Atmospheric Environment: X最新文献

{"title":"Physicochemical characterization of welding and grinding fine particulates at a machinery plant: A comprehensive case study of workers’ health risk assessment","authors":"Po-Hsuan Yen ,&nbsp;Hsin-Nan Chung ,&nbsp;Wen-Hsi Cheng ,&nbsp;Chung-Shin Yuan ,&nbsp;Yu-Lun Tseng ,&nbsp;Chin-Ko Yeh ,&nbsp;Chang-Hua Lien ,&nbsp;Su-Wen Cheng","doi":"10.1016/j.aeaoa.2025.100319","DOIUrl":"10.1016/j.aeaoa.2025.100319","url":null,"abstract":"<div><div>Electric welding is a well-developed technology and an indispensable process in today's metal processing-related manufacturing industries. However, high-temperature operation could easily expose welding personnel to metal fume containing particle matters (PM) with aerodynamic diameters (<em>d</em>_<em>p</em>) ranging from sub-microns to microns. This investigation focused on a high-pressure vessel manufacturing factory in southern Taiwan, and workers in the same indoor factory building were divided into five similar exposure groups (SEGs), including design, layout/assembling, arc welding, grinding and pickling/painting, to implement personal sampling. According to the monitoring data on-site, high concentrations of PM are mainly found in the areas within 0.5 m of welding operations; the fume produced from ilmenite electrodes has the highest concentration of PM, and followed by those using high-fiber electrodes and high-tensile steel electrodes. Especially when welding with ilmenite electrodes at the current of 150 A, the concentrations of PM_2.5 and PM₁₀ reached the highest 1716 and 3024 μg/m³, respectively. The SEG of welding, who manually welded carbon steel and stainless steel, even exposed to submicron particles with <em>d</em>_<em>p</em> = 151–170 nm. Metal analysis of PM using inductively coupled plasma optical emission spectroscopy (ICP-OES) found that the cancer risk of cadmium (Cd) and chromium (Cr(VI)) in the SEG of pickling and painting was significantly the highest among all SEGs; the non-cancer risk (hazard index) of manganese (Mn) was high among all SEGs.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"25 ","pages":"Article 100319"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444946","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":"Emission characteristics of railway tread brakes","authors":"Daniel Fruhwirt ,&nbsp;Thomas Nöst ,&nbsp;Philipp Leonhardt ,&nbsp;Martin Leitner ,&nbsp;Peter Brunnhofer ,&nbsp;Gina Bode ,&nbsp;Sabrina Michael ,&nbsp;Johannes Rodler","doi":"10.1016/j.aeaoa.2025.100320","DOIUrl":"10.1016/j.aeaoa.2025.100320","url":null,"abstract":"<div><div>Braking of freight cars in rail transport mostly happens mechanically, as tread brakes are the only braking system. For this reason, non-exhaust particle (NEP) emissions can be expected. In order to quantify the emissions of such brakes, an extensive series of tests was conducted on a rail brake test bench at Graz University of Technology. The quantification of NEP emissions was done for grey cast iron as well as composite blocks and included the observation of emitted particle mass, particle size distribution, analysis of particle composition as well as the monitoring of gaseous substances. The test results indicated that grey cast iron brake shoes represent the highest emitter. PM10 emission factors for brake events were derived in the range of 2.65–28.66 g/km (km refers to braking distance). Iron and manganese were present in most of the tests. Barium and zinc could only be identified in tests with type K brake shoes.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"25 ","pages":"Article 100320"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529618","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":"Multi-scenario modeling of regional dual-carbon target achievement and air quality improvement: A case study of Zhejiang province","authors":"Yuxin Yang","doi":"10.1016/j.aeaoa.2025.100312","DOIUrl":"10.1016/j.aeaoa.2025.100312","url":null,"abstract":"<div><div>The rise in carbon emissions is a major driver of global warming, and China faces the dual challenge of reducing emissions while addressing air pollution. However, current research lacks comprehensive models that jointly address both carbon reduction and air quality improvement. In this study, we applied the Long-range Energy Alternatives Planning (LEAP) model to project energy consumption and CO₂ emissions for baseline, low-carbon, and deep low-carbon scenarios in Zhejiang Province from 2020 to 2060, focusing on six key sectors: residential, agricultural, building, commercial, industrial, and transportation. The Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model was also employed to simulate the spatial and temporal distribution of major air pollutants (NO₂, SO₂, O₃, PM_2.5, PM₁₀) in four representative months—April, July, October, and January—under each scenario. The results indicate that (1) the industrial, transport, and residential sectors contribute over 84.7% of energy consumption, CO₂ emissions, and air pollutant emissions from 2020 to 2060, identifying them as key targets for mitigation strategies; (2) carbon peaking is not projected under the baseline scenario, but is expected by 2040 in the low-carbon scenario and 2029 in the deep low-carbon scenario, with a peak emission of 208.821 million tons, primarily driven by reductions in the industrial sector; (3) air pollutant emissions across scenarios rank as O₃, NO₂, PM₁₀, PM_2.5, and SO₂, indicating the need for stronger O₃ control; and (4) the proportion of days meeting national air quality standards remains unchanged, implying further action is necessary to improve air quality. This study provides a theoretical basis and policy support for provincial energy conservation, emissions reduction, and air quality management.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"25 ","pages":"Article 100312"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158160","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":"Modelling the air quality impacts of a zero emission zone scenario in the city of Milan","authors":"Andrea Piccoli ,&nbsp;Valentina Agresti ,&nbsp;Marco Bedogni ,&nbsp;Giovanni Lonati ,&nbsp;Guido Pirovano","doi":"10.1016/j.aeaoa.2025.100318","DOIUrl":"10.1016/j.aeaoa.2025.100318","url":null,"abstract":"<div><div>One of the sustainable mobility policies aimed to reduce road traffic emissions in urban areas is the implementation of zero-emission zones (ZEZ) which are areas where only electric vehicles, pedestrians, and cyclists are allowed. The assessment of possible benefit of ZEZ on emissions, air quality and human health can be performed with a dedicated modelling chain. The goal of this work is to simulate a 2030 ZEZ mobility scenario in Milan city centre (Italy), introducing changes to the vehicle fleet composition within the ZEZ, as suggested by the Air Quality and Climate Plan of the municipality. Then we evaluated the benefits in terms of both air quality and citizens’ health. The assessment of the environmental and health effects is done thanks to a modelling suite composed of a road traffic, an emission, a hybrid air quality models, plus a fourth module for human health impacts. In this specific case study, the modelling chain is applied to the metropolitan city of Milan for the reference year 2017 first, and then for two hypothetical mobility scenarios referred to 2030: the temporal evolution of baseline simulation as planned by the NEC (National Emission reduction Commitments) directive and by local emission containment measures (BAS30) and the BAS30 scenario with the implementation of the mobility policy (ZEZ30). The impact of the policy can be inferred by comparing the two scenarios.</div><div>Road traffic emission in 2030 are expected to strongly decrease from the 2017 levels (−84% for NOx and −28% for PM2.5). The road traffic emission reduction introduced by the ZEZ30 scenario compared to the BAS30 scenario is high in the model cells contained within the ZEZ area (complete removal of NOx emissions and up to −45% PM2.5), but given the limited area of the ZEZ the citywide reduction of emissions is 2.24% for NOx and 0.91% for PM2.5. NO2 concentrations are expected to decrease of circa 54% inside the city of Milan under the BAS30 scenario compared with 2017 (35% for PM2.5). Again, the reduction introduced by the ZEZ are low and are mostly included within the ZEZ area (maximum 2% and 0.6 % for NO2 and PM2.5 respectively). Considering the ZEZ30 scenario, morbidity and mortality indicators are expected to decrease of 50% circa compared with 2017. In conclusion, the introduction of the ZEZ in 2030 is expected to have a marginal effect compared to the BAS30 scenario due to the limited spatial extent of the simulated ZEZ, the limited effect of electric vehicles on non-exhaust emissions, and the already strong emission reductions achieved using the projected 2030 vehicle fleet.