2024, Vol. 45
2. 贵州师范大学贵州省山地环境信息系统与生态环境保护重点试验室, 贵阳 550001;
3. 南京大学常熟生态研究院, 苏州 215500
2. Key Laboratory for Information System of Mountainous Area and Protection of Ecological Environment of Guizhou Province, Guizhou Normal University, Guiyang 550001, China;
3. Nanjing University Ecology Research Institute of Changshu (NJUecoRICH), Suzhou 215500, China
塑料污染已在世界范围内引起广泛重视[1]. 据预测, 到2050年全球塑料总产量将达到3亿t[2], 其中约有1.2亿t会进入自然环境[3]. 塑料能够在环境中转化为更小的塑料碎片, 尺寸小于5 mm的碎片称之为微塑料(microplastics, MPs)[4]. 水体中的微塑料威胁各营养级生物的健康, 如影响藻类的光合作用[5], 阻碍高等植物蛋白质合成[6], 鱼类摄食微塑料后会产生生长抑制和氧化应激等[7, 8]. 微塑料汇入沉积物后, 能够损伤底栖动物的组织并诱导炎症反应[9, 10]. 而微塑料通过食物和物理接触进入人体后, 也会对人体健康造成潜在威胁[11]. 此外, 微塑料具有的大比表面积、疏水性等物化特性会使其成为重金属[12]及有机污染物[13]的载体, 引发潜在的联合毒性[14]. 因此, 当下迫切需要对淡水水体和沉积物的微塑料进行风险评估.
微塑料的风险评估仍处于起步阶段, 目前国内外相关研究呈井喷式地增加, 生态风险指数[15]、Nemerow指数[16]和健康风险指数[17]等评估体系都被应用于微塑料的风险评估中. 污染负荷指数(pollution load index, PLI)法因其简单易行, 适用面广[18], 被广泛应用于各类水体和沉积物的微塑料风险评估中. 一些风险模型也受到学者们的关注, Zhang等[19]通过SPRC(source-pathway-receptor-consequence)模型对长江口微塑料风险进行了较为全面地评估. 但这些手段并未充分考虑微塑料对生物的影响, 且不同类型的微塑料会对结果造成较大影响, 在淡水领域开展的风险评估研究也相对匮乏[20 ~ 22]. 近年来, 物种敏感性分布(species sensitive distribution, SSD)模型被广泛应用于微塑料的风险评估中, 该模型通过累计分布函数来估算对x%的物种造成影响的污染物浓度阈值(HCx)[23], 并与环境实测值比较得到风险商(risk quotients, RQs), 有较高的灵活性和普适性. Jung等[24]利用SSD对韩国海域的微塑料进行了风险评估, 并强调了粒径的重要性. Kim等[25]计算土壤中的微塑料风险阈值, 绘制了不同组成和不同形状微塑料的SSD曲线. Liu等[26]结合土壤和水体暴露数据对大气微塑料进行了曲线绘制. 目前利用SSD建立风险阈值的方法评估河流水体和沉积物微塑料生态风险的研究仍然较少.
筑坝能够破坏河流的连通性, 改变流域水文条件和功能服务, 其形成的水库显著地增加了河流的滞留时间[27, 28], 拦截了水体和沉积物中的各种污染物[29]. 有研究表明闸坝的建设会截留河流水体中的微塑料[30], 流速的减缓增加了微塑料的沉降时间, 从而使水库表层沉积物中的微塑料丰度更高[31, 32], 水库沉积物成为了河流微塑料一个重要的汇, 但对其影响机制、不同种类微塑料截留效率及其造成的生态风险仍不了解. 因此, 研究闸坝影响下微塑料风险评估对淡水环境中的微塑料带来的生态影响有着重要意义, 本研究选取典型多闸坝河流——沙颍河上10个闸坝的上游河道和水库的水体及沉积物, 调查微塑料的丰度和组成, 利用两种体系进行生态风险评估, 试图厘清闸坝的建设对河流微塑料风险造成的影响.
