[Characteristics, Impact Factors and Potential Sources of PM2.5 Pollution for Hainan Island].

Based on the environmental monitoring data and meteorological observational data in Hainan Island from 2015 to 2021, the PM2.5-polluted characteristics, influencing factors, and potential contributing regions were analyzed using the backward trajectory simulation, cluster analysis, potential source analysis function （PSCF）, and concentration weight trajectory （CWT） methods. The results showed that PM2.5 in Hainan Island had an obvious seasonal variation, with the highest in winter （22.6 µg·m-3）, followed by that in autumn and spring （17.38 and 16.53 µg·m-3, respectively）, with the lowest in summer （9.79 µg·m-3）. In the past seven years, there were 30 days in Hainan Island in which PM2.5 concentration exceeded the standard. The annual average and four seasons of PM2.5 showed a significant downward trend, and the climatic change rates were -0.97 （annual mean）, -1.09 （spring）, -0.61 （summer）, -0.83 （autumn）, and -1.25 （winter） µg·ï¼ˆm3·a）-1. PM2.5 in Hainan Island was highly correlated with gaseous pollutants, with correlation coefficients of 0.471 （SO2）, 0.633 （NO2）, 0.479 （CO）, and 0.773 （O3-8h）, all passing a significance level of 0.01. PM2.5 was positively correlated with average wind speed and atmospheric pressure and negatively correlated with precipitation, relative humidity, sunshine duration, average temperature, and total solar radiation. Among them, average temperature, relative humidity, and total solar radiation were the main dominant meteorological factors on PM2.5 in Hainan Island. Backward trajectory and potential source analysis revealed that PM2.5 concentration was high （≥20 µg·m-3） in winter and autumn, which was influenced by airflow from inland regions, and Fujian, Zhejiang, Hunan, Jiangxi, Guangdong, and Guangxi provinces were the main potential sources of PM2.5 in Hainan Island.

Characteristics and sources of chemical composition in precipitation on the Loess Plateau of China.

Knowing the sources of precipitation chemical composition is essential to understand the biogeochemical cycle and control air pollution. Despite this issue has been directly investigated with precipitation ion contents, the effects of water vapor transport have not been fully considered. Taking the Loess Plateau of China (LPC) as an example study area, this study established nine precipitation monitoring sites considering the variability in topography and rainfall amounts, and collected 435 precipitation samples during 2020-2022 to measure the chemical composition. The correlation analysis, positive matrix factorization model and backward trajectory model were combined to analyze the characteristics, sources and vapor transport effects of precipitation chemical composition. Seasonally, except for NH4+, the concentration of other ions in the dry season was significantly higher than that in the rainy season. Spatially, the concentrations of Ca2+, Na+, K+, SO42- and NO3- peaked in the Mu Us Sandy Land and industrial areas, while the high level of NH4+ was concentrated in the agricultural areas. The source apportionment found that the primary source of precipitation ions was crust (33 %), followed by coal combustion/vehicle (30 %), aged sea salt (21 %) and agriculture (16 %). The trajectory analysis showed that water vapor paths significantly varied with the seasons, but were primarily dominated by the northwestern air mass with proportions of >40 %. The dust aerosols transported by the northwestern air mass were the main contributor to crust-source precipitation ions. The eastern and southeastern air masses transported anthropogenic pollutants to the LPC, and the southeastern air mass also carried sea-salt precipitation ions. This study provides a framework to incorporate hydrochemical method with vapor source identification method for precipitation chemical source identification, and the results can be a theoretical basis for the treatment of atmospheric environmental problems.

The impact of bioaerosol trajectories on microbial community assembly and physicochemical dynamics in the atmosphere.

This study explored the assembly mechanisms and physicochemical dynamics of microbial communities within atmospheric bioaerosols, focusing on the influence of different aerial trajectories. Over two years, samples near Seoul were classified into 'North', 'Southwest', and 'Others' categories based on their aerial trajectories. Physicochemical analysis of the PM2.5 particles revealed distinct ion compositions for each cluster, reflecting diverse environmental influences. Microbial community analysis revealed that shared dominant bacterial phyla were present in all clusters. However, distinct taxonomic profiles and biomarkers were also evident, such as coastal bacteria in the 'Southwest' cluster correlating with wind speed, and arid soil-originated bacteria in the 'North' cluster correlating with cations. These findings demonstrate that biomarkers in each cluster are representative of the distinct environments associated with their aerial trajectories. Notably, cluster 'Southwest' the highest microbial diversity and a strong alignment with the neutral community model, suggesting a large influence of passive dispersal from marine environments. Contrarily, 'North' and 'Others' were more influenced by niche-dependent factors. This study highlights the complex interplay between environmental factors and microbial dynamics in bioaerosols and provides important insights for environmental monitoring and public health risk assessment.

