2023, Vol. 44
2. 河北省污染防治生物技术实验室, 石家庄 050018
, ZHAO Bo1 , LU Meng-qi1 , ZHAO Xin-yu1 , CHEN Hui1 , CHEN Hao-da1 , GAO Sai1 , WANG Lin-jing1 , CUI Jian-sheng1,2 , ZHANG Lu-lu1,2
2. Biotechnology Laboratory for Pollution Control in Hebei Province, Shijiazhuang 050018, China
自1928年青霉素发现以来, 抗生素广泛用于疾病预防和治疗, 以及动物生长促进剂[1, 2].根据其化学结构, 抗生素可以分为喹诺酮类(quinolones, QNs)、磺胺类(sulfonamides, SAs)、大环内酯类(macrolides, MLs)、四环素类(tetracyclines, TCs)和β-内酰胺类(β-lactam)等[3].目前, 我国已成为抗生素最大的消费国和生产国, 如2013年我国抗生素的生产量已达24.8万t, 使用量高达16.2万t, 其中SAs、TCs和QNs的使用量分别占5%、7%和17%[4].然而, 抗生素不能完全被人体和动物吸收, 约有30%~90%会以母体化合物或代谢产物的形式随尿液或者粪便排出体外[5~7].随后, 抗生素通过各种途径源源不断进入水环境中[8], 例如：城市污水处理厂尾水排放、畜禽养殖废水、医疗废水和农业排水等[9~13].据统计我国每年约有2.47万t抗生素进入水环境中[4].在水环境中抗生素可通过食物网在生物体中进行生物累积和营养放大, 进而对水生生物和生态系统造成危害[14].此外, 抗生素还会诱导产生耐药菌(antibiotics resistance bacterias, ARBs)和耐药基因(antibiotics resistance genes, ARGs), 对人体健康和生态安全构成严重威胁[15].因此, 抗生素耐药性问题已成为21世纪人类面临的重大挑战之一[16].
目前, 已在河流[17]、河口[18]、海湾[19]和湖泊[20]等多种水环境中检出70余种抗生素.其中, 湖泊作为抗生素的重要储库, 在2019年排放到我国湖泊中抗生素总量高达5 711 t[21].整体而言, 我国湖泊中抗生素污染形势较为严峻, 如：洞庭湖[22]中已检出4大类12种抗生素(总浓度1.06~135.40 ng·L-1), 大通湖[23]已检出4大类抗生素(总浓度0.162~61.89 ng·L-1), 鄱阳湖[24]中已检出18种抗生素(总浓度ND~56.2 ng·L-1).湖泊中抗生素的研究多集中于其时空分布及其风险评价等[25~27], 而抗生素的源解析则较少关注.目前, 抗生素源解析的方法主要包括正定矩阵因子分解(positive matrix factor, PMF)模型[18]、Unmix模型[19]和多元线性回归主成分分析(PCA-MLR)模型[28]等.其中, PMF模型对解析结果具有非负约束的特点, 已广泛应用于抗生素的源解析.对环境管理者而言, 定量解析抗生素的污染源将有助于抗生素及其风险的科学精准管控.
白洋淀作为雄安新区的核心生态功能区, 将为新区的建设提供重要生态支撑[29].然而, 新区成立前, 白洋淀长期接收上游城市污水、工业废水、生活污水和周边养殖废水等, 导致淀区抗生素污染形势较为严峻.如水体中SAs检出率最高(78.1%), 其浓度为0.86~1 563 ng·L-1; 沉积物中QNs检出率最高(2.22%~100%), 其含量为65.5~1 166 ng·g-1 [30].然而, 目前有关白洋淀中抗生素的研究主要集中于其污染特征、生物累积及其风险评估等[31, 32].因此, 本研究选取白洋淀为研究区, 根据其土地利用和人为干扰特征, 选取13个样点, 分别采集水体和沉积物样品, 共选取3大类26种抗生素为目标物, 明晰白洋淀中典型抗生素的空间分异特征; 基于PMF模型定量解析水体和沉积物中抗生素的来源; 结合源解析和风险商值法(risk quotients, RQ)来评估特定源风险, 以期为白洋淀抗生素及其风险的精准管控提供理论支撑和科学依据.
