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To elucidate the characteristics of VOCs chemical components during heavy pollution episodes, hourly online VOCs data derived from 11 heavy pollution events in Tianjin from 2019 to 2020 were employed. The positive matrix factorization ï¼PMFï¼ and conditional bivariate probability function ï¼CBPFï¼ were employed to analyze the sources of VOCs during heavy pollution episodes. The results indicated that the average VOCs volume fraction during these episodes was recorded at 35.7×10-9. Furthermore, it was observed that during the winter emergency response period, there was a discernible increase in the volume fraction of VOCs when compared to that during the autumn season. Specifically, there was a notable upswing of 48% in the olefins category, whereas alkanes registered a 4% increase. Additionally, the VOCs component structure changed significantly during the heavy pollution episodes. During the orange warning period, the proportion of alkanes increased by 36%, and the proportion of acetylene decreased by 32%. During the yellow warning period, the proportion of alkanes increased by 14%, and the proportion of acetylene decreased by 5%. During the emergency response period, motor vehicle emission sources, natural gas evaporative sources, and solvent use sources were the main contributors of VOCs in environmental receptors, contributing 17.5%, 15.4%, and 15.2%, respectively. Compared with that during the period antecedent to the emergency response, the contribution of vehicle emission sources and diesel volatile sources to VOCs in environmental receptors decreased by 2.0% to 5.5% and 2.1% to 6.6%, respectively, and the contribution of solvent use sources decreased by 0.2% to 2.4% during the yellow warning period. During the orange warning period, the contribution of motor vehicle emission sources was reduced by 0.1% to 8.3%, and the contribution of solvent use sources was reduced by 0.5% to 6.2%.
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Contaminantes Atmosféricos , Monitoreo del Ambiente , Emisiones de Vehículos , Compuestos Orgánicos Volátiles , China , Monitoreo del Ambiente/métodos , Contaminantes Atmosféricos/análisis , Compuestos Orgánicos Volátiles/análisis , Emisiones de Vehículos/análisis , Contaminación del Aire/análisis , Estaciones del AñoRESUMEN
The contents of eight carbonaceous subfractions were determined by simultaneously collecting PM2.5 samples from four sites in different functional areas of Tianjin in 2021. The results showed that the organic carbon (OC) concentration was 3.7 µg·m-3 to 4.4 µg·m-3, and the elemental carbon (EC) concentration was 1.6 µg·m-3 to 1.7 µg·m-3, with the highest OC concentration in the central urban area. There was no significant difference in EC concentration. The concentration of PM2.5 showed the distribution characteristics of the surrounding city>central city>peripheral area. The OC/EC minimum ratio method was used to estimate the concentrations of secondary organic carbon (SOC) in PM2.5, and the results showed that the secondary pollution was more prominent in the surrounding city, with SOC accounting for 48.8%. The correlation between carbon subcomponents in each functional area showed the characteristics of the peripheral area>central area>surrounding area, all showing the strongest correlation between EC1 and OC2 and EC1 and OC4. By including the carbon component concentration into the positive definite matrix factorization (PMF) model for source apportionment, the results showed that road dust sources(9.7%-23.5%), coal-combustion sources (10.2%-13.3%), diesel vehicle exhaust (12.6%-20.2%)and gasoline vehicle exhaust (18.9%-38.8%)were the main sources of carbon components in PM2.5 in Tianjin. The pollution sources of carbon components were different in different functional areas, with the central city and peripheral areas mainly affected by gasoline vehicle exhaust; the surrounding city was more prominently affected by the secondary pollution and diesel vehicle exhaust.
