Atmospheric Environment: X最新文献

{"title":"Dependence on the lateral boundary condition of ozone concentration over East Asia simulated by the regional air quality model","authors":"Syuichi Itahashi","doi":"10.1016/j.aeaoa.2026.100464","DOIUrl":"10.1016/j.aeaoa.2026.100464","url":null,"abstract":"<div><div>Lateral boundary conditions are required to drive regional models and are typically prepared from global model results. However, uncertainties arise from the boundary conditions. In this study, the effects of boundary conditions on the performance in the regional model (Community Multiscale Air Quality; CMAQ) for tropospheric ozone (O₃), which has a long lifetime, were evaluated over East Asia, mainly Japan, where air quality is affected by local and transboundary air pollution. Here, four lateral boundary conditions were prepared, with a time-independent profile assuming ocean clean air; the Community Atmosphere Model with Chemistry (CAM-Chem) global model; and the Hemispheric CMAQ (H-CMAQ) model over the northern hemisphere with seasonal and daily resolutions. All configurations generally captured the O₃ concentration measured at the surface and by ozonesonde in 2018 because O₃ in East Asia is dominated by regional production. The time-independent profile failed to capture the surface seasonal O₃ variation close to the boundary and had difficulty capturing the vertical gradient in the free troposphere, particularly in southern East Asia. CAM-Chem gave an overestimation at the surface level (+16% biases), although this configuration better captured the O₃ vertical profile in the free troposphere compared with ozonesonde. H-CMAQ, seamlessly connected to the regional scale, gave better performance for surface-level (−1% and −3% biases), but underestimated the O₃ profile in the free troposphere. Although the results clarified the effects of boundary conditions on simulated O₃ concentration, other factors (i.e., photochemical production/loss and dry deposition) must be investigated to further improve the regional modeling performance.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"30 ","pages":"Article 100464"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148230598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of urban carbon flux patterns: A case study of Hefei station","authors":"Zhongwei Lu ,&nbsp;Renmin Yuan ,&nbsp;Jiajia Hua ,&nbsp;Jiaoxu Mei ,&nbsp;Xiaoming Gao ,&nbsp;Xueyong Shen","doi":"10.1016/j.aeaoa.2026.100451","DOIUrl":"10.1016/j.aeaoa.2026.100451","url":null,"abstract":"<div><div>Cities are now central to global efforts to reduce CO₂ emissions, as they represent the major anthropogenic sources of carbon. Consequently, compiling urban carbon inventories, refining monitoring techniques, and investigating the distribution of sources and sinks in complex urban environments have become increasingly urgent. This study is based on data from an Eddy Covariance (EC) tower operated by the University of Science and Technology of China in downtown Hefei. The site represents a campus-influenced urban environment, with campus lawns and low-rise buildings in the near field and major roads in the broader footprint. By integrating high-resolution spatial data with flux-footprint modeling, we analyze the spatiotemporal patterns of urban CO₂ fluxes and their key drivers. Results show that CO₂ fluxes on weekdays exhibit a distinct bimodal diurnal cycle, while weekend emissions are 31.93% lower. Seasonal variability is governed by human activity and vegetation photosynthesis; winter emissions are 4.8-fold higher than those in summer. Spatial analysis attributes 4.36 kg CO₂·m⁻²·yr⁻¹ to urban traffic, the dominant source in the study area. After gap-filling missing observations with an artificial neural network (ANN) and an improved marginal-distribution-sampling (iMDS) method, the annual regional emission is estimated at 4.81 kg CO₂·m⁻²·yr⁻¹. The fact that urban traffic emissions account for roughly 91% of the total indicates that non-traffic sources largely offset one another, such that, once traffic is excluded, the study area is close to a carbon-neutral state.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"30 ","pages":"Article 100451"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147740440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A conceptual framework for the thermodynamic displacement of semi-volatile organic compounds by extractable organic matter in polyurethane foam passive air samplers: A potential hypothesized source of underestimation in global monitoring","authors":"Ahsan Habib","doi":"10.1016/j.aeaoa.2026.100473","DOIUrl":"10.1016/j.aeaoa.2026.100473","url":null,"abstract":"<div><div>Polyurethane foam-based passive air samplers (PUF-PAS) are central to global monitoring networks, including the Global Atmospheric Passive Sampling (GAPS) network and the European Monitoring and Evaluation Programme (EMEP), for the determination of persistent organic pollutants (POPs). Current data models are largely predicted on the assumption of infinite capacity, treating the PUF-air partition coefficient (<em>K</em>_PUF-air) as a thermodynamic constant. However, field observations in high-aerosol environments, where high concentrations of aerosols are prevalent, indicate that the PUF matrix