World across, there is an increasing concern about river health. In India, Ganga River Basin Management Plan (GRBMP) 2015 considered River Ganga as an ecological entity. This paper attempts to present a framework for river health assessment in India and discusses its applicability for River Ganga near Varanasi. In the proposed framework, the River Health Condition (RHC) is assessed through the calculation of River Health Index (RHI) on a 0-100 scale and categorized as Acceptable or Poor. RHI is calculated by using selected parameters/indices normalized on the 0-5 scale based on their critical and target values. River Health is presented through a coloured circumscribed pentagon each of whose side represents one of five indicator groups: i. Organo-electrolytic-bacterial qualities. ii. Nutrients, iii. Algae, iv. Macroinvertebrates, and v. Fish. Application of the proposed framework has been tested and explained using observed data for four seasons per year for two years from five locations of River Ganga near Varanasi. The colour of circumscribing pentagon reflects overall river health condition at a given location and each side of pentagon reflects health score concerningto one indicator group. The analyses indicate that the health of River Ganga near Varanasi is improving with time. The river health is found at its best level during Spring season and unstable during Post Monsoon period at most of the locations. The severely reduced RHI indicate “Overstressed” condition of River Ganga at the confluence points of River Assi and Varuna, which are evidenced by the presence of pollution tolerant biotic species. There are clear stretches of the river near outfall points which are nutrients rich and organically polluted causing poor health of river showing a disturbed balance of biotic species. Indicator group score based RHI gives a clear identification of critical parameters which may be used in strategic planning for river health restoration.

Industrial economic development, which is characterized by high input, high pollution, high consumption, and low benefits, causes environmental pollution problems to be prominent due to the increase of industrial pollutants every year. Blind expansion of investment scale regardless of governance efficiency of industrial pollution can lead to a certain amount of resource wastage. Thus, improving the governance efficiency of industrial pollution is an important method to solve industrial pollution. In particular, applying big data on environmental protection for environmental pollution management can improve the efficiency and quality of pollution control, and ensure comprehensive examination and governance of environmental pollution. In this study, research on industrial pollution governance efficiency in foreign developed countries was reviewed. Furthermore, industrial pollution governance efficiency in Jiangsu Province, China was calculated and certain measures were proposed to prevent and control industrial pollution by applying big data on environmental protection. Results demonstrate that the trend in which industrial pollution governance of emerging technologies, such as big data, penetrate into environmental protection and human capital input can significantly decrease industrial wastewater and industrial solid wastes. Technological innovation makes remarkable contributions to industrial waste gas control. To increase industrial pollution governance efficiency, this study proposes certain measures, such as establishing a scientific and highly efficient big-data monitoring network system for ecological environment, fully utilizing block-chain technological advantages in industrial pollution control, constructing a perfect big-data sharing and processing platform for environmental protection, and increasing sharing and application values of big data on environmental protection. Research conclusions can provide important references to reduce industrial pollutant emissions, increase the efficiency of industrial pollution governance, increase applications of big-data on environmental protection, and construct and perfect a big-data sharing platform for environmental protection.

Biochar was prepared from the peanut shell. Then, it was mixed with the clay mineral kaolinite and stirred under the magnetic stirrer. The biochar derived from peanut shell supported with clay mineral kaolinite (B@K) was obtained. Adsorption experiments of dye Acid Orange 7 by B@K were conducted. The characteristics of B@K were determined by the elemental analyzer, specific surface area meter, scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, X-ray photoelectron spectrometer and the model axis-HS. The experimental results showed that a large number of oxygen-containing functional groups appear on the surface of B@K, which is beneficial for binding dye ions. The adsorption process fits well with the pseudo-second-order kinetic model. It indicated that the adsorption process was both physical adsorption and chemical adsorption. Chemical adsorption is the main adsorption process. Langmuir isotherm model can better describe the adsorption isothermal process of B@K on dye Acid Orange 7. The adsorption process is monolayer adsorption process.

