There are many potential risks to human health from heavy metal contamination of vegetables resulting from wastewater irrigated sites. This study was carried out to assess the concentration of chromium (Cr) and the risk to human health by chromium through the intake of locally grown vegetables collected from wastewater irrigated agricultural fields. Twenty-seven samples of 9 (nine) different types of vegetables were analyzed by an Atomic Absorption Spectrometer (AAS) (Varian AAS 240 F S). The range of chromium concentration in wastewater irrigated vegetables was ND (Not detected) -4.14mg/kg. The highest mean concentration of chromium (4.14 mg/kg) was detected in radish. The mean concentration of chromium in all the vegetables was within the safe limits of WHO/FAO except radish which was much higher than the standard. Health risk index for chromium contamination in all vegetables was less than 1 for both adults and children which cause no risk to the local population. Among all vegetables tested, the highest intake value of chromium was from consumption of radish for both adults and children. The lower values of health risk index indicated chromium contamination in the wastewater irrigated vegetables that cause less negative impact on human health.

Air pollution is one of the major threats to environment in the present time. Increase in degree of urbanization is a major cause of this air pollution. Due to urbanization, vehicular activities are continuously increasing at a tremendous rate. Mobile or vehicular pollution is predominantly degrading the air quality worldwide. Thus, air quality management is necessary for dealing with this severe problem. The first step to deal with this air pollution problem is to find out the existing concentration of air pollutants in the atmosphere due to vehicular activities. It is not possible to establish ambient air monitoring stations everywhere, especially in developing countries as it is a costly process. Hence, vehicular air quality models are used to predict the concentration of different pollutants in the atmosphere. This review covers the simulation of vehicular emission by different types of models for estimating the pollutant concentration in ambient air from vehicular emissions. The models predict concentrations of pollutants in time and space and relate it to the dependent variables. These can also be used to predict the concentration of pollutants in the future. These models can be useful for imposing regulations by governments and to test techniques for controlling pollutant emissions. This review also discusses where and how the respective models can be used.

In the present study, 45 rainwater samples were collected from February to December 2012 on event basis in East Bokaro coal mining environment. Physico-chemical and major ionic compositions of rainwater samples as well as water soluble major ion composition were analyzed to employ principle component analysis for source identification. The average pH value was recorded 6.1 and varied from 5.1 to 6.9 in the collected rainwater samples, indicating slightly acidic to alkaline in nature. The rainwater chemistry of the region showed high contribution of HCO3- (32%) followed by SO42- (30%), Cl- (20%), NO3- (15%) and F- (3%) in anionic abundance. In case of major cations, Ca2+ (29%) was dominant followed by Mg2+ (27%), NH4+ (22%), Na+ (18%) and K+ (4%). The ratio of Cl-/Na+ in the rainwater samples was found to be almost equal to sea water. Higher enrichment of Na+ and Cl- concentration may be due to marine contribution. The EFs were found to be high for HCO3-, Ca2+, SO42- and K+ indicating sources other than sea; i.e., coal mining and other anthropogenic activities. The principle component analysis for ionic source identification was synthesized into four factors with eigen values cut off at greater than unity and explained about 71.8 % of the total variance. The rainwater quality area is mainly influenced due to mining activities, vehicular pollution and industrialization in the East Bokaro coalfield area.

An ever-increasing drift of energy consumption, unequal geographical distribution of natural wealth and the quest for low carbon fuel for a cleaner environment are sparking the production and use of biodiesels in many countries around the globe. In this work, jatropha and karanja biodiesels were produced from the respective crude vegetable oils through transesterification, and the different physical properties of the produced biodiesels have been presented and found to be acceptable according to the ASTM biodiesel specification standard. This paper presents the experimental results of the research carried out to evaluate the BTH, BSFC exhaust emission characteristics of jatropha and karanja blends in a single-cylinder diesel engine at different engine load. Comparative measures of brake thermal efficiency, smoke opacity, HC, CO, and NOx have been presented and discussed. Engine performance, in terms of higher brake thermal efficiency and lower emissions (HC, CO, NOx) with jatropha-based biodiesel (JB50) operation, were observed compared to karanja-based biodiesel (KB50).

Groundwater quality regarding major anions and cations in the Birjand Plain located in the largest desert in Eastern Iran was monitored in this study. Fifteen boreholes were considered as sampling stations and the parameters pH, TDS, EC and major anions and cations were measured in groundwater samples. The dominant groundwater types can be introduced as sodium-chloride and magnesium-sulphate. The majority of samples were within the not-suitable category for drinking uses. Regarding agricultural use, around 80 and 50 per cent of samples indicated a very high salinity hazard and a very high sodium alkali hazard, respectively. Spatial distribution of salinity was also monitored within the study area. If the study area was considered to be a semicircle, the centre appeared to be the least polluted area, while towards the peripheral surroundings, an increasing behaviour was observed. Intrusion of salt water from eastern and western parts of the study area caused severe groundwater degradation. The relatively better quality of groundwater in southern areas may be attributed to a chain of mountains located along south of the study area. The prevention of uncontrolled groundwater withdrawal must be regarded to cease the salinization trend and to prepare the required infrastructure for implementing the artificial recharge projects.

