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We apply convolutional neural network (CNN) model for estimating daily 24-h averaged ground-level PM 2.5 of the conterminous United States in 2011 by incorporating aerosol optical depth (AOD) data, meteorological fields, and land-use data. Unlike some of the recent supervised learning-based approaches, which only utilized the predictors from the location of which PM 2.5 value is estimated, we naturally aggregate predictors from nearby locations such that the spatial correlation among the predictors can be exploited. We carefully evaluate the performance of our method via overall, temporally-separated, and spatially-separated cross-validations (CV) and show that our CNN achieves competitive estimation accuracy compared to the recently developed baselines. Furthermore, we develop a novel predictor importance metric for our CNN based on the recent neural network interpretation method, Layerwise Relevance Propagation (LRP), and identify several informative predictors for PM 2.5 estimation., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

There has been growing interest in extending the coverage of ground particulate matter with aerodynamic diameter ≤ 2.5 μm (PM 2.5 ) monitoring networks based on satellite remote sensing data. With broad spatial and temporal coverage, a satellite-based monitoring network has a strong potential to complement the ground monitor system in terms of the spatiotemporal availability of the air quality data. However, most existing calibration models focus on a relatively small spatial domain and cannot be generalized to a national study. In this paper, we proposed a statistically reliable and interpretable national modeling framework based on Bayesian downscaling methods to be applied to the calibration of the daily ground PM 2.5 concentrations across the conterminous United States using satellite-retrieved aerosol optical depth (AOD) and other ancillary predictors in 2011. Our approach flexibly models the PM 2.5 versus AOD and the potential related geographical factors varying across the climate regions and yields spatial- and temporal-specific parameters to enhance model interpretability. Moreover, our model accurately predicted the national PM 2.5 with an R ² at 70% and generated reliable annual and seasonal PM 2.5 concentration maps with its SD. Overall, this modeling framework can be applied to national-scale PM 2.5 exposure assessments and can also quantify the prediction errors.

Background: Associations between ambient particulate matter < 2.5 μm (PM 2.5 ) and asthma morbidity have been suggested in previous epidemiologic studies but results are inconsistent for areas with lower PM 2.5 levels. We estimated the associations between early-life short-term PM 2.5 exposure and the risk of asthma or wheeze clinical encounters among Massachusetts children in the innovative Pregnancy to Early Life Longitudinal (PELL) cohort data linkage system., Methods: We used a semi-bidirectional case-crossover study design with short-term exposure lags for asthma exacerbation using data from the PELL system. Cases included children up to 9 years of age who had a hospitalization, observational stay, or emergency department visit for asthma or wheeze between January 2001 and September 2009 (n = 33,387). Daily PM 2.5 concentrations were estimated at a 4-km resolution using satellite remote sensing, land use, and meteorological data. We applied conditional logistic regression models to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CI). We also stratified by potential effect modifiers., Results: The median PM 2.5 concentration among participants was 7.8 μg/m 3 with an interquartile range of 5.9 μg/m 3 . Overall, associations between PM 2.5 exposure and asthma clinical encounters among children at lags 0, 1 and 2 were close to the null value of OR = 1.0. Evidence of effect modification was observed by birthweight for lags 0, 1 and 2 (p < 0.05), and season of clinical encounter for lags 0 and 1 (p < 0.05). Children with low birthweight (LBW) (< 2500 g) had increased odds of having an asthma clinical encounter due to higher PM 2.5 exposure for lag 1 (OR: 1.08 per interquartile range (IQR) increase in PM 2.5 ; 95% CI: 1.01, 1.15)., Conclusion: Asthma or wheeze exacerbations among LBW children were associated with short-term increases in PM 2.5 concentrations at low levels in Massachusetts.

