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

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

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

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

Fine airborne particles (diameter <2.5 μm; PM 2.5 ) are recognized as a major threat to human health due to their physicochemical properties: composition, size, shape, etc. However, normally only size-fraction-specific particle concentrations are monitored. Interestingly, although the aerosol type is reported as part of the aerosol optical depth retrieval from satellite observations, it has not been utilized, to date, as an auxiliary information/co-variate for PM 2.5 prediction. We developed Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models that account for this information when predicting surface PM 2.5 . The models take as input only widely available data: satellite aerosol products with full cover and surface meteorological data. Distinct models were developed for AOD of specific aerosol types. Both the RF and XGBoost models performed well, showing moderate-to-high cross-validated adjusted R 2 (RF: 0.753-0.909; XGBoost: 0.741-0.903), depending on the aerosol type and other covariates. The weighted performance of the specific aerosol-type models was higher than of the RF and XGBoost baseline models, where all the AOD retrievals were used together (the common practice). Our approach can provide improved risk estimates due to exposure to PM 2.5 , better resolved radiative forcing calculations, and tailored abatement surveillance of specific pollutants/sources., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: David Broday reports financial support was provided by Israel Science Foundation., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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

Air pollution in the urban environment is a major concern. The ambient concentrations depend on the levels of transboundary imported pollution, the intensity of local sources and the prevailing atmospheric conditions. This work studies the relative impact of two atmospheric variables-atmospheric stability and regional scale turbulence-in determining the air pollution concentrations. We considered a setting (downtown Haifa, Israel) impacted by a large variety of sources, emitting pollutants with different chemical attributes and atmospheric life times. We found that traffic accounts for most of the locally produced pollution in the study location. However, the meteorological factors can overwhelm its impact and dictate the concentrations. The switch from stable to convective conditions and the more vigorous daytime wind are associated with a premature end of the morning peak concentrations that result from rush hour emissions of NO x , Black Carbon (BC) and ultra-fine particles. It results in daytime concentration which are lower than (winter) or equal to (summer) those at night, in spite of the much lower night-time traffic volumes. Similar, albeit weaker, impact was detected in the benzene and toluene concentrations. Sources outside the study area are responsible for most of the CO, PM 1 and PM 2.5 concentrations but during winter nights, characterised by strong atmospheric stability and low turbulence, their concentrations are elevated due to the local emissions. We developed a diagnostic statistical nonlinear model for the pollutant concentrations, which points to a stronger association of the atmospheric stability with the concentrations during stable conditions but turbulence dominating during convective conditions. Our findings explain the relatively low overall concentrations of locally emitted pollutants in the study area but warn of the potential for high concentrations during night-time in locations with comparable meteorological conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Very high spatially resolved satellite-derived ground-level concentrations of particulate matter with an aerodynamic diameter of less than 2.5 μm (PM 2.5 ) have multiple potential applications, especially in air quality modeling and epidemiological and climatological research. Satellite-derived aerosol optical depth (AOD) and columnar water vapor (CWV), meteorological parameters, and land use data were used as variables within the framework of a linear mixed effect model (LME) and a random forest (RF) model to predict daily ground-level concentrations of PM 2.5 at 1 km × 1 km grid resolution across the Indo-Gangetic Plain (IGP) in South Asia. The RF model exhibited superior performance and higher accuracy compared with the LME model, with better cross-validated explained variance ( R 2 = 0.87) and lower relative prediction error (RPE = 24.5%). The RF model revealed improved performance metrics for increasing averaging periods, from daily to weekly, monthly, seasonal, and annual means, which supported its use in estimating PM 2.5 exposure metrics across the IGP at varying temporal scales (i.e., both short and long terms). The RF-based PM 2.5 levels over the middle and lower IGP, with the annual mean exceeding 110 μg/m 2.5 levels over the middle and lower IGP, with the annual mean exceeding 110 μg/m 3 concentrations of >170 μg/m 2.5 concentrations of >170 μg/m 3 .

Background: Individuals with coronary heart disease are considered susceptible to traffic-related air pollution exposure. Yet, cohort-based evidence on whether preexisting coronary heart disease modifies the association of traffic-related air pollution with health outcomes is lacking., Aim: Using data of four Israeli cohorts, we compared associations of traffic-related air pollution with mortality and cancer between coronary heart disease patients and matched controls from the general population., Methods: Subjects hospitalized with acute coronary syndrome from two patient cohorts (inception years: 1992-1993 and 2006-2014) were age- and sex-matched to coronary heart disease-free participants of two cycles of the Israeli National Health and Nutrition Surveys (inception years: 1999-2001 and 2005-2006). Ambient concentrations of nitrogen oxides at the residential place served as a proxy for traffic-related air pollution exposure across all cohorts, based on a high-resolution national land use regression model (50 m). Data on all-cause mortality (last update: 2018) and cancer incidence (last update: 2016) were retrieved from national registries. Cox-derived stratum-specific hazard ratios with 95% confidence intervals were calculated, adjusted for harmonized covariates across cohorts, including age, sex, ethnicity, neighborhood socioeconomic status, smoking, diabetes, hypertension, prior stroke and prior malignancy (the latter only in the mortality analysis). Effect-modification was examined by testing nitrogen oxides-by-coronary heart disease interaction term in the entire matched cohort., Results: The cohort (mean (standard deviation) age 61.5 (14) years; 44% women) included 2393 matched pairs, among them 2040 were cancer-free at baseline. During a median (25th-75th percentiles) follow-up of 13 (10-19) and 11 (7-17) years, 1458 deaths and 536 new cancer cases were identified, respectively. In multivariable-adjusted models, a 10-parts per billion nitrogen oxides increment was positively associated with all-cause mortality among coronary heart disease patients (hazard ratio = 1.13, 95% confidence interval 1.05-1.22), but not among controls (hazard ratio = 1.00, 0.93-1.08) ( p interaction  = 0.003). A similar pattern was seen for all-cancer incidence (hazard ratio CHD  = 1.19 (1.03-1.37), hazard ratio CHD-Free  = 0.93 (0.84-1.04) ( p interaction  = 0.01)). Associations were robust to multiple sensitivity analyses., Conclusions: Coronary heart disease patients might be at increased risk for traffic-related air pollution-associated mortality and cancer, irrespective of their age and sex. Patients and clinicians should be more aware of the adverse health effects on coronary heart disease patients of chronic exposure to vehicle emissions.

