High temporal and spatial resolution PM2.5 dataset acquisition and pollution assessment based on FY-4A TOAR data and deep forest model in China

Song, Zhihao; Chen, Bin; Zhang, Peng; Guan, Xiaodan; Wang, Xin; Ge, Jinming; Hu, Xiuqing; Zhang, Xingying; Wang, Yixuan

doi:10.1016/j.atmosres.2022.106199

Cited by 13 publications

(7 citation statements)

References 87 publications

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“…The meteorological factors used in this study are from the European Centre for Medium-Range Weather Forecasts (ECMWF) EAR-5 reanalysis datasets (Hersbach et al, 2020), which have an hourly temporal resolution and a spatial resolution of 0.25 °×0.25 °or 0.1 °×0.1 °(as Table 2 showed). Meteorological factors used in this study mainly include boundary layer height (BLH), relative humidity (RH), surface pressure (SP), 2 m temperature (TM), and 10 m U and V winds (U 10 , V 10 ) (Li et al, 2019b;Song et al, 2022b). In addition to meteorological factors, geographic information also affects SO 2 concentrations.…”

Section: Meteorological Factors and Geographic Informationmentioning

confidence: 99%

Estimation of the ground-level SO2 concentration in eastern China based on the LightGBM model and Himawari-8 TOAR

et al. 2023

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Sulfur dioxide (SO2) is one of the main pollutants in China’s atmosphere, but the spatial distribution of ground-based SO2 monitors is too sparse to provide a complete coverage. Therefore, obtaining a high spatial resolution of SO2 concentration is of great significance for SO2 pollution control. In this study, based on the LightGBM machine learning model, combined with the top-of-atmosphere radiation (TOAR) of Himawari-8 and additional data such as meteorological factors and geographic information, a high temporal and spatial resolution TOAR-SO2 estimation model in eastern China (97–136°E, 15–54°N) is established. TOAR and meteorological factors are the two variables that contribute the most to the model, and both of their feature importance values exceed 30%. The TOAR-SO2 model has great performance in estimating ground-level SO2 concentrations with 10-fold cross validation R2 (RMSE) of 0.70 (16.26 μg/m3), 0.75 (12.51 μg/m3), 0.96 (2.75 μg/m3), 0.97 (2.16 μg/m3), and 0.97 (1.71 μg/m3) when estimating hourly, daily, monthly, seasonal, and annual average SO2. Taking North China as main study area, the annual average SO2 is estimated. The concentration of SO2 in North China showed a downward trend since 2016 and decreased to 15.19 μg/m3 in 2020. The good agreement between ground measured and model estimated SO2 concentrations highlights the capability and advantage of using the model to monitor spatiotemporal variations of SO2 in Eastern China.

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Section: Meteorological Factors and Geographic Informationmentioning

confidence: 99%

Estimation of the ground-level SO2 concentration in eastern China based on the LightGBM model and Himawari-8 TOAR

et al. 2023

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“…A research paper that investigated PM 2.5 concentrations in China also mentioned a similar problem faced by us when collecting PM 2.5 concentration data in the United States, namely, the insufficient coverage of the ground‐measurement stations for measuring PM 2.5 (Song et al., 2022). However, when this study was done we did not have anything similar to FY‐4A, a group of Chinese geostationary weather satellites, in the United States which allows the capture of PM 2.5 data with great accuracy in temporal and spatial resolutions (Song et al., 2022). As mentioned above, satellite image data can fill the gaps where station data’s coverage is limited and will increase the accuracy of statistical and machine learning models.…”

Section: Introductionmentioning

confidence: 97%

“…This paper aimed to have better predictability of PM 2.5 values throughout different areas of the United States, as it is of great importance to know the future concentration of air pollutants such as PM 2.5 which can cause adverse health risks such as premature death and respiratory illnesses like ischemic heart disease (Apte et al, 2018). A research paper that investigated PM 2.5 concentrations in China also mentioned a similar problem faced by us when collecting PM 2.5 concentration data in the United States, namely, the insufficient coverage of the ground-measurement stations for measuring PM 2.5 (Song et al, 2022). However, when this study was done we did not have anything similar to FY-4A, a group of Chinese geostationary weather satellites, in the United States which allows the capture of PM 2.5 data with great accuracy in temporal and spatial resolutions (Song et al, 2022).…”

