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137Cs in outdoor air due to Chernobyl-contaminated wood combustion for residential heating in Thessaloniki, North Greece 希腊北部塞萨洛尼基因居民取暖燃烧受切尔诺贝利污染的木材而导致室外空气中的 137Cs
IF 4.2 2区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-13 DOI: 10.1016/j.atmosenv.2024.120929
S. Stoulos , E. Ioannidou , P. Koseoglou , E. Vagena , A. Ioannidou
Wood combustion was the key heating source in Greece during the first years at the beginning of the financial crisis. Signals of 137Cs were detected in Thessaloniki during the winter of 2013–2014 on weekends and holidays when the residents were at home burning the biggest amount of wood all day. 137Cs signals were >6–21 μBq m−3 detected using high-volume air filters and γ-spectrometry. No signals have been detected since then, as gas has replaced oil for residential heating, reducing forest wood. Besides, signal <6 μBq m−3 is undetectable because this is the minimum detectable activity. 40K concentrations were also measured, revealing a constant value of 143 ± 16 μBq m−3. The Cs-to-K ratio in air was 0.04–0.14 compared to 0.05 ± 0.01 measured before and after. Higher levels were measured when the air temperature was the lowest, but no correlation was observed with wind or pressure. Simulations using the HYSLIT model were applied on the dates on which the ratio was the highest. The model confirms the experimental results observed. 137Cs signals detected and related to the Chernobyl-contaminated biomass used for central heating indicate that contaminated forest ecosystems remain a source of unwanted radioactivity in the environment.
在金融危机爆发的最初几年,木材燃烧是希腊的主要供暖来源。塞萨洛尼基在 2013-2014 年冬季的周末和节假日检测到了 137Cs 信号,当时居民整天都在家燃烧大量木材。使用高容量空气过滤器和 γ 光谱仪检测到的 137Cs 信号为 6-21 μBq m-3。此后,由于天然气已取代石油用于居民取暖,减少了森林木材,因此没有再检测到任何信号。此外,6 μBq m-3 信号是检测不到的,因为这是最低检测活性。对 40K 浓度也进行了测量,结果显示其恒定值为 143 ± 16 μBq m-3。空气中的铯-钾比率为 0.04-0.14,而前后测得的比率为 0.05 ± 0.01。气温最低时测得的浓度水平较高,但与风力或气压没有相关性。使用 HYSLIT 模型对比率最高的日期进行了模拟。该模型证实了观察到的实验结果。检测到的 137Cs 信号与用于集中供暖的切尔诺贝利污染生物质有关,这表明受污染的森林生态系统仍然是环境中不必要的放射性来源。
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引用次数: 0
Aerosol retrievals derived from a low-cost Calitoo sun-photometer taken on board a research vessel 研究船上使用的低成本 Calitoo 太阳光度计得出的气溶胶检索结果
IF 4.2 2区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-12 DOI: 10.1016/j.atmosenv.2024.120888
Rosa D. García , África Barreto , Celia Rey , Eugenio Fraile-Nuez , Alba González-Vega , Sergio F. León-Luis , Antonio Alcantara , A. Fernando Almansa , Carmen Guirado-Fuentes , Pablo González-Sicilia , Victoria E. Cachorro , Frederic Bouchar
This study presents a comprehensive 5-year period assessment of aerosol optical depth (AOD) and Å ngströn Exponent (AE) data from a hand-held Calitoo sun photometer on board the Ángeles Alvariño research vessel. Observations spanned March 2018 to September 2023, focusing on key maritime regions such as the Canary Islands, coasts of North Africa, the Mediterranean, Portugal, the Cantabrian, and the Bay of Biscay. The Calitoo device measures solar irradiance at three wavelengths (465, 540, and 619 nm). Uncertainty analysis for Calitoo AOD retrievals was performed using the Monte Carlo method, yielding an expanded uncertainty (UAOD) ranging between 0.008 and 0.050 with a mean and standard deviation of 0.032 ± 0.008 for the three wavelengths. Our results also highlight the remarkable calibration stability of the Calitoo (< 2.6%) over this 5-year period. Calitoo AOD values were assessed using reference AOD data from Santa Cruz de Tenerife (the Canary Islands), El Arenosillo (Huelva), and Palma de Mallorca (the Balearic Islands) AERONET (Aerosol Robotic Network) stations. The comparison revealed a good agreement with correlation coefficients ranging from 0.727 to 0.917 and mean bias ranging from -0.030 to -0.001. Additionally, the Calitoo AOD data were compared with MODIS (Moderate Resolution Imaging Spectroradiometer) and CAMS-ECMWF (Copernicus Atmosphere Monitoring Service-European Centre for Medium-Range Weather Forecasts) aerosol products obtaining that Calitoo AOD values were generally lower, showing negative mean bias of -0.063 and -0.024, respectively.