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Ambient air pollution is a dominant determinant of health. The health effects and economic losses due to air pollution are very important for decision-making. Since the implementation of the "Air Pollution Prevention and Control Action Plan" and "blue sky defense war" policies, the air quality of Tianjin has changed significantly. Here, the health effects and economic losses attributable to ambient air pollution in Tianjin from 2013 to 2020 wereestimated. For the particulate matter which has complex components, we assessed the inhalation health risks of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in PM2.5. The variation in the concentration of the main components of PM2.5 was also analyzed. The results showed that improved air quality had positive health benefits. The health benefits from SO2 were the highest among the six air pollutants, and 3786 deaths were avoided in 2020 compared to in 2013 due to lower SO2 concentration. The economic losses caused by air pollutants ranged from several billion to ten billion yuan. Among the six air pollutants, particulate matter and ozone had higher health losses in recent years. The health risks of heavy metals and PAHs in PM2.5 showed a decreasing trend. However, Cr(â ¥), As, Cd, and Ni in PM2.5in the winter of 2020 still had respiratorysystem carcinogenic risk, whereas there was no health risk of PAHs in PM2.5in 2019-2020. The concentrations of main components of PM2.5 have decreased significantly. In the future, the reduction of health loss caused by air pollution depends on synergy governance of particulate matter and ozone and further research on health effects.
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Contaminantes Atmosféricos , Contaminación del Aire , Metales Pesados , Ozono , Hidrocarburos Policíclicos Aromáticos , Monitoreo del Ambiente/métodos , Contaminación del Aire/efectos adversos , Contaminación del Aire/prevención & control , Contaminación del Aire/análisis , Contaminantes Atmosféricos/efectos adversos , Contaminantes Atmosféricos/análisis , Material Particulado/efectos adversos , Material Particulado/análisis , Metales Pesados/análisis , Hidrocarburos Policíclicos Aromáticos/análisis , ChinaRESUMEN
Based on the hourly O3 concentration data of 337 prefectural-level divisions and simultaneous surface meteorological data in China, we applied empirical orthogonal function (EOF) analysis to analyze the main spatial patterns, variation trends, and main meteorological driving factors of O3 concentration in China from March to August in 2019-2021. In this study, a KZ (Kolmogorov-Zurbenko) filter was used to decompose the time series of O3 concentration and simultaneous meteorological factors into corresponding short-term, seasonal, and long-term components in 31 provincial capitals.Then, the stepwise regression was used to establish the relationship between O3 and meteorological factors. Ultimately, the long-term component of O3 concentration after "meteorological adjustment" was reconstructed. The results indicated that the first spatial patterns of O3 concentration showed a convergent change, that is, the volatility of O3 concentration was weakened in the high-value region of variability and enhanced in the low-value region.Before and after the meteorological adjustment, the variation trend of O3 concentration in different cities was different to some extent. The adjusted curve was "flatter" in most cities. Among them, Fuzhou, Haikou, Changsha, Taiyuan, Harbin, and Urumqi were greatly affected by emissions. Shijiazhuang, Jinan, and Guangzhou were greatly affected by meteorological conditions. Beijing, Tianjin, Changchun, and Kunming were greatly affected by emissions and meteorological conditions.
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Fugitive dust poses an important contribution to urban air particulate matter in China. To further improve the level of dust pollution prevention and control, the emission and contribution characteristics of urban fugitive dust were summarized; the main causes of dust pollution were analyzed; and the key links, key indicators, and main measures for prevention and control were clarified, so as to further improve the concept of "accurate dust control." Among all types of fugitive dust sources, road dust and construction dust were the main emission and contribution sources, among which road dust was more prominent. Production activities, vehicle disturbances, and wind erosion were the main dust-generating links of various dust sources. Silt loading was taken as the key control index for road dust prevention and control, whereas silt loading and bare soil (or material) areas were taken as the key control index for construction and other dust sources. Around the key indicators, three main ways to control the road dust and six main measures to control the construction and other dust sources were defined. In addition, some suggestions on the necessary supporting measures for dust control were put forward, so as to provide a comprehensive and beneficial reference for the practical application of dust control in Chinese cities.
