@article{libera_sankarasubramanian_sharma_reich_2018, title={A non-parametric bootstrapping framework embedded in a toolkit for assessing water quality model performance}, volume={107}, ISSN={1364-8152}, url={http://dx.doi.org/10.1016/j.envsoft.2018.05.013}, DOI={10.1016/j.envsoft.2018.05.013}, abstractNote={Assessing the ability to predict nutrient concentration in streams is important for determining compliance with the Numeric Nutrient Water Quality Criteria for Nitrogen in the U.S.A. Evaluation of the USGS's Load Estimator (LOADEST) and the Weighted Regression on Time, Discharge, and Season (WRTDS) models in predicting total nitrogen loads over 18 stations from the Water Quality Network show good performance (Nash-Sutcliffe Efficiency (NSE) > 0.8) in capturing the observed variability even for stations with limited data. However, both models captured only 40% of observed variance in total nitrogen (TN) concentration (NSE < 0.4). Thus, the same dataset performed differently in predicting two attributes – TN load and concentration – questioning the predictive skill of the models. This study proposes a non-parametric re-sampling approach for assessing the performance of water quality models particularly in predicting TN concentration. Null distributions for three common performance metrics belonging to populations of metrics with no skill in capturing the observed variability are constructed through a bootstrap resampling technique. Sample metrics from the LOADEST and WRTDS model in predicting TN concentration are used to calculate p-values for determining if the sample metrics belongs to the null distributions. .}, journal={Environmental Modelling & Software}, publisher={Elsevier BV}, author={Libera, Dominic A. and Sankarasubramanian, A. and Sharma, Ashish and Reich, Brian J.}, year={2018}, month={Sep}, pages={25–33} }
 @article{libera_sankarasubramanian_2018, title={Multivariate bias corrections of mechanistic water quality model predictions}, volume={564}, ISSN={0022-1694}, url={http://dx.doi.org/10.1016/j.jhydrol.2018.07.043}, DOI={10.1016/j.jhydrol.2018.07.043}, abstractNote={Water quality networks usually do not include observations on a continuous timescale over a long period. Statistical models that use streamflow and mechanistic models that use meteorological information and land-use are commonly employed to develop continuous streamflow and nutrient records. Given the availability of long meteorological records, mechanistic models have the potential to develop continuous water quality records, but such predictions suffer from systematic biases on both streamflow and water quality constituents. This study proposes a multivariate bias correction technique based on canonical correlation analysis (CCA) – a dimension reduction technique based on multivariate multiple regression – that reduces the bias in both streamflow and loadings simultaneously by preserving the cross-correlation. We compare the performance of CCA with linear regression (LR) in removing the systematic bias from the SWAT model forced with precipitation and temperature for three selected watersheds from the Southeastern US. First, we compare the performance of CCA with LR in removing the bias in SWAT model outputs in predicting the observed streamflow and total nitrogen (TN) loadings from the Water Quality Network (WQN) dataset. We also evaluate the potential of CCA in removing the bias in SWAT model predictions at daily and monthly time scales by considering the LOADEST model predicted loadings as the predictand for CCA and LR. Evaluation of CCA with the observed dataset and at daily and streamflow time scales shows that the proposed multivariate technique not only reduces the bias in the cross-correlation between streamflow and loadings, but also improves the joint probability of estimating observed streamflow and loadings. Potential implications of the proposed bias-correction technique, CCA, in water quality forecasting and management are also discussed.}, journal={Journal of Hydrology}, publisher={Elsevier BV}, author={Libera, Dominic A. and Sankarasubramanian, A.}, year={2018}, month={Sep}, pages={529–541} }
 @article{tung-thompson_libera_koch_de los reyes_jaykus_2015, title={Aerosolization of a Human Norovirus Surrogate, Bacteriophage MS2, during Simulated Vomiting}, volume={10}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0134277}, DOI={10.1371/journal.pone.0134277}, abstractNote={Human noroviruses (NoV) are the leading cause of acute gastroenteritis worldwide. Epidemiological studies of outbreaks have suggested that vomiting facilitates transmission of human NoV, but there have been no laboratory-based studies characterizing the degree of NoV release during a vomiting event. The purpose of this work was to demonstrate that virus aerosolization occurs in a simulated vomiting event, and to estimate the amount of virus that is released in those aerosols. A simulated vomiting device was constructed at one-quarter scale of the human body following similitude principles. Simulated vomitus matrices at low (6.24 mPa*s) and high (177.5 mPa*s) viscosities were inoculated with low (108 PFU/mL) and high (1010 PFU/mL) concentrations of bacteriophage MS2 and placed in the artificial “stomach” of the device, which was then subjected to scaled physiologically relevant pressures associated with vomiting. Bio aerosols were captured using an SKC Biosampler. In low viscosity artificial vomitus, there were notable differences between recovered aerosolized MS2 as a function of pressure (i.e., greater aerosolization with increased pressure), although this was not always statistically significant. This relationship disappeared when using high viscosity simulated vomitus. The amount of MS2 aerosolized as a percent of total virus “vomited” ranged from 7.2 x 10-5 to 2.67 x 10-2 (which corresponded to a range of 36 to 13,350 PFU total). To our knowledge, this is the first study to document and measure aerosolization of a NoV surrogate in a similitude-based physical model. This has implications for better understanding the transmission dynamics of human NoV and for risk modeling purposes, both of which can help in designing effective infection control measures.}, number={8}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Tung-Thompson, Grace and Libera, Dominic A. and Koch, Kenneth L. and de los Reyes, Francis L. and Jaykus, Lee-Ann}, editor={Bauch, Chris T.Editor}, year={2015}, month={Aug}, pages={e0134277} }