Author: "Van Donkelaar, Aaron" - Searchworks@Jio Institute Digital Library Search Results

934 results on '"Van Donkelaar, Aaron"'

1. Effect modification by statin use status on the association between fine particulate matter (PM2.5) and cardiovascular mortality.

Author: Bai, Li, Kwong, Jeffrey, Kaufman, Jay, Benmarhnia, Tarik, Chen, Chen, van Donkelaar, Aaron, Martin, Randall, Kim, JinHee, Lu, Hong, Burnett, Richard, and Chen, Hong
Subjects: Air pollution, cardiovascular health, effect modification, mortality, statins, Humans, Particulate Matter, Male, Aged, Female, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Case-Control Studies, Ontario, Cardiovascular Diseases, Aged, 80 and over, Coronary Disease, Stroke, Environmental Exposure, Logistic Models, Risk Factors, Independent Living, Odds Ratio
Abstract: BACKGROUND: Numerous studies have linked fine particulate matter (PM2.5) to increased cardiovascular mortality. Less is known how the PM2.5-cardiovascular mortality association varies by use of cardiovascular medications. This study sought to quantify effect modification by statin use status on the associations between long-term exposure to PM2.5 and mortality from any cardiovascular cause, coronary heart disease (CHD), and stroke. METHODS: In this nested case-control study, we followed 1.2 million community-dwelling adults aged ≥66 years who lived in Ontario, Canada from 2000 through 2018. Cases were patients who died from the three causes. Each case was individually matched to up to 30 randomly selected controls using incidence density sampling. Conditional logistic regression models were used to estimate odds ratios (ORs) for the associations between PM2.5 and mortality. We evaluated the presence of effect modification considering both multiplicative (ratio of ORs) and additive scales (the relative excess risk due to interaction, RERI). RESULTS: Exposure to PM2.5 increased the risks for cardiovascular, CHD, and stroke mortality. For all three causes of death, compared with statin users, stronger PM2.5-mortality associations were observed among non-users [e.g. for cardiovascular mortality corresponding to each interquartile range increase in PM2.5, OR = 1.042 (95% CI, 1.032-1.053) vs OR = 1.009 (95% CI, 0.996-1.022) in users, ratio of ORs = 1.033 (95% CI, 1.019-1.047), RERI = 0.039 (95% CI, 0.025-0.050)]. Among users, partially adherent users exhibited a higher risk of PM2.5-associated mortality than fully adherent users. CONCLUSIONS: The associations of chronic exposure to PM2.5 with cardiovascular and CHD mortality were stronger among statin non-users compared to users.
Published: 2024

2. Reversal of trends in global fine particulate matter air pollution.

Author: Li, Chi, van Donkelaar, Aaron, Hammer, Melanie, McDuffie, Erin, Burnett, Richard, Spadaro, Joseph, Chatterjee, Deepangsu, Cohen, Aaron, Southerland, Veronica, Anenberg, Susan, Brauer, Michael, Martin, Randall, and Apte, Joshua
Subjects: Air Pollution, Environmental Pollution, Africa, Particulate Matter
Abstract: Ambient fine particulate matter (PM2.5) is the worlds leading environmental health risk factor. Quantification is needed of regional contributions to changes in global PM2.5 exposure. Here we interpret satellite-derived PM2.5 estimates over 1998-2019 and find a reversal of previous growth in global PM2.5 air pollution, which is quantitatively attributed to contributions from 13 regions. Global population-weighted (PW) PM2.5 exposure, related to both pollution levels and population size, increased from 1998 (28.3 μg/m3) to a peak in 2011 (38.9 μg/m3) and decreased steadily afterwards (34.7 μg/m3 in 2019). Post-2011 change was related to exposure reduction in China and slowed exposure growth in other regions (especially South Asia, the Middle East and Africa). The post-2011 exposure reduction contributes to stagnation of growth in global PM2.5-attributable mortality and increasing health benefits per µg/m3 marginal reduction in exposure, implying increasing urgency and benefits of PM2.5 mitigation with aging population and cleaner air.
Published: 2023

3. Prenatal air pollution, maternal immune activation, and autism spectrum disorder.

Author: Yu, Xin, Mostafijur Rahman, Md, Carter, Sarah, Lin, Jane, Zhuang, Zimin, Chow, Ting, Lurmann, Frederick, Kleeman, Michael, Martinez, Mayra, van Donkelaar, Aaron, Martin, Randall, Eckel, Sandrah, Chen, Zhanghua, Levitt, Pat, Schwartz, Joel, Hackman, Daniel, Chen, Jiu-Chiuan, McConnell, Rob, and Xiang, Anny
Subjects: Autism spectrum disorders, Interaction, PM(2.5), PM(2.5) chemical components, Susceptibility, Female, Pregnancy, Humans, Child, Preschool, Autism Spectrum Disorder, Vitamins, Air Pollution, Asthma, Hypertension
Abstract: BACKGROUND: Autism Spectrum Disorder (ASD) risk is highly heritable, with potential additional non-genetic factors, such as prenatal exposure to ambient particulate matter with aerodynamic diameter
Published: 2023

4. Using Parametric g-Computation to Estimate the Effect of Long-Term Exposure to Air Pollution on Mortality Risk and Simulate the Benefits of Hypothetical Policies: The Canadian Community Health Survey Cohort (2005 to 2015)

Author: Chen, Chen, Chen, Hong, van Donkelaar, Aaron, Burnett, Richard T, Martin, Randall V, Chen, Li, Tjepkema, Michael, Kirby-McGregor, Megan, Li, Yi, Kaufman, Jay S, and Benmarhnia, Tarik
Subjects: Prevention, Clinical Research, Humans, Air Pollutants, Public Health, Reproducibility of Results, Canada, Air Pollution, Particulate Matter, Health Surveys, Environmental Exposure, Environmental Sciences, Medical and Health Sciences, Toxicology
Abstract: BackgroundNumerous epidemiological studies have documented the adverse health impact of long-term exposure to fine particulate matter [particulate matter ≤2.5μm in aerodynamic diameter (PM2.5)] on mortality even at relatively low levels. However, methodological challenges remain to consider potential regulatory intervention's complexity and provide actionable evidence on the predicted benefits of interventions. We propose the parametric g-computation as an alternative analytical approach to such challenges.MethodWe applied the parametric g-computation to estimate the cumulative risks of nonaccidental death under different hypothetical intervention strategies targeting long-term exposure to PM2.5 in the Canadian Community Health Survey cohort from 2005 to 2015. On both relative and absolute scales, we explored the benefits of hypothetical intervention strategies compared with the natural course that a) set the simulated exposure value at each follow-up year to a threshold value if exposure was above the threshold (8.8 μg/m3, 7.04 μg/m3, 5 μg/m3, and 4 μg/m3), and b) reduced the simulated exposure value by a percentage (5% and 10%) at each follow-up year. We used the 3-y average PM2.5 concentration with 1-y lag at the postal code of respondents' annual mailing addresses as their long-term exposure to PM2.5. We considered baseline and time-varying confounders, including demographics, behavior characteristics, income level, and neighborhood socioeconomic status. We also included the R syntax for reproducibility and replication.ResultsAll hypothetical intervention strategies explored led to lower 11-y cumulative mortality risks than the estimated value under the natural course without intervention, with the smallest reduction of 0.20 per 1,000 participants (95% CI: 0.06, 0.34) under the threshold of 8.8 μg/m3, and the largest reduction of 3.40 per 1,000 participants (95% CI: -0.23, 7.03) under the relative reduction of 10% per interval. The reductions in cumulative risk, or numbers of deaths that would have been prevented if the intervention was employed instead of maintaining the status quo, increased over time but flattened toward the end of the follow-up period. Estimates among those ≥65 years of age were greater with a similar pattern. Our estimates were robust to different model specifications.DiscussionWe found evidence that any intervention further reducing the long-term exposure to PM2.5 would reduce the cumulative mortality risk, with greater benefits in the older population, even in a population already exposed to low levels of ambient PM2.5. The parametric g-computation used in this study provides flexibilities in simulating real-world interventions, accommodates time-varying exposure and confounders, and estimates adjusted survival curves with clearer interpretation and more information than a single hazard ratio, making it a valuable analytical alternative in air pollution epidemiological research. https://doi.org/10.1289/EHP11095.
Published: 2023

5. Associations of Autism Spectrum Disorder with PM2.5 Components: A Comparative Study Using Two Different Exposure Models.

Author: Rahman, Md, Carter, Sarah, Lin, Jane, Chow, Ting, Yu, Xin, Martinez, Mayra, Chen, Zhanghua, Chen, Jiu-Chiuan, Rud, Daniel, Lewinger, Juan, van Donkelaar, Aaron, Martin, Randall, Eckel, Sandrah, Schwartz, Joel, Lurmann, Fred, McConnell, Rob, Xiang, Anny, and Kleeman, Michael
Subjects: PM2.5, PM2.5 chemical components, autism spectrum disorders, exposure models, prenatal exposures, Pregnancy, Female, Humans, Air Pollutants, Autism Spectrum Disorder, Retrospective Studies, Particulate Matter, Environmental Pollutants, Air Pollution, Environmental Exposure
Abstract: This retrospective cohort study examined associations of autism spectrum disorder (ASD) with prenatal exposure to major fine particulate matter (PM2.5) components estimated using two independent exposure models. The cohort included 318 750 mother-child pairs with singleton deliveries in Kaiser Permanente Southern California hospitals from 2001 to 2014 and followed until age five. ASD cases during follow-up (N = 4559) were identified by ICD codes. Prenatal exposures to PM2.5, elemental (EC) and black carbon (BC), organic matter (OM), nitrate (NO3-), and sulfate (SO42-) were constructed using (i) a source-oriented chemical transport model and (ii) a hybrid model. Exposures were assigned to each maternal address during the entire pregnancy, first, second, and third trimester. In single-pollutant models, ASD was associated with pregnancy-average PM2.5, EC/BC, OM, and SO42- exposures from both exposure models, after adjustment for covariates. The direction of effect estimates was consistent for EC/BC and OM and least consistent for NO3-. EC/BC, OM, and SO42- were generally robust to adjustment for other components and for PM2.5. EC/BC and OM effect estimates were generally larger and more consistent in the first and second trimester and SO42- in the third trimester. Future PM2.5 composition health effect studies might consider using multiple exposure models and a weight of evidence approach when interpreting effect estimates.
Published: 2023

6. Impact of lowering fine particulate matter from major emission sources on mortality in Canada: A nationwide causal analysis

Author: Chen, Hong, Quick, Matthew, Kaufman, Jay S, Chen, Chen, Kwong, Jeffrey C, van Donkelaar, Aaron, Meng, Jun, Martin, Randall V, Kim, JinHee, Lavigne, Eric, Bai, Li, Li, Yi, Tjepkema, Michael, Benmarhnia, Tarik, and Burnett, Richard T
Subjects: Good Health and Well Being, Male, Humans, Aged, Particulate Matter, Causality, Canada, Income, Transportation, fine particulate matter, emission source, survival, g-formula
Abstract: Emissions of fine particulate matter (PM2.5) from human activities have been linked to substantial disease burdens, but evidence regarding how reducing PM2.5 at its sources would improve public health is sparse. We followed a population-based cohort of 2.7 million adults across Canada from 2007 through 2016. For each participant, we estimated annual mean concentrations of PM2.5 and the fractional contributions to PM2.5 from the five leading anthropogenic sources at their residential address using satellite observations in combination with a global atmospheric chemistry transport model. For each source, we estimated the causal effects of six hypothetical interventions on 10-y nonaccidental mortality risk using the parametric g-formula, a structural causal model. We conducted stratified analyses by age, sex, and income. This cohort would have experienced tangible health gains had contributions to PM2.5 from any of the five sources been reduced. Compared with no intervention, a 10% annual reduction in PM2.5 contributions from transportation and power generation, Canada's largest and fifth-largest anthropogenic sources, would have prevented approximately 175 (95%CI: 123-226) and 90 (95%CI: 63-117) deaths per million by 2016, respectively. A more intensive 50% reduction per year in PM2.5 contributions from the two sources would have averted 360 and 185 deaths per million, respectively, by 2016. The potential health benefits were greater among men, older adults, and low-income earners. In Canada, where PM2.5 levels are among the lowest worldwide, reducing PM2.5 contributions from anthropogenic sources by as little as 10% annually would yield meaningful health gains.
Published: 2022

7. Chronic Exposure to Fine Particulate Matter Increases Mortality Through Pathways of Metabolic and Cardiovascular Disease: Insights From a Large Mediation Analysis

Author: Bai, Li, Benmarhnia, Tarik, Chen, Chen, Kwong, Jeffrey C, Burnett, Richard T, van Donkelaar, Aaron, Martin, Randall V, Kim, JinHee, Kaufman, Jay S, and Chen, Hong
Subjects: Diabetes, Aging, Heart Disease - Coronary Heart Disease, Climate-Related Exposures and Conditions, Heart Disease, Prevention, Cardiovascular, 2.2 Factors relating to the physical environment, 2.4 Surveillance and distribution, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Humans, Adult, Particulate Matter, Mediation Analysis, Cardiovascular Diseases, Air Pollutants, Cohort Studies, Air Pollution, Myocardial Infarction, Ontario, Stroke, Environmental Exposure, air pollutants, cardiovascular diseases, causality, cohort studies, mediation analysis, mortality, premature, population, mortality, premature, Cardiorespiratory Medicine and Haematology
Abstract: Background Long-term exposure to outdoor fine particulate matter (PM2.5) is the leading environmental risk factor for premature mortality worldwide. Characterizing important pathways through which PM2.5 increases individuals' mortality risk can clarify the PM2.5-mortality relationship and identify possible points of interventions. Recent evidence has linked PM2.5 to the onset of diabetes and cardiovascular disease, but to what extent these associations contribute to the effect of PM2.5 on mortality remains poorly understood. Methods and Results We conducted a population-based cohort study to investigate how the effect of PM2.5 on nonaccidental mortality is mediated by its impacts on incident diabetes, acute myocardial infarction, and stroke. Our study population comprised ≈200 000 individuals aged 20 to 90 years who participated in population-based health surveys in Ontario, Canada, from 1996 to 2014. Follow-up extended until December 2017. Using causal mediation analyses with Aalen additive hazards models, we decomposed the total effect of PM2.5 on mortality into a direct effect and several path-specific indirect effects mediated by diabetes, each cardiovascular event, or both combined. A series of sensitivity analyses were also conducted. After adjusting for various individual- and neighborhood-level covariates, we estimated that for every 1000 adults, each 10 μg/m3 increase in PM2.5 was associated with ≈2 incident cases of diabetes, ≈1 major cardiovascular event (acute myocardial infarction and stroke combined), and ≈2 deaths annually. Among PM2.5-related deaths, 31.7% (95% CI, 17.2%-53.2%) were attributable to diabetes and major cardiovascular events in relation to PM2.5. Specifically, 4.5% were explained by PM2.5-induced diabetes, 22.8% by PM2.5-induced major cardiovascular events, and 4.5% through their interaction. Conclusions This study suggests that a significant portion of the estimated effect of long-term exposure to PM2.5 on deaths can be attributed to its effect on diabetes and cardiovascular diseases, highlighting the significance of PM2.5 on deteriorating cardiovascular health. Our findings should raise awareness among professionals that improving metabolic and cardiovascular health may reduce mortality burden in areas with higher exposure to air pollution.
Published: 2022

