East Asian countries experience severe air pollution owing to their rapid development and urbanization induced by substantial economic activities. South Korea and China are among the most polluted ...East Asian countries with high mass concentrations of PM2.5. Although the occurrence of transboundary air pollution among neighboring countries has been recognized for a long time, studies involving simultaneous ground-based PM2.5 monitoring and source apportionment in South Korea and China have not been conducted to date. This study performed simultaneous daily ground-based monitoring of PM2.5 in Seoul and Beijing from January to December 2019. The mass concentrations of PM2.5 and its major chemical components were analyzed simultaneously during 2019. Positive matrix factorization (PMF) as well as dispersion normalized PMF (DN-PMF) were utilized for the source apportionment of ambient PM2.5 at the two sites. 23 h average ventilation coefficients were applied for daily PM2.5 chemical constituents' data. Nine sources were identified at both sites. While secondary nitrate, secondary sulfate, mobile, oil combustion, biomass burning, soil, and aged sea salt were commonly found at both sites, industry/coal combustion and incinerator were identified only at Seoul and incinerator/industry and coal combustion were identified only at Beijing. Reduction of the meteorological influences were found in DN-PMF compare to C-PMF but the effects of DN on mobile source were reduced by averaging over the 23 h sampling period. The DN-PMF results showed that Secondary nitrate (Seoul: 25.5%; Beijing: 31.7%) and secondary sulfate (Seoul: 20.5%; Beijing: 17.6%) were most dominant contributors to PM2.5 at both sites. Decreasing secondary sulfate contributions and increasing secondary nitrate contributions were observed at both sites.
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•PM2.5 measurements in Beijing and Seoul were made using comparable methods.•Conventional PMF and dispersion normalized PMF were applied for both sites.•Decreasing sulfate and increasing nitrate contributions were observed at both sites•Both sites were affected by regional and long-range transboundary air pollutants.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Fine particle (PM2.5) samples were collected simultaneously at three urban sites (Shanghai, Nanjing, and Hangzhou) and one rural site near Ningbo in the Yangtze River Delta (YRD) region, China, on a ...weekly basis from September 2013 to August 2014. In addition, high-frequency daily sampling was conducted in Shanghai and Nanjing for one month during each season. Severe regional PM2.5 pollution episodes were frequently observed in the YRD, with annual mean concentrations of 94.6 ± 55.9, 97.8 ± 40.5, 134 ± 54.3, and 94.0 ± 57.6 μg m−3 in Shanghai, Nanjing, Hangzhou, and Ningbo, respectively. The concentrations of PM2.5 and ambient trace metals at the four sites showed clear seasonal trends, with higher concentrations in winter and lower concentrations in summer. In Shanghai, similar seasonal patterns were found for organic carbon (OC), elemental carbon (EC), and water-soluble inorganic ions (K+, NH4+, Cl−, NO3−, and SO42-). Air mass backward trajectory and potential source contribution function (PSCF) analyses implied that areas of central and northern China contributed significantly to the concentration and chemical compositions of PM2.5 in Shanghai during winter. Three heavy pollution events in Shanghai were observed during autumn and winter. The modelling results of the Nested Air Quality Prediction Modeling System (NAQPMS) showed the sources and transport of PM2.5 in the YRD during the three pollution processes. The contribution of secondary species (SOC, NH4+, NO3−, and SO42-) in pollution event (PE) periods was much higher than in BPE (before pollution event) and APE (after pollution event) periods, suggesting the importance of secondary aerosol formation during the three pollution events. Furthermore, the bioavailability of Cu, and Zn in the wintertime PM2.5 samples from Shanghai was much higher during the pollution days than during the non-pollution days.
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Secondary aerosol formation is important and the bioavailability of trace metals is much higher during the pollution events.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
In recent years, PM2.5 and O3 pollutions are prevalent in the atmosphere in Beijing. The study on pollution characteristics of VOC, which are important precursors of O3 and secondary organic aerosols ...(SOA) contributing PM2.5, is of great significance for providing a reference to guide its reduction policy formulation. Herein, the seasonal variation of atmospheric VOCs and meteorological conditions at the sampling frequency of 1 time per hour were continuously measured from March 2016 to January 2017 in Beijing. Using the collected data combined with multiple models, the role of VOCs in SOA and O3 production was investigated. Alkanes were the most abundant species, contributing 54.1–64.7% of the total VOC concentration for four seasons, followed by aromatics, alkenes and acetylene. The SOA potential (SOAP) was highest in winter at 2885.1 μg m−3, followed by autumn, spring and summer. Aromatics were the main contributors to SOAP, accounting for ~98.2% of the total SOAP during the entire observation period. The empirical kinetic modeling approach results showed that O3 production featured the VOC-limited regime in Beijing. Alkenes and aromatics were major contributors to O3 formation potential (OFP), accounting for 33.1–45.6% and 27.2–45.2%, respectively, particularly ethylene and m,p-xylene. Positive matrix factorization results indicated that motor vehicle exhaust was still the largest local source of VOCs, but its proportion was considerably reduced. The potential source contribution function results revealed that regional transport sources of VOC pollution in Beijing mainly came from the northwest and southern areas. Thus, to control PM2.5 and O3 pollution in Beijing, the restriction of alkenes and aromatics emission, accompanied by regional cooperation combined with local control, is essential.
