Re-calibrating DIY: Testing digital participation across dust sensors, fry pans and environmental pollution

Pritchard, Helen; Gabrys, Jennifer; Houston, Lara

doi:10.1177/1461444818777473

Cited by 17 publications

(18 citation statements)

References 29 publications

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“…In the literature, many articles since 2015 have shown results from the PPD42NS alone or comparison between the PPD42NS and other sensors [4]- [14], [16], [18], [19], [25], [29]. None of those articles present or give any precise information about the first stage of data processing.…”

Section: Lpo Measurement Algorithm Issuesmentioning

confidence: 99%

“…While some studies conduct thorough evaluations [6], [8], [11], [18], some others describe approximate or inexact functioning information (http://aqicn.org/sensor/shinyei/). The problem is that many times, users, including re-searchers, simply reuse electric diagrams and algorithms directly downloaded from some websites without any adaptation (like in [19]). Many sensor users use data approximations based on linear regressions to get data from the Shinyei sensor, which works well for some applications, but, due to the lack of understanding of the sensor operation, it is impossible to extend those results to other situations.…”

Section: Introductionmentioning

confidence: 99%

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What does the Shinyei PPD42NS Low-Cost Dust Sensor Really Measure?

Canu¹,

Gálvis²,

Madelin³

2021

IJESD

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Deteriorating air quality is of great concern around the world. Recently, citizen scientists, researchers, and many others have used low-cost devices such as the Shinyei PPD42NS dust sensor to measure particulate matter pollution in both developed and under-developed countries. However, few articles exist specifically on the features and performance of these sensors. Some have shown mixed results in terms of precision, accuracy, and repeatability, especially for portable applications. Frequently, users assemble the electronics and the sensors applying simple guidelines, using electric schematics, and coding extraneous algorithms to get questionable data. There is a need to better understand how it works exactly, its limitations and the effect of the program used to interpret the outputs of this sensor. This article provides a short electronic analysis of the Shinyei PPD42NS dust sensor and shows that the internal sensor electronic design (filters and detection stage) as well as the used data processing algorithm, limit its precision and accuracy by generating nonlinearities and biases. These issues avoid some applications like moving ones and imply that the algorithm used to process the sensor signals must be clearly presented in future articles.

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Section: Lpo Measurement Algorithm Issuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

What does the Shinyei PPD42NS Low-Cost Dust Sensor Really Measure?

Canu¹,

Gálvis²,

Madelin³

2021

IJESD

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“…This pollutant is composed of small airborne particles that are particularly hazardous to human health (Grigg 2017) yet is only monitored by statutory instruments in select sites across London. The Dustbox includes an optical sensor that uses infrared light scattering technology to measure the density of PM in the air (for more detail, see Pritchard, Gabrys, and Houston 2018). The casing is 3-D printed in black ceramic shapes that resemble particles when viewed under an electron microscope and is designed to be a tactile and provocative device that would indicate its sensor function.…”

Section: Exploring Workarounds Through Urban-sensing Practicesmentioning

confidence: 99%

Breakdown in the Smart City: Exploring Workarounds with Urban-sensing Practices and Technologies

Houston

Gabrys

Pritchard

2019

Science, Technology, & Human Values

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Smart cities are now an established area of technological development and theoretical inquiry. Research on smart cities spans from investigations into its technological infrastructures and design scenarios, to critiques of its proposals for citizenship and sustainability. This article builds on this growing field, while at the same time accounting for expanded urban-sensing practices that take hold through citizen-sensing technologies. Detailing practice-based and participatory research that developed urban-sensing technologies for use in Southeast London, this article considers how the smart city as a large-scale and monolithic version of urban systems breaks down in practice to reveal much different concretizations of sensors, cities, and people. By working through the specific instances where sensor technologies required inventive workarounds to be setup and continue to operate, as well as moments of breakdown and maintenance where sensors required fixes or adjustments, this article argues that urban sensing can produce much different encounters with urban technologies through lived experiences. Rather than propose a “grassroots” approach to the smart city, however, this article instead suggests that the smart city as a figure for urban development be contested and even surpassed by attending to workarounds that account more fully for digital urban practices and technologies as they are formed and situated within urban projects and community initiatives.

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“…In this way, smart sensors are similarly framed as anticipatory sketches of a future technology (Kinsley 2012) and the material practices they create in the present are largely not evaluated. As a result, only a few researchers (Zandbergen 2017;Pritchard, Gabrys, and Houston 2018) have probed the ways these sensors are creating new kinds of sensing practices. What is needed is an overview of the material practices these smart sensing devices are creating for communities.…”

Section: Introductionmentioning

confidence: 99%