Risk based characterisation of contaminated industrial site using multivariate and geostatistical tools

Understanding the driving mechanisms of site contamination in China through a data-driven approach

China currently faces significant environmental risks stemming from contaminated sites. The driving mechanism of site contamination, influenced by various drivers, remain obscured due to a dearth of quantitative methodologies and comprehensive data. Here, we used a data-driven causality inference approach to construct an interpretable random forest (RF) model. Results show that: (1) the trained RF model demonstrated remarkable predictive accuracy for identifying contaminated sites, with an accuracy rate of 0.89. In contrast to conventional correlation analysis, the RF model excels in discerning the key drivers through non-linear and genuine causal relationships between these drivers and site contamination. (2) Among the 25 potential drivers, we identified 18 key drivers of site contamination. These drivers encompass a broad spectrum of factors, including production and operational data, pollutant control level, site protection capability, pollutant characteristics, and physical-geographical conditions. (3) Each key driver exerts varying impacts on site pollution, with diverse directions, intensities, and underlying patterns. The partial dependence plots (PDPs) illuminate the role of each key driver, its critical value contributing to site pollution, and the interplay between these drivers. The key drivers facilitate the realization of three primary contamination processes: uncontrolled release, effective migration, and persistent accumulation. In light of our findings, environmental managers can proactively prevent site contamination by regulating single, dual, and multiple key drivers to disrupt critical pollution processes. This research offers valuable insights for devising targeted strategies and interventions aimed at mitigating environmental risks associated with contaminated sites in China.

Assessing the ecological risk of croplands in loess drylands by combining environmental disturbance with ecosystem vulnerability

Agroecosystems suffer various ecological risks due to the intensive production of crops. However, comprehensive assessments of cropland ecological risks remain limited. This study developed an assessment method for cropland ecological risks by combining environmental disturbance with ecosystem vulnerability. Environmental disturbance reflects stresses caused by risk sources in an environment, while ecosystem vulnerability is the susceptibility of an ecosystem to adverse disturbances and its capacity to cope and adapt. The proposed method is conducive to understanding the complex exposure-response relationship between croplands and environmental stresses. Cropland ecological risk was evaluated by conducting a case study on a loess dryland region in Shaanxi. The hot spots and driving factors of risk were explored using spatial autocorrelation and quantile regression methods, respectively. Results show that overall cropland ecological risk is at medium low level. Risk hot spots are concentrated in the north of the loess dryland. Ecosystem vulnerability exerts greater effect on the distribution of hot spots than environmental disturbance in the study area. Road density (RDD), river density, and soil organic matter exert the most important effects on cropland ecological risk. Moreover, the same driving factor exhibits various effects on cropland ecological risk in different risk level areas. RDD, slope, precipitation, elevation, fertilizer application rate, gross domestic product, and distance to town center have greater effects on risk in regions with high cropland ecological risk than in regions with low cropland ecological risk. The findings of this study must be considered in formulating targeted policies for controlling cropland ecological risk in loess drylands to realize sustainable crop production.

Three-dimensional distribution characteristics of multiple pollutants in the soil at a steelworks mega-site based on multi-source information

Soil pollution at steelworks mega-sites has become a severe environmental issue worldwide. However, due to the complex production processes and hydrogeology, the soil pollution distribution at steelworks is still unclear. This study scientifically cognized the distribution characteristics of polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and heavy metals (HMs) at a steelworks mega-site based on multi-source information. Specifically, firstly, 3D distribution and spatial autocorrelation of pollutants were obtained by interpolation model and local indicators of spatial associations (LISA), respectively. Secondly, the characteristics of horizontal distribution, vertical distribution, and spatial autocorrelations of pollutants were identified by combining multi-source information such as production processes, soil layers, and properties of pollutants. Horizontal distribution showed that soil pollution in steelworks mainly occurred in the front end of the steel process chain. Over 47% of PAHs and VOCs pollution area were distributed in coking plants and over 69% of HMs in stockyards. Vertical distribution indicated that HMs, PAHs, and VOCs were enriched in the fill, silt, and clay layers, respectively. Spatial autocorrelation of pollutants was positively correlated with their mobility. This study clarified the soil pollution characteristics at steelworks mega-sites, which can support the investigation and remediation of steelworks mega-sites.

Deciphering the origin and controlling factors of mercury in reclaimed soils: a case study in Pingshuo opencast coalmine of China

Considering the significant influence of mercury (Hg) contamination on the land reclamation inopencast coalmine, the spatial distribution patterns and ecological risks of Hg were investigated and the regulating factors of Hg mobility were determined in the South Dump of the Pingshuo opencast coalmine. The results show that the total Hg (HgT) contents of most soil samples (83.7%) vary from 6 to 50 μg kg^-1, while the potential ecological risk index (EI_Hg) values of most samples (79.8%) are lower than 80, indicating that most reclaimed soils are in relatively good conditions and the soil samples at high to very high ecological risk are mainly collected near the backfilled coal gangue. Moreover, the kriging maps of the geo-accumulation index (I_geo) indicate that the uncontaminated areas (I_geo < 0) and Hg-contaminated areas (I_geo > 0) in topsoil (0-10 cm) are roughly divided by an "east-west arc" while the Hg-contaminated areas in other soil horizons are characterized by a "point distribution pattern". The slight Hg contamination in topsoil is mainly triggered by the atmospheric Hg deposition from the nearby coal-fired power plant, while the Hg contamination in other soil horizons should be attributed to the weathering and spontaneous combustion of coal gangue. On the other hand, Pearson's correlation analyses show that HgT contents were positively correlated with clay (r = 0.31, P < 0.01) and SOC (r = 0.53, P < 0.01) contents. This study can provide some insight for the land reclamation measures in the opencast coalmine.

Effectiveness of predicting the spatial distributions of target contaminants of a coking plant based on their related pollutants

The prediction accuracy of the spatial distribution of soil pollutants at a site is relatively low. Related pollutants can be used as auxiliary variables to improve the prediction accuracy. However, little relevant research has been conducted on site soil pollution. To analyze the prediction accuracy of target pollutants combined with auxiliary pollutants, Cu, toluene, and phenanthrene were selected as the target pollutants for this study. Based on geostatistical analysis and spatial analysis, the following results were obtained. (1) The reduction in the root mean square errors (RMSEs) for Cu, toluene, and phenanthrene with multivariable cokriging was 68.4%, 81.6%, and 81.2%, respectively, which are proportional to the correlation coefficient of the relationship between the auxiliary pollutants and the target pollutants. (2) The RMSEs calculated for the multivariable cokriging were lower than those obtained by only combining one related pollutants, and two co-variables should be better. (3) The predicted results for Cu, phenanthrene, and toluene and their corresponding related pollutants are more accurate than the results obtained not using the related pollutants. (4) In the interpolation process, the RMSEs for Cu, toluene, and phenanthrene with multivariable cokriging basically increase as the neighborhood sample data increases, and then they become stable. (5) When 84, 61, and 34 sample points were removed, the RMSEs for Cu, toluene, and phenanthrene, respectively, with multivariable cokriging were close to the RMSEs of the target pollutants based on the total samples. The results are of great significance to improving the prediction accuracy of the spatial distribution of soil pollutants at coking plant sites.

