Using 2017 data, 54 volatile organic compounds (VOCs) in Taiwan's atmospheric environment from nine photochemical assessment monitoring stations (PAMSs) were collected and their characteristics and ...sources were analyzed. High VOC concentration spots were exposed in urban and industrial areas (Wanhua, Tucheng, Chungming, Tainan, Qiaotou, and Xiaogang). The major categories of VOC were alkanes and aromatics. Among them, benzene, toluene, ethylbenzene, and m,p-xylene (BTEX) predominated and contributed to ozone formation potential (OFP) and secondary organic aerosol formation (SOAF). T/B and X/E ratios showed that VOCs in urban and industrial areas originated from fresh, mixed sources of local emissions. Positive matrix factorization (PMF) analysis indicated that industrial emissions (solvent usage, industry, and petrochemical plant) were the primary contributors, followed secondarily by traffic emissions (vehicular emission and vehicular fuel evaporation), and aged air-mass. Analysis by potential source contribution function (PSCF) was conducted to confirm the effects of aged air-mass sources. The estimated VOC sources OFP and SOAF indicated that industrial emissions were the greatest contributors to OFP and SOAF. Results from this study imply that VOC control measures should prioritize control of industrial emissions.
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•BTEX was the major VOC species, especially toluene, which was the dominant potential contributor to ozone formation potential (OFP) and secondary organic aerosol formation (SOAF).•Positive matrix factorization (PMF) and potential source contribution function (PSCF) are used for tracking regional aged air-mass transport pathways which confirm the results of PMF approach.•The analysis of backward trajectories by PSCF showed that the contribution of aged air-masses affects VOC distribution mainly in the northeast to southwest Taiwan.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Located in the transitional region between the Sichuan Basin (SCB) and Qinghai-Tibetan Plateau (QTP), the West China Rain Zone (WCRZ) is a large-scale ecotone and partially belongs to the Southwest ...China Mountains, which is one of the world’s 34 biodiversity hotspots. Using observation data from national air quality stations and our own monitoring data, we investigated the risk from O3 to vegetation and the major source-region of O3 for two UNESCO (i.e., United Nations Educational, Scientific and Cultural Organization) world heritage properties (Mt. Qingcheng and Mt. Emei) and one city (Ya’an) in the WCRZ. The results show that the annual mean maximum daily 8-h average (MDA8) O3 concentration in Mt. Qingcheng (54 ppb) was higher than that in the adjacent SCB cities (38–48 ppb). The acute and chronic risk levels from O3 to vegetation were also higher in Mt. Qingcheng than at all the other sites. The mean MDA8 O3 concentrations and the O3 risk levels to vegetation in Mt. Emei and Ya’an fell in the range of that at the SCB and QTP cities. However, O3 exposures at all the WCRZ, SCB, and QTP sites exceeded the empirical critical loads for natural ecosystems, forest trees, and highly O3-sensitive plants. The SCB was identified as the largest source-region of O3 for Mt. Qingcheng and Mt. Emei but other Chinese regions and northern India also had considerable contributions. To protect biodiversity and ecosystem services, there is a need to further systematically study O3 and its ecological impacts for the entire WCRZ.