Characterizing the regional distribution, interaction with microorganisms, and sources of dissolved organic matter for summer rainfall: Insights from spectroscopy, community structure, and back-trajectory analyses.

Dissolved organic matter (DOM) in rainfall participates in many biogeochemical cycles in aquatic environments and affects biological activities in water bodies. Revealing the characteristics of rainfall DOM could broaden our understanding of the carbon cycle. Therefore, the distribution characteristics and response mechanisms of DOM to microorganisms were investigated in different regions of Hebei. The results indicated that the water quality of the northern region was worse than that of the middle and southern regions. The two protein like components (C1, C2) and one humic like component (C3) were obtained; at high molecular weight (MW), the fluorescence intensity is high in the northern region (0.03 ± 0.02 R.U.), while at low MW, the fluorescence intensity is highest in the southern region (0.50 ± 0.18 R.U.). Furthermore, C2 is significantly positively correlated with C1 (P < 0.01), while C2 is significantly negatively correlated with C3 (P < 0.05) was observed. The spectral index results indicated that rainfall DOM exhibited low humification and highly autochthonous characteristics. The southern region obtained higher richness and diversity of microbial species than northern region (P < 0.05). The community exhibits significant spatiotemporal differences, and the Acinetobacter, Enterobacter, and Massilia, were dominant genus. Redundancy and network analyses showed that the effects of C1, C2, and nitrate on microorganisms increased with decreasing MW, while low MW exhibited a more complex network between DOM and microorganisms than high MW. Meanwhile, C1, C2 had a large total effect on ß-diversity and function through structural equation modeling. The backward trajectory model indicates that the sources of air masses are from the northwest, local area, and sea in the northern, middle, and southern regions, respectively. This study broadened the understanding of the composition of summer rainfall DOM and its interactions with microorganisms during rainfall.

Source and respiratory deposition of trace elements in PM2.5 at an urban location in Dhaka city.

Air pollution has been creating severe environmental crises in Dhaka. This city ranks at the top among the major cities of the world. A multidimensional study is needed to assess the severity of this crisis. This study aims to determine the sources of trace elements in PM2.5 and their effects on health. We measured concentrations of 15 trace elements in PM2.5 every hour for eight days using a well-equipped mobile air quality monitoring system integrated with an automatic sampling system (AQMS, Horiba, Japan). We analyzed the concentrations of the trace elements to identify their potential sources and diurnal variation and to compute the respiratory deposition dose of the trace elements to estimate the health risks they pose. The daily average concentration of PM2.5 was higher than the allowable limit set by the World Health Organization (WHO). Among the trace elements, sulfur had the highest concentration and vanadium was the lowest. We found out that concentrations of the elements were the highest during the middle of the day and the lowest during midnight. Four source profiles of PM2.5 were identified by positive matrix factorization (PMF). Soil dust with sulfur-rich petroleum contributed about 65 %, industrial and non-exhaust emissions about 5 % each, and heavy engine oil combustion about 25 % to air pollution. Air mass backward trajectory analysis indicated that Dhaka's air contains both local and transboundary pollution. According to the determined respiratory deposition dose of the elements, males had higher deposition than females during heavy exercise. Sulfur and vanadium have the highest and lowest respiratory deposition dose, respectively. The highest amount of deposition occurred in the upper airways. We expect that this study will help professionals develop effective strategies to prevent and mitigate the emission of air pollutants.

[Characteristics and Sources of PM2.5 Pollution During Winter in Handan City from 2016 to 2020].

