Groundwater salinity is a major problem in India and is mainly confined to arid and semiarid areas. The present study was conducted to assess the groundwater salinity, hydrochemical facies, and corrosion indices in the Mewat district of Haryana, India. This study is based on the groundwater samples collected during intervals of field campaigns conducted in the year 2018 from three blocks, namely, Nuh, Nagina, and Ferozpur Jhirka of Mewat. It was found that the electrical conductivity (EC) of the water in the regions varies from 353–10,181 μS/cm and the majority of the samples belong to Mg-Cl hydrochemical facies. Various corrosion indices such as Langelier Saturation Index (LSI), Puckorius Scaling Index (PSI), Ryznar Scaling Index (RSI), and Aggressive Index (AI) were evaluated based on the analyzed physicochemical parameters. It was observed that the water is slightly noncorrosive to LSI and corrosive to intensely corrosive to RSI, while AI values indicate the nonaggressive nature of water. This study suggests that the aquifers are highly saline, but a few freshwater pockets do exist in the aquifers of Mewat. Thus, a judicious approach toward the usage of saline water must be inducted and a regular supply of safe drinking water should be provided to villagers in the area for their sustenance in daily life and agriculture.

Different sewerage treatment plants (STPs) were evaluated for a period of five years to verify their performance in Delhi, India. It focuses upon the comparative analysis of the STPs to get the best STP among all in terms of its performance and to foresee the impact of seasonal variations affecting their performance. In order to figure out the seasonal performance of these STPs they were subjected to analytical analysis for pre and post monsoon months. Results revealed that Dr. Sen Nursing Home STP have shown the best result out of all. Hence the best STP in terms of performance evaluation is Dr. Sen Nursing Home than Vasant Kunj Phase-I than Okhla Phase-I STP.

Unsupervised learning techniques such as principal component analysis (PCA), cluster analysis (CA) were applied to the groundwater data of Mewat region (Haryana, India) collected in the pre monsoon season to identify the geochemical processes controlling groundwater chemistry. Thirteen physicochemical parameters were analyzed and were found to be above the permissible limits. The order of cation and anion concentration were found to be Na+  > Mg2+ > Ca2+ > K+ and Cl− > $${\text{NO}}_{3}^{ - }$$  > $${\text{SO}}_{4}^{2 - }$$  > $${\text{HCO}}_{3}^{ - }$$  > $${\text{CO}}_{3}^{2 - }$$. The dominance of Na+ and Cl− in groundwater chemistry showed the salinity factor in the groundwater. PCA applied to the data set reduced the dimensionality to four significant factors accounting 76.66% of the total variance in the data set. The first factor can be assigned to alkalinity which originates due to the dissolution of geological minerals into the groundwater, second factor is assigned to salinity (due to salt water intrusion) and hardness which is caused by weathering of sedimentary rocks and calcium bearing minerals and other factors originate as a result of industrial wastes, domestic wastes and wastes from agricultural activities. CA classified 30 sampling sites into three clusters with relatively low salinity region, high salinity and very high salinity regions based on similar water quality characteristics.

Among the various modeling techniques applied to dataset, multiple linear regression (MLR) analysis is the most efficient way to figure out the relationship between the response variable and the predictive variables. This study emphasizes on establishment of multiple linear regression models to analyze Biochemical Oxygen Demand (BOD) removal efficiency for technologies, namely Densadeck, Extended Aeration and Activated Sludge Process. Assumptions of multiple linear regression like linear relationship, multivariate normality, multicollinearity and Homoscedasticity were examined. The data that verify the assumptions were analyzed with multiple linear regression. Time series plots indicate drastic decline in BOD removal efficiency in the month of Feb and March during the years 2012 and 2013. This study was significant as it gives the technology having the best-fit regression equation based upon multiple correlation coefficient (R), coefficient of determination (R2), standard error, residual and F-ratio value. Societal benefits include enhancement in the performance of sewage treatment plants.

Background: Water plays an important role for healthy well-being of all living beings. During last decade, due to human interference ground water get polluted drastically and resulted into many health hazards. Objective: This study is done to understand the seasonal variations in the physiochemical parameters of the groundwater of three sites of Amber Tehsil of Jaipur district, Rajasthan using statistical tools. Methodology: To carry out the research, ground water samples were collected once a month throughout a year. Three samples were collected from each site and chemical analysis was conducted. With the help of one-way ANOVA test the difference between the three sites based on the parameters was calculated. Findings: This paper reveals that groundwater of these three sites shows seasonal variations in all twelve parameters using statistical methods like paired t-test and Analysis of Variance (ANOVA) tests. The groundwater of all the sites is not suitable for drinking & industrial purposes which will help the local government to take necessary action. Keywords: Anova, Bureau of Indian Standard, Seasonal Variation, Water Quality Assessment

Tourists’ get attracted towards India because of its diverse culture and geography. Apart from heritage and culture, the tourists from all over the world come here for various other purposes like medical, business, education and sports. The tourism industry of India is economically important and is growing rapidly. The tourism industry in India helps in the growth of other sectors like agriculture, small scale industries, self-employment, etc. This makes forecasting of tourists’ arrivals in India a prime focus of the government Forecasting is the process of making predictions of the future based on past and present data and analysis of trends. Tourism forecasting plays an important role in providing awareness and support for future development of the Indian tourism industry. In this paper, an attempt has been made to forecast tourists’ arrival using statistical time series modeling techniques with the help of secondary data.