Trace heavy metals, such as arsenic, cadmium, lead, chromium, nickel, and mercury, are important environmental pollutants, particularly in areas with high anthropogenic pressure. In addition to these metals, copper, manganese, iron, and zinc are also important trace micronutrients. The presence of trace heavy metals in the atmosphere, soil, and water can cause serious problems to all organisms, and the ubiquitous bioavailability of these heavy metal can result in bioaccumulation in the food chain which especially can be highly dangerous to human health. This study reviews the heavy metal contamination in several areas of Pakistan over the past few years, particularly to assess the heavy metal contamination in water (ground water, surface water, and waste water), soil, sediments, particulate matter, and vegetables. The listed contaminations affect the drinking water quality, ecological environment, and food chain. Moreover, the toxicity induced by contaminated water, soil, and vegetables poses serious threat to human health.

Adsorption kinetics of malachite green and methylene blue from aqueous solutions using surfactant-modified organoclays

The time series movements of Bitcoin prices are commonly characterized as highly nonlinear and volatile in nature across economic periods, when compared to the characteristics of traditional asset classes, such as equities and commodities. From a risk management perspective, such behaviors pose challenges, given the difficulty in quantifying and modeling Bitcoin’s price volatility. In this study, we propose hybrid analytical techniques that combine the strengths of the non-stationary properties of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with the nonlinear modeling capabilities of deep learning algorithms, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) algorithms with single, double, and triple layer network architectures to forecast Bitcoin’s realized price volatility. Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of Bitcoin’s price volatility.

Ridge regression is a variant of linear regression that aims to circumvent the issue of collinearity among predictors. The ridge parameter $k$ has an important role in the bias-variance tradeoff. In this article, we introduce a new approach to select the ridge parameter to deal with the multicollinearity problem with different behavior of the error term. The proposed ridge estimator is a function of the number of predictors and the standard error of the regression model. An extensive simulation study is conducted to assess the performance of the estimators for the linear regression model with different error terms, which include normally distributed, non-normal and heteroscedastic or autocorrelated errors. Based upon the criterion of mean square error (MSE), it is found that the new proposed estimator outperforms OLS, commonly used and closely related estimators. Further, the application of the proposed estimator is provided on the COVID-19 data of India.

Regression analysis is used to predict a dependent variable using one or more independent variables. In the linear regression model, when the independent variables are highly correlated, it leads toward the problem of multicollinearity. Subsequently, the ordinary least squares estimates become inconsistent and may lead to wrong inferences. In such a situation, ridge regression is the most commonly adopted technique. In this paper, we propose some new bootstrap aggregation (bagging) based ridge estimators. The performance of the proposed estimators is evaluated by a simulation study in terms of minimum mean squared error. The simulation results indicate that in the presence of multicollinearity with non-normal or heteroscedastic errors, the bagging-based ridge estimators perform better than conventional ridge estimators. The estimation of biasing parameters using bagging approach promotes the performance of the conventional ridge estimators. Finally, the real-life example is used to demonstrate the application of proposed estimators.