水土污染空间分析及源辨析
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摘要
土壤和水资源是人类生存的物质基础。它们的环境质量直接影响着人类的生活和生产；同时,人类活动也直接影响着土水环境。近几十年来,随着社会、经济的发展,土地利用与土地覆被方式的改变,以及工业化进程的加快,我国不少地方的土、水环境不断恶化。钱塘江流域和海宁市,经济发展快速,产业类型多样,结构复杂,土地利用方式变化频繁,对土水环境的影响较复杂,并最终导致这两个地方的污染水平、污染方式和污染的空间模式与众不同。本文分析了两地的污染源类型和空间分布模式,特别对海宁的重金属污染模式进行了分析和探讨。
在钱塘江流域水污染分析方面,应用模糊综合评估方法,对41个监测点的13个变量进行评估,根据评估结果划分高、中、低的三大污染区；应用因子分析法,判别出主要影响因子。同时结合研究区的实际情况,给各污染源确定名称；最后,用UNMIX方法,进行定量的污染源源解析,计算出每个污染源对主要污染变量的贡献及对每个监测点的综合贡献。结果发现,在低污染区,面源污染,比如农田径流和城市径流,是主要的污染源；在中污染区和高污染区,以混合污染源为主。在低、中、高三个污染区可辨别的潜在污染源分别为2个、2个和3个,因子分析分别解释了三个污染区的67%、73%和80%总体方差。大体上,小支流污染比大支流和干流严重。
在海宁土壤重金属污染分析及源辨析中,富集因子法定量分析了各变量的污染程度,判别出主要影响因子；聚类分析方法验证了因子分析的结论；地统计学方法对各变量进行空间分析和预测；GIS分析了道路对部分变量的影响；最后分别用指数法和概率法对生态风险进行了评价。结果显示,锡和汞在土壤中属中等程度的污染,其它的为低污染。Hg、Pb、Sn主要受人类活动的影响,其它的几个变量主要受自然因子的控制,局部地区受到人类不同程度的影响。空间预测图显示,明显受自然源影响的变量(Co、Cr、Ni、Cu和Zn)的空间分布相对规则和平滑,Cd、Pb、Sn和Hg的高值区分布相对分散。总体上,高值区分布在人类活动相对频繁的城镇周边区域。由于较低的有机质含量或砂性的土壤质地,除了汞以外的其它变量的平均含量在渗育水稻土和滨海盐土中较低。由于较低的施肥水平和较少农用化学用品的使用,除了汞以外的其它变量在苗圃中的浓度水平较低。点或线污染源对Pb、Sn和Hg的影响较高。受交通线影响的元素主要包括Pb、Cu,、Ni和Zn。Pb受到汽车排放的尾气的影响,Cu、Ni和Zn受到来自汽车零部件和轮胎磨损的影响。As, Cr, Ni, Pb, Cu和Zn的生态危害轻微；Cd,Hg的中等程度生态危害区分别占土地面积的98.4%,63.1%,Hg的强危害区占了33.7%,其余为轻危害区。As对生物种类的风险概率为43.7%,属中等危害；As和Cd对生态过程的风险概率分别为25.2%、25.3%,属中等危害,其余为轻微危害。
在海宁土壤重金属污染区域的模糊识别方面,用模糊聚类法对复合污染区进行了划分。结果表明,对10个变量分为三组较合适：组Ⅰ(As、Cd、Co、Cr和Ni),组Ⅱ(Cu、Zn),和组Ⅲ(Hg、Pb和Sn)。每组又分别分为三个污染程度不同的区。方差分析显示不同污染区之间污染物的平均浓度有明显差别。组Ⅰ和组Ⅱ在研究区内总体复合污染程度较低,土壤污染程度隶属关系较明确；组Ⅲ在南部沿江地带极小片区域属重污染区,西部大部分地区和海宁市区附近属中度污染区,中部大部分和东南部为低污染区,地理空间上土壤对组Ⅲ的隶属关系也较明确。土壤类型对分区的影响跟样点设计有关,如大部分样点来自水稻土。另外,跟土壤本身的性质也有一定关系。有机质含量是影响前两组分区的主要因素；第三组分区的因子中,有机质含量所起的作用并不大。土地利用类型在分区中所起的作用并不大,pH值在不同类别之间的差别并不明显。
在对研究区域的污染热点辨析方面,用局部Moran'sⅠ指数法辨别了污染热点(p=0.05)。权重函数,数据转换方法及极值都影响热点的数量和空间位置。极值剔除后的热点辨析结果与数据转换后的结果类似。高一高热点的数量明显比未去除极值时的数量要多,在空间上热点的覆盖面也相对更加广泛。
在污染区划分的不确定性评估方面,对比普通克立格法和序贯高斯模拟在污染制图上的差别,用指示克立格和序贯高斯模拟产生超过某一概率水平的概率分布图,将模拟插值图和模拟概率图叠置划分可靠的污染区。结果发现克立格法产生的插值图趋于平滑,局部细节不明显。而模拟方法产生的预测图空间结构明显,信息丰富。Hg的污染区主要分布在西南靠近钱塘江的几个镇；Pb的污染区星星点点分布在整个区域,污染区总面积比例很小；Sn的高置信污染区主要在西南靠近钱塘江地带和东南部的两个镇的局部地区。
最后,比较了Bayesian Maximum Entropy(贝叶斯最大熵)和克立格的空间预测精度。综合使用软、硬数据的BME (BME_HS),在对有高偏值存在的、非正态分布的环境变量的预测中,其预测偏差低于普通克立格,但预测的波动性表现趋势不一致；只使用硬数据的BME(BME_H)预测偏差小于普通克立格,对波动性的预测则表现不一致的趋势。在对极值的预测上,BME_HS对所有极高值的预测结果更接近实际值。
本文主要在以下几个方面取得了进展：
1)将模糊集方法、多元统计分析和GIS结合起来,共同探讨水体污染程度和污染空间模式。多种方法的综合使用,可以从不同角度了解水体污染状况,从而更全面、客观地认识水污染的实际情况,避免了单一方法可能产生的片面性。
2)以往,土壤中是否存在重金属污染及污染水平的界定标准不一。本文同时使用多种界定标准(多元统计、富集因子法、地统计学和GIS)来分析研究区的土壤污染水平,从而使分析结果更加可靠。同时,分析了土壤类型、土地利用方式等与重金属污染的相互关系。
