Pub Date : 2025-04-01DOI: 10.1016/j.aeaoa.2025.100328
Yihong Ning , Ruixiao Sun , David Hitchcock , Gurcan Comert , Yuche Chen
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 (PM1.0, PM2.5, 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.
{"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 , Ruixiao Sun , David Hitchcock , Gurcan Comert , 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<sub>1.0</sub>, PM<sub>2.5</sub>, PM<sub>10</sub>) concentrations, while traffic speed significantly affects nitrogen dioxide (NO<sub>2</sub>) levels. Specifically, higher average traffic speeds (indicative of smoother flow) correspond to lower NO<sub>2</sub> concentrations, suggesting that less congested conditions reduce NO<sub>2</sub> 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 NO<sub>2</sub> 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}
Pub Date : 2025-04-01DOI: 10.1016/j.aeaoa.2025.100340
Ahamed Ibrahim S.N. , Ramachandran A. , Pavithrapriya S. , Palanivelu K.
On-road vehicular emissions constitute a substantial source of air pollution within densely populated metropolitan areas, giving rise to substantial concerns related to public health and environmental integrity. This study analysed Particulate Matter (PM2.5), Black Carbon (BC) and Organic Carbon (OC) emission inventory observed trends from 2003 to 2020 and projected trends up to 2070 under different e-vehicle usage rate scenarios in Chennai City. Based on the projected vehicular growth rate, the annual inventory of PM2.5 could peak at 17 Gg in 2035, a 112 % increase from 2020 levels. Likewise, the BC and OC would increase at 6.5 Gg and 5.3 Gg, respectively. Also, compared to conventional fossil fuels at the end of 2040, pollution inventory would decrease by approximately 43 %, 66 %, 85 %, and 100 % under low (2 %/yr), medium (3 %/yr), high (4 %/yr), and very high (5 %/yr) usage rate scenarios for electric vehicles. The study also predicts emission intensity disparities in various traffic conditions across the city, highlighting the urgent need for transitioning to electric vehicles and targeted interventions in congested areas. The core city, particularly zones like Royapuram, Valasaravakkam, and Tondiarpet, exhibits severe emission risk, driven by key indicators such as population, bus stops, road density, omnibus, heavy vehicle flow, and congested traffic conditions. The outcome of this study underscores timely action is needed to address the projected rise in vehicular emissions and associated health burdens in fast-growing megacities like Chennai. The study provides critical insights for policymakers to mitigate air pollution through targeted interventions.
{"title":"Emission risk assessment of carbonaceous aerosols from road transport in the megacity of Chennai, India","authors":"Ahamed Ibrahim S.N. , Ramachandran A. , Pavithrapriya S. , Palanivelu K.","doi":"10.1016/j.aeaoa.2025.100340","DOIUrl":"10.1016/j.aeaoa.2025.100340","url":null,"abstract":"<div><div>On-road vehicular emissions constitute a substantial source of air pollution within densely populated metropolitan areas, giving rise to substantial concerns related to public health and environmental integrity. This study analysed Particulate Matter (PM<sub>2.5</sub>), Black Carbon (BC) and Organic Carbon (OC) emission inventory observed trends from 2003 to 2020 and projected trends up to 2070 under different e-vehicle usage rate scenarios in Chennai City. Based on the projected vehicular growth rate, the annual inventory of PM<sub>2.5</sub> could peak at 17 Gg in 2035, a 112 % increase from 2020 levels. Likewise, the BC and OC would increase at 6.5 Gg and 5.3 Gg, respectively. Also, compared to conventional fossil fuels at the end of 2040, pollution inventory would decrease by approximately 43 %, 66 %, 85 %, and 100 % under low (2 %/yr), medium (3 %/yr), high (4 %/yr), and very high (5 %/yr) usage rate scenarios for electric vehicles. The study also predicts emission intensity disparities in various traffic conditions across the city, highlighting the urgent need for transitioning to electric vehicles and targeted interventions in congested areas. The core city, particularly zones like Royapuram, Valasaravakkam, and Tondiarpet, exhibits severe emission risk, driven by key indicators such as population, bus stops, road density, omnibus, heavy vehicle flow, and congested traffic conditions. The outcome of this study underscores timely action is needed to address the projected rise in vehicular emissions and associated health burdens in fast-growing megacities like Chennai. The study provides critical insights for policymakers to mitigate air pollution through targeted interventions.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100340"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144270780","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}
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.