8. A comparison of PM2.5 exposure estimates from different estimation methods and their associations with cognitive testing and brain MRI outcomes

Author: Power, Melinda C., Lynch, Katie M., Bennett, Erin E., Ying, Qi, Park, Eun Sug, Xu, Xiaohui, Smith, Richard L., Stewart, James D., Yanosky, Jeff D., Liao, Duanping, van Donkelaar, Aaron, Kaufman, Joel D., Sheppard, Lianne, Szpiro, Adam A., and Whitsel, Eric A.
Published: 2024
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9. Associations of exposure to PM2.5 and its compounds with carotid intima-media thickness among middle-aged adults

Author: Kim, Kyeezu, Yao, Jie, Jacobs, David R., Jr, Martin, Randall V., van Donkelaar, Aaron, Su, Wei-Chung, Zhang, Chunchao, Chen, Xiang, Murthy, Venkatesh L., Zhang, Xuehong, Lloyd-Jones, Donald, Hou, Lifang, and Zhang, Kai
Published: 2024
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10. Association between long-term exposure to ambient air pollution and COVID-19 severity: a prospective cohort study

Author: Chen, Chen, Wang, John, Kwong, Jeff, Kim, JinHee, van Donkelaar, Aaron, Martin, Randall V, Hystad, Perry, Su, Yushan, Lavigne, Eric, Kirby-McGregor, Megan, Kaufman, Jay S, Benmarhnia, Tarik, and Chen, Hong
Subjects: Climate-Related Exposures and Conditions, Lung, Prevention, Clinical Research, 2.2 Factors relating to the physical environment, Aetiology, Respiratory, Good Health and Well Being, Air Pollutants, Air Pollution, COVID-19, Cohort Studies, Environmental Exposure, Humans, Nitrogen Dioxide, Ontario, Particulate Matter, Prospective Studies, SARS-CoV-2, Medical and Health Sciences, General & Internal Medicine
Abstract: BackgroundThe tremendous global health burden related to COVID-19 means that identifying determinants of COVID-19 severity is important for prevention and intervention. We aimed to explore long-term exposure to ambient air pollution as a potential contributor to COVID-19 severity, given its known impact on the respiratory system.MethodsWe used a cohort of all people with confirmed SARS-CoV-2 infection, aged 20 years and older and not residing in a long-term care facility in Ontario, Canada, during 2020. We evaluated the association between long-term exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ground-level ozone (O3), and risk of COVID-19-related hospital admission, intensive care unit (ICU) admission and death. We ascertained individuals' long-term exposures to each air pollutant based on their residence from 2015 to 2019. We used logistic regression and adjusted for confounders and selection bias using various individual and contextual covariates obtained through data linkage.ResultsAmong the 151 105 people with confirmed SARS-CoV-2 infection in Ontario in 2020, we observed 8630 hospital admissions, 1912 ICU admissions and 2137 deaths related to COVID-19. For each interquartile range increase in exposure to PM2.5 (1.70 μg/m3), we estimated odds ratios of 1.06 (95% confidence interval [CI] 1.01-1.12), 1.09 (95% CI 0.98-1.21) and 1.00 (95% CI 0.90-1.11) for hospital admission, ICU admission and death, respectively. Estimates were smaller for NO2. We also estimated odds ratios of 1.15 (95% CI 1.06-1.23), 1.30 (95% CI 1.12-1.50) and 1.18 (95% CI 1.02-1.36) per interquartile range increase of 5.14 ppb in O3 for hospital admission, ICU admission and death, respectively.InterpretationChronic exposure to air pollution may contribute to severe outcomes after SARS-CoV-2 infection, particularly exposure to O3.
Published: 2022

11. Increasing Racial and Ethnic Disparities in Ambient Air Pollution-Attributable Morbidity and Mortality in the United States

