Global Journal of Environmental Science and Management
Volume:5 Issue: 4, Autumn 2019

The study aimed to determine the impact of sound land use management to the runoff in Kabuntalan, Maguindanao in Tamontaka river basin. This was done through simulation and the comparison of the three land cover scenarios such as 2015 land cover, 2025 projected land cover and desired land use to determine its difference in terms of runoff. Hydrologic Engineering Center-Hydrologic Modeling System was used for simulating runoff. The geographic Information system was used for the preparation of the land use/cover and as an interface between GIS and HEC-HMS. Based on the result, 2025 land cover values in runoff volume and peak outflow increase from 2015 land cover while decrease in desired land use. There is shorter time to peak in 2025 land cover than desired land use. The 2025 land cover, represented the land cover without intervention, showed that agriculture will dominate the area with 78.28% of the total area, closed canopy forest and open forest cover will reduce with 4.57% and 6.78% of the total area respectively. The desired land use represented the sound land use management, showed that there will be 13.9% decreased in agriculture. This can, however, result to increase in close canopy forest (112.3%), grassland (125.7%), and open forest (4.3%). The study showed that desired land use will most likely reduce the magnitude of the flood than the 2015 and 2025 land covers. Thus, adopting sound land use management in Tamontaka river basin is crucial to reduce runoff and thereby mitigate flooding in the study area.

The use of constructed wetlands for purifying pre-treated wastewater is a cost effective technology that has been found to be more appropriate for many developing countries. The technology is also environmentally friendly with the wetlands being habitats for many water birds and other aquatic organisms. This study assessed nutrient removal efficiency of two floating macrophytes (Lemna minor and Azolla pinnata). The data generated was analyzed using both descriptive and inferential statistics. The significance level was maintained at 0.05. The results showed that the wastewater physicochemical parameters did not vary during the study period. The concentrations of nitrites and nitrates increased over the experimental period in all the treatments (Azolla pinnata, Lemna minor and control), and the increase between the sampling occasions was statistically significant for the two nutrients (Nitrates: F=24.78, P= 0.00; Nitrates: F=198.26, P= 0.00). To the contrary, in all the treatments the concentrations of ammonia, total phosphorous, soluble reactive phosphorous and total nitrogen, decreased over the experimental period. The decrease in concentration for these nutrients between the sampling occasions was statistically significant (ammonia: F=195.57, p= 0.00; total phosphorous: F= 56.50, p= 0.00; soluble reactive phosphorous: F= 37.11, p= 0.00; total phosphorous: F= 104.025, p= 0.00). Azolla pinnata proved to be better than Lemna minor in the uptake of the nutrients particularly for the soluble reactive phosphorous (F= 35.18, P= 0.044). We conclude that the two macrophytes are good for wastewater treatment. It is recommended introduction and/or multiplication of Azolla pinnata in the constructed wetlands meant for wastewater treatment especially within the tropics.

Plastic pollution has universally known accumulated in all environment compartments and accelerating threat to the sustainability of earth. Field survey to examine the occurrence of microplastics in ancient sea water evaporation technology of ponds at Pallengu-Jeneponto, was conducted. From this sea salt producing ponds, samples of water, sediment and freshly harvested salts were collected. Sixteen samples each of water and sediments and 12 salts were collected. From 16 water and sediment samples there are 31 microplastics item discovered in 11 water samples (68.75% of total contamination) and 41 microplastics item observed in 10 sediment samples (62.5% total contamination), respectively. Interestingly, sampling points at sedimentation/heating pools were found to be the locations with highest occurrence of microplastics in both water and sediment. There are 7 salt  samples positively contaminated with 29 microplastics or 58.3% of total contamination, which predominated by line and fragment forms. Fourier transform infra-red spectroscopy analysis has revealed polymers of polyvinyl acetate (41.7%), polyethylene (33.3%) and polystyrene (25%). There was no significant difference found on microplasticss occurrence from 3 kinds of samples collected, although there was a decreasing trend of total microplastics found from water, sediment and salt. Microplasticss abundance were ranged 7-55 items/L water, 14.6-50 items/kg sediments and 6.7-53.3 items/kg salt. With microplastics abundance reached over 53 microplastics items/kg salt, it is believed that continuous consumption by people will end up with possible accumulation of potentially absorbed of various toxic chemical pollutants which present in sea water as salt raw materials. The need for robust and practical strategy in water quality management for reduction of microplasticss contamination in consumed salts is a must.

