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International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 05 | May 2022

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e-ISSN: 2395-0056 p-ISSN: 2395-0072

Air Quality Visualization Ankita Mayekar1, Tejaswari Bhamare2, Rutuja Cheke3, Swati Narwane4 1,2,3BE

student, Information Technology, Datta Meghe College of Engineering, Navi Mumbai, India Professor, Information Technology, Datta Meghe College of Engineering, Navi Mumbai, India ---------------------------------------------------------------------***--------------------------------------------------------------------4Asst.

Abstract - With the advances of industry, air pollution is

observed in different studies include decreased pulmonary function, asthma attacks, development of respiratory diseases and premature death. Protecting the atmospheric environment, including air quality management, intervention policies, health impact and risk evaluation, as well as air pollution modeling would be impossible without quantitative description of air quality with measurable quantities.

increasingly becoming serious, and most governments in the world have deployed many devices to monitor daily air quality. Predicting and visualizing air quality has also become an important issue to improve the quality of people's lives. As far as we know, bad air quality does not only affect the health of the respiratory tract, it may also even cause mental illness. Many researchers have investigated different approaches to work on air quality forecasts, and the visualization becomes important. In this project, we present an architecture for visualizing air quality. Data will be collected, analyzed, and preprocessed of collected data. We’ll use an API, and finally we use the browser to get the data by predefined API and to present the visualization results with chart.js. It reveals that the visualization of the framework can work well for air quality analysis.

The aim of the air quality visualization is to keep the ambient air clean enough so that it is safe for the public health and the environment. In order to assess the status of the air, current air quality must be monitored. Public awareness of air pollution can help reduce both emission levels and exposure. In addition, scientists, policymakers, and regional and national planners need information on air quality to make informed decisions. Air quality monitoring provides a necessary scientific basis for developing policies, setting objectives and planning 10 enforcement actions. Despite the importance of measurements, in many cases, visualizing one may be insufficient for the purpose of fully defining population exposure in the environment. Therefore, monitoring should often be combined with other objective evaluation techniques, including modeling, customization and visualization of measurements. Traditionally air quality visualization is based on air quality stations operated by national environmental protection agencies. These stations provide very precise measurements; however, the coverage zone is limited. As a result, many new approaches are emerging to offer high-resolution air quality visualization.

Key Words: Air quality visualization, Air Quality Index, Machine learning, ReactJS, real-time, python.

1. INTRODUCTION Air pollution is a rapidly evolving concern in the past decades with the increase of pollution sources worldwide. According to the CPCB pollutants are released to the air from a wide range of sources including transport, agriculture, industry, waste management and households. Rapid urbanization and industrial growth exacerbate the problem and the pressure is felt severely in big cities. However, air pollution does not respect borders. Atmospheric pollutants and heavy metals are carried by the wind, polluting water and soil away from their source. Therefore, air pollution is a problem of industrial regions and a global burden which affects overall parts of society.

1.1 Objective and Purpose The main objective of this project is to implement a supervised algorithm for prediction of air quality index automatic and further classifying AQI into healthy, moderate and hazardous. Performing analysis of data to determine air quality index and then representing it in a graphical representation. The aim is to analyze the data and visualize the quality of air in the particular area. However, before performing visualization the information is exposed to different preprocessing procedures which finally give the desired optimized output. This will allow people to know the air quality in their area. This summarization also helps government organizations to work on areas where air quality is not good.

According to the World Health Organization (WHO), 92% of the population in our planet lives in the areas that exceed ambient air quality limits. In addition, the report states that air pollution is the largest environmental risk to health, being responsible for nine deaths per year. In addition, statistics show that external air pollution alone accounts for 3 million deaths a year. Depending on the duration of exposure to air pollution, type of pollutant and the toxicity level of the pollutant, it may cause different health issues to humans. The WHO presents air quality guidelines to explain in detail the health effects of various pollutants. It includes difficulty in breathing, nausea, skin irritation, cancer, etc. The most prevalent health effects

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