Abstract
Degraded air quality by PM2.5 can cause various health problems. Satellite observations provide abundant data for monitoring PM2.5 pollution. While satellite-derived products, such as aerosol optical depth (AOD) and normalized difference vegetation index (NDVI), have been widely used in estimating PM2.5 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 PM2.5 at a regional scale. Geographically weighted regression (GWR) was employed to estimate the PM2.5 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 PM2.5 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 PM2.5 prediction. Results showed that Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NTL imagery could be an important dataset for more accurately estimating PM2.5 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 PM2.5 levels clearly revealed patterns consistent with those densely populated areas and high traffic areas, implying a close and positive correlation between VANUI and PM2.5 concentration. In general, the DMSP/OLS NTL satellite imagery is promising for providing additional information for PM2.5 monitoring and prediction.
Highlights
Fine particulate matter is hazardous to human health [1,2]
This study aimed to evaluate the potential of using DMSP/OLS nighttime light (NTL) data, together with satellite-retrieved aerosol optical depth (AOD) data and meteorological data, to estimate the ground-level PM2.5 concentration
By adding normalized difference vegetation index (NDVI) as a predictor to the geographically weighted regression (GWR)-basic model, the prediction performance of the GWR-NDVI was improved by 10.5%, 3.9%, 10.08% and 3.26% in terms of root mean squared error (RMSE), mean absolute error (MAE), relative root mean squared error (RRMSE), and relative mean absolute error (RMAE), respectively, for the warm season; and by 1.8%, 1.18%, 2.26% and 0.98% for the cold season, respectively
Summary
Fine particulate matter (known as PM2.5 , with an aerodynamic diameter less than 2.5 μm) is hazardous to human health [1,2]. Its spatial coverage is limited and observed data are only available at certain times due to sampling frequencies These point measurements are insufficient to explain regional variations, and are inevitably subject to errors when estimating PM2.5 concentration at a regional scale [9]. The RS approach uses satellite-retrieved aerosol optical depth (AOD) to estimate PM2.5 pollution in areas where ground-based monitors are too sparsely distributed [10,11,12]. The DMSP/OLS NTL data were used as the only input variable in these studies for PM2.5 prediction, and the potential contributions of meteorological variables and AOD measurements were ignored. This study aimed to evaluate the potential of using DMSP/OLS NTL data, together with satellite-retrieved AOD data and meteorological data, to estimate the ground-level PM2.5 concentration. The performance of the constructed GWR models with different input variables was cross-validated, and the spatiotemporal variability of the predicted PM2.5 was demonstrated for the year 2013
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