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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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The change trend, relationship, and influencing factors of PM2.5 and O3 concentrations were analyzed by using a Kolmogorov-Zurbenko (KZ) filter coupled with stepwise multiple linear regression analysis and the spatiotemporal resolution monitoring data of PM2.5 and O3 and meteorological data observed in Tianjin from 2013 to 2020. The results showed that a significant decreasing trend of PM2.5 concentrations by 50.0% was observed from 2013 to 2020, whereas an increasing trend for O3 concentrations by 25.8% was observed from 2013 to 2020. Compared with that in 2013 to 2017, the monthly difference in PM2.5 concentrations gradually narrowed from 2018 to 2020, whereas the concentration of O3 had increased significantly since April, and the occurrence time of O3 pollution was advanced. The correlation coefficient patterns of O3 and PM2.5 showed obvious seasonal distribution characteristics. The correlation coefficients were negatively correlated in winter and positively correlated in the summer, and the correlation coefficients in summer were generally higher than those in other seasons. The correlation coefficients between O3 and PM2.5 in different seasons were positively proportional to the fitting slope. The ratios of the fitting slope to correlation coefficients showed an increasing trend, which might reflect that the inhibitory effect of PM2.5 on O3 formation in the PM2.5-O3 interaction mechanism might have been weakened due to the impact of emission reduction. A significant decreasing trend was observed for the long-term trend components of the PM2.5 concentration time series; emission reduction played a leading role, and meteorological factors contributed -3 to 6 µg·m-3. The changes in the relationship between the PM2.5/CO ratio versus NO2/SO2 from negative to positive were observed from 2013-2017 to 2018-2020 in Tianjin, which could indicate the enhanced contribution potential of nitrogen oxides to the main secondary component formation of PM2.5 under the current emission reduction scenarios, and the main secondary components of PM2.5in Tianjin gradually changed from sulfate to nitrate. An overall upward trend was observed for the long-term trend components of the O3 concentration time series from 2013 to 2020, and the contribution of precursor emissions to the long-term component of O3 increased from 2013 to 2018 and began to decrease after 2019. The contribution of meteorological factors to the long-term component of O3 presented an obvious stage change, showing a downward trend from 2013 to 2016 and an upward trend from 2016 to 2020. The O3 concentration presented a non-linear relationship with NO2 during the period of intense atmospheric photochemical processes (11:00-16:00) in summer. Compared with that in 2013-2015, the fitting curve of O3 and NO2 showed an obvious offset to the low value of NO2 from 2016 to 2020, which reflected that the NOx emission reduction in this period achieved certain results. Compared with that in 2018, the fitting curve of O3 and NO2 moved downward from 2019 to 2020, which may reflect that NOx and VOCs emission reduction had a non-negligible effect on the O3 decline at this stage.

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The spatial distribution, accumulation features, and driving factors of O3 pollution were analyzed using spatial autocorrelation and hotspot analysis and the STIRPAT model based on the high spatiotemporal resolution online monitoring data from 2016 to 2020 in Tianjin. The results showed that the variation characteristics of O3 concentration in Tianjin from 2016 to 2020 had the trend of pollution occurring in advance and the scope of the pollution expanding. The distribution of O3 pollution showed significant aggregation from June to October. High-high value clustering areas included six urban districts, Beichen District, Jinnan District, and Jinghai District. O3 concentration formed high value hot spots in the southwest and low value cold spots in the northeast. Meteorological factors such as temperature, breeze percentage, and sunshine duration, as well as social factors such as NOx emission, VOCs emission, and motor vehicle ownership had significant effects on O3 concentration. The regression fitting effect of the integrated drive STIRPAT model was better than that of the single meteorological factor or social factor models. In order to promote scientific and efficient prevention and control of ozone pollution during the 14th Five-Year Plan period, meteorological conditions require attention; under the goal of "peaking carbon dioxide emissions and achieving carbon neutrality," it is necessary for Tianjin to further improve the emission performance of steel, petrochemicals, thermal power, building materials, and other industries, Additionally, clean upgrading, transformation, and green development should be guided for enterprises to reduce VOCs and NOx emissions. At same time, the increase in fuel vehicle numbers should be controlled, and new energy vehicles should be vigorously promoted to reduce vehicle emissions.

