Aerosol Science and Engineering最新文献

{"title":"Heavy Metals in Urban and Rural Indoor Dust in Plateau Homes: Levels, Sources, and Health Risks","authors":"Weiying Hou,&nbsp;Ruijing Hu,&nbsp;Zhanpeng Cui,&nbsp;Jinze Wang,&nbsp;Jie Sun,&nbsp;Bingyan Zhang,&nbsp;Xiaoyu Han,&nbsp;Yuanchen Chen,&nbsp;Yong Zhang,&nbsp;Dong Yang,&nbsp;Wei Du","doi":"10.1007/s41810-025-00308-1","DOIUrl":"10.1007/s41810-025-00308-1","url":null,"abstract":"<div><p>The significant amount of people spend time indoors necessitates attention to health risks associated with heavy metals in indoor dust. This study quantified the concentrations of nine heavy metals, including manganese (Mn), zinc (Zn), arsenic (As), copper (Cu), lead (Pb), chromium (Cr), nickel (Ni), selenium (Se), and cadmium (Cd), and evaluated their rural–urban distribution, potential sources, and associated health risks through the analysis of indoor dust samples collected from Yunnan Province. The results showed that the heavy metal concentrations in indoor rural households were approximately 2–3 times higher than those in urban households. The application of Positive Matrix Factorization (PMF) analysis indicated that traffic is the dominant source, contributing 37.92% in rural and 44.53% in urban areas, and that indoor sources contributed more to urban than rural areas (41.09% vs 24.64%). Furthermore, the carcinogenic risk of indoor dust for children is seven times higher than that in adults (7.68 and 1.00 in rural areas and 7.41 and 0.96 in urban areas, respectively). Among the heavy metals, As presents the most significant risk to human health, warranting a priority focus on its mitigation. The study findings are intended to provide guidance on the prevention measures necessary to control heavy metal indoor pollution.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture><span>The alternative text for this image may have been generated using AI.</span></div></div></figure></div></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 3","pages":"450 - 462"},"PeriodicalIF":2.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Meteorological Influences and Machine Learning Approaches in Assessing Air Quality in Brasília","authors":"Amaury de Souza,&nbsp;Marcel Carvalho Abreu,&nbsp;Elias Silva de Medeiros,&nbsp;Marcos Antônio Alves,&nbsp;José Francisco de Oliveira Júnior,&nbsp;Sneha Gautam","doi":"10.1007/s41810-025-00312-5","DOIUrl":"10.1007/s41810-025-00312-5","url":null,"abstract":"<div><p>This study investigates the temporal variations of ozone (O₃), carbon monoxide (CO), sulfur dioxide (SO₂), and particulate matter (PM₂.₅) concentrations in Brasília, Brazilian Midwest, focusing on the critical dry season and forest fire periods. Utilizing real data from 2000 to 2018, nonparametric trend tests (Mann-Kendall and Pettitt) and Principal Component Analysis (PCA) were applied to evaluate pollutant levels and their relationships with meteorological factors. Machine learning (ML) models were employed to predict O₃ concentrations. Random Forest and XGBoost models achieved good predictive results with MSE scores of 20.57 and 18.34, MAE 3.22 and 3.11, R² 0.69 and 0.72, and SMAPE 17.55 and 16.89, respectively. XGBoost model highlighted relative humidity as the most important feature, followed by CO, SO₂, wind speed, air temperature, PM₂.₅, and wind speed. While no significant long-term trends were detected, CO concentrations showed an upward tendency during the dry season, and O₃ and SO₂ were positively correlated with wind speed but negatively with humidity and precipitation. These findings underscore the transient influence of meteorological systems on pollutant levels and demonstrate the potential of ML models for advancing air quality research by complementing traditional monitoring systems and providing actionable insights for policymakers. The study provides actionable insights for developing targeted air quality management strategies and expanding monitoring networks to protect public health in Brasília.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 4","pages":"509 - 520"},"PeriodicalIF":2.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148459055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Seasonal Prediction of Ozone Pollution in Central-East China Using Machine Learning","authors":"Sheng Yang,&nbsp;Xin Hao,&nbsp;Nan Li,&nbsp;Hong Liao,&nbsp;Jiandong Li","doi":"10.1007/s41810-025-00310-7","DOIUrl":"10.1007/s41810-025-00310-7","url":null,"abstract":"<div><p>Machine learning models have been extensively employed for predicting ground-level ozone (O₃) in China, primarily focusing on short-term (1–3 days) forecasts of O₃ pollution. This study established a dynamical–statistical model employing Random Forest (RF) and evaluated its capacity in forecasting O₃ pollution at a seasonal timescale. Observations indicated O₃ pollution was most frequent from April to September between 2014 and 2023 in Central-East China, with the monthly mean MDA8 O₃ exceeding 100 μg m⁻³ for the North China Plain (NCP), the Yangtze River Delta (YRD) and the Fen-Wei Plain (FWP). The seasonal forecast model predicted first-month monthly mean O₃ concentrations with the lowest NMB from the current month to the following three months. The RF model was tested using several statistical criteria and found to be reliable. In test datasets for NCP, YRD, and FWP, R² ranged from 0.68 to 0.77, with MAE and RMSE ranging from 16.3 to 17.2 and 21.2 to 22.1 μg m⁻³. From April to September 2023, independent hindcasts showed that approximately 77% of cities had NMBs less than 10%. Our investigation showed that the RF model could forecast O₃ monthly mean concentrations one month in advance, greatly improve early warning and fortify systems for preparing for mitigation actions.