Assessment of seasonal variations of chemical characteristics in surface water using multivariate statistical methods

Water pollution has become a growing threat to human society and natural ecosystems in the recent decades. Assessment of seasonal changes in water quality is important for evaluating temporal variations of river pollution. In this study, seasonal variations of chemical characteristics of surface water for the Chehelchay watershed in northeast of Iran was investigated. Various multivariate statistical techniques, including multivariate analysis of variance, discriminant analysis, principal component analysis and factor analysis were applied to analyze river water quality data set containing 12 parameters recorded during 13 years within 1995–2008. The results showed that river water quality has significant seasonal changes. Discriminant analysis identified most important parameters contributing to seasonal variations of river water quality. The analysis rendered a dramatic data reduction using only five parameters: electrical conductivity, chloride, bicarbonate, sulfate and hardness, which correctly assigned 70.2 % of the observations to their respective seasonal groups. Principal component analysis / factor analysis assisted to recognize the factors or origins responsible for seasonal water quality variations. It was determined that in each season more than 80 % of the total variance is explained by three latent factors standing for salinity, weathering-related processes and alkalinity, respectively. Generally, the analysis of water quality data revealed that the Chehelchay River water chemistry is strongly affected by rock water interaction, hydrologic processes and anthropogenic activities. This study demonstrates the usefulness of multivariate statistical approaches for analysis and interpretation of water quality data, identification of pollution sources and understanding of temporal variations in water quality for effective river water quality management.     

Application of multivariate statistical techniques in the assessment of water quality in Sakarya River,Turkey

In this paper, the surface water quality of the Sakarya River in Turkey is assessed by using multivariate statistical techniques. These techniques were applied to the chemical parameters obtained from the five different surface water quality observation stations. Factor and principal component analysis results reveal that the agricultural, anthropogenic and domestic pollution caused differences in terms of water quality. Cluster analysis revealed two different clusters of similarities between the stations, reflecting different chemical properties and pollution levels in the studied river. Surface water quality downstream of the river was different from the water quality upstream. Thus, this study shows the usefulness of multivariate statistical techniques for analysis and interpretation in the surface water quality problem.     

Spatial characteristics of surface water quality in the Haicheng River (Liao River basin) in Northeast China

Spatial variations of the water quality in the Haicheng River during April and October 2009 were evaluated for the national monitoring program on water pollution control and treatment in China. The spatial autocorrelation analysis with lower Moran’s I values displayed the spatial heterogeneity of the 12 physicochemical parameters among all the sampling sites of the river. The one-way ANOVA showed that all variables at different sampling sites had significant spatial differences (p < 0.01). Based on the similarity of water quality characteristics, cluster analysis grouped the 20 sampling sites into three clusters, related with less polluted, moderately polluted and highly polluted sites. The factor analysis extracted three major factors explaining 76.4 % of the total variance in the water quality data set, i.e., integrated pollution factor, nitrogen pollution factor and physical factor. The results revealed that the river has been severely polluted by organic matter and nitrogen. The major sources leading to water quality deterioration are complex and ascribed to anthropogenic activities, e.g., domestic and industrial wastewater discharges, agricultural runoff, and animal rearing practices.     

Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

In this study, spatial and seasonal variations of water quality in Haraz River Basin were evaluated using multivariate statistical techniques, such as cluster analysis, principal component analysis and factor analysis. Water quality data collected from 8 sampling stations in river during 4 seasons (Summer and Autumn of 2007, Winter and Spring of 2008) were analyzed for 10 parameters (dissolved oxygen, Fecal Coliform, pH, water temperature, biochemical oxygen demand, nitrate, total phosphate, turbidity, total solid and discharge). Cluster analysis grouped eight sampling stations into three clusters of similar water quality features and thereupon the whole river basin may be categorized into three zones, i.e. low, moderate and high pollution. The principle component analysis/factor analysis assisted to extract and recognize the factors or origins responsible for water quality variations in four seasons of the year. The natural parameters (temperature and discharge), the inorganic parameter (total solid) and the organic nutrients (nitrate) were the most significant parameters contributing to water quality variations for all seasons. Result of principal component analysis and factor analysis evinced that, a parameter that can be significant in contribution to water quality variations in river for one season, may less or not be significant for another one.     

