Satellite-derived aerosol optical depth (AOD) has been widely used to predict ground-level fine particulate matter (PM2.5) concentrations, although its utility can be limited due to missing values. ...Despite recent attempts to address this issue by imputing missing satellite AOD values, the uncertainty associated with the AOD imputation and its impacts on PM2.5 predictions have been understudied. To fill this gap, we developed a missing data imputation model for the AOD derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and PM2.5 prediction models using several machine learning methods. We also examined how the uncertainty associated with the imputed AOD and a choice of machine learning algorithm were propagated to PM2.5 predictions. The application of the proposed imputation model to the data from New York State in the U.S. achieved a superior performance than those related studies, with a cross-validated R2 of 0.94 and a Root Mean Square Error of 0.017. We also found that there was considerable uncertainty in PM2.5 predictions associated with the use of imputed AOD values, although it was not as high as the uncertainty from the machine learning algorithms used in PM2.5 prediction models. We concluded that the quantification of uncertainties for both AOD imputation and its propagation to AOD-based PM2.5 prediction is necessary for accurate and reliable PM2.5 predictions.
Display omitted
•Imputation of missing satellite AOD and quantification of imputation uncertainty.•AOD-based daily PM2.5 predictions using multiple machine learning algorithms.•Uncertainty analysis in PM2.5 predictions propagated from imputation.
We found that non-trivial levels of uncertainty existed in imputed satellite AOD, which was propagated to the uncertainty in AOD-based daily PM2.5 predictions.
We used the Community Multiscale Air Quality (CMAQ) simulation model to predict daily average of fine particulate matter (PM2.5) concentrations. The primary focus of our study was to investigate the ...sensitivity of CMAQ prediction accuracy associated with the horizontal grid resolutions and assess its impact on human health studies. To illustrate our point we ran CMAQ model at 4 km and 12 km resolutions over New York State for the year 2011, and systematically assessed the differences between two modeled PM2.5 concentrations. Model performance was evaluated against PM2.5 measured values at monitoring stations. The results indicated that simulations at both 4 km and 12 km resolutions reproduced measured PM2.5 values with fractional error (54.41% for 4 km and 52.28% for 12 km) that are within the recommend performance criteria except for summer seasons and rural areas. Additionally, model results at 12 km compared to 4 km resolution generally performed better and had substantially lower computational burden. In our health impact assessment study, we found that estimated adverse health outcomes associated with PM2.5 exposure derived from the two CMAQ models were compatible, especially in rural areas. Based on our findings, we conclude that the CMAQ simulation at 12 km resolution with further calibration and/or downscaling is a viable option than 4 km simulation to estimate small-scale within-city variations of air pollution concentrations.
Display omitted
•The effect of spatial resolution of air quality simulations was assessed using CMAQ model.•CMAQ model substantially underestimated PM2.5 concentrations in both rural areas and summer season at both resolutions.•The effect of CMAQ model resolution on health impact assessment studies was examined.•The effect of CMAQ model resolution on computational burdens was quantified in terms of computational time.
Despite a growing interest in the satellite derived estimation of ground-level PM2.5 concentrations, modeling hourly PM2.5 levels at high spatial resolution with complete coverage for a large study ...domain remains a challenge. The primary modeling challenges lie in the presence of missing data in aerosol optical depth (AOD) and the limited data resolution for a single-platformed satellite AOD product. To address these issues, we developed a gap-filling hybrid approach to estimate full coverage hourly ground-level PM2.5 concentrations at a high spatial resolution of 1 km using multi-platformed and multi-scale satellite derived AOD products. Specifically, we filled the gaps and downscaled the multi-sourced AOD from Geostationary Ocean Color Imager (GOCI), Multi-Angle Implementation of Atmospheric Correction (MAIAC), and Modern-Era Retrospective Analysis for Research and Applications - version 2 (MERRA-2), using a hybrid data fusion approach. The fused hourly AOD with full coverage was then used for hourly PM2.5 predictions at a high spatial resolution of 1 km. We demonstrated the application of the proposed approach and assessed its performance using the data collected from northeastern Asia from 2015 to 2019. Our fused hourly AOD data showed high accuracy with the mean absolute error of 0.14 and correlation coefficient of 0.94, in validation against Aerosol Robotic Network (AERONET) AOD. Our AOD-based PM2.5 prediction model showed a good prediction accuracy with cross-validated R2 of 0.85 and root mean squared error of 12.40 μg/m3, respectively. Given that the highly resolved PM2.5 predictions captured both the temporal trend and the peak of PM2.5 pollution scenarios, we concluded that the proposed hybrid approach can effectively combine multi-sourced satellite AOD and derive subsequent PM2.5 distributions at high spatial and temporal resolutions.
