城郊土水界面污染流污染特征、空间分布及其生态风险
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摘要
源于污染土壤的土水界面污染流是非点源污染发生的一种特殊形式,其是在降雨或溶雪等冲刷作用下,土壤中的污染物通过扩散、弥散、解吸、解离等化学反应和多种作用过程进入地表径流中,最终形成携带着包括重金属、持久性有机污染物以及N、P营养元素在内的多种污染物的一种特殊污染流体。城市郊区是介于城市与乡村的交错、过渡地带,具有农业生产化程度高,土地利用结构复杂,受城市城市化、工业化发展和乡村多重影响等特点,且受人类高强度活动的干扰和改造,是区域响应最为敏感的地带。本文以我国北方重要工业城市天津市郊西青区为典型研究区,通过资料收集、大规模野外定点采样和试验分析,将常规数据统计与数据挖掘技术相结合,深入系统地分析了研究区土水界面污染流重金属和多环芳烃等污染物污染特征、分布规律及其污染来源。以地统计学的理论与方法为基础,对重金属和多环芳烃的空间变异和分布规律进行了研究,构建了基于Monte-Carlo模型的土水界面污染流的生态风险评价方法,并对其生态风险进行了评价。主要研究结论如下:
(1)通过大规模的系统取样调查和分析,揭示了研究区土水界面污染流重金属及多环芳烃等污染物的污染特征。统计分析结果显示:
Cr、Ni、Cd、Zn等四项重金属服从对数正态分布,Cu、As、Pb等三项重金属为偏态分布。重金属变异系数普遍较高,表明重金属数据离散程度较大,各监测点污染流重金属含量的差异性较大。除As外,重金属之间具有显著相关性;主成分分析结果显示研究区土水界面污染流重金属可提取3个主成分,第一主成分包含Cr、Ni和Zn,第二主成分包含Cu、Cd、Pb,第三主成分则集中反映了As的作用。
研究区土水界面污染流中的多环芳烃以三环、四环和五环多环芳烃为主,分别占总量的36.49%、26.34%和27.85%,两环和六环的多环芳烃含量则相对较低。除高环多环芳烃外,低环、中环和多环芳烃总量均服从对数正态分布,各多环芳烃组分变异系数较高,在中等变异性以上。除Dba和Inp外,其他14种组分的多环芳烃与总量之间存在显著相关性,其中四环的Flu、Pyr、BaA和Chr与总量的相关性最强,在0.01的置信水平上,相关系数分别达到了0.90、0.84、0.87和0.89,而低环的Nap、Acy、Ace和Phe与总量的相关性相对较弱。
利用主成分分析技术、比值法和典型源三角图判别法对研究区土水界面污染流中多环芳烃来源进行了解析。主成分分析结果显示,可以提取5个主成分,第一主成分主要由高环数的多环芳烃组成,是交通源和燃煤源共同作用的综合体现:第二主成分主要由低环数的多环芳烃组成,主要代表气态多环芳烃的沉降过程;第三主成分集中反映了Flu、Phe和Pyr三种多环芳烃的作用,具有典型焚烧源的特征;第四主成分集中反映了Acy和Inp两种多环芳烃的作用,集中代表了秸秆燃烧和油燃烧源的多环芳烃来源;第五主成分则反映了Dba的作用。
研究区Phe/Ant的比值分析结果表明,多环芳烃主要来源于不完全燃烧;所有样点Flu/Pyr的比值均大于1,表明多环芳烃主要来源于煤炭/生物质的不完全燃烧等;研究区部分样点BaA/Chr的比值大于0.5,表明这部分样点的多环芳烃主要来源于燃烧源;部分样点BaA/Chr的比值在0.25～0.35之间,表明多环芳烃来源于石油和燃烧的混合源;极少部分样点Bar/Chr的比值小于0.25主要指示石油源。
典型源三角图判别法的分析结果表明,多环芳烃主要来自燃烧过程,除煤炭燃烧外,还存在木质材料如秸秆焚烧,发动机高温燃烧、油燃烧以及生物质燃烧等。低、中、高环多环芳烃各组分在多环芳烃总量中都没有占绝对优势,表明研究区污染流各监测样点多环芳烃不是来自于单一污染源,而可能是多污染源共同复合累加的结果。
(2)利用地统计技术对研究区土水界面污染流重金属和多环芳烃的空间变异和空间分布进行了研究。
研究表明,七项重金属元素均表现为各向同性,变程在4040～7404米之间,As和Cd在小区域范围内表现出极强的空间相关性,Zn、Ni、Cr为中等程度的空间相关。除As外,其他重金属在原点处均表现出明显的块金效应,As、Zn、Cr、Pb和Cu等采用球形模型拟合半变异函数,Ni和Cd采用指数模型拟合半变异函数。Cu和Pb具有明显的空间趋势,采用具有趋势的克里格进行插值,其他重金属采用普通克里格进行插值。
低环和高环多环芳烃表现为各向同性,均采用球形模型拟合其经验半变异函数。低环多环芳烃为弱空间相关性,高环多环芳烃为中等程度空间相关,而中环多环芳烃空间相关极弱。采用普通克里格插值方法对低环和高环多环芳烃的空间分布进行预测,采用反距离插值对中环多环芳烃的空间分布进行预测,利用叠置分析技术获取多环芳烃总量的空间预测结果。
