title: Quantitative identification of riverine nitrogen from point, direct runoff and base flow sources.
abstract: We present a methodological example for quantifying the contributions of riverine total nitrogen (TN) from point, direct runoff and base flow sources by combining a recursive digital filter technique and statistical methods. First, we separated daily riverine flow into direct runoff and base flow using a recursive digital filter technique; then, a statistical model was established using daily simultaneous data for TN load, direct runoff rate, base flow rate, and temperature; and finally, the TN loading from direct runoff and base flow sources could be inversely estimated. As a case study, this approach was adopted to identify the TN source contributions in Changle River, eastern China. Results showed that, during 2005-2009, the total annual TN input to the river was 1,700.4±250.2 ton, and the contributions of point, direct runoff and base flow sources were 17.8±2.8%, 45.0±3.6%, and 37.2±3.9%, respectively. The innovation of the approach is that the nitrogen from direct runoff and base flow sources could be separately quantified. The approach is simple but detailed enough to take the major factors into account, providing an effective and reliable method for riverine nitrogen loading estimation and source apportionment.
authors.affiliation: Department of Natural Resources, College of Environment and Natural Resources, Zhejiang University, Hangzhou, China 310058 E-mail: jlu@zju.edu.cn.
authors.forename: Hong
authors.initials: H
authors.lastname: Huang
authors.affiliation: Department of Natural Resources, College of Environment and Natural Resources, Zhejiang University, Hangzhou, China 310058 E-mail: jlu@zju.edu.cn.
authors.forename: Baifa
authors.initials: B
authors.lastname: Zhang
authors.affiliation: Department of Natural Resources, College of Environment and Natural Resources, Zhejiang University, Hangzhou, China 310058 E-mail: jlu@zju.edu.cn; China Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, Zhejiang University, Hangzhou, China 310058.
authors.forename: Jun
authors.initials: J
authors.lastname: Lu
mesh_terms: D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D015233:Models, Statistical; D009584:Nitrogen / Q000032:analysis*; D045483:Rivers / Q000737:chemistry*; D014874:Water Pollutants, Chemical / Q000032:analysis*
chemical_list: D014874:Water Pollutants, Chemical; D009584:Nitrogen