{"title":"Performance and applicability of low-cost PM sensors to assess global pollution variability through machine learning techniques","authors":"Rajat Sharma , Andry Razakamanantsoa , Ashutosh Kumar , Thaseem Thajudeen , 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}
Pub Date : 2025-04-01DOI: 10.1016/j.aeaoa.2025.100335
Muhammad Hassan , Khabat Khosravi , Travis J. Esau , Gurjit S. Randhawa , Aitazaz A. Farooque , Seyyed Ebrahim Hashemi Garmdareh , Yulin Hu , Nauman Yaqoob , Asad T. Jappa
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.
{"title":"Nitrous oxide prediction through machine learning and field-based experimentation: A novel strategy for data-driven insights","authors":"Muhammad Hassan , Khabat Khosravi , Travis J. Esau , Gurjit S. Randhawa , Aitazaz A. Farooque , Seyyed Ebrahim Hashemi Garmdareh , Yulin Hu , Nauman Yaqoob , 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 (N<sub>2</sub>O) and water vapor (H<sub>2</sub>O) 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 N<sub>2</sub>O and H<sub>2</sub>O 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 (R<sup>2</sup>), 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 N<sub>2</sub>O and H<sub>2</sub>O predictions in PEI and NB, followed by the RBD-RF, ASC-RF, and SVR models. The new models demonstrated good performance according to R<sup>2</sup> 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 N<sub>2</sub>O and H<sub>2</sub>O 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}
The spatial allocation of emissions in air quality models introduces uncertainties that significantly impact pollution exposure assessments. This study quantified the effects of emission allocation uncertainty on atmospheric concentrations and exposure levels using the CMAQ modeling system. The research focused on the Afşin-Elbistan Power Plant (AP), with substantial emissions of SO2 (∼300,000 t/y) and PM2.5 (∼6000 t/y), evaluating the variability in concentrations from emission allocation in gridded inventories. 13 model simulations were conducted, including a base case (c0) where emissions were spatially allocated based on intersection ratios and 12 scenario cases (c1–c12) where emissions were assigned to different grids for 2018. Results showed significant variability in pollution levels and population exposures across scenario cases. In the Maximum Impact Zone (MIZ), annual mean PM2.5 concentrations ranged from 5.0 to 41.3 μg/m3, with differences up to 24.9 μg/m3 from the base case. SO2 exhibited even greater variability, with maximum differences reaching 338.2 μg/m3. The 95 % probability range of uncertainty for PM2.5 was estimated at −45 % to +96 %, while for SO2, it reached −84 % to +240 %. Grids A–F represent six selected regions with high population density, used to evaluate differences in concentration and exposure across scenarios. In Grid A-F, meteorology influenced these patterns, with low wind speeds causing pollutant build-up in Grid A, while pollutant transport affected Grids D–F in summer. Annual population exposure in Grid C ranged from 1.0 to 2.1 kg/y for PM2.5 and from 3.9 to 16.7 kg/y for SO2. This paper highlights the importance of not only absolute emission inventories but also spatial emission allocation in air quality models to enhance regulatory effectiveness and protect public health.