Spatial Distribution and Regulating Factors of Soil Nutrient Stocks in Afforested Dump of Pingshuo Opencast Coalmine, China

Determining the regulating factors of soil nutrient variations can guide the implementation of land reclamation measures in opencast coalmine regions. In this study, 132 soil samples were collected at 22 sample sites in the South Dump of Pingshuo opencast coalmine, and soil physicochemical properties were separately measured to obtain the related soil information. Geostatistical analyses were employed to analyze the spatial distribution patterns of soil organic carbon stocks (SOCD), total nitrogen stocks (TND), available phosphorus stocks (APD), and available potassium stocks (AKD) at 0–60 cm. The results showed that the spatial distributions of these soil nutrient stocks were characterized by moderate (TND) to strong (SOCD, APD, and AKD) spatial dependence. Meanwhile, the values of SOCD (16.4–60.1 Mg ha−1) and TND (1.9–15.5 Mg ha−1) were much higher than those of APD (0.022–0.095 Mg ha−1) and AKD (0.31–1.40 Mg ha−1). The statistical analyses indicated that the influence of afforestation on SOCD, TND, APD, and AKD was not significant, and the dynamic variations of soil nutrient contents were mainly regulated by soil pH in the South Dump. The findings of this study can provide some scientific guidance for soil nutrient management in the opencast coalmine regions of similar ecosystems.

Spatiotemporal Assessment of Air Quality and Heat Island Effect Due to Industrial Activities and Urbanization in Southern Riyadh, Saudi Arabia

The aim of this paper is to evaluate the air and thermal pollution in the southern suburbs of Riyadh, where people are suffering from poor air quality due to the rapid development of the industrial facilities in the area. The study involved the distribution of questionnaires to 405 residents living in that area in order to obtain their opinions about air pollution. In addition, land surface temperature and 12 components of air were measured at 18 points. In addition, the air quality data from 2016 to 2020 for Al Khaldya and Southern Ring Road air stations were assessed. Al Misfat (Oil Refinery) and the Second Industry City are significant contributors to air pollution and also result in the urban heat island effect from high temperature due to factories and industrial activities. However, all the measured components of air quality are lower than the standard limits except the element particulate matter (PM)10, which exceeds the standard limits in several parts of the study area and during several months of the year. This can surely have a negative impact on the health of residents. At the same time, this study found that the preventive measures taken to stop the spread of COVID-19 infections have led to a positive impact in the area in terms of improvement in air quality.

Distribution and origin of potentially toxic elements in a multi-aquifer system

Pollution of the potentially toxic elements (PTEs) is a major concern in the metal ore-mining environment. Active polymetallic industries and mines cause great continuous devastation of both terrestrial and aquatic environments on a local and regional scale. This study investigated the pollution of surface water and groundwater in the area containing six large-scale iron ore mines, which have been in operation for more than a few decades. In order to assess the PTEs pollution, the spatial and temporal distributions of 13 different PTEs (Al, As, Co, Li, Mn, Mo, Ni, Pb, Rb, Se, Si, Sr, and Zn) were measured in 42 water samples collected from the multi-aquifer system including three distinct aquifers (upper alluvial aquifer (UAA), lower alluvial aquifer (LAA), and hard-rock aquifer (HRA)) of the Gohar-Zamin mining area in Iran. The highest concentrations of total dissolved solids (TDS = 164,000 mg/l) and PTEs were measured in HRA. Three trends were identified between the PTE concentration and increasing of TDS based on Spearman correlation analysis: (1) an increasing trend for Al, Co, Li, Mn, Rb, Se, Sr, and Ni; there were strong positive correlations in HRA between TDS and Mn (0.83), Al (0.65), Co (0.74), Li (0.90), Ni (0.83), Rb (0.91), Se (0.82), and Sr (0.84), suggesting a common origin for these elements; (2) no obvious trend for As and Mo, no correlation was founded between As and Mo with other PTEs and TDS, indicating a natural geogenic origin and mutual dependencies of these elements; and (3) a decreasing trend for Si, Zn, and Pb; TDS had a significantly negative correlation with the PTEs and attributing to different chemical properties of infiltrated groundwater. In the principal component analysis (PCA), the first PC that covers 85.09% of the total observed variance is mainly attributed the groundwater salinization. This component is composed of Al, Co, Li, Mn, Rb, Se, Sr, and Ni. The second PC contains elements As and Mo. This PC explain 14.4% of total variance and may be referred to natural origin of PTEs. Si, Zn, and Pb are in the third principal component and cover 9.64% of the variance of the data. Third PC have been attributed to lithogenic and/or primary water chemistry factors. The PTE pollution were evaluated based on heavy metal evaluation index (HEI), heavy metal pollution index (HPI), and degree of contamination (C_d). The results indicated that all of the groundwater samples collected from HRA had HEI, HPI, and C_d values greater than 21, 264, and 14 (highly pollution limits of indices), respectively, and were classified as highly polluted groundwater. HPI values within the UAA, LAA, and salt playa (SP) were lower than the critical level of 100, suggesting a threshold for the drinking water pollution. Moreover, HEI and C_d with values of less than 10 and 7 suggested low-level pollution in UAA, LAA, and SP. However, the contaminated level of PTEs exceeded the WHO standard for drinking water in HRA only. Since groundwater in HRA is a brine with the high values of PTEs, pumping of this water out to the surrounding natural environment may cause harmful impacts on the environment and perhaps living species in Bahram-e-Goor protected area. Graphical abstract.

Multivariate Geostatistical Modeling and Risk Analysis of Beach Litter: A Case Study of Playa Blanca Beach, Chile

Beach litter is a worldwide problem that has several negative effects. A first step in preventing an environmental hazard is to determine and model the level of contamination. In this paper, geostatistical simulation is used to model two main forms of beach litter (cigarette butts and sharp items) in one of the most contaminated beaches in Antofagasta, Chile. A hundred realizations of cigarette butts and broken glass are generated to emulate their joint spatial distribution. The simulation results are used to classify the beach into different areas with respect to the risk of injury by broken glass and the level of contamination by cigarette butts. The models obtained can be used by local authorities in beach clean-up programs and by visitors to beaches in choosing the safest and cleanest areas. The results demonstrate the capability of geostatistical simulation algorithms to model different types of beach litter.

Improving spatial prediction of health risk assessment for Hg, As, Cu, and Pb in soil based on land-use regression

Heavy-metal pollution is a significant health and environmental concern in areas of rapid industrialization in China. The accuracy of spatial mapping of pollutant is the main constraint on spatial prediction of health risks. Our study addressed the possibility of improving spatial prediction accuracy of risk assessment. We developed land-use regression (LUR) models for Hg, As, Cu, and Pb based on surface soil sampling, land-use data, and soil properties. The regression results suggested that LUR was more accurate than ordinary kriging method. Spatial prediction accuracy of Hg, As, Cu, and Pb were improved by 15%, 59%, 36%, and 20%, respectively. Then, spatial distribution of health risk was assessed by using distributions of heavy metal and exposure parameters. Chronic risk of children was controlled by distribution of Pb and carcinogenic controlled by As. The result indicated that Pb and As were the main sources of health risk for children in Kunshan. Chronic and carcinogenic risk maps could clearly show where we should pay attention to and control the risk. This study provided a simple approach to draw spatially explicit maps of health risk which were useful for pollution control and public health risk management.

