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Tracking nitrogen sources, transformation and transport at a basin scale with complex plain river networks Qitao Yi, Qiuwen Chen, Liuming Hu, and Wenqing Shi Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 20 Apr 2017 Downloaded from http://pubs.acs.org on April 20, 2017
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Tracking nitrogen sources, transformation and transport at a basin scale with complex
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plain river networks
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Qitao Yi2,3, Qiuwen Chen1,2*, Liuming Hu1, Wenqing Shi1
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210098, China
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100085, China
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232001, China
Center for Eco-Environment Research, Nanjing Hydraulic Research Institute, Nanjing
Research Center for Eco-Environment Sciences, Chinese Academy of Sciences, Beijing
School of Earth and Environment, Anhui University of Science and Technology, Huainan
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*
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E-mail:
[email protected] Corresponding author: Hujuguan 34, Nanjing 210098, China. Tel./Fax: +86 25 85829765,
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Table of contents
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ABSTRACT: This research developed an innovative approach to reveal nitrogen sources,
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transformation and transport in large and complex river networks in the Taihu Lake basin
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using measurement of dual stable isotopes of nitrate. The spatial patterns of δ15N
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corresponded to the urbanization level, and the nitrogen cycle was associated with the
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hydrological regime at the basin level. During the high flow season of summer, nonpoint
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sources from fertilizer/soils and atmospheric deposition constituted the highest proportion of
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the total nitrogen load. The point sources from sewage/manure, with high ammonium
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concentrations and high δ15N and δ18O contents in the form of nitrate, accounted for the
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largest inputs among all sources during the low flow season of winter. Hot spot areas with
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heavy point source pollution were identified, and the pollutant transport routes were revealed.
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Nitrification occurred widely during the warm seasons, with decreased δ18O values; whereas
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great potential for denitrification existed during the low flow seasons of autumn and spring.
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The study showed that point source reduction could have effects over the short term; however,
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long-term efforts to substantially control agriculture nonpoint sources are essential to
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eutrophication alleviation for the receiving lake, which clarifies the relationship between
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point and non-point source control.
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INTRODUCTION
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Over the past century, surplus nitrogen (N) has been loaded into the biosphere due to intensive
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anthropogenic activities, resulting in excessive nitrogen in surface water and contributing to water
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quality impairment, eutrophication and ecological disasters.1,2 Reduction of extra nitrogen load is
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the fundamental way to improve water quality and restore aquatic ecosystems. Strong efforts to
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identify nitrogen pollution sources entering into rivers and lakes have been attempted in the past
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several decades.2-4 Nitrogen sources are classified into point sources and nonpoint sources.
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Traditionally, point sources come from domestic sewage, industry discharge and livestock manure,
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and nonpoint sources come from agricultural fertilizer, soil erosion, and atmospheric dry and wet
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deposition. There are many methods for identifying nitrogen sources and load from a watershed to
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the receiving waters. In general, the point source pollution load can be obtained through detailed
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statistics, whereas the nonpoint source pollution load from a catchment is estimated by watershed
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models.5-7
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Despite great efforts to identify point source or nonpoint source pollution related to different land
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use,8,9 nitrogen source identification remains challenging in urbanized and industrialized areas with
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complex land use. The problem is intensified where densely crisscrossed river networks are
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concerned; complicated flow patterns make nitrogen transport processes difficult to trace.10
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Although some researchers correlate nitrogen in waters with land use using statistical
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approaches,11,12 the results are qualitative with great uncertainty. The relationship between complex
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source loading and water quality in plain river networks is not sufficient to support basin-scale
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nitrogen management. Consequently, many lakes suffer high external nutrient inputs and algal
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blooms.13,14
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The dual isotope (combination of 15N and 18O in nitrate) approach has been applied to trace sources 4
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of nitrate and their potential transformation from atmospheric deposition, soils, chemical fertilizers,
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and sewage or manure, both in surface and ground water.2,15-22 The application fields cover different
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types of land use, including forested,15,16 agricultural,17 urbanized and hybrid areas.18-22 During
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recent years, the analytical methodologies for both δ15N and δ18O have improved considerably, and
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have become an attractive technique to identify nitrogen sources in surface or groundwater.23,24
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However, adapting the method to identify nitrogen sources in complex plain river networks, which
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have variable
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remains highly ambitious.
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The main objectives of this work are to: (1) develop a comprehensive approach by combining
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analysis of water quality and dual isotopes of nitrate to identify the dominant sources and
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transformation processes of nitrogen in complex river networks of a lake basin; (2) reveal spatial
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distribution patterns and transport routes of nitrate in complex river networks with relation to land
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use and hydrological regime; (3) quantify the potential for reduction of nitrogen, and evaluate the
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effectiveness of nutrient management strategy at a basin scale for alleviation of lake eutrophication.