{"title":"Quantifying the impact of the uncertainty arising from spatial allocation on public health using CMAQ","authors":"Fulya Cingiroglu , Ezgi Akyuz , Mete Tayanc , Alper Unal","doi":"10.1016/j.aeaoa.2025.100338","DOIUrl":"10.1016/j.aeaoa.2025.100338","url":null,"abstract":"<div><div>The spatial allocation of emissions in air quality models introduces uncertainties that significantly impact pollution exposure assessments. This study quantified the effects of emission allocation uncertainty on atmospheric concentrations and exposure levels using the CMAQ modeling system. The research focused on the Afşin-Elbistan Power Plant (AP), with substantial emissions of SO<sub>2</sub> (∼300,000 t/y) and PM<sub>2.5</sub> (∼6000 t/y), evaluating the variability in concentrations from emission allocation in gridded inventories. 13 model simulations were conducted, including a base case (<strong>c0</strong>) where emissions were spatially allocated based on intersection ratios and 12 scenario cases (<strong>c1–c12</strong>) where emissions were assigned to different grids for 2018. Results showed significant variability in pollution levels and population exposures across scenario cases. In the Maximum Impact Zone (MIZ), annual mean PM<sub>2.5</sub> concentrations ranged from 5.0 to 41.3 μg/m<sup>3</sup>, with differences up to 24.9 μg/m<sup>3</sup> from the base case. SO<sub>2</sub> exhibited even greater variability, with maximum differences reaching 338.2 μg/m<sup>3</sup>. The 95 % probability range of uncertainty for PM<sub>2.5</sub> was estimated at −45 % to +96 %, while for SO<sub>2</sub>, it reached −84 % to +240 %. Grids A–F represent six selected regions with high population density, used to evaluate differences in concentration and exposure across scenarios. In Grid A-F, meteorology influenced these patterns, with low wind speeds causing pollutant build-up in Grid A, while pollutant transport affected Grids D–F in summer. Annual population exposure in Grid C ranged from 1.0 to 2.1 kg/y for PM<sub>2.5</sub> and from 3.9 to 16.7 kg/y for SO<sub>2</sub>. This paper highlights the importance of not only absolute emission inventories but also spatial emission allocation in air quality models to enhance regulatory effectiveness and protect public health.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100338"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240853","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}
Pub Date : 2025-04-01DOI: 10.1016/j.aeaoa.2025.100325
Chieh-Sen Tsai , Ping-Chieh Huang , Hsin-Chih Lai , John C. Lin , Hui-Ming Hung
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.
{"title":"Addressing underestimated carbon monoxide emissions in Taiwan using CMAQ and impacts on local ozone concentration","authors":"Chieh-Sen Tsai , Ping-Chieh Huang , Hsin-Chih Lai , John C. Lin , 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 O<sub>3</sub> 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}
This study presents the first integrated source apportionment and health risk assessment of PM2.5-bound metals in Southern Thailand using Positive Matrix Factorization (PMF) and Principal Component Analysis (PCA). PM2.5 samples were collected across three urban-industrial provinces, Nakhon Si Thammarat (NST), Phuket (PKT), and Songkhla (SKA), during multiple months in 2023. PMF successfully resolved five major emission sources, including industrial processes, vehicular traffic, maritime fuel combustion, waste incineration, and fossil fuel combustion, explaining 58.4 % of the variance in the dataset. PCA offered complementary insight but lacked the resolution to isolate mixed-source tracers such as vanadium (V) and nickel (Ni), with lower total explained variance. Metal concentrations and source contributions exhibited distinct spatial and seasonal patterns, reflecting dynamic emission influences across the region. Phuket emerged as a hotspot for toxic metal exposure, with the highest hazard index (HI = 1.63) and cancer risk (4.79 × 10−4), exceeding international safety thresholds. In contrast, NST showed elevated Zn and Ag from traffic-related sources, while SKA was dominated by V and Ni from maritime emissions. Enrichment factor analysis further highlighted anthropogenic signatures, with exceptionally high values for Hg (Log EF = 6.09) in PKT and arsenic (As) (39 % of total metal mass) in SKA. Our findings provide new regional-scale evidence of metal-specific health risks and emission patterns in an understudied Southeast Asian context. This work supports the urgent need for strengthened regulatory policies targeting industrial and vehicular emissions, improved waste management, and expanded air quality monitoring to mitigate public health impacts from PM2.5-bound metals in Southern Thailand.
