[Revealing Driving Factors of Urban O3 Based on Explainable Machine Learning].

Driven by precursor emissions, meteorological conditions, and other factors, atmospheric ozone (O3) has become the main pollutant affecting urban air quality in summer. The current deductive models driven by physical and chemical mechanisms require a large number of parameters for the analysis of O3 pollution, and the calculation timeliness is poor. The data-driven inductive models are efficient but have problems such as poor explanation. In this study, an explainable model of data-driven Correlation-ML-SHAP was established to reveal the strongly correlated influencing factors of O3 concentration. Additionally, the machine learning ML module coupled with the explainable SHAP module was used to calculate the contributions of driving factors to O3 concentration, so as to realize the quantitative analysis of driving factors. The O3 pollution process in the summer of 2021 in Jincheng City was used as an example to carry out the application research. The results showed that the Correlation-ML-SHAP model could reveal and use strong driving factors to simulate O3 concentration and quantify influence contribution, and the ML module used the XGBoost model to achieve the best simulation accuracy. Air temperature, solar radiation, relative humidity, and precursor emission level were the strong driving factors of O3 pollution in Jincheng City in summer 2021, and the contribution weights were 32.1%, 21.3%, 16.5%, and 15.6%. The contribution weights of air temperature, solar radiation, and precursor emission level increased by 3.4%, 1.2%, and 1.2% on polluted days, respectively, and the contribution weights of precursor emission level rose to third place on polluted days. Each driving factor had a nonlinear interaction effect on O3 concentration. When the air temperature exceeded 24℃, or the relative humidity was lower than 70%, there was a 94.9% and 94.1% probability of positive contribution to O3 pollution, respectively. Under such meteorological conditions, ρ(NO2) exceeded 9 µg·m-3, or ρ(CO) exceeded 0.7 mg·m-3, and there was a 94.9% and 99.3% probability of positive contribution to O3 pollution, respectively. The southeast wind speed was lower than 5.8 m·s-1, or the south wind speed was lower than 5.3 m·s-1, both of which contributed positively to O3 pollution. The model quantitatively analyzed the influence contribution of various driving factors on urban O3 concentration, which could provide a basis for the prevention and control of urban atmospheric O3 pollution in summer.

[Pollution Characteristics and Influencing Factors of PM2.5 in Shanxi Province Based on Wavelet Transform].

Based on the daily average concentration of PM2.5, social influencing factor data, and meteorological data of 11 cities in Shanxi Province from 2015 to 2019, the concentration period of PM2.5 was determined using wavelet transform. The correlation between PM2.5 and social influencing factors and meteorological factors was explored respectively through Spearman correlation and the wavelet coherence spectrum, and the main influencing factors of long-term and short-term management and control of PM2.5 were determined. The results showed that the concentration of PM2.5 in Shanxi Province showed an upward trend from 2015 to 2017, with an average annual increase rate of 4.3% and a downward trend from 2018 to 2019, with an average annual decrease rate of 4.2%. The average concentration of PM2.5 showed a "U" distribution, with the highest value in January (95 µg·m-3) and the lowest in August (34 µg·m-3); the average value in winter was approximately twice that in summer. The ρ(PM2.5) in southern cities such as Linfen was 62 µg·m-3, and the average value in Datong and other northern cities was 45 µg·m-3, which was high in the south and low in the north. There were significant periodic changes in PM2.5 concentration in the 11 cities, including a long period of approximately 293 d and a short period of approximately 27 d. Among them, the energy consumption level and industrial structure were the strong driving factors affecting the PM2.5 concentration in the long period of Shanxi Province. In the short period, it was greatly affected by the change in atmospheric circulation, and different cities were affected by typical meteorological factors. Linfen, Yuncheng, Datong, Shuozhou, and Xinzhou were vulnerable to wind speed; Jinzhong and Luliang were vulnerable to temperature; and Taiyuan, Jincheng, Yangquan, and Changzhi were uniquely and significantly affected by relative humidity. Therefore, industrial structure adjustment and energy structure adjustment are key to the long-term control of atmospheric PM2.5 and the long-term improvement of air quality in Shanxi Province. The differential impact of different urban meteorological factors on PM2.5 should be considered when carrying out short-term regional joint prevention and control.