Attenuated backscatter profiles retrieved by the space borne active lidar CALIOP on-board CALIPSO satellite were used to measure the vertical distribution of smoke aerosols and to compare it against the ECMWF planetary boundary layer height (PBLH) over the smoke dominated region of Indo-Gangetic Plain (IGP), South Asia. Initially, the relative abundance of smoke aerosols was investigated considering multiple satellite retrieved aerosol optical properties. Only the upper IGP was selectively considered for CALIPSO retrieval based on prevalence of smoke aerosols. Smoke extinction was found to contribute 2-50% of the total aerosol extinction, with strong seasonal and altitudinal attributes. During winter (DJF), smoke aerosols contribute almost 50% of total aerosol extinction only near to the surface while in post-monsoon (ON) and monsoon (JJAS), relative contribution of smoke aerosols to total extinction was highest at about 8 km height. There was strong diurnal variation in smoke extinction, evident throughout the year, with frequent abundance of smoke particles at lower height (<4 km) during daytime compared to higher height during night (>4 km). Smoke injection height also varied considerably during rice (ON: 0.71 ± 0.65 km) and wheat (AM: 2.34 ± 1.34 km) residue burning period having a significant positive correlation with prevailing PBLH. Partitioning smoke AOD against PBLH into the free troposphere (FT) and boundary layer (BL) yield interesting results. BL contribute 36% (16%) of smoke AOD during daytime (nighttime) and the BL-FT distinction increased particularly at night. There was evidence that despite travelling efficiently to FT, major proportion of smoke AOD (50-80%) continue to remain close to the surface (<3 km) thereby, may have greater implications on regional climate, air quality, smoke transport and AOD-particulate modelling., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background: Moderate correlations were previously observed between individual estimates of traffic-related air pollution (TRAP) produced by different exposure modeling approaches. This induces exposure misclassification for a substantial fraction of subjects., Aim: We used an ensemble of well-established modeling approaches to increase certainty of exposure classification and reevaluated the association with cancers previously linked to TRAP (lung, breast and prostate), other cancers, and all-cause mortality in a cohort of coronary patients., Methods: Patients undergoing percutaneous coronary interventions in a major Israeli medical center from 2004 to 2014 (n = 10,627) were followed for cancer (through 2015) and mortality (through 2017) via national registries. Residential exposure to nitrogen oxides (NO x ) -a proxy for TRAP- was estimated by optimized dispersion model (ODM) and land use regression (LUR) (r Pearson  = 0.50). Mutually exclusive groups of subjects classified as exposed by none of the methods (high-certainty low-exposed), ODM alone, LUR alone, or both methods (high-certainty high-exposed) were created. Associations were examined using Cox regression models., Results: During follow-up, 741 incident cancer cases were diagnosed and 3051 deaths occurred. Using a ≥25 ppb cutoff, compared with high-certainty low exposed, the multivariable-adjusted hazard ratios (95% confidence intervals) for lung, breast and prostate cancer were 1.56 (1.13-2.15) in high-certainty exposed, 1.27 (0.86-1.86) in LUR-exposed alone, and 1.13 (0.77-1.65) in ODM-exposed alone. The association of the former category was strengthened using more extreme NO x cutoffs. A similar pattern, albeit less strong, was observed for mortality, whereas no association was shown for cancers not previously linked to TRAP., Conclusions: Use of an ensemble of TRAP exposure estimates may improve classification, resulting in a stronger association with outcomes., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Traffic-related air pollution has been linked to multiple adverse pregnancy outcomes. However, few studies have examined pregnancy loss, targeting losses identified by hospital records, a large limitation as it does not capture events not reported to the medical system., Methods: We used a novel variation of the time-series design to determine the association, and identify the critical window of vulnerability, between week-to-week traffic-related air pollution and conceptions resulting in live births, using nitrogen dioxide (NO2) as a traffic emissions tracer. We used information from all live births recorded at Beth Israel Deaconess Medical Center in Boston, MA (2000-2013) and all live births in Tel Aviv District, Israel (2010-2013)., Results: In Boston (68,969 live births), the strongest association was during the 15th week of gestation; for every 10 ppb of NO2 increase during that week, we observed a lower rate of live births (rate ratio [RR] = 0.87; 95% confidence interval [CI], 0.78, 0.97), using live birth-identified conceptions to infer pregnancy losses. In the Tel Aviv District (95,053 live births), the strongest estimate was during the 16th gestational week gestation (RR = 0.82; 95% CI, 0.76, 0.90 per 10 ppb of NO2)., Conclusions: Using weekly conceptions ending in live birth rather than identified pregnancy losses, we comprehensively analyzed the relationship between air pollution and all pregnancy loss throughout gestation. The observed results, with remarkable similarity in two independent locations, suggest that higher traffic-related air pollution levels are associated with pregnancy loss, with strongest estimates between the 10th and 20th gestational weeks.

Polybrominated diphenyl ethers (PBDEs) are commercial flame retardants that have been commonly used in vehicle interior to reduce fire-related hazards. Due to high temperatures and intense insolation that can be attained inside cars parked in the sun, additive PBDEs are prone to leach out and attach to in-vehicle dust, as well as to photo-debrominate. This study examines seasonal variations of concentrations of three common PBDE congeners (BDE-47, BDE-99 and BDE-209) in car dust in Israel. The overall concentrations of these BDEs ranged from 1 to 29μg/g, and were higher in the summer than in the winter (average of 10.2 and 5.3μg/g, respectively). Congener-specific concentrations showed distinct seasonal pattern, representing the interplay between leaching, evaporation and photodebromination. Photolysis of the three congeners, while adsorbed on glass filters and exposed to solar radiation, revealed first order kinetics with debromination rates on the order of 10 -2 /min. Hence, seasonal variations of the meteorological conditions were found to affect the in-vehicle PBDE concentrations, and are therefore expected also to affect the exposure of passengers to PBDEs., (Copyright © 2017. Published by Elsevier B.V.)

Background Exposure to traffic-related air pollution (TRAP) is considered to have a carcinogenic effect. The authors previously reported a nonsignificant association between TRAP and cancer risk in a relatively small cohort of myocardial infarction survivors. This study assessed whether TRAP exposure is associated with subsequent cancer in a large cohort of coronary patients. Methods & results Consecutive patients undergoing percutaneous coronary interventions in a major medical centre in central Israel from 2004 to 2014 were followed for cancer through 2015. Residential levels of nitrogen oxides (NO x ) - a proxy for TRAP - were estimated based on a high-resolution national land use regression model. Cox proportional hazards models were constructed to study relationships with cancer. Among 12,784 candidate patients, 9816 had available exposure data and no history of cancer (mean age, 68 years; 77% men). During a median (25th-75th percentiles) follow-up of 7.0 (3.9-9.3) years, 773 incident cases of cancer (8%) were diagnosed. In a multivariable-adjusted model, a 10-ppb increase in mean NO x exposure was associated with hazard ratios (HRs) of 1.07 (95% confidence interval [CI] 1.00-1.15) for all-site cancer and 1.16 (95% CI 1.05-1.28) for cancers previously linked to TRAP (lung, breast, prostate, kidney and bladder). A stronger association was observed for breast cancer (HR = 1.43; 95% CI 1.12-1.83). Associations were slightly strengthened after limiting the cohort to patients with more precise exposure assessment. Conclusion Coronary patients exposed to TRAP are at increased risk of several types of cancer, particularly lung, prostate and breast. As these cancers are amenable to prevention strategies, identifying highly exposed patients may provide an opportunity to improve clinical care.