3)土壤系统和人类活动本身的复杂性和模糊性决定了污染区的界定是个难题。本研究从土壤污染的特点出发,用模糊数学分析了污染水平的模糊性,定量分析了影响模糊分区的因素,再运用GIS确定最终的污染模糊分区,这和其它类似研究中所用的方法不同。同时,运用联合模拟概率图和模拟预测图,共同划分某一置信水平上的污染区,使污染区的划分更加真实可靠。
4)对偏态性较大的Hg来说,BME预测效果比克立格预测效果好。类似地,用BME预测Pb,其误差比克立格小
Soil and wate are the most important resources for humanbeing. They have direct impact on the quality of human life, while human activities directly affect soil and water environment. With great social economic development and land use/land cover changes in recent decades, soil and water environment have gradually deteriorated. Qiantang River Basin and Haining City locate in the regions with fast-growing economy, rich industrial type, and frequently changing of land use/land cover, which all affect soil and water environment, and eventually lead to various pollution patterns. This dissertation characterized the spatial patterns of pollutions and identified the potential sources of pollution types.
Spatial analysis and source apportionment of water pollution in Qiantang River Basin were conducted. In this work, we considered data for 13 water quality variables collected during the year 2004 at 46 monitoring sites along the Qiantang River (China). Fuzzy comprehensive analysis categorized the data into three major pollution zones (low, moderate, and high) based on national quality standards for surface waters, China. Most sites classified as "low pollution zones" (LP) occurred in the main river channel, whereas those classified as "moderate and high pollution zones" (MP and HP, respectively) occurred in the tributaries. Factor analysis identified two potential pollution sources that explained 67% of the total variance in LP, two potential pollution sources that explained 73% of the total variance in MP, and three potential pollution sources that explained 80% of the total variance in HP, respectively. UNMIX was used to estimate contributions from identified pollution sources to each water quality variable and each monitoring site. Most water quality variables were influenced primarily by pollutants from industrial wastewater, agricultural activities and urban runoff. In LP, non-point source pollution such as agricultural runoff and urban runoff dominated; in MP and HP, mixed source pollution dominated. The pollution in the small tributaries was more serious than that in the main channel. These results provide important information for developing better pollution control strategies of the Qiantang River.