1 材料与方法 1.1 研究区概况与样品采集白洋淀位于华北平原, 共有8条入淀河流, 包括府河、瀑河和潴龙河等.淀内主要有143个大小不等的淀泊和3 700条沟壕, 且此前淀内养殖业发达, 人口密集, 大量生活污水和养殖废水直排入淀, 加剧了白洋淀中抗生素的污染形势[33, 34].
2018年4月, 根据白洋淀土地利用类型和人为干扰特征并结合现场实际情况共设置13个采样点(图 1), 分别为：南刘庄(S1)、鸳鸯岛(S2)、烧车淀(S3)、王家寨(S4)、寨南(S5)、杨庄子(S6)、枣林庄(S7)、圈头(S8)、东田庄(S9)、后塘(S10)、采蒲台(S11)、范峪淀(S12)和金龙淀(S13).水体样品使用5 L棕色玻璃瓶采集, 密封保存, 使用保温箱避光运回实验室, 进行适当过滤后存放在4℃冰箱内, 待后续分析[35, 36].沉积物使用彼得森采泥器在各样点0~10 cm处采集表层约500 g样品, 去除碎屑和石子等异物, 装入聚乙烯密封袋中并进行标记[35, 37], 低温保存运回实验室, 放在-20℃冰箱中, 保存备用.使用便携式水质分析仪(HYDROLAB, DS5X)对水温(temperature, T)、pH和溶解性固体总量(total dissolved solids, TDS)等基本水质参数进行现场测定.
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图 1 白洋淀采样点 Fig. 1 Sampling sites of Baiyangdian Lake |
目标抗生素共3大类26种：包括6种QNs[氧氟沙星(ofloxacin, OFL)、恩诺沙星(enrofloxacin, ENR)、氟甲喹(flumequine, FLU)、氟罗沙星(fleroxacin, FLE)、诺氟沙星(norfloxacin, NOR)和马波沙星(marbofloxacin, MAR)], 11种SAs[磺胺嘧啶(sulfadiazine, SDZ)、磺胺噻唑(sulfathiazole, STZ)、磺胺吡啶(sulfapyridine, SPY)、磺胺甲基嘧啶(sulfamerazine, SMR)、磺胺二甲嘧啶(sulfamethazine, SMT)、磺胺对甲氧嘧啶(sulfameter, SME)、磺胺间甲氧嘧啶(sulfamonomethoxine, SMM)、磺胺氯哒嗪(sulfachloropyridazine, SCP)、磺胺甲基异
准确量取1 L水样经0.45 μm玻璃纤维滤膜过滤, 加入0.2 g的乙二胺四乙酸二钠(Na2EDTA), 用1 mol·L-1的硫酸溶液调节pH为3.0.用6 mL甲醇、3 mL盐酸(0.5 mol·L-1)和6 mL超纯水液使HLB柱活化, 然后以2~5 mL·min-1的流速通过HLB小柱进行萃取.上样后, 用10 mL超纯水淋洗并弃去淋洗液, 负压条件下抽空干燥30 min, 然后依次用6 mL体积比为2%的氨水甲醇溶液和6 mL纯甲醇溶液进行洗脱, 洗脱液经氮吹(40℃)至近干后, 用甲醇水溶液(甲醇∶水=1∶1, 体积比)定容至1 mL, 过0.22 μm滤膜并转移至棕色瓶中, 待上机分析[38, 39].
取部分冷冻干燥后的沉积物样品进行粉碎和研磨, 过40目筛, 放入10 mL离心管中.准确称取样品1 g, 与适量干燥的硅藻土(Na2EDTA处理过的)充分混合, 并以乙腈-磷酸盐缓冲液(pH=3)作为萃取液.使用ASE 350快速溶剂萃取仪(Thermo, Germany)进行萃取, 循环2次, 再用平行浓缩蒸发仪(Buchi, Switzerland)将萃取液浓缩至萃取剂小于1 mL, 转移至锥形瓶中, 使用超纯水稀释至200 mL, 其他操作遵循上面水样品的步骤[35, 40].
