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Formaldehyde (HCHO), a key volatile organic compound (VOC) in the atmosphere, plays a crucial role in driving photochemical processes. Satellite-based observations of column concentrations of HCHO and other gaseous pollutants (e.g., NO2) have generally been used in previous studies to elucidate the mechanisms behind secondary organic aerosol (SOA) and ozone (O3) formation. This study aimed to investigate the characteristics of HCHO by retrieving its vertical profile over Nanjing during the warm season (May–June 2022) and analyzing the diurnal variation in vertical distribution and potential source regions on non-polluted (MDA8 O3 < 160 μg m−3, NO3P) and O3-polluted (MDA8 O3 ≥ 160 μg m−3, O3P) days. Under both conditions, HCHO was primarily concentrated below 1.5 km altitude, with average vertical profiles displaying similar Boltzmann-like distributions. However, HCHO concentrations on O3P days were 1.2–1.6 times higher than those on non-polluted days at the same altitude below 1.5 km. Maximum HCHO concentrations occurred in the afternoon, while the peak value in the 0.1–0.4 km layers was reached around noon (~11:00 a.m.). The variation rates (VR) of HCHO in the 0.3–1.2 km altitudes had a maximum on O3P days (approximately 0.33 ppbv h−1), and were significantly higher (p < 0.01) than the VR observed on NO3P days (0.14–0.20 ppbv h−1). The analysis of footprints showed that HCHO concentrations were jointly influenced by the upstream region and the surroundings of the study site. The study results improve the understanding of the vertical distribution and potential source regions of HCHO. [ABSTRACT FROM AUTHOR]

Black carbon (BC) is one of the main factors in global climate change and human health. Based on reanalysis data from the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), the historical trends in the variation of BC and the factors affecting this were investigated in core city of Central Plains Economic Zone in China-Zhengzhou. Based on correlation analysis with ground measurements of BC concentration, it was determined that the MERRA-2 reanalysis data could be used to reveal the variations in ground-level BC concentrations. According to the variation trends of BC concentration in 1980–2020 and typical fossil fuel consumption in 2008–2017, two periods of rapidly increasing BC concentration were identified in 1980–1989 and 1999–2006. The increase in BC concentration then slowed from 2007. Since 2012, due to the adjustment of the Zhengzhou strategy and the implementation of an emission control strategy throughout Henan Province, the consumption of coal and coke decreased by 4% every year, resulting in a downward trend in the total BC emissions. However, coal and coke were still the main factors affecting the BC concentration in Zhengzhou. The contribution of transportation to BC in Zhengzhou has been particularly important since 2016. Analysis of BC seasonal variation revealed that the highest BC concentration was in winter, mainly due to the increase in industry and residential emissions. Additionally, based on the local and regional emission inventories and combined with potential source contribution function (PSCF), concentration weighted trajectory (CWT), this study revealed the potential sources regions and source types of BC in Zhengzhou, and found that the intensification of energy consumption and BC emissions in Zhengzhou and the surrounding regions of Henan Province might be the reasons for the high BC loading in Zhengzhou through regional transport.

The methane emissions from the Hungarian Pannonian Basin are not well qualified, due to a lack of measurements of CH4 mole fraction and δ13CCH4 in the air. This study reports methane measurements in air samples from Hungary, placing them in the context of regional and global background data, to investigate the inputs to the methane burden in Central Europe. CH4 mole fraction and δ13CCH4 from the Hungarian tall tower station, Hegyhátsál, and additional data from Mace Head (Ireland) and Zeppelin (Svalbard) are used with back trajectory modeling to identify central European source areas and their seasonal variation between the summer vegetation and winter heating periods. Methane measurements in air masses sampled in the European interior, have significantly higher maxima and seasonal amplitudes than at the Mace Head and Zeppelin European background sites. The mean CH4 mole fraction value is about 80 ppb higher than the comparable marine background, and values above 2,000 ppb were frequently observed between February 2013 and December 2015. The mean δ13CCH4 value −47.5 ± 0.3‰ (2σ) was comparable to values at all three monitoring sites, but specific pollution events were detected at Hegyhátsál. Concentration weighted trajectory modeling, meteorological parameters, stable carbon isotopic composition (δ13CCH4), and Miller‐Tans analysis show that the main factors influencing CH4 at the Hegyhátsál, apart from diurnal and seasonal changes in the planetary boundary layer, are emissions from residential heating and industrial CH4 emissions during the winter. Key Points: The main factors influencing local CH4 values at Hegyhátsál (HUN) are local meteorology, and seasonal anthropogenic emissions related to heatingThe HUN station is valuable for monitoring local and regional methane emissions and tracking source inputsCH4 increments and isotopic seasonality in air at the Hungarian continental background site are comparable to other European regional sites [ABSTRACT FROM AUTHOR]