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Contaminantes Atmosféricos , Contaminación del Aire , Contaminantes Atmosféricos/análisis , Contaminación del Aire/análisis , Contaminación del Aire/prevención & control , China , Polvo/análisis , Monitoreo del Ambiente , Material Particulado/análisisRESUMEN
In order to understand the applicability of various new receptor models, four receptor models, including the positive matrix factorization/multilinear engine 2-species ratio (PMF/ME2-SR), partial target transformation-positive matrix factorization (PTT-PMF), positive matrix factorization (PMF), and chemical mass balance (CMB), were used to analyze and verify the atmospheric fine particulate matter (PM2.5) data of a typical city in northern China. It was found that coal combustion (25%-26%), dust (19%-21%), secondary nitrate (17%-19%), secondary sulfate (16%), vehicle emissions (13%-15%), biomass burning (4%-7%), and steel (1%-2%) had a contribution to PM2.5. By comparing the source profiles and source contributions obtained by different models and calculating the coefficient of differences (CD) and average absolute error (AAE) of each source, we found that although the source apportionment results of the four models were in good agreement (the average CD value was between 0.6 and 0.7), there were still slight differences in the identification of some components in each source. Compared with the traditional model (PMF), the PMF/ME2-SR model can better identify sources with similar source profile characteristics, which is due to the component ratios of sources that are introduced. For example, the CD and AAE of dust sources were 15% and 54% lower than those of PMF, respectively. The PTT-PMF model takes the measured primary source profiles and virtual secondary source profiles as a constraint target, and the calculated CD and AAE of secondary sulfate were 0.25 and 17%, respectively, which were 55% and 23% lower than PMF. The PTT-PMF model can obtain more "pure" secondary sources and identify the pollution sources that are not identified by other models, which has more advantages in the refined identification of sources.
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Contaminantes Atmosféricos , Contaminantes Atmosféricos/análisis , Polvo/análisis , Monitoreo del Ambiente , Material Particulado/análisis , Emisiones de Vehículos/análisisRESUMEN
Treatment of industrial atmospheric emission sources is an important way to improve air quality, but accurate pollution control remains still an urgent challenge. Taking Xiqing District of Tianjin as an example, based on the second national pollution source census, this study carried out a quantitative evaluation of the pollutant emission performance of industrial enterprises and explored the significance, feasibility, and challenges facing emission performance evaluation. The results show that the emission performance of various industries in Xiqing District vary greatly. The pollutant emission performance level is closely related to an industry's own attributes, development scale, and management level. On the whole, the emission performance level of industries with high production process emission coefficients and a high proportion of small and medium-sized enterprises (such as furniture manufacturing, the metal products industry, ferrous metal smelting, and the rolling processing industry) is worse, while the emission performance of high-end industries represented by computer communication and other electronic equipment manufacturing and automobile manufacturing is generally better. The emission performance of different enterprises in the same industry also varies greatly. For example, the 11 enterprises with the worst performance in the metal machinery manufacturing industry only contributed 0.06% of industrial output yet their PM emission contribution reached 8.50%. The 19 worst-performing enterprises in the rubber and plastic industry contributed 4.76% of industrial output yet their VOCs emissions accounted for 43.59% of the total. At the same time, this study presents an emissions reduction plan according to the relevant technical guidelines of the Ministry of Ecology and Environment. Based on this, the cost of emissions reduction could be cut by as much as 90% when the pollutant emissions reductions of the same scale are reduced. The gap in the pollutant emissions performance of various industries and enterprises, the incongruity between economic benefits and environmental costs, and the important guiding role of emission performance evaluation for emissions reductions demonstrate the necessity of performance evaluation. Overall, this research shows that pollutant emission performance evaluation can effectively support macro-industrial structure adjustment and the environmental governance of meso-micro industrial enterprises, providing an important reference for pollution control interventions.
