Health impact assessment of air pollution in an area of the largest coal mine in Brazil

Environmental Science and Pollution Research - Coal exploration and burning activities are among the activities with the greatest potential to cause atmospheric pollution due to the combustion...     

Intertidal macroinvertebrate community structure in a subtropical channel is driven by sediment properties across different land-use types

Environmental Science and Pollution Research - Macroinvertebrate community in the intertidal setup plays an important role in coastal ecosystem functions and biogeochemical cycle. However,...     

In silico screening of potent bioactive compounds from honeybee products against COVID-19 target enzymes

Environmental Science and Pollution Research - After the early advent of the Coronavirus Disease 2019 (COVID-19) pandemic, myriads of FDA-approved drugs have been massively repurposed for...     

Evaluation of environmental pollution and possible management options of heavy oil fly ash

Due to the presence of toxic metals, dumping of heavy oil fly ash (HOFA) is causing ever-growing environmental problem including the pollution of air, water and soil. The present study investigates the possible environmental impacts associated with the land disposal of HOFA generated in the power plant. Different modeling and laboratory analysis were integrated to address the real environmental problems. Leaching behavior of heavy metals within the HOFA were investigated by laboratory batch leaching tests, which confirmed that most of the toxic elements in the HOFA can easily leach into the environment with rain water. The level of atmospheric dust surrounding a HOFA dumping site was predicted using the Industrial Source Complex (ISC3) air dispersion model, and the results indicated that the dumping of HOFA could be a potential hazard for local air quality. The study also revealed different reuse options of HOFA. The characteristic analysis confirmed HOFA can be used as a natural adsorbent to remove pollutants from wastewater or as soil stabilizing material by blending with cement.     

Synergistic Effects of Multiple Metal Contaminants on Electrokinetic Remediation of Soils

This article presents a bench‐scale study performed to investigate the removal of heavy metals when they exist individually and in combination in soils. Electrokinetic experiments were conducted using two types of clayey soils, kaolin and glacial till. These soils were contaminated with Cr(VI) only, with Ni(II) only, and with Cr(VI), Ni(II), and Cd(II) combined. It was found that in kaolin, a significant pH variation occurred due to electric potential application, affecting the adsorption‐desorption and dissolution‐precipitation, as well as the extent of migration of the contaminants. In glacial till, however, pH changes were not affected significantly. In both kaolin and glacial till, the migration of Cr(VI) and Ni(II) was higher when they were present individually compared to when they existed together with Cd(II). Cr(VI) migration as single or combined contaminant was lower in kaolin as compared to that in glacial till. This result was due to the low pH conditions created near the anode region in kaolin that led to high Cr(VI) adsorption to the clay surfaces. In glacial till, however, nickel precipitated with or without the presence of co‐contaminants due to high pH conditions in the soil. Overall, this study demonstrates that adsorption, precipitation, and reduction are the significant hindering mechanisms for the removal of heavy metals using electrokinetic remediation. The direction of the contaminant migration and overall removal efficiency depend on the polarity of the contaminant, the presence of co‐contaminants, and the type of soil. © 2001 John Wiley & Sons.     

Modelling Pesticide and Nutrient Transport in the Cameron Highlands, Malaysia Agro-Ecosystems

Cameron Highlands has a long history of intensive horticulture especially vegetable and flower cultivation. This industry uses large amounts of nutrients and pesticides. Several simulation models were used to assess the movement of pesticides and nutrients in relation to agronomic practices in farm plots cultivated with cabbage and chrysanthemum. It was shown that most of the pesticides are absorbed by the topsoils which are enriched by organic fertiliser, and are not leached beyond the top 10 cm layer. Methamidophos, dazomet, cyromazine, triforine and NO₃N were predicted to cause soil contamination. The application of high amounts of fertilisers can cause nitrate contamination to the groundwater in Cameron Highlands.     

A multiobjective modeling approach to locate multi-compartment containers for urban-sorted waste

The location of multi-compartment sorted waste containers for recycling purposes in cities is an important problem in the context of urban waste management. The costs associated with those facilities and the impacts placed on populations are important concerns. This paper introduces a mixed-integer, multiobjective programming approach to identify the locations and capacities of such facilities. The approach incorporates an optimization model in a Geographical Information System (GIS)-based interactive decision support system that includes four objectives. The first objective minimizes the total investment cost; the second one minimizes the average distance from dwellings to the respective multi-compartment container; the last two objectives address the “pull” and “push” characteristics of the decision problem, one by minimizing the number of individuals too close to any container, and the other by minimizing the number of dwellings too far from the respective multi-compartment container. The model determines the number of facilities to be opened, the respective container capacities, their locations, their respective shares of the total waste of each type to be collected, and the dwellings assigned to each facility. The approach proposed was tested with a case study for the historical center of Coimbra city, Portugal, where a large urban renovation project, addressing about 800 buildings, is being undertaken. This paper demonstrates that the models and techniques incorporated in the interactive decision support system (IDSS) can be used to assist a decision maker (DM) in analyzing this complex problem in a realistically sized urban application. Ten solutions consisting of different combinations of underground containers for the disposal of four types of sorted waste in 12 candidate sites, were generated. These solutions and tradeoffs among the objectives are presented to the DM via tables, graphs, color-coded maps and other graphics. The DM can then use this information to “guide” the IDSS in identifying additional solutions of potential interest. Nevertheless, this research showed that a particular solution with a better objective balance can be identified. The actual sequence of additional solutions generated will depend upon the objectives and preferences of the DM in a specific application.     

Predictive model for disinfection by-product in Alexandria drinking water, northern west of Egypt

Chlorine has been utilized in the early stages of water treatment processes as disinfectant. Disinfection for drinking water reduces the risk of pathogenic infection but may pose a chemical threat to human health due to disinfection residues and their by-products (DBP) when the organic and inorganic precursors are present in water. In the last two decades, many modeling attempts have been made to predict the occurrence of DBP in drinking water. Models have been developed based on data generated in laboratory-scale and field-scale investigations. The objective of this paper is to develop a predictive model for DBP formation in the Alexandria governorate located at the northern west of Egypt based on field-scale investigations as well as laboratory-controlled experimentations. The present study showed that the correlation coefficient between trihalomethanes (THM) predicted and THM measured was R ²?=?0.88 and the minimum deviation percentage between THM predicted and THM measured was 0.8 %, the maximum deviation percentage was 89.3 %, and the average deviation was 17.8 %, while the correlation coefficient between dichloroacetic acid (DCAA) predicted and DCAA measured was R ²?=?0.98 and the minimum deviation percentage between DCAA predicted and DCAA measured was 1.3 %, the maximum deviation percentage was 47.2 %, and the average deviation was 16.6 %. In addition, the correlation coefficient between trichloroacetic acid (TCAA) predicted and TCAA measured was R ²?=?0.98 and the minimum deviation percentage between TCAA predicted and TCAA measured was 4.9 %, the maximum deviation percentage was 43.0 %, and the average deviation was 16.0 %.