ABSTRACT
COVID-19 and its impact on society have raised concerns about scaling up mechanical ventilation (MV) systems and the energy consequences. This paper attempted to combine MV and portable air cleaners (PACs) to achieve acceptable indoor air quality (IAQ) and energy reduction in two scenarios: regular operation and mitigating the spread of respiratory infectious diseases (RIDs). We proposed a multi-objective optimization method that combined the NSGA-II and TOPSIS techniques to determine the total equivalent ventilation rate of the MV-PAC system in both scenarios. The concentrations of PM2.5 and CO2 were primary indicators for IAQ. The modified Wells-Riley equation was adopted to predict RID transmissions. An open office with an MV-PAC system was used to demonstrate the method's applicability. Meanwhile, a field study was conducted to validate the method and evaluate occupants' perceptions of the MV-PAC system. Results showed that optimal solutions of the combined system can be obtained based on various IAQ requirements, seasons, outdoor conditions, etc. For regular operation, PACs were generally prioritized to maintain IAQ while reducing energy consumption even when outdoor PM2.5 concentration was high. MV can remain constant or be reduced at low occupancies. In RID scenarios, it is possible to mitigate transmissions when the quanta were < 48 h-1. No significant difference was found in the subjective perception of the MV and PACs. Moreover, the effects of infiltration on the optimal solution can be substantial. Nonetheless, our results suggested that an MV-PAC system can replace the MV system for offices for daily use and RID mitigation. Electronic Supplementary Material ESM: The Appendix is available in the online version of this article at 10.1007/s12273-023-0999-z.
ABSTRACT
The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infections and better respond for community mitigation, by advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and up-to-date data generated from heterogeneous sources (e.g., disease related data, demographic, mobility and social media data), in this work, we propose and develop an AI-driven system (named α-Satellite), as an initial offering, to provide dynamic COVID-19 risk assessment in the United States. More specifically, given a point of interest (POI), the system will automatically provide risk indices associated with it in a hierarchical manner (e.g., state, county, POI) to enable people to select appropriate actions for protection while minimizing disruptions to daily life. To comprehensively evaluate our system for dynamic COVID-19 risk assessment, we first conduct a set of empirical studies; and then we validate it based on a real-world dataset consisting of 5,060 annotated POIs, which achieves the area of under curve (AUC) of 0.9202. As of June 18, 2020, α-Satellite has had 56,980 users. Based on the feedback from its large-scale users, we perform further analysis and have three key findings: i) people from more severe regions (i.e., with larger numbers of COVID-19 cases) have stronger interests using our system to assist with actionable information; ii) users are more concerned about their nearby areas in terms of COVID-19 risks; iii) the user feedback about their perceptions towards COVID-19 risks of their query POIs indicate the challenge of public concerns about the safety versus its negative effects on society and the economy. Our system and generated datasets have been made publicly accessible via our website.