Atmospheric Environment最新文献

Assessment of Pandora and S5P/TROPOMI observations for investigating tropospheric NO2 vertical column densities in Thailand Pandora和S5P/TROPOMI观测对泰国对流层NO2垂直柱密度的评估

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

Atmospheric Environment

Pub Date : 2025-12-10 DOI: 10.1016/j.atmosenv.2025.121738

Worapan Kanchanachat

This study examines tropospheric nitrogen dioxide (NO₂) vertical column densities (VCDs) over Thailand using ground-based Pandora observations in Bangkok and S5P/TROPOMI satellite data at the national scale. Pandora measurements reveal distinct diurnal and weekly cycles, with sharp rush-hour peaks and lower weekend values, reflecting strong anthropogenic influence. Monthly and seasonal analyses show that NO₂ is highest in winter and lowest during the monsoon, influenced by the height of the boundary layer, precipitation, and photolysis rates. A comparison of Pandora and S5P/TROPOMI monthly averages shows strong temporal agreement while revealing a consistent low bias in S5P/TROPOMI. A seasonal breakdown shows the highest agreement in summer, moderate agreement in winter, and the poorest performance during the rainy season. At the national scale, the highest NO₂ levels are concentrated in central Thailand, especially Bangkok (5.59 × 10¹⁵ molec/cm²). Elevated column amounts also appear across the Bangkok Metropolitan Region and the Eastern Economic Corridor, consistent with urban–industrial sources, while Lampang exhibits a distinct hotspot associated with emissions from the Mae Moh power plant. The seasonal variation of aerosol optical depth (AOD) also follows the NO₂ pattern, with high AOD corresponding to elevated NO₂ levels during winter in central Thailand and during summer in northern Thailand. In contrast, both AOD and NO₂ decrease substantially during the rainy season due to enhanced wet deposition and more efficient atmospheric cleansing. These results highlight the dominant influence of traffic, industry, and power generation on Thailand's NO₂ burden.

本研究利用曼谷的Pandora地面观测资料和全国范围内的S5P/TROPOMI卫星资料，研究了泰国上空对流层二氧化氮（NO2）垂直柱密度（vcd）。Pandora的测量结果显示出明显的日和周循环，高峰时段峰值明显，周末值较低，反映出强烈的人为影响。月和季节分析表明，受边界层高度、降水和光解速率的影响，NO2在冬季最高，在季风期间最低。Pandora和S5P/TROPOMI月平均值的比较显示出强烈的时间一致性，同时显示出S5P/TROPOMI的一致性低偏差。按季节划分，夏季一致性最高，冬季一致性中等，雨季最差。在全国范围内，NO2水平最高的地区集中在泰国中部，特别是曼谷（5.59 × 1015分子/cm2）。在曼谷大都会区和东部经济走廊也出现了高柱量，与城市工业来源一致，而南邦则表现出与Mae Moh发电厂排放相关的独特热点。气溶胶光学深度（AOD）的季节变化也遵循NO2模式，在泰国中部冬季和泰国北部夏季，AOD高对应NO2水平升高。相比之下，在雨季，由于湿沉降增强和更有效的大气净化，AOD和NO2都大幅减少。这些结果突出了交通、工业和发电对泰国二氧化氮负担的主要影响。

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引用次数: 0

High-efficiency in situ detection of atmospheric isoprene peroxyl radicals by furan-based fluorescent probe 呋喃基荧光探针高效原位检测大气中异戊二烯过氧基自由基

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

Atmospheric Environment

Pub Date : 2025-12-09 DOI: 10.1016/j.atmosenv.2025.121739

Xin Li , Jiaxian Li , Shiming jia , Guoying Wang

Organic peroxyl radicals (RO₂·) serve as crucial intermediates in atmospheric volatile organic compounds (VOCs) degradation process, playing a pivotal role in the formation of secondary air pollution in the atmosphere. Among naturally generated RO₂ radical species, the Isoprene peroxyl radical derived from the oxidation of isoprene produced by plants, exhibits a relatively high concentration. It can drive the formation of photochemical ozone (O₃), facilitate the formation of secondary organic aerosols (SOA) and organic nitrates, and is of critical importance in modulating the atmospheric oxidation capacity. Consequently, the accurate detection of the chemical behavior of isoprene peroxyl radical is of great significance for simulating and predicting regional air quality and evaluating the impact of nitrogen deposition on ecosystems. In this study, we for the first time report a furan - based fluorescent probe (IND-F). Through a specific capture reaction, this probe enables the in-situ quantitative detection of isoprene peroxyl radicals (ISOP34O2) in the atmosphere for the first time. This method features high sensitivity and offers a novel tool for the direct and convenient monitoring of specific radical species in the atmospheric environment.

