A Review of Current Status and Analysis Methods of Antibiotic Contamination in Groundwater in China (2012—2021)
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摘要:
抗生素作为一种新污染物，在不同环境介质中均有检出。未被人类或动物完全吸收和代谢的抗生素会通过废水和废弃物以原型或代谢产物的形式进入环境，并在土壤中积累或淋滤进入地下水。抗生素进入环境可能影响微生物生态, 产生抗性基因，甚至威胁人体健康，而地下水作为重要的饮用水源，其抗生素污染问题不容忽视。本文从抗生素的危害、使用情况、污染来源、污染现状、定性定量检测方法的优缺点及适应范围和形态分析及环境效应等方面对近十年来(2012—2021)中国地下水中抗生素的研究现状进行总结。经调查，中国常用28种抗生素检出浓度在0.1~1000ng/L以上，检出频率较高的抗生素为诺氟沙星、氧氟沙星、磺胺甲恶唑、恩诺沙星、磺胺嘧啶、红霉素等。从空间分布来看，对地下水中抗生素的研究主要集中在华北、西南地区，而对西北地区中地下水抗生素研究程度较低。目前为止受到分析方法检出限及检出种类的限制，对地下水中抗生素的调查及评价还不够全面。通过综述抗生素定性定量分析方法，发现HPLC-MS/MS法因其具有灵敏度高、选择性好和定性定量准确的优点是目前应用最广泛的抗生素定量分析方法，而且可利用该方法对环境中抗生素类型进行初步识别，针对主要类型开展定量分析或长期监测，为抗生素环境效应研究提供数据支撑。而当抗生素以不同的带电形态、络合形态、吸附形态存在时，因其理化性质不同会影响测定的准确性、环境行为和毒理学效应，因而开展抗生素的形态分析对进一步准确测定抗生素和评估其环境效应具有重要意义。本文认为，优化定性定量检测方法、分析抗生素的不同形态、全面调查地下水中抗生素和科学评价抗生素形态与生态毒理学效应的关系，是今后地下水中抗生素污染研究的重点课题。
要点(1) 中国地下水中常检出的28种抗生素的浓度变化超过4个数量级。
(2) HPLC-MS/MS法可以对地下水中的抗生素进行准确的定性定量分析。
(3) 抗生素的存在形态影响前处理的回收率、定性和定量结果的准确性。
HIGHLIGHTS(1) The concentrations of 28 antibiotics commonly detected in groundwater in China vary by more than 4 orders of magnitude.
(2) HPLC-MS/MS can perform accurate quantitative analysis of antibiotics in groundwater.
(3) The presence forms of antibiotics affect the recovery rate of pretreatment and the accuracy of qualitative and quantitative results.
Abstract: As a form of new emerging pollutant, antibiotics have been detected in soil, surface water, groundwater, sediment and other different environmental media. As a major country in the production and usage of antibiotics, China's production and usage are increasing year by year. However, most antibiotics used for humans or animals cannot be fully absorbed and metabolized and will enter the environment in the form of prototypes or metabolites through waste and wastewater accumulating in soil and leaching into groundwater. Antibiotics entering the environment may affect microbial ecology, produce resistance genes, and even threaten human health. Compared with surface water, polluted groundwater is hidden, lagging and difficult to recover. The pollution of antibiotics in groundwater, as the main source of drinking water, has attracted much attention.So far, the research on antibiotics in China is still mainly on surface water and soil, and there are few observations on antibiotics in groundwater. In order to systematically grasp the current pollution situation of antibiotics in groundwater in China, relevant literature on antibiotics in groundwater from 2012 to 2021 is reviewed in this paper. Twenty-eight antibiotics detected more than 100 times in environmental media in China were selected as target antibiotics, and the detected concentrations were summarized and analyzed. It was found that the concentrations of 28 antibiotics commonly detected in groundwater varied by more than 4 orders of magnitude, from 0.1ng/L to more than 1000ng/L. The most frequently detected antibiotics were norfloxacin, ofloxacin, sulfamethoxazole, sulfadiazine, enrofloxacin, and erythromycin. Through comparative analysis of the detection of antibiotics in various places, it can be seen that the concentration of antibiotics in groundwater is