Personal pollution monitoring: mobile real-time air quality in daily life

Abstract

Poor air quality is a growing global health concern that impacts millions of people worldwide. Although we are beginning to understand the health impacts of air pollution, it remains a challenge to provide people with the information they need to be able to make health-conscious choices. The CitiSense system gives individuals the realtime tools they need to be able to identify when and where they are exposed to poor air. We present the results of a qualitative study regarding a 4-week "in the wild" deployment of the CitiSense air-quality sensor and system. We focus on how the 16 participants responded to their new-found information about their environment, how they shared information, and what kinds of actions were enabled by having access to real-time air-quality data. Quantitative data gathered through the course of the study frames participant responses by showing what levels of pollution were experienced and what activities heightened exposure. We found that CitiSense's real-time graphical displays and everywhere monitoring provided a critical bridge between data and experience, enabling sophisticated in-the-world sensemaking and sharing with those nearby. This in turn affected behavior and attitudes, leading to shifts in how users reasoned about their world, and how they assessed their personal choices and impact.

Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution

“…These stations provide accurate measurements, generally on an hourly basis, for their exact location but as hourly mean levels of pollutants vary considerably over tens of metres, this level of coverage cannot provide accurate exposure data for citizens [12][13][14][15]. The rapid variation in urban pollution levels is due to the strong influence of local sources and meteorological conditions, as well as street canyon effects in these areas [16]. The traditional monitoring strategy does not account for indoor exposure which can be greatly elevated relative to outdoor, for instance through use of solid-fuels for heating and cooking, emissions from the cooking itself, and household aerosols.…”

CAVisAP: Context-Aware Visualization of Outdoor Air Pollution with IoT Platforms

“…Hence numerous works propose systems to monitor and predict ambient air pollution. Studies in [17], [18], [19] demonstrate various solutions for outdoor air quality, while other papers such as [20], [21], [22] and [23] consider both environment types.…”

“…Crowd-sensing is another widespread approach to monitor air pollution. For example, authors in [21], [32] and [33] collect data from participants, whereas [22] collects data from both, sensors and crowd. Open-source data, national weather and pollution monitoring centers and internet-connected monitoring stations are other forms of data sources in the literature [17], [23], [30], [34].…”

Can portable air quality monitors protect children from air pollution on the school run? An exploratory study

“…The few studies that do exist are insightful, providing preliminary conclusions to suggest that personal exposure information can challenge preconceptions and inform people's responses to air pollution. In relation to perceptions and emotions, the reported effects of monitor use range from an increased awareness of air pollution and intense emotional reaction (Oltra et al 2017), through to enjoyment and surprise (Bales et al 2019;Wong-Parodi et al 2018;Bales et al 2012). In relation to behaviour, Wong-Parodi et al (2018) and Zappi et al (2012) found minor behaviour changes to occur, which include actions like closing windows and ceasing to burn incense indoors, while Oltra et al (2017) did not witness any alteration-intentional or real-as a result of monitor use.…”

“…This leads to a one-dimensional account of how monitors may affect behaviour. Finally, there are a myriad of shared shortcomings pertaining to the methodologies deployed; the majority of existing studies draw on relatively small and homogenous samples, while Zappi et al (2012) and Bales et al (2019;2012) use figures from the same study conducted almost a decade ago, although the latest iteration is augmented with more recent data.…”