accumulates a significant mass of non-target extractable organic matter (EOM). This study hypothesizes that EOM loading acts as a co-solute that modulates the activity coefficients of target semi-volatile organic compounds (SVOCs), thereby inducing a thermodynamic shift that suppresses the effective <em>K</em>_PUF-air. The resulting matrix-induced suppression is hypothesized to lead to the premature termination of the linear uptake phase and a theoretically simulated bias in effective air volumes (<em>V</em>_air). While direct experimental verification is required, the present preliminary sensitivity analysis suggests a potential model-based underestimation of atmospheric concentrations by 20-50% in heavily contaminated environments under assumed conditions. This study proposes a conceptual capacity-correction framework to account for EOM-induced bias and suggests the strategic evaluation of sorbent-impregnated PUF (SIP-PAS) to maintain data integrity in heavily contaminated environments.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"30 ","pages":"Article 100473"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From sensor noise to spatial insight: A pipeline for detecting and visualizing air pollution events in urban environments","authors":"Sofia Zafeirelli ,&nbsp;Martin Dolejš ,&nbsp;Dimitris Kavroudakis","doi":"10.1016/j.aeaoa.2026.100474","DOIUrl":"10.1016/j.aeaoa.2026.100474","url":null,"abstract":"<div><div>Air pollution, and in particular fine particulate matter (PM_2.5), has significant health impacts, especially in urban environments. In this study, a comprehensive low-cost sensor analysis process of hourly PM_2.5 data is presented for the detection, characterization and visualization of pollution events in Athens, Greece. The methodology integrates data quality control, air pollution event detection and spatiotemporal analysis of air pollution dynamics using inverse distance weighting (IDW) spatial interpolation, centroid tracking and bootstrap-based uncertainty analysis. During the data quality control stage, 4.88% of the PM_2.5 observations were flagged as erroneous or anomalous. Between 2018 and 2022, a total of 424 air pollution events were identified, with 84% occurring during winter months. Most of the air pollution events occurred during winter evenings and night hours, possibly due to residential heating and temperature inversions. The methodology successfully captured known extreme events, including the August 2021 wildfire smoke episode affecting Athens. The pipeline demonstrates that dense low-cost sensor networks, when combined with systematic quality control, event detection, and uncertainty-aware spatial analysis, can provide actionable high-resolution information for urban air-pollution monitoring, urban environmental management, and early-warning applications.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"30 ","pages":"Article 100474"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of nucleation mode traffic emission on modelled high-resolution particle number concentrations in Beijing","authors":"Xiaoyu Li ,&nbsp;Pauli Paasonen ,&nbsp;Jenni Kontkanen ,&nbsp;Dian Ding ,&nbsp;Yifan Wen ,&nbsp;Chao Yan ,&nbsp;Leena Järvi","doi":"10.1016/j.aeaoa.2026.100453","DOIUrl":"10.1016/j.aeaoa.2026.100453","url":null,"abstract":"<div><div>The size distribution of particles emitted from road traffic substantially influences urban air quality and human health. There is however a limited understanding on the importance of the emission number size distributions, especially for particles smaller than 50 nm, in neighbourhood-scale air quality modelling. We used the large-eddy simulation PALM–SALSA model to investigate the influence of traffic emissions and their number size distributions on pedestrian-level particle concentrations in Beijing. The simulations were driven by particle size distributions from emission model GAINS, roadside field measurements, and engine laboratory experiments. With constant total traffic emissions and only modifying the shape of the emission number size distribution to better represent smaller particles, the modelled total particle number concentrations (<span><math><msub><mrow><mi>N</mi></mrow><mrow><mi>t</mi><mi>o</mi><mi>t</mi></mrow></msub></math></span>) decreased less than 4% compared to the simulation using GAINS. When experimental-based nucleation mode vehicle emissions were introduced, the total particle number emissions increased to 12-fold when compared to GAINS, leading to a pronounced rise in <span><math><msub><mrow><mi>N</mi></mrow><mrow><mi>t</mi><mi>o</mi><mi>t</mi></mrow></msub></math></span> (up to 560%), particularly in the number concentration of smallest particles. Owing to aerosol dynamics, the increase in <span><math><msub><mrow><mi>N</mi></mrow><mrow><mi>t</mi><mi>o</mi><mi>t</mi></mrow></msub></math></span> within the street canyon was lower than the relative increase in vehicle emissions. PM₁ (Particulate Matter with a diameter smaller than 1 <span><math><mi>μ</mi></math></span>m) remained largely unchanged. These results highlight the importance of nucleation-mode traffic emissions for modelling urban <span><math><msub><mrow><mi>N</mi></mrow><mrow><mi>t</mi><mi>o</mi><mi>t</mi></mrow></msub></math></span>, whereas their influence on PM₁ and LDSA (Lung-Deposited Surface Area) appears comparatively minor at the neighbourhood scale.