1 材料与方法 1.1 研究区域及采样点布设沙颍河是淮河的最大一条支流, 流域面积超过3.6万km2, 覆盖超过40个县(市), 以纺织、建材等轻工业为主的布局产生了相当的塑料污染[33]. 流域内降水在时间和空间上分布不均匀, 全年65%以上的降水集中于6~9月[34], 因此流域内修建了多座闸坝以控制流量. 根据闸坝的代表性和可行性, 在尽量涵盖全流域的上下游的情况下, 在沙颍河主要支流及其干流上共选取了10个大坝作为样点(表 1和图 1), 分别在大坝水库(RSV)和大坝上游3 km (UP)左右进行采样, 两个取样点之间避开排污口, 共计20个采样点.
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表 1 采样点位信息1) Table 1 Locations of sampling sites |
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图 1 采样点位示意 Fig. 1 Map of sampling sites |
使用5 L的不锈钢采水器在每个采样点收集25 L地表水(0~25 cm深), 原位用1 340目不锈钢筛网过滤后用超纯水冲洗筛网3次, 冲洗水用0.22 μm聚四氟乙烯(PTFE)滤膜进行抽滤, 并将滤膜转移至玻璃瓶. 沉积物样品使用Van Veen抓斗进行采集, 每个点位收集3次, 每个样品由环刀在抓斗中采集的3个沉积物芯(100 cm3)混合而成. 所收集的样品分别装入玻璃瓶中, 为方便后续分析, 将沉积物样品储存在-20℃的冰箱中.
1.3 微塑料分离及鉴定微塑料的分离在实验室完成. 将滤膜上的微塑料用蒸馏水冲洗至玻璃瓶中, 在室温遮光的条件下用30%的H2O2处理1 d, 并用0.22 μm PTFE滤纸进行抽滤. 沉积物样品用锡纸包裹于50℃下烘干至恒重后, 依次使用饱和NaCl和ZnCl2溶液进行浮选, 两次浮选各重复至少3次. 之后, 将沉积物上清液和收集到的水样利用NaOH消化8 h, 最后同样用0.22 μm PTFE滤纸进行抽滤[33]. 使用微型傅里叶变换红外光谱仪(μFT-IR)与衰减全反射(ATR)附件(Nicolet iN10 MX, 透射模式, Thermo Fisher Scientific, 美国)进行组成分析, 波长范围为400~4 000 cm-1. 样品聚合物类型根据特征红外峰的存在和FTIR聚合物光谱库中匹配光谱的相似性来确定.
1.4 风险评估 1.4.1 污染负荷指数PLI是由Tomlinson等[36]提出的一种衡量地区重金属污染的评估指标, 可以用来综合评估区域的微塑料污染情况. 其评估公式如下[37, 38]：
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(1) |
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(2) |
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(3) |
式中, ci为采样点i的实测丰度, c0为微塑料背景污染丰度, 其比值CFi表示污染因子, n为水库中采样点的数量. 本研究中c0为实测的受到污染较少的上游河段中微塑料丰度, 对沉积物而言为2 n·kg-1, 对水而言是0.28 n·L-1. 将PLI低于10称为低生态风险, 10~20称为中等生态风险, 20~30称为高生态风险, 大于30则称为极高生态风险[39].
1.4.2 风险商RQ采用欧盟建议的体系进行评价, 该方法原理是利用预测环境浓度(PEC)和预测无效应浓度(PNEC)进行比较, 从而判断环境中的污染物是否会对本土生物造成不利影响. 其中, PEC用环境实测浓度(MEC)替代, 计算公式如下[40, 41]：
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(4) |
当RQ在0.1~1时, 表示存在中度生态风险, 当RQ大于1时, 表明当前污染物对环境造成了较大影响, 应当及时治理, 以防对当地生态系统造成破坏.
