Global population growth, urbanization, and climate change worsen the immediate environment of many individuals. Elevated concentrations of air pollutants, higher levels of acoustic noise, and more heat days, as well as increasingly complex mixtures of pollutants pose health risks for urban inhabitants. There is a growing awareness of the need to record personal environmental conditions (“the human exposome”) and to study options and implications of adaptive and protective behavior of individuals. The vast progress in smart technologies created wearable sensors that record environmental as well as spatio-temporal data while accompanying a person. Wearable sensing has two aspects: firstly, the exposure of an individual is recorded, and secondly, individuals act as explorers of the urban area. A literature review was undertaken using scientific literature databases with the objective to illustrate the state-of-the-art of person-based environmental sensing in urban settings. We give an overview of the study designs, highlight and compare limitations as well as results, and present the results of a keyword analysis. We identify current trends in the field, suggest possible future advancements, and lay out take-home messages for the readers. There is a trend towards studies that involve various environmental parameters and it is becoming increasingly important to identify and quantify the influence of various conditions (e.g., weather, urban structure, travel mode) on people’s exposure.

Citizens in urban areas are exposed to multiple environmental stressors like noise, heat, and air pollution, with impact on human health. There is a great deal of evidence that connects human health, objective environmental exposure, and place of residence. However, little is known about subjective and objective multiple personal exposures while being mobile. To address this research gap, this paper presents results from a mixed-methods exploratory study with cyclists in the City of Leipzig, Germany. In the summer of 2017, cyclists (n = 66) wore a unique combination of sensors that measured particle number counts (PNC), noise, humidity, temperature, geolocation, and the subjective perception of each exposure on everyday routes for one week (n = 730). A smartphone application was developed to question participants about their perception of subjective exposure. The data were analyzed with three aims: (i) to compare the multiple exposure profiles of the cyclists, (ii) to contrast the objective data and subjective individual perception, and (iii) to examine the role of route decision-making and awareness of health impacts for healthier route choices. The results indicate distinct differences between the exposure profiles of cyclists. Over 80% of the cyclists underestimated their exposure to noise and air pollution. Except for heat, no significant associations between the objective and subjective data were found. This reveals an exposure awareness gap that needs to be considered in urban health planning and risk communication. It is argued that knowledge about health impacts and route characteristics plays a crucial role in decision-making about route choices. The paper concludes with suggestions to harness smart sensing for exposure mitigation and research in health geography.

Individuals are simultaneously exposed to multiple environmental stressors during their daily life. Studies of adverse health effects and their etiology as well as recommendations for a healthier life style demand for an assessment of multifactorial personal exposure, according to the exposome concept. A challenge is to record exposure while people are moving in heterogeneous urban environments. Therefore wearable sensor technologies are becoming a promising way to measure personal exposure continuously: indoors, outdoors and even on the move. So far, studies which test the accuracy and usability of wearable sensors for multiple stressors are lacking. Performance evaluations are important and should take place beforehand, especially to ensure the success of citizens-oriented studies. For the first time we rigorously examined the accuracy and application suitability of wearable sensors for acoustic noise, heat (temp), particle number counts (PNC) and geo-location (GPS) in different environments. We present an extensive device inter-comparison and a ranking of the sensors based on performance measures, Taylor diagrams, Bland-Altman plots, and ease-of-use aspects. The sensors showed moderate to high correlations with precision reference devices (r = 0.4-0.99). Differences between errors outdoors and indoors suggest that environmental conditions have impact upon the accuracy of the sensors. Reaction time, recording interval, and sensor ventilation are features that play a crucial role for both ease-of-use and accuracy. We conclude with a final performance () ranking: (GPS) > (noise) > (temp) > (PNC). The results are relevant for future epidemiological studies of multifactorial exposure of individuals and their health and should guide the selection of wearables when persons are involved that are technically untaught. Inferences from multifactorial data are based on the performance of all sensors and the weakest chain links are PNC and temp sensors for which our article recommends urgent improvements.

Cities worldwide are facing problems to mitigate the impact of urban stormwater runoff caused by the increasing occurrence of heavy rainfall events and urban re-densification. This study presents a new approach for estimating the potential of the Management of Urban STormwater at Block-level (MUST-B) by decentralized blue-green infrastructures here called low-impact developments (LIDs) for already existing urban environments. The MUST-B method was applied to a study area in the northern part of the City of Leipzig, Germany. The Study areas was divided into blocks smallest functional units and considering two different soil permeability and three different rainfall events, seven scenarios have been developed: current situation, surface infiltration, swale infiltration, trench infiltration, trough-trench infiltration, and three different combinations of extensive roof greening, trough-trench infiltration, and shaft infiltration. The LIDs have been simulated and their maximum retention/infiltration potential and the required area have been estimated together with a cost calculation. The results showed that even stormwater of a 100 year rainfall event can be fully retained and infiltrated within the blocks on a soil with low permeability (k_f = 10⁻⁶ m/s). The cost and the required area for the LIDs differed depending on the scenario and responded to the soil permeability and rainfall events. It is shown that the MUST-B method allows a simple down- and up-scaling process for different urban settings and facilitates decision making for implementing decentralized blue-green-infrastructure that retain, store, and infiltrate stormwater at block level.

Smartphone-based sensing is becoming a convenient way to collect data in science, especially in environmental research. Recent studies that use smartphone sensing methods focus predominantly on single sensors that provide quantitative measurements. However, interdisciplinary projects call for study designs that connect both, quantitative and qualitative data gathered by smartphone sensors. Therefore, we present a novel open-source task automation solution and its evaluation in a personal exposure study with cyclists. We designed an automation script that advances the sensing process with regard to data collection, management and storage of acoustic noise, geolocation, light level, timestamp, and qualitative user perception. The benefits of this approach are highlighted based on data visualization and user handling evaluation. Even though the automation script is limited by the technical features of the smartphone and the quality of the sensor data, we conclude that task automation is a reliable and smart solution to integrate passive and active smartphone sensing methods that involve data processing and transfer. Such an application is a smart tool gathering data in population studies.