Identifying impact of international trade and renewable energy consumption on environmental quality improvement and their role in global warming

There is a lack of proper research that highlights the impact of institutional quality (IQ) and renewable energy consumption (REC) on the carbon emission (CE). The significance of IQ and REC in the achievement of zero CE is highlighted in this research. The current research reports the effects of these important factors on the consumption-based carbon emissions in the G-7 countries from 1995 to 2018. Based on the outcome of the cointegration test, the long-run connection is recognized between IQ, REC, GDP, exports, imports, and consumption-based CE. The findings also validated that there exist significant decrease and increase in the CE in both the short and long run; for instance, IQ, REC, and exports decrease the CE, while imports and GDP increase the CE. The estimates of causality test showed that policies aimed at improving IQ, REC, GDP, exports, and imports have a significant impact on the CE. Consequently, based on these results, policymakers in the G-7 must prioritize IQ and REC to enhance environmental quality and attain carbon neutrality.

     

Hydrogeochemical assessment of carcinogenic and non-carcinogenic health risks of potentially toxic elements in aquifers of the Hindukush ranges,Pakistan: insights from groundwater pollution indexing,GIS-based,and multivariate statistical approaches

Environmental Science and Pollution Research - Globally, potentially toxic elements (PTEs) and bacterial contamination pose health hazards, persistency, and genotoxicity in the groundwater aquifer....     

Energy efficiency and energy rebound,intensity, and output effects in transport sector of Pakistan

Environmental Science and Pollution Research - According to Jevon’s paradox, energy efficiency leads to more energy consumption instead of low. So, calculating the size of energy rebound...     

Heavy metals in three commercially valuable cyprinids in the river Panjkora,district Lower Dir,Khyber Pakhtunkhwa,Pakistan

This study was conducted in river Panjkora (Khall, upstream; Timergara, downstream) and its tributary (Konhaye stream, reference site) at district Lower Dir. The study was aimed to assess heavy metal load in gills, liver, kidneys, and muscles of three fish species, namely Schizothorax esocinus, Racoma labiata, and Crossocheilus diplocheilus. The heavy metals, including nickel, cadmium, lead, copper, zinc, chromium, iron, and manganese were determined using an atomic absorption spect-rophotometer. Nickel and cadmium were not detected in any of the collected specimens at all sampling sites. Concentration of the detected metals varied significantly depending upon fish species and tissue type. Crossocheilus diplocheilus showed higher accumulated concentration of heavy metals while R. labiata contained the least. Schizothorax esocinus and R. labiata displayed significant differences in their concentrations of metals. The order of accumulation of heavy metals in different tissues was liver>kidneys>muscles>gills. Data demonstrated that heavy metal levels in the studied species were within permissible limits and thus, apparently suitable for human consumption. Currently, the river contains a sewage burden, and continued heavy pollution may ultimately lead to serious problems in the future.     

Benzene removal by nano magnetic particles under continuous condition from aqueous solutions

Benzene removal evaluated using Fe304 nano continuous condition. A 44 initial benzene concentration, from aqueous solutions was magnetic particles （NM） in factorial design including NM dose, contact time and pH was investigated in 16 experiments （Taguchi OA design）. The results indicated that all factors were significant and the optimum condition was： pH 8, NM dose of 2000 mg.L-1, benzene concentrations of 100 mg.L-1 and contact time of 14min. The maximum benzene uptake and distribution ratio in the optimum situation were 49.4mg.g-1 and 38.4L.g-1, respectively. The nano particles were shown to capture 98.7% of the benzene in optimum batch condition and 94.5% in continuous condition. The isotherm data proved that the Bmnauer-Emmett-Teller model fit more closely and produced an isotherm constant （b） less than one, indicating favorable adsorption. Regeneration studies verified that the benzene adsorbed by the NM could be easily desorbed by temperature, and thereby, NM can be employed repeatedly in water and wastewater management.     