controlled by the properties of antibiotics, the location of pollution sources, hydrogeological structure and the amount of usage and emissions. From the perspective of spatial distribution, sulfonamide antibiotics are the most detected in northeast China, quinolones are the most detected in North and East China, quinolones and tetracyclines are the most detected in southwest China, and the research on antibiotics in groundwater in northwest China is relatively low. So far, restrained by the detection limits and detection types of the analysis methods, a comprehensive investigation and evaluation of antibiotics in groundwater is not possible.Due to the wide variety of antibiotics, their different structures lead to different physical andchemical properties. They exist in trace concentrations in the complex environment media, which also affects the accuracy of their qualitative and quantitative analysis. Therefore, the establishment of a sensitive and specific multi-component simultaneous analysis method has been a key issue for antibiotics research. The analysis methods of antibiotics are summarized, which are divided into qualitative analysis methods and quantitative analysis methods. The principle, advantages, disadvantages and application range of several antibiotic analytical methods are presented. These methods include microbial inhibition method (MIT), thin layer chromatography (TLC), gas chromatogram-mass spectrometry (GC-MS), high-performance liquid chromatogram-nuclear magnetic resonance (HPLC-NMR) and liquid chromatogram-mass spectrometry (LC-MS). Liquid chromatogram-mass spectrometry (LC-MS) is the most commonly used method for antibiotic analysis because of its high sensitivity, low detection limit and simultaneous determination of multiple antibiotics. With the rapid development of antibiotic analysis methods, some antibiotics in groundwater can be accurately quantified by using HPLC-MS/MS and other technologies. However, the number of antibiotics that can be analyzed and identified at one time is still limited. The research group of authors has established the qualitative spectrum library of common drugs by UPLC-MS/MS. In the future, the types of antibiotics that can be qualitatively identified in the spectrum library can be expanded by adding the mass spectrum information of antibiotics. Under specific conditions, the spectrum library can be used to carry out semi-qualitative identification of antibiotics in groundwater. At present, the commonly used quantitative detection methods include enzyme-linked immunoassay, capillary electrophoresis, and liquid chromatography-mass spectrometry. Compared with the other two methods, liquid chromatography-mass spectrometry has the advantages of high sensitivity, good selectivity and accurate quantitative ability. It is commonly used for the detection of trace antibiotics in reported water samples.Antibiotics exist in the environment at trace levels and the matrix of environmental samples is complex, so the pretreatment process, including antibiotic separation, purification and concentration, often becomes the key step of determination. For example, the samples to be tested should be adjusted to