Atmospheric Environment最新文献

{"title":"The coupling model of random forest and interpretable method quantifies the response relationship between PM2.5 and influencing factors","authors":"Jinxing Liu ,&nbsp;Hui Yu ,&nbsp;Yaqing Zhang ,&nbsp;Junjun Chen ,&nbsp;Shiyuan Feng ,&nbsp;Rui Guo ,&nbsp;Feng Wang ,&nbsp;Bo Xu ,&nbsp;Guoliang Shi ,&nbsp;Yinchang Feng","doi":"10.1016/j.atmosenv.2024.120925","DOIUrl":"10.1016/j.atmosenv.2024.120925","url":null,"abstract":"<div><div>Ambient fine particulate matter (PM_2.5) is affected by many factors, such as source emissions, meteorological conditions, and chemical reactions. Revealing the effects of these factors on PM_2.5 is essential to understand the causes of PM_2.5 pollution. The machine learning method can establish the non-linear relationship between influencing factors and PM_2.5. Here, a coupling model of machine learning and interpretation method was constructed to comprehensively quantify the importance of influencing factors to PM_2.5 from multiple dimensions and analyze the sensitivity of influencing factors. Among the primary indicators of influencing factors, the importance of emission, meteorological conditions, and atmospheric chemical reaction to PM_2.5 is 49%, 29%, and 22%, respectively. In the secondary indicator of influencing factors, the transmission effect is the most important meteorological condition, with an important degree of 15%. The liquid phase reaction is the most important atmospheric chemical reaction, with an importance of 7%. Among the three levels of influencing factors, emission, transport distance, liquid phase reaction coefficient, aerosol acidity, and accumulation promotion coefficient are important factors. The sensitivity of a single factor is complex and changeable, and the interaction between emission and other important factors is the strongest among the two factors. Of which the interaction between transmission distance and emission during the observation period is the strongest, and the interaction coefficient is 1.82. Our study focuses on the effect of influencing factors on PM_2.5, provides a basis for the analysis of the causes of PM_2.5 pollution, and technical support for the treatment of PM_2.5.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"341 ","pages":"Article 120925"},"PeriodicalIF":4.2,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calibration innovations to enhance the accuracy of proton-transfer-reaction mass spectrometry for volatile organic compounds measurements","authors":"Lingning Meng ,&nbsp;Song Gao ,&nbsp;Yun Sun ,&nbsp;Lipeng Liu ,&nbsp;Yong Ren ,&nbsp;Zheng Jiao","doi":"10.1016/j.atmosenv.2024.120923","DOIUrl":"10.1016/j.atmosenv.2024.120923","url":null,"abstract":"<div><div>Volatile organic compounds (VOCs) detection and analysis techniques are critical for understanding their emissions, transport, and impacts. Proton-transfer-reaction mass spectrometry (PTR-MS) is one of the most widely used methods for real-time monitoring VOCs emissions due to its high time resolution and low detection limits. Quantification of VOCs measurement data must be reliable to ensure data comparability, which heavily depends on measurement uncertainty. In this review, the definition of measurement uncertainty and its importance in VOCs measurements are present. Then, the sources of VOCs measurement uncertainty are discussed, and corresponding methods to reduce it are analyzed. Furthermore, several important innovations in PTR-MS calibration are detailed. These calibration innovations have enhanced the accuracy and efficiency of PTR-MS measurements. This review presents a recent calibration approach developed by the National Physics Laboratory (NPL) and the University of Utrecht, considered the most pragmatic for addressing PTR-MS measurement accuracy and comparability. Finally, perspectives for the PTR-MS are suggested: Technologies, such as electronics, optics, chemistry, mechanics, and so on, are anticipated to enhance PTR-MS systems, reduce costs, and increase their popularity.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"342 ","pages":"Article 120923"},"PeriodicalIF":4.