Development of machine learning multi-city model for municipal solid waste generation prediction

● A database of municipal solid waste (MSW) generation in China was established. ● An accurate MSW generation prediction model (WGMod) was constructed. ● Key factors affecting MSW generation were identified. ● MSW trends generation in Beijing and Shenzhen in the near future are projected. Integrated management of municipal solid waste (MSW) is a major environmental challenge encountered by many countries. To support waste treatment/management and national macroeconomic policy development, it is essential to develop a prediction model. With this motivation, a database of MSW generation and feature variables covering 130 cities across China is constructed. Based on the database, advanced machine learning (gradient boost regression tree) algorithm is adopted to build the waste generation prediction model, i.e., WGMod. In the model development process, the main influencing factors on MSW generation are identified by weight analysis. The selected key influencing factors are annual precipitation, population density and annual mean temperature with the weights of 13%, 11% and 10%, respectively. The WGMod shows good performance with R² = 0.939. Model prediction on MSW generation in Beijing and Shenzhen indicates that waste generation in Beijing would increase gradually in the next 3–5 years, while that in Shenzhen would grow rapidly in the next 3 years. The difference between the two is predominately driven by the different trends of population growth.     

Environment and economic feasibility of municipal solid waste central sorting strategy: a case study in Beijing

Although Beijing has carried out municipal solid waste (MSW) source separation since 1996, it has largely been ineffective. In 2012, a “Green House” program was established as a new attempt for central sorting. In this study, the authors used material flow analysis (MFA) and cost benefit analysis (CBA) methods to investigate Green House’s environment and economic feasibility. Results showed that the program did have significant environmental benefits on waste reduction, which reduced the amount of waste by 34%. If the Green House program is implemented in a residential community with wet waste ratio of 66%, the proportion of waste reduction can reach 37%. However, the Green House is now running with a monthly loss of 1982 CNY. This is mainly because most of its benefits come from waste reduction (i.e., 5878 CNY per month), which does not turn a monetary benefit, but is instead distributed to the whole of society as positive environmental externalities. Lack of government involvement, small program scale, and technical/managerial deficiency are three main barriers of the Green House. We, thus, make three recommendations: involve government authority and financial support, expand the program scale to separate 91.4 tons of waste every month, and use more professional equipment/technologies. If the Green House program can successfully adopt these suggestions, 33.8 tons of waste can be reduced monthly, and it would be able to flip the loss into a profit worth 35034 CNY.

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Assessment of key parameters in municipal solid waste management: a prerequisite for sustainability

Municipal solid waste management was studied for 1 year in a representative urban area of Lahore city for environmental sustainability. The effects of financial status of the household and the seasonal variations on generation rate and compositions of municipal solid waste (MSW) were determined. MSW generation rate and the economic status of the households were positively correlated (p < 0.05). Seasonal variations observed were significant only for organics (p = 0.001), plastics (p = 0.008) and food waste fractions (p = 0.009) in MSW. Response surface regression model developed and analysed by Minitab-15® showed that the interaction of season and different economic zones of the town on the MSW generation rate was non-significant (p = 0.334). Elemental and heating value analyses of the mixed organic fractions in kitchen waste had carbon 47.93%, hydrogen 6.20%, nitrogen 2.24%, sulphur 0.23%, oxygen 39.01%, and C and N ratio 27.78. Findings concluded that food waste was 56% of total MSW with 71.03% moisture content and a modest heating value of 5566 J/g. Existing temporary storage capacity of MSW is 51% of the total MSW generated considering the weighted MSW generation rate of 0.57 kg/person/day calculated in this study. Composting could be a possible final disposal option due to high moisture and organic content and can be studied in future research. Development of a transfer station, introduction of home composting programmes and awareness towards proper segregation and reduction of waste at the household level is suggested to attain sustainability in the MSW management system.