环境科学  2023, Vol. 44 Issue (1): 138-157   PDF    
引用本文
黄柱良, 蔡家伟, 王儒威. 中国沉积物多环芳烃的时空分布及驱动因子[J]. 环境科学, 2023, 44(1): 138-157.
HUANG Zhu-liang, CAI Jia-wei, WANG Ru-wei. Spatiotemporal Distribution and Driving Factors of Polycyclic Aromatic Hydrocarbons (PAHs) in Inland Sediments of China[J]. Environmental Science, 2023, 44(1): 138-157.
中国沉积物多环芳烃的时空分布及驱动因子
黄柱良, 蔡家伟, 王儒威     
暨南大学环境学院, 广州 511443
收稿日期: 2022-01-21; 修订日期: 2022-04-20
基金项目: 国家自然科学基金项目(41773099)
作者简介: 黄柱良(1998~), 男, 硕士研究生, 主要研究方向为水体沉积物中多环芳烃的环境地球化学, E-mail: 1742690718qq@stu2020.jnu.edu.cn
通信作者: 王儒威, E-mail: wangruwei@jnu.edu.cn
摘要: 根据2000~2020年中国湖泊、河流和河口沉积物中多环芳烃(PAHs)的时空分布, 并通过构建结构方程模型和重力模型分析了驱动PAHs时空分布的社会经济因素.结果表明, 不同地区沉积物中PAHs含量由高到低依次为: 北部沿海>东北>东部沿海>南部沿海>黄河中游>长江中游>西南>西北.南部沿海、长江中游和东部沿海地区高分子量PAHs的比例相对较高, 东北、西北、北部沿海和黄河中游地区低环PAHs的比例相对较高.沉积物中PAHs的含量从2000年起逐渐增加, 2006年以后逐渐减少, 且不同地区沉积物中PAHs含量到达峰值的年份有显著差异.经济发达地区沉积物中PAHs的含量在达到峰值后逐渐下降; 发展中地区由于经济发展较快导致污染物积累较快.偏远或欠发达地区PAHs含量逐渐增加, 但累积率低于发达地区.城市化和工业化对沉积物中PAHs的影响显著, 其中对沉积物中PAHs分布影响最大的因素为经济发展.
关键词: 多环芳烃(PAHs)      内陆沉积物      社会经济因素      贝叶斯模型      重力模型     
Spatiotemporal Distribution and Driving Factors of Polycyclic Aromatic Hydrocarbons (PAHs) in Inland Sediments of China
HUANG Zhu-liang , CAI Jia-wei , WANG Ru-wei     
School of Environment, Jinan University, Guangzhou 511443, China
Abstract: This study reviewed the spatial and temporal distributions of polycyclic aromatic hydrocarbons (PAHs) during 2000-2010 in inland sediments of China and quantified the underlying socioeconomic determinants based on the structural equation model and gravity model. We found that PAHs concentrations in the sediments of eight different regions followed the order of Northern coast>Northeast>Eastern coast>Southern coast>Middle Yellow River>Middle Yangtze River>Southwest>Northwest. The Southern coast, Middle Yangtze River, and Southern coast regions showed large contributions to the high-molecular weight PAHs, whereas the Northeast, Northwest, and Middle Yellow River regions showed high contributions to the low-molecular weight PAHs. PAHs concentrations continuously increased from the year 2000 followed by a gradual decline after 2006, with significant differences in the year when PAHs levels reached their peak. PAHs concentrations of sediment in developed regions declined in recent years following a continuous increase in the 2000s; however, they still increased rapidly in developing regions owing to fast economic development. In addition, the increment rate of PAHs concentrations in sediment at the remote or less-developed regions was slower than that at the developed regions. Urbanization and industrialization had an important effect on PAHs in the sediments, and the largest influencing factor was the economic development.
Key words: polycyclic aromatic hydrocarbons (PAHs)      inland sediment      socio-economic factors      Bayesian estimation      gravity model     

