Atmospheric Pollution Research最新文献

{"title":"Experimental and numerical characterization of the concentration distribution of particulate pollutants inside a full-scale car cabin","authors":"Mokhtar Djeddou ,&nbsp;Amine Mehel ,&nbsp;Georges Fokoua ,&nbsp;Anne Tanière ,&nbsp;Patrick Chevrier","doi":"10.1016/j.apr.2025.102516","DOIUrl":"10.1016/j.apr.2025.102516","url":null,"abstract":"<div><div>We report an investigation of particle dynamics through measurements of particle concentrations inside a full-scale car cabin and comparing the results to numerical predictions obtained using the ”Diffusion-Inertia Model” (DIM) for particle transport, coupled with the RANS approach for single-phase flow. Measurements were conducted by placing the vehicle in a closed chamber where a homogenized atmosphere was generated and controlled, enabling the study of fine and ultrafine particle infiltration by measuring the particle mass concentration distribution inside the vehicle’s cabin. A comparison between numerical and experimental results for particle concentration profiles of PM₁ and PM₁₀ showed that the numerical model reasonably reproduces the experimental results, particularly for low-inertia particles. Both numerical and experimental analyses revealed a tendency toward particle concentration homogeneity within the compartment. Additionally, the influence of ventilation velocity on the dynamics of <span><math><mrow><mn>1</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> and <span><math><mrow><mn>10</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> particles was investigated numerically. The results indicate that increasing airflow velocity accelerates the homogenization of particle concentrations, while inertia effects become more pronounced, leading to lower concentration levels due to particle deposition on cabin surfaces. The effect of thermal buoyancy on particle transport was also examined. While the overall dispersion patterns remained largely unchanged, localized variations were observed, particularly in the passenger breathing zone, where thermal effects reduced particle concentration.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102516"},"PeriodicalIF":3.9,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746894","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":"Meteorological and climatological conditions supportive for windblown dust formation in Poland","authors":"Filip Skop,&nbsp;Ewa Bednorz","doi":"10.1016/j.apr.2025.102521","DOIUrl":"10.1016/j.apr.2025.102521","url":null,"abstract":"<div><div>Windblown dust is considered a type of severe weather phenomena, causing low horizontal visibility, high particulate matter concentrations and economic loss. Although dust events mostly occur in arid and semiarid climates, they are also being reported in Poland during dry spells. Currently there are no comprehensive studies releted to windblown dust climatology of Poland, despite their abundance in the recent years. In order to identify significant windblown dust events in Poland, compiled data from meteorological stations, air quality stations and media/social media platforms was used. Hourly observations from 50 Polish meteorological stations were obtained in order to gather all windblown dust related reports. Hourly mean PM₁₀ concentrations were obtained in order to estimate the impact of windblown dust on air quality as well as to identify cases away from meteorological stations. Lastly, media and social media reports, depicting intense windblown dust, were included in the study in order to make the database more detailed. A total of 65 days with a windblown dust were identified for a period between 2001 and 2022. Each case was examined based on a type of a meteorological disturbance causing it (synoptic or convective).</div><div>Meteorological conditions present during windblown dust cases, including near-surface relative humidity, wind speed and visibility were also analyzed along with surface soil moisture and Standarized Precipitation Evapotranspiration Index (SPEI). Additionaly, atmospheric soundings and vertical tropospheric relative humidity profiles were simulated for convective windblown dust cases, based on ECMWF ERA5 Reanalysis. It was found that central and western regions of Poland are most prone to windblown dust, with April being by far the most active month for dust activity. Significant differences were also noted between the intensity of recorded windblown dust occurrences, with most cases being local and lasting less than 1 h to some covering large area of a Country and lasting for over 10 h. Recorded convective windblown dust most commonly formed as a result of thunderstorm's outflow, connected to cold fronts and low tropospheric convergence zones. High Lifted Condensation Level and low humidity in the lower troposphere strongly supported this type of events.