Assessment of groundwater quality using multivariate statistical techniques in Hashtgerd Plain, Iran

Multivariate statistical techniques, such as cluster analysis (CA), factor analysis (FA), principal component analysis (PCA), and discriminant analysis (DA), were applied for the evaluation of variations and the interpretation of a large complex groundwater quality data set of the Hashtgerd Plain. In view of this, 13 parameters were measured in groundwater of 26 different wells for two periods. Hierarchical CA grouped the 26 sampling sites into two clusters based on the similarity of groundwater quality characteristics. FA based on PCA, was applied to the data sets of the two different groups obtained from CA, and resulted in three and five effective factors explaining 79.56 and 81.57% of the total variance in groundwater quality data sets of the two clusters, respectively. The main factors obtained from FA indicate that the parameters influencing groundwater quality are mainly related to natural (dissolution of soil and rock), point source (domestic wastewater) and non-point source pollution (agriculture and orchard practices) in the sampling sites of Hashtgerd Plain. DA provided an important data reduction as it uses only three parameters, i.e., electrical conductivity (EC), magnesium (Mg²⁺) and pH, affording more than 98% correct assignations, to discriminate between the two clusters of groundwater wells in the plain. Overall, the results of this study present the effectiveness of the combined use of multivariate statistical techniques for interpretation and reduction of a large data set and for identification of sources for effective groundwater quality management.     

Assessment of physico-chemical and microbiological surface water quality using multivariate statistical techniques: a case study of the Wadi El-Bey River,Tunisia

Located in the northeastern part of Tunisia, Wadi El Bey drains the watershed through farmland, industrial, and urban areas of the region. It serves to discharge treated wastewater of different types. In this work, the variations of the water quality of Wadi El Bey were studied and evaluated, during 2 years (2012–2013), using multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA). In addition, the similarities or dissimilarities among the sampling points were as well analyzed to identify spatial and temporal variations. The results obtained based on the cluster analysis, led to identify three similar water quality zones: relatively polluted (LP), moderately polluted (MP), and highly polluted (HP). The inorganic and organic parameters, temperature, conductivity, dissolved oxygen, chemical oxygen demand, salmonella, and enterococcus, seemed to be the most significant parameters of water quality. Three factors were identified as responsible for the data structure, explaining 60.95% of the total variance. The first factor is the physical and non-organic chemical parameters explaining 23.48% of the total variance. The second and third factors are, respectively, the microbiological (21.26%) and organic-nutrient (16.2%).This study shows that multivariate statistical methods can help the water managers to understand the factors affecting the water quality.     

Key uncertainty sources analysis of water quality model using the first order error method

In this study, key uncertainty sources analysis was undertaken for a dynamic water model using a First order error analysis method. First, a dynamic water quality model for the Three Gorges Reservoir Regions was established using data after impoundment by the environmental fluid dynamics code model package. Model calibration and verification were then conducted using measured data collected during 2004 and 2006. Four statistical indices were employed to assess the modeling efficiency. The results indicated that the model simulated the variables well, with most relative error being less than 25 %. Next, input and parameter uncertainty analysis were conducted for ammonia nitrogen, nitrate nitrogen, total nitrogen, and dissolved oxygen at 3 grid cells located in the upper, middle and downstream portions of the research area. For the nitrogen related variables, input from Zhutuo Station, the Jialingjiang River, and the Wujiang River were the main sources of uncertainty. Point and nonpoint sources also accounted for a large ratio of uncertainty. Moreover, nitrification contributed some uncertainty to the estimated ammonia nitrogen and nitrate nitrogen. However, reaeration was found to be a key source of uncertainty for dissolved oxygen, especially at the middle and downstream reaches. The analysis conducted in this study gives a quantitative assessment for uncertainty sources of each variable, and provides guidance for further pollutant loading reduction in the Three Gorge Reservoir Region.     