有机过氧自由基（RO2·）是大气挥发性有机物（VOCs）降解过程中至关重要的中间体，在大气二次污染的形成中起着举足轻重的作用。在自然生成的RO2自由基种类中，由植物产生的异戊二烯氧化产生的异戊二烯过氧自由基浓度较高。它可以驱动光化学臭氧（O3）的形成，促进二次有机气溶胶（SOA）和有机硝酸盐的形成，对调节大气氧化能力至关重要。因此，准确检测异戊二烯过氧基的化学行为对于模拟和预测区域空气质量以及评价氮沉降对生态系统的影响具有重要意义。在这项研究中，我们首次报道了一种呋喃基荧光探针（IND-F）。通过特定的捕获反应，该探针首次实现了大气中异戊二烯过氧自由基（ISOP34O2）的原位定量检测。该方法灵敏度高，为直接、方便地监测大气环境中特定自由基提供了一种新的工具。

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引用次数: 0

Urbanization, air pollution, associated islands, and health hazards across six IGP cities 六个IGP城市的城市化、空气污染、相关岛屿和健康危害

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

Atmospheric Environment

Pub Date : 2025-12-04 DOI: 10.1016/j.atmosenv.2025.121716

Asmita Mukherjee, Jagabandhu Panda, Debjyoti Roy, Geo Tom, Ankan Sarkar

Uncontrolled urbanization has deep-rooted impacts on the environment, including weather, climate, and pollution, threatening the sustainability of cities. The current study focuses on six cities of the Indo-Gangetic Plain (IGP), which act as hotspots of air pollution across the country. Machine learning (ML) algorithms were utilized to analyze and forecast four major air pollutants, viz., NO₂, SO₂, O₃, and PM_2.5. The model performances based on RMSE, MAE, and R² values suggested that Linear Regression (LR) outperformed other models considered, and was more accurate in short-term prediction. In most of the considered cities, the overall trends (Mann-Kendall test) of four selected air pollutants are expected to remain unaltered in coming years, except for Delhi and Ludhiana. The PM_2.5 concentrations exhibit a strong increasing trend both during the observed and forecasted period, except for Chandigarh-Mohali. Notably, Kolkata exhibited an increasing trend for all four air pollutants while demonstrating higher values of PM_2.5 (200–450 μg/m³), which were greatly impacted by the pollution from nearby land and transport pathways over neighboring oceans. The analysis and forecasting (using ConvLSTM model) of urban land use was done to further associate urbanization with air pollution. Strong correlation signatures were noticed in all cities, either at the core or near the periphery (except for Chandigarh-Mohali), when the interplay was assessed through urban heat and pollution islands. Therefore, with prolonged exposure to air pollutants, the urban population is at higher risk of several pulmonary ailments, as the estimated mortality associated with PM_2.5 for cardiovascular, respiratory, and lung infections indicated a constant increase over the years.

