Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides

Autor: Li, Lianfa, Khalili, Roxana, Lurmann, Frederick, Pavlovic, Nathan, Wu, Jun, Xu, Yan, Liu, Yisi, O'Sharkey, Karl, Ritz, Beate, Oman, Luke, Franklin, Meredith, Bastain, Theresa, Farzan, Shohreh F., Breton, Carrie, Habre, Rima

Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects. Machine learning (ML) methods have significantly enhanced our capacity to predict NOx concentrations at ground-level

Externí odkaz: http://arxiv.org/abs/2308.07441

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Using Birth Cohort Data to Estimate Prenatal Chemical Exposures for All Births around the New Bedford Harbor Superfund Site in Massachusetts.

Autor: Khalili, Roxana¹, Bartell, Scott M.^1,2,3,4, Levy, Jonathan I.^5,6, Fabian, M. Patricia^5,6, Korrick, Susan^6,7, Vieira, Verónica M.^1,2 Vvieira@uci.edu

Publikováno v: Environmental Health Perspectives. Aug2019, Vol. 127 Issue 8, p087008-1-087008-11. 11p. 6 Charts, 1 Graph, 1 Map.

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Prenatal and Early-Life Environmental Exposures and Asthma Risk among Children

Autor: Khalili, Roxana

Publikováno v: Khalili, Roxana. (2018). Prenatal and Early-Life Environmental Exposures and Asthma Risk among Children. UC Irvine: Environmental Health Sciences. Retrieved from: http://www.escholarship.org/uc/item/7q1629tc

Asthma is a chronic lung disease in which the airways become inflamed and produce increased mucus making air flow in and out of the lungs difficult. With the prevalence of asthma higher among children compared to adults, possible environmental exposu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______325::d4d3ca828da823ca77e6aee2047ebbd2
http://www.escholarship.org/uc/item/7q1629tc