Over the past few decades, rivers have become severely polluted as a result of receiving vast quantities of domestic and industrial wastewater. The identification of the major factors that influence water quality is crucial to understand the interactions of anthropogenic and natural factors and develop river restoration projects. In this study, the QUAL2Kw water quality model was used to quantitatively evaluate the most critical factors for water quality at two sites with different meteorological conditions and urban scales. The genetic algorithm (GA) was used to optimize the parameters in the model. The Monte Carlo simulation (MCS) method was used to assess the model uncertainty and sensitivity in all reaches for five water quality outputs (temperature, CBOD, DO, TP, and TN) in two seasons. The K-means clustering method associated with the sensitivity results was used to identify the major factors influencing the water quality in all reaches from the input data and the model parameters. The results showed that CBOD, TN, and TP were most sensitive to headwater and tributary quality. DO tended to be affected by more natural reactions than the other water quality indicators. In the cold and dry seasons and the more urbanized areas, river pollution was more severe, and the impact of natural reactions was reduced. The simulation results revealed the reliability of QUAL2Kw in modeling the quantity and quality of all river reaches. The method applied in this study is beneficial for the improvement and management of the water environment.

The purpose of this study is to examine the role of economic structure of European countries into testing the Environmental Kuznets Curve (EKC) hypothesis for European countries for the period 1980 to 2014. This study is inspired by the work of Lin et al. (J Clean Prod 133:712–724, 2016), which made the first effort to investigate the phenomenon looking only at African countries. The main finding of the study is that the overall economic growth is the factor with which CO₂ emissions exhibit an inverted U-shaped relationship in the studied country group. On the contrary, when using their industrial share as a proxy to capture the countries’ economic structure, the EKC hypothesis is not confirmed – but a U-shaped relationship is confirmed. The industrial share decreases emissions through the development and absorption of technologies that are energy efficient and environmental friendly. The EKC hypothesis is confirmed when the aggregate GDP growth is considered, taking into account the improvement of the overall economic conditions of the countries regardless of the economic structure and role of industrialization.

Chemical sector contributes extensively to the economic development of countries, however, it is one of the main water-consuming industries. Considering that the corporate water accounting along complete SCs can help companies in improving their sustainable water managements along their value chains, this paper aims to study the water consumption in the entire supply chain of the chemical industry from a multi-regional input-output perspective. In this regard, six Asian countries including Indonesia, Taiwan, China, Japan, South Korea and India are selected to be studied based upon the availability of their data. In the following, the direct water consumed by themselves and the indirect water used by their suppliers are measured for each country using input-output analysis method. Moreover, to draw on the conclusions of selected Asian countries, the amount of consumed water in their supply chains is compared with each other using one-way analysis of variance method. The results from input-output analysis show that the indirect water consumption in the chemical SCs of Indonesia, China, India and South Korea are 20.66, 4.62, 1.37 and 1.08 times greater than their direct water use, while the indirect water consumption for Japan and Taiwan are 0.73 and 0.13 times less than their direct values. The final results from one-way analysis of variance indicate that direct and indirect amount of water consumptions in the chemical supply chains of the selected countries do not follow the same trend over 15 years. However, India and China are the top two countries in terms of both direct and indirect amounts of water consumption due to their higher population. This study presents valuable information for authorities and policymakers in terms of proper water consumption management in chemical industry and other industrial sectors. In this matter, technology-based or production-related actions are beneficiary for affecting direct water use, while consumption-based or end-use actions are more appropriate for affecting indirect water consumption.