多环芳烃(polycyclic aromatic hydrocarbons, PAHs)是含有2个以上苯环的芳香烃类化合物以及由它们所衍生出的各种化合物的总称, 其主要来源是以垃圾焚烧和燃煤为代表的能源和化工生产过程中有机物质不完全燃烧[1, 2].PAHs因其广泛的分布、持久性、毒性和生物积累能力而受到全世界的关注, 它们在环境介质中总致突变活性占很大比例(在空气悬浮颗粒中占35% ~82%, 在沉积物中占10% ~240%)[3, 4].根据现有的排放清单[5], 1995~2008年, 虽然全球PAHs排放量从59.2万t大幅下降至49.9万t, 但在许多原始地区(如深海表层沉积物, 北极和南极洲以及森林土壤), PAHs的含量没有显示显著下降的趋势.全球PAHs排放量下降速度放缓, 主要是由于快速工业化的发展中国家大量排放PAHs.中国人为排放的PAHs占东亚地区总排放量的四分之三, 超过了全球总排放量的四分之一[6].沉积物中PAHs的时空分布与经济活动和环境过程(如大气输送和沉降)密切相关, 深入理解PAHs的生物地球化学循环链过程有助于更科学地评估和防治沉积物的污染[7], 但目前的报道较少涉及社会经济因素与其的定量关系.本文基于2000~2020年已公布的国家沉积物类型PAHs数据, 分析影响PAHs时空分布的潜在人为和自然因素, 并通过构建结构方程模型和重力模型解析其与社会经济因素之间的关联.

1 材料与方法 1.1 数据排序

本文为采样时间在2000~2020年的中国沉积物(中国香港、澳门和台湾资料暂缺)中PAHs的浓度和组成数据(表 1), 文献检索工具为Web of Science和CNKI.通过数据筛选, 识别并丢弃噪声数据(即样品来自点源污染附近区域和远离人类活动的背景区域).本文所采集的PAHs数据90%以上为16种PAHs, 故统称为16种PAHs.所有PAHs分析的样品回收率在47.2% ~119.4%之间, 平行样品的相对标准偏差小于15%; 本研究采用柯尔莫哥洛夫-斯米尔诺夫检验法检验正态分布.当数据不符合正态性假设时, 使用曼-惠特尼和克鲁斯卡尔-沃利斯检验; 当数据不符合正态分布, 则进行斯皮尔曼检验.统计学显著性设定为P＜0.05水平.

表 1 中国沉积物中PAHs的采集数据1) Table 1 Acquisition data of PAHs in inland sediments in China

1.2 贝叶斯结构方程模型

为了避免高度相关变量之间的干扰, 首先采用主成分分析进行降维分析.从29个人为参数中提取6个主成分(表 2), 分别归为6个因素, 即能源消耗、经济发展、城市化水平、植被、工业化和森林火灾.从6个自然参数(平均气压、平均气温、极端最低气温、降水量、日照时数和相对湿度)中提取出1个成分, 归为气候因素.

表 2 人为参数的旋转分量矩阵1) Table 2 Rotation component matrix of man-made and natural parameters

为构建非信息先验贝叶斯结构方程模型, 假设：①所有参数均为截面数据, 利用气候因子与各种人为因素之间的潜在因果关系表征其空间和年际变化, 并综合考虑了不同地区气温和降水的差异; ②对于中间组分, 减小环境投资可能直接影响大气污染物排放.同时, 环境投资也会受到经济发展和城市化水平的影响, 而森林火灾也可能影响植被.其中, 经济发展用GDP、交通和用水量来衡量; ③气候因素对沉积物中PAHs的影响途径分为直接(大气沉降)和间接影响(改变人为因素的参数); ④人为因素会直接或间接影响PAHs的分布.贝叶斯估计依赖于Gelman-Rubin收敛.为了避免局部最优解, 将迭代次数设置为100 000次, 并丢弃前5 000次数据.潜在的规模折减系数小于1.1, 表明模型达到了收敛性[200].采用后验预测P值和卡方差值95%置信区间对模型拟合进行判断.当后验预测P值在0.5左右[201], 卡方差异值的95%置信区间以零为中心且基本对称, 说明模型拟合良好.若95%置信区间路径系数为零, 说明研究假设只有部分得到支持, 否则说明研究假设均可得到支持.

