[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.

[Assessment of Emission Reduction Effect of Major Air Pollution Control Measures on PM2.5 Concentrations During 13th Five-Year Period in Tianjin].

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.

[Characteristics and Meteorological Factors of PM2.5-O3 Compound Pollution in Tianjin].

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%.

[Characteristics and Sources of PM2.5-O3 Compound Pollution in Tianjin].

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.