Environmental concerns and switching toward electric vehicles: geographic and institutional perspectives

Environmental Science and Pollution Research - Smog pollution deteriorates environmental quality and has severe health risks. This affects the daily lives of people in China, particularly in urban...     

Trilemma association of energy consumption,carbon emission,and economic growth of BRICS and OECD regions: quantile regression estimation

Environmental Science and Pollution Research - Recent research has shown a huge impact of non-renewable energy (NRE) production on environmental health. In this context, this work analyzes the...     

Monitoring of cotton dust and health risk assessment in small-scale weaving industry

The present study describes the estimation of particulate matter (cotton dust) with different sizes, i.e., PM(1.0), PM(2.5), PM(4.0), and PM(10.0 μm) in small-scale weaving industry (power looms) situated in district Hafizabad, Punjab, Pakistan, and the assessment of health problems of workers associated with these pollutants. A significant difference was found in PM(1.0), PM(2.5), PM(4.0), and PM(10.0) with reference to nine different sampling stations with p values <0.05. Multiple comparisons of particulate matter with respect to size, i.e. PM(1.0), PM(2.5), PM(4.0), and PM(10.0), depict that PM(1.0) differs significantly from PM(2.5), PM(4.0), and PM(10.0), with p values <0.05 and that PM(2.5) differs significantly from PM(1.0) and PM(10.0), with p values <0.05, whereas PM(2.5) differs non-significantly from PM(4.0), with a p value >0.05 in defined sampling stations on an average basis. Majority of the workers were facing several diseases due to interaction with particulate matter (cotton dust) during working hours. Flue, cough, eye, and skin infections were the most common diseases among workers caused by particulate matter (cotton dust).     

The influence of personality traits on sustainability-oriented entrepreneurial intentions: the moderating role of servant leadership

Environment, Development and Sustainability - Economic, environmental, and social imperatives make sustainability-oriented entrepreneurship an indispensable phenomenon of the day, yet only sparse...     

Design and optimization of a hybrid process based on hollow-fiber membrane/coagulation for wastewater treatment

Environmental Science and Pollution Research - Treatment of textile wastewater using ultrafiltration membranes was carried out in this study. Since membrane fouling is a major operational problem...     

Using Paleo Reconstructions to Improve Streamflow Forecast Lead Time in the Western United States

In water stressed regions, water managers are exploring new horizons that would help in long‐range streamflow forecasts. Oceanic‐atmospheric oscillations have been shown to influence streamflow variability. In this study, long‐lead time streamflow forecasts are made using a multiclass kernel‐based data‐driven support vector machine (SVM) model. The extended streamflow records based on tree ring reconstructions were used to provide a longer time series data. Reconstructed data were used from 1658 to 1952 and the instrumental record was used from 1953 to 2007. Reconstructions for oceanic‐atmospheric oscillations included the El Niño‐Southern Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, and North Atlantic Oscillation. Streamflow forecasts using all four oscillations were made with one‐year to five‐year lead times for 21 gages in the western United States. This is the first study that uses both instrumental and reconstructed data of oscillations in SVM model to improve streamflow forecast lead time. SVM model was able to provide “satisfactory” to “very good” forecasts with one‐ to five‐year lead time for the selected gages. The use of all the oscillation indices helped in achieving better predictability compared to using individual oscillations. The SVM modeling results are better when compared with multiple linear regression model forecasts. The findings are statistical in nature and are expected to be useful for long‐term water resources planning and management.     