1.2.3 样品分析采用超高效液相色谱-三重四级杆串联质谱联用仪(HPLC-MS/MS)对样品进行测定, 使用Agilent 1200系列HPLC(色谱柱：C18, 2.1 mm×50 mm, 1.8 μm), 质谱为安捷伦6470三重四级杆质谱系统.流动相A为0.1%的甲酸水溶液, 流动相B为甲醇和0.1%的甲酸溶液, 流速0.3 mL·min-1, 进样量5 μL.质谱条件为电喷雾离子源, 采用多重选择检测模式(MRM), 干燥气温度为350℃, 干燥气体流速为11 L·min-1, 毛细管电压为±3 500 V, 雾化气压力为45 psi(310.5 kPa)[32].
1.2.4 质量控制采用内标法定量.配制浓度分别为0.1、0.5、1.0、5.0、10.0和100 ng·mL-1的6个系列标准溶液, 并设置空白组.经HPLC-MS分析获得质量浓度与峰面积的标准曲线, 相关系数均≥0.99, 各目标抗生素的回收率为72.4%~104.6%.
1.3 源解析方法PMF模型通过将样品数据分解成两个矩阵：因子贡献矩阵(G)和因子分布矩阵(F), 以及一个残差矩阵(E)[41], 其计算公式如式(1)：
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(1) |
式中, i为样品数; j为污染物种类; p为污染源数量.
通过PMF模型最小化累积残差Q值得到因子贡献与分布, 如公式(2)所示：
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(2) |
式中, n为样本数量; m为抗生素数量; uij为抗生素j的不确定度, 计算公式如下
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(3) |
式中, σi为抗生素浓度的相对标准偏差; c为抗生素浓度; MDL为方法的检出限.
本研究选取水体和沉积物中检出率大于30%的抗生素, 按(3)进行计算, 输入PMF模型进行源解析.随机选取20作为初始起点进行迭代计算, 取3~8个因子分别运算, 对比发现选择因子数为4时, R2均大于0.60且所有抗生素的残差都在-3和3之间并服从正态分布, 表明所选抗生素能够很好地被模拟.
1.4 生态风险评估多种抗生素同时存在于水体中会导致毒性作用加强[42], 因此本研究采取联合风险商(RQsum)来表征抗生素的生态风险, 其计算公式如下：
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(4) |
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(5) |
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(6) |
式中, RQ为单一抗生素的生态风险商[43, 44]; RQsum为联合风险商; MEC为实测浓度; PNEC(predicted no-effect concentration)为无效应浓度; LC50为半数致死浓度; EC50为半数有效浓度; AF(assessment factor)为评价因子.RQsum的分类标准：0.01 < RQsum≤0.1为低风险; 0.1 <RQsum≤1为中风险; RQsum>1为高风险.已有研究的相关数据见表 1.
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表 1 不同抗生素对应最敏感生物毒理数据1) Table 1 Data of the most sensitive biotoxicology for different antibiotics |
本研究中, 将结合不同样点的RQsum和源的贡献率Cp进行特定源风险评估, 如式(7)所示,
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(7) |
式中, Cp为水体中不同污染源的贡献率.
1.5 数据分析使用Microsoft Excel 2016和SPSS 26软件进行数据处理和统计分析; 用EPA PMF 5.0模型对抗生素进行源解析; 使用ArcGIS 10.7和Origin pro 2021软件进行绘图.
2 结果与分析 2.1 白洋淀中典型抗生素的污染特征在水体中(表 2), 共检出23种抗生素, 11种抗生素的检出率高达100%, 总浓度范围为252.1~2 957 ng·L-1, 均值为35.57 ng·L-1.就各类抗生素的平均检出率而言, TCs的检出率最高(100%), 其次为QNs(76.9%)和SAs(26.9%).就各类抗生素的浓度而言, ρ(QNs)范围为243.3~2 946 ng·L-1, 平均值为154.1 ng·L-1; ρ(SAs)范围为ND~9.75 ng·L-1, 平均值为0.25 ng·L-1; ρ(TCs)范围为8.48~13.77 ng·L-1, 平均值为1.14 ng·L-1.就单种抗生素的浓度平均值而言, 呈FLU>OFL>FLE>MAR>ENR>ATC>DMC>CTC>MCN>SMX的趋势.其中, ρ(FLU)最高, 其范围为144.5~2635 ng·L-1, 占抗生素总浓度的78.2%.
