Search

Background. Parkinson’s disease (PD) is believed to develop from epigenetic modulation of gene expression through environmental factors that accounts for up to 85% of all PD cases. The main objective of this study was to examine the association between PD onset and a cumulative exposure to potentially modifiable ambient exposures. Methods. The study population comprised 3343 incident PD cases and 31,324 non-PD controls in Southern Israel. The exposures were determined based on the monitoring stations and averaged per year. Their association with PD was modeled using a distributed lag non-linear model and presented as an effect of exposure to the 75th percentile as compared to the 50th percentile of each pollutant, accumulated over the span of 5 years prior to the PD. Results. We recorded an adverse effect of particulate matter of size ≤10 μm in diameter (PM10) and solar radiation (SR) with odds ratio (OR) = 1.06 (95%CI: 1.02; 1.10) and 1.23 (95%CI: 1.08; 1.39), respectively. Ozone (O3) was also adversely linked to PD, although with a borderline significance, OR: 1.12 (95%CI: 0.99; 1.25). Immigrants arriving in Israel after 1989 appeared to be more vulnerable to exposure to O3 and SR. The dose response effect of SR, non-existent for Israeli-born (OR = 0.67, 95%CI: 0.40; 1.13), moderate for immigrants before 1989 (OR = 1.17, 95%CI: 0.98; 1.40) and relatively high for new immigrants (OR = 1.25, 95%CI: 1.25; 2.38) indicates an adaptation ability to SR. Conclusions. Our findings supported previous reports on adverse association of PD with exposure to PM10 and O3. Additionally, we revealed a link of Parkinson’s Disease with SR that warrants an extensive analysis by research groups worldwide.

Stroke is the second most common cause of death and disability in the world. Many studies have found fine particulate matter (PM2.5) exposure to be associated with an increased risk of atherosclerotic cardiovascular disease, mostly focusing on ischemic heart disease and acute myocardial infarction. In a national analysis conducted in Israel—an area with unique climate conditions and high air pollution levels, we estimated the association between short-term PM2.5 exposure and ischemic stroke, intracerebral hemorrhage (ICH), or transient ischemic attacks (TIA). Using the Israeli National Stroke Registry, we obtained information on all stroke cases across Israel in 2014–2018. We obtained daily PM2.5 exposures from spatiotemporally resolved exposure models. We restricted the analytical data to days in which PM2.5 levels did not exceed the Israeli 24 h standard (37.5 µg/m3). We repeated the analysis with a stratification by sociodemographic characteristics and comorbidities. For all outcomes, the exposure–response curves were nonlinear. PM2.5 exposure was associated with a higher ischemic stroke risk, with larger effect estimates at higher exposure levels. Although nonsignificant, the exposure–response curve for TIA was similar. The associations with ICH were nonsignificant throughout the PM2.5 exposure distribution. The associations with ischemic stroke/TIA were larger among women, non-Jewish individuals, older adults, and individuals with diabetes, hypertension, and ischemic heart disease. In conclusion, short-term PM2.5 exposure is associated with a higher risk for ischemic stroke and possibly TIA, even when PM2.5 concentrations do not exceed the Israeli air quality guideline threshold. Vulnerability to the air pollution effects differed by age, sex, ethnicity, and comorbidities.

In the original article [...]

Exposure to PM2.5 has been associated with the prevalence of obesity. In the Greater Mexico City Area (GMCA), both are ranked among the highest in the world. Our aim was to analyze this association in children, adolescents, and adults in the GMCA. We used data from the 2006 and 2012 Mexican National Surveys of Health and Nutrition (ENSANUT). Participants’ past-year exposure to ambient PM2.5 was assessed using land use terms and satellite-derived aerosol optical depth estimates, weight and height were measured. We used survey-adjusted logistic regression models to estimate the odds ratios (ORs) of obesity (vs. normal-overweight) for every 10 µg/m3 increase in annual PM2.5 exposure for children, adolescents, and adults. Using a meta-analysis approach, we estimated the overall odds of obesity. We analyzed data representing 19.3 million and 20.9 million GMCA individuals from ENSANUT 2006 and 2012, respectively. The overall pooled estimate between PM2.5 exposure and obesity was OR = 1.96 (95% CI: 1.21, 3.18). For adolescents, a 10 µg/m3 increase in PM2.5 was associated with an OR of 3.53 (95% CI: 1.45, 8.58) and 3.79 (95% CI: 1.40, 10.24) in 2006 and 2012, respectively. More studies such as this are recommended in Latin American cities with similar air pollution and obesity conditions.