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•Mean MDA8 O3 concentrations at Mt. Emei and Mt. Qingcheng were highest in spring.•O3 exposures at the WCRZ sites exceeded relevant ecological critical loads.•SCB is the main source-region of O3 at the two rural WCRZ sites.•High O3 events (>70 ppb) were also mainly associated with SCB emissions.•Further observations are needed to study O3 effects on WCRZ ecosystem services.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Based on the continuous online measurements of volatile organic compounds (VOCs) at a suburban site in Jinan, Shandong province in the wintertime of 2021, characteristics, chemical transformations ...and sources of VOCs were analyzed. The total mixing ratio of VOCs was 30.35 ± 19.9 ppbv, with alkanes being the most abundant species. Alkenes and aromatics were the major contributors to the hydroxyl radical loss rate (LOH) and the ozone formation potential (OFP), accounting for 44.9% and 32.7% to LOH, and 43.2% and 33.4% to OFP, respectively. Aromatics had the primary contribution (98.1%) to the secondary organic aerosol potential (SOAP), and the high correlation between SOAP and PM2.5 concentrations (R = 0.70) suggested that VOCs were critical precursors of PM2.5. Coal/biomass burning and motor vehicle emissions were the main sources of VOCs (24.6% and 23.8%, respectively), followed by petrochemical and fuel evaporation (19.9%), industrial sources (16.9%), and liquefied petroleum gas/natural gas use (14.8%). Backward trajectories and the potential source contribution function (PSCF) analysis results suggested that short-range transport from the southeast was the primary potential source of VOC concentrations in suburban Jinan. The results can provide theoretical support for local governments to develop VOC emission control strategies in suburban areas.
•Daytime VOCs were significantly influenced by strong localized emissions.•SOAP of VOCs and PM2.5 were highly correlated.•Coal/biomass burning and vehicle emissions were the most significant sources of VOCs.•Short-distance transport from the southeast was important for VOCs control.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In this study, a total of 180 PM2.5 samples were collected from December 3, 2013 to October 20, 2016 in an urban area in Zhengzhou, China, and 16 polycyclic aromatic hydrocarbons (PAHs) in PM2.5 were ...analyzed. Diagnostic ratio and positive matrix factorization (PMF) were used for annual PAH source identification, and health risks and source regions of PM2.5-bound PAHs were also investigated. Results showed high pollution levels of PM2.5, in which all annual average concentrations substantially exceeded the Chinese standard. Although the PAH concentrations exhibited an evident decreasing trend, PAH pollution remained serious, especially in winter. Combustion, particularly coal combustion and vehicle emission, which were relative sources of 4–5-ring PAHs, played important roles in PAH pollution associated with PM2.5 by diagnostic ratios. PMF results showed that coal combustion had the highest contribution to PM2.5-bound PAHs at 39.6%, 39.6%, and 42.6% and traffic at 29.3%, 25.4%, and 27.9% in 2014–2016, respectively. Biomass burning and coking plants were also important sources of PAHs in PM2.5, with an average contribution of 16.4% ± 1.3% and 15.4% ± 3.5%, respectively. The surrounding region in Henan Province was the key potential source area for PM2.5. However, the northwest and adjoining regions of Zhengzhou were the vital potential sources for PAHs during the entire study period. The concentration levels of benzoapyrene (BaP), ∑16PAHsTEQ, and carcinogenic PAHs remained high, especially for BaP, which had an annual concentration (1.9–5.5 ng/m3) that was considerably higher than the Chinese standard. Carcinogenic risks existed in the order of ingestion > dermal absorption > inhalation and adults > children > seniors > adolescents (except for naphthalene). The risk for females was slightly high, and no remarkable non-carcinogenic risk from PAHs were found.
•The annual concentrations of BaP were considerably higher than the Chinese standard and have high risks.•Coal combustion and vehicle emission, which contributed approximately two-third of PAHs, was dominant.•PAHs in PM2.5 posed carcinogenic risks based on three years of data assessment.•Northwest and adjoining regions were the vital potential sources for PAHs.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Thirty-four air samples were collected from 2013 to 2015, at a semi-rural site in Eastern Mediterranean (Island of Crete), to study the atmospheric occurrence of polycyclic aromatic hydrocarbons ...(PAHs), polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) in the gas and particulate phase. Average levels (gaseous and particulate phase) of PAHs (36 compounds, 11–18 ng/m3), PCBs (49 congeners, 77–93 pg/m3) and OCPs (23 substances, 77–140 pg/m3) were comparable to those reported for similar locations worldwide. Multiple-linear regression analysis, performed to relate atmospheric concentrations with meteorological conditions, revealed as main controlling factors local sources for PAHs and long-range transport (LRT) for PCBs and OCPs. The consideration of parent-metabolite ratios for most OCPs excluded fresh inputs. The application of the potential source contribution function (PSCF) identified Black Sea and eastern Balkans as likely sources for PCBs and OCPs. Significant linear correlations (R2 = 0.79–0.98) were determined between the partitioning coefficients (logKp) and partial vapor pressures (logPL0) for most air samples for PAHs and PCBs excepting OCPs. Slope mr values were close to −1 for PAHs and OCPs indicating gas/particle partitioning close to equilibrium. The corresponding mr values for PCBs were shallower (<-0.60) denoting non-equilibrium conditions and potential sampling artefacts. The octanol-air partition coefficient absorption model, logKp-logKoa, did not offer robust evidence for the evaluation of the atmospheric partitioning of the studied compounds. Experimentally determined particle fractions (ϕ) fitted better with the typical remote and rural curves as predicted by the Junge-Pankow model for most PAHs and PCBs but not for OCPs. The Koa-fom absorptive model could not adequately simulate the measured ϕ values for the majority of the compounds.
