Atmospheric Environment最新文献_第2页

Integration of magnetic methods and chemical elemental analysis to differentiate the sources of dust in the indoor environment 结合磁法和化学元素分析，区分室内环境中的粉尘来源

IF 3.7 2区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Environment

Pub Date : 2026-01-17 DOI: 10.1016/j.atmosenv.2026.121808

Beata Górka-Kostrubiec, Katarzyna Dudzisz

Indoor dust (ID) is a key indicator of indoor air quality, reflecting both human activity and the infiltration of outdoor pollutants. This study integrates magnetic, geochemical, and microscopic methods to identify pollution sources by characterizing magnetic particles (MPs) in ID collected from residential locations in the Warsaw metropolitan area. Fine (<0.071 mm) and coarse (0.071–1.0 mm) dust fractions were analyzed alongside road dust (RD) and wood ash to differentiate between indoor and outdoor contributions. Magnetite was identified as the primary magnetic mineral, accompanied by metallic Fe and/or iron-based alloys. Decomposition of isothermal remanent magnetization acquisition curves revealed two main coercivity components in ID, while RD and ash exhibited distinct magnetic signatures. Scanning electron microscopy identified technogenic MPs, such as ferrospheres and abrasion-derived flakes, while geochemical analyses highlighted associations between Fe and heavy metals (e.g., Zn, Pb). Cluster analysis indicated both anthropogenic (traffic, biomass combustion, industry) and natural (soil, crustal) origins. Based on these findings, potentially toxic metals were correlated with their likely sources: sulfur from coal burning; zinc, copper, and lead from vehicle emissions; calcium from construction activities; and chromium from indoor sources such as chrome-plated surfaces. Variations in dust composition among apartments—especially in the fine fraction mass and magnetic properties—underscore the influence of ventilation, proximity to traffic, and resident behavior. This study confirms that magnetic methods provide a nondestructive, cost-effective approach for tracking external pollutants in ID and underscores their potential as a screening tool for assessing urban environmental health risks.

室内粉尘（ID）是室内空气质量的重要指标，反映了人类活动和室外污染物的渗入。本研究整合了磁学、地球化学和微观方法，通过表征从华沙大都市区居民区收集的ID中的磁性颗粒（MPs）来识别污染源。细粉尘（<0.071 mm）和粗粉尘（0.071 - 1.0 mm）与道路粉尘（RD）和木灰一起分析，以区分室内和室外的贡献。磁铁矿是主要的磁性矿物，伴生金属铁和/或铁基合金。等温剩磁采集曲线分解显示，ID中存在两种主要的矫顽力成分，而RD和灰分表现出明显的磁性特征。扫描电子显微镜发现了技术成因的MPs，如铁球和磨损产生的薄片，而地球化学分析强调了铁和重金属（如锌、铅）之间的联系。聚类分析表明，其成因既有人为因素（交通、生物质燃烧、工业），也有自然因素（土壤、地壳）。根据这些发现，潜在有毒金属与其可能的来源相关：燃煤产生的硫；汽车尾气中的锌、铜和铅；建筑活动中的钙；还有来自室内的铬，比如镀铬的表面。公寓间粉尘组成的变化——尤其是细颗粒质量和磁性——强调了通风、靠近交通和居民行为的影响。这项研究证实，磁性方法为跟踪ID中的外部污染物提供了一种非破坏性的、具有成本效益的方法，并强调了它们作为评估城市环境健康风险的筛选工具的潜力。

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引用次数: 0

Decadal shifts in aerosol hotspots and source attribution over IGP, north-east India and Himalayas: A 25-year (2000–2024) study IGP、印度东北部和喜马拉雅地区气溶胶热点和来源归属的年代际变化：25年（2000-2024）研究