Arsenic Distribution Assessment in a Residential Area Polluted with Mining Residues

Mining is a major source for metals and metalloids pollution, which could pose a risk for human health. In San Guillermo, Chihuahua, Mexico mining wastes are found adjacent to a residential area. A soil-surface sampling was performed, collecting 88 samples for arsenic determination by atomic absorption. Arsenic concentration data set was interpolated using the ArcGis models: inverse distance weighting (IDW), ordinary kriging (OK), and radial basis function (RBF). For method validation purposes, a set of the data was selected and two tests were performed (P1 and P2). In P1 the models were processed without the validation data; in P2 the validation data were removed one by one, models were processed every time that a data point was removed. An arsenic concentration range of 22.7 to 2190 mg/kg was reported. The 39% of data set was classified as contaminated soil and 61% as industrial land use. In P1 the method of interpolation with the lowest RMSE was RBF (0.80), the highest coefficient of E was RBF (46.25), and the highest Ceff value was with RBF (0.48). In P2 the method with the lowest RMSE was OK (0.76), the highest E value was 50.65 with OK, and the Ceff reported the highest value with OK (0.52). The high arsenic contamination in soil of the site indicates an abundant dispersion of this metalloid. Furthermore, the difference between the models was not very wide. The incorporation of more parameters would be of interest to observe the behavior of interpolation methods.

Spatial Patterns of Potentially Hazardous Metals in Soils of Lin'an City, Southeastern China

Urban soils are strongly related to human health. In this study, Lin’an city was chosen as a typical small-scale city with which to study the spatial variation of potentially hazardous metals (PHMs) in urban soils and their potential ecological risks using multivariate analysis, geostatistics and GIS techniques. A total of 62 soil samples were collected from the study area. The results showed that the average concentrations of total soil Mn, Cu, Zn, Pb, Cr, Cd were 439.42, 42.23, 196.80, 62.55, 63.65, 0.22 mg·kg⁻¹, respectively. Compared with the background values and the environmental quality standards, these PHMs were accumulated in urban soils to some extent. The single potential ecological risk indices of PHMs indicated that Pb and Cd had relatively high ecological risks. The pH and most of the PHMs had significant correlations (p < 0.05). The principle components analysis (PCA) showed that Pb, Zn and Cu had similar pollution sources related to the vehicles’ exhaust emission; Mn and Cr were mainly from the parent materials; while Cd was from the emission of industrial manufactories. The spatial structures and distributions of PHMs and their corresponding available fractions had strong/moderate spatial autocorrelation, which were influenced by human activities.

Estimation of Environmental Exposure: Interpolation, Kernel Density Estimation, or Snapshotting

In environmental health researches and practices, spatial analysis became an important approach to estimation of environmental exposure of human subjects under concern. A typical situation in this kind of application is that the data of pollution are available only at certain locations, and thus inference is needed to convert a limited number of values at discrete locations into a continuous surface. This paper intends to clarify the distinction among three methods that can be used to achieve this conversion, namely interpolation, kernel density estimation (KDE), and snapshotting. Due to the apparent similarity of the three, they may cause confusions that lead to misuses. We compare and contrast the three methods, in terms of nature of the input data, mathematical process of the inference, and essential meaning of the output. For each method we suggest appropriate applications within the context of estimation of environmental exposure.

Perspectives for integrating human and environmental exposure assessments

Integrated Risk Assessment (IRA) has been defined by the EU FP7 HEROIC Coordination action as "the mutual exploitation of Environmental Risk Assessment for Human Health Risk Assessment and vice versa in order to coherently and more efficiently characterize an overall risk to humans and the environment for better informing the risk analysis process" (Wilks et al., 2015). Since exposure assessment and hazard characterization are the pillars of risk assessment, integrating Environmental Exposure assessment (EEA) and Human Exposure assessment (HEA) is a major component of an IRA framework. EEA and HEA typically pursue different targets, protection goals and timeframe. However, human and wildlife species also share the same environment and they similarly inhale air and ingest water and food through often similar overlapping pathways of exposure. Fate models used in EEA and HEA to predict the chemicals distribution among physical and biological media are essentially based on common properties of chemicals, and internal concentration estimations are largely based on inter-species (i.e. biota-to-human) extrapolations. Also, both EEA and HEA are challenged by increasing scientific complexity and resources constraints. Altogether, these points create the need for a better exploitation of all currently existing data, experimental approaches and modeling tools and it is assumed that a more integrated approach of both EEA and HEA may be part of the solution. Based on the outcome of an Expert Workshop on Extrapolations in Integrated Exposure Assessment organized by the HEROIC project in January 2014, this paper identifies perspectives and recommendations to better harmonize and extrapolate exposure assessment data, models and methods between Human Health and Environmental Risk Assessments to support the further development and promotion of the concept of IRA. Ultimately, these recommendations may feed into guidance showing when and how to apply IRA in the regulatory decision-making process for chemicals.

Assessment of soil quality parameters using multivariate analysis in the Rawal Lake watershed

Soil providing a wide array of ecosystem services is subjected to quality deterioration due to natural and anthropogenic factors. Most of the soils in Pakistan have poor status of available plant nutrients and cannot support optimum levels of crop productivity. The present study statistically analyzed ten soil quality parameters in five subwatersheds (Bari Imam, Chattar, Rumli, Shahdra, and Shahpur) of the Rawal Lake. Analysis of variance (ANOVA), cluster analysis (CA), and principal component analysis (PCA) were performed to evaluate correlation in soil quality parameters on spatiotemporal and vertical scales. Soil organic matter, electrical conductivity, nitrates, and sulfates were found to be lower than that required for good quality soil. Soil pH showed significant difference (p < 0.05) in mean values at different sampling sites and sampling months indicating that it is affected and determined by land uses and seasons. Pearson correlation revealed a strong positive correlation (r = 0.437) between nitrates and organic matter. Application of principal component analysis resulted in three major factors contributing 76 % of the total variance. For factor 1, temperature, sand, silt, clay, and nitrates had the highest factor loading values (>0.75) and indicated that these were the most influential parameters of first factor or component. Cluster analysis separated five sampling sites into three statistically significant clusters: I (Shahdra-Bari Imam), II (Chattar), and III (Shahpur-Rumli). Among the five sites, Shahdra was found to have good quality soil followed by Bari Imam. The present study illustrated the usefulness of multivariate statistical approaches for the analysis and interpretation of complex datasets to understand variations in soil quality for effective watershed management.