an appropriate pH to enhance the enrichment of target antibiotics on HLB columns, and Na2EDTA should be added to inhibit its complexation with calcium and magnesium and other metal ions in groundwater. The accuracy of antibiotic determination will be improved, and the detection limit will be lowered for water samples by solid phase extraction and the subsequent concentration process. In addition to the detection limit and recovery rate of antibiotics affected by the analytical instrument, the presence states of antibiotics in water samples will also affect the accuracy and precision.Antibiotics may exist in the ionized state, complex state, adsorption state and other forms in groundwater. At different pH values, antibiotics may exist in neutral, cationic, anionic, orzwitterionic forms. When it coexists with metal ions, complexation reaction will occur under certain conditions to form antibiotic-metal complex which will reduce the peak area to a certain extent or cause tailing phenomenon on the reverse analytical column. The formation of the complex may also change the environmental behavior (migration, transformation, toxicity, etc.) and ecological effects of antibiotics. In addition, the analysis of antibiotics in different adsorption states can be used to evaluate the differences in microbial killing effects of different adsorption forms, especially the differences in ARG production and spreading. This will be helpful for accurately evaluating the potential effects on the environment or human beings and effectively controlling the risks of antibiotics in environmental media. Therefore, the existing form analysis of antibiotics is of great significance for the further accurate determination of antibiotics and the evaluation of environmental effects.Up to now, limited by the detection limits and detected types of antibiotics in analytical methods, there has not been a comprehensive national-scale investigation and evaluation of antibiotics in groundwater in China. Only by clarifying the concentration level and spatial distribution of antibiotic pollution in China's groundwater can it help to understand the contents of relevant laws and regulations on new emerging pollutants and support the establishment of a regulatory framework for natural resources and the environment. In conclusion, optimizing qualitative and quantitative detection methods, analyzing different existing forms of antibiotics, comprehensively investigating antibiotics in groundwater, and scientifically evaluating the relationship between antibiotic forms and ecotoxicological effects are the main contents of antibiotics research in groundwater in the future.
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Keywords:
- groundwater /
- antibiotics /
- pollution status /
- environmental behavior /
- speciation analysis /
- HPLC-MS/MS