Thenutrient medium used for the cultivation of microalgae adds more cost to its value-added product as well as the commercial scale application. Therefore in this study, focused feasibility of cattle urine as a cheap source of nutrients for microalgal growth, because, it contains various minerals and economical which may support the growth of microalgae and reduce the medium cost. To check this, fresh cattle urine was collected, characterized, diluted and inoculated microalgae species Oscillatoria-SRA (Stagnant rainwater algae), Oscillatoria-CWA (Cooum waste algae), Chlorella and Synecocystis separately and incubated under fluorescent light with 8 hours light and 16 hours dark cycle. The biomass was quantified after 15 days and found out variation in biomass quantity in all microalgae isolates. The maximum of 2.6 g/L biomass was produced in Chlorella sp., at 10% urine, followed by Synechocystis sp., (2.25 g/L in 10% urine), Oscillatoria sp.,-SRA (1.3 g/L in 5% urine) and Oscillatoria sp.,-CWA (0.3 g/L in 1% urine). Moreover, lipid quantity was shown at the maximum of 12% dry weight in Oscillatoria sp-SRA., trailed by the 10% in Chlorella sp., 7% in Synecocystis sp., and the least of 5% in Oscillatoria sp-CWA. This study divulged that cattle urine alone is being able to support microalgae growth at a significant amount, thus convalescing industrial production of microalgae ultimately will reduce the cost of microalgal value-added products.

This study aimed to evaluate concentrations of selected heavy metals in the soils of Ilokun dumpsite and adjourning farmlands, and to determine as well levels of heavy metals uptake in various parts of Carica papaya plant collected from the dumpsite. Twenty-two soil samples were collected within the dumpsites and farmlands at depths of 0-20 cm and 20-40 cm. Evaluations of heavy metals (Ni, Zn, Cd, Cr, Pb and Cu) in soil samples and in different parts of Carica papaya plants were carried out using atomic absorption spectrophotometer. The study revealed higher concentration of Pb in the dumpsite than the farmlands. Mean concentrations of Cd in the dumpsites; 2.98±1.93 (0-20 cm) and 3.22±2.14 (20-40 cm) were higher than their corresponding depth in farmlands (1.93±1.28 (0-20cm) and 1.94±1.59 (20 – 40 cm). The study established a strong correlation for Ni/Pb (0.948) at depth of 0.20cm; Cu/Cd (0.985) and Pb/Cd (0.918) at depth of 20-40cm. Heavy metal uptake was highest in the plant parts compared to the dumpsites and farmlands. The result showed that translocation factor arrangement is: Cu Zn Cd˃ Cr˃ Pb˃ Ni and Cu had the highest translocation factor of 4.698. Based on the results, the various heavy metal could be classified as slight contaminants (Pb, Cr and Ni), moderate contaminants (Cu and Cd) and severe contaminant (Zn). Although concentrations of heavy metals in the farmlands were below the Department of Petroleum Resources and World Health Organisation allowable limits, monitoring the concentration profile of these heavy metals concentrations in the area is recommended to prevent detrimental effects on the environment.

The use of amalgamation process to recover gold from mined ores by the small-scale gold miners in the Philippines and other developing countries produces and dispose of untreated wastewater to the receiving water bodies. In this study, a field-scale filter bed system was constructed to treat heavy metal metal-laden wastewater collected from small-scale gold mining site in Paracale, Camarines Norte, Philippines. The filter bed system was consists of sedimentation tank and filter bed with Cocopeat, a by-product of coconut husk, as adsorbent. Physico-chemical parameters (temperature, pH, oxidation-reduction potential, electrical conductivity, turbidity, dissolved oxygen, total dissolved solids, salinity, total suspended solids, color) and heavy metal (As, Ba, Cd, Hg, Pb) concentrations were monitored during the 50 days experiment at a flow rate of 40 Liter per hour for 3 hours daily wastewater application. Significant reduction was achieved on heavy metals; As (97.11%), Ba (39.75%), Cd (74.24%), Hg (97.02%), Pb (98.82%) from small-scale gold mining (SSGM)wastewater in sedimentation phase and further reductions on As (1.39%), Ba (28.00%), Cd (4.95%), Hg (2.91%), Pb (0.97%) were achieved by adsorption in the Cocopeat filter bed. Measured effluent physico-chemical parameters and heavy metal concentrations were within the respective regulatory limits. Other effluent parameters with strong correlation with total suspended solids such as turbidity and color, though not regulated, were reduced significantly. All adsorbed heavy metals accumulated in the upper 25 cm of the Cocopeat column in the filter bed. Measured heavy metal concentrations in Cocopeat suggest that the adsorbent was not saturated and further application of small-scale gold mining wastewater is recommended to determine its useful life.