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 3","pages":"479 - 487"},"PeriodicalIF":2.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Investigation of the Spatiotemporal Variation Characteristics and Potential Source Regions of PM2.5, PM10, and O3 in Shiyan, China","authors":"Ying Zhang,&nbsp;Shici Zhang,&nbsp;Rui Zhang,&nbsp;Hua Guo,&nbsp;Jinjin Peng,&nbsp;Yuqing Zhao,&nbsp;Zhiyan Zhang,&nbsp;Shuo Zhang,&nbsp;Shan Liu","doi":"10.1007/s41810-025-00307-2","DOIUrl":"10.1007/s41810-025-00307-2","url":null,"abstract":"<div><p>This study investigated the spatiotemporal distribution characteristics and seasonal potential sources of atmospheric pollutants in Shiyan, a typical inland mountainous city in China. Data were collected from four national control monitoring stations in Shiyan City between March 2021 and February 2022. The HYSPLIT model, backward trajectory airflow analysis, and cluster analysis were conducted to identify the main pollution processes for each season and determine the sources and migration paths of atmospheric pollutants in Shiyan City. The results indicated that PM_2.5 (autumn and winter), PM₁₀ (spring), and O₃ (summer) were the characteristic pollutants, with the air quality showing seasonal variations. Compared with spring, summer and autumn presented better air quality, whereas winter presented the poorest air quality. The potential sources of spring PM₁₀ pollution were primarily located in Jinan, Liaocheng, and Heze in southwestern Shandong Province and Xuchang, Kaifeng, and Nanyang in Henan Province. The main potential sources of O₃ pollution during summer were identified in Chongqing, Ankang, Yichang, Jingmen, and Xiangyang. In autumn, the primary potential sources of PM_2.5 pollution were local areas within Shiyan and surrounding regions such as Jingzhou and Xiangyang. For winter, the main potential sources of PM_2.5 pollution were from areas such as Enshi, which is located around Shiyan, as well as Zhoukou and Shangqiu in Henan Province. This study provides theoretical support for enhancing air pollution control measures and managing compound air pollutant emissions across different seasons.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 3","pages":"438 - 449"},"PeriodicalIF":2.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"A Study of the Transportation and Evaporation of a Teacher’s Respiratory Droplet Aerosols in a Ventilated Classroom","authors":"Hongmei Liu,&nbsp;Mu Chen,&nbsp;Qing Zhang,&nbsp;Xiao Jiang,&nbsp;Xuedong Liu","doi":"10.1007/s41810-025-00309-0","DOIUrl":"10.1007/s41810-025-00309-0","url":null,"abstract":"<div><p>The purpose of the present study is to explore the spread and distribution of droplet aerosols generated by a teacher in ventilated classrooms, which is important for understanding how infectious bacteria and viruses spread among people through respiratory activities. The large eddy simulation (LES) coupled with Lagrangian method is used in the simulation and experimental verification is carried out in an artificial climate chamber. The electromagnetic relay valve is innovatively installed in the aerosol transport pipeline to simulate the real human respiration process.The effects of ambient temperature, relative humidity, three respiratory behaviors and displacement ventilation mode on the spread of droplet aerosol are studied. The results show that the influence of ambient temperature on the evaporation rate of droplet aerosols in the classroom is greater than that of the relative humidity. Without ventilation, students in the front of the classroom are at higher risk for inhalation of pollutants when speaking, and students in the back of the classroom are at higher risk for inhalation of pollutants when coughing. The airflow from displacement ventilation has a significant effect on the blocking and removal of particles. Droplet aerosol removal rate for the coughing condition is 87%, which is the highest in comparison to breathing and speaking, while the particle removal rate is 60% for breathing and 79% for speaking. Unremoved particles are blocked and suspended away from the student’s breathing zone.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 3","pages":"463 - 478"},"PeriodicalIF":2.