14. Research for Big data - remote sensing applications in building AQI 24h air quality map of Hanoi

Abstract

Air pollution is directly related to dust and harmful chemical components in the form of gases in the air such as CO, SO2, NO2, CH4, etc. Air quality monitoring is currently being carried out by various methods such as direct observation via measuring stations, indirect monitoring (via satellite data, UAV, etc). However, each method of air quality monitoring has its advantages and disadvantages: Direct monitoring methods are often high reliable, but the display is only local, representing a small area; While indirect methods such as using remote sensing data have the advantage of large coverage, showing relatively clear trends in spatial distribution of air quality, but it has the limitation of the frequency of monitoring and reliability. The goal of this paper is to develop a map of the AQI 24h air quality index by combining direct and indirect monitoring data to generating a new technical solution with many
advantages than technical separates. However, this combination is currently facing difficulties in data processing, especially the spatial and temporal synchronization of the two data sources. To achieve this goal, we have developed a solution to handle Big data - remote sensing in monitoring some gas components in the air; On that basis, develop algorithms and tools on Google Earth Engine platform to build AQI 24h index map of Hanoi. The results show that the Big data - remote sensing application solution has provided new technical solution that can monitor air quality on a large area, with near-real-time frequency and ensure reliability for monitoring at the city scale