Integrating big data into a sustainable mobility policy 2.0 planning support system

It is estimated that each of us, on a daily basis, produces a bit more than 1 GB of digital content through our mobile phone and social networks activities, bank card payments, location-based positioning information, online activities, etc. However, the implementation of these large data amounts in city assets planning systems still remains a rather abstract idea for several reasons, including the fact that practical examples are still very strongly services-oriented, and are a largely unexplored and interdisciplinary field; hence, missing the cross-cutting dimension. In this paper, we describe the Policy 2.0 concept and integrate user generated content into Policy 2.0 platform for sustainable mobility planning. By means of a real-life example, we demonstrate the applicability of such a big data integration approach to smart cities planning process. Observed benefits range from improved timeliness of the data and reduced duration of the planning cycle to more informed and agile decision making, on both the citizens and the city planners end. The integration of big data into the planning process, at this stage, does not have uniform impact across all levels of decision making and planning process, therefore it should be performed gradually and with full awareness of existing limitations

AirKit: A Citizen-Sensing Toolkit for Monitoring Air Quality.

Increasing urbanisation and a better understanding of the negative health effects of air pollution have accelerated the use of Internet of Things (IoT)-based air quality sensors. Low-cost and low-power sensors are now readily available and commonly deployed by individuals and community groups. However, there are a wide range of such IoT devices in circulation that differently focus on problems of sensor validation, data reliability, or accessibility. In this paper, we present AirKit, which was developed as an integrated and open source "social IoT technology". AirKit enables a comprehensive approach to citizen-sensing air quality through several integrated components: (1) the Dustbox 2.0, a particulate matter sensor; (2) Airsift, a data analysis platform; (3) a reliable and automatic remote firmware update system; (4) a "Data Stories" method and tool for communicating citizen data; and (5) an AirKit logbook that provides a guide for designing and running air quality projects, along with instructions for building and using AirKit components. Developed as a social technology toolkit to foster open processes of research co-creation and environmental action, Airkit has the potential to generate expanded engagements with IoT and air quality by improving the accuracy, legibility and use of sensors, data analysis and data communication.This research was supported by the European Research Council under the European Union’s Seventh Framework Programme (FP/2007–2013)/ERC Grant Agreement n. 313347, “Citizen Sensing and Environmental Practice: Assessing Participatory Engagements with Environments through Sensor Technologies”, and from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (ERC Grant Agreement n. 779921), “AirKit: Citizen Sense Air Monitoring Kit”. The University of Cambridge provided additional support through the ESRC Impact Acceleration Account (2020) for enabling impact

AN INTEGRATED APPROACH FOR POLLUTION MONITORING: SMART ACQUIREMENT AND SMART INFORMATION

Air quality is a factor of primary importance for the quality of life. The increase of the pollutants percentage in the air can cause serious problems to the human and environmental health. For this reason it is essential to monitor its values to prevent the consequences of an excessive concentration, to reduce the pollution production or to avoid the contact with major pollutant concentration through the available tools. Some recently developed tools for the monitoring and sharing of the data in an effective system permit to manage the information in a smart way, in order to improve the knowledge of the problem and, consequently, to take preventing measures in favour of the urban air quality and human health. In this paper, the authors describe an innovative solution that implements geomatics sensors (GNSS) and pollutant measurement sensors to develop a low cost sensor for the acquisition of pollutants dynamic data using a mobile platform based on bicycles. The acquired data can be analysed to evaluate the local distribution of pollutant density and shared through web platforms that use standard protocols for an effective smart use

Ensemble learning of model hyperparameters and spatiotemporal data for calibration of low-cost PM2.5 sensors.

he PM2.5 air quality index (AQI) measurements from government-built supersites are accurate but cannot provide a dense coverage of monitoring areas. Low-cost PM2.5 sensors can be used to deploy a fine-grained internet-of-things (IoT) as a complement to government facilities. Calibration of low-cost sensors by reference to high-accuracy supersites is thus essential. Moreover, the imputation for missing-value in training data may affect the calibration result, the best performance of calibration model requires hyperparameter optimization, and the affecting factors of PM2.5 concentrations such as climate, geographical landscapes and anthropogenic activities are uncertain in spatial and temporal dimensions. In this paper, an ensemble learning for imputation method selection, calibration model hyperparameterization, and spatiotemporal training data composition is proposed. Three government supersites are chosen in central Taiwan for the deployment of low-cost sensors and hourly PM2.5 measurements are collected for 60 days for conducting experiments. Three optimizers, Sobol sequence, Nelder and Meads, and particle swarm optimization (PSO), are compared for evaluating their performances with various versions of ensembles. The best calibration results are obtained by using PSO, and the improvement ratios with respect to R2, RMSE, and NME, are 4.92%, 52.96%, and 56.85%, respectively

