Atmospheric Pollution Research最新文献

{"title":"Research on the impact of land use and meteorological factors on the spatial distribution characteristics of PM2.5 concentration","authors":"Xiaoxia Wang ,&nbsp;Hongtao Zhang ,&nbsp;Zhihai Fan ,&nbsp;Hong Ding","doi":"10.1016/j.apr.2025.102462","DOIUrl":"10.1016/j.apr.2025.102462","url":null,"abstract":"<div><div>Precisely capturing the spatial distribution characteristics of fine particulate matter (PM_2.5) is the key to air pollution prevention and control. Researches suggests that PM_2.5 concentrations is jointly affected by meteorological factors and urban land use. This study comprehensively considered these factors, with land cover, meteorology and road traffic as potential independent variables. The land use regression (LUR) model was used to identify the main influences on the spatial distribution of PM_2.5 concentration under different types of land use, and the importance of these factors was assessed using a random forest (RF) model. The research findings indicate that: (1) The fluctuations of PM_2.5 concentration in residential and industrial land use are relatively severe, ranging from 0 to 50 μg/m³. The changes in commercial and public service land use range from 0 to 40 μg/m³. And the in green space range from 15 to 33 μg/m³ (2) The distribution of PM_2.5 concentration in residential land is primarily influenced by precipitation and relative humidity, with relative importance of 36.3% and 23.9%, respectively. And that in industrial land use is primarily influenced by wind speed, with a relative importance of 30.6%. (3) The most influential determinant of spatial distribution of PM_2.5 concentration is meteorological factors, with relative importance exceeding 62%. The relative importance of road traffic ranges from 15.6% to 24.9%. And that of land cover factors ranges from 9.9% to 22.4%. This study analyzes the coupling connection between urban land use and spatial distribution of PM_2.5 concentration, and elaborates the specific influence of the former on the latter.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102462"},"PeriodicalIF":3.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Unprecedented impacts of meteorological and photolysis rates on ozone pollution in a coastal megacity of northern China","authors":"Jianli Yang ,&nbsp;Chaolong Wang ,&nbsp;Yisheng Zhang ,&nbsp;Sufan Zhang ,&nbsp;Xing Peng ,&nbsp;Xiaofei Qin ,&nbsp;Jianhui Bai ,&nbsp;Lian Xue ,&nbsp;Guan Wang ,&nbsp;Shanshan Cui ,&nbsp;Wenxin Tao ,&nbsp;Jinhua Du ,&nbsp;Dasa Gu ,&nbsp;Xiaohan Su","doi":"10.1016/j.apr.2025.102461","DOIUrl":"10.1016/j.apr.2025.102461","url":null,"abstract":"<div><div>This study investigates the seasonal variations in O₃ levels in Qingdao, a typical coastal city, and quantifies the effects of key photolysis rate constants (<em>J</em>[O¹D] and <em>J</em>[NO₂]), meteorological parameters (RH, TEMP, and SF), and pollutants (ΔCO, PM_2.5, and NO₂) on O₃ levels across different seasons using machine learning. Additionally, the summer months, when photochemical reactions are most active, were analyzed in detail. The results indicate that the factors contributing to summer O₃ levels in order of importance, were RH, ΔCO, SF, PM_2.5, <em>J</em>[O¹D], NO₂, TEMP, WS, and <em>J</em>[NO₂]. RH was the most significant factor, with high humidity levels (&gt;75%) inhibiting O₃ formation. ΔCO, representing regional transport, was the second most influential, suggesting that direct O₃ transport and the delivery of high concentrations of precursors significantly promoted local O₃ production and accumulation. While <em>J</em>[O¹D] and <em>J</em>[NO₂] had different roles in O₃ promotion and depletion, <em>J</em>[O¹D] had a greater impact overall. The temperature in the range of 26 °C–32 °C inhibits O₃ production, When RH exceeded 90%, <em>J</em>[O¹D] accelerates while other photolysis rate constants decline, further suppressing the production of O₃. For comparison, multiple linear regression models were used to develop empirical equations for calculating hourly O₃ concentrations across the four seasons. The results showed that these factors explained 50%, 64%, 61%, and 63% of the O₃ sources in Qingdao for spring, summer, autumn, and winter, respectively. Sensitivity tests on factors influencing summer O₃ concentrations found that MLR could not quantify their contributions to O₃ levels.