FAQ

The sensor is about 4”x 4”x 4” and weighs ¼ lb with the housing.

The AirU can hang from a hook under an overhang (like a plant), or mounted on a wall, like a smoke detector.

Ideally, the sensor should be mounted in a protected spot outdoors, like under an eave. Your sensor needs a strong Wi-Fi signal ( a minimum of 2 bars on a mobile device ). The AirU’s protective housing is water resistant but not water proof. The AirU should be roughly breathing level (5 ft high or higher). Avoid placing it near kitchen exhaust or dryer vents or less than 4 ft above the ground. The sensor needs to connect to power.

The AirU consumes approximately 1.5 Watts.  The cost of power varies by location, but it will cost less than 1 cent per month.

What if:

• My sensor goes offline?

• • • Ensure that your sensor has power. If not, provide power.  If it has power unplug it for 10 seconds, and plug it back in.  If it does not come back online contact us at info@tellus.

• My PM2.5 concentrations are 0 ug/m3 for more than 48 hours?

• Ensure that your sensor has power.  If not, provide power.  If it has power unplug it for 10 seconds, and plug it back in.  If it continues to read zero, contact us info@tellus.

• My PM2.5 concentrations remain constant (i.e., 4 ug/m3) for more than 24 hours?

• • • Unplug your sensor for 10 seconds, and plug it back in. If the numbers still do not change, contact us info@tellus.

• My PM2.5 concentrations fluctuate by more than 50% on a minute-by-minute, basis?

• • • PM2.5 levels can fluctuate greatly on a minute-by-minute basis.  Although we are interested in minute-by-minute readings.  It is more important to determine if your sensor generally follows air quality trends.

• My PM2.5 concentrations show an unhealthy level > 150 ug/m3 that lasts for a few minutes?

• • This can be normal, and there may be many causes.  One larger particle may have passed through the sensor and provided an erroneous reading, or a malfunctioning vehicle/an individual smoking a cigarette may be near the sensor.

Communities can collect spacially dense air quality measurements by having community members host sensor nodes, provide power, and connectivity (often WiFi). These sensor nodes push measurements to a cloud database where they are quality assured and can be visualized on a map and/or downloaded. Once a critical mass of sensors is deployed, these measurements can be integrated (see example). These visualizations can help communities make sense of the somewhat imperfect data from the sensors. Each sensor node may measure multiple pollutants, although particle pollution, a key driver of adverse health effects, is a common air-quality indicator and can (with proper quality control) provide good estimates of air quality. Depending on the pollutants being measured, sensor nodes typically cost hundreds to a few thousand dollars.

Regulatory measurements must meet strict siting and quality control standards, and they require dedicated maintenance. This equipment typically costs thousands to tens of thousands of dollars per pollutant. Its level of expense and quality control is required for regulatory purposes.

Data quality is one of the biggest challenges with crowd-sourced sensor measurements. The sensors can malfunction or located indoors rather than outdoors. Some pollutant measurements are sensitive to environmental conditions (i.e., temperature, humidity and composition of the species in the air). It is critical for malfunctioning sensors and to develop appropriate corrections for the environment. These corrections typically rely on state and regulatory agencies that allow co-location of the crowd-sourced sensor node with their high-quality regulatory monitors.

Crowd-sourced air quality measurements are typically presented as dots on a map, but this can be difficult to make sense of. Oftentimes, sensors in close proximity may read disparately, which may reflect reality or malfunctioning sensor nodes. One way to improve this difficult-to-interpret data is to incorporate the screened and environment-corrected measurements into a regression model, with appropriate functions s for distance, time and elevation to obtain continuous-valued spatio-temporal estimates of air quality throughout a region (see example).

Low-cost PM sensors use a laser scattering principle, which works by using a laser to radiate suspended particles in the air. The scattered light is then collected by an internal photodiode, and finally the curve of scattering light change with time is obtained. Finally, equivalent particle diameter and the number of particles with different diameter per unit volume can be calculated by microprocessor based on MIE theory (link).

The conversion from total light scattering to particle concentration measurements are based on the size of the particles and the number of particles passing through the laser, but also the optical properties of the particles. Different types of particles may scatter light in different ways due to their shapes or chemical makeup, which affects the amount of light received by the photodiode and ultimately the final concentration measurement. For example, optical PM sensors typically overestimate concentrations of wildfire smoke or pollution from wintertime inversions, but tend to underestimate firework smoke, sometimes by as much as 2x! Other environmental parameters such as humidity can have a profound impact on the sensor measurements as well. Therefore it’s very important to ensure that we keep the sensor calibrations up-to-date depending on the makeup of the current environment.

We perform multi-variable regional calibrations for all PM sensors using ground-truth data from local Federal Equivalement Method (FEM) or Federal Reference Method (FRM) sources. FEMs/FRMs are very high-quality monitoring instruments, typically maintained by the EPA, which produce reliable hourly PM concentration measurements. We collect measurements from these gold-standard sources and compare them to nearby low-cost monitors to provide accurate, up-to-date calibrations that are directly traceable to these ground-truth sources. We periodically recompute and update these calibrations to consider the latest environmental parameters of the region.