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Mole fractions of atmospheric CO2 (XCO2) have been continuously measured from October 2014 to March 2016 at the Guangzhou Panyu Atmospheric Composition Site (23.00°N, 113.21°E; 140 m MSL) in the Pearl River Delta (PRD) region using a cavity ring-down spectrometer. Approximately 66.63%, 19.28%, and 14.09% of the observed values were filtered as background, pollutant source, and sink due to biospheric uptake, respectively, by applying a robust local regression procedure. Their corresponding mean values were 424.12 ± 10.12 ppm (×10−6 mol mol−1), 447.83 ± 13.63 ppm, and 408.83 ± 7.75 ppm. The background XCO2 levels were highest in spring and winter, moderate in autumn, and lowest in summer. The diurnal XCO2 was at a minimum from 1400–1600 LST (Local Standard Time) and a maximum at 0500 LST the next day. The increase of XCO2 in spring and summer was mainly associated with polluted air masses from south coastal Vietnam, the South China Sea, and the southeast Pearl River Estuary. With the exception of summer, airflow primarily from marine regions southeast of Taiwan that passed over the Pearl River Estuary had a greater impact on XCO2, suggesting an important potential source region. [ABSTRACT FROM AUTHOR]

The evolution, transport characteristics, and potential source regions of PM2.5 and O3 were investigated from 1 January 2015 to 31 December 2020 in the coastal city of Nantong. The annual mean PM2.5 concentration declined obviously over the entire study period, and was 34.7 μg/m3 in 2020. O3 had a relatively smooth decreasing trend, but rebounded greatly during 2017 when the most frequent extreme high-temperature events occurred. Similar trends were observed for PM2.5 and O3 polluted hours. No PM2.5-O3 complex air pollution happened in 2019 and 2020, likely suggesting the preliminary results from the implementation of emission controls. Notable differences in transport pathways and frequencies were observed from the backward trajectory clusters in four seasons in Nantong. Clusters with the largest percentage of polluted PM2.5 and O3 trajectories were transported mostly over short distances rather than long distances. Analysis involving the potential source contribution function (PSCF) and concentration weighted trajectory (CWT) showed that PM2.5 polluted sources were from the adjacent western and northwestern provinces, whereas the influence of eastern marine sources was relatively small. O3 had a greatly different spatial distribution of polluted source regions from PM2.5, mostly covering the North China Plain, the Bohai Sea, and the Yellow Sea.