Satellite-retrieved aerosol optical properties have been extensively used to estimate ground-level fine particulate matter (PM 2.5 ) concentrations in support of air pollution health effects research and air quality assessment at the urban to global scales. However, a large proportion, ~70%, of satellite observations of aerosols are missing as a result of cloud-cover, surface brightness, and snow-cover. The resulting PM 2.5 estimates could therefore be biased due to this non-random data missingness. Cloud-cover in particular has the potential to impact ground-level PM 2.5 concentrations through complex chemical and physical processes. We developed a series of statistical models using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product at 1 km resolution with information from the MODIS cloud product and meteorological information to investigate the extent to which cloud parameters and associated meteorological conditions impact ground-level aerosols at two urban sites in the US: Atlanta and San Francisco. We find that changes in temperature, wind speed, relative humidity, planetary boundary layer height, convective available potential energy, precipitation, cloud effective radius, cloud optical depth, and cloud emissivity are associated with changes in PM 2.5 concentration and composition, and the changes differ by overpass time and cloud phase as well as between the San Francisco and Atlanta sites. A case-study at the San Francisco site confirmed that accounting for cloud-cover and associated meteorological conditions could substantially alter the spatial distribution of monthly ground-level PM 2.5 concentrations., Competing Interests: The authors declare no conflict of interest.

Chronic particulate matter less than 2.5μm in diameter (PM 2.5 ) exposure can leave infants more susceptible to illness. Our objective is to estimate associations of the chronic PM 2.5 exposure with infant bronchiolitis and otitis media (OM) clinical encounters. We obtained all first time bronchiolitis (n=18,029) and OM (n=40,042) clinical encounters among children less than 12 and 36 months of age, respectively, diagnosed from 2001 to 2009 and two controls per case matched on birthdate and gestational age from the Pregnancy to Early Life Longitudinal data linkage system in Massachusetts. We applied conditional logistic regression to estimate odds ratios (OR) and confidence intervals (CI) per 2-μg/m 3 increase in lifetime average satellite based PM 2.5 exposure . Effect modification was assessed by age, gestational age, frequency of clinical encounter, and income. We examined associations between residential distance to roadways, traffic density, and infant bronchiolitis and OM risk. PM 2.5 was not associated with infant bronchiolitis (OR=1.02, 95% CI=1.00, 1.04) and inversely associated with OM (OR=0.97, 95% CI=0.95, 0.99). There was no evidence of effect modification. Compared to infants living near low traffic density, infants residing in high traffic density had elevated risk of bronchiolitis (OR=1.23, 95% CI=1.14, 1.31) but not OM (OR=0.98, 95% CI=0.93, 1.02) clinical encounter. We did not find strong evidence to support an association between early-life long-term PM 2.5 exposure and infant bronchiolitis or OM. Bronchiolitis risk was increased among infants living near high traffic density., (Copyright © 2017 Elsevier GmbH. All rights reserved.)

To estimate PM 2.5 concentrations, many parametric regression models have been developed, while nonparametric machine learning algorithms are used less often and national-scale models are rare. In this paper, we develop a random forest model incorporating aerosol optical depth (AOD) data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM 2.5 concentrations over the conterminous United States in 2011. Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability. Our results achieve an overall cross-validation (CV) R 2 value of 0.80. Mean prediction error (MPE) and root mean squared prediction error (RMSPE) for daily predictions are 1.78 and 2.83 μg/m 3 , respectively, indicating a good agreement between CV predictions and observations. The prediction accuracy of our model is similar to those reported in previous studies using neural networks or regression models on both national and regional scales. In addition, the incorporation of convolutional layers for land use terms and nearby PM 2.5 measurements increase CV R 2 by ∼0.02 and ∼0.06, respectively, indicating their significant contributions to prediction accuracy. A pair of different variable importance measures both indicate that the convolutional layer for nearby PM 2.5 measurements and AOD values are among the most-important predictor variables for the training process.