To explore the characteristics and sources of PM2.5 pollution in winter of Handan City in the past five years, PM2.5 samples were collected in winter of 2016 to 2020, and eight types of water-soluble inorganic ions were analyzed. The principal component analysis(PCA) model was used to analyze the types of pollution sources, and the backward trajectory and potential source contribution factor(PSCF) were used to simulate the transport trajectory and pollution sources. The results showed that the PM2.5 concentration in winter of 2018 was the highest, increasing by 60.44%, 25.46%, 91.43%, and 21.53% compared with that in 2016, 2017, 2019, and 2020, respectively. In the winter of 2020, the concentration of water-soluble inorganic ions(WSIIs) decreased by 18.86% compared with that in 2016, and WSIIs/PM2.5 decreased to 26.69%. The PM2.5 concentration(110.20-209.65 µg·m-3) at night was higher than that in the daytime(95.21-193.00 µg·m-3). The concentration of NO3- and NH4+ increased more at night. On the contrary, the concentration and proportion of Cl-decreased annually. In the winter of 2020, the daytime concentrations of K+, Ca2+, Na+, and Mg2+ decreased by 69.72%, 97.10%, 90.91%, and 74.51% compared with that of 2018, and the night concentrations decreased by 66.67%, 95.38%, 91.67%, and 77.78%, respectively. In 2020, the concentrations of NO3-, SO42-, and NH4+ on polluted days were 4.90, 5.80, and 5.20 times those on non-polluted days, with the largest increase in five years. PCA results showed that the main sources of pollution were secondary sources, coal sources, biomass combustion sources, and road and building dust. The backward trajectory and PSCF analysis results showed that pollution transport continued to exist between south-central Mongolia and central Inner Mongolia in winter and was influenced by the transport between northern Henan and Handan and central Hebei and Handan in winter of 2016 and 2017, whereas the latter had a greater impact in winter of 2018-2020.

[Chemical Composition and Characterization of Nitroaromatic Compounds in Urban Areas of Shanghai].

The compositional characteristics, concentration of nitroaromatic compounds(NACs) in PM2.5 in urban Shanghai, and their correlation with gaseous precursors were investigated. A total of 39 winter and 46 summer PM2.5 samples from 2020 to 2021 were collected using a high-flow sampler and analyzed via ultra-performance liquid chromatography coupled with ESI-Orbitrap high-resolution mass spectrometry(UPLC-Orbitrap-HRMS). Quantitative analysis was performed on 12 NACs compounds, combined with backward trajectory meteorological elements, molecular composition, and classification analysis of CHON substances. The results showed that a total of 12 NACs had an average concentration in winter of 17.1 ng·m-3, which was three times higher than that in summer(5.7 ng·m-3), mainly due to air masses in winter coming primarily from the northern part of China with more biomass burning, whereas more air masses in summer came from the cleaner southeastern ocean. 4-Nitrophenol was the most abundant species of NACs in winter, whereas 4-nitrophenol(clean days) and 4-hydroxy-3-nitrobenzoic acid(polluted days) were the most abundant species in summer. Qualitative analysis based on features such as aromatic ring equivalence number(Xc), O/C, and H/C values for the identification and characterization of monocyclic and polycyclic aromatic compounds showed that CHON compounds were mainly aromatic compounds in winter and summer in urban Shanghai. The number and abundance of CHON compounds detected on PM2.5 polluted days were 2 and 1.5 times higher(winter) and 2.5 and 2 times higher(summer) than that on clean days, respectively. Comparing the analysis results of clean and polluted days in winter and summer, it was found that 80% of the CHON compounds with a relative abundance in the top 10 had O/N ≥ 3 and RDBE values between 5 and 8. The results suggest that these highly abundant CHON analogs may have had mononitro- or dinitro-substituted benzene rings. Correlation analysis between gaseous precursors and NACs indicated that oxidative reactive formation of VOCs(benzene, toluene, etc.) from anthropogenic emissions was the main source of NACs in summer. By contrast, it was influenced by a combination of biomass combustion emissions and secondary formation of oxidative NOx from anthropogenic VOCs in winter.

Analysis of variation characteristics, transport paths, and influencing factors of atmospheric NO2 pollution in Western Europe.

Climate change and air pollution are one of the global environmental problems. It is significant to grasp the air pollution situation of Western Europe in recent 10 years for its or the global pollution control. Based on the OMI tropospheric nitrogen dioxide (NO2) column density data, the spatial and temporal distribution characteristics, variation trend, transmission path, and influencing factors of NO2 in 15 countries in Western Europe from 2011 to 2022 are discussed in this paper. Meanwhile, the annual average spatial and temporal distribution in 2023 is predicted by the random forest (RF) model. The results showed that (1) the 12-year spatial distribution map showed an increasing trend from southwest to northeast, with the border area of the Netherlands and Germany and Milan as two high-value areas, and the overall trend over time was that the high-concentration area gradually shrank, the low-concentration area gradually expanded, and the annual average concentration gradually decreased. (2) The inter-month trend presents a "U" shape, with the mean NO2 pollution ranking in winter > autumn > spring > summer. (3) Natural factors are one of the reasons affecting NO2; for instance, NO2 pollution has a strong positive correlation with the lifted index, relative humidity, and wind speed and a moderately strong negative correlation with precipitable water and air temperature. (4) Exogenous atmospheric transport is another important factor affecting the change of NO2 pollution in Western Europe. The HYSPLIT model is used to analyze the backward trajectory of Milan, Italy, and Nijmegen, Netherlands, in the four seasons of 2022. Both are mainly influenced by westerly airflows, and therefore, the transport effect in the atmosphere brings air pollutants from westerly regions in the atmosphere.