Understanding the potential source regions of air pollutants and their relative contribution from surrounding areas are of great importance for air pollution control strategies in Northern China. Six years of measurement of air pollutants was conducted from 2005 to 2010 in Shangdianzi (SDZ) regional background station. During the study period, the annual average concentrations of sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide and ozone (Ox), and fine particle matter less than or equal to 2.5 μm (PM2.5) range from 15.7 to 20.0 μg/m3, 577.7 to 856.0 μg/m3, 90.4 to 101.8 μg/m3, and 39.8 to 62.4 μg/m3, respectively. In this work, Potential Source Contribution Function (PSCF) and Trajectory Sector Analysis (TSA) methods are applied to identify locations of sources and their relatively contribution of air pollutants at SDZ. PSCF analysis shows that central Inner Mongolia, north Shanxi, west and south Hebei, and west Liaoning are all potential sources of SO2. The North China Plain (NCP) region, especially south Hebei and north Shandong, are major potential source regions for CO, Ox, and PM2.5. Therefore, reducing anthropogenic emissions from the coal industry, biomass burning, agricultural activities, and vehicles in these areas could be an effective way of controlling air pollution at SDZ. Based on the TSA results, the contributions of SO2, CO, Ox, and PM2.5 from long-distance transport are 5.5 μg/m3, 301.4 μg/m3, 14.8 μg/m3, and 25.8 μg/m3, accounting for approximately 22.6%, 32.3%, 13.1%, and 37.5% of the respective air pollutant concentrations at SDZ. [ABSTRACT FROM AUTHOR]

Cluster analyses, potential source contribution function (PSCF) and concentration-weight trajectory (CWT) were used to identify the main transport pathways and potential source regions with hourly PM2.5 and PM10 concentrations in different seasons from January 2017 to December 2019 at Akedala Station, located in northwest China (Central Asia). The annual mean concentrations of PM2.5 and PM10 were 11.63 ± 9.31 and 19.99 ± 14.39 µg/m3, respectively. The air pollution was most polluted in winter, and the dominant part of PM10 (between 54 to 76%) constituted PM2.5 aerosols in Akedala. Particulate pollution in Akedala can be traced back to eastern Kazakhstan, northern Xinjiang, and western Mongolia. The cluster analyses showed that the Akedala atmosphere was mainly affected by air masses transported from the northwest. The PM2.5 and PM10 mainly came with air masses from the central and eastern regions of Kazakhstan, which are characterized by highly industrialized and semi-arid desert areas. In addition, the analyses of the pressure profile of back-trajectories showed that air mass distribution were mainly distributed above 840 hPa. This indicates that PM2.5 and PM10 concentrations were strongly affected by high altitude air masses. According to the results of the PSCF and CWT methods, the main potential source areas of PM2.5 were very similar to those of PM10. In winter and autumn, the main potential source areas with high weighted PSCF values were located in the eastern regions of Kazakhstan, northern Xinjiang, and western Mongolia. These areas contributed the highest PM2.5 concentrations from 25 to 40 µg/m3 and PM10 concentrations from 30 to 60 µg/m3 in these seasons. In spring and summer, the potential source areas with the high weighted PSCF values were distributed in eastern Kazakhstan, northern Xinjiang, the border between northeast Kazakhstan, and southern Russia. These areas contributed the highest PM2.5 concentrations from 10 to 20 µg/m3 and PM10 concentrations from 20 to 60 µg/m3 in these seasons.

An intensive sampling of PM2.5 was conducted at a rural site (Gucheng) in the North China Plain from 22 October to 23 November 2016. A total of 25 elements (Al, Na, Cl, Mg, P, S, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Sr, Cd, Ba, Pb, and Sb) from PM2.5 filter samples collected daily were measured using a wavelength dispersive X-ray fluorescence spectrometer. Cl, S, and K were the most abundant elements, with average concentrations of 2077.66 ng m−3 (range 118.88−4638.96 ng m−3), 1748.78 ng m−3 (range 276.67−4335.59 ng m−3), and 1287.07 ng m−3 (range 254.90−2748.63 ng m−3), respectively. Among noncrustal trace metal elements, the concentration of Zn was the highest, with an average of 397.74 ng m−3 (range 36.45−1602.96 ng m−3), followed by Sb and Pb, on average, of 299.20 ng m−3 and 184.52 ng m−3, respectively. The morphologies of PM2.5 samples were observed using scanning electron microscopy. The shape of the particles was predominantly spherical, chain-like, and irregular. Positive matrix factorization analysis revealed that soil dust, following by industry, secondary formation, vehicle emissions, biomass and waste burning, and coal combustion, were the main sources of PM2.5. The results of cluster, potential source contribution function, and concentration weighted trajectory analyses suggested that local emissions from Hebei Province, as well as regional transport from Beijing, Tianjin, Shandong, and Shanxi Province, and long-range transport from Inner Mongolia, were the main contributors to PM2.5 pollution.