Spatial analysis and source identification of heavy metal pollution in soils of Haining City were performed. A total of 309 topsoil samples were collected in 2005 from agricultural land. Each sample was analyzed for 10 elements:arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), tin (Sn) and zinc (Zn). With the combination of enrichment factor and multivariate statistics, the variables were classified into the high contaminating (Pb, Hg and Sn) and low contaminating elements (As, Co, Cr, Cu, Cd, Ni and Zn). The former were predominantly influenced by anthropic inputs, and the latter derived from natural sources or marginal human influence. Spatial analysis showed that high-value areas of Pb, Hg, Cd and Sn were isolated and mainly situated around the urban areas. The influence of soil type on uncontaminated or low contaminating elements was greater than on high contaminating elements, except for Hg. Land use had no distinct impact on most elements. Pb, Sn, and Hg were greatly influenced by point or linear pollution sources. Along the traffic lines, Pb was mainly from car exhausts, while Cu, Ni and Zn weremainly from wear and tear of mechanical parts and tires. For As, Cr, Ni, Pb, Cu and Zn, their ecological hazard was minor; The area with medium ecological hazards of Cd and Hg accounted for about 98.4% and 63.1%, respectively. The strongly hazardous area of Hg occupied 33.8%. The rest area was at slight risk. For biological types, risk probability of As with medium risk was 43.7%. For ecological process, risk probability of As and Cd was 25.2% and 25.3%, respectively. Risk level of the rest pollutants was low.
Fuzzy clustering analysis was used to delineate combined pollution zones. The ten elements were divided into three groups:Group I (As, Cd, Co, Cr and Ni), GroupⅡ(Cu, Zn), GroupⅢ(Hg, Pb, Sn). Each group was divided into three zones with different pollution levels. One way ANOVA showed that the significant differences of mean concentration existed among different pollution zones. The overall combined pollution levels in groupⅠand groupⅡwere low, and membership was relatively explicit in the two groups. In groupⅢ, severe pollution zone located in the south of Haining and along the Qiantang River; moderate pollution zone mainly located in the west and near the urban region; low pollution zone was mainly in central and southeastern regions. The impact of soil type on the delineation of pollution zones was related with the design of the sample points and soil properties. Land-use types were not important in the delineation of pollution zones. The differences of pH between different zones were not obvious. Organic matter contents were important factors in pollution delineation of groupⅠandⅡ. In GroupⅢ, organic matter contents of low-pollution area were higher than those in high-contaminated area.
Local Moran's I index was used to identify pollution hot spots. Weight function, data transformation methods and extreme values all affected the number and spatial location of hot spots. The results of removing the extreme values were similar to those in data transformation. High-high zones were increased after removing the extreme values and the area of high-high will also increase.
Uncertainty assessment of contaminated areas delineation was conducted. Ordinary kriging was compared with Sequential Gaussian Simulation in pollution mapping. Indicator Kriging and Sequential Gaussian Simulation were used to produce the probability distribution map exceeding certain probability level. Overlaying simulated interpolation maps and simulated probability maps produced reliable contaminated areas. The results showed that Kriging interpolation maps tended to be smooth, with less local details. However, the maps produced by simulation had rich spatial structure information. Contaminated areas of Hg were mainly distributed in several towns in the southwest along the Qiantang River; Pb contamination areas scattered throughout the region, and the proportion of contaminated areas was small; Contaminated areas with high confidence of Sn were mainly in the southwest along the river and the two towns in the southeast.
Spatial analysis was conducted based on Bayesian maximum entropy(BME). Integrating soft data and hard data in BME (BME_HS) had less bias than ordinary Kriging did in interpolating spatial data with highly skewed values. But, on prediction of variation (or fluctuation), BME_HS did not always perform better than ordinary Kriging. BME only with hard data also performeds better than ordinary Kriging. Predictions of extremely high values based on BME were closer to the real values than those based on ordinary Kriging.
In sumary, this dissertation made the following improvements:
1) Fuzzy method, multivariate statistics and GIS were used to explore the levels and spatial patterns of water pollution. The integrated uses of multiple techniques provided better understanding of water pollution, and the findings help developing better pollution control strategies of Qiantang River.
2) This study used a variety of methods (multivariate statistics, enrichment factor method, geostatistics and GIS) to charaterize the status of soil pollution, quantify the relationships between pollution levels and soil type, land use/cover.
3) The complexity of soil systems and human activity determined that the delineation of the contaminated area was difficult. In his study, fuzzy theory was used to analyze ambiguity of pollution levels, which could better reflect the reality.
