PMID- 38104806
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240117
IS  - 1879-1026 (Electronic)
IS  - 0048-9697 (Linking)
VI  - 912
DP  - 2024 Feb 20
TI  - From fuzzy-TOPSIS to machine learning: A holistic approach to understanding 
      groundwater fluoride contamination.
PG  - 169323
LID - S0048-9697(23)07953-6 [pii]
LID - 10.1016/j.scitotenv.2023.169323 [doi]
AB  - Fluoride (F(-)) contamination of groundwater is a prevalent environmental issue 
      threatening public health worldwide and in India. This study targets an 
      investigation into spatial distribution and contamination sources of fluoride in 
      Dhanbad, India, to help develop tailored mitigation strategies. A triad of Multi 
      Criteria Decision Making (MCDM) models (Fuzzy-TOPSIS), machine learning 
      algorithms logistic regression (LR), classification and regression tree (CART), 
      Random Forest (RF), and classical methods has been undertaken here. Groundwater 
      samples (n = 283) were collected for the purpose. Based on permissible limit 
      (1.5 ppm) of fluoride in drinking water as set by the World Health Organization, 
      samples were categorized as Unsafe (n = 67) and Safe (n = 216) groups. Mean 
      fluoride concentration in Safe (0.63 +/- 0.02 ppm) and Unsafe (3.69 +/- 0.3 ppm) 
      groups differed significantly (t-value = -10.04, p < 0.05). Physicochemical 
      parameters (pH, electrical conductivity, total dissolved solids, total hardness, 
      NO(3)(-), HCO(3)(-), SO(4)(2-), Cl(-), Ca(2+), Mg(2+), K(+), Na(+) and F(-)) were 
      recorded from samples of each group. The samples from 'Unsafe group' showed 
      alkaline pH, the abundance of Na(+) and HCO(3)(-) ions, prolonged rock water 
      interaction in the aquifer, silicate weathering, carbonate dissolution, lack of 
      Ca(2+) and calcite precipitation which together facilitated the F(-) abundance. 
      Aspatial distribution map of F(-) contamination was created, pinpointing the 
      "contaminated pockets." Fuzzy- TOPSIS identified that samples from group Safe 
      were closer to the ideal solution. Among these models, the LR proved superior, 
      achieving the highest AUC score of 95.6 % compared to RF (91.3 %) followed by 
      CART (69.4 %). This study successfully identified the primary contributors to 
      F(-) contamination in groundwater and the developed models can help predicting 
      fluoride contamination in other areas. The combination of different methodologies 
      (Fuzzy-TOPSIS, machine learning algorithms, and classical methods) results in a 
      synergistic effect where the strengths of each approach compensate for the 
      limitations of the other.
CI  - Copyright (c) 2023 Elsevier B.V. All rights reserved.
FAU - Nandi, Rupsha
AU  - Nandi R
AD  - Agricultural and Ecological Research Unit, Indian Statistical Institute, Giridih, 
      Jharkhand 815301, India.
FAU - Mondal, Sandip
AU  - Mondal S
AD  - Department of Plant Pathology, The Ohio State University, OH, Columbus 43210, 
      USA.
FAU - Mandal, Jajati
AU  - Mandal J
AD  - School of Sciences, Engineering & Environment, University of Salford, Manchester 
      M5 4WT, UK.
FAU - Bhattacharyya, Pradip
AU  - Bhattacharyya P
AD  - Agricultural and Ecological Research Unit, Indian Statistical Institute, Giridih, 
      Jharkhand 815301, India. Electronic address: pradip.bhattacharyya@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20231216
PL  - Netherlands
TA  - Sci Total Environ
JT  - The Science of the total environment
JID - 0330500
SB  - IM
OTO - NOTNLM
OT  - Fluoride
OT  - Fuzzy-TOPSIS
OT  - Health risk assessment
OT  - Hydrogeochemistry
OT  - Logistic regression
OT  - Water quality index
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/12/18 00:41
MHDA- 2023/12/18 00:42
CRDT- 2023/12/17 19:28
PHST- 2023/04/12 00:00 [received]
PHST- 2023/11/22 00:00 [revised]
PHST- 2023/12/10 00:00 [accepted]
PHST- 2023/12/18 00:42 [medline]
PHST- 2023/12/18 00:41 [pubmed]
PHST- 2023/12/17 19:28 [entrez]
AID - S0048-9697(23)07953-6 [pii]
AID - 10.1016/j.scitotenv.2023.169323 [doi]
PST - ppublish
SO  - Sci Total Environ. 2024 Feb 20;912:169323. doi: 10.1016/j.scitotenv.2023.169323. 
      Epub 2023 Dec 16.