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confidence: 99%

Predicting PM_2.5 Concentrations Across USA Using Machine Learning

Vignesh,

Jiang,

Kishore

2023

Earth and Space Science

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Economic growth, air pollution, and forest fires in some states in the United States have increased the concentration of particulate matter with a diameter less than or equal to 2.5 μm (PM2.5). Although previous studies have tried to observe PM2.5 both spatially and temporally using aerosol remote sensing and geostatistical estimation, they were limited in accuracy by coarse resolution. In this paper, the performance of machine learning models on predicting PM2.5 is assessed with linear regression (LR), decision tree (DT), gradient boosting regression (GBR), AdaBoost regression (ABR), XGBoost (XGB), k‐nearest neighbors (K‐NN), long short‐term memory (LSTM), random forest (RF), and support vector machine (SVM) using PM2.5 station data from 2017 to 2021. To compare the accuracy of all the nine machine learning models, the coefficient of determination (R2), root mean square error (RMSE), Nash‐Sutcliffe efficiency (NSE), root mean square error ratio (RSR), and percent bias (PBIAS) were evaluated. Among all nine models, the RF (100 decision trees with a max depth of 20) and support vector regression (SVR; nonlinear kernel, degree 3 polynomial) models were the best for predicting PM2.5 concentrations. Additionally, comparison of the PM2.5 performance metrics displayed that the models had better predictive behavior in the western United States than that in the eastern United States.

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“…Pollution caused by PM2.5 aerosols (particulate matter with a diameter less than 2.5 m) has long been a concern for the environment and for people's health [1,2]. The optical and microphysical properties of aerosols continue to be one of the largest sources of uncertainty when assessing the climatic forcing attributable to particles [2].…”

Section: Introductionmentioning

confidence: 99%

Seasonal variation in aerosol optical depth and study of PM2.5-AOD empirical relationship in Raipur, Chhattisgarh, India

Khobragade¹,

Ahirwar²

2023

Res. Eng. Struct. Mater.

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Owing to urbanization and industrialization, PM2.5 (particulate matter with a diameter of less than 2.5 m) pollution has developed into a severe environmental issue. The limited geographical precision and inadequate coverage of PM2.5 measurement stations hinders research into the sources of contamination and the associated health hazards. The complex link between PM2.5 and aerosol optical depth (AOD), which is again confounded by meteorological conditions, makes retrieving PM2.5 from space difficult. AOD dataset was obtained from multiple satellite data retrieval tools including MERAA and OMI for the year 2020. Assessment of annual, seasonal and monthly AOD variation was carried out. The average aerosol optical depth obtained from OMI and MERRA is 1.60±0.56 and 1.33±0.12 respectively. Maximum AOD value was recorded in the summer, while the lowest value was recorded in the winter. Both the OMI and the MERRA AOD monthly change trends were similar, according to the comparative data. The months with the highest AOD values were July and October, and the months with the lowest AOD values were April and November. In the region, aerosol optical thickness (AOT) shows a significant seasonal change. Back-trajectory analysis indicated the contribution from local as well as distant sources. A significant association between PM2.5-AOD was found with Rsquare =0.71 with validated model showing good model performance and can be used for predicting future ground-based PM2.5 concentrations in the region. Substantial site-specific AOD-PM2.5 associations enable OMI AOD to be used to monitor pollution levels.

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High temporal and spatial resolution PM2.5 dataset acquisition and pollution assessment based on FY-4A TOAR data and deep forest model in China

Cited by 13 publications

References 87 publications

Estimation of the ground-level SO2 concentration in eastern China based on the LightGBM model and Himawari-8 TOAR

Estimation of the ground-level SO2 concentration in eastern China based on the LightGBM model and Himawari-8 TOAR

Predicting PM_2.5 Concentrations Across USA Using Machine Learning

Seasonal variation in aerosol optical depth and study of PM2.5-AOD empirical relationship in Raipur, Chhattisgarh, India

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High temporal and spatial resolution PM2.5 dataset acquisition and pollution assessment based on FY-4A TOAR data and deep forest model in China

Cited by 13 publications

References 87 publications

Estimation of the ground-level SO2 concentration in eastern China based on the LightGBM model and Himawari-8 TOAR

Estimation of the ground-level SO2 concentration in eastern China based on the LightGBM model and Himawari-8 TOAR

Predicting PM2.5 Concentrations Across USA Using Machine Learning

Seasonal variation in aerosol optical depth and study of PM2.5-AOD empirical relationship in Raipur, Chhattisgarh, India

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Predicting PM_2.5 Concentrations Across USA Using Machine Learning