CoAN: A system framework correlating the air and noise pollution sensor data

Although existing works in the literature highlight the monitoring, characterization, and analysis of both air and noise pollution, they mainly focus on the two environmental pollutants independently. In this paper, we develop a system framework that includes sensing and allows the processing of the combined impact of air and noise samples together to design micro-services. Few of the existing works that studied the combined effect of the two environmental stressors merely calculated the correlation values without further inferring contextual information from it. In contrast, our work aims to draw further inferences about the demographic/traffic/spatio-temporal aspect of a location and thus identifies the context in which the samples are collected. To achieve the goal, a system framework CoAN is developed under which we performed in-house data collection with approx. 820 km trail, covering approx. 10 km road segment in Durgapur, a sub-urban city in India. We used a commercially available ‘Flow’ device, and developed an android-based application, ‘AudREC’ for air and noise sampling, respectively. An unsupervised K-means algorithm has been used to segregate the combined samples into disjoint clusters for analysis. In addition, feature selection, model training, and cluster interpretation using the LIME model are performed to draw some inferences about the sample data space. Several supervised models, like Decision Tree, Random Forest, Logistic Regression, SVM, and Kernel-SVM are used for training the system. Results show that Logistic Regression performs best over others achieving 99% accuracy. Furthermore, as a micro-service, a healthier route recommendation system is designed to avoid pollution exposure by taking into account both air and noise pollution exposure volumes. A sample result shows that our recommended route gives almost 12% lesser pollution exposures as compared to all other available routes suggested by Google map with the same source and destination. [Display omitted] •Correlation between air and noise pollution sensor data.•Different machine learning model is used to identify the context of a location.•Healthier route recommendation system based on our derive context.