Forecasting household packaging waste generation : a case study

Nowadays, house packaging waste (HPW) materials acquired a great deal of importance, due to environmental and economic reasons, and therefore waste collection companies place thousands of collection points (ecopontos) for people to deposit their HPW. In order to optimize HPW collection process, accurate forecasts of the waste generation rates are needed. Our objective is to develop forecasting models to predict the number of collections per year required for each ecoponto by evaluating the relevance of ten proposed explanatory factors for HPW generation. We developed models based on two approaches: multiple linear regression and artificial neural networks (ANN).The results obtained show that the best ANN model, which achieved an R 2 of 0.672 and MAD of 9.1, slightly outperforms the best regression model (R 2 of 0.636, MAD of 10.44). The most important factors to estimate HPW generation rates are related to ecoponto characteristics and to the population and economic activities around each ecoponto location.Fundação para a Ciência e a Tecnologia (FCT

Household packaging waste management

Household packaging waste (HPW) has an important environmental impact and economic relevance. Thus there are networks of collection points (named “ecopontos” in Portugal) where HPW may be deposited for collection by waste management companies. In order to optimize HPW logistics, accurate estimates of the waste generation rates are needed to calculate the number of collections required for each ecoponto in a given period of time. The most important factors to estimate HPW generation rates are linked to the characteristics of the population and the social and economic activities around each ecoponto location. We developed multiple linear regression models and artificial neural networks models to forecast the number of collections per year required for each location. For operational short term planning purposes, these forecasts need to be adjusted for seasonality in order to determine the required number of collections for the relevant planning period. In this paper we describe the methodology used to obtain these forecasts.This research has been partially supported by COMPETE: POCI-01-0145-FEDER007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Cooperative relative positioning

Many pervasive applications deal with relative positions between interacting entities rather than global coordinates. The Relate project developed sensing methods and a modular system architecture for peer-to-peer relative positioning. It studied the methods and architecture in application case studies on mobile spatial interaction, firefighter navigation, and wearable activity recognition

Forecasting Municipal Solid Waste Generation in Major European Cities

An understanding of the relationships between the quantity and quality of environmentally relevant outputs from human processes and regional characteristics is a prerequisite for planning and implementing ecologically sustainable strategies. Apart from process-related parameters, continuous and discontinuous socio-economic long-term trends often play a key role in the assessment of environmental impacts. This paper describes the development of a prognosis model for municipal solid waste (MSW) generation in European regions. The objective is to assess future municipal waste streams in major European cities. We therefore focussed on cities, which face significant social and economic changes, e.g. in central and east European (CEE) countries. The investigations covered waste-related data and a broad set of potential influencing parameters that contained commonly used social, economic and demographic indicators as well as previously proved waste generation factors. An extensive database was created with an annual time series up to 32 years from 55 European cities and 32 countries. The evaluation of this historic time series and the cross-sectional data by means of multivariate statistical methods has unveiled significant relationships between the status of regional development and municipal solid waste generation. We identified a core set of significant indicators, which can describe a long-term development path that predetermines the level of waste generation. These findings concerning this analogy have been integrated in an econometric model for European cities

Context Modelling and Management in Ambient-aware Pervasive Environments

Services in pervasive computing systems must evolve so that they become minimally intrusive and exhibit inherent proactiveness and dynamic adaptability to the current conditions, user preferences and environment. Context awareness has the potential to greatly reduce the human attention and interaction bottlenecks, to give the user the impression that services fade into the background, and to support intelligent personalization and adaptability features. To establish this functionality, an infrastructure is required to collect, manage, maintain, synchronize, infer and disseminate context information towards applications and users. This paper presents a context model and ambient context management system that have been integrated into a pervasive service platform. This research is being carried out in the DAIDALOS IST Integrated Project for pervasive environments. The final goal is to integrate the platform developed with a heterogeneous all-IP network, in order to provide intelligent pervasive services to mobile and non-mobile users based on a robust context-aware environment

Calibration of low-cost particulate matter Sensors with elastic weight consolidation (EWC) as an incremental deep learning method.

Urban air quality is an important problem of our time. Due to their high costs and therefore low spacial density, high precision monitoring stations cannot capture the temporal and spatial dynamics in the urban atmosphere, low-cost sensors must be used to setup dense measurement grids. However, low-cost sensors are imprecise, biased and susceptible to environmental influences. While neural networks have been explored for their calibration, issues include the amount of data needed for training, requiring sensors to be co-located with reference stations for extensive periods of time. Also re-calibrating them with new data can lead to catastrophic forgetting. We propose using Elastic Weight Consolidation (EWC) as an incremental calibration method. By exploiting the Fisher-Information-Matrix it enables the network to compensate for different sources of error, both pertaining to the sensor itself, as well as caused by varying environmental conditions. Models are pre-calibrated with data of 40 h measurement on a low-cost SDS011 PM sensor and then re-calibrated on another SDS011 sensor. Our evaluation on 1.5 years of real world data shows that a model using EWC with a time period of data of 6 h for re-calibration is more precise than models without EWC, even those with longer re-calibration periods. This demonstrates that EWC is suitable for on-the-fly collaborative calibration of low-cost sensors