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多环芳烃(PAHs)作为一种典型的持久型有机污染物，在环境中广泛存在，且稳定性高[1]。PAHs具有高毒性，以及致畸、致癌和致突变性，美国环境保护署已将16种PAHs单体列入优先控制污染物清单。环境中的PAHs来源广泛，既包括火山爆发和森林火灾等自然过程中释放的PAHs[2-3]，也包括人为活动排放源，如汽车尾气排放和工业生产排放等人为来源[4-5]。对于具有高密度人口的城市而言，人为输入显然是其环境中PAHs最重要的来源[6-7]。具体而言，人为来源涉及各种有机物质，例如生物质和化石燃料(煤炭、石油和天然气)等物质的不完全燃烧或热解，以及交通运输过程中石油的泄漏[6-10]。城市中发达的工业生产、高密度的交通运输以及居民日常生活等活动都离不开燃煤、石油以及天然气等化石燃料的大量使用[8-10]。人为活动所产生的PAHs会进入大气环境中，然后随着大气扩散以及干湿沉降等方式进一步进入水体、沉积物以及土壤等环境介质中[10]。土壤是城市环境中最重要的地表介质，同时由于PAHs具有高的亲脂疏水性，人为活动所输入的PAHs极易在土壤中发生累积[7-10]。已有大量研究集中于土壤中PAHs的含量分布与来源分析[11-16]，旨在为PAHs污染防控、生态环境改善及环保政策制定等提供技术依据。北京作为中国的首都，具有高度密集的人口和交通流量，城市能源消耗量大[17-22]。据资料统计，2019年北京市年能源消耗总量达7360.32万吨标准煤[23]。所以，对于北京这种典型的大型城市而言，环境中必然存在各种人为活动输入的PAHs。针对北京市不同范围内表层土壤中PAHs分布特征及来源分析已有相关研究报道，如沈亚婷等[9]于2008年利用因子分析-多元线性回归法解析了北京地区表土15种PAHs的3种来源，包括煤炭燃烧/交通排放、焦炉及石油，并定量计算了3种源的贡献。Qu等[20]于2020年分析了北京市六环范围内各大公园表层土壤PAHs含量、组成特征及来源，认为从组成特征来看四环PAHs是主要成分，其次是五环PAHs，二环PAHs含量最低，采用条件推理树模型识别出影响PAHs的主要因素是交通排放，其次为燃煤，以及公园的历史及位置。总体来看，以往研究侧重区域或单一功能区的PAHs分布特征及来源研究，针对不同功能区开展对比研究较少。
因此，为了进一步探求北京市不同功能区土壤环境中PAHs的含量、组成及来源，本文对北京市主城区(东城、西城、海淀、昌平、朝阳、丰台、通州)开展了大范围的采样，主要为了分析北京市主城区表层土壤中PAHs整体含量及组成情况；其次根据划分的功能区(工业区、居民区、农业种植区以及水源保护区)进行定点采样，通过对不同功能区表层土壤中PAHs含量及组成分析，系统揭示北京市表层土壤中PAHs的污染现状。并利用主成分分析-多元线性回归法判断北京市不同功能区表层土壤中PAHs的来源以及各来源贡献率，以期为控制和减少北京市PAHs污染、保护生态环境提供技术支撑和科学依据。
1. 实验部分
1.1 采样点及样品采集
本研究采样时间为2020年5月至7月，在全北京市共设置459个采样点，具体分布如图 1所示。在459个采样点中，215个位于北京市主城区，根据地理位置的分布，进一步分为东南区、中心区和西北区；57个位于工业区，包括东南化工厂、首钢工业区、大台煤矿区；44个位于居民区；124个位于农业种植区，包括昌平流村—南口地区、平谷峪口—刘家店地区、房山石楼地区以及通州牛堡屯地区；19个位于饮用水源地保护区，包括密云水库和怀柔水库。除主城区按照平均1个点/16km2的密度采样外，其他都为1~4km2平均1个点的密度采样。
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图 1 北京市表层土壤采样点分布图Figure 1. Distribution map of topsoil sampling sites in Beijing City为确保样品的代表性，每个点位采集柱状样品并上下均匀混合为一份，共采取了表层(0~20cm)土壤样品459件。土壤样品采集时使用干净的不锈钢铲，去除砾石及动植物的残体后，取1kg左右装入棕色玻璃瓶中带回实验室。最后将土壤样品经冷冻干燥处理后过60目筛，并于-5℃的冰箱中避光保存。
1.2 土壤样品前处理
准确称量土壤样品5.00g至预先装好硅藻土的萃取池中，加入已知浓度的替代物，经加速溶剂萃取仪提取目标化合物，利用多通道浓缩仪浓缩提取液，然后根据样品基体干扰情况选择是否用佛罗里硅土柱对浓缩液进行净化(若浓缩液颜色较深，需用铜粉脱硫)，净化后液体用氮吹仪浓缩至1mL，转移至2mL样品瓶中，加10μL合适浓度的内标溶液，最后用气相色谱-质谱(GC-MS)进行分析。
1.3 样品分析
样品分析测试由北京市一零一生态地质检测有限公司完成。采用气相色谱-质谱联用仪(7890B型，美国Agilent公司)进行定量测定。
所测的目标物为美国环境保护署列出的16种优控PAHs，包括萘、苊烯、苊、芴、菲、蒽、荧蒽、芘、苯并(a)蒽、䓛、苯并(b)荧蒽、苯并(k)荧蒽、苯并(a)芘、茚并(1, 2, 3-cd)芘、二苯并(a, h)蒽、苯并(g, h, i)苝，内标物质使用萘-d8、苊-d10、菲-d10和䓛-d12，替代标准物使用2, 4, 6-三溴苯酚、对三联苯-d14。
色谱条件：色谱柱为HP-5MS弹性石英毛细管柱(30m×0.25mm内径，膜厚0.25μm)，载气使用纯度为99.999%的氦气，载气流速1.5mL/min。样品以不分流方式进样，排气时间0.75min，进样器温度230℃。
升温程序：初始温度100℃，保持2 min；然后以10℃/min的速率上升到160℃，再以4℃/min的速率上升到230℃，最后以10℃/min上升到280℃，保持10min，直至样品完全流出色谱柱。
质谱条件：EI电流源为68eV，质量范围50~600amu，倍增器电压为1150V，离子源温度为230℃，四极杆温度150℃，扫描速度为4000~6000amu/s。接口温度260℃，采用全扫描方式进行定性分析，扫描范围为m/z 45~400。
1.4 质量控制与质量保证
为了保证目标物定性和定量的准确性，所有分析方法均采用严格的质量保证和控制措施进行监控。具体而言，实验过程中每5个样品为一批，每批样品中都设有一个程序空白，空白样品中未检出目标化合物。目标物的方法检出限设定为信噪比的5倍，即10μg/kg；低于方法检出限的浓度被报告为未检出。实验中所有的土壤样品均采用了3次重复样，重复分析结果的标准差小于6%，样品经提取、净化后，回收率指示物的回收率在76%~101%之间，满足痕量有机化合物残留分析要求。
1.5 数据分析
实验数据分析及制图采用SPSS 19和Section 2016等软件。
2. 结果与讨论
2.1 北京市不同功能区表层土壤PAHs的含量特征
为研究北京市不同功能区表层土壤中PAHs的含量特征，掌握北京市土壤PAHs污染现状，对主城区、工业区、农业种植区、水源保护区及居民区等功能区的表层土壤PAHs含量进行了分析。
2.1.1 主城区表层土壤PAHs含量
采集的北京市主城区215件表层土壤样品，包括东南区域71件、中心区域73件和西北区域71件。结果显示，东南区域16种PAHs单体总量(∑16PAHs，以干质量计，下同)的变化范围为ND(未检出，下同)~1319.3μg/kg，平均值为153.7μg/kg；中心区域∑16PAHs变化范围为ND~2730.1μg/kg，平均值高达333.2μg/kg；西北区域∑16PAHs变化范围为ND~1489.1μg/kg，平均值为142.9μg/kg。反映了中心区域表层土壤PAHs含量高，而东南和西北区域PAHs含量低的特点。
2.1.2 工业区表层土壤PAHs含量
采集了工业区57件表层土壤样品，包括东南化工厂区24件、首钢工业区10件和大台煤矿区23件。结果显示，东南化工厂区域内∑16PAHs变化范围为ND~6208.6μg/kg，平均值为1006.9μg/kg；首钢工业区∑16PAHs变化范围为ND~19466.5μg/kg，平均值为1379.4μg/kg；大台煤矿区∑16PAHs变化范围为ND~268.3μg/kg，平均值为146.8μg/kg。