Irrigation practice is one of the main factors affecting soil carbon dioxide (CO₂) emission from croplands and therefore on global warming. As a water-saving irrigation practice, the deficit irrigation has been widely used in summer maize fields and is expected to adapt to the shortage of water resources in Northwest China. In this study, we examined the impacts of deficit irrigation practices on soil CO₂ emissions through a plot experiment with different irrigation regimes in a summer maize field in Northwest China. The irrigation regimes consisted of three irrigation treatments: deficit irrigation treatments (T1: reduce the irrigation amount by 20%, T2: reduce the irrigation amount by 40%) and full irrigation (T0) treatments. The results showed that the soil CO₂ cumulative emissions with T1 and T2 were decreased by 9.8% (p < 0.05) and 14.3% (p < 0.05), respectively, compared with T0 treatment (1365.3 kg-C ha⁻¹). However, there were no significant differences between T1 and T2 treatments (p > 0.05). Soil CO₂ fluxes with different irrigation treatments showed significant correlations with soil moisture (p < 0.001) and soil temperature (p < 0.05). It was also observed that summer maize yields with T1 and T2 treatments were reduced by 4.9% (p > 0.05) and 30.9% (p < 0.05), compared with T0 (34.3 t ha⁻¹), respectively. The findings demonstrate that the deficit irrigation treatment (T1) resulted in a considerable decrease in soil CO₂ emissions without impacting the summer maize yields significantly. The results could be interpreted to develop better irrigation management practices aiming at reducing soil CO₂ emissions, saving water, and ensuring crop yield in the summer maize fields in Northwest China.

In the process of electrokinetic (EK) remediation of uranium-contaminated soil, the existence form of uranium in soil pore fluid will affect on its migration behavior. In this paper, a novel type of electrolyte (citric acid + ferric chloride, CA+ FeCl₃) has been investigated for the EK remediation of uranium-contaminated red soil. The effects of different electrolyte and the concentrations of FeCl₃ on migration behavior of U(VI) and environmental risks were investigated after EK remediation. The result showed that the optimum concentration was 0.1 mol/L CA mixed with 0.03 mol/L FeCl₃ in this study. At this time, the removal efficiency of uranium was about 61.55 ± 0.41%, and the cumulative energy consumption was 0.2559 kWh. Compared with deionized water and single CA, combined CA with FeCl₃ has the advantages of high removal efficiency, low leaching toxicity, and less damage to the soil after the electrokinetic remediation treatment.

In the context of global warming and environmental deterioration, the environment impact assessment is a crucial institutional guaranty to assure less or no pollution during the process of the economic growth and rapid social development. This paper selected the environmental regulation system in China as a research target and assesses the effectiveness of green tax on the environment through an empirical analysis. The panel data from 2005 to 2015 in different cities in China are employed to analyze with a two-way fixed regression model; it analyzes the impact of environmental instruments like resource tax, excise tax, vehicle purchase tax, and pollutant discharge fees on environmental pollution and finds that the modeling conditions with green tax reform can lead to higher effect on environmental pollutant constrain but is distributed differently among east, central, and west based on the empirical analysis in China. Moreover, resource tax has a significant effect in the east and midwest of China. The effectiveness of excise tax on integrated pollution is not statistically significant. Compared with the midwest, eastern China’s green tax had a much better performance on reducing environmental pollution. Pollutant discharge fees indicates a significant negative correlation to integrated pollution. The consumption of the other goods (like meat consumption) plays a mediating effect between vehicle purchase tax and integrated environmental pollution. In addition, the relevant policy recommendations are proposed against different tax types.

Nanotechnology is an emerging technology that deals with nanosized particles possessing crucial research roles and application. Disciplines like chemistry, biology, physics, engineering, materials science, and health sciences provide an accumulated knowledge of nanotechnology. Nonetheless, it has vast submissions precisely in biology, electronics, and medicine. Aimed at drug delivery system, nanoparticles are based on the mechanism of entrapment of the drugs or biomolecules into the interior structure of the particles; another mechanism could be that the drugs or the biomolecules can be absorbed onto the exterior surfaces of the particles. Currently, nanoparticles (NPs) are used in the delivery of drugs, proteins, genes, vaccines, polypeptides, nucleic acids, etc. In recent years, various applications of the drug delivery system via NPs have encountered an enormous position sector like pharmaceutical, medical, biological, and others. Considering the impact of NPs in drug delivery systems, this review focuses on the detailed profile of NPs, its impact on biology and medicine, and their commercialization prospects.