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Aerosol hygroscopic growth factors[g(RH)] are key for evaluating aerosol light extinction and direct radiative forcing. The hygroscopic tandem differential mobility analyzer (HTDMA) was utilized to measure the size-resolved gm(RH) under different polluted conditions in winter in Tianjin. Furthermore, based on the size distribution of aerosol water-soluble ions, the gκ(RH) across a wide size range (60 nm to 9.8 µm) was estimated using the κ-Köhler theory, which provides a basis for the estimation of aerosol optical parameters and direct radiative forcing under ambient conditions. Under clean conditions, ultrafine particles (<100 nm) were more hygroscopic and gm(RH=80%) was higher than 1.30 due to the active photolysis reaction. However, under severely polluted conditions, the proportion of water-soluble ions in aerosols increased with the increasing size; gm(RH) increased with particle size, where gm(RH=80%) and gm(RH=85%) for 300 nm particles was 1.39 and 1.46, respectively. For a wide size range (60 nm to 9.8 µm), the aerosols in the accumulation mode were more hygroscopic and aerosols in the Aitken mode were less hygroscopic, with coarse mode aerosols being the least hygroscopic. During the polluted period, the particulate size notably increased, and the mass fraction of NO3- and SO42- in the accumulation mode aerosols was significantly higher than during the clean period. Accordingly, the hygroscopicity of accumulation mode aerosols was strongly enhanced during the polluted period[gκ(RH)=1.3-1.4] and aerosols in the 0.18-3.1 µm size range all had a strong hygroscopicity. On polluted days, the synergistic effect of the increase in particle size, water-soluble ions, and aerosol hygroscopicity results in the considerable deterioration of visibility.
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From June to August 2018, a 1-hr resolution concentration dataset of ozone and its gaseous precursors (volatile organic compounds(VOCs) and NOx), and meteorological parameters were synchronously monitored by online instruments of the Nankai University Air Quality Research Supersite. The relationships and variation characteristics between ozone and its precursors were analyzed. According to the photochemical age, the initial concentrations of VOCs were calculated, and the photochemical loss of the concentration of VOCs during the daytime (06:00-24:00) was corrected. The initial and directly monitored concentrations of VOCs were incorporated into the PMF model for source apportionment. The results indicated that the mean concentration of O3 in Tianjin in summer was (41.3±25.7)×10-9, while that of VOCs was (13.9±12.3)×10-9. The average concentration of alkane (7.0±6.8)×10-9 was clearly higher than that of other VOC species. The species with high concentrations of alkanes were propane and ethane, accounting for 47% of the total alkane concentration. The average ozone formation potential (OFP) in summer was 52.1×10-9, and the OFP value of alkene was the highest and its contribution reached 57%. During the daytime, alkene loss accounted for 75% of the total VOC loss. The major sources of VOCs that were calculated based on the initial concentration data were the chemical industry and solvent usage (25%), automobile exhaust (22%), combustion source (19%), LPG/NG (19%), and gasoline volatilization (15%), respectively. Compared with the apportionment results based on directly monitored concentrations, the contribution of the chemical industry and solvent usage decreased by 4%, while automobile exhaust decreased by 5%. By combining the results of PMF apportionment and the OFP model to analyze the relative contributions of emission sources to ozone formation, and we found that the highest contribution source of ozone was the chemical industry and solvent usage (26%) in summer. Compared with the analysis results based on the directly monitored concentrations, the OFP values of the chemical industry and solvent usage decreased by 7%, while that of NG/LPG apparently decreased by 13%.