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MATERIALS AND METHODS
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Study area. Lake Taihu is the third largest freshwater lake in China. It covers an area of 2338 km2
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with an average depth of 1.9 meters and a corresponding volume of 4.4 billion m3. The Taihu basin
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has an area of approximately 36,895 km2 and is located in the downstream area of the Yangtze
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River (Figure 1). The basin is heavily populated and highly industrialized, with only 0.4% of
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China’s land area supporting 40 million residents and 11% of the Gross Domestic Product of the
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country. The lake suffers serious eutrophication and cyanobacterial blooms due to excessive
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external nutrient loading from the Taihu basin. The intensified land-use of industrialization,
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urbanization and agriculture in the basin has produced high nitrogen loading from multiple
spatiotemporal nitrogen loading patterns, from an integrated basin-scale perspective
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sources.10,12 Consequently, two-thirds of the lake area exceeds the level of 1.0 mg L-1 total nitrogen
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and 0.05 mg L-1 total phosphorus, the nutrient concentration thresholds for controlling
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eutrophication in Lake Taihu required by the government.
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The basin is divided into eight parts in terms of hydraulic characteristics (Figure 1b), and the river
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network is well developed, consisting of over 200 main rivers crisscrossing the basin. This study
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focuses on the upstream areas in the northwest of the Taihu basin, covering the whole area of the
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Huxi part and half the area of the Wu-Xi-Chen part (Figure 1b). These two parts are the most
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heavily polluted areas and account for over 70% of the pollution loads entering into the lake. The
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west and south are the upstream rivers along with mountains, and the north is bounded by the
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Yangtze River. Plain river networks characterize the hydraulics of the study areas, where west-east
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rivers crisscross with north-south rivers. Specific information on Taihu basin climate, hydrology
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and river networks in the study area can be found in the Supporting Information or literature.10
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Figure 1. Location of study areas (a, b), and sampling sites (c) in the upstream river network of Lake Taihu. (Note:
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the river network is simplified from more complex rivers; “Route Two” and “Route Three” refer to the Water
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Transfer Projects between the Yangtze River and Lake Taihu, and the arrows indicate water transfer directions.)
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Sampling design. Forty-eight sampling sites are located in the main rivers (Figure 1c). Specific
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information concerning sampling sites is listed in Table S1 (Supporting Information). Three 6
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sampling campaigns were conducted in the high flow season of summer, low flow season of autumn
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and low flow season of winter in late June 2015, October 2015 and January 2016, respectively.
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Monthly samplings from June 2015 to April 2016 were conducted in the thirteen main inflowing
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rivers to obtain more details on the nitrogen loading patterns. Water samples were collected at the
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Yangtze River sites in June of 2015 and January of 2016. The details of the hydrological regime of
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the study area are shown in Figure S1 in Supporting Information. Water volume at Lake Taihu
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reached its peak in the heavily rainy June of 2015, declined towards the low flow of autumn and
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winter, and increased with rainfall events in the spring of 2016.
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Water quality and stable isotope analysis. Surface water samples were taken at 0.5–1.0 m depth
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under the surface using a 5 L Plexiglas water sampler. The main analyzed parameters included
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water temperature, pH, dissolved oxygen, electricity conductivity, total nitrogen, total dissolved
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nitrogen, dissolved inorganic nitrogen forms of nitrate, nitrite and ammonium, and chloride. Details
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on sampling methods and water quality analysis are provided in Supporting Information.
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Collected river samples for stable isotopic analysis were filtered with 0.2-µm cellulose ester filters
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and frozen below -20oC until analysis. The denitrifier method at the Environmental Stable Isotope
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Lab in Chinese Academy of Agricultural Sciences (CAAS), Beijing was used for analyzing δ15N
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and δ18O in nitrate. Briefly, denitrifying bacteria (Pseudomonas auroeofaciens) convert nitrate to
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gaseous
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(Tracegas-Isoprime100, Germany). Isotopic ratio values are reported in parts per thousand (‰)
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relative to atmospheric N2 and Vienna Standard Mean Ocean water (VSMOW) for δ 15N and δ18O,
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respectively:
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∆sample (‰)=[(Rsample-Rstandard)/Rstandard] ×1000
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where ∆sample is the stable isotope ratio in the samples, Rsample is the ratio of 15N/14N or 18O/16O in the
nitrous
oxide
(N2O),
detected
using
an
isotope
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samples, Rstandard is the ratio of 15N/14N or 18O/16O in the standards. Sample analysis had an average
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precision of 0.2‰ for δ15N-nitrate and 0.7‰ for δ18O-nitrate.
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The land use of 2010, combined with the hydrological regime (Figure S1 and Table S2 in
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Supporting Information), of the study area was used to analyze their effects on spatial patterns of
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dual isotopes of nitrate. Sampling sites at the downstream of the flow direction were selected for
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statistical analysis. Pearson correlation coefficients, at confidence levels of 95% (p