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":"Influence of meteorological variables and human activities on precipitation chemistry in the Guanzhong Plain, Northwest China","authors":"Rui Sun ,&nbsp;Xiaofei Li ,&nbsp;Huayu Huang ,&nbsp;Chi Zhou ,&nbsp;Yibo Wang","doi":"10.1016/j.apr.2025.102523","DOIUrl":"10.1016/j.apr.2025.102523","url":null,"abstract":"<div><div>Precipitation chemistry can reflect the impacts of both anthropogenic and natural sources on air quality and provide insights into material cycles between the Earth's surface and atmosphere. We explored the chemical characteristics of precipitation in relation to meteorological and environmental factors in Weinan, a key hub for agriculture and ecological protection on the Guanzhong Plain in Northwest China. Precipitation samples (n = 291) collected in Weinan from 2021 to 2022 were analyzed for their chemical compositions using chemometric analysis, correlation analysis, the positive matrix factorization (PMF) model, and the backward trajectory model. The findings revealed that the primary ions in the precipitation were Ca²⁺, NH₄⁺, SO₄²⁻ and NO₃⁻. The concentrations of most ions were higher in winter and lower in summer due to changes in precipitation amount, humidity, PM_2.5 and PM₁₀. The PMF analysis identified six ion sources in precipitation, including crustal sources (24.9 %), secondary formation (20.7 %), waste incineration (16 %), marine sources (15 %), industrial emissions (11.8 %), and biomass burning (11.5 %). The backward trajectory analysis showed that water vapor transport varies seasonally and is primarily influenced by westerly, monsoonal, and regional circulations. The westerly circulation predominantly affects ion concentrations by transporting dust and anthropogenic pollutants to Weinan. The monsoonal circulation carries large amounts of water vapor and contributes the most to precipitation (54.38 %). This study reveals the impacts of natural factors, human activities, and water vapor sources on precipitation chemistry and offers decision support for air quality management and pollution control in Northwest China.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102523"},"PeriodicalIF":3.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799689","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":"Multi-scale characteristics and statistically associated factors of ozone pollution in Hangzhou based on machine learning","authors":"Yu-ting He ,&nbsp;Tao Ding ,&nbsp;Ru Yi ,&nbsp;Yuan-yuan Wang ,&nbsp;Yi Fu ,&nbsp;Cheng-kai Tu ,&nbsp;Hong-yan Fang ,&nbsp;Jin-ye Li ,&nbsp;Ming Zhang","doi":"10.1016/j.apr.2025.102522","DOIUrl":"10.1016/j.apr.2025.102522","url":null,"abstract":"<div><div>Ozone pollution poses a significant air quality challenge in Hangzhou in recent years. This study investigated the multi-scale characteristics and statistically associated factors of ozone pollution in Hangzhou based on O₃ data from 12 monitoring stations in the city from 2018 to 2022, along with data on other pollutants and meteorology. The analysis utilized the PAM (Partitioning Around Medoids) clustering method, KZ (Kolmogorov–Zurbenko) filtering method, and XGBoost (eXtreme Gradient Boosting) combined with the SHAP (Shapley additive explanations) model. The results indicate that: (1) Clustering of the MDA8-O₃ (Maximum Daily 8-Hour Average O₃) data from monitoring stations using PAM reveals that Hangzhou can be divided into three sub-regions: west, central, and east, with varying degrees of ozone pollution from low in the west to high in the east. (2) Decomposition of the MDA8-O₃ concentration time series into long-term, seasonal, and short-term components highlights that the short-term components primarily drive the fluctuations in the original sequence. (3) At both temporal and spatial scales, disparities in the statistically associated factors of ozone pollution exist. Temporally, temperature and relative humidity dominate seasonal and short-term components, while long-term components are statistically associated with both temperature and long-term emissions. Spatially, temperature is the main factor in the west, but diminishes in the central and eastern regions, where other pollutants become more influential. Regional differences in emission sources near monitoring sites also affect statistically associated factors. The findings of this study can offer valuable insights for developing targeted strategies for ozone pollution control in Hangzhou.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102522"},"PeriodicalIF":3.