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The emission reduction effect of major air pollution control measures on PM2.5 concentrations was assessed using air quality simulations based on the calculation data of emission reductions from different air pollution control measures and the high spatiotemporal resolution online monitoring data of PM2.5 during the 13th Five-Year Period in Tianjin. The results showed that the total emission reductions of SO2, NOx, VOCs, and PM2.5 from 2015 to 2020 were 4.77×104, 6.20×104, 5.37×104, and 3.53×104 t, respectively. SO2 emission reduction was mainly due to the prevention of process pollution, loose coal combustion, and thermal power. NOx emission reduction was mainly due to the prevention of process pollution, thermal power, and steel industry. VOCs emission reduction was mainly due to prevention of process pollution. PM2.5 emission reduction was mainly due to the prevention of process pollution, loose coal combustion, and the steel industry. The concentrations, pollution days, and heavy pollution days of PM2.5 decreased significantly from 2015 to 2020 by 31.4%, 51.2%, and 60.0% compared to those in 2015, respectively. The concentrations and pollution days of PM2.5 decreased slowly in the later stage (from 2018 to 2020)as compared with those in the early stage (from 2015 to 2017), and the days of heavy pollution remained for approximately 10 days. The results of air quality simulations showed that meteorological conditions contributed one-third to the reduction in PM2.5 concentrations, and the emission reductions of major air pollution control measures contributed two-thirds to the reduction in PM2.5 concentrations. For all air pollution control measures from 2015 to 2020, PM2.5 concentrations were reduced by the prevention of process pollution, loose coal combustion, the steel industry, and thermal power by 2.66, 2.18, 1.70, and 0.51 µg·m-3, respectively, accounting for 18.3%, 15.0%, 11.7%, and 3.5% of PM2.5 concentration reductions. In order to promote the continuous improvement in PM2.5 concentrations during the 14th Five-Year Plan period, under the total coal consumption control and the goal of "peaking carbon dioxide emissions and achieving carbon neutrality," Tianjin should continue to optimize and adjust the coal structure and further promote the coal consumption to the power industry with an advanced pollution control level. At the same time, it is necessary to further improve the emission performance of industrial sources in the whole process, taking environmental capacity as the constraint; design the technical route for industrial optimization, adjustment, transformation, and upgrading; and optimize the allocation of environmental capacity resources. Additionally, the orderly development model for key industries with limited environmental capacity should be proposed, and clean upgrading, transformation, and green development should be guided for enterprises.

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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(Ⅵ), 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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In order to explore the pollution characteristics and health risks of heavy metals in PM2.5 in Tianjin, heavy metal samples (Pb, Cd, Cr, As, Zn, Mn, Co, Ni, Cu, and V) in PM2.5 were analyzed from November 2020 to March 2021 using the Xact-625 heavy metal online analyzer. The spatial and temporal distribution characteristics were analyzed using the HYSPLIT model, and the health risks of heavy metals were analyzed using the US EPA risk assessment model. The results indicated that the average total concentration of the 10 heavy metal elements was (261.56±241.74) ng·m-3, among which the concentrations of Cr ï¼»converted Cr(Ⅵ)ï¼½ and As were higher than the annual average limit of the National Ambient Air Quality Standard (GB 3095-2012). According to the back trajectory results, the medium-distance transmissions from northwest areas (NO.1), the long-distance transmissions from northwest areas (NO.2), the transmissions from southwest areas (NO.3), and the transmissions from northeast areas (NO.4) were the major sources in Tianjin City. The heavy metals of different air masses presented different pollution characteristics and health risks; the concentration of PM2.5, the total concentration of the 10 heavy metal elements, and the total carcinogenic risk of the five heavy metal elements of the NO.3 air mass were the highest, whereas the total non-carcinogenic risk of the 10 heavy metal elements of the NO.2 air mass was higher than that of the other two air mass. The health risk assessment showed that Mn posed non-carcinogenic risks to children, and Cr and As presented carcinogenic risk. Meanwhile, Cd of the NO.3 air masses also presented carcinogenic risk.