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102461"},"PeriodicalIF":3.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"An integrated feature selection and machine learning framework for PM10 concentration prediction","authors":"Elham Kalantari ,&nbsp;Hamid Gholami ,&nbsp;Hossein Malakooti ,&nbsp;Dimitris G. Kaskaoutis ,&nbsp;Poorya Saneei","doi":"10.1016/j.apr.2025.102456","DOIUrl":"10.1016/j.apr.2025.102456","url":null,"abstract":"<div><div>The Sistan Basin, east Iran is a major dust source, presenting significant atmospheric, ecological, socio-economic, and health challenges. This study employed machine learning (ML) algorithms, including Random Forest (RF), K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), Support Vector Regression (SVR), and Least Absolute Shrinkage and Selection Operator (LASSO), to model and predict PM₁₀ concentrations in Zabol City (2013–2022), utilizing independent meteorological variables such as temperature, relative humidity, wind speed and direction. Feature selection methods — Filter (Information Gain, F-Test, Correlation Coefficient), Wrapper (Recursive Feature Elimination, Sequential Forward/Backward Selection), and Embedded (LASSO, Elastic Net, Ridge Regression, RF Importance) — were applied to identify significant predictors, with embedded methods providing the best balance of simplicity, accuracy, and cost-efficiency. Among the models, RF demonstrated the highest seasonal performance (R² = 0.75) during summer. RF's prediction R² values for PM₁₀ remained above 0.5 in all seasons, consistently outperformed the other models. The WKNN model performed reasonably well across all seasons, ranking second among the models, while the LASSO model demonstrated weaker performance. The SVR model showed satisfactory performance in specific seasons, such as summer and autumn. A common feature of all models was their better performance during summer. Importantly, the models relied solely on readily available meteorological data, enabling accurate predictions of PM₁₀ in this arid region of eastern Iran. The findings highlight the potential of ML techniques for developing air pollution prediction and warning systems, offering valuable support to policymakers in the design of effective pollution control strategies and safeguarding public health.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102456"},"PeriodicalIF":3.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"PM2.5 and PM10 concentrations in urban and peri-urban environments of two Pacific Island Countries","authors":"J.J. Hilly ,&nbsp;J. Sinha ,&nbsp;F.S. Mani ,&nbsp;A. Turagabeci ,&nbsp;P. Jagals ,&nbsp;D.S.G. Thomas ,&nbsp;G.F.S. Wiggs ,&nbsp;L. Morawska ,&nbsp;K. Singh ,&nbsp;J. Gucake ,&nbsp;M. Ashworth ,&nbsp;M. Mataki ,&nbsp;D. Hiba ,&nbsp;D. Bainivalu ,&nbsp;L.D. Knibbs ,&nbsp;R.M. Stuetz ,&nbsp;A.P. Dansie","doi":"10.1016/j.apr.2025.102454","DOIUrl":"10.1016/j.apr.2025.102454","url":null,"abstract":"<div><div>Air quality monitoring in most Pacific Island Countries, Territories, and States (PICTS) is minimal, with notable exceptions in Hawai'i and New Caledonia. However, air quality issues are increasingly significant in the region. Existing data on air quality, particularly regarding PM_2.5 and PM₁₀, are limited, with studies focusing on Fiji and New Caledonia. Our research provides the first continuous and comparative air quality monitoring in urban and peri-urban areas of Fiji and the Solomon Islands, and it is the first assessment since the introduction of the 2021 World Health Organization (WHO) Air Quality Guidelines (AQG). This study assesses health risks and air pollution trends to inform governmental recommendations. We collected PM_2.5, PM₁₀, and weather data from Honiara, Solomon Islands (February 2020–August 2023), and Suva, Fiji (April 2021–August 2023). In Honiara, PM_2.5 levels exceeded WHO AQG on 75% of days in urban areas and 51% in peri-urban areas, while PM₁₀ levels surpassed guidelines on 2% of days in both areas. In Suva, urban areas had a 10% exceedance of PM_2.5 guidelines, compared to 13% in peri-urban areas. Annual PM_2.5 averages exceeded WHO guidelines every year, with levels in Suva and Honiara exceeding guidelines by 2–4 times. PM₁₀ levels were 1.5 times higher than WHO AQG in urban Honiara and 1.2 times higher in peri-urban areas. These findings highlight the urgent need for governmental action to establish robust air quality standards and long-term monitoring programs in Fiji and the Solomon Islands to mitigate health risks from poor air quality.