Mass concentration data for particulate matter with an aerodynamic diameter less than or equal to 2.50 μm (PM2.5) combined with backward trajectory cluster analysis, potential source contribution function (PSCF), and concentration weighted trajectory (CWT) methods were used to investigate the transport pathways and potential source regions of PM2.5 on the west coast of Bohai Bay from 2009 to 2018. Two pathways responsible for the transportation of high PM2.5 levels were identified, namely a southerly pathway and a northwesterly pathway. The southerly pathway represented the major transport pathway of PM2.5 for all seasons. As a regional transport pathway, it had the greatest impact in winter, followed by autumn. The southerly transport pathway passed over the Shandong and Hebei provinces before reaching Tianjin: Air masses were transported within the boundary layer (below 925 hPa), representing a slow-moving air flow. The northwesterly pathway mostly occurred in winter and autumn and passed over desert and semidesert regions in Outer Mongolia, the sand lands of Inner Mongolia, and Hebei. The air masses associated with the northwesterly pathway represented fast-moving airflows responsible for long-range transportation of PM2.5. Two potential source regions that contributed to high PM2.5 loadings on the west coast of Bohai Bay were identified, “southerly source regions” and “northwesterly source regions”. The southerly source regions, with weighted CWT (WCWT) values in winter greater than 140.00 μg/m3, were anthropogenic source regions, including southern Hebei, western Shandong, eastern Henan, northern Anhui, and northern Jiangsu. The northwesterly source regions, with WCWT values in winter of 80.00−140.00 μg/m3, were natural source regions, encompassing central Inner Mongolia and southern Mongolia. In addition, the southerly transport pathway passed though anthropogenic source regions, while the northwesterly transport pathway passed though natural source regions. The impacts of anthropogenic source regions on PM2.5 loadings on the west coast of Bohai Bay were greater than those of natural source regions.

The evolution, transport characteristics, and potential source regions of PM2.5 and O3 were investigated from 1 January 2015 to 31 December 2020 in the coastal city of Nantong. The annual mean PM2.5 concentration declined obviously over the entire study period, and was 34.7 μg/m3 in 2020. O3 had a relatively smooth decreasing trend, but rebounded greatly during 2017 when the most frequent extreme high-temperature events occurred. Similar trends were observed for PM2.5 and O3 polluted hours. No PM2.5-O3 complex air pollution happened in 2019 and 2020, likely suggesting the preliminary results from the implementation of emission controls. Notable differences in transport pathways and frequencies were observed from the backward trajectory clusters in four seasons in Nantong. Clusters with the largest percentage of polluted PM2.5 and O3 trajectories were transported mostly over short distances rather than long distances. Analysis involving the potential source contribution function (PSCF) and concentration weighted trajectory (CWT) showed that PM2.5 polluted sources were from the adjacent western and northwestern provinces, whereas the influence of eastern marine sources was relatively small. O3 had a greatly different spatial distribution of polluted source regions from PM2.5, mostly covering the North China Plain, the Bohai Sea, and the Yellow Sea. [ABSTRACT FROM AUTHOR]