Accumulating evidence suggests that perinatal air pollutant exposures are associated with increased risk of autism spectrum disorder (ASD), but evidence for traffic pollutants outside the United States is inconclusive. We assessed the association between nitrogen dioxide, a traffic pollution tracer, and risk of ASD. We conducted a nested case-control study among the entire population of children born during 2005-2009 in the central coastal area of Israel. Cases were identified through the National Insurance Institute of Israel (n = 2,098). Controls were a 20% random sample of the remaining children (n = 54,191). Exposure was based on an optimized dispersion model. We estimated adjusted odds ratios and 95% confidence intervals using logistic regression and a distributed-lag model. In models mutually adjusted for the 2 periods, the odds ratio per 5.85-parts per billion (ppb) increment of nitrogen dioxide exposure during pregnancy (median, 16.8 ppb; range, 7.5-31.2 ppb) was 0.77 (95% confidence interval: 0.59, 1.00), and the odds ratio for exposure during the 9 months after birth was 1.40 (95% confidence interval: 1.09, 1.80). A distributed-lag model revealed reduced risk around week 13 of pregnancy and elevated risk around week 26 after birth. These findings suggest that postnatal exposure to nitrogen dioxide in Israel is associated with increased odds of ASD, and prenatal exposure with lower odds. The latter may relate to selection effects.

Appreciating the uncertainty margins of exposure assessment to air pollution requires good understanding of its variability throughout the daily activities. This study describes a modeling framework for estimating exposure to air pollutants for a representative sample of working Israeli adults ( N ∼ 168 000) for which both the residence and workplace addresses were available. Individual daily trajectories were simulated by accounting for five generic daily activities: at home, at work, while in commute from home to work and back, and during out-of-home leisure activities. The integrated daily exposure to nitrogen dioxide (NO 2 ) was estimated for each individual by tracking the daily trajectory through an NO 2 concentration map, obtained using a dynamic and highly resolved dispersion-like model (temporal resolution, half-hourly; spatial resolution, 500 m). Accounting for the subjects' daily mobility was found to affect their exposure more significantly than accounting solely for the diurnal concentration variability, yet a synergistic effect was noted when accounting for both factors simultaneously. Exposure misclassification varies along the day, with the work microenvironment found to contribute the most to it. In particular, regardless of the high concentrations encountered during the commute, their contribution to the integrated daily exposure is small due to the relatively short time spent in this activity by most people.

Low-cost air quality sensors offer high-resolution spatiotemporal measurements that can be used for air resources management and exposure estimation. Yet, such sensors require frequent calibration to provide reliable data, since even after a laboratory calibration they might not report correct values when they are deployed in the field, due to interference with other pollutants, as a result of sensitivity to environmental conditions and due to sensor aging and drift. Field calibration has been suggested as a means for overcoming these limitations, with the common strategy involving periodical collocations of the sensors at an air quality monitoring station. However, the cost and complexity involved in relocating numerous sensor nodes back and forth, and the loss of data during the repeated calibration periods make this strategy inefficient. This work examines an alternative approach, a node-to-node (N2N) calibration, where only one sensor in each chain is directly calibrated against the reference measurements and the rest of the sensors are calibrated sequentially one against the other while they are deployed and collocated in pairs. The calibration can be performed multiple times as a routine procedure. This procedure minimizes the total number of sensor relocations, and enables calibration while simultaneously collecting data at the deployment sites. We studied N2N chain calibration and the propagation of the calibration error analytically, computationally and experimentally. The in-situ N2N calibration is shown to be generic and applicable for different pollutants, sensing technologies, sensor platforms, chain lengths, and sensor order within the chain. In particular, we show that chain calibration of three nodes, each calibrated for a week, propagate calibration errors that are similar to those found in direct field calibration. Hence, N2N calibration is shown to be suitable for calibration of distributed sensor networks., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Models that are used to map air pollutant concentrations are not free of errors. A possible approach for improving the final concentration map is to interpolate the residuals of the initial model concentration estimates. Due to the possible spatial autocorrelation of the residuals of the initial model estimates, Bayesian inference schemes were suggested for this task, since they can correctly adjust the level of fitting of the residuals to the random measurement errors. However, the complexity of Bayesian methods often discourages their use. Here, we present an alternative and simpler approach, using a leave-one-out cross-validation to determine the optimal level of fitting of the residual correction. We show that the optimal correction level is related to the extent of the spatial autocorrelation of the cross-validated residuals. Namely, when the residuals are not autocorrelated residual correction is unnecessary, and if employed may actually degrade the quality of the final concentration map. Moreover, our approach enables to optimize the residual correction based on different target performance measures, with a possibly different optimal correction depending on the performance measure used. Hence, different target performance measures can be chosen to fit best the specific application of interest. The method is demonstrated using output of three different models used for estimating NO x and NO 2 concentrations over Israel. We show that our approach can be used as an exploratory step, for assessing the potential benefit of residual correction, and as a simple alternative to Bayesian schemes., (Copyright © 2017 Elsevier B.V. All rights reserved.)

