Analyzing Correlation Between Air and Noise Pollution with Influence on Air Quality Prediction

Air and noise pollution are two major factors that determine the quality of life of the people living in cities. The prime reasons for the rise of air and noise pollution are due to imbalanced urbanization, unregulated increase in traffic and inorganic industrialization. These have resulted in compromising the well-being of the citizens. In this context, the concept of smart cities has been developed. They inherently have the ability to sense and respond to the challenges which characterizes regular cities with the help of embedded intelligence. It has become important to monitor the environmental parameters for policy-making, planning and for making smart cities livable and sustainable. In a bid to make a smart city, in this work, we have studied the spatio-temporal relationship between air and noise pollution in four different locations and have also evaluated the effect of noise in predicting Air Quality(AQ). Data acquisition has been done using customized, self-developed & low-cost environment monitoring devices. These devices have an array of heterogeneous sensors that can sense the concentration of CO, CO2, NO2, PM2.5, humidity, temperature and intensity of noise. To determine the relationship between air and noise pollution, we have used Pearson correlation. Results show a strong association between the two types of pollution. For predicting the air quality, the impact of noise pollution as a feature has been investigated using three different machine learning models which are Decision Tree, Random Forest and K-Nearest Neighbors. When applicable, the results show that if noise pollution is used as a feature, we get a prediction accuracy of upto 95% which is an improvement of 5% on an average.