Using citizen observatory GPS data to trace changes in cycling practices: prospects and limitations

Understanding sustainability behavior is essential in tackling the global challenge of climate change. The importance of studying sustainability practices and their dynamics grows in light of recent global crises such as the COVID-19 pandemic and the energy crisis following the Ukraine war. These events both challenge and shape individual sustainable practices, offering opportunities for fostering individual sustainable practices and enhancing societal resilience. An online survey was conducted in Germany (n = 571, May 2023) to investigate sustainable behavior dynamics (mobility, energy-saving, and shopping habits) and to identify segments reflecting behavioral shifts. We found relative stability in sustainable mobility choices compared to pre-crisis times, with a tendency towards reduction, as well as an overall increase in energy-saving and sustainable shopping habits. Factor analyses revealed that sustainable mobility behavior (SMB) and sustainable consumer practices (SCP) formed two separate domains. Cluster analyses further identified four segments within each domain, each exhibiting unique behavioral patterns compared to pre-crisis practices. Examining individual variables, adopting more sustainable mobility practices was associated with sociodemographic factors (income, education, and area of living), higher levels of environmental awareness, institutional trust, and increased risk perceptions. Sociodemographic variables had less influence on sustainable consumer practices. Here, higher levels of knowledge, climate change awareness, trust, and risk perceptions played a significant role. Our findings highlight the importance of separately considering behavioral domains in understanding crises-induced changes in sustainability practices. Moreover, it is important to consider specific individual factors and to develop tailored interventions and policies to promote sustainable practices during volatile times.

Education for sustainable development (ESD) aims to empower future generations to address current global environmental threats, though it faces challenges to implementation, often linked to narrow perceptions of sustainability. To observe such changes in practice and draw their implications for ESD, we explore the effects of COVID-19 in perspectives and practices of sustainability across an education community. We reflect on how disruptions or threats can trigger a (re)imagination of individual and collective action. Our findings suggest that the COVID-19 pandemic and its effects on individuals and societies have altered perceptions and practices of sustainability through envisaging previously unimaginable global environmental restoration, and experiencing personal, professional and collective changes. Our study shows that the perceived restorative effects on the environment of the pandemic lifted the education community spirits and enhanced a willingness to change by leveraging the social network around the education community to promote collective action.

Low- and middle-income countries (LMICs) often have the highest levels of air pollution. At the same time, there is a serious lack of routinely collected data (e.g., traffic counts) to develop emission inventories and guide evidence-based policy interventions. The spatial resolution of emission inventories by international research groups (e.g., EDGAR) is often too coarse to represent within-city variation. There is an urgent need to identify cost-effective data sources and develop methods that can be readily applied across LMICs to generate emission inventories at high spatiotemporal resolution. We will present a review of potential big data sources, highlight their strengths and limitations, and propose methodological framework for data fusion to develop transport emissions inventory for an LMIC setting (New Delhi, India). While many transport inventories have been published for this setting in the past, they have limited reproducibility and often depend on data sources that are static in nature (e.g., vehicle registrations) and are annual estimates. The spatial resolution of these inventories is improved using assumed proxies (e.g., type of road), and temporal resolution using traffic count data or surveys. In some cases, the available data is supplemented by data- and time-intensive traffic simulation studies. We propose that these limitations can be overcome by big data sources combined with ground truth using context-specific low-cost observational surveys. Through our preliminary review, we identified the following typologies of big data sources: a) satellite or aerial imagery, b) street imagery (e.g., google street view), c) ground-based instrumentation (e.g., CCTV), and d) crowd-sourced GPS data trajectories. The satellite/aerial data, with varying image resolutions (as high as 0.1 m) and their update frequency (as frequent as 1 day), are promising in their potential for vehicle detection to estimate a spatial spread of traffic and to detect longitudinal changes. Street imagery can supplement overhead satellite imagery through accurate detection of smaller vehicles (e.g., motorcycles). GPS data can be used for routing of vehicles, and CCTV recordings (at limited number of locations) can provide diurnal variation and accurately identify types of vehicles. Use of such data has methodological challenges and requires multidisciplinary skills. Big data is analysed using machine learning methods and computer vision techniques, supported by high-performance computing resources. There is also a need to develop data fusion techniques to harmonise and integrate data across different sources (spatially detected vehicles, GPS routing, and time varying vehicle counts). Additional details of vehicle age, fuel type and emission factors are estimated from public datasets and literature. While challenging, this is usually a one-time procedure for a setting, after which revisions do not require the same amount of time or effort. Using New Delhi, India as a case study, the talk will discuss the application of these data sources and methods.

