Towards efficient digital governance of city air pollution using technique of big atmospheric environmental data

Abstract

The 11th goal of the United Nations Sustainable Development Goals (SDG) aims at making cities inclusive, safe, resilient and sustainable, to which the rising city air pollution poses a severe challenge. Achieving this goal necessitates governing city air quality at large scale. However, the lack of interconnection among air quality modelling, multi-source data, pollution diagnosis and control actions impedes scalable and efficient governance of city air pollution. Here we present a design of digital governance to make full use of science and information techniques, so that information and decisions can be well generated and processed to form scientific prevention and control against heavy city air pollution. We implement the desired interconnection based on open-architecture big atmospheric environmental data system, which supports digitizing the workflow and dataflow of governance practices. Through the network of digital governance, those cities with limited resources and capacities can henceforth access data and expertise, such as model-based air quality forecast and data-based mitigation actions, to realize regional joint prevention and control of heavy air pollution. With a demonstration case for the Wuhan city in China using the Nested Grid Air Quality Prediction Modelling System (NAQPMS) developed by the Institute of Atmospheric Physics of the Chinese Academy of Sciences, we show how our design of digital governance would empower scientific control of heavy city air pollution and help achieving the SDG goal 11.