巢湖二氧化碳排放特征及其潜在影响因素

李宇阳,朱俊羽,俞晓琴,陈慧敏,郭燕妮,周永强,3,周 蕾,3*

巢湖二氧化碳排放特征及其潜在影响因素

李宇阳1,2,朱俊羽1,俞晓琴1,陈慧敏1,郭燕妮1,周永强1,3,周 蕾1,3*

(1.中国科学院南京地理与湖泊研究所,江苏 南京 210008；2.南昌大学资源环境与化工学院,鄱阳湖环境与资源利用教育部重点实验室,江西 南昌 330031；3.中国科学院大学,北京 100049)

巢湖；二氧化碳(CO2)；通量；有色可溶性有机物(CDOM)；平行因子分析(PARAFAC)

CO2作为大气中最重要的温室气体成分[1],其温室效应增温贡献占所有温室气体总贡献的60%.虽然湖泊总表面积只覆盖了全球陆地面积的4%[2],但湖泊的CO2排放却在流域碳循环中发挥着重要作用[3].湖泊作为内陆水体有机碳循环的要冲与枢纽,承接了流域大量有机碳汇入.由于人类城镇化规模扩大,在人口和农业集中地区,湖泊富营养化现象尤为严重[4].巢湖是长江中下游典型的富营养化湖泊,由于入湖河流的营养负荷高,大量外源有机碳从湖泊西部入湖河口汇入.有研究显示,富营养化一般会导致浮游植物聚集与堆积,其呼吸矿化与光合作用均会影响CO2的变化[5],此外,富营养化也会增加藻生物量及藻源性有机质产生[5-6],从而影响湖泊温室气体的产生与排放.

溶解性有机物(DOM)是由腐殖质、富里酸、脂肪族及芳香烃类物质组成的[7],结构较为复杂,是天然有机质重要赋存形态和活性组分,富含有机碳、氮、磷、硫等生源要素[8-9],对水生生态系统具有重要意义.由于其结构的复杂性,传统方法难以准确解析其组成.有色可溶性有机物(CDOM)是DOM中可强烈吸收紫外和可见光的部分[10].通过CDOM能强烈吸收紫外辐射且紫外激发后能发出荧光的特性得出的荧光组分峰团信息可有效表征CDOM的组成及来源,从而揭示CDOM的组成对温室气体排放的影响.三维荧光光谱(EEMs)结合平行因子(PARAFAC)能有效揭示CDOM荧光组分峰团信息[7,10-11],由于富含CDOM来源组成“指纹”信息,因而被广泛应用于水生态系统中天然有机质来源组成的表征.

1 材料与方法

1.1 样品采集

巢湖地属安徽省合肥市,水域面积780km2,平均水深2.89m,环湖河流约33条,西经合肥,东入长江,湖水主要由地表径流补给,入湖年平均流量最高的河流分别为杭埠河、南淝河、兆河、派河、柘皋河、白石天河、十五里河[14-15].

根据环巢湖河流分布与湖区水文特征,在巢湖西部、中部、东部湖区均匀设置13个采样点,其中包括5个细菌计数采样点,野外采样分别开展于2018年1, 4, 7月,共计39个表层水与气体样品(图1).水样采集完成后,用聚乙烯瓶装存,置于黑暗冷冻环境下转移至实验室.气体采集采用顶空瓶法,将水样灌装在事先装有2g KCl (消除样品运输过程中潜在的微生物活动)的61mL棕色玻璃瓶中[16-17],排除气泡后用铝盖密封,每次野外采样均在湖心位置取湖面上方2m处的大气背景样品注入真空密封棕色玻璃瓶中用以消除季节变化大气本底CO2浓度变化,一同转移至实验室.

