Pollution
Volume:2 Issue: 4, Autumn 2016

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 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.

In the present study, air quality analyses for ozone (O3) were conducted in Shiraz, a city in the south of Iran. The measurements were taken from 2011 through 2012 in two different locations to prepare average data in the city. The average concentrations were calculated for every 24 hours, each month and each season. Results showed that the highest concentration of ozone occurs generally in the afternoon while the least concentration was found in the morning and at midnight. Monthly concentrations of ozone showed the highest value in August and June while the least value was in December. The seasonal concentrations showed the least amounts in autumn while the highest amounts were in spring. Relations between the air pollutant and some meteorological parameters were calculated statistically using the daily average data. The wind data (velocity, direction), relative humidity, temperature, sunshine periods, evaporation, dew point, and rainfall were considered as independent variables. The relationships between concentration of pollutant and meteorological parameters were expressed by multiple linear regression equations for both annual and seasonal conditions using SPSS software. Root mean square error (RMSE) test showed that among different prediction models, stepwise model is the best option.

Population’s exponential growth along with drought has increased water resources limitation, especially in arid and semi-arid area. Therefore, the use of non-conventional water is an important tool for water resource management. If unconventional water has no negative impact on soil properties and water, it can be used for irrigation coupled with desertification projects. So, this paper tries to present the effect of irrigation with municipal wastewater, salt water, brackish water, and combination of salty water and wastewater on some soil properties including nitrogen, phosphorus, and potassium in Qom plain. Soil samples were taken from agricultural land treated by wastewater, saline water, brackish water, combination of salty water, and wastewater and range land as control in five treatments from depths of 0-30 and 60-90 centimeter. The results showed that wastewater has increased the amount of N, P, and K to other treatments and control area. The concentration of potassium in surface layer of area treated by combination of salty water and wastewater with amount of 459.39 ppm has the most significant difference to control and other treatments. Also, the maximum amount of nitrogen was observed in sub layer of saline and brackish water treatment with amount of 0.08 percent.

The Karnafully is one of the most important rivers due to its profound influence on water chemistry and sediment characteristics. The present study intended to assess the quality of water and sediment from intertidal zone of this river in respect to the pollution index. Seasonal water and sediment samples were collected during four seasons (Monsoon, post-monsoon, winter, and pre-monsoon) of 2014. The result indicates that these investigated parameters ranged as water temperature (21.7-36 °C), pH (8.0-8.7), salinity (2.4-8.8‰), total suspended solid (0.08-0.8 g/L), dissolve oxygen (0.00-4.52 mg/L), soil temperature (21.3-33 °C), pH (5.0-6.8), sand (4.13-44.10%), silt (39.93-75.89%), clay (11.98-21.19%), soil organic matter (4.33-6.21%), organic carbon (2.5-3.6%), nitrite-nitrogen (0.69-3.97 µg/L), and phosphate-phosphorus (0.23-3.44 µg/L). Multivariate statistical analyses like post-hoc LSD test, Cluster Analysis (CA), and Principal Component analysis (PCA) brought out the spatial and temporal changing pattern of water chemistry and sediment characteristics with the effect of uprising pollution. CA ascertained the compatibility among different parameters and categorized the monitoring sites into highly and moderately polluted areas. Moreover, PCA brought out five primary components and highlighted the three dormant factors, enormously regulating the river water chemistry such as municipal waste, carbon based nitrogenous compound, and local geomorphological weathering process. This investigation provided an outline on deterioration of water and sediment quality by high anthropogenic impact and suggests national policy maker to take some initiatives for retaining the quality water and sediment properties.

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.

Surface ozone (Surface O3)is a secondary pollutant and there are only limited studies on ozone in South India. Studies have revealed a strong correlation between higher ozone levels and warmer days. Surface O3 along with its precursors like NO2, CO and CH4 are being measured at Kanyakumari (8.0780° N, 77.5410° E), TamilNadu, India and in this paper we present the summer time variation of ozone from 2010to 2014 . Surface O3 showed a clear diurnal variation, but an irregularity was observed during the night time for all the measuring days of Summer 2014.There was a formation of a well pronounced secondary peak in Surface O3 during 0230 hrs accompanied by relatively strong wind patterns. Since the normal diurnal variation cannot explain this phenomenon,this uncertain behavior is probably attributed tolow NOX titrations and the downward mixing of ozone in the ground layer.The daily mean of Surface O3showed an increasing trend in the study area during the summer monthsand a negative correlation was observed with its precursors. The correlation of Surface O3 with temperature and wind speed for the entire summer season was found as r= .68, p= 4.314E-05 and r= .63, p

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.

Air pollution is a challenging issue in some of the large cities in developing countries. Air quality monitoring and interpretation of data are two important factors for air quality management in urban areas. Several methods exist to analyze air quality. Among them, we applied the dynamic neural network (TDNN) and Radial Basis Function (RBF) methods to predict the concentrations of ground-level ozone in Karaj City in Iran. Input data included humidity, hour temperature, wind speed, wind direction, PM2.5, PM10 and benzene, which were monitored in 2014. The coefficient of determination between the observed and predicted data was 0.955 and 0.999 for the TDNN and RBF, respectively. The Index of Agreement (IA) between the observed and predicted data was 0.921 for TDNN and 0.9998 for RBF. Both methods determined reliable results. However, the RBF neural network performance had better results than the TDNN neural network. The sensitivity analysis related to the TDNN neural network indicated that the PM2.5 had the greatest and benzene had the minimum effect on prediction of ground-level ozone concentration in comparison with other parameters in the study area.

Bioretention or rain garden is a preferable low impact development (LID) approach due to its characteristics which reflect natural water cycle processes. However, this system is still little understood and quite complicated in terms of design and implementation due to many technical considerations. Hence, this paper gives a review of the challenges and developments for the use of bioretention facilities to enhance its capabilities in attenuating peak flow and treating stormwater runoff particularly in urban areas. This paper reviews the main aspects of bioretention which are stormwater hydrologic, hydraulic and treatment performance. Some of the limitations during the implementation of this natural approach are highlighted in design configuration and the public perception towards this new approach. It is concluded that the bioretention approach is one of the sustainable solutions for stormwater management that can be applied either for individual systems or regional systems.