Irrigation
N. Jafari; Y. Dinpashoh
Abstract
IntroductionThe study of surface water quality control in water resources and environment management programs is very important. Surface water is one of the most important water sources that have crucial impact on agricultural, industrial, drinking and electricity production activities. Due to ...
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IntroductionThe study of surface water quality control in water resources and environment management programs is very important. Surface water is one of the most important water sources that have crucial impact on agricultural, industrial, drinking and electricity production activities. Due to insufficient water sources with good quality and the increase in population growth rate and as a result of the increase in demand, the study of water quality parameters is very important. The Water Quality Index (WQI) serves as a prominent indicator in classifying surface water quality. Moreover, in recent years, the TOPSIS method has gained traction for evaluating water quality. This approach, known for its simplicity, is increasingly utilized in prioritizing river water and assessing its quality. Through this index, various components of water quality are condensed into a single numerical value, effectively expressing overall water quality. To ascertain the weight index, Shannon's entropy method was employed. Furthermore, to assess water suitability for drinking, agriculture, and industrial purposes, Schuler, Wilcox, and Piper diagrams were utilized. These diagrams provide valuable insights into the quality of water, aiding in decision-making processes regarding its utilization across different sectors. Therefore, the results of this study also confirmed the effectiveness of the TOPSIS method in identifying contaminated stations.Materials and MethodsThis research focuses on evaluating the water quality of three stations within the Aji Chai river watershed on an annual basis. These stations are identified as Arzanag, Akhola, and Markid. The assessment spans the years 2003 to 2021 and aims to classify water quality for both drinking and agricultural purposes. Utilizing the standards set forth by the World Health Organization, the surface water quality index of the Aji Chai basin is investigated to ascertain its suitability for drinking purposes. Shannon's entropy theory was used to prevent expert judgments in determining the weight of each parameter. TOPSIS method was used to classify eleven qualities including TDS, EC, pH, HCO3-, Cl-, , Ca2+, Mg2+, Na+ , K+ and TH. In all the three stations water quality were ranked, based on TOPSIS numerical values. Also, in order to check the quality of drinking, agricultural and industrial water, Schuler, Wilcox and Piper diagrams were used. Results and DiscussionThe initial findings from the %RE error analysis revealed that throughout the entire statistical period (2003-2021), the %RE values were consistently close to zero, with the majority being positive. This suggests that the total number of cations surpasses the total number. In terms of the Shannon water quality index, the results indicate that Markid station exhibited the highest index value at 945.92, while Arzanag station displayed the lowest value at 127.365 among the surveyed stations. The results of the water quality index showed that Arzanag and Akhola stations are in an average condition (100 < EWQI < 150) and Markid station is in a very poor condition (EWQI > 200). According to Schuler's diagram, it was found that the water of Arzanag station is in the average level in terms of water quality, which is in a good position in terms of quality compared to the other two stations, while the water of Akhola station is in a good position. In the range of poor quality, Markid water was undrinkable, which ranked worst among the three stations. According to the Wilcox diagram, it was found that the water quality of Markid is very poor, which is even outside the boundary of the Wilcox diagram, while the water of Arzanag station was ranked 1st in terms of quality. Arzanag water is in C4S2 class in terms of quality. Finally, the water class of Akhola station was placed in the C4S4 class (in the Wilcox chart), which shows very low water quality. According to the TOPSIS method, the first priority in terms of water quality pollution belonged to Markid station. Two other stations, including Akhola and Arzanag, were ranked second and third in this respect. Therefore, the most important station in this basin is Markid station. ConclusionThe results of Shannon water quality index showed that among the stations, the highest index value is related to Markid station with a value of 945.92 and the lowest one is related to Arzanag station with a value of 127.365. According to Schoeller diagram, it was found that the water quality of Arzanag station is average, compared to the other two stations, it was in the right place and the water of Akhola station was in the range of poor quality. The quality of Markid water was found to be undrinkable, which was the worst one among all the three stations. The range of TOPSIS values in different stations is between 0.054 and 0.894, which belonged to the Arzanag and Markid stations, respective ly. According to the results of the Arzanag station, the best water quality condition and the Markid station were assigned the worst water quality condition among all the three stations.
