Evidence for the Importance of Atmospheric Nitrogen Deposition to

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Evidence for the importance of atmospheric nitrogen deposition to eutrophic Lake Dianchi, China Xiaoying Zhan, Yan Bo, Feng Zhou, Xuejun Liu, Hans W. Paerl, Jianlin Shen, Rong Wang, Farong Li, Shu Tao, Yanjun Dong, and Xiaoyan Tang Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 01 Jun 2017 Downloaded from http://pubs.acs.org on June 2, 2017

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Environmental Science & Technology

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Evidence for the importance of atmospheric nitrogen

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deposition to eutrophic Lake Dianchi, China

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Xiaoying Zhan#†, Yan Bo#†, Feng Zhou*†, Xuejun Liu‡, Hans W. Paerl§|, Jianlin Shenǁ,

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Rong Wang┴, Farong Li , Shu Tao†, Yanjun Dong≡, Xiaoyan Tang+ ∥

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†

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Processes, College of Urban and Environmental Sciences, Peking University, Beijing,

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100871, P.R. China

Sino-France Institute of Earth Systems Science, Laboratory for Earth Surface

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‡

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Beijing, 100193, P.R. China

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§

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City, North Carolina, 28557, USA

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ǁ

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Subtropical Agriculture, Chinese Academy of Sciences, Changsha, Hunan, 410125,

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P.R. China

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┴

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94305 USA.

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College of Resources and Environmental Sciences, China Agricultural University,

Institute of Marine Sciences, University of North Carolina at Chapel Hill, Morehead

Key Laboratory of Agro-Ecological Processes in Subtropical Regions, Institute of

Department of Global Ecology, Carnegie Institution for Science, Stanford, California,

∥

Kunming Environmental Monitoring Center, Kunming, Yunnan, 650028, P.R. China

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+

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100871, P.R. China

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≡

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Pearl River Hydraulic Research Institute, Guangzhou 510611, P.R. China

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|

College of Environmental Sciences and Engineering, Peking University, Beijing,

Laboratory of the Pear River Estuarine Dynamics and Associated Process Regulation,

College of Environment, Hohai University, Nanjing, 210098, P.R. China

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* Corresponding author: Phone / fax: +86 10 62756511; Email: [email protected]

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#

X.Y.Z. and H.Y. contributed equally to this work.

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ACS Paragon Plus Environment


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TOC Art

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Abstract

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Elevated atmospheric nitrogen (N) deposition has significantly influenced aquatic

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ecosystems, especially with regard to their N budgets and phytoplankton growth

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potentials. Compared to a considerable number of studies on oligotrophic lakes and

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oceanic waters, little evidence for the importance of N deposition has been generated

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for eutrophic lakes, even though emphasis has been placed on reducing external N

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inputs to control eutrophication in these lakes. Our high-resolution observations of

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atmospheric depositions and riverine inputs of biologically-reactive N species into

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eutrophic Lake Dianchi (the 6th largest freshwater lake in China) shed new light onto

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the contribution of N deposition to total N loads. Annual N deposition accounted for

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15.7% to 16.6% of total N loads under variable precipitation conditions, twofold

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higher than previous estimates (7.6%) for the Lake Dianchi. The proportion of N

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deposition to total N loads further increased to 27~48% in May and June when toxic

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blooms of the ubiquitous non-N2 fixing cyanobacteria Microcystis spp. are initiated

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and proliferate. Our observations reveal that reduced N (59%) contributes a greater

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amount than oxidized N to total N deposition, reaching 56-83% from late spring to

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summer. Progress toward mitigating eutrophication in Lake Dianchi and other

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bloom-impacted eutrophic lakes will be difficult without reductions in ammonia

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emissions and subsequent N deposition.



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INTRODUCTION

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Human activities have significantly increased emissions of reactive nitrogen (N,

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including reduced and oxidized forms) to the atmosphere and their resultant

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deposition1, 2. Results of global atmospheric chemical transport models indicated that

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atmospheric deposition of N has increased by approximately three-fold since

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pre-industrial times, especially over the East and South Asia3. The ecological effects

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of elevated atmospheric N deposition have been observed or modeled for oligotrophic

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lakes and oceanic waters, showing that such N inputs can stimulate phytoplankton

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growth4-7. This also leads to an increase in the stoichiometric N/phosphorus (P) ratio,

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which can induce a shift from N-limited to P-limited phytoplankton3, 4. In contrast, the

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contribution made by atmospherically deposited N to the total N input for eutrophic

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lakes has rarely received attention8, 9, because it was commonly considered to be

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much less than N inputs from watersheds to these lakes10.