The aerosol characterizations using AE vs. AOD plots in the three maritime study regions using 5-years of non-routine Calitoo data are similar to the corresponding aerosol characterizations performed with simultaneous AERONET-Cimel data.
These findings underscore Calitoo’s reliability for aerosol studies in regions where AERONET instruments or other aerosol networks are unavailable. Likewise, given the low cost of Calitoo photometers, they could be deployed onboard a large number of merchant and passenger ships or in other remote or under-monitored areas, providing near real-time AOD/AE data to enhance our understanding of aerosols processes or for model or satellite assimilation/validation.
本研究对安赫尔斯-阿尔瓦里尼奥号研究船上的手持式 Calitoo 太阳光度计提供的气溶胶光学深度(AOD)和 Å ngströn 指数(AE)数据进行了为期 5 年的综合评估。观测时间跨度为 2018 年 3 月至 2023 年 9 月,重点是加那利群岛、北非海岸、地中海、葡萄牙、坎塔布连和比斯开湾等主要海洋区域。Calitoo 设备可测量三种波长(465、540 和 619 纳米)的太阳辐照度。使用蒙特卡洛方法对 Calitoo AOD 数据进行了不确定性分析,得出三个波长的扩展不确定性(UAOD)在 0.008 和 0.050 之间,平均值和标准偏差为 0.032 ± 0.008。我们的结果还突显了 Calitoo 在这 5 年中出色的校准稳定性(2.6%)。利用圣克鲁斯-德特内里费(加那利群岛)、埃尔阿雷诺西约(韦尔瓦)和帕尔马-德马略卡(巴利阿里群岛)AERONET(气溶胶机器人网络)站的参考气溶胶数据,对 Calitoo 的 AOD 值进行了评估。比较结果表明,两者的相关系数在 0.727 至 0.917 之间,平均偏差在-0.030 至-0.001 之间,具有良好的一致性。此外,将 Calitoo 的 AOD 数据与 MODIS(中分辨率成像分光仪)和 CAMS-ECMWF (哥白尼大气监测服务-欧洲中期天气预报中心)的气溶胶产品进行比较后发现,Calitoo 的 AOD 值普遍较低,平均偏差分别为-0.063 和-0.024。在三个海洋研究区域,使用 Calitoo 5 年非例行数据绘制的 AE vs. AOD 图进行的气溶胶特征描述与使用 AERONET-Cimel 同步数据进行的相应气溶胶特征描述相似。同样,鉴于 Calitoo 光度计的低成本,可以将其部署在大量商船和客船上或其他偏远或监测不足的地区,提供近乎实时的 AOD/AE 数据,以加深我们对气溶胶过程的了解,或用于模型或卫星同化/验证。
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引用次数: 0
Reaction between peracetic acid and carbonyl oxide: Quantitative kinetics and insight into implications in the atmosphere 过乙酸与羰基氧化物之间的反应:定量动力学和对大气中影响的见解
IF 4.2 2区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-12 DOI: 10.1016/j.atmosenv.2024.120928
Chao-Lu Xie , Hao Yang , Bo Long
Peracetic acid (PAA, CH3C(O)OOH) is one of the most abundant organic peroxyacid in the atmosphere. PAA is often assumed to be removed by hydroxyl radical in the gas phase of troposphere, but its reaction rate is quite low. Here, we investigated the new reaction between PAA and carbonyl oxide (CH2OO) by using quantum chemical methods, reaction kinetics in combination with atmospheric modeling. We first performed W3X-L calculations close to CCSDT(Q)/CBS accuracy with the reaction systems containing eight carbon and oxygen atoms. The present findings show that the post-CCSD(T) contribution is about 0.50 kcal/mol, which is important for obtaining quantitative relative enthalpy of activation at 0 K. We find that the recrossing effect reduces the rate constant by an order of magnitude for the mechanism of the hydrogen-shift coupled carbon-oxygen addition at low temperature. The calculated results reveal that the anharmonicity increases the rate constants of CH2OO + CH3C(O)OOH by a factor of 6.27 at 298 K. The present findings uncover that the PAA + CH2OO reaction is a dominant pathway for PAA sinks in the gas phase of troposphere at the lower nighttime OH concentrations at 298 K, since the rate of PAA + CH2OO is even an order of magnitude higher than the rate of the PAA + OH reaction. Moreover, atmospheric modeling simulations unveil that CH2OO can make certain contribution to the reduction of PAA in the Amazon.
