One of the major factors, contributing to the emission of greenhouse gases in the environment is generation of pollutant gases in municipal landfills. As for the design and building of a gas collecting system, it is necessary to properly estimate the amount and type of the landfill emissions. By means of LandGEM model, this study predicts the amount and type of the landfill gases, produced for 30 years (from 2016 to 2045) in Jiroft. Results show that in 2045, 3, 324, 274 tons of waste will be disposed in municipal landfills of Jiroft and the total amount of produced gas, methane, carbon dioxide, and non-methane organic compounds will be 32, 994, 8813, 24,181, and 378.8 tons/year, respectively. Furthermore, the rate of landfill gas emissions from 2016 to 2045 has been achieved. Maximum concentrations of methane, carbon dioxide and non-methane organic compounds in 2045, in 700 meters from landfill, will be 40, 590, 112, 700, and 1765 tons/m3 respectively. Based on the results, obtained from this article, landfill pollutants such as CH4, CO2, and NMOC's can reach up to 15 kilometers from landfill, thus social places should be located farther than 15 kilometers from the landfill site of Jiroft. The results, obtained in this paper, can be used to identify the effect of Jiroft landfill in global emission of greenhouse gases and proper management of the landfill gas not only reduces greenhouse gas emissions, diminishing their effects on public health, but can be also used as a sustainable energy source.

The performance of every plant in an environment is an indicator of how the plant can withstand the various environmental conditions. This study investigated the toxicity of crude oil on the growth performance, chlorophyll contents, enzymatic activities and oxidative stress biomarkers of eight accessions of Zea mays. The growth enzyme (amylase and invertase) activities, as well as oxidative stress biomarkers (malondialdehyde, peroxidase, superoxide dismutase and catalase) in Z. mays were assayed using spectrophotometric method. The maize accessions were grown in the experimental pots containing crude oil treatments (2%, 4%, 6%, 8% and 10%) and harvested after 14 days of seedling emergence. The results showed that the percentage seedling emergence, leaf area, root length, stem girth and shoot length of each accession were significantly reduced (p

The present research analyzes air quality in Ahvaz, a city in the south of Iran, paying special attention to sulfur dioxide (SO2). In order to prepare the average data in the city, measurements have been carried out between 2009 and 2010 in two different locations. Relations between sulfur dioxide and some meteorological parameters have been calculated statistically, using the daily average data. The wind data (velocity, direction), relative humidity, temperature, sunshine periods, evaporation and rainfall have been considered as independent variables. The RMSE Test showed that among different prediction models, the stepwise one is the best option. The average concentrations have been calculated for every 24 hours, during each month and each season. Results show that the highest concentration of sulfur dioxide occurs generally in the morning while the lowest concentration is found before the sunshine. In case of the monthly concentrations of sulfur dioxide, the highest value belongs to January, while the lowest one occurs in October. And as for the seasonal concentrations, it has been shown that the highest amounts belong to winter. Results show that quantities of SO2 in different seasons as well as the entire year can be estimated by climate parameters. Results also indicate that the relations between the SO2 and meteorological parameters are stronger than the entire year during the seasons.

Monitoring and controlling air quality parameters form an important subject of atmospheric and environmental research today due to the health impacts caused by the different pollutants present in the urban areas. The support vector machine (SVM), as a supervised learning analysis method, is considered an effective statistical tool for the prediction and analysis of air quality. The work presented here examines the feasibility of applying the SVM to predict the ozone and particle concentrations in two Tunisian cities, namely Tunis and Sfax. We used the SVM with the linear kernel, SVM with the polynomial kernel and SVM with the RBF kernel to predict the ozone and particle concentrations in Tunisia for one year. The RBF kernel produced good results for the two pollutants with 0% error rate. Polynomial and linear kernels produced sufficiently low errors for the pollutants, at 9.09% and 18.18%, respectively. Discriminant Analysis (DA) was selected to analyze the datasets of two air quality parameters, namely ozone O3 and Suspended Particles SP. The DA results show that the spatial characterization allows for the successful discrimination between the two cities with an error rate of 4.35% in the case of the linear DA and 0% in the case of the quadratic DA. A thematic map of Tunisia was created using the MapInfo software.

This study was carried out for the purpose of investigating the issue of tailings dams as a potential source of contamination by trace elements in soils at the Sidi Bou Othmane mine in the Marrakech Region, Morocco. Soil samples taken from depths of up to 15 cm and within a radius of 50 m from the tailings dams, were analyzed for Cd, Cu, Pb and Zn using atomic absorption spectrometry. Average concentrations of Cd, Cu, Pb and Zn in soils around the abandoned tailings dams were, respectively, 157.2±8.8, 969.1±38.7, 1640.7±42.7 and 2846.8±84.6 mg/kg. Soils in the vicinity of the decommissioned tailings dams registered increased values for Cd, Cu, Pb and Zn. Contamination factors (CF) and pollution index (PI) were calculated in order to estimate the anthropogenic contribution of target pollutants which determined Cd, Cu, Pb and Zn as the main pollutants in this region. The results revealed the polluted areas in the vicinity of the mine, especially two rural communities apparently linked to the lack of appropriate measures to counteract the effects of these mine tailings, which were causing a progressive contamination of the soil with residues of heavy metal emissions in this region under study. Improved strategies for the management of tailings, among other factors, might have influenced the reduced level of contamination by trace elements at the abandoned mine tailings dam sites.

