Benzothiazolium-based NIR AIE photosensitizers with type I and II ROS generation for efficient mitochondria-targeted photodynamic therapy.

In this work, a near-infrared emissive photosensitizer of 3,3-dimethyl-N,N-diphenyl-2-(thiophen-2-yl)-3H-indol-6-amine functionlized benzothiazolium (DPITT) was developed. DPITT exhibited aggregation-induced emission effect and potent type I and II reactive oxygen species generation capacities after white light irradiation. Taking advantage of the cationic feature, DPITT penetrated the cell membrane and selectively accumulated in the mitochondria in living cells. Upon white light irradiation, the photosensitized DPITT was able to induce mitochondrial dysfunction, leading to cell death. Photosensitized DPITT was further applied to disrupt the multicellular tumour spheroids, demonstrating its potential application in inhibiting hypoxic solid tumours.

[Spatial Distribution Characteristics of PM2.5 and O3 in Beijing-Tianjin-Hebei Region Based on Time Series Decomposition].

Notably, clear spatial differences occur in the distribution of air pollution among cities in the Beijing-Tianjin-Hebei (BTH) Region. Clarifying the concentration distribution of PM2.5 and O3 at different time scales is helpful to formulate scientific and effective pollution prevention and control measures. Here, the concentrations of PM2.5 and O3 were decomposed using a seasonal-trend decomposition procedure based on the loess (STL) method; their long-term, seasonal, and short-term components were obtained; and their temporal and spatial distribution characteristics were studied. The results showed that the decrease in PM2.5 concentration in the BTH Region from 2017 to 2021 was higher than that of O3. There was a positive correlation between PM2.5 and O3 concentrations in spring and summer and a negative correlation in autumn and winter. The short-term component and seasonal component had the greatest contribution to PM2.5 and O3 concentrations, respectively. There were two principal components in the seasonal and short-term components of PM2.5 and the long-term and short-term components of O3, corresponding to the central and southern part of Hebei Province and the northern part of the BTH Region. Sub-regional distribution of PM2.5 and O3 in the BTH Region at different time scales were found. Compared with that in the original series, the long-term component could better reflect the evolution trend of PM2.5 and O3 concentrations, and the standard deviation (SD) of the seasonal component and short-term component could be used to measure the fluctuation in PM2.5 and O3 concentrations in various cities. The SD of the seasonal and short-term components of the PM2.5 concentration in every city in front of Taihang Mountain was higher, and the SD of the short-term component of the O3 concentration in Tangshan was the highest.

[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.

[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.

[Spatio-temporal Distribution Characteristics of Secondary Aerosol in Beijing-Tianjin-Hebei Urban Agglomeration in Winter].

The secondary component is an important factor causing PM2.5 pollution in the Beijing-Tianjin-Hebei urban agglomeration in winter. In this study, the CO tracer method was used to estimate the secondary PM2.5 concentration of the Beijing-Tianjin-Hebei urban agglomeration in the winter of 2017-2021. The temporal and spatial distribution characteristics were analyzed, and the influencing factors of regional secondary PM2.5 were discussed. The results showed that the decreasing trend of PM2.5 concentration in the Beijing-Tianjin-Hebei Region in the winter of 2017-2021 was obvious, and the cities with the largest decline were located in the central and southern part of Hebei Province, mainly contributed by primary PM2.5. There was a good correlation between secondary PM2.5 and PM2.5 in all cities of the Beijing-Tianjin-Hebei urban agglomeration, and the proportion of secondary PM2.5 in Beijing and Tianjin was significantly higher than that in other cities. With the aggravation of pollution degree, the mass concentration of primary PM2.5 and secondary PM2.5 increased in varying degrees, and the proportion of secondary PM2.5 increased significantly. Compared with the direct measurement results, the estimated value obtained by this method was lower as a whole. The selection of appropriate primary aerosol reference value was the key to improving this method and estimating the secondary PM2.5 concentration.

[Observation Analyses of Aerosol Size Distribution Properties from February to March, 2019 in Tianjin Urban Area].

