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新冠疫情期间行动管制令下的地表水质状况及预测:马来西亚的案例研究

Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia.

作者信息

Najah A, Teo F Y, Chow M F, Huang Y F, Latif S D, Abdullah S, Ismail M, El-Shafie A

机构信息

Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), 43000 Kajang, Selangor Darul Ehsan Malaysia.

Faculty of Science and Engineering, University of Nottingham Malaysia, 43500 Semenyih, Selangor Malaysia.

出版信息

Int J Environ Sci Technol (Tehran). 2021;18(4):1009-1018. doi: 10.1007/s13762-021-03139-y. Epub 2021 Feb 3.

Abstract

Global concerns have been observed due to the outbreak and lockdown causal-based COVID-19, and hence, a global pandemic was announced by the World Health Organization (WHO) in January 2020. The Movement Control Order (MCO) in Malaysia acts to moderate the spread of COVID-19 through the enacted measures. Furthermore, massive industrial, agricultural activities and human encroachment were significantly reduced following the MCO guidelines. In this study, first, a reconnaissance survey was carried out on the effects of MCO on the health conditions of two urban rivers (i.e., Rivers of Klang and Penang) in Malaysia. Secondly, the effect of MCO lockdown on the water quality index (WQI) of a lake (Putrajaya Lake) in Malaysia is considered in this study. Finally, four machine learning algorithms have been investigated to predict WQI and the class in Putrajaya Lake. The main observations based on the analysis showed that noticeable enhancements of varying degrees in the WQI had occurred in the two investigated rivers. With regard to Putrajaya Lake, there is a significant increase in the WQI Class I, from 24% in February 2020 to 94% during the MCO month of March 2020. For WQI prediction, Multi-layer Perceptron (MLP) outperformed other models in predicting the changes in the index with a high level of accuracy. For sensitivity analysis results, it is shown that NH3-N and COD play vital rule and contributing significantly to predicting the class of WQI, followed by BOD, while the remaining three parameters (i.e. pH, DO, and TSS) exhibit a low level of importance.

摘要

由于基于因果关系的新冠疫情爆发和封锁措施,全球对此表示关切,因此,世界卫生组织(WHO)于2020年1月宣布这是一场全球大流行病。马来西亚的行动管制令(MCO)旨在通过已颁布的措施来减缓新冠病毒的传播。此外,根据MCO的指导方针,大规模的工业、农业活动以及人类对环境的侵扰显著减少。在本研究中,首先对MCO对马来西亚两条城市河流(即巴生河和槟城河)健康状况的影响进行了勘察调查。其次,本研究考虑了MCO封锁措施对马来西亚一个湖泊(布城湖)水质指数(WQI)的影响。最后,研究了四种机器学习算法来预测布城湖的水质指数和类别。基于分析的主要观察结果表明,在两条被调查的河流中,水质指数出现了不同程度的显著改善。对于布城湖,水质一类的占比显著增加,从2020年2月的24%增至2020年3月MCO实施期间的94%。对于水质指数预测,多层感知器(MLP)在预测指数变化方面的表现优于其他模型,准确率很高。对于敏感性分析结果,研究表明氨氮(NH3-N)和化学需氧量(COD)起着至关重要的作用,对预测水质指数类别有显著贡献,其次是生化需氧量(BOD),而其余三个参数(即pH值、溶解氧(DO)和总悬浮固体(TSS))的重要性较低。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4257/7857098/82b184a6964f/13762_2021_3139_Fig1_HTML.jpg

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