Estimation of PM2.5 Concentration across China Based on Multi-Source Remote Sensing Data and Machine Learning Methods

Abstract

Long-term exposure to high concentrations of fine particles can cause irreversible damage to people’s health. Therefore, it is of extreme significance to conduct large-scale continuous spatial fine particulate matter (PM2.5) concentration prediction for air pollution prevention and control in China. The distribution of PM2.5 ground monitoring stations in China is uneven with a larger number of stations in southeastern China, while the number of ground monitoring sites is also insufficient for air quality control. Remote sensing technology can obtain information quickly and macroscopically. Therefore, it is possible to predict PM2.5 concentration based on multi-source remote sensing data. Our study took China as the research area, using the Pearson correlation coefficient and GeoDetector to select auxiliary variables. In addition, a long short-term memory neural network and random forest regression model were established for PM2.5 concentration estimation. We finally selected the random forest regression model (R2 = 0.93, RMSE = 4.59 μg m−3) as our prediction model by the model evaluation index. The PM2.5 concentration distribution across China in 2021 was estimated, and then the influence factors of high-value regions were explored. It is clear that PM2.5 concentration is not only related to the local geographical and meteorological conditions, but also closely related to economic and social development.

Assessing the Nonlinear Relationship between Land Cover Change and PM10 Concentration Change in China

“…However, linear models exhibit significant uncertainty when faced with big data and complex nonlinear problems. In contrast, machine-learning models often perform with a higher computational efficiency and can express more complex relationships when solving pollution concentration estimation [13][14][15]. To further improve the accuracy of the model, meteorological auxiliary data such as the height of the boundary layer, humidity, temperature, and so on, which are related to the particulate matter, are added in the retrieval process, which can effectively improve the accuracy of the estimation results [16].…”

Enhancing daily PM2.5 air quality forecasts in Dakar, Senegal, through the integration of data from the automatic measuring station into a server using data assimilation

“…Therefore, it is important to have accurate forecasts at seasonal, weekly and daily scales to inform government decision-making, especially from a public health perspective. Zhang et al (2021) [9] study highlights the effectiveness of neural network models in accurately predicting PM2.5 concentrations in China, paving the way for similar applications in other parts of the world. In Africa, initiatives such as the African Union Air Quality Project (AU-QA) highlight the importance of data assimilation to increase forecast accuracy.…”