Global Journal of Environmental Science and Management
Volume:4 Issue: 4, Autumn 2018

The understanding of inland wetlands’ distribution and their level of vulnerability is important to enhance management and conservation efforts. The aim of the study was to map inland wetlands and assess their distribution pattern and vulnerability to natural and human disturbances such as climate change (temperature increase) and human activities by the year 2080. Inland wetland types i.e. forested/shrub, emergent and open water bodies were classified and mapped using maximum likelihood standard algorithm. The spatial distribution pattern of inland wetlands was examined using average nearest neighbor analysis. A weighted geospatial vulnerability analysis was developed using variables such as roads, land cover/ land use (developed and agricultural areas) and climate data (temperature) to predict potentially vulnerable inland wetland types. Inland wetlands were successfully classified and mapped with overall accuracy of about 73 percent. Clustered spatial distribution pattern was found among all inland wetland types with varied degree of clustering. The study found about 13 percent of open water bodies, 11 percent of forested/shrub and 7 percent of emergent wetlands potentially most vulnerable to human and natural stressors. This information could be used to improve wetland planning and management by wetland managers and other stakeholders.

Kinetic models which can express the behaviors of hydrolysis and biogas generation more precisely than the conventional models were developed. The developed models were evaluated based on the experimental data of six batch reactors. Anaerobic digestion test was co-digestion of food and vegetable waste with inoculating horse dung by 15% of the total wet weight, at the temperature of 37oC. For hydrolysis, the modified model was developed from an original first-order kinetic model. The modified first-order kinetic model was proved to be better than the original one with the hydrolysis rate constant in the range of 0.22-0.34/day and hydrolyzable rate of 0.80 to 0.84. Kinetics of carbon dioxide and methane were developed from a current potential model. The comparison between experimental data and modeling values had the high correlation of determination (0.9918-0.9998) and low root mean square errors (0.08-4.51) indicating the feasibility of the developed model. In which, the evolution of methane showed the rate constant in the range of 0.031-0.039/day. The carbon dioxide from fermentation accounted for 12-44% of the total observed carbon dioxide. Thus, separation of fermentation and methanogenesis by various reactors may reduce the price of methane enrichment significantly. There was a lag time between methanogenesis and fermentation in reactors (λ = 7-11 days). Also, the biogas yield was in the range of 431.6-596.9 Nml/g-VS with the CH4 concentration of 56.2-67.5%. The best methane yield (393.7 Nml/g-VS) was in a reactor with food waste to the vegetable waste ratio by 1.8:1 (wet basis) and C/N ratio by 25.4.

The present study investigated the coupling effect of biodegradation and media filtration in treating hydrocarbon contaminated water. The study recorded reductions in total petroleum hydrocarbon, total dissolved solids, turbidity and microbial load. The study was essentially a simulated pump and treat process that involved the pumping of hydrocarbon contaminated water for treatment in a locally designed multi-stage bioreactor incorporated with media filtration. A mixed consortium of hydrocarbon-eating microbes was applied in the study. Hydrocarbon-eating microbes were isolated from hydrocarbon contaminated soils obtained from selected mechanic workshops. Bamboo chips and coconut husk chips were applied as support media for microbial attachment within the bioreactor compartment of the treatment setup. Applied support media were approximately 2-4 cm in size. Media filters applied comprised three locally manufactured candle filters two of which were respectively impregnated with granular activated charcoal and sand. The coupling effect of biodegradation and media filtration recorded over 99 % (> 8.7 mg/L) total petroleum hydrocarbon removal. Microbial load reduction ranged from 3.57±0.11E+20 to 7.45±0.26E+20 Colony forming unit/mL, total dissolved solids reduction from 30.00±5.66 to 131.00±0.00 mg/L and turbidity reduction from 39.00±1.41 to 123.50±0.71 nephelometric turbidity units. Biodegradation accounted for 69.70±0.63 and 90.72±2.36 % total petroleum hydrocarbon removal respectively for bamboo chips and coconut husk chips.