Using "the crowd" as a means to collect observations within a certain area is currently explored in various fields. The European Commission funds a set of research and development projects, all flagged as "citizen observatories" projects. The goal is to provide best practices on data modeling and processing for citizen observatories. The best practices help create and maintain crowdsourcing systems in an effective and secure way. The Open Geospatial Consortium (OGC) has developed a suite of standards to describe, access, and process crowdsourcing data in an interoperable way. Concentrating on Biosphere Reserves in Wales, Germany and Greece, the main aim is to create a testbed environment, which will enable citizens living within Biosphere Reserves to collect environmental data using mobile devices. Information of use for policy formation and delivery is generated by quality controlling the crowdsourced data and aggregating with Spatial Data Infrastructure (SDI) reference data from authoritative sources. This paper describes how the Sensor Web Enablement suite of standards together with other OGC standards facilitates the setup efficient SDIs that integrate remote sensing data with in-situ crowd sourced data.

Soil moisture (SM) or soil water content is a critical variable in the climate system and a key parameter in earth surface processes. This study aimed to assess citizen observatory (CO) data's suitability to develop a method to estimate surface SM distribution using Sentinel-1B and Landsat 8 data; acquired between January 2019 and June 2019. Three approaches were developed and compared using multiple linear regression (MLR), regression-kriging (RK) and cokriging (CK). MLR provided more realistic spatial patterns over the landscape, even in a data-poor environment. RK was found to be a potential tool to refine the results, while CO was found to be less effective. The obtained results showed that CO data harmonised with Sentinel-1B SAR, Landsat 8, and terrain data could estimate and map soil moisture content.

Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.

Successful implementation of environmentally sustainable practices in the operating theatre (OT) requires stakeholder engagement. We evaluate the knowledge, attitudes, and practices of OT users in our institution towards sustainable practices in the OT. A survey was conducted among OT users in August 2022. Respondents completed a 44-question anonymised online survey covering sustainability in (1) knowledge, (2) attitude, (3) practice, and (4) proposed changes to current practices. Points were assigned to knowledge (15 points) and attitude (40 points) sections. Practice was evaluated based on proportion of measures practiced. Appropriate statistical tests were used, with significance p < 0.05. Data are presented as mean ± standard deviation (SD) or proportion (%). Among 149 respondents were doctors (n = 76), non-doctors (n = 45), and medical students (n = 28). Doctors scored highest in knowledge of sustainability (8.58 ± 1.83) compared to non-doctors and medical students (7.62 ± 1.75, 8.39 ± 1.87). Positive attitudes towards sustainability correlated with years of experience (p < 0.01). Non-doctors were more likely to practice sustainable measures, versus doctors (61 vs 52%, p < 0.01). Most (69%) respondents were unwilling to adopt any practice changes. The least popular measure was replacement of standard hand scrubbing with alcohol-based hand rub, considered unsafe by 74% of respondents. Despite positive attitudes towards sustainable measures, there remains wariness of practice changes, which should be addressed by educational campaigns.