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•VOC profiles in Beijing were studied using monitor data and multiple models.•Reductions of alkenes and aromatics are important for SOA and O3 control.•O3 production is VOC-limited in Beijing.•Vehicle exhaust, solvent usage and fuel evaporation were the main local sources.•Regional transport sources of VOC in Beijing mainly came from northwest and south.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Haze pollution in Beijing is rather deteriorated. Long-term measurement of PM2.5 from 2005 to 2010 at an urban site in Beijing showed very high concentration level with an annual average 74±55μg/m3. ...The contribution of regional sources is one of the most important factors; thus, transport and regional sources of PM2.5 in Beijing are investigated using the trajectory cluster and receptor models (potential source contribution function and trajectory sector analysis). The results indicated that the highest concentrations of PM2.5 (76–120μg/m3) were associated with south, southeast, and short northwest trajectories, and moderate concentrations (46–67μg/m3) with long northwest and short north trajectories, and the lowest concentrations (20–33μg/m3) with long north trajectories. During the relatively polluted periods, the probable locations of regional emission sources were mainly in the south and the west of Beijing and varied according to different seasons. Between 2005 and 2010, the annual mean contribution of 35.5% (32.8μg/m3) for PM2.5 was attributed to long-distance transportation. The transported contribution percentages from 2005 to 2010 for PM2.5 showed an increasing tendency with a linear rate of 1.2/year.
•Long-term measurement of PM2.5 for 6years at an urban site in Beijing was implemented.•Transport and regional sources of PM2.5 in Beijing were investigated using receptor models.•South, southeast, and short northwest air parcels trajectories had larger PM2.5 contributions.•The trends of transported contribution percentages increased.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
This work provides the first continuous measurements of atmospheric mercury (Hg) at the EMMA Station in Patagonia (Argentina), within the Southern Volcanic Zone of South America. This monitoring site ...was set up by the Global Mercury Observation System (GMOS project) and is located inside Nahuel Huapi National Park (41°07′43.33″S, 71°25′12.03″W; 800 m a.s.l) in a forested valley of the Andes upwind of San Carlos de Bariloche city.
This study aimed at describing atmospheric Hg levels and trends of variation using concentration data of Gaseous Elemental Mercury (GEM) collected from October 2012 to July 2017 and, Gaseous Oxidized Mercury (GOM) and Particle-Bound Mercury (PBM) recorded from March 2014 until July 2017. During the studied period the mean GEM concentration was 0.86 ± 0.16 ng m−3; with the highest level in the austral spring (0.95 ± 0.13 ng m−3) and the lowest in the autumn (0.80 ± 0.15 ng m−3). Mean GOM concentration was 4.61 ± 4.00 pg m−3, displaying the highest level in autumn (5.47 ± 4.39 pg m−3) and the lowest in winter (1.24 ± 0.90 pg m−3). Mean PBM computed for the whole period was 3.74 ± 3.41 pg m−3; with the highest mean levels recorded in autumn (6.32 ± 3.41 pg m−3) and the lowest in spring (1.18 ± 0.92 pg m−3). Daytime levels of GEM, GOM and PBM were higher than nighttime concentrations, although in the case GOM and PBM similar levels were computed in autumn and summer, respectively. Westerly winds along with temperature and relative humidity influenced the dynamics of GEM, GOM and PBM at the EMMA Station. The HYSPLIT model showed that the area of the EMMA station was simultaneously affected by local and regional sources (forest fires and volcanoes) while the lowest values were recorded with the inflow of clean oceanic air masses coming from the free troposphere and corresponding to a long-range transport. Moreover, the Potential Source Contribution Function analysis showed that emissions in the Marine Boundary Layer, deriving from remote areas of Pacific Ocean, are sources of GEM and GOM.