[Temporal and Spatial Distributions of O3 Concentration and Potential Source Area Analysis of Hexi Corridor Based on Satellite and Ground Monitoring].

Based on 2005-2020 O3 column concentration data of OMI remote sensing satellite, combined with air pollutant data from 10 nationally controlled environmental automatic monitoring stations in the Hexi Corridor and global data assimilation system meteorological data, we used Kriging interpolation, correlation analysis, and backward trajectory (HYSPLIT) models to explore the temporal and spatial distribution characteristics, meteorological factors, transmission paths, and potential sources of O3 in the Hexi Corridor. The results showed the following:① in terms of temporal distribution, O3 column concentration showed an upward trend in 2005-2010 and 2014-2020 and downward trend in 2010-2014; the maximum and minimum values were reached in 2010 and 2014 (332.31 DU and 301.00 DU), respectively, and seasonal changes showed that those in spring and winter were significantly higher than those in summer and autumn. ② In terms of spatial distribution, O3 column concentration showed a latitudinal band distribution characteristic of increasing from southwest to northeast; the high-value areas were primarily distributed in urban areas with low terrain, and the median zone was latitudinally striped with the basic alignment of the Qilian foothills. ③ The analysis of meteorological conditions revealed that temperature, wind speed, and sunshine hours were positively correlated with O3, and relative humidity was negatively correlated with O3. ④ By simulating the airflow transportation trajectory of the receiving point in Wuwei City, it was found that the direction of the O3 conveying path was relatively singular; the dominant airflow in each season was primarily in the west and northwest; and the proportions were 71.62%, 66.85%, 61.22%, and 77.78%, respectively. There were certain seasonal differences in the source areas of O3 potential contribution:the high-value areas of O3 potential sources in spring, summer, and autumn were distributed in Baiyin City and Lanzhou City, which were southeast wind sources, and the high-value areas in winter were distributed between the Badain Jaran Desert and the Tengger Desert, which was the north wind source.

[Distribution, Respiratory Exposure, and Traceability of Atmospheric Microplastics in Yichang City].

As an emerging environmental pollutant, microplastics have attracted much attention, but the sources and health hazards of atmospheric microplastics (AMPs) remain unclear. In order to explore the distribution characteristics, assess the risk of human respiratory exposure, and analyze the sources of AMPs in different functional areas of Yichang City, AMPs samples from 16 observation points were collected and analyzed, and the HYSPLIT model was used. The results showed that the main shapes of AMPs in Yichang City were fiber, fragment, and film, and six colors were observed including transparent, red, black, green, yellow, and purple. The smallest size was 10.42 µm, and the largest was 4761.42 µm. The deposition flux of AMPs was (4400±474) n·(m2·d)-1. The types of APMs were polyester fiber (PET), acrylonitrile-butadiene-styrene copolymer (ABS), polyamide (PA), rubber (Rubber), polyethylene (PE), cellulose acetate (CA), and polyacrylonitrile (PAN). The order of the subsidence flux in each functional area was as follows:urban residential area>agricultural production area>landfill>chemical industrial park>town residential area. The human respiratory exposure risk assessment models showed that the daily intake of AMPs (EDI) for adults and children in urban residential areas was higher than in town residential areas. The atmospheric backward trajectory simulation showed that the AMPs in the districts and counties of Yichang City mainly came from the surrounding areas via short-distance transportation. This study provided basic data support for the research on AMPs in the middle reaches of the Yangtze River and was of great significance for the traceability and health risk research of AMPs pollution.

Relationship between atmospheric pollution and polycyclic aromatic hydrocarbons in fresh snow during heavy pollution episodes in a cold city, northeast China.