Hydrological components in a river basin can get adversely affected by climate change in coming future. Manipur River basin lies in the extreme northeast region of India nestled in the lesser Himalayan ranges and it is under severe pressure from anthropogenic and natural factors. Basin is un-gauged as it lies in remote location and suffering from large data scarcity. This paper explores the impact of climate change towards understanding the inter-relationships between various complex hydrological factors in the river basin. An integrated approach is applied by coupling Soil and Water Assessment Hydrological Model and Hadley Center Coupled Model based on temperature, rainfall and geospatial data. Future representative concentration pathways 2.6, 4.5 and 8.5 scenarios for 2050s and 2090s decades were used to evaluate the effects of climatic changes on hydrological parameters. Both annual mean temperature and annual precipitation is predicted to be increased by 2.07oC and 62% under RCP 8.5 by the end of 21st century. This study highlights that change in meteorological parameters will lead to significant change in the hydrological regime of the basin. Runoff, actual evapotranspiration and water yield are expected to be increased by 40.96 m3/s, 52.2% and 86.8% respectively under RCP 8.5. This study shows that water yield and evapotranspiration will be most affected by increase in precipitation and temperature in the upper and middle sub-basins. Different region within the basin is likely to be affected by frequent landslides and flood in coming decades.

Four diverse chlorides layered double hydroxides with diverse ratios, i.e. Mg-Al (3:1), Mg-Al (4:1), Zn-Al (4:1), and Zn-Al (3:1) LDHs, were prepared to evaluate their efficiency and selectivity towards nitrate removal from aquatic solutions. A batch experiment was done at the initial nitrate concentration of 5-1000 mg/L, pH 5 to 12, and contact time of 5-180 min. Isotherms of nitrate adsorption on LDHs, soil and soil-LDH mixtures were studied. Kinetics of adsorption, temperature effect, nitrate adsorption in nitrate adsorption, simulated soil solution and desorption on Mg-Al-LDH (4:1) were measured. At an optimum speed of 250 rpm, pH value of 7 and adsorbent dosage of 2 g/L, the amounts of nitrate adsorption on Mg-Al- LDH (3:1) and Mg-Zn-LDH (3:1) and also on Mg-Al- LDH (4:1) and Mg-Zn-LDH (4:1) were obtained after 30 and 60 min, respectively. Isotherm studies indicated that nitrate adsorption on soil, soil-LDH mixture, and LDH fitted Langmuir linear isotherm. The highest nitrate adsorption on Mg-Al-LDH (4:1) and a mixture of soil-Mg-Al-LDH (4:1) were 188.67 and 107.52 mg/g, respectively. Among the studied kinetic equations for nitrate adsorption on Mg-Al-LDH (4:1), the pseudo-second-order with R2=0.998 had the best fitness. Negative values of ∆H in different nitrate concentrations indicated the exothermic process of nitrate adsorption on Mg-Al-LDH (4:1). In the presence of other anions, Mg-Al-LDH (4:1) removed nitrate preferentially. Moreover, Mg-Al-LDH (4:1) could exchange nitrate 20 times in different concentrations with no reduction in its adsorption capacity.

This study seeks to provide insights on understanding the contemporary problems of energy efficiency in Ukrainian universities by developing a comprehensive energy efficiency management framework that encompasses its participating subjects, objects and key drivers along with suggesting its implementation mechanism and tools. Emphasis should be given that the current situation of inefficient and irrational consumption of energy resources within the system of higher education in Ukraine challenges the development of an integrative approach to energy saving and energy efficiency management. It is argued that the key elements of this integrative approach to energy management are energy auditing, energy certification and energy monitoring based on the consistent use of ISO 9000 international standards. Over the last 10 years energy consumption in Ukrainian higher education institutions against the world best practice exceed by 30-40%. This triggers a critical need to building an integrative approach to energy saving and energy efficiency management. The findings revealed that disincentives reduce the degree of energy efficiency by 25%. Constructing energy profiles by a hierarchical clustering method demonstrated that 68% of the campus buildings belong to a 5th class out of 7, i. e. being highly energy intensive. Following the DGNB (German Sustainable Building Council) approach to evaluate energy efficiency has enabled to eliminate 17% of the G category classrooms (extra energy intensive) from the University heating facilities. The clustering method to assess 15 University buildings by 16 performance indicators identified 5 clusters in terms of energy consumption and energy efficiency.

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affect the performance of an algorithm, however, it is yet to be known why an algorithm is preferred over the other for a certain task. The work aims at highlighting the underlying principles of machine learning techniques and about their role in enhancing the prediction performance. The study adopts, 38 most relevant studies in the field of environmental science and engineering which have applied machine learning techniques during last 6 years. The review conducted explores several aspects of the studies such as: 1) the role of input predictors to improve the prediction accuracy; 2) geographically where these studies were conducted; 3) the major techniques applied for pollutant concentration estimation or forecasting; and 4) whether these techniques were based on Linear Regression, Neural Network, Support Vector Machine or Ensemble learning algorithms. The results obtained suggest that, machine learning techniques are mainly conducted in continent Europe and America. Furthermore a factorial analysis named multi-component analysis performed show that pollution estimation is generally performed by using ensemble learning and linear regression based approaches, whereas, forecasting tasks tend to implement neural networks and support vector machines based algorithms.