Most frequent questions and answers
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.
- 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.
The AirU works only with personal 2.4 GHz networks.
The AirU device needs a strong (more than 1 bar) 2.4 GHz WiFi signal. It cannot use a captive portal (i.e., network that requires manual acceptance of terms through an HTML page before allowing access, like hotels or airports). It is not capable of WPA-Enterprise, where a certificate and/or username is needed to connect. The system administrator may need to allow access to the device’s mac address (posted on the outside of the housing and the board itself).
The AirU communicates with the following endpoints, which must be open:
- mqtt.2030.ltsapis.goog:8883 – data packets every 2 minutes
- ota.tetradsensors.com:443– Over-the-Air firmware updates
This is actually a really challenging question, and researchers are actually evaluating this. This depends on what types of questions you are interested in answering and the topography and meteorology of your area. In our experience (mountain valley), we were able to capture significant differences how sensors correlate with each other for particulate matter concentrations associated with a variety of pollution events (“inversions”, wildfires, and fireworks) at spatial distances of one sensor per 1.5 to 2.5 mi2 and elevation differences ranging from 200 to 400 ft.
Setting up a crowd-sourced, air quality measurement network can be challenging. The sensors themselves are relatively inexpensive, but installing, maintaining, and ensuring equitable distribution of the sensor nodes requires effort. For example, crowd-sourced sensor networks that rely on community members to purchase and install their own sensors leads to few sensors being located in under-resourced (and often poorer air quality) areas.
The AirU map displayed in tellusensors’s home and dashboard page is populated with icons representing public sensors. We currently display 2 type of sensors, AirU’s and PurpleAir sensors. Their color on the indicates their PM2.5 reading on the US EPA Air Quality Index scale from the last 15 to 30 minutes. Sensors that appear with a grey color didn’t return a PM2.5 value in the last 15-30 minutes.
Colormap overlays are modelisation of PM2.5 real time measures in specific areas where we do have enough sensors to extrapolate their data to nearby areas.
Map overlays are only available in an area where we have sufficient number of sensors.
In the website dashboard, the time zone is UTC, coordinated universal time.
In the data studio, the timezone is local.
To create a “nickname” for your device, you can use the following URL:
Be sure to URL-encode your nickname string before you call this URL. URL-encoding is the act of replacing characters that are not allowed in a URL with characters that are allowed. For example, a space is replaced with %20. You may use the following website to encode the nickname parameter:
For example, if you want to give the device 30F5205BF383 the nickname “Geirge T. Craig Air Quality Monitoring Site”, you would pass that phrase through the website’s encoder and it returns:
And the final URL to set it up looks like:
Just put this link into your browser and hit Enter, it’s name should be updated in the data studio after a reload.
The table below list the file you can access through the website by clicking on data file links.
|Data File||Correction Factors||Format||Description|
|AggregatedData||Applied||Json||Contains averaged PM2.5 for each sensor displayed on the map within the last 15 minutes.|
|GetSensorsData||Not Applied||Json||Contains all sensors PM2.5 reading within the last 15 minutes.|
|WeeklyMaps||Applied||Jpeg||Archive that contains weekly processed maps for each city.|
|LatestMaps||Applied||Json||Archive that contains the latest unprocessed map (raw json file) for each city|
|Correction factors||Not Applicable||Json||Contains the correction factors we are using to correct PM2.5 readings.|
You can also download the data from your local dashboard in google datastudio or through our api, which you can find the related documentation here.