Comprehensive evaluation method of urban air quality statistics based on environmental monitoring data and its application.

Air quality monitoring is effective for timely understanding of the current air quality status of a region or city. Currently, the huge volume of environmental monitoring data, which has reasonable real-time performance, provides strong support for in-depth analysis of air pollution characteristics and causes. However, in the era of big data, to meet current demands for fine management of the atmospheric environment, it is important to explore the characteristics and causes of air pollution from multiple aspects for comprehensive and scientific evaluation of air quality. This study reviewed and summarized air quality evaluation methods on the basis of environmental monitoring data statistics during the 13th Five-Year Plan period, and evaluated the level of air pollution in the Beijing-Tianjin-Hebei region and its surrounding areas (i.e., the "2+26" region) during the period of the three-year action plan to fight air pollution. We suggest that air quality should be comprehensively, deeply, and scientifically evaluated from the aspects of air pollution characteristics, causes, and influences of meteorological conditions and anthropogenic emissions. It is also suggested that a three-year moving average be introduced as one of the evaluation indexes of long-term change of pollutants. Additionally, both temporal and spatial differences should be considered when removing confounding meteorological factors.

Formation processes and source contributions of ground-level ozone in urban and suburban Beijing using the WRF-CMAQ modelling system.

Beijing faces the challenge of high levels of ozone (O3) pollution. In this study, the Weather Research and Forecasting model and Community Multiscale Air Quality model (CMAQ) were used to simulate atmospheric O3 concentrations in Beijing. To investigate the formation mechanisms and source contributions of O3 pollution in different regions of Beijing, process analysis and the integrated source apportionment method within the CMAQ were applied to O3 concentrations in the summer of 2018. The process analysis results showed that vertical diffusion was the major contributor to O3 concentrations at all receptor sites in Beijing, at > 65.94 µg/(m3·hr). Gas-phase chemical reactions consumed a significant amount of O3 in urban and inner suburban areas (> -5.57 µg/(m3·hr)), while near-surface chemical reactions made positive contributions in outer suburban areas (> 4.72 µg/(m3·hr)). The O3 formation chemical reactions indicated that NO titration, which removes O3 at night-time, mainly occurred in urban areas. The weaker chemical reactions occurring near the surface in outer suburbs suggested that suburban-area O3 was produced in the upper atmospheric layers and was transported vertically to the lower layers. The O3 source apportionment results showed that boundary contributions were the dominant contributor to O3 pollution in Beijing (> 40%). The contribution of non-local emissions to O3 levels was significantly greater in the outer suburbs than in urban and inner suburban areas due to topography. This study increases the understanding of the complex processes of O3 formation in different areas of Beijing and informs the implementation of O3 control plans.

Evaluating the meteorological normalized PM2.5 trend (2014-2019) in the "2+26" region of China using an ensemble learning technique.

In recent years, implementation of aggressive and strict clean air policies has resulted in significant decline in observed PM2.5 concentration in the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas (i.e., the "2 + 26" region). To eliminate the effects of interannual and seasonal meteorological variation, and to evaluate the effectiveness of emission abatement policies, we applied a boosted regression tree model to remove confounding meteorological factors. Results showed that the annual average PM2.5 concentration normalized by meteorology for the "2 + 26" region declined by 38% during 2014-2019 (i.e., from 96 to 60 µg/m3); however, the BTH region exhibited the most remarkable decrease in PM2.5 concentration (i.e., a 60% reduction). Certain seasonal trend in normalized PM2.5 level remained for four target subregions owing to the effects of anthropogenic emissions in autumn and winter. Although strong interannual variations of meteorological conditions were unfavorable for pollutant dispersion during the heating seasons of 2016-2018, the aggressive abatement policies were estimated to have contributed to reductions in normalized PM2.5 concentration of 19%, 10%, 19%, and 17% in the BTH, Henan, Shandong, and Shanxi subregions, respectively. Our study eliminated the meteorological contribution to concentration variation and confirmed the effectiveness of the implemented clean air policies.

Comparison of four methods for spatial interpolation of estimated atmospheric nitrogen deposition in South China.

