The nexus of renewable and nonrenewable energy consumption,trade openness,and CO2 emissions in the framework of EKC: evidence from emerging economies

Environmental Science and Pollution Research - Emerging economies are experiencing considerable economic changes due to change in energy demand and CO2 emissions. To explore the link between energy...     

Multivariate analysis of heavy metals concentrations in river estuary

Multivariate statistical techniques such as multivariate analysis of variance (MANOVA) and discriminant analysis (DA) were applied for analyzing the data obtained from two rivers in the Penang State of Malaysia for the concentration of heavy metal ions (As, Cr, Cd, Zn, Cu, Pb, and Hg) using a flame atomic absorption spectrometry (F-AAS) for Cr, Cd, Zn, Cu, Pb, As and cold vapor atomic absorption spectrometry (CV-AAS) for Hg. The two locations of interest with 20 sampling points of each location were Kuala Juru (Juru River) and Bukit Tambun (Jejawi River). MANOVA showed a strong significant difference between the two rivers in terms of heavy metal concentrations in water samples. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers. It provided an important data reduction as it used four parameters (Zn, Pb, Cd and Cr) affording 100% correct assignations. Results indicated that the two rivers were different in terms of heavy metals concentrations in water, and the major difference was due to the contribution of Zn. A negative correlation was found between discriminate functions (DF) and Cr and As, whereas positive correlation was exhibited with other heavy metals. Therefore, DA allowed a reduction in the dimensionality of the data set, delineating a few indicator parameters responsible for large variations in heavy metal concentrations. Correlation matrix between the parameters exhibited a strong evidence of mutual dependence of these metals.     

Approaches for prevention and environmental management of novel COVID-19

Environmental Science and Pollution Research - The World Health Organization (WHO) recognized a novel coronavirus as the causative agent of a new form of pneumonia. It was subsequently named...     

Assessment of major food crops production-based environmental efficiency in China,India, and Pakistan

Environmental Science and Pollution Research - Global warming and food security have led to increasing concern about agricultural crop production efficiency, especially wheat and rice farming. The...     

Determinants of the ecological footprint in Thailand: the influences of tourism,trade openness,and population density

Environmental Science and Pollution Research - This paper investigates the impact of economic growth, energy consumption, tourism, trade openness, and population density on the ecological...     

Efficacy of nanoparticles as nanofertilizer production: a review

Environmental Science and Pollution Research - Owing to the ever-increasing demand for food, the growing global population has forced farmers to increase fertilizer use. The overall use of...     

Indoor air pollution evaluation with emphasize on HDI and biological assessment of HDA in the polyurethane factories

Today, many raw materials used in factories may have a dangerous effect on the physiological system of workers. One of them which is widely used in the polyurethane factories is diisocyanates. These compounds are widely used in surface coatings, polyurethane foams, adhesives, resins, elastomers, binders, and sealants. Exposure to diisocyanates causes irritation to the skin, mucous membranes, eyes, and respiratory tract. Hexamethylene diamine (HDA) is metabolite of hexamethylene diisocyanate (HDI). It is an excretory material by worker’s urine who is exposed to HDI. Around 100 air samples were collected from five defined factories by midget impinger which contained dimethyl sulfoxide absorbent as a solvent and tryptamine as reagent. Samples were analyzed by high-performance liquid chromatography with EC\UV detector using NIOSH 5522 method of sampling. Also, 50 urine samples collected from workers were also analyzed using William’s biological analysis method. The concentration of HDI into all air samples were more than 88 xxxμg/m³, and they have shown high concentration of pollutant in the workplaces in comparison with NIOSH standard, and all of the workers’ urine were contaminated by HDA. The correlation and regression test were used to obtain statistical model for HDI and HDA, which is useful for the prediction of diisocyanates pollution situation in the polyurethane factories.     

Influence of alum on cyanobacterial blooms and water quality of earthen fish ponds

Eruption of blue-green algal blooms occurs frequently in eutrophic lakes and fish ponds, with associated unpleasant odor and horrid scums. In the present study, we conducted a pre-test experiment in 3 m³ outdoor concrete ponds to determine the optimum concentration of aluminum sulfate (alum) required for reduction of the cyanobacterial blooms without negative effect on fish growth. As a consequence, 10 mg L^?1 alum was named as the optimum concentration that was applied in 1000 m³ earthen fish ponds. Obtained results showed that Secchi disc values significantly increased from 10 to 24 cm after 14 days of alum application. Alum-treated ponds showed a reduction in total phytoplankton counts by 94 and 96 % compared to the corresponding controls after 10 and 14 days, respectively. Abundance of blue-green algae in the treated ponds was decreased by 98 % compared to the corresponding control after 14 days of alum application. Consequently, dissolved oxygen, pH, total phosphorus, orthophosphate, and chlorophyll “a” content declined significantly. Our study revealed that using 10 mg L^?1 of alum is an effective way to control cyanobacterial blooms in eutrophic waters, especially in fish ponds, without negative effect in water quality.     

Assessment of surface water quality of selected estuaries of Malaysia: multivariate statistical techniques

Multivariate statistical techniques such as cluster analysis (CA), factor analysis (FA) were used for the evaluation of spatial variations and the interpretation of a large complex water quality data set of two selected estuaries of Malaysia. The two locations of interest with 10 sites in each location were Kuala Juru (Juru estuary) and Bukit Tambun (Jejawi estuary). Cluster analysis showed that some sites in both locations have similar sources of pollution from point or non-point sources whereas FA yielded four factors which are responsible for water quality variations explaining more than 80% of the total variance of the data set and allowed to group the selected water quality. Correlation analysis of the data showed that some parameters have strong association with other parameters and they share a common origin source. This study illustrates the usefulness of multivariate statistical analysis for evaluation and interpretation of complex data sets to get better information about the pollution sources/factors and understanding the behavior of the parameters in water quality for effective river water quality management.