Optimal measurement strategy for air quality combining official and low-cost measurements

Air pollution affects the health of people and therefore monitoring of the air quality is important both for the public and policy makers. Efficient monitoring of air quality requires a combination of measurements and modelling. Both current and annual average concentrations as well as future concentrations on all locations where people live are required. This information on exposure to pollutants can only be achieved at high spatial resolution at all locations by using air-quality models. Therefore, model calibration is a major objective in air quality measurement strategies. Measurement results of reference instruments (or equivalent) as defined in the EU air quality directive offer a high-quality basis for model calibration and validation. Over the last years, low-cost sensors/samplers have shown a rapid development and promising results. In this paper, a statistical framework is presented to evaluate measurement strategies that apply a combination of reference measurement instruments and low-cost measurements, like diffusion tubes and sensors. For some practical situations the introduction of sensors at only twenty locations gives a significant improvement of the calibration of an air quality model. The calibration of the low-cost measurements themselves with respect to the reference instruments is critical for any application. This calibration largely determines the model quality improvement due to the addition of low-cost measurements. The results shown in this paper can be used to optimize measurement strategy using low-cost measurements and/or sensors with established performance characteristics. The results can also be used to define the quality of the low-cost measurements that is required for useful applications. Using low-cost measurements can improve the quality of the calibrated model, even with a simultaneous reduction of the number of reference instruments. I.e., improved quality of information, at reduced costs. • Optimizing the combined use of model results, reference - and low-cost measurements. • Combination of citizen science and operational monitoring of air quality. • Minimum uncertainty of model after bias removal.