Revealing causality in the associations between meteorological variables and air pollutant concentrations.

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 (NO2) 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 NO2 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.

Prenatal and postnatal exposure to NO2 and rapid infant weight gain - A population-based cohort study.

BACKGROUND: Childhood overweight and obesity is a global public health problem. Rapid infant weight gain is predictive of childhood overweight. Studies found that exposure to ambient air pollution is associated with childhood overweight, and have linked prenatal exposure to air pollution with rapid infant weight gain. OBJECTIVES: To examine the association between prenatal and postnatal ambient NO2 exposure, a traffic-related marker, with rapid weight gain in infants. METHODS: We carried out a population-based historical cohort study using data from the Israeli national network of maternal and child health clinics. The study included 474,136 infants born at term with birthweight ≥2500 g in 2011-2019 in central Israel. Weekly averages of NO2 concentration throughout pregnancy (prenatal) and the first 4 weeks of life (postnatal) were assessed using an optimized dispersion model and were linked to geocoded home addresses. We modelled weight gain velocity throughout infancy using the SuperImposition by Translation and Rotation (SITAR) method, a mixed-effects nonlinear model specialized for modelling growth curves, and defined rapid weight gain as the highest velocity tertile. Distributed-lag models were used to assess critical periods of risk and to measure relative risks for rapid weight gain. Adjustments were made for socioeconomic status, population group, subdistrict, month and year of birth, and the alternate exposure period - prenatal or postnatal. RESULTS: The cumulative adjusted relative risk for rapid weight gain of NO2 exposure was 1.02 (95% confidence intereval [CI] 1.00, 1.04) for exposure throughout pregnancy and 1.02 (95% CI 1.01, 1.04) for exposure during the first four postnatal weeks per NO2 interquartile range increase (7.3 ppb). An examination of weekly associations revealed that the critical period of risk for the prenatal exposure was from mid-pregnancy to birth. CONCLUSIONS: Prenatal and postnatal exposures to higher concentrations of traffic-related air pollution are each independently associated with rapid infant weight gain, a risk factor for childhood overweight and obesity.

Accounting for the aerosol type and additional satellite-borne aerosol products improves the prediction of PM2.5 concentrations.

Fine airborne particles (diameter <2.5 µm; PM2.5) 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 PM2.5 prediction. We developed Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models that account for this information when predicting surface PM2.5. 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 R2 (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 PM2.5, better resolved radiative forcing calculations, and tailored abatement surveillance of specific pollutants/sources.

Exposure errors due to inaccurate residential addresses and their impact on epidemiological associations: Evidence from a national neonate dataset.

BACKGROUND: Studies assessing the associations between prenatal air pollution exposures and birth outcomes commonly use maternal addresses at the time of delivery as a proxy for residency throughout pregnancy. Yet, in large-scale epidemiology studies, maternal addresses commonly originate from an administrative source. OBJECTIVE: This study aimed to examine the use of population registry addresses to assign exposure estimations and to evaluate the impact of inaccurate addresses on exposure estimates and association measures of prenatal exposures with congenital hypothyroidism. METHODS: We used morbidity data for congenital hypothyroidism from the national program for neonatal screening for 2009-2015 and address data from two sources: population registry and hospital records. We selected neonates with geocoded addresses from both sources (N = 685,491) and developed a comparison algorithm for these addresses. Next, we assigned neonates with exposures from ambient air pollution of PM and NO2/NOX, evaluated exposure assessment differences, and used multivariable logistic regression models to assess the impact that these differences have on association measures. RESULTS: We found that most of the exposure differences between neonates with addresses from both sources were around zero and had a leptokurtic distribution density, with most values being zero. Additionally, associations between exposure and congenital hypothyroidism were comparable, regardless of address source and when we limited the model to neonates with identical addresses. CONCLUSIONS: We found that ignoring residential inaccuracies results in only a small bias of the associations towards the null. These results strengthen the validity of addresses from population registries for exposure assessment, when detailed residential data during pregnancy are not available.

