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The area proportion of rural residential at the 100 m buffer zone was the dominant index influencing TP. It is very important to optimize the landscape pattern within a 300 m width of a riparian buffer zone. In particular, the reasonable allocation of cultivated land, forest, and grassland, to improve the connectivity and aggregation of agricultural landscapes, and the control of rural residential areas and pollutant discharge along the river bank, will enhance the ecological function of the water quality of the Baihe River in Beijing. This will ensure drinking water safety from the Miyun Reservoir.In this study, the pollution characteristics, photochemical effects, and sources of atmospheric volatile organic compounds (VOCs) in the urban areas of Wuhu were investigated from September 2018 to August 2019. The results showed that the annual average mixing ratio of ambient VOCs in Wuhu was 27.86×10-9, with the highest values in fall (31.16×10-9), followed by summer (28.70×10-9), winter (24.75×10-9), and spring (24.04×10-9). The diurnal patterns showed two VOC peaks, due to traffic exhaust, at 0800-0900 and 1800-1900. The estimated total OFP of VOCs was 255.29 μg·m-3, and aromatics, olefins, alkanes, OVOCs, and halocarbons contributed 48.83%, 21.04%, 18.32%, 11.47%, and 0.35% to the average OFP, respectively. The total AFP was 1.84 μg·m-3, among which aromatics and alkanes accounted for 87.69% and 12.31%, respectively. The ratios of B/T/E indicated that atmospheric aromatic hydrocarbons were mainly derived from vehicle exhaust, as well as industry emission and solvent usage. Source apportionment indicated that petroleum evaporation, vehicle exhaust, solvent evaporation, liquefied petroleum gas (LPG), biogenic source, and secondary source shared 11.57%, 34.53%, 16.63%, 20.76%, 3.54%, and 12.97% of ambient VOCs during the sampling period, respectively.To accurately identify and locate ambient volatile organic compounds(VOCs)emission sources in industrial parks, a continuous online GC-FID method was used to monitor 43 kinds of VOCs on an hourly basis during January 2017 at five sites in an industrial park. A statistical analysis and a PMF model were used to analyze the sources of VOCs, and by combining with CPF and enterprise emission information, the location of each pollution source was accurately identified. The average VOCs concentration was 56.40×10-9 and the highest concentration of alkanes was observed at four sites, with the exception of one site. Ethane, propane, ethylene, toluene, isobutane, n-butane, and acetylene were the main contributors. Ambient VOCs in the park mainly derives from five sourcesurban transmission, butane leakage, process emissions, storage tank emissions, and ethylene synthesis. The enterprises in the zone B1, A1-A3, C1-C2, F4, E4-E6, F4-F6, and the canal loading and unloading area are the main emission areas of the pollution sources. Using selleck chemical monitoring data, the research combined a PMF model, meteorological conditions, and corporate emissions information to achieve precise positioning of the pollution sources of VOCs in the industrial park, thus providing a basis for the supervision and management of corporate emissions in industrial parks.As a typical secondary pollutant, tropospheric ozone has become the primary pollutant in Beijing in spring and summer, and meteorological factors are one of the main factors affecting the change in concentration. #link# Using atmospheric composition and meteorological observation data from 2008 to 2017, the weather types in Beijing were divided into six categories by Lamb classification and Mann-Whitney U test. Among these, the mean and extreme values of ozone concentration of SWW and C types at Shangdianzi station were the highest, and the highest frequency was from April to September, with a total of 47.4%. The main contribution weights of the two types were determined by a multiple stepwise regression equation. The southwest wind prevailed in 54.0% of SWW and C types, and the newly discharged pollutants and secondary aging air masses were continuously transported by the southwest air flow. The vertical velocity zero layer appeared near 850 hPa. The horizontal and vertical meteorological conditions were conducive to the transport, accumulation, and secondary generation of ozone. The northeast wind prevailed in 64.7% of AN and ESN types, and the air masses source was clean. The same subsidence movement and air divergence prevailed above 1000 hPa. The discharged pollutants can also be diluted and diffused quickly, and the ozone concentration was at a low value. Taking the NW type on May 3, 2015 as an example, although the northwest air flow prevailed on the ground, with clean source, the residual high concentration of ozone above the boundary layer was transported to the near ground through the vertical subsidence of the atmosphere, resulting in the high concentration of ozone on some days.Regional transport is an important factor when considering the prevention and control of air pollution. The aim of this study was to provide support for the joint prevention and control of air pollution in the Beijing-Tianjin-Hebei region. With a focus on an analysis of the relationship between regional transport and meteorological conditions based on the weather background, an atmospheric chemical model was developed to quantitatively estimate the impact of regional transport on Tianjin from October 2016 to September 2017. The results showed that the contribution percentage of regional transport in cities in plains in the Beijing-Tianjin-Hebei region was significantly higher than in cities in mountains. The local contribution of PM2.5 in the Tianjin area was 62.9% and the contribution of regional transport was 37.1%. This was mainly affected by transmissions of Chanzhou, Langfang, central