Atmospheric fine particulate pollution has been a dramatically environmental concern in all seasons in the megacity Beijing in recent years. A long-term real-time monitoring campaign of non-refractory submicron aerosol (NR-PM 1) at a southeastern urban site in Beijing from July 13, 2017 to May 26, 2018 using an Aerodyne Aerosol Chemical Speciation Monitor (ACSM) was carried out to investigate the characteristics and evolution processes of NR-PM 1 and its potential source effects. The annual average concentration of NR-PM 1 during the entire study period was 40.8 μg/m3, lower than that of NR-PM 1 from July 2011 to June 2012, and its seasonal variation presented the order of autumn and spring > winter > summer. The average concentration of organic species Org in the entire study was 15.1 μg/m3 and accounted for 37% of NR-PM 1 , being consistently a significant component of NR-PM 1 in the four seasons. The average concentrations of SO 4 2− and NO 3 − were 8.6 and 11.1 μg/m3, respectively, accounting for 21% and 27% of NR-PM 1 , and NO 3 − and its contribution to NR-PM 1 exceeded those of SO 4 2−, suggesting that secondary nitrate became a more dramatic pollutant. Note that SO 4 2− in summer accounted for 27% of NR-PM 1 , higher than those in the other seasons, indicating that secondary sulfate formation was greatly important in summer, however, NO 3 − in summer accounted for 22% of NR-PM 1 , lower than those in the other seasons, more likely due to relatively high temperature in summer being favorable for dissociation of nitrate. NH 4 + and Cl− species presented lower mass concentrations and mass fractions, and the seasonal variation of NH 4 + was similar to NO 3 −, yet Cl− presented a typical seasonal change related to intense fossil fuel combustion during the heating period in winter and biomass burning in autumn with an order of winter > autumn > spring > summer. All aerosol species exhibited different diurnal cycles, of which Org presented a bimodal mode, SO 4 2− and Cl− showed a flatter diurnal pattern, however, NO 3 − and NH 4 + displayed more similar single-peak patterns. The conversion of SO 2 to SO 4 2− presented an obvious seasonal variation with summer > autumn > spring > winter driven by different factors. The conversion of NO 2 to NO 3 − also showed a significant seasonal change characterized by autumn > summer and spring > winter controlled by different factors. Relative humidity, temperature and wind speed during the study period were significant meteorological elements influencing NR-PM 1 and its species, and heavy haze pollution episodes largely occurred under high relative humidity and low wind speed. High potential source regions influencing NR-PM 1 pollution in Beijing in all seasons presented dramatically distributions related to wind directions. • The mean loadings of all NR-PM 1 species in the entire study followed the order of Org > NO 3 − > SO 4 2− > NH 4 + > Cl−. • All NR-PM 1 species obviously presented different seasonal variations. • RH and NH 3 could promote the conversions of SO 2 to SO 4 2− and NO 2 to NO 3 −in all seasons. • High potential source regions influencing Beijing NR-PM 1 showed distinct distributions related to wind directions. [ABSTRACT FROM AUTHOR]

Organic carbon (OC), elemental carbon (EC), and PM 2.5 concentration data obtained from Shanxi Super Station in Jiashan County of Jiaxing City, in the winter of 2018 and 2019, were analyzed to determine the variation and potential source areas of carbonaceous aerosols. The results show that OC concentrations in the winter of 2018 and 2019 were 6.90 μg·m -3 and 5.63 μg·m -3 , respectively, while EC concentrations were 2.47 μg·m -3 and 1.57 μg·m -3 , respectively. The concentrations of OC and EC in the winter of 2019 were lower than those in the winter of 2018, by approximately 18.4% and 36.4%, respectively. In 2018 and 2019, the concentrations of secondary organic carbon (SOC), calculated using the minimum R-squared (MRS) method, were 1.49 μg·m -3 and 1.97 μg·m -3 , respectively, and the concentrations of primary organic carbon (POC) were 5.41 μg·m -3 and 3.66 μg·m -3 , respectively. The proportion of POC in OC showed a downward trend, from 96.0% in December 2018 to 64.9% in February 2020, indicating a decrease of 31.1 percentage points. SOC showed an upward trend, increasing by 31.1 percentage points from 4.0% in December 2018 to 35.1% in February 2020. It is worth noting that with the increase in PM 2.5 concentration, the concentration of OC and EC increased by 474.7% and 408.2%, respectively, although the proportion of OC in PM 2.5 decreased from 18.8% to 12.3%. and the percentage of OC decreased from 5.8% to 3.3%. The contribution of POC to PM 2.5 did not fluctuate, and only decreased significantly above 150 μg·m -3 , while the contribution of SOC to PM 2.5 first decreased and then increased. In Jiaxing, the potential sources of OC and EC were mainly southern Jiangsu, southeastern Anhui, local Jiaxing, and northern Zhejiang. In the winter of the contribution concentrations of OC and EC in the main potential source regions were approximately 2 μg·m -3 and 6 μg·m -3 lower, respectively, than in winter 2018. The range of high values in the potential source regions also decreased in 2019. Before the COVID-19 epidemic, it was affected by both motor vehicle exhaust emissions and coal burning. During the Spring Festival and home isolation, due to traffic control and other reasons, motor vehicle emissions were reduced, which leaving coal burning as the main contributor.