Display omitted
•Data fusion to integrate AODs from polar-orbiting and geostationary satellites.•A method to derive spatially and temporally resolved AOD data with full coverage.•Hourly PM2.5 predictions at 1 km using fused AOD data over a large domain.•Mapping hourly PM2.5 helps capture the temporal trend and peak of PM2.5 pollution.
The quantification of PM2.5 concentrations solely stemming from both wildfire and prescribed burns (hereafter referred to as ‘fire’) is viable using the Community Multiscale Air Quality (CMAQ), ...although CMAQ outputs are subject to biases and uncertainties. To reduce the biases in CMAQ-based outputs, we propose a two-stage calibration strategy that improves the accuracy of CMAQ-based fire PM2.5 estimates. First, we calibrated CMAQ-based non-fire PM2.5 to ground PM2.5 observations retrieved during non-fire days using an ensemble-based model. We estimated fire PM2.5 concentrations in the second stage by multiplying the calibrated non-fire PM2.5 obtained from the first stage by location- and time-specific conversion ratios. In a case study, we estimated fire PM2.5 during the Washington 2016 fire season using the proposed calibration approach. The calibrated PM2.5 better agreed with ground PM2.5 observations with a 10-fold cross-validated (CV) R2 of 0.79 compared to CMAQ-based PM2.5 estimates with R2 of 0.12. In the health effect analysis, we found significant associations between calibrated fire PM2.5 and cardio-respiratory hospitalizations across the fire season: relative risk (RR) for cardiovascular disease = 1.074, 95% confidence interval (CI) = 1.021–1.130 in October; RR = 1.191, 95% CI = 1.099–1.291 in November; RR for respiratory disease = 1.078, 95% CI = 1.005–1.157 in October; RR = 1.153, 95% CI = 1.045–1.272 in November. However, the results were inconsistent when non-calibrated PM2.5 was used in the analysis. We found that calibration affected health effect assessments in the present study, but further research is needed to confirm our findings.
Display omitted
•A two-stage calibration method to adjust biases in CMAQ-based fire PM2.5 estimates.•Selective use of ground PM2.5 measurements of fire versus non-fire days.•Improved accuracy of PM2.5 estimates after calibration.•Impacts of calibration on fire PM2.5-related health effect analyses.
Wildland fire is a major emission source of fine particulate matter (PM2.5), which has serious adverse health effects. Most fire-related health studies have estimated human exposures to PM2.5 using ...ground observations, which have limited spatial/temporal coverage and could not separate PM2.5 emanating from wildland fires from other sources. The Community Multiscale Air Quality (CMAQ) model has the potential to fill the gaps left by ground observations and estimate wildland fire-specific PM2.5 concentrations, although the issues around systematic bias in CMAQ models remain to be resolved. To address these problems, we developed a two-step calibration strategy under the consideration of prediction uncertainties. In a case study of the eastern U.S. in 2014, we evaluated the calibration performance using three cross-validation methods, which consistently indicated that the prediction accuracy was improved with an R 2 of 0.47–0.64. In a health impact study based on the wildland fire-specific PM2.5 predictions, we identified regions with excess respiratory hospital admissions due to wildland fire events and quantified the estimation uncertainty propagated from multiple components in health impact function. We concluded that the proposed calibration strategy could provide reliable wildland fire-specific PM2.5 predictions and health burden estimates to support policy development for reducing fire-related risks.