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•Atmospheric mercury speciation (GEM, GOM, PBM) was measured in a Patagonian mountain station.•GEM varied seasonally (spring > summer > winter and autumn) and daily (day > night).•Pacific winds, temperature and humidity influenced GEM, GOM and PBM.•Long- and short-range atmospheric transport influence GEM, GOM and PBM.•Volcanoes and forest fires are regional/local mercury sources.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Ambient PM2.5 samples were collected in the urban area of Taian in China in August–September and November, 2014. The chemical compositions and emission sources of PM2.5 were analyzed. The results ...indicated that the mean concentration of PM2.5 reached 70.8μg/m3 during the non-heating period, and water soluble inorganic ions (WSIIs), carbonaceous materials, including elemental carbon (EC) and organic carbon (OC); and elements contributed 43.80%, 10.34% and 17.36%, respectively, to PM2.5. The mean concentrations of WSIIs at three sampling sites decreased in the same order: SO42−>NH4+>NO3−>Cl− during the non-heating period. NO3− and NH4+, SO42− and NH4+, showed extremely significant positive-correlations (r=0.79, 0.54; P<0.01). The variability of OC was larger than the variability of EC during the non-heating period. The high concentration of secondary organic carbon (SOC) could reduce correlation-level between the OC and EC. Moreover, the percentages and concentrations of the total detected elements (TDE) increased significantly, ranging from August–September to November (P<0.01). Major sources of PM2.5 identified from positive matrix factorization (PMF) model and enrichment factors (EFs) included secondary aerosol, coal combustion, metal manufacturing, soil dust/resuspended dust/construction dust and vehicle exhaust/biomass burning, which contributed 27.47%, 17.94%, 19.06%, 9.41% and 16.65%, respectively, to PM2.5. The backward trajectory analysis identified three transport pathways that originated from Mongolia (12% of the total trajectories), Inner Mongolia (2%), and southeast of Shandong Province (86%), and the potential source contribution function (PSCF) model identified southeast of Shandong Province was mainly a potential source-area that affected air quality in Taian.
•The PM2.5 pollution level and emission-sources in medium–small city are investigated.•The source apportionment was conducted by PMF in medium–small city of China.•The air mass transport pathway was identified by the backward trajectory analysis.•The potential source-areas affecting air quality were identified by PSCF model.•Effective emission control policies can be enacted in terms of the results of source apportionment.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Extreme PM2.5 and nonmethane hydrocarbon (NMHC) pollution often occurs simultaneously during the winter. To study the formation mechanism of two pollution events in Chengdu from 23 December 2016 to ...31 January 2017, we explored the weather conditions, chemical composition, secondary pollutant conversion, aerosol hygroscopic growth, and potential source contribution function (PSCF) during this period. During the study period, the humidity was high (67.9%), the wind speed was low (1.0 m s−1), the height of the planetary boundary layer was low (463.4 m), and the atmosphere remained stationary. The potential source regions of PM2.5 and NMHCs were locally polluted sites in the southwestern and southern regions of Chengdu, affected by the southwesterly air mass trajectories. PM2.5 and sulfur oxidation ratios (SOR), nitrogen oxidation ratios (NOR) and secondary organic aerosol formation potential (SOAP) showed a strong positive correlation. As pollution increased, the conversion from SO2, NOx and NMHCs to sulfate, nitrate and SOAs increased, resulting in an increase in the secondary aerosol concentration. As the relative humidity increases, aerosols begin to undergo rapid hygroscopic growth, which seriously affects the visibility of the atmosphere. In general, pollutant emissions, static weather, and secondary conversion, among other factors, lead to the occurrence of this persistent extreme haze pollution.
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•The three important factors contributing to the low visibility in Chengdu were identified.•Mobile sources in Chengdu air pollution are becoming increasingly significant.•Secondary transformation of organic matter (OM) plays an important role in the formation of haze.•High potential source contribution function (PSCF) values of pollutants are in the southwestern and southern regions.
Stationary and stable meteorological conditions, favorable secondary transformation and high relative humidity are the causes of extreme haze pollution events.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The sources of different pollutants contributing to ambient fine particles (PM2.5) on Daebu Island, Korea, were estimated. Twenty four hour integrated filter samples were collected from May ...21–November 1, 2016, and analyzed for organic carbon, elemental carbon, ions, and trace elements. Positive matrix factorization was conducted on the PM2.5 chemical speciation data from the samples to define the pathways and sources of PM2.5 at the sampling site. A total of 80 samples and 24 chemical species were used to run the model and a total of nine sources were identified: secondary sulfate (29.0%), mobile (22.0%), secondary nitrate (13.2%), oil combustion (10.1%), coal combustion (9.4%), aged sea salt (7.9%), soil (5.6%), non-ferrous smelting (1.7%), and industrial activity (1.1%). Conditional probability and potential source contribution functions were then used to determine whether these sources were local or came from pollutants transported over long-range distances. The anthropogenic sources came from local emissions and originated from both industrialized and metropolitan areas, whereas the secondary inorganic aerosols were strongly influenced by the long-range transport of air pollutants from Shandong and Jiangsu provinces in China.