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102521"},"PeriodicalIF":3.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143737950","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":"Development of a representative transient cycle for evaluating real driving emissions of heavy-duty diesel engines","authors":"Gengfei Liu ,&nbsp;Xiuhua Yang ,&nbsp;Bin Pei ,&nbsp;Huaimin Xu ,&nbsp;Binyang Wu ,&nbsp;Wanhua Su","doi":"10.1016/j.apr.2025.102520","DOIUrl":"10.1016/j.apr.2025.102520","url":null,"abstract":"<div><div>Accurately assessing real driving emissions is crucial for effectively controlling vehicle exhaust pollution. However, significant discrepancies exist between the World Harmonized Transient Cycle (WHTC) used for emission certification and real driving conditions of heavy-duty diesel engines in China. To address this issue, this study introduces a two-step method for developing representative transient cycles. In the first step, short strokes are classified using the k-means clustering algorithm with adaptive particle swarm optimization to identify key kinematic scenarios for heavy-duty diesel vehicles. The Markov Chain Monte Carlo method is then applied to simulate driving patterns for these scenarios, thereby constructing the heavy-duty real driving cycle (HRDC). In the second step, the heavy-duty real transient cycle (HRTC) for diesel engines is generated by integrating typical transmission system and gear matching rules based on the HRDC. The emission test results indicate that compared to WHTC, NOx, PM, and PN emissions under HRTC increased by 36.69 %, 4.57 %, and 78.73 %, respectively. Additionally, transient soot emissions under HRTC are 155.74 % higher than those predicted by steady-state interpolation. The primary factor leading to transient soot emission deterioration is a sudden torque increase exceeding 40 %/s, observed during idle or motoring conditions. These findings provide a solid foundation for reliably evaluating the road emission performance of heavy-duty diesel vehicles.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102520"},"PeriodicalIF":3.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738069","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 apportionment of rainwater, and its contribution to nutrient deposition at an urban site of the middle Indo-Gangetic Plain region","authors":"Sanny Rathore,&nbsp;Kirpa Ram ,&nbsp;Pramod Kumar,&nbsp;Arnab Mondal","doi":"10.1016/j.apr.2025.102518","DOIUrl":"10.1016/j.apr.2025.102518","url":null,"abstract":"<div><div>The study of rainwater chemistry plays an important role in understanding scavenging processes, sources of atmospheric pollutants, and impacts on ecosystems. This study investigated the chemical composition, variations, and potential sources influencing rainwater chemistry in Varanasi, India from 2018 to 2022. A total of 158 event-based monsoonal rainwater samples were analyzed for physical (pH, EC and TDS) and major ionic species (Cl^ˉ, F^ˉ, NO₃^ˉ, SO₄²⁻, PO₄³⁻, Mg²⁺, Ca²⁺, NH₄⁺, Na⁺ and K⁺). The average rainwater pH was 6.22 ± 0.45 (n = 158) with ∼92 % of the samples being alkaline with the dominance of Ca²⁺ and NH₄⁺ ions, whereas the rest of the samples were acidic in nature with high SO₄^2ˉ and NO₃^ˉ levels. NH₄⁺ concentrations increased significantly until 2020, while those of Ca²⁺, K⁺, and Mg²⁺ initially decreased and rose after 2020. The study highlighted significant deposition of dissolved inorganic nitrogen (in the form of NO₃⁻, NO₂⁻, and NH₄⁺). The average monsoonal nitrogen deposition flux was 8.04 kg ha⁻¹ with significant contributions from NO₃⁻ (3.36) and NH₄⁺ (4.67). In contrast, the deposition of inorganic phosphorus was significantly lower (∼0.72 kg ha⁻¹). Thus, the rainwater deposition contributed to overall nutrient deposition, specially N and P which could significantly impact the ecosystem. Neutralization and enrichment factors indicated influences from crustal and anthropogenic sources. This is also evident from the study as ∼99 % of Ca²⁺ and ∼98 % of SO₄²⁻ fractions were determined to be of non-marine origin. Over 800 brick kilns were identified around Varanasi and contributing to an increased NO₃⁻, SO₄²⁻ and particulate matter. The Positive Matrix Factorization (PMF) technique identified sea-salt, crustal dust, fossil fuel and biomass combustion, and agricultural emissions as potential sources of major ionic constituents over Varanasi.