Multivariate spatial analyses of the distribution and origin of trace and major elements in soils surrounding a secondary lead smelter

Major and trace elements in soils originate from natural processes and different anthropogenic activities which are difficult to discriminate. On a 17-ha impacted site in northern France, two industrial sources of soil contamination were xidentified: a former iron foundry and a current secondary lead smelter. To discriminate and map natural and anthropogenic sources of major and trace elements on this site, the rarely applied MULTISPATI-principal component analysis (PCA) method was used. Using a 20-m × 20-m grid, 247 topsoil horizons were sampled and analysed with a field-portable X-ray fluorescence analyser for screening soil contamination. The study site was heavily contaminated with Pb and, to a lesser degree, with Sn. Summary statistics and enrichment factors allowed the differentiation of the main lithogenic or anthropogenic origin of the elements. The MULTISPATI-PCA method, which explained 73.9 % of the variability with the three first factors, evidenced strong spatial structures. Those spatial structures were attributed to different natural and artificial processes in the study area. The first axis can be interpreted as a lithogenic effect. Axes 2 and 3 reflect the two different contamination sources. Pb, Sn and S originated from the secondary lead smelter while Fe and Ca were mainly derived from the old iron foundry activity and the old railway built with foundry sand. This study demonstrated that the MULTISPATI-PCA method can be successfully used to investigate multicontaminated sites to discriminate the various sources of contamination.

Multivariate analysis combined with GIS to source identification of heavy metals in soils around an abandoned industrial area, Eastern China

Heavy metals in soils polluted by industrial production are a meaningful topic worldwide. The purpose of this study is to understand the pollution status and spatial distribution of heavy metals in soils. The result can help decision-makers apportion possible soil heavy metals sources and formulate effective pollution control policies. In this paper, 155 soil samples (0-20 cm) were collected and analyzed for eight heavy metals (Cd, Hg, As, Cu, Pb, Cr, Zn, and Ni) from an abandoned industrial area of Tong County, located in Jiangsu Province of Eastern China. The multivariate analysis (including I(geo), Ei/RI, EF, PCA, and CA) and geostatistics (GIS) were used to assess the enrichment level and pollution level of soil heavy metals and identify their sources. The results indicated that eight heavy metals in soils had moderate variations, with CVs ranging from 19.63 to 63.34%. The pollution level of I(geo) of soil heavy metals decreased in the order of Cd~Zn > Cu > Hg~As~Pb~Cr~Ni. The enrichment level of soil heavy metals decreased in the order of Cd > Zn > Hg > Cu > Pb > Ni > As > Cr. According to the Ei, except Cd and Hg were in the significant and moderate ecological risk levels respectively, other soil heavy metals were in the clean or light ecological risk levels, the level of potential ecological risk (RI) of the whole industrial area was moderate. Finally, the source identification of soil heavy metals indicated that Cd and Zn were primarily controlled by human activities, and Hg and Cu were controlled by natural and anthropogenic sources, and As, Pb, Cr, and Ni were mainly controlled by soil parent materials.

Characterization of Chlorinated Aliphatic Hydrocarbons and Environmental Variables in a Shallow Groundwater in Shanghai Using Kriging Interpolation and Multifactorial Analysis

CAHs, as a cleaning solvent, widely contaminated shallow groundwater with the development of manufacturing in China's Yangtze River Delta. This study focused on the distribution of CAHs, and correlations between CAHs and environmental variables in a shallow groundwater in Shanghai, using kriging interpolation and multifactorial analysis. The results showed that the overall CAHs plume area (above DIV) was approximately 9,000 m(2) and located in the 2-4 m underground, DNAPL was accumulated at an area of approximately 1,400 m(2) and located in the 6-8m sandy silt layer on the top of the muddy silty clay. Heatmap of PPC for CAHs and environmental variables showed that the correlation between "Fe(2+)" and most CAHs such as "1,1,1-TCA", "1,1-DCA", "1,1-DCE" and "%TCA" were significantly positive (p<0.001), but "%CA" and/or "%VC" was not, and "Cl-" was significantly positive correlated with "1,1-DCA" and "1,1-DCE" (p<0.001). The PCA demonstrated that the relative proportions of CAHs in groundwater were mostly controlled by the sources and the natural attenuation. In conclusion, the combination of geographical and chemometrics was helpful to establishing an aerial perspective of CAHs and identifying reasons for the accumulation of toxic dechlorination intermediates, and could become a useful tool for characterizing contaminated sites in general.

Modern climate and vegetation variability recorded in organic compounds and carbon isotopic compositions in the Dianchi watershed

The aliphatic hydrocarbons distribution and compound-specific characteristics of carbon isotopic compositions in the sediments from the small catchment (197 km(2)) of the Dianchi watershed were investigated for identification of modern climate and vegetation variations in the study area. Results show that a regular bimodal n-alkanes distribution ranged from nC16 to nC33, with strong dominance at nC17 for short-chain n-alkanes and nC31 for long-chain n-alkanes. Mass chromatogram of total fatty acids also indicates corresponding mixed contribution of algae, hydrophilous non-emergent (C4 plants) and terrestrial plants (C3 plants) to sedimentary organic matter (OM). At the depth of -24 to -25 cm (early 1970s), nC31/nC17 and terrestrial to aquatic ratio of hydrocarbons (TAR) values decrease, suggesting a shift of OM origins from C3 terrestrial plants to algae-derived C4 plants. The highest water stage in 1971 was found to be recorded in the particle size (<4 μm). For long-chain alkanes, the values of δ (13)Corg and δ (13)Cn-alkanes varied from -26.9 to -22.4 and -33.4 to -27.9 ‰, respectively. Population growth and economic development led to a demand for abundant habitable and cultivable land. Due to unreasonable land expansion, the primordial forest sporadically distributed. A mixture of C3 and C4 plants probably replaced C3 plants as the sources of OM in the past 10 years. The changes of land-use types and severe drought resulted in the excessive OM inputs to the watershed.

Interaction of soil heavy metal pollution with industrialisation and the landscape pattern in Taiyuan city, China

Many studies indicated that industrialization and urbanization caused serious soil heavy metal pollution from industrialized age. However, fewer previous studies have conducted a combined analysis of the landscape pattern, urbanization, industrialization, and heavy metal pollution. This paper was aimed at exploring the relationships of heavy metals in the soil (Pb, Cu, Ni, As, Cd, Cr, Hg, and Zn) with landscape pattern, industrialisation, urbanisation in Taiyuan city using multivariate analysis. The multivariate analysis included correlation analysis, analysis of variance (ANOVA), independent-sample T test, and principal component analysis (PCA). Geographic information system (GIS) was also applied to determine the spatial distribution of the heavy metals. The spatial distribution maps showed that the heavy metal pollution of the soil was more serious in the centre of the study area. The results of the multivariate analysis indicated that the correlations among heavy metals were significant, and industrialisation could significantly affect the concentrations of some heavy metals. Landscape diversity showed a significant negative correlation with the heavy metal concentrations. The PCA showed that a two-factor model for heavy metal pollution, industrialisation, and the landscape pattern could effectively demonstrate the relationships between these variables. The model explained 86.71% of the total variance of the data. Moreover, the first factor was mainly loaded with the comprehensive pollution index (P), and the second factor was primarily loaded with landscape diversity and dominance (H and D). An ordination of 80 samples could show the pollution pattern of all the samples. The results revealed that local industrialisation caused heavy metal pollution of the soil, but such pollution could respond negatively to the landscape pattern. The results of the study could provide a basis for agricultural, suburban, and urban planning.