[Characteristics and Cause Analysis of Heavy Air Pollution in a Mountainous City During Winter].

Taking the typical heavy air pollution process in Yangquan from December 26, 2018 to January 20, 2019 as an example, the characteristics and cause analysis of heavy air pollution in a mountainous city in winter were analyzed in this study. The results showed that fine particle mass (PM2.5) was the primary pollutant during the heavy pollution period. The water-soluble ions and carbonaceous components were the main components of PM2.5. The secondary ions of SO42-, NO3-, and NH4+ had the lager contribution to water-soluble ions (87.7%), and the secondary organic carbon (SOC) was the main component of the carbonaceous components (71.6%). The concentration of the secondary ions during the heavy pollution period increased by 5.3 times compared to levels before the heavy pollution period, and was an important component resulting in the fast increase of PM2.5. An analysis of meteorological conditions showed that PM2.5 and its main components had a significantly positive relationship with humidity and a significantly negative relationship with wind speed. And that pollution became stronger with an increase in humidity and a decrease in wind speed. The typical meteorological characteristics of mountainous cities are high relative humidity and large temperature variations, which can accelerate the formation of secondary pollutants and are the main reasons for the rapid aggravation of PM2.5. In addition, the lower average wind speed caused by the relatively closed terrain in mountainous cities makes the diffusion conditions of air pollutants relatively poor, which is one of the reasons for the accumulation of pollutants. The source apportionment results showed that the secondary sources (46.0%) were the most important source of PM2.5, followed by coal combustion (32.6%), vehicle exhaust (19.8%), and fugitive dust (1.6%). Therefore, mountainous cities should pay more attention to controlling secondary components, especially secondary ions.

[Analysis of Chemical Components and Sources of PM2.5 During Autumn and Winter in Yangquan City].

PM2.5 samples were collected from October 15, 2017 to January 23, 2018 in the Yangquan urban area. The characteristics of PM2.5 and its main chemical components on clean and polluted days were analyzed, and source apportionment of PM2.5 was conducted using enrichment factor analysis (EF) and positive matrix factorization (PMF). The results showed that the ratios of secondary inorganic ions (SO42-, NO3-, and NH4+) to PM2.5 on polluted days was 23.83%, which is 2.43 times higher than that on clean days, indicating that secondary inorganic pollution was more significant on polluted days. The enrichment degree of anthropogenic elements Cd, Sb, Sn, Cu, Pb, Zn, and As on polluted days was higher than that on clean days. The results of the PMF source apportionment showed that the main sources of PM2.5 in Yangquan are coal combustion, dust, motor vehicles, secondary aerosols, and industry, with contributions of 29.26%, 23.83%, 19.34%, 16.01%, and 11.57%, respectively. The contribution of motor vehicle emissions to PM2.5 on polluted days is 20.57%, which is higher than that on clean days (17.82%), while the contribution of coal combustion sources to PM2.5 on polluted days is 23.04%, which is significantly lower than that on clean days (33.75%). The stationary weather on polluted days caused the contribution of motor vehicle emissions to PM2.5 to increase compared with on clean days, while the contribution of coal combustion sources to PM2.5 was decreased. The results show that air pollution control should pay more attention to the control of coal combustion and dust during autumn and winter in Yangquan, and further strengthen the control of motor vehicles to reduce their contribution to pollution.

[Characteristics, Source Apportionment, and Environmental Impact of Volatile Organic Compounds in Summer in Yangquan].

