Deployment and Evaluation of a Network of Open Low-Cost Air Quality Sensor Systems

Low-cost air quality sensors have the potential to complement the regulatory network of air quality monitoring stations, with respect to increased spatial density of observations, however, their data quality continues to be of concern. Here we report on our experience with a small network of open low-cost sensor systems for air quality, which was deployed in the region of Stavanger, Norway, under Nordic winter conditions. The network consisted of AirSensEUR sensor systems, equipped with sensors for, among others, nitrogen dioxide and fine particulate matter. The systems were co-located at an air quality monitoring station, for a period of approximately six weeks. A subset of the systems was subsequently deployed at various roadside locations for half a year, and finally co-located at the same air quality monitoring station again, for a post-deployment evaluation. For fine particulate matter, the co-location results indicate a good inter-unit consistency, but poor average out-of-the-box performance (R-2 = 0.25, RMSE = 9.6 mu g m(-3)). While Kohler correction did not significantly improve the accuracy in our study, filtering for high relative humidity conditions improved the results (R-2 = 0.63, RMSE = 7.09 mu g m(-3)). For nitrogen dioxide, the inter-unit consistency was found to be excellent, and calibration models were developed which showed good performance during the testing period (on average R-2 = 0.98, RMSE = 5.73 mu g m(-3)), however, due to the short training period, the calibration models are likely not able to capture the full annual variability in environmental conditions. A post-deployment co-location showed, respectively, a slight and significant decrease in inter-sensor consistency for fine particulate matter and nitrogen dioxide. We further demonstrate, how observations from even such a small network can be exploited by assimilation in a high-resolution air quality model, thus adding value to both the observations and the model, and ultimately providing a more comprehensive perspective of air quality than is possible from either of the two input datasets alone. Our study provides valuable insights on the operation and performance of an open sensor system for air quality, particularly under challenging Nordic environmental conditions.