Air quality index (AQI) and air pollutants during two typical pollution episodes, and polycyclic aromatic hydrocarbons (PAHs) in fresh snow after each episode in the winter 2019 across Harbin City in northeast China were investigated to explore the co-environmental behaviors. Significantly greater values of AQI and PAHs were found in the more serious atmospheric pollution episode (episode Ⅱ), demonstrating that PAHs in fresh snow is a robust indicator. PM2.5 was the primary air pollutant in both episodes based on PM2.5/PM10 ratios, which might be attributed to fine particulate converted from gas-to-particle process. PM2.5 and 4-ring PAHs significantly positive correlated, indicating that airborne particulate PAHs were co-emitted and co-transported with atmospheric fine particles released from coal combustion and vehicular emission under low temperature and high relative humidity. 3- and 4- rings PAHs were dominant in episode Ⅱ, while 5- and 6- rings PAHs were found the lowest in both episodes. These characteristics reflected that long-range transportation of coal and biomass burning were from the surrounding areas, while vehicle exhausts were mainly from local emissions. Except for the impact of local pollution source emissions, the regional transport could make a greater contribution in a more serious pollution event.

Analysis of Factors Influencing Air Quality in Different Periods during COVID-19: A Case Study of Tangshan, China.

This study aimed to analyze the main factors influencing air quality in Tangshan during COVID-19, covering three different periods: the COVID-19 period, the Level I response period, and the Spring Festival period. Comparative analysis and the difference-in-differences (DID) method were used to explore differences in air quality between different stages of the epidemic and different years. During the COVID-19 period, the air quality index (AQI) and the concentrations of six conventional air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3-8h) decreased significantly compared to 2017-2019. For the Level I response period, the reduction in AQI caused by COVID-19 control measures were 29.07%, 31.43%, and 20.04% in February, March, and April of 2020, respectively. During the Spring Festival, the concentrations of the six pollutants were significantly higher than those in 2019 and 2021, which may be related to heavy pollution events caused by unfavorable meteorological conditions and regional transport. As for the further improvement in air quality, it is necessary to take strict measures to prevent and control air pollution while paying attention to meteorological factors.

[Pollution Characteristics of PM2.5 Chemical Composition in Zhejiang Province].

To investigate the seasonal and regional pollution characteristics of PM2.5 chemical composition in Zhejiang province, this study was based on manual sampling monitoring data from 11 sampling sites of four regions in Zhejiang province from October 1, 2019 to September 30, 2020. The results showed that during the observation period, the average ρ(PM2.5) of the four regions ranged from 34.3 µg·m-3 to 46.4 µg·m-3. The PM2.5 mass concentrations in the hinterland areas of western Zhejiang and northern Zhejiang were relatively high, 15.1% and 13.2% higher than the mean value, respectively. The PM2.5 mass concentrations in the coastal areas of eastern Zhejiang and southern Zhejiang were relatively low, 8.4% and 14.9% lower than the average, respectively. The seasonal characteristics showed a higher concentration in autumn and winter and lowest concentration in summer. The seasonal variation in PM2.5 mass concentration from autumn to spring was not obvious in southern Zhejiang, whereas in western Zhejiang, the PM2.5 mass concentration followed a descending sequence of autumn>winter>spring>summer. In northern Zhejiang and eastern Zhejiang, the trend was winter>autumn>spring>summer. During the observation period in the inland area, the ρ(PM2.5) of the scenic area, administrative area, residential area, and mixed area of commercial traffic and residents were (40.2±10.2), (46.3±9.6), (50.1±10.6), and (46.7±10.2) µg·m-3, respectively. The highest value of ρ(PM2.5) was in the residential area. During the sampling period in coastal areas, the ρ(PM2.5) of the cultural and entertainment area and mixed area of commercial traffic and residents were (27.4±5.8) µg·m-3and (37.2±5.6) µg·m-3, respectively. The contribution rates of organic matter (OM), NO3-, SO42-, NH4+, trace elements, and crustal matter in PM2.5were 26.4%, 15.4%, 12.4%, 9.0%, 7.1%, and 5.7%, respectively. The SNA, including SO42-, NO3-, and NH4+, contributed 36.8% in PM2.5. In terms of seasons, the contribution of OM to PM2.5 in autumn, spring, and summer was higher than that of other compositions, which accounted for 28.3%, 27.7%, and 26.3%, respectively. The contribution rate of NO3- in PM2.5 was the largest in winter, reaching 24.3%. In terms of spatial distribution, SNA contributed the most to PM2.5 in all regions, ranging from 32.8% to 39.7%, with the highest in northern Zhejiang and the lowest in southern Zhejiang. The SNA of all regions presented NO3->SO42->NH4+. Based on the backward trajectory clustering analysis, the main air sources of northern Zhejiang were the Yellow Sea-southern Jiangsu (autumn), northern Anhui (winter), East China Sea (spring), and western Jiangsu (summer) areas, with contribution rates of 38.11%, 35.28%, 37.46%, and 27.87%, respectively. The main air sources of western Zhejiang were the Yellow Sea-southern Jiangsu (autumn), southern Anhui (winter), eastern Zhejiang (spring), and northern Zhejiang (summer), with contribution rates of 38.11%, 37.50%, 46.55%, and 32.58%, respectively. The air of autumn, winter, spring, and summer in eastern Zhejiang were influenced by air masses from northern Hebei (36.07%), eastern Shandong (38.06%), East China Sea (30.17%), and southern Guangdong (34.43%), respectively. In autumn, winter, spring, and summer, southern Zhejiang was affected by air masses from the Yellow Sea (35.66%), northeast Anhui (34.44%), East China Sea (26.72%), and southern Fujian coast (35.00%), respectively. The regions in Zhejiang province showed large seasonal differences. The difference value between the maximum value of ρ(PM2.5) in the northwest and the lowest value in the southeast was 21.0 µg·m-3 and 20.5 µg·m-3 in autumn and winter, respectively; the difference values in spring and summer were 10.4 µg·m-3 and 6.1 µg·m-3. Thus, the northern air mass had a certain exogenous contribution to PM2.5 in autumn and winter in Zhejiang province. However, with the weakening of the northern air mass trajectory in spring and summer and the increasing contribution of the southern and east China Sea air mass to the air flow in Zhejiang province, PM2.5 pollution showed a trend of improvement.

