Atmospheric Environment: X最新文献

{"title":"Impacts of wildfire smoke aerosols on radiation, clouds, precipitation, climate, and air quality","authors":"Rahele Barjeste Vaezi ,&nbsp;Michael R. Martin ,&nbsp;Farnaz Hosseinpour","doi":"10.1016/j.aeaoa.2025.100322","DOIUrl":"10.1016/j.aeaoa.2025.100322","url":null,"abstract":"<div><div>Wildfires have become increasingly prevalent, impacting ecosystems, climate, and human health on a global scale. This review aims to present a comprehensive analysis of current knowledge on the environmental factors and conditions driving wildfires, the characteristics and transport of smoke emissions, along the broader impacts of wildfire smoke on the weather and climate. These impacts include changes in atmospheric radiation, cloud formation, atmospheric circulation, precipitation patterns, and air quality, as well as their effects on land cover, safety, and public health. Wildfire emissions include various pollutants such as particulate matter that alter the Earth's energy balance, reduce air quality, and impact cloud microphysics. Key interactions, such as the direct and indirect effects of smoke aerosols, affect cloud cover and lifetime, precipitation, atmospheric stability, and ultimately induce changes in weather and climate dynamics. Moreover, smoke transport extends the effects of wildfires thousands of kilometers beyond their sources, which reduces agricultural productivity, deteriorates human health, and threatens the environment. Advances in satellite retrievals and modeling techniques have improved the ability to monitor, analyze, and predict these complex interactions. Moreover, this review highlights the critical need for advancing research to more precisely quantify and project multi-scale trends in wildfire smoke and its far-reaching impact on public health, safety, infrastructure, and ecosystems. Developing more robust adaptation strategies and resilience measures is essential to effectively mitigate these complex, adverse effects on communities and the environment.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100322"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil flooding increases greenhouse gas fluxes","authors":"Getachew A. Kefelegn ,&nbsp;Niguss S. Hailegnaw ,&nbsp;Haimanote K. Bayabil","doi":"10.1016/j.aeaoa.2025.100333","DOIUrl":"10.1016/j.aeaoa.2025.100333","url":null,"abstract":"<div><div>Soil flooding poses significant challenges to livelihoods, agriculture, and the environment by adversly affecting soil health. This study investigated the effects of flooding, flooding duration, and water source (seawater and freshwater) on greenhouse gas fluxes from two predominant soil types in South Florida—Krome and Biscayne. Experiments were conducted by flooding Krome and Biscayne soils with fresh and seawater, and greenhouse gas samples were collected using PVC chambers on the 1^st, 7^th, 14^th, and 28^th days of flooding. Samples were analyzed for soil carbon dioxide (CO₂), nitrous oxide (N₂O), and methane (CH₄) fluxes using a gas chromatograph. Results confirmed that CO₂ and N₂O fluxes exhibited a distinct pattern, peaking one day after flooding and sharply decreasing with the progression of flooding. Soil type, flooding duration, and water source were critical factors modulating CO₂ and N₂O fluxes, but CH₄ fluxes were consistently below the detection limit. Biscayne soil had the highest CO₂ and N₂O fluxes under seawater and freshwater flooding compared to Krome. These findings underscore the critical role of the initial flooding phase in driving greenhouse gas emissions, emphasizing the need for targeted mitigation strategies.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100333"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106498","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":"Task specific assessment of particle exposure and low-cost sensor performance in indoor construction environments","authors":"Anders Brostrøm ,&nbsp;Josephine Thalmann ,&nbsp;Jesper Baldtzer Liisberg ,&nbsp;Frederika Husovská ,&nbsp;Søren Hanghøj Møller ,&nbsp;Julie Tølbøl Rasmussen ,&nbsp;Thomas Nørregaard Jensen ,&nbsp;Søren Bendt Jensen ,&nbsp;Keld A. Jensen ,&nbsp;Thomas Cole-Hunter ,&nbsp;Ana S. Fonseca","doi":"10.1016/j.aeaoa.2025.100336","DOIUrl":"10.1016/j.aeaoa.2025.100336","url":null,"abstract":"<div><div>In this study, a workplace measurement campaign was conducted during indoor renovation of two apartments following panel removal, wallpaper removal (dry/wet), sweeping (dry/wet), and floor removal (including insulation) tasks. Measurements with a low-cost sensor (LCS; OPC-N3; Alphasense) was compared to a benchmark optical particle sizer (OPS, TSI Model 3330) to assess the applicability of this LCS in a construction worker environment. Additionally, ultrafine particle concentrations (&lt;0.1 μm) were measured using a mobility particle sizer (NanoScan, TSI Model 3091) and a diffusion size classifier (DiSCmini).