Atmospheric Environment最新文献

{"title":"HONO chemistry affected by relative humidity and ammonia in the North China Plain during winter","authors":"Haiyan Ran ,&nbsp;Jingwei Zhang ,&nbsp;Yu Qu ,&nbsp;Juan Yang ,&nbsp;Yong Chen ,&nbsp;Yele Sun ,&nbsp;Chaoyang Xue ,&nbsp;Yujing Mu ,&nbsp;Junling An","doi":"10.1016/j.atmosenv.2025.121114","DOIUrl":"10.1016/j.atmosenv.2025.121114","url":null,"abstract":"<div><div>Nitrous acid (HONO) is a critical precursor of the hydroxyl radical (OH) and plays a pivotal role in atmospheric photochemistry. Although nitrogen dioxide (NO₂) heterogeneous reactions (HET) on ground and aerosol surfaces are widely recognized as major paths of HONO production, their influencing factors are not well characterized in air quality models, limiting the understanding of HONO formation and the quantification of their regional impact. In this study, a novel parameterization scheme for the NO₂ uptake coefficient, including the effects of solar radiation, relative humidity (RH) and ammonia (NH₃), was developed and coupled into the Weather Research and Forecasting model with Chemistry. Nine simulation scenarios were designed to assess the impacts of RH and NH₃ on HONO chemistry and O₃ levels in the North China Plain (NCP). The results showed that the RH-impacted HET contributed 10−25% of HONO, with a significant increase of more than 35% during the haze periods; whereas the NH₃-impacted HET contributed 15% of nighttime HONO and &lt;5% of noontime HONO, playing a more significant role in rural areas. Vertically, the RH-impacted HET contribution to nighttime HONO concentrations remained 26−31% at an altitude of 700–900 m due to the higher RH levels (50−60%) during the haze periods; whereas the NH₃-impacted HET contribution was minor above 500 m owing to the fast-decreasing NH₃ concentrations with height. When RH exceeded the turning point (70%), nighttime HONO was suppressed by up to 1 ppb in eastern NCP. The combination of RH and NH₃ produced a ground daily maximum 8h averaged O₃ enhancement of 6–14 μg m⁻³ during the haze periods, exceeding the effect of solar radiation. These findings deepen our understanding of the role of RH and NH₃ in HONO chemistry and imply the importance of reasonably expressing HET in air quality models.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"348 ","pages":"Article 121114"},"PeriodicalIF":4.2,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453631","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":"Insights into bacteria characteristics and potential pathogen in rural indoor households in Fenwei Plain, China","authors":"Liu Yang ,&nbsp;Xiaoyan Hu ,&nbsp;Zhenxing Shen ,&nbsp;Yiming Yang ,&nbsp;Hongmei Xu ,&nbsp;Jian Sun","doi":"10.1016/j.atmosenv.2025.121111","DOIUrl":"10.1016/j.atmosenv.2025.121111","url":null,"abstract":"<div><div>Indoor air quality in rural households significantly impacts public health, yet bioaerosol characteristics in these environments remain poorly understood. This study investigates the characteristics of bioaerosols and bacterial communities in rural households of the Fenwei Plain, China, comparing indoor and outdoor environments and contrasting rural-urban differences. The peak concentrations of total airborne microbes (TAMs), viable bacteria, non-viable bacteria, and viability showed pronounced indoor/outdoor variations, meanwhile, rural areas exhibited significantly lower bioaerosol concentrations than urban areas. The bacterial communities displayed distinct indoor-outdoor patterns: <em>Actinobacteria</em> (40.5% and 27.3%) and <em>Proteobacteria</em> (34.5% and 40.3%) were the predominant phyla detected in kitchen and living room, respectively, while <em>Bacteroidetes</em> (41% and 51.5) for chimney and outdoor environment. Notably, rural areas showed 2.8 and 8 times higher relative abundances of <em>Firmicutes</em> and <em>Bacteroidetes</em> compared to urban areas, indicating fundamentally different microbial ecosystems. At the genus level, the top two bacteria were <em>Vibrio</em> and <em>Chloroplast</em> in indoor areas, whereas the predominant genera in outdoor areas included <em>Prevotella</em>, <em>Faecalibacterium</em>, and <em>Bacteroides</em>. Bacterial communities in urban and rural areas displayed significant heterogeneity. The peak and valley relative abundance of potential pathogenic bacteria in rural areas appeared in the chimney area (62.7%) and in the living room (18.3%), respectively. <em>Rhodococcus</em> and <em>Prevotella</em> were the indicator pathogenic bacteria for urban and rural sites, respectively, with links to pulmonary infections and intestinal diseases. This study provides valuable insights into the characteristics of bioaerosols and their implications for human health protection in rural areas.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"347 ","pages":"Article 121111"},"PeriodicalIF":4.