基于多元统计方法的河流水质空间分析

基于聚类分析和判别分析探讨了河流水质空间分析方法,旨在识别采样点的空间相似性与差异性,从而为水质监测网络优化提供支持。该方法首先利用kurtosis和Skewness检验数据分布特征和进行数据对数转化与标准化处理;然后利用聚类分析进行空间相似性分析,确定空间尺度分类情况;最后利用判别分析识别显著性污染指标,以此反映上述空间尺度分类的差异性。以香港后海湾水质管制区为例,结果表明:①通过对数转化显著改善数据分布特征,使绝大部分污染指标呈正态或接近正态分布;②该区域采样点在个案链锁距离与最大链锁距离之比(D_link/D_max)×100<35处明显分为3类,它们分别代表轻度、中度、重度污染3种类型,且后两者属于采样点主要属于营养盐和重金属污染类型,需要控制其生活污水、畜牧污染、工业污染和地表径流污染;③后退式判别分析具有良好的指标降维能力,仅需7个显著性污染指标(pH,NH₃-N,NO₃-N,F.coil,Fe,Ni和Zn)可以反映整体水质的空间差异性,且具有90.65%的正确判别能力;④归纳起来,从3类采样点中选择一个或多个、监测7个显著性污染指标即可全面反映后海湾水质管制区的水质空间特征,实现水质监测网络优化。     

渭河干流典型断面非点源污染监测与负荷估算

渭河水质在较大程度上受非点源污染的影响,因此,有必要对其负荷和比重进行研究。2009年至2010年,对渭河干流关中段咸阳和临潼断面进行了洪水期和非洪水期水质水量同步监测。根据监测结果及水文站实测流量资料,分别采用改进的水文分割法和平均浓度法对两断面的非点源污染负荷进行了计算,分析了非点源污染的特点。结果表明:渭河干流关中段主要污染物为COD、NH₃-N和TN,两断面洪水期间各指标的平均浓度基本都小于平时的平均浓度;各指标非洪水期浓度变化总体上小于洪水期浓度变化幅度,量级较大的洪水水质变化幅度相对较小;改进的水文分割法和已被检验并被广泛采用的平均浓度法计算结果符合良好。2009年(枯水年,P=68%)渭河咸阳和临潼站各指标非点源污染所占比例基本在20%～30%左右;2009年渭河干流咸阳-临潼河段污染以点源污染为主,构成比例在80%以上。对比2006年(枯水年,P=69%),2009年临潼站COD、NH₃-N和TN年点源负荷分别减少11937t、791t和29t,渭河点源治理取得一定成效;此外,临潼站这两年的污染构成比例基本相同。非点源污染在渭河水污染中占较大比重,其对渭河水质的影响不容忽视。     

Assessment of water quality and identification of pollution sources of three lakes in Kashmir, India, using multivariate analysis

Multivariate statistical techniques, such as cluster analysis, principal component analysis (PCA) and factor analysis (FA) were applied to evaluate and interpret the water quality data set for 13 parameters at 10 different sites of the three lakes in Kashmir, India. Physicochemical parameters varied significantly (p?<?0.05) among the sampling sites. Hierarchical cluster analysis grouped 10 sampling sites into three clusters of less polluted, moderately polluted and highly polluted sites, based on similarity of water quality characteristics. FA/PCA applied to data sets resulted in three principal components accounting for a cumulative variance of 69.84, 65.05 and 71.76% for Anchar Lake, Khushalsar Lake and Dal Lake, respectively. Factor analysis obtained from principal components (PCs) indicated that factors responsible for accelerated eutrophication of the three lakes are domestic waste waters, agricultural runoff and to some extent catchment geology. This study assesses water quality of three lakes through multivariate statistical analysis of data sets for effective management of these lakes.     

Evaluation of groundwater quality and pollution in Daqing Oilfield

Daqing Oilfield is located in the northeast of Songnen Plain in Daqing City, Heilongjiang Province, which is a petrochemical industry gathering place based on petroleum refining, chemical industry, chemical fiber and fertilizer. In recent years, the quantity demand of petroleum and petrochemical production for groundwater in Daqing Oilfield is growing, and it’s of great significance to analyze and study the quality and pollution degree of groundwater for groundwater exploitation, utilization and protection. In this paper, groundwater quality of Daqing Oilfield evaluated by Nemerow Index is poor, and most points are Class IV groundwater; When evaluating groundwater pollution by hierarchical ladder method, the results show that the severe and extremely severe pollution points account for 34.48% in shallow phreatic water and 20% in deep confined water, showing that shallow groundwater is more seriously polluted than the deep. The main components influencing the quality of groundwater in the study area are total hardness, total dissolved solids, Cl-, SO42- and so on, which are affected by both the native environment and human activities; The main pollution components in groundwater are nitrite and nitrate nitrogen which are affected by human activities. Daqing Oilfield groundwater pollution is characterized by inorganic pollution, while organic components related to human activities contribute less to the groundwater pollution currently.     

