Atmospheric Pollution Research最新文献_第9页

Mediating effect of sleep duration and depressive symptoms on the association of volatile organic compounds with cardiovascular disease in the general population 睡眠时间和抑郁症状对挥发性有机化合物与普通人群心血管疾病关联的中介作用

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2025-09-06 DOI: 10.1016/j.apr.2025.102737

Yue Zhu , Yinghui Ju , Menglin Wang , Guojun Hong , Rui Wu

Cardiovascular disease (CVD) is a leading global cause of mortality, with air pollution increasingly implicated as a risk factor. Volatile organic compounds (VOCs), widespread ambient pollutants, pose significant health risks; however, their specific association with CVD risk remains insufficiently explored. This study investigated associations between VOC exposure and CVD risk and evaluated the mediating roles of sleep duration and depressive symptoms. We analyzed data from 2918 U.S. adults in the National Health and Nutrition Examination Survey. Associations of six VOCs with CVD risk were assessed using weighted logistic regression, weighted quantile sum (WQS) regression, and quantile-based g-computation (QGC). Mediation analyses examined the contributions of sleep duration and depressive symptoms. After full covariate adjustment, benzene, styrene, and toluene were significantly associated with elevated CVD risk (odds ratios [ORs] = 1.77–2.09). Mixed-exposure analyses confirmed positive associations, with styrene contributing the highest weights (WQS: OR = 1.56, 95 % CI: 1.05, 2.30; QGC: OR = 1.62, 95 % CI: 1.25, 2.10). Subgroup analyses indicated stronger associations among women and smokers. Sleep duration and depressive symptoms partially mediated the relationships between individual VOCs (benzene, styrene, toluene), combined VOC exposure, and CVD risk, explaining 6.39 %–22.78 % of the total effect. Furthermore, a serial mediation pathway (sleep duration → depressive symptoms) mediated these associations (proportion mediated: 2.44 %–2.63 %). These findings suggest that VOC exposure may increase CVD risk, partly through adverse effects on sleep and mental health. Addressing sleep deficits and depressive symptoms in exposed populations could be critical for mitigating CVD burden and improving public health outcomes.

心血管疾病（CVD）是全球主要的死亡原因，空气污染日益成为一个危险因素。挥发性有机化合物（VOCs）是广泛存在的环境污染物，构成重大健康风险；然而，它们与心血管疾病风险的具体关系仍未得到充分探讨。本研究调查了挥发性有机化合物暴露与心血管疾病风险之间的关系，并评估了睡眠时间和抑郁症状的中介作用。我们分析了2918名美国成年人在国家健康和营养检查调查中的数据。采用加权逻辑回归、加权分位数和（WQS）回归和基于分位数的g-计算（QGC）评估6种VOCs与CVD风险的相关性。中介分析检验了睡眠时间和抑郁症状的影响。全协变量调整后，苯、苯乙烯和甲苯与心血管疾病风险升高显著相关（优势比[or] = 1.77-2.09）。混合暴露分析证实了正相关，苯乙烯贡献的权重最高（WQS: OR = 1.56, 95% CI: 1.05, 2.30; QGC: OR = 1.62, 95% CI: 1.25, 2.10）。亚组分析表明，女性和吸烟者之间的关联更强。睡眠时间和抑郁症状部分介导了个体挥发性有机化合物（苯、苯乙烯、甲苯）、综合挥发性有机化合物暴露和心血管疾病风险之间的关系，解释了总效应的6.39% - 22.78%。此外，一系列的中介途径（睡眠时间→抑郁症状）介导了这些关联（比例：2.44% - 2.63%）。这些发现表明，接触挥发性有机化合物可能会增加心血管疾病的风险，部分原因是对睡眠和心理健康产生不利影响。解决暴露人群的睡眠不足和抑郁症状对于减轻心血管疾病负担和改善公共卫生结果至关重要。

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引用次数: 0

Development of a spatiotemporal resolution enhancement method for satellite-observed XCH4 products based on spatiotemporal information and LightGBM-Bayesian Integration 基于时空信息和LightGBM-Bayesian积分的星载XCH4产品时空分辨率增强方法研究

