2022, Vol. 34 
(2: 中国科学院地理科学与资源研究所, 资源与环境信息系统国家重点实验室, 北京 100101)
(3: 中国科学院大学，北京 100049)
(4: 北京市生态环境监测中心，北京 100048)
(5: 新西兰农业科学院，汉密尔顿 3240)
(2: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, P.R.China)
(3: University of Chinese Academy of Sciences, Beijing 100049, P.R.China)
(4: Beijing Municipal Ecological and Environmental Monitoring Center, Beijing 100048, P.R.China)
(5: AgResearch Limited, Ruakura Research Centre, Hamilton 3240, New Zealand)
农业面源污染指在农业生产和农村生活区域, 氮、磷等营养盐及其他污染物受水力驱动以随机、分散、无组织方式进入受纳水体引起的水质恶化[1]. 农业面源污染对水体污染的贡献率在世界大部分国家超过了50 % [2]. 我国作为农业大国, 同样面临农业面源污染这一重大环境问题[3]. 两次全国污染源普查数据显示, 2007年我国农业源总氮、总磷排放量分别占排放总量的57.2 % 和67.4 %, 到2017年数值有所降低, 但仍占到46.5 % 和67.2 % [4-5], 这说明农业面源是我国当前乃至未来一段时期的重要污染源.
监测、规划、实施、评估等一系列系统性方法的综合使用是农业面源污染有效控制的前提和基础, 其中监测是极其重要的一环. 各国近年来开展了许多水质监测项目用于流域管理. 这些项目的制定一般遵循以下几个目的：1)摸清当前的水质现状[6]; 2)解析点源和面源污染[6]; 3)为流域模型提供数据支持[7-8]; 4)在确定边界条件和监测时间的前提下对河流的长期污染趋势进行评估[9-10]; 5)评估最佳管理措施(best management practices, BMPs)的有效性, 为政策和管理措施的制定提供指导[9-11].
相较于森林/草地主导的流域, 农业小流域受灌溉和施肥等农业活动的影响, 流域内河流的水文水质有较高的时空变异性[12]. 此外, 在农业主导的流域, 施肥期往往和高流量期(晚春季节或早夏)耦合, 导致季节性的通量估算偏差变大[13-16]. 在管道排水的流域(如城市和现代农牧区)[17-19], 由于雨污的收集设施较为完善, 可将样点布设在管道节点及出口处. 而传统农业主导的流域, 由于其污染物的广域分散性、运输途径的多样无序性和水文水质的高时空变异性, 采样断面的布设需要考虑的因素更为复杂[20]. 《流域农业面源污染监测技术规范》(NY/T 3824—2020)中规定了流域农业面源污染监测技术的监测断面设置与采样、监测指标及方法、流域农业面源污染的结果表达与质量控制等要求[1]. 尽管目前面源污染的控制取得了极大的进步, 但如何使面源管控措施兼顾经济性和有效性, 还需要进一步的研究.
河流断面污染物浓度测量及通量估算可以表征各类污染物对河流水质的影响, 是各类模型校验的主要数据源, 也是进行流域主要污染源和污染区识别的直接依据. 流域研究方案的制定, 是在了解流域基本情况的基础上, 对流域水质采样断面的位置、采样的时间和频率进行确定[21]. 本文从农业小流域水质监测的采样点的布设、采样频率的确定和通量估算方法的选择入手, 梳理了国内外研究进展, 旨在为我国的小流域农业面源污染监测设定和最佳通量估算方法的选择提供参考.
1 采样位置和采样方式的确定河流断面监测是获取流域水质基本特征, 制定管理决策的重要基础手段[22]. 具有代表性的样本采集, 对水质的准确表征和管理至关重要, 在流域决策中也具有重要意义[23-24]. 采样位置的选择可以影响监测的结果, 是能否达到监测目标的决定因素[25]. 水样的采集和分析需要巨大的经济投入, 这是我国河流水质监测点布设数量的主要限制因素[26]. 如何在资金预算有限的前提下, 合理布设水质监测断面, 优化各监测指标的采样频率, 是亟需解决的问题.