(3)建立了基于概率密度函数和Monte-Carlo模型的土水界面污染流的生态风险评价方法,并对研究区土水界面污染流重金属和多环芳烃的生态风险进行了评价。
结果表明,Cu造成生态风险的概率较高,生态风险的可能性为80.74%,Cd造成生态风险可能性为43.09%,而Pb造成生态风险总体的可能性概率为16.49%。基于Rapant指数的重金属生态风险评价结果显示,研究区50.3%的区域为无生态风险和低生态风险,13.75%的区域为中等生态风险,13.61%的区域为高生态风险区域。
Phe、Ant、Pyr、BkF、Bghip等多环芳烃组分生态风险较低,对生态系统的危害较小;Nap、Ncy、BbF、Bap等多环芳烃组分具有一定的生态风险,对生态系统有一定危害,但尚不足以产生严重的生态风险;Ace、Fl、Flu、BaA、Chr、DahA等多环芳烃组分,造成严重生物危害影响的概率非常高,已具有极为严重的生态风险。而多环芳烃总量高于ERL或ESQVL的风险概率极高超过了90%,而高于ERM或ISQVH的风险概率较低,为11.21%,已经具有一定的生态风险。
Contaminated flow from soil-water interfaces is a special form of non-point pollution source. Under erosion of rainfall or snow melt, chemical reactions as diffusion, dispersion, desorption, dissociation and multiple processes resulted in the entrance of soil pollutants into surface runoff and the special effluent occurred including multiple pollutants such as heavy metals, persistent organic pollutants, nitrogen and phosphorus. The suburb area, as a typical transitional zone between city and country, is a multifunctional and complex ecosystem with high level agricultural producing, complex land use structure. Due to the interaction of city and country, the suburb area is one of highly human activities zone and affected by urbanization、industrialization and country development. So taking the Xiqing area of Tianjin suburb as a research object, pollution characters of heavy metals and PAH, spatial distribution and pollution sources have been discussed in this work through information collection, extensive systematic investigation and analysis. Based on the geostatistical method, the spatial variability and spatial distribution of heavy metal and PAHs were analyzed in the paper. In addition, ecological risk method was designed by Monte-Carlo model and assessed the risk of contaminated flow from soil-water interfaces of the study area. The main findings as follows:
(1) Based on the extensive investigation and analysis, pollution characters of heavy metals and PAHs of contaminated flow from soil-water interfaces of the study area were discovered.
