Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA.
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ABSTRACT:
SUBMITTER: Wright N
PROVIDER: S-EPMC8223501 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Estimates of daily air pollution concentrations with complete spatial and temporal coverage are important for supporting epidemiologic studies and health impact assessments. While numerous approaches have been developed for modeling air pollution, they typically only consider each pollutant separately. We describe a spatial multipollutant data fusion model that combines monitoring measurements and chemical transport model simulations that leverages dependence between pollutants to improve spatial prediction. For the contiguous United States, we created a data product of daily concentration for 12 pollutants (CO, NOx, NO2, SO2, O3, PM10, and PM2.5 species EC, OC, NO3, NH4, SO4) during the period 2005 to 2014. Out-of-sample prediction showed good performance, particularly for daily PM2.5 species EC (R2 = 0.64), OC (R2 = 0.75), NH4 (R2 = 0.84), NO3 (R2 = 0.73), and SO4 (R2 = 0.80). By employing the integrated nested Laplace approximation (INLA) for Bayesian inference, our approach also provides model-based prediction error estimates. The daily data product at 12km spatial resolution will be publicly available immediately upon publication. To our knowledge this is the first publicly available data product for major PM2.5 species and several gases at this spatial and temporal resolution.
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Project description:Studies have linked exposure to air pollutants to short-term and sub-chronic health outcomes. However, individual-level air pollution exposure is difficult to measure at a high spatial and temporal resolution and for larger populations due to limitations in sampling techniques. We presented a hierarchical model to capture spatiotemporal variability of nitrogen dioxide (NO2) and nitrogen oxides (NOx) concentrations in Southern California by combining high temporal resolution data from routine monitoring stations with high spatial resolution data from investigator-initiated episodic measurements. In this model, the spatiotemporal field of concentrations was first decomposed into a mean and residual and the mean representing the seasonal trend was further decomposed into a constant and varying temporal basis functions. The mean of the spatially varying coefficients of temporal basis functions were modeled by local covariates using non-linear generalized additive model and least square fitting using measurements from both routine monitoring and additional episodic sampling locations, while the spatially-correlated residuals of the coefficients were co-kriged. We found traffic, land-use and wind accounted for a large portion of the variance (beyond 35%) for the long-term average trend of concentrations. Spatial residuals accounted for a large portion of the variance of the temporal components (about 30% for NO2 and 20% for NOx). Leave-one-out cross validation produced an R2 of 0.84 for NO2 and 0.81 for NOx when comparing the modeled weekly concentration with the observed trends at all routine monitoring stations.
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Project description:Most published epidemiology studies of long-term air pollution health effects have relied on central site monitoring to investigate regional-scale differences in exposure. Few cohort studies have had sufficient data to characterize localized variations in pollution, despite the fact that large gradients can exist over small spatial scales. Similarly, previous data have generally been limited to measurements of particle mass or several of the criteria gases. The Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) is an innovative investigation undertaken to link subclinical and clinical cardiovascular health effects with individual-level estimates of personal exposure to ambient-origin pollution. This project improves on prior work by implementing an extensive exposure assessment program to characterize long-term average concentrations of ambient-generated PM2.5, specific PM2.5 chemical components, and copollutants, with particular emphasis on capturing concentration gradients within cities. This paper describes exposure assessment in MESA Air, including questionnaires, community sampling, home monitoring, and personal sampling. Summary statistics describing the performance of the sampling methods are presented along with descriptive statistics of the air pollution concentrations by city.
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Project description:IntroductionWe investigated the extent to which associations of ambient air pollutant concentrations and birth weight varied across birth weight quantiles.MethodsWe analyzed singleton births ≥27 weeks of gestation from 20-county metropolitan Atlanta with conception dates between January 1, 2002 and February 28, 2006 (N = 273,711). Trimester-specific and total pregnancy average concentrations for 10 pollutants, obtained from ground observations that were interpolated using 12-km Community Multiscale Air Quality model outputs, were assigned using maternal residence at delivery. We estimated associations between interquartile range width (IQRw) increases in pollutant concentrations and changes in birth weight using quantile regression.ResultsGestational age-adjusted associations were of greater magnitude at higher percentiles of the birth weight distribution. Pollutants with large vehicle source contributions (carbon monoxide, nitrogen dioxide, PM2.5 elemental carbon, and total PM2.5 mass), as well as PM2.5 sulfate and PM2.5 ammonium, were associated with birth weight decreases for the higher birth weight percentiles. For example, whereas the decrease in mean birthweight per IQRw increase in PM2.5 averaged over pregnancy was -7.8 g (95% confidence interval = -13.6, -2.0 g), the quantile-specific associations were: 10th percentile -2.4 g (-11.5, 6.7 g); 50th percentile -8.9 g (-15.7, -2.0g); and 90th percentile -19.3 g (-30.6, -7.9 g). Associations for the intermediate and high birth weight quantiles were not sensitive to gestational age adjustment. For some pollutants, we saw associations at the lowest quantile (10th percentile) when not adjusting for gestational age.ConclusionsAssociations between air pollution and reduced birth weight were of greater magnitude for newborns at relatively heavy birth weights.