1.1 流域监测类型流域监测可根据监测需求划分为3种类型：第1类是常规监测采样设计或概要设计(probabilistic design), 即采用随机的方式在流域进行采样点设置, 该方式主要为了能够对流域有较为公正客观的认识和评价; 第2类是针对性监测设计(targeted design), 此种方式是较为常用的设计方式, 是根据对具体问题的认知或即将发生事件的认知进行采样点设计的监测类型, 比如研究最佳管理措施的应用效果; 第3类是融合前两种类型的监测采样设计(combination of targeted and probabilistic). 针对性监测设计的主要分类及优缺点的比较见表 1[27].
1.2 样点布设方法
自1970s以来, 地表水质监测网的布设便受到了广泛关注, 针对水质监测断面的设置也开展了很多研究[28]. 采样断面一般分为背景断面和控制断面两类：背景断面应布设在基本未受人类活动干扰的河流源头位置; 控制断面应布设于顺直河段、河床稳定、水流集中、无浅滩处, 避开死水区、回水区、排水口处[1]. 背景断面的布设考虑因素较为简单, 但控制断面的布设需要对流域特征和监测目的进行综合考虑, 样点布设的合理性是后续进行数据分析的前提[28-29], 如果采样点的代表性不足，采样频率、数据分析和呈现的特征就变得无关紧要[23].
最初水质样本的采集只考虑便捷性, 如在桥下布设采样点[30], 随着地理信息系统技术和数学算法的发展, 水质采样点的布设方法有了很大的进步, 如遗传算法[31]、模糊逻辑法[32]、熵值法[33]和模型法[34]等. 下面介绍几种常用的水质采样点设置和优化方法.
1.2.1 河网拓扑法该方法将流域上游最小的不分枝支流指定为一级支流, 将仅由一级支流组成的河流定义为二级支流, 从而确定河流的等级. 河流等级确定后, 通过寻找质心的方式找到采样点的位置, 并通过计算流域出口处河网的连接点数目获得流域采样点个数[35-36]. Asadi等考虑了每个采样点的潜在污染，并将此方法在伊朗进行了应用[37]. 该方法可有效地识别潜在的污染源[38], 简单且对历史水质数据的需求较低, 适用于河流分枝不多的大流域, 如果分枝的数量过多(如分枝超过100时)或没有分枝时, 采用此种方法会存在困难[23-24].
1.2.2 模糊逻辑法该方法主要根据模糊函数计算的值对监测站进行排序, 选取耕地面积百分比、建成面积百分比、面源污染量、绿化覆盖率、滑坡面积比、山坡过度利用率、水质监测站密度等指标, 采用加权法对子流域进行评分, 在得分较高的子流域增加水质监测站, 该理论结合多准则分析提高了数据分类和排序的客观性[32]. 该方法在中国台湾、伊朗等地区得到了很好的应用[39]. 模糊理论在复杂决策过程的情况下具有以下优势：1)理论规则简单, 易解释和理解; 2)是一种高级分类方法, 通过模糊隶属函数处理信息的不确定性; 3)适用于需要通过区分等级之间的实际差异来对优先级进行排名的决策过程[39]. 模糊方法被认为比传统的分类模型(如自然断裂法)更客观、更准确, 为了节省预算, 在模糊理论法指定为一级的子流域内新建水质监测站更为可行[32].
1.2.3 遗传算法该方法定义了适应度函数, 该函数为河流系统的代表性、水质标准、用水监督、污染源监测和水质变化检查5个对开发监测系统至关重要的标准的线性组合. 利用地理信息数据对适应度函数进行一系列计算, 得出适合度等级, 并对世代数、种群大小、交叉和变异概率等主要参数进行敏感性分析, 以确定良好的适应度水平和最优解的收敛性. Park等[31]利用遗传算法和地理信息系统相结合的方法, 设计了一个有效的大型河流水质监测网络. 虽然用遗传算法得到的监测网的优化设计优于传统的主观设计[22], 但还需要进一步的研究来定义适应度函数, 使得这些函数可以代表选择标准之间的综合关系或优先级.
1.2.4 熵值理论该方法利用熵值理论, 在考虑系统中最大不确定性和最小冗余信息的前提下, 选择监测网中采样点和采样频率的最佳组合. 先利用河流水质模拟模型生成河流沿岸几个潜在监测点的水质数据, 再利用熵理论对含有冗余信息的台站进行剔除. 该方法在伊朗南部的Karoon河进行了应用[33], 可以方便地对现有的监测网络进行评价和修正, 但要求现有采样网络有足够数据, 以获得具有统计显著性的结果[40].