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102518"},"PeriodicalIF":3.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715459","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":"Harnessing open remote sensing data and machine learning for daily ground-level ozone prediction models: Spatio-temporal insights in the continental biogeographical region","authors":"Luka Mamić ,&nbsp;Francesco Pirotti","doi":"10.1016/j.apr.2025.102514","DOIUrl":"10.1016/j.apr.2025.102514","url":null,"abstract":"<div><div>Ground-level ozone (O₃) pollution poses significant environmental and public health challenges and requires accurate predictive models for effective monitoring and management. In this study we observe that 91 % of the observed ground-level O₃ variance can potentially be explained using time-lagged data from Sentinel-5P TROPOMI and data from ERA5-Land datasets on a trained artificial intelligence (AI) model deployed by machine learning (ML) in the continental part of the Veneto region in Italy. Data from local air quality monitoring stations were used as ground truth data. The study period is from January 2019 to December 2022. Spatio-temporal ML models predicted daily O₃ concentrations with RMSE of 9.05 μg/m³, 8.87 μg/m³ and 10.87 μg/m³ respectively for RF, XGB and LSTM. Models without spatio-temporal information gave lower accuracy, with RMSE of 10.88 μg/m³, 11.45 μg/m³ and 12.06 μg/m³ respectively, showing that spatio-temporal information can improve performance more than 10 %. However, spatio-temporal independent models are more transferable across continental region and different seasons. Results provide spatially continuous maps of ground-level O₃ with a spatial resolution of ∼11.13 km (0.1°), helping to estimate pollution levels in areas without ground stations. Spatial analysis of the models’ performance showed consistent high accuracy across all stations, while temporal analysis revealed lower performance in summer months. Overall, while the spatial resolution of the models developed in this study is insufficient for risk management in urban areas, they have practical implications for daily ground-level O₃ monitoring in areas without ground stations in the continental region.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102514"},"PeriodicalIF":3.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679302","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":"Integrated monitoring of road traffic and airborne ultrafine particles at a camera-equipped urban intersection","authors":"Tobias Schripp ,&nbsp;Kay Gimm ,&nbsp;Tobias Grein ,&nbsp;Clemens Schicktanz ,&nbsp;Stephan Weber ,&nbsp;Markus Köhler","doi":"10.1016/j.apr.2025.102504","DOIUrl":"10.1016/j.apr.2025.102504","url":null,"abstract":"<div><div>This study investigated ultrafine particle concentrations at a busy intersection in Braunschweig, Germany, highlighting the influence of traffic as the primary source of elevated ultra-fine particle (UFP) concentrations. A mobile lab, equipped with online instruments for combustion gases and aerosols (SMPS, EEPS) was operated near to the intersection. By coupling a high-resolution particle size spectrometer with a catalytic stripper, it effectively characterized short-lived traffic-related particle events, outperforming traditional methods in resolving high-emission events. While total concentrations of particles with diameters between 4 nm and 3 μm were in the range of 6,000 #/cm³ at low-traffic night hours, peak concentrations up to 3∗10⁶ #/cm³ (10–20 s duration) could be observed under heavy traffic conditions. During daytime traffic, approximately 10 %–30 % of particles could not be evaporated at 350 °C. Traffic analysis was performed on a four-lane intersection equipped with 14 vertical stereo-camera-systems that allows a precise characterization of the traffic situation. While combining high-resolution particle measurements with a camera system showed potential for classifying transport modes, challenges such as limited differentiation between emission sources and complex data interpretation reduced its overall effectiveness compared to conventional methods.