Abstract

The present study describes a novel application for use in the monitoring of municipal solid waste, based on distributed sensor technology and geographical information systems. Original field testing and evaluation of the application were carried out in Pudong, Shanghai (PR China). The local waste management system in Pudong features particular requirements related to the rapidly increasing rate of waste production. In view of the fact that collected waste is currently deployed to landfills or to incineration plants within the context investigated, the key aspects to be taken into account in waste collection procedures include monitoring of the overall amount of waste produced, quantitative measurement of the waste present at each collection point and identification of classes of material present in the collected waste. The case study described herein focuses particularly on the above mentioned aspects, proposing the implementation of a network of sensorized waste containers linked to a data management system. Containers used were equipped with a set of sensors mounted onto standard waste bins. The design, implementation and validation procedures applied are subsequently described. The main aim to be achieved by data collection and evaluation was to provide for feasibility analysis of the final device. Data pertaining to the content of waste containers, sampled and processed by means of devices validated on two purpose-designed prototypes, were therefore uploaded to a central monitoring server using GPRS connection. The data monitoring and management modules are integrated into an existing application used by local municipal authorities. A field test campaign was performed in the Pudong area. The system was evaluated in terms of real data flow from the network nodes (containers) as well as in terms of optimization functions, such as collection vehicle routing and scheduling. The most important outcomes obtained were related to calculations of waste weight and volume. The latter data were subsequently used as parameters for the routing optimization of collection trucks and material density evaluation.

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