•Source apportionment of ambient PM2.5 is investigated in background area of Korea.•Air pollutants from China enhance secondary inorganic aerosols contributions.•Primary sources are effected by local sources of industrial and metropolitan areas.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
As critical precursors of tropospheric ozone (O3) and secondary organic aerosol (SOA), volatile organic compounds (VOCs) largely influence air quality in urban environments. In this study, ...measurements of 102 VOCs at all five major cities in the Guanzhong Plain (GZP) were conducted during Sep.09-Oct. 13, 2017 (autumn) and Nov. 14, 2017–Jan. 19, 2018 (winter) to investigate the characteristics of VOCs and their roles in O3 and SOA formation. The average concentrations of total VOCs (TVOCs) at Xi'an (XA), Weinan (WN), Xianyang (XY), Tongchuan (TC), and Baoji (BJ) sites were in the range of 55.2–110.2 ppbv in autumn and 42.4–74.3 ppbv in winter. TVOCs concentrations were reduced by 22.4%–43.5% from autumn to winter at XA, WN and BJ. Comparatively low concentrations of TVOCs were observed in XY and TC, ranging from 53.5 to 62.7 ppbv across the sampling period. Alkanes were the major components at all sites, accounting for 26.4%–48.9% of the TVOCs during the sampling campaign, followed by aromatics (4.2%–26.4%). The average concentration of acetylene increased by a factor of up to 4.8 from autumn to winter, indicating the fuel combustion in winter heating period significantly impacted on VOCs composition in the GZP. The OH radical loss rate and maximum incremental reactivity method were employed to determine photochemical reactivities and ozone formation potentials (OFPs) of VOCs, respectively. The VOCs in XA and WN exhibited the highest reactivities in O3 formation, with the OFP of 168–273 ppbv and the OH loss rates of 19.3–40.8 s−1. Alkenes and aromatics primarily related to on-road and industrial emissions contributed 57.8%–76.3% to the total OFP. The contribution of aromatics to the SOA formation at all sites reached 94.1%–98.6%. Considering the potential source-area of VOCs, regional transport of VOCs occurred within the GZP cities.
•Urban cities in the Guanzhong Plain exhibited the highest VOCs levels.•Fuel combustion and meteorological conditions impacted on VOCs composition.•VOCs levels were impacted by transport of air mass within the Guanzhong Plain.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This study aimed to investigate the characteristics of the water-soluble ions concentrations in atmospheric particulates. Highly time-resolved measurements of inorganic ions associated with PM2.5 ...were conducted from December 1, 2017 to February 28, 2018 in Zhengzhou. The hourly mean and standard deviation of PM2.5 concentration during the observation episodes were 108.2 ± 80.7 μg/m3. The hourly mass concentration of PM2.5 increased from 8 μg/m3 to 438 μg/m3 throughout the entire observation. The proportion of water-soluble inorganic ions in PM2.5 was 52.5% throughout the entire observation period. The ions existed mainly in the form of (NH4)2SO4 and NH4NO3. The average mass concentration ratio of NO3− to SO42− was 1.9 ± 0.8 throughout the entire observation period, which initially increased and then decreased with the increased pollution level. The average ratio of the molar equivalent concentration of NH4+ to that of NO3− + SO42− was 1.14 ± 0.27, which decreased with the increased pollution level. Homogeneous reactions played an important role in the formation of nitrate, while, the heterogeneous reactions were important in the formation of sulfate. Both of the values of sulfur oxidation ratios (SOR) and nitrogen oxidation ratios increased with relative high humidity (RH) condition; especially, the SOR values sharply increased when the RH was above 50%. The results of potential source contribution function model demonstrated that the western and northeastern regions of Zhengzhou had a greater influence on PM2.5 pollution in Zhengzhou. All these results suggested that reducing the emission of precursors of secondary inorganic ions was highly important in controlling PM2.5 pollution in Zhengzhou.
•The proportion of secondary inorganic species in PM2.5 increased with increased PM2.5 concentration.•Aerosol acidity increased with increased pollution level.•When the RH value was above 50%, the sulfur oxidation ratios increased sharply.•Regional transport exerted an important influence on air pollution in Zhengzhou.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
