Journal of Advances in Environmental Health Research
Volume:4 Issue: 2, Spring 2016

Due to the limited number of researches conducted globally on heavy metals in edible oil, this study was carried out for analysis and health risk assessment of As and Zn in some brands of canola and soybean oils marketed in Kermanshah City, Iran, in 2015. In this research, 18 samples of three popular brands of edible oil (canola and soybean) in the Iranian market were analyzed for levels of As and Zn after digestion with acids using atomic absorption spectroscopy (AAS) in 3 replications and the health index was obtained. In addition, all statistical analyses were performed using the SPSS statistical package. The results showed that the mean concentrations of As and Zn in oil samples were 0.06 ± 0.05 and 100.17 ± 21.94 µg/kg, respectively. Moreover, the mean concentration of As and Zn in oil samples were lower than the World Health Organization’s (WHO) maximum permissible limits (MPL). The health risk assessment showed no potential risk for children and adults by consumption of the studied vegetable oil samples. Although the results showed that the consumption of the analyzed vegetable oils did not have any adverse effects on the consumers’ health, concerning increased use of agricultural inputs by farmers and industrial development, it is very important that the appropriate measures be taken by companies during the production process and products be treated before marketing.

Nowadays 90% of the required water of Iran is secured with groundwater resources and forecasting of pollutants content in these resources is vital. Therefore, this research aimed to develop and employ the feedforward artificial neural network (ANN) to forecast the arsenic (As), lead (Pb), and zinc (Zn) concentration in groundwater resources of Asadabad plain. In this research, the ANN models were developed using MATLAB R2014 software program. The artificial intelligence models were trained with the data collected from field and then utilized as prediction tool. Levenberg-Marquardt (LM) and Bayesian regularization (BR) algorithms were employed as ANN training algorithms and their performance was evaluated using determination coefficient and the root mean square error. The results showed that the ANN models could potentially forecast heavy metals concentration in groundwater resources of the studied area. Coefficients of determination for ANN models for As, Pb and Zn in testing phase were 0.9288, 0.9823 and 0.8876, respectively. Finally, based on the simulation results, it was demonstrated that ANN could be applied effectively in forecasting the heavy metals concentration in groundwater resources of Asadabad plain.

Given the presence of toxic metals in some local Iranian as well as some imported rice varieties, it may be of help to focus on public awareness for the implementation of educational interventions. This study aimed to assess awareness and attitudes of women in Sanandaj, Iran, regarding toxic metal-contaminated rice based on the Health Belief Model (HBM). This cross-sectional study was conducted on 1450 women aged 18 and above. The questionnaire used in the study consisted of three parts; demographic information, awareness assessment, and HBM constructs. Data were analyzed using chi-square test, t-test, ANOVA, and the logistic regression analysis in SPSS. The mean age of the study participants was 40.55 ± 13.8 years. The level of awareness regarding the presence of toxic metals in daily-consumed rice was low in 78.2% and moderate in 21.8% of the participants. Among the attitude factors, risk perception was the only one that increased the probability of falling in the group with moderate awareness instead of the group with low awareness by 1.37 times. The results support the necessity of raising public awareness and increasing risk perception in the population about the adverse effects of toxic metals.

The sugar cane industry produces significant amounts of cane trash and bagasse. Inappropriate disposal of agro-wastes can lead to environmental problems. Converting wastes such as cane trash and bagasse (Bg) to a fertilizer and conditioner is the aim of sustainable waste management in sugar cane industry. Cow dung (CD), kitchen waste (KW), and sewage sludge (SS) were mixed with bagasse as amendment in different proportions: Bg:CD (1:1), Bg:CD (1:2), Bg:SS (1:1), Bg:SS (1:2), Bg:KW (1:1) and Bg:KW (1:2) in triplicate treatment with Eisenia fetida. Chemical analysis of the samples showed a significant decrease in total organic carbon (TOC) (20%-54%), total Kjeldahl nitrogen (TKN) (9.5%-39.7%) and C:N ratio (12%-31.2%), while total potassium (31.4%-54%) and available phosphorus (32%-55%) contents increased during vermicomposting. A significant difference was observed among weight and number of worms in control with other treatments at the end of vermicomposting. According to obtained results vermicomposting is an efficient method for sustainable recycling different classes of waste produced in sugar cane agro-industry.