PNEC的数值由物种敏感性分布(SSD)推导得到[42], 本研究采用环境保护部的《淡水水生生物水质基准制定技术指南》(HJ 831-2017)[43]推荐的慢性毒性终点无观测效应浓度NOEL和最低观察浓度LOEL, 最后利用该曲线获得一个能够保护系统大部分生物的污染阈值[44]. 一般来说, 5%的危害浓度(hazardous concentration for 5% species, HC5)带来的生态风险是能够接受的[45], 以HC5作为河流微塑料风险阈值, PNEC计算公式如下：
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(5) |
式中, AF为评估因子, 根据欧盟标准, AF选取5[46].
依据我国相关规范, 曲线构建至少选取3个营养级的5个不同物种的毒性值[43]. 使用WOS和ScienceDirect网站收集淡水中的微塑料毒性数据, 用Leusch等[47]提供的方法统一单位为n·L-1, 最终收集5门11科共14个数据, 毒性数据如表 2所示. 拟合方式选择澳大利亚和新西兰毒性物质水质标准推荐的BurrⅢ型函数进行曲线拟合[48], 该函数对慢性毒性数据拟合效果较好, 其表达式如下[49]：
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(6) |
式中, x为环境浓度, b、c和k为函数的3个不同参数.
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表 2 微塑料毒性数据 Table 2 Toxicity data for microplastics |
1.5 数据处理
经检验, 所有微塑料数据符合正态分布, 显著性分析使用IBM SPSS 26进行t检验. 此外, 为了分析整个沙颍河流域的微塑料存赋特征, 将S1~S6作为沙颍河上游流域, S7~S10作为下游流域, 地图绘制使用ArcGIS 10.6完成, 数据绘图使用Origin 2021完成.
2 结果与讨论 2.1 微塑料分布及组成沉积物和地表水样品中均检测到微塑料. 地表水的微塑料丰度范围在0.12 ~ 2.46 n·L-1, 沉积物的微塑料丰度在54 ~ 765 n·kg-1变化(图 2), 整个沙颍河流域地表水中呈现出流域下游微塑料丰度显著高于流域上游的趋势(P < 0.05), 这种趋势在沉积物的丰度变化中也得到体现, 但无显著性(P ≥ 0.05). 据此推测微塑料能够以地表水和沉积物为介质进行迁移积累, 这在先前的研究中也得到验证[61]. 大坝会增加微塑料的沉降时间[62], 导致水库沉积物中的微塑料丰度增加. 此外, 相关研究表明流域中微塑料分布也会受到用地类型和土壤条件等因素的强烈影响[63]. 沉积物中微塑料丰度显著高于地表水(P < 0.05), 说明无论是在上游河流还是水库中, 沉积物都是地表水微塑料重要的汇.
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图 2 沙颍河流域微塑料丰度 Fig. 2 Microplastics abundance in the Shaying River Basin |
通过对比闸坝上游河道(UP)和水库(RSV)微塑料丰度发现, 水库地表水和沉积物中的微塑料丰度[地表水微塑料丰度为(1.27 ± 0.30) n·L-1, 沉积物微塑料丰度为(421 ± 73) n·kg-1]均高于上游河道[(1.08 ± 0.23) n·L-1, (304 ± 56) n·kg-1], 这与以往的结果相似[30, 32, 64]. 这表明大坝拦截了水体和沉积物中的微塑料并储存在水库中, 其原因可能是大坝降低了河流流速. 因此, 未来应当聚焦于不同季节, 大坝的不同运行条件下对微塑料分布的影响. 值得注意的是, 现有的研究不能证明大坝会改变大尺度流域上下游之间的地表水和沉积物微塑料丰度[65, 66], 微塑料在闸坝影响下的大流域尺度赋存特征仍缺乏认识.
