‘Real-time’ air quality channels: a technology review of emerging environmental alert systems
Author(s)
Schulte, K
Type
Journal Article
Abstract
Poor air quality is a pressing global challenge contributing to adverse health impacts around the world. In the past decade, there has been a rapid proliferation of air quality information delivered via sensors, apps, websites or other media channels in near real-time and at increasingly localized geographic scales. This paper explores the growing emphasis on self-monitoring and digital platforms to supply informational interventions for reducing pollution exposures and improving health outcomes at the individual level. It presents a technological case study that characterizes emerging air quality information communication mechanisms, or ‘AQ channels’, while drawing upon examples throughout the literature. The questions are posed: which air quality channels are ‘freely’ available to individuals in London, UK, and when and where are they accessed? Digital trace data and metadata associated with 54 air quality channels are synthesized narratively and graphically. Results reveal air quality channels derive air pollution estimates using common data sources, display disparate messaging, adopt variable geographic scales for reporting ‘readings’ and maintain psychosocial barriers to access and adoption of exposure-reducing behaviours. The results also point to a clear association between the publication of a high-profile news article about air pollution and increased air quality channel access. These findings illuminate a need for greater transparency around how air quality channels generate personalized air pollution exposure estimates and tailor messaging. The paper concludes by calling for air quality channel developers to exercise co-creative methods that can support sustainable, democratic data and knowledge production around air quality, while critically approaching disproportionate patterns of both pollution and information exposure.
Date Issued
2022-06
Online Publication Date
2024-02-14T16:27:00Z
Date Acceptance
2022-06-01
ISSN
2053-9517
Publisher
SAGE Publishing
Journal / Book Title
Big Data & Society
Volume
9
Issue
1
Copyright Statement
© The Author(s) 2022 Creative Commons NonCommercial-NoDerivs CC BY-NC-ND: This article is distributed under the terms of the Creative Commons
Attribution-NonCommercial-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial
use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is
attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Attribution-NonCommercial-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial
use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is
attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Identifier
http://dx.doi.org/10.1177/20539517221101346
Publication Status
Published
Date Publish Online
2022-06-15