Groundwater aquifers are an important source of water supply for agriculture, industry and drinking. The present study investigates the changes in the quality of groundwater using geostatistical methods in the Ravar plain during a 10-year period. In this study, after selecting the appropriate spatial interpolation method to draw water quality parameters such as TDS, SAR and EC, zoning maps of Ravar plain were provided for three periods of time: the first period (2002-2005), an intermediate period (2006-2009) and the final period (2010-2012) in two seasons using ArcGIS 10.1. For this purpose, data were evaluated in GS+ 5.1 software, after calculation, the best model with the lowest estimated error was selected for zoning water quality parameters. Because of the lowest estimation error, Kriging, Gaussian and Spherical variogram models were selected as appropriate interpolation method for zoning the quality parameters. The results of the spatial analysis of TDS showed that TDS have been increased in the study area. Due to the amount of dissolved solids, EC amount was highly variable. According to the Wilcox classification, at the end of the period, water quality of agricultural was inappropriate in most of the area which represents the increase of electrical conductivity during the period.

Identifying Municipal Solid Waste (MSW) disposal sites and appropriately managing them is a challenging task to many developing countries like Bangladesh. It is a complex issue in an urban area, as increasing population levels, rapid economic growth and rise in community living standard, accelerates the generation rate of MSW. The study area is zone-2 (Mirpur-Pallabi) of Dhaka North City Corporation (DNCC) (10.40 km2) is a residential area, from where about 353.34 ton/day solid waste is generated and among them about 57.43% were managed by DNCC. There are 41 different size containers present at 17 locations in study area. The existing site and waste collecting containers are not sufficient, which deteriorates the environment due to illegal waste dumping and about 15 illegal dumping sites were identified. To identify proper waste dumping site and prevent contamination, Geographical Information System (GIS) was used to propose an efficient scenario with relocating the existing waste collecting containers and another scenario was proposed with number of containers (73) to attain an 93.68% waste collection efficiency including optimization and selection of waste collecting routes for the study area. This study also indicate that the application of GIS is an efficient and low cost tool to study and select appropriate dumping site so as to facilitate decision making processes.

Bioremoval and bioreduction activities of hydrocarbon (diesel) isolation from environmental samples were studied by the activity of biosurfactant production, and calculating emulsification index, gravimetric, and FTIR analysis along with the estimation of bacterial biomass. Sample from soil near petrol, diesel pumps and water sample from Thesjaswini River near Padannakad, Kasaragod, Kerala, India, were used to screen the potential diesel oil utilizing bacteria. Among the bacterial isolates (Staphylococcus, Bacillus and Corynebacterium strains), Staphylococcus sp was the potent degraders of diesel oil. Staphylococcus strain was observed to be maximum diesel oil utilizing ability (73% emulsification index) and change in the functional groups of the compound (FTIR analysis). The strain showed optimal growth at 37oC with pH 7, agitation of 150 rpm and time period (5days). The results revealed the possibility to use these strain for the reduction of complex hydrocarbon in ecosystems where they accumulate and cause pollution problems. The highest rate of hydrocarbon degradation occurred when the bacterial strain is a biosurfactants producer. The selective strain produces biosurfactants which increase the interfacial area for contact to give improved uptake of hydrophobic substrates. Bacterial strains capable of degrading complex hydrocarbons, present in the environment, have a potential to be used as an effective tool for removing ecotoxic compounds. Furthermore, results indicated that the bacterial strain Staphylococcus sp could be potentially used in biodegradation of diesel oil in waste water and had a promising application in bioremediation of hydrocarbon contaminated environments.

Evaluation of surface water quality is a complex process undertakingmultiple parameters. Converting great amount of parameters into a simpler expressionand enabling easy interpretation of data are the main purposes of water quality indices.The main aim of this study is to plan effective water resources management system forKarun River by combination of CCMEWQI and Geographic Information System (GIS).The investigation was carried out to set a management plan through exploratory andspatial analysis of physicochemical water parameters of collected samples from 10stations over one year period. Since all indices were obtained from index, river zoningwas conducted by GIS. Moreover, trace metals concentrations (As, Cr, Cd, Fe, Zn, Mn,and Al) ranged in safer limit. The highest values of F1 belonged to aquatic life and thelowest ones belonged to irrigation. Aquatic life and drinking uses received the maximumvalues of F2. The lowest values were devoted to livestock and then recreation uses. It wasinferred from index that the quality of the Karun River is principally impacted by highturbidity, TDS, NO3, SO4, and PO4 due to high suspended sediment loads. The maincause is incremental agricultural, industrial, and residential effluents. Amongst stations,station one only received the priority for drinking water supply and recreation.

The estimation of pollution fields, especially in densely populated areas, is an important application in the field of environmental science due to the significant effects of air pollution on public health. In this paper, we investigate the spatial distribution of three air pollutants in Tehran’s atmosphere: carbon monoxide (CO), nitrogen dioxide (NO2), and atmospheric particulate matters less than 10 μm in diameter (PM10μm). To do this, we use four geostatistical interpolation methods: Ordinary Kriging, Universal Kriging, Simple Kriging, and Ordinary Cokriging with Gaussian semivariogram, to estimate the spatial distribution surface for three mentioned air pollutants in Tehran’s atmosphere. The data were collected from 21 air quality monitoring stations located in different districts of Tehran during 2012 and 2013 for 00UTC. Finally, we evaluate the Kriging estimated surfaces using three statistical validation indexes: mean absolute error (MAE), root mean square error (RMSE) that can be divided into systematic and unsystematic errors (RMSES, RMSEU), and D-Willmot. Estimated standard errors surface or uncertainty band of each estimated pollutant surface was also developed. The results indicated that using two auxiliary variables that have significant correlation with CO, the ordinary Cokriginga scheme for CO consistently outperforms all interpolation methods for estimating this pollutant and simple Kriging is the best model for estimation of NO2 and PM10. According to optimal model, the highest concentrations of PM10 are observed in the marginal areas of Tehran while the highest concentrations of NO2 and CO are observed in the central and northern district of Tehran.