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Tselemponis A, Stefanis C, Giorgi E, Kalmpourtzi A, Olmpasalis I, Tselemponis A, Adam M, Kontogiorgis C, Dokas IM, Bezirtzoglou E, Constantinidis TC. Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6216. [PMID: 37444064 PMCID: PMC10341787 DOI: 10.3390/ijerph20136216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/19/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023]
Abstract
In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009-2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.
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Affiliation(s)
- Athanasios Tselemponis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Christos Stefanis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Elpida Giorgi
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Aikaterini Kalmpourtzi
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Ioannis Olmpasalis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Antonios Tselemponis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Maria Adam
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Christos Kontogiorgis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Ioannis M. Dokas
- Department of Civil Engineering, Democritus University of Thrace, 69100 Komotini, Greece;
| | - Eugenia Bezirtzoglou
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Theodoros C. Constantinidis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
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Yu C, Zhang X, Gao J. Multi-Dimensional Comparison of the Impact Mechanism of the Self-Rated Health Status of Urban and Rural Residents in Chinese Social Environments. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10625. [PMID: 36078341 PMCID: PMC9518462 DOI: 10.3390/ijerph191710625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/17/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
Self-rated health status (SRHS) reflects individuals' social environment, and the difference between urban and rural areas in China further highlights the impact of social environment on health. This paper aimed to systematically analyze and compare the impact mechanism of the SRHS of urban and rural residents from multiple dimensions, i.e., time, space, and scale. Drawing on data from the Chinese General Social Survey (CGSS) and China Statistical Yearbook, we used spatial, cross, and HLM analyses. Results indicate that: (1) From 2010 to 2017, the overall SRHS level of Chinese residents gradually declined; the gradient pattern of east, middle, and west became more marked, and the health level in rural areas generally fell behind that of urban areas. (2) The focus of SRHS moved toward mental health, and people's perceptions of the social environment gradually became a key factor affecting health. (3) In the long term, the gradient allocation of medical service resources could narrow the gap between urban and rural areas to comprehensively improve regional health levels.
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Affiliation(s)
- Chao Yu
- School of Geographic Sciences, Xinyang Normal University, Xinyang 464000, China
- The Center of Targeted Poverty Alleviation and Rural Revitalization, Xinyang Normal University, Xinyang 464000, China
| | - Xinyi Zhang
- School of Geographic Sciences, Xinyang Normal University, Xinyang 464000, China
- The Center of Targeted Poverty Alleviation and Rural Revitalization, Xinyang Normal University, Xinyang 464000, China
| | - Junbo Gao
- The Center of Targeted Poverty Alleviation and Rural Revitalization, Xinyang Normal University, Xinyang 464000, China
- School of Tourism, Xinyang Normal University, Xinyang 464000, China
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Striving with Global Stress on a Local Level: Has the COVID-19 Pandemic Changed the Relationship between People and Nature? SUSTAINABILITY 2022. [DOI: 10.3390/su14159496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
The COVID-19 pandemic had harsh consequences on human health and society across the globe. In addition to health effects, the pandemic also influenced people’s values, concerns, and ethics due to lockdowns and general limitations in societal activities. In this study, we examined changes in the relationship between people and nature caused by COVID-associated stress, as well as its consequences on life quality, by comparing questionnaire-based survey data before and during the pandemic. We found that the pandemic had positive effects on individual respondents’ relationships with nature. Respondents who were more affected by the pandemic rated their life quality lower than those who were less affected. In accordance, the pandemic had a negative effect on people’s life quality, especially for people living in areas where the environment (coastal water quality) was in poor condition. Our results support the prediction that environmental quality may buffer against global stress and improve societal wellbeing.
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