Abstract
With the rapid development of the economy and society, fine particulate matter (PM2.5) has not only caused severe environmental problems, but also posed a threat to public health. In order to improve the estimated accuracy of PM2.5, the input data fine mode fraction (FMF), a key parameter to the PM2.5 remote sensing method (PMRS), should be improved due to its significant errors. In this study, we merge the observations of the fine mode fraction (FMF) from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Aerosol Robotic Network (AERONET) and the Sun-sky radiometer Observation Network (SONET) using the universal kriging (UK) method to obtain accurate FMF distribution over eastern China. PM2.5 mass concentration is estimated by the fusion and MODIS FMF distributions using the PMRS model. The results show that the parameters in the variogram are relatively stable except for significant differences in correlation lengths in summer. The FMF in the Winter of 2015 shows that the mean error decreases from 0.38 to 0.13 compared with that from MODIS using leave-one-out cross-validation, with the maximum error decreasing from 0.75 to 0.34, indicating that the UK method can provide better estimates of FMF. We also find that PM2.5 estimated from FMF fusion results is closer to the in situ PM2.5 from the Ministry of Environmental Protection (MEP) (87.2 vs. 88.9 μg/m3).
Highlights
In recent years, atmospheric particulate matter has reached a high level due to human activities.PM2.5, which has a great influence on air quality and on human health, has drawn a wide range of attention [1,2,3]
We find that PM2.5 estimated from fine mode fraction (FMF) fusion results is closer to the in situ PM2.5 from the Ministry of Environmental
In order to improve the accuracy of Moderate Resolution Imaging Spectroradiometer (MODIS) FMF over land, we study the feasibility of merging satellite data (MODIS) with ground-based data
Summary
PM2.5 (atmospheric particulate matter with a mass median diameter less than 2.5 μm), which has a great influence on air quality and on human health, has drawn a wide range of attention [1,2,3]. In order to obtain the spatial distribution of PM2.5 mass concentration near the ground, many studies have developed three kinds of methods based on remote sensing observations. These include the statistical method [4,5,6,7,8,9], the simulated method coupled with the atmospheric chemical model [10,11,12], and the physical dependent method [13]. The physical dependent method is a semi-physical model based on remote sensing observations to estimate PM2.5 mass concentration near the ground, which effectively solves the limitations of the above two methods
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