The aerosol size distribution is an important physical parameter reflecting the source, formation process, and pollution characteristics of aerosol particles. In order to study the properties of aerosol number concentration and size distributions in the Tianjin urban area,the aerosol number concentration and size distributions ranging from 10-600 nm were detected using a scanning mobility particle sizer (SMPS) during February and March, 2019. The results showed that in the Tianjin urban area, the aerosol number concentration,surface area concentration. and volume concentration in the size range of 10-600 nm were 22188.22 cm-3, 1581.08 µm2·cm-3, and 70.76 µm3·cm-3,respectively, in late winter and early spring. The aerosol number concentration,surface area concentration, and volume concentration spectrum were all unimodally distributed,and the peak value sizes were 109.40, 269.00, and 429.40 nm. The number concentrations of the nucleation mode (10-20 nm),Aitken mode (20-100 nm), and accumulation mode (100-600 nm) aerosols accounted for 1.40%, 52.44%, and 46.16% of the total number concentration. The diurnal variation in aerosol number concentration showed three peaks (06:00-08:00, 12:00-14:00, and 18:00-20:00) on work days and two peaks (07:00-08:00 and 19:00-21:00) on weekends. The peaks appeared 1-2 hours later on weekends,and the increment of aerosol number concentration was attributed to vehicle exhaust emissions. Meteorological factors had a significant influence on the aerosol size distribution in Tianjin; aerosol number concentration values were high in east and southwest wind. On non-precipitation days,the aerosol number size distribution moved to larger size ranges with the increment of relative humidity (RH); as the RH increased from <20% to 50%-60%,the size peak increased from 50 nm to 131 nm. The precipitation removed 100-200 nm aerosol particles discernibly,which resulted in the size peak decreasing to 98 nm.

[Exploring Formation of Ozone in Typical Cities in Beijing-Tianjin-Hebei Region Using Process Analysis].

As the problem of O3 pollution in the Beijing-Tianjin-Hebei region becomes increasingly prominent, it is of great significance to explore and analyze the ozone variation characteristics and causes of the pollution process in the Beijing-Tianjin-Hebei region for regional air pollution prevention and control. The observations in this study showed that high O3 concentration in spring and summer of the Beijing-Tianjin-Hebei region was higher in the south and lower in the north. The high O3 concentration in Beijing, Tianjin, and Shijiazhuang was often accompanied by the influence of southern wind. Based on WRF-Chem model simulation and process analysis technology, the variation characteristics and causes of O3 in The Beijing-Tianjin-Hebei region in 2019 were deeply analyzed. The diurnal variations in chemical processes, vertical mixing, and transportation in typical cities showed distinct seasonal variations. In summer afternoons, chemical processes were the main source of O3 concentration increase in each city. Vertical mixing resulted in an increase in O3 concentration in Tianjin and Shijiazhuang but a decrease in Beijing. Tianjin and Shijiazhuang had a net output, whereas Beijing had a net inflow. In the polluted O3 process, the chemical process dominated the afternoon O3 concentration increasing in Beijing and Shijiazhuang, whereas vertical mixing dominated in Tianjin. In addition, there was a net input of O3 in Beijing and Shijiazhuang and a net output of O3 in Tianjin. In the clean O3 process, vertical mixing dominated the increase in O3 concentration in Beijing and Shijiazhuang in the afternoon, whereas in Tianjin it was chemical processes. At the same time, the net output of O3 existed in all three cities.

[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%.

[Analysis of Change and Driving Factors of PM2.5 Mass Concentration in Tianjin from 2000 to 2020].

Based on real-time tracking data, PM2.5 mass concentration, and meteorological observations of the Tianjin Meteorological Bureau and the Ecological Environment Bureau, combined with the fine particle meteorological condition diffusion index constructed using the environmental model, the change and driving factors of the PM2.5 mass concentration in Tianjin from 2000 to 2020 were studied to analyze the impact of meteorology on the atmospheric environment. The study showed that change in PM2.5 mass concentration in Tianjin took place in three stages from 2000 to 2020; the first stage showed a continuous increase from 2000 to 2007. The rapid increase in emissions in this stage was the dominant factor, and its effect was four times that of the annual fluctuation in meteorological conditions. The second stage was from 2007 to 2013, in which the PM2.5 mass concentration fluctuated, with two peak years (2007 and 2013). The emissions were stable in this stage. The annual fluctuation of meteorological conditions had an important influence on the annual fluctuation in PM2.5 mass concentration. The third stage was from 2013 to 2020; the PM2.5 mass concentration decreased rapidly, and the decline in emissions was decisive, which reduced the PM2.5 mass concentration by 40% to 50%. The improvement in the meteorological diffusion conditions also provided a positive contribution, which reduced the PM2.5 mass concentration by approximately 10%. Based on the analysis of the data over the past 20 years, the annual variation in atmospheric diffusion conditions caused by the annual variation in meteorological conditions was periodic, with trough values from 2003 to 2004 and 2013 to 2015 and peaks from 2008 to 2010 and 2018 to 2020; the distance between peaks and valleys was approximately 11 years. It was estimated that the next atmospheric diffusion condition valley stage will occur circa 2025. The average intensity of the annual fluctuation in atmospheric diffusion conditions caused by the annual variation in meteorological conditions was 4%, which can explain 25%-50% of the annual variation in PM2.5 mass concentration over the past 20 years, with a difference between peaks and valleys of 16%. The periodic fluctuations in meteorological diffusion conditions have an important impact on the future PM2.5 target setting and corresponding measures design.

