Statistical Assessment of Water Quality Parameters for Pollution Source Identification in Deepor Beel Area, Guwahati, Assam

This study was carried out in Deepor Beel to assess the water quality parameter by statistical analysis. The samples were collected from nine selected stations covering entire area. The lake's water quality was evaluated based on it’s the locations, and the variations of seasons .Water Pollution issues were identified and quality classes were developed. The adaptability of aquatic life forms has also been demonstrated. 13 physico-chemical and four heavy metal parameters were examined in the lake water for such reasons. The results of the study suggest that non-point pollution that is, soil leaching and agricultural contamination may be the primary cause of pollution in this region. These results at the temporal and spatial scales recommend that water monitoring efforts in the future should be scale-sensitive to water management.


INTRODUCTION
Deepor Beel is quite important in both ecological and economical aspects, the quality of lake water plays the pivotal role in proper sustenance of the biological resources and livelihood of the local people.Developing an understanding of changes in the water quality of the lake is essential for the proper management of the environment and economy of this region.Natural processes (such as alterations in the ecosystem) and anthropogenic influences (such as rising water resource consumption, industrial, and agricultural activities, urbanization, Surface waters are not adequate for drinking, industrial, agricultural, recreational, or other uses due to precipitation inputs, erosion, and weathering of crustal nutrients.This was a longitudinal study involving the testing of water samples from the lake over a period of one years to assess temporal water quality trends (both qualitative and quantitative).Physicochemical parameters of the lake were assessed for determining the quality of water (Roy & Majumder, 2019).This study provides insight into changes in water quality of Deepor Beel.It is hoped that this information will help in the development of an improved management system.Assessment of water quality trends in Deepor Beel, Assam.The aim of this paper is to carry out a systematic water quality analysis for all the entire seasons in order to know the seasonal temporal changes in water quality and analyze the concentration of water quality

Data treatment and multivariate statistical Analysis
The statistical analysis of the water analysis results was done using the SPSS statistical package software.Descriptive statistical analysis, including One-way ANOVA, significance (0.01 and 0.05) was done for the stations and seasonal.In addition, Hierarchical Cluster Analysis (HCA) techniques were utilized to perform multivariate analysis of the lake water quality data set (Singh et al  The above table is a demonstration of the concentration of the 13 different water parameters for the 9 different sampling locations for the fourth season of our study.

Principal Component Analysis(PCA):
Principal component analysis was used to decrease the dimensional space of the large dataset in order to improve the clustering.In PCA analysis contains three components, 13 physico-chemical parameters were categorized.PCA's classified the factor loadings as 'strong', 'moderate' and 'weak', matching to absolute loading values of >0.75, 0.75-0.50and 0.50-0.30,respectively (Liu et al., 2003).PCA is done for four different seasons i.e.Autumn, Winter, Spring and Summer.The results of calculations were shown in Table 3 2009), the choice of the number of major components to be retained in the number of major components released before a clear break between scree.PCA for Autumn PCA revealed that three components explain 84.620% of the total variance, with the salinization process and anthropogenic activities being the main factors controlling the surface water quality variability.The PCA results are shown in Table 3.1.1.The PCA approach identified three components that have the most critical loading (Fig 3 .1.2)

: Component plot
The first component (PCA1) explains 41.477% of the total variance and encompasses the following main parameters EC, TDS, salinity, and pH were strongly related.The significant variables (EC, TDS, salinity, pH) within PCA1 followed the same direction and showed a major increase related to salinity.PCA1 demonstrates that the salinization process is the main factor controlling the surface water quality variability and the importance of mineralization process.The second component (PCA2) explains 34.091% of the total variance and was assembled by DO , BOD, Chloride, Iron and Nitrate showing high correlations among themselves towards the same direction (Table ).PC 2 demonstrates the high concentration of BOD and sets an inverse correlation with DO which seems an increased cause of pollution and a higher concentration of iron and nitrate also effecting the water quality.The third component (PCA3) accounts for 9.052% of the total variance and describes the significant contributions of Lead and Turbidity (Table 3.1.1and Figure 3.1.2),disclosing good correlations among themselves.It shows that higher lead concentration effecting turbidity of surface water.

PCA for Winter
The winter PCA reveals the three components explaining 78.536% of the total variance.The PCA results are shown in Table 3.1.2.The PCA approach identified three components that have the most critical loading (Fig 3 .1.4)The first component (PCA1) explains 40.584% of the total variance and encompasses the following main parameters pH, BOD,iron ,nitrate were strongly related and DO is negatively related with BOD.
The second component (PCA2) explains 24.423% of the total variance and was assembled by TDS and EC showing high correlations among themselves towards the same direction (Table 3.1.4).
The third component (PCA 3) demonstrates a strong negative value of total hardness.