Evaluating Water Conservation and Reuse Policies Using a Dynamic Water Balance Model

A dynamic water balance model is created to examine the effects of different water conservation policies and recycled water use on water demand and supply in a region faced with water shortages and significant population growth, the Las Vegas Valley (LVV). The model, developed using system dynamics approach, includes an unusual component of the water system, return flow credits, where credits are accrued for returning treated wastewater to the water supply source. In LVV, Lake Mead serves as, both the drinking water source and the receiving body for treated wastewater. LVV has a consumptive use allocation from Lake Mead but return flow credits allow the water agency to pull out additional water equal to the amount returned as treated wastewater. This backdrop results in a scenario in which conservation may cause a decline in the available water supply. Current water use in LVV is 945 lpcd (250 gpcd), which the water agency aims to reduce to 752 lpcd (199 gpcd) by 2035, mainly through water conservation. Different conservation policies focused on indoor and outdoor water use, along with different population growth scenarios, are modeled for their effects on the water demand and supply. Major contribution of this study is in highlighting the importance of outdoor water conservation and the effectiveness of reducing population growth rate in addressing the future water shortages. The water agency target to decrease consumption, if met completely through outdoor conservation, coupled with lower population growth rate, can potentially satisfy the Valley’s water demands through 2035.     

A review on solar-powered cooling systems coupled with parabolic dish collector and linear Fresnel reflector

Environmental Science and Pollution Research - Solar energy is the most sustainable and free source to manage the world energy demand. One aspect of solar-driven energy supply can be observed in...     

Property-performance relationship of core-shell structured black TiO2 photocatalyst for environmental remediation

● Properties and performance relationship of CSBT photocatalyst were investigated. ● Properties of CSBT were controlled by simply manipulating glycerol content. ● Performance was linked to semiconducting and physicochemical properties. ● CSBT (W:G ratio 9:1) had better performance with lower energy consumption. ● Phenols were reduced by 48.30% at a cost of $2.4127 per unit volume of effluent. Understanding the relationship between the properties and performance of black titanium dioxide with core-shell structure (CSBT) for environmental remediation is crucial for improving its prospects in practical applications. In this study, CSBT was synthesized using a glycerol-assisted sol-gel approach. The effect of different water-to-glycerol ratios (W:G = 1:0, 9:1, 2:1, and 1:1) on the semiconducting and physicochemical properties of CSBT was investigated. The effectiveness of CSBT in removing phenolic compounds (PHCs) from real agro-industrial wastewater was studied. The CSBT synthesized with a W:G ratio of 9:1 has optimized properties for enhanced removal of PHCs. It has a distinct core-shell structure and an appropriate amount of Ti³⁺ cations (11.18%), which play a crucial role in enhancing the performance of CSBT. When exposed to visible light, the CSBT performed better: 48.30% of PHCs were removed after 180 min, compared to only 21.95% for TiO₂ without core-shell structure. The CSBT consumed only 45.5235 kWh/m³ of electrical energy per order of magnitude and cost $2.4127 per unit volume of treated agro-industrial wastewater. Under the conditions tested, the CSBT demonstrated exceptional stability and reusability. The CSBT showed promising results in the treatment of phenols-containing agro-industrial wastewater.     

The challenges of reducing greenhouse gas emissions and air pollution through energy sources: evidence from a panel of developed countries

The objective of the study is to investigate the long-run relationship between climatic factors (i.e., greenhouse gas emissions, agricultural methane emissions, and industrial nitrous oxide emission), air pollution (i.e., carbon dioxide emissions), and energy sources (i.e., nuclear energy; oil, gas, and coal energy; and fossil fuel energy) in the panel of 35 developed countries (including EU-15, new EU member states, G-7, and other countries) over a period of 1975–2012. In order to achieve this objective, the present study uses sophisticated panel econometric techniques including panel cointegration, panel fully modified OLS (FMOLS), and dynamic OLS (DOLS). The results show that there is a long-run relationship between the variables. Nuclear energy reduces greenhouse gases and carbon emissions; however, the other emissions, i.e., agricultural methane emissions and industrial nitrous oxide, are still to increase during the study period. Electricity production from oil, gas, and coal sources increases the greenhouse gases and carbon emissions; however, the intensity to increase emissions is far less than the intensity to increase emissions through fossil fuel. Policies that reduce emissions of greenhouse gases can simultaneously alter emissions of conventional pollutants that have deleterious effects on human health and the environment.