Sustainability in the livestock industry has been a significant focus in recent years, specifically in areas of environmental impact and animal welfare. However, research into how producers define sustainability or how to achieve it seems limited. Considering the amount of research and advancement in on-farm sustainable applications and management practices, it is important for Extension workers to assess the level of awareness, understanding and usage among beef producers of varied technologies, information, and practices. To align educational programming with areas of need or facilitate change in behavior and practices toward a more sustainable direction, it is important to understand producers’ willingness to interact with sustainable messaging and practices. An objective of our research study was to assess Alabama beef producers’ opinions of sustainability, sustainable practices within the industry, and their implementation rate of those practices. An IRB approved online survey with 36 questions was developed and distributed to Alabama beef producers from July to November 2024. Questions probed producer opinions on the importance of resources and their views about environmental impact and sustainability, knowledge of Climate-Smart Commodities programs, implementation of certain sustainable practices, and what tools or resources are considered necessary for their operation to adopt and/or further improve sustainability practices. Over one-hundred primarily cow-calf producers from 42 of the 67 counties in Alabama had varying implementation rates (~9-90%) of practices that can increase sustainability and resource management, specifically grazing management plans, growth promoting technologies, and animal handling and welfare training. Further research into sustainable practices and financial information are important for continuing sustainability practice implementation. Providing producers with financial information or counseling on decision making could increase adoption of sustainability practices. Extension educators may use results from this research to guide development of financial advisory programming and sustainability resource management information for producers.

ABSTRACTLane-level road network updating is crucial for urban traffic applications that use geographic information systems contributing to, for example, intelligent driving, route planning and traffic control. Researchers have developed various algorithms to update road networks using sensor data, such as high-definition images or GPS data; however, approaches that involve change detection for road networks at lane level using GPS data are less common. This paper presents a novel method for automatic change detection of lane-level road networks based on GPS trajectories of vehicles. The proposed method includes two steps: map matching at lane level and lane-level change recognition. To integrate the most up-to-date GPS data with a lane-level road network, this research uses a fuzzy logic road network matching method. The proposed map-matching method starts with a confirmation of candidate lane-level road segments that use error ellipses derived from the GPS data, and then computes the membership degree between GPS data and candidate lane-level segments. The GPS trajectory data is classified into successful or unsuccessful matches using a set of defuzzification rules. Any topological and geometrical changes to road networks are detected by analysing the two kinds of matching results and comparing their relationships with the original road network. Change detection results for road networks in Wuhan, China using collected GPS trajectories show that these methods can be successfully applied to detect lane-level road changes including added lanes, closed lanes and lane-changing and turning rules, while achieving a robust detection precision of above 80%.

&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;Citizen Observatories are becoming a more and more popular source of input data in many scientific domains. This includes for example research on biodiversity (e.g. counts of specific species in an area of interest), air quality monitoring (e.g. low-cost sensor boxes), or traffic flow analysis (e.g. apps collecting floating car data).&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;For the collection of such data, different approaches exist. Besides frameworks providing re-usable software building blocks (e.g. wq framework, Open Data Kit), many projects rely on custom developments. However, these solutions are mainly focused on providing the necessary software components. Further work is necessary to set-up the necessary IT infrastructure. In addition, aspects such as interoperability are usually less considered which often leads to the creation of isolated information silos.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;In our presentation, we will introduce selected activities of the European H2020 project COS4CLOUD (Co-designed citizen observatories for the EOS-Cloud). Among other objectives, COS4CLOUD aims at providing re-usable services for setting up Citizen Observatories based on the European Open Science (EOS) Cloud. We will especially discuss how it will make use of interoperability standards such as the Sensor Observation Service (SOS), SensorThings API as well as Observations and Measurements (O&amp;amp;M) of the Open Geospatial Consortium (OGC).&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;As a result, COS4CLOUD will not only facilitate the collection of Citizen Observatory data by reducing the work necessary to set-up a corresponding IT infrastructure. It will also support the exchange and integration of Citizen Observatory data between different projects as well as the integration with other authoritative data sources. This shall increase the sustainability of data collection efforts as Citizen Science data may be used as input for many data analysis processes beyond the project that originally collected the data.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;

Radiation Dose Reduction in the Invasive Cardiovascular Laboratory: Implementing a Culture and Philosophy of Radiation Safety