Evaluating the accuracy of ANN and LR models to estimate the water quality in Zarivar International Wetland,Iran

One of the most important qualitative aspects of wetland ecosystem management is preserving the natural quality of water in such environments. This would not be achievable unless continuous water quality monitoring is implemented. With the recent advances in remote sensing technology, this technology could assist us to produce accurate models for estimating water quality variables in the ecosystem of wetlands. The present study was carried out to evaluate the capability of remote sensing data to estimate the water quality variables [pH, total suspended solids (TSS), total dissolved solids (TDS), turbidity, nitrate, sulfate, phosphate, chloride and the concentration of chlorophyll a] in Zarivar International Wetland using linear regression (LR) and artificial neural network (ANN) models. For this purpose, spectral reflectance of bands 2, 3, 4 and 5 of the OLI sensor of Landsat 8 was utilized as the input data and the collected chemical and physical data of water samples were selected as the objective data for both ANN and LR models. Based on our results overall, ANN model was the proper model compared with LR model. The spectral reflectance in bands 5 and 4 of OLI sensor revealed the best results to estimate TDS, TSS, turbidity and chlorophyll in comparison with other used bands in ANN model, respectively. We conclude that OLI sensor data are an excellent means for studying physical properties of water quality and comparing its chemical properties.     

双塔水库水质对人类活动的响应分析

选取1993～2008年瓜州县人类活动数据及双塔水库水质指标年平均值进行主成分分析,去除冗余信息后进行多元线性回归分析,得到四组反映人类活动对水库水环境影响情况的回归方程。说明对双塔水库水质起主要影响作用的是农业活动和因农业活动引起的土地利用变化,农业活动的非点源污染是水库水质的主要污染源,水库水质仍存在富营养化的风险;工业活动对水库水质的影响没有在回归方程中体现出来。在农业活动过程中,耕地面积每增加10 000亩促使pH值上升0.062、溶解氧值下降0.046、高锰酸钾指数上升0.103、COD值上升0.617;而每增加10 000t的氮肥施用量会引起pH值升高1.837;总人口每增加1万人,COD值上升0.798。     

Statistical interpretation on seasonal variations of groundwater quality in Ramanathapuram coastal tract,Tamil Nadu,India

Groundwater availability depends on its accessibility, as well as on its quality. Factor analysis (FA) has been used to analyze quality problems and provide strategies for water resources exploitation. The present study demonstrated the use of factor analysis to evaluate temporal variations in groundwater quality and find latent sources of water pollution in coastal areas of Ramanathapuram District, Tamil Nadu, India. The data set included data of eleven water quality parameters viz., pH, electrical conductivity, salinity, total dissolved solids, total alkalinity, calcium hardness, magnesium hardness, total hardness, chloride and fluoride for two different seasons (pre- and post-monsoon) in 2012. FA of the two seasons resulted in two latent factors accounting for 80.38 % of total variance for pre-monsoon (summer) and 73.03 % for post-monsoon (winter) in the water quality data sets. The results obtained from FA prove that the groundwater quality in winter is better than that of summer. Langelier Saturation Index was used to find out scaling and corrosive tendency of the groundwater samples for the study area. Karl Pearson correlation matrix was used to study the correlation between the studied water quality parameters. Hence, the analysis suggests that FA techniques are useful tools for identification of influence of various quality parameters on overall nature of the groundwater.     

Water and sediment quality assessment in the mid-Black Sea coast of Turkey using multivariate statistical techniques

The aim of this study was to investigate the water and sediment quality in the mid-Black Sea coast of Turkey. The samples were collected from six stations during 2007. Investigated parameters were total carbon (TC), total inorganic carbon (TIC), total organic carbon (TOC), ammonium-nitrogen (NH₄-N), nitrate-nitrogen (NO₃-N), nitrite-nitrogen (NO₂-N), total phosphorus (TP), sulphate, total hardness, methylene blue active substances (MBAS), phenol, adsorbable organic halogens (AOX), dissolved oxygen (DO), pH and electrical conductivity (EC) in water samples and TC, TIC, TOC, TP, pH, electrical conductivity (EC), redox potential (Eh) and water content (WC) in sediment samples. Different multivariate statistical techniques were used to evaluate variations in surface water and sediment quality. Principal component analysis helped in identifying the factors or sources responsible for water and sediment quality variations. Five factors were found responsible for 87.63% of the total variance in the surface waters. In sediments, three factors explained 84.73% of the observed total variance. Cluster analysis classified the monitoring sites into two groups based on similarities of water and sediment quality characteristics.     

