Munromacleod7408

In this study, the aerosol number size distribution in the range of 10 nm-10 μm was collected from August 16 to October 04, 2019 at Ordos using a wide-range particle spectrometer (WPS). Combined with PM (PM2.5 and PM10), pollution gases, meteorological data, and the HYSPLIT model, the characteristics and impact factors of new particle formation (NPF) were discussed. The results indicated that there were 19 NPF events during the observation period, which have different effects on diurnal variation in aerosol number concentration in different modes. The NPF events caused a sharp increase in the number concentration of nucleation and Aitken mode aerosols, but had little effect on the number concentration of accumulation and coarse mode aerosols. The temperature, wind speed, and total solar radiation during NPF days were usually higher than those in non-NPF days, and the RH during NPF days was lower. On NPF days, the mass concentrations of PM2.5, PM10, CO, and NO2 were lower than those on non-NPF days, while the mass concentrations of O3 and SO2 were higher. NPF events were observed in 40.0% of northern air masses and 29.6% of southern air masses. There were significant differences in meteorological elements in different NPF event air mass types. The southern NPF event air mass type had the lowest wind speed and the highest RH, with averages of (2.4±1.5) m·s-1 and (48.8±10.8)%, respectively. The northern NPF event air mass type had the highest wind speed and total solar radiation, with averages of (4.2±1.9) m·s-1 and (664.5±255.6) W·m-2, respectively. The western air mass type of NPF event had the lowest RH, with an average of (29.8±12.7)%. The formation rates of new particles in the different air mass types of NPF events were similar, ranging from 1.5 to 1.8 cm-3·s-1. The largest growth rate was (12.7±13.6) nm·h-1 in the southern NPF event air mass type, which was 1.2 times and 1.4 times higher than the NPF events of northern air masses and western air masses.Pollution characteristics and risk of heavy metals in atmospheric deposition in core urban areas of Chongqing were investigated for one year from December 2017 to November 2018.Six functional zonessuburb, education area, residential area, commercial area, transportation hub, and industrial-residential area in Chongqing were selected for monthly atmospheric deposition collection. Concentrations of Cd, Cr, Ni, and Pb were analyzed using AAS. The potential ecological risk index and geoaccumulation index were used to evaluate the heavy metals pollution. Results show that the concentrations of Cd, Cr, Ni, and Pb in the atmospheric deposition were 1.59, 72.68, 20.99, and 101.17 mg·kg-1, respectively, and their annual deposition fluxes were 0.39, 8.04, 2.41, and 10.41 mg·(m2·a)-1, respectively. Concentrations of heavy metals in autumn were significantly higher than those in the other three seasons, especially for Cd, and their deposition fluxes in winter were lowest. The potential ecological risk index of Cd was biggest, achieving a very high ecological hazard level, while the ecological risk of Cd and Pb in industrial-residential area was highest, and that of Cr and Ni, respectively, were highest in transportation hub and residential area. The geoaccumulation index indicated that the pollution of Cd was the highest, and that of Cr, Ni, and Pb was very low. The pollution in industrial-residential area and transportation hub was high, while that in the suburb was relatively low.As important components of PM2.5, metal elements are extremely harmful to people and also have source specificity. Understanding the characteristics of PM2.5 metal pollution in the two different types of cities can help adjust the layout of regional industrial structure and improve the environment. PM2.5 samples during haze/non-haze periods were collected in Chengdu City and Renshou County. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to determine the mass concentrations of eighteen metal elements in collected PM2.5 samples. The positive matrix factorization (PMF) model was used for source apportionment analysis for metal elements in PM2.5. The analysis showed that the ratio of trace elements from fugitive dust, motor vehicle emissions, and coal burning to the total elements is greater in Chengdu City than that in Renshou County. The proportion of trace elements from biomass combustion, industrial, and fuel sources in Renshou County is higher than that in Chengdu City. In additiuction, while in suburban area such as Renshou County, where secondary or heavy industry are the focus for economic development, the pollution is mainly affected by energy consumption and industrial production.To study winter pollution characteristics and physicochemical properties of PM2.5 in a northwest industrial city, for example, Baiyin in Gansu Province, we used related instruments, such as single particle aerosol mass spectrometry to conduct real-time online PM2.5 chemical composition observations, compared with Lanzhou in the same period. The results showed that, during the observation, PM2.5 concentrations (44.89 μg·m-3) in Baiyin were significantly lower than the same period in Lanzhou (70.69 μg·m-3). However, the proportion of particles containing heavy metals (7.84%) was significantly higher than that of Lanzhou (2.92%), the chemical composition was complex, and PM2.5 was mainly contributed by particles with larger particle sizes. The particle size distribution range of Cu, Pb, and Zn particles in Baiyin was relatively wide, the number of Cu and Zn particles was higher, and the mixing ratio of particles was higher than in Lanzhou. The main pollution source was automobile exhaust 30.91% and a secondary inorganic source was 13.00%. The pollution event on January 4, 