The deterioration in air quality is a challenging problem worldwide. There is a need to raise awareness among the people and support informed decision making. Over the years, citizen science activities have been implemented for environmental monitoring and raising awareness but most of such works are contributory in nature, i.e. task design, planning and analysis are performed by professional researchers and citizens act as participants. Our objective is to demonstrate that citizen science can be used as a ?tool? to enhance public understanding of air pollution by engaging communities and local stakeholders. We present a co-creation based citizen science approach which incorporates the ideas of inclusion, where citizens are involved in most of the steps of the scientific process; collaboration, where the citizen scientists define research problems and methodologies, and reciprocation, where citizen scientists share their observations through storytelling. We integrate the use of interactive air quality quizzes, offline questionnaires and low-cost air quality monitoring sensors. The results show that such methods can generate insightful data which can assist in understanding people?s perception and exposure levels at a fine-grained level. It was also observed that community engagement in air quality monitoring can enhance partnerships between the community and research fraternity.
Observation of air pollution at high spatio-temporal resolution has become easy with the emergence of low-cost sensors (LCS). LCS provide new opportunities to enhance existing air quality monitoring frameworks but there are always questions asked about the data accuracy and quality. In this study, we assess the performance of LCS against industry-grade instruments. We use linear regression (LR), artificial neural networks (ANN), support vector regression (SVR) and random forest (RF) regression for development of calibration models for LCS, which were Smart Citizen (SC) kits developed in iSCAPE project. Initially, outdoor colocation experiments are conducted where ten SC kits are collocated with GRIMM, which is an industry-grade instrument. Quality check on the LCS data is performed and the data is used to develop calibration models. Model evaluation is done by testing them on 9 SC kits. We observed that the SVR model outperformed other three models for PM2.5 with an average root mean square error of 3.39 and average R2 of 0.87. Model validation is performed by testing it for PM10 and SVR model shows similar results. The results indicate that SVR can be considered as a promising approach for LCS calibration.