Air Pollution and Autism Spectrum Disorder in Israel: A Negative Control Analysis.

BACKGROUND: Residual confounding is a major concern for causal inference in observational studies on air pollution-autism spectrum disorder (ASD) associations. This study is aimed at assessing confounding in these associations using negative control exposures. METHODS: This nested case-control study included all children diagnosed with ASD (detected through 31 December 2016) born during 2007-2012 in Israel and residing in the study area (N = 3,843), and matched controls of the same age (N = 38,430). We assigned individual house-level exposure estimates for each child. We estimated associations using logistic regression models, mutually adjusted for all relevant exposure periods (prepregnancy, pregnancy, and postnatal). We assessed residual confounding using postoutcome negative control exposure at age 28-36 months. RESULTS: In mutually adjusted models, we observed positive associations with ASD for postnatal exposures to NOx (odds ratio per interquartile range, 95% confidence interval: 1.19, 1.02-1.38) and NO2 (1.20, 1.00-1.43), and gestational exposure to PM2.5-10 (1.08, 1.01-1.15). The result for the negative control period was 1.04, 0.99-1.10 for PM2.5, suggesting some residual confounding, but no associations for PM2.5-10 (0.98, 0.81-1.18), NOx (1.02, 0.84-1.25), or NO2 (0.98, 0.81-1.18), suggesting no residual confounding. CONCLUSIONS: Our results further support a hypothesized causal link with ASD that is specific to postnatal exposures to traffic-related pollution.

Associations Between Prenatal Exposure to Air Pollution and Congenital Hypothyroidism.

Adequate thyroid hormone availability is required for normal brain development. Studies have found associations between prenatal exposure to air pollutants and thyroid hormones in pregnant women and newborns. We aimed to examine associations of trimester-specific residential exposure to common air pollutants with congenital hypothyroidism (CHT). All term infants born in Israel during 2009-2015 were eligible for inclusion. We used data on CHT from the national neonatal screening lab of Israel, and exposure data from spatiotemporal air pollution models. We used multivariable logistic regression models to estimate associations of exposures with CHT, adjusting for ethnicity, socioeconomic status, geographical area, conception season, conception year, gestational age, birth weight, and child sex. To assess residual confounding, we used postnatal exposures to the same pollutants as negative controls. The study population included 696,461 neonates. We found a positive association between third-trimester nitrogen oxide exposure and CHT (per interquartile-range change, odds ratio = 1.23, 95% confidence interval: 1.08, 1.41) and a similar association for nitrogen dioxide. There was no evidence of residual confounding or bias by correlation among exposure periods for these associations.

Fusion of land use regression modeling output and wireless distributed sensor network measurements into a high spatiotemporally-resolved NO2 product.

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 (NO2) 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 NO2 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.

Estimation of High-Resolution PM2.5 over the Indo-Gangetic Plain by Fusion of Satellite Data, Meteorology, and Land Use Variables.

Very high spatially resolved satellite-derived ground-level concentrations of particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) 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 PM2.5 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 (R2 = 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 PM2.5 exposure metrics across the IGP at varying temporal scales (i.e., both short and long terms). The RF-based PM2.5 estimates showed high PM2.5 levels over the middle and lower IGP, with the annual mean exceeding 110 µg/m3. As for seasons, winter was the most polluted season, while monsoon was the cleanest. Spatially, the middle and lower IGP showed poorer air quality compared to the upper IGP. In winter, the middle and lower IGP experienced very poor air quality, with mean PM2.5 concentrations of >170 µg/m3.

Preexisting coronary heart disease and susceptibility to long-term effects of traffic-related air pollution: A matched cohort analysis.

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) (pinteraction = 0.003). A similar pattern was seen for all-cancer incidence (hazard ratioCHD = 1.19 (1.03-1.37), hazard ratioCHD-Free = 0.93 (0.84-1.04) (pinteraction = 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.