沉积物的微塑料的颜色和形状组成较水体更加丰富(图 3), 可能是因为沉积物作为微塑料的汇. 地表水条状和颗粒状微塑料各占据了40%, 而沉积物占据主导位置的是条状微塑料. 条状微塑料的来源可能是城市废水中居民的化妆品和塑料瓶等[21], 城市的雨水渠也会增加其比例[67]. 值得注意的是, 不同形状的微塑料对生物造成的毒性效应不同, 纤维状的微塑料更容易在水生生物的肠胃中积累[68], 而球状微塑料的生物毒性相对较低[69]. 未来微塑料监测中应特别注意纤维状微塑料. 在地表水和沉积物中, 黑色微塑料平均占比都超过了30%, 其次是白色和棕色, 而绿色、红色和紫色的微塑料仅在沉积物中检测到. 造成该结果的原因可能是降解过程中的褪色和分解过程产生的漂白性有机物[70], 此外, 水生生物也会将彩色的塑料颗粒当作低级有机物误食[71].
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图 3 沙颍河微塑料组成 Fig. 3 Shape, color and type distribution of microplastics in Shaying River |
闸坝的建设并没有改变地表水微塑料的形状、颜色分布, 上游河道(UP)和水库(RSV)中微塑料形状颜色几乎没有差异, 但水库沉积物中的微塑料较上游沉积物更加多样, 闸坝长时间的沉降作用可能丰富了水库沉积物中的微塑料.
在每个水库样点的沉积物和地表水中均检测到了聚乙烯PE和聚丙烯PP, 作为用途最广和产量最大的塑料材料, 它们被广泛应用于农业、建材和服装等[72]. 但可发性聚苯乙烯EPS[73]、涤纶树脂PET[74]和聚硅氧烷[75]等都能够成为河流中主导类型的微塑料, 这表明其种类组成受到当地工业及人口等因素的强烈影响. S10沉积物中检测到的涤纶树脂PET和尼龙PA并未在上游沉积物和地表水中检测到(图 3), 这可能是因为不同种类微塑料的密度差异[76]、协同沉积物迁移作用[61]及更长时间的沉降.
2.2 SSD曲线绘制根据表 1的风险数据进行地表水微塑料SSD绘制, 毒性数据使用对数处理. 拟合结果为b = 8.4, c = 6.3, k = 0.2, 决定系数R2 = 0.972 2, 大于HJ 831-2017要求的0.6[43]. 拟合曲线如图 4所示, 物种敏感性自下而上增高. 最终得到地表水预测无效应浓度PNECw = 38/5 = 7.6 n·L-1, 介于前人计算的4.92 ~12 n·L-1之间[24, 77], 差异可能来源于拟合函数及研究区域的不同. 值得注意的是, 微塑料毒性试验的单位不统一限制了毒性评价的研究, 以μg·L-1[78]和mg·kg-1[79]单位的研究均有报道, 造成这种结果的原因很大程度上来自于分析仪器选择的差异, 这将导致进行阈值对比时的困难, 相关毒性研究较少也是当前需要解决的主要问题之一.
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图 4 物种敏感性拟合曲线 Fig. 4 Species sensitivity distributions curve for organisms |
PLI和RQ的评估数据如表 3所示, 沙颍河的微塑料评估结果表明所有样点均未检测到极高生态风险(图 5). S4下游的所有点位为中等生态风险以上, 高等生态风险仅在下游地表水中检测到, 这是因为PLI的计算与环境本底值有关. 沉积物HC5采用文献[80]利用体积矫正后的结果(4.9×109 n·kg-1), AF同样选择为5, PNECs = 9.8×108 n·kg-1. 最终, RQ和PLI的计算结果如表 3所示, 评估结果如图 5所示.