图1 巢湖采样点分布

圆圈表示采样点,其中黑色对应细菌计数样品采集点位

1.2 主要水质参数与总细菌数的测定

水样-22℃冻存,运至实验室后,以孔径0.7µm Whatman GF/F玻璃纤维滤膜过滤水样,滤后水用Shimadzu TOC-L总有机碳分析仪对水样中溶解性有机碳(DOC)进行测定,测定时采用NPOC扫吹模式,且温度设定为680℃[18].用体积分数90%的乙醇高温萃取留存滤膜,再用分光光度法测定波长665与750nm的吸光度值以计算叶绿素(Chl-a)浓度.留取1mL原水加入60µL无颗粒甲醛固定,4℃下低温保存,通过流式细胞仪对总细菌数进行测定.总氮(TN)、总磷(TP)采用紫外分光光度计(Shimazdu UV-2550UV-Vis),测量方法均参照文献[19].常规水质参数如表层水温(),溶解氧(DO)均用多参数水质仪(YSI-EXO2)现场测定.

1.3 CDOM紫外吸收光谱与三维荧光光谱的测定

以孔径0.2µm Millipore滤膜对水样进行过滤,留存100mL滤后水.吸收光谱测定时,使用Shimazdu UV-2550UV-Vis,波长范围为200～800nm,间隔1nm,且通过扣除700nm吸光度以去除水样颗粒散射;254为CDOM在波长254nm处的吸收系数,可用以表征CDOM相对丰度.三维荧光光谱EEMs测定时,使用Hitachi F-7000荧光光度计,以Milli-Q超纯水为空白,激发波长230～450nm,间隔5nm,发射波长300～ 600nm,间隔1nm.扫描完成后对数据进行内滤波效应校正,再扣除超纯水空白数据消除拉曼散射,用drEEM工具包消除扫描中的瑞利散射峰,处理完成后,将数据导入MATLAB R2015b软件,结合平行因子分析(PARAFAC)得出组分信息[20].

腐殖化指数(HIX),生物鲜活指数(BIX)均为特定波长波段荧光强度积分比值,HIX越大,CDOM陆源来源信号越强;BIX越大,CDOM生物来源信号越强.比紫外吸收系数(SUVA254)为254与DOC浓度的比值[8],SUVA254越大表明CDOM芳香程度越高[21]. HIX与SUVA254可表征CDOM的陆源信号强弱.

1.4 水体CO2浓度及CO2扩散通量

式中:C为水气界面扩散通量,mg/(m2×d),water为水样中溶解性气体浓度;air为采样时空气中气体浓度,µmol/L.

式中:x为气体交换系数, cm/h;由风速决定,风速<3m/s时,=0.66,风速>3m/s时,=0.5;c是CO2施密特数,取决于温度,℃.

600为20℃淡水中CO2施密特数为600时标准值,由风速决定,计算公式为:

10为水面上高程10m处的风速,根据实测高度风速U决定,计算公式为[22]:

式中:10为水面高程10m处的风阻系数, 0.0013m/s;为冯卡门常数, 0.41.

1.5 水文情形的划分

根据巢湖流域地理水文特征及合肥市年均逐月降雨量(1961～2018年),将巢湖水文情形划分为枯水期、平水期及丰水期3种水文情形,对应降雨量分别为1月(35.6mm)、4月(90.9mm)、7月(173.8mm).

1.6 数据处理

2 结果与分析

2.1 巢湖表层水体CO2浓度与扩散通量时空变化特征

表1 不同水文情景巢湖水体CO2浓度()与扩散通量()

图2 巢湖1、4、7月表层水体、空间分布

2.2 巢湖主要水质参数均值与标准差分析

图3 巢湖1、4、7月不同湖区DO、TN、TP、DOC变幅

如图3所示,采样期间,巢湖DO实测值为4.7～15.9mg/L,月均DO高值与低值均出现在西部湖区,分别为1月(14.5mg/L)与7月(6.3mg/L),且随着月份的增加不断降低;湖区内TN、TP、Chl-a具有显著的季节差异,随月份的增加不断增加(表2).由于巢湖独特的水文特征,湖区TN与TP有较为明显的季节与时空差异,从西部湖区到东部湖区逐渐降低,在西部与中部湖区,TN、TP随着月份的变化显著升高,高值均出现在西部湖区的7月份(7.4mg/L,0.7mg/L),东部湖区TN 4月较高;DOC从西到东也有逐渐降低的趋势,但变化不显著,在营养负荷较重的西部湖区4月和7月较高.