本研究首次使用正矩阵分解（PMF）和主成分分析（PCA）对泰国南部地区pm2.5结合金属进行了综合来源分配和健康风险评估。在2023年的多个月里，我们收集了三个城市工业省份的PM2.5样本，即那空西塔玛拉省（NST）、普吉岛省（PKT）和宋卡省（SKA）。PMF成功地解决了五个主要的排放源，包括工业过程、车辆交通、海上燃料燃烧、垃圾焚烧和化石燃料燃烧，解释了数据集中58.4%的方差。PCA提供了互补的见解，但缺乏分离混合源示踪剂(如钒（V)和镍（Ni））的分辨率，总解释方差较低。金属浓度和源贡献表现出明显的空间和季节格局，反映了整个区域的动态排放影响。普吉岛成为有毒金属暴露的热点地区，危害指数最高（HI = 1.63），癌症风险最高（4.79 × 10−4），超过国际安全阈值。相比之下，NST显示来自交通相关源的Zn和Ag升高，而SKA则主要是来自海上排放的V和Ni。富集因子分析进一步强调了人为特征，PKT中Hg （Log EF = 6.09）和SKA中砷（As）（占总金属质量的39%）的值异常高。我们的研究结果提供了新的区域尺度的证据，表明在东南亚尚未得到充分研究的背景下，金属特异性健康风险和排放模式。这项工作支持了加强针对工业和车辆排放的监管政策、改进废物管理和扩大空气质量监测的迫切需要，以减轻泰国南部pm2.5结合金属对公共卫生的影响。
{"title":"Identifying PM2.5-bound metal pollution sources in Southern Thailand using positive matrix factorization and principal component analysis","authors":"Siwatt Pongpiachan , Sarunpron Khruengsai , Teerapong Sripahco , Radshadaporn Janta , Rungruang Janta , Jompob Waewsak , Danai Tipmanee , Saran Poshyachinda , Patcharee Pripdeevech","doi":"10.1016/j.aeaoa.2025.100337","DOIUrl":"10.1016/j.aeaoa.2025.100337","url":null,"abstract":"<div><div>This study presents the first integrated source apportionment and health risk assessment of PM<sub>2.5</sub>-bound metals in Southern Thailand using Positive Matrix Factorization (PMF) and Principal Component Analysis (PCA). PM<sub>2.5</sub> samples were collected across three urban-industrial provinces, Nakhon Si Thammarat (NST), Phuket (PKT), and Songkhla (SKA), during multiple months in 2023. PMF successfully resolved five major emission sources, including industrial processes, vehicular traffic, maritime fuel combustion, waste incineration, and fossil fuel combustion, explaining 58.4 % of the variance in the dataset. PCA offered complementary insight but lacked the resolution to isolate mixed-source tracers such as vanadium (V) and nickel (Ni), with lower total explained variance. Metal concentrations and source contributions exhibited distinct spatial and seasonal patterns, reflecting dynamic emission influences across the region. Phuket emerged as a hotspot for toxic metal exposure, with the highest hazard index (HI = 1.63) and cancer risk (4.79 × 10<sup>−4</sup>), exceeding international safety thresholds. In contrast, NST showed elevated Zn and Ag from traffic-related sources, while SKA was dominated by V and Ni from maritime emissions. Enrichment factor analysis further highlighted anthropogenic signatures, with exceptionally high values for Hg (Log EF = 6.09) in PKT and arsenic (As) (39 % of total metal mass) in SKA. Our findings provide new regional-scale evidence of metal-specific health risks and emission patterns in an understudied Southeast Asian context. This work supports the urgent need for strengthened regulatory policies targeting industrial and vehicular emissions, improved waste management, and expanded air quality monitoring to mitigate public health impacts from PM<sub>2.5</sub>-bound metals in Southern Thailand.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100337"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124961","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}
Pub Date : 2025-04-01DOI: 10.1016/j.aeaoa.2025.100321
Jana Moldanová , Åsa M. Hallquist , Michael Priestley , Kristoffer Danèl , Bengt Fallenius , Omar Abdalal , Annika Potter , Bo Strandberg
Aviation contributes to air pollution and significantly impacts climate change. Sustainable aviation fuels (SAFs) offer a potential solution to reduce CO2 emissions with the possible co-benefit of reducing emissions of particles. This study evaluates emissions of a turbojet engine using conventional Jet A1 fuel, Biojet fuel (alcohol-to-jet synthetic kerosene with aromatics, ATJ-SKA), hydrotreated vegetable oil (HVO), and their blends. Emissions of particulate matter, gaseous pollutants (NOx, CO, total hydrocarbons, THC), polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs) and aldehydes were measured across different engine loads (Taxi, Cruise and Take-Off). The results show that SAFs, particularly neat Biojet and HVO, significantly reduced particle emissions, by 20 – >99 % compared to Jet A1, especially in the Take-Off mode in case of the Biojet fuel. This reduction is likely connected to the differences in the chemical composition of the fuels including higher content of hydrogen and lower content of aromatics and naphthalenes. Emissions of VOCs, PAHs and aldehydes were reduced by 40–50 % in the Taxi mode, which has the highest emission factors and is also responsible for majority of emissions during the LTO cycle, while an increase was observed for the Take-Off mode. Biojet use exhibited improved engine performance at the Take-Off, but fuel blends showed mixed effects on efficiency. This study shows that SAFs present a promising route to reducing aviation's environmental footprint, with co-benefit of reduced impact on air pollution and non-CO2 climate forcing from reduced particle emissions. Further research is required especially on impact of fuel blends on engine performance and emission characterization.
