemin, Francis, Gunnell, David, Gupta, Rahul, Gupta, Rajeev, Gutiérrez, Reyna A., Hafezi-Nejad, Nima, Hagan, Holly, Hagstromer, Maria, Halasa, Yara A., Hamadeh, Randah R., Hammami, Mouhanad, Hankey, Graeme J., Hao, Yuantao, Harb, Hilda L., Haregu, Tilahun Nigatu, Haro, Josep Maria, Havmoeller, Rasmus, Hay, Simon I., Hedayati, Mohammad T., Heredia-Pi, Ileana B., Hernandez, Lucia, Heuton, Kyle R., Heydarpour, Pouria, Hijar, Martha, Hoek, Hans W., Hoffman, Howard J., Hornberger, John C., Hosgood, H. Dean, Hoy, Damian G., Hsairi, Mohamed, Hu, Guoqing, Hu, Howard, Huang, Cheng, Huang, John J., Hubbell, Bryan J., Huiart, Laetitia, Husseini, Abdullatif, Iannarone, Marissa L., Iburg, Kim M., Idrisov, Bulat T., Ikeda, Nayu, Innos, Kaire, Inoue, Manami, Islami, Farhad, Ismayilova, Samaya, Jacobsen, Kathryn H., Jansen, Henrica A., Jarvis, Deborah L., Jassal, Simerjot K., Jauregui, Alejandra, Jayaraman, Sudha, Jeemon, Panniyammakal, Jensen, Paul N., Jha, Vivekanand, Jiang, Fan, Jiang, Guohong, Jiang, Ying, Jonas, Jost B., Juel, Knud, Kan, Haidong, Roseline, Sidibe S Kany, Karam, Nadim E., Karch, André, Karema, Corine K., Karthikeyan, Ganesan, Kaul, Anil, Kawakami, Norito, Kazi, Dhruv S., Kemp, Andrew H., Kengne, Andre P., Keren, Andre, Khader, Yousef S., Khalifa, Shams Eldin Ali Hassan, Khan, Ejaz A., Khang, Young-Ho, Khatibzadeh, Shahab, Khonelidze, Irma, Kieling, Christian, Kim, Daniel, Kim, Sungroul, Kim, Yunjin, Kimokoti, Ruth W, Kinfu, Yohannes, Kinge, Jonas M., Kissela, Brett M., Kivipelto, Miia, Knibbs, Luke D., Knudsen, Ann Kristin, Kokubo, Yoshihiro, Kose, M. Rifat, Kosen, Soewarta, Kraemer, Alexander, Kravchenko, Michael, Krishnaswami, Sanjay, Kromhout, Hans, Ku, Tiffany, Defo, Barthelemy Kuate, Bicer, Burcu Kucuk, Kuipers, Ernst J., Kulkarni, Chanda, Kulkarni, Veena S., Kumar, G. Anil, Kwan, Gene F., Lai, Taavi, Balaji, Arjun Lakshmana, Lalloo, Ratilal, Lallukka, Tea, Lam, Hilton, Lan, Qing, Lansingh, Van C., Larson, Heidi J., Larsson, Anders, Laryea, Dennis O., Lavados, Pablo M., Lawrynowicz, Alicia E., Leasher, Janet L., Lee, Jong-Tae, Leigh, James, Leung, Ricky, Levi, Miriam, Li, Yichong, Li, Yongmei, Liang, Juan, Liang, Xiaofeng, Lim, Stephen S., Lindsay, M. Patrice, Lipshultz, Steven E., Liu, Shiwei, Liu, Yang, Lloyd, Belinda K., Logroscino, Giancarlo, London, Stephanie J., Lopez, Nancy, Lortet-Tieulent, Joannie, Lotufo, Paulo A., Lozano, Rafael, Lunevicius, Raimundas, Ma, Jixiang, Ma, Stefan, Machado, Vasco M. P., MacIntyre, Michael F., Magis-Rodriguez, Carlos, Mahdi, Abbas A., Majdan, Marek, Malekzadeh, Reza, Mangalam, Srikanth, Mapoma, Christopher C., Marape, Marape, Marcenes, Wagner, Margolis, David J, Margono, Christopher, Marks, Guy B., Martin, Randall V., Marzan, Melvin B., Mashal, Mohammad T., Masiye, Felix, Mason-Jones, Amanda J., Matsushita, Kunihiro, Matzopoulos, Richard, Mayosi, Bongani M., Mazorodze, Tasara T., McKay, Abigail C., McKee, Martin, McLain, Abigail, Meaney, Peter A, Medina, Catalina, Mehndiratta, Man Mohan, Mejia-Rodriguez, Fabiola, Mekonnen, Wubegzier, Melaku, Yohannes A., Meltzer, Michele, Memish, Ziad A., Mendoza, Walter, Mensah, George A., Meretoja, Atte, Mhimbira, Francis Apolinary, Micha, Renata, Miller, Ted R., Mills, Edward J., Misganaw, Awoke, Mishra, Santosh, Ibrahim, Norlinah Mohamed, Mohammad, Karzan A., Mokdad, Ali H., Mola, Glen L., Monasta, Lorenzo, Hernandez, Julio C. 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Abstract: Summary Background The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) is the fi rst of a series of annual updates of the GBD. Risk factor quantifi cation, particularly of modifi able risk factors, can help to identify emerging threats to population health and opportunities for prevention. The GBD 2013 provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution. Methods Attributable deaths, years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) have been estimated for 79 risks or clusters of risks using the GBD 2010 methods. Risk–outcome pairs meeting explicit evidence criteria were assessed for 188 countries for the period 1990–2013 by age and sex using three inputs: risk exposure, relative risks, and the theoretical minimum risk exposure level (TMREL). Risks are organised into a hierarchy with blocks of behavioural, environmental and occupational, and metabolic risks at the fi rst level of the hierarchy. The next level in the hierarchy includes nine clusters of related risks and two individual risks, with more detail provided at levels 3 and 4 of the hierarchy. Compared with GBD 2010, six new risk factors have been added: handwashing practices, occupational exposure to trichloroethylene, childhood wasting, childhood stunting, unsafe sex, and low glomerular fi ltration rate. For most risks, data for exposure were synthesised with a Bayesian metaregression method, DisMod-MR 2.0, or spatial-temporal Gaussian process regression. Relative risks were based on meta-regressions of published cohort and intervention studies. Attributable burden for clusters of risks and all risks combined took into account evidence on the mediation of some risks such as high body-mass index (BMI) through other risks such as high systolic blood pressure and high cholesterol. Findings All risks combined account

115. Frailty Status Modifies the Association Between Air Pollution and Post-Myocardial Infarction Mortality A 20-Year Follow-Up Study

Author: Gerber, Yariv, Myers, Vicki, Broday, David M., Steinberg, David M., Yuval, Koton, Silvia, and Drory, Yaacov
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116. Improving modeled air pollution concentration maps by residual interpolation.

Author: Yuval, null, Levy, Ilan, and Broday, David M.
Subjects: *AIR pollutants, *ERROR, *AUTOCORRELATION (Statistics), *RANDOM measures, *AIR pollution
Abstract: Models that are used to map air pollutant concentrations are not free of errors. A possible approach for improving the final concentration map is to interpolate the residuals of the initial model concentration estimates. Due to the possible spatial autocorrelation of the residuals of the initial model estimates, Bayesian inference schemes were suggested for this task, since they can correctly adjust the level of fitting of the residuals to the random measurement errors. However, the complexity of Bayesian methods often discourages their use. Here, we present an alternative and simpler approach, using a leave-one-out cross-validation to determine the optimal level of fitting of the residual correction. We show that the optimal correction level is related to the extent of the spatial autocorrelation of the cross-validated residuals. Namely, when the residuals are not autocorrelated residual correction is unnecessary, and if employed may actually degrade the quality of the final concentration map. Moreover, our approach enables to optimize the residual correction based on different target performance measures, with a possibly different optimal correction depending on the performance measure used. Hence, different target performance measures can be chosen to fit best the specific application of interest. The method is demonstrated using output of three different models used for estimating NO x and NO 2 concentrations over Israel. We show that our approach can be used as an exploratory step, for assessing the potential benefit of residual correction, and as a simple alternative to Bayesian schemes. [ABSTRACT FROM AUTHOR]
Published: 2017
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117. Air Pollution and Community Health: Quantifying Acute Cardio-Respiratory Events Attributable to SO2 and NO2 Exposure.

Author: Gdalevich, Michael, Broday, David M., Yuval, Scharf, Shimon, Bibi, Haim, and Huerta, Michael
Published: 2009
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118. Air Pollution and Community Health Quantifying Acute Cardio-Respiratory Events Attributable to SO2and NO2Exposure

Author: Gdalevich, Michael, Broday, David M., Yuval, Scharf, Shimon, Bibi, Haim, and Huerta, Michael
Published: 2009
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119. Data-driven nonlinear optimisation of a simple air pollution dispersion model generating high resolution spatiotemporal exposure.

Author: Yuval, Bekhor, Shlomo, and Broday, David M.
Subjects: *AIR pollutants, *DISPERSION (Atmospheric chemistry), *HIGH resolution imaging, *URBAN ecology, *NONLINEAR systems, *NITRIC oxide
Abstract: Abstract: Spatially detailed estimation of exposure to air pollutants in the urban environment is needed for many air pollution epidemiological studies. To benefit studies of acute effects of air pollution such exposure maps are required at high temporal resolution. This study introduces nonlinear optimisation framework that produces high resolution spatiotemporal exposure maps. An extensive traffic model output, serving as proxy for traffic emissions, is fitted via a nonlinear model embodying basic dispersion properties, to high temporal resolution routine observations of traffic-related air pollutant. An optimisation problem is formulated and solved at each time point to recover the unknown model parameters. These parameters are then used to produce a detailed concentration map of the pollutant for the whole area covered by the traffic model. Repeating the process for multiple time points results in the spatiotemporal concentration field. The exposure at any location and for any span of time can then be computed by temporal integration of the concentration time series at selected receptor locations for the durations of desired periods. The methodology is demonstrated for NO2 exposure using the output of a traffic model for the greater Tel Aviv area, Israel, and the half-hourly monitoring and meteorological data from the local air quality network. A leave-one-out cross-validation resulted in simulated half-hourly concentrations that are almost unbiased compared to the observations, with a mean error (ME) of 5.2 ppb, normalised mean error (NME) of 32%, 78% of the simulated values are within a factor of two (FAC2) of the observations, and the coefficient of determination (R 2) is 0.6. The whole study period integrated exposure estimations are also unbiased compared with their corresponding observations, with ME of 2.5 ppb, NME of 18%, FAC2 of 100% and R 2 that equals 0.62. [Copyright &y& Elsevier]
Published: 2013
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120. The impact of a forced reduction in traffic volumes on urban air pollution

Author: Yuval, Flicstein, Bernanda, and Broday, David M.
Subjects: *URBAN pollution, *TRAFFIC flow, *ARAB-Israeli conflict, 1993-, *AIR quality, *NITROGEN dioxide, *PARTICULATE matter, *HYDROCARBONS, *OZONE
Abstract: The Middle East military conflict of summer 2006 resulted in a few weeks in which the city of Haifa, Israel, and its environs experienced very profound variations in the commercial and personal activities. Large industrial plants continued almost normal operations but activities of small scale industry, shopping, and personal commuting were drastically reduced, leading to a dramatic decrease in the commercial and personal traffic volumes. This period of reduced activity serves as a real life experiment for assessment and demonstration of the impact that human activity, and mainly road traffic, may have on the air pollution levels in a bustling middle-sized city. The analysis is made especially sharp and reliable due to the abruptness of the beginning and the end of the reduced activity period, its length, and the stable summer meteorological conditions in the eastern Mediterranean region. The reduced traffic volumes resulted in lowered levels of NO2, hydrocarbons and particulate matter. The decrease in these pollutants’ mean concentration was significantly larger than the reduction in the mean traffic volume. Slightly higher mean O3 concentrations were observed during the reduced traffic period. [Copyright &y& Elsevier]
Published: 2008
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121. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013

