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Significant spatiotemporal changes in atmospheric particulate mercury pollution in China: Insights from meta-analysis and machine-learning

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Science of the Total environment 2024年 955卷 177184页

作者： Wang, Haolin Li, Tianshuai Wang, Guoqiang Peng, Yanbo Zhang, Qingzhu Wang, Xinfeng Ren, Yuchao Liu, Ruobing Yan, Shuwan Meng, Qingpeng Wang, Yujia Wang, Qiao Academician Workstation for Big Data Research in Ecology and Environment Environmental Research Institute Shandong University Qingdao266237 China Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation Shandong Academy for Environmental Planning Jinan250101 China

PM2.5 bound mercury (PBM2.5) in the atmosphere is a major component of total mercury, which is a pollutant of global concern and a potent neurotoxicant when converted to methylmercury. Despite its importance, comprehensive macroanalyses of PBM2.5 on large scales are still lacking. To explore the driving factors, spatiotemporal pollution distribution, and associated health risks, we compiled a comprehensive dataset consisting of PBM2.5 concentrations and spatiotemporal information across China from 2000 to 2023 that was collected from the published scientific literature with valid data. By incorporating corresponding multidimensional predicting variables, the best-fitted random forest model was applied to predict PBM2.5 concentrations with a high spatial resolution of 0.25° × 0.25°, and the health risk assessment model was used for subsequent health risk assessment. Our results indicated that population density and PM2.5 emissions from power generation were the main contributors to PBM2.5 concentrations. In 2020, the pollution was primarily concentrated in northern, central, and eastern China, with the highest annual average concentration of 815.91 pg/m3 in Shanghai. Beijing experienced the most significant seasonal increase, with PBM2.5 concentrations rising by 146.92 % from summer to winter. Nationally, the annual average PBM2.5 pollution decreased extensively and markedly from 2015 to 2020. The non-carcinogenic risk of PBM2.5 alone was negligible in 2020, with HQ values generally 2.5 pollution. © 2024 Elsevier B.V.

关键词： Health risks Machine learning Particulate mercury Spatiotemporal patterns

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Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning

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Science of the Total environment 2024年 948卷 174452页

作者： Wang, Haolin Guan, Xu Li, Jiao Peng, Yanbo Wang, Guoqiang Zhang, Qingzhu Li, Tianshuai Wang, Xinfeng Meng, Qingpeng Chen, Jiaqi Zhao, Min Wang, Qiao Academician Workstation for Big Data Research in Ecology and Environment Environmental Research Institute Shandong University Qingdao266237 China Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation Shandong Academy for Environmental Planning Jinan250101 China Shandong Tianve Engineering Technology Co. LTD China

Airborne trace elements (TEs) present in atmospheric fine particulate matter (PM2.5) exert notable threats to human health and ecosystems. To explore the impact of meteorological conditions on shaping the pollution characteristics of TEs and the associated health risks, we quantified the variations in pollution characteristics and health risks of TEs due to meteorological impacts using weather normalization and health risk assessment models, and analyzed the source-specific contributions and potential sources of primary TEs affecting health risks using source apportionment approaches at four sites in Shandong Province from September to December 2021. Our results indicated that TEs experience dual effects from meteorological conditions, with a tendency towards higher TE concentrations and related health risks during polluted period, while the opposite occurred during clean period. The total non-carcinogenic and carcinogenic risks of TEs during polluted period increased approximately by factors of 0.53–1.74 and 0.44–1.92, respectively. Selenium (Se), manganese (Mn), and lead (Pb) were found to be the most meteorologically influenced TEs, while chromium (Cr) and manganese (Mn) were identified as the dominant TEs posing health risks. Enhanced emissions of multiple sources for Cr and Mn were found during polluted period. Depending on specific wind speeds, industrialized and urbanized centers, as well as nearby road dusts, could be key sources for TEs. This study suggested that attentions should be paid to not only the TEs from primary emissions but also the meteorology impact on TEs especially during pollution episodes to reduce health risks in the future. © 2024 Elsevier B.V.

