End-User Feedback on a Low-Cost Portable Air Quality Sensor System—Are We There Yet?

Abstract

Low-cost sensors are a current trend in citizen science projects that focus on air quality. Until now, devices incorporating such sensors have been tested primarily for their technical capabilities and limitations, whereas their usability and acceptability amongst the public rarely goes beyond proof of concept, leaving user experience (UX) unstudied. The authors argue that UX should be taken into account to make sure that products and services are fit for purpose. Nineteen volunteers tested and evaluated a prototype device and provided feedback through semi-structured interviews and during focus group sessions. Their UX was then coded using mixed coding methods regarding device functionality and recommendations for future product development. The results indicate that UX can identify potentially problematic design aspects while giving deeper insights into user needs. For example, UX recognized that one of the most important aspects of user involvement and motivation was successful data harvesting, which frequently failed. This study recommends that future developers of low-cost portable air quality sensor systems prioritize reliable data transmission to minimize data loss. This will ensure an efficient and positive UX that supports user engagement in citizen science based research where collecting sensor-based data is the primary objective.

A GPS-enabled portable air pollution sensor and web-mapping technologies for field-based learning in health geography

“…Since most of them are designed for measurement only, they generally lack the features necessary for field-based learning, such as display screens and built-in GPS/data loggers/batteries. Many rely on a smartphone GPS to collect location data and use Bluetooth to connect the devices to smartphone applications to view and store air pollution data (Robinson et al, 2018). Furthermore, these mobile applications often work only on an Android phone or do not work well on low-quality phones, requiring instructors to purchase Android phones of reasonable quality in addition to air-monitoring devices to serve all students, and data are often lost when applications crash (Gaskins, A. J. and J. E. Hart, 2019;Robinson et al, 2018).…”

“…Many rely on a smartphone GPS to collect location data and use Bluetooth to connect the devices to smartphone applications to view and store air pollution data (Robinson et al, 2018). Furthermore, these mobile applications often work only on an Android phone or do not work well on low-quality phones, requiring instructors to purchase Android phones of reasonable quality in addition to air-monitoring devices to serve all students, and data are often lost when applications crash (Gaskins, A. J. and J. E. Hart, 2019;Robinson et al, 2018). A smartphone application connected to an air monitor often requires technical experience and/or a steep learning curve, causing students to struggle with unnecessarily complicated technical features, which may hinder students in achieving intended learning objectives.…”

GeoAir—A Novel Portable, GPS-Enabled, Low-Cost Air-Pollution Sensor: Design Strategies to Facilitate Citizen Science Research and Geospatial Assessments of Personal Exposure

“…For example, devices that are designed to mainly target the general public tend to be easy to use and visually pleasing, but they do not provide sufficient accuracy for credible data [ 16 ], lack the elements required for scientific research, such as data loggers, and are often suitable for measurement only at a fixed location (e.g., a user’s home). In contrast, devices that are widely used by researchers or scientists are often not ready for citizen science applications due to their poor user-friendliness and technical complexity [ 17 , 18 ]. However, they have been frequently employed in community-based participatory research, despite being designed for researchers, scientists, or those who are technically capable.…”

“…Moreover, stationary monitoring often has little impact on people’s awareness of or behaviors to improve their surrounding air conditions because it does not fully satisfy participants’ curiosity [ 14 ]. It has been reported that people are more interested in identifying the air quality in their own spaces of daily activity than in the general outdoor areas of their communities [ 18 , 21 ] and thus, are more motivated to change their behaviors to reduce air pollution/exposure when they are informed of the air quality in their immediate surroundings [ 14 ].…”

Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources

“…This study was also partially motivated by the problems understanding the outputs of low-cost sensors in the context of their increased noise and cross sensitivities relative to regulatory-grade instruments [15]. Our own field experience and recent papers from the low-cost air quality instrument community have consistently encountered difficulties understanding and interpreting the outputs of low-cost sensors, especially when communicating results to individuals who may not have a highly technical understanding of the instruments and/or atmospheric chemistry [16,17]. This study attempted to address some of these issues by providing an initial interpretation of the sensor outputs that can be produced in near real-time and are output in a way that a user could understand: the likelihood that a type of source is or is not affecting the measured air quality.…”