IF 3.7 2区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Environment

Pub Date : 2026-01-17 DOI: 10.1016/j.atmosenv.2026.121810

Soumen Raul, Monami Dutta , Sauryadeep Mukherjee , Abhijit Chatterjee

To examine decadal changes in aerosol pollution, trends, hotspots, and aerosol types across the Indo-Gangetic Plain (IGP), north-east India (NEI), and the Himalayas, a 25-year (2000–2024) analysis was conducted using MODIS and MERRA-2 datasets. The highest aerosol loading and trend (MODIS AOD) occurred over the lower IGP (0.71 ± 0.09; 0.016 yr⁻¹), followed by the middle IGP (0.65 ± 0.10; 0.009 yr⁻¹), upper IGP (0.51 ± 0.10; 0.003 yr⁻¹), and NEI (0.40 ± 0.10; 0.008 yr⁻¹). Within the Himalayas, the central region showed the highest AOD (0.24 ± 0.09) with low trends (0.001–0.003 yr⁻¹). Significant rising trends were observed in SO₄²⁻_AOD over the lower IGP (0.006 yr⁻¹) and OC_AOD over NEI (0.004 yr⁻¹). AOD across the entire IGP and NEI increased by more than 20 % in the 2010s relative to the 2000s. Notably, SO₄²⁻_AOD increased by ∼30–40 % over the lower IGP, while OC_AOD rose by over 50 % in NEI and the eastern Himalayas, with an additional 30–40 % rise during 2020–2024. Bangladesh, the lower IGP, and NEI consistently emerged as hotspots of carbonaceous and sulphate aerosols, shifting into highly polluted zones after 2020. The upper and middle IGP for the western and central Himalayas and NEI and the lower IGP acted as major source regions. Clean-continental and biomass/urban aerosols dominated the Himalayas, whereas anthropogenic aerosols prevailed over the IGP and NEI, with notable increases across the Himalayas after 2020. These insights can guide targeted mitigation strategies for the IGP and vulnerable Himalayan regions.

为了研究印度-恒河平原（IGP）、印度东北部（NEI）和喜马拉雅地区气溶胶污染的年代际变化、趋势、热点和气溶胶类型，利用MODIS和MERRA-2数据集进行了25年（2000-2024）分析。最高气溶胶负荷和趋势（MODIS AOD）出现在较低IGP(0.71±0.09;0.016 yr - 1)，其次是中等IGP(0.65±0.10;0.009 yr - 1)，较高IGP（0.51±0.10;0.003 yr - 1）和NEI（0.40±0.10;0.008 yr - 1）。在喜马拉雅地区，中部地区AOD最高（0.24±0.09），趋势较低（0.001 ~ 0.003 yr−1）。SO42 - AOD在IGP较低的地区呈显著上升趋势（0.006 yr - 1）， OCAOD在NEI地区呈显著上升趋势（0.004 yr - 1）。与2000年代相比，2010年代整个IGP和NEI的AOD增长了20%以上。值得注意的是，在IGP较低的地区，SO42−AOD增加了~ 30 - 40%，而NEI和喜马拉雅东部的OCAOD增加了50%以上，在2020-2024年期间还增加了30 - 40%。IGP较低的孟加拉国和NEI一直是碳质和硫酸盐气溶胶的热点地区，在2020年之后转变为高污染地区。喜马拉雅西部和中部的上、中部IGP和NEI以及下IGP是主要的源区。清洁大陆气溶胶和生物质/城市气溶胶在喜马拉雅地区占主导地位，而人为气溶胶在IGP和NEI中占主导地位，2020年后喜马拉雅地区的气溶胶显著增加。这些见解可以指导针对IGP和喜马拉雅脆弱地区的有针对性的缓解战略。

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引用次数: 0

Extension of AIRPACT5 forecasting to Day3 and evaluation of PM2.5 and ozone predictions across winter, spring and summer (2023–2024) 2023-2024年冬、春、夏三季AIRPACT5预报扩展至第3天及PM2.5和臭氧预报评价

IF 3.7 2区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Environment

Pub Date : 2026-01-17 DOI: 10.1016/j.atmosenv.2026.121806

Mohammadamin Vahidi Ghazvini, Joseph K. Vaughan, Jun Meng, Ana Carla Fernandez Valdes, Von P. Walden

