The recent studies report that the short-term fluctuations of air pollution levels are directly related to the hospital admissions of patients with pneumonia and bronchitis. In addition, the long-term exposure to air pollution causes significant health problems, including cardiovascular disease, lung cancer and respiratory disease such as emphysema. At the same time, a huge amount of air quality data is collected by public air quality monitoring systems in different parts of the world. Official reporting from government is one of reliable sources of data. The European Environment Agency for Europe’s Air Quality and Clean Air Asia databases, the Global Burden of Disease epidemiological study, and peer-reviewed journal articles are other sources. Multidimensional visualization of such big amounts of data, including temporal granularities and spatial distribution, is a challenging question. In order to address this challenge, the paper proposes a software solution for visual analytics of air quality data using the potential of Big Data technologies. Its architecture, implementation with Elasticsearch and Kibana, and actual results from data visualization are presented. The findings of the paper show that the proposed solution provides more intuitive perception and valuable insight through multi-perspective air pollution graphs.