K-MEANS ANALYSIS
The spatial distribution , the descriptive statistics (Table 3.1.5),and the graphical representation of the means (Fig. ) of the four detected seasons signify the spatial variability of the hydrochemistry among the seasons.The first season Autumn(9 observations) exhibits higher concentration of nitrate(0.07mg/l)and lead(0.07mg/l)relative to other seasons .The nitrate concentration is under permissible limit (<45mg/l) but lead concentration is seems to be beyond permissible limit (>0.01mg/l).The second season exhibits higher concentration of DO (5mg/l) which signifies lesser pollution in winter season.Total hardness is also found in higher concentration (119.11mg/l).As per BIS total hardness ranges between 60mg/l to 120mg/l are classified as soft water.The winter season also consumes higher concentration of iron (1.5mg/l) which goes beyond the permissible limit of iron (>0.Calculation of WQI by Weighted Arithmetic Water Quality Index Method Weighted arithmetic water quality index method classified the water quality according to the degree of purity by using the most commonly measured water quality variables.The methodhas been widely used by the various scientists and the calculation of WQI was made by using the following equation:

WQI =∑QiWi /∑Wi
The quality rating scale (Qi) for each parameter is calculated by using this expression: Qi =100[(Vi-Vo)/( Si-Vo)] Where, Vi is estimated concentration of i th parameter in the analysed water Vo is the ideal value of this parameter in pure water Vo = 0 (except pH =7.0 and DO = 14.6 mg/l) Si is recommended standard value of i th parameter The unit weight (Wi) for each water quality parameter is calculated by using the following formula: Wi =K /Si Where, K = proportionality constant and can also be calculated by using the following equation: K = 1/ ∑(1/Si ) The rating of water quality according to this WQI is given in table 3.1.6.Water Quality Rating as per Weighted Arithmetic Water Quality Index Method.3.18, it is seen that some sites are performing very poorly in terms of quality throughout all seasons, whereas, the two sites (site 6 and site 7) from Boragaon Dumping Station show the water quality rating as "Unfit for Consumption", which means water samples collected from these sites are not usable for any purpose, not good even for aquatic life.The analyses and statistical tests conducted have resulted in the protection of the lake water only depending on the control of the amount and content of the fertilizers used in agriculture activities and the effect of pH changes on the aquatic ecosystem due to the sudden temperature changes as a result of changing the climate.A suggested solution to the problems is "best environmental practice" principle should be applied to minimize the out-of-source pollution and to efficiently use and control stocks of freshwater resources.

Fig 3 . 1 . 1 :
Fig 3.1.1:Scree-plot for the principal component model of the monitoring data

Fig 3 . 1 . 3 :
Fig 3.1.3:Scree-plot for the principal component model of the monitoring data

Table 3 .1: Concentrations of Water Quality Parameters of the sampling sites for Autumn Season Water quality Parameters SITE 1 SITE 2 SITE 3 SITE 4 SITE 5 SITE 6 SITE 7 SITE 8
The above table is a demonstration of the concentration of the 13 different water parameters for the 9 different sampling locations.The values listed are based on laboratory analysis of the water samples for the first season of our study.

Table 3 .2: Concentrations of Water Quality Parameters of the sampling sites for Winter Season
The above table is a demonstration of the concentration of the 13 different water parameters for the different sampling locations for the second season of our study.

Table 3 .3: Concentrations of Water Quality Parameters of the sampling sites for Spring Season
The above table is a demonstration of the concentration of the 13 different water parameters for the different sampling locations for the third season of our study.

Table 3 .4: Concentrations of Water Quality Parameters of the sampling sites for Summer Season
.1.1,Table 3.1.2,Table 3.1.3and Table 3.1.4.According to Hair et al. (

Table 3 .1.6:Water Quality Rating as per Weighted Arithmetic Water Quality Index Method WQI RATING OF WATER QUALITY
0-25 excellent highly loaded with EC concentration (0.29 mg/l) and turbidity (9.78 NTU).Lastly the summer season's samples are bringing highest pH level (7.34), TDS (198.06 mg/l), salinity (0.27 mg/l), BOD (3.11 mg/l) and chloride content (79.11 mg/l).The highest BOD concentration notifies the highest pollution in summer season among all the four seasons.From the WQI analysis, we have checked the quality of water in nine different sites for four seasons.Sites are showing different water quality index in different season.From the table