Author: Kerr, Gaige Hunter, van Donkelaar, Aaron, Martin, Randall V., Brauer, Michael, Bukart, Katrin, Wozniak, Sarah, Goldberg, Daniel L., and Anenberg, Susan C.
Subjects: Environmentally induced diseases -- Social aspects, Environmental justice -- Health aspects, Air pollution -- Health aspects, Environmental issues, Health
Abstract: BACKGROUND: Ambient nitrogen dioxide (N[O.sub.2]) and fine particulate matter with aerodynamic diameter [less than or equal to]2.5 [micro]m (P[M.sub.2.5]) threaten public health in the US, and systemic racism has led to modern-day disparities in the distribution and associated health impacts of these pollutants. OBJECTIVES: Many studies on environmental injustices related to ambient air pollution focus only on disparities in pollutant concentrations or provide only an assessment of pollution or health disparities at a snapshot in time. In this study, we compare injustices in N[O.sub.2]- and P[M.sub.2.5]-attributable health burdens, considering N[O.sub.2]-attributable health impacts across the entire US; document changing disparities in these health burdens over time (20102019); and evaluate how more stringent air quality standards would reduce disparities in health impacts associated with these pollutants. METHODS: Through a health impact assessment, we quantified census tract-level variations in health outcomes attributable to N[O.sub.2] and P[M.sub.2.5] using health impact functions that combine demographic data from the US Census Bureau; two spatially resolved pollutant datasets, which fuse satellite data with physical and statistical models; and epidemiologically derived relative risk estimates and incidence rates from the Global Burden of Disease study. RESULTS: Despite overall decreases in the public health damages associated with N[O.sub.2] and P[M.sub.2.5], racial and ethnic relative disparities in N[O.sub.2]-attributable pediatric asthma and P[M.sub.2.5]-attributable premature mortality have widened in the US during the last decade. Racial relative disparities in P[M.sub.2.5] -attributable premature mortality and N[O.sub.2] -attributable pediatric asthma have increased by 16% and 19%, respectively, between 2010 and 2019. Similarly, ethnic relative disparities in P[M.sub.2.5]-attributable premature mortality have increased by 40% and N[O.sub.2]-attributable pediatric asthma by 10%. DISCUSSION: Enacting and attaining more stringent air quality standards for both pollutants could preferentially benefit the most marginalized and minoritized communities by greatly reducing racial and ethnic relative disparities in pollution-attributable health burdens in the US. Our methods provide a semi-observational approach to track changes in disparities in air pollution and associated health burdens across the US. https://doi.org/10.1289/EHP11900, Introduction Ambient nitrogen dioxide (N[O.sub.2]), a marker for the complex mixture of traffic-related pollution, and fine particulate matter with aerodynamic diameter [less than or equal to] 2:5 [micro]m (P[M.sub.2.5]) pose [...]
Published: 2024
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12. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Author: Brauer, Michael, Roth, Gregory A, Aravkin, Aleksandr Y, Zheng, Peng, Abate, Kalkidan Hassen, Abate, Yohannes Habtegiorgis, Abbafati, Cristiana, Abbasgholizadeh, Rouzbeh, Abbasi, Madineh Akram, Abbasian, Mohammadreza, Abbasifard, Mitra, Abbasi-Kangevari, Mohsen, Abd ElHafeez, Samar, Abd-Elsalam, Sherief, Abdi, Parsa, Abdollahi, Mohammad, Abdoun, Meriem, Abdulah, Deldar Morad, Abdullahi, Auwal, Abebe, Mesfin, Abedi, Aidin, Abedi, Armita, Abegaz, Tadesse M, Abeldaño Zuñiga, Roberto Ariel, Abiodun, Olumide, Abiso, Temesgen Lera, Aboagye, Richard Gyan, Abolhassani, Hassan, Abouzid, Mohamed, Aboye, Girma Beressa, Abreu, Lucas Guimarães, Abualruz, Hasan, Abubakar, Bilyaminu, Abu-Gharbieh, Eman, Abukhadijah, Hana Jihad Jihad, Aburuz, Salahdein, Abu-Zaid, Ahmed, Adane, Mesafint Molla, Addo, Isaac Yeboah, Addolorato, Giovanni, Adedoyin, Rufus Adesoji, Adekanmbi, Victor, Aden, Bashir, Adetunji, Juliana Bunmi, Adeyeoluwa, Temitayo Esther, Adha, Rishan, Adibi, Amin, Adnani, Qorinah Estiningtyas Sakilah, Adzigbli, Leticia Akua, Afolabi, Aanuoluwapo Adeyimika, Afolabi, Rotimi Felix, Afshin, Ashkan, Afyouni, Shadi, Afzal, Muhammad Sohail, Afzal, Saira, Agampodi, Suneth Buddhika, Agbozo, Faith, Aghamiri, Shahin, Agodi, Antonella, Agrawal, Anurag, Agyemang-Duah, Williams, Ahinkorah, Bright Opoku, Ahmad, Aqeel, Ahmad, Danish, Ahmad, Firdos, Ahmad, Noah, Ahmad, Shahzaib, Ahmad, Tauseef, Ahmed, Ali, Ahmed, Anisuddin, Ahmed, Ayman, Ahmed, Luai A, Ahmed, Muktar Beshir, Ahmed, Safoora, Ahmed, Syed Anees, Ajami, Marjan, Akalu, Gizachew Taddesse, Akara, Essona Matatom, Akbarialiabad, Hossein, Akhlaghi, Shiva, Akinosoglou, Karolina, Akinyemiju, Tomi, Akkaif, Mohammed Ahmed, Akkala, Sreelatha, Akombi-Inyang, Blessing, Al Awaidy, Salah, Al Hasan, Syed Mahfuz, Alahdab, Fares, AL-Ahdal, Tareq Mohammed Ali, Alalalmeh, Samer O, Alalwan, Tariq A, Al-Aly, Ziyad, Alam, Khurshid, Alam, Nazmul, Alanezi, Fahad Mashhour, Alanzi, Turki M, Albakri, Almaza, AlBataineh, Mohammad T, Aldhaleei, Wafa A, Aldridge, Robert W, Alemayohu, Mulubirhan Assefa, Alemu, Yihun Mulugeta, Al-Fatly, Bassam, Al-Gheethi, Adel Ali Saeed, Al-Habbal, Khairat, Alhabib, Khalid F, Alhassan, Robert Kaba, Ali, Abid, Ali, Amjad, Ali, Beriwan Abdulqadir, Ali, Iman, Ali, Liaqat, Ali, Mohammed Usman, Ali, Rafat, Ali, Syed Shujait Shujait, Ali, Waad, Alicandro, Gianfranco, Alif, Sheikh Mohammad, Aljunid, Syed Mohamed, Alla, François, Al-Marwani, Sabah, Al-Mekhlafi, Hesham M, Almustanyir, Sami, Alomari, Mahmoud A, Alonso, Jordi, Alqahtani, Jaber S, Alqutaibi, Ahmed Yaseen, Al-Raddadi, Rajaa M, Alrawashdeh, Ahmad, Al-Rifai, Rami Hani, Alrousan, Sahel Majed, Al-Sabah, Salman Khalifah, Alshahrani, Najim Z, Altaany, Zaid, Altaf, Awais, Al-Tawfiq, Jaffar A, Altirkawi, Khalid A, Aluh, Deborah Oyine, Alvis-Guzman, Nelson, Alvis-Zakzuk, Nelson J, Alwafi, Hassan, Al-Wardat, Mohammad Sami, Al-Worafi, Yaser Mohammed, Aly, Hany, Aly, Safwat, Alzoubi, Karem H, Al-Zyoud, Walid, Amaechi, Uchenna Anderson, Aman Mohammadi, Masous, Amani, Reza, Amiri, Sohrab, Amirzade-Iranaq, Mohammad Hosein, Ammirati, Enrico, Amu, Hubert, Amugsi, Dickson A, Amusa, Ganiyu Adeniyi, Ancuceanu, Robert, Anderlini, Deanna, Anderson, Jason A, Andrade, Pedro Prata, Andrei, Catalina Liliana, Andrei, Tudorel, Anenberg, Susan C, Angappan, Dhanalakshmi, Angus, Colin, Anil, Abhishek, Anil, Sneha, Anjum, Afifa, Anoushiravani, Amir, Antonazzo, Ippazio Cosimo, Antony, Catherine M, Antriyandarti, Ernoiz, Anuoluwa, Boluwatife Stephen, Anvari, Davood, Anvari, Saeid, Anwar, Saleha, Anwar, Sumadi Lukman, Anwer, Razique, Anyabolo, Ekenedilichukwu Emmanuel, Anyasodor, Anayochukwu Edward, Apostol, Geminn Louis Carace, Arabloo, Jalal, Arabzadeh Bahri, Razman, Arafat, Mosab, Areda, Demelash, Aregawi, Brhane Berhe, Aremu, Abdulfatai, Armocida, Benedetta, Arndt, Michael Benjamin, Ärnlöv, Johan, Arooj, Mahwish, Artamonov, Anton A, Artanti, Kurnia Dwi, Aruleba, Idowu Thomas, Arumugam, Ashokan, Asbeutah, Akram M, Asgary, Saeed, Asgedom, Akeza Awealom, Ashbaugh, Charlie, Ashemo, Mubarek Yesse, Ashraf, Tahira, Askarinejad, Amir, Assmus, Michael, Astell-Burt, Thomas, Athar, Mohammad, Athari, Seyyed Shamsadin, Atorkey, Prince, Atreya, Alok, Aujayeb, Avinash, Ausloos, Marcel, Avila-Burgos, Leticia, Awoke, Andargie Abate, Ayala Quintanilla, Beatriz Paulina, Ayatollahi, Haleh, Ayestas Portugal, Carlos, Ayuso-Mateos, Jose L, Azadnajafabad, Sina, Azevedo, Rui M S, Azhar, Gulrez Shah, Azizi, Hosein, Azzam, Ahmed Y, Backhaus, Insa Linnea, Badar, Muhammad, Badiye, Ashish D, Bagga, Arvind, Baghdadi, Soroush, Bagheri, Nasser, Bagherieh, Sara, Bahrami Taghanaki, Pegah, Bai, Ruhai, Baig, Atif Amin, Baker, Jennifer L, Bakkannavar, Shankar M, Balasubramanian, Madhan, Baltatu, Ovidiu Constantin, Bam, Kiran, Bandyopadhyay, Soham, Banik, Biswajit, Banik, Palash Chandra, Banke-Thomas, Aduragbemi, Bansal, Hansi, Barchitta, Martina, Bardhan, Mainak, Bardideh, Erfan, Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barone-Adesi, Francesco, Barqawi, Hiba Jawdat, Barrero, Lope H, Barrow, Amadou, Barteit, Sandra, Basharat, Zarrin, Basiru, Afisu, Basso, João Diogo, Bastan, Mohammad-Mahdi, Basu, Sanjay, Batchu, Sai, Batra, Kavita, Batra, Ravi, Baune, Bernhard T, Bayati, Mohsen, Bayileyegn, Nebiyou Simegnew, Beaney, Thomas, Behnoush, Amir Hossein, Beiranvand, Maryam, Béjot, Yannick, Bekele, Alehegn, Belgaumi, Uzma Iqbal, Bell, Arielle Wilder, Bell, Michelle L, Bello, Muhammad Bashir, Bello, Olorunjuwon Omolaja, Belo, Luis, Beloukas, Apostolos, Bendak, Salaheddine, Bennett, Derrick A, Bennitt, Fiona B, Bensenor, Isabela M, Benzian, Habib, Beran, Azizullah, Berezvai, Zombor, Bernabe, Eduardo, Bernstein, Robert S, Bettencourt, Paulo J G, Bhagavathula, Akshaya Srikanth, Bhala, Neeraj, Bhandari, Dinesh, Bhardwaj, Nikha, Bhardwaj, Pankaj, Bhaskar, Sonu, Bhat, Ajay Nagesh, Bhat, Vivek, Bhatti, Gurjit Kaur, Bhatti, Jasvinder Singh, Bhatti, Manpreet S, Bhatti, Rajbir, Bhuiyan, Mohiuddin Ahmed, Bhutta, Zulfiqar A, Bikbov, Boris, Bishai, Jessica 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Hai-Feng, Pana, Adrian, Panda, Sujogya K, Panda-Jonas, Songhomitra, Pandey, Ashok, Pandi-Perumal, Seithikurippu R, Pangaribuan, Helena Ullyartha, Pantazopoulos, Ioannis, Pantea Stoian, Anca Mihaela, Papadopoulou, Paraskevi, Parent, Marie C, Parija, Pragyan Paramita, Parikh, Romil R, Park, Seoyeon, Park, Sungchul, Parsons, Nicholas, Pashaei, Ava, Pasovic, Maja, Passera, Roberto, Patil, Shankargouda, Patoulias, Dimitrios, Patthipati, Venkata Suresh, Paudel, Uttam, Pawar, Shrikant, Pazoki Toroudi, Hamidreza, Peden, Amy E, Pedersini, Paolo, Peng, Minjin, Pensato, Umberto, Pepito, Veincent Christian Filipino, Peprah, Emmanuel K, Peprah, Prince, Peres, Mario F P, Perianayagam, Arokiasamy, Perico, Norberto, Perna, Simone, Pesudovs, Konrad, Petcu, Ionela-Roxana, Petermann-Rocha, Fanny Emily, Pham, Hoang Tran, Philip, Anil K, Phillips, Michael R, Pickering, Brandon V, Pierannunzio, Daniela, Pigeolet, Manon, Pigott, David M, Piracha, Zahra Zahid, Piradov, Michael A, Pisoni, Enrico, Piyasena, Mapa Prabhath, Plass, Dietrich, Plotnikov, Evgenii, Poddighe, Dimitri, Polkinghorne, Kevan R, Poluru, Ramesh, Pond, Constance Dimity, Popovic, Djordje S, Porru, Fabio, Postma, Maarten J, Poudel, Govinda Raj, Pour-Rashidi, Ahmad, Pourshams, Akram, Pourtaheri, Naeimeh, Prabhu, Disha, Prada, Sergio I, Pradhan, Jalandhar, Pradhan, Pranil Man Singh, Prasad, Manya, Prates, Elton Junio Sady, Purnobasuki, Hery, Purohit, Bharathi M, Puvvula, Jagadeesh, Qasim, Nameer Hashim, Qattea, Ibrahim, Qazi, Asma Saleem, Qian, Gangzhen, Qiu, Suli, Rabiee Rad, Mehrdad, Radfar, Amir, Radhakrishnan, Raghu Anekal, Radhakrishnan, Venkatraman, Raeisi Shahraki, Hadi, Rafferty, Quinn, Rafiei, Alireza, Raggi, Alberto, Raghav, Pankaja Raghav, Raheem, Nasiru, Rahim, Fakher, Rahim, Md Jillur, Rahimifard, Mahban, Rahimi-Movaghar, Vafa, Rahman, Md Obaidur, Rahman, Muhammad Aziz, Rahmani, Amir Masoud, Rahmani, Bita, Rahmanian, Mohammad, Rahmanian, Nazanin, Rahmanian, Vahid, Rahmati, Masoud, Rahmawaty, Setyaningrum, Raimondo, Diego, Rajaa, Sathish, Rajendran, Vinoth, Rajput, Prashant, Ramadan, Mahmoud Mohammed, Ramasamy, Shakthi Kumaran, Ramasubramani, Premkumar, Ramazanu, Sheena, Ramteke, Pramod W, Rana, Juwel, Rana, Kritika, Ranabhat, Chhabi Lal, Rane, Amey, Rani, Usha, Ranta, Annemarei, Rao, Chythra R, Rao, Mithun, Rao, Puja C, Rao, Sowmya J, Rasella, Davide, Rashedi, Sina, Rashedi, Vahid, Rashidi, Mahsa, Rashidi, Mohammad-Mahdi, Rasouli-Saravani, Ashkan, Ratan, Zubair Ahmed, Rathnaiah Babu, Giridhara, Rauniyar, Santosh Kumar, Rautalin, Ilari, Rawaf, David Laith, Rawaf, Salman, Rawassizadeh, Reza, Razo, Christian, Reda, Zinabu Ferede Ferede, Reddy, Murali Mohan Rama Krishna, Redwan, Elrashdy Moustafa Mohamed, Reifels, Lennart, Reitsma, Marissa B, Remuzzi, Giuseppe, Reshmi, Bhageerathy, Resnikoff, Serge, Restaino, Stefano, Reyes, Luis Felipe, Rezaei, Maryam, Rezaei, Nazila, Rezaei, Negar, Rezaeian, Mohsen, Rhee, Taeho Gregory, Riaz, Mavra A, Ribeiro, Antonio Luiz P, Rickard, Jennifer, Robinson-Oden, Hannah Elizabeth, Rodrigues, Célia Fortuna, Rodrigues, Mónica, Rodriguez, Jefferson Antonio Buendia, Roever, Leonardo, Romadlon, Debby Syahru, Ronfani, Luca, Rosauer, Jennifer Jacqueline, Roshandel, Gholamreza, Rostamian, Morteza, Rotimi, Kunle, Rout, Himanshu Sekhar, Roy, Bedanta, Roy, Nitai, Rubagotti, Enrico, Ruela, Guilherme de Andrade, Rumisha, Susan Fred, Runghien, Tilleye, Russo, Michele, Ruzzante, Sacha Walde, S N, Chandan, Saad, Aly M A, Saber, Korosh, Saber-Ayad, Maha Mohamed, Sabour, Siamak, Sacco, Simona, Sachdev, Perminder S, Sachdeva, Rajesh, Saddik, Basema, Saddler, Adam, Sadee, Bashdar Abuzed, Sadeghi, Ehsan, Sadeghi, Masoumeh, Sadeghi Majd, Elham, Saeb, Mohammad Reza, Saeed, Umar, Safari, Mehdi, Safi, Sare, Safi, Sher Zaman, Sagar, Rajesh, Sagoe, Dominic, Saheb Sharif-Askari, Fatemeh, Saheb Sharif-Askari, Narjes, Sahebkar, Amirhossein, Sahoo, Soumya Swaroop, Sahu, Monalisha, Saif, Zahra, Sajid, Mirza Rizwan, Sakshaug, Joseph W, Salam, Nasir, Salamati, Payman, Salami, Afeez Abolarinwa, Salaroli, Luciane B, Salehi, Leili, Salehi, Sana, Salem, Marwa Rashad, Salem, Mohammed Z Y, Salihu, Dauda, Salimi, Sohrab, Salum, Giovanni A, Samadi Kafil, Hossein, Samadzadeh, Sara, Samodra, Yoseph Leonardo, Samuel, Vijaya Paul, Samy, Abdallah M, Sanabria, Juan, Sanjeev, Rama Krishna, Sanna, Francesca, Santomauro, Damian Francesco, Santric-Milicevic, Milena M, Sarasmita, Made Ary, Saraswathy, Sivan Yegnanarayana Iyer, Saravanan, Aswini, Saravi, Babak, Sarikhani, Yaser, Sarmiento-Suárez, Rodrigo, Sarode, Gargi Sachin, Sarode, Sachin C, Sartorius, Benn, Sarveazad, Arash, Sathian, Brijesh, Sattin, Davide, Sawhney, Monika, Saya, Ganesh Kumar, Sayeed, Abu, Sayeed, Md Abu, Sayyah, Mehdi, Schinckus, Christophe, Schmidt, Maria Inês, Schuermans, Art, Schumacher, Austin E, Schutte, Aletta Elisabeth, Schwarzinger, Michaël, Schwebel, David C, Schwendicke, Falk, Selvaraj, Siddharthan, Semreen, Mohammad H, Senthilkumaran, Subramanian, Serban, Dragos, Serre, Marc L, Sethi, Yashendra, Shafie, Mahan, Shah, Humaira, Shah, Nilay S, Shah, Pritik A, Shah, Syed Mahboob, Shahbandi, Ataollah, Shaheen, Amira A, Shahid, Samiah, Shahid, Wajeehah, Shahsavari, Hamid R, Shahwan, Moyad Jamal, Shaikh, Masood Ali, Shaikh, Summaiya Zareen, Shalash, Ali S, Sham, Sunder, Shamim, Muhammad Aaqib, Shams-Beyranvand, Mehran, Shamshirgaran, Mohammad Ali, Shamsi, Mohammad Anas, Shanawaz, Mohd, Shankar, Abhishek, Sharfaei, Sadaf, Sharifan, Amin, Sharifi-Rad, Javad, Sharma, Manoj, Sharma, Ujjawal, Sharma, Vishal, Shastry, Rajesh P, Shavandi, Amin, Shehabeldine, Amr Mohamed Elsayed, Shehzadi, Somia, Sheikh, Aziz, Shen, Jiabin, Shetty, Adithi, Shetty, B Suresh Kumar, Shetty, Pavanchand H, Shiani, Amir, Shiferaw, Desalegn, Shigematsu, Mika, Shin, Min-Jeong, Shiri, Rahman, Shittu, Aminu, Shiue, Ivy, Shivakumar, K M, Shivarov, Velizar, Shool, Sina, Shorofi, Seyed Afshin, Shrestha, Rajan, Shrestha, Sunil, Shuja, Kanwar Hamza, Shuval, Kerem, Si, Yafei, Siddig, Emmanuel Edwar, Silva, Diego Augusto Santos, Silva, Luís Manuel Lopes Rodrigues, Silva, Soraia, Silva, Thales Philipe R, Simpson, Colin R, Singh, Abhinav, Singh, Balbir Bagicha, Singh, Baljinder, Singh, Garima, Singh, Harmanjit, Singh, Jasvinder A, Singh, Mahendra, Singh, Narinder Pal, Singh, Paramdeep, Singh, Surjit, Sinto, Robert, Sivakumar, Shravan, Siwal, Samarjeet Singh, Skhvitaridze, Natia, Skou, Søren T, Sleet, David A, Sobia, Farrukh, Soboka, Matiwos, Socea, Bogdan, Solaimanian, Shahabaddin, Solanki, Ranjan, Solanki, Shipra, Soliman, Sameh S M, Somayaji, Ranjani, Song, Yi, Sorensen, Reed J D, Soriano, Joan B, Soyiri, Ireneous N, Spartalis, Michael, Spearman, Sandra, Spencer, Cory N, Sreeramareddy, Chandrashekhar T, Stachteas, Panagiotis, Stafford, Lauryn K, Stanaway, Jeffrey D, Stanikzai, Muhammad Haroon, Stein, Caroline, Stein, Dan J, Steinbeis, Fridolin, Steiner, Caitlyn, Steinke, Sabine, Steiropoulos, Paschalis, Stockfelt, Leo, Stokes, Mark A, Straif, Kurt, Stranges, Saverio, Subedi, Narayan, Subramaniyan, Vetriselvan, Suleman, Muhammad, Suliankatchi Abdulkader, Rizwan, Sundström, Johan, Sunkersing, David, Sunnerhagen, Katharina S, Suresh, Vinay, Swain, Chandan Kumar, Szarpak, Lukasz, Szeto, Mindy D, Tabaee Damavandi, Payam, Tabarés-Seisdedos, Rafael, Tabatabaei, Seyyed Mohammad, Tabatabaei Malazy, Ozra, Tabatabaeizadeh, Seyed-Amir, Tabatabai, Shima, Tabche, Celine, Tabish, Mohammad, Tadakamadla, Santosh Kumar, Taheri Abkenar, Yasaman, Taheri Soodejani, Moslem, Taherkhani, Amir, Taiba, Jabeen, Takahashi, Ken, Talaat, Iman M, Tamuzi, Jacques Lukenze, Tan, Ker-Kan, Tang, Haosu, Tat, Nathan Y, Taveira, Nuno, Tefera, Yibekal Manaye, Tehrani-Banihashemi, Arash, Temesgen, Worku Animaw, Temsah, Mohamad-Hani, Teramoto, Masayuki, Terefa, Dufera Rikitu, Teye-Kwadjo, Enoch, Thakur, Ramna, Thangaraju, Pugazhenthan, Thankappan, Kavumpurathu Raman, Thapar, Rekha, Thayakaran, Rasiah, Thirunavukkarasu, Sathish, Thomas, Nihal, Thomas, Nikhil Kenny, Tian, Jing, Tichopad, Ales, Ticoalu, Jansje Henny Vera, Tiruye, Tenaw Yimer, Tobe-Gai, Ruoyan, Tolani, Musliu Adetola, Tolossa, Tadesse, Tonelli, Marcello, Topor-Madry, Roman, Topouzis, Fotis, Touvier, Mathilde, Tovani-Palone, Marcos Roberto, Trabelsi, Khaled, Tran, Jasmine T, Tran, Mai Thi Ngoc, Tran, Nghia Minh, Trico, Domenico, Trihandini, Indang, Troeger, Christopher E, Tromans, Samuel Joseph, Truyen, Thien Tan Tri Tai, Tsatsakis, Aristidis, Tsermpini, Evangelia Eirini, Tumurkhuu, Munkhtuya, Udoakang, Aniefiok John, Udoh, Arit, Ullah, Atta, Ullah, Saeed, Ullah, Sana, Umair, Muhammad, Umakanthan, Srikanth, Unim, Brigid, Unnikrishnan, Bhaskaran, Upadhyay, Era, Urso, Daniele, Usman, Jibrin Sammani, Vaithinathan, Asokan Govindaraj, Vakili, Omid, Valenti, Mario, Valizadeh, Rohollah, Van den Eynde, Jef, van Donkelaar, Aaron, Varga, Orsolya, Vart, Priya, Varthya, Shoban Babu, Vasankari, Tommi Juhani, Vasic, Milena, Vaziri, Siavash, Venketasubramanian, Narayanaswamy, Verghese, Nicholas Alexander, Verma, Madhur, Veroux, Massimiliano, Verras, Georgios-Ioannis, Vervoort, Dominique, Villafañe, Jorge Hugo, Villalobos-Daniel, Victor E, Villani, Leonardo, Villanueva, Gabriela Ines, Vinayak, Manish, Violante, Francesco S, Vlassov, Vasily, Vo, Bay, Vollset, Stein Emil, Volovat, Simona Ruxandra, Vos, Theo, Vujcic, Isidora S, Waheed, Yasir, Wang, Cong, Wang, Fang, Wang, Shu, Wang, Yanzhong, Wang, Yuan-Pang, Wanjau, Mary Njeri, Waqas, Muhammad, Ward, Paul, Waris, Abdul, Wassie, Emebet Gashaw, Weerakoon, Kosala Gayan, Weintraub, Robert G, Weiss, Daniel J, Weiss, Eli J, Weldetinsaa, Haftom Legese Legese, Wells, Katherine M, Wen, Yi Feng, Wiangkham, Taweewat, Wickramasinghe, Nuwan Darshana, Wilkerson, Caroline, Willeit, Peter, Wilson, Shadrach, Wong, Yen Jun, Wongsin, Utoomporn, Wozniak, Sarah, Wu, Chenkai, Wu, Dongze, Wu, Felicia, Wu, Zenghong, Xia, Juan, Xiao, Hong, Xu, Suowen, Xu, Xiaoyue, Xu, Yvonne Yiru, Yadav, Mukesh Kumar, Yaghoubi, Sajad, Yamagishi, Kazumasa, Yang, Lin, Yano, Yuichiro, Yaribeygi, Habib, Yasufuku, Yuichi, Ye, Pengpeng, Yesodharan, Renjulal, Yesuf, Subah Abderehim, Yezli, Saber, Yi, Siyan, Yiğit, Arzu, Yigzaw, Zeamanuel Anteneh, Yin, Dehui, Yip, Paul, Yismaw, Malede Berihun, Yon, Dong Keon, Yonemoto, Naohiro, You, Yuyi, Younis, Mustafa Z, Yousefi, Zabihollah, Yu, Chuanhua, Yu, Yong, Zadey, Siddhesh, Zadnik, Vesna, Zakham, Fathiah, Zaki, Nazar, Zakzuk, Josefina, Zamagni, Giulia, Zaman, Sojib Bin, Zandieh, Ghazal G Z, Zanghì, Aurora, Zar, Heather J, Zare, Iman, Zarimeidani, Fatemeh, Zastrozhin, Mikhail Sergeevich, Zeng, Youjie, Zhai, Chunxia, Zhang, Anthony Lin, Zhang, Haijun, Zhang, Liqun, Zhang, Meixin, Zhang, Yunquan, Zhang, Zhenyu, Zhang, Zhi-Jiang, Zhao, Hanqing, Zhao, Jeff T, Zhao, Xiu-Ju George, Zhao, Yang, Zhao, Yong, Zhong, Chenwen, Zhou, Jingjing, Zhou, Juexiao, Zhou, Shangcheng, Zhu, Bin, Zhu, Lei, Zhu, Zhaohua, Ziaeian, Boback, Ziafati, Makan, Zielińska, Magdalena, Zimsen, Stephanie R M, Zoghi, Ghazal, Zoller, Thomas, Zumla, Alimuddin, Zyoud, Sa'ed H, Zyoud, Samer H, Murray, Christopher J L, and Gakidou, Emmanuela
Published: 2024
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13. Examining PM2.5 concentrations and exposure using multiple models.

