Author page: michaela

One common criticism towards low-cost air quality sensors such as the ones used by hackAIR is that they cannot achieve the accuracy of more expensive reference stations. We set out to test this claim. The hackAIR air quality test station is now active in Athens since October 2017. 

Using the setup pictured on the right, we wanted to make a single installation site for all hackAIR sensing devices, including all supported hardware and sensors. In particular, we were interested in understanding the impact of humidity and temperature on the measurements and whether the measurements would shift over time due to ageing. In addition, we included a commercial air quality sensor.

Sensors in the hackAIR air quality test station

• hackAIR home v2 (Wemos, sensors used: DfRobot SEN0177 and DHT11)
• hackAIR home v1 (Arduino Ethernet, sensor used: DfRobot SDS011)
• hackAIR home v1 (Arduino WiFi, sensor used: DfRobot SEN0177)
• hackAIR cardboard
• Dylos DC1100 (commercial reference sensor)

What did we find out?

We saw the measurements of the hackAIR low-cost sensors correlate sufficiently well with the reference measurements. hackAIR’s power saving protocol proved to be effective in protecting sensors from rapid ageing: we did not detect any shift after almost 6 months of continuous measurements. 

Humidity and temperature constitute two significant factors that influence the quality of fine particles and thus impact the corresponding sensor recordings. hackAIR is using a normalisation function on the sensor itself to adjust the air measurements in these situations. 

During the testing period, the devices showed acceptable network stability: no disconnections were recorded. We were able to fix a minor problem with the ethernet http connection. 

While this initial test site in Athens is still operational, we want to set up an additional installation next to an official measuring sites to be able to directly compare hackAIR measurements with official air quality information. 

This week’s blog post features four citizen initiatives that are taking action to reduce air pollution – in Belgium Kosovo, Bulgaria and Great Britain. Better air quality information enables us to fight for better air in where we live: Get inspired by these stories – and share with us what you are up to!

Help – I can’t breathe: Parents take action on air pollution around schools in Belgium 

On March 14, 2018 a report in Belgian national television informed the public about the dangerous levels of air pollution around schools. This documentary showed the results of a large scale study on air quality and used two schools as examples. 

The next day, the first protest actions happened at school streets. Filter Cafe Filtre was born.

The invitation is simple and powerful: “Close off the street where your school is to traffic every Friday morning before school starts. Have a cup of coffee together and let the children play in the car-free street. Invite local politicians to come and join you for a coffee.”

The initiative is growing: on April 20, 60 parent groups across Belgium took part. The mission is to raise awareness and change mindsets of parents and children, and to convey the urgency to policymakers.

Get data – Drive change: Youth-led air quality campaign in Kosovo

Kosovo is one of the most polluted regions in Europe. The project “Science for Change Kosovo” is a youth-led environmental movement, investigating air pollution in Kosovo and mobilizing people to take actions. The project was one of the partners in the Making Sense project. 

It works with local youth around environmental policy issues, how to collect data about air quality, and how to use this data to advocate for change. Their approach was documented in the Making Sense toolkit for citizen science initiatives – have a look!

Bulgaria: New collaboration between citizen activists and government to reduce air pollution

Air pollution is much worse in Eastern Europe. In Sofia, air pollution norms were exceeded on 70 days between October 2017 and March 2018. Government and activists are working hand-in-hand to improve the situation. The citizens’ group “AirBG.info” was founded in April 2017 and has rapidly expanded their network in the first year of their activities.

This initiative maps air quality with low-budget sensors, built by citizens. The open data from this citizen science project are collected at the luftdaten platform. Currently, 600 air quality sensors are operating in Bulgaria, nearly 300 of them in the capital. AirBG.info has been very successful in establishing a close collaboration with the Bulgarian government and with the environmental executive agency. This collaboration led to the decision that Bulgaria will integrate all existing air quality monitoring systems. The Environment Minister said: “We are trying to integrate all [air quality monitoring] systems: those of non-government organizations, of which there is a myriad but are less precise, and those of the Executive Agency for Environment, which are fewer but are more precise, in order to get a clear picture about the quality of air we breathe.”

Parents campaign for clean air in London

Mums for Lungs is a London based group of parents who are seriously concerned about the dangerous impact air pollution is having on children’s health. They have set up air quality campaigns in various contexts.

In March 2018, they campaigned for the extension of the Ultra Low Emission Zone in London. Within this zone, most vehicles will have to meet emissions standards or pay a daily charge. In London, around half of emissions of particulate matter (PM) come from transport. The Ultra Low Emission Zone will reduce the most harmful emissions generated by road traffic. Mums for Lungs, together with a number of local partners, has been calling to extend this zone so that more inhabitants of London can benefit from cleaner air.