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102454"},"PeriodicalIF":3.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Experimental and kinetic analysis of Hg0 removal by CoFe2O4 nanoparticles as an efficient activator of persulfate","authors":"Qianqian Zhang,&nbsp;Haixing Du,&nbsp;Anchao Zhang,&nbsp;Hongyu Zheng,&nbsp;Haixia Li,&nbsp;Weiwei Zhang,&nbsp;Zhijun Sun","doi":"10.1016/j.apr.2025.102453","DOIUrl":"10.1016/j.apr.2025.102453","url":null,"abstract":"<div><div>The sulfate radical (SO₄^•−) and hydroxyl radical (^•OH), derived from the oxidation of persulfate (PS), are significant active substances in the treatment of pollution. In this study, magnetic CoFe₂O₄ nanoparticles (CFO NPs) were synthesized by a hydrothermal method and applied to activate PS for Hg⁰ removal from the simulated flue gas. The results exhibited that the Hg⁰ removal efficiency can reach as high as 99.5% within 60 min under the optimal condition of 6 mM of PS, 0.8 g/L of CFO dose, 20 °C of reaction temperature and 7 of initial pH. The characterizations demonstrated that the large surface area and coexistence of Co/Fe mixed valence were generated after the formation of CFO nanostructure, improving the amount of active sites and facilitating the adsorption and activation of PS. Scavenging tests indicated that SO₄^•− and ^•OH were the main active radicals on Hg⁰ removal, where the ^•OH radicals primarily originated from the conversion of SO₄^•−. Moreover, the circulation of ≡Co(III)/≡Co(II) and ≡Fe(III)/≡Fe(II) resulted in a superior Hg⁰ removal activity. Based on the experiments and characterization analysis, the reaction mechanism was proposed. In addition, the kinetic model for Hg⁰ removal was systematically analyzed, and the role of SO₄^•− and ^•OH was further verified. This study provided new insights toward efficient activation of persulfate for removal of Hg⁰ from coal-fired flue gas.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 4","pages":"Article 102453"},"PeriodicalIF":3.9,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Characterization identification and speciated emission inventory construction of anthropogenic volatile organic compounds (VOCs) in Beijing, China","authors":"Xiaoyu Liu ,&nbsp;Hanyu Zhang ,&nbsp;Zhe Lv ,&nbsp;Huahua Bai ,&nbsp;Guohao Li","doi":"10.1016/j.apr.2025.102452","DOIUrl":"10.1016/j.apr.2025.102452","url":null,"abstract":"<div><div>Volatile organic compounds (VOCs) significantly impact air quality and human health, garnering widespread attention. We conducted a comparative analysis of anthropogenic VOC emissions across different years, established a speciated VOC emission inventory for Beijing in 2020 and assessed the ozone formation potential (OFP). The VOC emissions showed a consistent downward trend, ranging from 10.19 × 10⁴ t to 28.36 × 10⁴ t in 2007–2020. The main sectors shifted from mobile sources (43.42%) and solvent utilization (26.35%) in 2007 to solvent utilization (55.99%) and mobile sources (24.00%) in 2020. The key contributing districts shifted from Fangshan (28.92%), Chaoyang (9.84%), and Daxing (7.33%) in 2013 to Chaoyang (14.29%), Haidian (11.42%), and Fangshan (10.33%) in 2020. The profile dataset encompasses 15 sectors and includes 117 VOC species, with an estimated total of 5.07 × 10⁴ t of VOC emissions in 2020, with alkanes, alkenes, alkyne, aromatics, halocarbons, and OVOCs accounting for 34.51%, 17.84%, 1.01%, 36.20%, 7.37%, and 3.07%, respectively. Significant differences were observed in the proportions of various VOC species across sectoral emissions. This dataset shows substantial deviations from the U.S. SPECIATE database, highlighting the significance of developing VOC source profiles. The uncertainties in VOC emission estimates primarily originate from variations in activity levels, emission factors and spatial distribution of emissions. Some sectors with high OFPs, like automobile manufacturing, barbecue and residential combustion are regarded as critical targets for emission control. Aromatics, alkenes, and OVOCs were identified as the major contributors to OFP, and controlling their emissions is essential for reducing ozone formation in Beijing.