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Aerosol acidity is closely related to particle properties and the explosive growth of secondary particles. Aerosol pH is difficult to measure directly but can be estimated indirectly by thermodynamic equilibrium modeling. ISORROPIA-â ¡ is one of the most commonly used thermodynamic models and includes different modes (forward and reverse) and aerosol states (stable and metastable). Studies have shown that the calculated pH results vary with the selected mode and phase state. In addition to the selection of modes and phases, there are also other factors that influence the modeling results. In order to explore the appropriate mode and phase selection of ISORROPIA-â ¡ as well as the factors influencing the model results under the air pollution characteristics of typical Chinese cities, the simulation results of different modes and aerosol states were analyzed by using online hourly data for Tianjin. The results showed that the pH calculations using the forward mode and metastable state were satisfactory at a higher RH. With increased temperature, the pH, aerosol water content, and concentration proportion in the aerosol phase of semi-volatile components all decreased. RH affected aerosol pH by influencing the aerosol water content and concentration of semi-volatile components. An increased cation concentration led to an increased pH and NH3 concentration but a decreased HNO3 concentration, whereas an increased anion concentration had the opposite effect. Ca2+, SO42-, NO3-, and NH4+ had a great influence on pH. Compared with SO42-, NO3- had less effect on pH. Sensitive areas exist in the influence of NH4+ on pH, and a high NH4+ concentration did not cause a continuous pH increase. This study can improve the understanding of aerosol pH simulation using ISORROPIA-â ¡, and provides reference for research on the pH-related secondary generation mechanism, semi-volatile component gas-particle distribution, and pollution control measures.
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Contaminantes Atmosféricos , Material Particulado , Aerosoles/análisis , Contaminantes Atmosféricos/análisis , Ciudades , Monitoreo del Ambiente , Concentración de Iones de Hidrógeno , Material Particulado/análisisRESUMEN
As flue gas desulfurization (FGD) was one of the most important purification processes of coal-fired boilers, we selected four boilers, which were equipped with wet limestone, furnace calcium injection, ammonia-based, and double-alkali FGDs, to research the influence of FGDs on the flue particulate matter (PM). The flue PM before and after the FGD were sampled using laboratory resuspension and dilution tunnel sampling methods, respectively, and the PM was analyzed for its chemical composition (i.e., ions, elements, and carbon). The results showed that the types of desulfurizers could influence the composition of the flue PM. After passing through the wet limestone, ammonia-based, and double-alkali FGDs, the proportion of Ca, NH4+, and Na in PM2.5 increased from 5.1% to 24.8%, from 0.8% to 7.3%, and from 0.9% to 1.7%, respectively. The influence of wet and dry FGDs on the flue PM were different. The fraction of ions in the PM emitted from the wet FGD were higher than those from the dry FGD. The proportion of SO42- in the flue PM2.5 increased from 2.0% and 6.7% to 9.6% and 11.9% using the wet limestone and ammonia-based FGDs, respectively, and Cl- increased from 0.4% and 1.2% to 3.8% and 5.2%. In addition, the amount of heavy metals (e.g., Cr, Pb, Cu, Ti, and Mn) in PM2.5 declined after the wet FGDs. The PM2.5 emitted from the dry FGD boiler was richer in crustal elements, such as Al, Si, and Fe, than that from the wet FGDs. The wet FGDs also effected the carbonaceous components of the flue PM. After passing through the wet limestone and ammonia-based FGDs, the proportion of elemental carbon in the flue PM2.5 decreased from 6.1% to 0.9% and from 3.6% to 0.7% respectively, but the organic carbon content did not decrease.