1.2.5 模型法TWSST(targeted watershed site selection tool)模型由美国威斯康辛州自然资源部研发[34], 该模型进行监测点位布设的原理是先利用秩相关检验和典型相关找到水质和参数的关系, 进而通过K值聚类分析对河网进行分组, 使得每组的各水质参数绝对差异化, 从而得到流域监测点位的分布. 该模型可以采用先验方法进行有效的流域监测设计, 也可以通过后验方法确定监测位置所代表的空间范围.
河流物理、化学以及生物特性受到自然和人为因素的干扰, 流域的监测断面位置和监测断面密度需要能够反映这些自然和人为因素, 这就要求设计者对流域内影响因素的分布和未来的变化趋势有清晰的了解[23, 41]. 因此, 流域采样点的空间布设, 需要结合地理和人为要素, 充分利用地统计分析方法的优势, 结合多变量统计方法和优化算法, 对采样点位置进行确定[38].
1.3 采样方式采样的方式可以分为4种类型：随机采样、复合采样、综合采样以及连续采样. 其中, 随机采样指的是在某个地方一次性采样, 并不适用于需要考虑采样对象的空间异质性的采样, 如分层的湖泊、水质随时间的变化(如降雨事件的影响); 复合采样方式是一系列随机采样的集合, 可以分为时间加权和流量加权两种采样类型, 时间加权指在固定时间间隔进行采样, 流量加权指流量到一定的峰值进行采样; 综合采样指考虑水体水质空间变化, 将不同位置的采样点混合后作为原始样品的采样方式; 连续采样则需借助自动监测设备进行, 一般用于溶解氧、pH、电导率等指标的监测. 表 2列出了不同采样方式能实现的研究目标, 从表中可以看出复合采样适合大多数研究[27, 42].
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表 2 采样方式及其主要特征 Tab. 2 Sampling methods and its main characteristics |
不同的研究目标对监测的频率、时间和数据分析强度的要求不同. 监测频率受研究对象的影响较大, 如措施的有效性监测需要比趋势监测采用更高的采样频次, 而趋势监测则需要更长的监测时间[43]. 表 2列出了不同监测类型所需要的监测频率、持续时间和数据分析强度[42].
1.4 采样频率的确定与调整在监测项目设计过程中, 设计者对监测位置的布设给予了高度关注, 但对监测频率的关注却并不充分, Nguyen等评估了59项研究, 只有1项研究考虑了监测频率和监测成本[44]. 当前, 流量已可以用现场仪器连续或近连续测量(水位-流量传感器可以通过水位和流量的特定关系将测量数据转换为流量数据)[45-46], 测量精度也较高. 例如, 美国地质勘探局的7个测站的流量的测量效果较好, 测量误差仅为5 %~10 % [47]. 而水质指标的浓度测量由于人力和财力的限制大部分还是离散采样(如1次/周或1次/月)[48-50], 这导致在通量估算过程中, 样品收集带来的不确定性较高, 达到了累计不确定性的±4 %~±50 % [51]. 虽然有研究表明采样频率越高, 对污染物动态变化过程的描述越精确[52-53], 但是高频率水质监测需要耗费较多的人力物力, 往往不太现实. Kovács等研究发现将采样频率从1次/d降低到1次/3 d, 可以节省大约50 % 的成本[54]. 因此, 合理地设置采样频率可以减少样品采集和测试中产生的成本消耗. 国内外对采样频率对通量估计影响的研究总结见表 3.
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表 3 采样频率和算法对流域通量估算影响研究总结 Tab. 3 Summary of the influence of sampling frequency and algorithm on fluxes estimation |
虽有部分学者提出采用时间序列自相关分析结合数据分布特征来设定较为现实合理的监测频率[49, 55], 但多数仍是采用偏差分析的方法比较不同采样频率相对于最高采样频率下平均浓度的误差值, 从而得到满足一定误差范围的合理采样频率[52, 56]. 通常采样频率降低时, 检测具有统计学意义趋势的能力会下降[57], 通量估计的准确度也会随着采样频率的降低而降低, 并且采样频率的降低对不同的水质指标造成的误差结果不同[58]. 河流水文监测的不确定性易受水文特征和流域特征的影响. 水文特征包括：水文响应关系、浓度-流量相关关系、基流指数(base flow index，BFI)和变率指数(flashiness index，FI)等[49, 59-61]; 流域特征包括人口密度、流域尺度等[62-63]. 因此, 采样频率的设定取决于流域特征[62], 尤其是水文条件[49]和浓度行为[64].