Author: Kelly, James T, Jang, Carey, Timin, Brian, Di, Qian, Schwartz, Joel, Liu, Yang, van Donkelaar, Aaron, Martin, Randall V, Berrocal, Veronica, and Bell, Michelle L
Subjects: Aerosols, Air Pollutants, Models, Statistical, Bayes Theorem, Air Pollution, Environmental Monitoring, Particulate Matter, Air quality modeling, Ensemble modeling, Exposure inequality, PM(2.5), Prevention, PM2.5, Chemical Sciences, Environmental Sciences, Biological Sciences, Toxicology
Abstract: Epidemiologic studies have found associations between fine particulate matter (PM2.5) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM2.5 concentration models (i.e., exposure models) that span a wide range of methods to investigate i) PM2.5 concentrations in 2011, ii) potential changes in PM2.5 concentrations between 2011 and 2028 due to on-the-books regulations, and iii) PM2.5 exposure for the U.S. population and four racial/ethnic groups. The exposure models included two geophysical chemical transport models (CTMs), two interpolation methods, a satellite-derived aerosol optical depth-based method, a Bayesian statistical regression model, and three data-rich machine learning methods. We focused on annual predictions that were regridded to 12-km resolution over the conterminous U.S., but also considered 1-km predictions in sensitivity analyses. The exposure models predicted broadly consistent PM2.5 concentrations, with relatively high concentrations on average over the eastern U.S. and greater variability in the western U.S. However, differences in national concentration distributions (median standard deviation: 1.00 μg m-3) and spatial distributions over urban areas were evident. Further exploration of these differences and their implications for specific applications would be valuable. PM2.5 concentrations were estimated to decrease by about 1 μg m-3 on average due to modeled emission changes between 2011 and 2028, with decreases of more than 3 μg m-3 in areas with relatively high 2011 concentrations that were projected to experience relatively large emission reductions. Agreement among models was closer for population-weighted than uniformly weighted averages across the domain. About 50% of the population was estimated to experience PM2.5 concentrations less than 10 μg m-3 in 2011 and PM2.5 improvements of about 2 μg m-3 due to modeled emission changes between 2011 and 2028. Two inequality metrics were used to characterize differences in exposure among the four racial/ethnic groups. The metrics generally yielded consistent information and suggest that the modeled emission reductions between 2011 and 2028 would reduce absolute exposure inequality on average.
Published: 2021

14. Comparison of P[M.sub.2.5] Air Pollution Exposures and Health Effects Associations Using 11 Different Modeling Approaches in the Women's Health Initiative Memory Study (WHIMS)

Author: Power, Melinda C., Bennett, Erin E., Lynch, Katie M., Stewart, James D., Xu, Xiaohui, Park, Eun Sug, Smith, Richard L., Vizuete, Will, Margolis, Helene G., Casanova, Ramon, Wallace, Robert, Sheppard, Lianne, Ying, Qi, Serre, Marc L., Szpiro, Adam A., Chen, Jiu-Chiuan, Liao, Duanping, Wellenius, Gregory A., van Donkelaar, Aaron, Yanosky, Jeff D., and Whitsel, Eric
Subjects: Women -- Health aspects, Medical research, Medicine, Experimental, Cardiovascular diseases -- Statistics -- Risk factors -- Demographic aspects, Air pollution -- Health aspects -- Statistics, Particles -- Health aspects -- Statistics, Environmental issues, Health
Abstract: Background: Many approaches to quantifying air pollution exposures have been developed. However, the impact of choice of approach on air pollution estimates and health-effects associations remains unclear. Objectives: Our objective is to compare particulate matter with aerodynamic diameter [less than or equal to]2.5 [micro]m (P[M.sub.2.5]) concentrations and resulting health effects associations using multiple estimation approaches previously used in epidemiologic analyses. Methods: We assigned annual P[M.sub.2.5] exposure estimates from 1999 to 2004 derived from 11 different approaches to Women's Health Initiative Memory Study (WHIMS) participant addresses within the contiguous US. Approaches included geostatistical interpolation approaches, land-use regression or spatiotemporal models, satellite-derived approaches, air dispersion and chemical transport models, and hybrid models. We used descriptive statistics and plots to assess relative and absolute agreement among exposure estimates and examined the impact of approach on associations between P[M.sub.2.5] and death due to natural causes, cardiovascular disease (CVD) mortality, and incident CVD events, adjusting for individual-level covariates and climate-based region. Results: With a few exceptions, relative agreement of approach-specific P[M.sub.2.5] exposure estimates was high for P[M.sub.2.5] concentrations across the contiguous US. Agreement among approach-specific exposure estimates was stronger near P[M.sub.2.5] monitors, in certain regions of the country, and in 2004 vs. 1999. Collectively, our results suggest but do not quantify lower agreement at local spatial scales for P[M.sub.2.5]. There was no evidence of large differences in health effects associations with P[M.sub.2.5] among estimation approaches in analyses adjusted for climate region. Conclusions: Different estimation approaches produced similar spatial patterns of P[M.sub.2.5] concentrations across the contiguous US and in areas with dense monitoring data, and P[M.sub.2.5]-health effects associations were similar among estimation approaches. P[M.sub.2.5] estimates and P[M.sub.2.5]-health effects associations may differ more in samples drawn from smaller areas or areas without substantial monitoring data, or in analyses with finer adjustment for participant location. Our results can inform decisions about P[M.sub.2.5] estimation approach in epidemiologic studies, as investigators balance concerns about bias, efficiency, and resource allocation. Future work is needed to understand whether these conclusions also apply in the context of other air pollutants of interest. https://doi.org/10.1289/EHP12995, Introduction Routine air pollution monitoring is primarily conducted for regulatory purposes. While existing monitor networks provide useful information on air pollution exposures, they do not necessarily provide the spatial resolution [...]
Published: 2024
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15. Assessment of the impact of discontinuity in satellite instruments and retrievals on global PM2.5 estimates

Author: Hammer, Melanie S., van Donkelaar, Aaron, Bindle, Liam, Sayer, Andrew M., Lee, Jaehwa, Hsu, N. Christina, Levy, Robert C., Sawyer, Virginia, Garay, Michael J., Kalashnikova, Olga V., Kahn, Ralph A., Lyapustin, Alexei, and Martin, Randall V.
Published: 2023
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16. A cross-sectional analysis of ambient fine particulate matter (PM2.5) exposure and haemoglobin levels in children aged under 5 years living in 36 countries

Author: Odo, Daniel B., Yang, Ian A., Dey, Sagnik, Hammer, Melanie S., van Donkelaar, Aaron, Martin, Randall V., Dong, Guang-Hui, Yang, Bo-Yi, Hystad, Perry, and Knibbs, Luke D.
Published: 2023
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17. Dust soiling effects on decentralized solar in West Africa

Author: Isaacs, Stewart, Kalashnikova, Olga, Garay, Michael J., van Donkelaar, Aaron, Hammer, Melanie S., Lee, Huikyo, and Wood, Danielle
Published: 2023
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18. Residential Greenness and Cardiovascular Disease Incidence, Readmission, and Mortality.

Author: Chen, Hong, Burnett, Richard T, Bai, Li, Kwong, Jeffrey C, Crouse, Dan L, Lavigne, Eric, Goldberg, Mark S, Copes, Ray, Benmarhnia, Tarik, Ilango, Sindana D, van Donkelaar, Aaron, Martin, Randall V, and Hystad, Perry
Subjects: Humans, Cardiovascular Diseases, Patient Readmission, Incidence, Proportional Hazards Models, Cohort Studies, Cities, Environmental Exposure, Residence Characteristics, Adult, Middle Aged, Ontario, Female, Male, Built Environment, Environmental Sciences, Medical and Health Sciences, Toxicology
Abstract: BackgroundLiving in greener areas of cities was linked to increased physical activity levels, improved mental well-being, and lowered harmful environmental exposures, all of which may affect human health. However, whether living in greener areas may be associated with lower risk of cardiovascular disease incidence, progression, and premature mortality is unclear.ObjectivesWe conducted a cohort study to examine the associations between residential green spaces and the incidence of acute myocardial infarction (AMI) and heart failure (HF), post-AMI and HF hospital readmissions, and mortality.MethodsWe simultaneously followed four large population-based cohorts in Ontario, Canada, including the entire adult population, adults free of AMI and HF, and survivors of AMI or HF from 2000 to 2014. We estimated residential exposure to green spaces using satellite-derived observations and ascertained health outcomes using validated disease registries. We estimated the associations using spatial random-effects Cox proportional hazards models. We conducted various sensitivity analyses, including further adjusting for property values and performing exploratory mediation analysis.ResultsEach interquartile range increase in residential greenness was associated with a 7% [95% confidence interval (CI): 4%, 9%] decrease in incident AMI and a 6% (95% CI: 4%, 7%) decrease in incident HF. Residential greenness was linked to a ∼10% decrease in cardiovascular mortality in both adults free of AMI and HF and the entire adult population. These associations remained consistent in sensitivity analyses and were accentuated among younger adults. Additionally, we estimated that the decreases in AMI and HF incidence associated with residential greenness explained ∼53% of the protective association between residential greenness and cardiovascular mortality. Conversely, residential greenness was not associated with any delay in readmission or mortality among AMI and HF patients.ConclusionsLiving in urban areas with more green spaces was associated with improved cardiovascular health in people free of AMI and HF but not among individuals who have already developed these conditions. https://doi.org/10.1289/EHP6161.
Published: 2020

19. The role of cardiovascular disease in the relationship between air pollution and incident dementia: a population-based cohort study