不受控制的城市化对环境造成了根深蒂固的影响，包括天气、气候和污染，威胁着城市的可持续性。目前的研究集中在印度恒河平原（IGP）的六个城市，这些城市是全国空气污染的热点。利用机器学习（ML）算法对NO2、SO2、O3和PM2.5四种主要空气污染物进行分析和预测。基于RMSE， MAE和R2值的模型性能表明，线性回归（LR）优于其他考虑的模型，并且在短期预测中更准确。在大多数被考虑的城市中，四种选定的空气污染物的总体趋势（曼-肯德尔测试）预计在未来几年将保持不变，除了德里和卢迪亚纳。除昌迪加尔-莫哈里外，PM2.5浓度在观测期内和预测期内均呈现较强的上升趋势。值得注意的是，加尔各答所有四种空气污染物均呈上升趋势，同时PM2.5值较高（200-450 μg/m3），这在很大程度上受到附近陆地污染和邻近海洋运输途径的影响。利用ConvLSTM模型对城市土地利用进行分析和预测，进一步将城市化与空气污染联系起来。当通过城市热岛和污染岛评估相互作用时，所有城市（除昌迪加尔-莫哈里外）都注意到强相关特征，无论是在核心还是在外围)。因此，随着长时间暴露于空气污染物中，城市人口患几种肺部疾病的风险更高，因为与PM2.5相关的心血管、呼吸和肺部感染的估计死亡率逐年上升。

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引用次数: 0

Retrieval of aerosol optical depth from INSAT-3DR for accurate geostationary monitoring of regional and temporal aerosol dynamics 从INSAT-3DR反演气溶胶光学深度，用于区域和时间气溶胶动力学的精确地球静止监测

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

Atmospheric Environment

Pub Date : 2025-12-03 DOI: 10.1016/j.atmosenv.2025.121730

Chakradhar Rao Tandule , Mukunda M. Gogoi , Shiba Shankar Gouda , S. Suresh Babu

Accurate, high-frequency monitoring of aerosols is essential for understanding their dynamic behaviour, regional transport pathways, environmental and climatic impacts. Geostationary satellites are particularly well-suited for this purpose. In this study, we utilized INSAT-3DR satellite observations to retrieve high-quality sub-hourly aerosol optical depth (AOD) using a data-driven approach guided by a physically meaningful and interpretable machine learning framework. For this, we constructed a novel fused AOD product by integrating ground-based and satellite-based observations through hybrid statistical approach. The fused AOD showed a strong correlation coefficient (r ∼ 0.95) and low root mean square error (RMSE ∼ 0.06) with ground-based observations. Making use of this fused AOD as the target variable, an XGBoost machine learning model is trained with features derived from INSAT-3DR visible and infrared channels. The resulting model demonstrates strong performance in retrieving AOD from INSAT-3DR (r ∼ 0.839 and RMSE ∼ 0.081; relative to fused AOD), effectively capturing the regional distribution of aerosol hotspots, sub-hourly variability, and key pollution events, indicating robust generalization across diverse aerosol regimes and surface types. Furthermore, INSAT-3DR AOD exhibits strong temporal consistency with the ground-based observations at sub-hourly and seasonal scales with site-specific RMSE generally within 0.10. These findings establish INSAT-3DR as a valuable platform for continuous aerosol monitoring and present a transferable framework for future geostationary missions aimed at environmental and climate applications.

对气溶胶进行精确的高频监测对于了解其动态行为、区域运输途径、环境和气候影响至关重要。地球同步卫星特别适合于这一目的。在这项研究中，我们利用INSAT-3DR卫星观测数据，在物理意义和可解释的机器学习框架的指导下，使用数据驱动的方法获取高质量的亚小时气溶胶光学深度（AOD）。为此，我们采用混合统计方法，将地基观测和星载观测相结合，构建了一种新型的融合AOD产品。融合后的AOD与地面观测结果具有很强的相关系数（r ~ 0.95）和较低的均方根误差（RMSE ~ 0.06）。利用这种融合的AOD作为目标变量，XGBoost机器学习模型使用来自INSAT-3DR可见光和红外通道的特征进行训练。所得模型在从INSAT-3DR中检索AOD方面表现出色（相对于融合AOD， r ~ 0.839和RMSE ~ 0.081），有效捕获气溶胶热点的区域分布、次小时变化和关键污染事件，表明在不同气溶胶状态和地表类型中具有强大的通用性。此外，INSAT-3DR AOD在次小时和季节尺度上与地面观测具有较强的时间一致性，站点特定的RMSE一般在0.10以内。这些发现确立了INSAT-3DR作为持续气溶胶监测的宝贵平台，并为未来旨在环境和气候应用的地球静止任务提供了一个可转移的框架。