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102504"},"PeriodicalIF":3.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704276","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":"Role of aerosols on prolonged extreme heatwave event over India and its implication to atmospheric boundary layer","authors":"K.B. Betsy,&nbsp;Sanjay Kumar Mehta","doi":"10.1016/j.apr.2025.102513","DOIUrl":"10.1016/j.apr.2025.102513","url":null,"abstract":"<div><div>The extreme heatwave event is a major threat to living beings in the warming climate which demands immediate quantification of the meteorological factors triggering its amplification. In this study, we explored the role of absorbing and scattering aerosols in the occurrence of extreme heatwave events as well as changes in the atmospheric boundary layer (ABL) over the northwest (NW) and east coast (EC) India during March–June 2017–2022. Ten dry (RH &lt; 33 %) and nine moist (RH &gt; 55 %) heatwave events are observed over the study period. Among these cases, a dry heatwave over NW region prolonged from 27 May to June 11, 2019 is explored in detail. In this case, the increased ABL height from ∼2.0 to 3.0 km to ∼4.0–5.0 km is observed and the entire ABL depth shows enhanced temperature by ∼4 K. The latent and sensible heat fluxes are found to be reduced by 50 W/m² and enhanced by 80 W/m² respectively during heatwave. The total aerosol optical depth (AOD) is gradually enhanced to 0.6 leading to enhanced atmospheric warming of ∼8.5–11.5 W/m² during the heatwave event. Furthermore, the heating rates for moist heatwave cases (∼2 K/day) are higher than those for dry heatwave cases (∼1.8 K/day). In addition, the moist heatwaves exhibit a higher concentration of PM2.5 (∼80–120 μg/m³) compared to the dry heatwave (∼60–100 μg/m³) posing a greater threat to public health and air quality.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102513"},"PeriodicalIF":3.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725785","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":"Characteristics, regional transport and control strategies of atmospheric ammonia in urban Taiyuan, Fenwei Plain, China","authors":"Mingquan Ren ,&nbsp;Lili Guo ,&nbsp;Yang Cui ,&nbsp;Qiusheng He ,&nbsp;Dongsheng Ji ,&nbsp;Yuesi Wang","doi":"10.1016/j.apr.2025.102505","DOIUrl":"10.1016/j.apr.2025.102505","url":null,"abstract":"<div><div>Ammonia (NH₃) is a significant precursor for secondary inorganic aerosol, in order to better study the impacts of NH₃ on PM_2.5 pollution in Fenwei Plain in China, hourly-resolved NH₃ and water-soluble ions (WSI) were measured at an urban site in Taiyuan from 1 December 2021 to 30 November 2022. Hourly NH₃ concentrations ranged from 0.7 to 40.2 μg m⁻³, with an average concentration of 10.2 ± 5.0 μg m⁻³. Due to the impacts of meteorology and emission sources, NH₃ exhibited apparent seasonal variations: summer &gt; autumn &gt; spring &gt; winter. Diurnal variations of NH₃ concentrations showed higher values during the daytime except in autumn. Cluster analysis of backward trajectories suggested that the southern short-distance air mass from Taiyuan Basin had the highest concentrations of TNHx (NH₃+NH₄⁺) and PM_2.5. The analysis by conditional probability function and weighted concentration weighted trajectory function showed the rough consistency between the distribution of the TNHx and PM_2.5 in four seasons. The analysis of hourly excess NH₃ showed that Taiyuan's atmosphere was always ammonia-sufficient. SOR (nSO₄²⁻/(nSO₄²⁻ + nSO₂)) and NOR (nNO₃^-/(nNO₃^- + nNO₂)) increased with NHR (nNH₃/(nNH₄⁺+nNH₃); n denotes the molar concentration) and RH in four seasons, indicating that the gas-particle conversion of NH₃ promoted the formation of SO₄²⁻ and NO₃⁻ under high RH condition. The critical total ammonia concentrations (CTACs) in spring, summer, autumn, and winter were 63 %, 61 %, 60 %, and 53 %, respectively. Considering the current difficulty in reducing NH₃ and WSI concentration decreased linearly with the reduction of TNO₃ (NO₃⁻ + HNO₃), controlling NOx emissions is more effective for PM_2.5 pollution mitigation in Taiyuan.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102505"},"PeriodicalIF":3.