Air pollution is a significant problem in densely cities with the higher rate of the population as well as in the developing countries The concentration of particles in the atmosphere is notably affected by air pollution, which arises when harmful gases, dust particles, and smoke are released into the air. This contamination makes it challenging for plants, animals, and humans to thrive in the polluted environment [1]. Additionally, atmospheric pollutants play a role in causing environmental issues like acid rain and ozone layer depletion [2]. The World Health Organization (WHO) surveyed about the mortality rate all over the world which estimated around 1.4 billion urban residents are living in areas with Air pollutant levels above air quality index standards and examined that it kills approximately 7 million. Pollutants such as PM10, PM2.5 having 10 & 2.5 micrometers or less diameter are being analyzed as it is hazardous to health [3].
Nowadays, air pollution is to be monitored by static air quality measurement stations as well as dynamic stations to collect the data of air pollution from steady state as well as non-steady state stations that are operated by social authorities [4]. These stations are commendable for their ability to accurately and precisely measure air pollutants using advanced analytical instruments like mass spectrometer. However, the significant expenses involved in purchasing and operating such stations restrict the installation numbers [5]. Low cost sensors are easy to deploy, that is the reason why the maintenance cost becomes easy to handle. So we deployed the low cost sensors within the campus to analyze the trend of the air pollutants around the campus [6]. Wireless sensor net- works (WSNs) are specifically important as it can be designed by relatively low cost and small sensors with low power consumption or depends on the service cost whose ability is to transmit data values remotely to centralized server or various servers that needs data for data analysis and allowing their deployment or implementation at a large variety of locations [7]. These networks are used to sense physical or environmental conditions like pressure, temperature, humidity including particulate matters and cooperatively pass 2 data through the network to a main location which is server which communicates directly through MySQL database or via cloud [8].This paper is revolving around the different problem statements in which it predicts the temporal as well as spatial data visualization [9].
There are different aspects of analyzing air quality that are as follows:
(1) To study the correlation between 2.5 & 10 PM values w.r.t various parameters i.e. weather parameters.
(2) To identify the effect of various environmental factors on air pollution level (Temperature, Humidity & weather parameters like wind speed, wind direction etc.).
(3) To identify the effect of change of height on air pollution levels.
(4) To identify various trends throughout the day as well for whole week.
(5) Predictive analysis (Using Regression model).
(6) Summer & winter season pollutant level.
Also, Increasing global populations are interrelated to the escalation of air pollution worldwide [10]. The utilization of technology to monitor and control air pollution in urban areas is essential for effectively addressing the issue. Consequently, numerous researchers worldwide have devoted their efforts to studying this matter [11]. According to the report, it was hypothesized that previous exposure to air pollution might heighten susceptibility to viruses like the coronavirus, potentially leading to an aggravation in viral transmission within the population [12]. In smart cities, the integration of sensors, information, and communication technology enables more effective management of city resources [13]. The early prediction of these type of environmental pollutions is mandatory to preserve our environment from harmful effects [14]. The Fig. 1, 2 mentions the wireless sensors and café coffee day sensors.
PMS & BME Sensors (Low Cost):