El Niño事件对河流入海COD的调控机制

黄全佳

doi:10.12284/hyxb2021142

El Niño事件对河流入海COD的调控机制基于高分辨率时间序列观测的证据

doi: 10.12284/hyxb2021142

黄全佳^1,

厦门市环境监测站，福建厦门 361012

详细信息

作者简介:
黄全佳(1970—)，男，福建省南安市人，高级工程师，主要从事环境科学研究、环境监测与分析。E-mail：18905925898@189.cn

中图分类号: P714⁺.4
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出版历程
- 收稿日期: 2020-06-04
- 修回日期: 2020-09-15
- 网络出版日期: 2021-07-05
- 刊出日期: 2021-06-30

Regulation of El Niño event on riverine COD export: Evidence from high frequency time-series buoy monitoring

Huang Quanjia^1
,

Xiamen Environmental Monitoring Station, Xiamen 361012, China

摘要

摘要: 入海污染物在线监测是实现海洋污染总量控制和测管协同的关键手段之一。本文基于布设在福建省九龙江口的多参数浮标，获取2014−2018年浮标盐度和荧光溶解有机质(FDOM)数据，结合人工采样测定化学需氧量(COD)浓度，建立了河口区COD浓度的快速反演模型。使用有效浓度法外推得到河端COD浓度，结合流量数据估算了高频率的COD入海通量，利用通量分解模型定量分析了COD入海通量的调控因素。结果表明：(1) COD河端有效浓度的拟合值与实测值的偏差为(10.4±8.8)%，模型能够较好地反演九龙江口河端的COD浓度；(2)在季节尺度上，受2015−2016年超强El Niño事件影响，2016年旱季的异常降雨降低了河流COD浓度，但是径流量的增加仍然显著提高了该季节COD的入海通量；(3)在年际尺度上，2015−2016年El Niño事件所引起的异常降雨事件导致2016年九龙江COD入海通量为(4.4 ± 0.9)×10⁴ t/a，显著高于2014年、2015年及2017年的3.0×10⁴～3.2×10⁴ t/a。上述研究结果表明，FDOM水质浮标在线监测系统，有助于实现对陆源COD入海通量及其调控因素的长期连续高频监测，可为海洋生态环境保护和管理提供重要技术支撑。
- 多参数浮标 /
- 荧光溶解有机质 /
- 化学需氧量 /
- 反演 /
- 入海通量 /
- El Niño事件
Abstract: Online monitoring of pollutants exporting into coastal waters is one of the key technique to realize marine pollution control and coordination of monitoring and management. In this study, based on salinity and fluorescence dissolved organic matter (FDOM) sensor data between 2014−2018 from a multi-parameter buoy deployed in Jiulong River Estuary, a rapid inversion model of chemical oxygen demand (COD) was established. Daily river end member COD concentrations were then extrapolated by effective concentration method. Combined with daily runoff data, daily estuarine export fluxes of COD between 2014 and 2017 were estimated. The factors controlling the flux variations were quantified by flux decomposition model. The results indicated: (1) The average concentration deviation of the inversion was (10.4±8.8)%, indicating the good reliability and stability of inversion model. (2) On seasonal scale, COD concentration in dry season of 2016 was lower than the other dry seasons, which was regulated by 2015−2016 super El Niño events. However, the sharp increase of runoff still significantly increased the COD export flux. (3) On annual scale, the COD export flux in 2016 from the Jiulong River 4.4 ×10⁴ t/a was significantly higher than the other three years (3.0−3.2)×10⁴ t/a. The abnormal precipitation in 2016 caused by 2015−2016 El Niño event was the major reason for such yearly variation. This study highlighted the application of online FDOM sensor system for high frequency monitoring of COD export. This would help to achieve long-term continuous high-frequency monitoring of land-based pollutants and their fluxes into the sea and their regulatory factors. This would provide important technical support for marine ecological environmental protection and management.
- multi-parameter buoys /
- fluorescence dissolved organic matter /
- chemical oxygen demand /
- inversion /
- flux to the sea /
- El Niño event

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图 1 航次调查站位与浮标布设地点

Fig. 1 Sampling stations and buoy locations

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图 2 不同季节浮标FDOM与实测COD数据的分布及二者的相关性分析

Fig. 2 Distribution of measured COD and sensor FDOM data in different seasons and their correlation analysis

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图 3 COD实测值与拟合值的相关性（a）以及根据拟合曲线反演的2014−2018年河端COD浓度（灰线）与根据实测值外推的河端COD浓度（红点）的比较（b）

Fig. 3 Correlation between measured COD and fitted COD (a) and comparison of river COD concentrations (red point) extrapolated from fitting curve and those extrapolated from measured COD (grey line) from 2014 to 2018 (b)