Volatile organic compounds (VOCs) were collected at three environmental sampling sites in Yangquan and quantified by gas chromatography-mass selective detector/flame ionization detector(GC-MSD/FID). The VOC sources were identified by diagnostic ratios and positive matrix factorization (PMF), and environmental impact of VOCs on O3 and secondary organic aerosol (SOA) were evaluated. The results showed that the average VOC concentration was (82.1±22.7) µg·m-3, with alkanes being the most abundant group (51.8%), followed by aromatics (17.8%), alkenes (8.0%), and alkynes (3.8%). The diurnal variation of VOCs exhibited a bimodal trend, with twin peaks appearing at 08:00-10:00 and 18:00-20:00, falling to a valley at 12:00-14:00. The results for benzene/toluene (2.1±1.3) and isopentane/n-pentane (1.7±0.6) showed that the ambient VOCs may be influenced by coal combustion and vehicular emissions. Six sources were extracted by PMF:coal combustion (34.9%), vehicle emissions (18.2%), gasoline evaporation (15.2%), industrial emissions (13.6%), biogenic emissions (9.2%), and solvent usage (9.0%). The average concentration of ozone formation potential (OFP) was 156.6 µg·m-3, with the highest contribution from alkenes, while the average concentration of secondary organic aerosol formation potential (SOAp) was 68.7 µg·m-3, mainly from aromatics (93.4%). In summary, coal combustion was the most abundant source of VOCs, and accelerating the management of coal gangue and energy structure readjustment are the key points to address. Meanwhile, restricting the VOCs from vehicle emissions, gasoline evaporation, and industrial emissions is also required.

[Stable Carbon Isotope Compositions and Source Apportionments of Volatile Aromatic Compounds in the Urban Atmosphere of Taiyuan, China].

This study used Tenax TA absorption tubes to sample volatile aromatic compounds from different emission sources and functional zones in Taiyuan City, Shanxi Province, China. Thermal desorption-gas chromatography-isotope ratio mass spectrometry (TD-GC-IRMS) was subsequently employed to analyze the stable carbon isotope characteristics of the volatile aromatic compounds. The results revealed that the stable carbon isotope ratio (δ13C) of the volatile aromatic compounds emitted through diesel, gasoline, and solvent volatilization, vehicle exhaust, and domestic coal combustion ranged from (-30.79±0.98)‰ to (-29.10±0.14)‰, (-30.96±0.88)‰ to (-28.02±1.77)‰, (-32.13±0.59)‰ to (-27.67±0.49)‰, (-27.58±0.16)‰ to (-25.50±0.75)‰, and (-25.14±0.93)‰ to (-23.44±1.32)‰, respectively. The δ13C value of styrene was (-23.44±1.32)‰, which was only detected in the fumes emitted through domestic coal combustion. Additionally, the sample analysis based on data collected from four different functional zones of Taiyuan City revealed the following:① the δ13C values of the atmospheric volatile aromatic compounds in the mixed residential and traffic zone ranged from (-25.61±2.20)‰ to (-23.91±0.78)‰. Compared with other functional zones, the emissions in this zone were enriched with13C; and ② the δ13C values measured in the industrial zone ranged from (-29.15±1.06)‰ to (-24.53±1.07)‰; the emissions in this functional zone were relatively low in 13C compared with other zones. A comparison of the δ13C values of the atmospheric volatile aromatic compounds and emission sources indicated that the main sources of volatile aromatic compounds at the four sampling points in Taiyuan were vehicle exhausts and domestic coal combustion, while the air sampled in the industrial functional zone was heavily affected by the volatilization of solvents.

[Characteristics and Source Apportionment of Ambient Volatile Organic Compounds in Winter in Jincheng].

Air samples were collected and analyzed by GC-MS to investigate the component characteristics of volatile organic compounds (VOCs) in winter in Jincheng. PMF, ratio analysis, and the backward trajectory model were used to investigate sources of VOCs. Ozone formation potential and secondary organic aerosol formation potential were calculated, in order to analyze the environmental implications of detected VOCs. Results showed that the average concentration of VOCs was 93.35 µg·m-3 in Jincheng, with the most abundant component being alkane (52.91 µg·m-3 and 56.68% of total VOCs). Based on PMF analysis, five sources of ambient VOCs in Jincheng were identified, namely industrial emission sources (33.71%), fuel combustion sources (30.27%), vehicle emissions (26.28%), solvent evaporation sources (9.00%), and plant emission sources (0.74%). Ratios of B/T and i-pentane/n-pentane were 1.58±0.68 and 2.07±0.43, indicating that VOCs were derived from the mixture of road and coal combustion sources. Clustered analysis of the air mass backward trajectory showed that three air masses cluster, which were accounting for 50%, 25% and 25% of the total back trajectories respectively, all came from the northwest, and industrial pollution from the northwest might therefore significantly influence VOCs in Jincheng. With low wind speed (<3 m·s-1), the air quality index, concentration of total VOCs, and contribution rate of vehicle emissions were 143, 162.48 µg·m-3, and 46.16%, respectively, higher than values at faster wind speeds (3-6.9 m·s-1). Ozone formation potential and secondary organic aerosol formation potential of aromatic hydrocarbons, which had the highest formation potential, were 98.89 µg·m-3 and 1.21 µg·m-3, respectively, accounting for 37.28% and 97.01% of total formation potential. To reduce the pollution of VOCs in Jincheng, it is important to control industrial emissions, vehicle emissions, and fuel combustion emissions.

