One of the main aims of water resource planners and managers is to estimate and predict the parameters of groundwater quality so that they can make managerial decisions. In this regard, there have many models developed, proposing better management in order to maintain water quality. Most of these models require input parameters that are either hardly available or time-consuming and expensive to measure. Among them, the Artificial Neural Network (ANN) Models, inspired from human brain, are a better choice. The present study has simulated the groundwater quality parameters of Ramhormoz Plain, including Sodium Adsorption Ratio (SAR), Electrical Conductivity (EC), and Total Dissolved Solids (TDS), via ANN and ANN+ Particle Swarm Optimization (PSO) Models and at the end has compared their results with the measured data. The input data for TDS quality parameter is consisted of EC, SAR, pH, SO4, Ca, Mg, and Na, while for SAR, it includes TDS, pH, Na, and Hco3, and as for EC, it involves So4, Ca, Mg, SAR, and pH; all of them, gathered from 2009 to 2015. Results indicate that the highest prediction accuracy for SAR, EC, and TDS is related to the ANN + PSO model with the tangent sigmoid activation function so that both MAE and RMSE statistics have the minimum and R2 the maximum value for the model. Also the highest prediction accuracy is respectively related to EC, TDS, and SAR parameters. Considering the high efficiency of artificial neural network model, by training the PSO algorithm, it can be used in order to make managerial decisions and ensure monitoring and cost reduction results.

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.

Groundwater makes up an important part of global freshwater resources, though it is often threatened by overuse of natural resources along with abundant production of wastes in modern society. This study aims to investigate the removal of three heavy metals including nickel, cadmium, and lead from groundwater via electrocoagulation, a suitable method for treatment of water-soluble compounds, dealing with the impact of this process on three major groundwater parameters, namely TDS (Total Dissolved Solid), TH, and EC at a laboratory scale. The experiments have been performed using four aluminum electrodes. In this research, the efficiency of contaminants removal and the parameters of interest have been investigated under several conditions such as the distance among the electrodes, potential difference among the electrodes, and different initial concentrations of heavy metals with a detention time of 20 min. Afterwards both results and observations have been analyzed, using diagrams and data tables. Results indicate that this method has had no significant effect on TDS and EC; however, in case of TH, the removal has increased by up to 29.17%. As for the heavy metals, all three contaminants have achieved an increase of the potential difference to 15 V, a decrease in the distance among the electrodes to 2.2 cm, as well as a removal of over 90%. Furthermore, result analysis shows that this process has had a better removal efficiency, concerning lead.

The present study has been conducted to determine the surface water quality of urban area in Savar, Dhaka, Bangladesh by determining some water quality parameters (Transparency, Temperature, pH, EC, Eh, DO, TSS, TDS, TS, BOD5, COD, TOC, Cl-, Br-, SO4-2, NO3-, NO2-, PO4-3, TP, HCO3- and Total alkalinity) as well as the status of phytoplankton’s community in the water from two lakes (Tiger Lake and AERE Lake) and one canal (Karnapara Canal). It has been shown that, with exception of BOD and COD, all water quality parameters of AERE Lake in the present study are within the acceptable limits, recommended by local and international standards. Among water quality parameters of Tiger Lake and Karnapara Canal, the concentration of transparency, temperature, EC, DO, TSS, TDS, BOD, COD, TOC, NO2-, and TP exceed the acceptable limits. Organic Pollution Index (OPI) demonstrate that the water bodies are severely polluted by organic matters. R mode Cluster Analysis (CA) suggests that common sources of water quality parameters are industrial, agricultural, and natural. The Principle Component Analysis/Factor Analysis (PCA/FA) identifies two dominant factors, responsible for data structure, explaining 100% of total variance in the data set. The PCA agrees with CA, suggesting that multiple anthropogenic and natural sources are responsible for the water quality parameters. The present study reflects the actual scenario of surface water quality of Savar urban area, thus helping the policy planers and makers of the People’s Republic of Bangladesh to take proper management and abatement strategies for the management of sustainable water resource in Bangladesh.

Predicting the quality of water and air is a particular challenge for forecasting systems that support them. In order to represent the small-scale phenomena, a high-resolution model needs accurate capture of air and sea circulations, significant for forecasting environmental pollution. Data assimilation is one of the state of the art methods to be used for this purpose. Due to the importance of thermal structure in monitoring the variations of environmental phenomena, the present study has used Sea Surface Temperature (SST) in data assimilation method to optimize this parameter. SST is one of the most important factors to conduct researches on the ocean, the atmosphere, and their interaction, not to mention monitoring and forecasting air and ocean phenomena as well as commercial and fishing communities and weather forecasts. This study has aimed to present a satellite-derived SST based on pathfinder advanced very high resolution radiometer (AVHRR) data assimilating in FVCOM (finite volume community ocean model) on the Persian Gulf to examine the effect of data assimilation by using the Cressman scheme. The performance of this method has been compared to the optimal interpolation SST (OISST) data, via both visual comparisons and statistical parameters. Applying assimilation method improves correlation coefficient of the model from 0.92 to 0.99. Results demonstrate that the modeled SST has been completely reconstructed by the data assimilated experiment via the Cressman scheme for this region. The spatial and temporal pattern of SST reveals a significant improvement in the entire domain during the investigated period in the gulf.