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表 2 白洋淀水体和沉积物中抗生素的检出情况1) Table 2 Detection of antibiotics in surface water and sediments in Baiyangdian Lake |
在沉积物中(表 2), 26种抗生素均检出, 检出率均在40%以上, 总含量范围为26.14~346.8 ng·g-1.其中, QNs的平均检出率最低(87.7%), 但其含量平均值最高(141.1 ng·g-1), 远高于SAs(0.24 ng·g-1)和TCs(1.27 ng·g-1).就单个抗生素的含量平均值而言, 呈OFL>FLU>FLE>ENR>MAR>OTC>DMC>ATC>CTC>MCN的趋势.其中ω(OFL)最高, 其范围为4.54~259.8 ng·g-1, 占抗生素总含量的59.8%.
2.2 抗生素的空间分异特征抗生素在水体和沉积物中的空间分布特征如图 2和图 3所示, 相关分析表明水体中各类抗生素均无相关关系, 而沉积物中QNs和TCs显著正相关(P < 0.01).
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图 2 白洋淀水体中喹诺酮类、磺胺类和四环素类抗生素的空间分布 Fig. 2 Spatial distribution of QNs, SAs, and TCs in the surface water of Baiyangdian Lake |
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图 3 白洋淀沉积物中喹诺酮类、磺胺类和四环素类抗生素的空间分布 Fig. 3 Spatial distribution of QNs, SAs, and TCs in the sediments of Baiyangdian Lake |
就水体中抗生素的空间分布而言, QNs在S5浓度最高(2 946 ng·L-1), 最小值出现在S12(243.3 ng·L-1), 总体呈“西高东低”的分布特征.SAs在S6浓度最高(9.75 ng·L-1), 而在S12未检出, 呈“中部高, 南北低”的分布特征.TCs浓度总体呈“中部低, 南北高”的分布特征, 其浓度最大和最小值分别出现在S13(13.8 ng·L-1)和S8(8.48 ng·L-1).
2.2.2 沉积物中抗生素的空间分异特征就沉积物中抗生素的空间分布而言, ω(QNs)最大值在S2(334.6 ng·g-1), 最小值出现在S5(15.68 ng·g-1), 总体呈“中部高, 东西低”的空间分布规律.SAs和TCs均呈“西高东低”的空间分布规律, 且最大值均出现在S1(8.54 ng·g-1和19.47 ng·g-1), 而最小值分别出现在S12(0.69 ng·g-1)和S5(9.20 ng·g-1).
2.3 抗生素源解析及相对贡献 2.3.1 水体中抗生素源解析水体中抗生素源解析结果如图 4和图 5所示.因子1对CTC的贡献相对较高, 贡献率为56.8%, 且相关性分析显示CTC与其他抗生素相关性较弱, 则其可能具有单一来源.CTC作为抗菌药物在渔业养殖中广泛应用, 检出率较高[53~55].丁慧君对环鄱阳湖水产养殖区的研究表明ρ(CTC)高达162.68 ng·L-1[56], 而淀内有众多养殖区, 因子1推断为水产养殖.
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图 4 基于PMF模型水体中抗生素源解析 Fig. 4 Source profiles of antibiotics in surface water obtained with the PMF model |
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图 5 水体中各因子对抗生素总浓度的贡献 Fig. 5 Contribution of each factor to total antibiotic concentrations in surface water |
因子2对SDZ和SMX的贡献率较高, 分别为62.4%和62.8%, 且二者显著正相关(P < 0.01), 表明其可能具有相同的来源.其中, SDZ是家用抗菌药的主要成分, 已有研究表明SDZ主要通过生活污水进入环境, 其浓度高达5.0×104 ng·L-1[57].而SMX用于治疗人类尿道感染, 其在各种废水中均占主导地位[58~60].淀内有众多的村落且人口密集, 此前大量生活污水直接排放入淀[61], 因子2推断为生活污水.
