MOMA Technique

Data quality is often the Achilles heel of air quality sensor networks. While some sensors may be lower cost on a per unit basis, the ongoing maintenance needed to get meaningful air quality management data can be prohibitively expensive.

MOMA is a virtual network calibration technique used to improve data quality from outdoor air monitoring sensors and solutions.

MOMA (or “MOment MAtching”) uses specific moments in time to virtually compare sensor data to regulatory monitors. By automatically detecting and correcting for sensor drift, MOMA produces more accurate results, building trust in air quality network data via virtual calibration.

The MOMA technique applies a mixed-asset model to virtual calibration, using data from high-grade reference instruments (or “proxies”) to improve air sensor network performance.

First, an appropriate proxy network is selected. When deciding on a proxy, a range of factors, including land-use similarity, are taken into account. Sensor network data is compared with proxy data over a set period (long enough to reduce short-term bias, yet short enough to preserve local differences.) By matching the mean and standard deviations of data across air quality networks over time, our data experts derive slope and offset estimates forming calibration coefficients that provide a robust defense against sensor drift and ensure accurate network calibration.

Rigorous and regular network calibration is required to ensure data is up to regulatory standards. However, current methods of calibration can be prohibitively expensive. In eliminating the need for on-site sensor calibration, the MOMA technique frees up more budget for additional monitoring systems. This enables you to create denser, more expansive air quality management networks, delivering a more detailed, localized picture of air quality in your area.

Every virtual calibration is transparent, and quality controlled by our team of data experts and executed using our suite of proprietary MOMA software tools. Auditable outputs include:

All network calibration data is easily accessible via our purpose-built app. By updating network calibration factors at regular intervals, the MOMA technique offers a closer match to regulatory data when compared with a single correction factor approach. You may wish to initiate this process at set intervals or have it auto-initiate in response to drift using our drift detection algorithm. We maintain all raw and corrected data as part of these calibration services, for full transparency.

The MOMA method has been shown to be effective in many scientific studies and peer-reviewed papers.