Integrated geostatistics and GIS techniques for assessing groundwater contamination in Al Arish area, Sinai, Egypt

Sustainable development in El Arish area of North Sinai, Egypt, is retarded by serious environmental problems, where the land-use and land cover of the region is changing over present time. The impact of human activities in the study area is accompanied by the destruction and over-exploitation of the environment. This study applies multivariate statistics (factor and cluster analyses) and GIS techniques to identify both anthropogenic and natural processes affecting the groundwater quality in the Quaternary sands aquifer. The aim of this study was to investigate the impacts on groundwater resources, the potential pollution sources, and to identify the main anthropogenic inputs of both nutrients and trace metal. Since the depth to the water table is shallow especially in the northern part (<4?m), and the aquifer was exposed on the ground surface, it has poor buffering capacity and the pollution risk is very high. Groundwater chemistry in this coastal region has complex contaminant sources, where intensive farming activities and untreated wastes put stress on groundwater quality. Several areal distribution maps were constructed for correlating water quality with possible contributing factors such as location, land-use, and aquifer depth. These maps identified both anthropogenic and natural processes affecting groundwater quality of the studied aquifer. Cluster analysis was used to classify water chemistry and determine the hydrochemical groups, Q-mode dendrogram is interpreted and there are three main clusters. Factor analyses identify the potential contamination sources affecting groundwater hydrochemistry such as: nitrate, sulfate, phosphate and potassium fertilizers, pesticides, sewage pond wastes, and salinization due to circulation of dissolved salts in the irrigation water itself.     

Assessment of shallow groundwater in Lake Nyos catchment (Cameroon,Central-Africa): implications for hydrogeochemical controls and uses

In 1986, carbon dioxide gas exploded from Lake Nyos and killed about 1,800 people. After that disaster, various administrative and research activities have been conducted to mitigate subsequent disasters. However, none of those endeavors have characterized the groundwater chemistry to identify hydrogeochemical processes that control the water chemistry, and the quality of the water for domestic and agricultural uses that support the lives of un-official resettlers around Lake Nyos. Conventional hydrochemical techniques coupled with statistical and graphical analysis were therefore employed to establish the baseline hydrochemical conditions, assess processes controlling solutes distribution in shallow groundwater in the Lake Nyos catchment and explore its usability. Groundwater samples were analyzed for their physical and chemical properties. The wide ranges of electrical conductivity and total dissolved solid values reveal the heterogeneous distribution of groundwater within the watershed. The relative abundance of major dissolved species was Ca > Mg > Na > K for cations and HCO₃ >>> Cl > SO₄ > NO₃ for anions. Piper diagram classified almost all water samples into mixed CaMg–HCO₃ water type. Major ion geochemistry reveals that, in addition to silicates weathering (water–rock interaction), ion exchange processes regulate the groundwater chemistry. Principal component analysis supports the occurrence of water rock interaction. Hierarchical cluster analysis showed that the chemistry of groundwater in the study area is controlled by three main factors, and suggests no hydraulic connectivity between deep lake water and groundwater in the catchment. The quality assessment of the groundwater showed that groundwater parameters are within the acceptable limit of the World Health Organization and Nigeria guidelines for drinking and domestic uses, and water found to be good for irrigation.     

Hydrochemical properties of transition zone between fresh groundwater and seawater in karst environment of the Adriatic islands,Croatia

Eastern coast of the Adriatic Sea consists of karstified carbonates. It belongs to the well-known Dinaric karst region. The coast is extremely indented and there are 718 islands with numerous rock crags and reefs. Some of the inhabited islands use their own water resources for public water supply, or plan to do it in the future. Since karst rocks are extremely permeable, the seawater intrudes into underground water resources, thereby forming the wedge. A wide transition zone occurs between this seawater wedge and fresh water aquifers. Consequently, island groundwater reserves turn brackish to a certain extent. In this study, 77 water samples were collected from a wide variety of water resources. Comprehensive statistical and mathematical multivariate analysis of these data was performed. Simple statistical approach showed several useful correlations among some parameters, and more complex multivariate techniques extracted three factors in connection with three natural processes: (1) mixing with the seawater, (2) carbonate dissolution and (3) human influence (pollution) and nitrogen transformation processes. The results of this study demonstrate that in situ measurement of electrical conductivity is adequate for the very rough field estimation of numerous parameters.     