| S-EPMC6691899 | biostudies-literature
Project description:We introduce spatial (DLfuse) and spatiotemporal (DLfuseST) distributed lag data fusion methods for predicting point-level ambient air pollution concentrations, using, as input, gridded average pollution estimates from a deterministic numerical air quality model. The methods incorporate predictive information from grid cells surrounding the prediction location of interest and are shown to collapse to existing downscaling approaches when this information adds no benefit. The spatial lagged parameters are allowed to vary spatially/spatiotemporally to accommodate the setting where surrounding geographic information is useful in one area/time but not in another. We apply the new methods to predict ambient concentrations of eight-hour maximum ozone and 24-hour average PM2.5 at unobserved spatial locations and times, and compare the predictions with those from several state-of-the-art data fusion approaches. Results show that DLfuse and DLfuseST often provide improved model fit and predictive accuracy when the lagged information is shown to be beneficial. Code to apply the methods is available in the R package DLfuse.
| S-EPMC8189329 | biostudies-literature
Project description:Background:Limited epidemiological studies have investigated the relationship between prenatal exposure to ambient particulate matter and risk of abnormal fetal growth, and have reached inconclusive results. No study has been conducted in areas with very high air pollution levels. We investigated the hypothesis that exposure to high levels of particulate matter with aerodynamic diameter no larger than 10 µm (PM10) during pregnancy increases the risk of abnormal fetal growth. Methods:A birth cohort study was performed in Lanzhou, China, 2010-12, including 8877 pregnant women with 18 583 ultrasound measurements of four fetal growth parameters during pregnancy, including biparietal diameter (BPD), femur length (FL), head circumference (HC) and abdominal circumference (AC). Mixed-effects modelling was used to examine the associations between PM10 exposure and risk of abnormal fetal growth. Results:When average PM10 exposure from conception until the ultrasound examination exceeded 150 µg/m3, there were significant increases in standardized FL (? = 0.095, P = 0.0012) and HC (? = 0.090, P = 0.0078) measures. When average PM10 exposure was treated as continuous variable, we found a significant decrease in standardized BPD (? = -0.018, P = 0.0016) as per 10 µg/m3 increase in PM10. After examining the associations by various exposure windows, positive associations between higher levels of PM10 and fetal overgrowth were consistently seen for HC measures. Conclusions:Our study suggested that prenatal exposure to high levels of ambient PM10 increased the risk of abnormal fetal growth. The findings from our study have important public health implications and also call for future studies to explore the underlying mechanisms and post-natal consequences of these findings.
| S-EPMC6124630 | biostudies-literature
Project description:In time-series studies of ambient air pollution and health in large urban areas, measurement errors associated with instrument precision and spatial variability vary widely across pollutants. In this paper, we characterize these errors for selected air pollutants and estimate their impacts on epidemiologic results from an ongoing study of air pollution and emergency department visits in Atlanta. Error was modeled for daily measures of 12 air pollutants using collocated monitor data to characterize instrument precision and data from multiple study area monitors to estimate population-weighted spatial variance. Time-series simulations of instrument and spatial error were generated for each pollutant, added to a reference pollutant time-series, and used in a Poisson generalized linear model of air pollution and cardiovascular emergency department visits. Reductions in risk ratio due to instrument precision error were less than 6%. Error due to spatial variability resulted in average risk ratio reductions of less than 16% for secondary pollutants (O(3), PM(2.5) sulfate, nitrate and ammonium) and between 43% and 68% for primary pollutants (NO(x), NO(2), SO(2), CO, PM(2.5) elemental carbon); pollutants of mixed origin (PM(10), PM(2.5), PM(2.5) organic carbon) had intermediate impacts. Quantifying impacts of measurement error on health effect estimates improves interpretation across ambient pollutants.