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Tianjin is located in the Beijing-Tianjin-Hebei region. Recently, particulate matter pollution has received wide attention; therefore, studying the chemical composition and sources of particulate matter in the atmospheric environment is of great significance. To clarify the mixed state and possible sources of particulate matter in the summer ambient air in Tianjin, this study used single particle aerosol mass spectrometer (SPAMS) to collect 209887 samples. Particle size and complete spectrometry information were collected in July 2017. A total of 369 particle classes were obtained with respect to clustering particles with similarities in mass spectrometry characteristics using ART-2a. Then, according to the similarity in the chemical composition (mass spectrometry) of the categories, 19 particulate matter categories were artificially merged: K-EC (0.20%), K-EC-Sec (0.18%), K-NO3-PO3(12.00%), K-NO3-SiO3(2.98%), K-Sec (0.16%), EC (39.60%), EC-Sec (3.46%), EC-HM-Sec (3.93%), HEC (1.49%), HEC-Sec (1.38%), OC-Amine-Sec (3.58%), OC-Sec (0.36%), OCEC-Sec (0.71%), Dust-HEC (21.35%), Dust-Sec (0.72%), Cl-EC-NO3(1.22%), Na-Cl-NO3(3.20%), HM-Sec (2.58%), and PAH-Sec (0.90%). The obtained particle classes can be attributed to different sources of aerosol particles and different transmission and reaction processes. According to comprehensive analysis, the collected particle contribution sources were found to mainly include motor vehicle emission sources, biomass combustion sources, process sources, dust sources, and secondary processes. Among them, K-EC, EC, HEC, and Dust-HEC particles were mainly from direct emissions of primary sources. K-Sec, OC-Amine-Sec, OC-Sec, OCEC-Sec, Na-Cl-NO3, and PAH-Sec particles mainly undergo different degrees of aging or mixed with secondary components.
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Based on the source apportionment by positive matrix factorization (PMF) model, we analyze the main sources and characteristics of aerosol fine particulate matter (PM2.5) during winter and summer in the Hohhot-Baotou-Ordos region, China. We found that organic (19.9%-44.6%) and crustal compositions (9.7%-46.2%) accounted for a large proportion of aerosol PM2.5 according to the results of mass closure. The results of source apportionment showed that the contribution of sources rank as:secondary inorganic aerosol (26.7%) > coal (26.1%) > motor vehicle (19.1%) > dust (18.1%) during winter, and as:secondary inorganic aerosol (26.7%) > dust (22.3%) > coal (16.6%) > vehicle exhaust (15.1%) > SOC (8.7%) during summer. Findings suggest that the contribution of sources with secondary inorganic aerosol were the largest sources both in winter and summer, and that the Hohhot-Baotou-Ordos region was also affected by coal during the winter and dust during the summer. Corresponding to the source apportionment, analysis of typical heavy pollution episodes in winter and summer showed that the pollution sources during the winter were mainly secondary inorganic aerosol and coal, whereas they were mainly secondary inorganic aerosol during the summer.
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The analysis of the sources of atmospheric particulate pollution can provide scientific support for the prevention and control of air pollution. Most particulate matter (PM) source analysis studies are based on the chemical composition of PM. In addition, particle size characteristics are also one of the important properties of PM. The accuracy of analytical results can be improved by analyzing the particle size characteristics of chemical components. In this study we aim to to solve the problem of insufficient utilization of component particle size information by using a the three-dimensional multi-particle size factor analysis model (ABB), where the particle size distribution of marked components is regarded as the constraint limit, and a multi-particle size source analytical model (SDABB) based on the characteristics of the components particle size distribution is constructed. The sensitivity of the SDABB model to the collinearity of the source spectrum and the similarity of the particle size distribution of the source contributions are investigated by evaluating the model through the simulation of the data set. The results showed that the ABB model was sensitive to the collinearity of the source spectrum and to the similarity of the particle size distribution of the source contributions. When particle size distribution rules were incorporated into the SDABB model, the effects of the two scenarios were significantly improved, that is, the SDABB model was able to better analyze collinear source spectrum and was insensitive to the similarity of the contribution particle size distribution.
