A city is "smart" if it uses different types of sensors and devices to collect data and provide information that is used to efficiently manage resources. The data can be processed and analyzed in order to monitor and manage traffic and transportation systems, waste recycling, water supply networks, air pollution, and other public services. The use of sensors would enable the collection of these data and their transfer in real time to the users. Based on the information provided online by a Swiss company called IQAir, it appears that Tirana has significant air pollution. Through a web application we could enable the possibility to receive and display data from the sensors and send them to a database so we can use them in different situations. In this paper we will show the use of the Internet of Things (IOT) to monitor pollution levels, especially levels in many positions in the Faculty of Natural Science where the number of students can be considered high. The aim of this paper has a correlation with people heath especially for this case study is students' health. It serves for more detailed studies or evaluate whose are the element causing the greatest air pollution and a prediction of what measures should be taken to prevent it. Creating a suitable environment to make this information to be detailed in real-time of course will affect other fields and aspects of researchers in the future.
Air pollution is the greatest
environmental threat to public health globally and
accounts for an estimated 7 million premature
deaths every year. In 2019, 99% of the world
population was living in places where the WHO’s
strictest 2021 air quality guideline levels were not
met. Air pollution is the presence of extra
unwanted biological molecules, particulates or
other harmful things into the earth's
atmosphere. It is a major cause of infections,
allergies, and eventually reasons of death to some
people [
The air problem is something that should
not be neglected but more thought should be given
to ensure a healthy future for the population. The
World Health Organization (WHO) estimated
that, in the year 2012, ambient air pollution was
responsible for nearly seven million deaths,
representing more than 10% of all-cause deaths
and more than doubling previous estimates
[
Advanced technological tools such as artificial
intelligence (AI), machine learning, blockchain
technology, IoT, and geographic information
systems are some of the powerful tools that help
humanity to effectively address climate goals and
have a better picture of the actual air quality
situation [
It is important to analyze the relationship
between student activities and PM2.5 in the
classroom to provide guidance for air quality
improvement. However, modeling and predicting
PM2.5 in classrooms remains a challenging
problem. Previous studies have mainly
concentrated on residential or uninhabited rooms
without large-scale crowd conditions and
analyzed issues such as temperature, humidity,
and outdoor PM2.5 concentrations. Limited
studies have focused on quantifying the effect of
student activities on PM2.5 in classrooms.[
Below, two graphs show the level of the PM2.5 parameter in outdoor environments near the Faculty of Natural Sciences. These results belong to three consecutive days of March 2023 respectively by hours and days. The data is obtained from the website IQair [5] via the published station with longitude 41° north and latitude 19° east at 150 m distance from the buildings of the Faculty of Natural Sciences. Previous studies have indicated that indoor PM 2.5 concentrations are strongly correlated with outdoor PM2.5 concentrations.
In this paper we will show the use of the Internet of Things (IOT) to monitor pollution levels, especially at different points inside the buildings accompanied by different volumes of students at the Faculty of Natural Science. The aim of this paper is directly related to the health of the students. It serves for more detailed studies of which elements causes the greatest air pollution and a prediction of what measures should be taken to prevent it. Also, we will show a comparison between IQ Air devices with Arduino which is the best for monitoring air quality. This demonstration creates the suitable environment for this information to be detailed and used in other fields and aspects of research in the future.
Air pollution is one of the main environmental problems in major Albanian cities, especially in Tirana. The pollution has come as a result of the increase in cars, the reduction of urban greenery, the burning of garbage, the economic activities of enterprises, the use of low-quality fuels, etc. The concentration of PM10 and NO2 particles in Tirana exceeds the national standards and those of the World Health Organization [6]. Air pollution has a direct impact on human health and has longterm consequences. Technology has an important role in shaping the world, countries, society, economy and also the environment. All the organizations are working on how to reduce pollution, and scientists are thinking about using technology to reduce pollution.
The methodology used is mainly based on the initial study of the environment where the measurements were made. The two important moments were the location of the points where the measurements would be made and their frequency. Subsequently, other helpful attributes were determined, for example, for the location of the building, the area, the floors, etc. and for the frequency, it includes the weather forecast, measurements at different times of the day, halls filled with students. All these analyzes are presented with the schematic coded below
Air quality index is a parameter calculated based on other values gathered during data collection. Our tools include USA AQI.