因子3对DMC和MCN的贡献率相对较高, 分别为58.4%和44.3%, 且二者显著正相关(P < 0.01), 则其可能具有相同的来源.研究表明DMC和MCN在制药工艺中常作为原料药和中间体[62, 63], 而河北省作为制药大省且研究区长期接收来自上游城市医药和工业等废水[64], 加之, 目前的污水处理技术不完善, 使得未完全降解的抗生素排入自然水体[65, 66], 因子3推断为污水处理厂.
因子4对MAR、FLE和OTC的贡献率相对较高, 分别为63.5%、64.0%和35.7%, 且MAR、FLE和OTC两两之间显著正相关(P < 0.01), 表明其可能具有相同的来源.作为典型的兽用药物, MAR、FLE和OTC在畜禽养殖中常用作饲料添加剂来预防和治疗疾病[67~69].淀区内家禽畜牧业的废弃物、粪便直接或间接入淀[70], 因子4推断为畜禽养殖.
2.3.2 沉积物中抗生素源解析沉积物中抗生素源解析结果如图 6和图 7所示.因子1对SMX(64.9%)贡献较高, 与水体中因子2相同, 且相关性分析显示SMX与其他抗生素相关性较弱, 则其可能具有单一来源, 因子1推断为生活污水.
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图 6 基于PMF模型沉积物中抗生素源解析 Fig. 6 Source profiles of antibiotics in sediments obtained with the PMF model |
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图 7 沉积物中各因子对抗生素总含量的贡献 Fig. 7 Contribution of each factor to total antibiotic contents in sediment |
因子2对FLE(71.0%)和ENR(50.3%)的贡献相对较高, 且二者显著正相关(P < 0.01).作为典型的QNs, 在养殖场常用来预防家禽疾病和感染, 研究表明ENR在养殖场中的检出量(1.27×104ng·g-1)远高于其他的抗菌药物[71], 因子2推断为畜禽养殖.
因子3对SPY(93.0%)的贡献较高, 且相关性分析显示SPY与其他抗生素相关性较弱, 则其可能具有单一来源.因SPY本身很少用作抗菌, 多源于其相关的代谢物[72].而目前的处理工艺对其去除率较低, 研究表明SPY在污水处理厂中的检出浓度高达35.9~64.8 ng·L-1[73], 因子3推断为污水处理厂.
因子4对STZ(64.9%)和SCP(54.7%)的贡献相对较高, 且二者显著正相关(P < 0.01).STZ和SCP因其成本低, 常作为鱼用饲料以治疗和预防水产品疾病[74, 75].阮悦斐等[76]已在天津近郊水产养殖区沉积物中检出STZ和SCP, 因子4推断为水产养殖.综上所述, 白洋淀中抗生素的主要来源为水产养殖.
2.4 风险评价 2.4.1 生态风险评价本研究对水体中抗生素进行风险评估(图 8).就各抗生素的生态风险而言, FLU在S5(RQ>1.0)处于高风险水平, 其余样点均为中低风险水平; ENR在S1(RQ>1.0)为高风险水平, S4、S5、S9和S11的RQ处于0.1~1.0之间, 为中风险水平; CTC的RQ均处于0.1~1.0之间, 为中风险水平; SMX在S4和S6的RQ处于0.1~1.0之间, 为中风险水平, 其余样点均为低风险水平; 其余抗生素均处于低风险水平.