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The characteristics, pollutant concentration distribution, and key meteorological factors of PM2.5-O3 compound pollution in Tianjin were analyzed based on the high-resolution online monitoring data of PM2.5, O3,and meteorological data observed in Tianjin from 2013 to 2019. Total PM2.5-O3 compound pollution was 94 days and showed a decreasing trend by year; a significant decreasing trend of PM2.5-O3 compound pollution days were observed in the early stage, with a decline rate of 52.2% from 2013 to 2015. By contrast, in the later period from 2016 to 2019, a fluctuating increasing trend of PM2.5-O3 compound pollution days of 16.7% was observed. PM2.5-O3 compound pollution days mainly occurred from March to September each year with substantial variation by year, mainly occurring in June to August from 2013 to 2016 and in April and September from 2017 to 2019. The peak value of ρ(O3) (301-326 µg·m-3) appeared when ρ(PM2.5) ranged from 75 µg·m-3 to 85 µg·m-3. PM2.5-O3 compound pollution days accounted for 34.4% of total O3 pollution events in Tianjin, which showed a decreasing trend by year. The peak O3 concentration and average O3 concentration during PM2.5-O3 compound pollution were higher than those during simplex O3 pollution, and the number of days with PM2.5 and O3 as the primary pollutant decreased and increased in compound pollution days by year, respectively. The weather situation of PM2.5-O3 compound pollution was categorized into five weather types, namely low pressure, weak high pressure, rear of high pressure, front of cold front, and equalized pressure. The low pressure, front of cold front, and weak high pressure were observed most frequently, accounting for 92.5% of the total weather situation. The occurrence of PM2.5-O3 compound pollution was most probable when the dominant wind direction was the southwest and south, the average wind speed was less than 2 m·s-1, the temperature was between 20-35℃, and the humidity was between 40%-60%.

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The characteristics and sources of PM2.5-O3 compound pollution were analyzed based on the high-resolution online monitoring data of PM2.5, O3 and volatile organic compounds(VOCs) observed in Tianjin from 2017 to 2019. The results showed that total PM2.5-O3 compound pollution was 34 days, which only appeared between March and September and slightly increased by year. The peak value of ρ(O3)(301-326 µg·m-3) appeared when ρ(PM2.5) ranged from 75 µg·m-3 to 85 µg·m-3. During PM2.5-O3 compound pollution, the average ρ(VOCs) was 72.59 µg·m-3, and the chemical compositions of VOCs were alkanes, aromatics, alkenes, and alkynes, accounting for 61.51%, 20.38%, 11.54%, and 6.57% of VOCs concentration on average, respectively. The concentration of the top 20 species of VOCs increased, among which the proportion of alkane species such as ethane, n-butane, isobutane, and isopentane increased; the proportion of alkenes and alkynes decreased slightly; and the proportion of benzene and 1,2,3-trimethylbenzene of aromatic hydrocarbons increased slightly. The ozone formation potential(OFP) contribution of alkanes, alkenes, aromatics, and alkynes were 19.68%, 39.99%, 38.08%, and 2.25%, respectively; the contributions of alkanes, alkenes, and aromatics to secondary organic aerosol(SOA) formation potential were 7.94%, 2.17%, and 89.89%, respectively. Compared with that of non-compound pollution, the contribution of alkanes and aromatics to OFP increased 13.8% and 4.3%, and that to SOA formation potential increased 2.3% and 0.2%, respectively. The contribution of alkenes to OFP and SOA formation potential decreased 9.4% and 15.6%, respectively, and the contribution of alkynes to OFP increased 7.7% in compound pollution. The contributions of main species such as 1-pentene, n-butane, methyl cyclopentane, isopentane, 1,2,3-trimethylene, propane, toluene, acetylene, o-xylene, ethylbenzene, m-ethyltoluene, and m/p-xylene to OFP increased, and that of isoprene to OFP decreased. The contribution of benzene, 1,2,3-trimethylbenzene, toluene, and o-xylene to the potential formation of SOA increased during compound pollution. Positive matrix factorization was applied to estimate the contributions of sources to OFP and SOA formation potential in compound pollution, solvent usage, automobile exhaust, petrochemical industrial emission, natural source, liquefied petroleum gas(LPG) evaporation, combustion source, gasoline evaporation, and other industrial process sources were identified as major sources of OFP and SOA formation potential; the contributions of each source to OFP were 21.9%, 16.9%, 16.7%, 12.4%, 8.3%, 7.7%, 2.9%, and 13.2%, respectively, and to SOA formation potentials were 46.8%, 14.4%, 7.1%, 11.9%, 5.9%, 6.6%, 1.6%, and 5.7%, respectively. Solvent usage, automobile exhaust, and petrochemical industrial emissions were main sources for PM2.5-O3 compound pollution.

