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This study aims to identify the influential parameters and heavy metals in water and assess the water quality classification at the Alpine glacial lakes and rivers in three districts of Pakistan. For this purpose, nine water quality parameters (Cd, Cr, Pb, Ni, Fe, As, and TDS) in mg/L, pH, Ec µS/Cm are used to compute the Water Quality Index (WQI). The Boruta approach was utilized for the identification of influential parameters associated with the water quality classes. Moreover, we employed supervised machine learning models, including a decision tree, the k-nearest neighbor method, a neural network model (multi-layer perception), a support vector machine, and a random forest, to predict and validate the water quality class. The performance of all algorithms is assessed by an accuracy metric. The accuracy rates for the validation set were observed to be 83% for the decision tree model, 75% for the K-nearest neighbor method, 83% for the neural network, 88% for the support vector machine, and 88% for the random forest model. Water quality assessments for observed locations specify significant insights, revealing that 49% of the locations exhibit low water quality. According to the current study, the government should address problems with water quality in Pakistan’s impacted areas by implementing suitable measures designed water monitoring systems and innovative technologies.

The current research aimed to monitor and assess the heavy metal contamination in the surface water of 53 sampling sites along the selected rivers using principal component analysis and cluster analysis. For this purpose, both physiochemical parameters such as the temperature (T), the potential of hydrogen (pH), total dissolved solids (TDS) and electroconductivity (EC), and heavy metals such as iron (Fe), chromium (Cr), nickel (Ni), cadmium (Cd), lead (Pb) and arsenic (As) are analyzed as potential water contaminants. The average values of pH, TDS, EC and T are found at 7.75, 70.89 mg/L, 139.11 µs/cm and 20.29 °C, respectively, and heavy metals including Cr, Ni, Cd, Pb, As and Fe are observed at 0.04, 0.04, 0.04, 0.03, 0.001 and 0.04 mg/L, respectively. Moreover, it is found that in both rivers hazardous metals, including Cr (100%), Cd (92.30%), Pb (100%), Ni (100%) and Fe (91%), exceed the permissible limits of the WHO.

The study mainly focuses on heavy metals’ quantification, investigation of physicochemical characteristics, source apportionments, human health risk assessment, and connectivity between the water of alpine glacial lakes and rivers. In this regard, twenty-six water samples are taken from sixteen lakes and two lakes-fed rivers. The physiochemical parameters, particularly heavy metals are quantified using atomic absorption spectrophotometer. The Pearson correlations are used to find out the relationship between various heavy metals and physicochemical parameters. The multivariate methods, Principal component analysis (PCA), and Hierarchal cluster analysis (HCA) are conducted for contamination source apportionment. In the findings of the current analysis, the mean concentration of Lead (Pb 0.51), Cadmium (Cd 0.04), Nickel (Ni 0.06), Chromium (Cr 0.08), Iron (Fe 0.06), and Manganese (Mn 0.09) is the most common heavy metal contaminants found in both lakes and river water. Copper (Cu 0.00) and Arsenic (As 0.001) concentrations are lower in both lakes and rivers. Other tested physicochemical characteristics, such as pH, Hardness, Chemical oxygen demands (COD), total dissolved solids (TDS), dissolved oxygen (DO) chloride (Cl-), sulfate (SO4), nitrate (NO3), and bicarbonates (HCO3) are within the maximum permissible limits of World Health Organization’s (WHO).

The current study proposes a new framework for quantifying and monitoring the severity of meteorological drought. The proposed framework consists of three phases. The first phase of the framework uses K-component Gaussian Mixture Distribution (GMD) in the computation. The second phase is mainly based on the dissimilarity matrix-based clustering using C-index and Monte Carlo Feature-based Selection (MCFS) method. The third phase uses the Markov chain, transition probabilities and a non-homogeneous Poisson process under the Bayesian estimation. The Relative Importance (RI) values are used to choose appropriate stations. The Deviance Information Criteria (DIC) is used to check model suitability, and Root Mean Square Error (RMSE) is utilized for determining model performance. The proposed framework is validated to the 52 meteorological stations in Pakistan for 49 years from 1968 to 2016. Moreover, the outcomes of the current analysis provide insight to quantify and monitor meteorological drought comprehensively and accurately.

Precipitation has a dominant role in agriculture, and a regular rain pattern is usually vital to agriculture; excessive or inadequate rainfall can be harmful. In this paper, an agricultural drought index is utilized to study the agricultural drought periods and analyze them with their intensities at various locations. Some nonhomogeneous Poisson models are also used to calculate the probability of agricultural droughts (number of times occurred) in a time interval of interest. It is to be assumed that the number of agricultural drought event occurrences is a Nonhomogeneous Poisson Process (NHPP) has a rate function, which depends on some parameters that must be estimated. Two cases of these functions are considered: the Weibull and linear intensity function. The Bayesian approach with Gibbs sampling under the Markov Chain Monte Carlo (MCMC) algorithm is used to estimate the parameters of these functions. The most appropriate fitted model is selected by using Deviance Information Criteria (DIC) and use that appropriately fitted model to calculate the accumulated events of agricultural drought in a time interval of interest at each location. Ordinary Kriging (OK) is used to regionalize the parameters and present its spatial behavior. The results based on the DIC indicate that the Power Law Process (PLP) performs better than the linear intensity function, NHPP model. The interpolated parameter values for the appropriate model, their patterns, and fluctuations for the study area are efficiently presented using contour maps. It is a novel and straightforward approach to assess the selected model parameter values used to predict the accumulated drought events at un-sampled locations. The proposed framework might also help to analyze other spatial variables of interest and can be used for climate-change study, ecosystem modeling, etc. The findings can also help to make decisions for sustainable environmental management in Pakistan.

The coronavirus disease spread out rapidly in China and then in the whole world. Kuwait is one of those countries which are positively affected by this pandemic. Objective: The current study aims to provide an appropriate and novel framework for the analysis of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infected patient's counts and rate of change in these counts with respect to time. Therefore, we considered the number of SARS- CoV-2 patients, i.e., confirmed cases, deaths, and recoveries for Kuwait, ranging from the 24th of February 2020 to the 25th of August 2020. Method: Here, we used the Markov Chain Monte Carlo (MCMC) simulation methods for the data analysis of SARS-CoV-2 to develop the Bayesian analysis of the Non-Homogeneous Poisson Process (NHPP). For this purpose, we used the two unique models of NHPP: the linear intensity function and the power law process. The discrimination methods are also discussed to select a better model for daily basis data of confirmed cases, deaths, and recoveries of SARS-CoV-2 patients. The appropriate model is selected based on the Deviance Information Criteria (DIC). Results: The value of DIC indicates that the power-law process performs better than the linear intensity functions for estimating and presenting all the study variables. The current study explored the usefulness and significance of the proposed research framework to analyze the SARS-CoV-2 new confirmed cases, recoveries, and deaths in a specific area. Conclusion: The findings of the study will be helpful for the health organizations or authorities to develop the approaches based on the current resources and situations due to the pandemic. The provided framework could be beneficial in analyzing the second and third layers of COVID-19 in the area. The analysis of the counts for each study variable and for each variable a comparative analysis of all the three layers is the aim of our future study.