关键词： Health risks

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Evolutionary Trends and Driving Analysis of Ulva Prolifera Green Tides: A Study Based on Random Forest Algorithm and Multi-Source Remote Sensing Images

SSRN

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SSRN 2023年

作者： Hou, Wenlong Chen, Jinyue He, Maoxia Wang, Guoqiang Jiang, Peng Ren, Shilong Fang, Lei Wang, Wanting Wang, Qiao Academician Workstation for Big Data in Ecology and Environment Environment Research Institute Shandong University Qingdao266237 China Shenzhen Research Institute of Shandong University Shenzhen518057 China School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China

Mapping the long-term spatial-temporal evolution and analyzing causes of Ulva prolifera green tides are important for management, restoration, and sustainable development of marine ecosystems. Satellite remote sensing is the primary choice for monitoring Ulva prolifera green tides due to its advantage of long-term and large-scale ocean monitoring. This study developed a remote sensing extraction model using an integrated method of random forest (RF) and optical Algae Cloud Index (ACI) to map the distribution pattern of Ulva prolifera. The integrated ACI-RF method outperformed other methods such as support vector machines (SVM), K-nearest neighbors (KNN), and decision *** Ulva prolifera green tides area exhibited an overall downward trend from 2011 to 2022, with an average annual reduction rate of 3%. The maximum annual biomass ranged between 0.12 and 15.9Kt. Over 75% of Ulva prolifera areas drifted northward influenced by northern currents and wind fields. The areas most prone to Ulva prolifera green tides are located in seaweed cultivation and water eutrophication areas. The driving mechanism analysis suggests that NO3-, Si, and the Lianyungang Porphyra planting area have a significant impact on the occurrence and growth of Ulva *** average daily biological growth rate of Ulva prolifera is greatly influenced by factors such as maximum sea surface temperature, ocean current speed, precipitation, and nutrient concentrations. Coastal land use change and surface runoff also significantly impact the growth rate of Ulva prolifera in Jiangsu Province. Based on the distribution patterns and cause analysis of Ulva prolifera green tides, two management strategies are proposed for the initial area and early outbreak period. The application of remote sensing big data in mapping Ulva prolifera green tides can be extended to other areas affected by marine ecological disasters. The selection criteria of driving factors provide important reference for implementing

关键词： Mapping

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Remote sensing identification and model-based prediction of harmful algal blooms in inland waters: Current insights and future perspectives

Water Research X

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Water research X 2025年 28卷

作者： Wanting Wang Guoqiang Wang Jie Li Jinyue Chen Zhenyu Gao Lei Fang Shilong Ren Qiao Wang Academician Workstation for Big Data in Ecology and Environment Environment Research Institute Shandong University Qingdao 266237 China Innovation Research Center of Satellite Application Faculty of Geographical Science Beijing Normal University Beijing 100875 China

Harmful algal blooms (HABs) in freshwater systems pose significant threats to water quality, ecological stability, and public health. Managing these blooms requires substantial resources, making early and accurate prediction essential. Remote sensing technologies have emerged as powerful tools for HAB identification and forecasting, providing critical data to support predictive modeling. However, forecasting HABs remains challenging due to inherent uncertainties in bloom dynamics. Recent advances in data science and computational methods have facilitated the widespread application of both data-driven (DD) and process-based (PB) models for HAB prediction. DD models, particularly machine learning techniques such as artificial neural networks (ANN), random forest (RF), and long short-term memory (LSTM), effectively capture relationships between environmental variables and bloom events from historical data, enabling accurate short-term predictions. In contrast, PB models simulate the biochemical processes driving algal growth, such as photosynthesis, nutrient uptake, and cell division, providing mechanistic insights and supporting targeted management strategies. Despite these advancements, challenges remain, including the selection of optimal input variables, model transferability across diverse water bodies, and the interpretability of complex machine learning models. Future research should focus on developing adaptive hybrid models, integrating interpretable artificial intelligence (XAI) techniques, and enhancing the synergy between remote sensing and predictive modeling. This comprehensive approach has the potential to provide robust early warning systems for HABs, contributing to sustainable freshwater management.

关键词： Harmful algal blooms Prediction models Machine learning Remote sensing

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