2020 was mainly caused by the increased contributions of automobile exhaust and secondary inorganic sources, and the poor meteorological diffusion conditions in the early stage. AMI-1 inhibitor The control of PM2.5 pollution in Baiyin in winter should be dominated by emission reduction of automobile exhaust and secondary inorganic sources, and control of heavy metal pollution in the atmosphere should be strengthened.To reveal the process and cause of air pollution in winter in Zhengzhou, Zhengfangji Station was selected as the sampling point to discuss the concentration of air pollutants and the characteristics of meteorological parameters in December 2019, in Zhengzhou. The concentration changes in PM2.5 water-soluble ions, elements, and carbon components in different pollution stages were compared, and air quality model simulation results were used to analyze emissions from pollution sources and regional transmission during sampling of the PM2.5 mass concentration at the sampling point. The results showed that there was a slight difference in the process of formation and dissipation of the first and second heavy pollution occurrences, showing the characteristics of "slow accumulation, slow removal" and "slow accumulation, fast removal", respectively. The mass concentration of NO3-, SO42-, and NH4+ accounted for 41.5% and 46.2% of PM2.5, and the OC/EC ratios were 4.0 and 4.5 in the first and second heavy pollution periods, respectively. The formation of secondary aerosol particles was the main reason for the formation of heavy pollution. During the sampling period, the average contributions of local, eastern, southern, western, and northern regions to the PM2.5 concentration of the sampling point were 58.0%, 2.4%, 6.7%, 6.9%, and 12.7%, respectively. The first heavy pollution period was the result of combined locally sourced pollutant emission and externally sourced regional transmission, during which the contribution from western and southern regions and external industrial sources increased. The second heavy pollution period was mainly affected by the accumulation of local air pollutants, during which the contribution of traffic, dust, and coal-fired sources increased sharply, and the impact of external areas on the PM2.5 concentration of sampling point decreased.In recent years, atmospheric pollution represented by fine particulate matter PM2.5 pollution has seriously threatened human health. Therefore, it is important to identify the risk level of population exposure to PM2.5. Based on PM2.5 remote sensing inversion data and population distribution, this study measured the risk level of population exposure to PM2.5 in the Guanzhong area. Furthermore, the methods of Theil-Sen Median trend analysis, Mann-Kendall test, and geo-spatial analysis were used to reveal the temporal and spatial characteristics of population exposures risk to PM2.5 in the Guanzhong area from 2000 to 2016. The results show that①The years with heavy pollution and wide range in the Guanzhong area are 2006, 2007, and 2013, and the annual average concentration of PM2.5 exceeded 35 μg·m-3 in more than 40% of the Guanzhong area. From 2000 to 2016, the spatial distribution range of PM2.5 in the Guanzhong area continued to expand, forming a continuous belt-shaped concentrated distribution area from the center to the northeast. ②More than 60% of the population in the Guanzhong area was exposed in the areas with annual average concentration of PM2.5 above 35 μg·m-3 from 2000 to 2016, and the population exposure risk continued to increase, especially after 2011, the range of the high-risk area expanded dramatically. ③The pattern of population exposure risk to PM2.5 in the Guanzhong area was generally similar from 2000 to 2016. The areas with higher risk levels were mainly concentrated in the central Guanzhong area, forming a continuous belt-shaped distribution area from west to east. The areas with the highest value were distributed in the urban areas of several major cities, while the areas with the lowest value were mainly concentrated in the surrounding areas of Guanzhong.It is of great significance for joint prevention and control of air pollution to understand the spatial and temporal differentiation characteristics and regional driving factors of PM2.5 in China. In this study, from a multi-scale perspective, the spatial pattern analysis and geographical detectors are used to explore the spatial and temporal distribution pattern and causes of PM2.5 pollution in China mainland from 2011 to 2017. The results show that① the annual average PM2.5 concentration is relatively stable from 2011 to 2017, and there is no obvious trend. The change characteristics of regional PM2.5 are similar to those of national PM2.5, showing a "W" shaped fluctuation. Overall, the order of pollution degree from high to low iscentral, eastern, western, and northeastern. ② From the spatial pattern analysis results, we can see that the high-value cluster mainly appears in east China, middle China, and southwest of Xinjiang, while the low-value cluster appears in Qinghai-Tibet, Yunnan, Guizhou, Plateau, and Daxinganling regions. ③ The results of geographic detector analysis show that the population factor is the leading factor nationally; meanwhile, the industrial, energy consumption, and traffic factors all contribute to the distribution pattern of PM2.5 in varying degrees. Regionally, besides the population factor, the proportion of secondary production and urban green space rate have the greatest impact on the northeast, the industrial smoke and dust and road area in the east, and the total industrial electricity and buses in the central area. The impact of social and economic factors does not significantly affect the PM2.5 in the western region.