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2025-09-04 DOI: 10.1016/j.apr.2025.102736

Yong Wan, Yuhang Liu, Yu Liu, Qian Xiao

High spatiotemporal resolution XCH₄ observational data are crucial for the comprehensive prevention and control of CH₄ pollution. Satellite remote sensing has emerged as a key approach for XCH₄ monitoring; however, its effectiveness is constrained by satellite observation tracks, atmospheric disturbances, and sensor limitations. Consequently, data gaps persist in certain regions. Machine learning models have demonstrated remarkable success in generating high spatiotemporal resolution data for gases such as CO₂, yet research on their application to XCH₄ remains limited. Moreover, most existing studies fail to fully capture the complex spatiotemporal characteristics of XCH₄ due to insufficient feature selection. Therefore, this study proposes a novel spatiotemporal resolution enhancement method for satellite-derived XCH₄ data, integrating spatiotemporal information with a LightGBM-Bayesian framework. This approach establishes the latent relationships between satellite-derived XCH₄ measurements, auxiliary data, and precise spatiotemporal information. Using this method, we generated XCH₄ distribution maps for the Beijing-Tianjin-Hebei region from 2020 to 2022. Experimental results indicate that: (1) The LightGBM-Bayesian model outperforms traditional models such as LightGBM, XGBoost, and RF, demonstrating superior accuracy; (2) Model predictions exhibit strong consistency with TCCON station observations, validating its high precision; (3) Incorporating precise spatiotemporal information as input features significantly enhances the model's predictive performance; and (4) The spatiotemporal distribution of XCH₄ in the Beijing-Tianjin-Hebei region from 2020 to 2022 reveals a seasonal trend of higher concentrations in summer and autumn and lower concentrations in spring and winter, along with a year-on-year increase. Spatial patterns indicate elevated levels in the southwest and lower levels in the northeast.

高时空分辨率的XCH4观测数据对CH4污染的综合防治至关重要。卫星遥感已成为XCH4监测的关键方法；然而，其有效性受到卫星观测轨迹、大气干扰和传感器限制的制约。因此，某些地区的数据差距仍然存在。机器学习模型在生成二氧化碳等气体的高时空分辨率数据方面取得了显著成功，但将其应用于XCH4的研究仍然有限。此外，由于特征选择不足，现有研究大多未能充分捕捉到XCH4复杂的时空特征。因此，本研究提出了一种将时空信息与LightGBM-Bayesian框架相结合的星载XCH4数据时空分辨率增强新方法。该方法建立了卫星衍生的XCH4测量值、辅助数据和精确时空信息之间的潜在关系。利用该方法生成了2020 - 2022年京津冀地区XCH4的分布图。实验结果表明：(1)LightGBM- bayesian模型优于传统的LightGBM、XGBoost和RF模型，具有更好的准确率；(2)模式预测结果与TCCON台站观测结果具有较强的一致性，验证了模式预测精度较高；(3)将精确的时空信息作为输入特征显著提高了模型的预测性能；(4) 2020 - 2022年京津冀地区XCH4的时空分布呈现夏秋季浓度较高、春冬季浓度较低的季节变化趋势，且呈逐年上升趋势。空间格局显示西南高，东北低。

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引用次数: 0

Quantifying the drivers of light extinction due to PM2.5 at a regional site in the Indo-Gangetic Plain 在印度恒河平原的一个区域站点量化PM2.5造成的光消失的驱动因素

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2025-09-03 DOI: 10.1016/j.apr.2025.102735

Adnan Mateen Qadri , Saifi Izhar , Tarun Gupta , Sauryadeep Mukherjee , Abhijit Chatterjee