{"title":"Characterization of emissions from a turbojet engine running on sustainable aviation fuels, blends and conventional jet A1","authors":"Jana Moldanová , Åsa M. Hallquist , Michael Priestley , Kristoffer Danèl , Bengt Fallenius , Omar Abdalal , Annika Potter , Bo Strandberg","doi":"10.1016/j.aeaoa.2025.100321","DOIUrl":"10.1016/j.aeaoa.2025.100321","url":null,"abstract":"<div><div>Aviation contributes to air pollution and significantly impacts climate change. Sustainable aviation fuels (SAFs) offer a potential solution to reduce CO<sub>2</sub> emissions with the possible co-benefit of reducing emissions of particles. This study evaluates emissions of a turbojet engine using conventional Jet A1 fuel, Biojet fuel (alcohol-to-jet synthetic kerosene with aromatics, ATJ-SKA), hydrotreated vegetable oil (HVO), and their blends. Emissions of particulate matter, gaseous pollutants (NOx, CO, total hydrocarbons, THC), polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs) and aldehydes were measured across different engine loads (Taxi, Cruise and Take-Off). The results show that SAFs, particularly neat Biojet and HVO, significantly reduced particle emissions, by 20 – >99 % compared to Jet A1, especially in the Take-Off mode in case of the Biojet fuel. This reduction is likely connected to the differences in the chemical composition of the fuels including higher content of hydrogen and lower content of aromatics and naphthalenes. Emissions of VOCs, PAHs and aldehydes were reduced by 40–50 % in the Taxi mode, which has the highest emission factors and is also responsible for majority of emissions during the LTO cycle, while an increase was observed for the Take-Off mode. Biojet use exhibited improved engine performance at the Take-Off, but fuel blends showed mixed effects on efficiency. This study shows that SAFs present a promising route to reducing aviation's environmental footprint, with co-benefit of reduced impact on air pollution and non-CO<sub>2</sub> climate forcing from reduced particle emissions. Further research is required especially on impact of fuel blends on engine performance and emission characterization.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100321"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740115","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}
Pub Date : 2025-04-01DOI: 10.1016/j.aeaoa.2025.100329
Izzat Najmi Yaacob , Ezanee Gires , Adi Azrif Basri , Kamarul Arifin Ahmad , Kamsani Kamal , Nor Afizah Salleh , Suraya Shahedi , Fikri Rasih , Zakwan Azizi , Sharul Sham Dol , Norkhairunnisa Mazlan
The conventional propellant used in rocket boosters and tactical missiles commonly utilises ammonium perchlorate (AP). However, the release of harmful chloride fumes resulting from the combustion of AP during rocket launches contributes to a 1 % increase in air pollution. Generally, the perchlorate-based propellants contribute to pollution by hydrochloric acid (HCl), which can contaminate launch sites and potentially deplete the ozone layer. Several techniques have been employed to convert standard AP-based composite solid propellant (CSP) into green propellant. This study designed a minimum chlorine content propellant containing potassium permanganate, KMnO4/AP as a dual oxidiser and examined its properties in CSP formulation. This novel KMnO4/AP was characterised in terms of morphological structure, elemental analysis, and thermal decomposition properties by using scanning electron microscope (SEM), energy dispersive X-ray analysis (EDX), and differential scanning calorimetry (DSC) and thermogravimetry analysis (TGA), respectively. Additionally, heat of combustion analysis was examined through bomb calorimetry. The chlorine content investigations were experimentally carried out, and the results were compared with conventional AP-based CSP using gas bubbling and Mohr’s titration method. From thermal decomposition analysis, sample KMnO4/AP (40:60) shifted high temperature decomposition (HTD) of AP to a lowest temperature of 331.32 °C with the highest heat release of 3721.2 J/g. Besides, sample KMnO4/AP (80:20) shows the highest reduction of chlorine concentration content with a reduction percentage of 58.25 %. Besides, KMnO4/AP (40:60) sample resulted in the highest value of heat of combustion with a value of 1254.01 cal/g. This study contributes to the development of environmentally friendly and sustainable oxidisers for solid rocket propellant applications.
