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The part of industrial ecology known as industrial symbiosis engages traditionally separate industries in a collective approach to competitive advantage involving physical exchange of materials, energy, water and by-products (Dounavis et al. 2015, Dounavis et al. 2016, Ravindran and Jaiswal 2016, Kourmentza et al. 2018, Keskisaari and Kärki 2018, Ayadi et al. 2018). The keys to industrial symbiosis are collaboration and the synergistic possibilities offered by geographic proximity. Industrial symbiosis includes initiatives in which two or more industrial entities develop both beneficial relationships and the circular economy (Chowdhury et al. 2018). Since, the symbiosis encourages the sustainable development and promotes the economy growth (de Jesus et al. 2018). This paper presents an online industrial symbiosis and circular economy platform created by a LIFE project (still ongoing) which aims to demonstrate the usefulness of a digital on-line platform at European level for the cataloguing, use and exploitation of industrial waste produced in a local area in order to improve the overall performance of industrial processes and circular economy in a local level. All this will happen both through the increased use of waste within the area itself and by the less dependence on the disposal of waste to be disposed of, taking into account the reduction of used raw materials and the reduction of waste itself. The platform will be created through the European LIFE M3P Project (Material Match Making Platform for the promotion of the use of industrial waste in local networks) which is still on-going and ends at the end of September 2019.

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.

Samples of soil acquired from electrical and electronic waste (e-waste) dumpsite in Kansuwan gwari, Minna, Nigeria were utilized for the isolation of microorganisms utilized as a part of lead (Pb) biosorption studies. The soil specimen was cultured for Saccharomyces cerevisiae and Candida tropicalis. Box Behnken plan was utilized to advance the removal of lead by the organisms. The factors utilized as a part of the Box Behnken plan were temperature, pH and concentration of Pb. Saccharomyces cerevisiae was enhanced by the Box Behnken design, a temperature of 250C, pH of 6 and a lead of 2ppm concentration prompted a 70.8% lead removal. The investigation of variance for Candida tropicalis demonstrated that the reaction had a probability estimation of 0.851 for the regression. This likewise showed the model was not a solid match for the plan since the p-value (0.851) was more prominent than 0.05. S. cerevisiae gave a probability estimation of 0.029 for the regression, which demonstrated that the model was a solid match for the outline since the p-value was less than 0.05. More models may be connected to different fields in microbiology as it diminishes the ideal opportunity for protracted examinations and tends to limit the test to how successfully the outcomes can be accomplished. This study can be connected to the master plan of the effective pH, temperature and different parameters for the clean up of contamination in the environment.

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.