Author: Ilango, Sindana D, Chen, Hong, Hystad, Perry, van Donkelaar, Aaron, Kwong, Jeffrey C, Tu, Karen, Martin, Randall V, and Benmarhnia, Tarik
Subjects: Epidemiology, Health Sciences, Aging, Heart Disease, Acquired Cognitive Impairment, Cardiovascular, Clinical Research, Dementia, Brain Disorders, Climate-Related Exposures and Conditions, Prevention, Neurological, Good Health and Well Being, Aged, Air Pollutants, Air Pollution, Cardiovascular Diseases, Cohort Studies, Environmental Exposure, Female, Health Surveys, Humans, Male, Middle Aged, Ontario, Outcome Assessment, Health Care, Ozone, Particulate Matter, Population Surveillance, Air pollution, dementia, cardiovascular disease, mediation analysis, Statistics, Public Health and Health Services, Public health
Abstract: BackgroundEvidence suggests a link between air pollution and dementia. Cardiovascular disease (CVD) may be a potential determinant of dementia. This motivated us to quantify the contribution of CVD to the association between air pollution and dementia.MethodsA cohort of Canadian-born residents of Ontario, who participated in the 1996-2003 Canadian Community Health Surveys, was followed through 2013 or until dementia diagnosis. Exposure to nitrogen dioxide (NO2) and fine particulate matter (PM2.5) was estimated with a 3-year average and 5-year lag before dementia diagnosis. Incident CVD was evaluated as a mediator. We used multi-level Cox proportional and Aalen additive hazard regression models, adjusting for individual- and neighbourhood-level risk factors to estimate associations with NO2 and PM2.5. We estimated the total, direct and indirect effects of air pollution on dementia through cardiovascular disease.ResultsThis study included 34 391 older adults. At baseline, the mean age of this cohort was 59 years. The risk of dementia was moderately higher among those more exposed to NO2 (hazard ratio (HR) 1.10, 95% confidence interval (CI) 0.99-1.19; and 100 additional cases per 100 000 [standard error (SE)
Published: 2020

20. A cross-sectional analysis of long-term exposure to ambient air pollution and cognitive development in children aged 3–4 years living in 12 low- and middle-income countries

Author: Odo, Daniel B., Yang, Ian A., Dey, Sagnik, Hammer, Melanie S., van Donkelaar, Aaron, Martin, Randall V., Dong, Guang-Hui, Yang, Bo-Yi, Hystad, Perry, and Knibbs, Luke D.
Published: 2023
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21. Fine particulate matter composition in American Indian vs. Non-American Indian communities

Author: Li, Maggie, Do, Vivian, Brooks, Jada L., Hilpert, Markus, Goldsmith, Jeff, Chillrud, Steven N., Ali, Tauqeer, Best, Lyle G., Yracheta, Joseph, Umans, Jason G., van Donkelaar, Aaron, Martin, Randall V., Navas-Acien, Ana, and Kioumourtzoglou, Marianthi-Anna
Published: 2023
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22. Composition of fine particulate matter and risk of preterm birth: A nationwide birth cohort study in 336 Chinese cities

Author: He, Yuan, Jiang, Yixuan, Yang, Ying, Xu, Jihong, Zhang, Ya, Wang, Qiaomei, Shen, Haiping, Zhang, Yiping, Yan, Donghai, Peng, Zuoqi, Liu, Cong, Wang, Weidong, Schikowski, Tamara, Li, Huichu, Yan, Beizhan, Ji, John S., Chen, Aimin, van Donkelaar, Aaron, Martin, Randall, Chen, Renjie, Kan, Haidong, Cai, Jing, and Ma, Xu
Published: 2022
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23. Ambient air pollution and the risk of acute myocardial infarction and stroke: A national cohort study

Author: Olaniyan, Toyib, Pinault, Lauren, Li, Chi, van Donkelaar, Aaron, Meng, Jun, Martin, Randall V., Hystad, Perry, Robichaud, Alain, Ménard, Richard, Tjepkema, Michael, Bai, Li, Kwong, Jeffrey C., Lavigne, Eric, Burnett, Richard T., and Chen, Hong
Published: 2022
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24. Importance of aerosol composition and aerosol vertical profiles in global spatial variation in the relationship between PM2.5 and aerosol optical depth.

Author: Zhu, Haihui, Martin, Randall V., van Donkelaar, Aaron, Hammer, Melanie S., Li, Chi, Meng, Jun, Oxford, Christopher R., Liu, Xuan, Li, Yanshun, Zhang, Dandan, Singh, Inderjeet, and Lyapustin, Alexei
Abstract: Ambient fine particulate matter (PM2.5) is the leading global environmental determinant of mortality. However, large gaps exist in ground-based PM2.5 monitoring. Satellite remote sensing of aerosol optical depth (AOD) offers information to help fill these gaps worldwide when augmented with a modeled PM2.5 –AOD relationship. This study aims to understand the spatial pattern and driving factors of this relationship by examining η (PM2.5AOD) using both observations and modeling. A global observational estimate of η for the year 2019 is inferred from 6870 ground-based PM2.5 measurement sites and satellite-retrieved AOD. The global chemical transport model GEOS-Chem, in its high-performance configuration (GCHP), is used to interpret the observed spatial pattern of annual mean η. Measurements and the GCHP simulation consistently identify a global population-weighted mean η value of 96–98 µgm-3 , with regional values ranging from 59.8 µgm-3 in North America to more than 190 µgm-3 in Africa. The highest η value is found in arid regions, where aerosols are less hygroscopic due to mineral dust, followed by regions strongly influenced by surface aerosol sources. Relatively low η values are found over regions distant from strong aerosol sources. The spatial correlation of observed η values with meteorological fields, aerosol vertical profiles, and aerosol chemical composition reveals that spatial variation in η is strongly influenced by aerosol composition and aerosol vertical profiles. Sensitivity tests with globally uniform parameters quantify the effects of aerosol composition and aerosol vertical profiles on spatial variability in η , exhibiting a population-weighted mean difference in aerosol composition of 12.3 µgm-3 , which reflects the determinant effects of composition on aerosol hygroscopicity and aerosol optical properties, and a population-weighted mean difference in the aerosol vertical profile of 8.4 µgm-3 , which reflects spatial variation in the column–surface relationship. [ABSTRACT FROM AUTHOR]
Published: 2024
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25. Toward Fine Horizontal Resolution Global Simulations of Aerosol Sectional Microphysics: Advances Enabled by GCHP‐TOMAS.

Author: Croft, Betty, Martin, Randall V., Chang, Rachel Y.‐W., Bindle, Liam, Eastham, Sebastian D., Estrada, Lucas A., Ford, Bonne, Li, Chi, Long, Michael S., Lundgren, Elizabeth W., Sinha, Saptarshi, Sulprizio, Melissa P., Tang, Yidan, van Donkelaar, Aaron, Yantosca, Robert M., Zhang, Dandan, Zhu, Haihui, and Pierce, Jeffrey R.
Subjects: AIR quality, ATMOSPHERIC composition, COLUMNS, MICROPHYSICS, AEROSOLS, TROPOSPHERIC aerosols
Abstract: Global modeling of aerosol‐particle number and size is important for understanding aerosol effects on Earth's climate and air quality. Fine‐resolution global models are desirable for representing nonlinear aerosol‐microphysical processes, their nonlinear interactions with dynamics and chemistry, and spatial heterogeneity. However, aerosol‐microphysical simulations are computationally demanding, which can limit the achievable global horizontal resolution. Here, we present the first coupling of the TwO‐Moment Aerosol Sectional (TOMAS) microphysics scheme with the High‐Performance configuration of the GEOS‐Chem model of atmospheric composition (GCHP), a coupling termed GCHP‐TOMAS. GCHP's architecture allows massively parallel GCHP‐TOMAS simulations including on the cloud, using hundreds of computing cores, faster runtimes, more memory, and finer global horizontal resolution (e.g., 25 km × 25 km, 7.8 × 105 model columns) versus the previous single‐node capability of GEOS‐Chem‐TOMAS (tens of cores, 200 km × 250 km, 1.3 × 104 model columns). GCHP‐TOMAS runtimes have near‐ideal scalability with computing‐core number. Simulated global‐mean number concentrations increase (dominated by free‐tropospheric over‐ocean sub‐10‐nm‐diameter particles) toward finer GCHP‐TOMAS horizontal resolution. Increasing the horizontal resolution from 200 km × 200–50 km × 50 km increases the global monthly mean free‐tropospheric total particle number by 18.5%, and over‐ocean sub‐10‐nm‐diameter particles by 39.8% at 4‐km altitude. With a cascade of contributing factors, free‐tropospheric particle‐precursor concentrations increase (32.6% at 4‐km altitude) with resolution, promoting new‐particle formation and growth that outweigh coagulation changes. These nonlinear effects have the potential to revise current understanding of processes controlling global aerosol number and aerosol impacts on Earth's climate and air quality. Plain Language Summary: Small particles in the air have important effects on Earth's climate and air quality. Representing the number and size of these particles in global models is challenging because their processes are complex. This factor has often limited global‐model horizontal resolution because fine global resolution models (e.g., 25 km × 25 km or smaller) generally ran too slowly but would be useful for representing details missed at traditional coarse resolution (e.g., 200 km × 250 km). We start with a detailed particle scheme that previously only ran at coarse global resolution because fine resolution would take too long. We present the initial use of this scheme in an updated model version, with a structure allowing a fast‐running, high‐memory model with fine resolution, by using hundreds to thousands of computer cores. In the updated structure, model speed increases with the number of cores used. We find that the total number of particles in the model is more with fine compared to coarse model resolution. These increases are most in Earth's remote regions and for particles which come from gas. Using fine model resolution globally when representing particles could change our understanding of how they impact Earth's climate and air quality. Key Points: We couple aerosol microphysics with GEOS‐Chem's High‐Performance configuration for fine (25 km × 25 km) global‐resolution capabilityGlobal‐mean aerosol number increases with model resolution, dominated by particles smaller than 10 nm in the over‐ocean free troposphereToward finer horizontal resolution, enhanced particle precursor loading in the free troposphere promotes particle formation and growth [ABSTRACT FROM AUTHOR]
Published: 2024
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26. Global urban temporal trends in fine particulate matter (PM2·5) and attributable health burdens: estimates from global datasets

Author: Southerland, Veronica A, Brauer, Michael, Mohegh, Arash, Hammer, Melanie S, van Donkelaar, Aaron, Martin, Randall V, Apte, Joshua S, and Anenberg, Susan C
Published: 2022
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27. Long-term exposure to PM2.5 major components and mortality in the southeastern United States

Author: Wang, Yifan, Xiao, Siyao, Zhang, Yuhan, Chang, Howard, Martin, Randall V., Van Donkelaar, Aaron, Gaskins, Audrey, Liu, Yang, Liu, Pengfei, and Shi, Liuhua
Published: 2022
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28. Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.

Author: Burnett, Richard, Chen, Hong, Szyszkowicz, Mieczysław, Fann, Neal, Hubbell, Bryan, Pope, C Arden, Apte, Joshua S, Brauer, Michael, Cohen, Aaron, Weichenthal, Scott, Coggins, Jay, Di, Qian, Brunekreef, Bert, Frostad, Joseph, Lim, Stephen S, Kan, Haidong, Walker, Katherine D, Thurston, George D, Hayes, Richard B, Lim, Chris C, Turner, Michelle C, Jerrett, Michael, Krewski, Daniel, Gapstur, Susan M, Diver, W Ryan, Ostro, Bart, Goldberg, Debbie, Crouse, Daniel L, Martin, Randall V, Peters, Paul, Pinault, Lauren, Tjepkema, Michael, van Donkelaar, Aaron, Villeneuve, Paul J, Miller, Anthony B, Yin, Peng, Zhou, Maigeng, Wang, Lijun, Janssen, Nicole AH, Marra, Marten, Atkinson, Richard W, Tsang, Hilda, Quoc Thach, Thuan, Cannon, John B, Allen, Ryan T, Hart, Jaime E, Laden, Francine, Cesaroni, Giulia, Forastiere, Francesco, Weinmayr, Gudrun, Jaensch, Andrea, Nagel, Gabriele, Concin, Hans, and Spadaro, Joseph V
Subjects: Humans, Air Pollutants, Proportional Hazards Models, Bayes Theorem, Risk Assessment, Cohort Studies, Air Pollution, Environmental Exposure, Time Factors, Particulate Matter, Global Health, Global Burden of Disease, Noncommunicable Diseases, concentration, exposure, fine particulate matter, mortality, risk
Abstract: Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations.
Published: 2018

29. Long-term exposure to ambient ultrafine particles and respiratory disease incidence in in Toronto, Canada: a cohort study

Author: Weichenthal, Scott, Bai, Li, Hatzopoulou, Marianne, Van Ryswyk, Keith, Kwong, Jeffrey C, Jerrett, Michael, van Donkelaar, Aaron, Martin, Randall V, Burnett, Richard T, Lu, Hong, and Chen, Hong
Subjects: Epidemiology, Health Sciences, Clinical Research, Chronic Obstructive Pulmonary Disease, Lung, Climate-Related Exposures and Conditions, Cancer, 2.2 Factors relating to the physical environment, Aetiology, Respiratory, Life on Land, Adult, Aged, Air Pollutants, Cohort Studies, Environmental Exposure, Female, Humans, Incidence, Male, Middle Aged, Nitrogen Dioxide, Ontario, Particle Size, Particulate Matter, Respiratory Tract Diseases, Ultrafine particles, Cohort study, Asthma, Copd, Lung cancer, Public Health and Health Services, Toxicology, Public health
Abstract: BackgroundLittle is known about the long-term health effects of ambient ultrafine particles (
Published: 2017

30. Understanding Reductions of PM2.5 Concentration and Its Chemical Composition in the United States: Implications for Mitigation Strategies

Author: Li, Chi, primary, Martin, Randall V., additional, and van Donkelaar, Aaron, additional
Published: 2024
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31. Quantifying Prescribed‐Fire Smoke Exposure Using Low‐Cost Sensors and Satellites: Springtime Burning in Eastern Kansas

Author: Sablan, Olivia, primary, Ford, Bonne, additional, Gargulinski, Emily, additional, Hammer, Melanie S., additional, Henery, Giovanna, additional, Kondragunta, Shobha, additional, Martin, Randall V., additional, Rosen, Zoey, additional, Slater, Kellin, additional, van Donkelaar, Aaron, additional, Zhang, Hai, additional, Soja, Amber J., additional, Magzamen, Sheryl, additional, Pierce, Jeffrey R., additional, and Fischer, Emily V., additional
Published: 2024
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32. Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning

Author: Shen, Siyuan, primary, Li, Chi, additional, van Donkelaar, Aaron, additional, Jacobs, Nathan, additional, Wang, Chenguang, additional, and Martin, Randall V., additional
Published: 2024
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33. The prospective effects of long-term exposure to ambient PM2.5 and constituents on mortality in rural East China

Author: Chen, Yun, Chen, Renjie, Chen, Yue, Dong, Xiaolian, Zhu, Jianfu, Liu, Cong, van Donkelaar, Aaron, Martin, Randall V., Li, Huichu, Kan, Haidong, Jiang, Qingwu, and Fu, Chaowei
Published: 2021
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34. Data Integration Model for Air Quality: A Hierarchical Approach to the Global Estimation of Exposures to Ambient Air Pollution