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

Accurate estimation of exposure to air pollution is necessary for assessing the impact of air pollution on the public health. Most environmental epidemiology studies assign the home address exposure to the study subjects. Here, we quantify the exposure estimation error at the population scale due to assigning it solely at the residence place. A cohort of most schoolchildren in Israel (~950,000), age 6-18, and a representative cohort of Israeli adults (~380,000), age 24-65, were used. For each subject the home and the work or school addresses were geocoded. Together, these two microenvironments account for the locations at which people are present during most of the weekdays. For each subject, we estimated ambient nitrogen oxide concentrations at the home and work or school addresses using two air quality models: a stationary land use regression model and a dynamic dispersion-like model. On average, accounting for the subjects' work or school address as well as for the daily pollutant variation reduced the estimation error of exposure to ambient NO x /NO 2 by 5-10ppb, since daytime concentrations at work/school and at home can differ significantly. These results were consistent regardless which air quality model as used and even for subjects that work or study close to their home. Yet, due to their usually short commute, assigning schoolchildren exposure solely at their residential place seems to be a reasonable estimation. In contrast, since adults commute for longer distances, assigning exposure of adults only at the residential place has a lower correlation with the daily weighted exposure, resulting in larger exposure estimation errors. We show that exposure misclassification can result from not accounting for the subjects' time-location trajectories through the spatiotemporally varying pollutant concentrations field., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Airborne particulate matter (PM) is derived from diverse sources-natural and anthropogenic. Climate change processes and remote sensing measurements are affected by the PM properties, which are often lumped into homogeneous size fractions that show spatiotemporal variation. Since different sources are attributed to different geographic locations and show specific spatial and temporal PM patterns, we explored the spatiotemporal characteristics of the PM 2.5 /PM 10 ratio in different areas. Furthermore, we examined the statistical relationships between AERONET aerosol optical depth (AOD) products, satellite-based AOD, and the PM ratio, as well as the specific PM size fractions. PM data from the northeastern United States, from San Joaquin Valley, CA, and from Italy, Israel, and France were analyzed, as well as the spatial and temporal co-measured AOD products obtained from the MultiAngle Implementation of Atmospheric Correction (MAIAC) algorithm. Our results suggest that when both the AERONET AOD and the AERONET fine-mode AOD are available, the AERONET AOD ratio can be a fair proxy for the ground PM ratio. Therefore, we recommend incorporating the fine-mode AERONET AOD in the calibration of MAIAC. Along with a relatively large variation in the observed PM ratio (especially in the northeastern United States), this shows the need to revisit MAIAC assumptions on aerosol microphysical properties, and perhaps their seasonal variability, which are used to generate the look-up tables and conduct aerosol retrievals. Our results call for further scrutiny of satellite-borne AOD, in particular its errors, limitations, and relation to the vertical aerosol profile and the particle size, shape, and composition distribution. This work is one step of the required analyses to gain better understanding of what the satellite-based AOD represents., Implications: The analysis results recommend incorporating the fine-mode AERONET AOD in MAIAC calibration. Specifically, they indicate the need to revisit MAIAC regional aerosol microphysical model assumptions used to generate look-up tables (LUTs) and conduct retrievals. Furthermore, relatively large variations in measured PM ratio shows that adding seasonality in aerosol microphysics used in LUTs, which is currently static, could also help improve accuracy of MAIAC retrievals. These results call for further scrutiny of satellite-borne AOD for better understanding of its limitations and relation to the vertical aerosol profile and particle size, shape, and composition.

Recent developments in sensory and communication technologies have made the development of portable air-quality (AQ) micro-sensing units (MSUs) feasible. These MSUs allow AQ measurements in many new applications, such as ambulatory exposure analyses and citizen science. Typically, the performance of these devices is assessed using the mean error or correlation coefficients with respect to a laboratory equipment. However, these criteria do not represent how such sensors perform outside of laboratory conditions in large-scale field applications, and do not cover all aspects of possible differences in performance between the sensor-based and standardized equipment, or changes in performance over time. This paper presents a comprehensive Sensor Evaluation Toolbox (SET) for evaluating AQ MSUs by a range of criteria, to better assess their performance in varied applications and environments. Within the SET are included four new schemes for evaluating sensors' capability to: locate pollution sources; represent the pollution level on a coarse scale; capture the high temporal variability of the observed pollutant and their reliability. Each of the evaluation criteria allows for assessing sensors' performance in a different way, together constituting a holistic evaluation of the suitability and usability of the sensors in a wide range of applications. Application of the SET on measurements acquired by 25 MSUs deployed in eight cities across Europe showed that the suggested schemes facilitates a comprehensive cross platform analysis that can be used to determine and compare the sensors' performance. The SET was implemented in R and the code is available on the first author's website., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Background: Previous studies suggested a carcinogenic effect of exposure to traffic-related air pollution. Recently, higher rates of cancer incidence were observed among myocardial infarction survivors compared with the general population. We examined the association between chronic exposure to nitrogen oxides, a proxy measure for traffic-related air pollution, and cancer incidence and mortality in a cohort of myocardial infarction patients., Methods: Patients aged ≤65 years admitted to hospital in central Israel with a first myocardial infarction in 1992-1993 were followed to 2013 for cancer incidence and cause-specific mortality. Data on sociodemographic and cancer risk factors were obtained, including time-varying information on smoking. Using land use regression models, annual averages of nitrogen oxides during follow-up were estimated individually according to home addresses. Cox proportional hazards models were constructed to study the relationships with cancer outcomes., Results: During a mean follow-up of 16 (SD 7) years, 262 incident cancers and 105 cancer deaths were identified among 1393 cancer-free patients at baseline (mean age 54 years; 81% men). In adjusted models, a 10 ppb increase in mean nitrogen oxide exposure was associated with a hazard ratio (HR) of 1.06 (95% confidence interval (CI) 0.96-1.18) for cancer incidence and HR of 1.08 (95% CI 0.93-1.26) for cancer mortality. The association with lung, bladder, kidney or prostate cancer (previously linked to air pollution) was stronger (HR 1.16; 95% CI 1.00-1.33)., Conclusions: Chronic exposure to traffic-related air pollution may constitute an environmental risk factor for cancer post-myocardial infarction. Variation in the strength of association between specific cancers needs to be explored further., (© The European Society of Cardiology 2016.)

There is a need for efficient techniques to assess abnormalities in the peripheral regions of the lungs, for example, for diagnosis of pulmonary emphysema. Considerable scientific efforts have been directed toward measuring lung morphology by studying recovery of inhaled micron-sized aerosol particles (0.4-1.5 µm). In contrast, it is suggested that the recovery of inhaled airborne nanoparticles may be more useful for diagnosis. The objective of this work is to provide a theoretical background for the use of nanoparticles in measuring lung morphology and to assess their applicability based on a review of the literature. Using nanoparticles for studying distal airspace dimensions is shown to have several advantages over other aerosol-based methods. 1) Nanoparticles deposit almost exclusively by diffusion, which allows a simpler breathing maneuver with minor artifacts from particle losses in the oropharyngeal and upper airways. 2) A higher breathing flow rate can be utilized, making it possible to rapidly inhale from residual volume to total lung capacity (TLC), thereby eliminating the need to determine the TLC before measurement. 3) Recent studies indicate better penetration of nanoparticles than micron-sized particles into poorly ventilated and diseased regions of the lungs; thus, a stronger signal from the abnormal parts is expected. 4) Changes in airspace dimensions have a larger impact on the recovery of nanoparticles. Compared to current diagnostic techniques with high specificity for morphometric changes of the lungs, computed tomography and magnetic resonance imaging with hyperpolarized gases, an aerosol-based method is likely to be less time consuming, considerably cheaper, simpler to use, and easier to interpret (providing a single value rather than an image that has to be analyzed). Compared to diagnosis by carbon monoxide ( D L,CO ), the uptake of nanoparticles in the lung is not affected by blood flow, hemoglobin concentration or alterations of the alveolar membranes, but relies only on lung morphology., Competing Interests: Jakob Löndahl and Per Wollmer have a patent pending named “Device and method for pulmonary function measurement”. The other authors report no conflicts of interest in this work.