Air quality in the classroom is crucial to the student's health. In the beginning data were obtained by two sources. The first was the perception of the air quality by the students and the second was the concrete evaluation for air quality through estimating the level of CO2, humidity and temperature.
The methodology used helps clarify the correlation between human perception and real measurement.
Air quality perception data was collected through an online questionnaire fulfilled by students. The questionnaire included not only the students' opinion on the current air quality but also a general framework of their health situation. After analyzing the environment, the workflow continued with two parallel processes. 1. Assessment of air perception by students by completing an online questionnaire. 2. Concrete air quality measurement using two devices, one built-in Arduino tool and the other standardized device for measuring air quality
The built-in air quality Arduino tool used to measure air quality included the assessment of PM 2.5, PM 1.0, PM 10 and AQI (air quality index). The constituent elements part of this builtin tool is:
Grove - Laser PM2.5 Dust Sensor Arduino Compatible - HM3301 which is a sensor for measuring the level of PM 2.5 particles classified as dangerous particles for human health when they exceed the limit of normality.
Grove Base Shield V2.0 for Arduino serving as a base that connects Arduino Uno devices and other versions such as Arduino Leonardo and Mega. 4-pin connectors ensure simple and quick connections.
Seeeduino V4.2(ATMega328P) The Arduino board that comes with the development environment
Grove - Universal 4 Pin Buckled 20cm Cable Connections between Base Shield, Arduino and sensor 2.5
Lipo Rider Plus (Charger/Booster) 5V/2.4A USB Type C Connecting part between the board and the lithium battery. The following components and their relationship is presented through figure below
The environment where Arduino code is developed is divided into two important groups: • void Setup, • void Loop One serves for the initialization of the functionalities while the other for the execution of these functions depending on the situations. The results of code execution can be displayed in the Serial Monitor using the basic command for example:
Serial.println("Message").
The main part that helped display some test metrics is: aqi_pm = mapPMAQIValues(&pmReadings); Serial.println(aqi_pm);
Where the variable that is taken as a parameter pmReadings reads the information from the sensor, and by means of the mapping function mapPMAQIValues connects the value in mg of PM and converts it to AQI.
There are some libraries that need to be included at Arduino IDE as described by the picture below.
The second equipment used was a standardized one. AirVisual is a device that measures pollution to PM1.0 (μg/m3), PM2.5 (μg/m3), PM10 (μg/m3) particles and derives the AQI (Air Quality Index) result from them. Additionally, the device measures temperature (˚C), humidity (%) and air pressure (mbar). It is designed to be used for particle pollution up to 1,000 µg/m3. AirVisual performs these measurements through two already installed sensors.
Certificate used FCC, IC, UL, ROHS, CE. The AirVisual Outdoor is manufactured in IQAir’s state-of-the-art manufacturing location in Southern Germany. AirVisual provides three Internet connection options. Ethernet, Wi-Fi and USB 4G. Results collected by air visual device are displayed by the application installed on phones:
In the market there are so many air quality sensors with different features and functions. It is hard to pick which air quality sensor will fit the best. For this reason, we have done a comparison between the Arduino air quality built-in device and Air Visual Device that we bought. The IoT devices are divided into three execution types: • Processing time (the system is working) • Sending processing time (the system activated the GPS/GPRS sensor and sends data to the Edge). • Resting time (the system is in sleep mode, saving battery).
We must consider this specification when choosing the smart device.
• Operating Voltage • Operating Temperature • Operating Humidity • Particle Size 3 channels
During the working process which includes the two devices, several changes were identified, starting from the moment of configuration until receiving the results. Those changes are summarized in the table below.
Based on the data collected from the questionnaire on the perception of air quality and the concrete measured results performed by the air quality standardized devices, a direct correlation was noticed between them. It means that what is perceived by people for the quality of air is in the right way with the results of the equipment.
As a sum up the advantages and disadvantages for using smart devices to monitor air quality are summarized in the table below [8].
: Table 2
The real-time measurement results were included on a website created by us[9] , by which students will identify air pollution sources in the school environment, analyze air pollution data collected in real-time and think about ways to reduce exposure to air pollution on their journey to school and use them for further analysis and projects. Using the API key from the commercial device a JavaScript code was involved on www.airqualityfshn.info website.