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引用次数: 0

Evaluation of GOSAT-IM, NASA-GEOS and NOAA-CT terrestrial ecosystem and oceanic flux datasets using the GEOS-Chem model to simulate the seasonal cycle of tropospheric CO2 基于GEOS-Chem模式对GOSAT-IM、NASA-GEOS和NOAA-CT陆地生态系统和海洋通量数据集对流层CO2季节循环的评价

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

Atmospheric Environment

Pub Date : 2025-12-03 DOI: 10.1016/j.atmosenv.2025.121719

M. Krishnapriya , A. Bhuvana Chandra , Rabindra Kumar Nayak , S. Allahudheen , Yogesh Tiwari , V.K. Dadhwal , M.V. Ramana , G. Srinivasrao , Prakash Chauhan

This study evaluates the sensitivity of the GEOS-Chem atmospheric transport model to three CO₂ surface flux databases—GOSAT-IM, NASA-GEOS, and NOAA CarbonTracker (NOAA-CT)—in simulating the tropospheric CO₂ seasonal cycle during 2010–2014. Model outputs, generated using common anthropogenic emissions and meteorological forcing, were compared against NOAA-ESRL GLOBALVIEW in situ observations from 54 stations worldwide. All simulations coherently reproduced the observed latitudinal gradient, with high CO₂ concentrations (greater than 395 ppm) in the northern sub-polar region driven by fossil fuel emissions and shallow winter boundary layers, and lower concentrations (below 390 ppm) over the southern oceans due to strong uptake and vertical mixing. Among the simulations, the one using NOAA-CT input data showed the highest correlation (greater than 0.95) with observations in the northern mid-latitudes. This high correlation is attributed to the fluxes being constrained by actual observations. The simulation with GOSAT also captured the seasonal amplitudes in the northern hemisphere, although with lesser agreement, while the simulation with NASA-GEOS underestimated observed amplitudes in the tropics. Regional discrepancies, especially in the tropical and southern hemispheric regions, highlight the influence of convective processes and uncertainties in the flux datasets. Horizontal advection dominated seasonal variability at polar stations, whereas vertical transport was more important in the tropics. These findings emphasize the need for improved time-varying flux products, better parameterizations for vertical mixing, and the integration of new satellite observations to reduce model biases. This study highlights the crucial role of surface flux datasets, particularly the spatiotemporal resolution and observational constraints embedded in them in simulating the global CO₂ seasonal cycle. Among the evaluated datasets, NOAA-CT most accurately captured the phase, amplitude, and transport-driven dynamics of atmospheric CO₂, reinforcing its utility in model-data for carbon cycle assessments.

本研究评估了GEOS-Chem大气输送模型对三个CO2地表通量数据库（gosat - im、NASA-GEOS和NOAA CarbonTracker (NOAA- ct)）在模拟2010-2014年对流层CO2季节循环中的敏感性。利用共同的人为排放和气象强迫产生的模式输出与NOAA-ESRL GLOBALVIEW在全球54个站点的现场观测结果进行了比较。所有模拟都一致地再现了观测到的纬度梯度，在化石燃料排放和冬季浅层边界层的驱动下，北部亚极地地区的二氧化碳浓度较高（大于395 ppm），而由于强吸收和垂直混合，南部海洋的二氧化碳浓度较低（低于390 ppm）。其中，使用NOAA-CT输入数据的模拟与中纬度北部观测值的相关性最高（> 0.95）。这种高相关性归因于通量受到实际观测值的限制。GOSAT模拟也捕获了北半球的季节振幅，尽管一致性较差，而NASA-GEOS模拟低估了热带地区观测到的振幅。区域差异，特别是在热带和南半球区域，突出了对流过程的影响和通量数据集的不确定性。水平平流主导极地站的季节变化，而垂直输送在热带地区更为重要。这些发现强调需要改进时变通量产品，改进垂直混合的参数化，以及整合新的卫星观测以减少模式偏差。本研究强调了地表通量数据集在模拟全球CO2季节循环中的关键作用，特别是其中包含的时空分辨率和观测约束。在评估的数据集中，NOAA-CT最准确地捕获了大气CO2的相位、振幅和运输驱动的动态，增强了其在碳循环评估模型数据中的实用性。