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 4","pages":"Article 102452"},"PeriodicalIF":3.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Research of two dust transport pollution in northern China in 2023: Perspectives from LiDAR and multi source data","authors":"Hao Yang ,&nbsp;Xiaomeng Zhu ,&nbsp;Duoyang Qiu ,&nbsp;Zhiyuan Fang ,&nbsp;Yalin Hu ,&nbsp;Xianyang Li","doi":"10.1016/j.apr.2025.102441","DOIUrl":"10.1016/j.apr.2025.102441","url":null,"abstract":"<div><div>In March and April 2023, two dust events occurred in northern China, which had a huge impact on the travel and health of the public in northern China. During the two dust events (the first dust event, FD; the second dust event, SD), the peak PM₁₀ concentrations at the Handan station were 2407 μg/m³ and 829 μg/m³, respectively. According to observations from the Mie scattering LiDAR located in Handan City, during the period of FD and SD, there were differences in depolarization ratio, pollution duration, and spatial distribution. Based on multi-source data including the HYSPLIT model, MODIS sensor data, CALIPSO data, ERA5, CAMS and MERRA-2 reanalysis data, the transport process of two dust events and the causes of pollution were analyzed. The source of the FD dust air mass is twofold, On the one hand, the dust at 3000 m originates from the ground in southern Gansu, On the other hand, the dust at 1000–2000 m comes from the transport and deposition of high-altitude dust in Xinjiang. SD dust originated from deserts and Gobi regions within Mongolia. During the transboundary process, it is shown that part of FD sand dust is blown above the troposphere. When it reached Handan area, the dust above the troposphere settled. The prolonged duration of SD and the occurrence of secondary pollution were caused by calm surface winds and specific high-altitude atmospheric conditions.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 4","pages":"Article 102441"},"PeriodicalIF":3.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Differential impacts of the 2015–2020 El Niño/El Niño Modoki on seasonal ozone levels across China","authors":"Xiaohong Wang ,&nbsp;Qingheng Lu ,&nbsp;Shiyuan Zhong ,&nbsp;Yinchen Chen ,&nbsp;Zunli Dai ,&nbsp;Lejiang Yu","doi":"10.1016/j.apr.2025.102449","DOIUrl":"10.1016/j.apr.2025.102449","url":null,"abstract":"<div><div>Utilizing daily data from nearly 1500 ground stations within a nationwide air-quality monitoring network spanning 2015 to 2020, this study identifies three primary spatiotemporal modes of ozone (O₃) concentration anomalies and explores the sensitivity of surface O₃ to both Eastern Pacific El Niño and Central Pacific El Niño (El Niño Modoki) across distinct Chinese regions and seasons. Results reveal limited sensitivity during spring and winter, wherein negative (positive) O₃ anomalies predominate across China during the positive (negative) phase of either ENSO type, with spring magnitudes approximately doubling those observed in winter. In contrast, during summer and autumn, O₃ anomalies exhibit significant variations depending on El Niño type and geographical location. Positive anomalies are notably pronounced in summer and autumn, particularly during El Niño Modoki in eastern China, while negative anomalies concentrate in western China during autumn, with a scattered distribution in summer. On average, magnitudes of O₃ anomalies during El Niño Modoki surpass those during El Niño thoroughly, with the most pronounced differences in magnitude during summer for positive anomalies. The differences in O₃ anomalies between El Niño and El Niño Modoki are attributed to variations in anomalous atmospheric circulations, wherein El Niño Modoki stands out with stronger anomalous 850-hPa highs and lows, along with stronger associated anomalous circulations, as well as larger extents and magnitudes of anomalous precipitation and solar radiation. These variations induce changes in low-level wind directions that influence regional O₃ transport, and alterations in solar radiation and precipitation, impacting both O₃ generation and removal processes.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102449"},"PeriodicalIF":3.