Satellite-Based Estimation of Daily Ground-Level PM2.5 Concentrations over Urban Agglomeration of Chengdu Plain

Exploring the relationships between ground-measured particulate matter and satellite-retrieved aerosol parameters in China

In this study, the PM_2.5 and PM₁₀ concentrations from 367 cities in China were integrated with MODIS-retrieved aerosol optical depth (AOD) and Angstrom exponent (AE) data to explore the relationship between ground-measured surface particle concentrations and remote-sensing aerosol parameters. The impact of meteorological and topographical factors and seasonality were also taken into consideration and the partial least squares (PLS) regression model was adopted to evaluate the effects of surface particulate matter (PM) concentration and meteorological factors on the variation of aerosol parameters. PM concentrations and aerosol parameters all presented strong spatial disparity and seasonal patterns in China. After implementation of stringent clean air actions and policies, both the ground-measured and satellite-retrieved aerosol parameters revealed that the concentrations of suspended particles in China's cities declined dramatically from 2015 to 2018. The PM/AOD ratio showed conspicuous south-north and west-east differences. The ratio was strongly correlated to meteorological and topographic factors, and it tended to be higher in arid and less polluted regions. Moreover, the dominant factors affecting seasonal PM/AOD ratios varied among China's five regions. The correlations of daily PM-AOD were always strong in southwest China and in basin terrain (e.g., Sichuan Basin and Tarim Basin). In contrast, the PM-AOD correlation was found to be negative in some cities on the Tibetan Plateau because local relative humidity makes a greater contribution to AOD variation. Since the climate is arid and the ratio of coarse particles (e.g., PM₁₀) is much higher, PM tended to have a significantly negative correlation with AE in northwestern cities. Whereas in many southern cities, PM was positively correlated with AE because of the area's high relative humidity and aerosol hygroscopic properties.

Population exposure across central India to PM2.5 derived using remotely sensed products in a three-stage statistical model

Surface PM_2.5 concentrations are required for exposure assessment studies. Remotely sensed Aerosol Optical Depth (AOD) has been used to derive PM_2.5 where ground data is unavailable. However, two key challenges in estimating surface PM_2.5 from AOD using statistical models are (i) Satellite data gaps, and (ii) spatio-temporal variability in AOD-PM_2.5 relationships. In this study, we estimated spatially continuous (0.03° × 0.03°) daily surface PM_2.5 concentrations using MAIAC AOD over Madhya Pradesh (MP), central India for 2018 and 2019, and validated our results against surface measurements. Daily MAIAC AOD gaps were filled using MERRA-2 AOD. Imputed AOD together with MERRA-2 meteorology and land use information were then used to develop a linear mixed effect (LME) model. Finally, a geographically weighted regression was developed using the LME output to capture spatial variability in AOD-PM_2.5 relationship. Final Cross-Validation (CV) correlation coefficient, r², between modelled and observed PM_2.5 varied from 0.359 to 0.689 while the Root Mean Squared Error (RMSE) varied from 15.83 to 35.85 µg m^-3, over the entire study region during the study period. Strong seasonality was observed with winter seasons (2018 and 2019) PM_2.5 concentration (mean value 82.54 µg m^-3) being the highest and monsoon seasons being the lowest (mean value of 32.10 µg m^-3). Our results show that MP had a mean PM_2.5 concentration of 58.19 µg m^-3 and 56.32 µg m^-3 for 2018 and 2019, respectively, which likely caused total premature deaths of 0.106 million (0.086, 0.128) at the 95% confidence interval including 0.056 million (0.045, 0.067) deaths due to Ischemic Heart Disease (IHD), 0.037 million (0.031, 0.045) due to strokes, 0.012 million (0.009, 0.014) due to Chronic Obstructive Pulmonary Disease (COPD), and 1.2 thousand (1.0, 1.5) due to lung cancer (LNC) during this period.

Satellite measurements of aerosol optical depth and carbon monoxide and comparison with ground data

Satellite data of aerosol optical depths (AODs) from the moderate resolution imaging spectroradiometer (MODIS) and carbon monoxide (CO) columns from the measurements of pollution in the troposphere (MOPITT) were collected for the study in Northern Thailand. Comparative analyses were conducted of MODIS (Terra and Aqua) AODs with ground particulate matter with diameter below 10 microns (PM₁₀) concentrations and MOPITT CO surface/total columns with ground CO concentrations for 2014-2017. Temporal variations in both the satellite and ground datasets were in good agreement. High levels of air pollutants were common during March-April. The annual analysis of both satellite and ground datasets revealed the highest levels of air pollutants in 2016 and the lowest levels in 2017. The AODs and PM₁₀ concentrations were at higher levels in the morning than in the afternoon. The comparison between satellite products showed that AODs correlated better with the CO total columns than the CO surface columns. The regression analysis presented better performance of Aqua AODs-PM₁₀ than Terra AODs-PM₁₀ with correlation coefficients (r) of 0.72-0.83 and 0.57-0.79, respectively. Ground CO concentrations correlated better with MOPITT CO surface columns (r = 0.65-0.73) than with CO total columns (r = 0.56-0.72). The r values of satellite and ground datasets were greatest when the analysis was restricted to November-March (dry weather periods with possible low mixing height (MH)). Overall, the results suggested that the relationships between satellite and ground data can be used to develop predictive models for ground PM₁₀ and CO in northern Thailand, particularly during air pollution episodes located where ground monitoring stations are limited.

PM2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression

Fine aerosols with a diameter of less than 2.5 microns (PM_2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM_2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM_2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM_2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM_2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM_2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan’s government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM_2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper.