Application of best subset method for river water quality modeling: A study on Godavari River,India

The utilization of water quality analysis to inform optimal decision-making is imperative to achieve sustainable management of river water quality. A multitude of research works in the past has focused on river water quality modeling. Despite being a precise statistical regression technique that allows for fitting separate models for all potential combinations of predictors and selecting the optimal subset model, the application of best subset method in river water quality modeling is not widely adopted. The current research aims to validate the use of best subset method in evaluating the water quality parameters of the Godavari River, one of the largest rivers in India, by developing regression equations for different combinations of its physicochemical parameters. The study involves in formulating best subset regression equations to estimate the concentrations of river water quality parameters while also identifying and quantifying their variations. A total of 17 water quality parameters are analyzed at 13 monitoring sites using 13 years (1993–2005) of observed data for the monsoon (June–October) period and post-monsoon (November–February) period. The final subset model is selected among model combinations that are developed for each year's dataset through widely used statistical criteria such as R², F value, adjusted R²_a, AIC_c, and RSS. The final best subset model across all parameters exhibits R² values surpassing 0.8, indicating that the models possess the ability to account for over 80% of the variations in the concentrations of dependent parameters. Therefore, the findings demonstrated the appropriateness of this method in evaluating the water quality parameters in extensive rivers. This work is very useful for decision-making and in the management of river water quality for its sustainable use in the study area.     

Multi-drug resistance,integron and transposon-mediated gene transfer in heterotrophic bacteria from Penaeus vannamei and its culture environment

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Do pilot free trade zones improve the green total factor productivity? Evidence from a quasi-natural experiment in China

China’s pilot free trade zone (PFTZ) is an important national strategy to achieve high-quality development, so it is necessary to discuss the effect of PFTZ implementation on green total factor productivity (GTFP). Based on the data from 279 cities in China from 2004 to 2018, this study takes the establishment of PFTZ as a quasi-natural experiment and uses the difference-in-differences (DID) method to systematically evaluate the effect of PFTZ on urban GTFP. The empirical results of this paper are shown as follows: Firstly, the construction of PFTZ has a significant effect on urban GTFP, and this effect has increased gradually over time. Secondly, the construction of PFTZ mainly promotes the urban GTFP by increasing the level of science and technology innovation, reducing government intervention and improving the level of human capital. Thirdly, the effect of PFTZ construction on GTFP is more pronounced in regions with earlier waves of PFTZ and in western regions where environmental concerns are higher. In addition, there is a significant positive linkage between the construction of the PFTZ and the Belt and Road Initiative to improve the urban GTFP. The findings of this paper enrich the relevant literature on PFTAs and sustainable development and provide a theoretical basis for further promotion of PFTZ construction.

     

Novel coronavirus (COVID-19) in environmental engineering perspective

Environmental Science and Pollution Research -     

Effects of tetracycline on the secondary metabolites and nutritional value of oilseed rape (Brassica napus L.)

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Weather integrated malaria prediction system using Bayesian structural time series model for northeast states of India

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