对Loire河的研究结果表明, 当采样频率定为1次/月时, 总氮(TN)年通量估算的均方根误差(RMSE)范围为6.6 %~20.4 %, 硝态氮(NO3--N)为4 %~26 %、溶解态活性磷(SRP)为5 %~33 %、颗粒态磷(PP)为6 %~18 %, 总磷(TP)为13 %~42 %; 此外, TN的年通量估算精度(standard deviation, SD)为8.8 %, NO3--N的SD为13 %, TP的SD为20 %, SRP的SD为26 %, PP的SD为34 % [65]. 因此, 对于不同指标, 最佳采样频率的设定略有不同. 在大型流域, 采样频率对误差的影响不大, 但对精度有近线性影响, 若要达到10 % 精度, NO3--N采样间隔为15天, SRP和TP采样间隔为10天, SS采样间隔为5天[65]; 而在管道排水的流域, SRP的频率设置为13~26 h、NO3--N设置为2.7~17.5天, 可以达到±10 % 的准确性[66]; 相较于森林流域, 农业流域的水文水质时空变异性较高, 因此准确地进行通量估算更为困难[12], 在面积为217 km2的山地农业小流域, 1次/2周的采样频率可以使TN年通量的RMSE控制在5 % 左右, 但这个频率却不足以分析通量的周期或季节特征(4个季节的平均RMSE高达17 %)[67].
1.4.2 采样频率的优化浓度与流量的复杂关系将会对通量估算产生影响[12, 68], 在高流量事件中, 流量和浓度之间通常存在强相关关系[16, 64], 水位上升期间, 物质浓度可能达到最大, 在第一次冲刷之后则有可能产生稀释效应, 浓度逐渐降低[69-70], 大量悬浮沉积物的运输可能只发生在高流量时期[71], 高流量事件贡献了营养物的主要年通量[46, 72]. 因此, 基于水文变异的采样方案对通量估算来说是最准确的[63], 并且河流监测采样应尽可能多地捕捉高流量事件, 至少累积涵盖年流量的80 %~85 % [46, 73-74], 这就要求在暴雨径流等事件发生时进行加密观测[56].
相较于自然植被覆盖的流域, 农业流域由于灌溉和施肥等农业活动的影响, 河流水文水质的时空变异性较高[12], 且存在特定的季节特征, 导致季节性的通量估算变异变大[13-16]. 此外, 在较小的流域或者流域上游水文响应关系较强, 水文特征的时空变异较大, 通量估算的偏差也较大[49, 62, 75-77]. 在这类站点, 低频率的采样不能捕获短时内发生的水文的快速响应[61, 75, 78].
由于变异因素的存在, 如研究区域独特的特征和特殊的河流水文状况、不同的算法、不同的污染物和研究持续时间等, 现有研究结果很难被普适化[48, 79]. 对于时间自相关性强的污染物序列, 其浓度变化稳定, 可在保证精度的前提下, 减少频率来降低成本; 而对于污染物时间变异性较大的序列, 若要反映真实的水质变化过程, 则需要提高采样频率[49, 55]. 因此, 需要及时进行数据分析, 对于时间序列变化相对不稳定的指标进行加密监测和频率调整. 《流域农业面源污染监测技术规范》(NY/T 3824—2020)中规定了加密监测时段, 分别为农业面源污染剧增期(耕作、施肥等农事活动密集, 且发生径流排水并引起控制断面水位明显变化的时段)和人口剧增时段(如重要节假日或旅游季节). 此外, 规范中还规定汛期监测频率应高于非汛期[1].
