<p>MOMA Technique</p>

MOMA Technique

Hyperlocal air sensor data you can trust

Build trust in air quality sensor network 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 air quality network performance.

Expert support

Every network calibration is run by our team of data experts.

Expand your network

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

Product overview

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.

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. 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.

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Robust calibration methodology

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

Aeroqual

Virtual calibration service (MOMA)

The MOMA technique 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.

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How is calibration data managed?

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:

  • Scatterplots

  • Time-series

  • Pre-and post-calibration reporting

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.

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Aeroqual
Aeroqual

"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 air quality network data, improving operational efficiency.