A high resolution spatiotemporal model for in-vehicle black carbon exposure : quantifying the in-vehicle exposure reduction due to the Euro 5 particulate matter standard legislation

Several studies have shown that a significant amount of daily air pollution exposure is inhaled during trips. In this study, car drivers assessed their own black carbon exposure under real-life conditions (223 h of data from 2013). The spatiotemporal exposure of the car drivers is modeled using a data science approach, referred to as microscopic land-use regression (mu LUR). In-vehicle exposure is highly dynamical and is strongly related to the local traffic dynamics. An extensive set of potential covariates was used to model the in-vehicle black carbon exposure in a temporal resolution of 10 s. Traffic was retrieved directly from traffic databases and indirectly by attributing the trips through a noise map as an alternative traffic source. Modeling by generalized additive models (GAM) shows non-linear effects for meteorology and diurnal traffic patterns. A fitted diurnal pattern explains indirectly the complex diurnal variability of the exposure due to the non-linear interaction between traffic density and distance to the preceding vehicles. Comparing the strength of direct traffic attribution and indirect noise map-based traffic attribution reveals the potential of noise maps as a proxy for traffic-related air pollution exposure. An external validation, based on a dataset gathered in 2010-2011, quantifies the exposure reduction inside the vehicles at 33% (mean) and 50% (median). The EU PM Euro 5 PM emission standard (in force since 2009) explains the largest part of the discrepancy between the measurement campaign in 2013 and the validation dataset. The mu LUR methodology provides a high resolution, route-sensitive, seasonal and meteorology-sensitive personal exposure estimate for epidemiologists and policy makers

Exploring the use of mobile sensors for noise and black carbon measurements in an urban environment

Mobile measurements have been collected on a bicycle equipped with a global positioning system (GPS) in a few connecting streets in Gent (Belgium). The 1-s sound pressure levels and 1-s black carbon concentrations were measured. In addition, 5 continuous monitoring fixed stations connected to building facades were used. Different processing methods are compared, based on different temporal and spatial weighting aggregations. The possibility to take profit of the fixed stations to refine estimations is tested, according to the noise levels collected at fixed stations and the distance between mobile and fixed sensors. In a last step, route selection based on travel distance, noise levels and black-carbon measurements is explored based on the data obtained

Public Participation GIS for sustainable urban mobility planning: methods, applications and challenges

Sustainable mobility planning is a new approach to planning, and as such it requires new methods of public participation, data collection and data aggregation. In the article we present an overview of Public Participation GIS (PPGIS) methods with potential use in sustainable urban mobility planning. We present the methods using examples from two recent case studies conducted in Polish cities of Poznań and Łodź. Sustainable urban mobility planning is a cyclical process, and each stage has different data and participatory requirements. Consequently, we situate the PPGIS methods in appropriate stages of planning, based on potential benefits they may bring into the planning process. We discuss key issues related to participant recruitment and provide guidelines for planners interested in implementing methods presented in the paper. The article outlines future research directions stressing the need for systematic case study evaluation

Smart Cities and M³: Rapid Research, Meaningful Metrics and Co-Design

The research described in this paper is undertaken under the banner of the smart city, a concept that captures the way urban spaces are re-made by the incursion of new technology. Much of smart is centred on converting everyday activities into data, and using this data to generate knowledge mediated by technology. Ordinary citizens, those that may have their lives impacted by the technology, usually are not properly involved in the ‘smartification’ process. Their perceptions, concerns and expectations should inform the conception and development of smart technologies at the same extent. How to engage general public with smart cities research is the central challenge for the Making Metrics Meaningful (MMM) project. Applying a rapid participatory method, ‘Imagine’ over a five-month period (March – July) the research sought to gain insights from the general public into novel forms of information system innovation. This brief paper describes the nature of the accelerated research undertaken and explores some of the themes which emerged in the analysis. Generic themes, beyond the remit of an explicit transport focus, are developed and pointers towards further research directions are discussed. Participatory methods, including engaging with self- selected transport users actively through both picture creation and programmatically specific musical ‘signatures’ as well as group discussion, were found to be effective in eliciting users’ own concerns, needs and ideas for novel information systems

Measurements of a Real-time Transit Feed Service Architecture for Mobile Participatory Sensing

Abstract—We spend a substantial part of our time with traveling, in crowded cities usually taking public transportation. It is important, making travel planning easier, to have accurate information about vehicle arrival times at the stops. Most of the public transport operators make their timetables freely available either on the web or in some special format, like GTFS (General Transit Feed Specification). However, they contain static information only, not reflecting the actual traffic conditions. Mobile participatory sensing can help extend the basic service with real-time updates letting the crowd collect the required data. With this respect we believe that such participatory sensing based application must offer a day zero service following incremental service extension. In this paper, we discuss how to realize real-time refinements to static GTFS data based on mobile participatory sensing. We show how this service can be implemented by an XMPP (Extensible Messaging and Presence Protocol) based mobile participatory sensing architecture and we evaluate its performance

Value chain analysis for sea cucumber in the Philippines

This study examined the sea cucumber industry in the Philippines through the value chain lens. The intent was to identify effective pathways for the successful introduction of sandfish culture as livelihood support for coastal communities. Value chain analysis is a high-resolution analytical tool that enables industry examination at a detailed level. Previous industry assessments have provided a general picture of the sea cucumber industry in the country. The present study builds on the earlier work and supplies additional details for a better understanding of the industry's status and problems, especially their implications for the Australian Center for International Agricultural Research (ACIAR) funded sandfish project "Culture of sandfish (Holothuria scabra) in Asia- Pacific" (FIS/2003/059). (PDF contains 54 pages

Accessibility and urban design - Knowledge matters

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Surveillance strategies for Classical Swine Fever in wild boar – a comprehensive evaluation study to ensure powerful surveillance

Surveillance of Classical Swine Fever (CSF) should not only focus on livestock, but must also include wild boar. To prevent disease transmission into commercial pig herds, it is therefore vital to have knowledge about the disease status in wild boar. In the present study, we performed a comprehensive evaluation of alternative surveillance strategies for Classical Swine Fever (CSF) in wild boar and compared them with the currently implemented conventional approach. The evaluation protocol was designed using the EVA tool, a decision support tool to help in the development of an economic and epidemiological evaluation protocol for surveillance. To evaluate the effectiveness of the surveillance strategies, we investigated their sensitivity and timeliness. Acceptability was analysed and finally, the cost-effectiveness of the surveillance strategies was determined. We developed 69 surveillance strategies for comparative evaluation between the existing approach and the novel proposed strategies. Sampling only within sub-adults resulted in a better acceptability and timeliness than the currently implemented strategy. Strategies that were completely based on passive surveillance performance did not achieve the desired detection probability of 95%. In conclusion, the results of the study suggest that risk-based approaches can be an option to design more effective CSF surveillance strategies in wild boar