Author: Yong Zhao, Hadi Danawi, Bach Xuan Tran, Gene Bukhman, Vasiliki Stathopoulou, Taavi Tillmann, Nelson Alvis-Guzman, Yongmei Li, Jerry Puthenpurakal Abraham, Sudan Prasad Neupane, Jack Caravanos, Ben Schöttker, Rafael Lozano, Damian G Hoy, Yoshihiro Kokubo, Nicholas J K Breitborde, Sergey Soshnikov, Yukito Shinohara, Randall V. Martin, Michael Brainin, Fernando Perez-Ruiz, Yingfeng Zheng, Santosh Mishra, Julio Cesar Montañez Hernandez, Michael Phillips, Belinda J. Gabbe, Hebe N. Gouda, Ziad A. Memish, Rupert R A Bourne, Guoqing Hu, Emmanuel A. Ameh, Abigail McLain, Michelle L. Bell, Christopher Margono, Marissa Iannarone, Wilkister N. Moturi, Donald H. Silberberg, Carl Abelardo T. Antonio, Nataliya Foigt, Anand Dayama, Yanping Wang, Amanda J. Mason-Jones, Bolajoko O. Olusanya, Monica S. Vavilala, Katherine B Gibney, David Tanne, Sidibe S Kany Roseline, Marcella Montico, Abhishek Singh, Sarah Derrett, Alireza Esteghamati, Seok Jun Yoon, Corine Karema, Rakhi Dandona, David M. Pereira, Kazem Rahimi, Gitanjali M. Singh, Vivekanand Jha, John Hornberger, Anne M. Riederer, Kathryn H. Jacobsen, Andrea Pedroza, Lily Alexander, Fiona M. Blyth, Tommi Vasankari, Kyle J Foreman, Rana J. Asghar, Tilahun Nigatu Haregu, Yousef Khader, Rafael Alfonso-Cristancho, Suzanne Barker-Collo, Lydia S. Atkins, Simerjot K. Jassal, Mohammad Ali Sahraian, Peter Scarborough, Hans W. Hoek, E. Ray Dorsey, Muluken Dessalegn, David C. Schwebel, Gavin Shaddick, Thomas D. Fleming, Mohammad Tavakkoli, Dorairaj Prabhakaran, Mohammad H. Forouzanfar, Christopher C. Mapoma, Jost B. Jonas, Erin C Mullany, Gene F. Kwan, Johanna M. Geleijnse, Antonio Luiz Pinho Ribeiro, Roberto Tchio Talongwa, Tolesa Bekele, Jed D. Blore, Gunn Marit Aasvang, Philimon Gona, Miguel Angel Alegretti, Babak Eshrati, Mitsuru Mukaigawara, Richard F. Gillum, Odgerel Chimed-Ochir, Ubai Alsharif, Richard C. Franklin, Felix Masiye, Richard T. Burnett, Sanjay Krishnaswami, Martin McKee, John J Huang, Lucía Cuevas-Nasu, Wagner Marcenes, Walid Ammar, Knud Juel, Joseph R. Zunt, Martha M Téllez Rojo, Mamta Swaroop, Noela M Prasad, Azmeraw T. Amare, Tim Driscoll, Michael Kravchenko, Heresh Amini, Amir Sapkota, Theo Vos, Charlotte Watts, Dennis Odai Laryea, D. Alex Quistberg, Justin Beardsley, Cheng Huang, Adnan M. Durrani, Sarah V Thackway, Rita Van Dingenen, Manami Inoue, Martha Híjar, Honglei Chen, Amany H. Refaat, Yichong Li, Vineet K. Chadha, Wenzhi Wang, Louisa Degenhardt, Kingsley N. Ukwaja, Nayu Ikeda, James D. Wilkinson, Linh N Bui, Maria Hagströmer, Gonghuan Yang, Ann Kristin Knudsen, David J. Margolis, Soewarta Kosen, Hans Kromhout, Atsushi Goto, Man Mohan Mehndiratta, Thomas N. Williams, Michael Soljak, Yun Jin Kim, Hideaki Toyoshima, Jeyaraj D Pandian, Borja del Pozo-Cruz, Soufiane Boufous, Ivy Shiue, Anders Larsson, Guilherme V. Polanczyk, John Powles, Yara A. Halasa, Robin Room, Ratilal Lalloo, Carolina Batis Ruvalcaba, Panniyammakal Jeemon, Elisabete Weiderpass, Jürgen Rehm, Ejaz Ahmad Khan, Alicia Aleman, Zacharie Tsala Dimbuene, Elena Alvarez, Rachelle Buchbinder, Randah R. Hamadeh, Bryan Hubbell, Sadaf G. Sepanlou, Farhad Islami, Costas A. Christophi, Heidi Stöckl, Ismael R. Campos-Nonato, Nigel Bruce, Edward J Mills, Samuel A L Perry, Taavi Lai, Baffour Awuah, Mete Saylan, Karen J. Courville, Arindam Basu, Vanessa De la Cruz-Góngora, Teresita González de Cosío, Naohiro Yonemoto, Frida Namnyak Ngalesoni, Muluken Azage Yenesew, Atte Meretoja, Michael Brauer, Cyrus Cooper, Giorgia Giussani, Valentina S. Arsic Arsenijevic, Vasiliy Victorovich Vlassov, André Karch, Leilei Duan, Matthew M Coates, Omid Ameli, Gelin Xu, Matthias Endres, Ganesan Karthikeyan, Ione Jayce Ceola Schneider, Mohamed Hsairi, Palwasha Anwari, Mazin J. Al Khabouri, Dariush Mozaffarian, Juan R. Sanabria, Pablo M. Lavados, Sumeet S. Chugh, Johan Ärnlöv, Ivo Rakovac, Maurice Giroud, Haidong Kan, Ibrahim Abdelmageem Mohamed Ginawi, José Luis Texcalac-Sangrador, Luigi Naldi, Erica Leigh Slepak, Deena Alasfoor, James E. Saunders, Richard Matzopoulos, Talal Bakfalouni, Stein Emil Vollset, Andrea Werdecker, Lennert J. Veerman, Lorenzo Monasta, Henrica A. F. M. Jansen, Reyna A Gutiérrez, Brittany Wurtz, Luz Maria Sanchez, Lijing L. Yan, M. Patrice Lindsay, Michele Meltzer, Sanjay Basu, Steven van de Vijver, Alaa Badawi, Thomas Claßen, Young-Ho Khang, Brett M. Kissela, Jun Zhu, In-Hwan Oh, Fiona J Charlson, Maria Cecilia Bahit, Dinorah González-Castell, Rosario Cárdenas, Dan Poenaru, Sayed Saidul Alam, Mitchell T. Wallin, Harish Chander Gugnani, James Leigh, Ferrán Catalá-López, Lidia Morawska, Jim van Os, Stephanie J. London, Kaire Innos, Isabelle Romieu, Fiorella Cavalleri, Adrian Davis, Hwee Pin Phua, Chakib Nejjari, Héctor Gómez Dantés, Boris I. Pavlin, Karen Sliwa, Lynne Gaffikin, Constance D. Pond, Michael F. 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Jensen, Fabiola Mejía-Rodríguez, Xiaonong Zou, Bradford D. Gessner, Dhruv S. Kazi, Dragos Virgil Davitoiu, Alejandra Jáuregui, Pouria Heydarpour, Megan Bohensky, Harvey Whiteford, Berrak Bora Basara, Zhengming Chen, Gregory R. Wagner, Paul I. Dargan, Hermann Brenner, Nima Hafezi-Nejad, John Nelson Opio, Scott Weichenthal, Deborah Salvo, Jun She, Tea Lallukka, Carolyn C. Gotay, Stephen G. Waller, Christian Kieling, Shivanthi Balalla, Valery L. Feigin, Qing Lan, Matias Trillini, Adam D M Briggs, Sungroul Kim, Niveen M E Abu-Rmeileh, Renata Micha, Sergey Petrovich Ermakov, Ole Frithjof Norheim, Zulfiqar A Bhutta, Paul S. F. Yip, Grant Nguyen, Ralph L. Sacco, Biju Abraham, Ken Takahashi, Jixiang Ma, Peter A. Meaney, Ayse Abbasoglu Ozgoren, Kimberly Cooperrider, M Rifat Kose, Shams Eldin Ali Hassan Khalifa, Rasmus Havmoeller, Alize J. Ferrari, Kebede Deribe, Nadim E. Karam, George A. Mensah, Bongani M. Mayosi, Konrad Pesudovs, Joanna Moschandreas, Ziad Nahas, James Damsere-Derry, Nsanzimana Sabin, Tonatiuh Barrientos-Gutiérrez, Ying Jiang, Andre Pascal Kengne, Peter Allebeck, Jonas Minet Kinge, Shankuan Zhu, Guy B. Marks, Daniel C Casey, Marco A Avila, Anna Roca, Lalit Dandona, Ami R. Moore, Adansi A. Amankwaa, David Gunnell, Andre Keren, Yohannes Adama Melaku, Nhung T Nguyen, Anthony D. Woolf, Mayuree Rao, Peter J. Allen, Christina Papachristou, Karzan Abdulmuhsin Mohammad, Ravi Kumar Balu, Marie Ng, Marcello Tonelli, Maziar Moradi-Lakeh, Maigeng Zhou, Emmanuela Gakidou, Mohammed K. Ali, Amanda W Pain, Dan J. Stein, Kawkab Shishani, Fortuné Gbètoho Gankpé, Howard J. Hoffman, James Scott, Nadine Steckling, Samia Alhabib, Deborah Jarvis, Kara Estep, Arsène Kouablan Adou, Ricardo Orozco, Holly Hagan, K. C. Astha, Reza Malekzadeh, Klara Dokova, Aliya Naheed, Ernst J. Kuipers, Valeria Caso, Derrick A Bennett, Andrea B. Feigl, Uche S. Uchendu, Holly E. Erskine, Shireen Sindi, Arjun Lakshmana Balaji, Francesco Saverio Violante, Monika Sawhney, Alejandra Cantoral, Ketevan Goginashvili, Raghib Ali, Fan Jiang, Robert G. Weintraub, Homie Razavi, Myriam Tobollik, Howard Hu, Emerito Jose A. Faraon, Irma Khonelidze, Patricia M. Riccio, Eun-Kee Park, Julio Cesar Campuzano, Ibrahim Abubakar, Jürgen C Schmidt, Konstantinos Stroumpoulis, Aref A. Bin Abdulhak, Graeme J. Hankey, Natan M. Bornstein, Mouhanad Hammami, Lee Richardson, Rintaro Mori, Alanur Çavlin, Ruth W Kimokoti, Samir Soneji, Mark J. Nieuwenhuijsen, John Q. Wong, Joseph Frostad, Tom Achoki, Rahman Shiri, Ashish Bhalla, Kurt Straif, Simon I. Hay, Scott B. Patten, Kalpana Balakrishnan, Awoke Misganaw Temesgen, Chandrashekhar T Sreeramareddy, Ryan M Barber, Rosana E. Norman, JianLi Wang, Siyi Shangguan, Luke Nyakarahuka, Kovin Naidoo, Charles D. H. Parry, Mercedes Colomar, H. Ross Anderson, Carlos Magis-Rodriguez, Joan M. Nolla, Muhammad Imran Nisar, Karen Devries, Andrew L. Thorne-Lyman, Denis Nash, Marape Marape, Rajiv Chowdhury, Dima M. Qato, Luca Ronfani, Nobhojit Roy, Daniel Kim, Yuichiro Yano, Luke D. Knibbs, Margaret Robinson, Hilda L Harb, Rogelio Pérez-Padilla, Janet L Leasher, Jonathan L. Wright, Peter Brooks, Cristiana Abbafati, Belinda K Lloyd, Victor Aboyans, Nikhil Tandon, Charles R. Newton, Simón Barquera, Ted R. Miller, Kinnari S. Murthy, Habib Benzian, Glen Mola, Paulo A. Lotufo, Burcu Kucuk Bicer, Peggy Pei-Chia Chiang, Alexander Kraemer, Solomon Meseret Woldeyohannes, Saman Fahimi, Lesley Rushton, Kim Moesgaard Iburg, Vafa Rahimi-Movaghar, Logan Sandar, Bruce Neal, Teresa Shamah Levy, Karen M. Tabb, Jeffrey A. Towbin, Christopher J L Murray, Ramesh Sahathevan, Aaron Cohen, Chanda Kulkarni, Van C. Lansingh, François Alla, Tasara T. Mazorodze, Murugesan Raju, Saeid Shahraz, Uchechukwu K.A. Sampson, Rajeev Gupta, Neil Pearce, Mustafa Z. Younis, Veena S. Kulkarni, Francisco A García-Guerra, Amanda G. Thrift, Stefan Ma, Samaya Ismayilova, Evariste Gasana, Amitava Banerjee, Aslam Pervaiz, Emilie Agardh, Abraham D. Flaxman, Farshad Farzadfar, Peter W. Gething, Ileana Heredia-Pi, Boris Bikbov, Wanqing Chen, Saad B. Omer, Ruben Castro, Neeraj Bhala, Sara Sheikhbahaei, Hilton Lam, Urbano Fra Paleo, Lidia Sanchez-Riera, Alicia Elena Beatriz Lawrynowicz, Kristen Delwiche, Richard G. Ellenbogen, Max Petzold, Yuri Y Varakin, Guilherme Borges, Guohong Jiang, Francis Guillemin, Kyle R. Heuton, Yohannes Kinfu, Victoria F Bachman, Joseph A Wagner, Carlos A Castañeda-Orjuela, Leonardo Trasande, Abbas Ali Mahdi, Josef Coresh, Chuanhua Yu, Kenji Shibuya, Berrin Serdar, Laetitia Huiart, Xiaofeng Liang, Jean de Dieu Ngirabega, Takayoshi Ohkubo, Natalie Stephens, Francis Apolinary Mhimbira, Jefferson Traebert, Amiran Gamkrelidze, Kjetil Søreide, Samath D Dharmaratne, Robert P. Dellavalle, George Mugambage Ruhago, Lakshmi Vijayakumar, Joannie Lortet-Tieulent, Dipan Bose, Tania C Aburto, Saleem M Rana, Miriam Levi, Mohammad Taghi Hedayati, Rodolfo S Pagcatipunan, Ron T. Gansevoort, H. D. Hosgood, Michael Burch, Mohsen Naghavi, Vegard Skirbekk, Ayfer Pekericli, Walter Mendoza, Pengpeng Ye, Gabrielle deVeber, Ali H. Mokdad, David M. Broday, Koranteng Adofo, Zewdie Aderaw Alemu, Shifalika Goenka, Carrie Beth Peterson, Nicolas J. C. Stapelberg, Edson Serván-Mori, Anil Kaul, Foad Abd-Allah, Marek Majdan, Rahul Gupta, Giancarlo Logroscino, Kardiyoloji, Peterson, Carrie B., Laboratoire de Physique des Solides (LPS), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Wisconsin Division of Public Health, Laboratoire de psychologie sociale et de psychologie cognitive (LAPSCO), Centre National de la Recherche Scientifique (CNRS)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Erosion torrentielle neige et avalanches (UR ETGR (ETNA)), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), London South Bank University, Metropolitan Police Service, Hong Kong Baptist University (HKBU), Institut für Informatik [München/Munich] (LMU), Ludwig-Maximilians-Universität München (LMU), Neuroépidémiologie Tropicale (NET), Institut Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST), Université de Limoges (UNILIM)-Université de Limoges (UNILIM)-CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de Chirurgie Thoracique et Vasculaire - Médecine vasculaire [CHU Limoges], CHU Limoges, Insight Centre for Data Analytics [Galway] (INSIGHT), National University of Ireland [Galway] (NUI Galway), Maladies chroniques, santé perçue, et processus d'adaptation (APEMAC), Université Paris Descartes - Paris 5 (UPD5)-Université de Lorraine (UL), Centre d'Investigation Clinique - Epidemiologie Clinique/essais Cliniques Nancy, Cancéropôle du Grand Est-Institut National de la Santé et de la Recherche Médicale (INSERM), Karolinska Institute, karolinska institute, Department of Molecular Biosciences, Department of Molecular Biosciences [Oslo], Faculty of Mathematics and Natural Sciences [Oslo], University of Oslo (UiO)-University of Oslo (UiO)-Faculty of Mathematics and Natural Sciences [Oslo], University of Oslo (UiO)-University of Oslo (UiO), Centro de Estudios Avanzados en Zonas Aridas (CEAZA), Ecole Polytechnique Fédérale de Lausanne (EPFL), Regional Genetic Service, St Mary's Hospital, Manchester, Laboratoire d'Ingénierie des Matériaux (LIM), Centre National de la Recherche Scientifique (CNRS), Computer Science Department [Bristol], University of Bristol [Bristol], Universität Mannheim [Mannheim], Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Samsung Research &Development Institute India - Bangalore (Groupe Samsung) (SRI-B), Computational Science and Engineering Department [Daresbury] (STFC), Science & Technologie Facilities Council, Multimedia Research Center (MRC), University of Alberta, Division of Biostatistics (Biostat - MINNEAPOLIS), University of Minnesota [Twin Cities], University of Minnesota System-University of Minnesota System, University of Southampton, Imperial College London, Neurology Department, Ichilov Medical Center, Interactions, transferts, ruptures artistiques et culturels - EA 6301 (InTRu), Université de Tours, Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Julius Center for Health Sciences and Primary Care, University Medical Center [Utrecht], Risk Assessment Sciences Institute, Utrecht University [Utrecht], Unité de Biologie Fonctionnelle et Adaptative (BFA (UMR_8251 / U1133)), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Respiratory Epidemiology and Public Health, Imperial College London-Royal Brompton Hospital-National Heart and Lung Institute [UK], CIBER de Epidemiología y Salud Pública (CIBERESP), STAR laboratory, Stanford University [Stanford], Unité de recherche Virologie et Immunologie Moléculaires (VIM), Institut National de la Recherche Agronomique (INRA), Tennent Institute of Ophthalmology, National University of Singapore (NUS), Centre de Robotique (CAOR), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Multidisciplinary Nanotechnology Centre, Swansea University, Cyprus International Institute for the Environment and Public Health, Harvard School of Public Health, Genomics Research Center, Academia Sinica, Neuro Rehab Services LLP [New Delhi], Department of Signal Theory and Communications (TSC), Univ. Politec. de Catalunya, King‘s College London, Dept. Mat. Engn. De Ma, Sao Carlos, Fed. Univ. Sao Carlos UFSCar, RESPEC (RESPEC), RESPEC, Advanced Laboratories on Embedded Systems [Roma] (ALES), Department of Biology [Miami], University of Miami [Coral Gables], Health Care, Minister Of Labour-Ministry of Labor, Health and Social Affairs, Department of Nephrology, University Medical Center, University of Groningen, Division of Human Nutrition, Wageningen University and Research Centre [Wageningen] (WUR), Spatial Ecology and Epidemiology Group, University of Oxford [Oxford], College of Medicine, University of Hail, Saudi Arabia, Laboratory of Neurologic Diseases, Mario Negri Institute, Milan, Department of Civil Engineering [Hamirpur], National Institute of Technology [Hamirpur], GEMMA — Environmental Engineering and Microbiology Research Group, Department of Hydraulic, Maritime and Environmental Engineering, Universitat Politècnica de Catalunya [Barcelona] (UPC), Department of Biosciences and Nutrition, Karolinska Institutet [Stockholm], Institut National de Recherche et d'Analyse Physico-Chimique (INRAP), Institut National de Recherche et d'Analyse Physico-chimique (Ariana, Tunisie) (INRAP), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Technologie de Belfort-Montbeliard (UTBM), Obesity Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences [Tehran] (SBUMS), Shahid Beheshti University-Shahid Beheshti University, Center for Applied Medical Research (CIMA), University of Tehran, Secretariat of the Pacific Community, Public Health Division, Sociétés, Acteurs, Gouvernement en Europe (SAGE), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), College of Precision Instrument and Optoelectronics Engineering, Tianjin University, George Washington University (GW), Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale (SESSTIM - U912 INSERM - Aix Marseille Univ - IRD), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Universiteit Gent [Ghent], Washington State University (WSU), Laboratoire de Physique de l'ENS Lyon (Phys-ENS), École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, School of Computer Science - China University of Geosciences (China University of Geosciences (East Area)), Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Université Catholique de Louvain (UCL), Fachbereich Physik [Berlin], Freie Universität Berlin, Div Cyclotron & Radiopharmaceut Sci (DRDO, INMAS), Univ New Delhi, School of Physics and Astronomy, University of St Andrews [Scotland], University of Cape Town, Department of Neuroscience, Department of Computer Science and Engineering [Daejeon] (Chungnam National University), Lawrence University, Gastroenterology & Hepatology, Tata Research Development and Design Center (TRDDC), TCS Innovation Labs, University of Helsinki, Google Inc [Mountain View], Research at Google, Swedish Defense Research Agency (FOI), Servicio de Neurologia (SANTIAGO - Neurologie), Universidad del Desarrollo, Novartis Pharmaceutical Corporation, Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Observatoire des Sciences de l'Univers de Grenoble [1985-2015] (OSUG [1985-2015]), Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Department of Mechanical and Automation Engineering (CAD Laboratory), The Chinese University of Hong Kong [Hong Kong], Università degli studi di Bari, Department of Health and Human Services, National Institutes of Health [Bethesda] (NIH), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS), College of Information and Electrical Engineering [Beijing] (CIEE), China Agricultural University (CAU), Thales Research and Technology [Palaiseau], THALES, Department of Rheumatology and Connective Tissue Diseases, Medical University of Lublin, Barts and The London School of Medicine and Dentistry, Queen Mary University of London (QMUL), Centre d'économie de la Sorbonne (CES), Université Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS), Paris School of Economics (PSE), École supérieure du professorat et de l'éducation - Académie de Grenoble [2013-2019] (ESPE Grenoble [2013-2019]), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Department of Mathematics, University of Iowa [Iowa City], College of Medicine, Alfaisal University, Saudi Ministry of Health, Institut national des recherches agricoles du Bénin, Centre de Recherches agricoles du Sud, Departments of Epidemiology and Nutrition, Unit of Human Nutrition, Agricultural University of Athens, Department of Animal Science, Pennsylvania State University (Penn State), Penn State System-Penn State System, University of Virginia, University of Virginia [Charlottesville], Epidemiology and Biostatistics Unit, Institute for Maternal and Child Health - IRCCS ‘‘Burlo Garofolo', Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), Division of Cardiovascular Medicine and Channing Division of Network Medicine, Brigham and Women's Hospital [Boston], American University of Beirut [Beyrouth] (AUB), Department of Chemistry, Scientific Computing Research Unit, Department of dermatology, Milano University-Azienda Ospedaleria Ospedali Riuniti di Bergamo, Mailman School of Public Health, Columbia University [New York], The Georges Institute for International Health, The University of Sydney, Department of epidemiology and Public Health, Faculty of Medicine, Département Optique (OPT), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Universitat Pompeu Fabra [Barcelona], Center for Research in Environmental Epidemiology (CREAL), Universitat Pompeu Fabra [Barcelona]-Catalunya ministerio de salud, Nutriments Lipidiques et Prévention des Maladies Métaboliques, Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université de la Méditerranée - Aix-Marseille 2, Department of Neurology Lunds University Hospital Lund, Unit of Functional Bionanomaterials, School of Biosciences, University of Birmingham [Birmingham], Electrical Engineering and Computer Science Department - Case Western Reserve University, Case Western Reserve University [Cleveland], World Health Organization, Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), Nordic School of Public Health, The James Hutton Institute, Horia Hulubei National Institute for Physics and Nuclear Engineering, Department of Pediatrics, University of Washington [Seattle], Institute of Public Health, Department of Physics, Clarendon Laboratory, Center for TeleInFrastruktur (CTIF), Aalborg University [Denmark] (AAU), Physikalisches Institut [Freiburg], Albert-Ludwigs-Universität Freiburg, Savoirs, Textes, Langage (STL) - UMR 8163 (STL), Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Dept.of Computer Science, Indian Institute of Technology Madras (IIT Madras), Istituto Mario Negri Bergamo, Centro Ricerche e Trapianti Villa Camozzi, Universidade Estadual Paulista Júlio de Mesquita Filho [São José do Rio Preto] (UNESP), Laboratoire de Génie Informatique et Ingénierie de Production (LGI2P), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut geològic de Catalunya (IGC), Institut Geològic de Catalunya-IGC, Institut Cochin (IC UM3 (UMR 8104 / U1016)), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physiologie et Génomique des Poissons (LPGP), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Institut National de la Recherche Agronomique (INRA), Politecnico di Milano [Milan] (POLIMI), Symantec, University of Oviedo, European Microsoft Innovation Center (EMIC), Microsoft Corporation [Redmond, Wash.], Technion - Israel Institute of Technology [Haifa], Laboratoire de Mécanique, Physique et Géosciences (LMPG), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU), Department of Physics, Cavendish Laboratory, University of Cambridge [UK] (CAM), Laboratoire de Probabilités et Modèles Aléatoires (LPMA), Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Université Pierre et Marie Curie - Paris 6 (UPMC), Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Departments of Applied Physics [New Haven], Yale University [New Haven], Center for Mathematical Modeling (CMM), Department of Global Health and Development, London School of Hygiene and Tropical Medicine (LSHTM), University of Occupational and Environmental Health [Kitakyushu] (UEOH), Department of Computer Science and Engineering [New Delhi], Indian Institute of Technology Delhi (IIT Delhi), Institut de Recherche sur les Phénomènes Hors Equilibre (IRPHE), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), GlaxoSmithKline, Imperial College London-Clinical Imaging Center, Universidade Federal de Pernambuco [Recife] (UFPE), Maclean Building, Benson Lane, Crowmarsh Gifford, Centre for Ecology and Hydrology, Nanoscience Institute (NEST), Dipartimento di Fisica, Università di Pisa, Aristotle University of Thessaloniki, Laboratory Of Immune Cell Biology (LICB), JRC Institute for Environment and Sustainability (IES), European Commission - Joint Research Centre [Ispra] (JRC), Institute of Human Genetics, Rheinische Friedrich-Wilhelms-Universität Bonn, Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] (LNCC / MCT), Occupational Health Unit, Bologna University Hospital-Sant'Orsola-Malpighi Polyclinic, Royal Institute of Technology [Stockholm] (KTH ), NICTA [Eveleigh], National ICT Australia [Sydney] (NICTA), Division of Solid Mechanics, Lund University [Lund], University of Calgary, BioWare Corp, Manchester Academic Health Sciences Centre, Institut d'Histoire et de Philosophie des Sciences et des Techniques (IHPST), Université Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS)-Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Ghent University [Belgium] (UGENT), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Children’s Hospital of Philadelphia (CHOP ), Perelman School of Medicine, University of Pennsylvania [Philadelphia], Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique (LHEEA), École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche en Génie Civil et Mécanique (GeM), Université de Nantes - Faculté des Sciences et des Techniques, Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Department of Physiology, Augusta University - Medical College of Georgia, University System of Georgia (USG)-University System of Georgia (USG), Neurorestoration Group, Wolfson Centre for Age-related Diseases-King‘s College London, Electronic Navigation Research Institute (ENRI), Ministry of Land, Infrastructure, Transport and Tourism, Department of Computer Science [KAIST] (CS), Korea Advanced Institute of Science and Technology (KAIST), Centre for Undergraduate Studies, University of the Punjab, Siemens Corporate Research, Siemens AG [Munich], University of Massachusetts [Boston] (UMass Boston), University of Massachusetts System (UMASS), Department of Materials Science, Sichuan University [Chengdu] (SCU), Natl Engn Res Ctr Vegetables, Key Lab Biol & Genet Improvement Hort Crops N Chi, Beijing Acad Agr & Forestry Sci, University Hospital Puerta de Hierro, Madrid, Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University (PSL), Université de Franche-Comté (UFC)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Technologie de Belfort-Montbeliard (UTBM), Shahid Beheshti University of Medical Sciences, École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), MOLTECH-ANJOU (MOLTECH-ANJOU), Université d'Angers (UA)-Centre National de la Recherche Scientifique (CNRS), Universidad de Santiago de Chile [Santiago] (USACH)-Universidad del Desarrollo, Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS), École supérieure du professorat et de l'éducation - Académie de Grenoble (ESPE Grenoble), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Université Grenoble Alpes (UGA), California Institute of Technology (CALTECH)-NASA, Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Sud - Paris 11 (UP11), Université de la Méditerranée - Aix-Marseille 2-Institut National de la Recherche Agronomique (INRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Universidad de Santiago de Chile [Santiago] (USACH), École normale supérieure - Paris (ENS Paris)-École normale supérieure - Paris (ENS Paris), King‘s College London-Wolfson Centre for Age-related Diseases, Sichuan University, Universitat de Barcelona, Interne Geneeskunde, Medische Sociologie, MUMC+: MA Psychiatrie (3), MUMC+: Hersen en Zenuw Centrum (3), Psychiatrie & Neuropsychologie, CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST), Université de Limoges (UNILIM)-Université de Limoges (UNILIM), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut Mines-Télécom [Paris] (IMT)-Télécom Bretagne-Université européenne de Bretagne - European University of Brittany (UEB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Descartes - Paris 5 (UPD5)-Centre National de la Recherche Scientifique (CNRS), Cardiovascular Centre (CVC), Groningen Kidney Center (GKC), RS: MHeNs - R2 - Mental Health, Laboratoire de Physique des Solides ( LPS ), Université Paris-Sud - Paris 11 ( UP11 ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de psychologie sociale et de psychologie cognitive ( LAPSCO ), Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ) -Centre National de la Recherche Scientifique ( CNRS ), Erosion torrentielle neige et avalanches ( UR ETGR ), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture ( IRSTEA ), Hong Kong Baptist University ( HKBU ), Institut für Informatik [München/Munich] ( LMU ), Ludwig-Maximilians-Universität München, Neuroépidémiologie Tropicale ( NET ), CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Institut Génomique, Environnement, Immunité, Santé, Thérapeutique ( GEIST ), Université de Limoges ( UNILIM ) -Université de Limoges ( UNILIM ), Insight Centre for Data Analytics (National University of Ireland Galway (NUIG)) ( INSIGHT ), Maladies chroniques, santé perçue, et processus d'adaptation ( APEMAC ), Université Paris Descartes - Paris 5 ( UPD5 ) -Université de Lorraine ( UL ), Cancéropôle du Grand Est-Institut National de la Santé et de la Recherche Médicale ( INSERM ), University of Oslo ( UiO ) -University of Oslo ( UiO ), Centro de Estudios Avanzados en Zonas Aridas ( CEAZA ), Ecole Polytechnique Fédérale de Lausanne ( EPFL ), Laboratoire d'Ingénierie des Matériaux ( LIM ), Centre National de la Recherche Scientifique ( CNRS ), Lawrence Berkeley National Laboratory [Berkeley] ( LBNL ), Samsung Research &Development Institute India - Bangalore (Groupe Samsung) ( SRI-B ), Computational Science and Engineering Department [Daresbury] ( STFC ), Multimedia Research Center ( MRC ), University of Alberta [Edmonton], Division of Biostatistics ( Biostat - MINNEAPOLIS ), University of Minnesota [Minneapolis], University of Southampton [Southampton], Interactions, transferts, ruptures artistiques et culturels - EA 6301 ( InTRu ), Institut Jacques Monod ( IJM ), Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), University Medical Center Utrecht, Biologie Fonctionnelle et Adaptative ( BFA ), Imperial College London-Royal Brompton Hospital-National Heart and Lung Institute, Unité de recherche Virologie et Immunologie Moléculaires ( VIM ), Institut National de la Recherche Agronomique ( INRA ), National University of Singapore ( NUS ), Centre de Robotique ( CAOR ), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University ( PSL ), Department of Signal Theory and Communications ( TSC ), RESPEC ( RESPEC ), Advanced Laboratories on Embedded Systems [Roma] ( ALES ), Wageningen University and Research Centre [Wageningen] ( WUR ), Universitat Politècnica de Catalunya [Barcelona] ( UPC ), Institut National de Recherche et d'Analyse Physico-Chimique ( INRAP ), Institut National de Recherche et d'Analyse Physico-chimique (INRAP-Tunisie), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) ( FEMTO-ST ), Université de Franche-Comté ( UFC ) -Centre National de la Recherche Scientifique ( CNRS ) -Ecole Nationale Supérieure de Mécanique et des Microtechniques ( ENSMM ) -Université de Technologie de Belfort-Montbeliard ( UTBM ), Tehran University, Sociétés, Acteurs, Gouvernement en Europe ( SAGE ), Université de Strasbourg ( UNISTRA ) -Centre National de la Recherche Scientifique ( CNRS ), Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale ( SESSTIM - U912 INSERM - AMU - IRD ), Institut de Recherche pour le Développement ( IRD ) -Aix Marseille Université ( AMU ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Washington State University ( WSU ), Laboratoire de Physique de l'ENS Lyon ( Phys-ENS ), École normale supérieure - Lyon ( ENS Lyon ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ), Institut des Sciences Chimiques de Rennes ( ISCR ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Ecole Nationale Supérieure de Chimie de Rennes-Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de recherche en informatique de Toulouse ( IRIT ), Institut National Polytechnique [Toulouse] ( INP ) -Université Toulouse 1 Capitole ( UT1 ) -Université Toulouse - Jean Jaurès ( UT2J ) -Université Toulouse III - Paul Sabatier ( UPS ), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique ( CNRS ), Centre de Recherche en Automatique de Nancy ( CRAN ), Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ), Université Catholique de Louvain ( UCL ), Freie Universität Berlin [Berlin], Div Cyclotron & Radiopharmaceut Sci ( DRDO, INMAS ), Institut de Physique Nucléaire d'Orsay ( IPNO ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Centre National de la Recherche Scientifique ( CNRS ), Tata Research Development and Design Center ( TRDDC ), Laboratoire MOLTECH-Anjou [Angers] ( MOLTECH ANJOU ), Université d'Angers ( UA ) -Centre National de la Recherche Scientifique ( CNRS ), University of Helsinki [Helsinki], Swedish Defense Research Agency ( FOI ), Servicio de Neurologia ( SANTIAGO - Neurologie ), Universidad de Santiago de Chile [Santiago] ( USACH ) -Universidad del Desarrollo, Novartis Pharmaceutical Corp., East Hanover NJ 07936, USA, Laboratoire de glaciologie et géophysique de l'environnement ( LGGE ), Observatoire des Sciences de l'Univers de Grenoble ( OSUG ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Mechanical and Automation Engineering ( CAD Laboratory ), National Institutes of Health ( NIH ), Centre d'étude et de recherche en informatique et communications ( CEDRIC ), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise ( ENSIIE ) -Conservatoire National des Arts et Métiers [CNAM] ( CNAM ), Heuristique et Diagnostic des Systèmes Complexes [Compiègne] ( Heudiasyc ), Université de Technologie de Compiègne ( UTC ) -Centre National de la Recherche Scientifique ( CNRS ), College of Information and Electrical Engineering [Beijing] ( CIEE ), China Agricultural University ( CAU ), Queen Mary University of London ( QMUL ), Centre d'économie de la Sorbonne ( CES ), Université Panthéon-Sorbonne ( UP1 ) -Centre National de la Recherche Scientifique ( CNRS ), Paris School of Economics ( PSE ), École supérieure du professorat et de l'éducation - Académie de Grenoble ( ESPE Grenoble ), Université Savoie Mont Blanc ( USMB [Université de Savoie] [Université de Chambéry] ) -Université Grenoble Alpes ( UGA ), PennState University [Pennsylvania] ( PSU ), Jet Propulsion Laboratory ( JPL ), NASA-California Institute of Technology ( CALTECH ), American University of Beirut [Beyrouth], The University of Sydney [Sydney], Département Optique ( OPT ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], Laboratoire des signaux et systèmes ( L2S ), Université Paris-Sud - Paris 11 ( UP11 ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ), Center for Research in Environmental Epidemiology ( CREAL ), Université de la Méditerranée - Aix-Marseille 2-Institut National de la Recherche Agronomique ( INRA ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Center for TeleInFrastruktur ( CTIF ), Aalborg University [Denmark] ( AAU ), Savoirs, Textes, Langage (STL) - UMR 8163 ( STL ), Université de Lille-Centre National de la Recherche Scientifique ( CNRS ), Indian Institute of Technology Madras ( IIT Madras ), Universidade Estadual Paulista Júlio de Mesquita ( UNESP ), Laboratoire de Génie Informatique et Ingénierie de Production ( LGI2P ), IMT - Mines Alès Ecole Mines - Télécom ( IMT - MINES ALES ), Institut geològic de Catalunya ( IGC ), Institut Cochin ( UM3 (UMR 8104 / U1016) ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de Physiologie et Génomique des Poissons ( LPGP ), Institut National de la Recherche Agronomique ( INRA ) -Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Politecnico di Milano [Milan], European Microsoft Innovation Center ( EMIC ), Laboratoire de Mécanique, Physique et Géosciences ( LMPG ), Université Le Havre Normandie ( ULH ), Normandie Université ( NU ) -Normandie Université ( NU ), University of Cambridge [UK] ( CAM ), Laboratoire de Probabilités et Modèles Aléatoires ( LPMA ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Génétique et de Biologie Moléculaire et Cellulaire ( IGBMC ), Université de Strasbourg ( UNISTRA ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Center for Mathematical Modeling ( CMM ), Universidad de Santiago de Chile [Santiago] ( USACH ), London School of Hygiene and Tropical Medicine ( LSHTM ), University of Occupational and Environmental Health [Kitakyushu] ( UEOH ), Indian Institute of Technology Delhi ( IIT Delhi ), Institut de Recherche sur les Phénomènes Hors Equilibre ( IRPHE ), Aix Marseille Université ( AMU ) -Ecole Centrale de Marseille ( ECM ) -Centre National de la Recherche Scientifique ( CNRS ), Universidade Federal de Pernambuco [Recife] ( UFPE ), Nanoscience Institute ( NEST ), Laboratory Of Immune Cell Biology ( LICB ), JRC Institute for Environment and Sustainability ( IES ), European Commission - Joint Research Centre [Ispra] ( JRC ), Bonn Universität [Bonn], Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] ( LNCC / MCT ), Royal Institute of Technology [Stockholm] ( KTH ), National ICT Australia [Sydney] ( NICTA ), Institut d'Histoire et de Philosophie des Sciences et des Techniques ( IHPST ), Université Panthéon-Sorbonne ( UP1 ) -Département d'Etudes Cognitives - ENS Paris ( DEC ), École normale supérieure - Paris ( ENS Paris ) -École normale supérieure - Paris ( ENS Paris ) -Centre National de la Recherche Scientifique ( CNRS ), Ghent University [Belgium] ( UGENT ), Children’s Hospital of Philadelphia ( CHOP ), Univeristy of Pennsylvania Medical School, Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique ( LHEEA ), École Centrale de Nantes ( ECN ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Recherche en Génie Civil et Mécanique ( GeM ), Université de Nantes ( UN ) -École Centrale de Nantes ( ECN ) -Centre National de la Recherche Scientifique ( CNRS ), Medical College of Georgia, Electronic Navigation Research Institute ( ENRI ), Department of Computer Science [KAIST] ( CS ), Korea Advanced Institute of Science and Technology ( KAIST ), Laboratoire de l'Accélérateur Linéaire ( LAL ), University of Massachusetts [Boston] ( UMass Boston ), Forouzanfar, Mohammad H, Alexander, Lily, Anderson, H. Ro, Bachman, Victoria F, Biryukov, Stan, Brauer, Michael, Burnett, Richard, Casey, Daniel, Coates, Matthew M, Cohen, Aaron, Delwiche, Kristen, Estep, Kara, Frostad, Joseph J, Kc, Astha, Kyu, Hmwe H, Moradi Lakeh, Maziar, Ng, Marie, Slepak, Erica Leigh, Thomas, Bernadette A, Wagner, Joseph, Aasvang, Gunn Marit, Abbafati, Cristiana, Ozgoren, Ayse Abbasoglu, Abd Allah, Foad, Abera, Semaw F, Aboyans, Victor, Abraham, Biju, Abraham, Jerry Puthenpurakal, Abubakar, Ibrahim, Abu Rmeileh, Niveen M. E, Aburto, Tania C, Achoki, Tom, Adelekan, Ademola, Adofo, Koranteng, Adou, Arsène K, Adsuar, José C, Afshin, Ashkan, Agardh, Emilie E, Al Khabouri, Mazin J, Al Lami, Faris H, Alam, Sayed Saidul, Alasfoor, Deena, Albittar, Mohammed I, Alegretti, Miguel A, Aleman, Alicia V, Alemu, Zewdie A, Alfonso Cristancho, Rafael, Alhabib, Samia, Ali, Raghib, Ali, Mohammed K, Alla, Françoi, Allebeck, Peter, Allen, Peter J, Alsharif, Ubai, Alvarez, Elena, Alvis Guzman, Nelson, Amankwaa, Adansi A, Amare, Azmeraw T, Ameh, Emmanuel A, Ameli, Omid, Amini, Heresh, Ammar, Walid, Anderson, Benjamin O, Antonio, Carl Abelardo T, Anwari, Palwasha, Cunningham, Solveig Argeseanu, Arnlöv, Johan, Arsenijevic, Valentina S. Arsic, Artaman, Al, Asghar, Rana J, Assadi, Reza, Atkins, Lydia S, Atkinson, Charle, Avila, Marco A, Awuah, Baffour, Badawi, Alaa, Bahit, Maria C, Bakfalouni, Talal, Balakrishnan, Kalpana, Balalla, Shivanthi, Balu, Ravi Kumar, Banerjee, Amitava, Barber, Ryan M, Barker Collo, Suzanne L, Barquera, Simon, Barregard, Lar, Barrero, Lope H, Barrientos Gutierrez, Tonatiuh, Basto Abreu, Ana C, Basu, Arindam, Basu, Sanjay, Basulaiman, Mohammed O, Ruvalcaba, Carolina Bati, Beardsley, Justin, Bedi, Neeraj, Bekele, Tolesa, Bell, Michelle L, Benjet, Corina, Bennett, Derrick A, Benzian, Habib, Bernabé, Eduardo, Beyene, Tariku J, Bhala, Neeraj, Bhalla, Ashish, Bhutta, Zulfiqar A, Bikbov, Bori, Abdulhak, Aref A. Bin, Blore, Jed D, Blyth, Fiona M, Bohensky, Megan A, Başara, Berrak Bora, Borges, Guilherme, Bornstein, Natan M, Bose, Dipan, Boufous, Soufiane, Bourne, Rupert R, Brainin, Michael, Brazinova, Alexandra, Breitborde, Nicholas J, Brenner, Hermann, Briggs, Adam D. M, Broday, David M, Brooks, Peter M, Bruce, Nigel G, Brugha, Traolach S, Brunekreef, Bert, Buchbinder, Rachelle, Bui, Linh N, Bukhman, Gene, Bulloch, Andrew G, Burch, Michael, Burney, Peter G. J, Campos Nonato, Ismael R, Campuzano, Julio C, Cantoral, Alejandra J, Caravanos, Jack, Cárdenas, Rosario, Cardis, Elisabeth, Carpenter, David O, Caso, Valeria, Castañeda Orjuela, Carlos A, Castro, Ruben E, Catalá López, Ferrán, Cavalleri, Fiorella, Çavlin, Alanur, Chadha, Vineet K, Chang, Jung Chen, Charlson, Fiona J, Chen, Honglei, Chen, Wanqing, Chen, Zhengming, Chiang, Peggy P, Chimed Ochir, Odgerel, Chowdhury, Rajiv, Christophi, Costas A, Chuang, Ting Wu, Chugh, Sumeet S, Cirillo, Massimo, Claßen, Thomas Kd, Colistro, Valentina, Colomar, Mercede, Colquhoun, Samantha M, Contreras, Alejandra G, Cooper, Cyru, Cooperrider, Kimberly, Cooper, Leslie T, Coresh, Josef, Courville, Karen J, Criqui, Michael H, Cuevas Nasu, Lucia, Damsere Derry, Jame, Danawi, Hadi, Dandona, Lalit, Dandona, Rakhi, Dargan, Paul I, Davis, Adrian, Davitoiu, Dragos V, Dayama, Anand, de Castro, E. Filipa, De la Cruz Góngora, Vanessa, De Leo, Diego, de Lima, Graça, Degenhardt, Louisa, Del Pozo Cruz, Borja, Dellavalle, Robert P, Deribe, Kebede, Derrett, Sarah, Jarlais, Don C. De, Dessalegn, Muluken, Deveber, Gabrielle A, Devries, Karen M, Dharmaratne, Samath D, Dherani, Mukesh K, Dicker, Daniel, Ding, Eric L, Dokova, Klara, Dorsey, E. Ray, Driscoll, Tim R, Duan, Leilei, Durrani, Adnan M, Ebel, Beth E, Ellenbogen, Richard G, Elshrek, Yousef M, Endres, Matthia, Ermakov, Sergey P, Erskine, Holly E, Eshrati, Babak, Esteghamati, Alireza, Fahimi, Saman, Faraon, Emerito Jose A, Farzadfar, Farshad, Fay, Derek F. J, Feigin, Valery L, Feigl, Andrea B, Fereshtehnejad, Seyed Mohammad, Ferrari, Alize J, Ferri, Cleusa P, Flaxman, Abraham D, Fleming, Thomas D, Foigt, Nataliya, Foreman, Kyle J, Paleo, Urbano Fra, Franklin, Richard C, Gabbe, Belinda, Gaffikin, Lynne, Gakidou, Emmanuela, Gamkrelidze, Amiran, Gankpé, Fortuné G, Gansevoort, Ron T, García Guerra, Francisco A, Gasana, Evariste, Geleijnse, Johanna M, Gessner, Bradford D, Gething, Pete, Gibney, Katherine B, Gillum, Richard F, Ginawi, Ibrahim A. M, Giroud, Maurice, Giussani, Giorgia, Goenka, Shifalika, Goginashvili, Ketevan, Dantes, Hector Gomez, Gona, Philimon, de Cosio, Teresita Gonzalez, González Castell, Dinorah, Gotay, Carolyn C, Goto, Atsushi, Gouda, Hebe N, Guerrant, Richard L, Gugnani, Harish C, Guillemin, Franci, Gunnell, David, Gupta, Rahul, Gupta, Rajeev, Gutiérrez, Reyna A, Hafezi Nejad, Nima, Hagan, Holly, Hagstromer, Maria, Halasa, Yara A, Hamadeh, Randah R, Hammami, Mouhanad, Hankey, Graeme J, Hao, Yuantao, Harb, Hilda L, Haregu, Tilahun Nigatu, Haro, Josep Maria, Havmoeller, Rasmu, Hay, Simon I, Hedayati, Mohammad T, Heredia Pi, Ileana B, Hernandez, Lucia, Heuton, Kyle R, Heydarpour, Pouria, Hijar, Martha, Hoek, Hans W, Hoffman, Howard J, Hornberger, John C, Hosgood, H. Dean, Hoy, Damian G, Hsairi, Mohamed, Hu, Guoqing, Hu, Howard, Huang, Cheng, Huang, John J, Hubbell, Bryan J, Huiart, Laetitia, Husseini, Abdullatif, Iannarone, Marissa L, Iburg, Kim M, Idrisov, Bulat T, Ikeda, Nayu, Innos, Kaire, Inoue, Manami, Islami, Farhad, Ismayilova, Samaya, Jacobsen, Kathryn H, Jansen, Henrica A, Jarvis, Deborah L, Jassal, Simerjot K, Jauregui, Alejandra, Jayaraman, Sudha, Jeemon, Panniyammakal, Jensen, Paul N, Jha, Vivekanand, Jiang, Fan, Jiang, Guohong, Jiang, Ying, Jonas, Jost B, Juel, Knud, Kan, Haidong, Roseline, Sidibe S. Kany, Karam, Nadim E, Karch, André, Karema, Corine K, Karthikeyan, Ganesan, Kaul, Anil, Kawakami, Norito, Kazi, Dhruv S, Kemp, Andrew H, Kengne, Andre P, Keren, Andre, Khader, Yousef S, Khalifa, Shams Eldin Ali Hassan, Khan, Ejaz A, Khang, Young Ho, Khatibzadeh, Shahab, Khonelidze, Irma, Kieling, Christian, Kim, Daniel, Kim, Sungroul, Kim, Yunjin, Kimokoti, Ruth W, Kinfu, Yohanne, Kinge, Jonas M, Kissela, Brett M, Kivipelto, Miia, Knibbs, Luke D, Knudsen, Ann Kristin, Kokubo, Yoshihiro, Kose, M. Rifat, Kosen, Soewarta, Kraemer, Alexander, Kravchenko, Michael, Krishnaswami, Sanjay, Kromhout, Han, Ku, Tiffany, Defo, Barthelemy Kuate, Bicer, Burcu Kucuk, Kuipers, Ernst J, Kulkarni, Chanda, Kulkarni, Veena S, Kumar, G. Anil, Kwan, Gene F, Lai, Taavi, Balaji, Arjun Lakshmana, Lalloo, Ratilal, Lallukka, Tea, Lam, Hilton, Lan, Qing, Lansingh, Van C, Larson, Heidi J, Larsson, Ander, Laryea, Dennis O, Lavados, Pablo M, Lawrynowicz, Alicia E, Leasher, Janet L, Lee, Jong Tae, Leigh, Jame, Leung, Ricky, Levi, Miriam, Li, Yichong, Li, Yongmei, Liang, Juan, Liang, Xiaofeng, Lim, Stephen S, Lindsay, M. Patrice, Lipshultz, Steven E, Liu, Shiwei, Liu, Yang, Lloyd, Belinda K, Logroscino, Giancarlo, London, Stephanie J, Lopez, Nancy, Lortet Tieulent, Joannie, Lotufo, Paulo A, Lozano, Rafael, Lunevicius, Raimunda, Ma, Jixiang, Ma, Stefan, Machado, Vasco M. P, Macintyre, Michael F, Magis Rodriguez, Carlo, Mahdi, Abbas A, Majdan, Marek, Malekzadeh, Reza, Mangalam, Srikanth, Mapoma, Christopher C, Marape, Marape, Marcenes, Wagner, Margolis, David J, Margono, Christopher, Marks, Guy B, Martin, Randall V, Marzan, Melvin B, Mashal, Mohammad T, Masiye, Felix, Mason Jones, Amanda J, Matsushita, Kunihiro, Matzopoulos, Richard, Mayosi, Bongani M, Mazorodze, Tasara T, Mckay, Abigail C, Mckee, Martin, Mclain, Abigail, Meaney, Peter A, Medina, Catalina, Mehndiratta, Man Mohan, Mejia Rodriguez, Fabiola, Mekonnen, Wubegzier, Melaku, Yohannes A, Meltzer, Michele, Memish, Ziad A, Mendoza, Walter, Mensah, George A, Meretoja, Atte, Mhimbira, Francis Apolinary, Micha, Renata, Miller, Ted R, Mills, Edward J, Misganaw, Awoke, Mishra, Santosh, Ibrahim, Norlinah Mohamed, Mohammad, Karzan A, Mokdad, Ali H, Mola, Glen L, Monasta, Lorenzo, Hernandez, Julio C. 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Venkat, Neupane, Sudan P., Newton, Charles R., Ngalesoni, Frida N., Ngirabega, Jean De Dieu, Nguyen, Nhung T., Nieuwenhuijsen, Mark J., Nisar, Muhammad I., Nogueira, José R., Nolla, Joan M., Norheim, Ole F., Norman, Rosana E., Oh, In-Hwan, Olusanya, Bolajoko O., Omer, Saad B., Pagcatipunan, Rodolfo S., Pain, Amanda W., Pandian, Jeyaraj D., Panelo, Carlo Irwin A., Park, Eun-Kee, Parry, Charles D., Paternina Caicedo, Angel J., Patten, Scott B., Paul, Vinod K., Pavlin, Boris I., Pedraza, Lilia S., Pejin Stokic, Ljiljana, Pereira, David M., Perez-Padilla, Rogelio, Perez-Ruiz, Fernando, Perry, Samuel A.L., Phillips, Michael R., Polanczyk, Guilherme V., Pond, Constance D., Prasad, Noela M., Qato, Dima M., Quezada, Amado D., Quistberg, D. Alex A., Rahimi-Movaghar, Vafa, Ur Rahman, Sajjad, Rana, Saleem M., Refaat, Amany H., Ribeiro, Antonio L., Riccio, Patricia M., Rojas-Rueda, David, Ruhago, George M., Sacco, Ralph L., Salomon, Joshua A., Sampson, Uchechukwu K., Sanabria, Juan R., Sánchez-Pimienta, Tania G., Sanchez-Riera, Lidia, Santos, Itamar S., Saunders, James E., Saylan, Mete I., Schmidt, Jürgen C., Schneider, Ione J.C., Schwebel, David C., Scott, James G., Sepanlou, Sadaf G., Servan-Mori, Edson E., Shamah Levy, Teresa, Shin, Hwashin H., Sigfusdottir, Inga D., Silberberg, Donald H., Simard, Edgar P., Singh, Gitanjali M., Singh, Jasvinder A., Sposato, Luciano A., Sreeramareddy, Chandrashekhar T., Stapelberg, Nicolas J.C., Stein, Dan J., Stein, Murray B., Sunguya, Bruno F., Sykes, Bryan L., Tabb, Karen M., Talongwa, Roberto T., Te Ao, Braden J., Teixeira, Carolina M., Téllez Rojo, Martha M., Terkawi, Abdullah S., Texcalac-Sangrador, José Lui, Thackway, Sarah V., Thorne-Lyman, Andrew L., Thrift, Amanda G., Thurston, George D., Towbin, Jeffrey A., Tran, Bach X., Tsala Dimbuene, Zacharie, Uchendu, Uche S., Ukwaja, Kingsley N., Uzun, Selen B., Van De Vijver, Steven, Van Gool, Coen H., Van Os, Jim, Varakin, Yuri Y., Vasankari, Tommi J., Vasconcelos, Ana Maria N., Vavilala, Monica S., Veerman, Lennert J., Velasquez-Melendez, Gustavo, Venketasubramanian, N., Violante, Francesco S., Victorovich Vlassov, Vasiliy, Wagner, Gregory R., Waller, Stephen G., Wallin, Mitchell T., Warouw, Tati S., Watts, Charlotte H., Weintraub, Robert G., Whiteford, Harvey A., Wilkinson, James D., Williams, Hywel C., Williams, Thomas N., Woldeyohannes, Solomon M., Wolfe, Charles D.A., Wong, John Q., Woolf, Anthony D., Wright, Jonathan L., Yan, Lijing L., Yentür, Gökalp K., Yoon, Seok-Jun, Younis, Mustafa Z., Zaki, Maysaa E., Zunt, Joseph R., Lopez, Alan D., and Temesgen, A.M.
Subjects: Male, Fine particulate matter, Nutrition and Disease, MESH : Sanitation, Health Behavior, Diseases, MESH: Metabolic Diseases, MESH: Global Health, 030204 cardiovascular system & hematology, MESH: Risk Assessment, Global Health, MESH : Nutritional Status, MESH: Occupational Exposure, 0302 clinical medicine, Unsafe Sex, MESH: Risk Factors, Risk Factors, Voeding en Ziekte, Medicine, Air-pollution, MESH : Female, 030212 general & internal medicine, MESH : Risk Assessment, Sanitation, Wasting, 2. Zero hunger, Factors de risc en les malalties, Medicine (all), [ SDV.SPEE ] Life Sciences [q-bio]/Santé publique et épidémiologie, Public Health, Global Health, Social Medicine and Epidemiology, General Medicine, MESH : Occupational Diseases, MESH: Nutritional Status, All-cause mortality, MESH : Risk Factors, humanities, Environmental Exposure, Female, Humans, Metabolic Diseases, Nutritional Status, Occupational Diseases, Occupational Exposure, Risk Assessment, Tobacco smoking, 3. Good health, Nutritional Statu, MESH : Occupational Exposure, MESH : Metabolic Diseases, Cohort, medicine.symptom, Risk assessment, Blood-pressure, Human, MESH: Occupational Diseases, Risk factors in diseases, Coronary-heart-disease, MESH : Male, MESH: Health Behavior, MESH: Environmental Exposure, Population health, Body-mass index, 03 medical and health sciences, Household cooking, Cardiovascular-disease, Environmental health, General & Internal Medicine, parasitic diseases, Life Science, MESH: Sanitation, Risk factor, MESH : Health Behavior, VLAG, GBD2013, MESH: Humans, business.industry, Risk Factor, Global Burden of Disease Study, 79 behavioural, environmental and occupational, and metabolic risks, Long-term exposure, MESH : Humans, CAUSE-SPECIFIC MORTALITY, MESH: Male, Metabolic Disease, Occupational Disease, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, MALE BRITISH DOCTORS, Years of potential life lost, Relative risk, Malalties, MESH : Global Health, OUTDOOR AIR-POLLUTION, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, MESH : Environmental Exposure, MESH: Female
Abstract: Summary Background The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) is the first of a series of annual updates of the GBD. Risk factor quantification, particularly of modifiable risk factors, can help to identify emerging threats to population health and opportunities for prevention. The GBD 2013 provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution. Methods Attributable deaths, years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) have been estimated for 79 risks or clusters of risks using the GBD 2010 methods. Risk–outcome pairs meeting explicit evidence criteria were assessed for 188 countries for the period 1990–2013 by age and sex using three inputs: risk exposure, relative risks, and the theoretical minimum risk exposure level (TMREL). Risks are organised into a hierarchy with blocks of behavioural, environmental and occupational, and metabolic risks at the first level of the hierarchy. The next level in the hierarchy includes nine clusters of related risks and two individual risks, with more detail provided at levels 3 and 4 of the hierarchy. Compared with GBD 2010, six new risk factors have been added: handwashing practices, occupational exposure to trichloroethylene, childhood wasting, childhood stunting, unsafe sex, and low glomerular filtration rate. For most risks, data for exposure were synthesised with a Bayesian metaregression method, DisMod-MR 2.0, or spatial-temporal Gaussian process regression. Relative risks were based on meta-regressions of published cohort and intervention studies. Attributable burden for clusters of risks and all risks combined took into account evidence on the mediation of some risks such as high body-mass index (BMI) through other risks such as high systolic blood pressure and high cholesterol. Findings All risks combined account for 57·2% (95% uncertainty interval [UI] 55·8–58·5) of deaths and 41·6% (40·1–43·0) of DALYs. Risks quantified account for 87·9% (86·5−89·3) of cardiovascular disease DALYs, ranging to a low of 0% for neonatal disorders and neglected tropical diseases and malaria. In terms of global DALYs in 2013, six risks or clusters of risks each caused more than 5% of DALYs: dietary risks accounting for 11·3 million deaths and 241·4 million DALYs, high systolic blood pressure for 10·4 million deaths and 208·1 million DALYs, child and maternal malnutrition for 1·7 million deaths and 176·9 million DALYs, tobacco smoke for 6·1 million deaths and 143·5 million DALYs, air pollution for 5·5 million deaths and 141·5 million DALYs, and high BMI for 4·4 million deaths and 134·0 million DALYs. Risk factor patterns vary across regions and countries and with time. In sub-Saharan Africa, the leading risk factors are child and maternal malnutrition, unsafe sex, and unsafe water, sanitation, and handwashing. In women, in nearly all countries in the Americas, north Africa, and the Middle East, and in many other high-income countries, high BMI is the leading risk factor, with high systolic blood pressure as the leading risk in most of Central and Eastern Europe and south and east Asia. For men, high systolic blood pressure or tobacco use are the leading risks in nearly all high-income countries, in north Africa and the Middle East, Europe, and Asia. For men and women, unsafe sex is the leading risk in a corridor from Kenya to South Africa. Interpretation Behavioural, environmental and occupational, and metabolic risks can explain half of global mortality and more than one-third of global DALYs providing many opportunities for prevention. Of the larger risks, the attributable burden of high BMI has increased in the past 23 years. In view of the prominence of behavioural risk factors, behavioural and social science research on interventions for these risks should be strengthened. Many prevention and primary care policy options are available now to act on key risks. Funding Bill & Melinda Gates Foundation. Background The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) is the fi rst of a series of annual updates of the GBD. Risk factor quantifi cation, particularly of modifi able risk factors, can help to identify emerging threats to population health and opportunities for prevention. The GBD 2013 provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution. Methods Attributable deaths, years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) have been estimated for 79 risks or clusters of risks using the GBD 2010 methods. Risk–outcome pairs meeting explicit evidence criteria were assessed for 188 countries for the period 1990–2013 by age and sex using three inputs: risk exposure, relative risks, and the theoretical minimum risk exposure level (TMREL). Risks are organised into a hierarchy with blocks of behavioural, environmental and occupational, and metabolic risks at the fi rst level of the hierarchy. The next level in the hierarchy includes nine clusters of related risks and two individual risks, with more detail provided at levels 3 and 4 of the hierarchy. Compared with GBD 2010, six new risk factors have been added: handwashing practices, occupational exposure to trichloroethylene, childhood wasting, childhood stunting, unsafe sex, and low glomerular fi ltration rate. For most risks, data for exposure were synthesised with a Bayesian metaregression method, DisMod-MR 2.0, or spatial-temporal Gaussian process regression. Relative risks were based on meta-regressions of published cohort and intervention studies. Attributable burden for clusters of risks and all risks combined took into account evidence on the mediation of some risks such as high body-mass index (BMI) through other risks such as high systolic blood pressure and high cholesterol. Findings All risks combined account for 57·2% (95% uncertainty interval [UI] 55·8–58·5) of deaths and 41·6% (40·1–43·0) of DALYs. Risks quantified account for 87·9% (86·5−89·3) of cardiovascular disease DALYs, ranging to a low of 0% for neonatal disorders and neglected tropical diseases and malaria. In terms of global DALYs in 2013, six risks or clusters of risks each caused more than 5% of DALYs: dietary risks accounting for 11·3 million deaths and 241·4 million DALYs, high systolic blood pressure for 10·4 million deaths and 208·1 million DALYs, child and maternal malnutrition for 1·7 million deaths and 176·9 million DALYs, tobacco smoke for 6·1 million deaths and 143·5 million DALYs, air pollution for 5·5 million deaths and 141·5 million DALYs, and high BMI for 4·4 million deaths and 134·0 million DALYs. Risk factor patterns vary across regions and countries and with time. In sub-Saharan Africa, the leading risk factors are child and maternal malnutrition, unsafe sex, and unsafe water, sanitation, and handwashing. In women, in nearly all countries in the Americas, north Africa, and the Middle East, and in many other high-income countries, high BMI is the leading risk factor, with high systolic blood pressure as the leading risk in most of Central and Eastern Europe and south and east Asia. For men, high systolic blood pressure or tobacco use are the leading risks in nearly all high-income countries, in north Africa and the Middle East, Europe, and Asia. For men and women, unsafe sex is the leading risk in a corridor from Kenya to South Africa. Interpretation Behavioural, environmental and occupational, and metabolic risks can explain half of global mortality and more than one-third of global DALYs providing many opportunities for prevention. Of the larger risks, the attributable burden of high BMI has increased in the past 23 years. In view of the prominence of behavioural risk factors, behavioural and social science research on interventions for these risks should be strengthened. Many prevention and primary care policy options are available now to act on key risks. Funding Bill & Melinda Gates Foundation.
Published: 2015