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This study examined high-resolution online monitoring data from January to February 2020 to study the extinction characteristics and sources of heavy pollution episodes during winter in Tianjin. Heavy pollution episodes occurred during this period from January 16 to 18 (episode Ⅰ), from January 24 to 26 (episode Ⅱ), and from February 9 to 10 (episode Ⅲ). The results showed that the concentrations of PM2.5 during the three heavy pollution episodes were (229±52), (219±48), and (161±25) µg·m-3, respectively, with NO3-, SO42-, NH4+, OC, EC, Cl-, and K+ comprising the main species. The values of the scattering coefficient(Bsp550) during the three heavy pollution episodes were (1055.65±250.17), (1054.26±263.22), and (704.44±109.89) Mm-1, respectively, while the absorption coefficient(Bap550) showed much lower values of (52.96±13.15), (39.72±8.21), and (34.50±8.53) Mm-1, respectively. PM2.5 played a major role in atmospheric extinction during heavy pollution episodes. Specifically, nitrate (38.9%-48.8%), sulfate (31.1%-40.7%), and OM (9.9%-21.8%) were the most important extinction components. The contribution of PM2.5 chemical components to the extinction coefficient varied significantly between the three episodes; the percentage of nitrate was higher in episode Ⅰ than in the other two episodes; in episode Ⅱ, the percentage of OM was highest, significantly affected by the discharge of fireworks; in episode Ⅲ, as traffic decreased but coal combustion emissions remained constant, the contribution of nitrate to the extinction coefficient decreased, while that of sulfate increased. Source apportionment of extinction coefficients was performed using PMF model combined with IMPROVE. Various pollution sources contributed to the extinction coefficient, namely: secondary sources (37.1%-42.0%), industrial and coal combustion (22.9%-24.2%), vehicle exhaust (23.9%-27.2%), crustal dust (5.0%-6.4%), and fireworks and biomass burning (3.9%-6.2%). Compared with episode Ⅰ, the contribution of fireworks and biomass burning increased significantly during episode Ⅱ, while the contribution of vehicle exhaust decreased significantly during episode Ⅲ. The contribution of industrial and coal combustion was similar during all three heavy pollution episodes. According to backward analysis, the small-scale and short-distance transmissions from Hebei provinces, as well as the medium and short-distance transmissions from central Inner Mongolia, were the major sources during heavy pollution episodes in the winter in Tianjin City.