[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.

[Impact of Air Humidity on PM2.5 Mass Concentration and Visibility During Winter in Tianjin].

Air humidity is a key meteorological factor in regulating visibility changes and haze episodes. Based on multi-year historical data of PM2.5 mass concentration, visibility, relative humidity(RH), and specific humidity(q) during winter in Tianjin, the impact of air humidity on PM2.5 mass concentration and visibility was investigated. Between 2015 and 2020, the PM2.5 mass concentration showed an overall decline of 28.0%. The frequency of visibility above 10 km significantly increased between 2015 and 2018, indicating an improvement in visibility during this period. However, the visibility deteriorated again in the winter of 2019 and 2020, with a decreased frequency of visibility above 10 km. Specifically, the mean RH in January and February in 2020 of Tianjin reached 63% and 67%, respectively, which were higher than the historical 30-year average for the same period. The frequency of extremely low visibility(lower than 2 km) rebounded to a level equivalent to that during the winter of 2016. The enhanced air humidity visually obscured the reduction effect of PM2.5. For Tianjin, the external sources of water vapor are southwestern and eastern transport. Particularly, water vapor transported from eastern Bohai Bay(59%) is significantly greater than that from southwestern direction(25%). However, the eastern air mass is generally clean, hence, although the condensed water may increase the PM2.5 mass concentration in the humid air, the eastern air mass affects visibility to a greater extent. On the other hand, the haze episodes during winter frequently occurred when the southwestern wind dominated and specific humidity was greater than 2.0 g·kg-1, with a frequency of 83.6%. In a short period of time, the variation of specific humidity is less significant than RH, therefore, the relationship between specific humidity and PM2.5 mass concentration or air quality can be utilized to predict the occurrence of haze episodes and pollution during winter. When the average RH is higher than 80% or the mean specific humidity is greater than 3.0 g·kg-1, the frequency of PM2.5 mass concentration greater than 75 µg·m-3 is 78% and 80%, respectively. For the air quality forecast during winter, weather conditions with specific humidity greater than 3.0 g·kg-1 should be carefully monitored.

[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.

Haze episodes before and during the COVID-19 shutdown in Tianjin, China: Contribution of fireworks and residential burning.

Potential health benefits from improved ambient air quality during the COVID-19 shutdown have been recently reported and discussed. Despite the shutdown measures being in place, northern China still suffered severe haze episodes (HE) that are not yet fully understood, particularly how the source emissions changed. Thus, the meteorological conditions and source emissions in processing five HEs occurred in Beijing-Tianjin-Hebei area were investigated by analyzing a comprehensive real-time measurement dataset including air quality data, particle physics, optical properties, chemistry, aerosol lidar remote sensing, and meteorology. Three HEs recorded before the shutdown began were related to accumulated primary pollutants and secondary aerosol formation under unfavorable dispersion conditions. The common "business as usual" emissions from local primary sources in this highly polluted area exceeded the wintertime atmospheric diffusive capacity to disperse them. Thus, an intensive haze formed under these adverse meteorological conditions such as in the first HE, with coal combustion to be the predominant source. Positive responses to the shutdown measures were demonstrated by reduced contributions from traffic and dust during the final two HEs that overlapped the Spring and Lantern Festivals, respectively. Local meteorological dispersion during the Spring Festival was the poorest among the five HEs. Increased residential burning plus fireworks emissions contributed to the elevated PM2.5 with the potential of enhancing the HEs. Our results highlight that reductions from shutdown measures alone do not prevent the occurrence of HEs. To further reduce air pollution and thus improve public health, abatement strategies with an emphasis on residential burning are needed.

The effects of heatwaves and cold spells on patients admitted with acute ischemic stroke.