122. Re-framing the Gaussian dispersion model as a nonlinear regression scheme for retrospective air quality assessment at a high spatial and temporal resolution.

Author: Chen, Shimon, Yuval, and Broday, David M.
Subjects: *NONLINEAR regression, *AIR quality, *REGRESSION analysis, *TRAFFIC density, *AIR pollution, *STANDARD deviations
Abstract: Regression models (e.g. Land-Use Regression) are currently the most popular way to estimate retrospective exposures to air pollution. However, these models lack important features of atmospheric dispersion. We developed a new non-linear air quality regression model which is based on the physical grounds of the well-established and commonly applied Gaussian dispersion model. This was achieved through parametrization of the basic Gaussian model (including its standard deviations) and optimizing the parameters to provide a least-squares fit with ambient measurements at each individual time-point. The new model (GaussODM) outperformed both a simpler regression model and a benchmark interpolation model in predicting spatial ambient nitrogen oxides (NO x) concentrations. The GaussODM enables a deeper understanding of the relationship between air pollution and adverse health effects. This is partly because it is better adapted at incorporating meteorological data and the effects of elevated emissions compared with previously available air pollution regression models. • The Gaussian dispersion model was reformulated as a non-linear regression model. • The model incorporates high spatial density traffic volume input and shows its impact. • The new model is highly adapted at manifesting the effects of elevated emissions. • Our results agree with physical understandings of atmospheric dispersion processes. • Exposure estimates were responsive to varying emissions and dispersion conditions. [ABSTRACT FROM AUTHOR]
Published: 2020
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123. Application of a sensor network of low cost optical particle counters for assessing the impact of quarry emissions on its vicinity.