An innovative atmospheric moisture harvesting system is proposed, where water vapor is separated from the air prior to cooling and condensation. The system was studied using a model that simulates its three interconnected cycles (air, desiccant, and water) over a range of ambient conditions, and optimal configurations are reported for different operation conditions. Model results were compared to specifications of commercial atmospheric moisture harvesting systems and found to represent saving of 5-65% of the electrical energy requirements due to the vapor separation process. We show that the liquid desiccant separation stage that is integrated into atmospheric moisture harvesting systems can work under a wide range of environmental conditions using low grade or solar heating as a supplementary energy source, and that the performance of the combined system is superior.

To date, phosphate surface mining suffers from lack of reliable emission factors. Due to complete absence of data to derive emissions factors, we developed a methodology for estimating them indirectly by studying a range of possible emission factors for surface phosphate mining operations and comparing AERMOD calculated concentrations to concentrations measured around the mine. We applied this approach for the Khneifiss phosphate mine, Syria, and the Al-Hassa and Al-Abyad phosphate mines, Jordan. The work accounts for numerous model unknowns and parameter uncertainties by applying prudent assumptions concerning the parameter values. Our results suggest that the net mining operations (bulldozing, grading and dragline) contribute rather little to ambient TSP concentrations in comparison to phosphate processing and transport. Based on our results, the common practice of deriving the emission rates for phosphate mining operations from the US EPA emission factors for surface coal mining or from the default emission factor of the EEA seems to be reasonable. Yet, since multiple factors affect dispersion from surface phosphate mines, a range of emission factors, rather than only a single value, was found to satisfy the model performance., (Copyright © 2016 Elsevier B.V. All rights reserved.)

This study evaluates AERMOD and CALPUFF dispersion calculations of particulate matter emissions from stone quarries in two mountainous regions against TSP and PM10 measurements, using both observational and WRF-modeled meteorological data. Due to different model parameterization, AERMOD dispersion predictions were in better agreement with the measured concentrations than those obtained by CALPUFF. As expected, the smaller the distance between the meteorological station, the source (quarry) and the receptors, the better the predictions of both AERMOD and CALPUFF. In contrast, using in-situ wind field obtained by runs of the WRF meteorological model for the complex terrain study area provided, in general, less accurate dispersion estimates than when using (even remote) meteorological observations. In particular, using the three-dimensional WRF-modeled wind field within CALPUFF did not provide any advantage over using the two-dimensional wind field, which is the common procedure of AERMOD and CALPUFF. Dry deposition was more significant for ambient concentration estimation in AERMOD than in CALPUFF., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Background: The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) is the first of a series of annual updates of the GBD. Risk factor quantification, particularly of modifiable risk factors, can help to identify emerging threats to population health and opportunities for prevention. The GBD 2013 provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution., Methods: Attributable deaths, years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) have been estimated for 79 risks or clusters of risks using the GBD 2010 methods. Risk-outcome pairs meeting explicit evidence criteria were assessed for 188 countries for the period 1990-2013 by age and sex using three inputs: risk exposure, relative risks, and the theoretical minimum risk exposure level (TMREL). Risks are organised into a hierarchy with blocks of behavioural, environmental and occupational, and metabolic risks at the first level of the hierarchy. The next level in the hierarchy includes nine clusters of related risks and two individual risks, with more detail provided at levels 3 and 4 of the hierarchy. Compared with GBD 2010, six new risk factors have been added: handwashing practices, occupational exposure to trichloroethylene, childhood wasting, childhood stunting, unsafe sex, and low glomerular filtration rate. For most risks, data for exposure were synthesised with a Bayesian meta-regression method, DisMod-MR 2.0, or spatial-temporal Gaussian process regression. Relative risks were based on meta-regressions of published cohort and intervention studies. Attributable burden for clusters of risks and all risks combined took into account evidence on the mediation of some risks such as high body-mass index (BMI) through other risks such as high systolic blood pressure and high cholesterol., Findings: All risks combined account for 57·2% (95% uncertainty interval [UI] 55·8-58·5) of deaths and 41·6% (40·1-43·0) of DALYs. Risks quantified account for 87·9% (86·5-89·3) of cardiovascular disease DALYs, ranging to a low of 0% for neonatal disorders and neglected tropical diseases and malaria. In terms of global DALYs in 2013, six risks or clusters of risks each caused more than 5% of DALYs: dietary risks accounting for 11·3 million deaths and 241·4 million DALYs, high systolic blood pressure for 10·4 million deaths and 208·1 million DALYs, child and maternal malnutrition for 1·7 million deaths and 176·9 million DALYs, tobacco smoke for 6·1 million deaths and 143·5 million DALYs, air pollution for 5·5 million deaths and 141·5 million DALYs, and high BMI for 4·4 million deaths and 134·0 million DALYs. Risk factor patterns vary across regions and countries and with time. In sub-Saharan Africa, the leading risk factors are child and maternal malnutrition, unsafe sex, and unsafe water, sanitation, and handwashing. In women, in nearly all countries in the Americas, north Africa, and the Middle East, and in many other high-income countries, high BMI is the leading risk factor, with high systolic blood pressure as the leading risk in most of Central and Eastern Europe and south and east Asia. For men, high systolic blood pressure or tobacco use are the leading risks in nearly all high-income countries, in north Africa and the Middle East, Europe, and Asia. For men and women, unsafe sex is the leading risk in a corridor from Kenya to South Africa., Interpretation: Behavioural, environmental and occupational, and metabolic risks can explain half of global mortality and more than one-third of global DALYs providing many opportunities for prevention. Of the larger risks, the attributable burden of high BMI has increased in the past 23 years. In view of the prominence of behavioural risk factors, behavioural and social science research on interventions for these risks should be strengthened. Many prevention and primary care policy options are available now to act on key risks., Funding: Bill & Melinda Gates Foundation., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Estimates of exposure to PM 2.5 are often derived from geographic characteristics based on land-use regression or from a limited number of fixed ground monitors. Remote sensing advances have integrated these approaches with satellite-based measures of aerosol optical depth (AOD), which is spatially and temporally resolved, allowing greater coverage for PM 2.5 estimations. Israel is situated in a complex geo-climatic region with contrasting geographic and weather patterns, including both dark and bright surfaces within a relatively small area. Our goal was to examine the use of MODIS-based MAIAC data in Israel, and to explore the reliability of predicted PM 2.5 and PM 10 at a high spatiotemporal resolution. We applied a three stage process, including a daily calibration method based on a mixed effects model, to predict ground PM 2.5 and PM 10 over Israel. We later constructed daily predictions across Israel for 2003-2013 using spatial and temporal smoothing, to estimate AOD when satellite data were missing. Good model performance was achieved, with out-of-sample cross validation R 2 values of 0.79 and 0.72 for PM 10 and PM 2.5 , respectively. Model predictions had little bias, with cross-validated slopes (predicted vs. observed) of 0.99 for both the PM 2.5 and PM 10 models. To our knowledge, this is the first study that utilizes high resolution 1km MAIAC AOD retrievals for PM prediction while accounting for geo-climate complexities, such as experienced in Israel. This novel model allowed the reconstruction of long- and short-term spatially resolved exposure to PM 2.5 and PM 10 in Israel, which could be used in the future for epidemiological studies.