The Indo-Gangetic Plain (IGP), one of the most polluted and densely populated regions globally, frequently experiences severe haze and visibility degradation linked to fine particulate matter (PM_2.5). This study evaluates the influence of PM_2.5 on light extinction by examining the contributions of its chemical components and emission sources. Light extinction coefficients (bext) were calculated using the revised IMPROVE algorithm. Organic matter (OM) was the dominant contributor to light extinction (43 %), followed by ammonium sulfate seasonally, OM had the largest impact in winter (50 %) and post-monsoon (44 %), while sulfate dominated during the pre-monsoon and monsoon periods (43 % each). Source apportionment identified six major contributors to PM_2.5: biomass burning, vehicular emissions, resuspended dust, secondary aerosols, a chloride-rich source, and industrial emissions. Biomass burning (39 %), secondary aerosols (23 %), and vehicular emissions (15 %) were the most significant contributors overall, with biomass burning peaking during winter and post-monsoon. A multiple linear regression analysis linked secondary aerosols (43 %), vehicular emissions (30 %), and biomass burning (25 %) to light extinction. These findings underscore the critical need for targeted air quality management strategies and highlight how emissions from regional combustion sources can drive atmospheric visibility degradation—offering insights relevant to other polluted regions worldwide.

印度恒河平原（IGP）是全球污染最严重、人口最密集的地区之一，经常出现与细颗粒物（PM2.5）有关的严重雾霾和能见度下降。本研究通过考察PM2.5的化学成分和排放源对光消的贡献来评估PM2.5对光消的影响。采用改进的改进算法计算消光系数（bet）。有机质（OM）是主要的消光因子（43%），其次是硫酸铵，在冬季（50%）和季风后（44%）影响最大，而硫酸盐在季风前和季风期（43%）占主导地位。来源分析确定了PM2.5的六个主要来源：生物质燃烧、车辆排放、悬浮粉尘、二次气溶胶、富含氯化物的来源和工业排放。总体而言，生物质燃烧（39%）、二次气溶胶（23%）和车辆排放（15%）是最重要的贡献者，生物质燃烧在冬季和季风后达到峰值。多元线性回归分析将二次气溶胶（43%）、车辆排放（30%）和生物质燃烧（25%）与光灭联系起来。这些发现强调了有针对性的空气质量管理战略的迫切需要，并强调了区域燃烧源的排放如何导致大气能见度下降，为全球其他污染地区提供了相关见解。

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引用次数: 0

Variations in the characteristics and sources of PM2.5 during the COVID-19 lockdown in Xiangyang, Hubei Province, central China 湖北省襄阳新冠疫情防控期间PM2.5特征及来源变化

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2025-08-29 DOI: 10.1016/j.apr.2025.102723

Yao Mao , Tianpeng Hu , Weijie Liu , Mingming Shi , Ying Liu , Changlin Zhan , Jiaquan Zhang , Xinli Xing , Shihua Qi

This study investigated the variations in the characteristics and sources of fine particulate matter (PM_2.5) during the COVID-19 lockdown. The research was conducted in Xiangyang, a city in Hubei Province, central China, which is influenced by air pollutant transport from northern China and was the last city in Hubei to implement lockdown measures following the COVID-19 outbreak. PM_2.5 samples were obtained between 19 January and April 19, 2020. A significant reduction in the average PM_2.5 concentration was observed, falling from 148 ± 33 μg m⁻³ prior to the lockdown to 93 ± 40 μg m⁻³ after its implementation. Consistent with expectations, concentrations of polycyclic aromatic hydrocarbons (PAHs) and elemental carbon (EC) also gradually declined. Interestingly, 1 p.m._2.5 pollution event was observed during the lockdown, attributed to enhanced atmospheric oxidation capacity, as indicated by the elevated sulfur oxidation ratio (SOR), nitrogen oxidation ratio (NOR) and secondary organic carbon (SOC) levels. Results of diagnostic ratios and principal component analysis multiple linear regression (PCA-MLR) indicated an increased contribution from secondary aerosol formation and residential combustion emission during the lockdown, while vehicle emissions decreased. Geographical and meteorological data further suggested non-negligible influence from regional transport. Our findings reveal a complicated evolution of PM_2.5 species and sources amidst the enforcement of pollution control strategies. This underscores the necessity for regional coordinated source management and synergistic control of O₃ and PM_2.5, and provides a critical case for the design of differentiated emission control strategies.