[Pollution Characteristics and Source Analysis of Carbon and Nitrogen Components in Ambient PM2.5 in Huangshi City].

Based on stable isotope technology and a PMF model, the pollution characteristics and sources of carbon and nitrogen components in ambient PM2.5 in Huangshi City were explored. The results showed that the total carbon concentration[ρ(TC)] and the total carbon isotopic composition (δ13CTC) in ambient PM2.5 in Huangshi City both showed seasonal variation characteristics of being high in winter and low in summer, with values of (4.4±1.2) µg·m-3 and (-26.3±0.5)‰ in summer and (9.9±3.5) µg·m-3 and (-25.5±0.5)‰ in winter, respectively. The total nitrogen concentration[ρ(TN)]was significantly lower in summer[(9.1±9.1) µg·m-3]than that in winter[(62.4±26.4) µg·m-3], whereas the total nitrogen isotopic composition (δ15NTN) was obviously enriched in summer[(12.8±1.9)‰]compared with that in winter[(2.9±4.0)‰]. In addition to the contribution from local sources, the carbon and nitrogen components were mainly affected by the short-range regional emission in northern Hunan and the long-distance transport in the northwest. The MixSIAR model and the PMF model indicated that the vehicle emission source was the main source of carbon components in PM2.5, with contribution rates of 38.9% and 39.3%, respectively. MixSIAR results showed that NOx emission sources had a greater impact on nitrogen components in PM2.5 of different seasons than NH3 emission sources, and their contribution was higher in summer (80%) than that in winter (66.8%), among which the NOx emissions from coal combustion (summer:36.1%; winter:20.2%) had the largest contribution. By contrast, the PMF model indicated that the main source of nitrogen components was vehicle emissions (59.8%). Combining multiple models to overcome the uncertainty and subjectivity of single-model analysis can provide a theoretical basis for actively controlling and reducing fine particulate matter emissions and effectively dealing with urban aerosol pollution.

Long-range transport of CO and aerosols from Siberian biomass burning over northern Japan during 18-20 May 2016.

High CO concentration and dense aerosol layers at 1-6 km altitude in the free troposphere were observed over Rikubetsu, Japan, in ground-based Fourier transform spectrometer (FTS) and lidar measurements during 18-20 May 2016, days after intense wildfires east of Lake Baikal, Siberia. The column-averaged dry-air mole fraction of CO (XCO) was observed to be ∼150 ppb from 11:15 to 13:50 JST on 19 May, and peak aerosol optical depths (AODs) of 1.41 and 1.28 were observed at 15:40 JST 18 May and 11:20 JST 19 May, respectively. We used the HYSPLIT model to calculate five-day backward trajectories from Rikubetsu on May 18, 2016 at 2, 3 and 5 km altitude. The results show that the air parcels passed over the Siberian wildfires during 16-17 May. It was found that the high CO concentrations originated from forest fires were transported to the upper layers of Hokkaido. This will contribute to the understanding of the regional effects of air pollution in northern Japan due to air masses originating from forest fires. By combining these independent datasets such as AERONET aerosol optical thickness (AOT), MODIS fire data, and Infrared Atmospheric Sounding Interferometer (IASI) total CO columns, we confirmed that the lidar measurements of enhanced aerosol concentrations and FTS measurements of maximum XCO over Rikubetsu resulted from a persistent smoke plume transported from Siberian wildfires. Relatively large-scale forest fires have been frequently occurring in Siberia recently. However, the effects of CO and other gases released from them over northern Japan are not well known. We observed high concentrations of CO over the TCCON station in Rikubetsu, Japan, which we believe to be of forest fire origin. Therefore, we analyzed it as a case study to confirm its origin and impact on the upper atmosphere over northern Japan.

