Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements

Kloog, Itai; Koutrakis, Petros; Coull, Brent A.; Lee, Hyung Joo; Schwartz, Joel

doi:10.1016/j.atmosenv.2011.08.066

Cited by 329 publications

(283 citation statements)

References 48 publications

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“…According to the error data (represented by RMSE, MAE, RRMSE, and RMAE), GWR-basic performed the worst. In addition, the models performed relatively better for the warm season than for the cold season, which is consistent with the previous study of Kloog et al [51]. Here, the GWR-basic is considered as the benchmark to evaluate the predictive capabilities of GWR-NDVI and GWR-VANUI.…”

Section: Descriptive Statisticssupporting

confidence: 75%

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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Section: Descriptive Statisticssupporting

confidence: 75%

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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“…Recently, several studies proposed that the effects of time-varying parameters influencing the AOD-PM 2.5 relationship can be taken into account by using daily adjustments Kloog et al, 2011;Chudnovsky et al, 2012). Kloog et al (2011Kloog et al ( , 2012 introduced a day-specific calibration of AOD data using ground PM 2.5 measurements and incorporated commonly used land use variables and meteorological parameters to produce much higher R 2 values than previously reported in the literature, ranging from 0.83 to 0.92. The next step would be to apply a similar model to MAIAC data to investigate the spatial patterns of PM 2.5 at high resolution, especially in urban areas where MAIAC data promise improvements.…”

Section: Discussionmentioning

confidence: 90%

A critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions

Chudnovsky

Tang²,

Lyapustin

et al. 2013

Atmos. Chem. Phys.

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Abstract. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for the MODerate Resolution Imaging Spectroradiometer (MODIS), which provides aerosol optical depth (AOD) at 1 km resolution. The relationship between MAIAC AOD and PM 2.5 as measured by 84 EPA ground monitoring stations in the entire New England and the Harvard super site during 2002-2008 was investigated and also compared to the AOD-PM 2.5 relationship using conventional MODIS 10 km AOD retrieval from Aqua platform (MYD04) for the same days and locations. The correlations for MYD04 and for MA-IAC are r = 0.62 and 0.65, respectively, suggesting that AOD is a reasonable proxy for PM 2.5 ground concentrations. The slightly higher correlation coefficient (r) for MAIAC can be related to its finer resolution resulting in better correspondence between AOD and EPA monitoring sites. Regardless of resolution, AOD-PM 2.5 relationship varies daily, and under certain conditions it can be negative (due to several factors such as an EPA site location (proximity to road) and the lack of information about the aerosol vertical profile). By investigating MAIAC AOD data, we found a substantial increase, by 50-70 % in the number of collocated AOD-PM 2.5 pairs, as compared to MYD04, suggesting that MAIAC AOD data are more capable in capturing spatial patterns of PM 2.5 . Importantly, the performance of MAIAC AOD retrievals is slightly degraded but remains reliable under partly cloudy conditions when MYD04 data are not available, and it can be used to increase significantly the number of days for PM 2.5 spatial pattern prediction based on satellite observations.

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“…In general, our experimental results illustrate that the daily AOD-PM 2.5 relationships closely correlate with meteorological factors rather than geographical factors. The explanation for this is that the shifting meteorology conditions highly affect the generation or diffusion of daily PM 2.5 concentrations in the short term whereas geographical factors are crucial to long term prediction [30].…”

Section: Discussionmentioning

confidence: 99%

Modelling Seasonal GWR of Daily PM2.5 with Proper Auxiliary Variables for the Yangtze River Delta

et al. 2017

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Abstract:Over the past decades, regional haze episodes have frequently occurred in eastern China, especially in the Yangtze River Delta (YRD). Satellite derived Aerosol Optical Depth (AOD) has been used to retrieve the spatial coverage of PM 2.5 concentrations. To improve the retrieval accuracy of the daily AOD-PM 2.5 model, various auxiliary variables like meteorological or geographical factors have been adopted into the Geographically Weighted Regression (GWR) model. However, these variables are always arbitrarily selected without deep consideration of their potentially varying temporal or spatial contributions in the model performance. In this manuscript, we put forward an automatic procedure to select proper auxiliary variables from meteorological and geographical factors and obtain their optimal combinations to construct four seasonal GWR models. We employ two different schemes to comprehensively test the performance of our proposed GWR models: (1) comparison with other regular GWR models by varying the number of auxiliary variables; and (2) comparison with observed ground-level PM 2.5 concentrations. The result shows that our GWR models of "AOD + 3" with three common meteorological variables generally perform better than all the other GWR models involved. Our models also show powerful prediction capabilities in PM 2.5 concentrations with only slight overfitting. The determination coefficients R 2 of our seasonal models are 0.8259 in spring, 0.7818 in summer, 0.8407 in autumn, and 0.7689 in winter. Also, the seasonal models in summer and autumn behave better than those in spring and winter. The comparison between seasonal and yearly models further validates the specific seasonal pattern of auxiliary variables of the GWR model in the YRD. We also stress the importance of key variables and propose a selection process in the AOD-PM 2.5 model. Our work validates the significance of proper auxiliary variables in modelling the AOD-PM 2.5 relationships and provides a good alternative in retrieving daily PM 2.5 concentrations from remote sensing images in the YRD.

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Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements

Cited by 329 publications

References 48 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 critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions

Modelling Seasonal GWR of Daily PM2.5 with Proper Auxiliary Variables for the Yangtze River Delta

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