The main objective of this study was to reveal the impact of nine climate indices on temperature changes and climate oscillations in Golestan Province along the southern coast of the Caspian Sea. Climate indices data from across the Atlantic-Eurasian sector were collected from the NCEP/NCAR, the Climate Prediction Centre (CPC) and the Climatic Research Unit (CRU) over a period of 40 years (1971-2010). The climate indices are then compared and correlated with temperature observations from 47 weather stations collected from meteorological and energy organizations. The correlations are based on the 12-month moving average. The study results show a significant increasing temperature trend in most months over different regions of Golestan. For maximum temperature, a significant increasing trend was seen in 55.64, 41.8 and 40% of the land area in the province during August, June and July, respectively. In general, summer had the most significant maximum-temperature trends, with an average of 37.8% of the land area. On the other hand, increasing minimum-temperature trends were seen in 58% of the land area of the province compared to the other seasons. It was concluded that there is high correlation between climate indices and temperature components. The correlation coefficients obtained for various indices including North Atlantic Oscillation (NAO), North Sea Caspian Pattern (NCP), Arctic Oscillation Index (AO), East Atlantic (EA), East Atlantic/West Russia (EATL/WRUS), Atlantic Multi-decadal Oscillation (AMO), North Tropical Atlantic (NTA), Polar/Eurasia (PE), and Scandinavia teleconnection index (SCAND) suggest an inverse relationship between these indices and temperature components. Therefore, the higher the values of these indices, the lower the temperature values, and vice versa.

Bedload transport is an essential component of river dynamics and estimation of its rate is important to many aspects of river management. In this study, measured bedload by Helley- Smith sampler was used to estimate the bedload transport of Kurau River in Malaysia. An artificial neural network, genetic programming and a combination of genetic programming and a neural network were used to estimate the bedload carried in Kurau River, based on bedload transport measurement data and hydraulic variables. A statistical analysis was carried out to validate methods by computing RMSE, MARE and inequality ratio (U). In general, the ability of the artificial neural network combined with genetic programming with R2 equal to 0.95, RMSE equal to 0.1 as a precipitation predictive tool for predicting the bedload transport rate was observed as being acceptable.

Regarding the importance of the prevention of hazards and adverse environmental impacts in industrial and populated areas such as southern parts of Tehran city, the response of impulsive period ground-supported tanks were assessed. Having considered the study area's soil properties, the response of ground-supported tanks was modelled. Regarding the soil properties of southern parts of Tehran, the soil structure interaction method explained in FEMA 368 revealed that the interactional impulsive period (~T) was greater than non-interactional one (T). In addition, results showed that Poisson's ratio and stiffness ratio (K/Kx) were more effective regarding the response of the interactional period of ground-supported tank systems. According to the achieved results, the liquid mass density effect on impulsive period was as low as the thickness of the ground-supported walls effect. Results showed that wall materials significantly affected the variation within the impulsive period. Generally, concrete materials were shown to be more periodic than steel materials. Overall, in southern parts of Tehran, when the soil fluid structure interaction method was used, the period increased from 1 to up to 3.6 times greater than the normal impulsive period.

Rajasthan is one of the main mineral potential state of India. During the last 30 years it has witnessed enormous expansion of mining industries, but mining of most of the minor minerals coupled with changing climate has posed serious problems to the environmental fabric in the state, apart from base metal beneficiation plants. Groundwater is also being polluted day-by-day by effluents generated from mineral wastes and beneficiation processes in the vicinity of mining sites such as Khetri. Pollutant concentrations were measured in groundwater at the vicinity of Khetri copper mining project, Rajasthan to investigate the influence of copper mining on environment. Pollutant concentrations in groundwater were investigated. Copper metal concentration in water samples were found above the maximum desirable limit in two sources: G4 and G5, due to washing away of mineral with water. Mining industry has deteriorated quality of groundwater resources in the state of Rajasthan, and these industries are becoming centers of pollution sources which need timely actions at government level so that natural resources such as groundwater can be protected.

To determine the suitable indices for vegetation cover and production assessment based on the remote sensing data, simultaneous digital data with field data belonging to the spring rangeland of the Semirom-Isfahan province were analyzed. During two years of monitoring the annual, grass, forb, and shrub vegetation cover and the total production data from 86 were collected. The Global Positioning System (GPS) was used to measure the coordinates of plots and transects. Geometric correction and histogram equalization were applied in image processing, and image digital numbers were converted to reflectance numbers. In the next stage, all vegetation indices were calculated from the Advanced Wide Field Sensor (AWiFS) image data and compared with the vegetation cover estimates, at monitoring points, made during field assessments. A linear regression model was used to select suitable vegetation indices. The results showed that there were significant relationships between the satellite data and the vegetative characteristics. Among the indices, the Normalized Difference Vegetation Index (NDVI) consistently showed significant relationships with the vegetation cover. The estimation of the vegetation cover with the NDVI vegetation index was more accurately predicted within rangeland systems. Using the produced model from the NDVI index vegetation crown cover, percentage maps were produced in three class percentages for each image. Generally introduced indices provided accurate quantitative estimation of the parameters. Therefore, it was possible to estimate cover and production as important factors for range monitoring using the AWiFS data. The Remote sensing data and the Geographic Information System are the most effective tools in natural resource management.