<p>MOMA Technique</p>

MOMA Technique

Hyperlocal air sensor data you can trust

Build trust in air quality sensor data using MOMA’s network calibration service, reducing sensor drift and delivering more accurate results.

Data you can trust

Leverage high-grade proxy data, improving network performance.

Expert support

Every calibration is run by our team of data experts.

Expand your network

Track network performance and get the most out of your sensors.

Product overview

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

MOMA is a technique used to virtually calibrate outdoor air monitoring networks and improve data quality from air monitoring sensors and devices.

MOMA (or “MOment MAtching”) uses specific moments in time to remotely compare sensor data to regulatory monitors. By automatically detecting and correcting for sensor drift, MOMA produces more accurate results and builds trust in air sensor data.

How does it work?

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. Data from the sensor network 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 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 calibration across your network.

Rigorous and regular 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 calibration, the MOMA technique frees up more budget for additional instrumentation. This enables you to create denser, more expansive monitoring networks, delivering a more detailed, localized picture of air quality in your area.

Robust calibration methodology

Every calibration is quality assured and quality controlled by our expert team. Raw and corrected values maintained for full transparency.

Virtual calibration service (MOMA)

Method derives slope and offset estimates by matching the statistical moments (e.g. mean and variance) of the sensor data distribution to those of the proxy distribution over the same time period.

How is calibration data managed?

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

  • Scatterplots

  • Time-series

  • Pre-and post-calibration reporting

All calibration data is easily accessible via our purpose-built app. By updating 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, for full transparency.

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

Air quality sensor network solutions for...

Communities

Communities

Protect your environment and influence positive change through community air quality monitoring projects.

Read more
Government Authorities

Government Authorities

Build healthier cities with a trusted ambient air quality monitoring network.

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"Aeroqual gives us and our stakeholders confidence in the air quality data we collect.”

Boris Lukanov

Senior Scientist, PSE Healthy Energy

Optimize your network

Get in touch for more on how MOMA can help build trust in your data, improving operational efficiency.