Background: In 2012, Colorado experienced one of its worst wildfire seasons of the past decade. The goal of this study was to investigate the relationship of local PM2.5 levels, modeled using the Weather Research and Forecasting Model with Chemistry, with emergency department visits and acute hospitalizations for respiratory and cardiovascular outcomes during the 2012 Colorado wildfires., Methods: Conditional logistic regression was used to assess the relationship between both continuous and categorical PM2.5 and emergency department visits during the wildfire period, from June 5(th) to July 6(th) 2012., Results: For respiratory outcomes, we observed positive relationships between lag 0 PM2.5 and asthma/wheeze (1 h max OR 1.01, 95 % CI (1.00, 1.01) per 10 μg/m(3); 24 h mean OR 1.04 95 % CI (1.02, 1.06) per 5 μg/m(3)), and COPD (1 h max OR 1.01 95 % CI (1.00, 1.02) per 10 μg/m(3); 24 h mean OR 1.05 95 % CI (1.02, 1.08) per 5 μg/m(3)). These associations were also positive for 2-day and 3-day moving average lag periods. When PM2.5 was modeled as a categorical variable, bronchitis also showed elevated effect estimates over the referent groups for lag 0 24 h average concentration. Cardiovascular results were consistent with no association., Conclusions: We observed positive associations between PM2.5 from wildfire and respiratory diseases, supporting evidence from previous research that wildfire PM2.5 is an important source for adverse respiratory health outcomes.

Background: Associations between pediatric emergency department (ED) visits and ambient concentrations of particulate matter ≤ 2.5 μm in diameter (PM2.5) have been reported in previous studies, although few were performed in nonmetropolitan areas., Objective: We estimated associations between daily PM2.5 concentrations, using a two-stage model that included land use parameters and satellite aerosol optical depth measurements at 1-km resolution, and ED visits for six pediatric conditions in the U.S. state of Georgia by urbanicity classification., Methods: We obtained pediatric ED visits geocoded to residential ZIP codes for visits with nonmissing PM2.5 estimates and admission dates during 1 January 2002-30 June 2010 for 2- to 18-year-olds for asthma or wheeze (n = 189,816), and for 0- to 18-year-olds for bronchitis (n = 76,243), chronic sinusitis (n = 15,745), otitis media (n = 237,833), pneumonia (n = 52,946), and upper respiratory infections (n = 414,556). Daily ZIP code-level estimates of 24-hr average PM2.5 were calculated by averaging concentrations within ZIP code boundaries. We used time-stratified case-crossover models stratified on ZIP code, year, and month to estimate odds ratios (ORs) between ED visits and same-day and previous-day PM2.5 concentrations at the ZIP code level, and we investigated effect modification by county-level urbanicity., Results: A 10-μg/m3 increase in same-day PM2.5 concentrations was associated with ED visits for asthma or wheeze (OR = 1.013; 95% CI: 1.003, 1.023) and upper respiratory infections (OR = 1.015; 95% CI: 1.008, 1.022); associations with previous-day PM2.5 concentrations were lower. Differences in the association estimates across levels of urbanicity were not statistically significant., Conclusion: Pediatric ED visits for asthma or wheeze and for upper respiratory infections were associated with PM2.5 concentrations in Georgia., Citation: Strickland MJ, Hao H, Hu X, Chang HH, Darrow LA, Liu Y. 2016. Pediatric emergency visits and short-term changes in PM2.5 concentrations in the U.S. state of Georgia. Environ Health Perspect 124:690-696; http://dx.doi.org/10.1289/ehp.1509856.

Exposures to particulate matter with diameter of 2.5µm or less (PM2.5) may influence risk of birth defects. We estimated associations between maternal exposure to prenatal traffic-related air pollution and risk of cardiac, orofacial, and neural tube defects among Massachusetts births conceived 2001 through 2008. Our analyses included 2729 cardiac, 255 neural tube, and 729 orofacial defects. We used satellite remote sensing, meteorological and land use data to assess PM2.5 and traffic-related exposures (distance to roads and traffic density) at geocoded birth addresses. We calculated adjusted odds ratios (OR) and confidence intervals (CI) using logistic regression models. Generalized additive models were used to assess spatial patterns of birth defect risk. There were positive but non-significant associations for a 10µg/m(3) increase in PM2.5 and perimembranous ventricular septal defects (OR=1.34, 95% CI: 0.98, 1.83), patent foramen ovale (OR=1.19, 95% CI: 0.92, 1.54) and patent ductus arteriosus (OR=1.20, 95% CI: 0.95, 1.62). There was a non-significant inverse association between PM2.5 and cleft lip with or without palate (OR=0.76, 95% CI: 0.50, 1.10), cleft palate only (OR=0.89, 95% CI: 0.54, 1.46) and neural tube defects (OR=0.77, 95% CI: 0.46, 1.05). Results for traffic related exposure were similar. Only ostium secundum atrial septal defects displayed significant spatial variation after accounting for known risk factors., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background: Three decades of rapid economic development is causing severe and widespread PM2.5 (particulate matter ≤ 2.5 μm) pollution in China. However, research on the health impacts of PM2.5 exposure has been hindered by limited historical PM2.5 concentration data., Objectives: We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1° resolution using the most recent satellite data and evaluated model performance with available ground observations., Methods: We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model-predicted PM2.5 concentrations from 2004 to early 2014 using ground observations., Results: The overall model cross-validation R(2) and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM2.5 concentrations with little bias at the monthly (R(2) = 0.73, regression slope = 0.91) and seasonal (R(2) = 0.79, regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 μg/m(3) between 2004 and 2007 and a decrease of 0.46 μg/m(3) between 2008 and 2013., Conclusions: Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.