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However, recent evidence from the few field observations demonstrated that the

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contribution of N deposition cannot be neglected compared to riverine inputs for

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eutrophic lakes. For instance, Luo et al9. measured wet N deposition in mesotrophic

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Lake Taihu, the third largest freshwater lake in China, indicating that atmospheric

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inputs of N accounted for approximately 14% of total N inputs. A similar percentage

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was reported for a mesotrophic lake in Venezuela11 (Lake Maracaibo, wet deposition,

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19%). Furthermore, the percentage increased considerably when both dry and wet

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deposition (i.e., gaseous, particulate and dissolved N) were included12. With long-term

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controls of point and non-point sources from watersheds taking place in developed

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countries, atmospheric N deposition is becoming an increasingly dominant N source

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to these waters (e.g., 15-42% of the total N inputs13 in the northeastern and


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mid-Atlantic regions of the United States).

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However, the contribution made by atmospheric N deposition to total inputs remains

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challenging to accurately assess, especially at a high-resolution temporal scale.

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Multiple factors are associated with the difficulty in assessment, especially when

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quantifying dry deposition and N inputs from watersheds to receiving waters. First,

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gaseous N (i.e., ammonia [NH3], nitrogen dioxide [NO2], nitric acid and nitrous acid

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[HNO2/HNO3]) deposition have generally been ignored or are only partially

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considered by current observation networks globally (e.g., EANET14, CASTNET15,

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AMoN15, IMPROVE NHx15, EMEP16) and in previous regional assessments12.

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Likewise, wet or bulk deposition of inorganic N (NH4+-N+NO3−-N) has been

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measured systematically in China, but organic N was seldom analyzed12. Those

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omissions may result in a considerable underestimation of direct atmospheric N

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deposition to these waters. Second, most measurements applying passive samplers

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yield ambient exposures on a monthly interval, mainly due to low concentrations of

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gaseous or particulate N12. Although active samplers (e.g., denuder systems) can be

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deployed to determine hourly or daily concentrations of gaseous constituents and

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fine-fraction particulates17, they are unsuitable for long-term field observations

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without a power supply12. In addition, riverine N inputs (i.e., N concentration × river

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discharge) were generally determined by using process-based models that simulate the

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N available for transport to the waters from different watershed N sources (runoff

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from agriculture, urban areas and upland forests, point sources)13,

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limitation in such models is the large uncertainties arising from model structure and

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parameter choices20. Such uncertainties can be reduced by performing observations of

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river discharge and N concentrations at high spatial and temporal resolutions.


18, 19

. A key


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The primary objective of this study is to test the hypothesis that atmospheric N

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deposition makes an important contribution to total N loads for eutrophic Lake

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Dianchi, the sixth largest freshwater lake in China, through high-resolution (daily or

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biweekly) systematic observations of N deposition and inputs. Dry and wet deposition

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fluxes of all N species and riverine N inputs were quantified and their annual and

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biweekly contributions to total N loads were determined. In addition, we considered

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the reliability of the fluxes as well as the importance of N deposition on lake

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eutrophication, and implications for lake ecosystem management.

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MATERIALS AND METHODS

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Study area. Lake Dianchi is located in Southwest China (24º29′ to 25º28′N,

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102º29′to 103º01′E). It has a surface area of 310 km2 at the elevation of 1887.5 m, a

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mean depth of 5.3 m, a shoreline of 150 km, and water retention time of 3.5 years21

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(Fig. 1). Watershed area for Lake Dianchi is 2920 km2, of which 1088.6 km2 is

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controlled by 10 large to medium-size reservoirs that have no outflows (Fig. 1). The

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lake is artificially divided into two segments by an embankment and a dam, where the

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area of the northern part (Caohai) is ~12 km2 surrounded by urban area of Kunming

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city and the southern part (Waihai, 298 km2) is bordered by an intensively managed

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farmland. Land use is primarily forest (47%), cropland (20%), and urban areas (16%;

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Fig. 1). The total population in the Lake Dianchi Watershed has increased steadily

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from 1.8 million in 1992 to 4.1 million in 2015. Consequently, the lake has suffered

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from severe eutrophication, despite the fact that approximately 52 billion RMB (equal

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to 7.7 billion in USD) has been invested to control lake eutrophication by the Chinese

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Central Government during 1996-201522.


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Figure 1. Locations of Lake Dianchi watershed and sampling sites. Nineteen

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rivers, accounting for 95% of lake’s inflow, were chosen for measuring N

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concentration and discharge. The rivers numbered 1 to 6 flowed into Caohai, while

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the rest flowed into Waihai. Hatch marks indicate the area controlled by 10 large to

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medium-size reservoirs.