BACKGROUND: This study aimed to explore the effects of heatwaves and cold spells on blood pressure, thrombus formation, and systemic inflammation at admission in patients with ischemic stroke. METHODS: Data of patients with ischemic stroke who were admitted to the Second Hospital of Tianjin Medical University between May 2014 and March 2019 were reviewed, along with meteorological data from the same time period. A total of 806 clinically confirmed patients with ischemic stroke (34-97 years old) were included in the final analysis. Heatwaves and cold spells were defined as ≥2 consecutive days with average temperature >95th percentile (May-August) and <5th percentile (November-March), respectively. Coagulation parameters, inflammation indices, blood pressure, and neurological impairment were evaluated within 24 hours of admission. General linear and logistic regression models were created to investigate the relationships of heatwaves and cold spells with the examination results of patients with ischemic stroke at admission. RESULTS: After adjustment for potential environmental confounders, heatwaves were positively associated with high systolic blood pressure (SBP) (ß=8.693, P=0.019), diastolic blood pressure (DBP) (ß=3.665, P=0.040), reduced thrombin time (TT) (ß=-0.642, P=0.027), and activated partial thromboplastin time (APTT) (ß=-1.572, P=0.027) in ischemic stroke patients at admission. Cold spells were positively associated with high SBP (ß=5.277, P=0.028), DBP (ß=4.672, P=0.012), fibrinogen (ß=0.315, P=0.011), globulin (ß=1.523, P=0.011), and reduced TT (ß=-0.784, P<0.001) and APTT (ß=-1.062, P=0.024). Cold spells were also associated with a higher risk of respiratory infection [odds ratio (OR) =2.677, P=0.001]. CONCLUSIONS: Exposure to heatwaves or cold spells was associated with blood pressure and coagulation at admission in patients with ischemic stroke. Cold spells also resulted in higher levels of inflammation. These findings suggest that changes in coagulation, blood pressure, and inflammation may be the potential biological mechanisms underlying the cerebrovascular effects of exposure to extreme temperatures.

[Heavy Pollution Episode in Tianjin Based on UAV Meteorological Sounding and Numerical Model].

Pollution occurs in the boundary layer, and the thermal and dynamic vertical structure of the boundary layer has a significant influence on the formation of heavy pollution episodes. Based on unmanned aerial vehicle (UAV) sounding, ground-based remote sensing and numerical modeling, this paper analyzes the vertical structure of the boundary layer and the causes of pollution during the heavy pollution episode in Tianjin from January 10 to 15, 2019, with a view to strengthening the understanding of the influence law of boundary layer processes on heavy pollution in northern coastal cities and improving the accuracy of weather forecasts and heavy pollution warnings. The results show that atmospheric temperature stratification had a significant influence on the formation, persistence, and dissipation of heavy pollution episodes. During an episode, accompanied by the development and dissipation of the inversion layer, a high PM2.5 concentration area developed to the upper atmosphere with a height of over 300 m in the daytime and compressed to the ground at night with a height about 100 m. When fog appeared and continued in the daytime, the vertical structure characteristics of the boundary layer changed. A temperature inversion above the fog restrained the diffusion of pollutants to the upper air and made the contribution of turbulence vertical mixing process decrease significantly in the daytime, leading to the persistence and development of heavy pollution near the surface. Regional pollution transport accounted for 66.6% during the episode, which was closely related to regional pollution transport. Regional pollution transport mainly appeared at the top of the boundary layer and above the fog inversion layer where high wind speeds occurred. Pollutants were transported to the ground by a sinking motion as the boundary layer and fog height changed. This is how regional pollution transport occurred when Tianjin was controlled by a weak high pressure field in the north. The vertical structure of the boundary layer also affected the improvement of air quality by cold air. The strong temperature inversion at the top of the fog resulted in the failure of the cold air to transmit to the ground through turbulent shear stress in the S3 stage. There was an obvious difference in wind speed between the upper and lower air. The influence of cold air on the ground was delayed, and the effect of it was weakened. Thus, the heavy pollution episode could not be alleviated completely.

[Transport Characteristics of Air Pollution in Tianjin Based on Weather Background].

