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Due to the country's rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary particulate matter in air pollution, so an accurate estimation of PM2.5 distribution is of great significance. Although previous studies have attempted to retrieve PM2.5 using geostatistical or aerosol remote sensing retrieval methods, the current rough resolution and accuracy remain as limitations of such methods. This paper proposes a fine-grained spatiotemporal PM2.5 retrieval method that comprehensively considers various datasets, such as Landsat 8 satellite images, ground monitoring station data, and socio-economic data, to explore the applicability of different machine learning algorithms in PM2.5 retrieval. Six typical algorithms were used to train the multi-dimensional elements in a series of experiments. The characteristics of retrieval accuracy in different scenarios were clarified mainly according to the validation index, R2. The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated R2 of 0.86 and a category retrieval accuracy of 0.83, while both maintained excellent retrieval accuracy and achieved a high spatiotemporal resolution. Based on this retrieval model, we evaluated the PM2.5 distribution characteristics and hourly variation in the sample area, as well as the functions of different input variables in the model. The PM2.5 retrieval method proposed in this paper provides a new model for fine-grained PM2.5 concentration estimation to determine the distribution laws of air pollutants and thereby specify more effective measures to realize the high-quality development of the city. [ABSTRACT FROM AUTHOR]

Machine learning is widely used to infer ground-level concentrations of air pollutants from satellite observations. However, a single pollutant is commonly targeted in previous explorations, which would lead to duplication of efforts and ignoration of interactions considering the interactive nature of air pollutants and their common influencing factors. We aim to build a unified model to offer a synchronized estimation of ground-level air pollution levels. We constructed a multi-output random forest (MORF) model and achieved simultaneous estimation of hourly concentrations of PM2.5, PM10, O3, NO2, CO, and SO2 in China, benefiting from the world's first geostationary air-quality monitoring instrument Geostationary Environment Monitoring Spectrometer. MORF yielded a high accuracy with cross-validated R2 reaching 0.94. Meanwhile, model efficiency was significantly improved compared to single-output models. Based on retrieved results, the spatial distributions, seasonality, and diurnal variations of six air pollutants were analyzed and two typical pollution events were tracked. [ABSTRACT FROM AUTHOR]

Having a high-quality historical air pollutant dataset is critical for environmental and epidemiological research. In this study, a novel deep learning model based on convolutional neural network architecture was developed to estimate ground-level ozone concentrations across eastern China. A high-resolution maximum daily average 8-h (MDA8) surface ground ozone concentration dataset was generated with the support of the total ozone column from the satellite Tropospheric Monitoring Instrument, meteorological data from the China Meteorological Administration Land Data Assimilation System, and simulations of the WRF-Chem model. The modeled results were compared with in situ measurements in five cities that were not involved in model training, and the mean R2 of predicted ozone with observed values was 0.9, indicating the good robustness of our model. In addition, we compared the model results with some widely used machine learning techniques (e.g., random forest) and recently published ozone datasets, showing that the accuracy of our model is higher and that the spatial distributions of predicted ozone are more coherent. This study provides an efficient and exact method to estimate ground-level ozone and offers a new perspective for modeling spatiotemporal air pollutants. [ABSTRACT FROM AUTHOR]

Negative air ion (NAI) plays a vital role in promoting the psychological and physiological functions of the human body and is an essential indicator for measuring the air cleanliness of a given area. In this paper, we presented and compared the results of two methods for identifying the main environmental factors affecting changes of NAI in a warm-temperate region of China. NAI concentration was estimated based on measured data during the main growing season in a warm-temperate forest and was used as the dependent variable in the traditional multiple linear regression and random forest models. Air pollutants and certain weather, radiation, and soil factors were selected as predictors based on their potential influence on NAI. Two methods were applied for the analysis, and the latter was a non-parametric alternative based on an ensemble of classification and regression trees. We compared the precision of the two models, and the variables of each method on the basis of their levels of importance; Independent samples was used in model validation, then we discussed the important environmental factors affecting changes of NAI concentration for both linear and nonlinear perspectives, along with the potential implications of environmental factors on NAI. The random forest model showed a higher accuracy comparison with the multiple linear regression model. Furthermore, the analysis also indicated its better performance by using independent test data for 10-fold cross-validation of the random forest model, and showing that this method has potential for broad-scale application in the assessment of environmental-factor influence on NAI. Certain selected variables that were common to both models (particulate matter 2.5, soil moisture, and relative humidity) appeared to influence NAI to a relatively large extent, demonstrating the decidedly influential role of these parameters on NAI concentration. • Two models are tested to optimize application in NAI–environmental factor influence. • Particulate matter, soil moisture, and relative humidity strongly influence NAI. • Random forest has high predictive ability and better model performance. [ABSTRACT FROM AUTHOR]