In-Situ, Real-Time and High Performance Optical Analyzer for Low Cost IAQ Diagnoses

With better insulation of buildings, indoor air pollution is becoming a growing concern for human well-being and human health. Today there is no easy way to determine in real-time indoor air quality because of two main reasons. First, there are thousands of pollutants in indoor air with diverse impacts on human comfort, health and safety. Second, the concentration of these pollutants are generally very low (few ppb). In order to monitor Indoor Air Quality, one can use relatively low cost sensors that give an estimation of the amount of pollution in situ and in real-time but without discriminating harmful pollutants from non-harmful pollutants. It is thus difficult to give accurate advice to reduce the level of indoor pollution. Another way is to sample air and analyze the samples in laboratories with very sensitive but also expensive devices. The last case presents some drawbacks; it takes time, both for sampling (1 hour up to 5 days) and for analyzing (~2 weeks) before knowing if there is any indoor air problem. Then, if there is one, more air samples have to be taken and analyzed in order to identify and locate the pollution emission source and begin to look for a solution to prevent pollution. It is costly. French government estimated indoor air diagnoses cost at around 3,000 euros per school building; It requires a specialist operator to identify the pollution origin. Here, we propose a new way to perform fast, low-cost indoor air quality diagnoses at high added value in buildings by using an innovative photo-acoustic analyzer. Thanks to its ability to measure: a) multiple individual chemical pollutants, b) in real-time c) and in-situ at ppb level. Indoor air diagnosis results can be obtained in few minutes only. In addition the emission sources of air pollution can be easily located. Thanks to the fact that this tool can be used by a non specialist and diagnosis results given in less than an hour, the diagnosis price can be divided by two compared to traditional indoor air diagnoses. The idea is also to save in a cloud contextual collected data during diagnoses (concentration of each monitored pollutant associated with the indoor environment) so that predictions (and some remediation solutions) of indoor air pollution can be made in similar environments.