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427701","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":"Weather history-based parameterization of the G-93 isoprene emission formula for the tropical plant Ficus septica","authors":"Ishmael Mutanda ,&nbsp;Masashi Inafuku ,&nbsp;Hirosuke Oku","doi":"10.1016/j.atmosenv.2025.121102","DOIUrl":"10.1016/j.atmosenv.2025.121102","url":null,"abstract":"<div><div>The Guenther 1993 (G-93) formula is the most extensively used algorithm for predicting leaf-scale isoprene emissions driven by temperature and light intensity, and has been incorporated into many isoprene emission models. The temperature and light response variables of the G-93 define the rate of increase (ascend) and decrease (descend) of emissions as driven by temperature and light intensity. Our previous study on the tropical tree <em>Ficus septica</em> noted that hot weather in the previous days impacted unevenly the ascend and descend changes of isoprene emission, causing a significant deviation between the G-93 prediction and observations. Separate parameterization of the ascend and the descend phases successfully ameliorated this deviation, however, the relationship between weather history and parameters for individual ascend and descend phase still warrants more detailed studies to inform their reliable use in emission algorithms. We herein further examined the relationship between weather history and G-93 parameters for individual ascending and descending phase responses. We found that among the G-93 parameters, <em>C</em>_<em>T1</em> and <em>α</em> correlated with cumulative temperature or PPFD, whilst <em>C</em>_<em>T2</em> essentially remained constant for both the ascending and descending phases. These correlations allowed us to parameterize the G-93 formula based on weather history for the first time, and direct modification of <em>C</em>_<em>T1</em> and <em>α</em> in terms of cumulative temperature and light intensity captured 96.6% of variation in the ascending and 98.1% in the descending phase in the study period. Separate parameterization of the upward and the downward changes was found to be effective in improving our ability to predict isoprene emission from plants that experienced hot weather in the previous days. More importantly, this result implies that the assumption of a symmetric response of isoprene emission across the maxima temperature and light intensity needs revision especially under circumstances of a warming climate.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"347 ","pages":"Article 121102"},"PeriodicalIF":4.2,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420454","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 controlling variation of δ2H and δ18O in precipitation in Southern Bohemia, Central Europe","authors":"Marek Kopáček ,&nbsp;Petr Porcal ,&nbsp;Jiří Kopáček ,&nbsp;Yuliya Vystavna","doi":"10.1016/j.atmosenv.2025.121101","DOIUrl":"10.1016/j.atmosenv.2025.121101","url":null,"abstract":"<div><div>The effects of local climate parameters, seasonality and sources of air masses on the stable water isotopes [hydrogen (δ²H) and oxygen (δ¹⁸O)] in precipitation were investigated along an altitudinal gradient (381–1118 m a.s.l.) in southern Bohemia, Central Europe, from December 2021 to November 2023. The relationship between the observed δ²H and δ¹⁸O values was consistent with the Global Meteoric Water Line. The isotopic composition of precipitation changed with increasing altitude by −6.5 and −1.2‰ km⁻¹ for δ²H and δ¹⁸O, respectively. The δ values differed between seasons, with the values being most enriched in summer and most depleted in winter. An analysis of the air mass trajectories using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model showed that the main sources of precipitation were the North Atlantic (from 44% in spring to 70% in fall and winter), the Arctic Ocean (from 15% in summer to 38% in spring) and the Mediterranean Sea (from 12% in winter to 34% in summer). Throughout the study period, average δ values differed significantly between air masses (<em>p</em> &lt; 0.05) and along the altitudinal gradient, with the most enriched values (from −56 to −37‰ for δ²H and from −8.2 to −5.5‰ for δ¹⁸O) and the most depleted values (from −91 to −84‰ for δ²H and from −12.7 to −12.0‰ for δ¹⁸O) occurring in the Mediterranean and Arctic air masses, respectively. However, for individual daily samples, strong correlations occurred between the δ values and air temperature (the strongest), humidity, precipitation, time of sunshine and solar radiation, while the influence of air mass directions was not significant. A stepwise regression analysis showed that most of the variation in daily δ values (43–48% and 47–52% of δ²H and δ¹⁸O, respectively) was explained by the combination of air temperature and humidity, precipitation amount, and season.