过乙酸(PAA,CH3C(O)OOH)是大气中含量最高的有机过氧酸之一。人们通常认为 PAA 在对流层气相中会被羟基自由基清除,但其反应速率相当低。在此,我们采用量子化学方法、反应动力学和大气模型相结合的方法研究了 PAA 和羰基氧化物(CH2OO)之间的新反应。我们首先对含有八个碳原子和氧原子的反应体系进行了接近 CCSDT(Q)/CBS 精确度的 W3X-L 计算。本研究结果表明,CCSD(T) 后贡献约为 0.50 kcal/mol,这对于获得 0 K 时的定量相对活化焓非常重要。我们发现,对于低温下氢移耦合碳氧加成的机理而言,交叉效应将速率常数降低了一个数量级。计算结果显示,在 298 K 时,非谐波使 CH2OO + CH3C(O)OOH 的速率常数增加了 6.27 倍。本研究结果发现,在 298 K 的较低夜间 OH 浓度条件下,PAA + CH2OO 反应是对流层气相中 PAA 吸收的主要途径,因为 PAA + CH2OO 的速率甚至比 PAA + OH 反应的速率高出一个数量级。此外,大气模型模拟显示,CH2OO 对亚马逊河流域 PAA 的减少有一定的贡献。
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引用次数: 0
Development of an online cloud fog monitor: Design, laboratory, and field deployment at an unoccupied coastal site in Eastern China 开发在线云雾监测仪:设计、实验室和在中国东部沿海无人区的实地部署
IF 4.2 2区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-12 DOI: 10.1016/j.atmosenv.2024.120927
Ping Du , Xinghui Liu , Xiaoling Nie , Tao Li , Haoran He , Jianing Zhang , Xinfeng Wang , Yan Wang , Jianmin Chen
Online detection of cloud water chemistry is a pressing issue in atmospheric outfield observation, with online detection modules representing a significant development direction for cloud water observation. Addressing the common problem of time-delayed errors in manual detection, particularly in the context of cloud water acidity, has remained challenging, with limited understanding and effective solutions available. We developed an Online Cloud Fog Monitor (OCFM) featuring automatic pH and electrical conductivity (EC) detection capabilities, and conducted comprehensive laboratory and field tests. The OCFM utilizes a peristaltic pump, water pipe, and diversion chamber to direct cloud samples to distinct detection chambers, enabling real-time analysis. The diversion chamber is equipped with dual liquid level sensors to segregate and preserve samples once the volume exceeds a predetermined threshold. Calibration results indicate that the instrument's background metal elements do not affect cloud water analysis, and detection occurs within the designed response time. Field tests demonstrate that the OCFM can collect over 50 ml of cloud water, with a response accuracy exceeding 63.6%, though influenced by meteorological conditions. The time-delay error for pH was notably larger than for EC. Comparative analysis with the Caltech Active Strand Cloudwater Collector (CASCC) revealed that the OCFM's sampling process does not introduce errors, and the online detection accuracy of pH and EC is comparable to manual methods. Additionally, water-soluble ions in samples collected by the OCFM showed no significant differences compared to those collected by CASCC. Overall, the OCFM effectively replaces manual testing, mitigating time-delay errors in chemical property testing. The introduction of this cloud water detector promises to significantly reduce labor costs and economic consumption associated with cloud water observation, thereby facilitating long-term, multi-site observation of cloud water chemistry.
