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

被引:55
|
作者
Najah, A. [1 ]
Teo, F. Y. [2 ]
Chow, M. F. [3 ]
Huang, Y. F. [4 ]
Latif, S. D. [5 ]
Abdullah, S. [6 ]
Ismail, M. [7 ,8 ]
El-Shafie, A. [9 ,10 ]
机构
[1] Univ Tenaga Nas, Inst Energy Infrastruct IEI, Kajang 43000, Selangor Darul, Malaysia
[2] Univ Nottingham Malaysia, Fac Sci & Engn, Semenyih 43500, Selangor, Malaysia
[3] Univ Tenaga Nas, Inst Sustainable Energy ISE, Kajang 43000, Selangor, Malaysia
[4] Univ Tunku Abdul Rahman, Lee Kong Chian Fac Engn & Sci, Dept Civil Engn, Kajang, Selangor, Malaysia
[5] Univ Tenaga Nas, Coll Engn, Dept Civil Engn, Kajang 43000, Selangor, Malaysia
[6] Univ Malaysia Terengganu, Fac Ocean Engn Technol & Informat, Air Qual & Environm Res Grp, Terengganu 21030, Malaysia
[7] Univ Malaysia Terengganu, Fac Sci & Marine Environm, Terengganu 21030, Malaysia
[8] Univ Malaysia Terengganu, Inst Trop Biodivers & Sustainable Dev, Kuala Nerus 21030, Malaysia
[9] Univ Malaya UM, Fac Engn, Dept Civil Engn, Kuala Lumpur 50603, Malaysia
[10] United Arab Emirates Univ, Natl Water & Energy Ctr NWC, POB 15551, Al Ain, U Arab Emirates
关键词
Water quality index; Surface water quality; Movement control operation; COVID-19; pandemic;
D O I
10.1007/s13762-021-03139-y
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页码:1009 / 1018
页数:10
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