Emissions vs. turbulence and atmospheric stability: A study of their relative importance in determining air pollutant concentrations.

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 NOx, 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, PM1 and PM2.5 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.

Unconventional oil and gas development and health outcomes: A scoping review of the epidemiological research.

BACKGROUND: Hydraulic fracturing together with directional and horizontal well drilling (unconventional oil and gas (UOG) development) has increased substantially over the last decade. UOG development is a complex process presenting many potential environmental health hazards, raising serious public concern. AIM: To conduct a scoping review to assess what is known about the human health outcomes associated with exposure to UOG development. METHODS: We performed a literature search in MEDLINE and SCOPUS for epidemiological studies of exposure to UOG development and verified human health outcomes published through August 15, 2019. For each eligible study we extracted data on the study design, study population, health outcomes, exposure assessment approach, statistical methodology, and potential confounders. We reviewed the articles based on categories of health outcomes. RESULTS: We identified 806 published articles, most of which were published during the last three years. After screening, 40 peer-reviewed articles were selected for full text evaluation and of these, 29 articles met our inclusion criteria. Studies evaluated pregnancy outcomes, cancer incidence, hospitalizations, asthma exacerbations, sexually transmitted diseases, and injuries or mortality from traffic accidents. Our review found that 25 of the 29 studies reported at least one statistically significant association between the UOG exposure metric and an adverse health outcome. The most commonly studied endpoint was adverse birth outcomes, particularly preterm deliveries and low birth weight. Few studies evaluated the mediating pathways that may underpin these associations, highlighting a clear need for research on the potential exposure pathways and mechanisms underlying observed relationships. CONCLUSIONS: This review highlights the heterogeneity among studies with respect to study design, outcome of interest, and exposure assessment methodology. Though replication in other populations is important, current research points to a growing body of evidence of health problems in communities living near UOG sites.

Vertical distribution of smoke aerosols over upper Indo-Gangetic Plain.

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 ±â€¯0.65 km) and wheat (AM: 2.34 ±â€¯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.

Cancer and mortality in relation to traffic-related air pollution among coronary patients: Using an ensemble of exposure estimates to identify high-risk individuals.

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 (NOx) -a proxy for TRAP- was estimated by optimized dispersion model (ODM) and land use regression (LUR) (rPearson = 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 NOx 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.

Traffic-related Air Pollution and Pregnancy Loss.

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.

Mean and variance of greenness and pregnancy outcomes in Tel Aviv during 2000-14: longitudinal and cross-sectional approaches.

BACKGROUND: Natural environments may have beneficial impacts on pregnancy outcomes. However, longitudinal evidence is limited and the associations with variance in surrounding greenness is unknown. Our objective was to evaluate these associations among 73 221 live births in Tel Aviv, Israel. METHODS: Longitudinal exposure to mean of greenness during pregnancy and trimesters were calculated using satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) Normalised Difference Vegetation Index (NDVI) data. In addition, exposure to mean and variation of NDVI from high-resolution satellite and percentage of tree cover [Vegetation Continuous Fields (VCF)] at 300-m buffer were evaluated in a cross-sectional approach. Generalized linear models were used to estimate the crude and adjusted associations. We explore the possible mediating role of ambient exposures and distance to 'outdoor gyms' located in parks. RESULTS: Crude beneficial associations between exposure to higher mean NDVI during pregnancy and pregnancy outcomes were observed [for birthweight, 3rd/1st tertile exposure increased the mean by 25.5 g, 95% confidence intervals (CIs): 15.4, 35.5] and decreased the odds of low birthweight, small for gestational age, preterm deliveries (PTD) and very PTD. Adjustment for individual and neighbourhood-level markers of socio-economic status (SES) attenuated all the associations. Strongest associations were observed during the first and second trimesters. Cross-sectional associations for mean greenness were similar with narrower CIs, and associations with NDVI were stronger than with tree cover and stronger for mean compared with variance of greenness. Associations were consistent for term births, different buffer sizes and for further adjustment to maternal education. Stronger associations were observed for lowest SES. Distance to 'outdoor gyms' and variance of greenness had the largest estimates of mediation. CONCLUSION: This study adds to the limited information on when exposure to greenness is most beneficial, on the association with variance of greenness and the possible pathways. These observations require confirmation in other populations.

