Stofradar
| Project Stofradar | |
|---|---|
| |
| Visualizing airborne particulate matter concentrations on a map | |
| Status | Completed |
| Contact | bertrik |
| Last Update | 2022-05-06 |
Introduction
This page is about creating a 'stofradar' image of atmospheric particulate matter concentrations based on the raw data measured by the sensor.community network, see www.stofradar.nl.
The focus is on raw visualisation of the source data, only the most minimal attempt is made to "validate" the data. Sensor measurements and sensor locations are basically uncontrolled, since we cannot tell if a particular sensor is defective or has an unusual position that affects its measurements.
See also my DustSensor page.
The website sensor.community is an initiative to allow citizens to participate in measuring atmospheric particulate matter concentration using an inexpensive and easy to build sensor. They collect this data, calculate 5 minute and daily averages and publish it again as open data. The total number of sensors is > 12000 worldwide, most of them in Germany, Bulgaria, Belgium, Austria, Sweden. The Netherlands has > 2000 sensors. See also [1].
Future activities:
- add a water mark
- add some kind of slider to indicate progress on the GIF image, or even allow the user to slide back and forth
Visualisation
The general idea is to create an image, with a map at the background and the atmospheric particulate matter concentration overlaid on top.
Background map
The map background on stofradar.nl is based on https://mapsvg.com/maps/netherlands
The map projection used is the equirectangular projection (EPSG-32662), so I can easily map a pixel back to a latitude/longitude.
Data filtering
Data is raw PM2.5 data taken from:
- sensor.commnunity (5 minute data)
- RIVM samenmeten data portal (60 minute data)
There is only very minimal data filtering. Sensor measurements are taken into account as follows:
- Sensors from an area 2x2 times bigger than the area visualized are considered for visualisation
- Sensors marked as 'indoor' are ignored
- Sensors with a measurement value smaller than 0 are ignored
- The top percent of highest PM2.5 concentrations is discarded, this mostly takes care of outliers caused by defective sensors
- When sensor data is not available in the past 5 minutes, data from a previous measurement interval is used, up to 1 hour old
- A (small) number of sensors that are known to always report a very high value are not considered (blacklisted)
Interpolation
Since we only have data at a set of discrete points, the concentration at other points is estimated by combining data from all sensors using inverse distance weighting, in particular using the distance *squared* as the weighing factor in a weighted average. So a nearby sensor has a large effect and a far away sensor has very little effect, contributing only a little bit to the global average.
To calculate the distance, I use a very simple approximation:
- calculate the "middle" of the map (average latitude/longitude between top-left and bottom-right);
- calculate the "km-per-degree-latitude" at the middle for latitude as 40075 km / 360 degrees;
- calculate the "km-per-degree-longitude" at the middle for longitude as the number above multiplied with cos(latitude);
- determine the difference in longitude and the difference in latitude;
- convert both to km using the factors calculated earlier;
- calculate the euclidean distance.
Pixels that are not within a certain distance of any sensor station (e.g. 10 km) are rendered as grayscale, to indicate a geographic limit of each sensor.
Only sensors within a reasonable range of the map are taken into account, currently this is an area of 4 times (2x2) the visible area.
Colour range
The colours I'm using are based on the scale used for air quality index from luchtmeetnet with data from RIVM, see https://www.luchtmeetnet.nl/informatie/luchtkwaliteit/luchtkwaliteitsindex-(lki)
The input value is the PM2.5 concentration.
Values in between these levels are interpolated linearly with respect to the RGB colour value and alpha channel.
Correction for high humidity
The map is currently not corrected for high humidity, however the median of a subset of humidity sensors in the map area is determined and displayed on the image. Only humidity sensors of type BME280 are considered, they are considered to be of better quality than a DHT11 or DHT22 sensor. Not all particulate matter measurement stations have a humidity sensor onboard.
Humidity generally seems to cause an overestimation of PM measurements for measurements done with a "particle counting" type of PM sensor. The effect becomes really significant above approximately 70% humidity.
An interesting idea is to try to compensate for this effect, since the sensor.community sensor has an onboard humidity-sensor. Some papers/links about this:
- https://www.samenmetenaanluchtkwaliteit.nl/sites/default/files/2018-07/Status_SDS011_12juli18.pdf
- https://www.researchgate.net/publication/320474792_Influence_of_Humidity_on_the_Accuracy_of_Low-Cost_Particulate_Matter_Sensors
- https://github.com/opendata-stuttgart/meta/wiki/Luftfeuchte-Korrektur
However, I see the following problems with the formulas and coefficient in the opendata-stuttgart link above:
- it combines formulas and coefficients from different sources where relative humidity has different units. One paper seems to use an RH-value from 0 to 100, while another uses a kind of normalized relative humidity (from 0 to 1). You cannot just use the same coefficients if the unit is different.
- it claims a humidity correction for PM10 with coefficients that is not found in the source paper.
Animation
Besides an image with current data from the last 5 minutes, every hour two animations are created:
- GIF animation composed of hourly images over the past 24 hours
- WEBM animation composed of 5-minute images over the past 24 hours
You can click on the GIF animation to view the WEBM animation.
Software
See the github page for the source code.