</div><div>The highest particle number concentrations (PNC) were found during floor removal, dry sweeping, and wallpaper removal, where 63 % of particles were ultrafine (&lt;0.1 μm) and 96 % were smaller than 2.5 μm (PM_2.5). The PM₁₀ (particulate matter with a diameter &lt;10 μm) concentrations measured during some tasks exceeded the occupational exposure limit of 10 mg m⁻³ for total dust with values from 0.3 to 11 mg m⁻³. Analytical electron microscopy analysis revealed exposure to compounds such as talc, titania, quartz, and potential asbestos. Water-based dust control methods reduced PNC by at least 84 %, highlighting their effectiveness in mitigating exposure. LCS generally underestimated particle concentrations, particularly for PM₁, which was underestimated ranging from 31 % to 92 %. The largest discrepancies occurred during high concentrations in the presence of ultrafine particles, such as floor removal and dry wallpaper removal. This study also emphasizes the importance of multi-metric measurements and breathing zone assessments to accurately evaluate worker exposure and improve occupational safety.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100336"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134430","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":"Cement and brick factories contribute elevated levels of NO2 pollution in Nepal: Evidence of high-resolution view from space","authors":"Madhu S. Gyawali ,&nbsp;Lok N. Lamsal ,&nbsp;Sujan Neupane ,&nbsp;Bimal Gyawali ,&nbsp;Keshav Bhattarai ,&nbsp;Bradford Fisher ,&nbsp;Nickolay Krotkov ,&nbsp;Jos van Geffen ,&nbsp;Henk Eskes ,&nbsp;Shriram Sharma ,&nbsp;Cameron Brunt ,&nbsp;Rudra Aryal","doi":"10.1016/j.aeaoa.2025.100324","DOIUrl":"10.1016/j.aeaoa.2025.100324","url":null,"abstract":"<div><div>An upsurge in the pollution level in areas with a high concentration of brick and cement factories in Nepal is concerning. Nitrogen dioxide (NO₂), a key air quality indicator, can be effectively monitored from space. This study utilizes high-resolution satellite observations of NO₂ from the TROPOspheric Monitoring Instrument (TROPOMI). It examines the NO₂ distribution over areas with emerging sources of nitrogen oxides from brick and cement factories from 2018 to 2021. Rapid growth of brick and cement factories has turned the Lumbini-Butwal-Palpa corridor, in Midwest Nepal, more polluted than the capital city Kathmandu. Between 2019 and 2021, NO₂ levels in this corridor rose considerably, while it remained steady in the Kathmandu Valley. TROPOMI-derived NO₂ levels and inferred NO_x emissions over the corridor nearly doubled in the span of three years. Conversely, Kathmandu Valley exhibited no significant changes except in 2020 when NO₂ and NO_x levels declined. This drop coincided with COVID-19-related travel restrictions and other reduced activities. NO₂ pollution recorded by the Ozone Monitoring Instrument (OMI) from 2005 to 2019 shows an annual NO₂ increase of ∼3.5 % over both regions. A comparison between NO_x emissions from the 2018 EDGAR inventory and TROPOMI-derived estimates for 2019 reveal comparable values over the Lumbini-Butwal-Palpa corridor but around 35 % higher estimates over Kathmandu. This discrepancy over the capital city, as well as the rapid rise in emissions over the Lumbini-Butwal-Palpa corridor due to a large-scale development of cement and brick industries, highlights the need for timely updates in bottom-up emission inventory.