The concentrations of Cr, Ni, Cd and Zn in the contaminated flow from soil-water interfaces were in lognormal distribution. The concentration of Cu, As and Pb were in skew distribution. The coefficients of variation of heavy metals were almost big, which indicated that the degrees of scatter of havy metal datum were greater and the concentrations of heavy metal varied greatly. There were significant relationships between heavy metals except As. The results of principal component analysis revealed that three components could be chosen to represent the data set. The first factor include Cr, Ni and Zn, the second factor include Pb and Hg, while As was strongly related to the third component
PAHs with 3-ring, 4-ring and 5-ring numbers were dominated in the contaminated flow from soil-water interfaces of study area, the contribution of which were quantified as 36.49%、26.34% and 27.85%, respectively. Except PAHs with high-ring, the concentrations of PAHs with low-ring, median-ring and total PAHs were in lognormal distribution. The coefficients of variation of each PAHs were almost big. The relationships between fourteen PAHs and total PAHs were significant except for Dba and Inp. The relationships of Flu, Pyr, BaA and Chr to total PAHs were the strongest, which belongs to PAHs with 4-ring. At confidence levels of 0.01, the relationships coefficients were 0.90、0.84, 0.87 and 0.89 respectively.
PAHs sources of contaminated flow from soil-water interfaces of study area were examined using principal component analysis, ration method and typical source of triangle plot. The results of principal component analysis revealed that five components could be chosen to represent the data set. The first factor mainly includes PAHs with high-ring, which reveals the synthetical contribution by traffic exhausts and coal combust, ion source. The second factor mainly includes PAHs with low-ring, which represents the process of atmospheric deposition. The third factor reflected the effect of Flu、Phe and Pyr and shows the typical character of combustion source. The forth factor include Acy and Inp, which reflected the straw combustion and petrolic source. In the end, Dba was especially reflected by the fifth component.
Phe/Ant ratio showed that incomplete combustion was the primary source of PAHs. All Flu/Pyr ration was bigger than 1.0 in the study area, which inferred that coal combustion and biomass burning were the main sources. Bar/Chr ration was bigger than 0.5 in some of the samples, which concluded that the primary source of these samples was combustion source. Some Bar/Chr ration was 0.25-0.35 reflected that mixed source of petroleum and combustion. Few Bar/Chr ration was lower than 0.25 that indicated petroleum source.
The analysis result of typical source of triangle plot included that combustion process was the primary source that contains coal combustion, straw burning, high-temperature combustion of engine, oil combustion and biomass burning. It was clear that low-ring, median-ring and high-ring PAHs did not dominate total PAH burden. The results showed that PAHs in the samples were not come from the single source and may be affected by the joint of multiple pollution sources.
(2) Based on geostatistical technique, spatial variability and distribution of heavy metal and PAHs were analyzed.
The results showed that the spatial structures of seven heavy metals were isotropy and the ranges were 4040-7404 meters. As and Cd were strong spatial relationship in a small scale and it was median spatial heterogeneity of Zn, Ni, Cr. Except As, other heavy metals obvious showed nugget effect. Semivariogram of As, Zn, Cr, Pb and Cu could be fitted well with spherical model. And semivariogram of Ni and Cd could be fitted well with exponential model. The spatial structure of Cu and Pb were obvious spatial trend, kriging with trend was used to estimate spatial distribution.
The spatial structures of low-ring and high-ring PAHs were isotropy and semivariogram could be fitted well with spherical model. Result form geostatistical analysis demonstrated that low-ring PAHs have a weak spatial heterogeneity, high-ring PAHs have a median spatial heterogeneity and median-ring PAHs have no spatial heterogeneity. Ordinary kriging was used to estimate spatial distribution of low-ring and high-ring PAHs. IDW was used to estimate spatial distribution of median-ring PAHs. Overlay analysis was used to attain spatial distribution of total PAH.
(3) Based on probability density function and Monte-carlo model, ecological risk method was constructed to assess the ecological risk of heavy metal and PAHs in study area.
The results showed that the ecological risk probability of Cu, Cd and Pb were 80.75%, 43.09% and 16.49%, respectively. Based on Rapant index, the results of ecological risk assessment showed that 50.3% of study area was in degree of none or low ecological risk, 13.75% of study area was in degree of moderate ecological risk and 13.61% of study area was in degree of high ecological risk.