本研究调查了新冠肺炎封锁期间细颗粒物（PM2.5）特征和来源的变化。该研究是在中国中部湖北省襄阳进行的，襄阳受中国北方空气污染物运输的影响，是湖北新冠肺炎疫情爆发后最后一个实施封锁措施的城市。PM2.5样本采集时间为2020年1月19日至4月19日。PM2.5平均浓度显著下降，从封城前的148±33 μ m - 3降至封城后的93±40 μ m - 3。与预期一致，多环芳烃（PAHs）和单质碳（EC）的浓度也逐渐下降。有趣的是，在封锁期间观察到1 pm .2.5污染事件，这归因于大气氧化能力增强，如硫氧化比（SOR）、氮氧化比（NOR）和二次有机碳（SOC）水平升高。诊断比率和主成分分析多元线性回归（PCA-MLR）结果表明，二次气溶胶形成和住宅燃烧排放在封城期间的贡献增加，而车辆排放减少。地理和气象数据进一步表明，区域运输的影响不容忽视。我们的研究结果揭示了在污染控制策略的实施过程中PM2.5种类和来源的复杂演变。这凸显了区域协调源管理、协同控制O3和PM2.5的必要性，并为设计差异化排放控制策略提供了重要案例。

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引用次数: 0

Evaluating the impact of anthropogenic drivers and meteorological factors on air pollutants by explainable machine learning in Shandong Province, China 利用可解释的机器学习评估人为驱动因素和气象因素对山东省空气污染物的影响

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2025-08-14 DOI: 10.1016/j.apr.2025.102694

Yue Yuan , Fuzhen Shen , Chunyan Sheng , Zeming Zhang , Weihua Guo , Wengang Zhu , Hui Zhu

Unexpected haze in North China Plain during the COVID-19 lockdown has been regarded as a natural window to explore the meteorological impact on formatting PM_2.5 pollution but with limitations in quantifying weather elements’ contributions. In this study, daily data of six air pollutants (including PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO) and six meteorological factors (including temperature, pressure, relative humidity (RH), wind speed (WS), wind direction (WD), and precipitation) from 2015 to 2020 across 16 capital cities in Shandong province, China, was used to drive the Machine Learning and the SHapley Additive exPlanation (SHAP) models. By applying these models, contributions from anthropogenic drivers to pollutant reductions and contributions from meteorological factors to the haze event were investigated. Results show that the COVID-19 lockdown measures reduced concentrations of NO₂, PM_2.5, PM₁₀, CO and SO₂ by −52.1 %, −40.0 %, −45.5 %, −29.4 % and −38.7 % respectively. On average, an 18.9 % increase in O₃ was observed. PM_2.5 pollution was mainly driven by temperature with a SHAP value of 19.7 μg/m³, followed by RH (5.8 μg/m³), precipitation (0.9 μg/m³), WD (0.3 μg/m³), pressure (0.1 μg/m³) and WS (0.1 μg/m³) during the haze period. Relative to the post-haze period, high-pressure systems coupled with lower temperatures and weakened surface winds hindered the dispersion of PM_2.5 whilst higher RH was in favour of PM_2.5 production during the haze period. This study underscores the intricate interplay between emissions, meteorological conditions, and regulatory measures in air pollution, offering critical insights into future air quality management strategies by air pollution prediction.

新冠肺炎防控期间华北平原的意外雾霾被视为探索气象对PM2.5污染形成影响的天然窗口，但在量化天气要素的贡献方面存在局限性。在这项研究中，使用2015年至2020年中国山东省16个省会城市的六种空气污染物（包括PM2.5, PM10, SO2, NO2， O3和CO）和六种气象因子（包括温度，压力，相对湿度（RH），风速（WS），风向（WD）和降水）的日常数据来驱动机器学习和SHapley加性解释（SHAP）模型。通过应用这些模型，研究了人为驱动因素对污染物减排的贡献和气象因子对雾霾事件的贡献。结果表明，新冠肺炎防控措施使北京NO2、PM2.5、PM10、CO和SO2的浓度分别下降了52.1%、40.0%、45.5%、29.4%和38.7%。平均而言，O3增加了18.9%。霾期PM2.5污染主要受温度驱动，其SHAP值为19.7 μg/m3，其次是湿度（5.8 μg/m3）、降水（0.9 μg/m3）、WD （0.3 μg/m3）、气压（0.1 μg/m3）和WS （0.1 μg/m3）。与霾后相比，高压系统加上较低的温度和减弱的地面风阻碍了PM2.5的扩散，而较高的相对湿度有利于霾期PM2.5的产生。这项研究强调了排放、气象条件和空气污染监管措施之间复杂的相互作用，通过空气污染预测为未来的空气质量管理策略提供了重要见解。