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引用次数: 0

Characterization and influencing factors of hydroxymethanesulfonate (HMS) in the North China plain: integration of field measurements and theoretical calculations 华北平原羟甲磺酸盐（HMS）表征及影响因素：野外实测与理论计算的结合

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

Atmospheric Environment

Pub Date : 2025-12-03 DOI: 10.1016/j.atmosenv.2025.121731

Chun Chen , Haijie Zhang , Yele Sun , Junling Li , Zhiqiang Zhang , Jian Gao , Rui Gao , Lianfang Wei , Nan Ma , Wanyun Xu , Pingqing Fu

Hydroxymethanesulfonate (HMS) is a key secondary organosulfur compound formed through aqueous-phase or heterogeneous atmospheric processes, significantly affecting the atmospheric sulfur budget. However, the relationship between HMS and PM_2.5 components, as well as its formation mechanism, remains insufficiently explored. This study presents a comprehensive analysis of HMS at two sites in the North China Plain (NCP). Using offline filter analysis combined with theoretical calculations, we found that the HMS mass concentration in Gucheng during the winter of 2019 averaged 2.58 ± 2.56 μg/m³, approximately 1.5 times of that in Beijing during winter and 5.0 times of that in Beijing during autumn. This elevated concentration in Gucheng is attributed to higher relative humidity, elevated particle pH, and higher precursor concentrations. Correlation analyses demonstrated that aerosol liquid water content (AWC) is a key factor influencing HMS formation, while ammonium and nitrate may also contribute significantly. Theoretical calculations suggest that HMS formation primarily occurs through the reaction between SO₃²⁻ and HCHO, with an additional contribution from the reaction of HSO₃⁻ with HCHO. Aqueous H₂O and NH₃ can act as catalysts, reducing the reaction barrier between HSO₃⁻ and HCHO from 9.76 kcal/mol (uncatalyzed) to 5.01 and 6.48 kcal/mol, respectively, thus promoting HMS formation. Increasing concentrations of aqueous H₃O⁺ and NH₄⁺ during polluted periods do not exhibit a catalytic effect but instead strongly inhibit HMS formation. These results indicate that a comprehensive consideration of the combined effects of various aqueous species is essential in atmospheric models.

羟甲磺酸盐（HMS）是一种重要的次生有机硫化合物，通过水相或非均相大气过程形成，对大气硫收支有重要影响。然而，HMS与PM2.5组分之间的关系及其形成机制尚未得到充分探讨。本文对华北平原两个站点的HMS进行了综合分析。通过离线过滤分析结合理论计算发现，2019年冬季古城HMS质量浓度均值为2.58±2.56 μg/m3，约为冬季北京的1.5倍，秋季北京的5.0倍。古城的浓度升高是由于较高的相对湿度、颗粒pH值升高和前体浓度升高。相关分析表明，气溶胶液态水含量（AWC）是影响HMS形成的关键因素，而铵态氮和硝态氮可能也有显著贡献。理论计算表明，HMS的形成主要是通过SO32−和HCHO之间的反应，HSO3−与HCHO的反应也有额外的贡献。水溶液H2O和NH3可以作为催化剂，将HSO3−和HCHO之间的反应势垒分别从9.76 kcal/mol（未催化）降低到5.01和6.48 kcal/mol，从而促进HMS的形成。在污染期间，水溶液中h30 +和NH4+浓度的增加不表现出催化作用，反而强烈抑制HMS的形成。这些结果表明，在大气模式中综合考虑各种含水物种的综合效应是必要的。

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引用次数: 0

Unveiling the causes of winter haze in southern Sichuan via observations and explainable machine learning 通过观测和可解释的机器学习揭示川南冬季雾霾的原因

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

Atmospheric Environment

Pub Date : 2025-12-03 DOI: 10.1016/j.atmosenv.2025.121728

Gaofeng Zhou , Junjie Wang , Wenting Lv , Yingjie Li , Yuan Li , Xuefeng Zhang , Li Han , Tao Jiang

