Volume 11, Issue 4 (2023)
ECOPERSIA 2023, 11(4): 337-347 |
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Homayoonnezhad I, Amirian P, Mahdavi Lasibi M J. Water Quality Modeling in the Zabol Chahnimeh using
Multivariate Statistical Methods. ECOPERSIA 2023; 11 (4) :337-347
URL: http://ecopersia.modares.ac.ir/article-24-70986-en.html
URL: http://ecopersia.modares.ac.ir/article-24-70986-en.html
1- Ph.D., Assistant Prof, Faculty of Agriculture, Payam Nour University (PNU), Tehran, Iran, , homayoonnezhad@gmail.com
2- Environmental Health, Waste Management Organization of Shiraz Municipality, Shiraz.IRAN
3- Assistant Prof, Faculty of Agriculture, Payam Nour University (PNU), Tehran, Iran
2- Environmental Health, Waste Management Organization of Shiraz Municipality, Shiraz.IRAN
3- Assistant Prof, Faculty of Agriculture, Payam Nour University (PNU), Tehran, Iran
Abstract: (858 Views)
Aim: The purpose of this research is to formulate a program and design a suitable model for environmental monitoring of water quality in order to reduce the costs of water quality monitoring and evaluation in No.1 Zabol Chahnimeh.
Material & Methods: In this study eight sampling stations were selected in the bed of the reservoir No.1 in order to monitoring and measuring physical, chemical and microbial parameters. Sampling was conducted systematically on a month basis for one year. Then the data were analyzed with the help of multivariate statistical methods such as cluster analysis, Principal Component Analysis (PCA) and factor analysis.
Findings: Using multivariate statistical methods, No. 4, 5, 6 stations were selected as the main stations for sampling and factors of temperature, turbidity, DO, COD, magnesium, sodium, calcium, phosphate, chlorine, nitrite, sodium Absorption Ratio and pH was determined as the main water quality indicators. It was revealed that temperature, DO, nitrate and turbidity parameters should be sampled monthly, but the other parameters could be sampled periodically or even quarterly to model an optimal monitoring of water quality in reservoir No.1 during a year.
Conclusion: In general, the evaluation of the quality status of this valuable water resource by reducing the number of sampling stations, eliminating some unnecessary parameters and reducing the frequency of sampling completely and without defects and at a much lower cost is done for this reason, having the lowest costs and proper timing, management of the main reservoir will be possible considering all effective parameters.
Material & Methods: In this study eight sampling stations were selected in the bed of the reservoir No.1 in order to monitoring and measuring physical, chemical and microbial parameters. Sampling was conducted systematically on a month basis for one year. Then the data were analyzed with the help of multivariate statistical methods such as cluster analysis, Principal Component Analysis (PCA) and factor analysis.
Findings: Using multivariate statistical methods, No. 4, 5, 6 stations were selected as the main stations for sampling and factors of temperature, turbidity, DO, COD, magnesium, sodium, calcium, phosphate, chlorine, nitrite, sodium Absorption Ratio and pH was determined as the main water quality indicators. It was revealed that temperature, DO, nitrate and turbidity parameters should be sampled monthly, but the other parameters could be sampled periodically or even quarterly to model an optimal monitoring of water quality in reservoir No.1 during a year.
Conclusion: In general, the evaluation of the quality status of this valuable water resource by reducing the number of sampling stations, eliminating some unnecessary parameters and reducing the frequency of sampling completely and without defects and at a much lower cost is done for this reason, having the lowest costs and proper timing, management of the main reservoir will be possible considering all effective parameters.
Keywords: Chahnimeh reservoirs, Environmental monitoring, indicator stations, sampling frequencies, water quality parameters
Article Type: Original Research |
Subject:
Water Management
Received: 2023/08/9 | Accepted: 2023/10/30 | Published: 2023/12/7
Received: 2023/08/9 | Accepted: 2023/10/30 | Published: 2023/12/7
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