PMID- 33763768
OWN - NLM
STAT- MEDLINE
DCOM- 20210326
LR  - 20210429
IS  - 1573-2959 (Electronic)
IS  - 0167-6369 (Linking)
VI  - 193
IP  - 4
DP  - 2021 Mar 25
TI  - An integrated regional water quality assessment method considering 
      interrelationships among monitoring indicators.
PG  - 223
LID - 10.1007/s10661-021-08992-5 [doi]
AB  - To monitor and manage water environments, China developed a centralized 
      multi-level administrative system where governments and agencies at each level 
      are responsible for water quality within their regions. In this case, regional 
      water quality assessment has become a critical issue. However, as a complex 
      multi-criteria decision making (MCDM) problem, it faces many challenges such as 
      diverse implement indicator framework, complicated indicator interrelations, and 
      lack of reliable assessment methods. Therefore, this paper constructs a novel 
      multistage decision support framework for regional water quality assessment. In 
      phase I, we determine indicator framework strictly according to the national 
      standards, involving PH, dissolved oxygen (DO), chemical oxygen demand (COD), 
      etc., totally 21 water quality indicators where the temperature indicator is 
      excluded due to its lack of assessment standard. In addition, considering the 
      matching between the characteristic of water quality data and the probabilistic 
      linguistic term set (PLTS) technique, we employ PLTS theory to process massive 
      monitoring data. In phase II, relative weight considering indicators' 
      interrelationship is produced by the proposed regression-based decision-making 
      trial and evaluation laboratory (DEMATEL) method, and further forms combined 
      weight by balancing single-factor weight. In phase III, we present a new PLTS 
      measure and extend the fuzzy technique for order performance by similarity to 
      ideal solution (FTOPSIS) method to generate assessment results. Then, we 
      investigate water quality status of 16 administrative districts in Shanghai, 
      China, with the proposed method. The collected data are derived from 26 water 
      quality monitoring sites and covers the period during September 2018 to February 
      2019. The results confirm a hypothesis that the statistically significant 
      interrelationship does exist among indicators, and point out that Huang Pu 
      District remains the best water quality with highest values of [Formula: see 
      text] in the range of (0.79-0.85) over the 6 months. Moreover, the parameter 
      analysis and comparative analysis are further given that verifies the robustness 
      and reliability of the model in details.
FAU - Li, Yu
AU  - Li Y
AD  - School of Business, Central South University, Changsha, 410083, People's Republic 
      of China.
FAU - Wang, Xiao-Kang
AU  - Wang XK
AD  - School of Business, Central South University, Changsha, 410083, People's Republic 
      of China.
FAU - Zhang, Hong-Yu
AU  - Zhang HY
AD  - School of Business, Central South University, Changsha, 410083, People's Republic 
      of China.
FAU - Wang, Jian-Qiang
AU  - Wang JQ
AD  - School of Business, Central South University, Changsha, 410083, People's Republic 
      of China. jqwang@csu.edu.cn.
FAU - Li, Lin
AU  - Li L
AD  - School of Business, Hunan University, Changsha, 410082, People's Republic of 
      China.
LA  - eng
PT  - Journal Article
DEP - 20210325
PL  - Netherlands
TA  - Environ Monit Assess
JT  - Environmental monitoring and assessment
JID - 8508350
SB  - IM
MH  - Biological Oxygen Demand Analysis
MH  - China
MH  - *Environmental Monitoring
MH  - Reproducibility of Results
MH  - *Water Quality
OTO - NOTNLM
OT  - Decision-making trial and evaluation laboratory (DEMETEL)
OT  - Multi-criteria decision making (MCDM)
OT  - Multiple regression method
OT  - Probabilistic linguistic term set (PLTS)
OT  - Water quality assessment
EDAT- 2021/03/26 06:00
MHDA- 2021/03/27 06:00
CRDT- 2021/03/25 06:40
PHST- 2020/07/20 00:00 [received]
PHST- 2021/03/02 00:00 [accepted]
PHST- 2021/03/25 06:40 [entrez]
PHST- 2021/03/26 06:00 [pubmed]
PHST- 2021/03/27 06:00 [medline]
AID - 10.1007/s10661-021-08992-5 [pii]
AID - 10.1007/s10661-021-08992-5 [doi]
PST - epublish
SO  - Environ Monit Assess. 2021 Mar 25;193(4):223. doi: 10.1007/s10661-021-08992-5.