Facing serious air pollution problems, the Chinese government has taken numerous measures to prevent and control air pollution. Understanding the sources of pollutants is crucial to the prevention of air pollution. Using numerical simulation method, this study analysed the contributions of the total local emissions and local emissions from different sectors (such as industrial, traffic, resident, agricultural, and power plant emissions) to PM 2.5 concentration, backward trajectory, and potential source regions in Tangshan, a typical heavy industrial city in north China. The impact of multi-scale meteorological conditions on source apportionment was investigated. From October 2016 to March 2017, total local emissions accounted for 46.0% of the near-surface PM 2.5 concentration. In terms of emissions from different sectors, local industrial emissions which accounted for 23.1% of the near-surface PM 2.5 concentration in Tangshan, were the most important pollutant source. Agricultural emissions were the second most important source, accounting for 10.3% of the near-surface PM 2.5 concentration. The contributions of emissions from power plants, traffic, residential sources were 2.0%, 3.0%, and 7.2%, respectively. The contributions of total local emissions and emissions from different sectors depended on multi-scale meteorological conditions, and static weather significantly enhanced the contribution of regional transport to the near-surface PM 2.5 concentration. Eight cluster backward trajectories were identified for Tangshan. The PM 2.5 concentration for the 8 cluster trajectories significantly differed. The near-surface PM 2.5 in urban Tangshan (receptor point) was mainly from the local emissions, and another important potential source region was Tianjin. The results of the source apportionment suggested the importance of joint prevention and control of air pollution in some areas where cities or industrial regions are densely distributed. Image 1 • The contribution of local and different sectors emissions on PM 2.5 concentration. • Backward trajectory and potential source regions of PM 2.5. • The impact of multi-scale meteorological conditions on source apportionment. • Local emissions accounted for 46.0% of the near-surface PM 2.5 concentration. • Static weather significantly enhanced the contribution of regional transport. The contributions of local emissions depended on multi-scale meteorological conditions, and static weather significantly enhanced the contribution of regional transport in Tangshan. [ABSTRACT FROM AUTHOR]

Cluster analyses, potential source contribution function (PSCF) and concentration-weight trajectory (CWT) were used to identify the main transport pathways and potential source regions with hourly PM2.5 and PM10 concentrations in different seasons from January 2017 to December 2019 at Akedala Station, located in northwest China (Central Asia). The annual mean concentrations of PM2.5 and PM10 were 11.63 ± 9.31 and 19.99 ± 14.39 µg/m3, respectively. The air pollution was most polluted in winter, and the dominant part of PM10 (between 54 to 76%) constituted PM2.5 aerosols in Akedala. Particulate pollution in Akedala can be traced back to eastern Kazakhstan, northern Xinjiang, and western Mongolia. The cluster analyses showed that the Akedala atmosphere was mainly affected by air masses transported from the northwest. The PM2.5 and PM10 mainly came with air masses from the central and eastern regions of Kazakhstan, which are characterized by highly industrialized and semi-arid desert areas. In addition, the analyses of the pressure profile of back-trajectories showed that air mass distribution were mainly distributed above 840 hPa. This indicates that PM2.5 and PM10 concentrations were strongly affected by high altitude air masses. According to the results of the PSCF and CWT methods, the main potential source areas of PM2.5 were very similar to those of PM10. In winter and autumn, the main potential source areas with high weighted PSCF values were located in the eastern regions of Kazakhstan, northern Xinjiang, and western Mongolia. These areas contributed the highest PM2.5 concentrations from 25 to 40 µg/m3 and PM10 concentrations from 30 to 60 µg/m3 in these seasons. In spring and summer, the potential source areas with the high weighted PSCF values were distributed in eastern Kazakhstan, northern Xinjiang, the border between northeast Kazakhstan, and southern Russia. These areas contributed the highest PM2.5 concentrations from 10 to 20 µg/m3 and PM10 concentrations from 20 to 60 µg/m3 in these seasons. [ABSTRACT FROM AUTHOR]