无论是从含量整体变化还是均值，均可以看出，东南化工厂和首钢工业区的PAHs污染水平远高于大台煤矿区。北京焦化厂位于北京市东南的朝阳区，曾是中国规模最大的独立焦化厂和最大的商品焦炭供应及出口基地，年产焦炭200多万吨，占全国总产量的1.67%[24]。首钢是中国最早成立的大型钢厂，集烧结、炼铁、炼钢以及发电等为一体，年产量可达到800万吨[25-27]。在长期的工业生产活动中，特别是焦化厂、炼钢及炼铁厂的煤干馏和燃烧，会导致大量携带有PAHs的粉尘被释放到大气中，经过大气干湿沉降最终降落在周边区域，造成厂区及周边地区土壤PAHs污染。北京市西南的门头沟区，拥有煤矿、石灰石、页岩等多种资源，而大台煤矿只是位于该地区的一座小型煤矿区[28-29]。北京焦化厂和首钢钢厂的规模大于大台煤矿，这也就导致了前两个地区表层土壤中PAHs的污染程度远高于大台煤矿。
2.1.3 其他功能区表层土壤PAHs含量
采集的农业种植区124件表层土壤样品，包括昌平28件、平谷35件、房山25件和通州36件。结果显示，农业种植区中昌平表层土壤中∑16PAHs的变化范围为ND~361.4μg/kg，平均值为109.0μg/kg；平谷表层土壤中∑16PAHs的变化范围为ND~456.8μg/kg，平均值为118.3μg/kg；房山表层土壤中∑16PAHs的变化范围为ND~210.5μg/kg，平均值为106.8μg/kg；通州表层土壤中∑16PAHs的变化范围为ND~251.2μg/kg，平均值为94.2μg/kg。
采集的水源保护区19件表层土壤样品，包括怀柔10件和密云9件。水源保护区怀柔表层土壤中∑16PAHs的变化范围为ND~113.5μg/kg，平均值为86.4μg/kg；密云表层土壤中∑16PAHs的变化范围为ND~399.4μg/kg，平均值为154.5μg/kg。
居民区44件表层土壤PAHs结果显示，∑16PAHs的变化范围为ND~1407.1μg/kg，平均值为131.1μg/kg。
除密云外，以上功能区表层土壤中PAHs的含量(均值)均低于北京市主城区的平均水平，轻环占比明显高于工业区和主城区的中心城区。显然，农业种植区和水源保护区都属于人类活动非密集区，没有直接的PAHs燃烧源输入，与其他典型排放源区相比，其PAHs污染程度较低。
2.1.4 不同区域范围内表层土壤PAHs含量对比
表 1归纳了不同研究中北京市各区域采集的表层土壤PAHs含量。Tang等[21]于2005年在北京市海淀区、石景山区、朝阳区以及房山区的主要居民活动区共采集了31件表层土壤样品，其∑16PAHs的变化范围较大(219.0~27825.0μg/kg)，平均值高达3917.0μg/kg。与此同时，Ma等[19]在北京市四环以外区域所采集的47件表层土壤样品中测得∑16PAHs的变化范围为14.0~4238.0μg/kg，平均值为1056.0μg/kg。Li等[18]于2006年在北京市四环内收集了30件表层土壤样品，测得∑16PAHs的变化范围为467.0~5470.0μg/kg，平均值为1637.0μg/kg。沈亚婷等[9]于2008年在北京全市范围内进行了网格布点采样，共采集了138件表层土壤样品，∑15PAHs(萘除外)的平均值为262.3μg/kg。Peng等[17]于2011年在北京市五环以内地区采集了233件表层土壤样品，其∑16PAHs的变化范围为93.3~13141.5μg/kg，平均值为1228.1μg/kg。Qu等[20]于2020年在北京市六环范围内的各大公园采集了122件表层土壤样品，其∑16PAHs的变化范围为66.0~6867.0μg/kg，平均值为460.0μg/kg，反映了公园土壤中具有相对较高的PAHs含量。对比可知，本研究中仅有工业区中东南工厂区和首钢工业区表层土壤中PAHs含量(1006.9μg/kg和1379.4μg/kg)是接近于Ma等[19]于北京市四环以外区域所采集样品中的含量(1056.0μg/kg)。这也符合实际情况，因为本研究的工业区都位于北京市五环以外。本研究其余功能区以及主城区(中心区除外)PAHs含量(均值)低于2008年北京全市范围内表层土壤中PAHs含量(262.3μg/kg)，这可能与近年来北京市能源使用结构的变化有关。由北京市2020统计年鉴数据可以看出，近年来北京市煤炭占能源消耗总量的比重呈明显下降趋势。2010年至2019年间，北京市石油和煤炭占能源消费总量的比重从60.53%下降至36.36%，而天然气的比重则从14.58%上升至34.01%[23]。因此，北京市能源结构的改变使得源区释放量逐渐减少，从而导致最终沉降到表层土壤中的PAHs呈减少趋势。
表 1 北京市不同区域内表层土壤中多环芳烃含量统计Table 1. Statistics of PAHs concentration in topsoil of different regions in Beijing City北京市区域 样品数(件) 采样深度(cm) 所测PAHs单体数(种) PAHs总量范围(μg/kg) PAHs总量均值(μg/kg) 参考文献 居民区 31 0~5 16 219~27825 3917 Tang等(2005)[21] 四环外 47 5~30 16 14~4238 1056 Ma等(2005)[19] 四环内 30 5~30 16 467~5470 1637 Li等(2006)[18] 全市 138 0~10 15(萘除外) - 262.3 沈亚婷等(2008)[9] 五环内 233 0~10 16 93.3~13141.5 1228.1 Peng等(2011)[17] 六环内公园 122 0-10 16 66-6867 460 Qu等(2020)[20] 主城区 215 0~20 16 ND~2730.1 210.4 本文研究 工业区 57 0~20 16 ND~19466.5 1006.3 本文研究 居民区 44 0~20 16 ND~1407.1 131.1 本文研究 水源保护区 19 0~20 16 ND~399.4 118.6 本文研究 农业种植区 124 0~20 16 ND~456.8 106.9 本文研究 注：“-”代表无相应参考数据。 2.2 北京市不同功能区表层土壤PAHs的组成特征
一般而言，2~3环PAHs(萘、苊烯、苊、芴、菲及蒽)被视为轻环PAHs，4环PAHs(荧蒽、芘、苯并[a]蒽及䓛)被看作中环PAHs，而5~6环PAHs(苯并[b]荧蒽、苯并[k]荧蒽、苯并[a]芘、茚并[1, 2, 3-c, d]芘、二苯并[a, h]蒽及苯并[g, h, i]苝)则被认定为重环PAHs[1]。重环及部分中环PAHs，分子量高，迁移能力差，经点源释放进入大气后多以干湿沉降的方式累积在该地区土壤中[1, 30]。
图 2展示了主城区、工业区、其他功能区内∑16PAHs的含量(均值)及轻环、中环、重环PAHs的组成特征。从总体情况来看，工业区的首钢工业区、东南化工厂∑16PAHs的含量明显高于其他区域，主城区的中心区域∑16PAHs的含量也相对高于其他区域。
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图 2 北京市主城区PAHs含量(均值)及组成Figure 2. Concentration (mean value) and composition of PAHs in the main area of Beijing City在主城区的中心区域，中环和重环PAHs所占比例合计高达80%以上，东南和西北区域则是接近于70%。对于中心区域而言，其∑16PAHs的均值达到东南和西北区域的两倍以上，同时中环及重环PAHs呈现的高占比，说明该区域是一个重要的PAHs释放源区。中心区域主要是北京市五环内及其周边城区，该区域人口密集程度高，交通流量大，人为活动输入的PAHs自然高于非中心区。所以，从整体上看来，北京市主城区PAHs污染水平呈现明显的中心区高，沿东南和西北两个方向扩散而降低的特点。
