COVID-19 Secure and Clean Indoor Air Environment for Schools (COVID-SIS)

As children across the UK are preparing to return to schools (i.e. where they spend the majority of their time after their home), it is more important than ever to ensure that children's exposure to harmful pollutants is minimised. For instance, outdoor air pollutants from vehicle emissions (e.g. PM2.5, NOx) can enter classrooms through open doors and windows during peak times. Similarly, harmful air pollutants (e.g. Aerosols, CO2) which are known to be detrimental to children's health are also being generated inside classrooms. In particular, a growing body of epidemiological evidence (e.g. Wang & Du, 2020; Huffman et al., 2020) suggests that aerosols (i.e. chemicals emitted by building materials, furnishings, electronics) can become active transporters of COVID19\. To ensure that schools have a Clear-Air learning environment and are COVID-secure, it is vital to monitor the presence of these harmful indoor air pollutants (e.g. Aerosols, PM, SO2, CO2, NOx). Even though schools have introduced several pandemic precautions (e.g. physical distancing, staggered mealtimes, regular cleaning), no school currently has a digital tool/platform that allows **school administrators** to monitor and take proactive actions on the presence of indoor air pollutants. It is on this premise that this solution will for the first-time combine state-of-the-art technologies such as IoT and AI to develop a **COVID-19 Secure and Clean Indoor Air Learning Environment for Schools (COVID-SIS)**. COVID-SIS will have three modules; they include: 1. **IoT-Multi Pollutant Sensing Module (IoT-PSM):** will leverage low-cost, off-the-shelf and state-of-the-art IoT sensors to monitor the real-time trace amounts of indoor air pollutants in schools. IoT-PSM will collect and transmit indoor air pollutants data from areas of interests to the cloud server for insightful analyses. 2. **AI-Multi Pollutant Analytics Module (AI-PAM):** will directly communicate with the data being streamed from IoT-PSM to facilitate advanced AI analytics of indoor air pollutants data. They include: * **(a) Descriptive-AI:** will provide in-depth information on the concentration of pollutants in areas of interests. * **(b) Diagnostic-AI:** will provide factors responsible for specific pollutant trends. * **(c) Predictive-AI:** will understand hidden trends in historical and current pollutant data and providing step-change day-ahead forecasting of pollutant concentration. * **(d) Prescriptive-AI:** will provide the proven best course of action to improve the health and safety of indoor air to school administrators. 3\. **Web Based-Multi Pollutant Visualisation Platform (Web-PVP):** will allow school administrators with little-or-no expert knowledge to quickly visualise the trace amounts and advanced AI analyses of indoor air pollutants in areas of interest.