THE LOOPER CO-CREATION METHODOLOGY: ENHANCING URBAN TRANSFORMATION THROUGH PARTICIPATORY SENSING AND URBAN LIVING LABS IN LEARNING LOOPS

My research aims to test how the participatory co-creation methodology can help to solve different urban issues, and wants to show some practicalities to organisers about how to set up a Urban Living Lab to involve stakeholders in a co-creation process. This research involved both the study of the state of the art, but also some practical work to experience which are the positive results and found criticalities. The study of the state of the art gave me a more complete comprehension of the situation in which my research is framed, and it included: the Scandinavian \u2018cooperative design\u2019 in the \u201860s; De Carlo participatory design of the Terni project; the concept of \u2018Participatory design\u2019 in the USA during the \u201870s; Siza and the SAAL process in the \u201870s; the \u2018User-centred design\u2019 concept by Donald Dorman in the \u201880s; the idea of \u2018Participatory budgeting\u2019 in Portugal from the 2000 on. The methodology has been that of \u2018practice-led\u2019. In my work, I applied the co-creation methodology in different urban environments to: check which practices can be considered good or bad; cross data collected from the state of the art and the field research; compare collected data. The research I have done focused on an European Research Project, funded under the JPI Urban Europe, called LOOPER (Learning Loops in the Public Realm) which applies the learning loop to the co-design process. A comparison background case was used as well: the planning of the City of Sports in San Don\ue0 di Piave (Italy). This research has the ambition of creating a new way of decision-making which brings together all stakeholders, including policymakers, that iteratively learn how to address urban challenges. This then results in an implemented co-design process since stakeholders in the end are called to evaluate what they have done. Future implementations of my research would allow the creation of a complete set of guidelines that can be used to solve different urban issues, by triggering the co-creation methodology applied within Urban Living Labs

Research Priorities of Applying Low-Cost PM2.5 Sensors in Southeast Asian Countries

The low-cost and easy-to-use nature of rapidly developed PM2.5 sensors provide an opportunity to bring breakthroughs in PM2.5 research to resource-limited countries in Southeast Asia (SEA). This review provides an evaluation of the currently available literature and identifies research priorities in applying low-cost sensors (LCS) in PM2.5 environmental and health research in SEA. The research priority is an outcome of a series of participatory workshops under the umbrella of the International Global Atmospheric Chemistry Project–Monsoon Asia and Oceania Networking Group (IGAC–MANGO). A literature review and research prioritization are conducted with a transdisciplinary perspective of providing useful scientific evidence in assisting authorities in formulating targeted strategies to reduce severe PM2.5 pollution and health risks in this region. The PM2.5 research gaps that could be filled by LCS application are identified in five categories: source evaluation, especially for the distinctive sources in the SEA countries; hot spot investigation; peak exposure assessment; exposure–health evaluation on acute health impacts; and short-term standards. The affordability of LCS, methodology transferability, international collaboration, and stakeholder engagement are keys to success in such transdisciplinary PM2.5 research. Unique contributions to the international science community and challenges with LCS application in PM2.5 research in SEA are also discussed

Sustainability Assessment in Social Housing Environments: An Inclusive Indicators Selection in Colombian Post-Pandemic Cities

The use of indicators for sustainability assessment in the urban planning process is a widely used approach. With the definition of the Agenda 2030 and the role of cities in achieving sustainable development goals, much work has been devoted to the definition of evaluation frameworks and indicators to assess policies and plans and support decision-making in the transition to sustainable urban environments. Therefore, there is currently a wide range of indicator frameworks for the sustainability assessment of human settlements. However, considering the effects of the COVID-19 pandemic on the urban sustainability paradigm, the need to reassess the relevance of existing assessment frameworks in the post-pandemic context has been highlighted. Thus, this article aims to illustrate a selection of indicators to evaluate urban sustainability in developing countries’ post-pandemic contexts, using Colombia as a case study. This work comprises the characterization of the post-pandemic relevance of a set of sustainability indicators through the participation of stakeholders associated with the development process of social housing in urban environments in Colombia. Within a Delphi process, the initial indicators were taken from local and international sustainability frameworks validated before the pandemic. Further, a final selection was made through the evaluation of a survey from a sample of 45 stakeholders, and different participatory mechanisms with experts. These results acknowledged the relevance of factors, such as atmospheric conditions, risk management, the performance of public transport systems, and the availability and accessibility to key services, in the achievement of urban sustainability. These results will support the sustainability assessment of the development of post-pandemic recovery policies in Colombia and serve as a reference for other contexts in developing countries