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1.TC, 2.OTC, 3.CTC, 4.DC, 5.SDZ, 6.SPY, 7.SMT, 8.SMP, 9.SMM, 10.SCP, 11.SMX, 12.MAR, 13.FLE, 14.OFL, 15.ENR, 16.FLU, 17.RQsum 图 8 白洋淀水体中抗生素的生态风险评价 Fig. 8 Ecological risk assessment of antibiotics in the surface water of Baiyangdian Lake |
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图 9 白洋淀水体中特定源的生态风险(RQp) Fig. 9 RQp of antibiotics in the surface water of Baiyangdian Lake |
就抗生素联合生态风险而言, S1、S5和S11样点的RQsum>1.0, 处于高风险水平, 最大值出现在S1(3.03); 其余样点的RQsum处于0.1~1.0之间, 为中风险水平.
2.4.2 特定源生态风险评价就各特定源生态风险的空间分布而言, 水产养殖在S1处的RQp>1.0, 处于高风险水平, 其余样点均为中风险水平; 生活污水和畜禽养殖在S1、S2、S4、S5、S6、S9、S10和S11样点的RQp处于0.1~1.0之间, 为中风险水平, 其余样点均为低风险水平; 而污水处理厂在所有样点的RQp均处于0.1~1.0之间, 为中风险水平.
3 讨论 3.1 国内外湖泊和河流中抗生素的污染特征目前, 在国内外河流和湖泊中QNs、TCs和SAs均已有检出.就水体中各抗生素的浓度而言(表 3), OFL浓度最大值远高于洞庭湖(0.53 ng·L-1)[78]、南四湖(10.30 ng·L-1)[26]和骆马湖(13.30 ng·L-1)[25]; FLU浓度(最大值为2635 ng·L-1)远高于石家庄河流(最大值645.7 ng·L-1)[45]、潮白河(最大值95.59 ng·L-1)[80]和塞纳河(最大值32.0 ng·L-1)[82]; ENR的浓度则略高于巢湖(最大值82.7 ng·L-1)[77].此外, SMX、SDZ、DC和CTC的浓度均低于其它河流, 如白洋淀中SMX浓度最大值(6.47 ng·L-1)远低于大通湖(50.90 ng·L-1)[23]、洞庭湖(47.41 ng·L-1)[78]、潮白河(63.78 ng·L-1)[80]和辽河(16.40 ng·L-1)[81], 而与鄱阳湖浓度大致相近(5.10 ng·L-1)[24]; DC的浓度最大值(1.45 ng·L-1)与巢湖(5.70 ng·L-1)[77]、鄱阳湖(8.10 ng·L-1)[24]处于一个量级, 但远低于南四湖(49.20 ng·L-1)[26]; CTC的浓度最大值为2.00 ng·L-1, 与巢湖(4.00 ng·L-1)[77]、南四湖(3.24 ng·L-1)[26]、洞庭湖(6.50 ng·L-1)[78]、鄱阳湖(8.40 ng·L-1)[24]和辽河(9.50 ng·L-1)[81]相当.
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表 3 国内外河流和湖泊水体中典型抗生素浓度比较1) Table 3 Comparison of typical antibiotic concentrations in the surface water of global rivers and lakes |
就沉积物中各抗生素含量而言(表 4), OFL含量最大值(259.8 ng·g-1)与滇池(108.9 ng·g-1)[79]大致接近, 远高于太湖(16.50 ng·g-1)[84]、南湖(5.56 ng·g-1)[85]、辽河(51.60 ng·g-1)[89]和黄河(49.69 ng·g-1)流域[90], 而低于海河(653 ng·g-1)[88], 王同飞等[83]也证实白洋淀沉积物中OFL含量最高(52.90 ng·g-1).OTC含量最大值为7.53 ng·g-1, 与南湖(4.71 ng·g-1)[85]、黄河(5.33 ng·g-1)[90]和乌伦古湖(6.60 ng·g-1)[87]含量水平相当, 而低于太湖(52.80 ng·g-1)[84]、东洞庭湖(98.50 ng·g-1)[86]、洪湖(74.73 ng·g-1)[78]和辽河(384.6 ng·g-1)[89].SMX和CTC含量均处于较低水平(0.81 ng·g-1和2.12 ng·g-1), 远低于太湖(16.10 ng·g-1和19.00 ng·g-1)[84]、洪湖(115.8 ng·g-1和55.57 ng·g-1)[78]和海河(2.59 ng·g-1和10.9 ng·g-1)[88]; SPY含量最大值为5.59 ng·g-1, 远高于太湖(0.32 ng·g-1)[84]和辽河(0.68 ng·g-1)[89].