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引用次数: 0

Biomonitoring of airborne magnetic particles over time: an in situ magnetic susceptibility-based methodology 空气中磁性粒子的生物监测：一种基于原位磁化率的方法

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2025-08-13 DOI: 10.1016/j.apr.2025.102687

Daniela Buitrago Posada , Marcos A.E. Chaparro , Harald N. Böhnel , José Duque-Trujillo

We introduce a novel methodology that utilizes in situ magnetic susceptibility (κ_is) measurements through transplants of Tillandsia capillaris, which facilitates the quantification of (sub)micron-sized magnetite particles that may pose health risks. Scanning Electron Microscopy with Energy Dispersive Spectroscopy analysis reveals the presence of iron-rich spherical, semi-spherical, and irregular particles, along with potentially toxic elements such as chromium, cobalt, and manganese. Over one year, we tested two in situ measuring protocols—direct-contact methodology (DCM) and Petri-wood methodology (PWM)—on thirty-nine samples. The κ_is values obtained were comparable; however, the DCM exhibited a higher coefficient of variation (CV ≈ 1–97 %) compared to the PWM (CV ≈ 0–10 %). The PWM demonstrated low dispersion in its results, with a standard error of the mean of 0–3 × 10⁻⁷ SI, which is comparable to the instrument's sensitivity of 1 × 10⁻⁷ SI. The maximum change in κ_is observed in the transplants during the year of exposure across various sites ranged from 2.8 to 13.1 × 10⁻⁶ SI, indicating an accumulation of airborne magnetic particles (AMP) between 0.13 and 0.63 mg on the transplants. The analysis over one year suggests that traffic avenues corresponded with high AMP accumulation, while most other sites exhibited moderate accumulation. This insight is crucial for developing more accurate in situ measurement protocols and for understanding the role of epiphytic plants as biomonitors of air particle pollution.

我们介绍了一种新的方法，利用原位磁化率（κis）测量通过移植的Tillandsia capillaris，这有助于量化（亚）微米大小的磁铁矿颗粒，可能会造成健康风险。扫描电子显微镜和能量色散光谱分析揭示了富含铁的球形、半球形和不规则颗粒的存在，以及潜在的有毒元素，如铬、钴和锰。在一年多的时间里，我们在39个样品上测试了两种原位测量方案-直接接触法（DCM）和Petri-wood法（PWM）。获得的κis值具有可比性；然而，与PWM （CV≈0 - 10%）相比，DCM表现出更高的变异系数（CV≈1 - 97%）。结果表明，PWM的色散较低，平均标准误差为0-3 × 10 - 7 SI，与仪器的灵敏度1 × 10 - 7 SI相当。在暴露的一年中，移植体中κ的最大变化在2.8至13.1 × 10 - 6 SI之间，表明空气中磁性颗粒（AMP）在移植体上的积累在0.13至0.63 mg之间。一年多的分析表明，流量途径对应于高AMP积累，而大多数其他站点显示中等积累。这一见解对于制定更准确的原位测量方案和理解附生植物作为空气颗粒污染生物监测仪的作用至关重要。

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引用次数: 0

Unveiling the dynamics of multi-scale carbon emissions in urban agglomerations: A hierarchical causal framework for China's strategic economic corridor 揭示城市群多尺度碳排放动态：中国战略经济走廊的层次因果框架

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2025-08-13 DOI: 10.1016/j.apr.2025.102698