Characteristics and source origin analysis of halogenated hydrocarbons in Hong Kong.

Despite being regulated globally for almost three decades, halocarbon continues to play a vital role in climate change and ozone layer because of its long lifetime in the ambient air. In recent years, unexpected halocarbon emissions have been found in Asia, raising concerns about ozone recovery. As a number of studies focused on halocarbon variations and source profiles, there is an increasing need to identify halocarbon source origins. In this study, an eight-month regular air sampling was conducted at a coastal site in Hong Kong from November 2020 to June 2021, and seventeen halocarbon species were selected for extensive investigation after advanced sample analysis in our laboratory. The temporal variations of halocarbon mixing ratio enhancements were analyzed, and the spatial variations of source origins were investigated by wind sectors and backward trajectory statistics. Our results indicate lower enhancements beyond the background values for major regulated CFCs and CCl4 than later controlled HCFCs and HFCs, suggesting the greater progress of Montreal Protocol implementation for the former species. The notable high enhancement values of non-regulated halocarbons from the north direction indicate their widespread usage in China. The source apportionment analysis estimates the contributions from six emission sectors on measured halocarbons, including solvent usage (43.57 ± 4.08 %), refrigerant residues (17.05 ± 5.71 %), cleaning agent/chemical production (13.18 ± 4.76 %), refrigerant replacements (13.06 ± 2.13 %), solvent residues (8.65 ± 3.28 %), and foaming agent (4.49 ± 1.08 %). Trajectories statistical analysis suggests that industrial solvent was mainly contributed by eastern China (i.e., Shandong and YRD), cleaning agent/chemical production was spread over southeast China (i.e., YRD and Fujian), and refrigeration replacements were dominant in Hong Kong surrounding regions. This work provides insight into the progress made in implementing the Montreal Protocol in Hong Kong and the surrounding region and the importance of continuous emission control.

Characterization, reactivity, source apportionment, and potential source areas of ambient volatile organic compounds in a typical tropical city.

Based on one-year observation, the concentration, sources, and potential source areas of volatile organic compounds (VOCs) were comprehensively analyzed to investigate the pollution characteristics of ambient VOCs in Haikou, China. The results showed that the annual average concentration of total VOCs (TVOCs) was 11.4 ppbV, and the composition was dominated by alkanes (8.2 ppbV, 71.4%) and alkenes (1.3 ppbV, 20.5%). The diurnal variation in the concentration of dominant VOC species showed a distinct bimodal distribution with peaks in the morning and evening. The greatest contribution to ozone formation potential (OFP) was made by alkenes (51.6%), followed by alkanes (27.2%). The concentrations of VOCs and nitrogen dioxide (NO2) in spring and summer were low, and it was difficult to generate high ozone (O3) concentrations through photochemical reactions. The significant increase in O3 concentrations in autumn and winter was mainly related to the transmission of pollutants from the northeast. Traffic sources (40.1%), industrial sources (19.4%), combustion sources (18.6%), solvent usage sources (15.5%) and plant sources (6.4%) were identified as major sources of VOCs through the positive matrix factorization (PMF) model. The southeastern coastal areas of China were identified as major potential source areas of VOCs through the potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) models. Overall, the concentration of ambient VOCs in Haikou was strongly influenced by traffic sources and long-distance transport, and the control of VOCs emitted from vehicles should be strengthened to reduce the active species of ambient VOCs in Haikou, thereby reducing the generation of O3.

Pollution characteristics and sources of carbonyl compounds in a typical city of Fenwei Plain, Linfen, in summer.