Author: Shaddick, Gavin, Thomas, Matthew L., Jobling, Amelia, Brauer, Michael, van Donkelaar, Aaron, Burnett, Rick, Chang, Howard, Cohen, Aaron, Van Dingenen, Rita, Dora, Carlos, Gumy, Sophie, Liu, Yang, Martin, Randall, Waller, Lance A., West, Jason, Zidek, James V., and Prüss-Ustün, Annette
Subjects: Statistics - Applications
Abstract: Air pollution is a major risk factor for global health, with both ambient and household air pollution contributing substantial components of the overall global disease burden. One of the key drivers of adverse health effects is fine particulate matter ambient pollution (PM$_{2.5}$) to which an estimated 3 million deaths can be attributed annually. The primary source of information for estimating exposures has been measurements from ground monitoring networks but, although coverage is increasing, there remain regions in which monitoring is limited. Ground monitoring data therefore needs to be supplemented with information from other sources, such as satellite retrievals of aerosol optical depth and chemical transport models. A hierarchical modelling approach for integrating data from multiple sources is proposed allowing spatially-varying relationships between ground measurements and other factors that estimate air quality. Set within a Bayesian framework, the resulting Data Integration Model for Air Quality (DIMAQ) is used to estimate exposures, together with associated measures of uncertainty, on a high resolution grid covering the entire world. Bayesian analysis on this scale can be computationally challenging and here approximate Bayesian inference is performed using Integrated Nested Laplace Approximations. Model selection and assessment is performed by cross-validation with the final model offering substantial increases in predictive accuracy, particularly in regions where there is sparse ground monitoring, when compared to current approaches: root mean square error (RMSE) reduced from 17.1 to 10.7, and population weighted RMSE from 23.1 to 12.1 $\mu$gm$^{-3}$. Based on summaries of the posterior distributions for each grid cell, it is estimated that 92% of the world's population reside in areas exceeding the World Health Organization's Air Quality Guidelines., Comment: 23 pages, 9 figures, 2 tables
Published: 2016

35. Comparing the Health Effects of Ambient Particulate Matter Estimated Using Ground-Based versus Remote Sensing Exposure Estimates

Author: Jerrett, Michael, Turner, Michelle C, Beckerman, Bernardo S, Pope, C Arden, van Donkelaar, Aaron, Martin, Randall V, Serre, Marc, Crouse, Dan, Gapstur, Susan M, Krewski, Daniel, Diver, W Ryan, Coogan, Patricia F, Thurston, George D, and Burnett, Richard T
Subjects: Epidemiology, Environmental Sciences, Pollution and Contamination, Health Sciences, Climate-Related Exposures and Conditions, Prevention, 2.2 Factors relating to the physical environment, Aetiology, Good Health and Well Being, Life on Land, Air Pollutants, Air Pollution, Environmental Exposure, Health Status, Humans, Models, Theoretical, Particulate Matter, Remote Sensing Technology, Risk Assessment, Medical and Health Sciences, Toxicology, Biomedical and clinical sciences, Environmental sciences, Health sciences
Abstract: BackgroundRemote sensing (RS) is increasingly used for exposure assessment in epidemiological and burden of disease studies, including those investigating whether chronic exposure to ambient fine particulate matter (PM2.5) is associated with mortality.ObjectivesWe compared relative risk estimates of mortality from diseases of the circulatory system for PM2.5 modeled from RS with that for PM2.5 modeled using ground-level information.MethodsWe geocoded the baseline residence of 668,629 American Cancer Society Cancer Prevention Study II (CPS-II) cohort participants followed from 1982 to 2004 and assigned PM2.5 levels to all participants using seven different exposure models. Most of the exposure models were averaged for the years 2002-2004, and one RS estimate was for a longer, contemporaneous period. We used Cox proportional hazards regression to estimate relative risks (RRs) for the association of PM2.5 with circulatory mortality and ischemic heart disease.ResultsEstimates of mortality risk differed among exposure models. The smallest relative risk was observed for the RS estimates that excluded ground-based monitors for circulatory deaths [RR = 1.02, 95% confidence interval (CI): 1.00, 1.04 per 10 μg/m3 increment in PM2.5]. The largest relative risk was observed for the land-use regression model that included traffic information (RR = 1.14, 95% CI: 1.11, 1.17 per 10 μg/m3 increment in PM2.5).ConclusionsWe found significant associations between PM2.5 and mortality in every model; however, relative risks estimated from exposure models using ground-based information were generally larger than those estimated using RS alone.
Published: 2017

36. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Author: Brauer, M, Roth, G, Aravkin, A, Zheng, P, Abate, K, Abate, Y, Abbafati, C, Abbasgholizadeh, R, Abbasi, M, Abbasian, M, Abbasifard, M, Abbasi-Kangevari, M, Abd ElHafeez, S, Abd-Elsalam, S, Abdi, P, Abdollahi, M, Abdoun, M, Abdulah, D, Abdullahi, A, Abebe, M, Abedi, A, Abegaz, T, Abeldaño Zuñiga, R, Abiodun, O, Abiso, T, Aboagye, R, Abolhassani, H, Abouzid, M, Aboye, G, Abreu, L, Abualruz, H, Abubakar, B, Abu-Gharbieh, E, Abukhadijah, H, Aburuz, S, Abu-Zaid, A, Adane, M, Addo, I, Addolorato, G, Adedoyin, R, Adekanmbi, V, Aden, B, Adetunji, J, Adeyeoluwa, T, Adha, R, Adibi, A, Adnani, Q, Adzigbli, L, Afolabi, A, Afolabi, R, Afshin, A, Afyouni, S, Afzal, M, Afzal, S, Agampodi, S, Agbozo, F, Aghamiri, S, Agodi, A, Agrawal, A, Agyemang-Duah, W, Ahinkorah, B, Ahmad, A, Ahmad, D, Ahmad, F, Ahmad, N, Ahmad, S, Ahmad, T, Ahmed, A, Ahmed, L, Ahmed, M, Ahmed, S, Ajami, M, Akalu, G, Akara, E, Akbarialiabad, H, Akhlaghi, S, Akinosoglou, K, Akinyemiju, T, Akkaif, M, Akkala, S, Akombi-Inyang, B, Al Awaidy, S, Al Hasan, S, Alahdab, F, AL-Ahdal, T, Alalalmeh, S, Alalwan, T, Al-Aly, Z, Alam, K, Alam, N, Alanezi, F, Alanzi, T, Albakri, A, Albataineh, M, Aldhaleei, W, Aldridge, R, Alemayohu, M, Alemu, Y, Al-Fatly, B, Al-Gheethi, A, Al-Habbal, K, Alhabib, K, Alhassan, R, Ali, A, Ali, B, Ali, I, Ali, L, Ali, M, Ali, R, Ali, S, Ali, W, Alicandro, G, Alif, S, Aljunid, S, Alla, F, Al-Marwani, S, Al-Mekhlafi, H, Almustanyir, S, Alomari, M, Alonso, J, Alqahtani, J, Alqutaibi, A, Al-Raddadi, R, Alrawashdeh, A, Al-Rifai, R, Alrousan, S, Al-Sabah, S, Alshahrani, N, Altaany, Z, Altaf, A, Al-Tawfiq, J, Altirkawi, K, Aluh, D, Alvis-Guzman, N, Alvis-Zakzuk, N, Alwafi, H, Al-Wardat, M, Al-Worafi, Y, Aly, H, Aly, S, Alzoubi, K, Al-Zyoud, W, Amaechi, U, Aman Mohammadi, M, Amani, R, Amiri, S, Amirzade-Iranaq, M, Ammirati, E, Amu, H, Amugsi, D, Amusa, G, Ancuceanu, R, Anderlini, D, Anderson, J, Andrade, P, Andrei, C, Andrei, T, Anenberg, S, Angappan, D, Angus, C, Anil, A, Anil, S, Anjum, A, Anoushiravani, A, Antonazzo, I, Antony, C, Antriyandarti, E, Anuoluwa, B, Anvari, D, Anvari, S, Anwar, S, Anwer, R, Anyabolo, E, Anyasodor, A, Apostol, G, Arabloo, J, Arabzadeh Bahri, R, Arafat, M, Areda, D, Aregawi, B, Aremu, A, Armocida, B, Arndt, M, Ärnlöv, J, Arooj, M, Artamonov, A, Artanti, K, Aruleba, I, Arumugam, A, Asbeutah, A, Asgary, S, Asgedom, A, Ashbaugh, C, Ashemo, M, Ashraf, T, Askarinejad, A, Assmus, M, Astell-Burt, T, Athar, M, Athari, S, Atorkey, P, Atreya, A, Aujayeb, A, Ausloos, M, Avila-Burgos, L, Awoke, A, Ayala Quintanilla, B, Ayatollahi, H, Ayestas Portugal, C, Ayuso-Mateos, J, Azadnajafabad, S, Azevedo, R, Azhar, G, Azizi, H, Azzam, A, Backhaus, I, Badar, M, Badiye, A, Bagga, A, Baghdadi, S, Bagheri, N, Bagherieh, S, Bahrami Taghanaki, P, Bai, R, Baig, A, Baker, J, Bakkannavar, S, Balasubramanian, M, Baltatu, O, Bam, K, Bandyopadhyay, S, Banik, B, Banik, P, Banke-Thomas, A, Bansal, H, Barchitta, M, Bardhan, M, Bardideh, E, Barker-Collo, S, Bärnighausen, T, Barone-Adesi, F, Barqawi, H, Barrero, L, Barrow, A, Barteit, S, Basharat, Z, Basiru, A, Basso, J, Bastan, M, Basu, S, Batchu, S, Batra, K, Batra, R, Baune, B, Bayati, M, Bayileyegn, N, Beaney, T, Behnoush, A, Beiranvand, M, Béjot, Y, Bekele, A, Belgaumi, U, Bell, A, Bell, M, Bello, M, Bello, O, Belo, L, Beloukas, A, Bendak, S, Bennett, D, Bennitt, F, Bensenor, I, Benzian, H, Beran, A, Berezvai, Z, Bernabe, E, Bernstein, R, Bettencourt, P, Bhagavathula, A, Bhala, N, Bhandari, D, Bhardwaj, N, Bhardwaj, P, Bhaskar, S, Bhat, A, Bhat, V, Bhatti, G, Bhatti, J, Bhatti, M, Bhatti, R, Bhuiyan, M, Bhutta, Z, Bikbov, B, Bishai, J, Bisignano, C, Biswas, A, Biswas, B, Biswas, R, Bjørge, T, Boachie, M, Boakye, H, Bockarie, M, Bodolica, V, Bodunrin, A, Bogale, E, Bolla, S, Boloor, A, Bonakdar Hashemi, M, Boppana, S, Bora Basara, B, Borhany, H, Botero Carvajal, A, Bouaoud, S, Boufous, S, Bourne, R, Boxe, C, Braithwaite, D, Brant, L, Brar, A, Breitborde, N, Breitner, S, Brenner, H, 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Pandey, Ashok, Pandi-Perumal, Seithikurippu R, Pangaribuan, Helena Ullyartha, Pantazopoulos, Ioannis, Pantea Stoian, Anca Mihaela, Papadopoulou, Paraskevi, Parent, Marie C, Parija, Pragyan Paramita, Parikh, Romil R, Park, Seoyeon, Park, Sungchul, Parsons, Nicholas, Pashaei, Ava, Pasovic, Maja, Passera, Roberto, Patil, Shankargouda, Patoulias, Dimitrios, Patthipati, Venkata Suresh, Paudel, Uttam, Pawar, Shrikant, Pazoki Toroudi, Hamidreza, Peden, Amy E, Pedersini, Paolo, Peng, Minjin, Pensato, Umberto, Pepito, Veincent Christian Filipino, Peprah, Emmanuel K, Peprah, Prince, Peres, Mario F P, Perianayagam, Arokiasamy, Perico, Norberto, Perna, Simone, Pesudovs, Konrad, Petcu, Ionela-Roxana, Petermann-Rocha, Fanny Emily, Pham, Hoang Tran, Philip, Anil K, Phillips, Michael R, Pickering, Brandon V, Pierannunzio, Daniela, Pigeolet, Manon, Pigott, David M, Piracha, Zahra Zahid, Piradov, Michael A, Pisoni, Enrico, Piyasena, Mapa Prabhath, Plass, Dietrich, Plotnikov, Evgenii, Poddighe, Dimitri, Polkinghorne, Kevan R, Poluru, Ramesh, Pond, Constance Dimity, Popovic, Djordje S, Porru, Fabio, Postma, Maarten J, Poudel, Govinda Raj, Pour-Rashidi, Ahmad, Pourshams, Akram, Pourtaheri, Naeimeh, Prabhu, Disha, Prada, Sergio I, Pradhan, Jalandhar, Pradhan, Pranil Man Singh, Prasad, Manya, Prates, Elton Junio Sady, Purnobasuki, Hery, Purohit, Bharathi M, Puvvula, Jagadeesh, Qasim, Nameer Hashim, Qattea, Ibrahim, Qazi, Asma Saleem, Qian, Gangzhen, Qiu, Suli, Rabiee Rad, Mehrdad, Radfar, Amir, Radhakrishnan, Raghu Anekal, Radhakrishnan, Venkatraman, Raeisi Shahraki, Hadi, Rafferty, Quinn, Rafiei, Alireza, Raggi, Alberto, Raghav, Pankaja Raghav, Raheem, Nasiru, Rahim, Fakher, Rahim, Md Jillur, Rahimifard, Mahban, Rahimi-Movaghar, Vafa, Rahman, Md Obaidur, Rahman, Muhammad Aziz, Rahmani, Amir Masoud, Rahmani, Bita, Rahmanian, Mohammad, Rahmanian, Nazanin, Rahmanian, Vahid, Rahmati, Masoud, Rahmawaty, Setyaningrum, Raimondo, Diego, Rajaa, Sathish, Rajendran, Vinoth, Rajput, Prashant, Ramadan, Mahmoud Mohammed, Ramasamy, Shakthi Kumaran, Ramasubramani, Premkumar, Ramazanu, Sheena, Ramteke, Pramod W, Rana, Juwel, Rana, Kritika, Ranabhat, Chhabi Lal, Rane, Amey, Rani, Usha, Ranta, Annemarei, Rao, Chythra R, Rao, Mithun, Rao, Puja C, Rao, Sowmya J, Rasella, Davide, Rashedi, Sina, Rashedi, Vahid, Rashidi, Mahsa, Rashidi, Mohammad-Mahdi, Rasouli-Saravani, Ashkan, Ratan, Zubair Ahmed, Rathnaiah Babu, Giridhara, Rauniyar, Santosh Kumar, Rautalin, Ilari, Rawaf, David Laith, Rawaf, Salman, Rawassizadeh, Reza, Razo, Christian, Reda, Zinabu Ferede Ferede, Reddy, Murali Mohan Rama Krishna, Redwan, Elrashdy Moustafa Mohamed, Reifels, Lennart, Reitsma, Marissa B, Remuzzi, Giuseppe, Reshmi, Bhageerathy, Resnikoff, Serge, Restaino, Stefano, Reyes, Luis Felipe, Rezaei, Maryam, Rezaei, Nazila, Rezaei, Negar, Rezaeian, Mohsen, Rhee, Taeho Gregory, Riaz, Mavra A, Ribeiro, Antonio Luiz P, Rickard, Jennifer, Robinson-Oden, Hannah Elizabeth, Rodrigues, Célia Fortuna, Rodrigues, Mónica, Rodriguez, Jefferson Antonio Buendia, Roever, Leonardo, Romadlon, Debby Syahru, Ronfani, Luca, Rosauer, Jennifer Jacqueline, Roshandel, Gholamreza, Rostamian, Morteza, Rotimi, Kunle, Rout, Himanshu Sekhar, Roy, Bedanta, Roy, Nitai, Rubagotti, Enrico, Ruela, Guilherme de Andrade, Rumisha, Susan Fred, Runghien, Tilleye, Russo, Michele, Ruzzante, Sacha Walde, S N, Chandan, Saad, Aly M A, Saber, Korosh, Saber-Ayad, Maha Mohamed, Sabour, Siamak, Sacco, Simona, Sachdev, Perminder S, Sachdeva, Rajesh, Saddik, Basema, Saddler, Adam, Sadee, Bashdar Abuzed, Sadeghi, Ehsan, Sadeghi, Masoumeh, Sadeghi Majd, Elham, Saeb, Mohammad Reza, Saeed, Umar, Safari, Mehdi, Safi, Sare, Safi, Sher Zaman, Sagar, Rajesh, Sagoe, Dominic, Saheb Sharif-Askari, Fatemeh, Saheb Sharif-Askari, Narjes, Sahebkar, Amirhossein, Sahoo, Soumya Swaroop, Sahu, Monalisha, Saif, Zahra, Sajid, Mirza Rizwan, Sakshaug, Joseph W, Salam, Nasir, Salamati, Payman, Salami, Afeez Abolarinwa, Salaroli, Luciane B, Salehi, Leili, Salehi, Sana, Salem, Marwa Rashad, Salem, Mohammed Z Y, Salihu, Dauda, Salimi, Sohrab, Salum, Giovanni A, Samadi Kafil, Hossein, Samadzadeh, Sara, Samodra, Yoseph Leonardo, Samuel, Vijaya Paul, Samy, Abdallah M, Sanabria, Juan, Sanjeev, Rama Krishna, Sanna, Francesca, Santomauro, Damian Francesco, Santric-Milicevic, Milena M, Sarasmita, Made Ary, Saraswathy, Sivan Yegnanarayana Iyer, Saravanan, Aswini, Saravi, Babak, Sarikhani, Yaser, Sarmiento-Suárez, Rodrigo, Sarode, Gargi Sachin, Sarode, Sachin C, Sartorius, Benn, Sarveazad, Arash, Sathian, Brijesh, Sattin, Davide, Sawhney, Monika, Saya, Ganesh Kumar, Sayeed, Abu, Sayeed, Md Abu, Sayyah, Mehdi, Schinckus, Christophe, Schmidt, Maria Inês, Schuermans, Art, Schumacher, Austin E, Schutte, Aletta Elisabeth, Schwarzinger, Michaël, Schwebel, David C, Schwendicke, Falk, Selvaraj, Siddharthan, Semreen, Mohammad H, Senthilkumaran, Subramanian, Serban, Dragos, Serre, Marc L, Sethi, Yashendra, Shafie, Mahan, Shah, Humaira, Shah, Nilay S, Shah, Pritik A, Shah, Syed Mahboob, Shahbandi, Ataollah, Shaheen, Amira A, Shahid, Samiah, Shahid, Wajeehah, Shahsavari, Hamid R, Shahwan, Moyad Jamal, Shaikh, Masood Ali, Shaikh, Summaiya Zareen, Shalash, Ali S, Sham, Sunder, Shamim, Muhammad Aaqib, Shams-Beyranvand, Mehran, Shamshirgaran, Mohammad Ali, Shamsi, Mohammad Anas, Shanawaz, Mohd, Shankar, Abhishek, Sharfaei, Sadaf, Sharifan, Amin, Sharifi-Rad, Javad, Sharma, Manoj, Sharma, Ujjawal, Sharma, Vishal, Shastry, Rajesh P, Shavandi, Amin, Shehabeldine, Amr Mohamed Elsayed, Shehzadi, Somia, Sheikh, Aziz, Shen, Jiabin, Shetty, Adithi, Shetty, B Suresh Kumar, Shetty, Pavanchand H, Shiani, Amir, Shiferaw, Desalegn, Shigematsu, Mika, Shin, Min-Jeong, Shiri, Rahman, Shittu, Aminu, Shiue, Ivy, Shivakumar, K M, Shivarov, Velizar, Shool, Sina, Shorofi, Seyed Afshin, Shrestha, Rajan, Shrestha, Sunil, Shuja, Kanwar Hamza, Shuval, Kerem, Si, Yafei, Siddig, Emmanuel Edwar, Silva, Diego Augusto Santos, Silva, Luís Manuel Lopes Rodrigues, Silva, Soraia, Silva, Thales Philipe R, Simpson, Colin R, Singh, Abhinav, Singh, Balbir Bagicha, Singh, Baljinder, Singh, Garima, Singh, Harmanjit, Singh, Jasvinder A, Singh, Mahendra, Singh, Narinder Pal, Singh, Paramdeep, Singh, Surjit, Sinto, Robert, Sivakumar, Shravan, Siwal, Samarjeet Singh, Skhvitaridze, Natia, Skou, Søren T, Sleet, David A, Sobia, Farrukh, Soboka, Matiwos, Socea, Bogdan, Solaimanian, Shahabaddin, Solanki, Ranjan, Solanki, Shipra, Soliman, Sameh S M, Somayaji, Ranjani, Song, Yi, Sorensen, Reed J D, Soriano, Joan B, Soyiri, Ireneous N, Spartalis, Michael, Spearman, Sandra, Spencer, Cory N, Sreeramareddy, Chandrashekhar T, Stachteas, Panagiotis, Stafford, Lauryn K, Stanaway, Jeffrey D, Stanikzai, Muhammad Haroon, Stein, Caroline, Stein, Dan J, Steinbeis, Fridolin, Steiner, Caitlyn, Steinke, Sabine, Steiropoulos, Paschalis, Stockfelt, Leo, Stokes, Mark A, Straif, Kurt, Stranges, Saverio, Subedi, Narayan, Subramaniyan, Vetriselvan, Suleman, Muhammad, Suliankatchi Abdulkader, Rizwan, Sundström, Johan, Sunkersing, David, Sunnerhagen, Katharina S, Suresh, Vinay, Swain, Chandan Kumar, Szarpak, Lukasz, Szeto, Mindy D, Tabaee Damavandi, Payam, Tabarés-Seisdedos, Rafael, Tabatabaei, Seyyed Mohammad, Tabatabaei Malazy, Ozra, Tabatabaeizadeh, Seyed-Amir, Tabatabai, Shima, Tabche, Celine, Tabish, Mohammad, Tadakamadla, Santosh Kumar, Taheri Abkenar, Yasaman, Taheri Soodejani, Moslem, Taherkhani, Amir, Taiba, Jabeen, Takahashi, Ken, Talaat, Iman M, Tamuzi, Jacques Lukenze, Tan, Ker-Kan, Tang, Haosu, Tat, Nathan Y, Taveira, Nuno, Tefera, Yibekal Manaye, Tehrani-Banihashemi, Arash, Temesgen, Worku Animaw, Temsah, Mohamad-Hani, Teramoto, Masayuki, Terefa, Dufera Rikitu, Teye-Kwadjo, Enoch, Thakur, Ramna, Thangaraju, Pugazhenthan, Thankappan, Kavumpurathu Raman, Thapar, Rekha, Thayakaran, Rasiah, Thirunavukkarasu, Sathish, Thomas, Nihal, Thomas, Nikhil Kenny, Tian, Jing, Tichopad, Ales, Ticoalu, Jansje Henny Vera, Tiruye, Tenaw Yimer, Tobe-Gai, Ruoyan, Tolani, Musliu Adetola, Tolossa, Tadesse, Tonelli, Marcello, Topor-Madry, Roman, Topouzis, Fotis, Touvier, Mathilde, Tovani-Palone, Marcos Roberto, Trabelsi, Khaled, Tran, Jasmine T, Tran, Mai Thi Ngoc, Tran, Nghia Minh, Trico, Domenico, Trihandini, Indang, Troeger, Christopher E, Tromans, Samuel Joseph, Truyen, Thien Tan Tri Tai, Tsatsakis, Aristidis, Tsermpini, Evangelia Eirini, Tumurkhuu, Munkhtuya, Udoakang, Aniefiok John, Udoh, Arit, Ullah, Atta, Ullah, Saeed, Ullah, Sana, Umair, Muhammad, Umakanthan, Srikanth, Unim, Brigid, Unnikrishnan, Bhaskaran, Upadhyay, Era, Urso, Daniele, Usman, Jibrin Sammani, Vaithinathan, Asokan Govindaraj, Vakili, Omid, Valenti, Mario, Valizadeh, Rohollah, Van den Eynde, Jef, van Donkelaar, Aaron, Varga, Orsolya, Vart, Priya, Varthya, Shoban Babu, Vasankari, Tommi Juhani, Vasic, Milena, Vaziri, Siavash, Venketasubramanian, Narayanaswamy, Verghese, Nicholas Alexander, Verma, Madhur, Veroux, Massimiliano, Verras, Georgios-Ioannis, Vervoort, Dominique, Villafañe, Jorge Hugo, Villalobos-Daniel, Victor E, Villani, Leonardo, Villanueva, Gabriela Ines, Vinayak, Manish, Violante, Francesco S, Vlassov, Vasily, Vo, Bay, Vollset, Stein Emil, Volovat, Simona Ruxandra, Vos, Theo, Vujcic, Isidora S, Waheed, Yasir, Wang, Cong, Wang, Fang, Wang, Shu, Wang, Yanzhong, Wang, Yuan-Pang, Wanjau, Mary Njeri, Waqas, Muhammad, Ward, Paul, Waris, Abdul, Wassie, Emebet Gashaw, Weerakoon, Kosala Gayan, Weintraub, Robert G, Weiss, Daniel J, Weiss, Eli J, Weldetinsaa, Haftom Legese Legese, Wells, Katherine M, Wen, Yi Feng, Wiangkham, Taweewat, Wickramasinghe, Nuwan Darshana, Wilkerson, Caroline, Willeit, Peter, Wilson, Shadrach, Wong, Yen Jun, Wongsin, Utoomporn, Wozniak, Sarah, Wu, Chenkai, Wu, Dongze, Wu, Felicia, Wu, Zenghong, Xia, Juan, Xiao, Hong, Xu, Suowen, Xu, Xiaoyue, Xu, Yvonne Yiru, Yadav, Mukesh Kumar, Yaghoubi, Sajad, Yamagishi, Kazumasa, Yang, Lin, Yano, Yuichiro, Yaribeygi, Habib, Yasufuku, Yuichi, Ye, Pengpeng, Yesodharan, Renjulal, Yesuf, Subah Abderehim, Yezli, Saber, Yi, Siyan, Yiğit, Arzu, Yigzaw, Zeamanuel Anteneh, Yin, Dehui, Yip, Paul, Yismaw, Malede Berihun, Yon, Dong Keon, Yonemoto, Naohiro, You, Yuyi, Younis, Mustafa Z, Yousefi, Zabihollah, Yu, Chuanhua, Yu, Yong, Zadey, Siddhesh, Zadnik, Vesna, Zakham, Fathiah, Zaki, Nazar, Zakzuk, Josefina, Zamagni, Giulia, Zaman, Sojib Bin, Zandieh, Ghazal G Z, Zanghì, Aurora, Zar, Heather J, Zare, Iman, Zarimeidani, Fatemeh, Zastrozhin, Mikhail Sergeevich, Zeng, Youjie, Zhai, Chunxia, Zhang, Anthony Lin, Zhang, Haijun, Zhang, Liqun, Zhang, Meixin, Zhang, Yunquan, Zhang, Zhenyu, Zhang, Zhi-Jiang, Zhao, Hanqing, Zhao, Jeff T, Zhao, Xiu-Ju George, Zhao, Yang, Zhao, Yong, Zhong, Chenwen, Zhou, Jingjing, Zhou, Juexiao, Zhou, Shangcheng, Zhu, Bin, Zhu, Lei, Zhu, Zhaohua, Ziaeian, Boback, Ziafati, Makan, Zielińska, Magdalena, Zimsen, Stephanie R M, Zoghi, Ghazal, Zoller, Thomas, Zumla, Alimuddin, Zyoud, Sa'ed H, Zyoud, Samer H, Murray, Christopher J L, and Gakidou, Emmanuela
Published: 2024

37. Evaluation of CMIP6 model simulations of PM2.5 and its components over China.

Author: Ren, Fangxuan, Lin, Jintai, Xu, Chenghao, Adeniran, Jamiu A., Wang, Jingxu, Martin, Randall V., van Donkelaar, Aaron, Hammer, Melanie S., Horowitz, Larry W., Turnock, Steven T., Oshima, Naga, Zhang, Jie, Bauer, Susanne, Tsigaridis, Kostas, Seland, Øyvind, Nabat, Pierre, Neubauer, David, Strand, Gary, van Noije, Twan, and Le Sager, Philippe
Subjects: PARTICULATE matter, SOOT, RADIATIVE forcing, CARBON-black, AMMONIUM nitrate, SIMULATION methods & models
Abstract: Earth system models (ESMs) participating in the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate various components of fine particulate matter (PM2.5) as major climate forcers. Yet the model performance for PM2.5 components remains little evaluated due in part to a lack of observational data. Here, we evaluate near-surface concentrations of PM2.5 and its five main components over China as simulated by 14 CMIP6 models, including organic carbon (OC; available in 14 models), black carbon (BC; 14 models), sulfate (14 models), nitrate (4 models), and ammonium (5 models). For this purpose, we collect observational data between 2000 and 2014 from a satellite-based dataset for total PM2.5 and from 2469 measurement records in the literature for PM2.5 components. Seven models output total PM2.5 concentrations, and they all underestimate the observed total PM2.5 over eastern China, with GFDL-ESM4 (- 1.5 %) and MPI-ESM-1-2-HAM (- 1.1 %) exhibiting the smallest biases averaged over the whole country. The other seven models, for which we recalculate total PM2.5 from the available component output, underestimate the total PM2.5 concentrations partly because of the missing model representations of nitrate and ammonium. Concentrations of the five individual components are underestimated in almost all models, except that sulfate is overestimated in MPI-ESM-1-2-HAM by 12.6 % and in MRI-ESM2-0 by 24.5 %. The underestimation is the largest for OC (by - 71.2 % to - 37.8 % across the 14 models) and the smallest for BC (- 47.9 % to - 12.1 %). The multi-model mean (MMM) reproduces the observed spatial pattern for OC (R = 0.51), sulfate (R = 0.57), nitrate (R = 0.70) and ammonium (R = 0.74) fairly well, yet the agreement is poorer for BC (R = 0.39). The varying performances of ESMs on total PM2.5 and its components have important implications for the modeled magnitude and spatial pattern of aerosol radiative forcing. [ABSTRACT FROM AUTHOR]
Published: 2024
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38. Correction to “Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty”

