Analyzing ozone formation sensitivity in a typical industrial city in China: Implications for effective source control in the chemical transition regime.

Volatile organic compounds (VOCs) play a major role in O3 formation in urban environments. However, the complexity in the emissions of VOCs and nitrogen oxides (NOx) in industrial cities has made it challenging to identify the key factors influencing O3 formation. This study used observation-based-model (OBM) to analyze O3 sensitivities to VOCs and NOx during summer in a typical industrial city in China. The OBM model results were coupled with a receptor model to analyze the sources of O3. Higher concentrations of O3 precursors were observed during polluted periods indicating that precursor accumulation contributed to the higher maxima of the net ozone formation rate and HOx concentrations. Analyses of ROx· budgets and relative incremental reactivity (RIR) indicated that O3 production is in a chemical transition regime and was sensitive to both VOCs and NOx. Results from Positive Matrix Factorization (PMF) analysis indicated that gasoline vehicle emissions, industrial processes, and coal combustion were major sources of O3 precursors. The sensitivities of O3 production to these sources depend on if both VOC and NOx sensitivities are considered. If only VOCs sensitivity is considered, in contrast, the contribution of anthropogenic sources to O3 production was significantly underestimated. This study highlights the importance of accounting for both VOCs and NOx sensitivities when O3 chemistry is in a transition regime in O3 production attribution studies.

[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.

Impact of primary emission variations on secondary inorganic aerosol formation: Prospective from COVID-19 lockdown in a typical northern China city.

Hourly observations in northern China city of Taiyuan were performed to compare secondary inorganic aerosol (SIA) reaction mechanisms, and emission effects on SIA during the pre-lock and COVID-19 lock days. Emission control implemented and meteorological conditions during lock days both caused beneficial impact on air quality. NO2 showed the highest decrease ratio of -49.5%, while the relative fraction of NO3- in PM2.5 increased the most (2.7%). Source apportionment revealed the top three contributors to PM2.5 were secondary formation (SF), coal combustion (CC), and vehicle exhaust (VE) during both pre-lock and lock days. EC lock/pre were all lower than 1, suggesting the overall reduction of primary emissions during lock days, while the higher ratio of (SIA/EC) lock/pre (1.01-1.36) indicated the enhanced secondary formation in lock days. The ratio of SIA of pollution to clean days during lock periods considerably higher by 23.7% compared with that in pre-lock periods, which was indicated SIA secondary formation was more pronounced and contributed great to pollution days in lock periods though secondary formation existed in pre-lock and lock periods. Enhanced secondary formation of NO3- and SO42- during lock days might be mainly due to the increased in aqueous and gas-phase reactions, respectively. Except for SF, high contribution of VE and CC were also important for high SIA concentration in pre-lock and lock days, respectively. The decreased contribution of VE weakens its contribution to SIA formation, indicating the effectiveness of VE emission control, as confirmed during the COVID-19 pandemic. This study highlights the aqueous and gas-phase reactions for nitrate and sulfate, respectively, which contributed to heavy pollution, as well as indicated the important role of VE on SIA formation, suggesting the urgent need to further strengthen controls on vehicle emissions.

Effects of regional transport from different potential pollution areas on volatile organic compounds (VOCs) in Northern Beijing during non-heating and heating periods.

Despite the adoption of air quality control measures, the influence of regional transport on volatile organic compounds (VOCs) pollution has gradually increased in Beijing. In this study, the whole observation period (September 24 to December 12, 2020) was divided into heating period and non-heating period to explore the impact of changing VOCs sources in different observation periods, and also classified into "Type-N" and "Type-S" periods both in non-heating period and heating period to explore the impact of regional transport from the northern and southern regions of sampling site, respectively. The average VOCs concentrations in northern Beijing during observation period were 22.6 ± 12.6 ppbv, which showed a decrease trend in recent years compared with other studies. And higher VOCs concentrations were observed in Type-S than in Type-N period. The positive matrix factorization results showed that vehicular exhaust dominated VOCs (26.1%-33.7%), but coal combustion could not be ignored in heating period, when it was twice that in non-heating period. In particular, coal combustion dominated VOCs in southern trajectories (30.9%) in heating period. The analysis of concentration weighted trajectory showed that coal combustion was affected by regional transport from the southeast regions of Beijing, while vehicular exhaust was affected by urban and the southeast regions of Beijing. Regarding human health risks, the carcinogenic risks of benzene and ethylbenzene exceeded the acceptable cancer risk value (1 × 10-6), and were higher in Type-S than in Type-N period. The results indicated that regional transport from urban areas and the areas south of Beijing had a significant impact on VOCs in northern Beijing. Thus, targeted control measures for different potential pollution regions are important for controlling VOCs pollution in Beijing.

Establishment and verification of anthropogenic volatile organic compound emission inventory in a typical coal resource-based city.

A few studies on volatile organic compound (VOC) emission inventories in coal resource-based cities have been reported, and previous emission inventories lacked verification. Herein, using Yangquan as a case study, emission factor (EF) method and "(tracer ratio) TR - positive matrix factorization (PMF)" combined method based on atmospheric data were used to establish and verify the VOC emission inventory in coal resource-based cities, respectively. The total VOC emissions in Yangquan were 9283.2 t [-40.0%, 62.1%] in 2018, with industrial processes being the major contributors. Alkanes (35.8%), aromatics (25.0%), and alkenes (19.8%) were the main compounds in the emission inventory. The verification results for both species emission and source structure were in agreement, indicating the accuracy of VOC emission inventory based on EF method to a certain extent. However, for some species (ethane, propane, benzene, and acetylene), the EF method indicated emissions lower than those obtained from the TR results. Furthermore, the summer-time emission contribution from fossil fuel combustion indicated by the EF method (23.4%) was lower than that obtained from the PMF results (38.4%). Overall, these discrepancies could be attributed to the absence of a coal gangue source in the EF method. The verification results determined the accuracy of the VOC emission inventory and identified existing problems in the estimation of the VOC emission inventory in coal resource-based cities. In particular, not accounting for the coal gangue emissions may result in an underestimation of VOC emissions in coal resource-based cities. Thus, coal gangue emissions should be considered in future research.

[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.