Water and sediment samples were collected from six different stations, located along the Nil River between February and June 2015. Concentrations of cadmium, lead, zinc, and copper were determined. The extent of the sediment pollution was assessed, using the multiple pollution indices, namely contamination factor (CF), pollution load index (PLI), and the geoaccumulation index (Igeo). The results showed that the level of metals in water samples exceeded background concentrations for Cd and Pb, and the average values for those elements were higher than those of Zn and Cu, ranked as the following: Pb (0.58)> Zn (0.38)> Cd (0.32)> Cu (0.061). For sedimentary samples, the results showed that Zn and Pb concentrations were greater than the concentrations of Cu and Cd, exceeding the background values (except for Cu). The concentration of the tested heavy metals decreased to the following order: Zn (96.2)> Pb (61.5)> Cu (38.83)> Cd (2.34). The Igeo values revealed that Cd (2.87) and Pb (1.61) had accumulated significantly in the Nil River. Contamination factor (CF) confirmed that the sedimentary samples were moderate to very high in terms of Cd, Pb, and Zn contamination. The Pollution Load Index (PLI) values were above one (>1), indicating an advanced decline of the sediment quality. Also, the results showed that the stations, located at the middle portion of the river (S3, S4, and S5) have higher levels of metals than the other stations (S1, S2, and S6). The assessment of heavy metals' levels in water and sedimentary samples indicated that river water and the sediments in the study area were strongly impacted by agricultural activities and domestic waste water.

The present paper has examined the degrading ability of phenol-oxidizing bacterium, Ralstonia eutropha, for biological removal of methylene blue (MB) from aqueous solutions under aerobic conditions. Results show that MB has been extensively eliminated as a co-metabolism in the presence of supplementary carbon (glucose) and nitrogen (yeast extract and peptone) sources and the experimental observations indicate that MB is initially adsorbed on the cell’s surface, in accordance to Langmuir Theory, then to be degraded by the cell. The type of nitrogen source, initial pH, aeration rate, and the presence of CaCl2 are all influential factors in the process of MB removal. The biodegradation kinetics modeling has determined that while playing an uncompetitive role, MB inhibits its biodegradation at high concentrations. According to the best fit Han-Levenspiel Model, the maximum MB specific biodegradation rate (rmax), half-saturation concentration of MB (KS), maximum allowable MB concentration (Sm), and the shape factors (n and m) have been 7.37 mg gcell-1 h-1, 32.13 mg/L, 158.8 mg/L, 0.27, and 0.76, respectively.

The Nematodes are roundworms that are found in every environment of the earth. While some species are harmful parasites, others play a vital role in nutrient cycle and medical research. Nematode infestation in the fields is poly-specific; however, depending on the agro-climatic conditions, one or two species are dominant over the rest. The present studies attempts to observe and control the root knot infestation on spinach (Spinacea oleracea), which belong to Chenopodiaceous family and is extensively cultivated in India for its nutritious leaves. Various organic metabolites have been estimated in root knot nematode infested spinach, including chlorophyll, total carbohydrates, and total free amino acids. Spinach plants, infested with root knot nematode, have been treated with peels of lemon, which proved beneficial in terms of increased chlorophyll content. Altered total carbohydrate and total free amino acid content have been found with S/4 of lemon-peel-treated spinach plants. It has been found that the infested spinach shows 137.5% carbohydrate content over the normal plants. The kaghzi neemboo amended spinach contains lower carbohydrate than normal-control. Rate of carbohydrate contents has been found to be inversely proportional to the rate of extracts concentrations as S/4, S/2, and S show 92.5%, 55%, and 37.5% increase over normal-control spinach. The bio-amendment of citrus aurantifolia, (kaghzi neemboo) helps controlling root knot nematodes, which is more beneficial than using chemicals to control the same, as the chemical fertilizer causes pollution, exerting negative impacts on flora and fauna.

The management of Municipal Solid Waste (MSW) in Nigeria and most developing countries has remained a major public health challenge, thus creating the need for reliable and environmentally-acceptable alternatives. This study focuses on composting assessment as a viable recovery alternative for MSW in six States of Southwest Nigeria, namely: Ekiti, Osun, Ondo, Ogun, Oyo, and Lagos. Extensive literature review has been carried out to understand the waste generation patterns in these states. Reported literature data has been assessed for sustainability of composting strategy in terms of organic waste streams, amenable of biotransformation as well as in terms of return through energy saving and material recovery. A life-cycle framework has been used to estimate GHG emissions, available nutrients, and potential compost production, instead of landfill in each region. Results show significant potential compost production of 895,659-, 255,267-, 153,423-, 117,468-, 113,094- and 112, 397-m3/yr for Lagos, Oyo, Ogun, Osun, Ondo, and Ekiti, respectively. It has been deduced from the study that composting would be very beneficial to the economy as its product would boost agriculture production while reducing the budget spent on fertilizer annually.

The present study attempts to introduce a method of statistical downscaling with a synoptic view. The precipitation data of Golestan Province has been used for the years 1971 to 2010. Employing multivariable regression, this study models the precipitation gauges in the station scale, by making use of 26 predicting components of model HadCM3, on the basis of two A2 and B2 scenarios. However, the minimum predicting components for precipitation in station scale included 26 components for one grid to 390 atmosphere circulation components for the 15 suggested grids. Nevertheless, results indicate minimum error, related to the precipitation models, based on projecting components of the studies of 15 grids. By applying this selected method, the precipitation gauges for 2020 to 2040 has been simulated. General results of the precipitation changes for the yearly decennial average of Golestan Province indicates additive stream of this component, based on both A2 and B2 scenarios. Yet this yearly decennial addition of precipitation go with seasonal and annual changes, i.e. getting drier in summer as well as its subsequent increase in draught issue on one hand, and increased centralization of precipitations in the winter and lack of its proper distribution during year on the other. As a result, changes in local patterns of precipitations throughout the province is promising for maximum increase of precipitation for the farthest southwest area of Golestan, greatly potential for decreasing precipitation of sub eastern area.