Leaching characteristics of the fine fraction from an excavated landfill: physico-chemical characterization

Journal of Material Cycles and Waste Management - Leaching of fine fraction (<10 mm) obtained from landfill mining activities in an Estonian landfill was done. On-site excavation...     

Presence of heavy metals in drinking water resources of Iran: a systematic review and meta-analysis

Environmental Science and Pollution Research - Water quality is one of the most important indices for public health especially for drinking water consumptions. This study was conducted to survey...     

Effect of TiC Nanoparticles Reinforcement in Coir Fiber Based Bio/Synthetic Epoxy Hybrid Composites: Mechanical and Thermal Characteristics

Journal of Polymers and the Environment - The present investigation was performed to study the effect of titanium carbide (TiC) nanoparticles and coir fiber as hybrid reinforcements on the...     

Determination of heavy metal concentrations in street dusts in Istanbul E-5 highway

Components and quantity of street dust are environmental pollution indicators especially in big cities. Street dust is generally composed of car exhaust gas originated particles and wind-transported particles. Heavy metals, which are found in street dust, such as Pb, Cu, Mn, Zn, Cd and Ni are significant for environmental pollution. According to the kind of vehicle in traffic, quantity and type of heavy metals vary in street dust. The use of leaded gasoline gives a boost to the importance of lead level especially in street dust even at the start of 21st century. These metals possess bioaccumulation property, and the possibility of the amount of these metals reaching a critical value and threatening human health increases the importance of this issue. In this study, street dusts have been collected from E-5 Highway from Topkapi to Avcilar regions that spans about 18 km in Istanbul, Turkey, and Pb, Cu, Mn, Zn, Cd and Ni concentrations have been detected in street dust. Twenty-two street dust samples were taken from a total of 22 different points at previously decided 14 main areas. Analyses were conducted using Leeds Public Analyst method. According to the results of this study, Pb, Cu and Zn concentrations in E-5 Highway between Topkapi and Avcilar region in Istanbul were higher than maximum concentration levels of these heavy metals in normal soil. This situation indicates that there is heavy metal pollution in the inspected area in E-5 Highway in Istanbul.     

Seasonal changes of CO(2), CH(4) and N(2)O fluxes in relation to land-use change in tropical peatlands located in coastal area of South Kalimantan

Tropical peatland could be a source of greenhouse gases emission because it contains large amounts of soil carbon and nitrogen. However these emissions are strongly influenced by soil moisture conditions. Tropical climate is characterized typically by wet and dry seasons. Seasonal changes in the emission of carbon dioxide (CO(2)), methane (CH(4)) and nitrous oxide (N(2)O) were investigated over a year at three sites (secondary forest, paddy field and upland field) in the tropical peatland in South Kalimantan, Indonesia. The amount of these gases emitted from the fields varied widely according to the seasonal pattern of precipitation, especially methane emission rates were positively correlated with precipitation. Converting from secondary forest peatland to paddy field tended to increase annual emissions of CO(2) and CH(4) to the atmosphere (from 1.2 to 1.5 kg CO(2)-C m(-2)y(-1) and from 1.2 to 1.9 g CH(4)-C m(-2)y(-1)), while changing land-use from secondary forest to upland tended to decrease these gases emissions (from 1.2 to 1.0 kg CO(2)-C m(-2)y(-1) and from 1.2 to 0.6 g CH(4)-C m(-2)y(-1)), but no clear trend was observed for N(2)O which kept negative value as annual rates at three sites.     

Modeling of SO<Subscript>2</Subscript> distribution in Istanbul using artificial neural networks

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO₂) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO₂ distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO₂ concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO₂ concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO₂. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO₂ is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.     

Association of environmental exposure with hematological and oxidative stress alteration in gasoline station attendants

Environmental Science and Pollution Research - Gasoline station attendants spend a great deal of their time in the direct exposure to noxious substances such as benzene and byproducts of gasoline...     

Manual Material Handling Assessment Among Workers of Iranian Casting Workshops

Manual material handling (MMH) tasks can be found in most workplaces and they may constitute a risk factor for work-related musculoskeletal disorders (WMSDs). This study was conducted to determine the prevalence of WMSDs and to compare MMH loads with the acceptable weight and force limits among Iranian casting workers. Data were collected from 50 workers of casting workshops who performed MMH tasks. The Nordic musculoskeletal disorders questionnaire and the Snook tables were used as tools for data collection. Hand/ wrist symptoms were the most prevalent problems among the workers (84%). The results of the Snook tables showed that the loads in lifting (84%), lowering (86%), carrying (66%), pushing with initial (43%) and sustained force (59%), and pulling tasks with initial (48%) and sustained force (93%) exceeded recommended limits. WMSDs occurred in high rates among the workers and, thus, ergonomics interventions should focus on decreasing WMSDs and redesigning MMH tasks.     

Quantification of human adenovirus in irrigation water-soil-crop continuum: are consumers of wastewater-irrigated vegetables at risk?

Environmental Science and Pollution Research - Because of health concerns regarding the presence of enteric viruses in wastewater effluents, this study was designed to investigate the occurrence of...     

Economic complexity,ICT, biomass energy consumption,and environmental degradation: evidence from Iran

Economic complexity, biomass energy consumption, and information communication technology (ICT) have diverse impacts on energy consumption and carbon dioxide (CO₂) emissions. Nevertheless, analysis of these variable effects is not addressed in the previous literature; the antiqueness of this article is stuffing this gap. This study assessed the relationship between gross domestic product (GDP) per capita, biomass consumption, economic complexity index (ECI), ICT, and CO₂ emissions in Iran in 1994–2018. The autoregressive distributed lag (ARDL) model and the quantile regression (QR) econometric technique were used to investigate the factors affecting CO₂ emissions in the tails of the conditional distribution. The share of each influential factor was predicted through the variance decomposition analysis (VD) for the next 10 years. The empirical results showed a long-run relationship between the variables. So, the variables of biomass consumption, ECI, and ICT improve the quality of the environment in Iran by reducing CO₂ emissions, and the per capita GDP variable increases CO₂ emissions. Results suggest no evidence indicating the presence of environmental Kuznets curve (EKC); however, QR demonstrated the existence of EKCs in the lower quantiles of the conditional distribution. The ECI will have the most share to change the CO₂ emissions in the future. The income threshold should be determined at the turning point of the EKC to increase economic development. Moreover, investing in increasing biomass consumption is vital. Policymakers also need to consider strict added value for the export of products.