Moslem Akbarzadeh; Bijan Ghahraman; Kamran Davary
Abstract
Introduction: For water resources monitoring, Evaluation of groundwater quality obtained via detailed analysis of pollution data. The most fundamental analysis is to identify the exact measurement of dangerous zones and homogenous station identification in terms of pollution. In case of quality evaluation, ...
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Introduction: For water resources monitoring, Evaluation of groundwater quality obtained via detailed analysis of pollution data. The most fundamental analysis is to identify the exact measurement of dangerous zones and homogenous station identification in terms of pollution. In case of quality evaluation, the monitoring improvement could be achieved via identifying homogenous wells in terms of pollution. Presenting a method for clustering is essential in large amounts of quality data for aquifer monitoring and quality evaluation, including identification of homogeneous stations of monitoring network and their clustering based on pollution. In this study, with the purpose of Mashhad aquifer quality evaluation, clustering have been studied based on Euclidean distance and Entropy criteria. Cluster analysis is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). SNI as a combined entropy measure for clustering calculated from dividing mutual information of two values (pollution index values) to the joint entropy. These measures apply as similar distance criteria for monitoring stations clustering.
Materials and Methods: First, nitrate data (as pollution index) and electrical conductivity (EC) (as covariate) collected from the related locational situation of 287 wells in statistical period 2002 to 2011. Having identified the outlying data and estimating non-observed points by spatial-temporal Kriging method and then standardizes them, the clustering process was carried out. A similar distance of wells calculated through a clustering process based on Euclidean distance and Entropy (SNI) criteria. This difference explained by characteristics such as the location of wells (longitude & latitude) and the pollution index (nitrate). Having obtained a similar distance of each well to others, the hierarchical clustering was used. After calculating the distance matrix, clustering of 287 monitoring stations (wells) was conducted. The optimal number of clusters was proposed. Finally, in order to compare methods, the validation criteria of homogeneity (linear-moment) were used. The research process, including spatial-temporal Kriging, clustering, silhouette score and homogeneity test was performed using R software (version 3.1.2). R is a programming language and software environment for statistical computing and graphics supported by R foundation for statistical computing.
Results and Discussion: Considering 4 clusters, the silhouette score for Euclidean distance criteria was obtained 0.989 and for entropy (SNI) was 0.746. In both methods, excellent structure was obtained by 4 clusters. Since the values of H1 and H2 are less, clusters will be more homogeneous. So the results show the superiority of clustering based on entropy (SNI) criteria. However, according to the results, it seems there is more homogeneity of clustering with Euclidean distance in terms of geography, but the measure of entropy (SNI) has better performance in terms of variability of nitrate pollution index. To prove the nitrate pollution index effectiveness in clusters with entropy criteria, the removal of nitrate index, the results was influenced by location index. Also, by removing index locations from clustering process it was found that in clusters with Euclidean distance criteria, the influence of nitrate values is much less. Also, compared to Euclidean distance, better performance was obtained by Entropy based on probability occurrence of nitrate values.
Conclusion: Results showed that the best clustering structure will obtain by 4 homogenous clusters. Considering wells distribution and average of the linear-moment, the method based on entropy criteria is superior to the Euclidean distance method. Nitrate variability also played a significant role in identification of homogeneous stations based on entropy. Therefore, we could identify homogenous wells in terms of nitrate pollution index variability based on entropy clustering, which would be an important and effective step in Mashhad aquifer monitoring and evaluation of its quality. Also, in order to evaluate and optimize the monitoring network, it could be emphasized on network optimization necessity and approach selection. Accordingly, less monitoring network clusters lead more homogeneous. Therefore the optimization approach will be justified from increasing to decreasing. In this case the monitoring costs, including drilling, equipment, sampling, maintenance and laboratory analysis, also reduce.