4) Comparing with kriging alone, the combination of simulation probability and simulations values produced better prediction to delineate the contaminated area.
5) Mean error and mean squared error showed that prediction results of Hg based on BME were better than those based on Kriging. Mean error showed that prediction results of Pb based on BME were better than those based on Kriging, while mean squared error of Pb based on BME was greater than that based on kriging.
在钱塘江流域水污染分析方面,应用模糊综合评估方法,对41个监测点的13个变量进行评估,根据评估结果划分高、中、低的三大污染区；应用因子分析法,判别出主要影响因子。同时结合研究区的实际情况,给各污染源确定名称；最后,用UNMIX方法,进行定量的污染源源解析,计算出每个污染源对主要污染变量的贡献及对每个监测点的综合贡献。结果发现,在低污染区,面源污染,比如农田径流和城市径流,是主要的污染源；在中污染区和高污染区,以混合污染源为主。在低、中、高三个污染区可辨别的潜在污染源分别为2个、2个和3个,因子分析分别解释了三个污染区的67%、73%和80%总体方差。大体上,小支流污染比大支流和干流严重。
在海宁土壤重金属污染分析及源辨析中,富集因子法定量分析了各变量的污染程度,判别出主要影响因子；聚类分析方法验证了因子分析的结论；地统计学方法对各变量进行空间分析和预测；GIS分析了道路对部分变量的影响；最后分别用指数法和概率法对生态风险进行了评价。结果显示,锡和汞在土壤中属中等程度的污染,其它的为低污染。Hg、Pb、Sn主要受人类活动的影响,其它的几个变量主要受自然因子的控制,局部地区受到人类不同程度的影响。空间预测图显示,明显受自然源影响的变量(Co、Cr、Ni、Cu和Zn)的空间分布相对规则和平滑,Cd、Pb、Sn和Hg的高值区分布相对分散。总体上,高值区分布在人类活动相对频繁的城镇周边区域。由于较低的有机质含量或砂性的土壤质地,除了汞以外的其它变量的平均含量在渗育水稻土和滨海盐土中较低。由于较低的施肥水平和较少农用化学用品的使用,除了汞以外的其它变量在苗圃中的浓度水平较低。点或线污染源对Pb、Sn和Hg的影响较高。受交通线影响的元素主要包括Pb、Cu,、Ni和Zn。Pb受到汽车排放的尾气的影响,Cu、Ni和Zn受到来自汽车零部件和轮胎磨损的影响。As, Cr, Ni, Pb, Cu和Zn的生态危害轻微；Cd,Hg的中等程度生态危害区分别占土地面积的98.4%,63.1%,Hg的强危害区占了33.7%,其余为轻危害区。As对生物种类的风险概率为43.7%,属中等危害；As和Cd对生态过程的风险概率分别为25.2%、25.3%,属中等危害,其余为轻微危害。
在海宁土壤重金属污染区域的模糊识别方面,用模糊聚类法对复合污染区进行了划分。结果表明,对10个变量分为三组较合适：组Ⅰ(As、Cd、Co、Cr和Ni),组Ⅱ(Cu、Zn),和组Ⅲ(Hg、Pb和Sn)。每组又分别分为三个污染程度不同的区。方差分析显示不同污染区之间污染物的平均浓度有明显差别。组Ⅰ和组Ⅱ在研究区内总体复合污染程度较低,土壤污染程度隶属关系较明确；组Ⅲ在南部沿江地带极小片区域属重污染区,西部大部分地区和海宁市区附近属中度污染区,中部大部分和东南部为低污染区,地理空间上土壤对组Ⅲ的隶属关系也较明确。土壤类型对分区的影响跟样点设计有关,如大部分样点来自水稻土。另外,跟土壤本身的性质也有一定关系。有机质含量是影响前两组分区的主要因素；第三组分区的因子中,有机质含量所起的作用并不大。土地利用类型在分区中所起的作用并不大,pH值在不同类别之间的差别并不明显。
在对研究区域的污染热点辨析方面,用局部Moran'sⅠ指数法辨别了污染热点(p=0.05)。权重函数,数据转换方法及极值都影响热点的数量和空间位置。极值剔除后的热点辨析结果与数据转换后的结果类似。高一高热点的数量明显比未去除极值时的数量要多,在空间上热点的覆盖面也相对更加广泛。
在污染区划分的不确定性评估方面,对比普通克立格法和序贯高斯模拟在污染制图上的差别,用指示克立格和序贯高斯模拟产生超过某一概率水平的概率分布图,将模拟插值图和模拟概率图叠置划分可靠的污染区。结果发现克立格法产生的插值图趋于平滑,局部细节不明显。而模拟方法产生的预测图空间结构明显,信息丰富。Hg的污染区主要分布在西南靠近钱塘江的几个镇；Pb的污染区星星点点分布在整个区域,污染区总面积比例很小；Sn的高置信污染区主要在西南靠近钱塘江地带和东南部的两个镇的局部地区。
最后,比较了Bayesian Maximum Entropy(贝叶斯最大熵)和克立格的空间预测精度。综合使用软、硬数据的BME (BME_HS),在对有高偏值存在的、非正态分布的环境变量的预测中,其预测偏差低于普通克立格,但预测的波动性表现趋势不一致；只使用硬数据的BME(BME_H)预测偏差小于普通克立格,对波动性的预测则表现不一致的趋势。在对极值的预测上,BME_HS对所有极高值的预测结果更接近实际值。
本文主要在以下几个方面取得了进展：
1)将模糊集方法、多元统计分析和GIS结合起来,共同探讨水体污染程度和污染空间模式。多种方法的综合使用,可以从不同角度了解水体污染状况,从而更全面、客观地认识水污染的实际情况,避免了单一方法可能产生的片面性。