Author: Yuval, Magen Molho, Hadas, Zivan, Ohad, Broday, David M., and Raz, Raanan
Subjects: *AIR quality monitoring stations, *SENSOR networks, *QUARRIES & quarrying, *AIR quality monitoring, *AIR quality standards, *PARTICULATE matter
Abstract: The adverse health effects of inhaled particulate matter (PM) are global concern. Yet, in general, estimating the exposure to PM is challenging due to the sparsity of standard air quality monitoring stations and the low accuracy of dispersion models when high spatial resolution is required. The city of Elad, Israel, is situated less than 1 km from the Migdal Tzedek stone quarry, and public concerns were raised regarding the impact of the quarry's PM emissions on the city air quality. This work describes a year-long campaign of continuous measurements of particle number concentration (PNC) in few locations in the city and its vicinity, using a network of low cost optical particle counters (OPCs). To assess the possible impact of the quarry on the city we examined the OPCs' accuracy, coherency, and their capability to detect the quarry's impact on the PNC levels in the city. Using PNC time series in two size channels from a network of five nodes, PM10 and PM2.5 records from a nearby reference air quality monitoring station, and meteorological data, we could conclude that the quarry's impact on the city was small relative to the background PNC levels. Yet, more importantly, this work demonstrates the use of a network of low cost OPCs for responding to an environmental query for which the sparse standard air quality monitoring observations were not sufficient. The trade-offs between deployment of a network of cheap low-quality instruments and the use of a single high-end device are discussed. Image 1 • A year-long PNC measurements were collected using a cheap OPC network. • The possible impact of a quarry on the air quality of a nearby city was examined. • The OPCs' were shown to capture spatial variability of airborne dust. • No impact of the quarry on the city was found. • Trade-offs between using low-cost sensor networks and reference AQM are presented. [ABSTRACT FROM AUTHOR]
Published: 2019
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124. Comparison and evaluation of MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia.

Author: Mhawish, Alaa, Banerjee, Tirthankar, Sorek-Hamer, Meytar, Lyapustin, Alexei, Broday, David M., and Chatfield, Robert
Subjects: *ATMOSPHERIC aerosols, *MODIS (Spectroradiometer), *SMOKING, *STANDARD deviations
Abstract: Abstract The Multiangle Implementation of Atmospheric Correction (MAIAC) is a new generic algorithm applied to collection 6 (C6) MODIS measurements to retrieve Aerosol Optical Depth (AOD) over land at high spatial resolution (1 km). This study is the first evaluation of the MAIAC AOD from MODIS Aqua (A) and Terra (T) satellites between 2006 and 2016 over South Asia. The retrieval accuracy of MAIAC was assessed by comparing it to ground-truth AErosol RObotic NETwork (AERONET) AOD, as well as to AOD retrieved by the two operational MODIS algorithms: Dark Target (DT) and Deep Blue (DB). MAIAC AOD showed higher spatial coverage and retrieval frequency than either the DT or the DB AOD retrievals. The high spatial resolution of the MAIAC retrievals enhances the capability to distinguish aerosol sources and to determine fine aerosol features, such as wildfire smoke plumes and haze over complex geographical regions, and provides more retrievals in conditions that are cloudy or when the surface is partially covered by snow. In comparison to AERONET AOD, MAIAC AOD shows a better accuracy than both DT and DB AOD. A higher number of MAIAC-AERONET AOD matchups demonstrate the capability of MAIAC to retrieve AOD over varied surfaces, different aerosol types and loadings. Our results demonstrate high retrieval accuracy in term of the Expected Error (EE) (A/T, EE: 72.22%, 73.50%), and low root mean square error (A/T, RMSE: 0.148, 0.164), root mean bias (RMB) (A/T, RMB: 0.978, 1.049) and mean absolute error (MAE) (A/T, MAE: 0.098, 0.096). Moreover, MAIAC has a lower bias as a function of the viewing geometry and the aerosol type among the three retrieval algorithms. MAIAC performed well over bright and vegetated land surfaces, showing the highest retrieval accuracy over dense vegetation and particularly well in retrieving smoke AOD, yet it underestimated dust AOD. In conclusion, MAIAC's ability to provide AOD at high spatial resolution appears promising over South Asia, thus having advantage over contemporary aerosol retrieval algorithms for epidemiological and climatological studies. Capsule In comparison with MODIS DT and DB AOD, and AERONET AOD, MAIAC shows improved accuracy and lower bias over South Asia, as well as with greater spatial coverage. Graphical abstract Distribution of Aqua and Terra MODIS MAIAC, DT and DB AOD bias with respect to AERONET over South Asia. Unlabelled Image Highlights • Comprehensive evaluation of MAIAC AOD vs AERONET AOD and DT/DB AOD over South Asia • High-resolution MAIAC is capable to distinguish aerosol sources and fine feature. • MAIAC retrieval accuracy is higher than DT/DB with more accuracy over dark surface. • MAIAC has less sensitivity to variation in aerosol types across the seasons. • MAIAC best retrieve AOD for smoke events while underestimate AOD for dust. [ABSTRACT FROM AUTHOR]
Published: 2019
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125. Estimating daily and intra-daily PM10 and PM2.5 in Israel using a spatio-temporal hybrid modeling approach.

Author: Shtein, Alexandra, Karnieli, Arnon, Katra, Itzhak, Raz, Raanan, Levy, Ilan, Lyapustin, Alexei, Dorman, Michael, Broday, David M., and Kloog, Itai
Subjects: *PARTICULATE matter, *ATMOSPHERIC aerosols, *AIR pollution, *EPIDEMIOLOGICAL research, *MODIS (Spectroradiometer)
Abstract: Abstract Satellite-based particulate matter (PM) models provide spatially and temporally resolved estimations, allowing greater spatial-temporal coverage compared to sparse ground monitoring stations. The spatio-temporal resolution of these models can be improved using aerosol optical depth (AOD) products from various satellite platforms with different overpass times which can capture possible changes in diurnal aerosol concentrations. Israel is characterized by diverse geo-climatic regions and it is subjected to frequent dust storms events. Our goal was to estimate PM 10 and PM 2.5 concentrations in Israel on daily and intra-daily (mean PM around the Aqua and Terra overpasses) temporal resolutions and to assess the differences between these time windows. A hybrid modeling approach that consists of three stages was used enabling spatially continuous PM estimations at 1 × 1 km spatial resolution. The model was calibrated on a daily basis applying a mixed modeling approach using MODIS-based MAIAC AOD and various spatial and temporal predictors. It was found that in certain urban areas the measured and estimated PM concentrations during the satellite overpass (Terra or Aqua) were higher than the mean daily PM. The models performed well showing cross-validated R2 that ranged between 0.82 and 0.92. Mean estimated PM for the study period (2005–2015) during days with no dust events showed different spatial patterns for the daily and intra-daily estimations and revealed areas in Israel that are affected by high PM concentrations (mainly industrial or dense urban areas). Estimations from these models are useful for epidemiological research and might contribute to environmental regulatory purposes by focusing the efforts of PM pollution reduction at the identified polluted areas. Highlights • Daily and intra-daily PM 10 and PM 2.5 concentrations were estimated in Israel. • The model used satellite-based MAIAC AOD data and various spatio-temporal predictors. • Intra-daily PM concentrations were higher than daily concentrations in urban areas. • The models performed well with cross-validated R2 that ranged between 0.82 and 0.92. [ABSTRACT FROM AUTHOR]
Published: 2018
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126. Frailty Status Modifies the Association Between Air Pollution and Post-Myocardial Infarction Mortality: A 20-Year Follow-Up Study.

Author: Gerber, Yariv, Myers, Vicki, Broday, David M., Steinberg, David M., Yuval, Koton, Silvia, and Drory, Yaacov
Published: 2014
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127. Exposure estimation errors to nitrogen oxides on a population scale due to daytime activity away from home.

Author: Shafran-Nathan, Rakefet, Yuval, null, Levy, Ilan, and Broday, David M.
Subjects: *NITROGEN oxides & the environment, *SAMPLING errors, *AIR pollution, *PUBLIC health, *MATHEMATICAL models of air quality
Abstract: Accurate estimation of exposure to air pollution is necessary for assessing the impact of air pollution on the public health. Most environmental epidemiology studies assign the home address exposure to the study subjects. Here, we quantify the exposure estimation error at the population scale due to assigning it solely at the residence place. A cohort of most schoolchildren in Israel (~ 950,000), age 6–18, and a representative cohort of Israeli adults (~ 380,000), age 24–65, were used. For each subject the home and the work or school addresses were geocoded. Together, these two microenvironments account for the locations at which people are present during most of the weekdays. For each subject, we estimated ambient nitrogen oxide concentrations at the home and work or school addresses using two air quality models: a stationary land use regression model and a dynamic dispersion-like model. On average, accounting for the subjects' work or school address as well as for the daily pollutant variation reduced the estimation error of exposure to ambient NO x /NO 2 by 5–10 ppb, since daytime concentrations at work/school and at home can differ significantly. These results were consistent regardless which air quality model as used and even for subjects that work or study close to their home. Yet, due to their usually short commute, assigning schoolchildren exposure solely at their residential place seems to be a reasonable estimation. In contrast, since adults commute for longer distances, assigning exposure of adults only at the residential place has a lower correlation with the daily weighted exposure, resulting in larger exposure estimation errors. We show that exposure misclassification can result from not accounting for the subjects' time-location trajectories through the spatiotemporally varying pollutant concentrations field. [ABSTRACT FROM AUTHOR]
Published: 2017
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128. Aggregated GPS tracking of vehicles and its use as a proxy of traffic-related air pollution emissions.