Estimating ground-level PM2.5 from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM2.5 exposure in a large geographic region. However, studies outside North America have been limited due to the lack of ground PM2.5 measurements to calibrate the model. Taking advantage of the newly established national monitoring network, we developed a national-scale geographically weighted regression (GWR) model to estimate daily PM2.5 concentrations in China with fused satellite AOD as the primary predictor. The results showed that the meteorological and land use information can greatly improve model performance. The overall cross-validation (CV) R(2) is 0.64 and root mean squared prediction error (RMSE) is 32.98 μg/m(3). The mean prediction error (MPE) of the predicted annual PM2.5 is 8.28 μg/m(3). Our predicted annual PM2.5 concentrations indicated that over 96% of the Chinese population lives in areas that exceed the Chinese National Ambient Air Quality Standard (CNAAQS) Level 2 standard. Our results also confirmed satellite-derived AOD in conjunction with meteorological fields and land use information can be successfully applied to extend the ground PM2.5 monitoring network in China.

There has been a growing interest in the use of satellite-retrieved aerosol optical depth (AOD) to estimate ambient concentrations of PM2.5 (particulate matter <2.5 μm in aerodynamic diameter). With their broad spatial coverage, satellite data can increase the spatial-temporal availability of air quality data beyond ground monitoring measurements and potentially improve exposure assessment for population-based health studies. This paper describes a statistical downscaling approach that brings together (1) recent advances in PM2.5 land use regression models utilizing AOD and (2) statistical data fusion techniques for combining air quality data sets that have different spatial resolutions. Statistical downscaling assumes the associations between AOD and PM2.5 concentrations to be spatially and temporally dependent and offers two key advantages. First, it enables us to use gridded AOD data to predict PM2.5 concentrations at spatial point locations. Second, the unified hierarchical framework provides straightforward uncertainty quantification in the predicted PM2.5 concentrations. The proposed methodology is applied to a data set of daily AOD values in southeastern United States during the period 2003-2005. Via cross-validation experiments, our model had an out-of-sample prediction R(2) of 0.78 and a root mean-squared error (RMSE) of 3.61 μg/m(3) between observed and predicted daily PM2.5 concentrations. This corresponds to a 10% decrease in RMSE compared with the same land use regression model without AOD as a predictor. Prediction performances of spatial-temporal interpolations to locations and on days without monitoring PM2.5 measurements were also examined.

Most of currently reported models for predicting PM(2.5) concentrations from satellite retrievals of aerosol optical depth are global methods without considering local variations, which might introduce significant biases into prediction results. In this paper, a geographically weighted regression model was developed to examine the relationship among PM(2.5), aerosol optical depth, meteorological parameters, and land use information. Additionally, two meteorological datasets, North American Regional Reanalysis and North American Land Data Assimilation System, were fitted into the model separately to compare their performances. The study area is centered at the Atlanta Metro area, and data were collected from various sources for the year 2003. The results showed that the mean local R(2) of the models using North American Regional Reanalysis was 0.60 and those using North American Land Data Assimilation System reached 0.61. The root mean squared prediction error showed that the prediction accuracy was 82.7% and 83.0% for North American Regional Reanalysis and North American Land Data Assimilation System in model fitting, respectively, and 69.7% and 72.1% in cross validation. The results indicated that geographically weighted regression combined with aerosol optical depth, meteorological parameters, and land use information as the predictor variables could generate a better fit and achieve high accuracy in PM(2.5) exposure estimation, and North American Land Data Assimilation System could be used as an alternative of North American Regional Reanalysis to provide some of the meteorological fields., (Copyright © 2012 Elsevier Inc. All rights reserved.)

