{"accessLevel": "public", "bureauCode": ["020:00"], "contactPoint": {"fn": "Mohamed Hantush", "hasEmail": "mailto:hantush.mohamed@epa.gov"}, "description": "The dataset is lake dissolved oxygen concentrations obtained form plots published by Gelda et al. (1996) and lake reaeration model simulated values using Bayesian Monte Carlo methods (Chaudhary and Hantush, 2017). The data also includes measured (Gelda et al., 1996 and references therein) versus estimated liquid film transfer coefficient values (KL) by Chaudhary and Hantush (2017). \n\nThis dataset is associated with the following publication:\nChaudhary, A., and M. Hantush. Bayesian Monte Carlo and Maximum Likelihood Approach for Uncertainty Estimation and Risk Management:  Application to Lake Oxygen Recovery Model.  Mark van Loosdrecht  WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 108: 301-311, (2017).", "distribution": [{"downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1405325/ScienceHub_Lake%20reaeration%20paper_data.xlsx", "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", "title": "ScienceHub_Lake reaeration paper_data.xlsx"}], "identifier": "https://doi.org/10.23719/1405325", "keyword": ["Liquid film transfer coefficient", "Observed dissolved oxygen concentration", "Simulated dissolved oxygen concentration", "95% Confidence interval", "Bayesian statistics", "Monte Carlo method", "model calibration", "Uncertainty Estimation", "Markov Chain Monte Carlo", "Water Quality Management", "TMDL", "Margin of safety"], "license": "https://pasteur.epa.gov/license/sciencehub-license.html", "modified": "2016-06-29", "programCode": ["020:000"], "publisher": {"name": "U.S. EPA Office of Research and Development (ORD)", "subOrganizationOf": {"name": "U.S. Environmental Protection Agency", "subOrganizationOf": {"name": "U.S. Government"}}}, "references": ["https://doi.org/10.1016/j.watres.2016.11.012"], "rights": null, "title": "Digitized Onondaga Lake Dissolved Oxygen Concentrations and Model Simulated Values using Bayesian Monte Carlo Methods   "}