Water quality in the Mexican Caribbean is affected by increases in tourism infrastructure and poor wastewater treatment. Additionally, karst geomorphology facilitates the infiltration of organic matter to subterranean water and coastal fresh water that originates from submarine groundwater discharges (SGDs), altering the environment. The tourism infrastructure grows at different rates along the Caribbean coast, characterizing zones with diverse levels of tourism impact. The aim of this work was to measure nutrient concentrations in superficial coastal water and fresh water to evaluate the water quality through different zones along a gradient from intermediate- (Riviera Maya) to low-tourism (Costa Maya) development regions. Furthermore, this study aimed to compare the measured nutrient concentrations with the Mexican ecological criteria of water quality 001/89 (CE-CCA-001/89), detect possible contributions by SGDs, and determine whether the nitrogen (N) sources are anthropogenic using stable nitrogen isotopes in the seagrass Thalassia testudinum. According to the results, nutrient concentrations (ammonium, nitrate, nitrite, and orthophosphate) differed significantly between the Riviera Maya and Costa Maya (P = 0.0001). Sites such as Shambala, Chávez, Tankah, Mahahual 2, Tulum, Akumal, and Xahuayxol exceeded the upper levels set by the CE-CCA-001/89. Tankah, Shambala, and Chávez were influenced by SGDs. The nitrogen isotope ratio in Akumal and Tulum coast water shows that they are under N loading derived to the sewage percentage of δ¹⁵N in Akumal, Tulum, and Mahahual, showing that these sites suffer N loading due to sewage. Our study recommends continuous monitoring and coastal characterization to detect SGD and to regulate, treat, and dispose of sewage.

Metal(loid) contamination of vital food grains such as wheat and rice is a very serious problem throughout the world because consumption of such contaminated food can lead to severe health effects in humans. Metal(loid) contamination of food crops can occur from different sources such as contaminated soil, irrigation water, and aerial deposition. Therefore, the present study was conducted to analyze potential non-carcinogenic and carcinogenic health impacts posed by different metal(loid)s (As Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Se, and Zn) via consumption of wheat and rice grown on metal(loid)-contaminated soils in areas around rivers (Beas and Sutlej) of Punjab, India. Among the metal(loid)s analyzed in wheat and rice samples, contents of As, Cd, Cr, Ni, and Pb were found to be above the international (FAO/WHO and EU) maximum permissible limits. The non-carcinogenic and carcinogenic health risk assessment of individual metal(loid)s revealed that As posed highest risk followed by Cd, Cu, Fe, Mn, and Pb. The values of indices calculated for analysis of combined non-carcinogenic, i.e., (hazard index; range 3.49–15.94) and carcinogenic (total carcinogenic risk index; range 8.30 × 10⁻⁴–131.62 × 10⁻⁴) risks for both crops were found to be many fold higher than the prescribed limits of 1.0 and 1.0 × 10⁻⁴, respectively. Thus, the analysis of combined risks posed by metal(loid)s indicated that human population consuming wheat and rice from the study area faced both non-carcinogenic and carcinogenic health risks. Therefore, immediate steps must be taken to reduce the levels of metal(loid)s in wheat and rice from the study area.

With an exponential industrial growth, an accurate demand forecasting of energy is of prime importance for strategic decision-making and new power policies regarding generation and distribution in the power sector. This is a great impediment in economic development as well as shattering people’s daily life. Hence, forecasting of energy demand in emerging markets is one of the most important policy tool used by decision-makers all over the world. This study focused on the forecasting approach of electricity consumption in Pakistan by developing a model that is called ANFIS (Adaptive neuro-fuzzy inference system). A framework was developed comprising economic and demographic variables as input. Previous historical data of GDP, population, industry efficiency, and weather (annual average temperature) was collected as input to the model and electricity consumption as output of the model. By developing ANFIS model, forecasting was done up to 2045. The increasing trends with respect to predictors showed significant association with electricity consumption. The overall least error proved this model best for forecasting and planning electricity demand to achieve sustainability in the power sector.