Re-inventing the Nation's Urban Water Infrastructure (ReNUWIt), National Science Foundation Engineering Research Center, Stanford, California 94305-4020, United States.
Department of Civil & Environmental Engineering, Stanford University , Stanford, California 94305-4020, United States.
Environ Sci Technol. 2017 May 2;51(9):5156-5164. doi: 10.1021/acs.est.7b01025. Epub 2017 Apr 13.
Woodchip bioreactors (WBRs) are increasingly being applied to remove nitrate from runoff. In this study, replicate columns with aged woodchips were subjected to a range of measured flow rates and influent nitrate concentrations with an artificial stormwater matrix. Dissolved oxygen (DO), nitrate, and dissolved organic carbon (DOC) were measured along the length of the columns. A multispecies reactive transport model with Michaelis-Menten kinetics was developed to explain the concentration profiles of DO, nitrate, and DOC. Four additional models were developed based on simplifying assumptions, and all five models were tested for their ability to predict nitrate concentrations in the experimental columns. Global sensitivity analysis and constrained optimization determined the set of parameters that minimized the root-mean-squared error (RMSE) between the model and the experimental data. A k-fold validation test revealed no statistical difference in RMSE for predicting nitrate concentrations between a zero-order model and the other multispecies reactive transport models tested. Additionally, the multispecies reactive transport models demonstrated no significant differences in predicting DO and DOC concentrations. These results suggest that denitrification in an aged woodchip bioreactor at constant temperature can effectively be modeled using zero-order kinetics when nitrate concentrations are >2 mg-N L. A multispecies model may be used if predicting DOC or DO concentrations is desired.
木屑生物反应器（WBR）越来越多地被应用于去除径流水中的硝酸盐。在这项研究中，使用人工雨水基质，用老化的木屑填充的重复柱经历了一系列测量的流速和入口硝酸盐浓度。在柱的长度上测量溶解氧（DO）、硝酸盐和溶解有机碳（DOC）。开发了一个具有米氏动力学的多物种反应传输模型来解释 DO、硝酸盐和 DOC 的浓度分布。基于简化假设，还开发了另外四个模型，并测试了所有五个模型预测实验柱中硝酸盐浓度的能力。全局敏感性分析和约束优化确定了一组参数，这些参数使模型和实验数据之间的均方根误差（RMSE）最小化。k 折验证测试表明，对于预测硝酸盐浓度，零级模型和测试的其他多物种反应传输模型之间的 RMSE 没有统计学差异。此外，多物种反应传输模型在预测 DO 和 DOC 浓度方面没有显著差异。这些结果表明，当硝酸盐浓度>2mg-N L 时，在恒定温度下老化的木屑生物反应器中的反硝化可以有效地使用零级动力学进行建模。如果需要预测 DOC 或 DO 浓度，则可以使用多物种模型。
