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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.

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 (pre-pregnancy, pregnancy, and postnatal). We assessed residual confounding using post-outcome 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 [OR] per interquartile range, 95% confidence interval [CI]: 1.19, 1.02-1.38) and NO(2) (1.20, 1.00-1.43), and gestational exposure to PM(2.5-10) (1.08, 1.01-1.15). The result for the negative control period was 1.04, 0.99-1.10 for PM(2.5,) suggesting some residual confounding, but no associations for PM(2.5-10) (0.98, 0.81-1.18), NOx (1.02, 0.84-1.25) or NO(2) (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.

Exposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed.We aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration-response relationships between annual exposure to fine particulate matter [PMIn this simulation study, we use dailyWe simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration-response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels.Our findings suggest that the causal determination for long-term

Nitrogen dioxide (NO2) remains an important traffic-related pollutant associated with both short- and long-term health effects. We aim to model daily average NO2 concentrations in Switzerland in a multistage framework with mixed-effect and random forest models to respectively downscale satellite measurements and incorporate local sources. Spatial and temporal predictor variables include data from the Ozone Monitoring Instrument, Copernicus Atmosphere Monitoring Service, land use, and meteorological variables. We derived robust models explaining ∼58% (R2 range, 0.56-0.64) of the variation in measured NO2 concentrations using mixed-effect models at a 1 × 1 km resolution. The random forest models explained ∼73% (R2 range, 0.70-0.75) of the overall variation in the residuals at a 100 × 100 m resolution. This is one of the first studies showing the potential of using earth observation data to develop robust models with fine-scale spatial (100 × 100 m) and temporal (daily) variation of NO2 across Switzerland from 2005 to 2016. The novelty of this study is in demonstrating that methods originally developed for particulate matter can also successfully be applied to NO2. The predicted NO2 concentrations will be made available to facilitate health research in Switzerland.

International audience; Understanding the health impacts of particulate matter (PM) requires spatiotemporally continuous exposure estimates. We developed a multi-stage ensemble model that estimates daily mean PM2.5 and PM10 at 1 km spatial resolution across France from 2000 to 2019. First, we alleviated the sparsity of PM2.5 monitors by imputing PM2.5 at more common PM10 monitors. We also imputed missing satellite aerosol optical depth (AOD) based on modelled AOD from atmospheric reanalyses. Next, we trained three base learners (mixed models, Gaussian Markov random fields, and random forests) to predict daily PM concentrations based on AOD, meteorology, and other variables. Finally, we generated ensemble predictions using a generalized additive model with spatiotemporally varying weights that exploit the strengths and weaknesses of each base learner. The Gaussian Markov random field dominated the ensemble, outperforming mixed models and random forests at most locations on most days. Rigorous cross-validation showed that the ensemble predictions were quite accurate, with mean absolute error (MAE) of 2.72 µg/m 3 and R 2 of 0.76 for PM2.5; PM10 MAE was 4.26 µg/m 3 and R 2 0.71. Our predictions are available to improve epidemiological studies of acute and chronic PM exposure in urban and rural

Background Numerous studies have confirmed the association of ambient temperature and air pollution with a higher risk of morbidities, yet few have addressed their effect on the ocular system. The purpose of this study was to assess the association between temperature, air pollution, and emergency room visits for conjunctivitis. Methods In this case-crossover study, the records of all emergency room visits to Soroka University Medical Center (SUMC) from 2009 to 2014 were reviewed for patients with conjunctivitis. Daily exposure to fine and coarse particulate matter and temperature were determined by a hybrid model involving satellite sensors. Mean relative humidity was obtained from the Ministry of Environmental Protection meteorological monitoring station located in Beer-Sheva. Results Six hundred one patients were diagnosed with conjunctivitis in the SUMC emergency room. We discovered a positive association between temperature increments and incidence of conjunctivitis. The strongest effect was found during summer and autumn, with an immediate (lag0) incidence increase of 8.1% for each 1 °C increase in temperature (OR = 1.088, 95%CI: 1.046–1.132) between 24 and 28 °C in the summer and 7.2% for each 1 °C increase in temperature (OR = 1.072, 95%CI: 1.036–1.108) between 13 and 23 °C in the autumn. There was no statistically significant association between fine and coarse particulate matter and conjunctivitis incidence. Conclusion Temperature increases during summer and autumn are significantly associated with an increased risk of conjunctivitis. Conjunctivitis is not associated with non-anthropogenic air pollution. These findings may help community clinics and hospital emergency rooms better predict conjunctivitis cases and will hopefully lead to improved prevention efforts that will lower the financial burden on both the individual and the public.