云水化学在线探测是大气外场观测中的一个紧迫问题,在线探测模块是云水观测的一个重要发展方向。解决人工检测中常见的延时误差问题,尤其是云水酸度方面的问题,仍然具有挑战性,人们对这一问题的理解和有效解决方案都很有限。我们开发了一种在线云雾监测仪(OCFM),具有自动 pH 值和导电率(EC)检测功能,并进行了全面的实验室和实地测试。OCFM 利用蠕动泵、水管和分流室将云雾样本导入不同的检测室,从而实现实时分析。分流室配备了双液面传感器,一旦体积超过预定阈值,就会隔离并保存样本。校准结果表明,仪器的本底金属元素不会影响云水分析,检测也在设计的响应时间内完成。现场测试表明,OCFM 可以采集 50 毫升以上的云水,响应精度超过 63.6%,但会受到气象条件的影响。pH 值的延时误差明显大于 EC 值。与加州理工学院主动式云水采集器(CASCC)的比较分析表明,OCFM 的采样过程不会产生误差,pH 值和 EC 值的在线检测精度与人工方法相当。此外,OCFM 采集的样本中的水溶性离子与 CASCC 采集的样本相比没有明显差异。总之,OCFM 能有效取代人工检测,减少化学性质检测中的时间延迟误差。这种云水检测器的引入有望大幅降低与云水观测相关的人工成本和经济消耗,从而促进对云水化学的长期、多站点观测。
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引用次数: 0
The coupling model of random forest and interpretable method quantifies the response relationship between PM2.5 and influencing factors 随机森林与可解释方法耦合模型量化了 PM2.5 与影响因素之间的响应关系
IF 4.2 2区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-09 DOI: 10.1016/j.atmosenv.2024.120925
Jinxing Liu , Hui Yu , Yaqing Zhang , Junjun Chen , Shiyuan Feng , Rui Guo , Feng Wang , Bo Xu , Guoliang Shi , Yinchang Feng
Ambient fine particulate matter (PM2.5) is affected by many factors, such as source emissions, meteorological conditions, and chemical reactions. Revealing the effects of these factors on PM2.5 is essential to understand the causes of PM2.5 pollution. The machine learning method can establish the non-linear relationship between influencing factors and PM2.5. Here, a coupling model of machine learning and interpretation method was constructed to comprehensively quantify the importance of influencing factors to PM2.5 from multiple dimensions and analyze the sensitivity of influencing factors. Among the primary indicators of influencing factors, the importance of emission, meteorological conditions, and atmospheric chemical reaction to PM2.5 is 49%, 29%, and 22%, respectively. In the secondary indicator of influencing factors, the transmission effect is the most important meteorological condition, with an important degree of 15%. The liquid phase reaction is the most important atmospheric chemical reaction, with an importance of 7%. Among the three levels of influencing factors, emission, transport distance, liquid phase reaction coefficient, aerosol acidity, and accumulation promotion coefficient are important factors. The sensitivity of a single factor is complex and changeable, and the interaction between emission and other important factors is the strongest among the two factors. Of which the interaction between transmission distance and emission during the observation period is the strongest, and the interaction coefficient is 1.82. Our study focuses on the effect of influencing factors on PM2.5, provides a basis for the analysis of the causes of PM2.5 pollution, and technical support for the treatment of PM2.5.
环境细颗粒物(PM2.5)受多种因素的影响,如污染源排放、气象条件和化学反应。揭示这些因素对 PM2.5 的影响对于了解 PM2.5 污染的成因至关重要。机器学习方法可以建立影响因素与 PM2.5 之间的非线性关系。本文构建了机器学习与解释方法的耦合模型,从多个维度全面量化影响因素对PM2.5的重要性,分析影响因素的敏感性。在影响因素一级指标中,排放、气象条件和大气化学反应对PM2.5的重要程度分别为49%、29%和22%。在影响因素的二级指标中,传输效应是最重要的气象条件,其重要程度为 15%。液相反应是最重要的大气化学反应,重要程度为 7%。在三个层次的影响因素中,排放、传输距离、液相反应系数、气溶胶酸度和积聚促进系数是重要因素。单个因素的敏感性复杂多变,排放与其他重要因素的交互作用是两个因素中最强的。其中,观测期内传输距离与排放量的交互作用最强,交互作用系数为 1.82。我们的研究重点关注影响因素对PM2.5的影响,为PM2.5污染成因分析提供依据,为PM2.5治理提供技术支持。