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There are few analyses on the components of particulate matter emitted from waste incineration plants. In past studies, analyses of particle size distribution characteristics of the components were mainly targeted at particles with larger particle sizes. An electrical low pressure impactor (ELPI) was used in this study to collect the particulate matter emitted from a waste incineration plant, and the elements and carbonaceous components of these samples were analyzed. The particle size characteristics of organic carbon (OC), elemental carbon (EC), and heavy metal elements in 14 particle size segments were analyzed and composition profiles of elements and carbonaceous components of PM1, PM2.5, and PM10 from the waste incineration plant were established to provide a reference for refined source apportionment research. The results showed that the main components of the waste incineration plant included Al, Si, S, Ca, Cr, Fe, OC, EC, etc. OC and Ca were dominating components, and mass fractions of these components in the PM2.5 profile were 10.15% and 12.37%, respectively. The contents of heavy metals were ranked as Cr > Pb > Zn > Mn > Cu > Cd > Ni, and the mass fractions of Cr and Pb in PM2.5 amounted to 1.83% and 0.74%, respectively. OC in the range of 2.39-3.99 and 6.68-9.91 µm accounted for 15.02% and 20.45% of the total OC content, respectively, and the content of OC in fine particles was higher than that in coarse particles. The content of EC in fine particles was much higher than that in coarse particles, and it accounted for 14.8% in the 0.382-0.613 µm particle size. Heavy metal elements such as Cr, Mn, Ni, Cu, Zn, Cd, and Pb were mainly concentrated in the fine particles.
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PM2.5 samples were collected at three sites in Yantai City during all four seasons of 2016-2017, and the mass concentration and chemical composition characteristics were analyzed. The CMB model was used to calculate source apportionment of ambient PM2.5, and the backward trajectory cluster and PSCF were used to analyze the transport flow and potential source regions. The results showed that the average mass concentrations of PM2.5 in winter, spring, summer, and autumn in Yantai were (89.45±56.80), (76.78±28.44), (32.65±17.92) and (57.32±24.60) µg·m-3, respectively. The PM2.5 concentration showed a significant seasonal variation (P<0.01). The contribution of PM2.5 sources was as follows:secondary nitrate (20.3%) > crustal dust (15.7%) > vehicle exhaust (14.9%) > coal combustion (13.8%) > secondary sulphate (12.8%) > SOC (6.1%) > cement dust (5.5%) > sea salt source (2.9%). It can be seen that the predominant sources were secondary sources, crustal dust, vehicle exhaust, and coal combustion. The sources of nitrate in spring and of crustal dust were important contributors. The contribution of sulfate in summer was prominent, and the proportion of coal combustion was high in autumn and winter. Yantai City's airflow transport and potential source regions also showed significant seasonal changes:winters were mainly affected by short-distance transport in Yantai City; summers were mainly affected by the coastal of eastern Yantai City and local areas; springs and autumns were mainly affected by regional transmission in the northeast and in the eastern coastal areas of Shandong Province and by local sources in Yantai City.
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Recently, a new method combining positive matrix factorization (PMF) and heavy metal health risk (HMHR) assessment was proposed to apportion sources of heavy metals in ambient particulate matter and the associated heavy metal cancer health risk (HMCR), which has been applied to data collected in Yangzhou, China. The annual average concentrations of six measured heavy metals were Pb (64.4 ng·m-3), followed by Cr (25.24 ng·m-3), As (6.36 ng·m-3), Ni (5.36 ng·m-3), Cd (3.34 ng·m-3), and Co (1.21 ng·m-3). The results showed that the major sources of PM2.5 were secondary sources (37.7%), followed by coal combustion (19.4%), resuspended dust (17.5%), vehicle emissions (16.9%), construction dust (5.2%), and industrial emissions (3.4%). As was primarily emitted from coal combustion, vehicle emissions, and resuspended dust. Co originated from industry emissions. Pb was mainly emitted from coal combustion. Ni and Cd were from industrial emissions. The major sources that contributed to HMCR were resuspended dust, coal combustion, vehicle emissions, industry emissions, and construction dust. The high contributions of resuspended dust and coal to HMCR were likely due to the high heavy metals concentrations in coal and the resuspended dust profile as well as high emissions of these sources.