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To further study the effect of volatile organic compounds (VOCs) on ozone pollution, the characteristics and sources of VOCs at different ozone (O3) concentration levels were analyzed, using high-resolution online monitoring data obtained from Tianjin in the summer of 2019. Results showed that VOCs concentrations were 32.94, 38.10, 42.41, and 47.12 µg ·m-3, when the O3 concentration levels were categorized as excellent, good, light pollution, and moderate pollution, respectively. VOCs were composed of alkanes, alkenes, alkynes and aromatics, which accounted for 61.72%-63.36%, 14.96%-15.51%, 2.73%-4.13%, and 18.53%-19.10%, respectively, of VOCs concentrations at different O3 concentration levels. Among them, the proportion of alkanes was slightly higher when O3 concentration was categorized as good or light pollution, alkenes and alkynes accounted for the highest proportion when O3 concentration was excellent, and the proportion of aromatics was highest during periods of moderate pollution. The main VOCs species were propane, ethane, ethylene, toluent, n-butane, isopentane, m/p-xylene, propylene, acetylene, n-hexane, isobutene, benzene, n-pentane, isoprene, and 1,2,3-trimethylbenzene. The concentration percentage of isopentane, n-pentane, benzene, ethylene, propylene, n-butane, and isobutane increased gradually as O3 concentration increased. Significant increases in isoprene and 1,2,3-trimethylbenzene were observed during periods of light and moderate pollution. Alkenes and aromatics had higher ozone formation potential (OFP), and the contribution of alkenes to OFP declined as the O3 level rose, whereas that of aromatics increased. Ethylene, propylene, m/p-xylene, 1,2,3-trimethylbenzene, toluene, isoprene, trans-2-butene, and cis-2-pentene were the key species for O3 generation, and the contribution ratio of 1,2,3-trimethylbenzene, isoprene, propylene, and ethylene to OFP increased significantly during light or moderate O3 pollution. Positive matrix factorization was applied to estimate the source contributions of VOCs. Automobile exhaust, solvent usage, liquefied petroleum gas (LPG)/gasoline evaporation, combustion, petrochemical industrial emissions, natural sources, and other industrial emissions were identified as major sources of VOCs in summer. As O3 concentration level rose, the contribution percentage of automobile exhaust, LPG/gasoline evaporation, petrochemical industrial emissions, and natural sources increased gradually, whereas the contribution of combustion and other industrial emissions decreased overall. The contribution of solvent usage was lower when O3 levels indicated light or moderate pollution than when it was good.

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To study the formation and approaches to controlling secondary nitrate in PM2.5, the ionic compositions of PM2.5, pH of aerosols, variations in NH3-NH4+ and HNO3-NO3- concentrations, and the joint NH3/HNO3 sensitivity regime map of ammonium nitrate were investigated based on high-resolution online monitoring data for an urban site in central Tianjin from 2018 to 2019. The results showed that the average concentration of PM2.5 was 58µg·m-3, and the main ionic compositions of PM2.5 were nitrate (NO3-), ammonium (NH4+), sulfate (SO42-), Cl-, and K+ with corresponding mass percentages of 18.4%, 11.6%, 10.3%, 3.3%, and 2.6%, respectively. Concentrations of PM2.5 and the main components were relatively high during the heating season and relatively low during the non-heating season. The aerosols were weakly acidity with an average pH of 5.21; pH was higher in spring and winter and lower in summer and autumn, and diurnal variations pH were lower in the morning (00:00-08:00) and slightly higher at other times. The concentrations of NH3(g) (gas NH3) and HNO3(g) (gas HNO3) were 16.7µg·m-3and 1.2µg·m-3, respectively. The concentrations of NH3(g) were relatively higher from April to September and lower from October to February of the following year. HNO3(g) concentrations did not show any clear monthly pattern. Except during the summer, NH3(g) concentrations were higher in the morning and evening, and HNO3(g) concentrations were higher during the day. No clear linear relationships were observed between the concentrations of NH3(g) and NH4+ nor the concentrations of HNO3(g) and NO3- at different pH levels. Higher concentrations of NO3- and NH4+ were observed in the morning and evening, while no linear relationships were observed between the pH and concentrations of NH3(g)-NH4+ and HNO3(g)-NO3-. The joint NH3/HNO3 sensitivity regime map showed that most of the points were located in the HNO3 sensitive region with some in the NH3 & HNO3 sensitive region. In spring, autumn, and winter, most of the points were located in the HNO3 sensitive region while in summer, a significant quantity of the points were located in the NH3 & HNO3 sensitive region. Therefore, the precursors of HNO3 (such as NOx) should be controlled in the spring, autumn, and winter, and attention should be given to the control of the precursors of HNO3 (NOx) and NH3 in the summer to effectively control nitrate and ammonium aerosols in PM2.5 in Tianjin.