Adsorptive removal of direct dyes by PEI-treated peanut husk biomass: Box–Behnken experimental design

This paper reports the application of Box-Behnken experimental design to illustrate the adsorption of direct dyes (Indosol Black NF and Indosol Orange RSN) using polyethyleneimine (PEI)-treated peanut husk biomass. The effect of three independent variables (initial dyes concentration, biosorbent dose and pH) was investigated during the study. Maximum biosorption capacity (141 and 98.2 mg/g) of PEI-pretreated biomass was achieved with 200 mg/L initial dye concentration and 0.05 g/50 mL biomass dose for Indosol Black NF and Indosol Orange RSN, respectively. Acidic pH was found to be favourable for maximum dyes removal. Characterisation of biosorbent was carried out through Fourier transform-infrared spectroscopy (FT-IR), scanning electron microscopy, thermogravimetric analysis (TGA) and point of zero charge determination. FT-IR analyses confirmed the involvement of carboxylic and carbonyl groups. The desorption study was also conducted to check out the possibility of regeneration of dyes and adsorbent and it was found that 51.58 and 76.6% of Indosol Black NF and Indosol Orange RSN, respectively, can be desorbed from the loaded biosorbent by using 1 M NaOH solution. The results indicated that PEI-treated peanut husk biomass can be used as an efficient biosorbent for the removal of Indosol Black NF and Indosol Orange RSN dyes from aqueous solutions.     

Effects of gasoline engine emissions on health status of auto rickshaw drivers at DIR (L) Khyber Pukhtunkhwa

A total of 50 blood samples were collected from auto rickshaw drivers who were exposed to environmental gasoline in different sites of Dir (L), Khyber Pukhtunkhwa, Pakistan, in July and August, 2009. Control group was selected among the individuals who were not regularly exposed to gasoline emissions. The samples were analyzed for calcium, cholesterol, glucose, phosphorus, potassium, and triglycerides levels. A significant increase was found in the blood levels of calcium, glucose, phosphorus, and triglycerides. A significant decrease was detected in the blood levels of potassium without any significant effect on the blood levels of cholesterol and magnesium.     

Effect of numerical schemes on advection terms for large eddy simulation of inclined dense jets

Environmental Fluid Mechanics - Discharged jet from desalination plants has harmful effects on the surrounding environment so that it is essential to investigate its behavior in detail. The aim of...     

Coastal scenic assessment: unlocking the potential for coastal tourism in rural Pakistan via Mediterranean developed techniques

Originally developed within the Mediterranean, a tested coastal management and planning tool was applied outside the region to evaluate its international scope, with particular reference to a less developed country. Seven coastal sites in Pakistan were investigated for their scenic values through a 26 item checklist grouped as physical and human parameters analysed though weighted parameters and fuzzy logic matrices. With respect to a five-class evaluation system, results indicated: Jiwani, Miani Hor and Pasni-Astola Island as extremely attractive natural sites with very high landscape value (classified as Class 1 sites); Mubarak village as an attractive natural site with high landscape value (classified as a Class 2 site); Kaka pir village, Harjana village and Keti Bandar as having little outstanding landscape features or with urban environment and classified as Class 3 sites. Rural and/or urban environmental influences were observed to be critical factors which influenced major human parameters that ultimately affect sites’ scenic classification. However, notwithstanding natural attraction, unless political and social development barriers are tackled in conjunction with recognising areas that possess inherent tourism potential, economic potential is unlikely to be realised.     

Forecasting the impact of meteorological parameters on air pollutants in Andhra Pradesh using machine learning techniques

In the 21st century, air pollution has emerged as a significant problem all over the globe due to a variety of activities carried out by humans, such as the acceleration of industrialization and urbanization. SO₂, NO₂, and NH₃ are the key components contributing to air pollution. Moreover, these air pollutants have a significant connection to several climatic characteristics, such as the speed of the wind, the relative humidity, the temperature, the amount of precipitation, and the surface pressure. As a result, machine learning (ML) is regarded as a more effective strategy for predicting air quality than more conventional approaches such as probability and statistics, among others. In the research, Decision Tree (DT), Support Vector Regression (SVR), Random Forest (RF), and Multi-Linear Regression (MLR) algorithms are used to make predictions about air quality, and MSE (Mean Squared Error), RMSE (Root Mean Square Error), MAE (Mean Squared error), and R² are used to determine how accurate the predictions are.