Jianan Wang , Wei Fang , Haizhong An , Yujia Fu

Understanding multi-scale spatiotemporal dynamics of urban carbon emissions is critical for crafting targeted decarbonization strategies. However, existing studies predominantly examine emissions at singular scales, overlooking cross-scale interactions and causal spatial dependencies. This study proposes a hierarchical analytical framework integrating urban agglomeration, county, and 500m grid levels to dissect carbon emission patterns across China's Yangtze River economic belt (YREB) from 2010 to 2020. Leveraging convergent advances in satellite-derived NPP-VIIRS-like nighttime light data and provincial energy inventories, we develop an ensemble approach combining geographical convergent cross mapping (GCCM) with multi-scale geographically weighted regression (MGWR) to unravel causal mechanisms and scale-dependent drivers. Our findings reveal three insights: (1) Emission trajectories exhibit strong path dependency, with the Yangtze River delta agglomeration contributing 60.4 % of total YREB emissions through 2020, while emerging hotspots demonstrate spatial decoupling from traditional economic cores; (2) Causal analysis identifies technology-intensity and tertiary sector growth as dominant mitigation factors, contrasting with persistent carbon lock-in effects from legacy infrastructure; (3) MGWR exposes paradoxical regional dynamics where urbanization drives emission reductions in advanced economies yet accelerates emissions in developing regions. The framework advances spatial econometrics by reconciling Simpson's paradox in cross-scale analysis while providing actionable intelligence for tiered carbon governance. This contribution establishes a replicable paradigm for transboundary emission management in mega-economic corridors globally.

了解城市碳排放的多尺度时空动态对于制定有针对性的脱碳战略至关重要。然而，现有的研究主要是在单一尺度上检查排放，忽略了跨尺度的相互作用和因果空间依赖性。本文提出了城市群、县域和500米网格层次的分层分析框架，分析了2010 - 2020年中国长江经济带碳排放格局。利用卫星衍生的npp - viirs类夜间灯光数据和省级能源清单的收敛性进展，我们开发了一种将地理收敛交叉映射（GCCM）与多尺度地理加权回归（MGWR）相结合的集成方法，以揭示因果机制和尺度相关驱动因素。结果表明：(1)排放轨迹表现出强烈的路径依赖性，到2020年，长三角城市群对长江经济带排放总量的贡献率为60.4%，新兴热点地区与传统经济核心呈现空间脱钩；(2)因果分析表明，技术强度和第三产业增长是主要的缓解因素，与传统基础设施的持续碳锁定效应形成对比；(3) MGWR揭示了城市化在发达经济体推动减排但在发展中地区加速减排的矛盾区域动态。该框架通过协调跨尺度分析中的辛普森悖论来推进空间计量经济学，同时为分层碳治理提供可操作的情报。这一贡献为全球大型经济走廊的跨界排放管理建立了一个可复制的范例。

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引用次数: 0

Online observation of PM2.5 during a persistent haze in the Yangtze River Delta: chemical components, health effect and light extinction 长三角持续雾霾期间PM2.5的在线观测：化学成分、健康效应和消光

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2025-08-13 DOI: 10.1016/j.apr.2025.102695

Zelin Ao , Honglei Wang , Yinglong Zhang , Li Li , Yue Ke , Zihao Wu , Zhizhen Peng

Haze impacts visibility and health. To understand the effects of haze chemical components on health risks and atmospheric optics, PM_2.5 chemical components during a haze event in Jiaxing (Dec 15, 2023–Jan 8, 2024) were analyzed. NO₃⁻ was the main component of water-soluble ions (WSIs) (45.07 %–58.20 %). Primary organic carbon (POC) was the main component of total carbon (TC) (55.12 %–78.21 %). In the clean and developing stages, Fe was the main component of metal. K was the main component in the maintenance and dissipating stages. In the developing stage, NO₃⁻ and secondary organic carbon (SOC) concentrations increased sharply, 3.86 and 18.75 times that of the previous stage. Among them, the increase of NO₃⁻ played an essential role in light extinction. In the maintenance stage, SO₄²⁻ played a more critical role. Its contribution to PM_2.5 was 1.68 times that of the previous stage, and the contribution of sulfate (Sul) to light extinction was 1.98 times that of the previous stage. The decrease in NO₃⁻ concentration mainly caused haze dissipating, but the contribution of Nitrate (Nit) to light extinction increased (62.30 %). From clean to maintenance stage, the contribution of WSIs to PM_2.5 increased, while that of TC decreased. At the same time, the enrichment factors (EF) of heavy metals such as Fe, Ca, and Zn, as well as the non-carcinogenic and carcinogenic risk for kids, decreased but increased in the dissipating stage. The non-carcinogenic and carcinogenic risk for adults increased in the developing stage. Kids were more sensitive to PM_2.5 than adults.