BACKGROUND:Associations between ambient particulate matter

Our aim is to estimate associations between acute increases in particulate matter with diameter of 2.5 µm or less (PM2.5) concentrations and risk of infant bronchiolitis and otitis media among Massachusetts births born 2001 through 2008.Our case-crossover study included 20,017 infant bronchiolitis and 42,336 otitis media clinical encounter visits. PM2.5 was modeled using satellite, remote sensing, meteorological and land use data. We applied conditional logistic regression to estimate odds ratios (ORs) and confidence intervals (CIs) per 10-µg/m3 increase in PM2.5. We assessed effect modification to determine the most susceptible subgroups. Infant bronchiolitis risk was elevated for PM2.5 exposure 1 day (OR = 1.07, 95% CI = 1.03-1.11) and 4 days (OR = 1.04, 95% CI = 0.99-1.08) prior to clinical encounter, but not 7 days. Non-significant associations with otitis media varied depending on lag. Preterm infants were at substantially increased risk of bronchiolitis 1 day prior to clinical encounter (OR = 1.17, 95% CI = 1.08-1.28) and otitis media 4 and 7 days prior to clinical encounter (OR = 1.09, 95% CI = 1.02-1.16 and OR = 1.08, 95% CI = 1.02-1.15, respectively). In conclusion, preterm infants are most susceptible to infant bronchiolitis and otitis media associated with acute PM2.5 exposures.

Chronic particulate matter less than 2.5μm in diameter (PM2.5) exposure can leave infants more susceptible to illness. Our objective is to estimate associations of the chronic PM2.5 exposure with infant bronchiolitis and otitis media (OM) clinical encounters. We obtained all first time bronchiolitis (n=18,029) and OM (n=40,042) clinical encounters among children less than 12 and 36 months of age, respectively, diagnosed from 2001 to 2009 and two controls per case matched on birthdate and gestational age from the Pregnancy to Early Life Longitudinal data linkage system in Massachusetts. We applied conditional logistic regression to estimate odds ratios (OR) and confidence intervals (CI) per 2-μg/m3 increase in lifetime average satellite based PM2.5 exposure. Effect modification was assessed by age, gestational age, frequency of clinical encounter, and income. We examined associations between residential distance to roadways, traffic density, and infant bronchiolitis and OM risk. PM2.5 was not associated with infant bronchiolitis (OR=1.02, 95% CI=1.00, 1.04) and inversely associated with OM (OR=0.97, 95% CI=0.95, 0.99). There was no evidence of effect modification. Compared to infants living near low traffic density, infants residing in high traffic density had elevated risk of bronchiolitis (OR=1.23, 95% CI=1.14, 1.31) but not OM (OR=0.98, 95% CI=0.93, 1.02) clinical encounter. We did not find strong evidence to support an association between early-life long-term PM2.5 exposure and infant bronchiolitis or OM. Bronchiolitis risk was increased among infants living near high traffic density.