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"25 ","pages":"Article 100318"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377647","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":"Optimisation and validation of Plume Chasing for robust and automated NOx and particle vehicle emission measurements","authors":"Christina Schmidt ,&nbsp;C. David Carslaw ,&nbsp;J. Naomi Farren ,&nbsp;N. René Gijlswijk ,&nbsp;Markus Knoll ,&nbsp;E. Norbert Ligterink ,&nbsp;Jan Pieter Lollinga ,&nbsp;Martin Pechout ,&nbsp;Stefan Schmitt ,&nbsp;Michal Vojtíšek ,&nbsp;Quinn Vroom ,&nbsp;Denis Pöhler","doi":"10.1016/j.aeaoa.2025.100317","DOIUrl":"10.1016/j.aeaoa.2025.100317","url":null,"abstract":"<div><div>High-emitting vehicles comprise a small proportion (<span><math><mrow><mo>&lt;</mo><mn>20</mn><mspace></mspace><mstyle><mtext>%</mtext></mstyle></mrow></math></span>) of the vehicle fleet, yet are responsible for the majority (<span><math><mrow><mo>&gt;</mo><mn>50</mn><mspace></mspace><mstyle><mtext>%</mtext></mstyle></mrow></math></span>) of vehicle emissions. Plume Chasing is a reliable, high-precision measurement technique that derives emissions without interfering with the vehicle being tested. Its characteristics make it well suited for high emitter identification. In this study, the influence of several Plume Chasing measurement and data processing methods on the results of derived on-road <span><math><msub><mrow><mi>NO</mi></mrow><mrow><mi>x</mi></mrow></msub></math></span> and particle fuel-specific emission factors are investigated. A range of vehicles, representative of a common vehicle fleet, were tested under different driving conditions on a test track. The derived results were evaluated against on-board SEMS (Smart Emission Measurement System) emission measurements. We found that one of the best performing Plume Chasing data processing methods is based on the use of a rolling minimum for background determination. The average absolute deviation of the determined <span><math><mrow><msub><mrow><mi>NO</mi></mrow><mrow><mi>x</mi></mrow></msub><mo>/</mo><msub><mrow><mi>CO</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span> emission ratios from the reference was <span><math><mrow><mo>−</mo><mn>0</mn><mo>.</mo><mn>2</mn><mrow><mo>(</mo><mn>46</mn><mo>)</mo></mrow><mspace></mspace><mstyle><mi>p</mi><mi>p</mi><mi>m</mi></mstyle><mo>/</mo><mstyle><mtext>%</mtext></mstyle></mrow></math></span> for the heavy duty vehicle and <span><math><mrow><mn>0</mn><mo>.</mo><mn>3</mn><mrow><mo>(</mo><mn>29</mn><mo>)</mo></mrow><mspace></mspace><mstyle><mi>p</mi><mi>p</mi><mi>m</mi></mstyle><mo>/</mo><mstyle><mtext>%</mtext></mstyle></mrow></math></span> for the light duty vehicles tested. The methods were easy to automate and suitable for high emitter detection and quantification of emissions from two-wheeled vehicles. Inaccurate emission factors derived from Plume Chasing measurements occurred only in situations when emissions were significantly influenced by a strong plume from vehicles driving directly ahead of the vehicle of interest.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"25 ","pages":"Article 100317"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143237363","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":"Urgent issues regarding real-time air quality monitoring data in India: Unveiling solutions and implications for policy and health","authors":"Karn Vohra ,&nbsp;Madhumitha S. ,&nbsp;Abhishek Chakraborty ,&nbsp;Hitansh Shah ,&nbsp;Bharrathi AS. ,&nbsp;Jayaraju Pakki","doi":"10.1016/j.aeaoa.2024.100308","DOIUrl":"10.1016/j.aeaoa.2024.100308","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Deteriorating air quality in India has heightened the emphasis on air quality monitoring. This has resulted in a 16-fold increase in the number of Continuous Ambient Air Quality Monitoring Sites (CAAQMS) across the country over the last decade. The CAAQMS datasets are used globally, but concerns about data quality have also been raised. Missing is a comprehensive assessment quantifying the scale of these air quality data issues and