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PM2.5 and PM10 in the atmosphere seriously affect human health and air quality, a situation which has aroused widespread concern. In this paper, we analyze the temporal and spatial distribution of PM2.5 and PM10 concentrations from 2016 to 2021 based on real-time monitoring data. In addition, we also explore the influence of meteorological conditions on pollutants. The results show that PM2.5 and PM10 concentrations are similarly distribution in temporal and spatial from 2016 to 2021, and the average concentrations of both show a decreasing trend. The ratio of PM2.5 to PM10 is decreasing, indicating that the proportion of fine particles is declining. PM2.5 and PM10 concentrations are higher in spring and winter, but lower in summer. Spatially, it shows a gradual shift from the characteristic of "high in the south and low in the north" to a uniform homogenization across districts. The spatial distribution of PM2.5 and PM10 mass concentrations is synchronous by applying empirical orthogonal functions (EOF). The first EOF pattern exhibits a consistent characteristic of high in the southeast and low in the northwest. The second pattern EOF reflects the effect of impairing PM2.5 concentrations in the southeast during the winter of 2016–2018. The PM2.5 and PM10 concentrations are significantly negatively correlated with wind speed and precipitation in both spring and winter. On the other hand, from the perspective of the circulation situation, the southeasterly and weak westerly wind in spring produce convergence resulting in higher particulate matter concentrations in the south than in the north in Beijing. The westerly wind is flatter at 700 hPa geopotential height, which is conducive to the formation of stationary weather. The vertical direction of airflow in spring and winter is dominated by convergence and sinking, indicating the weak dispersion ability of the atmosphere. The reason for the accumulation of particulate matter at the surface is investigated, which is beneficial to provide the theoretical basis for air quality management and pollution control in Beijing. [ABSTRACT FROM AUTHOR]

The concentration series of PM2.5 (particulate matter ≤ 2.5 μm) is nonlinear, nonstationary, and noisy, making it difficult to predict accurately. This paper presents a new PM2.5 concentration prediction method based on a hybrid model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bi-directional long short-term memory (BiLSTM). The new method was applied to predict the same kind of particulate pollutant PM10 and heterogeneous gas pollutant O3, proving that the prediction method has strong generalization ability. First, CEEMDAN was used to decompose PM2.5 concentrations at different frequencies. Then, the fuzzy entropy (FE) value of each decomposed wave was calculated, and the near waves were combined by K-means clustering to generate the input sequence. Finally, the combined sequences were put into the BiLSTM model with multiple hidden layers for training. We predicted the PM2.5 concentrations of Seoul Station 116 by the hour, with values of the root mean square error (RMSE), the mean absolute error (MAE), and the symmetric mean absolute percentage error (SMAPE) as low as 2.74, 1.90, and 13.59%, respectively, and an R2 value as high as 96.34%. The "CEEMDAN-FE" decomposition-merging technology proposed in this paper can effectively reduce the instability and high volatility of the original data, overcome data noise, and significantly improve the model's performance in predicting the real-time concentrations of PM2.5. [ABSTRACT FROM AUTHOR]

While the district of Mae Moh, Thailand has been well known for its atmospheric pollution associated with coal power production, economic assessment of demand for improved air quality has not been conducted to date. This study estimated local residents' individual and aggregate willingness to pay (WTP) for mitigation of atmospheric pollution in Mae Moh using the contingent valuation method (CVM), and analyzed the factors associated with the individual WTP using the bivariate tobit and double-hurdle regression techniques. Primary data were collected through face-to-face interviews with a stratified sample of 200 residents. The hypothetical scenarios used in the CVM module were 50% and 80% mitigation of atmospheric concentrations of major pollutants. The weighted average WTP was found to be THB 251.3 and 307.9 per annum (USD 8.4 and 10.3) for the 50% and 80% reduction scenarios, respectively. The aggregate WTP for the entire population of Mae Moh was THB 10,008,733 and 12,264,761 per annum (USD 336,294 and 412,096), respectively. Education, occupation type, income, expenses, satisfaction with ambient quality, and perceived sources of pollution had significant associations with the individual WTP. The paper concludes by discussing policy implications for atmospheric pollution management and avenues for future research. [ABSTRACT FROM AUTHOR]

Studies on air quality frequently adopt clustering, in particular the k-means technique, owing to its simplicity, ease of implementation and efficiency. The aim of the present paper was the assessment of air quality in a winter season (December–February) in the conditions of temperature inversion using the k-means method, representing a non-hierarchical algorithm of cluster analysis. The air quality was assessed on the basis of the concentrations of particulate matter (PM10, PM2.5). The studies were conducted in four winter seasons (2015/16, 2016/17, 2017/18, 2019/20) in Wrocław (Poland). As a result of the application of the v-fold cross test, six clusters for each fraction of PM were identified. Even though the analysis covers only four winter seasons, the applied method has unequivocally revealed that the characteristics of surface-based (SBI) and elevated inversions (ELI) affect the concentration level of both fractions of particulate matter. In the case of PM10, the average lowest daily concentration (15.5 µg·m−3) was recorded in the conditions of approx. 205 m in thickness, 0.5 °C intensity of the SBI and at the height of the base of the ELI at approx. 1700 m a.g.l., a thickness of 148 m and an intensity of 1.2 °C. In turn, the average highest concentration (136 µg·m−3) was recorded at a thickness of SBI of approx. 400 m and an intensity of 1.4 °C. Such high concentration occurred when the lowest location of ELI formed at 764 m a.g.l. with a thickness of 308 m and an intensity of 0.96 °C. A marked role of the thickness of the SBI and ELI as well as the height of the base of the lowest location of ELI was also manifested with respect to PM2.5 concentrations. [ABSTRACT FROM AUTHOR]