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"347 ","pages":"Article 121101"},"PeriodicalIF":4.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420453","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":"Explainable deep learning hybrid modeling framework for total suspended particles concentrations prediction","authors":"Sujan Ghimire ,&nbsp;Ravinesh C. Deo ,&nbsp;Ningbo Jiang ,&nbsp;A.A. Masrur Ahmed ,&nbsp;Salvin S. Prasad ,&nbsp;David Casillas-Pérez ,&nbsp;Sancho Salcedo-Sanz ,&nbsp;Zaher Mundher Yaseen","doi":"10.1016/j.atmosenv.2025.121079","DOIUrl":"10.1016/j.atmosenv.2025.121079","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Total Suspended Particles (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;S&lt;/mi&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) is an important indicator of air quality, yet traditional prediction models lack comprehensive consideration of spatio-temporal interactions of different meteorological and air pollution phenomena. To address these limitations, this study introduces an explainable (X) deep hybrid (H) network, integrating Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BGRU), for hourly &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;S&lt;/mi&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; concentration prediction. The model was trained and evaluated using meteorological and air quality data from Canon Hill, Australia. By combining CNN’s spatial feature extraction capabilities with BGRU’s temporal dependencies, the model effectively captures complex spatial–temporal patterns in the data. The X-H-CBGRU model outperforms fifteen competing benchmark models such as deep neural network, extreme learning machine, multilayer perceptron, support vector regression, random forest regression, light gradient boosting, gradient boosting regression, long short-term memory network, as well as their hybrid CNN counterparts in terms of the accuracy evidenced by a lower Root Mean Square Error (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;mi&gt;M&lt;/mi&gt;&lt;mi&gt;S&lt;/mi&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;≈&lt;/mo&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mn&gt;6&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;302&lt;/mn&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mi&gt;μ&lt;/mi&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;g/m&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) and higher Correlation Coefficient (&lt;span&gt;&lt;math&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;≈&lt;/mo&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;91&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) compared to other models. Moreover, the model demonstrates strong probabilistic performance with a high Prediction Interval Coverage Probability (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;≈&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;98&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) and low Prediction Interval Normalized Average Width (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;mi&gt;N&lt;/mi&gt;&lt;mi&gt;A&lt;/mi&gt;&lt;mi&gt;W&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;≈&lt;/mo&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;18&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;), indicating its reliable prediction intervals. To enhance model interpretability, Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) methods were employed, revealing &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;M&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;10&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; concentration, relative humidity, air temperature, and wind speed as key predictors of &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;S&lt;/mi&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; concentrations. The Diebold–Mariano statistical test further confirmed the model’s superior performance. This study contributes towards advancing &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;S&lt;/mi&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; prediction by providing a robust, ac","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"347 ","pages":"Article 121079"},"PeriodicalIF":4.