2)以往,土壤中是否存在重金属污染及污染水平的界定标准不一。本文同时使用多种界定标准(多元统计、富集因子法、地统计学和GIS)来分析研究区的土壤污染水平,从而使分析结果更加可靠。同时,分析了土壤类型、土地利用方式等与重金属污染的相互关系。
3)土壤系统和人类活动本身的复杂性和模糊性决定了污染区的界定是个难题。本研究从土壤污染的特点出发,用模糊数学分析了污染水平的模糊性,定量分析了影响模糊分区的因素,再运用GIS确定最终的污染模糊分区,这和其它类似研究中所用的方法不同。同时,运用联合模拟概率图和模拟预测图,共同划分某一置信水平上的污染区,使污染区的划分更加真实可靠。
4)对偏态性较大的Hg来说,BME预测效果比克立格预测效果好。类似地,用BME预测Pb,其误差比克立格小
Soil and wate are the most important resources for humanbeing. They have direct impact on the quality of human life, while human activities directly affect soil and water environment. With great social economic development and land use/land cover changes in recent decades, soil and water environment have gradually deteriorated. Qiantang River Basin and Haining City locate in the regions with fast-growing economy, rich industrial type, and frequently changing of land use/land cover, which all affect soil and water environment, and eventually lead to various pollution patterns. This dissertation characterized the spatial patterns of pollutions and identified the potential sources of pollution types.
Spatial analysis and source apportionment of water pollution in Qiantang River Basin were conducted. In this work, we considered data for 13 water quality variables collected during the year 2004 at 46 monitoring sites along the Qiantang River (China). Fuzzy comprehensive analysis categorized the data into three major pollution zones (low, moderate, and high) based on national quality standards for surface waters, China. Most sites classified as "low pollution zones" (LP) occurred in the main river channel, whereas those classified as "moderate and high pollution zones" (MP and HP, respectively) occurred in the tributaries. Factor analysis identified two potential pollution sources that explained 67% of the total variance in LP, two potential pollution sources that explained 73% of the total variance in MP, and three potential pollution sources that explained 80% of the total variance in HP, respectively. UNMIX was used to estimate contributions from identified pollution sources to each water quality variable and each monitoring site. Most water quality variables were influenced primarily by pollutants from industrial wastewater, agricultural activities and urban runoff. In LP, non-point source pollution such as agricultural runoff and urban runoff dominated; in MP and HP, mixed source pollution dominated. The pollution in the small tributaries was more serious than that in the main channel. These results provide important information for developing better pollution control strategies of the Qiantang River.