Author: Chen, Shimon, Bekhor, Shlomo, Yuval, null, and Broday, David M.
Subjects: *GLOBAL Positioning System, *EMISSIONS (Air pollution), *TRACKING & trailing, *MATHEMATICAL models of air quality, *INFORMATION & communication technologies, *DISPERSION (Atmospheric chemistry)
Abstract: Most air quality models use traffic-related variables as an input. Previous studies estimated nearby vehicular activity through sporadic traffic counts or via traffic assignment models. Both methods have previously produced poor or no data for nights, weekends and holidays. Emerging technologies allow the estimation of traffic through passive monitoring of location-aware devices. Examples of such devices are GPS transceivers installed in vehicles. In this work, we studied traffic volumes that were derived from such data. Additionally, we used these data for estimating ambient nitrogen dioxide concentrations, using a non-linear optimisation model that includes basic dispersion properties. The GPS-derived data show great potential for use as a proxy for pollutant emissions from motor-vehicles. [ABSTRACT FROM AUTHOR]
Published: 2016
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129. Estimating daily PM2.5 and PM10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data.

Author: Kloog, Itai, Sorek-Hamer, Meytar, Lyapustin, Alexei, Coull, Brent, Wang, Yujie, Just, Allan C., Schwartz, Joel, and Broday, David M.
Subjects: *PARTICULATE matter, *CLIMATE change, *ATMOSPHERIC aerosols, *EPIDEMIOLOGICAL research, *PREDICTION models
Abstract: Estimates of exposure to PM 2.5 are often derived from geographic characteristics based on land-use regression or from a limited number of fixed ground monitors. Remote sensing advances have integrated these approaches with satellite-based measures of aerosol optical depth (AOD), which is spatially and temporally resolved, allowing greater coverage for PM 2.5 estimations. Israel is situated in a complex geo-climatic region with contrasting geographic and weather patterns, including both dark and bright surfaces within a relatively small area. Our goal was to examine the use of MODIS-based MAIAC data in Israel, and to explore the reliability of predicted PM 2.5 and PM 10 at a high spatiotemporal resolution. We applied a three stage process, including a daily calibration method based on a mixed effects model, to predict ground PM 2.5 and PM 10 over Israel. We later constructed daily predictions across Israel for 2003–2013 using spatial and temporal smoothing, to estimate AOD when satellite data were missing. Good model performance was achieved, with out-of-sample cross validation R 2 values of 0.79 and 0.72 for PM 10 and PM 2.5 , respectively. Model predictions had little bias, with cross-validated slopes (predicted vs. observed) of 0.99 for both the PM 2.5 and PM 10 models. To our knowledge, this is the first study that utilizes high resolution 1 km MAIAC AOD retrievals for PM prediction while accounting for geo-climate complexities, such as experienced in Israel. This novel model allowed the reconstruction of long- and short-term spatially resolved exposure to PM 2.5 and PM 10 in Israel, which could be used in the future for epidemiological studies. [ABSTRACT FROM AUTHOR]
Published: 2015
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130. Spatial analysis of air pollution and cancer incidence rates in Haifa Bay, Israel

Author: Eitan, Ori, Yuval, Barchana, Micha, Dubnov, Jonathan, Linn, Shai, Carmel, Yohay, and Broday, David M.
Subjects: *SPATIAL analysis (Statistics), *LUNG cancer, *AIR pollution, *DISEASE incidence, *EMISSION control, *HODGKIN'S disease
Abstract: Abstract: The Israel National Cancer Registry reported in 2001 that cancer incidence rates in the Haifa area are roughly 20% above the national average. Since Haifa has been the major industrial center in Israel since 1930, concern has been raised that the elevated cancer rates may be associated with historically high air pollution levels. This work tests whether persistent spatial patterns of metrics of chronic exposure to air pollutants are associated with the observed patterns of cancer incidence rates. Risk metrics of chronic exposure to PM10, emitted both by industry and traffic, and to SO2, a marker of industrial emissions, was developed. Ward-based maps of standardized incidence rates of three prevalent cancers: Non-Hodgkin''s lymphoma, lung cancer and bladder cancer were also produced. Global clustering tests were employed to filter out those cancers that show sufficiently random spatial distribution to have a nil probability of being related to the spatial non-random risk maps. A Bayesian method was employed to assess possible associations between the morbidity and risk patterns, accounting for the ward-based socioeconomic status ranking. Lung cancer in males and bladder cancer in both genders showed non-random spatial patterns. No significant associations between the SO2-based risk maps and any of the cancers were found. Lung cancer in males was found to be associated with PM10, with the relative risk associated with an increase of 1μg/m3 of PM10 being 12%. Special consideration of wards with expected rates <1 improved the results by decreasing the variance of the spatially correlated residual log-relative risk. [Copyright &y& Elsevier]
Published: 2010
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131. Impact of environmental attributes on the uncertainty in MAIAC/MODIS AOD retrievals: A comparative analysis.

Author: Falah, Somaya, Mhawish, Alaa, Sorek-Hamer, Meytar, Lyapustin, Alexei I., Kloog, Itai, Banerjee, Tirthankar, Kizel, Fadi, and Broday, David M.
Subjects: *THERMOPHYSICAL properties, *COMPARATIVE studies, *UNCERTAINTY, *AEROSOLS, *OPTICAL properties
Abstract: This work examines the impact of different environmental attributes on the uncertainty in satellite-based Aerosol Optical Depth (AOD) retrieval against the benchmark Aerosol Robotic Network (AERONET) AOD measurements at 21 sites across North Africa, California and Germany, in the years 2007–2017. As a first step, we studied the effects of spatial averaging the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD retrievals, and of temporal averaging the AERONET AOD around the satellite (Aqua) overpass, on the agreement between the two products. AERONET AOD averaging over a time-window of ±15 min around the satellite overpass and the 1 × 1 km2 spatial grid of MAIAC were found to provide the best AOD retrieval performance. Next, MAIAC AOD were stratified according to different co-measured environmental attributes (aerosol loading, dominant particle size, vegetation cover, and prevailing particle type) and analyzed against the AERONET AOD. The envelope of the expected retrieval error varied considerably among different environmental attributes categories, with more accurate AOD retrievals obtained over highly vegetated areas (i.e. less surface reflectance) than over arid areas. Moreover, the retrieval accuracy was found to be sensitive to the aerosol loading and particle size, with a large bias between the MAIAC and AERONET AOD during high aerosol loading of coarse particles. In addition, the retrieval accuracy of MAIAC AOD was found to depend on the aerosol type due to the aerosol model assumptions regarding their optical properties. [Display omitted] • The MAIAC AOD retrieval uncertainty varies with the scene attributes. • Scene attributes include the surface reflectance and aerosol characteristics. • The latter accounts for the aerosol loading and thermophysical properties. • Large AOD retrieval bias & low expected error were noted during certain conditions. • Improved retrieval performance requires enhanced regional aerosol models. [ABSTRACT FROM AUTHOR]
Published: 2021
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132. Emissions vs. turbulence and atmospheric stability: A study of their relative importance in determining air pollutant concentrations.

Author: Yuval, Tritscher, Torsten, Raz, Raanan, Levi, Yoav, Levy, Ilan, and Broday, David M.
Abstract: Air pollution in the urban environment is a major concern. The ambient concentrations depend on the levels of transboundary imported pollution, the intensity of local sources and the prevailing atmospheric conditions. This work studies the relative impact of two atmospheric variables–atmospheric stability and regional scale turbulence–in determining the air pollution concentrations. We considered a setting (downtown Haifa, Israel) impacted by a large variety of sources, emitting pollutants with different chemical attributes and atmospheric life times. We found that traffic accounts for most of the locally produced pollution in the study location. However, the meteorological factors can overwhelm its impact and dictate the concentrations. The switch from stable to convective conditions and the more vigorous daytime wind are associated with a premature end of the morning peak concentrations that result from rush hour emissions of NO x , Black Carbon (BC) and ultra–fine particles. It results in daytime concentration which are lower than (winter) or equal to (summer) those at night, in spite of the much lower night–time traffic volumes. Similar, albeit weaker, impact was detected in the benzene and toluene concentrations. Sources outside the study area are responsible for most of the CO, PM 1 and PM 2.5 concentrations but during winter nights, characterised by strong atmospheric stability and low turbulence, their concentrations are elevated due to the local emissions. We developed a diagnostic statistical nonlinear model for the pollutant concentrations, which points to a stronger association of the atmospheric stability with the concentrations during stable conditions but turbulence dominating during convective conditions. Our findings explain the relatively low overall concentrations of locally emitted pollutants in the study area but warn of the potential for high concentrations during night–time in locations with comparable meteorological conditions. Unlabelled Image • Atmospheric stability and turbulence override the impact of local pollution sources. • The relative importance of sources and meteorology depends on the stability state. • Large contribution of external sources to CO and PM concentrations. • Meteorology's impact explains the relatively low concentrations in the study area. • Our conclusions can be useful for air pollution management and exposure modelling. [ABSTRACT FROM AUTHOR]
Published: 2020
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133. Air quality impacts of the low emission zone policy in Haifa.

Author: Tartakovsky, Dmitry, Kordova – Biezuner, Levana, Berlin, Ella, and Broday, David M.
Subjects: *AIR quality, *CARBON-black, *PARTICULATE matter, *AIR pollution, *TRAFFIC flow, *SOOT
Abstract: This study analyzes the performance of the first year of implementation of the low emission zone policy in Haifa (February 2018–January 2019), looking at its effects on ambient black carbon, NO X and particulate matter concentrations. Statistically significant reduction of black carbon and NO X concentrations, and a decline in the black carbon fraction in PM 1 was observed at the Atzmaut monitoring station, suggesting that the measures taken to reduce air pollution as part of the Haifa low emission zone were successful. Regarding NO X concentrations, the reported results for Haifa after the first year of the low emission zone policy (12.9%) are better than those previously reported in Western Europe (mostly 0–7%). As for black carbon, the concentration reduction (10.6%) is less than that reported in Western Europe (mostly 14–60%). Yet, traffic volumes in general and, the fraction of diesel vehicles in the total fleet in particular, differ considerably among different cities. Comparison of trends of black carbon and NO X showed good correlations on both daily (0.96) and half-hourly (0.92) basis. Trends of black carbon, benzene, PM 2.5 and PM 1 agreed relatively nicely on a daily basis (correlation coefficients of 0.84, 0.52 and 0.54, respectively) but only partially on a half-hourly basis (correlation coefficients of 0.52, 0.31 and 0.37, respectively). Thus, in spite of the LEZ starting only one year ago, observations suggest that it has considerable effect of the air quality in Haifa. • Performance of the implementation of the low emission zone in Haifa was studied. • Statistically significant reduction of BC and NO X concentrations was observed. • NO X reduction was more pronounced while BC reduction was less than formerly reported. • The measures taken to reduce air pollution in Haifa were successful. [ABSTRACT FROM AUTHOR]
Published: 2020
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134. On the association between characteristics of the atmospheric boundary layer and air pollution concentrations.

Author: Yuval, Levi, Yoav, Dayan, Uri, Levy, Ilan, and Broday, David M.
Subjects: *AIR pollution, *TEMPERATURE lapse rate, *BOUNDARY layer (Aerodynamics), *ATMOSPHERIC boundary layer, *RICHARDSON number, *SPACE-based radar
Abstract: Air pollution in the lower atmosphere is a global concern. Characteristics of the planetary boundary layer (PBL) are considered important determinants of the dispersion and the concentrations of pollutants. This work explores the association between characteristics of the PBL and corresponding air pollution data, separately for different PBL states. Five years (2014–2018) of twice daily (01:30 and 13:30 local time) high resolution radiosonde data were used to estimate the convective and stable boundary layer heights (CBL and SBL, respectively), and the temperature lapse rate, in Bet Dagan, Israel. In 23% of the nights, mostly cloudy ones, no indication for an SBL was found but a clear top of a ground–based residual layer (RBL) was detected. The concentrations of NO, NO 2 , NO x and PM 2.5 were negatively correlated with the heights of the CBL and RBL but positively correlated with the SBL height. Their association with the lapse rate was always negative, weak in daytime (CBL) but very strong during the night (SBL and RBL). The associations with the O 3 concentrations were opposite in sign to those with the primary pollutants. The PBL height using the bulk Richardson number method was most strongly associated with the CBL and RBL concentrations but we found it unsuitable for estimating the height of the SBL. Our results demonstrate the importance of accounting for the different states of the PBL. In particular, they suggest that during SBL conditions, the common practice of using the PBL volume as a determinant of pollutant concentrations might not be appropriate. [ABSTRACT FROM AUTHOR]
Published: 2020
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135. Estimating the spatial variability of fine particles at the neighborhood scale using a distributed network of particle sensors.

Author: Shafran-Nathan, Rakefet, Etzion, Yael, Zivan, Ohad, and Broday, David M.
Subjects: *PARTICULATE matter, *SENSOR networks, *NEIGHBORHOODS, *SENSOR placement, *ENVIRONMENTAL health, *AIR quality, *AIR pollutants
Abstract: Small-scale heterogeneity of airborne pollutants may have implications for accurate exposure estimation in environmental health studies. However, it has been studied thus far mainly near main roads and over relatively short periods. The emergence of low-cost miniature particle sensors enables deployment of multi-sensor nodes for studying the spatial variability of ambient pollutants at fine spatial scales for extended time periods. We carried out measurements of fine ambient particles, both in terms of particle number concentrations (PNC; 0.3 < d < 3 μm) and particle mass concentrations (PM 2.5 ; 0.3 < d < 2.5 μm), for more than three months (Dec 2015–Mar 2016; N = 1953 h) using a distributed network of optical particle counters. The network consisted of seven nodes that were deployed in a residential urban area, five nodes in one neighborhood (~1.5 km2) and two nodes in neighboring neighborhoods. While collocated with a reference monitoring instrument the sensors' readings were highly correlated (Pearson's r > 0.9; RMSE ~5 μg m−3) and the variance of the observations when the reference PM 2.5 measurements were <20 μg m−3 (~90% of the records) was very low. Significantly higher heterogeneity was observed during the sensor deployment in the neighborhood, suggesting spatial variability of airborne particles at the neighborhood scale. Studying the spatial variability during different conditions (meteorological, day of the week, time of day) revealed signatures of human activity, suggesting specific sources that possibly contribute to the observed inner-neighborhood variability. Image 1 • We measured PNC and PM 2.5 of fine ambient particles using a distributed network of OPCs. • Marginal inter-sensor variability was found during collocation. • Considerable spatial variability was observed during neighborhood-scale sensor deployment. • The latter proves that there is sub-urban scale particle concentration variability. • Urban heterogeneity of particle concentrations exists even during good air quality conditions. [ABSTRACT FROM AUTHOR]
Published: 2019
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136. Cancer and mortality in relation to traffic-related air pollution among coronary patients: Using an ensemble of exposure estimates to identify high-risk individuals.

Author: Cohen, Gali, Steinberg, David M., Yuval, Levy, Ilan, Chen, Shimon, Kark, Jeremy D., Levin, Noam, Witberg, Guy, Bental, Tamir, Broday, David M., Kornowski, Ran, and Gerber, Yariv
Subjects: *CANCER-related mortality, *AIR pollution, *DRUG-eluting stents, *PERCUTANEOUS coronary intervention, *PROSTATE cancer, *REGRESSION analysis
Abstract: Moderate correlations were previously observed between individual estimates of traffic-related air pollution (TRAP) produced by different exposure modeling approaches. This induces exposure misclassification for a substantial fraction of subjects. We used an ensemble of well-established modeling approaches to increase certainty of exposure classification and reevaluated the association with cancers previously linked to TRAP (lung, breast and prostate), other cancers, and all-cause mortality in a cohort of coronary patients. Patients undergoing percutaneous coronary interventions in a major Israeli medical center from 2004 to 2014 (n = 10,627) were followed for cancer (through 2015) and mortality (through 2017) via national registries. Residential exposure to nitrogen oxides (NO x) –a proxy for TRAP– was estimated by optimized dispersion model (ODM) and land use regression (LUR) (r Pearson = 0.50). Mutually exclusive groups of subjects classified as exposed by none of the methods (high-certainty low-exposed), ODM alone, LUR alone, or both methods (high-certainty high-exposed) were created. Associations were examined using Cox regression models. During follow-up, 741 incident cancer cases were diagnosed and 3051 deaths occurred. Using a ≥25 ppb cutoff, compared with high-certainty low exposed, the multivariable-adjusted hazard ratios (95% confidence intervals) for lung, breast and prostate cancer were 1.56 (1.13–2.15) in high-certainty exposed, 1.27 (0.86–1.86) in LUR-exposed alone, and 1.13 (0.77–1.65) in ODM-exposed alone. The association of the former category was strengthened using more extreme NO x cutoffs. A similar pattern, albeit less strong, was observed for mortality, whereas no association was shown for cancers not previously linked to TRAP. Use of an ensemble of TRAP exposure estimates may improve classification, resulting in a stronger association with outcomes. • We conducted a cohort study of NO x and cancer and mortality among coronary patients. • Exposure was assessed via an ensemble of land use regression and dispersion model. • We used a consensus estimation scheme to increase certainty of exposure classification. • Robust associations were seen between NO x and lung, breast, and prostate cancer. • Exposure certainty was related to the strength of exposure-outcomes associations. [ABSTRACT FROM AUTHOR]
Published: 2019
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137. Greenness Exposure and Mortality Risk in a Cardio-Oncologic Population.

