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Prediction of air pollutant concentrations is currently one of the most important methods for the prevention and control of urban air pollution in most countries, and accurate and timely prediction of pollutant concentrations is of great significance for urban pollution control. Using Taiyuan, China, as a case study, this study examines how to predict hourly air pollutant concentrations over longer periods of time while ensuring their accuracy. In this paper, an air pollutant concentration prediction method based on improved inverse distance interpolation and Informer model (EIDW-Informer), and hour-by-hour prediction of PM2.5, NO2, and O3 concentrations in Taiyuan, China is carried out. In this study, historical data from seven environmental monitoring stations in Taiyuan City were used to build multidimensional environmental vectors and calculate the similarity between sample points. Then, the missing values in the dataset were interpolated according to the similarity and distance weights, and the long series prediction was performed by Informer. The experimental results show that the EIDW-Informer method has advantages in hour-by-hour prediction compared to LSTM, CNN-LSTM, and Attention-LSTM models, which improves by 20%, 27%, and 43% on 1 h, 8 h, and 72 h time scales, respectively. [ABSTRACT FROM AUTHOR]

To characterize seasonal carbonaceous aerosol pollution in Taiyuan, a typical city in North China that mainly relies heavily on coal, a total of 124 PM2.5 samples were collected from August 2018 to the next May. The annual mean PM2.5 concentration was 83.8 ± 48.5 μg m−3, with a seasonal rank of winter (117.4 ± 47.6 μg m−3) > spring (79.2 ± 34.3 μg m−3) > fall (67.3 ± 34.7 μg m−3) > summer (31.8 ± 6.5 μg m−3), suggesting that fine particulate pollution was still serious in cold seasons. Organic carbon (OC) and elemental carbon (EC) showed similar seasonal patterns with PM2.5. The mean concentration values of OC in summer, fall, winter, and spring were 5.1 ± 0.9, 11.8 ± 6.4, 22.1 ± 14.9, and 12.2 ± 6.7 μg m−3, respectively. The mean concentration values of EC in summer, fall, winter, and spring were 1.5 ± 0.3, 2.5 ± 1.6, 4.4 ± 2.8, and 2.4 ± 1.5 μg m−3, respectively. The proportion of total carbon aerosol (TCA) was about 31.7%, 33.8%, 30.0%, and 27.0% in PM2.5 in summer, fall, winter, and spring, respectively. The good correlation between OC vs. EC and the high value of OC/EC suggests that coal and biomass combustion were the main emissions in cold seasons, aggravated by adverse meteorological conditions and the dustpan-shaped terrain. The mean annual secondary organic carbon (SOC) concentration was 6.1 ± 7.1μg m−3, representing 38.7% of the OC content. The present results presented the serious carbonaceous particulate pollution, which might affect haze pollution in cold seasons. [ABSTRACT FROM AUTHOR]

Based on environmental monitoring data and meteorological observation data of the Chinese major energy province, Shanxi, from 2015 to 2020, using the satellite remote sensing data of Atmospheric Infrared Sounder Instrument (AIRS) and Ozone Monitoring Instrument (OMI) in 2017, we analyzed the characteristics of surface ozone (O3) pollution and its correlation with meteorological factors, as well as the vertical distribution of O3 in typical pollution cities in Shanxi Province. The results showed that surface O3 became the primary pollutant in Shanxi. Surface O3 has shown a zonal distribution with a high level in the south and a low level in the north region since 2017. Surface O3 pollution was severe in 2019, and the maximum daily 8 h running average of O3 (MDA8 O3) decreased, but annual mean O3 in northern and central regions showed a slow rising trend in 2020. Comprehensive analyses of the influence of meteorological factors on surface O3 indicated that O3 pollution in Linfen, Yuncheng and Taiyuan was mainly caused by local photochemical reactions, while that in Jincheng, Xinzhou, Lvliang and Yangquan resulted from regional transports. O3 volume mixing ratios (VMR) in the middle and lower troposphere generally increased with altitude, peaking at 120 ppbv at approximately 400 hPa. The positive vertical gradient of O3 in the boundary layer was obvious in Taiyuan in summer and significant in the surface layer in Taiyuan and Linfen during winter and spring, which was associated with greater atmospheric dynamic stability and suppressed vertical mixing. Due to the lack of direct detection of O3 in the lower troposphere in this region, O3 vertical distribution retrieved by satellite observation is critical for the study of vertical mixing and transport of local O3, as well as its regional transport characteristics. [ABSTRACT FROM AUTHOR]

To mitigate the adverse effects of climate warming, the Paris Agreement proposed the goal to reduce global warming up to an increase of 1.5 °C above the preindustrial level. Study of the population and GDP exposure to precipitation extreme events under the 1.5 °C warming target is fundamental for disaster risk mitigation and adaptation on the Loess Plateau. This study projected the population and GDP exposure to extreme precipitation events under the 1.5 °C global warming level on the Loess Plateau using daily precipitation data from CMIP6 outputs and population and GDP data under a Shared Socioeconomic Pathway 1(SSP1) 2.6 scenario. The population and GDP exposure were evaluated by combing the frequency and the areal coverage of the extreme precipitation events. Results show that population and GDP exposure to extreme precipitation events on the Loess Plateau will increase under the 1.5 °C global warming level. The population exposure was projected from 1.32 × 106 to 2.68 × 106 person-year. The population exposure of eastern and southern Loess Plateau is significantly higher than that of the northern region. The annual exposure of GDP ranges from USD 2.9 to 12.3 billion, and the regions with the highest GDP exposure are Zhengzhou, Xi'an, Taiyuan, and Lanzhou. Our results reveal that limiting the increase of global mean temperature to 1.5 °C warming level is of great significance to reduce the social and economic exposure to extreme precipitation events on the Loess Plateau. [ABSTRACT FROM AUTHOR]