REPOSITORY > RESULTS

Abstract

A paradigm shift is occurring in the assessment of exposure to urban environmental stressors. Improved accuracy of personal monitors has enabled researchers to study exposure at the individual level. Monitoring stations are the primary reference point for air quality in urban environments, although there is a need to assess exposure in greater spatial and temporal detail. In this work, an evaluation of personal monitors is conducted in the context of assessing individual exposure to urban stressors, validating results, and using advanced exposure assessment methods. An initial overview of the field analysed a broader range of urban stressors and the use of personal monitors for exposure assessment. Poor air quality, particularly elevated particulate matter (PM) levels, were identified as of particular concern and were most commonly assessed using personal monitors. However, gaps were identified in data collection, dissemination of results, communication, assessment of exposure in different areas, participant involvement, etc. This work addresses several of these gaps and evaluates some approaches and tools to gain more information from personal monitor-based exposure assessments.
Within the community of users of personal monitors, a consensus has formed that validation is required before the device is deployed. In some cases, validation and calibration is also required during and after sampling. The results of this work have shown that simple collocation of the personal monitor with a research-grade reference sensor can be sufficient. The Personal PM Monitor (PPM) used in the ICARUS H2020 project (icarus2020.eu), which recorded PM exposure of 82 participants as part of the sampling campaign in Ljubljana, Slovenia, was found to be fit for purpose by collocation with a reference sensor. In addition, each participant was provided with an activity and biometric monitoring device, and required to complete an activity diary for each hour each day. Combining these datasets provided an extraordinary amount of detailed data on exposure, activities, movement, routines, and behaviours. On the other hand, the collection of these data also had shortcomings. Manual collection of activity data by participants was shown to be tedious and error-prone. Therefore, a machine learning approach was used to compensate for this shortcoming, and attempts were made to predict or recognize certain complex activities using only personal monitors. The results showed higher accuracy when a combination of personal monitors was used.
In this work, two assessments of performance and applicability of personal monitors were performed: (i) by comparing indoor and outdoor exposure during a high PM concentration event, i.e., an atmospheric thermal inversion, and (ii) using an agent-based model (ABM) to assess exposure and compare it with data from ICARUS. Although these are two different assessments and approaches, similar conclusions can be drawn. A personal monitor provides insight into indoor and outdoor exposure with high spatial and temporal granularity. The results show that while outdoor PM concentrations account for most of the exposure, indoor activities are a significant contributor. In addition, the ABM yielded results comparable to the ICARUS data, indicating that simulations informed by personal monitor data can assess exposure in various scenarios. Personal interactions in the model were shown to influence exposure and dose of particulate matter. These results could help decision makers develop data-driven and effective policies.
Dissemination and communication are critical in participatory projects, as indicated by the literature review and the ICARUS project. One aspect, a personalized report of participant data, was demonstrated in this work. The communication and visualization was based on participant feedback and multiple iterations. Data accuracy and validity considerations must be directly communicated not only in the report but also in the visualizations. An automated approach allowed for the compilation of over 600 individual reports. Overall, a well-structured report was shown to guide participants through the data and help them gain useful information.