AI-assisted Air Quality Monitor w/ IoT Surveillance

1 DFRobot FireBeetle ESP32
1 DFRobot FireBeetle Covers - Camera&Audio Media Board
1 Arduino Mega
1 LattePanda 3 Delta 864
1 DFRobot Gravity: Electrochemical Nitrogen Dioxide Sensor
1 DFRobot Gravity: Electrochemical Ozone Sensor
1 DFRobot Anemometer Kit
1 DHT22 Temperature and Humidity Sensor
1 SH1106 OLED Display (128x64)
1 Creality Sermoon V1 3D Printer
1 Creality Sonic Pad
1 Creality CR-200B 3D Printer
1 Keyes 10mm RGB LED Module (140C05)
1 Logic Level Converter (Bi-Directional)
3 Button (6x6)
2 Half-sized Breadboard
1 Mini Breadboard
1 DFRobot 8.9" 1920x1200 IPS Touch Display (Optional)
1 Xiaomi 20000 mAh 3 Pro Type-C Power Bank
1 USB Buck-Boost Converter Board
1 Jumper Wires

Due to the ever-growing industrialization, forest degradation, and pollution, the delicate balance of ambient gases shifted. Thus, hazardous air pollutants impinge on the human respiratory system detrimentally, in addition to engendering climate change and poisoning wildlife. Even though nations realized that it was incumbent on them to act in order to prevent destructive air contaminants from pervading the ecosystem, we are too far away from obviating human-made air pollutants during the following decades. Therefore, it is still crucial to detect air pollutants to inform people with prescient warnings.


Since some air pollutants can react with each other and spread very rapidly, precedence must be given to detecting highly reactive gases (air contaminants), such as ozone (O3) and nitrogen compounds (NOx, NOy). Thus, in this project, I decided to focus on ozone (O3) and nitrogen dioxide (NO2) concentrations, which denote dangerous air pollution.


In ambient air, nitrogen oxides can occur from diverse combinations of oxygen and nitrogen. The higher combustion temperatures cause more nitric oxide reactions. In ambient conditions, nitric oxide is rapidly oxidized in air to form nitrogen dioxide by available oxidants, for instance, oxygen, ozone, and VOCs (volatile organic compounds). Hence, nitrogen dioxide (NO2) is widely known as a primary air pollutant (contaminant). Since road traffic is considered the principal outdoor source of nitrogen dioxide[1], densely populated areas are most susceptible to its detrimental effects. Nitrogen dioxide causes a range of harmful effects on the respiratory system, for example, increased inflammation of the airways, reduced lung function, increased asthma attacks, and cardiovascular harm[2].


Tropospheric, or ground-level ozone (O3), is formed by chemical reactions between oxides of nitrogen (NOx) and volatile organic compounds (VOCs). This chemical reaction is triggered by sunlight between the mentioned air pollutants emitted by cars, power plants, industrial boilers, refineries, and chemical plants[3]. Depending on the level of exposure, ground-level ozone (O3) can have various effects on the respiratory system, for instance, coughing, sore throat, airway inflammation, increased frequency of asthma attacks, and increased lung infection risk. Some of these detrimental effects have been found even in healthy people, but symptoms can be more severe in people with lung diseases such as asthma[4].

Since nitrogen dioxide (NO2), ozone (O3), and other photochemical oxidant reactions and transmission rates are inextricably related to air flow, heat, and ambient humidity, I decided to collect the following data parameters to create a meticulous data set:


Nitrogen dioxide concentration (PPM)


Ozone concentration (PPB)


Temperature (°C)


Humidity (%)


Wind speed 


After perusing recent research papers on ambient air pollution, I noticed there are very few appliances focusing on collecting air quality data, detecting air pollution levels with machine learning, and providing surveillance footage for further examination. Therefore, I decided to build a budget-friendly and easy-to-use air station to forecast air pollution levels with machine learning and inform the user of the model detection results with surveillance footage consecutively, in the hope of forfending the plight of hazardous gases.


To predict air pollution levels, I needed to collect precise ambient hazardous gas concentrations in order to train my neural network model with notable validity. Therefore, I decided to utilize DFRobot electrochemical gas sensors. To obtain the additional weather data, I employed an anemometer kit and a DHT22 sensor. Since FireBeetle ESP32 is a compact and powerful IoT-purposed development board providing numerous features with its budget-friendly media (camera) board, I decided to use FireBeetle ESP32 in combination with its media board so as to run my neural network model and inform the user of the model detection results with surveillance footage. Due to the memory allocation issues, I connected all sensors to Arduino Mega to collect and transmit air quality data to FireBeetle ESP32 via serial communication. Also, I connected three control buttons to Arduino Mega to send commands to FireBeetle ESP32 via serial communication.


Since the FireBeetle media board supports reading and writing information from/to files on an SD card, I stored the collected air quality data in separate CSV files on the SD card, named according to the selected air pollution class, to create a pre-formatted data set. In this regard, I was able to save and process data records via FireBeetle ESP32 without requiring any additional procedures.


After completing my data set, I built my artificial neural network model (ANN) with Edge Impulse to make predictions on air pollution levels (classes). Since Edge Impulse is nearly compatible with all microcontrollers and development boards, I had not encountered any issues while uploading and running my model on FireBeetle ESP32. As labels, I utilized the empirically assigned air pollution levels in accordance with the Air Quality Index (AQI) estimations provided by IQAir:








After training and testing my neural network model, I deployed and uploaded the model on FireBeetle ESP32 as an Arduino library. Therefore, the air station is capable of detecting air pollution levels by running the model independently without any additional procedures or latency.


Since I focused on building a full-fledged AIoT air station predicting air pollution and informing the user of the model detection results with surveillance footage, I decided to develop a web application from scratch to obtain the detection results with surveillance footage from FireBeetle ESP32 via HTTP POST requests, save the received information to a MySQL database table, and display the stored air quality data with model detection results in descending order simultaneously.


Due to the fact that the FireBeetle media board can only generate raw image data, this complementing web application executes a Python script to convert the obtained raw image data to a JPG file automatically before saving it to the server as surveillance footage. After saving the converted image successfully, the web application shows the most recently obtained surveillance footage consecutively and allows the user to inspect previous surveillance footage in descending order.


Lastly, to make the device as robust and compact as possible while operating outdoors, I designed a metallic air station case with a sliding front cover and a mountable camera holder (3D printable) for the OV7725 camera connected to the FireBeetle media board.


So, this is my project in a nutshell 😃


Click here to inspect code files, STL files, and instructions.











All Rights