Spatial interpolation methods have been applied in many environmental research studies. However, it is still a controversial issue to select an appropriate interpolation method for the conversion of discrete sampling sites into continuous maps. This study aimed at selecting an optimal interpolation method to analyze the spatial pattern of atmospheric N deposition in South China. N deposition was calculated by 259 moss sample data. Four spatial interpolation methods, including inverse distance weighting (IDW), radial basis function (RBF), ordinary kriging (OK), and universal kriging (UK), were utilized for modeling the spatial distribution of N deposition. It is the first time that these methods were applied to analyze N deposition in South China. Validation method was used to evaluate the interpolation precision of the various methods, and the cross-validation method was used to evaluate their interpolation accuracy. Comparison of predicted values with measured values indicated that OK was the optimal method for analyzing the spatial distribution of N deposition in this study; it had the highest precision (mean error (ME) = -0.059, root-mean-square error (RMSE) = 5.240, mean relative error (MRE) = 0.129, mean absolute error (MAE) = 4.007) and the lowest uncertainties (standard deviation (SD) = 5.47, coefficient of variation (CV) = 0.15). RBF produced similar results as good as OK, while the worst performed interpolation method was UK. By using the OK method for analyzing N deposition, this work revealed systematic temporal and spatial variations in atmospheric N deposition in South China.

[N% and S% in Leaves of Vascular Plants Cinnamomum camphora and Pinus massoniana Lamb. for Indicating the Spatial Variation of Atmospheric Nitrogen and Sulfur Deposition].

N and S contents in Cinnamomum camphora leaves, Pinus massoniana Lamb. leaves, epilithic Haplocladium microphyllum(Hedw.) Broth. and rhizosphere soil collected along 3 directions from urban area to rural area at Guiyang city in a total of 296 samples were investigated systematically. The level of atmospheric N deposition and atmospheric SO2 concentrations at each sampling site were calculated according to the quantitative relationship between moss N content and atmospheric N deposition and the quantitative relationship between moss S content and atmospheric SO2 concentration. Leaves N content in Cinnamomum camphora(1.01%-2.37%) and Pinus massoniana Lamb.(0.99%-2.42%)showed significant decrease from urban area with the highest atmospheric nitrogen deposition to suburb, while slightly higher leaves N reemerged at rural area beyond 24 km, suggesting increased N deposition occurred in rural area. Leaves S content in Cinnamomum camphora(0.16%-0.43%) and Pinus massoniana Lamb.(0.18%-0.32%) showed significant decrease from urban area to suburb, the highest level at urban was mainly contributed by the high sulfur released from the production and living of urban areas into the atmosphere, and the lowest level occurred at rural area(30-36km). No significant difference was seen for soil N and S contents. The relationships between the estimated input of atmospheric N deposition and the leaves N content of Cinnamomum camphora and Pinus massoniana Lamb. at the sites investigated were found to be significant linear correlations, and the relationships between the estimated atmospheric SO2 concentration and the leaves S content of Cinnamomum camphora and Pinus massoniana Lamb. were also significant linear correlations(P<0.05). The results indicated that N and S contents in Cinnamomum camphora and Pinus massoniana Lamb. leaves can be used to show the spatial variation of atmospheric N and S deposition.

[Sulfur isotopic signatures in leaves of Pinus massoniana Lamb. and source apportionment].

This study analyzed the inorganic sulfur (SSO4) and total sulfur (ST) content as well as the isotopic signatures (delta34SSO4 and 834ST) in leaves of Pinus massoniana lamb. collected from Guizhou and Yunnan areas. The results indicated that the SSO4 and ST content in leaves at Guiyang areas was significantly higher than that at Yunnan areas, and the content of inorganic sulfur in the leaves was found to be directly related to the concentration of ambient sulfur dioxide, but no correlation was seen between the ST content and the ambient sulfur dioxide, showing the SSO4 content in leaves was more reliable to reflect the ambient sulfur input. The average value of delta34SSO4 in leaves at Guiyang areas ( -7. 22%o) was significantly lower than that at Yunnan areas(3. 85 per thousand) , which was related to the fact that the sulfur isotopic composition of coal at Guiyang areas is lower than that at Yunnan areas. The SSO4 and ST content in leaves around Kunming steel and Qujing power plant was inversely proportional to the distance from the factories, while around Kunming steel plant the value of 83SSO4 in leaves became more negative when the distance became larger while around Qujing power plant the value of 834Sso4 became more positive when the distance became larger, indicating that the SSO4 content and delta34SSO4, in leaves around Kunming steel and Qujing power plant were controlled by coal sources of atmospheric sulfur deposition.