In the present study, carbon species including organic carbon (OC), elemental carbon (EC), and water-soluble organic carbon (WSOC) concentration in PM2.5 were assessed at an urban site of Tehran, Iran during March to June 2014. The PM2.5 samples were collected using an frmOMNITM Ambient Air Sampler. Thermal gravimetric analysis (TGA) was used to analyze OC and EC. The results showed that PM2.5 concentrations varied from 14.32 to 74.45 µg/m3 with an average value of 41.39 µg/m3. The results also showed that carbon species varied from 5.52 to 23.21 (15.35 ± 6.05) µg/m3 for OC and 1.03 to 4.16 (2.25 ± 0.65) µg/m3 for EC. As the findings indicated, the mean PM2.5 level in the sampling area was higher than the annual average determined by the United States Environmental Protection Agency (EPA) as the ambient air quality standard. On average, carbon species (OC, EC, and WSOC) account for almost 60% of PM2.5 mass in the atmospheric outflow from a downwind site. OC and EC concentrations in atmospheric PM2.5 collected at the sampling site were lower than the values reported for other urban areas with high or medium vehicular traffic and/or industrial sources. Moreover, the results obtained in this research can provide a valuable data base for health risk evaluation of the local residents and prioritization of control actions.

The purpose of present study was to evaluate fluoride, nitrate, iron, manganese and total hardness in drinking water of wells and reservoirs in Fereidonkenar, Mazandaran, Northern Iran and compare the results with national and international standards. This cross-sectional descriptive study was carried out on data during five years from the spring of 2008 until the autumn of 2013. Studies were performed on 430 samples in the different seasons and years taken from water and wastewater company (WWC). The results showed that the average fluoride, nitrate, iron, manganese, total hardness concentrations obtained were 0.42, 10.2, 0.136, 0.03, 382.28 mg/l, respectively. The analysis showed a negative correlation between nitrate and fluoride, iron and manganese and a positive correlation with the hardness. The mean fluoride concentration was less than the standard. Total hardness value was more than recommend standard. Nitrate was below 50 mg/l, in accordance with national and international standards. The amount of iron and manganese in drinking water were acceptable. So, except for low fluoride and high total hardness, there was no any problem in other investigated parameters.

The purpose of this study was to evaluate the spatial changes of groundwater phosphate concentrations using geostatistical methods based on data from 10 groundwater wells. One of the conventional tools in decision making on the groundwater management is geostatistical method. To evaluate the spatial changes of phosphate concentrations in groundwater, the universal kriging method with cross-validation was used for mapping and estimating groundwater phosphate concentrations in Eyvan Plain, Iran. Phosphate concentration followed a log-normal distribution and demonstrated a moderate spatial dependence according to the nugget ratio (60%). The experimental variogram of groundwater phosphate concentration was best-fitted by a spherical model. Cross-validation errors were within an acceptable level. According to the spatial distribution map, phosphate pollution in the groundwater occurred mostly in the west of the plain because of the phosphate discharge from the industrial effluents.

In this study, a combined electro-(Fenton/coagulation/flotation) (EF/EC/El) process was studied via degradation of Disperse Orange 25 (DO25) organic dye as a case study. Influences of seven operational parameters on the dye removal efficiency (DR%) were measured: initial pH of the solution (pH0), applied voltage between the anode and cathode (V), initial ferrous ion concentration (CFe), initial hydrogen peroxide concentration (CH2O2), initial DO25 concentration (C0), applied aeration flow rate (FAir), and process time (tP). Combined design of experiments (DOE) was applied, and experiments were conducted in accordance with the design. The experimental data were collected in a hand-made laboratory-scaleglass cylindrical batch reactor equipped with four graphite barcathodes, an aluminum sheet anode, an aeration pump equipped with an air filter and air distributer, a 150-rpm mixer, and a DC power supply. A DR% of 98 was achieved with a pH0 of 4, V of 10, CFe of 7.5, CH2O2 of 0, C0 of 140, and FAir of 0. The data were used for modeling using normal and reduced multiple regression models (MLR & r-MLR) and artificial neural networks (ANN & r-ANN). Further statistical tests were applied to determine the models’ goodness and to compare the models. Based on statistical comparison, ANN models clearly outperformed the stepwise multiple linear regression (SMLR) models. Finally, an optimization process was carried out using a genetic algorithm (GA) over the outperformed ANN model. The optimization procedure was used to determine the optimal operating conditions of the combined process.