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Dry N deposition samples around the lake. Sampling of dry N deposition, including

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gaseous and particulate N, was conducted from April 1, 2010 to March 31, 2011. Five

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deposition monitoring stations were evenly located around Lake Dianchi (Fig.1),

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including Kunming (KM), Baofeng (BF), Chenggong (CG), Jinning (JN), and Haikou

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(HK). For each station, Ogawa passive samplers (Ogawa & Co., FL, USA) were used

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to collect ammonia [NH3] and nitrogen dioxide [NO2] samples, which showed no



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significant differences from the continuous active samplers used in previous studies23.

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Another passive sampler (USDA Forest Service, CA, USA) was used for collecting

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nitrous acid/nitric acid [HNO2/HNO3] samples. Previous comparative experiments

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showed that this sampler was reliable for determining wide ranges of ambient

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HNO2/HNO324. Detailed descriptions of these passive samplers can be found in

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Roadman et al.25 and Bytnerowicz et al.26. High volume aerosol samplers (Laoshan

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Elec. Inc., Qingdao, China) were applied to collect total suspended particulates (TSP)

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at a flow rate of 1.05 m3 min−1. Three replicate samplers for dry N deposition were

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installed at a height of 3.5 m from the ground and at a distance of 2 mm. All collected samples were frozen at −18°C at each site until delivery to

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the laboratory for analysis. Collectors were cleaned for each event with 1% HCl and

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Nanopure water.

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River discharge and water samples. Although river discharges data were not

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available except for Panlongjiang (No.8) and Baoxiang (No.10) Rivers, water levels

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in the other 17 rivers were continuously measured in this study (Fig. 1). These 19

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rivers contribute more than 95% of lake’s streamflow27. The rating curves were

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developed by simultaneously measuring water levels and the current velocities for a

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cross-section of each river following the USGS guideline for the current-meter

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method28, where the sizes of cross-section (i.e., width, depth, area) were measured for

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each river (Table S1). To ensure rating curves over a wide range of flow rates,

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enhanced observations were conducted for three stormwater events (precipitation > 30

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mm day−1). Each sampling procedure lasted more than 48 h per event. The 10-minute

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water levels were continuously observed for each tributary by HOBOTM data logger

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(U20-001-01, Onset Computer Corporation, MA, USA), and the current velocities

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were measured by the Global WaterTM flow probe (FP201) in each subsection of a

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channel cross-section. Water level was further converted into daily discharge based on

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the derived rating curves for each river. In addition, vertical water samplers were used

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to collect water samples of 19 rivers on biweekly interval. The procedure was

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referenced to the Technical Specifications Requirements for Monitoring of Surface

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Water and Waste Water in China (HJ/T91-2002). As all rivers have width of ≤ 50m

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and depth of ≤ 5m (Table S1), one sample was collected in the river centerline at 0.5



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m below or half depth of the river. All water samples were rapidly transported to the

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KEMC for analysis.

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Chemical analysis. After the exposures, all filters for gaseous and particulate N were

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placed in polyethylene tubes into which 8 ml and 20 ml distilled/deionized water was

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added, respectively. The tubes were shaken on a shaker for 15 min at mid-range speed.

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The extracted solutions of dry deposition, wet deposition samples, and water samples

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were then filtered with a 0.45 µm syringe filter. N quantities in the filtered solutions

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were analyzed following the Standard Methods for the Examination of Water and

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Wastewater29 in China. Ammonium nitrogen [NH4+-N], nitrate nitrogen [NO3−-N],

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total nitrogen (TN), and Kjeldahl nitrogen (KTN) were measured by Nessler's reagent

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colorimetric

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oxidation-ultraviolet spectrophotometric method, as well as Semi-Micro-Kjeldahl

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method, respectively. Organic N was determined by the difference between KTN and

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NH4+-N. The detection limits for NH4+-N, NO3−-N, TN, and KTN are 0.025, 0.02,

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0.05, and 0.025 mg N L−1, respectively. For gaseous N deposition, ambient

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concentrations of NH3, NO2, and HNO2/HNO3 were calculated as product of their

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quantities collected in passive samplers and conversion coefficients divided by

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exposure time (see details at http://ogawausa.com/wp-content/uploads/2014/04/

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ConcentrationInAmbientAir.pdf and Bytnerowicz et al.26). Additionally, for each

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batch of field exposed samples for gaseous and particulate N, three blank filters were

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also prepared to represent extracts from unexposed samplers that are prepared in the

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laboratory and transported between laboratory and monitoring sites. The blank

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samples were extracted and analyzed using the same methods as the exposed samples.