1.3 重力模型

重力模型计算公式如下：

式中, Iij为地区i和j之间的重力, Pi和Pj分别为地区i和j的质量, dij为两个地区之间的距离, G为重力系数, r为重力衰减系数(r =2).

沉积物PAHs表征网络计算公式如下:

式中, C为沉积物中16种PAHs总含量的平均值.

人类活动关系(国内生产总值、人口、用水量、道路客运和货物运输)的计算公式如下[202]：

式中, E为国内生产总值, N为总人口, W为用水量, T为公路客运量, F为公路货运量, H为社会经济发展指数, d为两个地区重力点之间的距离[203].

2 结果与讨论 2.1 沉积物中PAHs的时空分布

中国沉积物中16种PAHs总量的地理分布见图 1.污染水平最高的是2003年天津市河流(775~255 372 ng·g-1, 平均值：27 074 ng·g-1), 3环和4环约占总含量的比例分别为44%和41%, 主要来源于附近的矿物和煤炭燃烧[68].污染水平最低的是2017年江苏省苏北浅滩(0~25.2 ng·g-1, 平均值：5.88 ng·g-1), 4环PAHs占42%, 其次为5环PAHs(占35%), 主要来自高温燃烧[148].根据国务院发展研究中心《地区协调发展的战略和政策报告》将中国大陆划分的8个经济区, 不同地区沉积物中PAHs含量分布由高到低依次为：北部沿海>东北>东部沿海>南部沿海>黄河中游>长江中游>西南>西北.总体而言, 东部高于西部; 河口低于河流; 北方高于南方.

图 1 沉积物中PAHs含量地理分布 Fig. 1 Geographic distribution map of PAHs concentration in sediments

PAHs含量的时空变化趋势表明, 沉积物中PAHs的含量从2000年起逐渐增加, 2006年以后逐渐减少(图 2). 2006年以前, PAHs高污染区域主要集中在沿海地区, 如东北、京津冀、长三角和珠三角地区, 这些地区的城市化水平很高, 人口和工业集中.东北地区污染网络的中心位于辽河流域, 该地区的经济发展以重工业为主.北部沿海地区污染最严重的地方是海河下游.长江中游污染网中心点位于京杭大运河的安徽段[15], 表明航运对内河运河中沉积物的污染较大.而东部沿海地区和南部沿海地区的污染网络中心是黄浦江和珠江, 分别位于上海和广州, 这些地区作为经济发达地区, 机动车排放源的污染贡献率相对较大[77, 120]. 2006年以后, 长江中游和黄河中游污染网络密度增加, 黄河中游的污染网络节点位于三门峡段(渭河)[176].上游地区宝鸡、西安等城市沉积物中PAHs含量也随之增加, 可能与当地化学工业排放和煤炭燃烧有关[177].

图 2 沉积物中PAHs含量的历史趋势 Fig. 2 Historical trends of the PAHs concentrations in sediments

不同地区沉积物中PAHs含量到达峰值的年份有显著差异.北部沿海地区如白洋淀的ω(PAHs)在2009年时出现最大值为2 402 ng·g-1, 在2016年最大值为861 ng·g-1[10, 11].东北松花江ω(PAHs)在2005年的最大值为15 310 ng·g-1, 2010年后, 最大值为3 877 ng·g-1[204, 205].东北辽河ω(PAHs)的中位数由2009年的3 281 ng·g-1下降至2014年的178 ng·g-1[157, 206].长江中游地区如长江武汉段沉积物中ω(PAHs)在2005年范围为26~7 136 ng·g-1, 在2016年范围为46~424 ng·g-1[192, 207].东部地区巢湖沉积物中ω(PAHs)在2009年最大值为10 200 ng·g-1, 但2016年时最大值降至1 032 ng·g-1[27, 28]. 2003、2005和2011年太湖梅梁湾沉积物中ω(PAHs)分别为2 563、3 498和944 ng·g-1[168~170], 说明该地区湖泊污染的治理效果显著.青海湖、博斯腾湖等偏远地区沉积物中的PAHs含量总体较低, 但沉积物中的PAHs含量呈逐年增加的趋势, 且高环PAHs的比例逐年上升[208].

