Inclusion Reciprocation Cocreation Citizen Science Collaboration Communication and engagement Problem formulation Dissemination of results Debate over key issues Contribution to community Contribution to science Research highlights A multi-component citizen science approach is designed for community engagement. Our approach revolves around the ideas of inclusion, collaboration and reciprocation. Behavioural changes can be promoted using tools like interactive air quality quiz. Citizen science workshops can result in establishing community-led air sensing schemes. Citizen scientists permit detection of events with wider spatial and temporal coverage.

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Air pollution is a global problem and can be perceived as a modern-day curse. One way of dealing with it is by finding economical ways to monitor and forecast air quality. Accurately monitoring and forecasting fine particulate matter (PM2.5) concentrations is a challenging prediction task but Internet of Things (IoT) can help in developing economical and agile ways to design such systems. In this paper, we use a historical data-based approach to perform PM2.5 forecasting. A forecasting method is developed which uses exponential smoothing with drift. Experiments and evaluation were performed using the real-time PM2.5 data obtained from large scale deployment of IoT devices in Taichung region in Taiwan. We used the data from 132 monitoring stations to evaluate our model’s performance. A comparison of prediction accuracy and computation time between the proposed model and three widely used forecasting models was done. The results suggest that our method can perform PM2.5 forecast for 132 monitoring stations with error as low as 0.16 μg/normalm3 and also with an acceptable computation time of 30 s. Further evaluation was done by forecasting PM2.5 for next 3 h. The results show that 90 % of the monitoring stations have error under 1.5 μg/normalm3 which is significantly low.