the impact these have on policy- and health-relevant metrics. So, we develop the first open-source automated tool to identify and address data issues and apply it to six pollutants (PM&lt;sub&gt;2.5&lt;/sub&gt;, PM&lt;sub&gt;10&lt;/sub&gt;, NO, NO&lt;sub&gt;2&lt;/sub&gt;, NO&lt;sub&gt;x&lt;/sub&gt;, and O&lt;sub&gt;3&lt;/sub&gt;) from 213 CAAQMS in 2019–2023. Typical issues in CAAQMS datasets include similar values that repeat continuously for durations exceeding 24 h and outliers that occur at almost the same time every day. We also reveal hidden issues for nitrogen oxides (NO&lt;sub&gt;x&lt;/sub&gt; ≈ NO + NO&lt;sub&gt;2&lt;/sub&gt;) that include (1) reporting of NO and NO&lt;sub&gt;2&lt;/sub&gt; in units not compliant with the Central Pollution Control Board parameter reporting protocol and (2) inconsistency in data reporting when either NO or NO&lt;sub&gt;2&lt;/sub&gt; is recorded as “Not Available” but valid NO&lt;sub&gt;x&lt;/sub&gt; data is reported. The proportion of data influenced by consecutively similar observations and outliers has remained fairly consistent but sites affected by unit inconsistency issues have grown between 2019 and 2023. No significant difference in data quality issues was observed between CAAQMS maintained by central and state pollution control boards illustrating the country-wide extent of these issues. We find that removing consecutively similar observations and outliers changes annual mean pollutant concentrations by only &lt;5% but correcting for the yet unaddressed issue of unit inconsistency increases annual mean NO&lt;sub&gt;2&lt;/sub&gt; concentrations by a dramatic &gt;80% for sites affected by it. We conducted a separate analysis to confirm that the unit inconsistency issue was not identified and addressed in multiple peer-reviewed studies examining the impact of the COVID-19 lockdown, and this is likely to have resulted in reporting of inaccurate absolute air quality improvements.&lt;/div&gt;&lt;div&gt;A substantial impact of data cleaning on air quality-derived metrics is observed for nitrogen oxides. The impact is marginal for other pollutants. We find that after data cleaning, 23 sites in 2019 became non-compliant with national ambient air quality standards for NO&lt;sub&gt;2&lt;/sub&gt;. Worsening of NO&lt;sub&gt;2&lt;/sub&gt; data quality over the years increased the number of non-compliant sites to 45 in 2023 after using our tool. For PM&lt;sub&gt;2.5&lt;/sub&gt; and PM&lt;sub&gt;10&lt;/sub&gt;, fewer than 5 sites changed compliance post-data cleaning. Given marginal changes in concentrations of PM&lt;sub&gt;2.5&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt;, premature mortality attributable to exposure to these in Delhi, Mumbai, and Kolkata changed only by &lt;10% after data c","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"25 ","pages":"Article 100308"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158866","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":"A real world assessment of European medium-duty vehicle emissions and fuel consumption","authors":"Nikiforos Zacharof ,&nbsp;Stijn Broekaert ,&nbsp;Theodoros Grigoratos ,&nbsp;Evangelos Bitsanis ,&nbsp;Georgios Fontaras","doi":"10.1016/j.aeaoa.2024.100307","DOIUrl":"10.1016/j.aeaoa.2024.100307","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Emissions of road vehicles have a significant impact on climate change and air quality and in order to address these problems there have been regulatory actions globally in the last decades. Such actions have focused mainly on light and heavy-duty vehicles, which comprise the highest share of the fleet and are responsible for the majority of emissions in the field. However, there are also medium-duty vehicles with a maximum permissible mass between 3.5 and 12 tonnes in the European categories, which have been mostly overlooked until recently. These vehicles could have a low market share, but they are important as they circulate mainly in urban and suburban areas under transient conditions and often with congestion. This has a detrimental impact on the environment and human health due to greenhouse gas and pollutant emissions. However, there are limited studies for this vehicle category. The current work undertook to address this issue by focusing on medium-duty vehicles in Europe by attempting to establish a