Using statistical methods to carry out in field calibrations of low cost air quality sensors

The poor air quality found in big cities is harmful to human health. The aim of the LIFE PHOTOSCALING Project (LPP) is to assess the effectiveness of different photocatalytic pavements in reducing NO2 pollution. The objective of this preliminary study is to determine how well low cost AQmesh sensors can accurately enough measure NO2 concentrations to be able to determine the effects of using photocatalytic pavements. Data was collected from AQmesh sensors that were installed in two Air Quality Stations in order to monitor NO2 under traffic and urban background conditions. The NO2 measurements were unreliable, resulting in an unsatisfactory level of accuracy. A two-step calibration method was devised in order to overcome this limitation. This method consisted of supervised statistical machine learning regression algorithms. A first Multivariate Linear Regression provided a new explanatory variable that contained valuable information about the error. This variable was fed into more sophisticated equations, such as Random Forests, Support Vector Machines and Artificial Neural Networks. The various models were evaluated by calculating statistics of errors and relative expanded uncertainties, and through Taylor Diagrams. After a careful calibration, AQmesh sensors met the Air Quality Directive's standards of accuracy at high concentrations of NO2. However, we found that each individual sensor behaves differently and thus, each unit requires the development and application of a specific calibration model.