Data Visualization for Air Quality Analysis on Bigdata Platform

With the advances of industry, air pollution is increasingly becoming serious, and most of governments in the world has deployed many devices to monitor daily air quality. Monitoring and forecasting of 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 but also even cause mental illness. Many researchers have investigated different approaches to work on air quality forecast, and the visualization of forecasting becomes important. In this paper, we present an architecture for visualizing forecasted air quality on a big data platform. We implemented an ETL (Extract-Transform-Load) based framework in the platform, which includes computing nodes and storage nodes. Computational nodes are used for data collection and for air quality forecasting over the next 1 to 8 hours through machine learning and deep learning. Storage nodes are used to retrieve, analyze, and preprocess of collected data. We use the RESTful Web Service as an API, and finally we use the browser to get the data by predefined API and to present the forecasted and monitored results with Google Map API and D3 JavaScript library. It reveals that the visualization on big data framework can work well for air quality analysis.