Themed collection A collection on dense networks and low-cost sensors, including work presented at ASIC 2022
Evaluation of low-cost gas sensors to quantify intra-urban variability of atmospheric pollutants
Low-cost electrochemical air quality sensors can provide deep insights into the intra-urban variability of different air pollutants after proper calibration using field co-location with regulatory Air Quality Monitoring stations.
An analysis of degradation in low-cost particulate matter sensors
PurpleAir sensors are widely used to measure PM2.5 levels in cities around the world. However, little is known about the change in sensor performance over time. This paper fills this gap.
Estimation of hourly black carbon aerosol concentrations from glass fiber filter tapes using image reflectance-based method
Laboratory and field evaluation of a low-cost methane sensor and key environmental factors for sensor calibration
Low-cost electrochemical methane sensor shows improved measurement accuracy after corrections for carbon monoxide, absolute humidity, temperature, and adjusting for time of day in an urban environment.
Investigation of indoor air quality in university residences using low-cost sensors
Indoor air quality (IAQ) is crucial for the wellbeing of university students. Yet, IAQ in student residences is highly variable and challenging to monitor. This work is the first to monitor IAQ in student residence with a low-cost sensor network.
Improving the performance of portable aerosol size spectrometers for building dense monitoring networks
A new charging method is deployed to improve the accuracy of portable size spectrometers with reduced size and maintenance, thus, more suitable for building dense monitoring networks.
Particulate matter in a lockdown home: evaluation, calibration, results and health risk from an IoT enabled low-cost sensor network for residential air quality monitoring
Low-cost sensor analysis of indoor air quality.
Application of machine learning and statistical modeling to identify sources of air pollutant levels in Kitchener, Ontario, Canada
Machine learning is used in air quality research to identify complex relations between pollutant levels, emission sources, and meteorological variables.
Ambient characterisation of PurpleAir particulate matter monitors for measurements to be considered as indicative
Using low-cost systems to obtain indicative measurements when no calibration is possible.
About this collection
For over a decade the advent of low-cost sensors has promised a paradigm shift in the way air pollution is measured. Although the full potential of these devices may not yet have been realised, a significant amount of work has now been done to demonstrate their capabilities. In contrast to the traditional model of air pollution monitoring, advances have predominantly come from novel software developments instead of hardware.
Guest-edited by R. Subramanian, Albert Presto, Peter Edwards, and Mei Zheng, this collection explores this rapidly developing and exciting area of research, from operating dense networks of devices to enhancing the information content of sensor data and making measurements in previously difficult to access environments (e.g. residential properties).