methodology

Chianese, E., Camastra, F., Ciaramella, A., Landi, T. C., Staiano, A., & Riccio, A. (2019). Spatio-temporal learning in predicting ambient particulate matter concentration by multi-layer perceptron. Ecological Informatics. http://doi.org/10.1016/j.ecoinf.2018.12.001
Conte, M., Donateo, A., & Contini, D. (2018). Characterisation of particle size distributions and corresponding size-segregated turbulent fluxes simultaneously with CO2 exchange in an urban area. Science Of The Total Environment. http://doi.org/10.1016/j.scitotenv.2017.12.040
Barnaba, F., Bolignano, A., Di Liberto, L., Morelli, M., Lucarelli, F., Nava, S., et al. (2017). Desert dust contribution to PM10 loads in Italy: Methods and recommendations addressing the relevant European Commission Guidelines in support to the Air Quality Directive 2008/50. Atmospheric Environment. http://doi.org/10.1016/j.atmosenv.2017.04.038
Barnaba, F., Bolignano, A., Di Liberto, L., Morelli, M., Lucarelli, F., Nava, S., et al. (2017). Desert dust contribution to PM10 loads in Italy: Methods and recommendations addressing the relevant European Commission Guidelines in support to the Air Quality Directive 2008/50. Atmospheric Environment. http://doi.org/10.1016/j.atmosenv.2017.04.038
Belis, C. A., Karagulian, F., Amato, F., Almeida, M., Artaxo, P., Beddows, D. C. S., et al. (2015). A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises. Atmospheric Environment. http://doi.org/10.1016/j.atmosenv.2015.10.068