Reference: https://library.wmo.int/doc_num.php?explnum_id=10620
Richard E. Peltier ; Núria Castell, Andrea L Clements, Tim Dye, Christoph Hüglin, Jesse H Kroll, Shih-Chun Candice Lung, Zhi Ning, Matthew Parsons, Michele Penza, Fabienne Reisen and Erika von Schneidemesser. (2021). WMO. Low-cost sensors for the measurement of atmospheric composition: overview of topic and future applications.
Key Findings
- LCS are on a trajectory of rapid growth
- The devices are not yet suitable for replacing reference monitoring networks
- LCS should be operated under a protocol of rigorous quality assurance and quality control that meets or exceeds the objectives of the research application
- Most LCS have known and still-unknown issues that affect their precision and accuracy
- Community engagement and outreach, and the use of LCS in educational contexts, are a particular strength of these devices
- LCS represent a logical tool to assess air quality in regions of the world that lack high quality atmospheric composition observations, and which are also understudied. However it is important to support these methods with appropriate calibration and validation platforms to ensure high quality data
Short Summary
- users should strive to co-locate LCS applications in close proximity to equivalent reference monitoring instruments
- It is crucial to go beyond factory calibration and assess calibration robustness within local conditions in order to retain the highest possible accuracy, precision, and reproducibility.
- Sensor systems and their application in the atmospheric sciences need to be evaluated not only in terms of the technical performance of individual devices, but also in terms of the hardware, software, and data analysis frameworks that are required to support their use for specific kinds of applications
| Strengths |
Weaknesses |
| potential for deployment in large numbers as networks or as part of networks and in locations where reference instrumentation use is impractical |
large networks introduce additional logistical complexity |
| high spatiotemporal granularity increases average precision & better spatial understanding of pollutants |
high spatiotemporal granularity might require improved data handling infrastructure |
Applications
Citizen science & grass roots movements
- to provide a greater sense of ownership of issues related to local air quality and climate change
- the users may not be experiences in measurement science, air quality monitoring, or data interpretation