Exposures to particulate matter with diameter of 2.5µm or less (PM2.5) may influence risk of birth defects. We estimated associations between maternal exposure to prenatal traffic-related air pollution and risk of cardiac, orofacial, and neural tube defects among Massachusetts births conceived 2001 through 2008. Our analyses included 2729 cardiac, 255 neural tube, and 729 orofacial defects. We used satellite remote sensing, meteorological and land use data to assess PM2.5 and traffic-related exposures (distance to roads and traffic density) at geocoded birth addresses. We calculated adjusted odds ratios (OR) and confidence intervals (CI) using logistic regression models. Generalized additive models were used to assess spatial patterns of birth defect risk. There were positive but non-significant associations for a 10µg/m(3) increase in PM2.5 and perimembranous ventricular septal defects (OR=1.34, 95% CI: 0.98, 1.83), patent foramen ovale (OR=1.19, 95% CI: 0.92, 1.54) and patent ductus arteriosus (OR=1.20, 95% CI: 0.95, 1.62). There was a non-significant inverse association between PM2.5 and cleft lip with or without palate (OR=0.76, 95% CI: 0.50, 1.10), cleft palate only (OR=0.89, 95% CI: 0.54, 1.46) and neural tube defects (OR=0.77, 95% CI: 0.46, 1.05). Results for traffic related exposure were similar. Only ostium secundum atrial septal defects displayed significant spatial variation after accounting for known risk factors.

Background: Associations between ambient particulate matter < 2.5 μm (PM2.5) and asthma morbidity have been suggested in previous epidemiologic studies but results are inconsistent for areas with lower PM2.5 levels. We estimated the associations between early-life short-term PM2.5 exposure and the risk of asthma or wheeze clinical encounters among Massachusetts children in the innovative Pregnancy to Early Life Longitudinal (PELL) cohort data linkage system.Methods: We used a semi-bidirectional case-crossover study design with short-term exposure lags for asthma exacerbation using data from the PELL system. Cases included children up to 9 years of age who had a hospitalization, observational stay, or emergency department visit for asthma or wheeze between January 2001 and September 2009 (n = 33,387). Daily PM2.5 concentrations were estimated at a 4-km resolution using satellite remote sensing, land use, and meteorological data. We applied conditional logistic regression models to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CI). We also stratified by potential effect modifiers.Results: The median PM2.5 concentration among participants was 7.8 μg/m3 with an interquartile range of 5.9 μg/m3. Overall, associations between PM2.5 exposure and asthma clinical encounters among children at lags 0, 1 and 2 were close to the null value of OR = 1.0. Evidence of effect modification was observed by birthweight for lags 0, 1 and 2 (p < 0.05), and season of clinical encounter for lags 0 and 1 (p < 0.05). Children with low birthweight (LBW) (< 2500 g) had increased odds of having an asthma clinical encounter due to higher PM2.5 exposure for lag 1 (OR: 1.08 per interquartile range (IQR) increase in PM2.5; 95% CI: 1.01, 1.15).Conclusion: Asthma or wheeze exacerbations among LBW children were associated with short-term increases in PM2.5 concentrations at low levels in Massachusetts. [ABSTRACT FROM AUTHOR]

Abstract: Previous studies showed that fine particulate matter (PM2.5, particles smaller than 2.5μm in aerodynamic diameter) is associated with various health outcomes. Ground in situ measurements of PM2.5 concentrations are considered to be the gold standard, but are time-consuming and costly. Satellite-retrieved aerosol optical depth (AOD) products have the potential to supplement the ground monitoring networks to provide spatiotemporally-resolved PM2.5 exposure estimates. However, the coarse resolutions (e.g., 10km) of the satellite AOD products used in previous studies make it very difficult to estimate urban-scale PM2.5 characteristics that are crucial to population-based PM2.5 health effects research. In this paper, a new aerosol product with 1km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields (e.g., wind speed) and land use parameters (e.g., forest cover, road length, elevation, and point emissions) as ancillary variables to estimate daily mean PM2.5 concentrations. The study area is the southeastern U.S., and data for 2003 were collected from various sources. A cross validation approach was implemented for model validation. We obtained R2 of 0.83, mean prediction error (MPE) of 1.89μg/m3, and square root of the mean squared prediction errors (RMSPE) of 2.73μg/m3 in model fitting, and R2 of 0.67, MPE of 2.54μg/m3, and RMSPE of 3.88μg/m3 in cross validation. Both model fitting and cross validation indicate a good fit between the dependent variable and predictor variables. The results showed that 1km spatial resolution MAIAC AOD can be used to estimate PM2.5 concentrations. [Copyright &y& Elsevier]

