Nitrate source apportionment in a subtropical watershed using Bayesian model

Abstract

Nitrate (NO3-) pollution in aquatic system is a worldwide problem. The temporal distribution pattern and sources of nitrate are of great concern for water quality. The nitrogen (N) cycling processes in a subtropical watershed located in Changxing County, Zhejiang Province, China were greatly influenced by the temporal variations of precipitation and temperature during the study period (September 2011 to July 2012). The highest NO3- concentration in water was in May (wet season, mean±SD=17.45±9.50 mg L(-1)) and the lowest concentration occurred in December (dry season, mean±SD=10.54±6.28 mg L(-1)). Nevertheless, no water sample in the study area exceeds the WHO drinking water limit of 50 mg L(-1) NO3-. Four sources of NO3(-) (atmospheric deposition, AD; soil N, SN; synthetic fertilizer, SF; manure & sewage, M&S) were identified using both hydrochemical characteristics [Cl-, NO3-, HCO3-, SO42-, Ca2+, K+, Mg2+, Na+, dissolved oxygen (DO)] and dual isotope approach (δ15N-NO3- and δ(18)O-NO3-). Both chemical and isotopic characteristics indicated that denitrification was not the main N cycling process in the study area. Using a Bayesian model (stable isotope analysis in R, SIAR), the contribution of each source was apportioned. Source apportionment results showed that source contributions differed significantly between the dry and wet season, AD and M&S contributed more in December than in May. In contrast, SN and SF contributed more NO3- to water in May than that in December. M&S and SF were the major contributors in December and May, respectively. Moreover, the shortcomings and uncertainties of SIAR were discussed to provide implications for future works. With the assessment of temporal variation and sources of NO3-, better agricultural management practices and sewage disposal programs can be implemented to sustain water quality in subtropical watersheds.

Keywords: Nitrogen isotope; Oxygen isotope; Source apportionment; Stable isotope analysis in R (SIAR); Temporal variation.