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图 4 2014−2017年Niño 3.4指数与九龙江径流量变化趋势

Fig. 4 Variation of Niño 3.4 index and seasonal runoff of the Jiulong River watershed from 2014 to 2017

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图 5 2014−2017年暴雨事件与非暴雨事件对COD入海通量的相对贡献^[19]

Fig. 5 Relative contribution of rainstorm and non-rainstorm events on COD export flux from 2014 to 2017^[19]

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图 6 2014−2017年COD入海通量的通量分解模型结果

Fig. 6 Flux decomposition model of COD export flux from 2014 to 2017

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表 1 各季节浮标FDOM及实测COD的混合曲线及相关性方程

Tab. 1 Mixed curves of sensor FDOM and measured COD and their correlation equations in different seasons

时间	盐度−FDOM		盐度−COD		FDOM−COD
时间	混合曲线	r²	混合曲线	r²	相关性方程	r²
2016年12月	y=−0.5x+14.1	0.98	y=−0.07x+1.9	0.91	y=5.7x+0.7	0.91
2017年2月	y=−0.5x+15.3	0.90	y=−0.06x+2.4	0.98	y=8.8x−6.15	0.94
2017年4月	y=−1.1x+28.2	0.91	y=−0.07x+2.6	0.96	y=14x−8	0.96
2018年4月	y=−0.8x+24.3	0.81	y=−0.1x+3.9	0.96	y=6.4x−2	0.96
2018年7月17日	y=−0.9x+27.9	0.98	y=−0.08x+3.3	0.96	y=8.2x−5.3	0.98
2018年7月23日	y=−0.7x+24.1	0.98	y=−0.08x+2.7	0.93	y=8.2x+1.1	0.91
2018年10月	−	−	y=−0.1x+3.9	0.97	−	−

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表 2 2014−2017年不同季节COD河端表观浓度、径流量及COD入海通量

Tab. 2 Seasonal effective COD concentrations, total runoff and total export fluxes between 2014−2017

时间		COD浓度/ (mg·L⁻¹)	径流量/ (10⁹ m³)	COD入海通量/ (10⁴ t)
2014年	旱季（前）	2.51±0.37	1.64	0.41
	梅雨期	2.81±0.48	4.87	1.37
	台风期	2.49±0.42	3.38	0.84
	旱季（后）	2.94±0.33	1.35	0.39
2015年	旱季（前）	3.08±0.51	0.96	0.30
	梅雨期	2.96±0.69	3.00	0.93
	台风期	2.51±0.42	5.68	1.39
	旱季（后）	2.25±0.33	2.47	0.56
2016年	旱季（前）	1.78±0.42	4.81	0.81
	梅雨期	1.80±0.32	7.02	1.25
	台风期	2.04±0.65	6.20	1.24
	旱季（后）	2.10±0.45	5.24	1.14
2017年	旱季（前）	2.35±0.43	2.42	0.57
	梅雨期	2.68±0.43	5.21	1.27
	台风期	2.49±0.52	3.37	0.84
	旱季（后）	3.00±0.35	1.33	0.40

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表 3 通量分解模型解析的各组分相对贡献

Tab. 3 Quantification for each component of flux decomposition model

年度	时期	C_mQ_m	C_mQ′	C′Q_m	C′Q′	CQ
2014	旱季（前）	180.2%	−73.3%	−12.35	5.4%	100%
	梅雨期	55.0%	40.6%	2.5%	1.9%	100%
	台风期	90.6%	17.6%	−6.8%	−1.4%	100%
	旱季（后）	194.9%	−102.3%	19.0%	−11.6%	100%
2015	旱季（前）	250.6%	−163.7%	36.1%	−23.0%	100%
	梅雨期	80.9%	5.8%	8.1%	5.2%	100%
	台风期	54.8%	55.1%	−3.7%	−6.2%	100%
	旱季（后）	135.5%	−17.3%	−22.1%	3.9%	100%
2016	旱季（前）	93.0%	66.6%	−31.6%	−28.1%	100%
	梅雨期	60.3%	90.9%	−20.1%	−31.1%	100%
	台风期	61.3%	72.8%	−14.8%	−19.2%	100%
	旱季（后）	66.7%	56.7%	−14.6%	−8.8%	100%
2017	旱季（前）	130.0%	−16.4%	−16.3%	2.7%	100%
	梅雨期	59.5%	51.2%	−0.4%	−10.3%	100%
	台风期	91.1%	17.2%	−6.9%	−1.4%	100%
	旱季（后）	189.4%	−100.6%	22.1%	−10.9%	100%

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