Blanchardewing6688

The performance of waste management system has been recently interrupted and encountered a very serious situation due to the epidemic outbreak of the novel Coronavirus (COVID-19). To this end, the handling of infectious medical waste has been particularly more vital than ever. Therefore, in this study, a novel mixed-integer linear programming (MILP) model is developed to formulate the sustainable multi-trip location-routing problem with time windows (MTLRP-TW) for medical waste management in the COVID-19 pandemic. The objectives are to concurrently minimize the total traveling time, total violation from time windows/service priorities and total infection/environmental risk imposed on the population around disposal sites. Here, the time windows play a key role to define the priority of services for hospitals with a different range of risks. To deal with the uncertainty, a fuzzy chance-constrained programming approach is applied to the proposed model. A real case study is investigated in Sari city of Iran to test the performance and applicability of the proposed model. Accordingly, the optimal planning of vehicles is determined to be implemented by the municipality, which takes 19.733 h to complete the processes of collection, transportation and disposal. Finally, several sensitivity analyses are performed to examine the behavior of the objective functions against the changes of controllable parameters and evaluate optimal policies and suggest useful managerial insights under different conditions.In recent years, geospatial data (e.g. remote sensing imagery), and other relevant ancillary datasets (e.g. Tacrolimus chemical structure land use land cover, climate conditions) have been utilized through sophisticated algorithms to produce global population datasets. With a handful of such datasets, their performances and skill in flood exposure assessment have not been explored. This study proposes a comprehensive framework to understand the dynamics and differences in population flood exposure over Canada by employing four global population datasets alongside the census data from Statistics Canada as the reference. The flood exposure is quantified based on a set of floodplain maps (for 2015, 1 in 100-yr and 1 in 200-yr event) for Canada derived from the CaMa-Flood global flood model. To obtain further insights at the regional level, the methodology is implemented over six flood-prone River Basins in Canada. We find that about 9% (3.31 million) and 11% (3.90 million) of the Canadian population resides within 1 in 100-yr and 1 in 200-yr floodplains. We notice an excellent performance of WorldPop, and LandScan in most of the cases, which is unaffected by the representation of flood hazard, while Global Human Settlement and Gridded Population of the World showed large deviations. At last, we determined the long-term dynamics of population flood exposure and vulnerability from 2006 to 2019. Through this analysis, we also identify the regions that contain a significantly larger population exposed to floods. The relevant conclusions derived from the study highlight the need for careful selection of population datasets for preventing further amplification of uncertainties in flood risk. We recommend a detailed assessment of the severely exposed regions by including precise ground-level information. The results derived from this study may be useful not only for flood risk management but also contribute to understanding other disaster impacts on human-environment interrelationships.Magnetic particles (MPs) assisted powdered activated carbon (PAC) is a promising composite material for adsorption removal of micropollutants. The fractional amount of Fe3O4 impacts the balance between adsorption capacity and magnetic property of magnetic activated carbons (MPACs), and therefore it affects the extent of sulfamethoxazole (SMX) removal. Here, five MPACs with different mass ratios of Fe3O4 PAC (11, 12, 14, 16, and 18) were prepared using a hydrothermal method and characterized by various spectroscopic methods. The spherical shaped MPs were monolayerly deposited on PAC with fewer pores blocked when the mass ratio of Fe3O4 was comparatively low (≤ 20%). MPAC6 (14.3 wt% of Fe3O4) had the best overall performance, with good Langmuir adsorption capacities for SMX (173.0 mg g-1) and excellent magnetic properties (9.0 emu g-1). Corresponding adsorption kinetics fitted well with the pseudo second-order kinetic model. The negative ΔG0 (-25.6 to -27.2 KJ mol-1) and ΔH0 (-9.14 KJ mol-1), and positive ΔS0 (0.55 KJ mol-1 K-1) properties indicated the spontaneous and exothermic nature of the adsorption process accompanied by an increase in entropy. The strong cation-assisted electron donor-acceptor and hydrophobic interactions were contributed to a high extent of SMX removal in the pH range of 2-4. Formation of negative charge-assisted H-bonds was responsible for the adsorption of hydrophilic SMX- on negatively charged MPAC6 in alkaline solution. Desorption and regeneration experiments showed SMX removal was still 92.3% in the 5th cycle. These findings give valuable insights into the interactions between SMX and MPACs and guide for choosing sustainable magnetic adsorbents for environmental applications.The increasing prevalence of antibiotic-resistant microorganisms in both clinical and environmental samples is of great concern for public health. In the present study, environmental samples from seven different sites, heavily contaminated with petroleum hydrocarbons has been examined for the antimicrobial resistome through metagenomic approach. The soil samples were found to be contaminated with high concentration of total petroleum hydrocarbons (average 45 g/kg), polyaromatic hydrocarbons (average ∑16PAH = 280 mg/kg), and heavy metals, which shapes the microbial community and their function. Proteobacteria was found to be predominant phylum in the contaminated habitat with the highest diversity (55.91%) followed by Actinobacteria (9.86%). Although the taxonomical abundance of the non-contaminated sample was not significantly different from contaminated samples, the functional abundance of genes related to antibiotic resistance was found to be higher up to 2 fold in contaminated samples. The comparative metagenomic analysis revealed a higher abundance of different antibiotic resistance genes, especially genes for fluoroquinolones was found to be higher up to 10 fold in contaminated samples.