Time-location data collected from location-sensing technologies have the potential to advance our understanding of human mobility. Existing human activity studies tend to ignore a critical issue in ...data collection-the time period for which the activity data will be collected. Our study investigated this significant gap in the literature on temporal aspects of human mobility behavior-how many days constitute a period long enough to capture individuals' highly organized activity episodes and how they vary among individuals with heterogeneous demographic and social-economic characteristics. To determine a minimum number of days to capture individuals' highly organized activity episodes in activity space, we examined a distribution of Kullback-Leibler divergence indexes. To evaluate the differences in the minimal number of observation days per subgroup whose demographic and economic characteristics are heterogenous, we used a Bayesian profile regression model. Our study showed that the estimated minimum number of days required to capture routine activity patterns was 13.5 days with a standard deviation of 6.64. We found that participant's age, employment status, size of household, and accessibility to downtown, food, and physical activity, as well as economic status of residential environment, are important factors that affect temporal aspects of mobility behavior. Key Words: Bayesian profile regression, human mobility, Kullback-Leibler divergence, mobile phone data, temporal regularity.
Most previous studies on air pollution exposure disparities among racial and ethnic groups in the US have been limited to residence-based exposure and have given little consideration to population ...mobility and spatial patterns of residences, workplaces, and air pollution. This study aimed to examine air pollution exposure disparities by racial and ethnic groups while explicitly accounting for both the work-related activity of the population and localized spatial patterns of residential segregation, clustering of workplaces, and variability of air pollutant concentration.
In the present study, we assessed population-level exposure to air pollution using tabulated residence and workplace addresses of formally employed workers from LEHD Origin-Destination Employment Statistics (LODES) data at the census tract level across eight Metropolitan Statistical Areas (MSAs). Combined with annual-averaged predictions for three air pollutants (PM2.5, NO2, O3), we investigated racial and ethnic disparities in air pollution exposures at home and workplaces using pooled (i.e., across eight MSAs) and regional (i.e., with each MSA) data.
We found that non-White groups consistently had the highest levels of exposure to all three air pollutants, at both their residential and workplace locations. Narrower exposure disparities were found at workplaces than residences across all three air pollutants in the pooled estimates, due to substantially lower workplace segregation than residential segregation. We also observed that racial disparities in air pollution exposure and the effect of considering work-related activity in the exposure assessment varied by region, due to both the levels and patterns of segregation in the environments where people spend their time and the local heterogeneity of air pollutants.
The results indicated that accounting for workplace activity illuminates important variation between home- and workplace-based air pollution exposure among racial and ethnic groups, especially in the case of NO2. Our findings suggest that consideration of both activity patterns and place-based exposure is important to improve our understanding of population-level air pollution exposure disparities, and consequently to health disparities that are closely linked to air pollution exposure.
•Home- and workplace-based air pollution exposures were estimated for populations.•Population level exposures to multiple air pollutants were estimated.•Air Pollution exposure disparity was smaller at workplace than residence.•Spatial patterns of residence and workplace clusters determine exposure estimates.•Spatial variability of air pollutants contributes to the racial exposure disparities.
Accessibility to healthcare has a direct bearing on the overall well-being of the population. Poor access to healthcare has serious consequences particularly in low and low-middle income countries ...(LMIC) in sub-Saharan Africa. The lack of detailed and up-to-date spatial data and health information in these regions further challenges both the accurate assessment of spatial accessibility and the determination of optimal locations of healthcare facilities that would improve health service planning. In the present study, we proposed a systematic approach to assess the spatial accessibility to healthcare and to identify optimal locations for additional healthcare facilities based on the accessibility measures. Results from a raster-based accessibility measurement showed that majority of population could not reach the nearest hospitals within 2-hours; only 25%, 50%, and 44% of population reached the nearest hospital within 2-hours under walking, motor and, bus travel scenarios, respectively. Our results also showed that the five newly proposed hospitals whose optimal locations were determined using a location-allocation model could potentially increase 11.41%, 8.29%, and 8.95% of additional population coverage for the three travel scenarios. The proposed health system evaluation approach and the health care planning based on open-source data derived from remote sensing and crowdsourcing and the spatial modeling approach has the potential to be useful in LMIC to improve overall population health.