Author: Ashri S, Cohen G, Zadok OIB, Moran M, Broday DM, Steinberg DM, Keinan-Boker L, Witberg G, Bental T, Golan L, Shafran I, Kornowski R, and Gerber Y
Abstract: Background and Aims: Knowledge is lacking on the relationship between greenness and mortality in cancer survivors who experience coronary artery disease (CAD), a cardio-oncologic population. We aimed to investigate the association between residential greenness exposure and all-cause mortality in a cardio-oncologic population., Methods: Cancer survivors undergoing percutaneous coronary intervention at the Rabin Medical Center in Israel between 2004 and 2014 were included in the study. Clinical data were collected from medical records during index hospitalization and from the Israeli National Cancer Registry. Residential greenness was estimated by the normalized difference vegetation index (NDVI), a satellite-based index derived from Landsat imagery at a 30-meter spatial resolution, with larger values indicating higher levels of vegetative density (ranging between -1 to 1). Mortality follow-up data were obtained through the end of 2021. Cox models were used to assess the hazard ratios (HRs) for all-cause mortality per 1SD increase in NDVI., Results: Among 1,331 patients analyzed [mean (SD) age, 75.6 (10.2) years, 373 (28%) females], the mean (SD) NDVI within a 300-meter radius was 0.12 (0.03). During a median follow-up period of 12.0 (IQR 9.2-14.7) years, 883 (66%) participants died. After adjustment for potential confounding factors, including residential socioeconomic status, air pollution, and smoking, NDVI was inversely associated with mortality hazard [HR (95% CI) = 0.93 (0.86, 0.99); p=.042]. The association was stronger among individuals with more recently (<10 years) diagnosed cancer [HR (95% CI) = 0.89 (0.81, 0.98); p=.019]., Conclusion: In a cohort of cardio-oncologic patients, greenness was independently associated with lower mortality., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)
Published: 2024
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138. Decomposing PM 2.5 concentrations in urban environments into meaningful factors 2. Extracting the contribution of traffic-related exhaust emissions.

Author: Belachsen I and Broday DM
Abstract: Vehicle-emitted fine particulate matter (PM 2.5 ) has been associated with significant health outcomes and environmental risks. This study estimates the contribution of traffic-related exhaust emissions (TREE) to observed PM 2.5 using a novel factorization framework. Specifically, co-measured nitrogen oxides (NO x ) concentrations served as a marker of vehicle-tailpipe emissions and were integrated into the optimization of a Non-negative Matrix Factorization (NMF) analysis to guide the factor extraction. The novel TREE-NMF approach was applied to long-term (2012-2019) PM 2.5 observations from air quality monitoring (AQM) stations in two urban areas. The extracted TREE factor was evaluated against co-measured black carbon (BC) and PM 2.5 species to which the TREE-NMF optimization was blind. The contribution of the TREE factor to the observed PM 2.5 concentrations at an AQM station from the first location showed close agreement (R 2 =0.79) with monitored BC data. In the second location, a comparison of the extracted TREE factor with measurements at a nearby Surface PARTiculate mAtter Network (SPARTAN) station revealed moderate correlations with PM 2.5 species commonly associated with fuel combustion, and a good linear regression fit with measured equivalent BC concentrations. The estimated concentrations of the TREE factor at the second location accounted for 7-11 % of the observed PM 2.5 in the AQM stations. Moreover, analysis of specific days known to be characterized by little traffic emissions suggested that approximately 60-78 % of the traffic-related PM 2.5 concentrations could be attributed to particulate traffic-exhaust emissions. The methodology applied in this study holds great potential in areas with limited monitoring of PM 2.5 speciation, in particular BC, and its results could be valuable for both future environmental health research, regional radiative forcing estimates, and promulgation of tailored regulations for traffic-related air pollution abatement., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: David Broday reports financial support was provided by Israel Science Foundation. David Broday reports financial support was provided by Israel Ministry of Environmental Protection. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)
Published: 2024
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139. Decomposing PM 2.5 concentrations in urban environments into meaningful factors: 1. Separating the contribution of local anthropogenic activities from background and long-range transport.

Author: Belachsen I and Broday DM
Abstract: Fine particulate matter (PM 2.5 ) is a complex mixture of aerosol particles with varying properties and sources, both local and distant. In areas lacking detailed monitoring of PM 2.5 speciation, the common source-apportionment analyses are not applicable. This study demonstrates an alternative framework for estimating sources and processes that affect observed PM 2.5 concentrations when information on the particle composition is unavailable. Eight years (2012-2019) of half-hourly PM 2.5 observations from 10 air quality monitoring (AQM) stations, clustered according to their airmass transport sector were analyzed, using Non-negative Matrix Factorization (NMF). Factors were determined based on their variation in time, space, and between airmass sectors. Employing a supervised machine-learning model provided insights into the relationships between the extracted factors, meteorological parameters and co-measured airborne pollutants. Factor interpretations were evaluated through comparisons with measurements of PM 2.5 species from a nearby Surface PARTiculate mAtter Network (SPARTAN) station. The NMF successfully separated background factors from an urban anthropogenic-activity factor, with the latter accounting for approximately 60 % of the observed PM 2.5 levels in Tel Aviv (∼10±6μg/m 3 ). Positive monotonic relationships were observed between the PM 2.5 urban anthropogenic-activity factor and measurements of nitrogen oxides (NO x ) and absolute humidity (AH), representing the impact of traffic emissions and hygroscopic growth, respectively. The summer background factor was found to represent long-range transport (LRT) from Europe, showing a good agreement (R 2 = 0.81) with ammonium sulphate concentrations. Our results demonstrate that a spatial NMF analysis can reliably estimate contributions of different sources with distinct compositions and properties to the total observed PM 2.5 . Using such an analysis, future environmental health studies could assess health risks associated with exposure to distinct PM 2.5 fractions. This information may assist decision makers to set environmental targets for abating PM 2.5 with specific compositions and properties., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: David Broday reports financial support was provided by Israel Science Foundation. David Broday reports financial support was provided by Israel Ministry of Environmental Protection. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)
Published: 2024
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140. Revealing causality in the associations between meteorological variables and air pollutant concentrations.

Author: Yuval, Levi Y, and Broday DM
Subjects: Nitrogen Dioxide analysis, Meteorology, Weather, Environmental Monitoring methods, Particulate Matter analysis, China, Meteorological Concepts, Air Pollutants analysis, Air Pollution analysis
Abstract: Understanding the role of meteorology in determining air pollutant concentrations is an important goal for better comprehension of air pollution dispersion and fate. It requires estimating the strength of the causal associations between all the relevant meteorological variables and the pollutant concentrations. Unfortunately, many of the meteorological variables are not routinely observed. Furthermore, the common analysis methods cannot establish causality. Here we use the output of a numerical weather prediction model as a proxy for real meteorological data, and study the causal relationships between a large suite of its meteorological variables, including some rarely observed ones, and the corresponding nitrogen dioxide (NO 2 ) concentrations at multiple observation locations. Time-lagged convergent cross mapping analysis is used to ascertain causality and its strength, and the Pearson and Spearman correlations are used to study the direction of the associations. The solar radiation, temperature lapse rate, boundary layer height, horizontal wind speed and wind shear were found to be causally associated with the NO 2 concentrations, with mean time lags of their maximal impact at -3, -1, -2 and -3 hours, respectively. The nature of the association with the vertical wind speed was found to be uncertain and region-dependent. No causal association was found with relative humidity, temperature and precipitation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)
Published: 2024
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141. Accounting for the aerosol type and additional satellite-borne aerosol products improves the prediction of PM 2.5 concentrations.

Author: Falah S, Kizel F, Banerjee T, and Broday DM
Subjects: Humans, Particulate Matter analysis, Environmental Monitoring, Aerosols analysis, Air Pollutants analysis, Air Pollution analysis
Abstract: Fine airborne particles (diameter <2.5 μm; PM 2.5 ) are recognized as a major threat to human health due to their physicochemical properties: composition, size, shape, etc. However, normally only size-fraction-specific particle concentrations are monitored. Interestingly, although the aerosol type is reported as part of the aerosol optical depth retrieval from satellite observations, it has not been utilized, to date, as an auxiliary information/co-variate for PM 2.5 prediction. We developed Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models that account for this information when predicting surface PM 2.5 . The models take as input only widely available data: satellite aerosol products with full cover and surface meteorological data. Distinct models were developed for AOD of specific aerosol types. Both the RF and XGBoost models performed well, showing moderate-to-high cross-validated adjusted R 2 (RF: 0.753-0.909; XGBoost: 0.741-0.903), depending on the aerosol type and other covariates. The weighted performance of the specific aerosol-type models was higher than of the RF and XGBoost baseline models, where all the AOD retrievals were used together (the common practice). Our approach can provide improved risk estimates due to exposure to PM 2.5 , better resolved radiative forcing calculations, and tailored abatement surveillance of specific pollutants/sources., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: David Broday reports financial support was provided by Israel Science Foundation., (Copyright © 2023 Elsevier Ltd. All rights reserved.)
Published: 2023
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142. Fusion of land use regression modeling output and wireless distributed sensor network measurements into a high spatiotemporally-resolved NO 2 product.

Author: Shafran-Nathan R, Etzion Y, and Broday DM
Subjects: Environmental Monitoring, Models, Theoretical, Nitrogen Dioxide analysis, Particulate Matter analysis, Reproducibility of Results, Air Pollutants analysis, Air Pollution analysis
Abstract: Land use regression modeling is a common method for assessing exposure to ambient pollutants, yet it suffers from very coarse temporal resolution. Wireless distributed sensor networks (WDSN) is a promising technology that can provide extremely high spatiotemporal pollutant patterns but is known to suffer from several limitations that put into question its data reliability. This study examines the advantages of fusing data from these two methods and obtaining high spatiotemporally-resolved product that can be used for exposure assessment. We demonstrate this approach by estimating nitrogen dioxide (NO 2 ) concentrations at a sub-urban scale, with the study area limited by the deployment of the WDSN nodes. Specifically, hourly-resolved fused-data estimates were obtained by combining a stationary traffic-based land use regression (LUR) model with observations (15 min sampling frequency) made by an array of low-cost sensor nodes, with the sensors' readings mapped over the whole study area. Data fusion was performed by merging the two independent information products using a fuzzy logic approach. The performance of the fused product was examined against reference hourly observations at four air quality monitoring (AQM) stations situated within the study area, with the AQM data not used for the development of any of the underlying information layers. The mean hourly RMSE between the fused data product and the AQM records was 9.3 ppb, smaller than the RMSE of the two base products independently (LUR: 14.87 ppb, WDSN: 10.45 ppb). The normalized Moran's I of the fused product indicates that the data-fusion product reveals more realistic spatial patterns than those of the base products. The fused NO 2 concentration product shows considerable spatial variability relative to that evident by interpolation of both the WDSN records and the AQM stations data, with significant non-random patterns in 74% of the study period., Competing Interests: Declaration of competing interest All the authors confirm that they have no conflicting interests in relation to this work and the results reported in this manuscript. In particular none of the authors has a primary or a secondary interests in relation to this work., (Copyright © 2020 Elsevier Ltd. All rights reserved.)
Published: 2021
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143. Estimation of High-Resolution PM 2.5 over the Indo-Gangetic Plain by Fusion of Satellite Data, Meteorology, and Land Use Variables.

Author: Mhawish A, Banerjee T, Sorek-Hamer M, Bilal M, Lyapustin AI, Chatfield R, and Broday DM
Subjects: Aerosols analysis, Asia, Environmental Monitoring, Meteorology, Particulate Matter analysis, Air Pollutants analysis, Air Pollution analysis
Abstract: Very high spatially resolved satellite-derived ground-level concentrations of particulate matter with an aerodynamic diameter of less than 2.5 μm (PM 2.5 ) have multiple potential applications, especially in air quality modeling and epidemiological and climatological research. Satellite-derived aerosol optical depth (AOD) and columnar water vapor (CWV), meteorological parameters, and land use data were used as variables within the framework of a linear mixed effect model (LME) and a random forest (RF) model to predict daily ground-level concentrations of PM 2.5 at 1 km × 1 km grid resolution across the Indo-Gangetic Plain (IGP) in South Asia. The RF model exhibited superior performance and higher accuracy compared with the LME model, with better cross-validated explained variance ( R 2 = 0.87) and lower relative prediction error (RPE = 24.5%). The RF model revealed improved performance metrics for increasing averaging periods, from daily to weekly, monthly, seasonal, and annual means, which supported its use in estimating PM 2.5 exposure metrics across the IGP at varying temporal scales (i.e., both short and long terms). The RF-based PM 2.5 levels over the middle and lower IGP, with the annual mean exceeding 110 μg/m 2.5 levels over the middle and lower IGP, with the annual mean exceeding 110 μg/m 3 concentrations of >170 μg/m 2.5 concentrations of >170 μg/m 3 .
Published: 2020
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144. Wireless Distributed Environmental Sensor Networks for Air Pollution Measurement-The Promise and the Current Reality.

Author: Broday DM
Abstract: The evaluation of the effects of air pollution on public health and human-wellbeing requires reliable data. Standard air quality monitoring stations provide accurate measurements of airborne pollutant levels, but, due to their sparse distribution, they cannot capture accurately the spatial variability of air pollutant concentrations within cities. Dedicated in-depth field campaigns have dense spatial coverage of the measurements but are held for relatively short time periods. Hence, their representativeness is limited. Moreover, the oftentimes integrated measurements represent time-averaged records. Recent advances in communication and sensor technologies enable the deployment of dense grids of Wireless Distributed Environmental Sensor Networks for air quality monitoring, yet their capability to capture urban-scale spatiotemporal pollutant patterns has not been thoroughly examined to date. Here, we summarize our studies on the practicalities of using data streams from sensor nodes for air quality measurement and the required methods to tune the results to different stakeholders and applications. We summarize the results from eight cities across Europe, five sensor technologies-three stationary (with one tested also while moving) and two personal sensor platforms, and eight ambient pollutants. Overall, few sensors showed an exceptional and consistent performance, which can shed light on the fine spatiotemporal urban variability of pollutant concentrations. Stationary sensor nodes were more reliable than personal nodes. In general, the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. This calls for the development of suitable field calibration procedures, and several such in situ field calibrations are presented., Competing Interests: The authors declare no conflicts of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in writing of the manuscript; or in the decision to publish the results.
Published: 2017
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145. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.