The study provides a statistical trend analysis of different air pollutants using Mann-Kendall and Sen’s slope estimator approach on past pollutants statistics from air quality index station of Varanasi, India. Further, using autoregressive integrated moving average model, future values of air pollutant levels are predicted. Carbon monoxide, nitrogen dioxide, sulphur dioxide, particulate matter particles as PM2.5 and PM10 are the pollutants on which the study focuses. Mann-Kendall and Sen’s slope estimator tests are used on summer (February-May), monsoon (June-September) and winter (October-January) seasonal data from year 2013 to 2016 and trend results and power of the slopes are estimated. For predictive analysis, different autoregressive integrated moving average models are compared with goodness of fit statistics, and the observed results stated autoregressive integrated moving average (1,1,1) as the best-suited for forecast modeling of different pollutants in Varanasi. Autoregressive integrated moving average model (1,1,1) is also used on the annual concentration levels to predict forthcoming year's annual pollutants value. Study reveals that PM 10 shows a rising trend with predicted approximate annual concentration of 273 µg/m3 and PM2.5, carbon monoxide, nitrogen dioxide and sulphur dioxide show a reducing trend with approximate annual concentration of 139 µg/m3, 1.37 mg/ m3, 38 µg/m3 and 17 µg/m3, respectively, by the year 2030. The study predicted carbon monoxide, nitrogen dioxide andsulphur dioxide concentrations are lower and PM10 and PM2.5 concentrations are much higher to the standard permissible limits in future years also, and specific measures are required to control emissions of these pollutants in Varanasi.

ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water temperature at upper, middle and downstream of the river. To predict outlet of dissolved oxygen of the river in each station, considering different input combinations as i) 11 inputs parameters for all three locations except, dissolved oxygen at the downstream ii) 7 inputs for middle and downstream except dissolved oxygen, at the target location and lastly iii) 3 inputs for downstream location. To determine the accuracy of the model, root mean square error and determination coefficient were employed. The simulated results of dissolved oxygen at three stations indicated that, multi-linear regression is found not to be efficient for predicting dissolved oxygen. In addition, both artificial intelligence models were found to be more capable and satisfactory for the prediction. Adaptive neuro fuzzy inference system model demonstrated high prediction ability as compared to feed forward neural network model. The results indicated that adaptive neuro fuzzy inference system model has a slight increment in performance than feed forward neural network model in validation step. Adaptive neuro fuzzy inference system proved high improvement in efficiency performance over multi-linear regression modeling up to 18% in calibration phase and 27% in validation phase for the best models.

The current study deals with the physicochemical characterization, temporal variability and trophic state evaluation of a post glacial mountain lake in eastern Himalaya during the period of 2014-2016. Notable seasonal variations are recorded for physicochemical parameters of lake water. The values for electrical conductivity, total suspended solids, total dissolved solids, total alkalinity and Chloride are higher during the rainy season. Concentrations of total phosphorous (136.78±29.14 µg/L), total nitrogen (7177.78±1346.70 µg/L) and Chlorophyll-a (38.54±21.67 µg/L) in lake water are distinctly higher than the recommended standards for eutrophic condition of lake/surface water. Application of multivariate tools such as cluster analysis and principal component analysis reveals that ionic constituents of lake water are majorly associated to the geogenic and exogenic factors, with minor seasonal influences. Trophic state indices based on water transparency (3.15±1.57), total phosphorous (74.72±3.39), total nitrogen (82.64±2.83) exhibit hypertrophic nature of lake water; while trophic state index for chlorophyll-a indicate eutrophic condition. Deviations between Trophic state indices (TSICHLa-TSISD: -14±7.88, TSICHLa-TSITP: -9.17±3.33, and TSICHLa-TSITN: -17.56±5.29) infer that the nutrients (phosphorus and nitrogen) are not limiting factors for the algal biomass, and non-algal components such as suspended solids soil/sediment particles affects the light attenuation in the monitored lake. The observations reveal that trophic condition of monitored lake is in alarming stage. Therefore, measures should be taken on urgent basis in order to intercept the increasing trend in eutrophication, and for the restoration of water quality and integrated lake ecosystem.