In this study, the forecast period of AIRPACT5 (Air Indicator Report for Public Awareness and Community Tracking) model was extended from two days to three days, and the accuracy of the third-day forecasts was subsequently evaluated. Two primary pollutants, PM_2.5 and O₃, were considered for validation. The model's third-day forecasts were compared with observed data from AirNow monitoring stations, as well as with the model's first and second-day forecasts. The evaluation covered a nine-month period from December 2023 to August 2024, encompassing the winter, spring, and summer seasons. Additionally, the model domain was categorized into urban, suburban and rural areas, and results were analyzed separately for each category. The comparison indicates that the performance trends of the third-day forecasts closely align with those of the first and second days. For PM_2.5, model predictions were generally consistent with observational data, particularly in rural areas and across all seasons except during the wildfire season. In the case of O₃, model performance was satisfactory in the summer but showed significant discrepancies in winter, especially in rural regions.

本研究将AIRPACT5 （Air Indicator Report for Public Awareness and Community Tracking）模型的预测周期从2天延长至3天，并对第三天预测的准确性进行了评估。两种主要污染物PM2.5和O3被考虑用于验证。该模型的第三天预报与AirNow监测站的观测数据以及该模型的第一天和第二天预报进行了比较。评估涵盖了从2023年12月到2024年8月的9个月，包括冬季、春季和夏季。此外，将模型域划分为城市、郊区和农村地区，并对每个类别的结果分别进行分析。比较表明，第三天预测的表现趋势与第一天和第二天的预测密切一致。对于PM2.5，模型预测总体上与观测数据一致，特别是在农村地区和除野火季节外的所有季节。在O3的情况下，模型在夏季的表现令人满意，但在冬季，特别是在农村地区，表现出显著的差异。

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引用次数: 0

Advancing VOC management: A mobile and drone-based approach for industrial emission monitoring 推进VOC管理：基于移动和无人机的工业排放监测方法

IF 3.7 2区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Environment

Pub Date : 2026-01-16 DOI: 10.1016/j.atmosenv.2026.121807

Cheonwoong Kang , Hyunjun Shin , Hyunjeong Seo , Hanjin Yoo , Ki-Joon Jeon

Effective management of volatile organic compounds (VOCs) in large, dense industrial complexes remains a critical challenge, as conventional monitoring methods are often too slow, limited in spatial coverage, and insufficiently resolved to pinpoint specific emission sources among thousands of facilities. This study introduces and validates a novel, multi-stage methodological framework which synergistically combines mobile Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) with drone-based systems to enable rapid and precise source identification. The methodology begins with a wide-area mobile SIFT-MS survey, utilizing spatial statistics (Getis-Ord Gi∗) to identify hotspots, followed by targeted drone deployment for high-resolution aerial screening. This multi-stage approach successfully resolved distinct chemical signatures from adjacent sources that were not separable by ground-based monitoring alone. For instance, the framework differentiated a xylene- and ethylbenzene-rich profile near chemical manufacturing facilities, a MEK (methyl ethyl ketone)-rich profile near paint production, and a toluene- and acrolein-rich profile originating from a painting facility. The quantitative reliability of the drone-based sampling was validated through concurrent ground-level measurements, which demonstrated a high consistency in capturing the unique chemical signature at each location. This study demonstrates that the integrated mobile-drone framework provides a scientifically robust and efficient approach for characterizing industrial VOC emissions. By progressing from broad spatial screening to precise, evidence-based source identification, the methodology offers a powerful tool for targeted air quality management and has strong potential for application in highly complex industrial environments worldwide.

在大型、密集的工业综合体中，有效管理挥发性有机化合物（VOCs）仍然是一项重大挑战，因为传统的监测方法往往太慢，空间覆盖范围有限，并且无法在数千个设施中确定特定的排放源。本研究介绍并验证了一种新的多阶段方法框架，该框架将移动选择离子流管质谱（SIFT-MS）与无人机系统协同结合，以实现快速准确的源识别。该方法首先进行广域移动SIFT-MS调查，利用空间统计数据（Getis-Ord Gi∗）确定热点，然后部署有针对性的无人机进行高分辨率空中筛查。这种多阶段的方法成功地解决了相邻源的不同化学特征，这些特征仅通过地面监测是无法分离的。例如，该框架区分了靠近化学生产设施的富含二甲苯和乙苯的剖面，靠近油漆生产设施的富含MEK（甲基乙基酮）的剖面，以及来自油漆设施的富含甲苯和丙烯醛的剖面。通过同时进行地面测量，验证了无人机采样的定量可靠性，证明了在每个位置捕获独特化学特征的高度一致性。本研究表明，集成的移动无人机框架为表征工业VOC排放提供了一种科学可靠且有效的方法。通过从广泛的空间筛选到精确的、基于证据的来源识别，该方法为有针对性的空气质量管理提供了强大的工具，并在全球高度复杂的工业环境中具有强大的应用潜力。

