Precipitation patterns are influenced by many factors, such as global atmospheric circulations to name but one. Precipitation patterns in Iran have always had great fluctuations even in a smaller scale like the Alburz Mountain Range. The present research has tried to find the relationship between global atmospheric patterns and the pervasive precipitation ones in Alburz. For doing so, 17 climate indices have been chosen with the correlation between these indices and the precipitation data calculated in different lag times, using a backward correlation method (from the present time to 3 months earlier). Based on the obtained correlation results, a regression modeling has been conducted that employs a backward method. As for each lag time, one equation has been offered to estimate the amount of precipitation for every single region. Results have shown that the Bivariate ENSO Time Series (BEST) and the East Pacific Oscillation (EPO) provide the highest correlation with the pervasive precipitation time series. Also, it has been demonstrated that in multivariate correlation, the efficient index to model the relation among these indices as well as precipitation in southern Alburz alters in each lag time. Both MBE and RMSE, employed to evaluate the modeling, show relatively acceptable values, implying that the equations are acceptably capable of predicting the amount of precipitation in both northern and southern Alburz.

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.

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.

Fertilizers usually enhance potassium (K) content and other naturally occurring radioactive materials in agricultural fields that eventually enter the human food chain through plants. In this study, pot-grown rice plants planted in soil that is relatively high in natural radioactive content was used to estimate the individual influence of fertilizer applications on the uptake of 226Ra, 232Th, and 40K using gamma-ray spectrometry. Three types of common fertilizers used in rice cultivation (with percentages) 17.5N:15.5P:10K, 17N:3P:25K+2MgO, and 46N (i.e., urea) were separately added to the potted-rice plants which were in three different growth stages: emergence stage (10 days), maximum tillering stage (40 days), and initiation stage (70 days). Fertilizers at various concentrations (0, 100, 200, 300, and 400 mg kg–1) were applied in the first stage of plant growth, whereas only 200 mg kg–1 fertilizer was applied in the second and third stages. Results showed that the uptake of 226Ra, 232Th, and 40K by rice grains was affected by different concentrations of fertilizer and its application time. However, these findings suggested insignificant health risk related to the ingestion dose of grains treated with selected fertilizers.

The goal of this research is to study the effect of benzo-alpha-pyrene (BαP) as a pollutant on the plasma levels of cortisol, thyroxin (T4), and triiodothyronine (T3) hormones, and the T3/T4 ratio in the yellowfin sea bream, Acanthopagrus latus. The BαP (50 mg kg-1) in vegetable oil was peritoneally injected. Blood samples were obtained from the treated and control groups after 3 and 72 hr, respectively. The amounts of cortisol, T3, and T4 were measured using the ELISA techniques. The results showed that during both the 3 and 72 hr BαP exposures, the T4 hormone levels significantly decreased, although the cortisol levels increased (P<0.05). However, the T3 hormone levels and T3/T4 ratios compared with their control groups showed a significant difference just after 72 hr (P<0.05). The disruptive effects of the BαP exposure on T4 was stronger than that on the T3, being more evident in long-term stress. Thus, the BαP exerts a significant effect on the thyroid endocrine system and consequently on fish metabolism and growth.

The present study has been undertaken in a laboratory scale to characterize and investigate pollution potential of textile effluents from DEPZ area in Dhaka, Bangladesh. Collected effluent samples from five different industries were analyzed for physico-chemical parameters using field kits and Standard Methods, and for metals using Flame Atomic Absorption Spectrophotometer (FAAS). The average physico-chemical parameters such as temperature, color, pH, DO, EC, BOD, COD, TS, total alkalinity and total hardness were found 52.4 ºC, 2646 PCU, 9.788, 1.492 mg/L, 7473.2 μS/cm, 157 mg/L, 508.8 mg/L, 9140.8 mg/L, 761.2 mg/L and 189.6 mg/L, respectively. The average concentrations of metal found in the textile effluents were in the order of Na (4611.762 mg/L) > Ca (9.166 mg/L) > Mg (3.578 mg/L) > Zn (0.113 mg/L) > Ni (0.0074 mg/L) > Cu (0.0032 mg/L). All the measured physico-chemical parameter values are negatively deviated but metal concentrations (except Na) are positively deviated from standard limits of wastewater discharge set by Department of Environment and US Environmental Protection Agency. In view of those characteristics, the textile industry effluents should be considered to be treated by setting eco-friendly effluent treatment plant (ETP) before directly discharging into the water bodies.