{"title":"The coupling model of random forest and interpretable method quantifies the response relationship between PM2.5 and influencing factors","authors":"Jinxing Liu ,&nbsp;Hui Yu ,&nbsp;Yaqing Zhang ,&nbsp;Junjun Chen ,&nbsp;Shiyuan Feng ,&nbsp;Rui Guo ,&nbsp;Feng Wang ,&nbsp;Bo Xu ,&nbsp;Guoliang Shi ,&nbsp;Yinchang Feng","doi":"10.1016/j.atmosenv.2024.120925","DOIUrl":"10.1016/j.atmosenv.2024.120925","url":null,"abstract":"<div><div>Ambient fine particulate matter (PM<sub>2.5</sub>) is affected by many factors, such as source emissions, meteorological conditions, and chemical reactions. Revealing the effects of these factors on PM<sub>2.5</sub> is essential to understand the causes of PM<sub>2.5</sub> pollution. The machine learning method can establish the non-linear relationship between influencing factors and PM<sub>2.5</sub>. Here, a coupling model of machine learning and interpretation method was constructed to comprehensively quantify the importance of influencing factors to PM<sub>2.5</sub> from multiple dimensions and analyze the sensitivity of influencing factors. Among the primary indicators of influencing factors, the importance of emission, meteorological conditions, and atmospheric chemical reaction to PM<sub>2.5</sub> is 49%, 29%, and 22%, respectively. In the secondary indicator of influencing factors, the transmission effect is the most important meteorological condition, with an important degree of 15%. The liquid phase reaction is the most important atmospheric chemical reaction, with an importance of 7%. Among the three levels of influencing factors, emission, transport distance, liquid phase reaction coefficient, aerosol acidity, and accumulation promotion coefficient are important factors. The sensitivity of a single factor is complex and changeable, and the interaction between emission and other important factors is the strongest among the two factors. Of which the interaction between transmission distance and emission during the observation period is the strongest, and the interaction coefficient is 1.82. Our study focuses on the effect of influencing factors on PM<sub>2.5</sub>, provides a basis for the analysis of the causes of PM<sub>2.5</sub> pollution, and technical support for the treatment of PM<sub>2.5</sub>.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"341 ","pages":"Article 120925"},"PeriodicalIF":4.2,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Seasonal trends and light extinction effects of PM2.5 chemical composition from 2021 to 2022 in a typical industrial city of central China 2021 至 2022 年中国中部典型工业城市 PM2.5 化学成分的季节变化趋势和光消散效应
IF 4.2 2区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-07 DOI: 10.1016/j.atmosenv.2024.120922
Changlin Zhan , Chong Wei , Ziguo Liu , Hongxia Liu , Xuefen Yang , Jingru Zheng , Shan Liu , Jihong Quan , Yong Zhang , Qiyuan Wang , Nan Li , Junji Cao
This study investigates the concentrations, chemical compositions, and sources of PM2.5 in Huangshi, China. Daily average PM2.5 levels ranged from 8.43 to 193.08 μg m−3, with an annual mean of 54.13 μg m−3, exceeding China's annual secondary standard of 35 μg m−3. Seasonal mean concentrations peaked in winter and were lowest in summer. Organic carbon (OC) and elemental carbon (EC) had annual means of 4.89 μg m−3 and 0.94 μg m−3, respectively. Water-soluble inorganic ions (WSIIs) accounted for 52.17% of PM2.5, with NO3, SO42−, and NH4+ being the major components. The NO3/SO42− ratio averaged 1.65, indicating a transition from coal combustion to vehicle emissions as the primary pollution source. Chemical mass reconstruction revealed that NH4NO3, (NH4)2SO4, and organic matter (OM) accounted for 65.3% of PM2.5 mass. Seasonal variations in light extinction (bext) highlighted the impact of secondary inorganic salts on visibility, with an annual average bext of 346.30 ± 246.98 Mm−1. Airmass clusters and potential source region analysis suggested PM2.5 and its components were primarily originated from local and nearby regions. These findings underscore the effectiveness of local pollution control measures, changing pollution sources, and the necessity for targeted emission controls to improve air quality and visibility in urban areas.