Accurate evaluation of air pollution on human-wellbeing requires high-resolution measurements. Standard air quality monitoring stations provide accurate pollution levels but due to their sparse distribution they cannot capture the highly resolved spatial variations within cities. Similarly, dedicated field campaigns can use tens of measurement devices and obtain highly dense spatial coverage but normally deployment has been limited to short periods of no more than few weeks. Nowadays, advances in communication and sensory technologies enable the deployment of dense grids of wireless distributed air monitoring nodes, yet their sensor ability to capture the spatiotemporal pollutant variability at the sub-neighborhood scale has never been thoroughly tested. This study reports ambient measurements of gaseous air pollutants by a network of six wireless multi-sensor miniature nodes that have been deployed in three urban sites, about 150 m apart. We demonstrate the network's capability to capture spatiotemporal concentration variations at an exceptional fine resolution but highlight the need for a frequent in-situ calibration to maintain the consistency of some sensors. Accordingly, a procedure for a field calibration is proposed and shown to improve the system's performance. Overall, our results support the compatibility of wireless distributed sensor networks for measuring urban air pollution at a sub-neighborhood spatial resolution, which suits the requirement for highly spatiotemporal resolved measurements at the breathing-height when assessing exposure to urban air pollution., (Copyright © 2014 Elsevier B.V. All rights reserved.)

The adverse consequences of particulate matter (PM) inhalation on human health are well documented. Most of the epidemiological work assessing the health impacts of PM exposure concentrates on PM2.5, however, many of the PM monitoring records available for retrospective studies are of coarser PM10 data. This study explores the possibility to simulate PM2.5 data using PM10 records and assesses the trade-off between introduction of the unavoidable simulation errors and the improved spatial coverage enabled by the simulated series. Several modelling approaches are studied, bearing in mind the range of exposure time scales typical to environmental epidemiology studies. The modelling capability is evaluated by cross-testing between three stations observing both the PM2.5 and PM10 fractions. The trade-off between improved spatial coverage and simulation errors is assessed using leave-one-out cross-validation interpolations. Interpolations including both original and simulated data resulted in richer spatial patterns. Their cross-validated performance was comparable to or slightly better than that obtained using the original PM2.5 data only. The study provides methodologies and guidelines for scientists and practitioners on how to properly exploit their PM10 data to enhance their PM2.5 spatial coverage and minimise investment in new PM2.5 monitoring.

This study investigates the association between exposure to ambient NOx and SO2 originating from power plant emissions and prevalence of obstructive pulmonary disease and related symptoms. The Orot Rabin coal-fired power plant is the largest power generating facility in the Eastern Mediterranean. Two novel methods assessing exposure to power plant-specific emissions were estimated for 2244 participants who completed the European Community Respiratory Health Survey. The "source approach" modeled emissions traced back to the power plant while the "event approach" identified peak exposures from power plant plume events. Respiratory symptoms, but not prevalence of asthma and COPD, were associated with estimates of power plant NOx emissions. The "source approach" yielded a better estimate of exposure to power plant emissions and showed a stronger dose-response relationship with outcomes. Calculating the portion of ambient pollution attributed to power plants emissions can be useful for air quality management purposes and targeted abatement programs., (Copyright © 2013. Published by Elsevier Ltd.)

Retrieval of aerosol loading in vertical atmospheric columns is a common product of satellite and ground instruments that measure spectral extinction of solar radiation throughout the entire atmosphere. Here we study ground hyperspectral imaging of artificial light sources as a complementary method for retrieving fine aerosol concentrations along quazi-horizontal ambient open paths. Previously, we reported hyperspectral measurements of the aerosol optical thickness in the 500-900 nm range over urban-scale distances (180 m to 4 km), measuring the extinction of radiation emitted from a halogen source. Here we confirm in a laboratory-setup the basic premise that different accumulation-size aerosols generate distinct hyperspectral signatures in this spectral range. Measured hyperspectral attenuation signatures of fine aerosols were comparable to calculated Mie scattering signatures, suggesting that modal aerosol concentrations can be retrieved. A genetic algorithm was adapted to estimate the aerosol modal concentrations from its hyperspectral extinction signature. Retrievals of aerosol concentrations from measured and synthetic hyperspectral signatures indicated a robust algorithm, with an expected retrieval error of 0.2-22% for typical ambient concentrations along an urban-scale open path. The retrieval accuracy was found to depend on the relative aerosol modal concentrations, especially when there is a substantial overlap between the modal spectral signatures.