工业区的首钢工业区重环PAHs占比更是高达50%，轻环占比则明显低于东南化工厂和大台煤矿区。从能源使用情况来看，首钢工业区内炼铁、炼钢及发电等生产活动都使用了大量的煤炭，而荧蒽、芘、䓛、苯并[b]荧蒽和苯并[a]芘等中环及重环PAHs都是典型的煤炭燃烧指示物[31-32]。同时，工业产品运输又会涉及大量的货运车辆往来，苯并[k]荧蒽、茚并[1, 2, 3-c, d]芘和二苯并[a, h]蒽等重环PAHs则是典型汽车尾气排放物[33-34]。所以，无论是煤炭燃烧还是车辆尾气排放，这些高温燃烧活动所产生的主要是重环及中环PAHs，这也是首钢工业区重环及中环PAHs的主要来源。东南化工厂主要是炼焦工业，涉及焦炭的高温干馏生产及煤气、煤焦油等其他化工产品的获取[24]。炼焦过程除高温燃烧的产物外，还包括萘、菲等轻环PAHs[35]。所以，东南化工厂表层土壤中轻环所占百分比高于首钢工业区。
轻环PAHs由于其分子质量轻，蒸气压高易挥发，在环境中具有更大的迁移潜力[36-37]，所以轻环PAHs占比高可以表明大气迁移输入源的存在。本研究中的居民区位于通州郊区，人口数量远低于城区，轻环PAHs占比较高，反映该居民区PAHs污染水平相对较低。密云水源保护区表层土壤中PAHs的含量水平要高于其他非工业功能区，其中重环PAHs的占比更是接近于工业区。这是因为密云水库上游金矿和铁矿资源丰富，矿山开采活动、矿石采选及冶炼所产生的废弃尾矿对该地区的土壤环境已经造成了一定的影响[38-39]。所以，采矿活动可能加重了密云水库地区表层土壤中PAHs的污染，并影响了该地区PAHs成分组成。
2.3 北京市不同功能区表层土壤PAHs来源识别
2.3.1 主成分分析结果
主成分分析作为一种多元统计工具被广泛使用，其可以将大数据集中的原始变量转换为有限的成分因子，有效地揭示原始变量之间的关系。主成分分析在环境研究方面得到了广泛的应用，其可以通过识别污染物之间的内在联系，从而有效判断环境中污染物的可能来源[40]。
在主成分分析中，输入变量为北京市表层土壤样品中16种PAHs单体的含量(未检出单体的含量按检出限50%处理)。由于北京市主城区、工业区以及居民区人类活动密度以及人为输入PAHs明显高于水源保护区及农业种植区，所以重点讨论以上三个功能区。分别对以上三个功能区表层土壤样品中16种PAHs单体含量采用主成分法提取主要成分因子，原始因子负荷系数经具有Kaiser标准化的最大方差法旋转后，最终三个功能区提取出特征值大于1的主要成分因子均为2个，分析结果如表 2所示。
表 2 北京市表层土壤中PAHs主成分载荷及累积方差Table 2. Component loading and cumulative variance of principal components for PAHs in topsoil of Beijing CityPAHs物质 主城区 工业区 居民区 PC1 PC2 PC1 PC2 PC1 PC2 萘 0.089 0.788 0.875 0.433 0.845 0.525 苊烯 0.229 0.858 0.265 0.918 0.855 0.517 苊 0.688 0.334 0.793 0.462 0.517 0.855 芴 0.841 0.211 0.294 0.888 0.517 0.855 菲 0.930 0.184 0.859 0.471 0.763 0.643 蒽 0.809 0.481 0.481 0.866 0.855 0.517 荧蒽 0.948 0.251 0.820 0.566 0.657 0.753 芘 0.940 0.258 0.759 0.632 0.625 0.778 苯并[a]蒽 0.973 0.175 0.462 0.885 0.654 0.745 䓛 0.974 0.183 0.931 0.351 0.512 0.847 苯并[b]荧蒽 0.964 0.214 0.944 0.323 0.864 0.474 苯并[k]荧蒽 0.979 0.176 0.318 0.944 0.579 0.808 苯并[a]芘 0.978 0.173 0.363 0.926 0.781 0.606 茚并[1, 2, 3-c, d]芘 0.969 0.189 0.449 0.882 0.824 0.552 二苯并[a, h]蒽 0.941 0.117 0.626 0.373 0.855 0.517 苯并[g, h, i]苝 0.913 0.193 0.383 0.920 0.763 0.621 方差(%) 74.63 13.53 58.17 35.35 59.35 39.56 累积方差(%) 74.63 88.16 58.17 93.52 59.35 98.91 2.3.2 主城区表层土壤PAHs的来源分析
主城区表层土壤样品主成分分析结果表明，PC1和PC2所贡献的方差分别为74.63%和13.53%，两者累积方差高达88.16%，可用于分析该区域的PAHs来源。
除萘和苊烯两个轻环单体在第二因子(PC2)上具有高载荷外，其他单体均在第一因子(PC1)上呈现高载荷，尤其是中环和重环PAHs呈现高度统一。结合该区域实际情况，主城区是北京市人流量最大的区域，各种交通工具行驶过程中尾气的释放充当了该地区PAHs的主要来源，这与在PC1上呈现高载荷的高温燃烧产物包括芘、苯并[a]蒽、䓛、苯并[b]荧蒽、苯并[k]荧蒽、苯并[a]芘、茚并[1, 2, 3-c, d]芘、二苯并[a, h]蒽和苯并[g, h, i]苝是一致的，所以PC1代表了该区域PAHs的尾气排放源。与此同时，萘和苊烯是典型的石油泄漏产物[41]，表征了石油生产、储存、运输等过程中的PAHs泄漏和排放[15]，所以PC2明显指示了该区域PAHs的石油泄漏来源。总体上，北京市主城区PAHs的主要来源是由尾气排放和石油泄漏组成的交通释放源。
2.3.3 工业区表层土壤PAHs的来源分析
工业区分析结果表明，第一因子和第二因子(PC1和PC2)的方差贡献率分别为58.17%和35.35%，包含了原始数据信息的90%以上，因此用这2个因子来分析PAHs的来源比较可靠。
在PC1中，具有高载荷的单体为萘、苊、菲、荧蒽、芘、䓛、苯并[b]荧蒽、苯并[a]芘以及苯并[g, h, i]苝，其中菲、荧蒽、芘、䓛、苯并[b]荧蒽和苯并[a]芘都是典型的煤炭燃烧指示物[31-32]，因此判断PC1代表了煤炭燃烧源；在PC2中，占有高载荷的单体有苊烯、芴、蒽、苯并[a]蒽、苯并[k]荧蒽、苯并[a]芘、茚并[1, 2, 3-c, d]芘以及二苯并[a, h]蒽，而苯并[a]蒽、苯并[k]荧蒽、茚并[1, 2, 3-c, d]芘和二苯并[a, h]蒽等高分子量PAHs是高温燃烧的产物，尤其茚并[1, 2, 3-c, d]芘和二苯并[a, h]蒽是石油燃烧的标记物[33-34]，所以推测PC2指示了尾气排放源。