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Unveiling Urban Heat Islands: A Path to Cleaner Urban Air","authors":"M. P. Akhtar,&nbsp;Satya Prakash Maurya","doi":"10.1007/s41810-025-00305-4","DOIUrl":"10.1007/s41810-025-00305-4","url":null,"abstract":"<div><p>Urban Heat Islands (UHIs) are a pressing environmental challenge, characterized by elevated temperatures in urban areas compared to rural surroundings. This phenomenon, exacerbated by urbanization and human activities, poses significant risks to public health, energy consumption, and air quality. Addressing UHIs requires a multifaceted approach involving green infrastructure, urban planning, technological innovations, and policy interventions. Strategies such as green roofs, cool surfaces, and compact urban design offer promising solutions to mitigate UHIs and improve urban sustainability. Collaboration among policymakers, urban planners, researchers, and communities is essential to implement effective mitigation strategies. This article highlights the complex relationship between UHIs and air pollution, underscores the need for evidence-based policies, and identifies future research directions to address this critical issue. Moreover, the UHIs measurement methods and the workflow to reduce the impact of air pollution on UHIs are also discussed.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 3","pages":"409 - 418"},"PeriodicalIF":2.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Improving the Spatiotemporal Forecasting of PM2.5 by Coupling a Deep Learning Method with a Chemical Transport Model: A Severe Haze Case in China","authors":"Zheng Zhao,&nbsp;Huangjian Wu,&nbsp;Keyi Chen,&nbsp;Lei Kong,&nbsp;Xiao Tang","doi":"10.1007/s41810-025-00302-7","DOIUrl":"10.1007/s41810-025-00302-7","url":null,"abstract":"<div><p>The chemical transport model (CTM) is often used for PM_2.5 forecasting. However, due to the uncertainty sources in modelling processes such as meteorology, emissions, and chemical reactions, the PM_2.5 forecasting of the CTM still has high uncertainty, especially during severe air pollution episodes with complex meteorological and chemical coupling. In this study, by using low-memory GPU, we developed a Model-Coupled Deep Learning method (MCDL method) by coupling a deep learning method with the CTM to improve the spatiotemporal forecasting of PM_2.5 for 3 days in China during a severe haze episode. This method assimilates surface-observed PM_2.5 into the CTM to produce an analysis field of PM_2.5. Then, an encoder-decoder-based deep learning algorithm (ConvGRU) was used to predict the bias between the PM_2.5 of the CTM and the analysis field, therefore improving the accuracy of the forecasting. The PM_2.5 of the CTM and meteorological variables of the WRF model were added as auxiliary variables for training. The results suggest that the MCDL method can significantly improve the PM_2.5 forecast of the CTM, where the root-mean-square error (RMSE) decreased by 24.5%. The MCDL method also performed better than the data-driven deep learning method with 20% lower RMSE. During this severe haze episode, the MCDL method effectively corrected the significant underestimation or overestimation of the CTM spatiotemporal forecast results and has better long-term spatiotemporal forecasting performance than the data-driven deep learning method. Besides, the MCDL method has the highest accuracy in forecasting pollution levels of PM_2.5 when daily concentrations exceed 75 µg/m³.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 3","pages":"354 - 369"},"PeriodicalIF":2.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Unveiling the Drivers of Cloud Condensation Nuclei Activity: Chemical Transformations and Biomass Burning over Rural India","authors":"Tamada Jayasri,&nbsp;V. Ravi Kiran,&nbsp;M. Venkat Ratnam,&nbsp;Chaithanya D. Jain,&nbsp;B. L. Madhavan,&nbsp;P. R. Sinha","doi":"10.1007/s41810-025-00304-5","DOIUrl":"10.1007/s41810-025-00304-5","url":null,"abstract":"<div><p>Cloud Condensation Nuclei (CCN) are the fraction of the aerosol that participate in the formation and modification of clouds. Comprehensive knowledge of CCN is key to understanding aerosol-cloud interactions, and climate change. The ability of aerosols to act as CCN depends on their size and composition. However, various location-specific factors also determine the CCN activity. To investigate this, long-term observations of CCN, along with collocated measurements of aerosols, gases and meteorological parameters, were analyzed from a rural location, Gadanki (13.5⁰N, 79.2⁰E, 375 m AMSL) in India. The results suggest biomass burning activities, represented by high values of BC, and CO, contribute to the highest CCN during March–April. Conversely, chemical transformations involving O₃, SO₂, NH₃, and VOCs, driven by solar radiation, are linked to high CCN levels observed during April as well as daytime (post 10:00 h). The similarity between CCN and small particle concentrations (&lt; 0.5 µm, CN) suggests the dominant role of small particles in CCN activation. Furthermore, a detailed multiple linear regression (MLR) analysis was conducted to quantify the factors influencing CCN. Findings indicate that CCN is infleuenced by a combination of factors rather than a single one, with T, and RH having indirect effects. Although the MLR results support the observed variations in CCN, they exhibit bias due to multicollinearity in the dataset. Therefore, a machine learning-based framework is proposed for future research. Nevertheless, the results emphasize the role of chemical transformations and biomass burning in CCN activity in the rural location, Gadanki.