In order to monitor the air quality in real- time and to have a history for each measurement that is made, we built a database whose main purpose was to feed our website with the data obtained from the devices. The database we used was MySQL and it has two tables (devices, air_cities). The table device saves all data that identifies in a unique way the air quality commercial equipment with their location. The table air_cities hold the necessary information sent to us by the air quality device with the elements that identify the air quality.
After creating the database, it was necessary to develop a script which receives the data automatically from the device and stores it on the database. The device has an API key (provided by the commercial). By sending a request to POSTMAN using API Key: 4a28fd8e-1d3d-4aeca0b4-8b98d416e532 we could get the necessary data to save on our database
The technology used to send the device request is the Php programming language. We build the AirQuanity class which sets the device's API key and the get methods for receiving data. API key sends the data showing the air quality in JSON format. With a function save(data) we read data from the smart device in real time and insert it into the database as soon as we have a new request from the API key, that is, when a new measurement is encountered. When the database is populated with real-time air quality data, we present this data on a website by using a Php script. The figure below shows the request to the Air Quality Database to read the air quality data and present them on the website. The general architecture used during these activities is presented in a schematic way below.
The results after executing the code are displayed according to the table below as a data collection.
The work process cannot be called complete, considering that the work consists of continuous measurements, and by means of them, the difference in the accuracy of the measurements using the two devices can be clearly distinguished and of course analyzed in a convenient way.
Normally, the addition of commercial and DIY Arduino devices in other points of the faculty will bring parallel evaluation of the measurements, including the effect of other external factors.
In the future, there will also be the construction of a platform with alpha numerical and geographical data to create an image as user friendly as possible for every user of the airqualityfshn.info website.
The main purpose of this paper is to convey the work done during the development of a project for air quality monitoring system by using smart devices. Retrieve data from IOT devices (air quality monitoring device and Air Visual monitor) handle for further analysis and correlation between air pollution factors and the impact it has on different fields, but also serves as a source for the development of an online application that apprises in real time air quality for the internal and external environments at the Faculty of Natural Sciences. An important role is played by the creation of an air quality device based on Arduino at an acceptable cost and which also empowers the management of the application created in C language. No comprehensive air quality legislation exists in Albania. The most commonly measured compounds for air pollution are: • Particulate matter (PM2.5-PM10) • • •
Volatile organic compounds (VOCs) Carbon dioxide (CO2)
Radon gas
The development of an online platform which currently presents some air quality elements, in a standardized format by IQAir, will be an important impetus to build an online real-time application using webservices.
This study shows measurements of carbon dioxide (CO2), particulate matter (PM2.5) and volatile organic compounds (VOCs) on indoor environment at Faculty of Natural Science and discusses the concentration levels of these parameters on indoor air quality. The data is made actionable by performing advanced analytics. The sensor-based systems are low-cost, compact, and easy to install compared to conventional analyzerbased systems, making them an ideal system for scalable monitoring. Levels of carbon dioxide and volatile organic compounds in some points we measure in faculty were not too polluted. There are some future improvements that could be made to this project including monitoring the air quality not only at faculty but also in other areas that are more popular and in we look forward to implementing cameras and robust ML analysis to both improve the obtained results and test the maximum number of allowed sensors
https://www.sciencedirect.com/science/artic le/abs/pii/S095965262102672X [5] IQAir, Air quality in Albanian, 2023. URL: https://www.iqair.com/albania [6] The Clean Air Act: Solving Air Pollution Problems with Science and Technology, 2023. URL:https://www.epa.gov/clean-airact-overview/clean-air-act-solving-airpollution-problems-science-and-technology [7] The James Dyson Foundation: Engineering solutions: Air Pollution-Teacher pack. Pg 48. 2021. URL: https://www.jamesdysonfoundation.com/res ources/engineering-soluytions-airpollution.html. [8] Cartieaux, E., Rzepka, M.A. and Cunny, D.
Indoor Air Quality in Schools. Archives de Pediatrie. Pg 791-792. 2011. [9] Air quality Faculty of Natural Science. http://airqualityfshn.info