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402496","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 recent mercury trends associated with the National Atmospheric Deposition Program Mercury Litterfall Network","authors":"Mae Sexauer Gustin ,&nbsp;David A. Gay ,&nbsp;Nicole Choma","doi":"10.1016/j.atmosenv.2025.121097","DOIUrl":"10.1016/j.atmosenv.2025.121097","url":null,"abstract":"<div><div>The National Atmospheric Deposition Program established the Mercury (Hg) Litterfall Network in 2007 to assist with estimating changes in dry deposition of Hg. These measurements represent primarily gaseous elemental mercury (Hg⁰) taken up by foliage actively during the growing season through stomata. Hg deposition is driven by litterfall mass; thus concentrations are a better indicator of trends. Previous work assessed trends from 2007 to 2014 from 27 locations in the eastern U.S. and found that litterfall total Hg concentrations declined. Here, data from the same area representing 2017 to 2021, 2013 to 2021, and 2007 to 2021 were compiled. For the first two time periods no significant trends in litter concentrations were observed; however, values measured at locations impacted by local/regional sources had higher concentrations and showed increasing trends, but these were not significant. Using all sites for which data were available from 2017 to 2021, total Hg concentration in litterfall for 2017 to 2018 was significantly greater than 2020 to 2021. Using all data from 2007 to 2021 Hg concentrations in litter have declined, as have precipitation concentrations. In general, from 2013 to 2021 Mid-Atlantic, East Coast, and Mid-Western concentration in foliage declined due to controls on sources; while the Great Lakes Region and Southeast did not change. Methylmercury was measured in litterfall at all locations. MeHg concentrations generally declined from 2007 to 2021, but have not changed since 2017. However, concentrations for 2021 were higher than for 2020 for most sites. Methylmercury in litterfall has been demonstrated to bioaccumulate in terrestrial ecosystems raising concerns for songbirds.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"347 ","pages":"Article 121097"},"PeriodicalIF":4.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402748","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":"A review of measurements and model simulations of atmospheric nitrous acid","authors":"Like Wang ,&nbsp;Jiajue Chai ,&nbsp;Benjamin Gaubert ,&nbsp;Yaoxian Huang","doi":"10.1016/j.atmosenv.2025.121094","DOIUrl":"10.1016/j.atmosenv.2025.121094","url":null,"abstract":"<div><div>Ambient nitrous acid (HONO) plays a crucial role in the atmosphere's oxidative capacity, significantly impacting air quality and climate. This study reviews the current understanding of HONO formation mechanisms, including in-situ and vertical gradient measurements, as well as the temporal, spatial, and vertical characteristics of HONO and its modeling approaches. HONO concentrations exhibit significant diurnal variation based on sources and sinks in different environments. Typically, concentrations are higher near the ground and decrease with altitude. Additionally, this study examines the incorporation of contemporary HONO chemical mechanisms into box models, regional and global chemical transport models (CTMs), and chemistry-climate models. Models often underestimate observations due to uncertainties in heterogeneous HONO formation and varying measurement techniques. Finally, this review identifies key challenges for future HONO measurements and modeling efforts. Significant opportunities remain to enhance our fundamental understanding of HONO. Precision and accuracy are important for advancing HONO observation measurement techniques. Simultaneously, the representation of HONO in state-of-the-art models helps us better quantify atmospheric oxidation capacity and air quality impacts.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"347 ","pages":"Article 121094"},"PeriodicalIF":4.2,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420452","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":"Validation of multi-model decadal simulations of present-day central European air-quality","authors":"Alvaro Patricio Prieto Perez,&nbsp;Peter Huszár,&nbsp;Jan Karlický","doi":"10.1016/j.atmosenv.2025.121077","DOIUrl":"10.1016/j.atmosenv.2025.121077","url":null,"abstract":"<div><div>Although air quality has improved in Europe, most of its population is still exposed to levels of pollutants that are harmful to health, such as nitrogen dioxide (NO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>) or sulphur dioxide (SO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>). Studying the processes that drive atmospheric chemistry is key to understanding their contribution to air quality. However, since many pollutants are secondary, since meteorology influences the chemical evolution of pollutants, disperses them and transport them and their precursors, among other factors, this task is extremely difficult. This makes thus the use of models essential for the study of air quality. In this work, we present the first long-term validation of air quality simulations in Central Europe for the 2010–2019 decade. The simulations were carried out using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the Comprehensive Air Quality Model with Extensions (CAMx). Using the AirBase dataset, stations inside the model domain were classified into three categories according to their pollution burden, and the validation of the models was performed independently in each group. Our research shows that, generally, simulations underestimate pollutant concentrations – except ozone (O<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>) – most likely because of an incorrect monthly and hourly emissions profile and an overestimation of vertical mixing.