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•Concentrations of PCBs and OCPs in the atmosphere were generally low.•The use of parent-metabolite ratios for OCPs excluded fresh inputs.•PSCF indicated local sources for PAHs, and Black Sea and Balkans for PCBs and OCPs.•Experimentalϕ fractions for PAHs and PCBs fitted with typical remote/rural curves.•The Koa-fom model is a poor descriptor for the G/P partitioning of the analytes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Measurements of PM2.5 concentrations in five major Greek cities over a two-year period using calibrated low-cost sensor-based particulate matter (PM) monitors (Purple Air PA-II) were combined with ...local meteorological parameters, synoptic patterns and air mass residence time models to investigate the factors controlling PM2.5 spatiotemporal variability over continental Greece. Fourteen sensors nodes in Athens, Patras, Ioannina, Xanthi, and Thermi (in the Metropolitan Area of Thessaloniki) were selected out of more than 100 of a countrywide network for detailed analysis. The cities have populations ranging from 65k to 3M inhabitants and cover different latitudes along the South-North axis. High correlations between the daily average PM2.5 levels were observed among all sites, indicating strong intra- and inter-city covariance of concentrations, both in cold and warm periods. Higher PM2.5 concentrations in all cities during the cold period were primarily associated with low temperatures and stagnant anticyclonic conditions, favoring the entrapment of residential heating emissions from biomass burning. Anticyclonic conditions were also connected to an increased frequency of PM2.5 episodes, exceeding the updated daily guideline value (15 μg m−3) of the World Health Organization (WHO). During the warm period, nearly uniform PM2.5 levels were encountered across continental Greece, independently of their population size. This uniformity strongly suggests the importance of long-range transport and regional secondary aerosol formation for PM2.5 during this period. Peak concentrations were associated mainly with regional northern air flows over Greece and the Balkan Peninsula. The use of the measurements from dense air quality sensor networks, provided that a robust calibration protocol and continuous data quality assurance practices are followed, appears to be an efficient tool to gain insights on the levels and variability of PM2.5 concentrations, underpinning the characterization of spatial and seasonal particularities and supporting real-time public information and warning.
