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.

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.

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.

A four year long research programme divided into four phases has been designed for the first time to assess the combined Noise-Air quality and combined exposure to Noise-Air pollution at the curbside open-air microenvironment of Kolkata city, India. The results and the findings of the different phases of study had already been published. The objective of the review work is to fuse the findings of the different phases of studies; to identify the factor(s) influences the degree of correlation between concentration of the air pollutant(s) and traffic noise level at the microenvironment of the city. Incidence of moderate to very strong positive Pearson’s correlation coefficients between concentration of the air pollutant(s) and traffic noise level indicated chances of correlated exposure to these environmental stressors at the microenvironment of the city. Average combined Noise-Air quality and combined exposure to Noise-Air pollution of the microenvironment of the city was very poor (0.17) to fair (0.50) and poor (-0.06) to excellent (3.33) respectively. The best combined Noise-Air quality and the best level of combined exposure to Noise-Air pollution at the microenvironment of the city was prominently evidenced in after-noon, in summer and under variable road geometry. However, the worst combined Noise-Air quality and the worst level of combined exposure to Noise-Air pollution at the microenvironment of the city was prominently evidenced in evening, in winter and under constant road geometry. A principal component analysis revealed that intensity of wind determines the degree of correlation between the environmental stressors at the microenvironment.

Climate change is a complicated issue with many factors playing role in its formation and distribution. Considering this complication, a comprehensive and holistic approach is needed for a better understanding and management of those factors. The causal frameworks are among systemic and integrated methods for addressing the causes of environmental problems and the relationships that exist between the environmental systems in order to propose proper solutions. The DPSIR model is a functional analysis framework that depicts the cause-effect relationships that exist in creating environmental problems. Tehran is one of the major megacities in the Middle East that faces environmental consequences of overpopulation and unplanned urban sprawl, and being located in an arid region, makes it vulnerable to rise of temperature and reduction of precipitation. In this research, by using the DPSIR framework, different aspects of climate condition of Tehran are analyzed and later with the help of this conceptual framework, strategies for controlling climate change are presented.

Today, ecotourism is a major tourist activity around the world. It is based on environment potential through which suitable utilization and conservation of sites under management practices including considering accurate planning, potential, and peoples’ preferences are realized. The present study was conducted to determine people’s recreational preferences using questionnaires to evaluate the ecotourism potentials (recreational activities that choice in questioner by visitors) for site selection and land use planning, and to analyze the functional relationships among zones in the MianKaleh wildlife sanctuary, south of the Caspian Sea in Mazandaran and Golestan Provinces of Iran. Recreational preferences of people were found to be bird watching, swimming, camping, sightseeing, horse riding, and boating. Multi Criteria Evaluation was used to assess the ecotourism potential. For land use planning, the Multi Objective Land Allocation function included environmental suitability maps, zone weighting, and a set of desirable areas for each zone. Post processing functions (filters, zone size, and distance to other zones) and functional relationship diagrams were applied to amend the zoning maps. The functional relationship diagram concept was applied to the amended maps for optimizing access and identifying the relationships among zones. Overall, the results revealed that MCE and MOLA methods are capable of evaluating and zoning the wildlife sanctuary. Furthermore, post-processing and functional relationship diagrams were effective in selecting recreational sites. The results of this research revealed the recreational potential of MianKaleh wildlife sanctuary. Land planning for ecotourism can now be implemented using the results of this study that will upgrade the conservation status in the area.

Bioremoval and bioreduction activities of hydrocarbon (diesel) isolation from environmental samples were studied by the activity of biosurfactant production, and calculating emulsification index, gravimetric, and FTIR analysis along with the estimation of bacterial biomass. Sample from soil near petrol, diesel pumps and water sample from Thesjaswini River near Padannakad, Kasaragod, Kerala, India, were used to screen the potential diesel oil utilizing bacteria. Among the bacterial isolates (Staphylococcus, Bacillus and Corynebacterium strains), Staphylococcus sp was the potent degraders of diesel oil. Staphylococcus strain was observed to be maximum diesel oil utilizing ability (73% emulsification index) and change in the functional groups of the compound (FTIR analysis). The strain showed optimal growth at 37oC with pH 7, agitation of 150 rpm and time period (5days). The results revealed the possibility to use these strain for the reduction of complex hydrocarbon in ecosystems where they accumulate and cause pollution problems. The highest rate of hydrocarbon degradation occurred when the bacterial strain is a biosurfactants producer. The selective strain produces biosurfactants which increase the interfacial area for contact to give improved uptake of hydrophobic substrates. Bacterial strains capable of degrading complex hydrocarbons, present in the environment, have a potential to be used as an effective tool for removing ecotoxic compounds. Furthermore, results indicated that the bacterial strain Staphylococcus sp could be potentially used in biodegradation of diesel oil in waste water and had a promising application in bioremediation of hydrocarbon contaminated environments.

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.

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.