methodology to calculate reference emission values for CO&lt;sub&gt;2&lt;/sub&gt;, NO&lt;sub&gt;x&lt;/sub&gt; and CO to improve fleet monitoring. For this reason, two state-of-the-art vehicles were measured on-road under the EU verification test procedure. Naturally, the measurements represented the anticipated average European conditions of the route in terms of speed profile, road grade and distance. In order to provide emissions values that are representative of the European conditions a normalization process was needed. For this reason, the measurements were used to set up vehicle simulations in VECTO, the official simulation tool of the European Commission for calculating type-approval fuel consumption and CO&lt;sub&gt;2&lt;/sub&gt; emissions. In this way, the simulations provided values ranging from 297 g/km to 373 g/km. Using the ratio of fuel consumption for NO&lt;sub&gt;x&lt;/sub&gt; and CO from the measurements, it was possible to derive reference pollutant values. For NO&lt;sub&gt;x&lt;/sub&gt;, they were found to be between 0.0557 and 0.0963 g/km, while for CO the values were at 0.047 g/km. These values could be used as emissions factors as in the Guidebook, which is the official tool for monitoring fleet emissions of the European Commission. The Guidebook offers several approaches to calculate emissions, depending on data availability with the most sophisticated being a calculation method using vehicle speed, loading share and road grade. Taking this into consideration, the current work developed a similar methodology using the simulation time-series to derive regression coefficients that enable the calculation of CO&lt;sub&gt;2&lt;/sub&gt;, NO&lt;sub&gt;x&lt;/sub&gt; and CO emissions under different operating conditions. In this way, this methodology can be applied to representative vehicles of the medium and heavy-duty categories that have been through the verification test procedure to determine representative emission factors for these vehicles. This methodology could be used to improve fleet emiss","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"25 ","pages":"Article 100307"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158447","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":"Real-world emission characteristics and driving factors of diesel trucks: Insights from plume chasing experiments","authors":"Jiahao Yang ,&nbsp;Xiang Che ,&nbsp;Jiani Tan ,&nbsp;Xiaoliang Qin ,&nbsp;Jiahao Duan ,&nbsp;Dengguo Liu ,&nbsp;Yusen Duan ,&nbsp;Sheng Xiang ,&nbsp;Nanchi Shen ,&nbsp;Xi Zhai ,&nbsp;Yi Zhang ,&nbsp;Zhi Ning ,&nbsp;Li Li","doi":"10.1016/j.aeaoa.2025.100311","DOIUrl":"10.1016/j.aeaoa.2025.100311","url":null,"abstract":"<div><div>On-road transportation is gradually becoming one of the major source contributors to air pollutants and carbon emissions in China. In this study, a chasing experiment was conducted on 487 diesel trucks in Shanghai using a mobile plume chasing and analysis system to obtain the real-world emission characteristics of air pollutants and carbon dioxide (CO₂). The average emission factors (EFs) of nitrogen oxide (NOx), fine particulate matter (PM_2.5), volatile organic compounds (VOCs) and carbon monoxide (CO) of the measured vehicles are 22.8 ± 13.5, 0.38 ± 0.26, 5.6 ± 4.9, and 4.5 ± 4.0 g/kg fuel, respectively. The observed decrease in EFs of air pollutants from China IV to China VI suggests a potential correlation between stricter emission standards and reduced emissions in diesel truck fleets. Additionally, the EFs increase with the gross vehicle weights (GVW). The driving speed, registration year and wind direction were the main drivers of NOx EFs. The measured CO₂ EFs is 3182.2 ± 5.8 g/kg fuel, showing little variations with emission standards and GVW, which is different from the abovementioned air pollutants. We found a significant negative correlation between the EFs of CO₂ and NO_x (p &lt; 0.05), indicating that as NOx emissions decrease, CO₂ emissions tend to increase. Therefore, we recommend integrating CO₂ emission limits into new standards to achieve synergistic control of pollutants and greenhouse gases.