基于级别特征值的岩溶含水层水质模糊综合评价修正

为改进传统水质模糊综合评价中存在评价指标不能全面反映水质状况、评价结果不清晰,评价等级区分度不明显等缺陷,文章以位于六枝特区威宁-郎岱褶皱群的第一层岩溶含水层水质为例,根据岩溶区水质评价特点和研究区含水层超标因子特征,建立涵盖物理、化学和微生物等因子的评价指标体系,利用级别特征值对传统模糊综合评价结果进行了适当修正,并与综合污染指数法、传统模糊评价综合法的评价精度进行定量化比较。研究结果表明:六枝特区的研究区探采点水质综合评价等级都达到Ⅲ类生活饮用水卫生标准,但仍有71%的探采点存在氨氮（NH4+）、氟化物(F-)、高锰酸盐指数(CODMn)、溶解性总固体（TDS）和大肠杆菌等指标超标,且超标因子浓度呈点状扩散分布于三叠系中下统地层;另外,传统模糊综合评价中有57.1%的水质达到Ⅰ类标准,与多个探采点存在因子超标的情况不符,而通过级别特征值修正的模糊综合评价结果中分别有57.1%和28.6%的探采点水质为Ⅱ类或Ⅲ类标准,与着重突出最大超标因子权重的综合指数法类别标准差低0.011。因此,基于级别特征值的模糊综合评价能有效的反映水质整体水平,探采点水样超标因子浓度和同类水质的区分度,评价结果合理、可信。      

Characterization of underground tunnel water hydrochemical system and uses through multivariate statistical methods: a case study from Maddhapara Granite Mine,Dinajpur, Bangladesh

A quality study of the drained water from Maddhapara Granite Mine underground tunnel was undertaken to study their hydrochemical variations and suitability for various uses employing chemical analysis, basic statistics, correlation matrix (r), cluster analysis, principal component/factor analyses, and ANOVA as the multivariate statistical methods. The results of chemical analysis of water show the modest variation in their ionic assemblage among different sampling points of the tunnel where Ca–HCO₃ type of hydrochemical facies is principally dominated. The correlation matrix shows a very strong to very weak positive, even negative, correlation relationship, suggesting the influence of different processes such as geochemical, biochemical processes, and multiple anthropogenic sources on controlling the hydrochemical evolution and variations of water in the mine area. Cluster analysis confirms that cluster 1 contains 68.75% of total samples, whereas cluster 2 contains 31.25%. On the whole, the dominated chemical ions of first cluster groups are Ca and HCO₃, suggesting a natural process similar to dissolution of carbonate minerals. The second cluster group consisted of Cl^? and SO₄ ^2? ions representing natural and anthropogenic hydrochemical process. The results of PCA/FA analysis illustrate that different processes are involved in controlling the chemical composition of groundwater in the mine area. The factor 1 loadings showed that pH, EC, TDS, Na, Mg, chloride, and sulfate which have high loading in this factor are expected to come from carbonate dissolution to oxidation conditions. One-way ANOVA describes the significance of dependent variables with respect to independent variables. ANOVA gives us the idea that EC, K⁺, Fe^total, SO ₄ ² , As, and Pb are the most important factors in controlling spatial differences in water quality in this tunnel. But different results have been encountered for different independent variables which might be due to dissimilar sources of water. From the qualitative analysis, it is clear that water quality is not very favorable for aquatic creatures as well as for drinking purposes. The water can be used for irrigation purposes without any doubt as SAR and RSC analysis provides good results. Moreover, the results of this research confirmed that the application of multivariate statistical analysis methods is apposite to inferring complex water quality data sets with its possible pollution sources. At the end, this research recommends (1) as water becomes more and more important, water treatment plants should be built before the water being used; (2) a detailed water step utilization plan should be set beforehand to guarantee tunnel water being used effectively; and (3) after the water being used for agriculture, elements in crops should be monitored continuously to ensure that ions and compounds that come from the tunnel water are lower than guideline values for human beings health.