雾霾影响能见度和健康。为了了解雾霾化学成分对健康风险和大气光学的影响，我们分析了嘉兴一次雾霾事件（2023年12月15日至2024年1月8日）期间PM2.5的化学成分。NO3−是水溶性离子（wsi）的主要成分（45.07% ~ 58.20%）。原生有机碳（POC）是总碳（TC）的主要组成部分（55.12% ~ 78.21%）。在清洁和发育阶段，铁是金属的主要成分。K是维持和耗散阶段的主要成分。在发育阶段，NO3−和二级有机碳（SOC）浓度急剧上升，分别是前一阶段的3.86和18.75倍。其中，NO3−的增加对光灭起着至关重要的作用。在维护阶段，SO42−起着更为关键的作用。其对PM2.5的贡献是前一阶段的1.68倍，硫酸盐（Sul）对消光的贡献是前一阶段的1.98倍。NO3−浓度的降低主要引起雾霾消散，但硝态氮（Nit）对消光的贡献增加（62.30%）。从清洁阶段到维护阶段，wsi对PM2.5的贡献增大，TC的贡献减小。同时，Fe、Ca、Zn等重金属的富集因子（EF）以及儿童的非致癌性和致癌性风险在消散阶段呈下降趋势，但在消散阶段呈上升趋势。成人的非致癌性和致癌性风险在发育阶段增加。儿童对PM2.5的敏感度高于成年人。

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引用次数: 0

Environmental disinfection to inactivate airborne viruses by aerosolized surfactants 利用表面活性剂雾化灭活空气传播病毒的环境消毒

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2025-08-08 DOI: 10.1016/j.apr.2025.102684

Isaura Yáñez Noguez , Ignacio Monje Ramírez , Jesús Gracia Fadrique , Lidia Alicia López Vega , María Teresa Orta Ledesma

Providing effective alternatives for the detection and inactivation of airborne viruses is the focus of this research in response to the emergence of new variants of the virus, such as SARS-CoV-2. The use of environmentally friendly surfactants has led to the development of a promising technique with high efficacy for the inactivation of viruses in indoor bioaerosols, with no by-products and no health risks. Environments contaminated with infectious bioaerosols were simulated under controlled laboratory conditions (contact chamber) and in test rooms. The Sampling Bio-Aerosol Button by Double Agar Layer (SBAB-DAL) was developed and validated for virus recovery from infectious bioaerosols. Three new custom surfactants (CS) (patent pending): CS formulated with benzalkonium chloride (CSBC), CS based on pyridinium (CSPB) and CS prepared from cetyltrimethylammonium chloride (CSCTAC), were evaluated for inactivation of bacteriophage MS2 ATCC 15597-B1 as a surrogate for SARS-CoV-2. Non-inactivated viruses were analysed by SBAB-DAL to assess reduction percentages (% reduction) in contact chamber. CSBC, CSPB and CSCTAC were consistently able to inactivate up to 99.810, 99.986 and 99.500 % of the MS2 surrogate respectively in a 5-min contact time. The three surfactants were able to inactivate up to and 99.999 % over a 10-min contact time. The highest inactivation (up to 5 log₁₀ reduction) by application of the customised surfactant treatments supplied in aerosol form has a high potential for the inactivation of environmental viruses such as SARS-CoV-2. The SBAB-DAL methodology is a simple and effective test that can be applied to the monitoring of infectious bioaerosols as vehicles of primary virus transmission in indoor environments.