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100324"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817089","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":"Influence factors on airborne pollen dispersal in a tropical island over China: morphology and meteorology","authors":"Mengyuan Pang,&nbsp;Ningyan Fu,&nbsp;Siyang Li","doi":"10.1016/j.aeaoa.2025.100323","DOIUrl":"10.1016/j.aeaoa.2025.100323","url":null,"abstract":"<div><div>Airborne pollen is an important primary biological aerosol particle in tropical regions, greatly impacting climate and human health. However, the pollen morphology in tropical areas, particularly their impact on pollen dispersal, remains unknown. To determine the relationship between the dispersal and morphology of airborne pollens, we collected the airborne pollen by Durham samplers at three vertical heights, including 1.5 m, ∼18.5 m, and ∼55 m in Haikou City, China. Pollen particles showed higher concentrations at higher heights above ground level. The quantitative analysis of single pollen particles based on the size index showed that the airborne pollen sizes in the tropics were mainly small (10–25 μm) (45.9%) and medium (25–50 μm) (32.2%). That's consistent with the pollen morphology of spring and summer flowering plants in the surrounding areas. The proportions of very small (&lt;10 μm) and small (10–25 μm) pollen particles increased significantly with the vertical height. The shape index showed the prominent shape of airborne pollen was subspheroidal/spheroidal (∼80%). The pollen concentration of other shapes, like prolate or oblate, slightly increased with height. The Pearson correlation analysis showed that local meteorological conditions had an important role in influencing pollen amounts, with some associations found to be statistically significant. Temperature variables had significant positive correlation with pollen amounts, especially the maximum temperature (r = 0.71, P &lt; 0.01). The rainfall and relative humidity exhibited a negative correlation with pollen concentration. Notably, pollen release was influenced by meteorological factors with a 1–7 day lagged effect. This study provided a near-ground vertical profile of tropical pollen concentration and morphology. These findings also offer a comprehensive understanding of how airborne pollen morphology and meteorological factors influence their transport and deposition characteristics on a tropical island.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100323"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emissions from fuel-operated heaters in battery-electric buses","authors":"Åsa M. Hallquist,&nbsp;Håkan Salberg","doi":"10.1016/j.aeaoa.2025.100332","DOIUrl":"10.1016/j.aeaoa.2025.100332","url":null,"abstract":"<div><div>Battery-electric buses have become more common in the urban environment. At low ambient temperatures the energy consumption due to heating of the passenger and driving compartment can be significant, and to preserve the range of the battery fuel-operated heaters can be used. The legislation regarding these heaters is less stringent compared to engine exhaust emission legislation e.g., Euro VI, and knowledge about these emissions is scarce. In this study, emissions from 34 fuel-operated heaters, running on hydrotreated vegetable oil (HVO), in battery-electric buses from an in-use bus fleet have been characterised both regarding gaseous (total hydrocarbon (THC) and nitrogen oxides (NOx)) and particle pollutants (particle number (PN), particle mass (PM), black carbon (BC) and size) during real-world dilution. The median PM and PN emissions varied between 0.96 and 8.4 mg (kg fuel)⁻¹ and 4.4–127 × 10¹³ # (kg fuel)⁻¹ for the heater types studied. Additionally, the significance of the heater emissions compared to engine exhaust emissions was analysed.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100332"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137980","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":"Bayesian modeling of traffic-related air pollutants: A case study of urban transportation and air quality dynamics in Columbia, South Carolina","authors":"Yihong Ning ,&nbsp;Ruixiao Sun ,&nbsp;David Hitchcock ,&nbsp;Gurcan Comert ,&nbsp;Yuche Chen","doi":"10.1016/j.aeaoa.2025.100328","DOIUrl":"10.1016/j.aeaoa.2025.100328","url":null,"abstract":"<div><div>Traffic emissions significantly impact near-road air quality and public health. This research applies a Bayesian modeling framework to investigate these impacts using high-resolution traffic and air pollutant data from an urban corridor in Columbia, South Carolina. Despite a data collection period truncated by the COVID-19 lockdown, the Bayesian approach successfully identified significant predictors and quantified model uncertainty. Employing