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•A network of low cost PM2.5 sensors in five Greek continental cities was used.•An intra- and inter-city positive covariance of PM2.5 concentrations was detected.•Anticyclonic conditions caused daily PM2.5 episodes across Greece in cold seasons.•Uniform PM2.5 levels (8.0–10.8 μg m−3) were measured by all sensors in warm seasons.•CWT and PSCF models indicated regional PM2.5 contributions in warm seasons.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The 24-h PM2.5 samples were collected at the site of East China University of Science and Technology (ECUST) in Shanghai from 2011 to 2012, representing winter, spring, summer and autumn, ...respectively. And PM2.5 and its chemical components including organic carbon (OC), elemental carbon (EC), water-soluble organic carbon (WSOC), humic-like substance carbon (HULIS-C) and water-soluble ions were analyzed. The results suggested that the average PM2.5 concentrations were (70.35±43.75) μg/m3, (69.76±38.67) μg/m3, (51.26±28.25) μg/m3 and (82.37±48.70) μg/m3 in winter, spring, summer and autumn, respectively. Secondary inorganic ions (sulfate, nitrate and ammonium) were the dominant pollutants of PM2.5 in the four seasons. Total carbon (TC) was an important component explaining above 15% of PM2.5. OC/EC ratios were all above 2 ranging from 4.31 to 6.35; particularly in winter it reached the highest 6.35 which demonstrated that secondary organic carbon (SOC) should be a significant composition of PM2.5. The SOC calculated based on the OC/EC ratio method had stronger correlation with WSOC in summer and autumn (summer: R2=0.73 and autumn: R2=0.75). The HULIS-C and SOC most significantly correlated in autumn (R2=0.83). The data showed that PM2.5 atmospheric aerosols were more acidic in autumn and the concentrations of PM2.5 and its chemical components were much higher. Factor analysis (FA), cluster analysis of air mass back trajectories, potential source contribution function (PSCF) model and concentration weighted trajectory (CWT) model were used to investigate the transport pathways and identify potential source areas of PM2.5 in different seasons. FA identified various sources of PM2.5: secondary aerosol reactions, the aged sea salts and road dusts. The results of cluster analysis, PSCF model and CWT model demonstrated that the local sources in the Yangtze River Delta Region (YRDR) made significant contributions to PM2.5. During winter and autumn long-time transport from the Circum-Bohai-Sea Region (CBSR) and northwestern China including the Inner Mongol had adverse effects.
•The characteristics of carbonaceous components especially HULIS-C were analyzed.•The levels of OC, EC and WSOC in acidic and alkaline aerosols were compared.•FA results explained the sources of PM2.5 in Shanghai.•PSCF model identified the likely source areas affecting air quality in Shanghai.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Dust storms are a common phenomenon in arid and semi-arid regions in West Asia, which has led to high levels of PM10 in local and remote area. The Yazd city in Iran with a high PM10 level located ...downstream of dust sources in the Middle East and Central Asia. In this study, based on meteorological and PM10 monitoring data, backward trajectory modeling of air parcels related to dust events at Yazd station was performed using the HYSPLIT model in 2012–2019. The trajectory cluster analysis was used to identify the main dust transport pathways and wind systems. Three methods of Cross-referencing Backward Trajectory (CBT), Potential Source Contribution Function (PSCF) and Concentration Weighted Trajectory (CWT) were used to identify the most critical dust sources. Multi-Criteria Decision Making (MCDM) methods were also used to integrate the results. Nine dust sources affecting central Iran were determined, and six criteria from different aspects were considered. To prioritize the dust sources affecting central Iran from four new MCDM methods, including WASPAS, EDAS, ARAS and TOPSIS were used. The results showed that the Levar wind system (51%), the Shamal wind system (32%) and the Prefrontal wind system (18%) were the most important wind systems to cause dust events in central Iran. The MCDM approach to identify dust sources also showed that Dasht-e-Kavir in central Iran was the most critical dust source. The results also showed that in hot seasons (spring and summer), local and Central Asia dust sources and cold seasons (autumn and winter), Middle East dust sources have the greatest impact on dust events in central Iran. Also, a comparison of common receptor-based methods for identifying dust sources showed that CBT, CWT and PSCF were the most appropriate methods for identifying dust sources, respectively.