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.

Intrauterine growth has health implications both in childhood and adulthood. Birthweight is partially determined by prenatal environmental exposures. We aim to identify important predictors of birthweight out of a set of environmental, built environment exposures, and socioeconomic environment variables during pregnancy (i.e., fine particulate matter (PM2.5), temperature, greenness, walkability, noise, and economic indices). We included all singleton live births of mothers who resided in urban census block-groups and delivered in Massachusetts between 2001 and 2011 (n = 640,659). We used an elastic-net model to select important predictors of birthweight and constructed a multivariate model including the selected predictors, with adjustment for confounders. We additionally used a weighted quantile sum regression to assess the contribution of each exposure to differences in birthweight. All exposures were selected as important predictors of birthweight. In the multivariate model, lower birthweight was significantly associated with lower greenness and with higher temperature, walkability, noise, and segregation of the &ldquo, high income&rdquo, group. Treating the exposures individually, nighttime noise had the highest weight in its contribution to lower birthweight. In conclusion, after accounting for individual confounders, maternal environmental exposures, built environment exposures, and socioeconomic environment during pregnancy were important predictors of birthweight, emphasizing the role of these exposures in fetal growth and development.

We aimed to estimate the association of urinary metal content with the exposure to hazardous household factors and ambient air pollution in pregnant women. We analyzed urine samples of 143 women enrolled at delivery and assessed household exposure by questionnaire. Air pollution estimates were based on monitoring stations and satellite-based models. Complaints about noise, rubbish, and sewage in the neighborhood were associated with higher uranium concentrations (prevalence ratio (PR) = 1.42, 1.46, and 1.18, respectively). Complaints about noise were more frequent in women with higher cobalt, nickel, molybdenum, cadmium, and vanadium urine concentrations (PRs = 2.42&ndash, 9.43), and complaints about smell in women with higher cobalt and strontium (PRs = 2.55&ndash, 6.27). Exposure to nitrogen oxides was associated with higher concentrations of nickel, aluminum, lead, and chromium in urine (PRs = 1.80&ndash, 5.85). Women exposed to elevated levels of particulate matter were more likely to have higher concentrations of cobalt, nickel, thallium, lead, iron, strontium, barium, silver, and chromium (PRs = 1.97&ndash, 13.64). Exposure to high levels of sulfur dioxide was positively associated with cobalt and nickel (PR = 1.98 and 1.88, respectively). The current analysis suggests the possibility of a related simultaneous exposure to multiple pollutants. Further studies are warranted to corroborate the findings and reveal the multiple exposure effect on human health.

Natural vegetation, or greenness, may benefit maternal health and consequently, fetal growth, by providing opportunities for physical activity and psychological restoration, and decreasing detrimental environmental exposures. We retrieved Massachusetts Birth Registry data from 2001&ndash, 2013 and investigated the association between residential greenness and birthweight in full-term births (&ge, 37 weeks gestation). We calculated average residential greenness during pregnancy using 250 m normalized difference vegetation index (NDVI) from satellites. We estimated associations between greenness and continuous birthweight, term low birthweight (TLBW: &lt, 2500 g), and small for gestational age (SGA: &lt, 10th percentile of birthweight stratified by sex and gestational age) adjusted for individual and neighborhood covariates and considered nonlinearity and effect modification. Higher greenness exposure was associated with higher birthweight with stronger associations in the lower than higher range of greenness. Greenness was associated with lower odds of TLBW (OR 0.98, 95% CI 0.97, 0.99 per 0.1 increase in NDVI) and SGA (OR 0.98, 95% 0.97, 0.99) and associations varied by population density (TLBW) and socioeconomic status (TLBW, SGA). Our results suggest that greenness is beneficial to fetal growth exhibited by higher birthweight and lower odds of TLBW and SGA. Unlike prior studies, associations with TLBW and SGA appeared stronger among those with higher socioeconomic status.

