Assessing PM<sub>2.5</sub>Exposures with High Spatiotemporal Resolution across the Continental United States

Di, Qian; Kloog, Itai; Koutrakis, Petros; Lyapustin, A.; Wang, Yujie; Schwartz, Joel

doi:10.1021/acs.est.5b06121

Cited by 416 publications

(338 citation statements)

References 79 publications

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“…The results are in line with other recent studies [52][53][54], which all indicated that land use information such as NDVI can help to predict the PM 2.5 concentration.…”

Section: Model Validationsupporting

confidence: 92%

Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States

Zhang

et al. 2017

Remote Sensing

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Degraded air quality by PM 2.5 can cause various health problems. Satellite observations provide abundant data for monitoring PM 2.5 pollution. While satellite-derived products, such as aerosol optical depth (AOD) and normalized difference vegetation index (NDVI), have been widely used in estimating PM 2.5 concentration, little research was focused on the use of remotely sensed nighttime light (NTL) imagery. This study evaluated the merits of using NTL satellite images in predicting ground-level PM 2.5 at a regional scale. Geographically weighted regression (GWR) was employed to estimate the PM 2.5 concentration and analyze its relationships with AOD, meteorological variables, and NTL data across the New England region. Observed data in 2013 were used to test the constructed GWR models for PM 2.5 prediction. The Vegetation Adjusted NTL Urban Index (VANUI), which incorporates Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI into NTL to overcome the defects of NTL data, was used as a predictor variable for final PM 2.5 prediction. Results showed that Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NTL imagery could be an important dataset for more accurately estimating PM 2.5 exposure, especially in urbanized and densely populated areas. VANUI data could obviously improve the performance of GWR for the warm season (GWR model with VANUI performed 17% better than GWR model without NDVI and NTL data and 7.26% better than GWR model without NTL data in terms of RMSE), while its improvements were less obvious for the cold season (GWR model with VANUI performed 3.6% better than the GWR model without NDVI and NTL data and 1.83% better than the GWR model without NTL data in terms of RMSE). Moreover, the spatial distribution of the estimated PM 2.5 levels clearly revealed patterns consistent with those densely populated areas and high traffic areas, implying a close and positive correlation between VANUI and PM 2.5 concentration. In general, the DMSP/OLS NTL satellite imagery is promising for providing additional information for PM 2.5 monitoring and prediction.

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“…The results are in line with other recent studies [52][53][54], which all indicated that land use information such as NDVI can help to predict the PM 2.5 concentration.…”

Section: Model Validationsupporting

confidence: 92%

Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States

Zhang

et al. 2017

Remote Sensing

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“…A similar hybrid approach has been applied to assess human exposures to PM 2.5 mass and chemical components (Di et al 2015, Di et al 2016, Kloog et al 2014, Kloog et al 2011). This study applies a hybrid approach similar to the previous model of PM 2.5 , but incorporates additional variables due to ozone’s distinct gaseous nature and chemical characteristics.…”

Section: Introductionmentioning

confidence: 99%

A hybrid model for spatially and temporally resolved ozone exposures in the continental United States

Rowland

Koutrakis

et al. 2016

Journal of the Air & Waste Management Association

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113

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Ground-level ozone is an important atmospheric oxidant, which exhibits considerable spatial and temporal variability in its concentration level. Existing modeling approaches for ground-level ozone include chemical transport models, land-use regression, Kriging, and data fusion of chemical transport models with monitoring data. Each of these methods has both strengths and weaknesses. Combining those complementary approaches could improve model performance. Meanwhile, satellite-based total column ozone, combined with ozone vertical profile, is another potential input. We propose a hybrid model that integrates the above variables to achieve spatially and temporally resolved exposure assessments for ground-level ozone. We used a neural network for its capacity to model interactions and nonlinearity. Convolutional layers, which use convolution kernels to aggregate nearby information, were added to the neural network to account for spatial and temporal autocorrelation. We trained the model with AQS 8-hour daily maximum ozone in the continental United States from 2000 to 2012 and tested it with left out monitoring sites. Cross-validated R2 on the left out monitoring sites ranged from 0.74 to 0.80 (mean 0.76) for predictions on 1 km×1 km grid cells, which indicates good model performance. Model performance remains good even at low ozone concentrations. The prediction results facilitate epidemiological studies to assess the health effect of ozone in the long term and the short term.