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•The characteristics of dust events in central Iran during 2012–2019 were examined.•The main corridors of dust transport were identified using cluster analysis.•The performance of receptor-based models for dust source detection was evaluated.•To integrate the results of receptor-based models, MCDM methods were used.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
In this paper, two backward air mass trajectory-based models (Potential Source Contribution Function PSCF and Concentration Weighted Trajectory CWT) were combined, aiming to identify sources and ...factors defining the load of PM in the city of Limassol (Cyprus). The study also focused on the determination of atmospheric pathways enriching the aerosol phase of four carcinogenic Polycyclic Aromatic Hydrocarbons (PAHs): Benzo(a)pyrene (BaP), Benzo(a)anthracene (BaA), Benzo(b)fluoranthene (BbF) and Benzo(k)fluoranthene (BkF), in PM10 mass. The analysis was performed on a 0.5°·0.5° resolution grid for the two-year period 2011–2012. During cold seasons, regional airflows triggered the accumulation of locally produced PM2.5, while the impact of dust plumes originated from deserts in NE Africa, Syria and the Middle East, was apparent on PM2.5 and principally on PMCOARSE levels. On the contrary, within warm seasons, weaker dust PMCOARSE contributions were detected in Limassol from areas in Egypt and Libya. Raised particulate-phase PAH concentrations in Limassol were clearly related to air parcels reaching Cyprus via continental areas. The use of outdated technologies for heating and transportation in Turkey and Syria, and fire events in central Turkey, are possible sources of exogenous PAHs throughout cold and warm period respectively. The influence of clean marine air masses dropped the levels of PAH compounds in all seasons.
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•During cold seasons, regional airflows entrapped locally produced PM2.5 in Cyprus.•The impact of dust from NE Africa and the Middle East was apparent on PMCOARSE.•Continental airflows increased particulate-phase PAHs in Limassol.•Clean marine air masses dropped the levels of PAH compounds in all seasons.•Possible PAH intrusions from fire events in Central Turkey were indicated.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The concentrations of particulate mercury (PHg) and other trace elements in PM2.5 and PM10 in the atmosphere were measured at the summit of Mount Tai during the time period of 15 June – 11 August ...2015. The average PHg concentrations were 83.33 ± 119.1 pg/m3 for PM2.5 and 174.92 ± 210.5 pg/m3 for PM10. Average concentrations for other trace elements, including Al, Ca, Fe, K, Mg, Na, Pb, As, Se, Cu, Cd, Cr, V, Mo, Co, Ag, Ba, Mn, Zn and Ni ranged from 0.06 ng/m3 (Ag) to 354.33 ng/m3 (Ca) in PM2.5 and 0.11 ng/m3 (Co) to 592.66 ng/m3 (Ca) in PM10. The average concentrations of PHg were higher than those at other domestic mountain sites and cities in other counties, lower than those at domestic city sites. Other trace elements showed concentrations lower than those at the domestic mountain sites. Due possibly to increased control of emissions and the proportion of new energy, the PHg and trace element concentrations decreased, but the PHg showed concentrations higher than those at the Mountain sites, this showed that the reasons was not only severely affected by anthropogenic emissions, but also associated with other sources. The concentration changed trend of the main trace elements indicated that PHg, trace elements and particle matters present positive correlation and fine particulate matter has a greater surface area which was conductive to adsorption of Hg and trace elements to particles. On June 19, June 27 and July 6, according to the peak of mercury and trace elements, we can predict the potential sources of these three days. The results of principal component analysis (PCA) suggested that, crustal dust, coal combustion, and vehicle emissions were the main emission sources of PHg and other trace elements in Mount Tai. The 24-h backward trajectories and potential source contribution function (PSCF) analysis revealed that air masses arriving at Mount Tai were mainly affected by Shandong province. Mount Tai was subjected to five main airflow trajectories. Clusters 1, 2, 3, and 5 represented four pathways for local and regional sources and cluster 4 originated long-distance transportation. Central Shandong was the main source regions of PHg, Pb, Se, As, Cu and Cd. Southeastern and northwestern Shandong province and northern Jiangsu province were the most polluted source regions of Mn, Zn, and Ni. The crustal elements Fe and Ca had similar distributions of potential source regions, suggested by the highest PSCF values in southeastern Shandong and northern Jiangsu.
•PHg characteristic was first determined in the high coal-fired area near Mount Tai.•The distributions of elements in PM2.5 and PM10 at high altitude were compared.•The transport pathways and potential source regions of risk elements were analyzed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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