Brain affection is a common symptom of liver insufficiency. This study aimed to evaluate the role of low-dose γ irradiation (LDR) as a potential therapeutic agent in thioacetamide (TAA)-induced hepatic encephalopathy (HE) in rats. Effects of local and whole-body irradiation (0.5 Gy) on rat brain/liver were evaluated following the induction of HE by TAA (200 mg/kg/day/for 3 successive days). Serum activities of aspartate transaminase (AST) and alanine transaminase (ALT) and ammonia level were assessed. The effect of HE on brain was evaluated through the determination of brain contents of malondialdehyde (MDA), reduced glutathione (GSH), tumor necrosis factor-alpha (TNF-α), and interleukin-1beta (IL-1β) and glutathione peroxidase (GPx) activity. Moreover, apoptotic and inflammatory changes in brain and liver tissues were assessed together with alpha-smooth muscle actin (α-SMA); fibrosis marker. Results showed correction of the biochemical parameters which was supported by the results of the immunohistochemical examinations. LDR is a promising hepato- and neurotherapy against HE.

The main objective of this study is to lower the greenhouse gases by developing and optimizing a solar flat plate collector. The rifled tube is integrated into the collector to increase the thermal heat transfer thereby improving its performance. Two flat plate collectors, one with in-housed longitudinal fins and another without fins of 0.5 m² collector area, have been intended and fabricated with provisions for K-type thermocouples to examine the temperature variations inside the collector for different working fluids. This current study reveals using CuO and Al₂O₃ nanoparticles in varying weight fractions in incremental order to study the effect of weight fractions on the efficiency of the collector. The simulation was done using computational fluid dynamics both for the finned and without finned tube collectors separately and the outcome of the results for the collector outlet temperatures is compared with the experimental one and results show a valuable outcome for the intended collectors. Initially, the test was conducted with pure distilled water as working fluid and further nanoparticles were opted and doped inside the collector side for varying weight fractions of 0.2% and 0.4% and their results are compared. The experimental results showed an improved heat transfer was pragmatic in the collector side for using nanoparticles. Mixing the nanofluids exhibited superior efficiency on the collector side. The results showed after successful trials of experimentation, doping of CuO nanoparticles by varying weight fractions of 0.2% and 0.4%, augmentation of the collector (unfinned) efficiency is 2.1% and 4.05%, and similarly for finned tube collector, it is 3.02% and 5.5% for same weight fractions. In order to improve the thermal efficiency of collector, CuO is replaced by Al₂O₃ nanoparticles; for dissimilar weight fractions, the efficiency is enhanced nearly by 3.7% and 6.54% for unfinned tube collector, and for the finned tube, the collector is 4.8% and 7.8% respectively, compared with the base working fluid (water). Experimentation of the collectors with finned tube type achieved a superior efficiency compared with that of unfinned tube collectors which is proved to be higher when used for nanofluids to that of the base working fluid water.

Cadmium (Cd) and zinc (Zn) toxicity causes physiological disorders and harms plants, interfering with the rehabilitation of areas affected by mining activities. This study evaluated how the exposure to Zn and/or Cd affects the growth of native andropogon grass (Andropogon gayanus Kunth) plants originally found in areas contaminated with Cd and/or Zn due to zinc mining activities. Plants were cultivated for 7 weeks in a nutrient solution treated with Zn (142.3–854.0 μM) or Cd (0.9–13.3 μM) separately or combined with a molar ratio of 64:1 (Zn:Cd). A control treatment was grown in a complete Hoagland and Arnon solution (without Cd). Plant height, stem diameter, internode length, dry weight, Cd and Zn concentration, and accumulation in shoots/roots, as well as ultrastructure of roots and leaves were analyzed at the end of the experiment. The root dry weight was not significantly affected by the addition of the metals. Moreover, Zn provided higher shoot dry weight (up to 160%) relative to control. Andropogon grass tolerated both metals better separately than when applied together. Transmission electron microscopy analyses showed modifications such as vesiculation and vacuolation in the ultrastructure of andropogon tissues by Cd and/or Zn. The andropogon grass was tolerant to the doses tested, evidencing that it has potential for recovering areas contaminated with Zn and/or Cd.

Securing a moderate level of social acceptance for obnoxious facilities, public facilities that have negative effects, such as odors, noise, or other disruptions, is critical to infrastructure plans. For wastewater treatment plant (WWTP), also obnoxious facilities, upgrading and expanding the capacity of existing WWTP, are more important than the construction of new plants, in some regions. This study analyzes and compares the social acceptance of different types of WWTP upgrades and capacity expansion projects. Contingent valuation method is used to elicit South Korean households’ willingness to pay (WTP) for preventing the expansion of a WWTP. The aggregated WTP is interpreted from the perspective of social conflict costs. The results show that the annual mean WTP of South Korean households to prevent WWTP expansion ranges from KRW 32,058 (US $27.61) to KRW 45,793 (US $39.44) depending on spatial location, which implies that the social conflict costs for the WWTP expansion in South Korea are considerable. It is also found that an underground WWTP at current site is a best alternative to lower the social conflict costs; it is even better than relocation an existing WWTP to another area. Several related policy implications are provided based on the analysis results.