Author: Forouzanfar MH, Alexander L, Anderson HR, Bachman VF, Biryukov S, Brauer M, Burnett R, Casey D, Coates MM, Cohen A, Delwiche K, Estep K, Frostad JJ, Astha KC, Kyu HH, Moradi-Lakeh M, Ng M, Slepak EL, Thomas BA, Wagner J, Aasvang GM, Abbafati C, Abbasoglu Ozgoren A, Abd-Allah F, Abera SF, Aboyans V, Abraham B, Abraham JP, Abubakar I, Abu-Rmeileh NM, Aburto TC, Achoki T, Adelekan A, Adofo K, Adou AK, Adsuar JC, Afshin A, Agardh EE, Al Khabouri MJ, Al Lami FH, Alam SS, Alasfoor D, Albittar MI, Alegretti MA, Aleman AV, Alemu ZA, Alfonso-Cristancho R, Alhabib S, Ali R, Ali MK, Alla F, Allebeck P, Allen PJ, Alsharif U, Alvarez E, Alvis-Guzman N, Amankwaa AA, Amare AT, Ameh EA, Ameli O, Amini H, Ammar W, Anderson BO, Antonio CA, Anwari P, Argeseanu Cunningham S, Arnlöv J, Arsenijevic VS, Artaman A, Asghar RJ, Assadi R, Atkins LS, Atkinson C, Avila MA, Awuah B, Badawi A, Bahit MC, Bakfalouni T, Balakrishnan K, Balalla S, Balu RK, Banerjee A, Barber RM, Barker-Collo SL, Barquera S, Barregard L, Barrero LH, Barrientos-Gutierrez T, Basto-Abreu AC, Basu A, Basu S, Basulaiman MO, Batis Ruvalcaba C, Beardsley J, Bedi N, Bekele T, Bell ML, Benjet C, Bennett DA, Benzian H, Bernabé E, Beyene TJ, Bhala N, Bhalla A, Bhutta ZA, Bikbov B, Bin Abdulhak AA, Blore JD, Blyth FM, Bohensky MA, Bora Başara B, Borges G, Bornstein NM, Bose D, Boufous S, Bourne RR, Brainin M, Brazinova A, Breitborde NJ, Brenner H, Briggs AD, Broday DM, Brooks PM, Bruce NG, Brugha TS, Brunekreef B, Buchbinder R, Bui LN, Bukhman G, Bulloch AG, Burch M, Burney PG, Campos-Nonato IR, Campuzano JC, Cantoral AJ, Caravanos J, Cárdenas R, Cardis E, Carpenter DO, Caso V, Castañeda-Orjuela CA, Castro RE, Catalá-López F, Cavalleri F, Çavlin A, Chadha VK, Chang JC, Charlson FJ, Chen H, Chen W, Chen Z, Chiang PP, Chimed-Ochir O, Chowdhury R, Christophi CA, Chuang TW, Chugh SS, Cirillo M, Claßen TK, Colistro V, Colomar M, Colquhoun SM, Contreras AG, Cooper C, Cooperrider K, Cooper LT, Coresh J, Courville KJ, Criqui MH, Cuevas-Nasu L, Damsere-Derry J, Danawi H, Dandona L, Dandona R, Dargan PI, Davis A, Davitoiu DV, Dayama A, de Castro EF, De la Cruz-Góngora V, De Leo D, de Lima G, Degenhardt L, del Pozo-Cruz B, Dellavalle RP, Deribe K, Derrett S, Des Jarlais DC, Dessalegn M, deVeber GA, Devries KM, Dharmaratne SD, Dherani MK, Dicker D, Ding EL, Dokova K, Dorsey ER, Driscoll TR, Duan L, Durrani AM, Ebel BE, Ellenbogen RG, Elshrek YM, Endres M, Ermakov SP, Erskine HE, Eshrati B, Esteghamati A, Fahimi S, Faraon EJ, Farzadfar F, Fay DF, Feigin VL, Feigl AB, Fereshtehnejad SM, Ferrari AJ, Ferri CP, Flaxman AD, Fleming TD, Foigt N, Foreman KJ, Paleo UF, Franklin RC, Gabbe B, Gaffikin L, Gakidou E, Gamkrelidze A, Gankpé FG, Gansevoort RT, García-Guerra FA, Gasana E, Geleijnse JM, Gessner BD, Gething P, Gibney KB, Gillum RF, Ginawi IA, Giroud M, Giussani G, Goenka S, Goginashvili K, Gomez Dantes H, Gona P, Gonzalez de Cosio T, González-Castell D, Gotay CC, Goto A, Gouda HN, Guerrant RL, Gugnani HC, Guillemin F, Gunnell D, Gupta R, Gupta R, Gutiérrez RA, Hafezi-Nejad N, Hagan H, Hagstromer M, Halasa YA, Hamadeh RR, Hammami M, Hankey GJ, Hao Y, Harb HL, Haregu TN, Haro JM, Havmoeller R, Hay SI, Hedayati MT, Heredia-Pi IB, Hernandez L, Heuton KR, Heydarpour P, Hijar M, Hoek HW, Hoffman HJ, Hornberger JC, Hosgood HD, Hoy DG, Hsairi M, Hu G, Hu H, Huang C, Huang JJ, Hubbell BJ, Huiart L, Husseini A, Iannarone ML, Iburg KM, Idrisov BT, Ikeda N, Innos K, Inoue M, Islami F, Ismayilova S, Jacobsen KH, Jansen HA, Jarvis DL, Jassal SK, Jauregui A, Jayaraman S, Jeemon P, Jensen PN, Jha V, Jiang F, Jiang G, Jiang Y, Jonas JB, Juel K, Kan H, Kany Roseline SS, Karam NE, Karch A, Karema CK, Karthikeyan G, Kaul A, Kawakami N, Kazi DS, Kemp AH, Kengne AP, Keren A, Khader YS, Khalifa SE, Khan EA, Khang YH, Khatibzadeh S, Khonelidze I, Kieling C, Kim D, Kim S, Kim Y, Kimokoti RW, Kinfu Y, Kinge JM, Kissela BM, Kivipelto M, Knibbs LD, Knudsen AK, Kokubo Y, Kose MR, Kosen S, Kraemer A, Kravchenko M, Krishnaswami S, Kromhout H, Ku T, Kuate Defo B, Kucuk Bicer B, Kuipers EJ, Kulkarni C, Kulkarni VS, Kumar GA, Kwan GF, Lai T, Lakshmana Balaji A, Lalloo R, Lallukka T, Lam H, Lan Q, Lansingh VC, Larson HJ, Larsson A, Laryea DO, Lavados PM, Lawrynowicz AE, Leasher JL, Lee JT, Leigh J, Leung R, Levi M, Li Y, Li Y, Liang J, Liang X, Lim SS, Lindsay MP, Lipshultz SE, Liu S, Liu Y, Lloyd BK, Logroscino G, London SJ, Lopez N, Lortet-Tieulent J, Lotufo PA, Lozano R, Lunevicius R, Ma J, Ma S, Machado VM, MacIntyre MF, Magis-Rodriguez C, Mahdi AA, Majdan M, Malekzadeh R, Mangalam S, Mapoma CC, Marape M, Marcenes W, Margolis DJ, Margono C, Marks GB, Martin RV, Marzan MB, Mashal MT, Masiye F, Mason-Jones AJ, Matsushita K, Matzopoulos R, Mayosi BM, Mazorodze TT, McKay AC, McKee M, McLain A, Meaney PA, Medina C, Mehndiratta MM, Mejia-Rodriguez F, Mekonnen W, Melaku YA, Meltzer M, Memish ZA, Mendoza W, Mensah GA, Meretoja A, Mhimbira FA, Micha R, Miller TR, Mills EJ, Misganaw A, Mishra S, Mohamed Ibrahim N, Mohammad KA, Mokdad AH, Mola GL, Monasta L, Montañez Hernandez JC, Montico M, Moore AR, Morawska L, Mori R, Moschandreas J, Moturi WN, Mozaffarian D, Mueller UO, Mukaigawara M, Mullany EC, Murthy KS, Naghavi M, Nahas Z, Naheed A, Naidoo KS, Naldi L, Nand D, Nangia V, Narayan KM, Nash D, Neal B, Nejjari C, Neupane SP, Newton CR, Ngalesoni FN, Ngirabega Jde D, Nguyen G, Nguyen NT, Nieuwenhuijsen MJ, Nisar MI, Nogueira JR, Nolla JM, Nolte S, Norheim OF, Norman RE, Norrving B, Nyakarahuka L, Oh IH, Ohkubo T, Olusanya BO, Omer SB, Opio JN, Orozco R, Pagcatipunan RS Jr, Pain AW, Pandian JD, Panelo CI, Papachristou C, Park EK, Parry CD, Paternina Caicedo AJ, Patten SB, Paul VK, Pavlin BI, Pearce N, Pedraza LS, Pedroza A, Pejin Stokic L, Pekericli A, Pereira DM, Perez-Padilla R, Perez-Ruiz F, Perico N, Perry SA, Pervaiz A, Pesudovs K, Peterson CB, Petzold M, Phillips MR, Phua HP, Plass D, Poenaru D, Polanczyk GV, Polinder S, Pond CD, Pope CA, Pope D, Popova S, Pourmalek F, Powles J, Prabhakaran D, Prasad NM, Qato DM, Quezada AD, Quistberg DA, Racapé L, Rafay A, Rahimi K, Rahimi-Movaghar V, Rahman SU, Raju M, Rakovac I, Rana SM, Rao M, Razavi H, Reddy KS, Refaat AH, Rehm J, Remuzzi G, Ribeiro AL, Riccio PM, Richardson L, Riederer A, Robinson M, Roca A, Rodriguez A, Rojas-Rueda D, Romieu I, Ronfani L, Room R, Roy N, Ruhago GM, Rushton L, Sabin N, Sacco RL, Saha S, Sahathevan R, Sahraian MA, Salomon JA, Salvo D, Sampson UK, Sanabria JR, Sanchez LM, Sánchez-Pimienta TG, Sanchez-Riera L, Sandar L, Santos IS, Sapkota A, Satpathy M, Saunders JE, Sawhney M, Saylan MI, Scarborough P, Schmidt JC, Schneider IJ, Schöttker B, Schwebel DC, Scott JG, Seedat S, Sepanlou SG, Serdar B, Servan-Mori EE, Shaddick G, Shahraz S, Levy TS, Shangguan S, She J, Sheikhbahaei S, Shibuya K, Shin HH, Shinohara Y, Shiri R, Shishani K, Shiue I, Sigfusdottir ID, Silberberg DH, Simard EP, Sindi S, Singh A, Singh GM, Singh JA, Skirbekk V, Sliwa K, Soljak M, Soneji S, Søreide K, Soshnikov S, Sposato LA, Sreeramareddy CT, Stapelberg NJ, Stathopoulou V, Steckling N, Stein DJ, Stein MB, Stephens N, Stöckl H, Straif K, Stroumpoulis K, Sturua L, Sunguya BF, Swaminathan S, Swaroop M, Sykes BL, Tabb KM, Takahashi K, Talongwa RT, Tandon N, Tanne D, Tanner M, Tavakkoli M, Te Ao BJ, Teixeira CM, Téllez Rojo MM, Terkawi AS, Texcalac-Sangrador JL, Thackway SV, Thomson B, Thorne-Lyman AL, Thrift AG, Thurston GD, Tillmann T, Tobollik M, Tonelli M, Topouzis F, Towbin JA, Toyoshima H, Traebert J, Tran BX, Trasande L, Trillini M, Trujillo U, Dimbuene ZT, Tsilimbaris M, Tuzcu EM, Uchendu US, Ukwaja KN, Uzun SB, van de Vijver S, Van Dingenen R, van Gool CH, van Os J, Varakin YY, Vasankari TJ, Vasconcelos AM, Vavilala MS, Veerman LJ, Velasquez-Melendez G, Venketasubramanian N, Vijayakumar L, Villalpando S, Violante FS, Vlassov VV, Vollset SE, Wagner GR, Waller SG, Wallin MT, Wan X, Wang H, Wang J, Wang L, Wang W, Wang Y, Warouw TS, Watts CH, Weichenthal S, Weiderpass E, Weintraub RG, Werdecker A, Wessells KR, Westerman R, Whiteford HA, Wilkinson JD, Williams HC, Williams TN, Woldeyohannes SM, Wolfe CD, Wong JQ, Woolf AD, Wright JL, Wurtz B, Xu G, Yan LL, Yang G, Yano Y, Ye P, Yenesew M, Yentür GK, Yip P, Yonemoto N, Yoon SJ, Younis MZ, Younoussi Z, Yu C, Zaki ME, Zhao Y, Zheng Y, Zhou M, Zhu J, Zhu S, Zou X, Zunt JR, Lopez AD, Vos T, and Murray CJ
Subjects: Female, Global Health statistics & numerical data, Health Behavior, Humans, Male, Nutritional Status, Occupational Exposure adverse effects, Risk Assessment methods, Risk Factors, Sanitation trends, Environmental Exposure adverse effects, Global Health trends, Metabolic Diseases epidemiology, Occupational Diseases epidemiology
Abstract: Background: The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) is the first of a series of annual updates of the GBD. Risk factor quantification, particularly of modifiable risk factors, can help to identify emerging threats to population health and opportunities for prevention. The GBD 2013 provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution., Methods: Attributable deaths, years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) have been estimated for 79 risks or clusters of risks using the GBD 2010 methods. Risk-outcome pairs meeting explicit evidence criteria were assessed for 188 countries for the period 1990-2013 by age and sex using three inputs: risk exposure, relative risks, and the theoretical minimum risk exposure level (TMREL). Risks are organised into a hierarchy with blocks of behavioural, environmental and occupational, and metabolic risks at the first level of the hierarchy. The next level in the hierarchy includes nine clusters of related risks and two individual risks, with more detail provided at levels 3 and 4 of the hierarchy. Compared with GBD 2010, six new risk factors have been added: handwashing practices, occupational exposure to trichloroethylene, childhood wasting, childhood stunting, unsafe sex, and low glomerular filtration rate. For most risks, data for exposure were synthesised with a Bayesian meta-regression method, DisMod-MR 2.0, or spatial-temporal Gaussian process regression. Relative risks were based on meta-regressions of published cohort and intervention studies. Attributable burden for clusters of risks and all risks combined took into account evidence on the mediation of some risks such as high body-mass index (BMI) through other risks such as high systolic blood pressure and high cholesterol., Findings: All risks combined account for 57·2% (95% uncertainty interval [UI] 55·8-58·5) of deaths and 41·6% (40·1-43·0) of DALYs. Risks quantified account for 87·9% (86·5-89·3) of cardiovascular disease DALYs, ranging to a low of 0% for neonatal disorders and neglected tropical diseases and malaria. In terms of global DALYs in 2013, six risks or clusters of risks each caused more than 5% of DALYs: dietary risks accounting for 11·3 million deaths and 241·4 million DALYs, high systolic blood pressure for 10·4 million deaths and 208·1 million DALYs, child and maternal malnutrition for 1·7 million deaths and 176·9 million DALYs, tobacco smoke for 6·1 million deaths and 143·5 million DALYs, air pollution for 5·5 million deaths and 141·5 million DALYs, and high BMI for 4·4 million deaths and 134·0 million DALYs. Risk factor patterns vary across regions and countries and with time. In sub-Saharan Africa, the leading risk factors are child and maternal malnutrition, unsafe sex, and unsafe water, sanitation, and handwashing. In women, in nearly all countries in the Americas, north Africa, and the Middle East, and in many other high-income countries, high BMI is the leading risk factor, with high systolic blood pressure as the leading risk in most of Central and Eastern Europe and south and east Asia. For men, high systolic blood pressure or tobacco use are the leading risks in nearly all high-income countries, in north Africa and the Middle East, Europe, and Asia. For men and women, unsafe sex is the leading risk in a corridor from Kenya to South Africa., Interpretation: Behavioural, environmental and occupational, and metabolic risks can explain half of global mortality and more than one-third of global DALYs providing many opportunities for prevention. Of the larger risks, the attributable burden of high BMI has increased in the past 23 years. In view of the prominence of behavioural risk factors, behavioural and social science research on interventions for these risks should be strengthened. Many prevention and primary care policy options are available now to act on key risks., Funding: Bill & Melinda Gates Foundation., (Copyright © 2015 Elsevier Ltd. All rights reserved.)
Published: 2015
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146. Enhancement of PM2.5 exposure estimation using PM10 observations.

Author: Yuval and Broday DM
Subjects: Environmental Monitoring, Particle Size, Air Pollutants analysis, Air Pollution statistics & numerical data, Environmental Exposure statistics & numerical data, Particulate Matter analysis
Abstract: The adverse consequences of particulate matter (PM) inhalation on human health are well documented. Most of the epidemiological work assessing the health impacts of PM exposure concentrates on PM2.5, however, many of the PM monitoring records available for retrospective studies are of coarser PM10 data. This study explores the possibility to simulate PM2.5 data using PM10 records and assesses the trade-off between introduction of the unavoidable simulation errors and the improved spatial coverage enabled by the simulated series. Several modelling approaches are studied, bearing in mind the range of exposure time scales typical to environmental epidemiology studies. The modelling capability is evaluated by cross-testing between three stations observing both the PM2.5 and PM10 fractions. The trade-off between improved spatial coverage and simulation errors is assessed using leave-one-out cross-validation interpolations. Interpolations including both original and simulated data resulted in richer spatial patterns. Their cross-validated performance was comparable to or slightly better than that obtained using the original PM2.5 data only. The study provides methodologies and guidelines for scientists and practitioners on how to properly exploit their PM10 data to enhance their PM2.5 spatial coverage and minimise investment in new PM2.5 monitoring.
Published: 2014
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147. Assessing the long term impact of power plant emissions on regional air pollution using extensive monitoring data.

Author: Yuval and Broday DM
Subjects: Humans, Israel, Power Plants, Temperature, Time Factors, Wind, Air Pollution analysis, Industrial Waste analysis, Nitrogen Oxides analysis, Sulfur Dioxide analysis
Abstract: In spite of the recent increasing interest in energy production from renewable sources, polluting hydrocarbon-fueled power plants will continue to provide most of the electricity to the world's population in the coming decades. This work studies the long term impact on the regional ambient air which can be attributable to three plants with different power outputs, fuel types, and stack heights. The study is carried out in an area with relatively flat topography and typical coastal meteorology. A dense air pollution monitoring network, operating for many years, makes this area a real life laboratory for studying the pollution routes, the impact of the sources at different directions and distances, and the effects of transition to cleaner fuel. The direct impact of each of the two large power plants on the ambient SO2 levels could be clearly detected in most of the monitoring stations at distances up to 40 km away. Interestingly, a relatively large impact can also be attributed to the indirect effect of emissions that are recirculated back to the region with the land breeze. The transition from using fuel oil to natural gas in one of the large power plants resulted in a dramatic reduction in the mean SO2 levels in all of the monitoring stations. The contribution of the industrial emissions to the ambient NO2 levels seems to be very modest relative to that from traffic. An analysis of the NO, NO2 and O3 records suggests that the highest mean NO2 concentrations, and a large proportion of the total NO2 encountered in the study area, are probably due to recirculated NOx emitted by traffic in a densely populated region north of it.
Published: 2009
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148. Allocation of routinely monitored mixing ratios of nitrogen oxides to their sources.

Author: Yuval, Dubowski Y, and Broday DM
Subjects: Environmental Monitoring, Industrial Waste, Israel, Vehicle Emissions, Air Pollutants analysis, Nitrogen Oxides analysis
Abstract: In many urban areas throughout the world, the rising mean and peak levels of nitrogen oxides (NOx) are a concern. Road traffic and local industry are usually the major NOx sources in urban environments, buttheir relative contributions to the spatial distribution of the NOx volumetric mixing ratio is normally unknown. This missing piece of information is required for designing effective abatement measures to reduce ambient NOx levels. A new method for estimating the shares of which traffic and industry contribute to the mean ambient NOx mixing ratios observed in urban environments is proposed in this paper. The estimation is based on data obtained by routine air pollution monitoring, using a few assumptions aboutthe temporal emission patterns of NOx and SO2 in the area of study. A set of equations is formulated for the unknown industry and traffic contributions to the NOx mixing ratios at each monitoring site. These equations are solved using the gradient projection optimization method. The bootstrap technique is used to estimate the errors in the process. Spatial maps of the separate shares of industry and traffic in the total ambient NOx levels can be obtained where a sufficient aerial coverage of stations is available. An application of the method to the allocation of NOx mixing ratios to traffic and industrial sources in the Haifa Bay area, Israel, is demonstrated. The results are expected to be useful for future planning of traffic thoroughfares and industrial development in the area.
Published: 2007
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