Background: Accumulating evidence suggest that perinatal exposure to particulate matter (PM), nitrogen dioxide (NO2) and nitrogen oxides (NOx), is associated with risk of autism spectrum disorder (...

Satellite-based particulate matter (PM) models provide spatially and temporally resolved estimations, allowing greater spatial-temporal coverage compared to sparse ground monitoring stations. The spatio-temporal resolution of these models can be improved using aerosol optical depth (AOD) products from various satellite platforms with different overpass times which can capture possible changes in diurnal aerosol concentrations. Israel is characterized by diverse geo-climatic regions and it is subjected to frequent dust storms events. Our goal was to estimate PM10 and PM2.5 concentrations in Israel on daily and intra-daily (mean PM around the Aqua and Terra overpasses) temporal resolutions and to assess the differences between these time windows. A hybrid modeling approach that consists of three stages was used enabling spatially continuous PM estimations at 1 × 1 km spatial resolution. The model was calibrated on a daily basis applying a mixed modeling approach using MODIS-based MAIAC AOD and various spatial and temporal predictors. It was found that in certain urban areas the measured and estimated PM concentrations during the satellite overpass (Terra or Aqua) were higher than the mean daily PM. The models performed well showing cross-validated R2 that ranged between 0.82 and 0.92. Mean estimated PM for the study period (2005–2015) during days with no dust events showed different spatial patterns for the daily and intra-daily estimations and revealed areas in Israel that are affected by high PM concentrations (mainly industrial or dense urban areas). Estimations from these models are useful for epidemiological research and might contribute to environmental regulatory purposes by focusing the efforts of PM pollution reduction at the identified polluted areas.

Air pollution is one of the leading causes of mortality worldwide. An accurate assessment of its spatial and temporal distribution is mandatory to conduct epidemiological studies able to estimate long-term (e.g., annual) and short-term (e.g., daily) health effects. While spatiotemporal models for particulate matter (PM) have been developed in several countries, estimates of daily nitrogen dioxide (NO2) and ozone (O3) concentrations at high spatial resolution are lacking, and no such models have been developed in Sweden. We collected data on daily air pollutant concentrations from routine monitoring networks over the period 2005&ndash, 2016 and matched them with satellite data, dispersion models, meteorological parameters, and land-use variables. We developed a machine-learning approach, the random forest (RF), to estimate daily concentrations of PM10 (PM&lt, 10 microns), PM2.5 (PM&lt, 2.5 microns), PM2.5&ndash, 10 (PM between 2.5 and 10 microns), NO2, and O3 for each squared kilometer of Sweden over the period 2005&ndash, 2016. Our models were able to describe between 64% (PM10) and 78% (O3) of air pollutant variability in held-out observations, and between 37% (NO2) and 61% (O3) in held-out monitors, with no major differences across years and seasons and better performance in larger cities such as Stockholm. These estimates will allow to investigate air pollution effects across the whole of Sweden, including suburban and rural areas, previously neglected by epidemiological investigations.

Spatiotemporally resolved particulate matter (PM) estimates are essential for reconstructing long and short-term exposures in epidemiological research. Improved estimates of PM2.5 and PM10 concentrations were produced over Italy for 2013-2015 using satellite remote-sensing data and an ensemble modeling approach. The following modeling stages were used: (1) missing values of the satellite-based aerosol optical depth (AOD) product were imputed using a spatiotemporal land-use random-forest (RF) model incorporating AOD data from atmospheric ensemble models; (2) daily PM estimations were produced using four modeling approaches: linear mixed effects, RF, extreme gradient boosting, and a chemical transport model, the flexible air quality regional model. The filled-in MAIAC AOD together with additional spatial and temporal predictors were used as inputs in the three first models; (3) a geographically weighted generalized additive model (GAM) ensemble model was used to fuse the estimations from the four models by allowing the weights of each model to vary over space and time. The GAM ensemble model outperformed the four separate models, decreasing the cross-validated root mean squared error by 1-42%, depending on the model. The spatiotemporally resolved PM estimations produced by the suggested model can be applied in future epidemiological studies across Italy.

