Can low-cost sensors be trusted? Why performance classification matters for IAQ monitoring
Low-cost sensors are transforming the air quality monitoring landscape. Their relatively low cost, small size, and ease of deployment allow organisations to collect environmental data at a scale that would have been difficult to achieve using only traditional reference-grade instruments. As a result, sensor networks are increasingly being used to support air quality assessments, urban monitoring initiatives, research projects, and smart building applications.
However, the rapid growth of sensor-based monitoring has also highlighted a key challenge: not all sensors perform in the same way. Differences in technologies, environmental conditions, maintenance practices, and calibration approaches can lead to significant variations in data quality. For users and decision-makers, this creates an important question: how much confidence can be placed in measurements generated by low-cost devices?
Recent European standardisation efforts are helping to address this issue. The technical specification CEN/TS 17660-2 establishes a framework for evaluating and classifying the performance of sensor systems used to monitor particulate matter, including PM10 and PM2.5. The specification defines testing principles and requirements that allow sensor performance to be assessed under controlled conditions and compared using a common methodology.
Importantly, the standard also recognises that sensor performance is context-dependent. Results obtained during testing do not necessarily guarantee the same level of performance when devices are deployed in different environments or climatic conditions. The specification further highlights that long-term stability cannot be assumed and should be supported through ongoing quality assurance and quality control procedures.
These considerations are increasingly relevant as air quality monitoring networks become more widespread. Many organisations are moving beyond isolated measurements and deploying dozens or even hundreds of sensors across buildings, campuses, cities, or regions. In such settings, consistency and comparability of data are essential. Without clear information on sensor performance, it becomes difficult to interpret results, compare measurements across locations, or generate reliable evidence for decision-making.
The discussion is also closely linked to broader debates about transparency and trust in environmental data. Recent years have seen growing attention on advanced calibration techniques, including artificial intelligence and machine learning approaches. While these methods offer significant opportunities to improve data quality, their effectiveness still depends on a clear understanding of the underlying performance of the sensors themselves. Performance classification therefore complements calibration efforts by providing a more transparent basis for assessing the strengths and limitations of monitoring devices.
Looking ahead, performance standards may play an increasingly important role in supporting the adoption of air quality monitoring technologies. As organisations seek to integrate sensor data into building management systems, public information platforms, and evidence-based policy processes, confidence in the reliability of measurements becomes a fundamental requirement. Standardised evaluation frameworks can help bridge the gap between technological innovation and practical deployment by providing users with clearer information about how sensors perform and under which conditions their data can be used.
Ultimately, the future of air quality monitoring will depend not only on making sensors more accessible, but also on ensuring that the data they generate can be trusted. In this context, performance classification, comparability, and continuous quality control are emerging as essential building blocks for the next generation of IAQ monitoring solutions.