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142697630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Environmental drivers of tropospheric bromine and mercury variability in coastal East Antarctica","authors":"Neil C. Page ,&nbsp;Jenny A. Fisher ,&nbsp;Stephen R. Wilson ,&nbsp;Robyn Schofield ,&nbsp;Robert G. Ryan ,&nbsp;Sean Gribben ,&nbsp;Andrew R. Klekociuk ,&nbsp;Grant C. Edwards ,&nbsp;Anthony Morrison","doi":"10.1016/j.atmosenv.2024.120918","DOIUrl":"10.1016/j.atmosenv.2024.120918","url":null,"abstract":"<div><div>Bromine radicals released from sea ice, snow, and marine sources play a critical role in the atmospheric chemistry of polar regions. The Chemical and Mesoscale Mechanisms of Polar Cell Aerosol Nucleation (CAMMPCAN) ship campaign conducted in coastal East Antarctica over two 6-month periods in 2017–18 and 2018–19 provides a unique dataset to identify the environmental drivers of bromine variability in Antarctic spring and summer. In this study, we used CAMMPCAN chemical and meteorological observations combined with reanalysis data from the Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) and satellite-based sea ice data from the National Snow and Ice Data Center to select variables that showed statistically significant correlation with bromine monoxide (BrO) partial columns measured during CAMMPCAN. We then used those variables in principal component analysis and subsequent principal component regression to identify dominant modes of Antarctic environmental variability and their impacts on lower tropospheric BrO. Comparing our three dominant Antarctic principal components to those from a similar analysis conducted previously for the Arctic (Swanson et al., 2020), we found only one mode with clear overlap, representing a vertical mixing mode in which low-pressure systems mix BrO and its precursors into the lower troposphere. We also identified an Antarctic mode describing conditions favourable for blowing snow, similar to the combined effect of two modes from the Arctic analysis but more clearly disambiguated here due to the inclusion of sea ice data in our analysis. The third Antarctic mode, attributed to an ocean source (biological activity and/or sea salt aerosol), was particularly important in summer. The principal component regression model developed from these modes showed moderate skill in predicting BrO partial columns in the lowest 2 km of the troposphere (<em>R</em> = 0.51), a significant improvement over the Arctic-based regression model (<em>R</em> = 0.08). Neither model could reproduce the observed variability in BrO in the lowest 200 m. Finally, we applied the same analysis to coincident CAMMPCAN observations of gaseous elemental mercury and found regression of our three dominant modes could explain nearly 50% of observed mercury variability (<em>R</em> = 0.69). Our results reinforce the importance of sea ice and ocean processes in bromine cycling in coastal East Antarctica and highlight the need to consider Antarctic-specific processes in mechanistic models of atmospheric bromine chemistry.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"342 ","pages":"Article 120918"},"PeriodicalIF":4.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142697627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seasonal trends and light extinction effects of PM2.5 chemical composition from 2021 to 2022 in a typical industrial city of central China","authors":"Changlin Zhan ,&nbsp;Chong Wei ,&nbsp;Ziguo Liu ,&nbsp;Hongxia Liu ,&nbsp;Xuefen Yang ,&nbsp;Jingru Zheng ,&nbsp;Shan Liu ,&nbsp;Jihong Quan ,&nbsp;Yong Zhang ,&nbsp;Qiyuan Wang ,&nbsp;Nan Li ,&nbsp;Junji Cao","doi":"10.1016/j.atmosenv.2024.120922","DOIUrl":"10.1016/j.atmosenv.2024.120922","url":null,"abstract":"<div><div>This study investigates the concentrations, chemical compositions, and sources of PM_2.5 in Huangshi, China. Daily average PM_2.5 levels ranged from 8.43 to 193.08 μg m⁻³, with an annual mean of 54.13 μg m⁻³, exceeding China's annual secondary standard of 35 μg m⁻³. Seasonal mean concentrations peaked in winter and were lowest in summer. Organic carbon (OC) and elemental carbon (EC) had annual means of 4.89 μg m⁻³ and 0.94 μg m⁻³, respectively. Water-soluble inorganic ions (WSIIs) accounted for 52.17% of PM_2.5, with NO₃⁻, SO₄²⁻, and NH₄⁺ being the major components. The NO₃⁻/SO₄²⁻ ratio averaged 1.65, indicating a transition from coal combustion to vehicle emissions as the primary pollution source. Chemical mass reconstruction revealed that NH₄NO₃, (NH₄)₂SO₄, and organic matter (OM) accounted for 65.3% of PM_2.5 mass. Seasonal variations in light extinction (<em>b</em>_ext) highlighted the impact of secondary inorganic salts on visibility, with an annual average <em>b</em>_ext of 346.30 ± 246.98 Mm⁻¹. Airmass clusters and potential source region analysis suggested PM_2.5 and its components were primarily originated from local and nearby regions. These findings underscore the effectiveness of local pollution control measures, changing pollution sources, and the necessity for targeted emission controls to improve air quality and visibility in urban areas.