Abstract
The satellite-retrieved Aerosol Optical Depth (AOD) is widely used to estimate the concentrations and analyze the spatiotemporal pattern of Particulate Matter that is less than or equal to 2.5 microns (PM2.5), also providing a way for the related research of air pollution. Many studies generated PM2.5 concentration networks with resolutions of 3 km or 10 km. However, the relatively coarse resolution of the satellite AOD products make it difficult to determine the fine-scale characteristics of PM2.5 distributions that are important for urban air quality analysis. In addition, the composition and chemical properties of PM2.5 are relatively complex and might be affected by many factors, such as meteorological and land cover type factors. In this paper, an AOD product with a 1 km spatial resolution derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, the PM2.5 measurements from ground sites and the meteorological data as the auxiliary variable, are integrated into the Modified Support Vector Regression (MSVR) model that proposed in this paper to estimate the PM2.5 concentrations and analyze the spatiotemporal pattern of PM2.5. Considering the relatively small dataset and the somewhat complex relationship between the variables, we propose a Modified Support Vector Regression (MSVR) model that based on SVR to fit and estimate the PM2.5 concentrations in Hubei province of China. In this paper, we obtained Cross Correlation Coefficient (R²) of 0.74 for the regression of independent and dependent variables, and the conventional SVR model obtained R² of 0.60 as comparison. We think our MSVR model obtained relatively good performance in spite of many complex factors that might impact the accuracy. We then utilized the optimal MSVR model to perform the PM2.5 estimating, analyze their spatiotemporal patterns, and try to explain the possible reasons for these patterns. The results showed that the PM2.5 estimations retrieved from 1 km MAIAC AOD could reflect more detailed spatial distribution characteristics of PM2.5 and have higher accuracy than that from 3 km MODIS AOD. Therefore, the proposed MSVR model can be a better method for PM2.5 estimating, especially when the dataset is relatively small.
Highlights
Air pollution and its related health problems have become research hot spots [1]
Before estimating the PM2.5 based on the constructed Modified Support Vector Regression (MSVR) method, we explored the relationships between PM2.5 and Aerosol Optical Depth (AOD)
A higher resolution (1 km) satellite AOD data is used to ensure that the obtained PM2.5 can reflect more accurate and detailed temporal and spatial characteristics
Summary
Air pollution and its related health problems have become research hot spots [1]. The Multiangle Implementation of Atmospheric Correction (MAIAC) is a new generic algorithm applied to collection 6 (C6) MODIS measurements to retrieve Aerosol Optical Depth (AOD) over land at high spatial resolution (1 km) [20]. Previous studies have shown that the relationship between PM2.5 and AOD is relatively complex and may be affected by a series of parameters, such as the aerosol type and the vertical structure of aerosol distribution [22], the relative humidity (RH) [23], planetary boundary layer height (PBLH) [24], wind speed and direction [25], the depth and temperature difference of the inversion layer [24], land cover [26], etc. The statistical characteristics of PM2.5 concentrations may vary over space and time This space–time anisotropy may violate the independent and identically distributed random variables in most of the machine learning methods [32]. McaotnetrriibaulstearneldatiMveelythmoodreoslioggniyficant influence, with MAIAC AOD as the primary predictor and the meteorological and land cover information as ancillary information
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