The treatment efficiency of Chlorella sorokiniana and Scenedesmus species, immobilized in sodium alginate, was evaluated for removing nitrate from groundwater. The experiments were performed initially in batch mode and the best-performing conditions were replicated in sequencing batch reactor mode. S. sp. showed a higher nitrate uptake in short term than C. sorokiniana. Immobilized S. sp. and C. sorokiniana cells showed 90% nitrate removal in 9 and 12 days, respectively. The optimal ratio of algal beads/water was found to be 12.5% (v:v). Comparatively, suspended S. sp. cells were able to remove only up to 35% of nitrate in 8 days. Alginate immobilized S. sp. beads were capable of uptaking nitrate for 100 consecutive days in sequencing batch reactor mode. When tested in actual groundwater, 90% of nitrate was eliminated in 2 days without need for any additional carbon source. Immobilized algal beads can be a low-cost alternative technique to remove nitrate from groundwater as they are water-insoluble, non-toxic, easy to harvest, and offer high removal efficiency.

Lake water-level fluctuation is a complex and dynamic process, characterized by high stochasticity and nonlinearity, and difficult to model and forecast. In recent years, applications of machine learning (ML) models have yielded substantial progress in forecasting lake water-level fluctuations. This paper presents a comprehensive review of the applications of ML models for modeling water-level dynamics in lakes. Among the many existing ML models, seven popular ML model types are reviewed: (1) artificial neural network (ANN); (2) support vector machine (SVM); (3) artificial neuro-fuzzy inference system (ANFIS); (4) hybrid models, such as hybrid wavelet-artificial neural network (WA-ANN) model, hybrid wavelet-artificial neuro-fuzzy inference system (WA-ANFIS) model, and hybrid wavelet-support vector machine (WA-SVM) model; (5) evolutionary models, such as gene expression programming (GEP) and genetic programming (GP); (6) extreme learning machine (ELM); and (7) deep learning (DL). Model inputs, data split, model performance criteria, and model inter-comparison as well as the associated issues are discussed. The advantages and limitations of the established ML models are also discussed. Some specific directions for future research are also offered. This review provides a new vision for hydrologists and water resources planners for sustainable management of lakes.

Climate change is persistently causing adverse effects to the agriculture sector of developing countries, specifically in Asia. Pakistan is no exception to this effect and is ranked among the top 10 countries, which are most vulnerable to climate change. A huge upcoming challenge is to sustain an equilibrium among production and environmental protection. In this context, adaptation to climate change is considered as a win-win strategy for agriculture sectors in developing countries. However, numerous studies have focused on current farm-level adaptation while a scant interest has been shown on the role of physiological factors in the process of shaping small livestock herders’ intentions towards environmental enrichment measures. A possible explanation of their lagging intentions is particular significance as they may comply with requisite climate adaptation measures or not. For deeper understanding, the current study investigates different psychological factors that affect the small livestock herder’s intentions on adopting climate smart practices by using theory of planned behavior (TPB) with additional constructs (moral norms, risk perception, and social attributes). To this end, 405 small livestock herders from Punjab, Pakistan, were selected on the basis of multistage random sampling. The results of structural equation model showed that all constructs accounted for 57% of the variances in small livestock herders’ adoption intentions. The outcome of this research offers a new indication regarding the interrelationship of numerous variables which are crucial to understand behavioral changes and psychological interventions. Overall attitude was the most prominent construct in the extended TPB model, which is mainly influenced by risk perception awareness. The results suggest that veterinary experts and extension agents should focus on psychological factors to explore different prospects to increase the involvement of livestock herders in environmental enrichment measures with little effort rather than tackling with traditional practices because it will be more likely to affect people’s consideration of the external obstructions to act. Findings also offer public and private intervention for enabling technical and policy environment and strengthen social networks to keep livestock herders on track of updates of running government policies to ensure them to adopt climate change measures for their prosperous future.