[Emission Inventory of Anthropogenic VOCs and Its Contribution to Ozone Formation in Shanxi Province].

Based on the activity levels, emission factors and composition characteristics of VOCs, which was obtained in statistic data and references, the emission amount of anthropogenic VOCs in Shanxi province in 2013 was calculated, and the ozone formation potential of VOCs was studied in this study. The results showed that the emission amount of anthropogenic VOCs in Shanxi province in 2013 was 723700 t, with the major sector of the industrial emission and vehicle emission, accounting for 36.47% and 24.28% of total emission amount, respectively. Coke and chemicals production, the major emission source of VOCs in industrial emission, emitting 190600 t and 38800 t VOCs in 2013, accounting for 72.22% and 14.72% of industrial emission, respectively. The emission amount of ozone precursor VOCs was 435900 t, and the total amount of ozone formation potential in Shanxi province in 2013 was 1769900 t. The sources of the greatest contribution to total ozone were vehicle emission, combustion sources and industrial emission. The results indicated that industrial emission was the major source of VOCs emission, which showed the simplification and heavy industrial structure. The increasing numbers of vehicles led to the huge emission of VOCs in recent years. In conclusion, the main measure of controlling the ozone pollution caused by VOCs emissions was controlling the VOCs emission from industrial emission and vehicle emission.

[Distribution Characteristics of Water-soluble Ions in Size-segregated Particulate Matters in Taiyuan].

The distributions of size-segregated particles (PM10) and water-soluble ions (WSIs) in Taiyuan were studied from July 2014 to April 2015 by TE-235 aerosol sampling and ion chromatography analyzing. As the results shown, the daily PM10 level was 173.7 µg·m-3, which exceeded the Grade Ⅱ limitation value in the Ambient Air Quality Standard (150 µg·m-3, GB 3095-2012). PM10 levels varied seasonally, and its were 199.1 and 194.2 µg·m-3 in winter and spring, respectively, which were much higher than those in summer. The PM10 size-segregated was bimodal distribution for the range of 0-0.95 and 3.0-7.2 µm. The concentration of WSIs was the highest in winter, followed by summer and spring. SO42-, NO3- and NH4+ were the main water soluble ions and accounted for 66% to 80% of the total WSIs. SO42-, K+, NH4+ and Cl- showed a unimodal distribution at <0.95 µm in all samples, while Ca2+ and Mg2+ showed a bimodal distribution at <0.95 µm and 3.0-7.2 µm. NO3- showed a unimodal size distribution at <0.95 µm in winter and spring, compared with a bimodal distribution at <0.95 µm and 3.0-7.2 µm in summer. By the correlation analysis, PM10 and WSIs decreased with the increase of wind speed in summer and winter other than in spring for the road-dust re-suspension by strong wind. Based on the ratio analysis of NO3-/SO42- and Mg2+/Ca2+, coal combustion was the main source of NO3- and SO42-, while Mg2+ and Ca2+ were mainly from the dust and coal combustion.

[Composition and variation characteristics of atmospheric carbonaceous species in PM 2.5 in Taiyuan, China].