Introduction: Chronic environmental exposure to particulate matter <2.5μm in diameter (PM2.5) has been associated with cardiovascular disease; however, the effect of air pollution on myocardial infarction (MI) survivors is not clear. We studied the association of chronic exposure to PM2.5 with death and recurrent cardiovascular events in MI survivors., Methods: Consecutive patients aged ≤65years admitted to all medical centers in central Israel after first-MI in 1992-1993 were followed through 2005 for cardiovascular events and 2011 for survival. Data on sociodemographic and prognostic factors were collected at baseline and during follow-up. Residential exposure to PM2.5 was estimated for each patient based on data recorded at air quality monitoring stations. Cox and Andersen-Gill proportional hazards models were used to study the pollution-outcome association., Results: Among the 1120 patients, 469 (41.9%) died and 541 (48.3%) experienced one or more recurrent cardiovascular event. The adjusted hazard ratios associated with a 10μg/m(3) increase in PM2.5 exposure were 1.3 (95% CI 0.8-2.1) for death and 1.5 (95% CI 1.1-1.9) for multiple recurrences of cardiovascular events (MI, heart failure and stroke)., Conclusion: When adjustment for socio-demographic factors is performed, cumulative chronic exposure to PM2.5 is positively associated with recurrence of cardiovascular events in patients after a first MI., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Concentrations of particulate emissions from a quarry located in hilly terrain were calculated by two common atmospheric dispersion models, AERMOD and CALPUFF. Evaluation of these models for emissions from quarries/open pit mines that are located in complex topography is missing from the literature. Due to severe uncertainties in the input parameters, numerous scenarios were simulated and model sensitivity was studied. Model results were compared among themselves, and to measured total suspended particulate (TSP). For a wide range of meteorological and topographical conditions studied, AERMOD predictions were in a better agreement with the measurements than those obtained by CALPUFF. The use of AERMOD's "Open pit" tool seems unnecessary when accurate digital topographic data are available. Onsite meteorological data are shown to be crucial for reliable dispersion calculations in complex terrain., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Purpose: Frailty, a multidimensional syndrome of increased vulnerability, is prevalent post-myocardial infarction (MI) and predicts mortality and recurrent events. We investigated whether chronic exposure to particulate matter ≤2.5 μm in diameter (PM2.5) is associated with the development of post-MI frailty., Methods: Participants (n = 1120) were aged 65 or less and admitted to hospital in central Israel with first MI in 1992 and 1993. Daily measures of PM2.5 recorded at air quality monitoring stations were summarized and chronic exposure was estimated individually using the geo-coded residential location. Frailty assessment was conducted via an index based on deficit accumulation, and those defined as frail (applying a threshold of ≥0.25) at baseline were excluded. Remaining participants who survived to follow-up 10 to 13 years post-MI (n = 848) were reassessed for frailty. Logistic regression models were constructed to evaluate the role of PM2.5 exposure in frailty risk prediction., Results: Mean exposure to PM2.5 was 24.2 μg/m(3) (range, 16.9-28.6). A total of 301 participants (35.5%) developed frailty during follow-up. Adjusting for sociodemographic and clinical variables, PM2.5 exposure was associated with increased odds of developing frailty (odds ratio, 1.53; 95% confidence interval, 1.22-1.91, comparing the 75th vs. 25th percentiles). Addition of PM2.5 exposure to the multivariable model resulted in an integrated discrimination improvement of 1.60% (P = .005) and a net reclassification index of 6.51% (P = .02)., Conclusions: An association was observed between exposure to PM2.5 and incidence of frailty, providing a potential intermediary between air pollution and post-MI outcomes., (Copyright © 2013 Elsevier Inc. All rights reserved.)

For a given emissions inventory, the general levels of air pollutants and the spatial distribution of their concentrations are determined by the physiochemical state of the atmosphere. Apart from the trivial seasonal and daily cycles, most of the variability is associated with the atmospheric synoptic scale. A simple methodology for assessing future levels of air pollutants' concentrations based on synoptic forecasts is presented. At short time scales the methodology is comparable and slightly better than persistence and seasonal forecasts at categorical classification of pollution levels. It's utility is shown for air quality studies at the long time scale of a changing climate scenario, where seasonality and persistence cannot be used. It is demonstrated that the air quality variability due to changes in the pollution emissions can be expected to be much larger than that associated with the effects of climatic changes., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The Israel National Cancer Registry reported in 2001 that cancer incidence rates in the Haifa area are roughly 20% above the national average. Since Haifa has been the major industrial center in Israel since 1930, concern has been raised that the elevated cancer rates may be associated with historically high air pollution levels. This work tests whether persistent spatial patterns of metrics of chronic exposure to air pollutants are associated with the observed patterns of cancer incidence rates. Risk metrics of chronic exposure to PM(10), emitted both by industry and traffic, and to SO(2), a marker of industrial emissions, was developed. Ward-based maps of standardized incidence rates of three prevalent cancers: Non-Hodgkin's lymphoma, lung cancer and bladder cancer were also produced. Global clustering tests were employed to filter out those cancers that show sufficiently random spatial distribution to have a nil probability of being related to the spatial non-random risk maps. A Bayesian method was employed to assess possible associations between the morbidity and risk patterns, accounting for the ward-based socioeconomic status ranking. Lung cancer in males and bladder cancer in both genders showed non-random spatial patterns. No significant associations between the SO(2)-based risk maps and any of the cancers were found. Lung cancer in males was found to be associated with PM(10), with the relative risk associated with an increase of 1 microg/m(3) of PM(10) being 12%. Special consideration of wards with expected rates <1 improved the results by decreasing the variance of the spatially correlated residual log-relative risk., (Copyright 2010 Elsevier B.V. All rights reserved.)

Background: Cardiovascular disease is a leading cause of morbidity and mortality worldwide and epidemiological studies have consistently shown an increased risk for cardiovascular events in relation to exposure to air pollution. The Israel Study of First Acute Myocardial Infarction was designed to longitudinally assess clinical outcomes, psychosocial adjustment and quality of life in patients hospitalized with myocardial infarction. The current study, by introducing retrospective air pollution data, will examine the association between exposure to air pollution and outcome in myocardial infarction survivors. This report will describe the methods implemented and measures employed. The study specifically aims to examine the relationship between residential exposure to air pollution and long-term risk of recurrent coronary event, heart failure, stroke, cardiac and all-cause death in a geographically defined cohort of patients with myocardial infarction., Methods/design: All 1521 patients aged < or =65 years, admitted with first myocardial infarction between February 1992 and February 1993 to the 8 hospitals serving the population of central Israel, were followed for a median of 13 years. Data were collected on sociodemographic, clinical and environmental factors. Data from air quality monitoring stations will be incorporated retrospectively. Daily measures of air pollution will be summarised, allowing detailed maps to be developed in order to reflect chronic exposure for each participant., Discussion: This study addresses some of the gaps in understanding of the prognostic importance of air pollution exposure after myocardial infarction, by allowing a sufficient follow-up period, using a well-defined community cohort, adequately controlling for multiple and multilevel confounding factors and providing extensive data on various outcomes.