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The characterization and source apportionment of atmospheric volatile organic compounds (VOCs) in Tianjin in 2019 were investigated based on high-resolution online monitoring data observed at an urban site in Tianjin. The results showed that the average annual concentration of VOCs was 48.9 µg·m-3, and seasonal concentrations followed with winter (66.9 µg·m-3) > autumn (47.9 µg·m-3) > summer (42.0 µg·m-3) > spring (34.6 µg·m-3). The chemical compositions of the VOCs were alkanes, aromatics, alkenes, and alkynes, which accounted for 65.0%, 17.4%, 14.6%, and 3.0% of the VOCs concentrations on average, respectively. The proportion of alkanes, aromatics, and alkynes was the highest in autumn, summer, and winter, respectively, while a higher alkenes proportion was observed in summer and winter. The ozone formation potential contribution of alkanes, alkenes, aromatics, and alkynes in spring and summer was 16.9%, 48.6%, 33.5%, and 1.0%, respectively, and the species with higher contributions were ethene, propylene, m,p-xylene, 1,2,3-trimethylbenzene, toluene, isoprene, trans-2-butene, cis-2-pentene, o-xylene, and m-ethyltoluene. During autumn and winter, the aromatics contributed as much as 91.5% to the secondary organic aerosol (SOA) formation potential, and o-xylene, toluene, m,p-xylene, ethylbenzene, o-ethyltoluene, and benzene were the main contributing species. Positive matrix factorization was applied to estimate VOCs source contributions, and automobile exhaust, liquefied petroleum gas/natural gas (LPG/NG) and gasoline evaporation, solvent usage, petrochemical industrial emissions, combustion, and natural sources were identified as major sources of VOCs in spring and summer, accounting for 29.2%, 19.9%, 16.4%, 10.3%, 7.3%, and 6.6%, respectively. While in autumn and winter, the contributions of LPG/NG and gasoline evaporation, automobile exhaust, combustion, solvent usage, and petrochemical industrial emissions were 32.4%, 21.9%, 18.5%, 13.3%, and 8.4%, respectively. Compared to the source contributions in spring and summer, a significant increase was observed for LPG/NG and combustion emission of 62.8% and 153.4%, respectively, and other sources decreased by 18.4%-25.0% in autumn and winter. Source composition spectrums showed that the petrochemical industry and solvent usage were the main emission sources of alkenes and aromatics in spring and summer, and combustion and solvent usage were the main emission sources of aromatics in autumn and winter. Thus, focus should be played on the petrochemical industry and solvent usage in spring and summer and on combustion and solvent usage in autumn and winter to further prevent and control ozone and SOA in Tianjin.

