Global NEST Journal

In the recent era, utmost attention is paid towards exploring the alternative source of energies, being more specific green energies. In modern days, when growing energy scarcity is a hot topic and considered as a global threat, in that context a green technology capable of sustainably addressing waste management issues and synthesize waste-derived end-product such bio-diesel from Jatropha Curcas linnaeus (JCL) and Chlorella vulgaris shall be paid ancillary attention. The attribution of the above-mentioned species and Lemnagibba L towards the purification of the municipal solid waste (MSW) leachate by utilizing its bioaccumulation properties and ultimately yielding several value-added co-products could be the solution of the era in terms of sustainable leachate treatment. A specially designed ridge furrow phytoremediation wetland was fabricated under the controlled environment associated with a pre-treatment facility and bottom lining system to captivate the downward percolation. A 1.5 m high Jatropha species were planted at an interval of 1 m on the ridges and the root for the same found to reach a depth of 0.8 m on an average over a period of 3 months. Leachate was supplied by means of free flow into the furrows with an initial addition rate of 1 m3 per day for a 10 m x 10 m remediation hybrid pond system and simultaneously the loading was elevated up to 80 m3/ha/day. Prior to the active remediation zone, a pre-treatment unit was installed which works on the bio-accumulation property of the vetiver grass (Chrysopogon zizanioides). A packed bed system of gravel, marble chips, sand, and coconut coir as a medium of filtration was installed to bring the concentrate waste juice up to the wetland treatment standard. Moreover, the bleaching influence of the sunlight also found to be effective against pathogen removal, active evaporation, and evapotranspiration. The ultimate treatment capacity for the designated size was assessed as ~ 0.03 m3 per day ascertained with an HRT value of 45 days. The ultimate outcome was on par with the influent characteristics obligatory for the tertiary units with a comprehensive efficacy value evaluated as approx. 60%. Thus, it’s quite evident to claim the feasibility of the present study towards the bio-remediation of the enormous quantity of leachate generation associated with the handling of solid waste which is otherwise, a costly affair.

Particulate matter (PM), classified according to aerodynamic diameter, is one of the harmful pollutants causing health damaging effects. It is considered as cancerogenic by the World Health Organization (WHO) because of the substances found in the chemical composition of PM. In this study, short-term prediction of PM2.5 pollution at 1, 2 and 3 hours was modelled using deep learning methods. Three deep learning algorithms and the combination thereof were evaluated: Long-short term memory units (LSTM), recurrent neural networks (RNN) and gated recurrent unit (GRU). Air Quality Monitoring Stations of the Ministry of Environment and Urbanization of Turkey were utilized to obtain the data. Specifically, meteorological and air pollution data were obtained from a monitoring station located in Keçiören District of Ankara. Several trials were conducted using different combinations of RNN, GRU and LSTM models. Pollutant concentrations and meteorological factors were integrated into the model as input parameters to predict PM2.5 concentration for 1,2 and 3 hours. Best results with R2 of 0.83, 0.7 and 0.63 for 1-, 2-, and 3-hour predictions, respectively, were obtained by using a combination of GRU and RNN models. The results of this study are promising for explaining the effect of different deep learning models on prediction performance.

In this study, an investigation was carried out in order to determine the potential of ordered mesoporous carbon including high ratio sucrose in its composition as a CO2 adsorbent. Structural characterizations of silica and carbon material were determined by using a scanning electron microscope (SEM), N2 adsorption/desorption and X-ray diffraction (XRD) analysis. CO2 adsorption capacities of developed adsorbent at various temperatures (25°C, 35°C and 50°C) were determined by using a thermogravimetric analyzer under CO2 flow. The maximum adsorption capacity was obtained as approximately 2.27 mmol g CO2 g-1 adsorbent which was remained almost the same in the desorption process. The first order reaction kinetic model was well fitted with the experimental data. Arrhenius equation was used to calculate activation energy which was obtained as -2.86 kJ mol-1 indicating barrier-less type adsorption. Results showed that ordered mesoporous carbon could be a potential source for an efficient and low-cost adsorbent for CO2 capture.

Spatio-temporal methods have been developed for the estimation of concentrations of pollutants such as particulate matter and nitrogen dioxide for application in epidemiological studies. A limited number of city-specific spatio-temporal ozone (O3) models have been proposed until today. Our aim was to develop a spatio-temporal land use regression (LUR) model that estimates daily concentrations of O3, for the whole year, as well as the warm (April-September) and cold season (October-March), within the greater Athens area. We developed models using a semiparametric approach including linear and smooth functions of spatial and temporal covariates and a bivariate smooth thin plate function. The final set of explanatory variables was selected based on the adjusted-R2. We tested the final model in temporal and spatial terms following a leave-one out monitor approach. The adjusted-R2 in the leave-one-out cross validation was 0.73 for the annual model (warm: 0.65 and cold: 0.70). The spatial terms in our annual model explained 32.9% and the temporal 63.2% of the variability in O3. The developed models showed good validity when comparing predicted and observed measurements for the 2015 data. Spatio-temporal LUR modeling provides a useful tool for estimating O3 spatio-temporal variability with adequate accuracy for subsequent use in epidemiological studies.