针对SARS-CoV-2等新病毒变体的出现，本研究的重点是提供检测和灭活空气传播病毒的有效替代方案。环境友好型表面活性剂的使用导致了一种有前途的技术的发展，这种技术高效地灭活室内生物气溶胶中的病毒，没有副产品，也没有健康风险。在受控的实验室条件（接触室）和试验室中模拟受感染性生物气溶胶污染的环境。建立了双琼脂层取样生物气溶胶按钮（SBAB-DAL），并验证了该按钮可用于从感染性生物气溶胶中回收病毒。研究了三种新型自定义表面活性剂（CS）（专利申请中）：由苯扎氯铵（CSBC）配制的CS，基于吡啶（CSPB）的CS和由十六烷基三甲基氯化铵（CSCTAC）制备的CS，用于灭活噬菌体MS2 ATCC 15597-B1作为SARS-CoV-2替代品。用SBAB-DAL分析未灭活的病毒，以评估接触室的减少百分比（%减少）。在5分钟的接触时间内，CSBC、CSPB和CSCTAC的灭活率分别为99.810、99.986和99.500%。在10分钟的接触时间内，这三种表面活性剂的失活率高达99.999%。通过应用以气溶胶形式提供的定制表面活性剂处理，最高的失活（高达5 log10降低）具有很高的灭活潜力，可灭活环境病毒，如SARS-CoV-2。SBAB-DAL方法是一种简单有效的测试方法，可用于监测室内环境中作为初级病毒传播载体的感染性生物气溶胶。

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引用次数: 0

The impact of different spatial sampling strategies on the spatial extrapolation prediction accuracy of PM2.5 concentrations in the Beijing-Tianjin-Hebei region, China 不同空间采样策略对京津冀地区PM2.5浓度空间外推预测精度的影响

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2025-08-07 DOI: 10.1016/j.apr.2025.102685

Lihang Fan , Guangjian Wu , Weimiao Li , Wei Wang , Fuxing Li

PM_2.5 monitoring station networks play a critical role in retrieving ground-level PM_2.5 concentrations using satellite remote sensing technology. However, the optimal spatial distribution of PM_2.5 monitoring stations is frequently overlooked during satellite-based PM_2.5 retrieval. Here, we employ the space-time linear mixed effects (STLME) model to assess the impact of different spatial sampling strategies on the spatial extrapolation prediction accuracy of PM_2.5 concentrations in the Beijing-Tianjin-Hebei region in 2020. Results demonstrate that the all-station strategy (national + provincial stations) achieves superior performance, with station- and county/district-based CV-R² values measuring 0.87 and 0.78 respectively, compared to the national-station strategy's corresponding values of 0.80 and 0.73. These findings suggest that both the all-station and national-station strategies generally reflect the model's spatial extrapolation prediction accuracy at the county and city scales. Furthermore, six strategies based on the all-station framework were developed through spatial stratified sampling strategy, demonstrating that strategic expansion of monitoring stations enhances model performance. However, model performance exhibits limited improvement when the number of stations exceeds 300. That indicate the all-station strategy can meet the estimation accuracy requirements for spatial extrapolation prediction models in this area. These findings suggest that the all-station strategy offers a relatively robust framework for PM₂.₅ extrapolation modeling in the BTH region, making it a preferred choice for supporting future air quality management and monitoring network optimization.

PM2.5监测站网络在利用卫星遥感技术反演地面PM2.5浓度方面发挥着关键作用。然而，在基于卫星的PM2.5反演中，PM2.5监测站的最佳空间分布往往被忽略。本文采用时空线性混合效应（STLME）模型，评估不同空间采样策略对2020年京津冀地区PM2.5浓度空间外推预测精度的影响。结果表明，全站策略（国家+省级站点）的CV-R2值分别为0.87和0.78，而全国站点策略的CV-R2值分别为0.80和0.73。这些结果表明，无论是全站策略还是国家站策略，总体上反映了模型在县市尺度上的空间外推预测精度。此外，通过空间分层采样策略，提出了基于全站框架的6种策略，证明了监测站的战略扩展提高了模型的性能。然而，当站点数量超过300个时，模型性能的改善有限。这表明全站策略可以满足该地区空间外推预测模型的估计精度要求。这些结果表明，全站策略为北京地区的PM2.5外推模型提供了一个相对稳健的框架，使其成为支持未来空气质量管理和监测网络优化的首选策略。

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引用次数: 0