In the ongoing local elections, Mums for Lungs has proposed a number of pledges to local parties – raising awareness about air quality. The pledges are also a way to hold policy makers accountable – after the elections.

Have you taken a sky photo with the hackAIR app? Are you curious how the app estimates the air quality, based on your photo? Do you want to know more about the science behind this feature?

Even with your bare eyes, you can see that the cloudless sky is not always the same blue. The color of the sky changes with the time of the year and time of the day, but it also changes when particles are present in the atmosphere. In very polluted cities the sky often does not appear very blue, while it can be deep blue when it has rained recently and it is very windy and it can also be deep blue in remote locations.

The feature

Once you have taken a sky photo with your hackAIR mobile app, the image is uploaded to the hackAIR server where we analyse the colour of the sky and estimate the current air quality. When the calculations have completed, the picture is shown on the hackAIR map with an estimate of the air quality.

Behind the scenes

• Step 1: A computer algorithm detects the portion of blue sky in the photo. As the lower parts of photos often show buildings or landscape, this part is discarded by the hackAIR system. From the recognised sky, the upper third is used for further analysis.
  
• Step 2: Using the average colour detected, hackAIR then calculates the ratio between red and green light bands (R/G ratio).
  
• Step 3: We then look up this ratio in a table that lists the corresponding air quality value in relation to the location and time of day at which the photo was taken.
  
• Step 4: The estimated rating of the air quality, together with the photo, is shown on the hackAIR platform.
  

The scientific background

The main idea is that the ratio of red and green band of the light (R/G) depends on the amount and type of aerosols in the atmosphere (R/G increases with increasing AOD).

Aerosols, which are tiny particles suspended in the atmosphere, are emitted by natural (e.g., volcanoes, desert dust, forest fires, sea salt) as well as human activities (e.g., biomass burning, combustion of fossil fuel, industrial activities). Aerosols affect the levels of surface solar radiation by scattering and absorbing the light coming from the sun reducing the visibility in the atmosphere.

By measuring the radiation that reaches the ground at specific wavelengths we can assess the degree to which aerosols prevent the transmission of light by absorption or scattering. This is expressed as aerosol optical depth (AOD). AOD is unitless and usually reported at a wavelength of 550 nm. A value of 0.01 corresponds to an extremely clean atmosphere (air quality = very good), and a value of 0.4 would correspond to a very hazy condition (air quality = bad).

To control for varying atmospheric conditions, we produced a set of look-up tables using a  radiative transfer model (RTM). RTMs calculate the intensity of the light transferred within the atmosphere under different user-input scenarios that include information about the position of sun (solar zenith angle) relative to Earth and various atmospheric parameters (e.g., clouds, aerosols, water vapour, ozone, surface albedo, etc.).

We then compare the measured light intensities with those expected conditions to retrieve an estimate of the current aerosol optical depth.

A similar approach has been used to examine the atmospheric effects of volcanic eruptions in historical paintings¹. Instead of the painter’s eyes, hackAIR uses mobile images and publicly available images from Flickr and webcams. We performed a number of tests to check the effect of the camera type on R/G ratios – as previous studies indicate, this is not a major issue.

Future potential

This analysis of sky photos makes it possible to monitor air quality in urban and rural environments with easily available tools: digital and mobile cameras. This estimation of air quality supports citizens in collecting more valuable information about the quality of the air: e.g. in their neighbourhood, around schools, in busy streets.

hackAIR uses the sky photo analysis not only for images provided by users through the mobile app. This image analysis is also used by the hackAIR tech team to analyse a very large number of photos from Flickr and webcams.

---

¹ Zerefos, C. S., Gerogiannis, V. T., Balis, D., Zerefos, S. C., and Kazantzidis, A.: Atmospheric effects of volcanic eruptions as seen by famous artists and depicted in their paintings, Atmos. Chem. Phys., 7, 4027-4042, https://doi.org/10.5194/acp-7-4027-2007, 2007.

Once you’ve set up your hackAIR sensors, the question is: what can you do with the data? Of course, you can check it day by day on the map, but wouldn’t it be cool to do more? Let’s see what’s already possible.

This blog post has been written for hackAIR users who are familiar with Excel and curious about data visualisation. Please check out what hackAIR communications lead Wiebke Herding has learned about visualising data! We’d also love to hear from you what you have been experimenting with.

From the sensor profile

Each sensor has its own profile page that you can find by going to Profile > Sensors. After a moment’s wait, you’ll get a list of the latest measurements translated as air quality ratings.