•A systematic approach to evaluate spatial accessibility in resource-poor regions using open-source spatial datasets.•Integrating spatial accessibility and location-allocation model for healthcare planning under specific travel scenarios.•A first attempt to investigate overall spatial accessibility to healthcare in North Kivu, Democratic Republic of Congo.•Model validation by comparing the model-based travel-time estimates to field survey responses.
There is growing evidence suggesting that extreme temperatures have an impact on mental disorders. We aimed to explore the effect of extreme temperatures on emergency room (ER) visits for mental ...health disorders using 2.8 million records from New York State, USA (2009–2016), and to examine potential effect modifications by individuals' age, sex, and race/ethnicity through a stratified analysis to determine if certain populations are more susceptible.
To assess the short-term impact of daily average temperature on ER visits related to mental disorders, we applied a quasi-Poisson generalized linear model combined with a distributed lag non-linear model (DLNM). The model was adjusted for day of the week, precipitation, as well as long-term and seasonal time trends. We also conducted a meta-analysis to pool the region-specific risk estimates and construct the overall cumulative exposure-response curves for all regions.
We found positive associations between short-term exposure to extreme heat (27.07 ∘C) and increased ER visits for total mental disorders, as well as substance abuse, mood and anxiety disorders, schizophrenia, and dementia. We did not find any statistically significant difference among any subgroups of the population being more susceptible to extreme heat than any other.
Our findings suggest that there is a positive association between short-term exposure to extreme heat and increased ER visits for total mental disorders. This extreme effect was also found across all sub-categories of mental disease, although further research is needed to confirm our finding for specific mental disorders, such as dementia, which accounted for less than 1% of the total mental disorders in this sample.
Overall cumulative effect of daily average temperature for specific mental disorders in NYS. Pooled temperature-ER visits related to anxiety disorder, mood disorder, substance abuse, schizophrenia, and dementia, respectively. Red lines denote relative risk and shaded area represent 95% CI. Dotted lines represent the optimal ER visit temperature, and dashed lines the 2.5 and the 97.5th percentile, respectively. Display omitted
•ER visits by mental disorders increase significantly with extreme hot temperatures.•Extreme heat effect was positively associated with sub-categories of mental disease.•Difference among any subgroups of the population was not significant.
One of the major challenges in conducting epidemiological studies of air pollution and health is the difficulty of estimating the degree of exposure accurately. Fine particulate matter (PM
2.5
) ...concentrations vary in space and time, which are difficult to estimate in rural, suburban and smaller urban areas due to the sparsity of the ground monitoring network. Satellite retrieved aerosol optical depth (AOD) has been increasingly used as a proxy of ground PM
2.5
observations, although it suffers from non-trivial missing data problems. To address these issues, we developed a multi-stage statistical model in which daily PM
2.5
concentrations can be obtained with complete spatial coverage. The model consists of three stages - an inverse probability weighting scheme to correct non-random missing patterns of AOD values, a spatio-temporal linear mixed effect model to account for the spatially and temporally varying PM
2.5
-AOD relationships, and a gap-filling model based on the integrated nested Laplace approximation-stochastic partial differential equations (INLA-SPDE). Good model performance was achieved from out-of-sample validation as shown in R
2
of 0.93 and root mean square error of 9.64 μg/m
3
. The results indicated that the multi-stage PM
2.5
prediction model proposed in the present study yielded highly accurate predictions, while gaining computational efficiency from the INLA-SPDE.