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"349 ","pages":"Article 121077"},"PeriodicalIF":4.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549748","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":"Study on global atmospheric aerosol type identification from combined satellite and ground observations","authors":"Xin Nie ,&nbsp;Leyi Yu ,&nbsp;Qianjun Mao ,&nbsp;Xiaoyan Zhang","doi":"10.1016/j.atmosenv.2025.121100","DOIUrl":"10.1016/j.atmosenv.2025.121100","url":null,"abstract":"<div><div>Accurate identification of aerosol types is essential for understanding the radiative properties of aerosols and further studying aerosol regional and global climate effects. However, aerosol type identification studies combining satellite and ground observations are rare. In this paper, an aerosol type identification model based on aerosol relative optical depth (AROD) and volume depolarization ratio (VDR) is developed by effectively matching satellite and ground observation data. The accuracy and applicability of the new model are verified by typical AERONET sites with dominant aerosols, and the spatial distribution characteristics of aerosol types in global continents are also studied and analyzed with joint observation data from 2018 to 2023 globally. The results show that the East Asian continent, North America, and Europe are the main source regions for continental aerosol emissions. Among the continents, densely populated East/Southeast Asia is more heavily polluted by anthropogenic aerosols than other continents, while North America and Europe are relatively less polluted. The Indo-China Peninsula, central and southern Africa, central South America, and central and northern North America are the main source emission regions of global biomass burning aerosol, while northern Africa, West Asia, and Central Asia emit most of the global dust and polluted dust aerosols. Additionally, marine aerosols are more frequent along the east coast of North America, the west coast of Africa, the Malay Archipelago, and some island sites. The present study provides a basis for aerosol type identification using joint observation data and effectively promotes research in related fields.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"347 ","pages":"Article 121100"},"PeriodicalIF":4.2,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402744","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":"The impact of sky conditions on gross primary production and methane flux from different rice paddies","authors":"Tingting Zhu ,&nbsp;Yanlian Zhou ,&nbsp;Weimin Ju","doi":"10.1016/j.atmosenv.2025.121098","DOIUrl":"10.1016/j.atmosenv.2025.121098","url":null,"abstract":"<div><div>The increase of diffuse radiation fraction has been reported to greatly impact carbon uptake in agroecosystems. However, it is unclear how radiation components affected methane (CH₄) emission. Based on eddy covariance measurement from six sites, the effects of sky conditions on gross primary productivity (GPP) and CH₄ emission were investigated at half-hourly and daily scales. The results showed diurnal patterns of GPP were similar under all sky conditions, while CH₄ emission displayed irregular unimodal curves with greater fluctuations under different sky conditions. GPP responded to the changing radiation more efficiently under overcast conditions than under sunny conditions, whereas CH₄ emission under sunny conditions was higher at the same radiation levels. Parameters describing GPP and CH₄ emission varied across sites and sky conditions. The maximum photosynthetic rate and CH₄ rate at turning point under cloudy and overcast skies were lower than those under sunny conditions. The values of initial light use efficiency from GPP and CH₄ emission were opposite with the increase of diffuse radiation fraction, respectively. Soil moisture, temperature (Ta), vapor pressure deficit, direct, and diffuse radiation (<em>R</em>_dif) were responsible for the variations of GPP and CH₄ emission under different skies, CH₄ emission depended heavily on GPP and Ta under the inhibition of <em>R</em>_dif. This study implies that the direct effects of sky conditions on GPP were greater than those on CH₄ emission and should be paid more attention in ecosystem carbon cycle models.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"346 ","pages":"Article 121098"},"PeriodicalIF":4.2,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378405","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}