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High-resolution online monitoring data from January to February in 2020 was used to study the characterization of two heavy pollution episodes in Tianjin in 2020; the heavy pollution episode that lasted from January 16 to 18, 2020 (referred to as episode Ⅰ) and that from February 9 to 10, 2020 (referred to as episode Ⅱ) were analyzed. The results showed that two heavy pollution episodes were influenced by regional transportation in the early stage and local adverse meteorological conditions in the later stage. During these episodes, the average wind speed was low, the average relative humidity was close to 70%, and relative humidity approached the saturated, the boundary layer heights were below 300 m, and the horizontal and vertical diffusion conditions were poor. Compared to episode Ⅰ, the concentration of pollutants decreased during episode Ⅱ, especially for the concentration of NO2. During the episode Ⅱ, the concentrations of PM2.5 and CO were higher in the north of Tianjin. The chemical component concentrations and their mass ratios to PM2.5 changed significantly in both episodes; the concentrations of secondary inorganic ions (NO3-, SO42-, and NH4+), elemental carbon (EC) and Ca2+were higher in episode Ⅰ, the concentrations of organic carbon (OC) and Cl- slightly increased in episode Ⅱ; and the concentrations of K+were higher in episode Ⅱ. Compared to episode Ⅰ, because of the increase in the combustion sources and significant reductions in the number of vehicles, the mass ratios of SO42-, OC, and K+ to PM2.5 increased while the mass ratios of NO3- and EC to PM2.5 decreased in episode Ⅱ; the mass ratios of NH4+ and Cl- to PM2.5 were relatively higher due to the continuity of the industrial production processes; the mass ratios of Ca2+ to PM2.5 were lower in two heavy pollution episodes because construction activities were halted. Source apportionment of PM2.5 was performed using the positive matrix factorization (PMF) model. In episode Ⅰ, the major sources of PM2.5 in Tianjin were secondary sources, industrial and coal combustion, vehicle exhaust, crustal dust, fireworks and biomass burning, with contributions of 53.8%, 20.2%, 18.6%, 6.3%, and 1.1%, respectively. In episode Ⅱ, the same sources were identified in the PMF analysis with contributions of 48.3%, 28.2%, 8.7%, 2.6%, and 12.2%, respectively. Compared to episode Ⅰ, the contributions of industrial and coal combustion, fireworks and biomass burning increased, and the contributions of secondary sources, vehicle exhaust, and crustal dust decreased in episode Ⅱ; contributions of vehicle exhaust and crustal dust decreased by 53.2% and 58.7%, respectively.

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The characteristics of secondary organic reactions were studied based on supersite monitoring data from January to March, 2019, in Tianjin. During heavy pollution episodes, SOC (secondary organic carbon) accounted for between 3.1% and 3.8% of PM2.5, and the growth rate of SOC was obviously higher than that of PM2.5, thus indicating that secondary organic reactions had a considerable effect on PM2.5. The growth rate of VOCs (volatile organic compounds) was lower than that of PM2.5, which was probably due to the fact that VOCs were consumed as precursors to secondary particles. The ratio of ethane to acetylene was higher than 2.0 during heavy pollution episodes indicating that air masses were old, and the ratio was lower than clean air days showing that the reaction activities were higher than before. During the heavy pollution episodes, the potential formation of SOA (secondary organic aerosol) from VOCs ranged from 0.49 to 1.21 µg·m-3. Among the species, aromatic hydrocarbons contributed the most, whereby the highest contribution exceeded 90%, and their growth rates were also the highest; hence, aromatic hydrocarbons were the VOCs species that had the greatest effect on SOA.

