Mapping urban air quality using mobile and fixed low cost sensors: a model comparison

Abstract

This study addresses the critical challenge of modeling and mapping urban air quality to ascertain pollutant concentrations in unmonitored locations. The advent of low-cost sensors, particularly those deployed in vehicular networks, presents novel datasets that hold the potential to enhance air quality modeling. This research conducts a comprehensive review of ten statistical models drawn from existing literature, using both fixed and mobile low-cost sensor data, alongside ancillary variables, within the urban confines of Nantes, France. Employing a methodology that includes cross-validation of data from low-cost sensors and validation on fixed air quality monitoring stations, this paper evaluates the models' performance in scenarios of temporal interpolation and prediction. Our findings reveal a pronounced bias in the model outputs when reliant on low-cost sensor data compared to the verification data obtained from fixed stations. Furthermore, machine learning models demonstrated superior performance in predictive scenarios, suggesting their enhanced suitability for forecasting tasks. The study conclusively indicates that reliance solely on data from low-cost mobile sensors compromises the reliability of air quality models, due to significant accuracy deficiencies. Consequently, we advocate for a directed focus towards the integration and calibration of low-cost sensor data with information from fixed monitoring stations. This approach, rather than an exclusive emphasis on the complexity of statistical modeling techniques, is pivotal for achieving the precision required for effective air quality management and policy-making.