{"title":"Enhanced thermal performance and hydrochloric acid gas (HCl) emission mitigation in ammonium perchlorate (AP)-Based solid propellants with potassium permanganate (KMnO4)/AP dual oxidisers","authors":"Izzat Najmi Yaacob , Ezanee Gires , Adi Azrif Basri , Kamarul Arifin Ahmad , Kamsani Kamal , Nor Afizah Salleh , Suraya Shahedi , Fikri Rasih , Zakwan Azizi , Sharul Sham Dol , Norkhairunnisa Mazlan","doi":"10.1016/j.aeaoa.2025.100329","DOIUrl":"10.1016/j.aeaoa.2025.100329","url":null,"abstract":"<div><div>The conventional propellant used in rocket boosters and tactical missiles commonly utilises ammonium perchlorate (AP). However, the release of harmful chloride fumes resulting from the combustion of AP during rocket launches contributes to a 1 % increase in air pollution. Generally, the perchlorate-based propellants contribute to pollution by hydrochloric acid (HCl), which can contaminate launch sites and potentially deplete the ozone layer. Several techniques have been employed to convert standard AP-based composite solid propellant (CSP) into green propellant. This study designed a minimum chlorine content propellant containing potassium permanganate, KMnO<sub>4</sub>/AP as a dual oxidiser and examined its properties in CSP formulation. This novel KMnO<sub>4</sub>/AP was characterised in terms of morphological structure, elemental analysis, and thermal decomposition properties by using scanning electron microscope (SEM), energy dispersive X-ray analysis (EDX), and differential scanning calorimetry (DSC) and thermogravimetry analysis (TGA), respectively. Additionally, heat of combustion analysis was examined through bomb calorimetry. The chlorine content investigations were experimentally carried out, and the results were compared with conventional AP-based CSP using gas bubbling and Mohr’s titration method. From thermal decomposition analysis, sample KMnO<sub>4</sub>/AP (40:60) shifted high temperature decomposition (HTD) of AP to a lowest temperature of 331.32 °C with the highest heat release of 3721.2 J/g. Besides, sample KMnO<sub>4</sub>/AP (80:20) shows the highest reduction of chlorine concentration content with a reduction percentage of 58.25 %. Besides, KMnO<sub>4</sub>/AP (40:60) sample resulted in the highest value of heat of combustion with a value of 1254.01 cal/g. This study contributes to the development of environmentally friendly and sustainable oxidisers for solid rocket propellant applications.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100329"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935806","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}
Pub Date : 2025-04-01DOI: 10.1016/j.aeaoa.2025.100330
Saeed Sharafi, Maryam Lorvand
Given the escalating environmental degradation and public health concerns resulting from severe air pollution in Iran, this study presents a comprehensive assessment of historical trends and future projections of air pollutant emissions. Focusing on key greenhouse gases (CO2 and CH4) and air pollutants (NO2 and SO2), the research employs Shared Socioeconomic Pathways (SSPs), Representative Concentration Pathways (RCPs), and the innovative Pattern Mining Engine (PME) model. Covering the period from 1960 to 2100, the study identifies pivotal trends and key change points in emission trajectories. The analysis reveals significant surges in CO2 and NO2 emissions, particularly during the late 1980s and mid-1990s, driven by socio-economic transitions and policy shifts. PME projections highlight a sharp increase in CO2 emissions from 96.6 to 359.08 Mt, and a substantial rise in NO2 emissions under the RCP1.9 scenario from 10.44 to 68.48 Mt. Similarly, SO2 emissions experienced notable growth, with critical inflection points in the late 1980s and mid-1990s, while CH4 emissions exhibited variable patterns, reflecting divergent source behaviors and mitigation measures. Future projections offer a nuanced perspective on potential emission pathways, indicating significant reductions in CH4 and SO2 under SSP1 and