Air Quality Index in India - R shiny dashboard

Project description :

Clean air is the basic amenity when it comes to healthy living for mankind. Today, inadequate air quality is one of the key causes for a variety of acute health problems. To determine its effect on our health, it is important to know the air quality of our locality, region, and country.

Problem Statement :

The project aims at analysing Air Quality Data for various cities in India over a time period of 2017-2019. The project analyses the same data in multiple ways using different statistical tools and visualization tools to derive insights from the changing trends over the years.

Dataset :

• finalDS.csv is the dataset we have used in the project. It is a pre-processed dataset of a dataset taken from Kaggle[link: https://www.kaggle.com/rohanrao/air-quality-data-in-india].
• pollutants table.csv is a dataset we have created that is used in the pollutant trends tab.

Work flow :

• We have used R shiny to develop a dashboard to explain our findings and insights. T
• The HOME tab introduces the topic and gives insight into the current affairs related to pollution .
• The MAP DISTRIBUTION tab shows the India map with the states we are analyzing and the AQI for that state, the distribution of various pollutants using bar graph was also presented in the same page.
• In the LINE GRAPH tab one can understand the trends/patterns of how pollutants change in different cities on a weekly basis.
• The POLLUTANT TRENDS tab showsthe correlation between the various pollutants and how they affect the value of AQI using heat map and correlation matrices. This tab also provides some suggestions for the people in those cities to have a better life against pollution.

Data Visualization tools used :

Leaflet, Corrplot, Hmisc, ggplot2, Performance Analytics

Result :

Through the graphs and statistical tools seen in the dashboard, we can see how AQI has changed over the course of years and which pollutant is the most significant contributor to a given city. In nearly all the 8 cities that we have analysed, the air quality is lower.The line graph illustrates how the data differs in various time frames. If we use a particular time frame, we will find the relation of the variation and correlate it with the incidence that produced it.

Conclusion :

Air pollution is one of the biggest challenges that our country is facing right now. It’s all the more important in our country where the common man is not quite familiar with the technical terminologies and measuring units (like ppm /ppb / or µg/mg3). Hence the AQI simplifies the understanding of their air quality by decoding the quality in terms of unitless numbers. By knowing which pollutant contributes more, that specific pollutant can be reduced from usage. By knowing the highest polluted city that city can take measures to avoid the air pollution in future.

Web Application of the project :

https://rishab-sarkar.shinyapps.io/AQI-India/