The potential ecological risk of Phe、Ant、Pyr、BkF and Bghip was low, which had small harm to ecosystem. The potential ecological risk of Nap、Ncy、BbF、Bap existed and could harm to ecosystem. By contrast, the potential of Ace、Fl、Flu、BaA、Chr、DahA was high and the probability harm to ecosystem was also high. For total PAH, the risk probability of 90% was higher than ERL or ESQVL and the risk probability of 11.21% was higher than ERM or ISQVH.
(1)通过大规模的系统取样调查和分析,揭示了研究区土水界面污染流重金属及多环芳烃等污染物的污染特征。统计分析结果显示:
Cr、Ni、Cd、Zn等四项重金属服从对数正态分布,Cu、As、Pb等三项重金属为偏态分布。重金属变异系数普遍较高,表明重金属数据离散程度较大,各监测点污染流重金属含量的差异性较大。除As外,重金属之间具有显著相关性;主成分分析结果显示研究区土水界面污染流重金属可提取3个主成分,第一主成分包含Cr、Ni和Zn,第二主成分包含Cu、Cd、Pb,第三主成分则集中反映了As的作用。
研究区土水界面污染流中的多环芳烃以三环、四环和五环多环芳烃为主,分别占总量的36.49%、26.34%和27.85%,两环和六环的多环芳烃含量则相对较低。除高环多环芳烃外,低环、中环和多环芳烃总量均服从对数正态分布,各多环芳烃组分变异系数较高,在中等变异性以上。除Dba和Inp外,其他14种组分的多环芳烃与总量之间存在显著相关性,其中四环的Flu、Pyr、BaA和Chr与总量的相关性最强,在0.01的置信水平上,相关系数分别达到了0.90、0.84、0.87和0.89,而低环的Nap、Acy、Ace和Phe与总量的相关性相对较弱。
利用主成分分析技术、比值法和典型源三角图判别法对研究区土水界面污染流中多环芳烃来源进行了解析。主成分分析结果显示,可以提取5个主成分,第一主成分主要由高环数的多环芳烃组成,是交通源和燃煤源共同作用的综合体现:第二主成分主要由低环数的多环芳烃组成,主要代表气态多环芳烃的沉降过程;第三主成分集中反映了Flu、Phe和Pyr三种多环芳烃的作用,具有典型焚烧源的特征;第四主成分集中反映了Acy和Inp两种多环芳烃的作用,集中代表了秸秆燃烧和油燃烧源的多环芳烃来源;第五主成分则反映了Dba的作用。
研究区Phe/Ant的比值分析结果表明,多环芳烃主要来源于不完全燃烧;所有样点Flu/Pyr的比值均大于1,表明多环芳烃主要来源于煤炭/生物质的不完全燃烧等;研究区部分样点BaA/Chr的比值大于0.5,表明这部分样点的多环芳烃主要来源于燃烧源;部分样点BaA/Chr的比值在0.25～0.35之间,表明多环芳烃来源于石油和燃烧的混合源;极少部分样点Bar/Chr的比值小于0.25主要指示石油源。
典型源三角图判别法的分析结果表明,多环芳烃主要来自燃烧过程,除煤炭燃烧外,还存在木质材料如秸秆焚烧,发动机高温燃烧、油燃烧以及生物质燃烧等。低、中、高环多环芳烃各组分在多环芳烃总量中都没有占绝对优势,表明研究区污染流各监测样点多环芳烃不是来自于单一污染源,而可能是多污染源共同复合累加的结果。
(2)利用地统计技术对研究区土水界面污染流重金属和多环芳烃的空间变异和空间分布进行了研究。
研究表明,七项重金属元素均表现为各向同性,变程在4040～7404米之间,As和Cd在小区域范围内表现出极强的空间相关性,Zn、Ni、Cr为中等程度的空间相关。除As外,其他重金属在原点处均表现出明显的块金效应,As、Zn、Cr、Pb和Cu等采用球形模型拟合半变异函数,Ni和Cd采用指数模型拟合半变异函数。Cu和Pb具有明显的空间趋势,采用具有趋势的克里格进行插值,其他重金属采用普通克里格进行插值。
低环和高环多环芳烃表现为各向同性,均采用球形模型拟合其经验半变异函数。低环多环芳烃为弱空间相关性,高环多环芳烃为中等程度空间相关,而中环多环芳烃空间相关极弱。采用普通克里格插值方法对低环和高环多环芳烃的空间分布进行预测,采用反距离插值对中环多环芳烃的空间分布进行预测,利用叠置分析技术获取多环芳烃总量的空间预测结果。
(3)建立了基于概率密度函数和Monte-Carlo模型的土水界面污染流的生态风险评价方法,并对研究区土水界面污染流重金属和多环芳烃的生态风险进行了评价。
结果表明,Cu造成生态风险的概率较高,生态风险的可能性为80.74%,Cd造成生态风险可能性为43.09%,而Pb造成生态风险总体的可能性概率为16.49%。基于Rapant指数的重金属生态风险评价结果显示,研究区50.3%的区域为无生态风险和低生态风险,13.75%的区域为中等生态风险,13.61%的区域为高生态风险区域。
Phe、Ant、Pyr、BkF、Bghip等多环芳烃组分生态风险较低,对生态系统的危害较小;Nap、Ncy、BbF、Bap等多环芳烃组分具有一定的生态风险,对生态系统有一定危害,但尚不足以产生严重的生态风险;Ace、Fl、Flu、BaA、Chr、DahA等多环芳烃组分,造成严重生物危害影响的概率非常高,已具有极为严重的生态风险。而多环芳烃总量高于ERL或ESQVL的风险概率极高超过了90%,而高于ERM或ISQVH的风险概率较低,为11.21%,已经具有一定的生态风险。