2.3 巢湖表层水体细菌丰度时空变化特征

通过流式细胞仪测定巢湖表层水体细菌丰度,结果表明,细菌总数大体上表现为7月>1月>4月,且在空间分布上大体表现为自西向东不断降低.高值出现在西部湖区的7月(2.6×107cells/mL),低值为东部湖区的4月(0.9×106cells/mL) (图4).

图4 巢湖不同水文情景下细菌计数结果

2.4 巢湖表层水体、与DO、DOC、lg(Chl-a)的相关性分析

图5 巢湖表层水体、与DO、DOC、lg(Chl-a)的相关性

2.5 巢湖表层水体可溶性有机物光谱组成特征以及各组分与、的相关性分析

用平行因子分析模型对巢湖表层水体可溶性有机物进行组分解析,最终得出四种主要荧光峰团,分别为短波类腐殖质峰C1(表征陆源类腐殖酸经微生物降解后产物,通常与入湖河流输入息息相关[23])、类色氨酸峰C2 (藻降解的内源产生亦或生活污水新近产生[24])、类酪氨酸峰C3 (生物降解内源产生或微生物矿化产物[25])和长波类腐殖质峰C4(外源地表有机碎屑输入[20]).模型结果详尽情况可参见文献[26].

表2 巢湖1、4、7月主要水质要素与CDOM光学组分比较

图6 巢湖表层水体、与四个荧光组分C1-C4相关性

2.6 巢湖主成分分析

图7 巢湖环境因子主成分分析(a)及主成分PC1与、的相关性分析(b)

3 讨论

3.1 巢湖富营养水平对、的影响

3.2 巢湖水体CDOM来源组成对、的影响

自然湖泊CDOM主要来源于有机质的外源输入,即土壤植被有机质或动植物残体分解,亦或者是污水的排放[32];也可能来源于浮游植物、微生物等一系列初级生产力降解分解产生.

CDOM来源组成对CO2产生的影响还未被充分研究,但CDOM对CO2的影响毋庸置疑.CDOM因能强烈吸收紫外辐射而快速被光降解产生CO2,同时由于对水体蓝光的强烈吸收从而直接影响浮游植物光合作用与呼吸作用及对CO2的吸收与释放[13,37].CDOM中陆源腐殖质组分直接表征外源有机质的输入,能直接影响水体营养水平及有机物的积累与降解,进而促进CO2排放,这也与主成分分析中的PC1结果相吻合,此外内源性组分由于生物活性强,与微生物新陈代谢过程息息相关,藻源性组分也可综合表征藻华的暴发与降解.基于此,今后可从CDOM来源组成与CO2的潜在关联出发,以期对湖泊碳循环以及二氧化碳排放作出更准确的评价.

4 结论

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致谢：感谢邹伟、施坤、李娜、叶小锐、张成英等同志在野外及室内实验过程中给予的帮助.

Emission of carbon dioxide from Lake Chaohu and the potential influencing factors.

LI Yu-yang1,2, ZHU Jun-yu1, YU Xiao-qin1, CHEN Hui-min1, GUO Yan-ni1, ZHOU Yong-qiang1,3, ZHOU Lei1,3*

(1.Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China；2.Key Laboratory of Poyang Lake Environment and Resource Utilization, Ministry of Education, School of Resources Environmental & Chemical Engineering, Nanchang University, Nanchang 330031, China；3.University of Chinese Academy of Sciences, Beijing 100049, China)., 2022,42(1)：425～433

Lake Chaohu；carbon dioxide (CO2)；flux；chromophoric dissolved organic matter (CDOM)；parallel factor analysis (PARAFAC)

X143,X171

A

1000-6923(2022)01-0425-09

李宇阳(1997-),男,安徽淮南人,南昌大学硕士研究生,主要从事有色可溶性有机物循环与水污染治理.发表论文2篇.

2021-05-27

国家自然科学基金资助项目(41807362);中国科学院青年创新促进会会员(2021312);江苏省自然科学基金资助项目(BK20181104);中国科学院南京地理与湖泊研究所青年科学家小组(E1SL002)

* 责任作者, 助理研究员, zhoulei16@mails.ucas.ac.cn