2.2 沉积物中PAHs的来源解析

在大多数地区, 沉积物中4环及以下PAHs的比例(20.5% ~45.1%)高于5环和6环PAHs(图 3), 这可能与我国以煤炭为主要能源有关[6].南部沿海、长江中游和东部沿海地区由于经济发达和交通繁忙, 导致高分子量PAHs的组成比例高于其他地区.西北地区低分子量PAHs的组成比例显著高于其他地区, 可能是由于该地区经济发展水平较低和人口数量较小.北部(东北、北部沿海和黄河中游)低环PAHs含量相对较高, 且主要为源自生物质和煤炭燃烧源的菲、芴、芘和萘, 这与北方地区的生产和生活能耗一致[5, 6].南部和东部的PAHs含量差异不明显, 但高环PAHs的比例普遍高于北部, 南方的强降雨、高温和长时间的日照是造成这种分布现象的重要因素[209].

1.萘(Nap), 2.苊烯(Acy), 3.二氢苊(Ace), 4.芴(Flu), 5.菲(Phe), 6.蒽(Ant), 7.荧蒽(Fla), 8.芘(Pyr), 9.苯并[a]蒽(BaA), 10. (Chry), 11.苯并[b]荧蒽(BbF), 12.苯并[k]荧蒽(BkF), 13.苯并[a]芘(BaP), 14.二苯并[a, h]蒽(DahA), 15.茚并[1, 2, 3-cd]芘(InP), 16.苯并[ghi]苝(BghiP) 图 3 中国8个地区的PAHs单体含量 Fig. 3 PAH concentrations in eight Chinese regions

为进一步解析不同地区沉积物中PAHs的来源, 采用特征比值法来确定PAHs的可能排放源.可以看出2006年前与2006年后8个区域的PAHs比值有显著变化, 不同地区的比值也不相同(图 4), 比如2006年前8个区域Ant/(Ant+Phe)的比值普遍比2006年后的大, 表明化石能源消费的增加加剧我国内陆沉积物中PAHs污染[210~212].黄河中游地区作为我国最大的煤炭开采和深加工基地, 沉积物中Ant/(Ant+Phe)比值最大且2006年后明显减少, 可能是由于2006年后我国对煤炭开采进行严格管理并修订《中华人民共和国煤炭法》, 此外, 2006年后西北地区BaA/(BaA+Chry)比值显著比2006年前大, 可能是由于2006年以后西部地区经济快速发展引起机动车数量剧增.西北地区和西南地区的InP/(InP+BghiP)和BaA/(BaA+Chry)的值较高, 可能是由于其经济发展水平较低, 大量燃烧草、木和煤作为供热和发电[213].而经济较为活跃的东部沿海地区的Fla/(Fla+Pyr)比值一直较高, 反映了其地区人口数量多和交通发达[214].