Economic evaluation of 12 MW grid-connected wind farms and PV power plants in two regions in Northern Cyprus for electricity generation was investigated. The wind speed, sunshine duration, and solar global radiation characteristics were analyzed using monthly data collected over 17 years (2000-2016) for Girne and nine years (2008-2016) for Lefkoşa, which were measured at various heights. The result showed that during 2000-2016, the mean wind speed at Girne was 2.505 m/s and during 2008-2016, the mean wind speed at Lefkoşa was 2.536 m/s. The result showed that both regions had annual mean wind speed greater than 2 m/s at 10 m height. Moreover, the annual mean sunshine duration and global solar radiation were higher than seven h/day and 15 MJ./m2/day at a height of 2 m for all studied regions, respectively. In this study, eight distribution functions were used to analyze the wind speeds and global solar radiation data in each region. The results indicated that Weibull and Logistic were the best distributions for analyzing the wind speeds and global solar radiation data of the studied regions, respectively. Furthermore, the capacity factors of the selected regions ranged between 1.92% and 48.53%. Based on the renewable energy cost results, it is found that the generation costs of the wind farm were between 0.023 and 0.04 Euro/kWh, while the PV plant was between 0.08 and 0.098 Euro/kWh.

Different methods have been designed to calculate the air quality index in form of mathematical formula. But the formula designed by Central Pollution Control Board in 2014 is more robust to find out the air quality category. The index has been calculated based upon four parameters like particulate matters (PM10, PM2.5), sulfur oxide and nitrogen oxide. The study area has affected by different sources like point, line and volume. Presence of different industries and mining activities polluting the natural environment of nearby areas more, although the industries taking mitigative measures proactively. In the present research, monitoring of ambient air quality has been carried out for a period from March 2013 to February 2016 for three years. It has been revealed from the study that the air quality status of the area has been declining from 2013 to 2016 i.e. 78.9 to 157.8 in summer, 49.4 to 84.3 in monsoon and 86.9 to 183.9 in winter season. It has also been found that, PM10 and PM2.5 were responsible for maximum sub-index as well as air quality index. During the study period 2015-16, out of the eight stations most comes under moderately polluted category especially in winter season followed by summer season. Statistical and Duncan’s multiple range test has been applied to the results with two-way and one-way analysis of variance based on different seasons and stations. In two-way analysis of variance, F-value was computed to be 30.105 based on seasons and stations and one-way analysis of variance test shows the F-values as 186.07 and 18.97 based on seasons and stations respectively which is found to be significant (P<0.01).The present research is important to assess the environmental quality of a mining- industrial complex area and can be a reference for similar study in other areas.

Anaerobic decomposition of organic compounds in landfills is responsible for generation of greenhouse gases. The present study aimed to determine the total gas and methane emission from a landfill located in Hamedan (west of Iran) from 2011 to 2030. LandGEM 3.02 model was used to estimate the gas emission with the volumetric methane percent of 60%, production potential of 107, and methane generation rate of 0.2. Spatial distribution of annual methane and total landfill gas emission rate in the study area at three decades were provided through ArcGIS software. The results showed that organic and food wastes had the maximum amounts in the solid waste stream (over 75%). The results showed that 4.371×108 m3 methane would be produced after 20 years, mostly (4.053×106m3) in the first year. In addition, methane production capacity in Hamedan landfill site was 107 m3/Mg. According to the results, the maximum and minimum gas generation rates are in summer (the hottest season) and winter (the coldest season) respectively. The results of the LandGEM model represented that the total gas and methane generation rates will be significant in the first 10 years. The potential of rapidly degradable organic compounds for gas emission will be higher than that of slowly degradable organic compounds. The results obtained in the present study can be beneficially used in planning for energy production and other applications in landfill sites.

Global warming is increasing permanently, because the concentration of CO2 in the atmosphere is rising continuously. According to National Oceanographic and Atmospheric Administration, the concentration of CO2 in the atmosphere was 407 ppm in June 2016 and 413 ppm in April 2017 as a last record for now. If the effects of other greenhouse gases, such as CH4, N2O, SF6, NF3, chlorofluorocarbons, hydrofluorocarbons, perfluorocarbons are added, the effective concentration may reach or exceed 550 ppm CO2-equivalent. According to the United Nations Intergovernmental Panel on Climate Change-2014 Climate Change Report, this is about two times higher than 278 ppm CO2 concentration in the pre-industrial year 1765. Thus, very urgent solutions must be found. The aim of this article is to suggest a vital, fast and very meticulous solution using NH3 gas in the atmosphere in order to decrease the atmospheric CO2 without delay. The laboratory experiments in the gas phase for (NH3+ CO2) reaction showed us that to use NH3 gas in the atmosphere will be a very fast, effective method for decreasing CO2 concentration of atmosphere. (NH3+ CO2) reaction is also quantitative in the cold atmosphere strata and there will be no more free ammonia in the atmosphere and no public health problem.