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The details of blank samples can be found in Text S1.

method,

ultraviolet

spectrophotometric


method,

K2S2O8

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Estimation of deposition fluxes and riverine inputs. Biweekly flux of dry

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deposition (mg km−2 s−1) was calculated as a product of N concentration and

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deposition velocity (Vd, m s−1) at each site. The concentrations of gaseous and

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particulate N were determined as described above. Vd of gaseous N and particulate N

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over lake surface was estimated by a well-tested deposition velocity model30

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combined with the on-site hourly meteorological data. One of the national

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meteorological stations was located at the north end of Lake Dianchi (Fig. 1), where

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meteorological variables used in the model included air temperature, relative humidity,

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air pressure, solar radiation, wind speed at 10-m height (v_10), precipitation, and dew

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point on hourly intervals. Prior to the estimate, v_10 was converted into wind speed at

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3.5-m height, v_3.5, according to a power law (i.e., v_3.5= v_10×(3.5/10)α, where α

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was set as 0.1)31. Gas Vd over Lake Dianchi was calculated using the big-leaf

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resistance analogy model32. In contrast to vegetation, roughness length was set as

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0.0003 m based on the results for the East China Sea33, and surface resistance was set

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as 0 s m−1 for NH3 and HNO3 but 20000 s m−1 for NO234. Particulate Vd was

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parameterized according to Slinn35, where parameters related to surface resistance and

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gravitational settling velocity were determined according to Zhang et al36. Details of

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these models were described in Text S2. In addition, the flux of wet deposition was

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calculated as a product of N concentration and rainfall amount for each rainfall event

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and monitoring site.

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Daily riverine N inputs were calculated as a function of river discharge, by means of

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Load Estimator (LOADEST)37:



ln ( Li ) = a0 + a1 ln Q + a2 ln Q2 + a3 sin ( 2π dtime ) +

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a4 cos ( 2π dtime ) + a5dtime + a6 dtime2 + ε

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(1)

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where Q is daily river discharge, dtime is decimal time, a0~a6 are the fitted

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coefficients in the multiple regression model, and ε is estimate error. Note that the

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biweekly TN concentration was multiplied by the corresponding daily discharge so

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that equation 1 is a function of load (Li) instead of concentration. Within LOADEST,

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the model to estimate N loads was set to be automatically selected from predefined

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regression models37. To select the best one, LOADEST calculated model coefficients

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using each calibration data set (i.e., observed TN loads in few of days), and models

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with the lowest Akaike information criterion (AIC) values were selected for load

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estimations.

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RESULTS

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Dry N deposition. The N flux of dry deposition over the Lake Dianchi was 42.1±10.3

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mg km−2 s−1 in 2010-2011 (1σ as the standard deviation of fluxes occurring in 5 sites;

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Fig. 2), with 37.1±10.5 mg km−2 s−1 for gaseous N and 5.0±0.7 mg km−2 s−1 for

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particulate N. The major species, accounting for 99% of the dry deposition, was NH3

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(32.8±10.1 mg km−2 s−1, 77.9%), HNO2/HNO3 (4.0±0.6 mg km−2 s−1, 9.5%),

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particulate ON (2.9±0.6 mg km−2 s−1, 6.8%), and particulate NO3−-N (2.0±0.1 mg

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km−2 s−1, 4.7%), while the rest of 1% were NO2 (0.7%) and particulate NH4+-N (0.3%).

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The corresponding concentrations and Vd of gases and TSP at 5 sites can be found in

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Fig. S1 and S2. Regardless of three missing samples in early April-June, the dry

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deposition N flux showed strong seasonality (Fig. 2b). High fluxes were observed

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from May to August, which were 1.7-13.3 times higher than the remaining periods.


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Dry deposition of NH3 was the major source (>86.0%) of total dry deposition N flux

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from May to August, with biweekly coefficient of variation [CV] of 115%. The NO2

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flux was highest in autumn and early winter (0.4±0.2 mg km-2 s-1) and remained low

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in early spring and summer (0.2±0.1 mg km-2 s-1). The remaining N fluxes were

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negligible with a seasonal variability with CV of ~27%.

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Wet N deposition. Wet deposition over the Lake Dianchi yielded an N flux of

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46.5±13.2 mg km−2 s−1, 10.5% higher than dry deposition (Fig. 2c). NO3−-N

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dominated the total flux of wet deposition (24.3±7.5 mg km−2 s−1, 52.2%), followed

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by ON (11.9±5.4 mg km−2 s−1, 25.6%) and NH4+-N (10.3±3.9 mg km−2 s−1, 22.2%).

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The corresponding concentrations of diverse N species at all 5 sites can be found in

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Fig. S1. Peak N fluxes of wet deposition occurred in the wet season (i.e., from later

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July to early October; Fig. 2d), following the dry deposition peaks. The N flux during

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this period were up to 172.9±72.2 mg km−2 s−1, six-fold greater than mean value of the

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rest of the year. N fluxes from January to March were only 4.4±7.7 mg km−2 s−1, due

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to this being the period of

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deposition fluxes was highly consistent with precipitation patterns over all 5 sites

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(r=0.83, P

Evidence for the Importance of Atmospheric Nitrogen Deposition to

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