Contaminated flow from soil-water interfaces is a special form of non-point pollution source. Under erosion of rainfall or snow melt, chemical reactions as diffusion, dispersion, desorption, dissociation and multiple processes resulted in the entrance of soil pollutants into surface runoff and the special effluent occurred including multiple pollutants such as heavy metals, persistent organic pollutants, nitrogen and phosphorus. The suburb area, as a typical transitional zone between city and country, is a multifunctional and complex ecosystem with high level agricultural producing, complex land use structure. Due to the interaction of city and country, the suburb area is one of highly human activities zone and affected by urbanization、industrialization and country development. So taking the Xiqing area of Tianjin suburb as a research object, pollution characters of heavy metals and PAH, spatial distribution and pollution sources have been discussed in this work through information collection, extensive systematic investigation and analysis. Based on the geostatistical method, the spatial variability and spatial distribution of heavy metal and PAHs were analyzed in the paper. In addition, ecological risk method was designed by Monte-Carlo model and assessed the risk of contaminated flow from soil-water interfaces of the study area. The main findings as follows:
(1) Based on the extensive investigation and analysis, pollution characters of heavy metals and PAHs of contaminated flow from soil-water interfaces of the study area were discovered.
The concentrations of Cr, Ni, Cd and Zn in the contaminated flow from soil-water interfaces were in lognormal distribution. The concentration of Cu, As and Pb were in skew distribution. The coefficients of variation of heavy metals were almost big, which indicated that the degrees of scatter of havy metal datum were greater and the concentrations of heavy metal varied greatly. There were significant relationships between heavy metals except As. The results of principal component analysis revealed that three components could be chosen to represent the data set. The first factor include Cr, Ni and Zn, the second factor include Pb and Hg, while As was strongly related to the third component
PAHs with 3-ring, 4-ring and 5-ring numbers were dominated in the contaminated flow from soil-water interfaces of study area, the contribution of which were quantified as 36.49%、26.34% and 27.85%, respectively. Except PAHs with high-ring, the concentrations of PAHs with low-ring, median-ring and total PAHs were in lognormal distribution. The coefficients of variation of each PAHs were almost big. The relationships between fourteen PAHs and total PAHs were significant except for Dba and Inp. The relationships of Flu, Pyr, BaA and Chr to total PAHs were the strongest, which belongs to PAHs with 4-ring. At confidence levels of 0.01, the relationships coefficients were 0.90、0.84, 0.87 and 0.89 respectively.