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

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

Management of ambient concentrations of Volatile Organic Compounds (VOCs) is essential for maintaining low ozone levels in urban areas where its formation is under a VOC-limited regime. The significant decrease in traffic-induced VOC emissions in many developed countries resulted in relatively comparable shares of traffic and non-traffic VOC emissions in urban airsheds. A key step for urban air quality management is allocating ambient VOC concentrations to their pertinent sources. This study presents an approach that can aid in identifying sources that contribute to observed BTEX concentrations in areas characterized by low BTEX concentrations, where traditional source apportionment techniques are not useful. Analysis of seasonal and diurnal variations of ambient BTEX concentrations from two monitoring stations located in distinct areas reveal the possibility to identify source categories. Specifically, the varying oxidation rates of airborne BTEX compounds are used to allocate contributions of traffic emissions and evaporative sources to observed BTEX concentrations.

A numerical investigation of a hood inhaler is presented, aiming at the assessment of the amount of aerosol that reaches the eyes of the patient when administering medications with such a device. Using a hood for aerosol therapy for infants was already found to be effective and friendly to handle over the commonly used face mask. Using a hood device may adversely deliver unwanted medications to the eyes of the infant. The current study addresses the extent of aerosol deposition at the infant's eye zone. We describe the development and utilization of a numerical simulation for studying the transport and fate of the aerosol particles within a 3D realistic representation of the hood and the infant's head, with a focus on the eye zone. The governing equations were solved using the commercial software, FLUENT 6.1, which is based on the finite volume method. The computational domain was created using the GAMBIT package. The computational geometry was built separately for each configuration of the hood and the infant. It is shown that under optimal working conditions (i.e., when the infant's head is aligned to the funnel) the percentage of aerosol reaching the eye zone is 0.48%. However, when the funnel is tilted toward the eyes the amount of aerosol reaching the eyes zone is predicted to be 4.7%. In general, the results obtained in this study are in good agreement with available in vitro data. It can be concluded that using the hood for aerosol therapy results in minimal deposition at the infant's eye area.

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

Using a hood for aerosol delivery to infants was found to be effective and user-friendly compared to the commonly used face mask. The currently available hood design has an even greater potential in terms of efficiency, and a numerical simulation can serve as a tool for its optimization. The present study describes the development and utilization of a numerical simulation for studying the transport and fate of the aerosol particles and the carrier gas within a three-dimensional realistic representation of the hood and the infant's head. The study further incorporates realistic breathing patterns, with a longer expiration phase than an inspiration phase. Both nose and mouth breathing are simulated. While the base case assumes that the funnel that delivers the aerosol within the hood is perpendicular to the infant's face, more realistic scenarios include a funnel that is slanted with respect to the infant face, the infant's head taking a general position with respect to the funnel, and the funnel and the head being both tilted. A good agreement is found between computation and experimental results. As expected, the most efficient drug delivery, 18%, is achieved when the funnel is normal to the infant's face. The quantitative evaluation of different scenarios presented in this work increases the knowledge of physicians, nurses, and parents regarding the efficacy of the treatment, in terms of the actual amount of drug inhaled under various modes of function of the device.

The use of a hood to administer therapeutic aerosols to wheezy infants has many advantages and was found as efficient as administration using a mask. The aim of the present study is to investigate numerically the airflow induced drug dispersion inside the hood. Drug droplet dispersion is examined with respect to three breathing phases: inspiration, expiration, and apnea. The governing equations describing the airflow and the trajectories of drug droplets were solved using the FLUENT 6.1 Computational Fluid Dynamics (CFD) software package. The geometry and mesh were generated with the GAMBIT package. The velocity field of the air and the trajectories of drug droplets inside the funnel--the tube that delivers the drug from the nebulizer to the infant's mouth--and close to its exit are robust and do not show any appreciable differences among the three breathing phases studied. However, in other parts of the hood, air velocity, and particle motion largely depend on the infant's breathing and physiological state. The efficiency of drug delivery to the mouth during inspiration is found to be as high as 84%, whereas it is much smaller in the other two (common) breathing phases examined. Our results may be utilized to improve the hood design and to increase its efficacy for administration of aerosolized medications to infants.

Drag and torque on nanobeads translating within the pericellular layer while attached to glycolipids of the plasma membrane are calculated by a novel hydrodynamic model. The model considers a bead that translates proximate to a rigid planar interface that separates two distinct Brinkman media. The hydrodynamic resistance is calculated numerically by a modified boundary integral equation formulation, where the pertinent boundary conditions result in a hybrid system of Fredholm integrals of the first and second kinds. The hydrodynamic resistance on the translating bead is calculated for different combinations of the Brinkman screening lengths in the two layers, and for different viscosity ratios. Depending on the bead-membrane separation and on the hydrodynamic properties of both the plasma membrane and the pericellular layer, the drag on the bead may be affected by the properties of the plasma membrane. The Stokes-Einstein relation is applied for calculating the diffusivity of probes (colloidal gold nanobeads attached to glycolipids) in the plasma membrane. This approach provides an alternative way for the interpretation of in vitro observations during single particle tracking procedure, and predicts new properties of the plasma membrane structure.

Air samples were collected at breathing height in the hulling-separation department of a modern cottonseed oil mill in Uzbekistan. The average elutriated mass concentration measured by standard cotton dust samplers was 4.6 mg/m3, much lower than the average total dust concentration measured by stationary personal samplers, 12.49 mg/m3, and by personal samplers attached to workers, 14.53 mg/m3. Differences in readings among the vertical elutriators, stationary personal samplers, and roving personal samplers are attributed to the distinct sampling nature and dynamics of these samplers. The data suggest that most of the dust consisted of particles larger than 15 microm, the particle size cutoff of the vertical elutriator. Differences in readings among stationary and roving personal samplers are statistically significant, presumably representing biased sampling by the roving personal samplers of regions characterized by high dust concentration (due to machines malfunctioning), the nonstatic nature of the sampling, and the interaction between the sampler and the worker (the personal cloud). Cotton dust concentrations in the hulling-separation room were nonuniform, peaking in front of and between the huller-separator pairs. The high total mass readings show that workers were exposed to very high levels of nonthoracic airborne dust, which upon inhalation tends to deposit in the extrathoracic airways. The high elutriated mass concentrations suggest that workers were exposed to respirable cotton dust at levels higher than 1 mg/m3 mean concentration, the current Occupational Safety and Health Administration (OSHA) Permissible Exposure Limit (PEL) for cotton dust. Regressions between dust concentrations measured by stationary vertical elutriators and by personal samplers attached to workers serve for estimating the potential occupational exposure to cotton dust of workers in the hulling-separation room.