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引用次数: 0

Impacts of period-specific particulate matter exposure on COPD phenotypes: Rapid lung function decline 特定时期颗粒物暴露对COPD表型的影响：肺功能快速下降

IF 3.7 2区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Environment

Pub Date : 2026-01-14 DOI: 10.1016/j.atmosenv.2026.121803

Po-Hao Feng , I-Jung Liu , Rachel Chien , Kang-Yun Lee , Kuan-Yuan Chen , Wen-Te Liu , Ying-Ying Chen , Yen-Ling Chen , Kun-Ta Lee , Shu-Chuan Ho , Arnab Majumdar , Jiunn-Horng Kang , Wun-Hao Cheng , Sheng-Ming Wu , Cheng-Yu Tsai

Rapid decline in lung function is a key phenotype of chronic obstructive pulmonary disease (COPD) and is associated with disease progression and a poor prognosis. However, time-specific dynamics of pollutant exposure and their impacts on this phenotype remain unclear. In this retrospective cohort study, we investigated how period-specific particulate matter (PM) exposure relates to lung function rapid decline in individuals with COPD. Clinical and individual factors and ambient PM exposures were collected, and participants were categorized into rapid-decline and non-rapid-decline groups based on forced expiratory volume in 1 s (FEV₁) trajectories. A modified exponentially weighted moving average approach was used for estimating PM_2.5 and PM₁₀ exposure levels across the baseline, early follow-up, and late follow-up periods. Regression and machine learning models assessed period-specific effects, with variable importance evaluated via Shapley additive explanation. The rapid-decline group had significantly higher baseline lung function but greater annual FEV₁ reductions than the non-rapid-decline group (both p < 0.01). After adjusting for confounding factors, PM exposures were associated with FEV₁ declines (PM_2.5: −280.62 to −267.38 mL/year; PM₁₀: −305.15 to −246.89 mL/year, all p < 0.01) and increased odds ratio of rapid FEV₁ decline (PM_2.5: 1.76–1.95; PM₁₀: 1.62–1.96, all p < 0.05), particularly evident in models emphasizing the baseline and late follow-up periods. Baseline PM_2.5 and PM₁₀ exposures, and age were identified as the most influential predictors. These findings suggest that period-specific PM exposure may critically contribute to the development of lung function rapid decline in COPD. Considering environmental, temporal, and individual-level factors may help improve disease management.

肺功能快速下降是慢性阻塞性肺疾病（COPD）的一个关键表型，并与疾病进展和不良预后相关。然而，污染物暴露的时间特异性动态及其对该表型的影响仍不清楚。在这项回顾性队列研究中，我们调查了特定时期颗粒物（PM）暴露与COPD患者肺功能快速下降的关系。收集临床和个人因素以及环境PM暴露，并根据1秒内用力呼气量（FEV1）轨迹将参与者分为快速下降组和非快速下降组。采用改良指数加权移动平均法估算基线、早期随访和后期随访期间的PM2.5和PM10暴露水平。回归和机器学习模型评估了特定时期的影响，通过沙普利加性解释评估了变量的重要性。与非快速衰退组相比，快速衰退组的基线肺功能明显更高，但年FEV1减少量更大（p < 0.01）。在调整混杂因素后，PM暴露与FEV1下降（PM2.5:−280.62至−267.38 mL/年；PM10:−305.15至−246.89 mL/年，均p <； 0.01）和FEV1快速下降的优势比增加（PM2.5: 1.76至1.95;PM10: 1.62至1.96，均p <； 0.05）相关，这在强调基线和随访后期的模型中尤为明显。PM2.5和PM10的基线暴露以及年龄被确定为最具影响力的预测因素。这些发现表明，特定时期的PM暴露可能对COPD肺功能的发展起关键作用。考虑环境、时间和个人因素可能有助于改善疾病管理。

