Sustainable strategy for municipal solid waste disposal in Hong Kong: current practices and future perspectives

Leveraging food waste for electricity: A low-carbon approach in energy sector for mitigating climate change and achieving net zero emission in Hong Kong (China)

In recent years, food waste has been a global concern that contributes to climate change. To deal with the rising impacts of climate change, in Hong Kong, food waste is converted into electricity in the framework of low-carbon approach. This work provides an overview of the conversion of food waste into electricity to achieve carbon neutrality. The production of methane and electricity from waste-to-energy (WTE) conversion are determined. Potential income from its sale and environmental benefits are also assessed quantitatively and qualitatively. It was found that the electricity generation from the food waste could reach 4.33 × 109 kWh annually, avoiding equivalent electricity charge worth USD 3.46 × 109 annually (based on US' 8/kWh). An equivalent CO2 mitigation of 9.9 × 108 kg annually was attained. The revenue from its electricity sale in market was USD 1.44×109 in the 1st year and USD 4.24 ×109 in the 15th year, respectively, according to the projected CH4 and electricity generation. The modelling study indicated that the electricity production is 0.8 kWh/kg of landfilled waste. The food waste could produce electricity as low as US' 8 per kW ∙ h. In spite of its promising results, there are techno-economic bottlenecks in commercial scale production and its application at comparable costs to conventional fossil fuels. Issues such as high GHG emissions and high production costs have been determined to be resolved later. Overall, this work not only leads to GHG avoidance, but also diversifies energy supply in providing power for homes in the future.

GIS-based approach and multivariate statistical analysis for identifying sources of heavy metals in marine sediments from the coast of Hong Kong

Hong Kong is an urbanized coastal city which experiences substantially different metal loads from anthropogenic activities. This study was aimed at analyzing the spatial distribution and pollution evaluation of ten selected heavy metals (As, Cd, Cr, Cu, Pb, Hg, Ni, Zn, Fe, V) in the coastal sediments of Hong Kong. The distribution of heavy metal pollution in sediments has been analyzed using the geographic information system (GIS) technique, and their pollution degrees, corresponding potential ecological risks and source identifications, have been studied by applying the enrichment factor (EF) analysis, contamination factor (CF) analysis, potential ecological risk index (PEI), and integrated multivariate statistical methods, respectively. Firstly, the GIS technique was used to access the spatial distribution of the heavy metals; the result revealed that pollution trend of these metals was decreased from the inner to the outer coast sites of the studied area. Secondly, combining the EF analysis and CF analysis, we found that the pollution degree of heavy metals followed the order of Cu > Cr > Cd > Zn > Pb > Hg > Ni > Fe > As > V. Thirdly, the PERI calculations showed that Cd, Hg, and Cu were the most potential ecological risk factors compared to other metals. Finally, cluster analysis combined with principal component analysis showed that Cr, Cu, Hg, and Ni might originate from the industrial discharges and shipping activities. V, As, and Fe were mainly derived from the natural origin, whereas Cd, Pb, and Zn were identified from the municipal discharges and industrial wastewater. In conclusion, this work should be helpful in the establishment of strategies for contamination control and optimization of industrial structures in Hong Kong.

Solid waste generation indicators, per capita, in Amazonian countries

Countries participating in the Amazon Cooperation Treaty Organization have few options for the environmentally appropriate final disposal of municipal solid waste. Thus, sustainable practices aimed at reducing the negative effects of such a disposal on the environment are complex and hard to accomplish, since solid waste generation per capita proportionally increases as populations grow (≈ 2.7% > world average), mainly in countries inserted in Amazon Cooperation Treaty Organization. Thus, demographic, socioeconomic, management, and ecological factors represented by 18 independent variables were statistically analyzed to explain waste per capita variation in Amazonian countries and sub-regions. Multiple Kruskal-Wallis tests were applied; 13 of them recorded significant results (p < 0.05). Subsequently, simple and multivariate regression analyses were carried out by taking into consideration waste per capita and significant variables. Simple regression results recorded for variables "IAC" and "Gini index" were significant (R_IAC² = 60.09%, R_Gini² = 30.83%), with emphasis on "Amazon biome" (DF = 33, p < 0.01, R_Biome² = 5.34%). Multivariate models resulted in wide explainability variation, depending on the number and type of available variable (54.47% ≤ R_aj² ≤ 70.83%), with emphasis on "IAC," "Ptot," "Purb," "Wton," "Lon," Area, "HDI," "Gini," and "SDG11" (p < 0.01). In conclusion, waste per capita estimation models can present variations and geographical interdependencies due to different variables and factors that reflect the current public policies and municipal solid waste management practices.