Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives

Bai, Kaixu; Ke, Li; Sun, Yuangong; Lv, Wu; Zhang, Ying; Chang, Ni‐Bin; Li, Zhengqiang

doi:10.1016/j.earscirev.2023.104461

Cited by 23 publications

(13 citation statements)

References 106 publications

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“…Machine learning methods, an extension of traditional statistical models, have been widely utilized in pollutant level estimation in recent years due to their excellent performances. [45][46][47] Furthermore, XGBoost, a popular statistical modeling method with fast training speed, excellent prediction accuracy, and ability to quantify the relative importance of input variables, was utilized to establish surface NO 2 , O 3 , and SO 2 estimation models. [48][49][50][51] The equations of three nationwide surface NO 2 , O 3 , and SO 2 estimation models using XGBoost model are described as follows:…”

Section: Gap-free Air Pollutants Concentration Mappingmentioning

confidence: 99%

Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning

Ma,

Liu,

Liu

et al. 2023

J. Appl. Rem. Sens.

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It is of great significance to explore the spatial-temporal variations and estimate the relative importance of the influencing factors of PM 2.5 and O 3 pollution. The study established nationwide surface O 3 , NO 2 , and SO 2 estimation models using the extreme gradient boosting model and the data fusion method. The cross-validation results indicated that the forecasted models performed well (R-values from 0.86 to 0.95). The results revealed that the pollution levels of O 3 , PM 2.5 , NO 2 , and SO 2 in the North China Plain (NCP) were the highest in China. Subsequently, a multi-task learning model was utilized to estimate the relative importance of influential factors on the PM 2.5 and O 3 pollution in the NCP. The sensitivity analysis results indicated that the O 3 pollution from 2010-2020 in the NCP was susceptible to meteorological factors such as ultraviolet radiation and temperature, as well as anthropogenic precursors such as NO X , and PM 2.5 pollution in the NCP was constrained by both meteorological factors (44.62%) and anthropogenic emissions (16.86%). The impact of NO 2 on PM 2.5 pollution was similar to its impact on O 3 pollution; therefore, the importance of NO 2 emission reduction to PM 2.5 pollution is as important as that of O 3 pollution, whereas the impact of SO 2 on PM 2.5 was much greater than its impact on O 3 pollution, so SO 2 emission reduction is more important for PM 2.5 .

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Section: Gap-free Air Pollutants Concentration Mappingmentioning

confidence: 99%

Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning

Ma,

Liu,

Liu

et al. 2023

J. Appl. Rem. Sens.

Self Cite

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“…Machine learning methods, an extension of the traditional statistical model, have been widely used in air pollutant estimation in recent years due to their excellent performances [48][49][50]. Furthermore, among these machine learning models, the eXtreme Gradient Boosting (XGBoost) model, a popular statistical modeling method, was utilized to establish the surface ozone estimation model for East China.…”

Section: Statistical Modeling Methodsmentioning

confidence: 99%

Spatiotemporal Patterns and Quantitative Analysis of Factors Influencing Surface Ozone over East China

Ma,

Liu,

Liu

et al. 2023

Sustainability

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Surface ozone pollution in China has been persistently becoming worse in recent years; therefore, it is of great importance to accurately estimate ozone pollution and explore the spatiotemporal variations in surface ozone in East China. By using S5P-TROPOMI-observed NO2, HCHO data (7 km × 3.5 km), and other surface-ozone-influencing factors, including VOCs, meteorological data, NOX emission inventory, NDVI, DEM, population, land use and land cover, and hourly in situ surface ozone observations, an extreme gradient boosting model was used to estimate the daily 0.05° × 0.05° gridded maximum daily average 8 h ozone (MDA8) in East China during 2019–2021. Four surface ozone estimation models were established by combining NO2 and HCHO data from S5P-TROPOMI observations and CAMS reanalysis data. The sample-based validation R2 values of these four models were all larger than 0.92, while their site-based validation R2 values were larger than 0.82. The results revealed that the coverage ratio of the model using CAMS NO2 and CAMS HCHO was the highest (100%), while the coverage ratio of the model using S5P-TROPOMI NO2 and CAMS HCHO was the second highest (96.26%). Furthermore, the MDA8 estimation results of these two models were averaged to produce the final surface ozone estimation dataset. It indicated that O3 pollution in East China during 2019–2021 was susceptible to anthropogenic precursors such as VOCs (22.55%) and NOX (8.97%), as well as meteorological factors (27.35%) such as wind direction, temperature, and wind speed. Subsequently, the spatiotemporal patterns of ozone pollution were analyzed. Ozone pollution in East China is mainly concentrated in the North China Plain (NCP), the Pearl River Delta (PRD), and the Yangtze River Delta (YRD). Among these three regions, ozone pollution in the NCP mainly occurs in June (summer), ozone pollution in the YRD mainly occurs in May (spring), and ozone pollution in the PRD mainly occurs in April (spring) and September (autumn). In addition, surface O3 concentration in East China decreased by 3.74% in 2020 compared to 2019, which may have been influenced by the COVID-19 epidemic and the implementation of the policy of synergistic management of PM2.5 and O3 pollution. The regions mostly affected by the COVID-19 epidemic and the policy of the synergistic management of PM2.5 and O3 pollution were the NCP (−2~−8%), the Middle and Lower of Yangtze Plain (−6~−10%), and the PRD (−4~−10%). Overall, the estimated 0.05° × 0.05° gridded surface ozone in East China from 2019 to 2021 provides a promising data source and data analysis basis for the related researchers. Meanwhile, it reveals the spatial and temporal patterns of O3 pollution and the main influencing factors, which provides a good basis for the control and management of O3 pollution, and also provides technical support for the sustainable development of the environment in East China.

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“…Currently, numerous studies have estimated the concentration of fine particulate matter by employing satellite-based aerosol optical depth (AOD) products, and the data quality is improved by integrating other multi-source data [7][8][9]. Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products are the most widely available data for PM 2.5 estimation [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Improving the Estimation of PM2.5 Concentration in the North China Area by Introducing an Attention Mechanism into Random Forest

Zhang,

Li,

Guang

et al. 2024

Atmosphere

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Fine particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) profoundly affects environmental systems, human health and economic structures. Multi-source data and advanced machine or deep-learning methods have provided a new chance for estimating the PM2.5 concentrations at a high spatiotemporal resolution. In this paper, the Random Forest (RF) algorithm was applied to estimate hourly PM2.5 of the North China area (Beijing–Tianjin–Hebei, BTH) based on the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) aerosol optical depth (AOD) products. To improve the estimation of PM2.5 concentration across large areas, we construct a method for co-weighting the environmental similarity and the geographical distances by using an attention mechanism so that it can efficiently characterize the influence of spatial–temporal information hidden in adjacent ground monitoring sites. In experiment results, the hourly PM2.5 estimates are well correlated with ground measurements in BTH, with a coefficient of determination (R2) of 0.887, a root-mean-square error (RMSE) of 18.31 μg/m3, and a mean absolute error (MAE) of 11.17 µg/m3, indicating good model performance. In addition, this paper makes a comprehensive analysis of the effectiveness of multi-source data in the estimation process, in this way, to simplify the model structure and improve the estimation efficiency of the model while ensuring its accuracy.

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Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives

Cited by 23 publications

References 106 publications

Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning

Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning

Spatiotemporal Patterns and Quantitative Analysis of Factors Influencing Surface Ozone over East China

Improving the Estimation of PM2.5 Concentration in the North China Area by Introducing an Attention Mechanism into Random Forest

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