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引用次数: 0

Efficient stacking ensemble machine learning for 1 km daily PM2.5 and PM10 mapping in Beijing-Tianjin-Hebei 京津冀1 km日PM2.5和PM10的高效叠加集成机器学习

IF 3.7 2区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Environment

Pub Date : 2026-01-13 DOI: 10.1016/j.atmosenv.2026.121804

Yumeng Li , Xin Su , Lunche Wang , Lan Feng , Xiaoyu Ma , Ming Zhang , Shikuan Jin

PM₂.₅ and PM₁₀ pollution in China's Beijing-Tianjin-Hebei (BTH) region poses severe environmental and health risks, yet sparse ground monitoring networks constrain high-resolution and spatial assessments despite policy-driven air quality improvements. This study developed a stacking ensemble machine learning model integrating multi-source data with 2014–2024 national station observations to generate gap-free 1 km daily PM₂.₅ and PM₁₀ grids across the BTH region. The ensemble model, integrating multiple machine learning models, performs comparably to or better than existing single models, achieving high accuracy (R² = 0.935 and 0.916, RMSE = 12.8 and 22.4 μg/m³ for PM₂.₅ and PM₁₀ under 10-fold cross-validation) and spatial generalization (R² = 0.89 and 0.87 for PM₂.₅ and PM₁₀ under leave-one-station-out validation). The products captured a 56.3 % decline (from 81.4 to 35.6 μg/m³) in PM₂.₅ and a 50.9 % reduction (from 136.7 to 67.1 μg/m³) in PM₁₀ from 2014 to 2024, followed by stabilization with interannual fluctuations in recent years. The analysis results indicate that anthropogenic emissions are the dominant factor driving the reduction in PM. Over 70 % of regions exhibit significant improvements in air quality, affirming the efficacy of the Air Pollution Prevention and Control Action Plan. In summary, the ensemble model delivers high accuracy and strong spatial generalization, supporting PM₂.₅ and PM₁₀ mapping and policy impact analysis across the entire BTH areas.

中国京津冀（BTH）地区的PM2.5和PM10污染构成了严重的环境和健康风险，尽管政策推动了空气质量的改善，但稀疏的地面监测网络限制了高分辨率和空间评估。本研究开发了一种叠加集成机器学习模型，将多源数据与2014-2024年国家台站观测数据相结合，生成了北京地区每天1公里的无间隙PM2.5和PM10网格。集成多个机器学习模型的集成模型具有与现有单一模型相当或更好的精度（10倍交叉验证下PM2.5和PM10的R2分别为0.935和0.916，RMSE分别为12.8和22.4 μg/m3）和空间泛化（留一站验证下PM2.5和PM10的R2分别为0.89和0.87）。从2014年到2024年，这些产品的PM2.5下降了56.3%（从81.4 μg/m3降至35.6 μg/m3）， PM10下降了50.9%（从136.7 μg/m3降至67.1 μg/m3），随后在近年的年际波动中趋于稳定。分析结果表明，人为排放是PM减少的主导因素。70%以上地区空气质量明显好转，大气污染防治行动计划成效明显。综上所述，该集成模型具有较高的精度和较强的空间泛化能力，可支持整个北京市区的PM2.5和PM10制图和政策影响分析。

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引用次数: 0

The transport pathways and sectoral potential source areas of PM2.5 in Tangshan, a typical heavy industrial city in the Beijing-Tianjin-Hebei region 京津冀典型重工业城市唐山市PM2.5运输路径及行业潜在源区

IF 3.7 2区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Environment

Pub Date : 2026-01-13 DOI: 10.1016/j.atmosenv.2026.121799

Aifang Gao , Xi You , Zhao Li , Qixian Liu , Aiguo Li , Zhao Liang , Aibin Kang , Baojun Zhang , Hongliang Zhang