Batiaghata Upazila, Khulna District in south-west coastal region of Bangladesh is the mostly saline affected area, where agriculture activities are mainly dependent on rainfall. 23 water samples from surface water and shallow tube well (STW) were collected in the monsoon season and analyzed for physico-chemical properties to classify them according to salinity hazard. Electrical Conductivity (EC) of both surface and groundwater samples were slightly higher than that of acceptable limit ( Ca2+ > Mg2+ > K+ in both surface and groundwater while the anions trend in both surface and groundwater of the study area were Cl¯ > SO42¯ > PO43¯. EC and TDS showed high positive correlation with Na+, K+ and Ca2+ with Cl¯ as confirmed from Correlation Matrix and Principal Component Analysis (PCA). Most of the STW water samples compared to the surface water had higher Soluble Sodium Percentage (SSP) values while Sodium Adsorption Ratio (SAR) indicated the surface water and ground water with low sodium hazard. The Kelly’s ratio of STW water is more subjected to sodium hazard compared to surface water in the study area.

The adsorption study was conducted on three Japanese soils with different soil properties to characterize the adsorption pattern of Arsenic (As). Double tube method was used to find out the effect of soil moisture levels on As adsorption. For this study, besides double tube method, conventional batch method also was used. The As adsorption showed two phase kinetics. An initial and rapid adsorption was found at first hour and then gradually preceded before equilibrium in all the three soils in the case of double tube method and in clay loam and light clay soil in the case of batch method. Adsorption seemed to reach equilibrium at 24 h in both methods, though the initial adsorbate load was not the same at the same applied concentration in the methods. Adsorption activity differed according to soil and as well as to the methods. The highest adsorption was found in clay loam soil followed by light clay and sandy loam soil. The linear model of Freundlich adsorption was found better fitted in the case of double tube method than batch method. Suggesting that, under the experimental conditions stated here, the double tube method is more appropriate to describe the adsorption of As in the three Japanese soils under normal field condition. The concentration of As in soil water was slightly increased at different moisture levels from 50%-80% which may contribute significantly to the bioavailability of As at the moisture level below maximum filed capacity.

The concentration of selected trace metals (Fe, Cd, Cu, Pb, and Zn) in 30surface sediments were measured using Atomic Absorption Spectrometer to investigatethe spatial and seasonal variations of trace metals along Kanyakumari coast, India. Toassess the environmental risk of trace metals, enrichment factor, geo-accumulation index,pollution load index, and ecological risk index have been calculated. According to thepollution load index and geo-accumulation index (Igeo) values, Kanyakumari coastalsediments were unpolluted by Fe, Cu, Pb, and Zn, whereas moderately polluted by Cdwith low to moderate ecological risk. The existence of the high hydrodynamic conditionduring the southwest monsoon is more favorable to the transport of sediments andenhance the accumulation of metals, whereas during the northeast monsoon theaccumulation of metals is less. The baseline data for spatial distribution and seasonalvariation of trace metals and their controlling factors found in this study will be useful forpollution monitoring program along the Kanyakumari coast.

We investigated the issue of noise pollution in the Bidboland gas refineryby noise measurement and surveys. The Bidboland gas refinery has several process units.The sound pressure levels were measured at several places, such as units of 200, 300,400, 500, boiler, powerhouse area, cooling towers pumps, and maintenance area, and thecorresponding noise maps were produced by using sound plan software. We identified thesources of noise pollution. We first measured the mean A-weighted sound pressure levelin each study unit and analyzed the obtained data in Microsoft Excel. The noisiest unitswere identified and some suggestions were offered to reduce the sound exposure level. Itwas specifically noted through the surveys that the noise levels detected in all testedindustries was much above 80 dBA limit specified by regulations. In lieu, variousmeasures to control the noise pollution were contemplated and discussed.