The challenge is to design a system that can monitor the surrounding environment and/or purify the air in an attempt to make it cleaner for people to breathe. Due to this the monitoring device is a low-cost device which can monitor a wide range of differing important environmental factors, this includes the humidity and temperature of a room, as well as being able to get information on the parts per million (ppm) and percentage in the air of Hydrogen and Nitrogen Dioxide. There is also a sensor which can sense the ppm and percentage of Carbon Monoxide, Ethanol, Hydrogen, Ammonia and Methane. This all will give a detailed idea of the air quality of the environment/room. With the cost of this system being around £32.36 (approx. $39.95) and for an extra £43.37 ($53.56) a sensor could be added which would be able to measure the CO2 ppm of the surroundings as well. As the Microcontroller (MCU) chosen is to be the ESP32, we have access to Wi-Fi Communication. Therefore, the system could easily be connected to the internet and report back to a central server, this data would could then be analysed to both suggest the ideal filters the user requires (as depending on location certain filters will be more important than in others). The prices were estimated using an order of 5 boards, the total price is likely to fall further when ordering a higher number, from around £77 down to less than £60 for 100 boards. This all combined means that we have a low-cost solution which could be produced in order to monitor the environment in a more localised way at an affordable cost.

The part of the project was also to develop a purification system. This purification system would be unique in that it would be modular in design. As many of the different types of air purifying systems have different strengths and weaknesses by having a modular system the best filters for the job could be chosen. This could involve (for example) a HEPA filter to capture large particulates, followed by a photocatalytic oxidation system to destroy smaller pollutants, this could then be followed by a small carbon capture module.

As the system would be modular many different modules could be designed such as activated carbon filters, modules filled with plants (see below), or a Biologial air filter using microorganisms could be investigated as well. Using the data from the sensor module a filtration system could then be recommended that could deal with the common pollutants found by the sensor module.

One type of filter that was investigated was plants, in which we looked into how effective plants were at removing pollutants from the air. In which we found a study from NASA showing a wide range of different plants and their ability to removing TCE, Benzene and Formaldehyde. From this data the best plant per surface area was found, it was seen that the Gerba daisy had the best overall score however there was no data for formaldehyde so that was discounted, because this suggests it was either low or none at all. The next was English Ivy but English Ivy is often found covering entire walls so could not be kept as a potted plant as easily. The 3rd best in this ranking was then the plant called “Pot mum” in the study which was removed for the same reason as the Daisy as it had no information on the TCE or Formaldehyde. Therefore, it was decided the “Mother in Laws tongue” also called “Snake Plant” or “Dracaena Trifasciata” was the best overall plant that can easily be stored as a potted plant.

When the system is fully installed the monitoring system and purifier could work in unison. This would operate through passing real-time data gathered by the sensor, the time of day and previous data into a machine learning system which would alter the power of the purification system could be adjusted. By using machine learning the system would become more efficient as the power levels it as it could be adjusted pre-emptively to prevent any impurities from ever reaching unsafe levels, in a power efficient way.