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表 3 各样点生态风险值 Table 3 Ecological risk values of sampling sites |
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红色、黄色和绿色分别表示高等、中等和低生态风险, 柱的形状和高度分别表示点位和风险 图 5 沙颍河流域生态风险 Fig. 5 Ecological risk in the Shaying River Basin |
两种评估结果均显示, 沙颍河流域的大坝修建并没有显著增加地表水(P ≥ 0.05)和沉积物(P ≥ 0.05)中的生态风险, 但风险商评估体系下的沉积物风险极低, 几乎可以认为沉积物中的生态风险为0. 地表水的微塑料风险显著高于沉积物(P < 0.05), 但PLI计算结果却显示出沉积物具有较高的生态风险, 推测是因为构建SSD曲线时着重考虑了微塑料对生物的影响, 而底栖动物可能从沉积物中摄食微塑料较少, 较高的微塑料丰度并不会对底栖动物造成影响, 在一些海洋沉积物的研究中, 证实了底栖动物并不会积累沉积物中高丰度的微塑料[81]. 因此, 在未来的生态监测中, 应当特别注意地表水中的微塑料丰度.
风险商的评估结果展示了沙颍河中微塑料对水生生物造成的生态影响, 这是一种重要的生态风险评估指标, 弥补了PLI等传统评估方式对生物多样性和生物毒性的忽视[82]. 但需要说明的是, 本研究建立的风险阈值并未充分考虑微塑料对人类的影响. 近几年的研究显示, 在人类的痰液、血液和内脏等均存在微塑料[11], 未来应当持续关注微塑料对人类的影响. 在SSD构建风险曲线时, 通过分析目标人群的食物来源和接触方式等对SSD进行目的性的优化[83]. 进一步结合地区与人群特性, SSD也能够被广泛用于水质标准的计算中[84], 为微塑料的环境基准及相关政策指定提供技术支持.
微塑料的形状、粒径大小和塑料种类都会对评估结果造成影响, Lithner等[85]对不同种类的塑料进行了毒性评级, 孙晓楠等[86]基于该等级对评估结果进行了调整, 表明不同种类的微塑料对风险商会造成影响, 尽管研究人员尝试归一化不同粒径和形状的微塑料带来的生态风险, 也尝试建立一些标准的评估体系[87], 但目前仍然缺少一个受广泛认可的归一化评估模型, 这导致微塑料评估体系较为繁杂. 此外, 采样方式的差异[88], 分析微塑料时不同仪器的使用[89], 毒性试验终点的选择, 都会造成评估风险误差. 这些因素使世界不同区域和历史不同时期的微塑料风险比较复杂化. 未来应当更加深入地探究微塑料的物理特性如何影响评估结果, 深入探究微塑料与其他污染物的联合生态风险, 并构建统一的采样分析体系和归一化的风险评估体系.
3 结论(1) 本研究对沙颍河流域的10座大坝的上游及水库的水体和沉积物的微塑料丰度和组成进行了研究, 并进行了生态风险评估. 在流域内地表水和沉积物中分别检测到0.12 ~ 2.46 n·L-1和54 ~ 765 n·kg-1的微塑料. 利用PLI和RQ进行风险评估后, 表明沙颍河流域的微塑料都存在一定程度的生态风险, 且微塑料对地表水带来的生态风险更为严峻, 未来仍需要对沙颍河的微塑料进行持续监测与治理, 并重点关注地表水的风险.
(2) 大坝的建设不会显著影响微塑料的组成和生态风险, 但大坝带来的水文条件改变会导致水库的沉积物成为微塑料的汇, 应当关注大坝沉积物的微塑料积累.
(3) SSD评估体系更加注重污染物对水生生物的毒性影响, 传统PLI等评估手段忽略了生物积累、摄食行为与微塑料丰度之间的关系, SSD能够弥补这种缺陷, 如能结合当地生态多样性及毒性数据, 则更能做到因地制宜地评估. 此外, 淡水中的微塑料毒理数据是匮乏的, 在沉积物中体现得尤为明显, 已有的数据同样缺乏规范性, 在微塑料的计量单位、粒径和种类选择都有体现, 这种差异可能阻碍SSD在微塑料风险评估中的应用, 未来还需要开展更多的研究来完善并规范微塑料的毒性数据库.
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