Utility of soil linear alkylbenzenes to assess regional anthropogenic influences with special reference to atmospheric transport

Tracing regional anthropogenic influences is important for assessing the magnitude of human interferences with the environment. In the present study, the utility of soil linear alkylbenzenes (LABs) as indicators of anthropogenic influences was examined, with the emphasis on the role of atmospheric transport in dissipating LABs from input sources to remote areas. The Pearl River Delta, South China, which has experienced rapid economic growth and urbanization, was selected as the study region. The concentrations of LABs (mean: 8.6 ng/g and median: 5.7 ng/g, with an outlier of 2,020 ng/g excluded) suggested that sewage contamination throughout the entire study region was generally light. The spatial variation of sewage pollution was significantly positively correlated with population density and per capita gross domestic product, with hot spots concentrated in the central PRD. Atmospheric deposition was hypothesized as an important input route for soil LABs in forestry and drinking water source areas with little impact of irrigation or direct wastewater discharge. This proposition could explain the opposite spatial patterns of LAB concentrations and values of a biodegradation index (5-C12+5-C13)/(5-C11+5-C10), where i-Cn defines a specific LAB congener with i and n indicating the position of the phenyl group and the number of carbon atoms on the alkyl chain, respectively. These findings somewhat validated LABs as tracers of regional anthropogenically derived contamination, with atmospheric transport of LABs as a viable dissipating mechanism.

Analysis of heavy metal sources in soil using kriging interpolation on principal components

Anniston, Alabama has a long history of operation of foundries and other heavy industry. We assessed the extent of heavy metal contamination in soils by determining the concentrations of 11 heavy metals (Pb, As, Cd, Cr, Co, Cu, Mn, Hg, Ni, V, and Zn) based on 2046 soil samples collected from 595 industrial and residential sites. Principal Component Analysis (PCA) was adopted to characterize the distribution of heavy metals in soil in this region. In addition, a geostatistical technique (kriging) was used to create regional distribution maps for the interpolation of nonpoint sources of heavy metal contamination using geographical information system (GIS) techniques. There were significant differences found between sampling zones in the concentrations of heavy metals, with the exception of the levels of Ni. Three main components explaining the heavy metal variability in soils were identified. The results suggest that Pb, Cd, Cu, and Zn were associated with anthropogenic activities, such as the operations of some foundries and major railroads, which released these heavy metals, whereas the presence of Co, Mn, and V were controlled by natural sources, such as soil texture, pedogenesis, and soil hydrology. In general terms, the soil levels of heavy metals analyzed in this study were higher than those reported in previous studies in other industrial and residential communities.

Assessment of PCDD/Fs levels in soil at a contaminated sawmill site in Sweden--a GIS and PCA approach to interpret the contamination pattern and distribution

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzo-p-furans (PCDD/Fs) were analysed in soil from a Swedish sawmill site where chlorophenols (CPs) had been used more than 40 years ago. The most contaminated area at the site was the preservation subarea where the PCDD/F WHO2005-TEQ level was 3450 times higher than the current Swedish guideline value of 200 ng TEQ/kg soil for land for industrial use. It was also shown that a fire which destroyed the sawmill might have affected the congener distribution at the concerned areas. To get a broader picture of the contamination both GIS (spatial interpolation analysis) and multivariate data analysis (PCA) were applied to visualize and compare PCDD/F levels as well as congener distributions at different areas at the site. It is shown that GIS and PCA are powerful tools in decisions on future investigations, risk assessments and remediation of contaminated sites.

Changes in toxicity during in situ bioremediation of weathered drill wastes contaminated with petroleum hydrocarbons

Bioremediation of weathered drill wastes severely contaminated with total petroleum hydrocarbons (TPH) (90,000-170,000 mg kg(-1)) and BTEX (51.2-95.5 mg kg(-1)) to soil standards was achieved over a 3-year period in three phases: initial remediation, basic bioremediation and inoculation with a biopreparation. Fourteen non-pathogenic indigenous bacteria species belonging mainly to the Actinomycetales were identified and shown to be able to degrade 63-75% of nC(9)-nC(20), 36-51% of nC(21)-nC(36), 36% of BTEX and 20% of PAHs (polycyclic aromatic hydrocarbons). Addition of five non-pathogenic fungi species to the bacterial consortium allowed degradation of 69-89% of nC(9)-nC(20), 47-80% of nC(21)-nC(36), 76% of BTEX, and 68% of PAHs. Microtox, Ostacodtoxkit, Phytotoxkit and Ames tests indicated that changes in toxicity were not connected with the decrease in TPH contents, possibly due to the formation of toxic indirect metabolites during bioremediation. No toxicity was found in the soil after bioremediation.

Use of geostatistics for remediation planning to transcend urban political boundaries

Soil remediation plans are often dictated by areas of jurisdiction or property lines instead of scientific information. This study exemplifies how geostatistically interpolated surfaces can substantially improve remediation planning. Ordinary kriging, ordinary co-kriging, and inverse distance weighting spatial interpolation methods were compared for analyzing surface and sub-surface soil sample data originally collected by the US EPA and researchers at the University at Buffalo in Hickory Woods, an industrial-residential neighborhood in Buffalo, NY, where both lead and arsenic contamination is present. Past clean-up efforts estimated contamination levels from point samples, but parcel and agency jurisdiction boundaries were used to define remediation sites, rather than geostatistical models estimating the spatial behavior of the contaminants in the soil. Residents were understandably dissatisfied with the arbitrariness of the remediation plan. In this study we show how geostatistical mapping and participatory assessment can make soil remediation scientifically defensible, socially acceptable, and economically feasible.

Integrated assessment of heavy metal contamination in sediments from a coastal industrial basin, NE China

The purpose of this study is to investigate the current status of metal pollution of the sediments from urban-stream, estuary and Jinzhou Bay of the coastal industrial city, NE China. Forty surface sediment samples from river, estuary and bay and one sediment core from Jinzhou bay were collected and analyzed for heavy metal concentrations of Cu, Zn, Pb, Cd, Ni and Mn. The data reveals that there was a remarkable change in the contents of heavy metals among the sampling sediments, and all the mean values of heavy metal concentration were higher than the national guideline values of marine sediment quality of China (GB 18668-2002). This is one of the most polluted of the world's impacted coastal systems. Both the correlation analyses and geostatistical analyses showed that Cu, Zn, Pb and Cd have a very similar spatial pattern and come from the industrial activities, and the concentration of Mn mainly caused by natural factors. The estuary is the most polluted area with extremely high potential ecological risk; however the contamination decreased with distance seaward of the river estuary. This study clearly highlights the urgent need to make great efforts to control the industrial emission and the exceptionally severe heavy metal pollution in the coastal area, and the immediate measures should be carried out to minimize the rate of contamination, and extent of future pollution problems.