本研究调查了中国黄石 PM2.5 的浓度、化学成分和来源。PM2.5 的日平均水平在 8.43 到 193.08 μg m-3 之间,年平均值为 54.13 μg m-3,超过了中国 35 μg m-3 的年二级标准。季节平均浓度在冬季达到峰值,夏季最低。有机碳(OC)和元素碳(EC)的年均值分别为 4.89 μg m-3 和 0.94 μg m-3。水溶性无机离子(WSIIs)占 PM2.5 的 52.17%,主要成分是 NO3-、SO42- 和 NH4+。NO3-/SO42- 的平均比值为 1.65,表明主要污染源已从燃煤过渡到汽车尾气排放。化学质量重建显示,NH4NO3、(NH4)2SO4 和有机物(OM)占 PM2.5 质量的 65.3%。光消光(bext)的季节变化凸显了次生无机盐对能见度的影响,年平均 bext 为 346.30 ± 246.98 Mm-1。空气质量集群和潜在来源地区分析表明,PM2.5 及其成分主要来自本地和附近地区。这些发现强调了当地污染控制措施的有效性、污染源的变化以及有针对性地控制排放以改善城市地区空气质量和能见度的必要性。
{"title":"Seasonal trends and light extinction effects of PM2.5 chemical composition from 2021 to 2022 in a typical industrial city of central China","authors":"Changlin Zhan ,&nbsp;Chong Wei ,&nbsp;Ziguo Liu ,&nbsp;Hongxia Liu ,&nbsp;Xuefen Yang ,&nbsp;Jingru Zheng ,&nbsp;Shan Liu ,&nbsp;Jihong Quan ,&nbsp;Yong Zhang ,&nbsp;Qiyuan Wang ,&nbsp;Nan Li ,&nbsp;Junji Cao","doi":"10.1016/j.atmosenv.2024.120922","DOIUrl":"10.1016/j.atmosenv.2024.120922","url":null,"abstract":"<div><div>This study investigates the concentrations, chemical compositions, and sources of PM<sub>2.5</sub> in Huangshi, China. Daily average PM<sub>2.5</sub> levels ranged from 8.43 to 193.08 μg m<sup>−3</sup>, with an annual mean of 54.13 μg m<sup>−3</sup>, exceeding China's annual secondary standard of 35 μg m<sup>−3</sup>. Seasonal mean concentrations peaked in winter and were lowest in summer. Organic carbon (OC) and elemental carbon (EC) had annual means of 4.89 μg m<sup>−3</sup> and 0.94 μg m<sup>−3</sup>, respectively. Water-soluble inorganic ions (WSIIs) accounted for 52.17% of PM<sub>2.5</sub>, with NO<sub>3</sub><sup>−</sup>, SO<sub>4</sub><sup>2−</sup>, and NH<sub>4</sub><sup>+</sup> being the major components. The NO<sub>3</sub><sup>−</sup>/SO<sub>4</sub><sup>2−</sup> ratio averaged 1.65, indicating a transition from coal combustion to vehicle emissions as the primary pollution source. Chemical mass reconstruction revealed that NH<sub>4</sub>NO<sub>3</sub>, (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub>, and organic matter (OM) accounted for 65.3% of PM<sub>2.5</sub> mass. Seasonal variations in light extinction (<em>b</em><sub>ext</sub>) highlighted the impact of secondary inorganic salts on visibility, with an annual average <em>b</em><sub>ext</sub> of 346.30 ± 246.98 Mm<sup>−1</sup>. Airmass clusters and potential source region analysis suggested PM<sub>2.5</sub> and its components were primarily originated from local and nearby regions. These findings underscore the effectiveness of local pollution control measures, changing pollution sources, and the necessity for targeted emission controls to improve air quality and visibility in urban areas.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"341 ","pages":"Article 120922"},"PeriodicalIF":4.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Assessment of potential sources of airborne pollen in a high-mountain mediterranean natural environment 评估地中海高山自然环境中空气传播花粉的潜在来源
IF 4.2 2区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-06 DOI: 10.1016/j.atmosenv.2024.120917
Paloma Cariñanos , Soledad Ruiz-Peñuela , Andrea Casans , Alberto Cazorla , Fernando Rejano , Alejandro Ontiveros , Pablo Ortiz-Amezcua , Juan Luis Guerrero-Rascado , Francisco José Olmo , Lucas Alados-Arboledas , Gloria Titos
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引用次数: 0
An attention-based CNN model integrating observational and simulation data for high-resolution spatial estimation of urban air quality 基于注意力的 CNN 模型整合了观测和模拟数据,用于高分辨率城市空气质量空间估算
IF 4.2 2区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-05 DOI: 10.1016/j.atmosenv.2024.120921
Shibao Wang , Yanxu Zhang
Machine learning, especially deep learning, can outperform traditional atmospheric models in air quality assessment, offering enhanced efficiency and accuracy without relying on detailed emission inventories and atmospheric chemical mechanisms. Despite their predictive power, deep learning models often grapple with the perception of being “black boxes” due to their intricate architectures. Here, we develop an attention-based convolutional neural network (CNN-attention) model that incorporates observational data, the parallelized large-eddy-simulation model (PALM), and urban morphology data for high-resolution spatial estimation of urban air quality. Our findings indicate that the CNN-attention model outperforms traditional CNN with higher accuracy and efficiency, achieving R2 = 0.987 and root mean square error (RMSE) = 0.15 mg/m3, while significantly reducing training time and memory usage. Compared to traditional machine learning models, the CNN exhibits higher R2 values and lower RMSE, showcasing its adeptness at capturing complex nonlinear patterns. The inclusion of attention layer further improves the model's performance by dynamically assigning attention scores to key features, enabling the model to focus on areas of critical emissions and distinctive urban features such as highways, arterial roads, intersections, and dense building clusters. This approach also reveals fluid dynamical principles, highlighting the significant disparities in pollutant concentration across roadways caused by atmospheric turbulence, and the distinct plume formations influenced by land use and topography. When applied to various urban settings, the CNN-attention model exhibits superior generalizability and transferability. This study provides valuable scientific insights and technical support for urban planning, air quality management, and exposure risk evaluation.