Spatiotemporally resolved particulate matter (PM) estimates are essential for reconstructing long and short-term exposures in epidemiological research. Improved estimates of PM

Nitrogen dioxide (NO

Background Numerous studies have confirmed that ambient temperature and air pollution are associated with higher risk of morbidities to different systems of the human body, yet few have addressed their effect on the ocular system. The purpose of this study is to determine the association between temperature, air pollution and emergency room visits for non-specific conjunctivitis Methods In this retrospective cohort study, the records of all emergency room visits to Soroka University Medical Center (SUMC) from 2009 to 2014 were reviewed for patients with conjunctivitis. Exposure to fine and coarse particulate matter and temperature were assessed by a hybrid model that incorporated daily satellite remote sensing. Results The records of the 6001 patients who visited the SUMC emergency room with conjunctivitis, together with the meteorological data, revealed a positive association between temperature increment and incidence of conjunctivitis. The strongest effect was found during summer and autumn: the incidence increased 8.1% for each 1oC rise in temperature between 24oC and 28oC in the summer, and 7.2% for each 1oC rise in temperature between 13oC and 23oC in autumn. The association between fine and coarse particulate matter and incidence of conjunctivitis was not statistically significant. Conclusion High ambient temperature is significantly associated with an increased risk of nonspecific conjunctivitis in summer and autumn and not in spring and winter. Conjunctivitis is not associated with air pollution. The findings can assist community clinics and hospital emergency rooms prepare for the upticks in the condition during certain seasons and acute rises in temperatures, lowering the financial costs to both the individual and the public.

The role played by unsustainable resource management in initiating international conflicts is well documented. The Syrian Civil War, commencing in March 2011, presents such a case. The prevailing opinion links the unrest with sequential droughts occurring from 2007&ndash, 2010. Our research, however, reveals that the winter-rainfed agricultural conditions before 2011, as detected by satellite-derived vegetation indices, were similar and even better for Syrian farmers than for those of their Turkish counterparts across the border. Concurrently, summer-irrigated crops, heavily dependent on Euphrates River water originating from Turkey, notably declined in Syria while flourishing in Turkey. These findings are firmly supported by other independent and validated datasets, including long-term cross-border discharge, the water level in Syrian and Turkish reservoirs, and transborder groundwater flow. We conclude that the Turkish policy of unilaterally diverting the Euphrates water was the main reason for the agricultural collapse and subsequent instability in Syria in 2011. The obvious inference is that while prolonged drought exacerbated conditions, unsustainable anthropogenic water management in Turkey was the proximate cause behind the Syrian uprising.

Introduction Previous research suggested that an out-of-hospital cardiac arrest (OHCA) may be triggered by an exposure to ambient pollutants. Objective We investigated the link between OHCA and a short-term exposure to particulate matter (PM) and other pollutants, within extreme climate conditions in Israel and high PM. Methods In a case-crossover analysis, we analyzed all adult cases of OHCA in Israel during 2016–2017. The air-pollution and meteorology data were retrieved from the 132 monitoring stations. All associations at study were investigated using a lag-distributed regression and adjusted to temperature and humidity. Results There were 12401 OHCA cases. Patients experiencing OHCA were likely to be exposed to elevated levels of pollutants, specifically, nitrogen dioxide (NO2) and particulate matter of size ≤2.5 μm (PM2.5) several hours prior to an event, although both at borderline significance, i.e. odds ratio (OR) = 1.20 (95%CI 0.96; 1.51) and OR = 1.15 (95%CI 0.84; 1.60), respectively. An exposure to NO2 was independently associated with OHCA among males (OR = 1.39, 95%CI 0.96; 2.01) and if occurred during the midweek (OR = 1.43, 95%CI 1.03; 1.97). The adverse effect of PM10 was more evident during a weekend (OR = 2.36, 95%CI 0.88; 6.28), as opposed to working days (OR = 0.81, 95%CI 0.45; 1.44). Analysis stratified by regions suggested a spatial variability in pollution associated with OHCA. Conclusions Short-term exposure to high levels of pollution is adversely associated with OHCA independently of meteorological conditions. The magnitude of the effect is modified by patients' demography. Main finding Short-term exposure to high levels of pollution is adversely associated with OHCA. This effect is independent of temperature and humidity.