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"30 ","pages":"Article 100453"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147740399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Connecting inland ship emissions from on-board sampling, onshore measurements and large-eddy simulations","authors":"Philipp Eger ,&nbsp;Daniel Weber ,&nbsp;Svenja Sommer ,&nbsp;Matthias Sühring ,&nbsp;Helge Knoop ,&nbsp;Alex Zavarsky","doi":"10.1016/j.aeaoa.2026.100449","DOIUrl":"10.1016/j.aeaoa.2026.100449","url":null,"abstract":"<div><div>We present the results of a comparison between inland ship exhaust emissions derived from onshore and on-board measurements, and numerical simulations. Our aim is to discuss the applicability and potential limitations of commonly used approaches to determine ship-related air pollutant emissions. For this purpose, we made a push boat pass a measurement station on an inland canal repeatedly under similar driving conditions. We observed a large variability in the detected pollutant concentration peaks with regard to their amplitude, shape and time of occurrence, both in measurement and model. We trace this back to randomly distributed turbulence, which strongly affects the plume dispersion close to the emission source. Consequently, it is hardly possible to reliably derive an emission rate based on a single ship plume measurement onshore. In contrast, an emission factor can be reliably deduced from individual measurements, as revealed by the good agreement between onshore and on-board measurement data.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"30 ","pages":"Article 100449"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147703243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From receptor models to machine learning: Advancing source apportionment of carbonaceous aerosol","authors":"Aruzhan Oshakbayeva ,&nbsp;Akmaral Bolatkyzy ,&nbsp;Azhar Serik ,&nbsp;Adeliya Seidaliyeva ,&nbsp;Azamat Mukhamediya ,&nbsp;Ferhat Karaca ,&nbsp;Nassiba Baimatova ,&nbsp;Akmaral Agibayeva","doi":"10.1016/j.aeaoa.2026.100470","DOIUrl":"10.1016/j.aeaoa.2026.100470","url":null,"abstract":"<div><div>Source apportionment of organic carbon (OC) and elemental carbon (EC) is essential for understanding combustion-related pollution, secondary aerosol formation, and their impacts on climate and human health. Conventional receptor models, such as Positive Matrix Factorization (PMF), remain widely used but are constrained by linear assumptions, sensitivity to input uncertainty, and limited ability to resolve overlapping and evolving sources. The integration of machine learning (ML) has emerged as a promising solution to these challenges, bridging the gap between linear statistical assumptions and the dynamic reality of ambient air quality monitoring. This review evaluates the role of ML in OC/EC source apportionment by organizing existing studies according to their functional roles within the workflow, including PMF-assisted hybrid approaches, data structuring methods, end-to-end frameworks, and physics-informed models. Particular attention is given to challenges related to data resolution, transferability, interpretability, and uncertainty quantification. The analysis shows that ML can help improve nonlinear source discrimination, high-resolution source prediction, and integration of heterogeneous datasets, yet most approaches remain dependent on receptor-model-derived labels and lack standardized validation protocols. Hybrid frameworks that combine receptor-model interpretability with ML predictive capability currently represent the most widely adopted approach. At the domain level, ML-assisted source apportionment can support improved characterization of emission sources and atmospheric processes, contributing to advances in aerosol chemistry and modeling. As a complementary tool that strengthens traditional receptor modeling, the integration of ML into source apportionment provides a more consistent evidence base for air quality policies and public health protection.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"30 ","pages":"Article 100470"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of the spatial-temporal variability of PM2.5 mass and fine-ultrafine particle concentration in two sites of Southern Italy","authors":"A. Dinoi ,&nbsp;F. Unga ,&nbsp;A. Pennetta ,&nbsp;D. Cesari ,&nbsp;G. DeLuca ,&nbsp;E. Bloise ,&nbsp;P. Semeraro ,&nbsp;A. Mangone ,&nbsp;L. Giotta ,&nbsp;M.G. Lionetto ,&nbsp;M.R. Guascito ,&nbsp;D. Contini","doi":"10.1016/j.aeaoa.2026.100472","DOIUrl":"10.1016/j.aeaoa.2026.100472","url":null,"abstract":"<div><div>Within the framework of the TOX-IN-AIR Project, two simultaneous measurement campaigns were conducted during cold and warm periods at urban site, MAGA, and suburban background sites, ECO, in Lecce (Puglia, Southern Italy). Daily PM₂.₅ samples were collected on quartz filters using a dual-channel low-volume automatic sampler (2.3 m³/h, SWAM - Fai Instruments). Concurrently, particle number concentrations in the 0.3-10 μm range (large fine particles) were measured using optical particle counters (OPC, Fai Instruments), while size distributions in the 0.01-0.8 μm range (ultrafine particles) were obtained with scanning mobility particle sizers (SMPS, Tropos and Grimm). Results showed comparable temporal variability of daily PM₂.