Community-Empowered Air Quality Monitoring System

Abstract

Developing information technology to democratize scientific knowledge and support citizen empowerment is a challenging task. In our case, a local community suffered from air pollution caused by industrial activity. The residents lacked the technological fluency to gather and curate diverse scientific data to advocate for regulatory change. We collaborated with the community in developing an air quality monitoring system which integrated heterogeneous data over a large spatial and temporal scale. The system afforded strong scientific evidence by using animated smoke images, air quality data, crowdsourced smell reports, and wind data. In our evaluation, we report patterns of sharing smoke images among stakeholders. Our survey study shows that the scientific knowledge provided by the system encourages agonistic discussions with regulators, empowers the community to support policy making, and rebalances the power relationship between stakeholders.

Citizen-Based Air Quality Monitoring: The Impact on Individual Citizen Scientists and How to Leverage the Benefits to Affect Whole Regions

“…The results led to the development of measures to avoid exposure to this pollutant. Furthermore, the European initiatives impacted the participants' knowledge, self-efficacy, and attitudes towards NO 2 pollution; they resulted in changed or adjusted behaviour of the involved citizens towards measures to exposure and contribution to air pollution, led to a higher sense of community, stimulated discussions with policy makers, and influenced political decisions in the involved regions (Francis and Stockwell 2014, Hsu et al 2017, Kloetzer et al 2017, Zannella 2017, Van Brussel and Huyse 2018, Haklay and Eleta 2019. Other projects, such as Luftdaten (https:// luftdaten.info/), HackAir (https://platform.hackair.eu/), and senseBox (https://sensebox.de/) experiment with self-made sensor kits and data platforms to measure particulate matter (PM), a mixture of solid particles and liquids found in the air which are either emitted directly from a source, like a construction site, or are the result of chemical reactions of other pollutants, such as NO 2 .…”

“…The project expected to achieve regional impacts in all three testbeds, but learned that this requires strong efforts in communication and community building (Van Brussel and Huyse 2018). In the case of ozone, the communication issue becomes highly complex as the source of pollutant is not identifiable nor addressable compared to other citizenbased air monitoring projects dealing with other pollutants (Hsu et al 2017, Kloetzer et al 2017, Van Brussel and Huyse 2018.…”

When human-computer interaction meets community citizen science

“…The results obtained by conducting a randomized experiment on two independent communities can be misleading. Figure 1: The left image (a) shows the user interface of our prior work, a community-empowered air quality monitoring system [6]. The right image (b) shows the user interface of our other prior work, Smell Pittsburgh [7].…”

“…Despite the small sample size in 5 the analysis of self-efficacy and sense of community, the survey study found that the capability of using data-driven evidence from multiple perspectives is an important reason that the communities felt more confident after interacting with the system. Our other work, Smell Pittsburgh , is a mobile application that enables residents to report 6 pollution odors and track where these odors are frequently concentrated [7]. From our previous work, we found that smell experiences were valuable in representing how local air pollution affected the living quality of communities.…”

Visualization Tool for Environmental Sensing and Public Health Data

“…Air quality and its impacts on public health are critical environmental issues for residents who live near oil and gas development sites [5]. A vital step towards addressing these issues is through the collection and dissemination of convincing scientific evidence of these impacts [8,9]. However, conveying this evidence, especially with multiple types of data at a large temporal and geographic scale, requires the assistance of computational tools.…”