Using the ‘Export Measurements’ button, you can then download the exact measurements from your sensor in CSV format. This way, you can export 5000 measurements at a time – if you need more, just run multiple exports. Note: the timestamp in the export is in GMT – depending on your own time zone, you might need to adjust this (e.g. add an hour if you are based in Berlin).

Air pollution over time with Excel

After downloading the files, you can open them in Excel. I then added three columns to be able to access the values I was interested in:

• Date CET: =<Date>+”01:00″
  
• PM10: =IF(<Pollutant_Q_Name>=”PM10_AirPollutantValue”,<Pollutant_Q_Value>,””)
  
• PM2.5: =IF(<Pollutant_Q_Name>=”PM2.5_AirPollutantValue”,<Pollutant_Q_Value>,””)
  

Note: these formulas assume that you use English localisation settings. If you use Excel in a different language, you might need to adjust quotation marks and commas.

Using Insert >PivotChart, I created a chart with Date CET as the axis and PM10 and PM2.5 as the values. I set both values to show the average PM measurement and changed the chart type to a line graph. The result was a time series of daily averages:

Using the report filter buttons at the bottom right, I could then zoom into the hourly averages and finally into the individual measurements.

As I am currently running two sensors (120 is a hackAIR home v1 at the front of my house, 255 is a hackAIR home v2 in a more protected space at the back), I can also compare the two:

 

On average, the sensor at the front of my house picked up 53% more PM10 particles, and 23% more PM2.5 particles in the testing period.

Building graphs that update themselves

As downloading the files can get tedious over time, we could also use the hackAIR API to access our data. We can use a service like data.world for that. After setting up an an account and creating a new project, you can add your own sensor data using Add data > Add from URL.

Paste the following link: https://api.hackair.eu/hackair_data?access_key=1234 (replace 1234 with your own sensor’s access key). Add the extension .json to the file name, and you’re good. 

To enable automatic updates, go to project settings, and enable the Automatic Sync Options. You can then explore your data and build graphs like the one below.

Data.world alone will not give you graphs that you can permanently link to, but it’s provides good access to the API data. One option for building graphs is Google Data Studio. Log in and add a data source, adding data.world as a community connector. You’ll need the URL of your data.world project. To import all data from your sensor, add “SELECT * FROM <table_name>” as your SQL query. To be able to use the data, you’ll need to make a few adjustments:

• date_str: set the type to Date Hour (YYYMMDDHH)
  
• pollutant_q_value: set the aggregation to None
  

Now add two new fields and set both of them to aggregation = Average.

• PM10 with the formula “CASE WHEN pollutant_q_name=”PM10_AirPollutantValue” THEN pollutant_q_value ELSE 0 END”
  
• PM2.5 with the formula “CASE WHEN pollutant_q_name=”PM2.5_AirPollutantValue” THEN pollutant_q_value ELSE 0 END”
  

Now you can set up your report, for example using the time series chart or the data table. Play around – and when you’re done, you can share the link to your sensor data. Here’s the view of my recent measurements: https://datastudio.google.com/open/11iG_TgonCmPy0nFmUS0ObVWLudstQk4M

How about measurements from a specific geographic area?

You can download the latest measurements from a rectangular geographic area as follows:

1. Determine the coordinates of the top left corner of your area, e.g. by locating it on OpenStreetMap and selecting show address. This will give you a pair of two numbers, the latitude (e.g.  52.6315 for Berlin) and the longitude (e.g. 13.1259 for Berlin). We’ll call them lat1 and lon1.
   
2. Determine the coordinates of the bottom right corner of your area, e.g.  52.3153, 13.7569 for Berlin. Again, we’ll call them lat2 and lon2.
   
3. Go to the following location: https://api.hackair.eu/hackair_data?location=lon1,lat1|lon2,lat2 (replacing the lon and lat variables with your actual values)
   

Now you can either add this link to data.world (as explained above) or transform it to a csv file using a JSON to CSV service (like Konklone.io/json). After you import or connect this data to Google Data Studio, you’ll again need to make a few adjustments:

• date_str: set the type to Date Hour (YYYMMDDHH)
  
• pollutant_q_value: set the aggregation to None
  

Now add two new fields:

• with the formula “CASE WHEN pollutant_q_name=”PM10_AirPollutantValue” THEN pollutant_q_value ELSE 0 END”. Set the aggregation to average.
  
• coordinate with the formula “CONCAT(loc_coordinates_1,”,”,loc_coordinates_0)”
  

You can now add a map to your report. Here are, for example, some of the latest measurements in the Netherlands:

Over to you!

• What other ideas do you have to visualise and use the data you collect through hackAIR? Any cool tools we’ve missed?