| Summary: | Effectively monitoring urban air quality, and analyzing the source terms of the main atmospheric pollutants is important for public authorities to take air quality management actions. Previous works, such as long-term observations by monitoring stations, cannot provide customized data services and in-time emergency response under urgent situations (gas leakage incidents). Therefore, we first review the up-to-date approaches (often machine learning and optimization methods) with respect to urban air quality monitoring and hazardous gas source analysis. To bridge the gap between present solutions and practical requirements, we design a conceptual framework, namely MAsmed (Multi-Agents for sensing, monitoring, estimating and determining), to provide fine-grained concentration maps, customized data services, and on-demand emergency management. In this framework, we leverage the hybrid design of wireless sensor networks (WSNs) and mobile crowdsensing (MCS) to sense urban air quality and relevant data (e.g. traffic data, meteorological data, etc.); Using the sensed data, we can create a fine-grained air quality map for the authorities and relevant stakeholders, and provide on-demand source term estimation and source searching methods to estimate, seek, and determine the sources, thereby aiding decision-makers in emergency response (e.g. for evacuation). In this paper, we also identify several potential opportunities for future research.
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