Day-night variation characteristics of organic carbon (OC) and elemental carbon (EC) in atmospheric fine particles (PM2.5) collected during winter of 2009 and spring of 2010 in Taiyuan city were analyzed using DRI Model 2001A Thermal/Optical Carbon Analyzer, and the sources of carbonaceous materials in PM2.5 were analyzed. The results showed that the average concentrations of PM2.5, OC, EC and average OC/EC ratios were all higher during winter [(289.2 ± 104.8) µg x m(-3), (65.2 ± 22.1) µg x m(-3), (23.5 ± 8.2) µg x m(-3) and 2.8 ± 0.3] than during spring [(248.6 ± 68.6) µg x m(-3), (29.7 ± 6.2) µg x m(-3), (20.2 ± 5.4) µg x m(-3) and 1.5 ± 0.3], higher in nighttime [(309.3 ± 150.0) µg x m(-3), (74.6 ± 19.5) µg x m(-3), (24.3 ± 6.6) µg x m(-3) and 3.1 ± 0.3] than in daytime [(234.9 ± 122.1) µg x m(-3), (54.9 ± 28.2) µg x m(-3), (22.6 ± 10.8) µg x m(-3) and 2.5 ± 0. 5] during winter while higher in daytime [(292.5 ± 120.8) µg x m(-3), (32.7 ± 10.5) µg x m(-3), (22.7 ± 10.1) µg x m(-3) and 1.6 ± 0.5] than in nighttime [(212.3 ± 36.7) µg x m(-3), (29.6 ± 6.6) µg x m(-3), (20.7 ± 6.4) µg x m(-3) and 1.5 ± 0.2] during spring. This result was explained by the fact that winter is a "heating season", especially in nighttime, emission of carbonaceous particles was increased because of the increase of coal and biomass combustion and diffusion of pollutants was difficult because of low atmospheric temperature and stable atmospheric conditions; and high OC/EC was caused by increase of OC emission but not contribution of secondary organic carbon (SOC) since low temperature and weak solar radiation were not favorable for the formation of SOC. The higher concentrations of PM2.5, OC and EC in daytime than in nighttime during spring might be due to more dust in daytime because of higher wind speed and lower relative humidity in daytime than in nighttime, and the higher OC/EC in daytime than in nighttime might be caused by higher temperature and stronger solar radiation in daytime, which were favorable for the formation of SOC. Comparing with other cities in China, Taiyuan showed high concentrations of PM2.5, OC and EC, indicating serious carbonaceous aerosol pollution which may significantly contribute to the formation of dust-haze.

A prospective randomized control clinical trial about clopidogrel combined with warfarin versus clopidogrel alone in the prevention of restenosis after femoral-popliteal artery angioplasty / 中华外科杂志

<p><b>OBJECTIVE</b>Using two antithrombotic treatment (clopidogrel vs. clopidogrel combined warfarin) strategies after femoral-popliteal artery angioplasty prospectively, to evaluate which strategy is more effective for the restenosis prevention.</p><p><b>METHODS</b>Totally 50 patients referred for endovascular treatment (including the percutaneous transluminal angioplasty (PTA) and stent implantation) of the superficial femoral artery and popliteal artery from January 2008 to May 2009 were randomly divided into clopidogrel group (group A, 25 cases, 30 limbs) and clopidogrel plus warfarin group (group B, 25 cases, 33 limbs) before operation. Clinical outcomes and restenosis rate of the target lesions were evaluated at 3, 6 and 12 months after operation.</p><p><b>RESULTS</b>Totally 88 patients were screened for participation in the study, 56 patients were included after the follow-up of 12 months. At 3 months, the rates of restenosis were 16.7% in group A and 18.2% in group B (χ² = 0.025, P = 0.874). At 6 months, the accumulated restenosis rates were 36.7% in group A and 36.4% in group B (χ² = 0.001, P = 0.98). At 12 months, the accumulated restenosis rates were 53.3% in group A and 42.4% in group B (χ² = 0.75, P = 0.387). Analysis for the critical limb ischemia sub-group showed that follow-up of 12 months, the accumulated restenosis rate was 8/10 in group A and 6/12 in group B (χ² = 1.023, P = 0.312).</p><p><b>CONCLUSION</b>The clopidogrel alone treatment for PTA or PTA plus stent implantation of femoral popliteal artery has no statistically significant difference in comparison with the clopidogrel combined warfarin treatment in terms of the cumulative vascular restenosis rate at 3, 6, 12 months postoperatively.</p>