The Sichuan Basin is one of China's most haze-prone regions due to its unique topography and stagnant meteorological conditions. To understand the winter haze formation mechanisms in the Sichuan Basin, a field campaign was conducted in a previously identified pollution hotspot (Southern Sichuan, December 2024 to February 2025). During this campaign, PM_2.5 concentration averaged 63.79 ± 42.92 μg m⁻³, and we identified three haze episodes (H1-H3) during this campaign. The chemical speciation of fine aerosol was characterized using a quadrupole aerosol chemical speciation monitor (Q-ACSM), and the apportionment of organic aerosols (OAs) was solved by EPA PMF 5.0. The results indicated that OAs dominated the PM_2.5 composition throughout this campaign, with OA concentrations particularly high in H1 (64.63 ± 20.77 μg m⁻³) and H2 (64.11 ± 19.05 μg m⁻³). Biomass burning organic aerosols (BBOA) and oxygenated organic aerosols (OOA) were the primary contributors to these high concentrations. The haze episodes resulted mainly from early-stage biomass burning emissions and less-aged OOA, occurring concurrently with the highest levels of aerosol liquid water content (ALWC), suggesting the humid air can favor the formation of haze. In addition to local emissions, regional transport synergized with meteorological conditions also significantly contributed to haze formation during this campaign, especially in H2 (38.2 %), quantified by a machine learning based deweathering analysis. This study underscores the intricate interplay between emissions, regional transport, and meteorology in driving haze formation in the Sichuan Basin and demonstrates the utility of machine learning aided diagnostics for source attribution and regulatory insights.

由于其独特的地形和停滞的气象条件，四川盆地是中国最容易发生雾霾的地区之一。为了了解四川盆地冬季雾霾的形成机制，在先前确定的污染热点地区（2024年12月至2025年2月）进行了实地调查。PM2.5平均浓度为63.79±42.92 μg m−3，共发现3次雾霾（H1-H3）。采用四极杆气溶胶化学形态监测仪（Q-ACSM）对细颗粒物的化学形态进行了表征，并用EPA pmf5.0对有机气溶胶（OAs）进行了解析。结果表明，在整个运动过程中，OA在PM2.5组成中占主导地位，其中H1（64.63±20.77 μ m−3）和H2（64.11±19.05 μ m−3）的OA浓度特别高。生物质燃烧有机气溶胶（BBOA）和氧化有机气溶胶（OOA）是这些高浓度的主要贡献者。雾霾主要由早期生物质燃烧排放和较短时间的OOA引起，与气溶胶液态水含量（ALWC）最高同时发生，表明潮湿的空气有利于雾霾的形成。除当地排放外，区域运输与气象条件的协同作用也显著促进了此次活动期间雾霾的形成，特别是H2(38.2%)，这是通过基于机器学习的风化分析量化的。本研究强调了排放、区域运输和气象在推动四川盆地雾霾形成中的复杂相互作用，并展示了机器学习辅助诊断在来源归属和监管见解方面的实用性。

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引用次数: 0

Impact of ambient air pollutants on influenza-like illness, influenza A and influenza B: A nationwide time-series study in China 环境空气污染物对流感样疾病、甲型流感和乙型流感的影响：中国全国时间序列研究

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

Atmospheric Environment

Pub Date : 2025-12-02 DOI: 10.1016/j.atmosenv.2025.121729

Xi Huang , Dina Wang , Qing Zhang , Dayan Wang , Yuelong Shu , Shenglan Xiao

Influenza is a serious respiratory infection that imposes significant public health challenges. However, the precise impact of pollutants on influenza virus activity remains unclear. In this study, we aimed to investigate the effects of different air pollutants on the incidence of influenza-like illness (ILI), influenza A (Flu A), and influenza B (Flu B) in China based on nationwide air pollution and influenza data from 554 sentinel hospitals across 30 provinces and municipalities from 2014 to 2017. A Distributed Lag Nonlinear Model (DLNM) was employed to discern the lagged effects of six distinct air pollutants, namely PM2.5, PM10, O₃, CO, SO₂, and NO₂, on the incidence of ILI, Flu A, and Flu B. Our analysis indicated that the relationship between air pollutants and influenza varied among ILI, Flu A, and Flu B, with Flu B being more sensitive to SO₂ than Flu A. Elevated levels of air pollutants were generally associated with an increased risk of influenza; however, relative risks declined slightly at extreme concentrations of PM2.5, SO₂, and NO₂. These results highlight the complex associations between air pollution and influenza.