2 断面通量估算方法估算算法对通量估计的影响不如采样频率大, Vries和Klavers[102]在荷兰一个研究区的研究表明, 河流污染物的通量估算首先应考虑采样策略, 其次是算法. 针对当前连续/高频的流量数据和离散/低频的水质数据, 主要形成了3类计算通量的方法：平均法、插值法和回归/曲线法[58, 62, 82, 103-104]. 平均法使用实测数据的平均值作为时段数据的代表值; 插值法假定数据(浓度和流量)由瞬时采样数据表示; 回归(外推)方法取决于经验关系和水文水质的变化特征[65].
2.1 常用估算方法河流监测断面的污染物通量是断面流量和浓度的函数, 污染物通量的变化与降雨-产流过程有很强的相关关系[50, 105]. 监测断面污染物浓度与流量有正相关、负相关和无关3种关系[106]. 当前各学者采用实测断面平均浓度、样品时间平均浓度、断面瞬时流量、采样期间平均流量、采样代表时段平均流量等概念构造了多种监测断面通量估算方法. 在此, 对应用较多的8种通量估算方法进行了分析比较, 各种方法的公式及物理意义如表 4所示.
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表 4 通量估算方法* Tab. 4 Fluxes estimation methods |
从环境水力学角度对表中的估算公式进行分析, 可将监测断面的流量及平均浓度表达为时间平均的形式[93-94]：
| $ Q(t)=\frac{1}{T} \int_T Q(t) \mathrm{d} t+Q^{\prime \prime}(t)=Q_{\mathrm{a}}+Q^{\prime \prime}(t) $ | (1) |
| $ C(t)=\frac{1}{T} \int_T C(t) \mathrm{d} t+C^{\prime \prime}(t)=C_{\mathrm{a}}+C^{\prime \prime}(t) $ | (2) |
式中, Qa为时段平均流量; Ca为时段平均浓度; T为估算时段; Q″和C″分别为流量和浓度时均距平值. 时段通量可以通过式(3)表达：
| $ W=\int_T F(t) \mathrm{d} t=\int_T Q(t) \cdot C(t) \mathrm{d} t=Q_a \cdot C_a \cdot T+\int_T Q^{\prime \prime} \cdot C^{\prime \prime} \mathrm{d} t \approx \sum\limits_{i=1}^n Q_i \cdot C_i \Delta t_i= \\ Q_{\mathrm{a}} \cdot C_{\mathrm{a}} \cdot T+\sum\limits_{i=1}^n Q^{\prime \prime}{ }_i \cdot C_i^{\prime \prime} \Delta t_i $ | (3) |
在8种算法中(表 4), 算法A和D使用时间加权平均浓度值, 在高流量时具有稀释效果; 而算法B和C使用部分流量加权浓度数据; 算法E、F和G则是应用通量的基本定义[110]. 这些算法通过不同的方程来关联浓度和流量数据, 估计给定时间段内的通量. 将表 4中各方法与式(3)进行对比, 可以看到方法A、D实际上只包含了(3)中的第1项(对流项), 而忽略了第2项(时均离散项), 在使用过程中会反映出时间加权平均浓度对流量加权平均浓度的高估或低估; 方法A和D的差别在于方法A采用的是离散的实测流量平均, 方法D采用的是时段的平均流量; 方法B、C、E、F和G则两项都包括, 方法B和C的差别在于采用离散的流量平均还是连续的流量平均; 方法E则采用了流量加权平均浓度, 与时段平均流量的乘积作为时段通量; 方法F采用插值方法, 将离散浓度数据插值为高频浓度数据, 与监测获得的高频流量数据的乘积作为时段通量; 方法G利用浓度与流量的相关关系, 用流量预测浓度, 结合监测获得的高频流量数据得到时段通量; 方法H利用连续的流量监测和离散的浓度监测, 用流量和时间预测瞬时通量, 瞬时通量加和得到时段通量. 显然, 方法A~G均是先得到对应的流量和浓度数据, 再进行通量的估算; 方法H是直接以通量与流量或事件的回归关系得到瞬时通量, 要求流量数据具有连续性; 方法A、D仅适用于推断流域断面(断面流速均匀)的平均污染通量的估算; 方法B适合点源占优的情况; 方法C、E适合面源贡献较大的通量估算; 方法F适用污染物排放短时变异不强的情况; 方法G和H要求流量与浓度或通量具有较好的回归关系(表 5).