This study investigated the transport pathways and sectoral potential source areas of PM_2.5 in Tangshan in January, April, July, and October of 2020. Utilizing the HYSPLIT model upon MeteoInfo software, cluster analysis was conducted on the backward trajectories of airflow at different starting heights and times. The sectoral potential source areas of PM_2.5 in Tangshan were determined by calculating the weighted potential source contribution function (WPSCF) and weighted concentration-weighted trajectory (WCWT) while examining pollution transport pathways. The findings are as follows: Firstly, the higher PM_2.5 concentration carried by pollution trajectories in January (114.8 μg m⁻³) and October (110.7 μg m⁻³) is mainly due to the impact of short-distance transport through Hebei and Tianjin, trajectory 1 (950 hPa) accounts for 30.52 % (124.8 μg m⁻³) of the contaminated airflow. Secondly, the potential source area for PM_2.5 pollution in Tangshan during January has the highest value of WPSCF and WCWT. WCWT high-value areas exceeding 90 μg m⁻³ were distributed in the Langfang, parts of Tianjin, Beijing, and the junction of the Hebei Beijing Tianjin. Thirdly, the contribution of various sector sources to Tangshan's PM_2.5 concentration varies across different months, with industrial and residential sources being the primary contributors. Fourthly, the airflow trajectories at 500 m and 1000 m heights were consistent. 24-h PM_2.5 was more concentrated, and the potential source area (24/72-h) results indicated the high-value areas were industrially dense areas. These findings underscore the importance of regional collaborative efforts and sector collaborative management to mitigate PM_2.5 pollution in Tangshan.

研究了2020年1月、4月、7月和10月唐山市PM2.5的输送路径和行业潜在源区。利用MeteoInfo软件上的HYSPLIT模型，对不同启动高度和时间下的气流反向轨迹进行聚类分析。通过计算加权潜在源贡献函数（WPSCF）和加权浓度-加权轨迹（WCWT），同时考察污染输送途径，确定了唐山市PM2.5的行业潜在源区。结果表明：①1月（114.8 μ m−3）和10月（110.7 μ m−3）污染轨迹携带的PM2.5浓度较高，主要是受河北、天津等地的短程输送影响，轨迹1 （950 hPa）占污染气流的30.52% (124.8 μ m−3)；②唐山市1月PM2.5污染潜在源区WPSCF和WCWT值最高；超过90 μg m−3的WCWT高值区主要分布在廊坊、天津、北京部分地区和冀京交界处。③不同行业污染源对唐山PM2.5浓度的贡献在不同月份有所不同，工业污染源和居民污染源是主要污染源。第四，500 m和1000 m高度气流轨迹基本一致。24 h PM2.5浓度较高，潜在源区（24/72 h）结果显示高值区为工业密集区。这些发现强调了区域协同努力和部门协同管理对减轻唐山市PM2.5污染的重要性。

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引用次数: 0

Personal dose during cardiovascular exercise: Links between PM2.5/PM10 concentration levels, activity intensity and health risk 心血管运动中的个人剂量：PM2.5/PM10浓度水平、活动强度和健康风险之间的联系

IF 3.7 2区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Environment

Pub Date : 2026-01-13 DOI: 10.1016/j.atmosenv.2026.121802

Sofia Eirini Chatoutsidou, Eleftheria Chalvatzaki, Mihalis Lazaridis

Cardiovascular exercise is a popular activity that aims to improve physical fitness and overall health, however practicing outdoors enhances pollutant inhalation. The main objective was to estimate the dose received by inhalation of airborne particles during cardiovascular exercise in urban environments. Dosimetry simulations used particle mass concentrations (PM_2.5, PM_2.5-10) to estimate the deposited dose in the human respiratory tract that assumed a young and healthy adult male and female train at variable activity intensities. Hourly dose rates were substantially increased with activity intensity due to increased inhaled volumes, with a 9.5-fold increase from rest (60 bpm) to high-intensity exercise (170 bpm). PM levels played also a crucial role as increased concentrations were linked with increased deposition rates. Heating, and Sahara events comprised the most burdened cases with unfavorable conditions for exercise. Higher % nasal contribution for female trainees was the reason for higher deposition in the anterior nose compared to male trainees. Linking these results with a health risk showed that females have an increased risk related to a health outcome in the upper respiratory tract whereas male trainees have increased risk for a health impact in the lungs. Overall, health risk analysis verified the negative impact of elevated PM concentrations and the enhanced risk accompanied by increased intensity for experienced trainees. To prevent negative health outcomes, trainees are recommended to practice in areas with reduced particulate pollution (e.g suburban areas) and during times of the day where concentrations are expected to be lower.