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Project description:BackgroundKawasaki disease (KD) is an acute and multi-systemic vasculitis that occurs predominantly in infants and young children. Although the etiological agent of KD remains unclear, limited studies have reported that windborne environmental factors may trigger KD.ObjectivesWe conducted a time-stratified case-crossover study to assess the associations between air pollutants and KD in Taiwan.MethodsWe identified children < 5 years old with a diagnosis of KD from the Longitudinal Health Insurance Database 2000 (LHID2000) between 2000 and 2010. We obtained data regarding carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), particulate matter with aerodynamic diameter < 10 μm (PM10), and sulfate dioxide (SO2) from 70 monitoring stations and used inverse distance weighting to calculate average daily exposures for the residential postal code of each case. We performed conditional logistic regression to estimate associations between KD and each air pollutant according to interquartile range (IQR) increases and quartiles of exposure on the day of hospitalization versus 3-4 reference days during the same month for each case. Additionally, we estimated associations with single-day exposures lagged 1-2 days.ResultsWe identified 695 KD hospital admissions during the study period. An IQR increase (28.73 ppb) of O3 was positively associated with KD after adjusting for temperature, humidity, northward wind, and eastward wind [adjusted odds ratio = 1.21; 95% confidence interval (CI): 1.01, 1.44]. There were no significant associations between KD and CO, NO2, PM10, or SO2. The association with O3 was limited to exposure on the day of hospitalization and to exposure during the summer months (June-August).ConclusionsOur results provide new evidence that exposure to O3 may increase the risk of KD in children. However, further investigation is needed to confirm the association and identify a potential biological mechanism.
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Project description:Increased ambient air pollutant concentrations during pregnancy have been associated with reduced birth weight, but the etiologically relevant pregnancy time window(s) is/are unclear. In 76,500 singleton births in Monroe County, NY (2005-2016), who were 37-42 gestational weeks at delivery, we used generalized linear models to regress term birth weight against mean gestational month pollutant concentrations, adjusting for mean temperature, and maternal, infant, and medical service use characteristics. Overall, there were no clear patterns of term birth weight change associated with increased concentrations of any pollutant across gestational months. However, among Hispanic women only, increases in all pollutants, except O3, in multiple gestational months, were associated with decreased term birth weight. Each 3.25 µg/m3 increase in PM2.5 concentration in the 6th gestational month was associated with a -20.4 g (95% CI = -34.0, -6.8) reduction in term birth weight among Hispanic women, but a 4.1 g (95% CI = -2.5, 10.8) increase among non-Hispanic mothers (p for interaction < 0.001). Although ambient air pollutant concentrations during pregnancy were not associated with reduced term birth weight among women of all ethnicities living in Monroe County, this observed association in Hispanic mothers may be a result of less exposure misclassification and bias (due to closer residential proximity to the monitoring site).
| S-EPMC6592743 | biostudies-literature
Project description:To control the spread of the 2019 novel coronavirus (COVID-19), China imposed rigorous restrictions, which resulted in great reductions in pollutant emissions. This study examines the characteristics of air pollutants, including PM2.5 (particles with aerodynamic diameters < 2.5 μm), gas pollutants, water-soluble ions (WSIs), black carbon (BC) and elements, as well as the source apportionment of PM2.5 in Suzhou before, during and after the Chinese New Year (CNY) holiday of 2020 (when China was under an unprecedented state of lockdown to restrict the COVID-19 outbreak). Compared to those before CNY, PM2.5, BC, SNA (sulfate, nitrate and ammonium), other ions, elements, and NO2 and CO mass concentrations decreased by 9.9%-64.0% during CNY. The lockdown policy had strong (weak) effects on the diurnal variations in aerosol chemical compositions (gas pollutants). Compared to those before CNY, source concentrations and contributions of vehicle exhaust during CNY decreased by 72.9% and 21.7%, respectively. In contrast, increased contributions from coal combustion and industry were observed during CNY, which were recorded to be 2.9 and 1.7 times higher than those before CNY, respectively. This study highlights that the lockdown policy that was imposed in Suzhou during CNY not only reduced the mass concentrations of air pollutants but also modified their diurnal variations and the source contributions of PM2.5, which revealed the complex responses of PM2.5 sources to the rare, low emissions of anthropogenic pollutants that occurred during the COVID-19 lockdown.
| S-EPMC7832523 | biostudies-literature