An accurate assessment of pollutants’ exposure and precise evaluation of the clinical outcomes pose two major challenges to the contemporary environmental health research. The common methods for exposure assessment are based on residential addresses and are prone to many biases. Pollution levels are defined based on monitoring stations that are sparsely distributed and frequently distanced far from residential addresses. In addition, the degree of an association between outdoor and indoor air pollution levels is not fully elucidated, making the exposure assessment all the more inaccurate. Clinical outcomes’ assessment, on the other hand, mostly relies on the access to medical records from hospital admissions and outpatients’ visits in clinics. This method differentiates by health care seeking behavior and is therefore, problematic in evaluation of an onset, duration, and severity of an outcome. In the current paper, we review a number of novel solutions aimed to mitigate the aforementioned biases. First, a hybrid satellite-based modeling approach provides daily continuous spatiotemporal estimations with improved spatial resolution of 1 × 1 km2 and 200 × 200 m2 grid, and thus allows a more accurate exposure assessment. Utilizing low-cost air pollution sensors allowing a direct measurement of indoor air pollution levels can further validate these models. Furthermore, the real temporal-spatial activity can be assessed by GPS tracking devices within the individuals’ smartphones. A widespread use of smart devices can help with obtaining objective measurements of some of the clinical outcomes such as vital signs and glucose levels. Finally, human biomonitoring can be efficiently done at a population level, providing accurate estimates of in-vivo absorbed pollutants and allowing for the evaluation of body responses, by biomarkers examination. We suggest that the adoption of these novel methods will change the research paradigm heavily relying on ecological methodology and support development of the new clinical practices preventing adverse environmental effects on human health.

Few studies examined the impact of maternal socioeconomic status and of its combined effects with environmental exposures on birthweight. Our goal was to examine the impact of maternal homelessness (mothers ever homeless or who lived in shelters during pregnancy) and participation in the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) during pregnancy in conjunction with air pollution exposure on birthweight in the Boston-based Children&rsquo, s HealthWatch cohort from 2007 through 2015 (n = 3366). Birthweight was obtained from electronic health records. Information on maternal homelessness and WIC participation during pregnancy were provided via a questionnaire. Prenatal fine particulate matter (PM2.5) exposures, estimated at the subject&rsquo, s residential address, were calculated for each trimester. We fit linear regression models adjusting for maternal and child characteristics, seasonality, and block-group-level median household income and examined the interactions between PM2.5 and each covariate. Prenatal maternal homelessness was associated with reduced birthweight (&minus, 55.7 g, 95% CI: &minus, 97.8 g, &minus, 13.7 g), while participating in WIC was marginally associated with increased birthweight (36.1 g, 95% CI: &minus, 7.3 g, 79.4 g). Only average PM2.5 during the second trimester was marginally associated with reduced birthweight (&minus, 8.5 g, 95% CI: &minus, 19.3, 2.3) for a 1 &micro, g/m3 increase in PM2.5. The association of PM2.5 during the second trimester with reduced birthweight was stronger among non-Hispanic Black mothers and trended toward significance among immigrants and single mothers. Our study emphasizes the independent and synergistic effects of social and environmental stressors on birthweight, particularly the potentially protective effect of participating in WIC for vulnerable populations.

Satellite-derived estimates of aerosol optical depth (AOD) are key predictors in particulate air pollution models. The multi-step retrieval algorithms that estimate AOD also produce quality control variables but these have not been systematically used to address the measurement error in AOD. We compare three machine-learning methods: random forests, gradient boosting, and extreme gradient boosting (XGBoost) to characterize and correct measurement error in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 × 1 km AOD product for Aqua and Terra satellites across the Northeastern/Mid-Atlantic USA versus collocated measures from 79 ground-based AERONET stations over 14 years. Models included 52 quality control, land use, meteorology, and spatially-derived features. Variable importance measures suggest relative azimuth, AOD uncertainty, and the AOD difference in 30–210 km moving windows are among the most important features for predicting measurement error. XGBoost outperformed the other machine-learning approaches, decreasing the root mean squared error in withheld testing data by 43% and 44% for Aqua and Terra. After correction using XGBoost, the correlation of collocated AOD and daily PM2.5 monitors across the region increased by 10 and 9 percentage points for Aqua and Terra. We demonstrate how machine learning with quality control and spatial features substantially improves satellite-derived AOD products for air pollution modeling.