₅ mass concentrations between the two sites, with combined mean concentrations of 15.7 ± 9.4 μg/m³ during the cold period and 14.0 ± 4.3 μg/m³ during the warm period. Despite similar temporal trends, the urban site consistently exhibited higher ultrafine particles number concentrations than suburban site, approximately 36% higher in winter and 32% higher in summer, reflecting a stronger influence of vehicular traffic emissions. Both sites recorded higher particle concentrations (&gt;25%) during the cold period, mainly attributable to biomass and wood combustion for domestic and residential heating, in addition to traffic-related emissions. The fine particle fraction followed a similar seasonal pattern, with ∼35% higher concentrations in winter. No statistically significant differences (p = 0.35) were observed for coarse particles (3.0-10 μm). A weak correlation between PM₂.₅ and ultrafine particles number concentration, r = 0.42-0.70 at MAGA, 0.41-0.43 at ECO, indicates that reductions in PM₂.₅ mass concentrations do not necessarily correspond to lower ultrafine particle exposure. Therefore, PM₂.₅ levels alone are not fully representative of population exposure to ultrafine particles.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"30 ","pages":"Article 100472"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Formulating ozone control strategiesin the Fenwei plain under large-scale circulation: Insights from integrated multi-source 3D observations and model simulations","authors":"Xin Liao ,&nbsp;Ming Liu ,&nbsp;Danni Xu ,&nbsp;Zibing Yuan ,&nbsp;Bo Wang ,&nbsp;Huipeng Zhang ,&nbsp;Kaihui Zhao","doi":"10.1016/j.aeaoa.2026.100469","DOIUrl":"10.1016/j.aeaoa.2026.100469","url":null,"abstract":"<div><div>Due to its unfavorable topographic conditions and energy structure, the Fenwei Plain (FWP) has been designated by the Chinese government as a key region for national ozone (O₃) pollution control. To address the lack of rapid and reliable O₃ despiking strategies, this study integrates a fused emission inventory that incorporates local anthropogenic emissions from Xianyang (XYA) into the CAMx model. It further couples large-scale circulation (LSC) regimes with O₃ precursor sensitivity (OPS) derived from Observation-Based Model (OBM) simulations influenced by in-situ VOC and NO_x measurements in XYA. CAMx simulations using this locally refined inventory show substantially improved performance, with the correlation coefficient (R) increasing by 7.5%, and the normalized mean error (NME), mean fractional bias (MFB), and mean fractional error (MFE) decreasing by 30.3%, 13.1%, and 22.6%, respectively. Three typical LSC patterns were identified: a uniform pressure field, a low-pressure reverse flow pattern, and a high-pressure control pattern. OSAT results under these regimes reveal distinct circulation-dependent source contributions to O₃ formation, with dominant inflows contributing 54.08% from BJI, 55.52% from GNI, and 46.78% from XAN across the three LSCs. Interestingly, associated by spatial heterogeneity in anthorpogenic emission, a close linkage between LSC and OPS was observed, with the three LSC patterns corresponding respectively to transitional, VOC-limited, and NO_x-limited regimes. The LSC-OPS framework established based on the local emission inventory and OBM simulations can accurately identify the underlying mechanisms driving OPS variation, offering scientific support for developing dynamic emergency response and control strategies for O₃ pollution in the FWP.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"30 ","pages":"Article 100469"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Queueing-driven transportation emissions and near-road exposure at a binational port of entry: An AERMOD assessment","authors":"Mayra Chavez,&nbsp;Leonardo Vazquez-Raygoza,&nbsp;Wen-Whai Li","doi":"10.1016/j.aeaoa.2026.100456","DOIUrl":"10.1016/j.aeaoa.2026.100456","url":null,"abstract":"<div><div>Queueing-driven transportation emissions at international ports of entry create localized air pollution hotspots affecting commuters, pedestrians, and facility workers. This study examines the Bridge of the Americas, a U.S.-Mexico port of entry in El Paso, Texas, using continuous Federal Equivalent Method monitoring and AERMOD dispersion modeling to assess traffic-related air pollutants. Monitored PM_2.5 and NO₂ concentrations were elevated at the port compared to a nearby reference site. Modeling showed that concentrations plateau once vehicle queues exceed 270 m, with levels declining tenfold within 200 m of the traffic lanes. Pedestrians experienced 75% of in-lane pollutant concentrations, and toll booth workers 66%. Modeled contributions accounted for 51% of PM_2.5, 147% of PM₁₀, and 7% of NO₂ relative to monitored averages. Findings demonstrate how queue length, traffic mix, and weather impact exposure and support strategies, such as queue management, lane design, and walkway placement, to reduce emissions at border crossings.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"30 ","pages":"Article 100456"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}