An intensive sampling of PM2.5 was conducted at a rural site (Gucheng) in the North China Plain from 22 October to 23 November 2016. A total of 25 elements (Al, Na, Cl, Mg, P, S, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Sr, Cd, Ba, Pb, and Sb) from PM2.5 filter samples collected daily were measured using a wavelength dispersive X-ray fluorescence spectrometer. Cl, S, and K were the most abundant elements, with average concentrations of 2077.66 ng m−3 (range 118.88–4638.96 ng m−3), 1748.78 ng m−3 (range 276.67–4335.59 ng m−3), and 1287.07 ng m−3 (range 254.90–2748.63 ng m−3), respectively. Among noncrustal trace metal elements, the concentration of Zn was the highest, with an average of 397.74 ng m−3 (range 36.45–1602.96 ng m−3), followed by Sb and Pb, on average, of 299.20 ng m−3 and 184.52 ng m−3, respectively. The morphologies of PM2.5 samples were observed using scanning electron microscopy. The shape of the particles was predominantly spherical, chain-like, and irregular. Positive matrix factorization analysis revealed that soil dust, following by industry, secondary formation, vehicle emissions, biomass and waste burning, and coal combustion, were the main sources of PM2.5. The results of cluster, potential source contribution function, and concentration weighted trajectory analyses suggested that local emissions from Hebei Province, as well as regional transport from Beijing, Tianjin, Shandong, and Shanxi Province, and long-range transport from Inner Mongolia, were the main contributors to PM2.5 pollution. [ABSTRACT FROM AUTHOR]

Mass concentration data for particulate matter with an aerodynamic diameter less than or equal to 2.50 μm (PM2.5) combined with backward trajectory cluster analysis, potential source contribution function (PSCF), and concentration weighted trajectory (CWT) methods were used to investigate the transport pathways and potential source regions of PM2.5 on the west coast of Bohai Bay from 2009 to 2018. Two pathways responsible for the transportation of high PM2.5 levels were identified, namely a southerly pathway and a northwesterly pathway. The southerly pathway represented the major transport pathway of PM2.5 for all seasons. As a regional transport pathway, it had the greatest impact in winter, followed by autumn. The southerly transport pathway passed over the Shandong and Hebei provinces before reaching Tianjin: Air masses were transported within the boundary layer (below 925 hPa), representing a slow-moving air flow. The northwesterly pathway mostly occurred in winter and autumn and passed over desert and semidesert regions in Outer Mongolia, the sand lands of Inner Mongolia, and Hebei. The air masses associated with the northwesterly pathway represented fast-moving airflows responsible for long-range transportation of PM2.5. Two potential source regions that contributed to high PM2.5 loadings on the west coast of Bohai Bay were identified, "southerly source regions" and "northwesterly source regions". The southerly source regions, with weighted CWT (WCWT) values in winter greater than 140.00 μg/m3, were anthropogenic source regions, including southern Hebei, western Shandong, eastern Henan, northern Anhui, and northern Jiangsu. The northwesterly source regions, with WCWT values in winter of 80.00–140.00 μg/m3, were natural source regions, encompassing central Inner Mongolia and southern Mongolia. In addition, the southerly transport pathway passed though anthropogenic source regions, while the northwesterly transport pathway passed though natural source regions. The impacts of anthropogenic source regions on PM2.5 loadings on the west coast of Bohai Bay were greater than those of natural source regions. [ABSTRACT FROM AUTHOR]