Relation between personal exposure and outdoor concentrations of carcinogenic polycyclic aromatic hydrocarbons during smog episode.

OBJECTIVES: To our knowledge this is the first study measuring personal exposure to carcinogenic polycyclic aromatic hydrocarbons (cPAHs) bound to airborne particulate matter ≤ 2.5 µm (PM2.5) in periods of high air pollution (smog episode) in which citizen were tracked. METHODS: Measurements were performed in industrial regions of the Czech Republic: Ostrava, Karviná, Havírov. The city of Prague served as a control. Personal monitoring was conducted by active personal monitors for 48 hours. Non-smoking city policemen from Prague, Karviná and Havírov, office workers from Ostrava city and volunteers from Ostrava-Radvanice and Bartovice participated in the study (N = 214). RESULTS: The average personal exposure to benzo[a]pyrene (B[a]P) was highest in Ostrava (17.2 ng/m3), followed by Karviná, Havírov, Radvanice and Bartovice, and Prague (14.2, 12.0, 9.3, and 2.8 ng/m3, respectively). We tested for association between the personal exposure to cPAHs and various health-related factors extracted from the questionnaires, including lifestyle factors and day-to-day activities. CONCLUSIONS: Exposure to outdoor cPAHs, environmental tobacco smoke (ETS), commuting, and time spent indoors (in restaurants, workplace or home) were found to be the main determinants of the personal exposure. Daily cPAHs measurements in highly polluted areas are needed for evaluating the personal exposure and to avoid its underestimation resulting from stationary monitoring.

Seasonal variations of polybrominated flame retardants bound to car dust under Mediterranean climate.

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.

Data Integration for the Assessment of Population Exposure to Ambient Air Pollution for Global Burden of Disease Assessment.

Air pollution is a leading global disease risk factor. Tracking progress (e.g., for Sustainable Development Goals) requires accurate, spatially resolved, routinely updated exposure estimates. A Bayesian hierarchical model was developed to estimate annual average fine particle (PM2.5) concentrations at 0.1° × 0.1° spatial resolution globally for 2010-2016. The model incorporated spatially varying relationships between 6003 ground measurements from 117 countries, satellite-based estimates, and other predictors. Model coefficients indicated larger contributions from satellite-based estimates in countries with low monitor density. Within and out-of-sample cross-validation indicated improved predictions of ground measurements compared to previous (Global Burden of Disease 2013) estimates (increased within-sample R2 from 0.64 to 0.91, reduced out-of-sample, global population-weighted root mean squared error from 23 µg/m3 to 12 µg/m3). In 2016, 95% of the world's population lived in areas where ambient PM2.5 levels exceeded the World Health Organization 10 µg/m3 (annual average) guideline; 58% resided in areas above the 35 µg/m3 Interim Target-1. Global population-weighted PM2.5 concentrations were 18% higher in 2016 (51.1 µg/m3) than in 2010 (43.2 µg/m3), reflecting in particular increases in populous South Asian countries and from Saharan dust transported to West Africa. Concentrations in China were high (2016 population-weighted mean: 56.4 µg/m3) but stable during this period.

Impacts of Personal Mobility and Diurnal Concentration Variability on Exposure Misclassification to Ambient Pollutants.

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 (NO2) was estimated for each individual by tracking the daily trajectory through an NO2 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.

Chronic exposure to traffic-related air pollution and cancer incidence among 10,000 patients undergoing percutaneous coronary interventions: A historical prospective study.

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 (NOx) - 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 NOx 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.