在空气质量评估中,机器学习,尤其是深度学习,可以超越传统的大气模型,提供更高的效率和准确性,而无需依赖详细的排放清单和大气化学机制。尽管深度学习模型具有强大的预测能力,但由于其复杂的架构,它们常常被认为是 "黑盒子"。在此,我们开发了一种基于注意力的卷积神经网络(CNN-attention)模型,该模型结合了观测数据、并行化大涡度模拟模型(PALM)和城市形态数据,可用于城市空气质量的高分辨率空间估算。我们的研究结果表明,CNN-注意力模型以更高的精度和效率超越了传统的 CNN,达到了 R2 = 0.987 和均方根误差 (RMSE) = 0.15 mg/m3,同时显著减少了训练时间和内存使用。与传统的机器学习模型相比,CNN 的 R2 值更高,均方根误差更小,这表明它善于捕捉复杂的非线性模式。注意力层的加入进一步提高了模型的性能,它可以动态地为关键特征分配注意力分数,使模型能够关注关键排放区域和独特的城市特征,如高速公路、主干道、十字路口和密集的建筑群。这种方法还揭示了流体动力学原理,突出了大气湍流造成的各条道路污染物浓度的显著差异,以及受土地利用和地形影响的独特羽流形态。在应用于各种城市环境时,CNN-注意力模型表现出卓越的普适性和可移植性。这项研究为城市规划、空气质量管理和暴露风险评估提供了宝贵的科学见解和技术支持。
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引用次数: 0
Improved tools for estimation of ammonia emission from field-applied animal slurry: Refinement of the ALFAM2 model and database 改进田间施用动物粪便的氨排放估算工具:改进 ALFAM2 模型和数据库
IF 4.2 2区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-02 DOI: 10.1016/j.atmosenv.2024.120910
Sasha D. Hafner , Johanna Pedersen , Roland Fuß , Jesper Nørlem Kamp , Frederik Rask Dalby , Barbara Amon , Andreas Pacholski , Anders Peter S. Adamsen , Sven Gjedde Sommer
Ammonia volatilization from animal slurry applied to agricultural fields reduces nitrogen use efficiency in agriculture and pollutes the environment. This work presents new versions of a model and database focused on this route of N loss. The public ALFAM2 database (https://github.com/AU-BCE-EE/ALFAM2-data) was expanded with ammonia emission and ancillary measurements for >700 additional field plots. The ALFAM2 model (https://github.com/AU-BCE-EE/ALFAM2, https://zenodo.org/records/13312251) was extended with the addition of an ammonia sink for more plausible predictions over extended durations and to better reflect the expected reduction in emission rate several days after slurry application. A new parameter set was developed for the model taking into account the newly available measurement data. Model efficiency improved to 0.67 for the parameter estimation subset (0.52 for cross-validation) and mean absolute error was around 10% of applied total ammoniacal nitrogen. As in earlier versions, predicted emission is sensitive to application method, slurry dry matter and pH, air temperature, and wind speed. A collection of parameter sets for estimating uncertainty in average predictions was developed using a bootstrap approach. Predicted uncertainty is not trivial, and is high for some variable combinations, highlighting the challenge of making predictions based on available measurement data. Still, this work has resulted in more accurate, comprehensive, transparent, and flexible tools for emission inventory and related work on ammonia loss from field-applied slurry.
