@article{1289,
  keywords = {Air quality, Environmental Protection Agency, Forecasting, Landforms, Mean square error, Monte Carlo methods, Particles (particulate matter), Ambient particulate Matter, Annual average concentration, Chemical transport models, data assimilation, Matter flows, Particulate Matter, Population exposure, World Health Organization, Population statistics, Air quality, ambient air, atmospheric dynamics, Atmospheric modeling, atmospheric pollution, atmospheric transport, chemical analysis, concentration (composition), data assimilation, Particulate Matter, pollution exposure, spatiotemporal analysis, World Health Organization, Italy, PO Valley},
  author = {E. Chianese and A. Galletti and G. Giunta and T.C. Landi and L. Marcellino and R. Montella and A. Riccio},
  title = {Spatiotemporally resolved ambient particulate matter concentration by fusing observational data and ensemble chemical transport model simulations},
  year = {2018},
  journal = {Ecological Modelling},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050980380&doi=10.1016%2fj.ecolmodel.2018.07.019&partnerID=40&md5=990bb6ad2a993fc7b486ea288c234aa5},
  doi = {10.1016/j.ecolmodel.2018.07.019},
}