本研究的工业区包括北京市首钢工业区、东南郊工厂区以及大台煤矿区，其中首钢工业区内钢铁冶炼过程需要燃烧大量的煤炭来提供足够的热量，以及东南郊工厂区一些化学产品的生产也需要煤炭充当燃料，所以煤炭燃烧是这一区域PAHs的主要来源之一。与此同时，工厂内原料和产品例如钢铁、化工品和燃煤等的运输又离不开各种大型车辆，这些交通工具排放出的尾气又构成了该地区PAHs另一个不可忽略的来源——尾气排放源。所以，北京市工业区表层土壤中PAHs的主要来源有煤炭燃烧以及尾气排放源。
2.3.4 居民区表层土壤PAHs的来源分析
居民区分析结果中，PC1和PC2所贡献的方差分别为59.35%和39.56%，两者累积方差高达98.91%，显然适用于分析该区域的PAHs来源。
在PC1上具有高载荷的单体为萘、苊烯、菲、蒽、苯并[b]荧蒽、苯并[a]芘、茚并[1, 2, 3-c, d]芘、二苯并[a, h]蒽和苯并[g, h, i]苝。同理，这些单体的组合特征表明PC1是尾气排放源。在PC2上呈现高载荷的单体有苊、芴、荧蒽、芘、苯并[a]蒽、䓛以及苯并[k]荧蒽，其中荧蒽、芘和䓛是典型的天然气燃烧指示物[42-43]，所以PC2代表了居民区表层土壤中PAHs的另一来源——天然气燃烧。居民区人类出行活动多，汽车排放的尾气自然是该地区PAHs的重要来源。同时，居民在室内的烹饪等活动离不开天然气的使用[44-45]，北京市天然气的年消耗量也是呈明显上升趋势，占能源消耗总量的比重从2010年的14.58%一直增加到2019年的34.01%[23]。所以，北京市居民区PAHs的主要来源由交通释放源和天然气燃烧组成。
2.4 北京市不同功能区表层土壤PAHs各来源贡献率
2.4.1 多元线性回归分析结果
线性回归分析是常用的数据分析方法之一，可以根据已得的试验结果来建立统计模型，并研究变量间的相关关系，从而建立起变量间关系的经验公式[46-47]。当根据试验结果判断与因变量有关的自变量不只一个时，则采用多元线性回归法。在前文主成分分析的基础上，利用多元线性回归分析进一步定量北京市不同功能区表层土壤中PAHs各来源的贡献值。多元线性回归中，SPSS默认对每个回归变量中的数据标准化后，再进行逐步回归，最终每个变量PCi都会得到相对应的系数(Ci)，∑16PAHs的标准化偏差(Z)的方程如表 3所示。然后，通过展开∑16PAHs的标准化偏差并且重新排列项，得到不同功能区表层土壤中PAHs总量的多元线性回归方程如表 4所示。此时，单个来源(PCi)的贡献率(%)可以由以下方程得出：
$ {\rm{PC}}_{i}贡献率=(C_{i}/∑C_{i})×100\% $
表 3 北京市表层土壤中PAHs多元线性回归分析的结果方程Table 3. Resulting equations of multiple linear regression for PAHs in topsoil of Beijing City北京市 Z R2 主城区 0.971PC1+0.221PC2 0.991 工业区 0.871PC1+0.487PC2 0.997 居民区 0.752PC1+0.659PC2 1.000 表 4 北京市不同功能区表层土壤中PAHs多元线性回归方程Table 4. Multiple linear regression equations for PAHs in topsoil of different functional areas in Beijing City北京市 PAHs总量(∑16PAHs) 主城区 0.971σPAHPC1+0.221σPAHPC2+mean∑16PAHs 工业区 0.871σPAHPC1+0.487σPAHPC2+mean∑16PAHs 居民区 0.752σPAHPC1+0.659σPAHPC2+mean∑16PAHs 注：PCi代表了北京市不同功能区表层土壤中PAHs的不同来源；σPAH和mean∑16PAHs分别代表不同功能区表层土壤中16种PAHs单体总量的标准偏差和平均值。 2.4.2 表层土壤PAHs各来源贡献率解析
通过以上分析，最终可以获得北京市不同功能区表层土壤中PAHs各来源的贡献率。北京市主城区表层土壤PAHs的主要来源中，尾气排放源贡献率很高，达到了81.46%，而石油泄漏则贡献了相对较小一部分(18.54%)。工业区表层土壤中PAHs的主要来源有煤炭燃烧以及交通释放源，其中煤炭燃烧的贡献率高达62.65%，而交通释放源的贡献率则为37.35%。与此同时，北京市居民区表层土壤中PAHs的来源中交通释放源依旧承担了较多的贡献率(53.30%)，而天然气燃烧来源贡献率(46.70%)也是不容忽视的一部分。因此，可以看出，北京市不同功能区土壤环境中PAHs的来源存在一定的差异性，显然在人类活动密集的区域，尾气排放源始终是土壤环境中PAHs的主要来源之一。
3. 结论
本研究针对北京市主城区、工业区、农业种植区、水源保护区以及居民区等不同功能区进行了分区采样，获得不同功能区表层土壤16种PAHs含量及组成特征。其中工业区(大台煤矿区除外)表层土壤中PAHs的含量明显高于其他功能区以及主城区。与前人研究对比，本研究揭示北京市表层土壤中PAHs的含量总体呈下降趋势，这与近些年北京市能源结构变化有关，随着煤炭能源减少、天然气等清洁能源增加，减少了PAHs排放。不同功能区PAHs成分组成存在一定的差异，工业区重环以及中环PAHs占比高，而居民区、农业种植区以及水源保护区的轻环PAHs占比要高于工业区，这与PAHs的来源有关，初始排放源的不同以及二次源的输入都会对其成分组成造成一定的影响。主成分分析-多元线性回归分析结果表明主城区PAHs的主要来源是尾气排放以及石油泄漏，贡献率分别为81.46%和18.54%；工业区表层土壤中PAHs的主要来源有煤炭燃烧以及尾气排放，贡献率分别为62.65%和37.35%；居民区PAHs的主要来源有尾气排放源和天然气燃烧源，贡献率分别为53.30%和46.70%。总体来看，北京市土壤环境中PAHs最重要的来源为车辆尾气的排放。建议进一步加强北京市交通管制，继续缩减煤炭在北京地区能源结构中的比重，增加清洁能源比重，从而有效减少PAHs排放。
本研究初步掌握了北京市不同功能区表层土壤PAHs含量及组成特征，并定量解析了PAHs的主要贡献来源，成果可以为北京市生态环境保护、能源结构优化等方面的政策制定提供技术支撑。受研究工作周期约束，本研究并没有开展不同功能区的土壤PAHs垂向分布特征研究，还难以全面反映北京市各功能区的土壤PAHs现状，仍需深入开展不同功能区表层和深层土壤PAHs含量特征的分析研究，以完善相关结论。
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图 1 地下水中抗生素的污染来源和迁移途径
Figure 1. The figure mainly describes the pollution sources and migration routes of antibiotics in groundwater. Antibiotics and their metabolites enter surface water or soil through direct or indirect means, and finally collect into groundwater through leaching or infiltration. The main sources of antibiotics in groundwater can be divided into production sources, use sources and discharge sources.