图 4 中国8个区域的PAHs比值 Fig. 4 Ratio of PAHs in eight Chinese regions

2.3 人为和自然因素与沉积物中PAHs分布的关系

利用贝叶斯结构方程模型计算得到各社会经济因素对PAHs含量影响的路径系数, 从而构建了人类活动强度和全国沉积物中PAHs污染分布网络.根据标准化路径系数(λ)从大至小, 影响沉积物中PAHs的分布的因素分别为：经济发展(λ=0.33±0.18)、能源消耗(λ=0.20±0.10)、城市化水平(λ=0.02±0.01)、植被(λ=-0.25±0.12)、森林火灾(λ=0.13±0.17)和工业化(λ=0.06±0.03).从环境投资和能源消费的角度看, 经济发展对PAHs的分布直接影响和间接影响均最大(图 5).此外, 气候因素和植被对沉积物中PAHs的分布显现负相关, 因为高温和降水较多的环境能够有效溶解和稀释PAHs[215].植被则具有吸附和降解功能, 同时还可以通过减少水土流失, 防止PAHs的环境迁移.本研究结果与中国潮间带PAHs分布模式影响的最新研究结果不同[209], 可能是不同的研究区域, 不同流域和省份的参数带来的差异.

图 5 贝叶斯结构方程模型的结果 Fig. 5 Results of Bayesian structural equation model

2.4 经济活动关联与PAHs分布的关系

中国不同省、市之间存在普遍的空间溢出效应.从2000~2019年, 点向和点出入度发生了显著变化, 区域经济活动由少中心向多中心转变(图 6).经济效益关联区逐年增加, 但增长的速度和方式各不相同.在北部沿海、东部沿海和南部沿海比较发达的地区, 增长速度明显加快, 而黄河中游和长江中游增长缓慢, 西南地区增长最慢.

图 6 2000~2019年中国31个地区的投入产出分析结果 Fig. 6 Input-output analysis results of 31 regions in China from 2000 to 2019

沉积物中PAHs含量的时空变化也显示出类似的规律.2005~2015年, 人为因素的强相关网络整体密度显著增加, 关联区域从2005年的沿海地区转移到2015年的内陆地区(图 7).与此同时, 沉积物中PAHs的时空分布发生了变化, 高污染区域也发生了转移. 2005年前后, 高污染区出现在东北辽河, 天津海河和广州珠江[38, 68, 119].2015年前后, 黄河中游的高污染区分别为河南黑石关、甘肃兰州、云南滇池、江苏太湖、漳渭新河和上海黄浦江[216].随着第二产业比重呈先上升后下降的趋势, 并在2010年前后出现拐点, PAHs的污染组分发生了变化, 高环PAHs的比例逐渐增加, 如东北辽河和巢湖[30, 94].在青海湖和滇池等也出现了类似的变化[45, 131].由此可见, 城市化和工业化对沉积物中PAHs的影响显著.此外, 人类活动与污染的相关网络图发生变化, 节点数量增加, 密集的强相关网络由沿海地区扩展至中西部地区.随着全国地区经济快速发展, 城市化水平高的地区有了足够的财政用于排放控制、清洁生产和专项污染控制等环境治理, 从而有效降低了污染水平.

图 7 改进的重力模型结果 Fig. 7 Improved gravity model results

总的来说, 各地区沉积物中PAHs污染程度的减少和增长各不相同, 表现出复杂的关联机制.随着区域经济的发展和关联性的增强, 经济发达地区加大了环境管控和治理措施, 并促进周边地区的经济活动.经济发达地区沉积物中PAHs的含量在达到峰值后逐渐下降.发展中地区的经济发展较快, 污染物积累较快.偏远或经济欠发达的地区, 污染物含量逐渐增加, 但累积率慢于快速发展地区.

3 结论

(1) 高污染区域主要集中在北部和东部沿海地区, 沉积物中4环PAHs占比较高, 燃煤和生物质燃烧是沉积物中PAHs的主要来源.

(2) 通过贝叶斯结构方程模型, 得出对沉积物中PAHs分布直接影响最大的因素是经济发展.

(3) 沉积物中PAHs的时空分布与经济发展有关.强关联区域的污染网络由沿海地区向内陆地区扩展, 内陆地区PAHs含量呈上升趋势, 而这与沿海地区情况相反, 这表明当区域社会经济发展到一定水平后, 由于环境投资的增加和能源结构的调整, 沉积物中PAHs含量变化趋势逐渐平稳.

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