Difference between revisions of "Stofradar"

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(Created page with "== Introduction == This is about my plan to create a 'stofradar' image of fine dust concentrations based on the raw data measured by the luftdaten.info network. luftdaten.inf...")
 
(Data filtering)
 
(100 intermediate revisions by the same user not shown)
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  {{Project
 +
  |Name=Stofradar
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  |Picture=stofradar.png
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  |Omschrijving=Visualizing airborne particulate matter concentrations on a map
 +
  |Status=Completed
 +
  |Contact=bertrik
 +
  }}
 +
 
== Introduction ==
 
== Introduction ==
This is about my plan to create a 'stofradar' image of fine dust concentrations based on the raw data measured by the luftdaten.info network.
+
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 [http://www.stofradar.nl www.stofradar.nl].
  
luftdaten.info is an initiative to allow citizens measure fine dust concentration using an inexpensive and easy to build fine dust sensor.
+
Visualisation of citizen-science data by RIVM in the samenmeten project can be found [https://samenmeten.rivm.nl/animatie/index-both.php here].
 +
 
 +
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 [https://sensor.community sensor.community] is an initiative to allow citizens to participate in measuring atmospheric particulate matter concentration using an inexpensive and [https://sensor.community/nl/sensor-bouwen/ easy to build sensor].
 
They collect this data, calculate 5 minute and daily averages and publish it again as open data.
 
They collect this data, calculate 5 minute and daily averages and publish it again as open data.
The total number of sensors is about 5000 worldwide, most of them in germany, bulgaria, belgium, austria, sweden.
+
The total number of sensors is > 12000 worldwide, most of them in Germany, Bulgaria, Belgium, Austria, Sweden.
The netherlands has about 100 sensors.
+
The Netherlands has > 2000 sensors. See also [https://stats.sensor.community/].
 +
 
 +
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
 +
* fix the problem of stale data
 +
 
 +
=== Stale data ===
 +
Perhaps the next things to tackle is how to handle stale data.
  
I want to create images / animations based on this data.
+
Some data sources are not as frequent (5 min) as the sensor.community data.
 +
For example, some data from samenmeten undergoes several stages of aggregation, is only updated hourly and is often over three hours old already.
 +
 
 +
Perhaps invent some kind of weighing factor so that stale/old data is still used, but does not deteriorate the overall quality when combined with other data:
 +
* there is already a mechanism in place to weigh local PM concentrations by distance to measurement points
 +
* perhaps also add a mechanism to account for weighing by age of the measurement?
 +
* for example simply use age as a weighing factor: a 5-minute old measurement from sensor.community is regarded as 24 times more important than a 2-hour old samenmeten measurement
  
 
== Visualisation ==
 
== Visualisation ==
The general idea is to create an image, with a map at the background and the fine dust concentration overlaid on top.
+
The general idea is to create an image, with a map at the background and the atmospheric particulate matter concentration overlaid on top.
  
TODO:
+
=== Background map ===
* get a nice background map, I prefer the equirectangular projection since that is easy to calculate. A black-and-white map would be nice so the overlay adds all the colour.
+
The map background on stofradar.nl is based on https://mapsvg.com/maps/netherlands
* find a way to combine the background with the dust concentration overlay: imagemagick?
+
 
* find a way to combine images over time into an animated gif: imagemagick?
+
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)
 +
* meetjestad
 +
 
 +
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 ===
 
=== 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
 
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
[https://en.wikipedia.org/wiki/Inverse_distance_weighting inverse distance weighting], in particular using the distance squared as a weighing factor in a weighted average.
+
[https://en.wikipedia.org/wiki/Inverse_distance_weighting 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 only contributing a bit to the global 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:
 
To calculate the distance, I use a very simple approximation:
* determine the difference in longitude and latitude
+
* calculate the "middle" of the map (average latitude/longitude between top-left and bottom-right);
* apply an 'aspect ratio' factor of cos(latitude) to the longitude difference. I use the latitude of the approximate middle of the netherlands to calculate this aspect ratio factor.  
+
* calculate the "km-per-degree-latitude" at the middle for latitude as 40075 km / 360 degrees;
* distance squared = (difference latitude)squared + (difference longitude)squared
+
* calculate the "km-per-degree-longitude" at the middle for longitude as the number above multiplied with cos(latitude);
A better way would be to use the 'great-circle-distance' and possibly even account for the fact that the earth is not perfectly spherical, but I think my approximation suffices and makes the calculation faster.
+
* determine the difference in longitude and the difference in latitude;
 +
* convert both to km using the factors calculated earlier;
 +
* calculate the [https://en.wikipedia.org/wiki/Euclidean_distance 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 ===
 
=== Colour range ===
 +
[[File:luchtmeetnet_lki.png|right|thumb|Luchtmeetnet ranges]]
 +
 +
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.
  
The colours I'll probably be using (a kind of spectral range):
+
=== Animation ===
*  0 ug/m3: fully transparent white (#FFFFFF.00)
+
Besides an image with current data from the last 5 minutes, every hour two animations are created:
* 25 ug/m3: quarter transparent cyan (#00FFFF.40)
+
* GIF animation composed of hourly images over the past 24 hours
* 50 ug/m3: quarter transparent yellow (#FFFF00.40)
+
* MP4 animation composed of 5-minute images over the past 24 hours
* 100 ug/m3: quarter transparent red (#FF0000.40)
 
* 200 ug/m3 and higher: quarter transparent purple (#FF00FF.40)
 
With interpolation for RGB and alpha for values between these levels.
 
  
The level of 50 ug/m3 has been agreed on as a level that should not be exceeded as the yearly average in the Netherlands.
+
You can click on the GIF animation to view the MP4 animation.
  
This scale is approximately logarithmic, with each step being twice as big as the previous one.
+
== Software ==
 +
See the [https://github.com/bertrik/stofradar github page] for the source code.

Latest revision as of 01:01, 11 June 2022

Project Stofradar
Stofradar.png
Visualizing airborne particulate matter concentrations on a map
Status Completed
Contact bertrik
Last Update 2022-06-11

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.

Visualisation of citizen-science data by RIVM in the samenmeten project can be found here.

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
  • fix the problem of stale data

Stale data

Perhaps the next things to tackle is how to handle stale data.

Some data sources are not as frequent (5 min) as the sensor.community data. For example, some data from samenmeten undergoes several stages of aggregation, is only updated hourly and is often over three hours old already.

Perhaps invent some kind of weighing factor so that stale/old data is still used, but does not deteriorate the overall quality when combined with other data:

  • there is already a mechanism in place to weigh local PM concentrations by distance to measurement points
  • perhaps also add a mechanism to account for weighing by age of the measurement?
  • for example simply use age as a weighing factor: a 5-minute old measurement from sensor.community is regarded as 24 times more important than a 2-hour old samenmeten measurement

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)
  • meetjestad

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

Luchtmeetnet ranges

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:

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
  • MP4 animation composed of 5-minute images over the past 24 hours

You can click on the GIF animation to view the MP4 animation.

Software

See the github page for the source code.