Regional transport is an important factor when considering the prevention and control of air pollution. The aim of this study was to provide support for the joint prevention and control of air pollution in the Beijing-Tianjin-Hebei region. With a focus on an analysis of the relationship between regional transport and meteorological conditions based on the weather background, an atmospheric chemical model was developed to quantitatively estimate the impact of regional transport on Tianjin from October 2016 to September 2017. The results showed that the contribution percentage of regional transport in cities in plains in the Beijing-Tianjin-Hebei region was significantly higher than in cities in mountains. The local contribution of PM2.5 in the Tianjin area was 62.9% and the contribution of regional transport was 37.1%. This was mainly affected by transmissions of Chanzhou, Langfang, central and southern Hebei, Beijing, Tanshan, and Shandong. Regional transport was the most significant from April to June, the weakest from July to August, and the highest contributor to local emissions. Regional transport was closely related to weather situation, wind field, precipitation, and other meteorological conditions. Post-high pressure and pre-frontal low pressure were the two types of pollution weather with the highest proportion in regional transport, and the impact of air pollution transport under the southwest wind, westerly wind and south wind was the most apparent. Wind speed of 2-3 m·s-1 was beneficial to the regional transport of PM2.5, and precipitation above 5 mm will effectively reduce the regional transport of air pollutants. For different pollution types and heavy pollution stages, the contribution of regional transport was the most apparent in light pollution weather, being 20.5% higher than the average. The heavy pollution weather was controlled by static stable air mass, and because of the migration of high PM2.5 concentrations, pollution air mass in the surrounding area had a significant impact on the accumulation of pollution and transport in the region. The contribution ratio of PM2.5 transport in the heavy pollution period was more than the average and was approximately 10% and 15% higher. In the process of heavy pollution, the proportion of transport contribution in the initial accumulation stage and peak stage were higher than in other periods, and 14.5% and 19.5% higher than in the outbreak stage. The contribution of local emissions in the outbreak stage was more significant, being 9.9% higher than average.

[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.

Short-term effects of outdoor air pollution on acute ischaemic stroke occurrence: a case-crossover study in Tianjin, China.

OBJECTIVE: Ambient air pollution is associated with ischaemic stroke incidence. However, most of the previous studies used stroke-related hospital admission rather than stroke onset itself. This study aimed to evaluate the relationship between ambient air pollutant exposures and acute ischaemic stroke based on the timing of symptom onset. METHODS: A time-stratified, case-crossover analysis was performed among 520 patients who had ischaemic stroke admitted to the Second Hospital of Tianjin Medical University (Tianjin, China) between 1 April 2018 and 31 March 2019 (365 days). Daily air pollutant concentrations of particulate matter with aerodynamic diameter 2.5 µm, particulate matter with aerodynamic diameter 10 µm (PM10), sulfur dioxide, nitrogen dioxide, carbon monoxide and ozone were obtained from fixed-site monitoring stations. We used conditional logistic regression to estimate OR and 95% CI corresponding to an increase in IQR of each air pollutant after adjusting for the effects of temperature and relative humidity. RESULTS: Overall, a higher risk of ischaemic stroke was found between April and September. During this period PM10 was associated with an increased risk of ischaemic stroke (1-day lag: OR=1.49, 95% CI 1.09 to 2.02; 3-day mean: OR=1.58, 95% CI 1.09 to 2.29) among patients between 34 and 70 years old. Positive associations were also observed between PM10 (1-day lag: OR=1.51, 95% CI 1.10 to 2.07; 3-day mean: OR=1.57, 95% CI 1.08 to 2.29), ozone (1-day lag: OR=1.83, 95% CI 1.16 to 2.87; 3-day mean: OR=1.90, 95% CI 1.06 to 3.42) and ischaemic stroke occurrence among those with hyperlipidaemia. CONCLUSION: Our results suggest that air pollution is associated with a higher risk of ischaemic stroke in younger people or people with hyperlipidemia. These findings still need to be further investigated.

[Effects of VOCs on Ozone Formation in the Tianjin Suburbs in Summer].

Volatile organic compounds (VOCs) play an important role in the formation of ozone. The concentrations of VOCs in the Jinnan District of Tianjin were monitored by the Syntech Spectras GC955 online monitoring system, and the ozone generation potential of VOCs was calculated by the maximum incremental activity factor. The results showed that the total concentration of VOCs in the Jinnan District was (32.33±23.77) µg·m-3, in which the mass concentration of alkane was the highest, and propylene, ethylene and toluene had the highest mass concentration. During the observation period, the ozone formation potential (OFP) of TVOC was 107.81 µg·m-3, and the contribution of alkenes to OFP was the largest, which was 55.80%. Ethylene, isoprene, and toluene accounted for the first three places of OFP contribution rate. The backward trajectory analysis showed that TVOC and its OFPs were different under different trajectories. The estimation of VOCs/NOx volume fraction ratio showed that O3 formation was sensitive to VOCs, which showed that atmospheric photochemical pollution has a considerable degree of regional characteristics. The concentration ratio of ethylbenzene/m,p-xylene, and ethane/acetylene can be used to measure the progress of atmospheric chemical reaction and photochemical age in the air mass, which was proved by the aging process of VOC.