流感是一种严重的呼吸道感染，对公共卫生构成重大挑战。然而，污染物对流感病毒活动的确切影响尚不清楚。在这项研究中，我们旨在研究不同空气污染物对中国流感样疾病（ILI）、甲型流感（Flu A）和乙型流感（Flu B）发病率的影响，基于2014年至2017年全国30个省市554家定点医院的空气污染和流感数据。采用分布滞后非线性模型（DLNM）分析了PM2.5、PM10、O3、CO、SO2和NO2 6种不同空气污染物对流感、甲型流感和乙型流感发病率的滞后效应。结果表明，空气污染物与流感的关系在流感、甲型流感和乙型流感中存在差异，乙型流感对SO2的敏感性高于甲型流感。然而，在PM2.5、SO2和NO2的极端浓度下，相对风险略有下降。这些结果强调了空气污染和流感之间的复杂联系。

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引用次数: 0

Lead sources detected in Manila's air after the phase-out of leaded gasoline 在逐步淘汰含铅汽油后，马尼拉的空气中检测到铅源

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

Atmospheric Environment

Pub Date : 2025-12-02 DOI: 10.1016/j.atmosenv.2025.121727

Mengli Chen , Jariya Kayee , Armin Sorooshian , Grace Betito , Paola Angela Bañaga , Rachel A. Braun , Maria Obiminda Cambaliza , Melliza Templonuevo Cruz , Alexander B. MacDonald , James Bernard Simpas , Connor Stahl , Iravati Ray , Reshmi Das , Zunya Wang , Xianfeng Wang

The global phase-out of leaded gasoline marked a major milestone in pollution control, yet modern uses of lead (Pb) continue to pose significant health risks, especially in low- and middle-income countries. In the Philippines, significant data gaps still exist despite increasing exposure. This study presents to the best of our knowledge, the first Pb isotopic fingerprinting of atmospheric aerosols in Metro Manila, Philippines, covering fine (0.56–1 μm) and coarse (5.6–10 μm) fractions, collected in 2018–2019. Results show that local sources, mainly industrial activities (45–62 %) and fossil fuel combustion (30–45 %), are now the dominant contributors to airborne Pb, while a minor legacy leaded gasoline and geogenic Pb persists (<18 %) through soil resuspension. Stable isotopes show no clear seasonal pattern. Together with 25 fold higher Pb concentration in the fine fraction, these indicate limited transboundary input. Regional comparison highlights overlapping Pb isotopic composition across Southeast Asia, but is distinct from areas farther north due to intensive coal use in China. The consistency between isotopic fingerprinting and Positive Matrix Factorization (PMF) results demonstrates the value of combining methods for robust source apportionment. These findings demonstrate the continuing importance of isotopic monitoring for distinguishing contemporary and legacy Pb sources and informing targeted air quality management in rapidly developing regions.

全球逐步淘汰含铅汽油标志着污染控制方面的一个重要里程碑，但铅的现代使用继续构成重大健康风险，特别是在低收入和中等收入国家。在菲律宾，尽管暴露程度越来越高，但仍然存在重大的数据缺口。据我们所知，本研究首次收集了菲律宾马尼拉大都会2018-2019年大气气溶胶的Pb同位素指纹图谱，覆盖细（0.56-1 μm）和粗（5.6-10 μm）组分。结果表明，本地来源，主要是工业活动（45 - 62%）和化石燃料燃烧（30 - 45%），现在是空气中铅的主要来源，而少量遗留的含铅汽油和地质Pb通过土壤再悬浮存在（< 18%）。稳定同位素显示不出明显的季节模式。再加上细粒Pb浓度高出25倍，表明越界输入有限。区域比较突出了整个东南亚地区重叠的铅同位素组成，但由于中国密集的煤炭使用，与更北的地区不同。同位素指纹图谱与正矩阵分解（PMF）结果的一致性证明了组合方法在鲁棒源分配中的价值。这些发现表明，同位素监测对于区分当代和遗留的铅源，并为快速发展地区的针对性空气质量管理提供信息，具有持续的重要性。