and southern Hebei, Beijing, Tanshan, and Shandong. Regional transport was the most significant from April to June, the wlation of pollution and transport in the region. The contribution ratio of PM2.5 transport in the heavy pollution period was more than the average and was approximately 10% and 15% higher. In the process of heavy pollution, the proportion of transport contribution in the initial accumulation stage and peak stage were higher than in other periods, and 14.5% and 19.5% higher than in the outbreak stage. The contribution of local emissions in the outbreak stage was more significant, being 9.9% higher than average.In this study, the hourly meteorological factors and PM2.5 concentrations during 2014-2019 in Beijing were analyzed, in order to explore the characteristics of the prevailing wind direction of pollution, and the corresponding long-term tendency. During the study period, 67% of pollution in Beijing occurred under the influence of southerly and easterly wind, and pollution was most likely to occur in winter, followed by spring and autumn. The average pollution probability of winter, spring, autumn and summer was 45.2%, 34.1%, 32.1%, and 26.1% and 47.0%, 45.8%, 39.7%, and 29.6% for southerly and easterly wind, respectively. In Beijing, the southerly wind appeared more frequently, but the pollution occurrence probability was higher under the control of easterly wind, with the maximum difference of 11.7% (2.8%-18.6%) in spring and the minimum difference of 1.8% (-7.6%-13.9%) in winter. During the past six years, the pollution probability decreased at a rate of 4.6%-8.0% and 5.5%-7.9% per year under the southerly aheating in winter, the air mass transported by the southerly wind may be more conducive to increased PM2.5 concentration. Furthermore, pollution in Beijing tended to be an "easterly wind type" in spring, summer and autumn, but remained a "southerly wind type" in winter.An ensemble estimation model of PM2.5 concentration was proposed on the basis of extreme gradient boosting, gradient boosting, random forest model, and stacking model fusion technology. Measured PM2.5 data, MERRA-2 AOD and PM2.5 reanalysis data, meteorological parameters, and night light data sets were used. On this basis, the spatiotemporal evolution features of PM2.5 concentration in China during 2000-2019 were analyzed at monthly, seasonal, and annual temporal scales. The results showed that① Monthly PM2.5 concentration in China from 2000-2019 can be estimated reliably by the ensemble model. ② PM2.5 annual concentration changed from rapid increase to remaining stable and then changed to significant decline from 2000-2019, with turning points in 2007 and 2014. The monthly variation of PM2.5 concentration showed a U shape that first decreased then increased, with the minimum value in July and the maximum value in December. ③ Natural geographic conditions and human activities laid the foundation for the annual spatial pattern change of PM2.5 concentration in China, and the main trend of monthly spatial pattern change of PM2.5 concentration was determined by meteorological conditions. ④ At an annual scale, the national PM2.5 concentration average center of standard deviation ellipse moved eastward from 2000-2014 and westward from 2014-2018. At a monthly scale, the average center shifted to the west from January to March, moved northward then southward from April to September, and shifted to the east from September to December.In order to investigate the pollution characteristics and sources of elements in PM2.5 in the Shanxi University Town in 2017, an energy dispersive X-ray fluorescence spectrometer (ED-XRF) was used to analyze 21 kinds of elements in PM2.5 samples. A health risk assessment was conducted for Mn, Zn, Cu, Sb, Pb, Cr, Co, and Ni. The main sources of elements were identified by the principal component analysis (PCA) and positive matrix factorization (PMF). The results found that, among the 21 kinds of elements in PM2.5 in Shanxi University Town, the mass concentration of Ca was the highest, followed by Si, Fe, Al, S, K, and Cl. These seven elements accounted for 95.71% of the total element concentrations. The concentration of Cr exceeded the annual average concentration limit of ambient air quality standards in China by 104 times. The concentration of Ca in PM2.5 was the highest in spring, summer, and winter, while in autumn the concentration of S was the highest. Mn was the element that had non-carcinogenic risks to the three population types, and the level of risks were in the order of children > adult men > adult women. Cr and Co had tolerable carcinogenic risks, and the risk levels were in the order of adult men > adult women > children. link2 The main sources of elements in PM2.5 in Shanxi University Town in 2017 were natural mineral dust, urban dust, coal combustion, and traffic.The aim of this study was to fully understand the pollution characteristics and sources of PM2.5 in Zhengzhou, and to investigate the differences in four seasons and between urban and suburban areas. link3 At the Zhengzhou environmental monitoring center (urban areas) and Zhengzhou University (suburban areas), 1284 environmental PM2.5 samples were collected in the four seasons of 2018. The concentrations of nine kinds of inorganic water-soluble ions, organic carbon, elemental carbon and 27 kinds of elements, were measured by ion chromatography, carbon analyzer, and X-ray fluorescence spectrometry, respectively. Enrichment factors (EF), index of geoaccumulation (Igeo), potential ecological risk index (RI), chemical mass balance model (CMB), backward trajectory, and potential source contribution function were the methods used to study the chemical component characteristics and source differences of PM2.5 in different seasons in the urban and suburban areas of Zhengzhou. The results showed that the annual average PM2.