Field measurements of atmospheric carbonyl compounds (carbonyls) and essential precursors of O3 were carried out in the urban area of Linfen City (Linfen) where serious O3 pollution has occurred in recent years due to its unique terrain. Carbonyls were sampled using an automatic carbonyl sampler in August 2019 to determine their pollution characteristics and sources. An average concentration of ten carbonyls was 27 ± 5.7 µg m-3 detected using an HPLC-UV system. The concentrations of most detected carbonyls in August were significantly higher than those in the winter months in China. Acetone, formaldehyde and acetaldehyde were the most abundant species, accounting for 73% of all detected carbonyls. Formaldehyde, acetaldehyde, and methacrolein (MACR) were the most significant contributors to OH• reactivity and ozone generation, indicating that these three carbonyls were the key species influencing the production of O3. The concentrations of formaldehyde, acetaldehyde, and MACR showed similar diurnal variations on most days, with high values during the daytime reaching a peak at 10:00. However, the concentrations of the latter two species varied less than that of formaldehyde during the day. The acetone concentration generally increased continuously from morning to night, with the maximum value around 22:00. The C1/C2 ratio in summer was higher than that in winter. These results indicated that the carbonyls in Linfen were not only affected by anthropogenic sources such as vehicle exhaust but also by secondary photochemical production. The results of formaldehyde source apportionment showed that the contributions of background, primary, and secondary sources to the observed formaldehyde concentration were 27.6%, 36.6%, and 35.8%, respectively. Additionally, this study revealed for the first time that the vertical transport of air masses containing high concentrations of O3 and NO3 radicals above the boundary layer could increase the secondary generation of formaldehyde at night in summer.

[Organic Aerosols and Source Analysis of Fine Particles in the Background of Shiwanda Mountain, Guangxi].

The non-polar compounds in atmospheric fine particles (PM2.5) mainly include polycyclic aromatic hydrocarbons (PAHs) and normal alkanes (n-alkanes), etc., which are usually used to identify the source of the air pollution and have a great important impact on human health and the environment. To study the pollution characteristics and sources of non-polar organic aerosols in the background point PM2.5 in Guangxi, from November 2017 to October 2018, 17 types of PAHs and 20 types of n-alkanes were analyzed on PM2.5 samples collected in the field. It was found that the annual average concentrations of PAHs and n-alkanes were 4.28±4.25 ng·m-3 and 13.7±14.72 ng·m-3, respectively. The seasonal change was as follows:winter[(7.86±5.19) ng·m-3, (27.51±16.90) ng·m-3]>spring[(2.73±1.76) ng·m-3, (7.64±4.71) ng·m-3]>autumn[(2.34±145) ng·m-3, (7.01±4.55) ng·m-3]>summer[(1.91±1.67) ng·m-3, (3.98±3.12) ng·m-3]. In PAHs, 5-ring and 6-ring molecules accounted for more than 60%, followed by low- and medium-ring molecules (4-ring and 3-ring). The high molecular weight of n-alkanes was relatively high (C29>C31>C27), and the odd and even carbon numbers were significantly different. In addition, combined with the feature ratio method, principal component analysis method, and backward trajectory joint verification, it was found that 41.5% of non-organic aerosols in winter were affected by maritime traffic emissions and ocean source transportation, and 36.7% of the pollution was explained by the coal burning and local biomass burning; 25.2% of the pollution in spring came from biomass combustion and transportation, and 45.0% was attributed to marine transportation in the southern part of the study area and higher plant wax emissions pollution; 53.4% of pollution in summer came from polluted ship emissions, and 10.6% of pollution came from transportation in Southwest Thailand Source transportation. The organic aerosols at the background sites in Guangxi were affected by the combined effects of local emissions and transmission sources.

First atmospheric mercury measurements at a coastal site in the Apulia region: seasonal variability and source analysis.

In the framework of the Italian Special Network for Mercury (ISNM) "Reti Speciali", a sampling campaign to monitor atmospheric mercury (Hg) was carried out at Monte Sant'Angelo (MSA). This is a coastal monitoring station in the Apulia region, representative of the Southern Adriatic area, within the Mediterranean basin. This work presents continuous Gaseous Elemental Mercury (GEM) measurements over about three years at MSA, using the Lumex RA-915AM mercury analyzer. The aim was to obtain a dataset suitable for the analysis of Hg concentrations in terms of source and transport variation. Diurnal cycles of GEM were evaluated to observe the influence of local atmospheric temperature and wind speed on potential re-emissions from surrounding sea and soil surfaces. Data were also analyzed in terms of long-range transport, using backward trajectory cluster analysis. The spatial distribution of potential sources, contributing to higher measured GEM values, was obtained employing Potential Source Contribution Function (PSCF) statistics. The influence of major Hg anthropogenic point sources, such as mining activities and coal-fuel power plants, both regionally and continentally, from mainland Europe, was observed. The role of the vegetation GEM uptake in modulating the seasonal GEM variability was also investigated. The potential of wildfire influence over the highest detected GEM levels was further examined using active fire data and the evaluation of the vegetation dryness index during the selected episodes.