Integrated assessment of personal monitor applications for evaluating exposure to urban stressors: A scoping review

Pressures of the urban environment on the endocrine system: Adverse effects and adaptation

With an increasing collective awareness of the rapid environmental changes, questions and theories regarding the adaptability of organisms are emerging. Global warming as well as chemical and non-chemical pollution have been identified as triggers of these adaptative changes, but can we link different kinds of stressors to certain phenotypic traits? The physiological adaptation, and particularly endocrine system adaptation, of living beings to urban environments is a fascinating way of studying urban endocrinology, which has emerged as a research field in 2007. In this paper, we stress how endocrine disruption in humans and environment can be studied in the urban environment by measuring the levels of pollution, endocrine activities or adversity. We broaden the focus to include not only exposure to the chemicals that have invaded our private spheres and their effects on wild and domestic species but also non-chemical effectors such as light, noise and climate change. We argue that taking into account the various urban stress factors and their effects on the endocrine system would enable the adoption of new approaches to protect living organisms.

Simulating the impact of particulate matter exposure on health-related behaviour: A comparative study of stochastic modelling and personal monitoring data

Epidemiological and exposure studies concerning particulate matter (PM) often rely on data from sparse governmental stations. While low-cost personal monitors have some drawbacks, recent developments have shown that they can provide fairly accurate and fit-for-purpose data. Comparing a stochastic, i.e., agent-based model (ABM), with environmental, biometric and activity data, collected with personal monitors, could provide insight into how the two approaches assess PM exposure and dose. An ABM was constructed, simulating a PM exposure/dose assessment of 100 agents. Their actions were governed by inherent probabilities of performing an activity, based on population data. Each activity was associated with an intensity level, and a PM pollution level. The ABM results were compared with real-world results. Both approaches had comparable results, showing similar trends and a mean dose. Discrepancies were seen in the activities with the highest mean dose values. A stochastic model, based on population data, does not capture well some specifics of a local population. Combined, personal sensors could provide input for calibration, and an ABM approach can help offset a low number of participants. Implementing a function of agents influencing others transport choice, increased the importance of cycling/walking in the overall dose estimate. Activists, agents with an increased transport influence, did not play an important role at low PM levels. As concentrations rose, higher shares of activists (and their influence) caused the dose to increase. Simulating a person's PM exposure/dose in different scenarios and activities in a virtual environment provides researchers and policymakers with a valuable tool.