心血管运动是一项很受欢迎的活动，旨在提高身体素质和整体健康，然而在户外锻炼会增加污染物的吸入。主要目的是估计在城市环境中心血管运动期间吸入空气中颗粒的剂量。剂量学模拟使用颗粒质量浓度（PM2.5、PM2.5-10）来估计假设年轻健康成年男性和女性在不同活动强度下训练时在人体呼吸道中的沉积剂量。由于吸入量的增加，每小时剂量率随着活动强度的增加而显著增加，从休息（60 bpm）到高强度运动（170 bpm）增加9.5倍。PM水平也起着至关重要的作用，因为浓度的增加与沉积速率的增加有关。炎热和撒哈拉事件是最不利于锻炼的情况。与男性学员相比，女性学员的鼻部贡献较高是前鼻沉积较高的原因。将这些结果与健康风险联系起来表明，女性上呼吸道健康风险增加，而男性受训者肺部健康风险增加。总体而言，健康风险分析证实，对有经验的受训人员来说，颗粒物浓度升高的负面影响以及伴随着强度增加的风险增加。为了防止对健康产生负面影响，建议学员在颗粒污染较少的地区（例如郊区）和一天中浓度预计较低的时间进行练习。

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引用次数: 0

A spatial regression analysis of the association between PM2.5 and early childhood development outcomes in South Africa using the 2021 Thrive by Five Index 使用2021年五大茁壮成长指数对PM2.5与南非儿童早期发展结果之间关系的空间回归分析

IF 3.7 2区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Environment

Pub Date : 2026-01-13 DOI: 10.1016/j.atmosenv.2026.121801

Junita Henry , Yazan Alwadi , Marcia C. Castro

Background

South Africa experiences high levels of ambient air pollution, with PM2.5 concentrations routinely exceeding WHO guidelines of 5 μg/m³. Early childhood represents a critical window of neurodevelopmental vulnerability, yet the impacts of postnatal PM2.5 exposure on developmental outcomes remain underexplored in this context.

Methods

We analysed nationally representative data on 5222 children aged 50–59 months attending 1248 Early Learning Programmes (ELPs) across South Africa, using the Early Learning Outcomes Measure 4&5 (ELOM) to assess development across five domains. Twelve-month cumulative PM2.5 exposure at each ELP was estimated using satellite-derived, ground-calibrated data. Spatial lag and error models were employed to account for residual spatial autocorrelation.

Results

A 10 μg/m³ increase in PM2.5 exposure was associated with lower total ELOM scores (β = −0.12 SD, p < .05), with the largest associations observed for gross motor development (β = −0.17 SD, p < .01) and fine motor coordination (β = −0.12 SD, p < .05). No significant associations were detected for executive functioning, literacy, or numeracy domains.

Conclusion

This study provides evidence of associations between ambient PM2.5 exposure and specific developmental domains in preschool-aged children in South Africa. The findings highlight the need to integrate air quality interventions into early childhood policy and to further research mechanisms, exposure timing, and mitigation.