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表 4 国内外河流和湖泊沉积物中主要抗生素的含量比较1) Table 4 Comparison of major antibiotic contents in the sediments of global rivers and lakes |
就各类抗生素而言, QNs为主要抗生素, 其次为TCs和SAs.在郴州市东江湖中, QNs亦为主要抗生素[91], 与本研究的结果一致.QNs作为一种人畜共用抗生素, 其消费量占位居抗菌药物前列, 因其具有较强的抗菌能力且价格低廉而被广泛应用[92].加之, QNs还具有较高的沉积物-水分配系数, 因此在水体和沉积物中被广泛检出[93].此外, 本研究中TCs的检出率均高达100%, 这可能与TCs不仅用于人类和动物疾病防治, 还广泛用于水产养殖等有关[94]; 且污水处理厂仅可去除废水中24%的TCs[95].其次, TCs具有一定持久性, 其稳定性与光照、微生物和沉积物的吸附作用等多种因素有关[88].
3.2 不同湖泊和河流源解析结果的成因与比较此前河湖中抗生素的源解析主要集中于水体, 因此将本研究水体中抗生素的源解析结果与其他研究进行比较(表 5).当地政府自2018年9月开始禁止水产养殖, 而本研究结果表明水产养殖(33.2%)为白洋淀中抗生素主要来源, 这一现象可能与其周边密集的养殖区有关.与其它结果比较, 不同河流或湖泊中抗生素的主要来源存在显著差异.例如：在东洞庭湖[28]中, 抗生素的主要来源为畜禽养殖(79.6%), 这可能与洞庭湖周围有众多畜禽生产基地有关.岳阳市作为洞庭湖第二大畜禽生产基地; 而东洞庭湖作为洞庭湖中最大的湖区, 会接收大量畜禽养殖废水[22].在汾河[57]中, 制药废水为主要源(30%).据统计2020年山西省医疗机构已达14 343个[96], 汾河作为山西省内最大河流, 接收大量的制药废水.而在潮白河中, 生活污水(31.5%)是主要源[100].此外, 湘江[98]和上海市周边河流[99]均以污水处理厂为主要源, 其贡献率分别为40.0%和66.8%.据统计, 上海每年处理的废水量高达26.6亿t[95].而与传统污染物(有机物和氧化物等)相比, 现有的污水处理工艺对抗生素的去除率较低, 使得污水处理厂也是抗生素的主要来源.此前, 基于PCA-MLR模型对白洋淀流域中SAs、QNs、TCs以及其他药物等PPCPs进行源解析, 结果表明生活污水为其主要污染源(63.5%)[97].因此, 源解析的方法也可能会影响抗生素的源解析结果; 此外, PPCPs与抗生素的种类也可能导致源解析的结果出现差异性.
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表 5 不同湖泊和河流中抗生素的源贡献率与方法比较 Table 5 Comparison of source contribution rate and methods for antibiotics in different lakes and rivers |
综上所述, 不同区域抗生素生产和使用情况存在差异, 导致不同河湖污染源不同; 不同的源解析方法, 也会造成结果存在差异.因此, 在进行源解析和污染防治时应根据实际情况选取和制定合适的方法和防治措施.
4 结论(1) 各类抗生素含量在水体和沉积物中存在显著差异, QNs为白洋淀主要抗生素; 各类抗生素具有不同的空间分布特征.
(2) 源解析研究结果表明, 水体和沉积物中抗生素各来源占比存在差异.水产养殖、污水处理厂、生活污水和畜禽养殖是其主要来源, 且均以水产养殖的贡献率最高.
(3) 除FLU和ENR处于高风险水平, 白洋淀水体中抗生素的生态风险整体处于中低风险水平.
(4) 特定源的风险评估结果表明, 除水产养殖源为中高风险水平, 白洋淀中其余各源整体处于中低风险水平.
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