Background Multiple Sclerosis (MS) is a chronic inflammatory disease of the central nervous system with both a genetic and environmental component. Objective In the current study, we examined an association between incidence of MS moderate to severe relapses and exposure to air pollutants and meteorological exposures. Methods We enrolled MS patients in Southern Israel during 2000-2017. Exposure assessment relied on satellite-based model of exposure to particulate matter of size Kloog et al., 2015 ). The information on exposure to nitrogen dioxide (NO2), sulfur dioxide (SO2) and ozone (O3) levels was completed from the database of the monitoring stations. We analyzed the data using a semi-ecological approach. The monthly incidence of MS-related relapses requiring hospitalization as a function of environmental factors was analyzed by time-series technique, adjusting to sex, age and smoking. We also used a case-crossover approach to compare environmental exposure of a patient on the day of the relapse with the exposure on the relapse-free days. All estimates were adjusted to the heat index and were divided by IQR. Results There were 287 MS patients in the study, with an average age of 52.8 ± 16.7 years, 37% of them (107) being under 40. Mostly female (66.2%), and 13.6% of the patients smoking (47% non-smoking and 39.4% unknown). PM2.5 was independently associated with MS relapses within the non-smoking population [Relative Risk (RR)=1.28, 95%CI:1.01-1.62]. O3 was found adversely associated with MS relapses among patients younger than 40 [RR=1.58, 95%CI 1.03-4.43]. Based on the case-crossover approach, relapses were associated with elevated levels of PM10 and NO2 in all subjects [Odds Ratio (OR)=1.05, 95%CI:1.00-1.11; OR=1.85, 95%CI: 1.28-2.68, respectively]. An adverse association with PM2.5 was observed in non-smokers [OR 1.12, 95%CI 1.00-1.25]. Conclusions The findings show that MS relapses are adversely associated with an ambient exposure to PM and NO2.

Reconstructing the distribution of fine particulate matter (PM(2.5)) in space and time, even far from ground monitoring sites, is an important exposure science contribution to epidemiologic analyses of PM(2.5) health impacts. Flexible statistical methods for prediction have demonstrated the integration of satellite observations with other predictors, yet these algorithms are susceptible to overfitting the spatiotemporal structure of the training datasets. We present a new approach for predicting PM(2.5) using machine-learning methods and evaluating prediction models for the goal of making predictions where they were not previously available. We apply extreme gradient boosting (XGBoost) modeling to predict daily PM(2.5) on a 1×1 km(2) resolution for a 13 state region in the Northeastern USA for the years 2000–2015 using satellite-derived aerosol optical depth and implement a recursive feature selection to develop a parsimonious model. We demonstrate excellent predictions of withheld observations but also contrast an RMSE of 3.11 μg/m(3) in our spatial cross-validation withholding nearby sites versus an overfit RMSE of 2.10 μg/m(3) using a more conventional random ten-fold splitting of the dataset. As the field of exposure science moves forward with the use of advanced machine-learning approaches for spatiotemporal modeling of air pollutants, our results show the importance of addressing data leakage in training, overfitting to spatiotemporal structure, and the impact of the predominance of ground monitoring sites in dense urban sub-networks on model evaluation. The strengths of our resultant modeling approach for exposure in epidemiologic studies of PM(2.5) include improved efficiency, parsimony, and interpretability with robust validation while still accommodating complex spatiotemporal relationships.

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Particulate matter (PM) air pollution is one of the major causes of death worldwide, with demonstrated adverse effects from both short-term and long-term exposure. Most of the epidemiological studies have been conducted in cities because of the lack of reliable spatiotemporal estimates of particles exposure in nonurban settings. The objective of this study is to estimate daily PM

The authors have observed that the function linking health outcomes with exposure to particulate-matter (PM) follows a biphasic pattern. It peaks around levels of PM10≤100 μg/m3, then weakens and rises again at PM10 levels in the range of hundreds. This could be due to a different nature of PM, the first peak reflecting a stronger anthropogenic and the second - weaker non-anthropogenic particles' effect. The current analysis is focused at the biphasic pattern on the association between PM levels with BG and asthma exacerbations. Pollutants were assessed by local monitoring stations and a satellitebased model. Local weekends/holidays were used to define nonanthropogenic levels of pollutants featured by lower Nitrogen Dioxide, the proxy for anthropogeneity. The association of PM10 with health outcomes within 24-48h lag was explored using spline functions of generalized additive models. Analysis of 546,420 BG tests (43,569 subjects) showed an almost linear association of PM10 with asthma with BG during the days with anthropogenic activity and no trend on other days. Analysis of asthmatic exacerbations within 1576 children showed no heterogeneity in association with PM10 by anthropogeneity levels, possibly indicating a mechanical impact on alveolar as the main trigger for exacerbations rather than PM10 chemical composition.