[Analysis of Change Trend and Influencing Factors of PM2.5-O3 Pollution in Tianjin from 2013 to 2020].

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.

[Characteristics and Driving Factors of O3 Pollution During 13th Five-Year Period in Tianjin].

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.

[Extinction Characteristics of Aerosols and the Contribution of Pollution Sources to Light Extinction During Three Heavy Pollution Episodes in the Winter of 2020 in Tianjin].

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.

[Size Distribution of Aerosol Hygroscopic Growth Factors in Winter in Tianjin].

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.

[Characterization of Two Heavy Pollution Episodes in Tianjin in 2020].

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.

[Characteristics of Secondary Organic Particles and the Potential Formation of SOA from VOCs During Wintertime Heavy Pollution Episodes in Tianjin].

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.

[Characterization and Source Apportionment of PM2.5 Based on the Online Observation in Tianjin].

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.

[Vertical Distribution and Transport of PM2.5 During Heavy Pollution Events in the Jing-Jin-Ji Region].

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.