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"25 ","pages":"Article 100311"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158867","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":"Distribution of polycyclic aromatic compounds among various phases in an urban road microenvironment of a tropical megacity","authors":"Dheeraj Alshetty ,&nbsp;S.M. Shiva Nagendra ,&nbsp;Andrea Mueller ,&nbsp;Uwe Schlink","doi":"10.1016/j.aeaoa.2024.100309","DOIUrl":"10.1016/j.aeaoa.2024.100309","url":null,"abstract":"<div><div>Quantitative research on characteristics of PAHs prevalent in urban road microenvironments (URM) is vital to emphasize the seriousness of health risks and reduce exposure among commuters and nearby residents, especially in Indian cities where the traffic emissions (exhaust and non-exhaust) are the major contributors to atmospheric particulates in an urban area. The present research investigates the distribution and correlation of Polycyclic Aromatic Hydrocarbons (PAHs) found in various phases (PM₁₀, PM_2.5, resupendable road dust (RRD) and gaseous phase) at urban road microenvironment (URM) using Two-way ANOVA analysis. The sources and health risk associated with PAH exposure is also estimated. PM₁₀ and PM_2.5 samples were collected on the kerbside using high-volume samplers, and RRD was collected at eight contrasting locations using EPA AP-42 methodology. A total of 64 PAHs were analyzed using GC-MS and incremented life cancer risk (ILCR) was estimated for children and adults by calculating toxicity equivalents using three different approaches. Average PAHs concentration varied from 75 to 175 μg/g for PM₁₀, 30–80 μg/g for PM_2.5, and 01–03 μg/g for RRD. Gaseous phase ∑PAH was found to be in the range of 0.5–2.75 μg/m³. It was found that high molecular weight PAHs such as Coronene, Pyrene, Indeno(1,2,3-c,d)pyrene and Benzo ghi perylene were the major contributing compounds in the urban road microenvironment. A strong correlation between PAHs bound to PM₁₀ and RRD was found at all the sampling locations. Further, ILCRs of total cancer risk due to inhalation of PM were in the range of 1.61E-05 to 2.05E-03. However, the risk due to exposure to RRD was within an acceptable risk of 1E-06. The current study highlights the scientific backing for RRD-specific regulations, which are currently absent, to control non-exhaust emissions in India.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"25 ","pages":"Article 100309"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158448","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":"A multiscale geographically weighted regression approach to emphasize the effects of traffic characteristics on vehicular emissions","authors":"Farzane Omrani,&nbsp;Rouzbeh Shad,&nbsp;Seyed Ali Ziaee","doi":"10.1016/j.aeaoa.2025.100315","DOIUrl":"10.1016/j.aeaoa.2025.100315","url":null,"abstract":"<div><div>The rapid growth in motor vehicle numbers over the years has notably increased air pollution levels, particularly in developing countries. According to the International Energy Agency, road transport significantly contributes to air pollution more than other transportation. This study aims to investigate the spatial distribution impact of various built environment, sociodemographic, meteorological, and traffic-related features across buffer distances on vehicular emissions from all vehicle types at the link level. Initially, this study restructured data to perform 25 combination models for five emissions from all vehicles, classified into five types. Secondly, regression models were created using Ordinary Least Squares (OLS) and Multiscale Geographically Weighted Regression (MGWR) in ArcGIS Pro, assessing the spatial impact of these features on emissions for each road segment in North Carolina in 2019. Model performance was evaluated using adjusted R-squared and R-squared metrics, with the MGWR model outperforming the OLS model, achieving adjusted R-squared values between 74% and 97%. Finally, it analyzes the spatial distribution impact of each feature on each emission from vehicle types at the link level. Particularly, the significant impact of traffic-related features on vehicular emission offers valuable insights for governments and decision-makers to develop targeted transportation planning strategies and meet air pollution targets set by the state.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"25 ","pages":"Article 100315"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158446","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}