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642040","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 on the impact of urban built environments on PM2.5 pollution based on machine learning methods","authors":"Xiaoxia Wang ,&nbsp;Zhihai Fan ,&nbsp;Xiaolong Yue ,&nbsp;Qianqian Zhou ,&nbsp;Danting Lin ,&nbsp;Hong Zou","doi":"10.1016/j.apr.2025.102503","DOIUrl":"10.1016/j.apr.2025.102503","url":null,"abstract":"<div><div>Since PM_2.5 pollution poses a serious threat to the environment and health, understanding its interaction with the urban built environment (UBE) is essential for effective mitigation. To assess the impact of UBE on PM_2.5 pollution, this study quantitatively evaluates the relationship between them. First, given the limitation that current PM_2.5 concentration collection mainly relies on fixed monitoring stations, this study set up a taxi mobile monitoring system. Second, aiming at the deficiency of traditional extraction mostly based on remote sensing imagery, this study proposed a deep learning-based method to calculate the green and sky visibility index. Pearson's preliminary correlation analysis showed that climate factors were most correlated to changes in PM_2.5 concentration. Furthermore, the prediction effects of nine mainstream machine learning methods were compared. The results showed that (1) The overall prediction performance of summer (<em>R</em>² = 0.92) and autumn (<em>R</em>² = 0.93) outperformed the one of spring (<em>R</em>² = 0.88) and winter (<em>R</em>² = 0.86) seasons. (2) The Random Forest and LightGBM models obtained optimal predictions with <em>R</em>² of 0.907 and 0.916, respectively. (3) The complex nonlinear relationship between the UBE and PM_2.5 concentration needed to be captured by the Shapley additive explanations method. The findings suggested controlling the space enclosure index between 0.08 and 0.15, plot area ratio within 0.5, and building density within 0.2. This study provided a general analytical framework for understanding the diffusion mechanism of PM_2.5 concentrations and a theoretical basis for green urban design.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102503"},"PeriodicalIF":3.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679303","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":"Soil emissions of HONO and other nitrogen-containing gases: Insights into microbial pathways and moisture effects","authors":"Peng Cheng ,&nbsp;Baobin Han ,&nbsp;Zhilin Tian ,&nbsp;Zhen Liu ,&nbsp;Wenda Yang ,&nbsp;Jianwei Gu ,&nbsp;Xiaofang Yu ,&nbsp;Hongli Wang ,&nbsp;Min Zhou","doi":"10.1016/j.apr.2025.102501","DOIUrl":"10.1016/j.apr.2025.102501","url":null,"abstract":"<div><div>Nitrous acid (HONO) greatly impacts tropospheric chemistry by producing hydroxyl radical (OH) through photolysis, and yet our knowledge about sources of HONO remains elusive. Emissions of nitrogen (N) containing gases from soils have long been a subject of research in biogeochemistry. Soil emissions of HONO have received greater attention recently, helping explain a missing source of observed atmospheric HONO. We conducted laboratory experiments to simultaneously measure emission fluxes of HONO along with other N containing gases including nitric oxide (NO), nitrous oxide (N₂O), and ammonia (NH₃) from lateritic red soil samples, and evaluated the contributions of microbiological processes to HONO emissions by conducting process inhibiting experiments. Results from monitoring emissions during a full wet-drying cycle showed that the emissions of HONO, NO and N₂O have a strong dependance on soil water content, with maximum fluxes for HONO (125 ± 17 ng N m⁻² s⁻¹), NO (115 ± 11 ng N m⁻² s⁻¹) and N₂O (453 ± 100 ng N m⁻² s⁻¹) observed at 17 % (HONO), 42 % (NO) and 94 % (N₂O) water filled pore space (WFPS), respectively, while NH₃ emission remains at ∼16 ng N m⁻² s⁻¹ in majority of the WFPS range. Results from process inhibiting experiments suggested ammonia oxidation to be the dominant pathway for HONO production in the low water-content range, while reduction of nitrate to NO₂⁻ appeared dominant in the high water-content range. Our study demonstrates the feasibility of studying emissions of HONO along with other N containing gases as connected network of processes as a whole.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102501"},"PeriodicalIF":3.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679304","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}