农田施用的动物粪便中的氨挥发会降低农业的氮利用效率并污染环境。这项工作介绍了针对这一氮损失途径的模型和数据库的新版本。公共 ALFAM2 数据库 (https://github.com/AU-BCE-EE/ALFAM2-data) 已扩充,增加了 700 块田地的氨排放和辅助测量数据。对 ALFAM2 模型(https://github.com/AU-BCE-EE/ALFAM2, https://zenodo.org/records/13312251)进行了扩展,增加了氨吸收汇,以便在更长的持续时间内进行更合理的预测,并更好地反映施用泥浆几天后排放率的预期降低。考虑到新获得的测量数据,为模型开发了新的参数集。参数估计子集的模型效率提高到 0.67(交叉验证为 0.52),平均绝对误差约为施用总氨氮的 10%。与早期版本一样,预测排放量对施用方法、泥浆干物质和 pH 值、气温和风速很敏感。使用自举法开发了用于估计平均预测不确定性的参数集。预测的不确定性并非微不足道,某些变量组合的不确定性很高,这凸显了根据现有测量数据进行预测所面临的挑战。尽管如此,这项工作还是为排放清单和田间施用泥浆的氨损失相关工作提供了更加准确、全面、透明和灵活的工具。
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引用次数: 0
Indoor ozone reaction products: Contributors to the respiratory health effects associated with low-level outdoor ozone 室内臭氧反应产物:与低浓度室外臭氧有关的呼吸系统健康影响因素
IF 4.2 2区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Pub Date : 2024-11-02 DOI: 10.1016/j.atmosenv.2024.120920
Linchen He , Zhiheng Hao , Charles J. Weschler , Feng Li , Yinping Zhang , Junfeng Jim Zhang
Low-level outdoor ozone (O3) exposure has been associated with adverse respiratory health effects, whereas substantially higher O3 concentrations have been required to exert measurable effects in controlled studies. This discrepancy remains poorly understood. After entering indoors, a substantial portion of O3 reacts with indoor chemicals to generate ozone reaction products that are potentially more toxic than O3 itself. We hypothesize that ozone reaction product exposures contribute to the adverse respiratory effects associated with low-level outdoor O3 exposure. In a panel study of 70 healthy adults, each was measured four times during a low-ozone season (maximum 8-h average: 29 ± 13 ppb). We found that higher average outdoor O3 concentrations, irrespective of whether participants were outdoors or indoors, were significantly associated with worsened spirometric lung function (i.e., FVC, FEV1, FEF25-75) and airway mechanics (i.e., R5, R20) indicators. Per interquartile range (IQR) increase in average outdoor O3 exposure when participants were indoors with windows closed (exposure proxy for ozone reaction products + indoor O3) was significantly associated with worsening of multiple respiratory function indicators including FVC, FEV1, FEF25-75, Z5, R5, and R20 by 0.56–3.08%. In contrast, per IQR increase in average outdoor O3 exposure when participants were outdoors or indoors with windows open (exposure proxy for O3 without ozone reaction products) was only significantly and adversely associated with worsening of one respiratory function indicator X5 by 1.4%. These findings support our hypothesis and suggest further evaluation of indoor ozone reaction products' contribution to adverse health effects induced by outdoor O3 exposure.
暴露于低浓度的室外臭氧(O3)会对呼吸系统健康产生不良影响,而在对照研究中,需要更高浓度的 O3 才能产生可测量的影响。人们对这一差异仍然知之甚少。进入室内后,相当一部分臭氧会与室内化学品发生反应,生成可能比臭氧本身毒性更强的臭氧反应产物。我们假设,臭氧反应产物的暴露会导致与低浓度室外臭氧暴露相关的呼吸系统不良反应。在一项由 70 名健康成年人组成的小组研究中,我们在低臭氧季节对每个人进行了四次测量(8 小时最大平均值:29 ± 13 ppb)。我们发现,无论参与者是在室外还是在室内,较高的室外臭氧平均浓度都与肺功能(即 FVC、FEV1、FEF25-75)和气道力学(即 R5、R20)指标的恶化密切相关。参与者在室内关窗时,室外 O3 平均暴露量(臭氧反应产物+室内 O3 暴露量)每增加一个四分位数间距(IQR),与多个呼吸功能指标(包括 FVC、FEV1、FEF25-75、Z5、R5 和 R20)恶化 0.56%-3.08% 显著相关。相比之下,参与者在室外或室内开窗时的室外 O3 平均暴露量(不含臭氧反应产物的 O3 暴露替代物)每增加一个 IQR 值,仅与一项呼吸功能指标 X5 的恶化有显著的不利关系,恶化幅度为 1.4%。这些发现支持了我们的假设,并建议进一步评估室内臭氧反应产物对室外臭氧暴露所引起的不良健康影响的贡献。
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Atmospheric Environment
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