Identification of inference fallacies in solid waste generation estimations of developing countries. A case-study in Panama.

The absence of sound sampling procedures and statistical analyses to estimate solid waste generation in many developing countries has resulted in incomplete historical records of waste quantity and composition. Data is often arbitrarily aggregated or disaggregated as a function of waste generators to obtain results at the desired spatial level of analysis. Inference fallacies arising from the generalization or individualization of results are almost never considered. In this paper, Panama, one of the fastest-growing developing countries, was used as a case-study to review the main methodological approaches to estimate solid waste generation per capita per day, and at different hierarchical levels (from households to the country). The solid waste generation intensity indicator is used by the Panamanian waste management authority to run the waste management system. It was also the main parameter employed by local and foreign companies to estimate solid waste generation in Panama between 2001 and 2008. The methodological approaches used by these companies were mathematically formalized and classified as per the expressions suggested by Subramanian et al. (2009). Seven inference fallacies (ecological, individualistic, stage, floating population, linear forecasting, average population and mixed spatial levels) were identified and allocated to the studies. Foreign companies committed three of the seven inference fallacies, while one was committed by the local entity. Endogenous knowledge played an important role in these studies to avoid spatial levels mismatch and multilevel measurements appear to produce more reliable information than studies obtained via other means.

Robust information for effective municipal solid waste policies: Identifying behaviour of waste generation across spatial levels of organization.

Existing studies have studied influencing factors of MSW generation behaviour at different spatial levels of organization, but always one at a time and not simultaneously. Income is a strong influencing factor, affecting MSW generation from the individual to the country level, capable of hiding the effects of the others. This study shows that when MSW generation behaviour is holistically analysed across multiple levels of organization (individuals, households, and communities) hierarchically organized as functional units of MSW generation within a specific study area, it is possible to identify influencing factors in addition to income (education, demographic, health, ethnic, economic activity and financial types) as explanatory variables. Increasing the number of influencing factors of MSW generation makes it possible to create a robust knowledge base for MSW management policies in fast-growing urban areas of developing countries, improving the information used to select proper policies and plans within their MSW management systems and avoiding overlapping policies causing legal gaps. Betania, an urban area of the Panama City district, has been chosen as a case study area. The results show that the household income explains 86% of its members MSW generation and the community indigenous population explains 21% of households MSW generation. It is concluded that MSW generation is not linear across levels, it has as many degrees of freedom as influencing factors shaping the levels of organization where functional units generating waste exist. Influencing factors appearing at each spatial level affects MSW generation in an interdependent manner in variable degrees of magnitude.

Landfill reactions to society actions: The case of local and global air pollutants of Cerro Patacón in Panama.

This paper studies landfill emissions and the related environmental and health risks in Panama City, with the aim to sensitize the population about the harmful effects of irresponsible resource consumption and non-deliberate solid waste generation that it is disposed of in an uncontrolled manner in landfills. Empirical data on Cerro Patacón, Panama City's landfill was obtained to describe the status of municipal waste disposal. Ten known methane generation models were used to estimate the yearly emission rate of methane from the landfill for a 100-year period starting from its inception in 1986. From the models used, the GasSIM model was chosen to estimate emission rates of six long-term hazardous air pollutants. The AERMOD source dispersion model was used to simulate their atmospheric downwind dispersion by levels of concentration over nearby affected communities; results were mapped in Google Earth. The relative contributions by population of the 32 towns making up Panama City to the forecasted waste generation in 2022 and related hazardous air pollutants emission rates from the landfill were assessed. It was found that Cerro Patacón will generate 45% of the countrywide methane generation by 2022; an average of 47 Gg. The solid waste generated by the 1.5 million inhabitants of Panama City impacts the health of ~73,600 inhabitants in nearby communities through the dispersion of hazardous atmospheric pollutants derived from the landfill. The highest emission rates were from hydrogen sulfide and dichloromethane, which can be largely attributed to the waste generated by the communities of Juan Diaz and Tocúmen. The concentration of hydrogen sulfide and benzene was over the reference concentration (uncertainty factor spanning three orders of magnitude) for all communities and years simulated. The concentration of vinyl chloride was over the RfC for all communities and years simulated, except in 2018 for 12 communities.