Bayesian Model Selection and Averaging enhanced prediction accuracy and evaluated model uncertainty. Findings indicate that higher temperatures and increased moisture levels elevate particulate matter (PM_1.0, PM_2.5, PM₁₀) concentrations, while traffic speed significantly affects nitrogen dioxide (NO₂) levels. Specifically, higher average traffic speeds (indicative of smoother flow) correspond to lower NO₂ concentrations, suggesting that less congested conditions reduce NO₂ emissions. This study highlights the robustness of Bayesian methods for generating reliable air quality insights even under data-constrained conditions. The findings underscore the importance of traffic flow management (e.g., reducing congestion) for mitigating near-road NO₂ exposure and provide a basis for developing targeted public health strategies.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100328"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936958","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":"Performance and applicability of low-cost PM sensors to assess global pollution variability through machine learning techniques","authors":"Rajat Sharma ,&nbsp;Andry Razakamanantsoa ,&nbsp;Ashutosh Kumar ,&nbsp;Thaseem Thajudeen ,&nbsp;Agnès Jullien","doi":"10.1016/j.aeaoa.2025.100331","DOIUrl":"10.1016/j.aeaoa.2025.100331","url":null,"abstract":"<div><div>Air quality monitoring and analyses became easy and affordable due to emergence of low-cost sensors. Recently, the efforts to improve the monitoring and understanding of region-specific air pollution events attracted immense global attention. Nevertheless, the applicability issues were observed due to data reliability and inconsistency, caused by reserve testing of performance parameters for better accuracy, selection and deployment of sensors without considering their fitness for the purpose, and area-specific requirements. This paper analyses and evaluates low-cost sensor deployments across lower, middle, and higher income group of countries, emphasizing variations in pollutant sources, performance parameters, and machine learning approaches for local source categorization. The performance parameters were analyzed using three Key parameters: (1) the Performance Index, (2) Sector Sensitivity Ratio, and (3) Data Reliability Indicator, that provide a comprehensive understanding of sensor efficiency in diverse environments. Our findings reveal distinct trends among income group countries. Higher income group countries exhibited the highest performance Index (0.35), followed by middle (0.33) and lower income group countries (0.27). However, the lower income group countries showed the highest data reliability indicator for maximum sector contribution (14.26), surpassing the higher (11.74) and middle income group (10.71) countries. Sector wise, transport (higher income), industry (middle income), and power (low income) demonstrated the highest data reliability based on its indicator. Additionally, it was observed that advanced machine learning algorithms helped to improve performance parameters, particularly in middle and lower income group countries where pollution variability is higher. These findings underscored the disparities in sensor performance and data reliability across diverse income groups.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100331"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089860","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":"Nitrous oxide prediction through machine learning and field-based experimentation: A novel strategy for data-driven insights","authors":"Muhammad Hassan ,&nbsp;Khabat Khosravi ,&nbsp;Travis J. Esau ,&nbsp;Gurjit S. Randhawa ,&nbsp;Aitazaz A. Farooque ,&nbsp;Seyyed Ebrahim Hashemi Garmdareh ,&nbsp;Yulin Hu ,&nbsp;Nauman Yaqoob ,&nbsp;Asad T. Jappa","doi":"10.1016/j.aeaoa.2025.100335","DOIUrl":"10.1016/j.aeaoa.2025.100335","url":null,"abstract":"<div><div>Applying machine learning to predict complex environmental phenomena like greenhouse gas emissions (GHG) is gaining significant attention. This study introduces innovative ensemble learning models that integrate the randomizable filter classifier (RFC), regression by discretization (RBD), and attribute-selected classifier (ASC) with the random forest (RF) algorithm, resulting in hybrid models (RFC-RF, RBD-RF, and ASC-RF). These models predicted nitrous oxide (N₂O) and water vapor (H₂O) emissions from agricultural soils. These model were benchmarked against a support vector regression (SVR) model. The dataset comprised 401 samples from potato fields in Prince Edward Island (PEI) and 122 samples from New Brunswick (NB), including measurements of N₂O and H₂O and related input variables such as soil moisture (SM), temperature ST, electrical conductivity (EC), wind speed, solar radiation, relative humidity, precipitation, air temperature (AT), dew point, vapor pressure deficit, and reference evapotranspiration. Feature selection and optimization of input scenarios were achieved using a combination of particle swarm optimization (PSO) and manual methods. Model performance was evaluated using multiple metrics: scatter plots, kite diagrams, density distribution histograms of relative percentage error, coefficient of determination (R²), Nash–Sutcliffe efficiency coefficient (NSE), Percent of BIAS (PBIAS), coefficient of uncertainty at the 95 % confidence level (U95 %), Kling–Gupta efficiency (KGE), Willmott index of agreement (WI), and Legates and McCabe coefficient of efficiency (LME). Results demonstrated that the hybrid RFC-RF model outperformed the other models for N₂O and H₂O predictions in PEI and NB, followed by the RBD-RF, ASC-RF, and SVR models. The new models demonstrated good performance according to R² values, while the SVR model ranged from unacceptable to good. The study found that combining soil and climatic variables improved prediction accuracy, with ST, AT, and soil EC being the most influential variables. SHapley Additive exPlanations (SHAP) analysis confirmed the importance of ST for both N₂O and H₂O predictions. The findings underscore the significance of dataset length over input-output correlation and indicate that combining soil and climatic variables enhances model prediction accuracy. The developed models offer reliable and cost-effective tools for researchers, policymakers, and stakeholders to effectively predict and manage GHG in agricultural contexts.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100335"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134431","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":"Addressing underestimated carbon monoxide emissions in Taiwan using CMAQ and impacts on local ozone concentration","authors":"Chieh-Sen Tsai ,&nbsp;Ping-Chieh Huang ,&nbsp;Hsin-Chih Lai ,&nbsp;John C. Lin ,&nbsp;Hui-Ming Hung","doi":"10.1016/j.aeaoa.2025.100325","DOIUrl":"10.1016/j.aeaoa.2025.100325","url":null,"abstract":"<div><div>Emission inventories play a crucial role in understanding and managing air quality. This research centers on carbon monoxide (CO), a low-reactivity species with a lifetime of 2 months, acting as a tracer for local pollutants. The investigation delves into the potential uncertainties within its emissions and the impacts. CO is significantly underestimated in the current air quality model using Taiwan Emission Data System 9.0 (TEDS 9.0) for Taiwan. The present CMAQ simulation underestimates CO in Taiwan by a factor of ∼3 compared to observations. With the minimum root mean square error (RMSE) analysis between simulation and observation, the optimal emission correction factors are estimated as 2, 4, and 3.6 for northern, central, and southern Taiwan, respectively. The simulated underestimation of CO concentrations, coupled with relatively consistent NOx concentrations compared to observations, might indicate possible uncertainties in emission sources, especially for sources with high CO/NOx ratios, such as vehicles. This discrepancy further suggests the possibility of underestimating other combustion chemical species, such as volatile organic compounds (VOCs), which are not adequately quantified in the ambient environment. Our findings indicate that the adjustment would increase local O₃ concentration (up to 3 ppbv), with a minor decreased influence on NOx (less than 0.5 ppbv), underscoring the importance of accurate emission inventories in air quality modeling and the reassessment of the validity of CO and NOx emissions in a NOx-saturated environment. Our analysis of the potential emission sources highlights the importance of implementing stricter local emission controls and monitoring.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100325"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829188","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}