Abstract
Previous studies have estimated ground-level concentrations of particulate matter 2.5 (PM2.5) using satellite-derived aerosol optical depth (AOD) in conjunction with meteorological and land use variables. However, the impacts of urbanization on air pollution for predicting PM2.5 are seldom considered. Nighttime light (NTL) data, acquired with the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite, could be useful for predictions because they have been shown to be good indicators of the urbanization and human activity that can affect PM2.5 concentrations. This study investigated the potential of incorporating VIIRS NTL data in statistical models for PM2.5 concentration predictions. We developed a mixed-effects model to derive daily estimations of surface PM2.5 levels in the Beijing–Tianjin–Hebei region using 3 km resolution satellite AOD and VIIRS NTL data. The results showed the addition of NTL information could improve the performance of the PM2.5 prediction model. The NTL data revealed additional details for predication results in areas with low PM2.5 concentrations and greater apparent seasonal variation due to the seasonal variability of human activity. Comparison showed prediction accuracy was improved more substantially for the model using NTL directly than for the model using the vegetation-adjusted NTL urban index that included NTL. Our findings indicate that VIIRS NTL data have potential for predicting PM2.5 and that they could constitute a useful supplemental data source for estimating ground-level PM2.5 distributions.
Highlights
In recent decades, rapid economic development in China has caused severe environmental pollution, air pollution [1]
We examined the impact of urbanization on the relationship between PM2.5 and aerosol optical depth (AOD); the urbanization indicator was expressed by the Visible Infrared Imaging Radiometer Suite (VIIRS) Nighttime light (NTL)
The results show that the patterns of PM2.5 estimates from the four models are largely similar
Summary
Rapid economic development in China has caused severe environmental pollution, air pollution [1]. As a major air pollutant in many regions of China, fine particulate matter (aerodynamic diameter < 2.5 μm; PM2.5 ) is associated with various adverse health outcomes including cardiovascular and respiratory diseases [2,3] and it has received considerable attention [4]. Measurements of PM2.5 concentrations can be acquired by ground-based monitoring networks and satellite remote sensing. PM2.5 measurements obtained from ground-based monitoring sites are accurate, such networks are typically sparse, which means the spatial coverage of routine measurements is limited [5,7,8]. Because of the large spatial coverage and reliability of repeated measurements, satellite remote sensing provides a potentially cost-effective method for predicting
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