SSP5 scenarios, contrasted with continued increases under SSP3 and SSP4 due to slower technological progress. Despite the implementation of ambitious climate policies, the anticipated CO2 reductions of 23–29 % by 2050 remain insufficient to meet climate targets, underscoring persistent challenges in aligning policy aspirations with tangible outcomes. Sectoral analyses further identify the industrial sector as a major contributor to NO2 and SO2 emissions, although SSP1 and SSP5 scenarios foresee marked declines by 2100, driven by cleaner technologies and stricter regulatory measures. This study distinguishes itself through its comprehensive analysis of emission dynamics across multiple scenarios and its critical insights into the effectiveness of various mitigation strategies. The findings emphasize the urgent need for more aggressive, integrated approaches to emission control and climate policy to effectively address rising pollutant levels and curb environmental degradation.
{"title":"Comprehensive assessment of air pollutant emissions, climate scenario projections, and mitigation strategies in Iran","authors":"Saeed Sharafi, Maryam Lorvand","doi":"10.1016/j.aeaoa.2025.100330","DOIUrl":"10.1016/j.aeaoa.2025.100330","url":null,"abstract":"<div><div>Given the escalating environmental degradation and public health concerns resulting from severe air pollution in Iran, this study presents a comprehensive assessment of historical trends and future projections of air pollutant emissions. Focusing on key greenhouse gases (CO<sub>2</sub> and CH<sub>4</sub>) and air pollutants (NO<sub>2</sub> and SO<sub>2</sub>), the research employs Shared Socioeconomic Pathways (SSPs), Representative Concentration Pathways (RCPs), and the innovative Pattern Mining Engine (PME) model. Covering the period from 1960 to 2100, the study identifies pivotal trends and key change points in emission trajectories. The analysis reveals significant surges in CO<sub>2</sub> and NO<sub>2</sub> emissions, particularly during the late 1980s and mid-1990s, driven by socio-economic transitions and policy shifts. PME projections highlight a sharp increase in CO<sub>2</sub> emissions from 96.6 to 359.08 Mt, and a substantial rise in NO<sub>2</sub> emissions under the RCP<sub>1.9</sub> scenario from 10.44 to 68.48 Mt. Similarly, SO<sub>2</sub> emissions experienced notable growth, with critical inflection points in the late 1980s and mid-1990s, while CH<sub>4</sub> emissions exhibited variable patterns, reflecting divergent source behaviors and mitigation measures. Future projections offer a nuanced perspective on potential emission pathways, indicating significant reductions in CH<sub>4</sub> and SO<sub>2</sub> under SSP1 and SSP5 scenarios, contrasted with continued increases under SSP3 and SSP4 due to slower technological progress. Despite the implementation of ambitious climate policies, the anticipated CO<sub>2</sub> reductions of 23–29 % by 2050 remain insufficient to meet climate targets, underscoring persistent challenges in aligning policy aspirations with tangible outcomes. Sectoral analyses further identify the industrial sector as a major contributor to NO<sub>2</sub> and SO<sub>2</sub> emissions, although SSP1 and SSP5 scenarios foresee marked declines by 2100, driven by cleaner technologies and stricter regulatory measures. This study distinguishes itself through its comprehensive analysis of emission dynamics across multiple scenarios and its critical insights into the effectiveness of various mitigation strategies. The findings emphasize the urgent need for more aggressive, integrated approaches to emission control and climate policy to effectively address rising pollutant levels and curb environmental degradation.</div></div>","PeriodicalId":37150,"journal":{"name":"Atmospheric Environment: X","volume":"26 ","pages":"Article 100330"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106497","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}
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