Smart Cities - Air Quality

Good air quality is a prerequisite for a healthy life, but the air in our cities is increasingly polluted. Students will discover which are important parameters used to establish the air quality index. Then they can build a real analyser using sensors and different open-source electronics platforms to measure the chosen air parameters and to transfer the data in real-time. From data analysis, students can conclude about the air quality in their own city and can alert the the public, community and administration, so that appropriate measures can be taken.

The SDG 11 (Sustainable Cities and Communities) stipulates to make cities inclusive, safe, resilient and sustainable, but in 2016 air pollution caused 4.2 million premature deaths. Some gas pollutants make a big contribution to climate change (SDG 13 - Climate Action). These are motives enough to understand the importance of studying air quality ^[1] [2].

The students can study the principles of physics that underlie the operation of sensors for air pollutants. For example, they learn about how the non-dispersive infrared (NDIR) carbon dioxide sensor ^[4] works or how the light scattering principle is implemented in a dust sensor ^[5].

Students may be challenged to discuss well known cases of pollution in modern history like the London pollution in 1952 ^[6], the increase of PM10 in Athens because of wildfires in August 2021 ^[7] or the presence of ozone in some Italian cities in June 2021 ^[8].

The air quality index considered by the European Environment Agency is based on measurements of up to five key pollutants. The index corresponds to the poorest level for any of the five pollutants according to the following table ^[9]:

The European Union has developed an extensive body of legislation that establishes standards for a number of pollutants present in the air. These standards are summarised in the table below. These apply over differing periods of time because the observed health impacts associated with the various pollutants occur over different exposure times ^[10].

The device is based on an ATmega2560 microcontroller board, ARDUINO MEGA 2560. A Wireless NodeMCU based ESP8266 WIFI module have been attached for sending the data to the server, using a PHP script, through an HTTP POST.

The power supply is made with two rechargeable 3.7V LiPo batteries, a step-up converter (3.3 V to 5V) to supply the Arduino, and a step-down converter to charge the battery.

We used components that are cheap, easy to connect with the Arduino board, with a low energy consumption (the device being autonomous), powered at low voltage and that provide the desired parameters:

• air temperature and humidity module DHT22, SEN0137;
• air pressure, BMP280, I2C barometer sensor, SEN 0226;
• air quality (CO₂ + TVOC) sensor CCS 811, SEN0318;
• analogue sound level meter SEN0232;

This time we measured more parameters in the air, using a multiple-in-one air quality module type ZPHS01B (as you see in Photo 2) and a Raspberry Pi 4B+ module: particulate matter with the size 0.3 - 10 μm (PM1.0, PM2.5, PM10), air temperature (-10 °C - 50 °C), air humidity (0 - 80 %RH), carbon dioxide (0 - 5000 ppm), carbon monoxide (0 - 500 ppm), ozone (0 - 10 ppm), nitrogen dioxide (0 - 10 ppm), formaldehyde (0 - 6.25 mg/m³) and TVOC (0 - 3 grades).

Advice

At the beginning it’s good to set one sensor at a time and check its correct operation and only after that to gather all sensors in a box.

In our experience, MQ series gas sensors were not ideal because they consumed a lot of power, as they have a powerful heater inside them to keep the sensing element warm (50 °C).

• a larger number of measured parameters for a better analysis of air quality
• the latest generation sensors for low electric energy consumption
• the use of fewer but more complex lines of code
• the high storage capacity of measured data on a server
• quick access to data on our website ^[11]
• the database is accessible to anyone
• the increase of the values of the measured parameters over the allowed values can be observed in real time in the graphs
• it is an IoT device.

As can be seen in the table for the European Air Quality Index, all parameter data is measured in μg/m³ but for example the CO₂ sensor gives the values in ppm and the TVOC sensor in ppb.

You can convert the sensor data units with this formula ^[12]:

concentration [mg/m³] = 0.0409 x concentration [ppm] x molar mass [g/mol]

The mean molar mass of the TVOC mixture is 110 g/mol and hence, 1 ppb TVOC corresponds to 4.5 µg/m^3 [13].

During the measurement you can use standard devices for temperature, humidity and pressure to check the validity of the registered data and the sensors can be calibrated in a specialised institute for this process.

To choose the representative places for the measurements you must know which are the most polluted ones. Smog and soot come from the combustion of fossil fuels in cars and trucks, factories, power plants, incinerators, engines or from central heating systems in buildings. In a city, the first source of air pollution is transportation and the second is the housing sector, with respect to energy generation to provide heating and hot water ^[14].

You have to calculate the average parameter values and compare them using graphs. By collecting data in both cold and warm conditions, in very busy places or in quiet ones, you can clearly see how the pollution varies.

In each chosen place, the students measure the parameters over a period of 3-5 minutes (to have enough values for the average calculation), 2-3 times on the same day and on different days (warm, cold, dry or rainy days). On a cold and humid day, it is expected that the amount of dust will be smaller.

The students can collaborate with students from other schools, where working groups can be set up, mentored by those who developed the analysers. Then a contest can be organised to find the best analyser. The students can enlarge their horizon, collaborating with students from other countries – to compare their analyses based on geographical, social or economic conditions.

The project can be presented to colleagues and an abstract can be put on the school website. Younger students can use the analysers. They can make more analyses in different places in the city and in this way, they can put more information about air pollution on the website^[11]. They can use the new measured data to combine them and to display other graphs for their study.

A dissemination can be done through the media. The specialised services of the town hall must find out about what the students have achieved. They can co-opt them into real projects and thus students will become responsible citizens. An online webinar can be held for a national recognition.

1. Air Quality