南非的环境空气污染水平很高，PM2.5浓度经常超过世卫组织5 μg/m3的指导标准。幼儿期是神经发育脆弱性的关键窗口期，但在此背景下，出生后PM2.5暴露对发育结果的影响仍未得到充分探讨。方法我们分析了南非参加1248个早期学习计划（elp）的5222名年龄在50-59个月的儿童的全国代表性数据，使用早期学习成果测量4和5 （ELOM）来评估五个领域的发展。每个ELP的12个月累积PM2.5暴露量是使用卫星衍生的地面校准数据估计的。利用空间滞后和误差模型来解释残差空间自相关。结果PM2.5暴露增加10 μg/m3与ELOM总评分降低相关（β = - 0.12 SD, p < 0.05），其中大运动发育（β = - 0.17 SD, p < 0.01）和精细运动协调（β = - 0.12 SD, p < 0.05）的相关性最大。在执行功能、读写能力或计算能力方面没有发现显著的关联。本研究提供了环境PM2.5暴露与南非学龄前儿童特定发育领域之间关联的证据。研究结果强调需要将空气质量干预措施纳入幼儿政策，并进一步研究机制、暴露时间和缓解措施。

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引用次数: 0

Unexpected larger NMVOCs emissions of Chinese stainless steel than carbon steel production: insights from field measurements 中国不锈钢的NMVOCs排放量意外大于碳钢生产：来自现场测量的见解

IF 3.7 2区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Environment

Pub Date : 2026-01-13 DOI: 10.1016/j.atmosenv.2026.121796

Yiting Li , Haotian Liu , Lei Zhang , Shuanzhu Sun , Rongrong Wu , Weizhe Zhou , Jiewen Zhu , Yu Zhao

The emissions of non-methane volatile organic compounds (NMVOCs) worsen air quality and pose potential health and environmental risks. The steel industry is an important NMVOCs source category, yet studies on NMVOCs emissions in steel industry remains limited, particularly for stainless steel manufacturing. This study presents a field investigation of NMVOCs emissions for different processes in both stainless steel and carbon steel plants, revealing clear differences in emission characteristics between them. The results show that the NMVOCs emissions were higher in stainless steel production, with notable different species profiles compared to carbon steel plant. The emission of carbon disulfide during the sintering process in stainless steel manufacturing was lower compared to carbon steel production. These discrepancies were attributed to the different raw materials used for production of the two types of steel, and higher fuel consumption and lower combustion efficiency for stainless steel production. The NMVOCs emission factor for stainless steel sintering was 40 times higher than that of carbon steel sintering, thus the NMVOCs emissions from steel production could be underestimated without consideration of this high emission factor. In addition, this study examined the impact of air pollution control devices (APCDs) on NMVOCs emissions for steel production. Selective catalytic reduction (SCR) and flue gas desulfurization (FGD) technologies demonstrated great NMVOCs removal efficiency, while fabric filter (FF) might elevate NMVOCs emissions. The diverse effects resulted primarily from the removal mechanisms of these APCDs and the physicochemical properties of NMVOCs. Through field measurements, this study improves the understanding of NMVOCs emission characteristics in the steel manufacturing industry and provides valuable insights for development of NMVOCs emission control strategies.

非甲烷挥发性有机化合物（NMVOCs）的排放使空气质量恶化，并构成潜在的健康和环境风险。钢铁行业是NMVOCs的重要来源类别，但对钢铁行业特别是不锈钢制造业NMVOCs排放的研究仍然有限。本研究对不锈钢和碳钢工厂不同工艺的NMVOCs排放进行了实地调查，发现两者在排放特征上存在明显差异。结果表明：不锈钢工厂的NMVOCs排放量高于碳钢工厂，且存在显著的物种分布差异；与碳钢生产相比，不锈钢生产中烧结过程中二硫化碳的排放量较低。这些差异归因于用于生产两种钢的不同原材料，以及不锈钢生产的高燃料消耗和低燃烧效率。不锈钢烧结的NMVOCs排放系数比碳钢烧结的NMVOCs排放系数高40倍，如果不考虑不锈钢烧结的NMVOCs排放系数高，钢铁生产的NMVOCs排放量可能会被低估。此外，本研究还考察了空气污染控制装置（apcd）对钢铁生产中NMVOCs排放的影响。选择性催化还原（SCR）和烟气脱硫（FGD）技术对NMVOCs的去除效果较好，而织物过滤器（FF）可能会增加NMVOCs的排放量。这些不同的影响主要是由apcd的去除机制和NMVOCs的物理化学性质决定的。通过现场测量，提高了对钢铁制造业NMVOCs排放特征的认识，为制定NMVOCs排放控制策略提供了有价值的见解。

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引用次数: 0