A reliable and easy-to-implement approach to estimate daily urban benzene levels

Assessing air benzene is mandatory in the European Union due to potentially harmful human effects. Current exposure data are lacking since there is a limited number of fixed stations. This research provides an easy approach to estimating daily urban benzene levels potentially applicable in other geographical areas. Madrid City was regarded as a case study. They were calculated mathematical models explaining the relationship between dependent and predictor inputs using an original 2019–2020 dataset (r from 0.881 to 0.903) and were tested using a 2021 dataset (r from 0.682 to 0.914). Similarly, the suggested mathematical expressions were satisfactorily implemented in another geographic region (r = 0.936 and 0.764 for urban traffic and rural background sites, respectively). Furthermore, the recommended approach helps identify the most representative fixed benzene measuring locations without lost spatial information, given that a similar spatial distribution gradient was sustained between estimated and the most representative benzene levels (r = 0.959), which is translated into acceptable quantitative differences (0.07 μg/m3, 0.01 μg/m3 and 5.01 % for RMSE, MAE, and MAPE, respectively). Applying the proposed approach furnishes enriched benzene exposure data, thus decreasing uncertainty generated by the lack of actual information and complements European Legislation directrices concerning air pollutants monitoring using air quality networks. [Display omitted] •Mathematical equations elucidated the relationship between C6H6 and NO2, O3.•Improved approach for predicting daily urban benzene levels within AQN was validated.•Additional data on benzene levels in poorly monitored areas within AQN were provided.•Representativeness of fixed benzene monitoring sites within AQM was assessed.