Spatial analysis and source identification of heavy metal pollution in soils of Haining City were performed. A total of 309 topsoil samples were collected in 2005 from agricultural land. Each sample was analyzed for 10 elements:arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), tin (Sn) and zinc (Zn). With the combination of enrichment factor and multivariate statistics, the variables were classified into the high contaminating (Pb, Hg and Sn) and low contaminating elements (As, Co, Cr, Cu, Cd, Ni and Zn). The former were predominantly influenced by anthropic inputs, and the latter derived from natural sources or marginal human influence. Spatial analysis showed that high-value areas of Pb, Hg, Cd and Sn were isolated and mainly situated around the urban areas. The influence of soil type on uncontaminated or low contaminating elements was greater than on high contaminating elements, except for Hg. Land use had no distinct impact on most elements. Pb, Sn, and Hg were greatly influenced by point or linear pollution sources. Along the traffic lines, Pb was mainly from car exhausts, while Cu, Ni and Zn weremainly from wear and tear of mechanical parts and tires. For As, Cr, Ni, Pb, Cu and Zn, their ecological hazard was minor; The area with medium ecological hazards of Cd and Hg accounted for about 98.4% and 63.1%, respectively. The strongly hazardous area of Hg occupied 33.8%. The rest area was at slight risk. For biological types, risk probability of As with medium risk was 43.7%. For ecological process, risk probability of As and Cd was 25.2% and 25.3%, respectively. Risk level of the rest pollutants was low.
Fuzzy clustering analysis was used to delineate combined pollution zones. The ten elements were divided into three groups:Group I (As, Cd, Co, Cr and Ni), GroupⅡ(Cu, Zn), GroupⅢ(Hg, Pb, Sn). Each group was divided into three zones with different pollution levels. One way ANOVA showed that the significant differences of mean concentration existed among different pollution zones. The overall combined pollution levels in groupⅠand groupⅡwere low, and membership was relatively explicit in the two groups. In groupⅢ, severe pollution zone located in the south of Haining and along the Qiantang River; moderate pollution zone mainly located in the west and near the urban region; low pollution zone was mainly in central and southeastern regions. The impact of soil type on the delineation of pollution zones was related with the design of the sample points and soil properties. Land-use types were not important in the delineation of pollution zones. The differences of pH between different zones were not obvious. Organic matter contents were important factors in pollution delineation of groupⅠandⅡ. In GroupⅢ, organic matter contents of low-pollution area were higher than those in high-contaminated area.
Local Moran's I index was used to identify pollution hot spots. Weight function, data transformation methods and extreme values all affected the number and spatial location of hot spots. The results of removing the extreme values were similar to those in data transformation. High-high zones were increased after removing the extreme values and the area of high-high will also increase.
Uncertainty assessment of contaminated areas delineation was conducted. Ordinary kriging was compared with Sequential Gaussian Simulation in pollution mapping. Indicator Kriging and Sequential Gaussian Simulation were used to produce the probability distribution map exceeding certain probability level. Overlaying simulated interpolation maps and simulated probability maps produced reliable contaminated areas. The results showed that Kriging interpolation maps tended to be smooth, with less local details. However, the maps produced by simulation had rich spatial structure information. Contaminated areas of Hg were mainly distributed in several towns in the southwest along the Qiantang River; Pb contamination areas scattered throughout the region, and the proportion of contaminated areas was small; Contaminated areas with high confidence of Sn were mainly in the southwest along the river and the two towns in the southeast.
Spatial analysis was conducted based on Bayesian maximum entropy(BME). Integrating soft data and hard data in BME (BME_HS) had less bias than ordinary Kriging did in interpolating spatial data with highly skewed values. But, on prediction of variation (or fluctuation), BME_HS did not always perform better than ordinary Kriging. BME only with hard data also performeds better than ordinary Kriging. Predictions of extremely high values based on BME were closer to the real values than those based on ordinary Kriging.
In sumary, this dissertation made the following improvements:
1) Fuzzy method, multivariate statistics and GIS were used to explore the levels and spatial patterns of water pollution. The integrated uses of multiple techniques provided better understanding of water pollution, and the findings help developing better pollution control strategies of Qiantang River.
2) This study used a variety of methods (multivariate statistics, enrichment factor method, geostatistics and GIS) to charaterize the status of soil pollution, quantify the relationships between pollution levels and soil type, land use/cover.
3) The complexity of soil systems and human activity determined that the delineation of the contaminated area was difficult. In his study, fuzzy theory was used to analyze ambiguity of pollution levels, which could better reflect the reality.
4) Comparing with kriging alone, the combination of simulation probability and simulations values produced better prediction to delineate the contaminated area.
5) Mean error and mean squared error showed that prediction results of Hg based on BME were better than those based on Kriging. Mean error showed that prediction results of Pb based on BME were better than those based on Kriging, while mean squared error of Pb based on BME was greater than that based on kriging.
引文
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