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图 2 中国地下水中28种常见抗生素的检出情况
参考来源：北京[48]、雄安新区[49]、滹沱河[50]、哈尔滨[51]、江西锦江流域[52]、徐州[53]、江汉平原[54]、桂林[55]、毕节[56]、开阳[57]、台湾[58]、青岛大沽河[59]。
Figure 2. Twenty-eight antibiotics with high detection frequency in environmental media in China were selected as target antibiotics, and the concentrations of antibiotics detected in groundwater in China in the past ten years were collected. The detected concentration value is calculated as the average value, or the maximum value is calculated if the average value is not given. It can be seen that the concentration of 28 antibiotics commonly detected in groundwater changes by more than 4 orders of magnitude, from 0.1 to more than 1000ng/L, and the antibiotics with high detection frequency are norfloxacin, ofloxacin, sulfamethoxazole, enrofloxacin, sulfadiazine, erythromycin other antibiotics.
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图 3 不同种类抗生素在中国不同地区地下水中的检出情况
Figure 3. The antibiotics in the same place were added according to the categories, showing the detection of different classes of antibiotics in groundwater in different areas. It can be seen that the research on antibiotics in groundwater is mainly concentrated in North and southwest China. Sulfa antibiotics are the most detected in northeast China, quinolones are the most detected in North and East China, quinolones and tetracycline antibiotics are the most detected in southwest China, but the research level of antibiotics in groundwater in northwest China is relatively low.
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图 4 抗生素在地下水中的三种存在形式及形成机理
吸附态抗生素表现形式基于秦晓鹏等[90]修改。
Figure 4. The figure mainly describes three existing forms and formation mechanisms of antibiotics in groundwater. In groundwater, antibiotics may exist in ionized form, complex state, adsorption state and so on. At different pH, antibiotics may exist in neutral, cationic, anionic or zwitterionic forms. When they coexist with metal ions, complexation reaction will occur under certain conditions to form antibiotic-metal complex. In addition, antibiotics may also exist in the adsorbed state, promoting or inhibiting antibiotic degradation. When antibiotics exist in charged form, complex form and adsorption form, they will also affect the accuracy of detection of antibiotic residue content in the environment.
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图 5 (a) 四环素类抗生素结构式及(b)四环素在水中随pH变化的物质形态分布
Figure 5. The figure shows (a) the structural formula of tetracycline antibiotics and (b) the material form distribution of tetracycline in water with pH change. It has three ionizable functional groups, so there are three acid dissociation constants. At different pH, tetracycline exists in the form of H3TC+, H2TC, HTC- and TC2-, respectively. The distribution of each component in aqueous phase depends on the pH-pKa relationship in aqueous phase.
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图 6 抗生素与水中金属共存的作用机理
基于黄翔峰等[103]修改。
Figure 6. The figure shows the mechanism of the coexistence of antibiotics and metal ions in water. Because most antibiotics contain atoms that provide lone pairs of electrons, they are prone to complexation with metal ions.The complexation between antibiotics and metals will reduce the content of free antibiotics, change their morphological distribution, and ultimately affect the adsorption, oxidative degradation, photodegradation and other behaviors of antibiotics.
表 1 抗生素定性方法
Table 1 Qualitative methods for antibiotics analysis
抗生素定性方法 方法原理 方法优缺点 方法适用范围 微生物抑制法
(Microbial Inhibition Technique，MIT)传统的测定方法，利用抗生素对微生物的生理机能、代谢的抑制作用，与阴性对照进行对比，判断是否存在抗生素[60] 操作简单，但灵敏度低，特异性差，且相似抗生素之间干扰性大[61] 动物性食品中抗生素残留[62] 薄层色谱法
(Thin Layer Chromatography，TLC)利用各成分对同一吸附剂吸附能力不同，从而达到各成分相互分离的目的 具有设备简单、操作简便等优点[63]，但样品处理复杂，且灵敏度低[64] 可用于快速分离和定性少量分析物质 气相色谱-质谱联用
(Gas Cluomatography- Mass Spectrometry，GC-MS)利用样品在色谱柱中气相和固定相间分配系数的不同，经过反复多次分配从而实现分离[65] 具有稳定性好、重复性强、操作简单和扩容性强及普适性大等优点，但不适用于极性大、难挥发的有机污染物[59] 应用于农药和易挥发性有机污染物的定性检测 高效液相色谱-核磁共振联用
(HPLC-NMR)利用HPLC分离复杂化合物，NMR波谱确证未知化合物的结构[66] 该方法相较于质谱检测技术，灵敏度较低，且分析成本高 可用于分析化合物的组成、结构及其变化规律，被广泛应用于化学、医学等行业[67] 
下载: 导出CSV
表 2 常见抗生素的酸解离常数及存在形式
Table 2 Acid dissociation constants and existing forms of common antibiotics
抗生素类别 抗生素名称 pKa pH范围 抗生素形式 参考文献 四环素类 四环素
(TC)pKa1=3.3 ＜3.30 H3TC+ [94] pKa2=7.7 3.30~7.70 HTC-/TC2- pKa3=9.7 ＞7.70 HTC-/TC2- 磺胺类 磺胺噻唑
(STZ)pKa1=2.0
pKa2=7.24＜2.00 STZ+ [95] 2.00~7.24 STZ0 ＞7.24 STZ- 大环内酯类 罗红霉素
(ROX)pKa1=9.08
pKa2=12.45＜9.08 ROX+ [96] 9.08~12.45 ROX0 ＞12.45 ROX- β-内酰胺类 头孢拉定
(CED)pKa1=2.63
pKa2=7.27＜2.63 CED+ [97] 2.63~7.27 CED0 ＞7.27 CED- 喹诺酮类 环丙沙星
(CIP)pKa1=6.1
pKa2=8.7＜6.10 CIP+ [98] 6.10~8.70 CIP0 ＞8.70 CIP-
下载: 导出CSV
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