Pollutant plume delineation from tree core sampling using standardized ranks

There are currently contradicting results in the literature about the way chloroethene (CE) concentrations from tree core sampling correlate with those from groundwater measurements. This paper addresses this issue by focusing on groundwater and tree core datasets in CE contaminated site, Czech Republic. Preliminary analyses revealed strongly and positively skewed distributions for the tree core dataset, with an intra-tree variability accounting for more than 80% of the total variability, while the spatial analyses based on variograms indicated no obvious spatial pattern for CE concentration. Using rank transformation, it is shown how the results were improved by revealing the initially hidden spatial structure for both variables when they are handled separately. However, bivariate analyses based on cross-covariance functions still failed to indicate a clear spatial correlation between groundwater and tree core measurements. Nonetheless, tree core sampling and analysis proved to be a quick and inexpensive semi-quantitative method and a useful tool.

Contribution of a municipal solid waste incinerator to the trace metals in the surrounding soil

This study analyses the pedological environment of the area near a municipal waste incinerator that has been operating in the south-east district of Pisa for approximately 20 years. There are many other industrial activities in the area besides the incinerator, which represent possible sources of pollution, as well as heavy road traffic. The study area was defined by a 0-4-km zone around the site with a population of approximately 12,000 residents. The study included the physical and chemical characterisation of 100 samples of soil and an analysis of trace metals such as Cr, Ni, Pb, Zn, Hg, As and Cd. The samples were grouped into soil use categories. The results showed Zn, Pb and Hg correlated with their potentially mobile fractions, and suggested an anthropic contribution to their presence in the soil. Ni, Cr and As showed values attributable to a lithological origin. This was consistent with the PCA results. The aim was to define the environmental state of the soil of the area in order to create a reference for future research and to verify the possible presence of pollution from other sources (local industrial activities and traffic).

Polycyclic aromatic hydrocarbons in urban soils of Beijing: status, sources, distribution and potential risk

We studied the source, concentration, spatial distribution and health risk of 16 polycyclic aromatic hydrocarbons (PAHs) in urban soils of Beijing. The total mass concentration of 16 PAHs ranged from 93 to 13,141 μg kg(-1) with a mean of 1228 μg kg(-1). The contour map of soil PAH concentrations showed that the industrial zone, the historical Hutong district and the university district of Beijing have significantly higher concentrations than those in remainder of the city. The results of sources identification suggested that the primary sources of PAHs were vehicle exhaust and coal combustion and the secondary source was the atmospheric deposition of long-range transported PAHs. The incremental lifetime cancer risks (ILCRs) of exposing to PAHs in the urban soils of Beijing for adult were 1.77 × 10(-6) and 2.48 × 10(-5), respectively under normal and extreme conditions. For child, they were 8.87 × 10(-7) and 6.72 × 10(-6), respectively under normal and extreme conditions.

Watershed-scale assessment of arsenic and metal contamination in the surface soils surrounding Miyun Reservoir, Beijing, China

Concentrations of As and selected metals were determined in surface soils of the Miyun Reservoir watershed of Beijing, China. The degree to which concentrations of As and metals exceeded the corresponding background concentration of soils was: Cr>Cu>Zn>As>Ni with no apparent anthropogenic contamination with Cd and Pb. Based on the results of a combination of multivariate statistics and geostatistical analysis, greater concentrations of Cr and Ni in soils were determined to be primarily from iron ore mining near where the Chaohe River enters the northeast portion of the reservoir. Agricultural activities were responsible for the observed elevated concentrations of Cu and Zn in soils. Relatively great concentrations of As were found in soils near the upstream regions of the Baihe River in Chicheng County where small gold mining activities have taken place. The greatest potential for adverse effects of Cr and Cu occurred along the eastern shore of Miyun Reservoir.

Multi-scale spatial structure of heavy metals in agricultural soils in Beijing

To effectively investigate the spatial variability of heavy metals in soil, produce a higher quality spatial distribution map, and identify the potential pollution sources of heavy metals, geostatistics was employed to evaluate the effect of scale on spatial variability of heavy metals in Beijing agricultural soils. The results revealed that spatial variability of Cr, Ni, Zn, and Hg was dependent on scale. Validation of the optimality of theoretical semivariance and comparative analysis of the estimation accuracy demonstrated that the multi-scale nested model can reveal the spatial structure of heavy metals effectively and improve the estimation accuracy better than the single-scale method, thereby enabling production a higher quality spatial interpolation map. Thus, the multi-scale kriging nested model is a useful tool for revealing spatial variability of heavy metals in soils, while the spatial distribution maps allow the identification of hot spots with high concentrations of heavy metals.

Use of principal components analysis (PCA) on estuarine sediment datasets: the effect of data pre-treatment

Principal components analysis (PCA) is a multivariate statistical technique capable of discerning patterns in large environmental datasets. Although widely used, there is disparity in the literature with respect to data pre-treatment prior to PCA. This research examines the influence of commonly reported data pre-treatment methods on PCA outputs, and hence data interpretation, using a typical environmental dataset comprising sediment geochemical data from an estuary in SE England. This study demonstrated that applying the routinely used log (x + 1) transformation skewed the data and masked important trends. Removing outlying samples and correcting for the influence of grain size had the most significant effect on PCA outputs and data interpretation. Reducing the influence of grain size using granulometric normalisation meant that other factors affecting metal variability, including mineralogy, anthropogenic sources and distance along the salinity transect could be identified and interpreted more clearly.

Distribution of heavy metals in agricultural soils near a petrochemical complex in Guangzhou, China

The aim of the study was to investigate influence of an industrialized environment on the accumulation of heavy metals in agricultural soils. Seventy soil samples collected from surface layers (0-20 cm) and horizons of five selected pedons in the vicinity area of petrochemical complex in Guangzhou, China were analyzed for Zn, Cu, Pb, Cd, Hg and As concentrations, the horizontal and vertical variation of these metals were studied and geographic information system (GIS)-based mapping techniques were applied to generate spatial distribution maps. The mean concentrations of these heavy metals in the topsoils did not exceed the maximum allowable concentrations in agricultural soil of China with the exception of Hg. Significant differences between land-use types showed that Cu, Pb, Cd, Hg and As concentrations in topsoils were strongly influenced by agricultural practices and soil management. Within a radius of 1,300 m there were no marked decreasing trends for these element concentrations (except for Zn) with the increase of distance from the complex boundary, which reflected little influence of petroleum air emission on soil heavy metal accumulation. Concentrations of Zn, Cu, Pb, Cd, Hg and As in the five pedons, particularly in cultivated vegetable field and orchard, decreased with soil depth, indicating these elements mainly originated from anthropogenic sources. GIS mapping was a useful tool for evaluating spatial variability of heavy metals in the affected soil. The spatial distribution maps allowed the identification of hot-spot areas with high metal concentration. Effective measures should be taken to avoid or minimize heavy metal further contamination of soils and to remediate the contaminated areas in order to prevent pollutants affecting human health through agricultural products.