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"341 ","pages":"Article 120922"},"PeriodicalIF":4.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of potential sources of airborne pollen in a high-mountain mediterranean natural environment","authors":"Paloma Cariñanos ,&nbsp;Soledad Ruiz-Peñuela ,&nbsp;Andrea Casans ,&nbsp;Alberto Cazorla ,&nbsp;Fernando Rejano ,&nbsp;Alejandro Ontiveros ,&nbsp;Pablo Ortiz-Amezcua ,&nbsp;Juan Luis Guerrero-Rascado ,&nbsp;Francisco José Olmo ,&nbsp;Lucas Alados-Arboledas ,&nbsp;Gloria Titos","doi":"10.1016/j.atmosenv.2024.120917","DOIUrl":"10.1016/j.atmosenv.2024.120917","url":null,"abstract":"","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"340 ","pages":"Article 120917"},"PeriodicalIF":4.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An attention-based CNN model integrating observational and simulation data for high-resolution spatial estimation of urban air quality","authors":"Shibao Wang ,&nbsp;Yanxu Zhang","doi":"10.1016/j.atmosenv.2024.120921","DOIUrl":"10.1016/j.atmosenv.2024.120921","url":null,"abstract":"<div><div>Machine learning, especially deep learning, can outperform traditional atmospheric models in air quality assessment, offering enhanced efficiency and accuracy without relying on detailed emission inventories and atmospheric chemical mechanisms. Despite their predictive power, deep learning models often grapple with the perception of being “black boxes” due to their intricate architectures. Here, we develop an attention-based convolutional neural network (CNN-attention) model that incorporates observational data, the parallelized large-eddy-simulation model (PALM), and urban morphology data for high-resolution spatial estimation of urban air quality. Our findings indicate that the CNN-attention model outperforms traditional CNN with higher accuracy and efficiency, achieving R² = 0.987 and root mean square error (RMSE) = 0.15 mg/m³, while significantly reducing training time and memory usage. Compared to traditional machine learning models, the CNN exhibits higher R² values and lower RMSE, showcasing its adeptness at capturing complex nonlinear patterns. The inclusion of attention layer further improves the model's performance by dynamically assigning attention scores to key features, enabling the model to focus on areas of critical emissions and distinctive urban features such as highways, arterial roads, intersections, and dense building clusters. This approach also reveals fluid dynamical principles, highlighting the significant disparities in pollutant concentration across roadways caused by atmospheric turbulence, and the distinct plume formations influenced by land use and topography. When applied to various urban settings, the CNN-attention model exhibits superior generalizability and transferability. This study provides valuable scientific insights and technical support for urban planning, air quality management, and exposure risk evaluation.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"340 ","pages":"Article 120921"},"PeriodicalIF":4.2,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved tools for estimation of ammonia emission from field-applied animal slurry: Refinement of the ALFAM2 model and database","authors":"Sasha D. Hafner ,&nbsp;Johanna Pedersen ,&nbsp;Roland Fuß ,&nbsp;Jesper Nørlem Kamp ,&nbsp;Frederik Rask Dalby ,&nbsp;Barbara Amon ,&nbsp;Andreas Pacholski ,&nbsp;Anders Peter S. Adamsen ,&nbsp;Sven Gjedde Sommer","doi":"10.1016/j.atmosenv.2024.120910","DOIUrl":"10.1016/j.atmosenv.2024.120910","url":null,"abstract":"<div><div>Ammonia volatilization from animal slurry applied to agricultural fields reduces nitrogen use efficiency in agriculture and pollutes the environment. This work presents new versions of a model and database focused on this route of N loss. The public ALFAM2 database (<span><span>https://github.com/AU-BCE-EE/ALFAM2-data</span><svg><path></path></svg></span>) was expanded with ammonia emission and ancillary measurements for &gt;700 additional field plots. The ALFAM2 model (<span><span>https://github.com/AU-BCE-EE/ALFAM2</span><svg><path></path></svg></span>, <span><span>https://zenodo.org/records/13312251</span><svg><path></path></svg></span>) was extended with the addition of an ammonia sink for more plausible predictions over extended durations and to better reflect the expected reduction in emission rate several days after slurry application. A new parameter set was developed for the model taking into account the newly available measurement data. Model efficiency improved to 0.67 for the parameter estimation subset (0.52 for cross-validation) and mean absolute error was around 10% of applied total ammoniacal nitrogen. As in earlier versions, predicted emission is sensitive to application method, slurry dry matter and pH, air temperature, and wind speed. A collection of parameter sets for estimating uncertainty in average predictions was developed using a bootstrap approach. Predicted uncertainty is not trivial, and is high for some variable combinations, highlighting the challenge of making predictions based on available measurement data. Still, this work has resulted in more accurate, comprehensive, transparent, and flexible tools for emission inventory and related work on ammonia loss from field-applied slurry.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"340 ","pages":"Article 120910"},"PeriodicalIF":4.2,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning integrated PMF model reveals influencing factors of ozone pollution in a coal chemical industry city at the Jiangsu-Shandong-Henan-Anhui boundary","authors":"Chaolong Wang ,&nbsp;Xiaofei Qin ,&nbsp;Yisheng Zhang ,&nbsp;Dantong Liu ,&nbsp;Wenxin Tao ,&nbsp;Ming Wang ,&nbsp;Sufan Zhang ,&nbsp;Jianli Yang ,&nbsp;Jinhua Du ,&nbsp;Shanshan Cui ,&nbsp;Dasa Gu ,&nbsp;Yingjie Sun ,&nbsp;Chenying Lv","doi":"10.1016/j.atmosenv.2024.120916","DOIUrl":"10.1016/j.atmosenv.2024.120916","url":null,"abstract":"<div><div>Zaozhuang, located at the center of the boundary between Jiangsu, Shandong, Henan, and Anhui, contains coal and heavy industries. Zaozhuang has experienced severe O₃ pollution in recent years and it is crucial to identify the key drivers. This study aims to deeply excavate and analyze the formation mechanism of O₃ in Zaozhuang based on hourly measured volatile organic compound (VOC) concentration data for the year 2023, combined with meteorological factors and other atmospheric pollutants, using a machine learning model in combination with the SHapley Additive Properties Interpretation method and Positive Matrix Factorization model. The results show the important contributions of meteorological factors to O₃ production, especially solar radiation and temperature. Among atmospheric pollutants, VOCs are the main contributors, with significant effects from alkene and oxygenated VOCs, whereas propene and acetone have the most critical individual impacts on local O₃ production. O₃ peaked in June and August, with June seeing added contributions from temperature, and a higher chemical variable contribution than meteorological factors in August, led by NO₂, OVOCs, and alkenes. The effects of the six emission sources on O₃ formation in Zaozhuang showed that chemical emission sources (5.98 μg/m³), combustion sources (3.75 μg/m³), and solvent use sources (3.06 μg/m³) were the main drivers. The solvent source exhibited the most significant change on the O₃ polluted day, with a relative increase of 115%. This relative increase was significantly higher than that of the other sources. During persistent pollution events with the highest levels of O₃, the use of solvents made the greatest contribution to the emission sources, representing 23% of the total impact of the emission sources. Therefore, an integrated approach using machine learning, SHapley Additive Properties Interpretation, and Positive Matrix Factorization rapidly diagnoses the causes of O₃ pollution at different timescales and provides a basis for targeted control measures.