Extending the spatial scale of land use regression models for ambient ultrafine particles using satellite images and deep convolutional neural networks.
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Pedro H. O. Pinheiro | Marianne Hatzopoulou | Scott Weichenthal | Laura Minet | S. Weichenthal | M. Hatzopoulou | L. Minet | Kris Y Hong | Pedro O Pinheiro | Kris Y. Hong
[1] Timothy Dozat,et al. Incorporating Nesterov Momentum into Adam , 2016 .
[2] Marianne Hatzopoulou,et al. Spatial variations in ambient ultrafine particle concentrations and the risk of incident prostate cancer: A case‐control study , 2017, Environmental research.
[3] R. Burnett,et al. Associations of Long-Term Exposure to Ultrafine Particles and Nitrogen Dioxide With Increased Incidence of Congestive Heart Failure and Acute Myocardial Infarction , 2018, American journal of epidemiology.
[4] Marianne Hatzopoulou,et al. A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach. , 2016, Environmental research.
[5] Hadley Wickham,et al. ggmap: Spatial Visualization with ggplot2 , 2013, R J..
[6] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[7] S. Hankey,et al. Using Mobile Monitoring to Develop Hourly Empirical Models of Particulate Air Pollution in a Rural Appalachian Community. , 2019, Environmental science & technology.
[8] R. Burnett,et al. Spatiotemporal Variations in Ambient Ultrafine Particles and the Incidence of Childhood Asthma. , 2019, American journal of respiratory and critical care medicine.
[9] Marianne Hatzopoulou,et al. Characterizing the spatial distribution of ambient ultrafine particles in Toronto, Canada: A land use regression model. , 2016, Environmental pollution.