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Chemical composition, source distribution and health risk assessment of PM2.5 and PM10 in Beijing","authors":"Shaosong Zhen ,&nbsp;Min Luo ,&nbsp;Futao Xin ,&nbsp;Lingling Ma ,&nbsp;Diandou Xu ,&nbsp;Xiaomeng Cheng ,&nbsp;Yang Shao","doi":"10.1016/j.apr.2025.102448","DOIUrl":"10.1016/j.apr.2025.102448","url":null,"abstract":"<div><div>Beijing's air quality has seen significant enhancements owing to the successful implementation of China's emission control measures. However, air pollution incidents continue to occur, and more attention should be paid to continuous air quality monitoring and control. Here, the PM_2.5 and PM₁₀ samples collected during the air pollution days in Beijing in 2022 were analyzed to study the air quality situation and understand the changes in air pollution sources. The average concentration of PM_2.5 was 84.3 μg/m³ and PM₁₀ was 128.7 μg/m³. NO₃⁻, SO₄²⁻ and NH₄⁺ were the primary constituents of water-soluble inorganic ions, with concentrations of 18.8 μg/m³, 7.9 μg/m³, 7.4 μg/m³ in PM_2.5, and 20.5 μg/m³, 9.3 μg/m³, and 8.1 μg/m³ in PM₁₀. The enrichment factor values for Sn, Sb, and Cd in PM_2.5 exceeded 100, indicating severe anthropogenic pollution. Five pollution factors for PM_2.5 and PM₁₀ were obtained from the analysis of PMF model: vehicle emissions and dust, industrial emissions, secondary inorganic aerosols, coal combustion, and electronic manufacturing. The highest contributing factors were vehicle emissions and dust (28 % for PM_2.5 and 34 % for PM₁₀). According to the health risk assessment, Mn presented a non-carcinogenic risk to humans. Cd, As, Ni, and Cr (VI) showed a low level of carcinogenic risk within the acceptable range. The backward trajectory analysis showed that air masses from nearby cities exhibited stronger pollutant capabilities. Combining the potential source contribution function and concentration weight trajectory diagrams, the main potential source areas of Beijing are in Hebei, Tianjin, Shanxi and Henan.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 4","pages":"Article 102448"},"PeriodicalIF":3.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143331263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"3DVAR meteorological data assimilation and aerosol impact on the simulation of heat wave 2022 over Haryana using WRF-Chem","authors":"Paushali Deb ,&nbsp;S.K. Panda ,&nbsp;Unashish Mondal ,&nbsp;Sushil K. Dash ,&nbsp;Devesh Sharma","doi":"10.1016/j.apr.2025.102440","DOIUrl":"10.1016/j.apr.2025.102440","url":null,"abstract":"<div><div>The rising frequency of heat waves in India presents significant risks to public health, agriculture, and the economy. In March 2022, temperatures reached a record-breaking 33.10 °C, the highest in 122 years resulting in two major heat wave events: March 11–21 and March 26–31, which claimed 33 lives. This study delves into the impact of anthropogenic emission/aerosols and meteorological data assimilation on model-predicted surface meteorological variables using “Weather Research and Forecasting (WRF) model” coupled with Chemistry (WRF-Chem). Four distinct simulation scenarios namely, WRF, WRFDA (WRF with meteorological Data Assimilation), WRF-Chem, and WRF-ChemDA (WRF-Chem with meteorological Data Assimilation) were executed across the Haryana domain to assess the sensitivity of model outputs. Analyses within the WRFDA and WRF-ChemDA frameworks utilized a 6-hourly cyclic 3-Dimensional Variational (3DVAR) Data Assimilation (DA) of NCEP ADP Surface Observational Fields. Most critically, the incorporation of aerosols and DA techniques markedly improved forecasts of key meteorological variables, including 2 m Temperature (T2), 2 m Relative Humidity (RH2), Planetary Boundary Layer Height (PBLH), and Outgoing Longwave Radiation (OLR). Skill assessment metrics, including the Heidke Skill Score (HSS ∼ 0.5), Accuracy (ACC &gt;0.9), and Probability of Detection (POD ∼ 1), demonstrate that WRF-ChemDA outperformed other models, especially during heat wave events. Conclusively, this study advocates for the meticulous selection of modeling approaches to accurately simulate heat wave events, ensuring that selected models adeptly capture the intricate dynamics and complexities of extreme temperature phenomena.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 4","pages":"Article 102440"},"PeriodicalIF":3.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}