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As an important megacity of the Beijing-Tianjin-Hebei air pollution transmission channel and the Bohai Sea Economic Zone, Tianjin is influenced by air pollution in recent years, thus research on the fine particulate matter in Tianjin is of vital value. In this study, single particle aerosol mass spectrometry (SPAMS) was used to measure data of Jinnan District in Tianjin during August 2017, to describe the chemical features of fine particles in summer ambient air and estimate the potential pollution sources of fine particles. By using the ART-2a clustering method, 12 classes of PM were acquired, such as elemental carbon particles, Fe-NO3 particles, Na-K particles, and metal particles. After monitoring the size distribution and diurnal variation of fine particles, it was concluded that the ratio of EC particles decreased as the size increased, whereas dust particles and Fe-NO3 particles showed the opposite trend; three types of EC particles varied differently in a day according to the photochemical reaction; and the ratio of Fe-NO3 particles was elevated in the daytime because of industrial production during that period. Backward trajectories of daily airflow at the measured spot were also calculated. When the monitoring site was affected by the air mass from the southwest, coal-burning particles may have contributed more; whereas, when the air mass from the southeast occurred more frequently, biomass burning and sea salt particles possibly contributed more.
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Samples were collected from six different organized industrial odor sources in Tianjin Binhai New Area, including pharmacy, paint spraying, oil refinery, petrochemical, resin synthesis, and rubber manufacturing. Chemical analysis was conducted to identify and quantify major odorous volatile organic compounds (VOCs) and subsequently, establish source profiles. Olfactory measurement was used to characterize the sensory stimulating intensity of odorous VOCs and express them as odor concentration. The TVOC mass concentrations of these six sources were between 10.9 and 225.3 mg/m3. Toluene was the most abundant component in profiles of both pharmacy and spraying sources with abundances of 79.1 and 94%, respectively. The petrochemical source was characterized by high levels of o,m,p-xylene (more than 60%). Sulfides were identified almost solely in the rubber manufacturing source. High levels of styrene were found in the resin synthesis source, whereas the oil refinery source was dominated by halocarbons. The odor concentration of oil refinery, spraying, rubber manufacturing, and resin synthesis all exceeded the Chinese emission standards for odor pollutants (GB14554-93) during the study period. Based on industrial processing analysis and factor analysis, toluene, m,p-xylene, carbon disulfide, toluene, three types of halogenated hydrocarbons, and styrene were the markers of pharmacy, petrochemical, rubber manufacturing, spraying, oil refinery, and resin synthesis sources, respectively. Production process and factor analysis methods were used to identify the markers of each odor source, which were based on instrument analysis and olfactory measurement.
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Contaminantes Atmosféricos/análisis , Industria Química , Monitoreo del Ambiente , Odorantes/análisis , Compuestos Orgánicos Volátiles/análisis , China , CiudadesRESUMEN
Considering the lack of numbers and updates of particulate matter (PM) source profiles, which show PM emitted from the Chinese iron and steel industry, a dilution tunnel system was used to sample PM discharged from the three main processes (sintering, puddling, and steelmaking) of an iron and steel company in Wuhan, China. Six source profiles for fine and coarse PM were established, and their characteristics were researched. The main conclusions were as follows:â For the sintering source profiles, SO42-, Al, and NH4+ were the dominant components, with mass fractions of 22.2%, 4.5%, and 3.5% in the PM2.5 profile and 36.0%, 5.2%, and 2.7% in the PM10 profile, respectively. Fe was abundant in puddling source profiles, the mass fractions of which reached 28.3% and 24.5% for PM2.5 profile and PM10 profile, respectively. As for steelmaking, the main components were Ca and Fe. â¡ For the element component features, S was enriched in the sintering source profiles. Metal elements, such as Pb and Cr, were more abundant in the puddling source profiles. ⢠The coefficients of divergence for profiles were calculated. Profiles of different sizes for the same processes showed similarities, whereas the diversities between the sintering and the other two profiles were higher. 4 Compared with research in other regions, similarities and differences were found and analyzed.