PAHs sources of contaminated flow from soil-water interfaces of study area were examined using principal component analysis, ration method and typical source of triangle plot. The results of principal component analysis revealed that five components could be chosen to represent the data set. The first factor mainly includes PAHs with high-ring, which reveals the synthetical contribution by traffic exhausts and coal combust, ion source. The second factor mainly includes PAHs with low-ring, which represents the process of atmospheric deposition. The third factor reflected the effect of Flu、Phe and Pyr and shows the typical character of combustion source. The forth factor include Acy and Inp, which reflected the straw combustion and petrolic source. In the end, Dba was especially reflected by the fifth component.
Phe/Ant ratio showed that incomplete combustion was the primary source of PAHs. All Flu/Pyr ration was bigger than 1.0 in the study area, which inferred that coal combustion and biomass burning were the main sources. Bar/Chr ration was bigger than 0.5 in some of the samples, which concluded that the primary source of these samples was combustion source. Some Bar/Chr ration was 0.25-0.35 reflected that mixed source of petroleum and combustion. Few Bar/Chr ration was lower than 0.25 that indicated petroleum source.
The analysis result of typical source of triangle plot included that combustion process was the primary source that contains coal combustion, straw burning, high-temperature combustion of engine, oil combustion and biomass burning. It was clear that low-ring, median-ring and high-ring PAHs did not dominate total PAH burden. The results showed that PAHs in the samples were not come from the single source and may be affected by the joint of multiple pollution sources.
(2) Based on geostatistical technique, spatial variability and distribution of heavy metal and PAHs were analyzed.
The results showed that the spatial structures of seven heavy metals were isotropy and the ranges were 4040-7404 meters. As and Cd were strong spatial relationship in a small scale and it was median spatial heterogeneity of Zn, Ni, Cr. Except As, other heavy metals obvious showed nugget effect. Semivariogram of As, Zn, Cr, Pb and Cu could be fitted well with spherical model. And semivariogram of Ni and Cd could be fitted well with exponential model. The spatial structure of Cu and Pb were obvious spatial trend, kriging with trend was used to estimate spatial distribution.
The spatial structures of low-ring and high-ring PAHs were isotropy and semivariogram could be fitted well with spherical model. Result form geostatistical analysis demonstrated that low-ring PAHs have a weak spatial heterogeneity, high-ring PAHs have a median spatial heterogeneity and median-ring PAHs have no spatial heterogeneity. Ordinary kriging was used to estimate spatial distribution of low-ring and high-ring PAHs. IDW was used to estimate spatial distribution of median-ring PAHs. Overlay analysis was used to attain spatial distribution of total PAH.
(3) Based on probability density function and Monte-carlo model, ecological risk method was constructed to assess the ecological risk of heavy metal and PAHs in study area.
The results showed that the ecological risk probability of Cu, Cd and Pb were 80.75%, 43.09% and 16.49%, respectively. Based on Rapant index, the results of ecological risk assessment showed that 50.3% of study area was in degree of none or low ecological risk, 13.75% of study area was in degree of moderate ecological risk and 13.61% of study area was in degree of high ecological risk.
The potential ecological risk of Phe、Ant、Pyr、BkF and Bghip was low, which had small harm to ecosystem. The potential ecological risk of Nap、Ncy、BbF、Bap existed and could harm to ecosystem. By contrast, the potential of Ace、Fl、Flu、BaA、Chr、DahA was high and the probability harm to ecosystem was also high. For total PAH, the risk probability of 90% was higher than ERL or ESQVL and the risk probability of 11.21% was higher than ERM or ISQVH.
引文
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