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引用次数: 0

Integrating surface-based in-situ and satellite observations to characterize CO2 and CH4 emission hotspots in Houston, USA 基于地面的原位和卫星观测综合表征美国休斯敦CO2和CH4排放热点

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

Atmospheric Environment

Pub Date : 2025-11-28 DOI: 10.1016/j.atmosenv.2025.121713

Irfan Karim , Bernhard Rappenglück

This study integrates in-situ and satellite observations to characterize urban greenhouse gas (GHG) emissions across Houston, Texas USA. Surface-based background concentrations show seasonal reductions from ∼435 to ∼410 ppm for carbon dioxide (CO₂) due to photosynthetic uptake, and from ∼2.02 to ∼1.88 ppm for methane (CH₄) predominantly due to oxidation by the hydroxyl radical (OH). Boundary layer height corrected excess CO₂ and CH₄ (ΔCO₂, ΔCH₄) peak in winter (∼139.4 ppm, ∼5.5 ppm) and drop in summer (∼5.6 ppm, ∼0.08 ppm), highlighting emission seasonality. The observed annual ΔCH₄/ΔCO₂ ratio (9.4 ppb ppm⁻¹) exceeds EDGAR and EPA inventory estimates by 65–70 %, and spatial mapping identifies key CH₄ hotspots - such as McCarty and Blue Ridge landfills - with ratios larger than 40–70 ppb ppm⁻¹, which are severely underrepresented in emission inventories.

Satellite-derived enhancements from OCO-3 and TROPOMI offer broader coverage but lack sensitivity to surface plumes. For example, in situ bivariate plots show sharp ΔCO₂ enhancements >30 ppm and ΔCH₄ up to ∼0.3 ppm over industrial zones like the Ship Channel, while satellite ΔXCO₂ (∼5–7 ppm) and ΔXCH₄ (∼0.03–0.04 ppm) show moderate enhancements over the urban core. TROPOMI NO₂ (∼1 × 10⁻⁴ μmol/m²) and HCHO (∼2.0 × 10⁻⁴ mol/m²) enhancements further confirm co-located industrial emissions. This synthesis underscores the value of combining surface and satellite data for robust urban emission assessments and improved emission inventory evaluation.

本研究综合了现场和卫星观测资料，对美国德克萨斯州休斯顿的城市温室气体（GHG）排放进行了表征。基于表面的背景浓度显示，由于光合作用的吸收，二氧化碳（CO2）从~ 435 ppm减少到~ 410 ppm，甲烷（CH4）从~ 2.02 ppm减少到~ 1.88 ppm，这主要是由于羟基自由基（OH）的氧化。边界层高度校正了过量的CO2和CH4 （ΔCO2， ΔCH4）在冬季达到峰值（~ 139.4 ppm, ~ 5.5 ppm），在夏季下降（~ 5.6 ppm, ~ 0.08 ppm），突出了排放的季节性。观测到的年ΔCH4/ΔCO2比值（9.4 ppb ppm−1）比EDGAR和EPA估算的清查值高出65 - 70%，并且空间制图确定了关键的CH4热点地区，如McCarty和Blue Ridge垃圾填埋场，其比值大于40-70 ppb ppm−1，这在排放清查表中被严重低估。来自OCO-3和TROPOMI的卫星增强提供了更广泛的覆盖范围，但对地表羽流缺乏敏感性。例如，原位双变量图显示，在像船舶航道这样的工业区上空，二氧化碳浓度急剧ΔCO2增强（30 ppm）和ΔCH4高达~ 0.3 ppm，而卫星ΔXCO2 （~ 5-7 ppm）和ΔXCH4 （~ 0.03-0.04 ppm）在城市核心上空显示适度增强。TROPOMI NO2 （~ 1 × 10−4 μmol/m2）和HCHO （~ 2.0 × 10−4 mol/m2）的增强进一步确认了工业排放的位置。这种综合强调了将地面和卫星数据结合起来进行可靠的城市排放评估和改进排放清单评估的价值。

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引用次数: 0