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838224","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":"Methodology for selecting near-surface CH4, CO, and CO2 observations reflecting atmospheric background conditions at the WMO/GAW station in Lamezia Terme, Italy","authors":"Luana Malacaria ,&nbsp;Salvatore Sinopoli ,&nbsp;Teresa Lo Feudo ,&nbsp;Giorgia De Benedetto ,&nbsp;Francesco D'Amico ,&nbsp;Ivano Ammoscato ,&nbsp;Paolo Cristofanelli ,&nbsp;Mariafrancesca De Pino ,&nbsp;Daniel Gullì ,&nbsp;Claudia Roberta Calidonna","doi":"10.1016/j.apr.2025.102515","DOIUrl":"10.1016/j.apr.2025.102515","url":null,"abstract":"<div><div>Since 2015, the permanent World Meteorological Organization/Global Atmosphere Watch (WMO/GAW) station of Lamezia Terme (LMT) in Calabria, Southern Italy, has been performing continuous measurements of atmospheric greenhouse gases (GHGs). As a coastal monitoring station, LMT allowed continuous data gathering of carbon dioxide (CO₂), carbon monoxide (CO) and methane (CH₄) mole fractions in a region characterized by a Mediterranean climate. This work aims to test the adoption of three different methods in the selection of observations representative of the atmospheric background conditions at LMT. In particular, we applied the Background Data Selection (BaDS) method, the smoothed minima baseflow separation method (SM), and the new “Wind” method. All the three selection methods appeared to be effective in retaining the background CH₄, CO, and CO₂ data. Wind, based on the analysis of the local wind regime, selected the lowest number of data. For all the gases considered, the monthly mean values obtained after the implementation of BaDS (SM) were the highest (lowest). Taking into account the complete datasets over the 2015–2023 period, Mann-Kendall and Sen's slope showed annual and seasonal increasing tendencies for CH₄ and CO₂ with significance levels of α = 0.05 and α = 0.001, respectively. For CO, a decreasing tendency was only observed for the winter season level of α = 0.05. The application of the three selection methods resulted in changes in the calculated annual and seasonal growth rates and non-negligible deviations were also found for the average annual growth rates calculated for the three background datasets. This indicates that growth rate calculations are sensitive to the choice of background selection methods, and we recommend that multiple selection methods could be applied to resolve the associated uncertainties.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102515"},"PeriodicalIF":3.9,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768540","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":"Street level PM2.5 over a major Indian metropolis using low-cost sensors mounted to bicycles: Elevated exposures to livelihood bicyclists","authors":"Sauryadeep Mukherjee ,&nbsp;Srijan Haldar ,&nbsp;Srodhya Paul ,&nbsp;Sandip Ghosh","doi":"10.1016/j.apr.2025.102517","DOIUrl":"10.1016/j.apr.2025.102517","url":null,"abstract":"<div><div>PM_2.5 is a major ambient air pollutant which is responsible for global mortality and morbidity. Vehicular emissions are one of the leading sources of air pollution, especially in urban areas of developing countries like India. Bicycling is an emission-free multipurpose mode of transport, encouraged globally to reduce vehicular emissions. However, bicyclists have higher risks of getting affected by air pollution in comparison to other travelling modes. The study was conducted in a metropolitan city of the Eastern IGP to understand the street-level PM₂.₅ in different road types, its relation to vehicular flow dynamics and the effects on cyclists. Highest PM₂.₅ were recorded during the colder seasons and the least in monsoon, yet the concentrations during the monsoon period were quite high with 14 % of trips surpassing the daily NAAQS values. It was found that primary roads having a higher number of vehicles had significantly higher values of PM₂.₅ across all seasons. Similarly, higher values were associated with office hours as well, clearly indicating the role played by the number of vehicles for heightening the PM_2.5 concentrations. Road-width plays a crucial role in hindering PM_2.5 dispersion, especially on narrow tertiary lanes. Constructional activities on roads were further found to escalate PM₂.₅ loads throughout the dry seasons. A survey involving livelihood and recreational cyclists revealed that the former are more prone to air pollution related diseases. Finally, the study represents the amount of risk faced by livelihood-bicyclists in metro cities and highlights the need for framing policies to control street-level air pollution.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102517"},"PeriodicalIF":3.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758962","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}