Social practice theories have attracted attention for their potential insights into how to change transport systems towards “healthier” states. However, most evidence is from small-scale qualitative case studies. We explored whether a synthesis of qualitative evidence on mobility practices in one country, informed by meta-ethnography and a Bourdieusian approach to practice, could produce theory that is of sufficient abstraction to be transferable, yet also capable of informing intervention planning. The synthesis identified three third order constructs: mobility practices result from habitus plus capital in fields; specific configurations of local mobility practices are shaped, but not determined, by material infrastructures and social structures; and changes in practice happen across a number of scales and temporalities. This body of evidence as a whole was then interpreted as an integrative “storyline”: Mobility systems are complex, in that outcomes from interventions are neither unilinear nor necessarily predictable from aggregations of individual practice changes. Infrastructure changes may be a necessary, but not sufficient, condition for change. Moving systems towards “healthier” states requires changing habitus such that “healthier” practices align with fields, and that interventions take sufficient account of the power relations that materially and symbolically constrain or enable attachments to and changes in mobility practices. Meta-ethnography is a useful approach for integrating qualitative evidence for informing policy.

Sustainable Anesthesia

Environmental and sustainability issues prevail in modern society. Southern Africa, where this study is based, is one of the regions most at risk from intersecting issues of climate health risk, and poverty-related ills. Education has the potential to facilitate catalytic transformation of society through development of understandings of these intersecting environment and sustainability concerns, and to support engagements in more sustainable social practices oriented towards the common good. This requires a rethinking of education within a wider common good frame. It also has implications for how quality education is considered. However, little is said of how this could be done, especially in teacher education. The paper shares two cases of teacher educators' change project experiences, as they emerged via professional development support and the mediatory processes applied in courses conducted by the Southern African Development Community Regional Environmental Education Programme (SADC REEP) aimed at enhancing professional capacity of teacher educators and other environmental educators for mainstreaming environment and sustainability education (ESE)1. These courses are framed using a change project approach, and involve teacher educators as main participants. In-depth data were generated from interviews with two teacher educators, their assignment write-ups, and observations of their teacher education practice. Realist social theory, particularly the principle of emergence, was used to trace the emergence of change in teacher education practice. Sociocultural learning theory was used to explain mediation of learning-oriented changes in teacher education practice. We illustrate how the change project model and approach contributed to mediating change in practice, showing emergent attributes of capacity for mainstreaming ESE and elements of a concept of quality education among course participants oriented towards the common good. In conclusion, we argue that ESE seems to be a sensitising construct for initiating and sustaining change for ESE in teacher education. In addition, the change project has proved to be a potential vehicle for mainstreaming the notion and practice of ESE into social systems and teacher education practices. We argue that reflexive ESE praxis provides a sensitising focus, initiating quality education with humanising properties necessary for the common good.

The COVID-19 pandemic has brought about debates on rethinking food and other socio-technical systems. While swiftly re-establishing normality has understandable appeal in a crisis, the landscape-level changes during the pandemic also hold windows of opportunity to “build back better” and to achieve sustainability transitions. In this article, we ask whether a cycle of disruption and adaptation results either in the rise of more sustainable niche practices or the consolidation of the socio-technical regimes in place. To approach this question, we consider the specific cases of charitable and on-the-go food provision and examine the extent to which COVID-induced adaptations have resulted in debates about, and implementations of, more just and sustainable practices. We draw on systems transitions and practice theoretical approaches to elucidate dynamics and elasticity and thus the effect of socio-technical practice changes. After describing the pre-COVID food regimes, we evaluate organizational practice adaptations during the lockdowns with regard to (1) changing cultural images of food security and provision, (2) socio-technical innovations, and (3) new forms of governance. We find that rather than justifying the public and policy frame of “building back better,” the effect of recovery measures reinforces the socio-technical regimes and omits wider sectoral and societal sustainability challenges such as the systemic reduction of poverty and waste.