Abstract: Most of currently reported models for predicting PM2.5 concentrations from satellite retrievals of aerosol optical depth are global methods without considering local variations, which might introduce significant biases into prediction results. In this paper, a geographically weighted regression model was developed to examine the relationship among PM2.5, aerosol optical depth, meteorological parameters, and land use information. Additionally, two meteorological datasets, North American Regional Reanalysis and North American Land Data Assimilation System, were fitted into the model separately to compare their performances. The study area is centered at the Atlanta Metro area, and data were collected from various sources for the year 2003. The results showed that the mean local R 2 of the models using North American Regional Reanalysis was 0.60 and those using North American Land Data Assimilation System reached 0.61. The root mean squared prediction error showed that the prediction accuracy was 82.7% and 83.0% for North American Regional Reanalysis and North American Land Data Assimilation System in model fitting, respectively, and 69.7% and 72.1% in cross validation. The results indicated that geographically weighted regression combined with aerosol optical depth, meteorological parameters, and land use information as the predictor variables could generate a better fit and achieve high accuracy in PM2.5 exposure estimation, and North American Land Data Assimilation System could be used as an alternative of North American Regional Reanalysis to provide some of the meteorological fields. [Copyright &y& Elsevier]

Background: In 2012, Colorado experienced one of its worst wildfire seasons of the past decade. The goal of this study was to investigate the relationship of local PM2.5 levels, modeled using the Weather Research and Forecasting Model with Chemistry, with emergency department visits and acute hospitalizations for respiratory and cardiovascular outcomes during the 2012 Colorado wildfires.Methods: Conditional logistic regression was used to assess the relationship between both continuous and categorical PM2.5 and emergency department visits during the wildfire period, from June 5(th) to July 6(th) 2012.Results: For respiratory outcomes, we observed positive relationships between lag 0 PM2.5 and asthma/wheeze (1 h max OR 1.01, 95 % CI (1.00, 1.01) per 10 μg/m(3); 24 h mean OR 1.04 95 % CI (1.02, 1.06) per 5 μg/m(3)), and COPD (1 h max OR 1.01 95 % CI (1.00, 1.02) per 10 μg/m(3); 24 h mean OR 1.05 95 % CI (1.02, 1.08) per 5 μg/m(3)). These associations were also positive for 2-day and 3-day moving average lag periods. When PM2.5 was modeled as a categorical variable, bronchitis also showed elevated effect estimates over the referent groups for lag 0 24 h average concentration. Cardiovascular results were consistent with no association.Conclusions: We observed positive associations between PM2.5 from wildfire and respiratory diseases, supporting evidence from previous research that wildfire PM2.5 is an important source for adverse respiratory health outcomes. [ABSTRACT FROM AUTHOR]

Chronic particulate matter less than 2.5μm in diameter (PM2.5) exposure can leave infants more susceptible to illness. Our objective is to estimate associations of the chronic PM2.5 exposure with infant bronchiolitis and otitis media (OM) clinical encounters. We obtained all first time bronchiolitis (n=18,029) and OM (n=40,042) clinical encounters among children less than 12 and 36 months of age, respectively, diagnosed from 2001 to 2009 and two controls per case matched on birthdate and gestational age from the Pregnancy to Early Life Longitudinal data linkage system in Massachusetts. We applied conditional logistic regression to estimate odds ratios (OR) and confidence intervals (CI) per 2-μg/m3 increase in lifetime average satellite based PM2.5 exposure. Effect modification was assessed by age, gestational age, frequency of clinical encounter, and income. We examined associations between residential distance to roadways, traffic density, and infant bronchiolitis and OM risk. PM2.5 was not associated with infant bronchiolitis (OR=1.02, 95% CI=1.00, 1.04) and inversely associated with OM (OR=0.97, 95% CI=0.95, 0.99). There was no evidence of effect modification. Compared to infants living near low traffic density, infants residing in high traffic density had elevated risk of bronchiolitis (OR=1.23, 95% CI=1.14, 1.31) but not OM (OR=0.98, 95% CI=0.93, 1.02) clinical encounter. We did not find strong evidence to support an association between early-life long-term PM2.5 exposure and infant bronchiolitis or OM. Bronchiolitis risk was increased among infants living near high traffic density. [ABSTRACT FROM AUTHOR]