Author: van Donkelaar, Aaron, primary, Hammer, Melanie S., additional, Bindle, Liam, additional, Brauer, Michael, additional, Brook, Jeffery R., additional, Garay, Michael J., additional, Hsu, N. Christina, additional, Kalashnikova, Olga V., additional, Kahn, Ralph A., additional, Lee, Colin, additional, Levy, Robert C., additional, Lyapustin, Alexei, additional, Sayer, Andrew M., additional, and Martin, Randall V., additional
Published: 2024
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39. Ambient Fine Particulate Matter and Mortality among Survivors of Myocardial Infarction: Population-Based Cohort Study

Author: Chen, Hong, Burnett, Richard T, Copes, Ray, Kwong, Jeffrey C, Villeneuve, Paul J, Goldberg, Mark S, Brook, Robert D, van Donkelaar, Aaron, Jerrett, Michael, Martin, Randall V, Brook, Jeffrey R, Kopp, Alexander, and Tu, Jack V
Subjects: Epidemiology, Environmental Sciences, Pollution and Contamination, Health Sciences, Clinical Research, Heart Disease, Climate-Related Exposures and Conditions, Cardiovascular, Heart Disease - Coronary Heart Disease, Good Health and Well Being, Air Pollutants, Cohort Studies, Myocardial Infarction, Ontario, Particle Size, Particulate Matter, Proportional Hazards Models, Seasons, Medical and Health Sciences, Toxicology, Biomedical and clinical sciences, Environmental sciences, Health sciences
Abstract: BackgroundSurvivors of acute myocardial infarction (AMI) are at increased risk of dying within several hours to days following exposure to elevated levels of ambient air pollution. Little is known, however, about the influence of long-term (months to years) air pollution exposure on survival after AMI.ObjectiveWe conducted a population-based cohort study to determine the impact of long-term exposure to fine particulate matter ≤ 2.5 μm in diameter (PM2.5) on post-AMI survival.MethodsWe assembled a cohort of 8,873 AMI patients who were admitted to 1 of 86 hospital corporations across Ontario, Canada in 1999-2001. Mortality follow-up for this cohort extended through 2011. Cumulative time-weighted exposures to PM2.5 were derived from satellite observations based on participants' annual residences during follow-up. We used standard and multilevel spatial random-effects Cox proportional hazards models and adjusted for potential confounders.ResultsBetween 1999 and 2011, we identified 4,016 nonaccidental deaths, of which 2,147 were from any cardiovascular disease, 1,650 from ischemic heart disease, and 675 from AMI. For each 10-μg/m3 increase in PM2.5, the adjusted hazard ratio (HR10) of nonaccidental mortality was 1.22 [95% confidence interval (CI): 1.03, 1.45]. The association with PM2.5 was robust to sensitivity analyses and appeared stronger for cardiovascular-related mortality: ischemic heart (HR10 = 1.43; 95% CI: 1.12, 1.83) and AMI (HR10 = 1.64; 95% CI: 1.13, 2.40). We estimated that 12.4% of nonaccidental deaths (or 497 deaths) could have been averted if the lowest measured concentration in an urban area (4 μg/m3) had been achieved at all locations over the course of the study.ConclusionsLong-term air pollution exposure adversely affects the survival of AMI patients.CitationChen H, Burnett RT, Copes R, Kwong JC, Villeneuve PJ, Goldberg MS, Brook RD, van Donkelaar A, Jerrett M, Martin RV, Brook JR, Kopp A, Tu JV. 2016. Ambient fine particulate matter and mortality among survivors of myocardial infarction: population-based cohort study. Environ Health Perspect 124:1421-1428; http://dx.doi.org/10.1289/EHP185.
Published: 2016

40. Associations of Pregnancy Outcomes and PM2.5 in a National Canadian Study

Author: Stieb, David M, Chen, Li, Beckerman, Bernardo S, Jerrett, Michael, Crouse, Daniel L, Omariba, D Walter Rasugu, Peters, Paul A, van Donkelaar, Aaron, Martin, Randall V, Burnett, Richard T, Gilbert, Nicolas L, Tjepkema, Michael, Liu, Shiliang, and Dugandzic, Rose M
Subjects: Biomedical and Clinical Sciences, Epidemiology, Public Health, Health Sciences, Paediatrics, Reproductive Medicine, Pediatric, Preterm, Low Birth Weight and Health of the Newborn, Perinatal Period - Conditions Originating in Perinatal Period, Climate-Related Exposures and Conditions, Infant Mortality, Clinical Research, Reproductive health and childbirth, Life on Land, Adolescent, Adult, Air Pollutants, Canada, Female, Humans, Infant, Low Birth Weight, Infant, Newborn, Infant, Small for Gestational Age, Male, Maternal Exposure, Particle Size, Particulate Matter, Pregnancy, Premature Birth, Rural Population, Urban Population, Young Adult, Environmental Sciences, Medical and Health Sciences, Toxicology, Biomedical and clinical sciences, Environmental sciences, Health sciences
Abstract: BackgroundNumerous studies have examined associations between air pollution and pregnancy outcomes, but most have been restricted to urban populations living near monitors.ObjectivesWe examined the association between pregnancy outcomes and fine particulate matter in a large national study including urban and rural areas.MethodsAnalyses were based on approximately 3 million singleton live births in Canada between 1999 and 2008. Exposures to PM2.5 (particles of median aerodynamic diameter ≤ 2.5 μm) were assigned by mapping the mother's postal code to a monthly surface based on a national land use regression model that incorporated observations from fixed-site monitoring stations and satellite-derived estimates of PM2.5. Generalized estimating equations were used to examine the association between PM2.5 and preterm birth (gestational age < 37 weeks), term low birth weight (< 2,500 g), small for gestational age (SGA; < 10th percentile of birth weight for gestational age), and term birth weight, adjusting for individual covariates and neighborhood socioeconomic status (SES).ResultsIn fully adjusted models, a 10-μg/m(3) increase in PM2.5 over the entire pregnancy was associated with SGA (odds ratio = 1.04; 95% CI 1.01, 1.07) and reduced term birth weight (-20.5 g; 95% CI -24.7, -16.4). Associations varied across subgroups based on maternal place of birth and period (1999-2003 vs. 2004-2008).ConclusionsThis study, based on approximately 3 million births across Canada and employing PM2.5 estimates from a national spatiotemporal model, provides further evidence linking PM2.5 and pregnancy outcomes.
Published: 2016

41. Variability in ambient ozone and fine particle concentrations and population susceptibility among Canadian health regions

Author: Stieb, David M., Yao, Jiayun, Henderson, Sarah B., Pinault, Lauren, Smith-Doiron, Marc H., Robichaud, Alain, van Donkelaar, Aaron, Martin, Randall V., Ménard, Richard, and Brook, Jeffrey R.
Published: 2019

42. Source sector and fuel contributions to ambient PM2.5 and attributable mortality across multiple spatial scales

Author: McDuffie, Erin E., Martin, Randall V., Spadaro, Joseph V., Burnett, Richard, Smith, Steven J., O’Rourke, Patrick, Hammer, Melanie S., van Donkelaar, Aaron, Bindle, Liam, Shah, Viral, Jaeglé, Lyatt, Luo, Gan, Yu, Fangqun, Adeniran, Jamiu A., Lin, Jintai, and Brauer, Michael
Published: 2021
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43. Critical Time Windows for Air Pollution Exposure and Birth Weight in a Multi-City Canadian Pregnancy Cohort

Author: Johnson, Markey, Shin, Hwashin Hyun, Roberts, Eric, Sun, Liu, Fisher, Mandy, Hystad, Perry, Van Donkelaar, Aaron, Martin, Randall V., Fraser, William D., Lavigne, Eric, Clark, Nina, Beaulac, Vanessa, and Arbuckle, Tye E.
Published: 2021
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44. Ambient PM2.5, O3, and NO2 Exposures and Associations with Mortality over 16 Years of Follow-Up in the Canadian Census Health and Environment Cohort (CanCHEC)

Author: Crouse, Dan L, Peters, Paul A, Hystad, Perry, Brook, Jeffrey R, van Donkelaar, Aaron, Martin, Randall V, Villeneuve, Paul J, Jerrett, Michael, Goldberg, Mark S, Pope, C Arden, Brauer, Michael, Brook, Robert D, Robichaud, Alain, Menard, Richard, and Burnett, Richard T
Subjects: Pollution and Contamination, Epidemiology, Environmental Sciences, Public Health, Health Sciences, Climate-Related Exposures and Conditions, Prevention, Aetiology, 2.2 Factors relating to the physical environment, Good Health and Well Being, Adult, Aged, Aged, 80 and over, Air Pollutants, Air Pollution, Canada, Cause of Death, Cohort Studies, Female, Follow-Up Studies, Humans, Male, Middle Aged, Mortality, Nitrogen Dioxide, Ozone, Particulate Matter, Medical and Health Sciences, Toxicology, Biomedical and clinical sciences, Environmental sciences, Health sciences
Abstract: BackgroundFew studies examining the associations between long-term exposure to ambient air pollution and mortality have considered multiple pollutants when assessing changes in exposure due to residential mobility during follow-up.ObjectiveWe investigated associations between cause-specific mortality and ambient concentrations of fine particulate matter (≤ 2.5 μm; PM2.5), ozone (O3), and nitrogen dioxide (NO2) in a national cohort of about 2.5 million Canadians.MethodsWe assigned estimates of annual concentrations of these pollutants to the residential postal codes of subjects for each year during 16 years of follow-up. Historical tax data allowed us to track subjects' residential postal code annually. We estimated hazard ratios (HRs) for each pollutant separately and adjusted for the other pollutants. We also estimated the product of the three HRs as a measure of the cumulative association with mortality for several causes of death for an increment of the mean minus the 5th percentile of each pollutant: 5.0 μg/m3 for PM2.5, 9.5 ppb for O3, and 8.1 ppb for NO2.ResultsPM2.5, O3, and NO2 were associated with nonaccidental and cause-specific mortality in single-pollutant models. Exposure to PM2.5 alone was not sufficient to fully characterize the toxicity of the atmospheric mix or to fully explain the risk of mortality associated with exposure to ambient pollution. Assuming additive associations, the estimated HR for nonaccidental mortality corresponding to a change in exposure from the mean to the 5th percentile for all three pollutants together was 1.075 (95% CI: 1.067, 1.084). Accounting for residential mobility had only a limited impact on the association between mortality and PM2.5 and O3, but increased associations with NO2.ConclusionsIn this large, national-level cohort, we found positive associations between several common causes of death and exposure to PM2.5, O3, and NO2.CitationCrouse DL, Peters PA, Hystad P, Brook JR, van Donkelaar A, Martin RV, Villeneuve PJ, Jerrett M, Goldberg MS, Pope CA III, Brauer M, Brook RD, Robichaud A, Menard R, Burnett RT. 2015. Ambient PM2.5, O3, and NO2 exposures and associations with mortality over 16 years of follow-up in the Canadian Census Health and Environment Cohort (CanCHEC). Environ Health Perspect 123:1180-1186; http://dx.doi.org/10.1289/ehp.1409276.
Published: 2015

45. No one knows which city has the highest concentration of fine particulate matter

Author: Martin, Randall V., Brauer, Michael, van Donkelaar, Aaron, Shaddick, Gavin, Narain, Urvashi, and Dey, Sagnik
Published: 2019
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46. Critical Time Windows for Air Pollution Exposure and Birth Weight in a Multicity Canadian Pregnancy Cohort

Author: Johnson, Markey, Shin, Hwashin Hyun, Roberts, Eric, Sun, Liu, Fisher, Mandy, Hystad, Perry, Van Donkelaar, Aaron, Martin, Randall V., Fraser, William D., Lavigne, Eric, Clark, Nina, Beaulac, Vanessa, and Arbuckle, Tye E.
Published: 2022
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47. PM2.5 data inputs alter identification of disadvantaged communities

Author: Carter, Therese S, primary, Kerr, Gaige Hunter, additional, Amini, Heresh, additional, Martin, Randall V, additional, Ovienmhada, Ufuoma, additional, Schwartz, Joel, additional, van Donkelaar, Aaron, additional, and Anenberg, Susan, additional
Published: 2023
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48. Development and evaluation of processes affecting simulation of diel fine particulate matter variation in the GEOS-Chem model

Author: Li, Yanshun, primary, Martin, Randall V., additional, Li, Chi, additional, Boys, Brian L., additional, van Donkelaar, Aaron, additional, Meng, Jun, additional, and Pierce, Jeffrey R., additional
Published: 2023
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49. Diabetes Status and Susceptibility to the Effects of PM 2.5 Exposure on Cardiovascular Mortality in a National Canadian Cohort

Author: Pinault, Lauren, Brauer, Michael, Crouse, Daniel L., Weichenthal, Scott, Erickson, Anders, van Donkelaar, Aaron, Martin, Randall V., Charbonneau, Shannon, Hystad, Perry, Brook, Jeffrey R., Tjepkema, Michael, Christidis, Tanya, Ménard, Richard, Robichaud, Alain, and Burnett, Richard T.
Published: 2018

50. Exposure to Ambient Ultrafine Particles and Nitrogen Dioxide and Incident Hypertension and Diabetes

Author: Bai, Li, Chen, Hong, Hatzopoulou, Marianne, Jerrett, Michael, Kwong, Jeffrey C., Burnett, Richard T., van Donkelaar, Aaron, Copes, Ray, Martin, Randall V., Van Ryswyk, Keith, Lu, Hong, Kopp, Alexander, and Weichenthal, Scott
Published: 2018

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