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“…Details have been published elsewhere. 12,13 Warm season is defined to be from April 1 to September 30, which is the specific time window to examine the association between ozone and mortality. Meteorological variables including air and dew point temperatures were retrieved from North American Regional Reanalysis data and estimated daily mean values were determined for each 32 km × 32 km grid in the continental US.…”

Section: Methodsmentioning

confidence: 99%

Association of Short-term Exposure to Air Pollution With Mortality in Older Adults

Dai

Wang

et al. 2017

JAMA

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528

370

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Importance The Environmental Protection Agency (EPA) is required to re-examine its National Ambient Air Quality Standards (NAAQS) every 5 years, but evidence of mortality risk is lacking at air pollution levels below the current daily NAAQS, in unmonitored areas and for sensitive subgroups. Objective To estimate the association between short-term exposures to ambient PM2.5 and ozone and at levels below the current daily NAAQS and mortality in the continental US. Design, Setting, and Participants Case-crossover design and conditional logistic regression to estimate the association between short-term exposures to PM2.5 and ozone (mean of daily exposure on the same day of death and one day prior) and mortality in 2-pollutant models. The study included the entire Medicare population from January 1, 2000 to December 31, 2012 residing in 39,182 zip codes. Exposures Daily PM2.5 and ozone levels in a 1 km × 1 km grid were estimated using published and validated air pollution prediction models based on land use, chemical transport modeling, and satellite remote sensing data. From these gridded exposures, daily exposures were calculated for every zip code in the US. Warm-season ozone was defined as ozone levels for the months April to September of each year. Main Outcome and Measure All-cause mortality in the entire Medicare population from 2000 to 2012. Results During the study period, there were 22,433,862 million case days and 76,143,209 control days. Of all case and control days, 93.6% had PM2.5 levels below 25 μg/m3, during which 95% of deaths occurred (21,353,817 of 22,433,862), and 91.1% of days had ozone levels below 60 ppb, during which 93.4% of deaths occurred (20,955,387 of 22,433,862). The baseline daily mortality rate was 137.33 and 129.44 (per 1 million persons at risk per day) for the entire year and for the warm season, respectively. Each short-term increase of 10 μg/m3 in PM2.5 (adjusted by ozone) and 10 ppb (parts-per-billion, 10−9) in warm-season ozone (adjusted by PM2.5) were statistically significantly associated with a relative increase of 1.05% (95% confidence interval [CI]: 0.95%, 1.15%) and 0.51% (95% CI: 0.41%, 0.61%) in daily mortality rate, respectively. Absolute risk differences in daily mortality rate were 1.42 (95% CI: 1.29, 1.56) and 0.66 (95% CI: 0.53, 0.78) per 1 million persons at risk per day. There was no evidence of a threshold in the exposure-response relationship. Conclusions and Relevance In the US Medicare population from 2000-2012, short-term exposures to PM2.5 and warm-season ozone were significantly associated with increased risk of mortality. This risk occurred at levels below current national air quality standards, suggesting that these standards may need to be reevaluated.

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Assessing PM_2.5Exposures with High Spatiotemporal Resolution across the Continental United States

Cited by 416 publications

References 79 publications

Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States

Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States

A hybrid model for spatially and temporally resolved ozone exposures in the continental United States

Association of Short-term Exposure to Air Pollution With Mortality in Older Adults

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Assessing PM2.5Exposures with High Spatiotemporal Resolution across the Continental United States

Cited by 416 publications

References 79 publications

Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States

Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States

A hybrid model for spatially and temporally resolved ozone exposures in the continental United States

Association of Short-term Exposure to Air Pollution With Mortality in Older Adults

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Assessing PM_2.5Exposures with High Spatiotemporal Resolution across the Continental United States