[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.

[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.

[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.