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 3","pages":"385 - 408"},"PeriodicalIF":2.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Intelligent Predictive Systems for Modeling PM10, Ozone, and Temperature Dynamics: A Forecasting Approach for Urban Air Quality in Delhi","authors":"Rahul Malik,&nbsp;Fasiur Rehman,&nbsp;Renu Dhupper,&nbsp;Bisma Nadeem,&nbsp;Harshita Jain,&nbsp;Arti Bhardwaj,&nbsp;Amit Awasthi,&nbsp;Ayan Sar,&nbsp;Tannupriya Choudhury","doi":"10.1007/s41810-025-00301-8","DOIUrl":"10.1007/s41810-025-00301-8","url":null,"abstract":"<div><p>Study investigates the relationship between PM₁₀, ozone and temperature in Delhi's metropolitan areas, a densely populated and industrialized city with significant air pollution problems. Understanding the complex interactions between PM₁₀ and ozone concentrations and climatic variables is the study's main objective. The data included PM₁₀ and ozone concentrations as well as daily readings of the temperature, relative humidity, and other relevant meteorological factors. Regression modelling and correlation analysis is used to examine the relationship between PM₁₀, ozone concentration and temperature. Machine learning approach is used to forecast the Ozone, PM₁₀ and temperature by using the algorithm of auto regressive integrated moving average (ARIMA). Preliminary data indicates a clear relationship between ozone, PM₁₀ concentration and temperature in Delhi's metropolitan areas. The results demonstrate a substantial correlation between these variables, where higher temperatures are linked to higher ozone concentrations and lower PM₁₀ concentrations. Precursor pollutants are implicated in heat-induced photochemical reactions that culminate in the synthesis and deposition of ozone. Study examines the influence of several meteorological factors, such as solar radiation and relative humidity, on the relationship between ozone, PM₁₀, and temperature by observing their past and predicated trends through the years 2018–2026. ARIMA model is used to forecast ozone, temperature, and PM₁₀ levels in Delhi’s urban environment that shows good accuracy. The predictions indicate a significant rise in ozone and PM₁₀ concentrations in the coming years, highlighting the urgent need for effective mitigation measures to maintain air quality within national and international standards.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 3","pages":"343 - 353"},"PeriodicalIF":2.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Monitoring and Dispersion Modelling of Particulate Matter (PM2.5) in Rwanda","authors":"Elisephane Irankunda,&nbsp;Zoltán Török","doi":"10.1007/s41810-025-00297-1","DOIUrl":"10.1007/s41810-025-00297-1","url":null,"abstract":"<div><p>Air quality degradation from particulate matter (PM_2.5) presents significant health and environmental risks, particularly in urban areas where traffic emissions are a primary source. Rapid urbanisation and increased vehicle use have worsened PM_2.5 levels, heightening respiratory and cardiovascular health threats, especially in developing cities with insufficient emission controls. This study examined the impact of local traffic emissions, regional pollution transport, and atmospheric circulation on PM_2.5 concentrations in Kigali, the capital of Rwanda, in 2021. Roadside PM_2.5 concentrations were compared with predictions from AERMOD and ISCST3 dispersion models, resulting in PM_2.5 dispersion maps. Model performance was assessed using statistical metrics, including fractional bias (FB), mean bias (MB), normalised mean bias (NMB), and normalised mean square error (NMSE). Annual PM_2.5 concentrations from real-time data ranged from 25.03 to 49.47 µg/m³, significantly surpassing the WHO safe yearly limit of 5 µg/m³. Both models under-predicted PM_2.5, with daily discrepancies of up to 27.6 µg/m³ for AERMOD and 11.5 µg/m³ for ISCST3, and annual under-predictions of 9.6 µg/m³ and 4.7 µg/m³, respectively. These discrepancies indicate unmodeled sources, such as residential emissions, influenced by air mass movements from Kigali’s south, southeast, and northwest at 3–6 m/s. These findings underscore air quality challenges in Kigali, which are driven by transboundary pollution during dry seasons and localised emissions. The results emphasise the need for regional collaboration, improved urban emission controls, and targeted pollution policies, providing insights pertinent to air quality management in similar urban contexts worldwide.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 2","pages":"283 - 300"},"PeriodicalIF":2.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}