A spatial approach to environmental risk assessment of PAH contamination

The extent of remediation of contaminated industrial sites depends on spatial heterogeneity of contaminant concentration and spatially explicit risk characterization. We used sequential Gaussian simulation (SGS) and indicator kriging (IK) to describe the spatial distribution of polycyclic aromatic hydrocarbons (PAHs), pH, electric conductivity, particle aggregate distribution, water holding capacity, and total organic carbon, and quantitative relations among them, in a creosote polluted soil in southern Sweden. The geostatistical analyses were combined with risk analyses, in which the total toxic equivalent concentration of the PAH mixture was calculated from the soil concentrations of individual PAHs and compared with ecotoxicological effect concentrations and regulatory threshold values in block sizes of 1.8 x 1.8 m. Most PAHs were spatially autocorrelated and appeared in several hot spots. The risk calculated by SGS was more confined to specific hot spot areas than the risk calculated by IK, and 40-50% of the site had PAH concentrations exceeding the threshold values with a probability of 80% and higher. The toxic equivalent concentration of the PAH mixture was dependent on the spatial distribution of organic carbon, showing the importance of assessing risk by a combination of measurements of PAH and organic carbon concentrations. Essentially, the same risk distribution pattern was maintained when Monte Carlo simulations were used for implementation of risk in larger (5 x 5 m), economically more feasible remediation blocks, but a smaller area became of great concern for remediation when the simulations included PAH partitioning to two separate sources, creosote and natural, of organic matter, rather than one general.

Long-term impact of acid resin waste deposits on soil quality of forest areas I. Contaminants and abiotic properties

Acid resins are residues characterised by elevated concentrations of hydrocarbons and trace elements, which were produced by mineral oil industries in Central Europe during the first half of the last century. Due to the lack of environmental legislation at that time, these wastes were dumped into excavated ponds in public areas without further protection. In this work, the long-term effects of such resin deposits on soil quality of two forest areas (Bayern, Germany) were assessed. We evaluated the distribution and accumulation of contaminants in the surroundings of the deposits, where the waste was disposed of about 60 years ago. General soil chemical properties such as pH, C, N and P content were also investigated. Chemical analysis of resin waste from the deposits revealed large amounts of potential contaminants such as hydrocarbons (93 g kg(-1)), As (63 mg kg(-1)), Cd (24 mg kg(-1)), Cu (1835 mg kg(-1)), Pb (8100 mg kg(-1)) and Zn (873 mg kg(-1)). Due to the location of the deposits on a hillside and the lack of adequate isolation, contaminants have been released downhill despite the solid nature of the waste. Five zones were investigated in each site: the deposit, three affected zones along the plume of contamination and a control zone. In affected zones, contaminants were 2 to 350 times higher than background levels depending on the site. In many cases, contaminants exceeded the German environmental guidelines for the soil-groundwater path and action levels based on extractable concentrations. Resin contamination yielded larger total C/total N ratios in affected zones, but no clear effect was observed on absolute C, N and P concentrations. In general, no major acidification effect was reported in affected zones.

Spatial distribution of Cd and Cu in soils in Shenyang Zhangshi Irrigation Area (SZIA), China

Heavy metal contamination of soils, derived from sewage irrigation, mining and inappropriate utilization of various agrochemicals and pesticides, and so on, has been of wide concern in the last several decades. The Shenyang Zhangshi Irrigation Area (SZIA) in China is a representative area of heavy metal contamination of soils resulting from sewage irrigation for about 30 years. This study investigated the spatial distribution and temporal variation of soil cadmium (Cd) and copper (Cu) contamination in the SZIA. The soil samples were collected from the SZIA in 1990 and 2004; Cd and Cu in soils was analyzed and then the spatial distribution and temporal variation of Cd and Cu in soils were modeled using Kriging methods. The results show that long-term sewage irrigation had caused serious Cd and Cu contamination in soils. The mean and the maximum of soil Cd are markedly higher than the levels in second grade standard soil (LSGSS) in China, and the maximum of soil Cu is close to the LSGSS in China in 2004 and is more than the LSGSS in China in 1990. The contamination magnitude of soil Cd and the soil extent of Cd contamination had evidently increased since sewage irrigation ceased in 1992. The contamination magnitude of soil Cu and the soil extent of Cu contamination had evidently increased in topsoil, but obviously decresed in subsoil. The soil contamination of Cd and Cu was mainly related to Cd and Cu reactivation of contaminated sediments in Shenyang Xi River and the import of Cd and Cu during irrigation. The eluviation of Cd and Cu in contaminated topsoil with rainfall and irrigation water was another factor of temporal-spatial variability of Cd and Cu contamination in soils.

A spatial risk assessment methodology to support the remediation of contaminated land

When soil and groundwater contaminations occur over large areas, remediation measures should be spatially prioritized on the basis of the risk posed to human health and in compliance with technological and budget constraints. Within this scope, the application of human health risk assessment algorithms in a spatially resolved environment raises a number of methodological and technical complexities. In this paper, a methodology is proposed and applied in a case study to support the entire formulation process of remediation plans, encompassing hazard assessment, exposure assessment, risk characterisation, uncertainty assessment and allocation of risk reduction measures. In the hazard assessment, it supports the selection of Contaminants of Concern (CoC) with regard to both their average concentrations and peak concentrations, i.e. hot spots. In the exposure assessment, it provides a zoning of the site based on the geostatistical mapping of contaminant. In the risk characterisation, it generates vector maps of Risk Factors on the basis of the risk posed by multiple substances and allows the interrogation of most relevant CoC and exposure pathways for each zone of the site. It also supports the Monte Carlo based probabilistic estimation of the Risk Factors and generates maps of the associated uncertainty. In the risk reduction phase, it supports the formulation of remediation plans based on the stepwise spatial allocation of remediation interventions and the on-time simulation of risk reduction performances. The application of this methodology is fully supported by an easy-to-use and customized Geographical Information System and does not require high expertise for interpretation. The proposed methodology is the core module of a Decision Support System (DSS) that was implemented in the DESYRE software aimed at supporting the risk-based remediation of megasites.

Factors affecting the spatial pattern of nitrate contamination in shallow groundwater

The elevated level of nitrate in groundwater is a serious problem in Korean agricultural areas. To control and manage groundwater quality, the characterization of groundwater contamination and identification of the factors affecting the nitrate concentration of groundwater are significant. The characterization of groundwater contamination at a hydrologically complex agricultural site in Yupori, Chuncheon (Korea) was undertaken by analyzing the hydrochemical data of groundwater within a statistical framework. Multivariate statistical tools such as cluster analyses and Tobit regression were applied to investigate the spatial variation of nitrate contamination and to analyze the factors affecting the NO3-N concentration in a shallow groundwater system. The groundwater groups from the cluster analysis were consistent with the land use pattern of the study area. The clustered group of a gentle-slope area with lower elevations showed higher NO3-N contamination of groundwater than groups on a hillside with higher elevations. Tobit regression results indicated that the agricultural activity in the vegetable fields and barns were the major factors affecting the elevated NO3-N concentration while the land slopes and elevations were negatively correlated with the NO3-N concentration. This shows that topographic characteristics such as land slopes and elevations should be considered to evaluate the land use impact on shallow groundwater quality.

