Reducing waste management scenario space for developing countries: A hierarchical clustering on principal components approach.

The complexity of waste management (WM) problems resulted in the explosion of scenarios that challenge focused discussion among stakeholders and hinder the integrity of policy responses in developing countries. Hence, drawing similarities is essential to reduce the number of scenarios to simplify the WM efforts. To extract similarities, measuring WM performance is not enough, but the background factors related to this performance should be incorporated. These factors form a unique system characteristic that facilitates or hinders WM functions. Thus, this study applied multivariate statistical analysis to clarify underlying characteristics that facilitate efficient WM scenario developments for developing countries. The study first analysed drivers associated with improved WM system performance using bivariate correlation analysis. As a result, twelve significant drivers associated with controlled solid waste were identified. Then, it mapped the countries based on their WM system characteristics using the combined principal component analysis and hierarchical clustering approach. Thirteen variables were examined to extract similarities between the countries. The results identified three homogenous clusters. The clusters were found considerably parallel to the global classifications based on income and human development index. Hence, the presented approach is efficient in explaining similarities that reduce WM scenarios and favours cooperation among countries.

Health risk assessment and cost-benefit analysis of agricultural soil remediation for tailing dam failure in Jinding mining area, SW China.

The impact of the tailing dams and the economic feasibility of the remediation process is significant for future risk management for tailing dams. In this research, we develop a hypothetical failure scenario for a tailing dam in the Jinding mining area, Southwest China. We assess the exposure with the Geo-Environmental Risk Assessment System, tier-1 model, and health impact with Disability-Adjusted Life Years (DALY). Cost and benefit are also analyzed for the following clean-up process. The result shows that the exposure dose (mg/kg-BW/d) of As, Cd, and Pb right after the dam failure is 1.07 × 10-2 for As, 1.76 × 10-4 for Cd, and 5.68 × 10-3 for Pb, respectively. The DALY caused by heavy metal exposure is 2.63 × 10-2 DALY per year, which significantly exceeds the tolerable level. This indicates that the tailing dam failure will pose a high health risk to the residents, and remediation is necessary. After remediation, the DALY is 1.24 × 10-8 DALY per year, indicating the clean-up process effectively reduces the resident's health impact. From the financial point of view, the net present value of the clean-up is $- 1.02 × 107. This indicates that the clean-up process is not economically feasible. Sensitivity analysis shows that the amount of released tailing influences the output result. The time span for benefit estimation is also an important issue. This research shows that the impact of a tailing dam failure will be severe, and remediation may be effective but economically infeasible. Therefore, preventing tailing dam failure is the most crucial task for the local government.

Application of fuzzy c-means clustering to PRTR chemicals uncovering their release and toxicity characteristics.

Increasing manufacture and usage of chemicals have not been matched by the increase in our understanding of their risks. Pollutant release and transfer register (PRTR) is becoming a popular measure for collecting chemical data and enhancing the public right to know. However, these data are usually in high dimensionality which restricts their wider use. The present study partitions Japanese PRTR chemicals into five fuzzy clusters by fuzzy c-mean clustering (FCM) to explore the implicit information. Each chemical with membership degrees belongs to each cluster. Cluster I features high releases from non-listed industries and the household sector and high environmental toxicity. Cluster II is characterized by high reported releases and transfers from 24 listed industries above the threshold, mutagenicity, and high environmental toxicity. Chemicals in cluster III have characteristics of high releases from non-listed industries and low toxicity. Cluster IV is characterized by high reported releases and transfers from 24 listed industries above the threshold and extremely high environmental toxicity. Cluster V is characterized by low releases yet mutagenicity and high carcinogenicity. Chemicals with the highest membership degree were identified as representatives for each cluster. For the highest membership degree, half of the chemicals have a value higher than 0.74. If we look at both the highest and the second highest membership degrees simultaneously, about 94% of the chemicals have a value higher than 0.5. FCM can serve as an approach to uncover the implicit information of highly complex chemical dataset, which subsequently supports the strategy development for efficient and effective chemical management.

Data uncertainties in material flow analysis: Municipal solid waste management system in Maputo City, Mozambique.

Material flow analysis can effectively trace and quantify the flows and stocks of materials such as solid wastes in urban environments. However, the integrity of material flow analysis results is compromised by data uncertainties, an occurrence that is particularly acute in low-and-middle-income study contexts. This article investigates the uncertainties in the input data and their effects in a material flow analysis study of municipal solid waste management in Maputo City, the capital of Mozambique. The analysis is based on data collected in 2007 and 2014. Initially, the uncertainties and their ranges were identified by the data classification model of Hedbrant and Sörme, followed by the application of sensitivity analysis. The average lower and upper bounds were 29% and 71%, respectively, in 2007, increasing to 41% and 96%, respectively, in 2014. This indicates higher data quality in 2007 than in 2014. Results also show that not only data are partially missing from the established flows such as waste generation to final disposal, but also that they are limited and inconsistent in emerging flows and processes such as waste generation to material recovery (hence the wider variation in the 2014 parameters). The sensitivity analysis further clarified the most influencing parameter and the degree of influence of each parameter on the waste flows and the interrelations among the parameters. The findings highlight the need for an integrated municipal solid waste management approach to avoid transferring or worsening the negative impacts among the parameters and flows.

Application of material flow analysis to municipal solid waste in Maputo City, Mozambique.

Understanding waste flows within an urban area is important for identifying the main problems and improvement opportunities for efficient waste management. Assessment tools such as material flow analysis (MFA), an extensively applied method in waste management studies, provide a structured and objective evaluating process to characterize the waste management system best, to identify its shortcomings and to propose suitable strategies. This paper presents the application of MFA to municipal solid waste management (MSWM) in Maputo City, the capital of Mozambique. The results included the identification and quantification of the main input and output flows of the MSWM system in 2007 and 2014, from the generation, material recovery and collection, to final disposal and the unaccounted flow of municipal solid waste (MSW). We estimated that the waste generation increased from 397×103 tonnes in 2007 to 437×103 tonnes in 2014, whereas the total material recovery was insignificant in both years - 3×103 and 7×103 tonnes, respectively. As for collection and final disposal, the official collection of waste to the local dumpsite in the inner city increased about threefold, from 76×103 to 253×106 tonnes. For waste unaccounted for, the estimates indicated a reduction during the study period from 300×103 to 158×103 tonnes, due to the increase of collection services. The emphasized aspects include the need for practical waste reduction strategies, the opportunity to explore the potential for material recovery, careful consideration regarding the growing trend of illegal dumping and the urgency in phasing-out from the harmful practice of open dumping.