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"342 ","pages":"Article 120916"},"PeriodicalIF":4.2,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142697623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Indoor ozone reaction products: Contributors to the respiratory health effects associated with low-level outdoor ozone","authors":"Linchen He ,&nbsp;Zhiheng Hao ,&nbsp;Charles J. Weschler ,&nbsp;Feng Li ,&nbsp;Yinping Zhang ,&nbsp;Junfeng Jim Zhang","doi":"10.1016/j.atmosenv.2024.120920","DOIUrl":"10.1016/j.atmosenv.2024.120920","url":null,"abstract":"<div><div>Low-level outdoor ozone (O₃) exposure has been associated with adverse respiratory health effects, whereas substantially higher O₃ concentrations have been required to exert measurable effects in controlled studies. This discrepancy remains poorly understood. After entering indoors, a substantial portion of O₃ reacts with indoor chemicals to generate ozone reaction products that are potentially more toxic than O₃ itself. We hypothesize that ozone reaction product exposures contribute to the adverse respiratory effects associated with low-level outdoor O₃ exposure. In a panel study of 70 healthy adults, each was measured four times during a low-ozone season (maximum 8-h average: 29 ± 13 ppb). We found that higher average outdoor O₃ concentrations, irrespective of whether participants were outdoors or indoors, were significantly associated with worsened spirometric lung function (i.e., FVC, FEV₁, FEF_25-75) and airway mechanics (i.e., R₅, R₂₀) indicators. Per interquartile range (IQR) increase in average outdoor O₃ exposure when participants were indoors with windows closed (exposure proxy for ozone reaction products + indoor O₃) was significantly associated with worsening of multiple respiratory function indicators including FVC, FEV₁, FEF_25-75, Z₅, R₅, and R₂₀ by 0.56–3.08%. In contrast, per IQR increase in average outdoor O₃ exposure when participants were outdoors or indoors with windows open (exposure proxy for O₃ without ozone reaction products) was only significantly and adversely associated with worsening of one respiratory function indicator X₅ by 1.4%. These findings support our hypothesis and suggest further evaluation of indoor ozone reaction products' contribution to adverse health effects induced by outdoor O₃ exposure.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"340 ","pages":"Article 120920"},"PeriodicalIF":4.2,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors affecting the different growth rates of PM2.5：Evidence from composition variation, formation mechanisms, and importance analysis of water-soluble inorganic ions with case study in northern China","authors":"Huan Li ,&nbsp;Ting Zhang ,&nbsp;Hui Su ,&nbsp;Sui Xin Liu ,&nbsp;Ying Qiang Shi ,&nbsp;Lu Yao Wang ,&nbsp;Dong Dong Xu ,&nbsp;Jia Mao Zhou ,&nbsp;Zhu Zi Zhao ,&nbsp;Qi Yuan Wang ,&nbsp;Steven Sai Hang Ho ,&nbsp;Yao Qu ,&nbsp;Jun Ji Cao","doi":"10.1016/j.atmosenv.2024.120913","DOIUrl":"10.1016/j.atmosenv.2024.120913","url":null,"abstract":"<div><div>PM_2.5 affects air quality, therefore, understanding the mechanism of PM_2.5 growth is essential to figure out mitigation measures. Hourly real-time concentrations of water-soluble inorganic ions (WSIIs), including anions and cations, in fine particulate matter (PM_2.5) were measured in Baoji, northwest China. During the winter monitoring period, the concentrations of PM_2.5 and most WSIIs exhibited similar trends. Mass proportions of SNA [i.e., sulfate (SO₄²⁻), nitrate (NO₃⁻), ammonium (NH₄⁺)] in PM_2.5 gradually increased with air deterioration, while equivalent ratios of anions to cations also increased. The heterogeneous aqueous reactions and/or gas-phase homogeneous reactions promoted the formation of secondary inorganics, especially during the haze events. Rapid transformations of primary gaseous precursors to secondary pollutants could lead to the substantial formation of SO₄²⁻ and NO₃⁻. In terms of particle growth rate, the mass proportions of SNA in PM_2.5 decreased from General Growth (GG) to Explosive Growth (EG) events. Furthermore, the particle growth rates did not coincide with the pollution levels, while it occurred most frequently during the Transition Period, instead of the Polluted Period. The diurnal variation of SNA at different PM_2.5 growth rates has been discussed. The results of the Random Forest (RF) model showed that RH was an important factor for EG of PM_2.5, while low RH was a reliable reason for the relatively low mass proportion of SNA. The results of this study could advance our understanding of particle growth and provide scientific evidence to support the establishment of unique air quality control measures under different pollution scenarios in Fenwei Plain, China.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"340 ","pages":"Article 120913"},"PeriodicalIF":4.2,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}