Accurate air quality data starts with calibration

Low-cost sensors, like those in the Clarity Node-S, require calibration because they can be affected by environmental factors like humidity, temperature, and particle type. For example, particulate sensors estimate PM₂.₅ using light scattering—not direct mass—so readings can vary with smoke or dust type. Similarly, electrochemical gas sensors can drift over time or react to multiple gases. Clarity corrects for these factors by calibrating sensors against regulatory monitors, ensuring you get decision-grade data suitable for health advisories and public communication.

Clarity uses a multi-step calibration process. This approach ensures high correlation between sensor readings and reference-grade data—even during extreme events like wildfires:

• All sensors are factory-calibrated before shipment.
• Clarity developed global calibration models by collocating hundreds of Node-S devices with Federal Equivalent Method (FEM) monitors worldwide.
  For higher accuracy, Clarity recommends a local collocation study near a regulatory monitor. That site-specific data is used to fine-tune a regional calibration, applied automatically in the cloud.
• All calibration happens remotely—no need to ship devices back—thanks to Clarity’s patented Remote Calibration approach.

Calibration is included as part of Clarity’s Sensing-as-a-Service model. Both global and local calibration support are covered, along with access to tools like Accuracy Reporting and optional QA/QC services for long-term data reliability.

Calibration is not just a one-time event for Clarity sensors – it’s an ongoing process. With Remote Calibration, the applied calibration factors can be updated whenever needed. Many users note that the data quality remains high over multi-year deployments, which is because Clarity monitors and updates calibration as required. It’s not something you have to schedule or pay extra for each time – it’s part of the service. In summary, initial calibration is done at deployment, and after that Clarity ensures calibrations are maintained and refreshed regularly (quietly in the background via the cloud) so the data doesn’t degrade over time.

Clarity strives to meet the data quality objectives set by agencies like the US EPA for sensors and indicative monitoring. They do this through a combination of calibration (as we discussed) and rigorous Quality Assurance/Quality Control (QA/QC) protocols. For instance, Clarity will perform collocation validation whenever possible: if you have a reference station in your project area, they’ll compare the Node-S air quality data to that station and compute performance metrics such as R² (correlation), mean bias, RMSE, etc. The Clarity Dashboard even has an Accuracy Reporting feature specifically for collocated device evaluation, showing metrics like R² and MAE (Mean Absolute Error) to quantify accuracy. If those metrics fall outside acceptable bounds (for example, EPA’s guidelines might be that sensor data should be within ±~5 µg/m³ or 20% of FEM readings for PM₂.₅, and Clarity will aim for that), they will investigate and recalibrate or service the sensor.

Furthermore, Clarity references third-party evaluations: by undergoing testing at places like AQ-SPEC and obtaining MCERTS, they’ve demonstrated that under those test conditions, Node-S data can meet certain performance criteria. For example, MCERTS certification indicates that the Node-S (with correct calibration) met defined accuracy and precision targets for indicative monitoring. Clarity uses those evaluations to refine their process as well, essentially benchmarking against regulatory standards. They also incorporate EPA’s performance targets for sensors (like those published in the U.S. for grant programs) into their QA. On their calibration page they note striving to meet US EPA, EU, UK MCERTS, etc., performance targets wherever possibleclarity.io.