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 ...
Read More
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.
Irrigation
M. Mohammadi Ghaleni; H. Kardan Moghaddam
Abstract
IntroductionThe water quantity and quality has always been one of the main challenges in the issue of allocating water resources for different uses. Water quality management requires the collection and analysis of large amounts of water quality parameters that will be evaluated and concluded. Many tools ...
Read More
IntroductionThe water quantity and quality has always been one of the main challenges in the issue of allocating water resources for different uses. Water quality management requires the collection and analysis of large amounts of water quality parameters that will be evaluated and concluded. Many tools have been found to simplify the evaluation of water quality data, and the water quality index (WQI) is one of these widely used tools. In summary, the WQI can be defined as a number obtained from the combination of several quality parameters based on standards for its extraction. The aim of this study was to develop and introduce the new Surface water Drinking Water Quality Index (SDWQI) adopt the water quality parameters measured on hydrometric stations of Iran. In developing this index, criteria such as the availability of required parameters in most rivers and simple and accurate methods have been considered. Also, the ability to calculate with the minimum general parameters of water quality, simple calculations and in terms of the international standard WHO for drinking is one of the advantages of the introduced index.Materials and MethodsFor this purpose, 12 water quality parameters including Total Dissolved Solids (TDS), Electrical Conductivity (EC), Total Hardness (TH), pH, Chloride (Cl-), Sulfate (SO42-), Carbonate (CO32-), Bicarbonate (HCO3-), Magnesium (Mg2+), Sodium (Na+), Calcium (Ca2+) and Potassium (K+) have been used from Rudbar and Astaneh hydrometric stations located on Sefidroud river. Then initial preprocessing on data e.g. correlation analysis, and multivariate statistical methods including cluster analysis (CA) and principal components analysis (PCA) are used to selecting and weighting of water quality parameters using the “clustering” and “factoextra” packages in R 4.1.1. In order to develop the SDWQI were performed four steps including, parameter selection, sub-indexing, weighting and aggregation of the index. Also, in order to evaluate the index of the present research, the results of the SDWQI have been compared with the WHO drinking water quality index and Schoeller drinking water quality classification.Results and DiscussionCorrelation analysis between water quality parameters shows a significant correlation between TDS, EC and TH parameters and also with Cl-, Ca2+ and Mg2+ parameters at the level of 1% in both Astaneh and Rudbar stations. On the other hand, the lowest values of Pearson correlation coefficient are related to pH and CO32- parameters with other quality parameters. The results of CA indicate that most of the water quality parameters are located in separate clusters. So only the parameters TDS, EC, Cl- and Na+ in both Rudbar and Astaneh stations are in the same cluster. The weights of the parameters showed that TDS and K+ are assigned with the highest and lowest weights equal to 0.163 and 0.031 based on PCA method. Also, PCA results show that first and second principal components covered 59.3% and 67.6% of the total variance of measured water quality parameters in Rudbar and Astaneh stations, respectively. Water quality classification results indicate that (40.5%, 16.4% and 23.7%) and (90.1%, 73.1% and 57.3%) of data in Rudbar and Astaneh stations, respectively, fell into the excellent and good categories for drinking purposes based on Schoeller classification, WHOWQI and SDWQI.ConclusionGenerally, the comparison of the SDWQI with the WHO index and the Schoeller classification shows the rigidity of the new index in the classification of water quality for drinking purposes. Each water quality index developed in order to evaluate the uncertainty of results, should be tested for data with different characteristics in terms of the range of variation with different limit values (minimum and maximum). The index developed in the present study is no exception to this rule and in order to better evaluate the results, it is suggested that to be evaluated and analyzed with data from other hydrometric stations. Another important points that should be considered in using any water quality index, including the present research index, is to examine the allowable limits of water quality parameters that are not considered in these indicators. The results of the study indicated that, two most important steps in the development of a quality index that have a great impact on its results are sub-indexing and weighting of parameters. According to the results, two ideas recommended for future research. One, choosing an appropriate method such as non-deterministic (fuzzy) and intelligent (machine learning) methods to sub-index the parameters and two, to weigh the parameters more effectively, multivariate statistical methods such as clustering, factor analysis and principal component analysis should be used.