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To study the characterization and source apportionment of PM2.5 in Tianjin, based on high-resolution online monitoring data from 2017 to 2019, the concentrations and its chemical compositions and sources of PM2.5 were analyzed. The results showed that the average concentration of PM2.5 was 61 µg ·m-3. The primary chemical compositions of PM2.5 were nitrate, organic carbon (OC), ammonium, sulfate, elemental carbon (EC), and Cl- and their corresponding mass percentages to PM2.5 were 17.7%, 12.6%, 11.5%, 10.7%, 3.4%, and 3.1%, respectively. From 2017 to 2019, the concentrations of PM2.5 and its main chemical compositions exhibited a decreasing trend; the mass ratios of NO3- and NH4+ to PM2.5 exhibited an increasing trend, while the mass ratios of SO42-, OC, and EC to PM2.5 exhibited a decreasing trend; further, the mass ratio of Cl- exhibited a slight increasing trend. The concentrations of K+, Ca2+, and Na+ and their mass percentages to PM2.5 increased. The concentrations of PM2.5 and its primary components were relatively higher during heating season, and relatively lower during non-heating season. High values of SOR and NOR indicated that the secondary transformation of nitrate and sulfate played an important role during summer and autumn, which resulted in higher mass percentages of secondary inorganic ions (NO3-, SO42-, and NH4+) to PM2.5 during summer and autumn. When the PM2.5 concentrations were at excellent levels, the mass ratios of the secondary inorganic ions to PM2.5 were relatively lower, the mass ratios of OC, Ca2+, and Na+ to PM2.5 were relatively higher, and secondary organic carbon (SOC) was high. When the PM2.5 concentrations were between light pollution to heavy pollution levels, as the pollution levels increased, the mass percentages of secondary inorganic ions, OC, EC, and Cl-, and other components (K+, Ca2+, and Na+) showed a significant increasing trend, relatively stable level, slightly increasing trend, and decreasing trend, respectively. When PM2.5 concentrations were between moderate pollution to heavy pollution levels, the influence of vehicle emission increased significantly. The source apportionment of PM2.5 were analyzed using the positive matrix factorization model. The major sources of PM2.5 in Tianjin were secondary source, vehicle exhaust, industrial and coal combustion emissions, and crustal dust. From 2017 to 2019, the contribution of vehicle exhaust increased, and the contribution of secondary source and crustal dust showed a slight increasing trend, while the contribution of industrial and coal combustion emissions decreased. For Tianjin, vehicle exhaust and industrial and coal combustion emissions were the primary sources of PM2.5. The adjustment of industrial and energy structure and management and control of vehicle exhaust are the main directions for air pollution control in Tianjin.

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RESUMEN

Based on monitoring data collected at the supersite of Tianjin in 2017, seven typical heavy pollution episodes were investigated. The concentrations of air pollutants and secondary inorganic transformation products were analyzed to study the secondary inorganic pollution characteristics during the heavy pollution episodes. Compared to clean weather, concentrations of NO3- and SO42- during the heavy pollution episodes increased at rapid growth rates. These rates were obviously higher than the rate for PM2.5 increases, which indicates that the secondary inorganic reactions had an important influence on PM2.5 pollution during the episodes. The concentrations of PM2.5 and SO2 during the episodes in the latter half of the year were lower than those in the first half of the year probably because a substantial amount of coal use had been controlled. During the heavy pollution episodes, the NO2/SO2 values were 1.5 to 19.6, with higher values in the latter half of the year than the first half of the year suggestive of a greater influence from mobile sources. During most episodes, NO3- concentrations were higher than SO42- concentrations, and SOR values were higher than NOR values, which shows that the secondary transformation of nitrate and sulfate both played important roles during the heavy pollution episodes. When SO2 concentrations decreased significantly, SO42- concentrations did not decrease obviously, thus indicating that besides the secondary inorganic reactions, other factors also had a large impact on the generation of sulfate.

RESUMEN

Based on vehicle-borne tethered balloon measurements, the vertical distribution of particulate matter (PM) concentrations were observed in Gaocun in the Wuqing District of Tianjin from December 17 to 19, 2016, during a period of heavy pollution. Using observational data, the transport flux of PM2.5 in the Jing-Jin-Ji region was calculated. The results showed that the mixed layer was low at only 200 m during the heavy pollution period. The vertical distribution of PM2.5 concentrations was closely associated with the heights of mixed layer whereby, below the mixed layer, PM2.5 concentrations were higher. Vertical variation was insignificant, forming a district pollution layer. Above the mixed layer, PM2.5 concentrations rapidly decreased and stabilized at low levels. During the observation period, higher concentrations of PM were found with particle sizes of less than 1.0 µm, and lower concentrations were observed for particle sizes larger than 2.2 µm. The size profiles of PM tallied with relative humidity and the height of the mixed layer. The size distribution was wider during periods of high humidity and with a lower mixed layer height. The greatest PM2.5 transport flux was from the southwest, accounting for 63.3% of the total flux; the highest fluxes occurred at the heights of 46-156 m and 156-296 m. The dominant transport direction was southwest below 300 m, while the dominant transport direction was dispersed over 300 m.