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表 5 时段通量估算方法的应用取向分析[94] Tab. 5 Application orientation analysis of the estimation methods for period-fluxes[94] |
由于不同方法的计算结果存在较大差异, 目前对估算方法的适用性研究是当前流域污染物通量研究的热点之一[110-112]. 研究表明, 由于污染物类型的差别, 在统计时段通量时(如年通量)对于不同的污染物应采用不同的估算方法[95, 97]. 在A~F这7种方法中, 基于流量的浓度估计算法(E)和插值算法(F)是大多数水质参数通量估算的首选算法[47, 65-66, 91]. 虽然对于NO3--N通量的估算采用方法E较为合适[58, 66], 但方法F的变异系数最小[47]. 同时, 方法F对于TN和TP的估算效果好于方法E[82]. 方法D对于TP(尤其是PP)的估算效果最好[65], 但在高流量时可能会由于稀释效应产生高估[82, 91]. 总体来看, 线性插值法(F)的表现效果是7种方法中最好的[65-66]. 由于方法H所采用的LOADEST模型在计算时会运用Akaike信息准则(AIC)和Schwarz后验概率准则(SPPC)对模型进行优化、检验, 因此, 在预测通量和观测趋势方面表现良好, 但普遍倾向于高估[113]. 基于方法H的通量估算的研究多采用低频的水质数据, 并且采样频率对结果误差的影响较小[100]. 此外, 对于G和H这类回归模型的估算效果不仅随水质参数的不同而不同, 也强烈依赖降雨事件样品所占的比例[114].
通常情况下, 使用污染物的年通量来评估当前水质的年内状况是无法实现的[6], 季节通量的估算往往不如年度通量的估算准确[60]. 污染源类型和丰/枯水期的不同也会影响估算方法的表现效果[92, 111], 夏季发生的暴雨事件会使污染物浓度产生更大的变异性, 导致通量估算的不确定性大于其他3个季节[66-67]. 此外, 在面源占优的情况下, 丰水期的监测采用较高的监测频率对提高长时间通量的估算精度是必要的[94, 115]. 因此, 有研究将丰水期和枯水期分别进行分析, 发现采用时段通量平均浓度与时段平均流量之积(方法E), 分别计算丰、平、枯季的污染通量, 然后相加求和来估算年通量, 是最简便精确的方法[93]; 也有学者通过比较不同水文阶段污染物通量估算结果, 提出枯水期采用以点源占优的方法B, 丰水期采用强调时段总径流量以及以面源占优的方法E, 平水期则结合相关的水文特征进行方法的选择[92]; 如果采样频率低且污染物输出与流量的相关性较好, 方法H是可以保证估算精准度的首选方法[100]. 此外, 也可以根据不同时期流域污染的点源和面源特征进行方法的选择[94, 96-97](表 5).
流域土地利用特征也会对通量估算效果产生影响. 由于农业活动具有明显的时空变异特征, 农业流域污染物输出的时空变异性也更高, 因此, 准确地进行通量估算要比森林流域更具挑战性[12, 16]; 此外, 通量估算方法的表现效果也受采样频率的影响, 一般采样频率越低, 表现效果会越差[67]. 由此可以看出导致通量测算结果不确定性的因素, 不仅包括通量估算方法, 还包括水文特征[80, 105]、污染源特征(点源或面源类型)[94, 115]、采样策略[116-117]、采样时间[98]和流域大小[82]等因素.
3 结语由于经济和地理因素, 当前我国农业面源污染监测断面设置数量较少, 尤其是偏远地区, 监测断面数量和监测频率有待提高, 监测断面布设和采样策略的制定也亟需科学化和标准化. 经济欠发达地区往往对先进技术和设备的投入不足, 这也在一定程度上影响到监测工作的质量和水平. 随着3S技术、信息技术和化学分析技术的发展, 我国农业面源污染监测在朝着标准化、自动化和智能化的方向发展. 未来还需建立科学的监测体系和标准, 加强人员培训, 改造和引进监测设备和技术, 促进监测站的标准化、规范化和自动化运营.
本文通过文献回顾的方式, 就农业小流域采样点布设、采样频率优化和断面通量估算方法3个主要环节进行了详细的分析和介绍. 虽然各流域的监测方案应在充分考虑监测目的、流域特征、水文特征和人类活动的前提下制定, 但通过文献回顾仍能发现一些共性的规律, 可为今后的监测项目提供参考.
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