DESYRE: DEcision Support sYstem for the REhabilitation of contaminated megasites

DESYRE (DEcision Support sYstem for the REqualification of contaminated sites) is a GIS-based decision support system (DSS) specifically developed to address the integrated management and remediation of contaminated megasites (i.e., large contaminated areas or impacted areas characterized by multiple site owners and multiple stakeholders). In the DESYRE conceptual design and development the main aspects pertaining to a remediation process--analysis of social and economic benefits and constrains, site characterization, risk assessment, selection of best available technologies, creation of sets of technologies to be applied, analysis of the residual risk, and comparison of different remediation scenarios--were included. The DESYRE DSS is a GIS-based software composed of 6 interconnected modules. In the characterization module, chemical and hydrogeological data are organized in a relational database and contaminants' distributions are mapped by using geostatistic tools. The socioeconomic module addresses the socioeconomic constraints though a fuzzy logic analysis to select the best land use. The risk assessment module is divided into 2 phases. In the preremediation phase, an original procedure allows assessing and representing the spatial distribution of risks posed by contaminants in soil and groundwater, providing a risk-based zoning of the site. Then, in the technology assessment module, a selection of suitable technologies and creation of different technology sets, taking into account both technical requirements and site-specific features, are performed by experts supported by multicriteria decision analysis tools. In the postremediation risk assessment, a simulation of applied technologies provides residual risk maps with related uncertainty maps. Finally, in the decision module, alternative remediation scenarios are described by a set of indices and can be compared and ranked by interested stakeholders using multicriteria decision analysis methodologies. The paper highlights original procedural steps and functionalities of DESYRE nd analyzes its main points of strength and potentialities, as well as limits.

Landscape ecology of the Guanting Reservoir, Beijing, China: multivariate and geostatistical analyses of metals in soils

Surface soil samples were collected from 52 sites around Guanting Reservoir in Beijing, China, and contents of 'total' metals (Cu, Zn, Pb, Cd, As, Ni and Cr) were determined. The results indicate that the degree of heavy metal pollution in the soils declines in the order of Cd>Cr>Zn>As>Cu>Ni>Pb. Based on the results of a combination of multivariate statistics and geostatiscal analyses, it was concluded that land application of phosphate fertilizer, wastewater and sludge were the primary sources of Cd and Zn in soils. Whereas As, Cu, Cr and Ni in some soils were due to natural rock weathering. The sources of Pb in soils only partially originated from land application of phosphate fertilizer, but mainly from vehicle exhaust. The greatest concentrations of all metals, except for Pb, were found in Huailai County and the towns of Yanghedaqiao and Guanting.

Characterization and classification of complex PAH samples using GC-qMS and GC-TOFMS

The aim of this study was to compare the polycyclic aromatic hydrocarbon (PAH) contents in a number of complex samples, including soil samples from industrial sites, anti-skid sand, urban dust and ash samples from municipal solid waste incinerators. The samples were characterized by routine analysis of PAHs (gas chromatography-quadrupole mass spectrometry) and gas chromatography-time of flight mass spectrometry (GC-TOFMS). Classification of the samples by principal component analysis (PCA) according to their composition of PAHs revealed that samples associated with traffic and the municipal incinerator formed homogeneous clusters, while the PAH-contaminated soils clustered in separate groups. Using spectral data to resolve co-eluting chromatographic peaks, 962 peaks could be identified in the GC-TOFMS analysis of a pooled sample and 123-527 peaks in the individual samples. Many of the studied extracts included a unique set of chemicals, indicating that they had a much more diverse contamination profile than their PAH contents suggested. Compared to routine analysis, GC-TOFMS provided more detailed information about each sample and in this study a large number of alkylated PAHs were found to be associated with the corresponding unsubstituted PAHs. The possibility to filter peaks according to different criteria (e.g. to include only peaks that were detected in the analysis of another sample) was explored and used to identify unique as well as common compounds within samples. This procedure could prove to be valuable for obtaining relevant chemical data for use in conjunction with results from various biological test systems.

Definition and GIS-based characterization of an integral risk index applied to a chemical/petrochemical area

A risk map of the chemical/petrochemical industrial area of Tarragona (Catalonia, Spain) was designed following a two-stage procedure. The first step was the creation of a ranking system (Hazard Index) for a number of different inorganic and organic pollutants: heavy metals, polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), polychlorinated biphenyls (PCBs) and polychlorinated aromatic hydrocarbons (PAHs) by applying self-organizing maps (SOM) to persistence, bioaccumulation and toxicity properties of the chemicals. PCBs seemed to be the most hazardous compounds, while the light PAHs showed the minimum values. Subsequently, an Integral Risk Index was developed taking into account the Hazard Index and the concentrations of all pollutants in soil samples collected in the assessed area of Tarragona. Finally, a risk map was elaborated by representing the spatial distribution of the Integral Risk Index with a geographic information system (GIS). The results of the present study seem to indicate that the development of an integral risk map might be useful to help in making-decision processes concerning environmental pollutants.

Heavy metals content in N. oleander leaves as urban pollution assessment

Nerium oleander L. (Oleander) leaves grown in Palermo city (Sicily, Italy) were collected from six sampling sites representing either areas of high traffic and urbanisation density or areas far away from traffic (e.g. city gardens). Concentration of Al, Ba, Cr, Cu, Fe, Pb, Mg, Mn, and Zn were determined in leaf samples during two years. Multivariate analysis classified the sampling sites in four groups based on the metal content in vegetal leaves in agreement with traffic and human activity site. Many elements studied (Al, Ba, Fe, Mn Mg) arise from the soil composition and others such as Cr, Cu, Pb and Zn as pollutant of the soil. On the other hand, about 30% of Al, Fe Cr, Cu and Pb originate from aerial deposition on leaves. Although the results presented should be handled with caution N. oleander can be considered as a means of assessing dust contamination in the urban environment.

Metal contamination in urban soils of coastal Tuscany (Italy)

Urban soils are often contaminated by metals deriving from human activities. Urban polluted soils can affect human health through direct physical contact or through the food chain. The behaviour of metals is affected by chemical and physical soil properties. Heavy metal concentration was determined in some soils of three medium sized towns of coastal Tuscany, Central Italy. Soil samples were collected in roadsides, urban agricultural soils (allotments), playgrounds and public parks. The analysis included total metal content (Pb, Cu, Zn, Ni, Cd), and sequential extraction. Lead reached the highest levels in the soils and was higher near roads. In urban agricultural soils and in allotments Cu was present in noticeable quantities (300 mg kg(-1)). The presence of Cu in urban soils seems to be typical of soils used for a long period as agricultural land, especially vineyards in the area covered by this study. Sequential extraction was performed to evaluate the mobility of the metals and to better understand the impact of the anthropogenic activity on urban sites. In the present study principal components analysis (PCA) is employed to describe the characteristics of urban soils.

Benthic foraminifera and heavy metals distribution: a case study from the Naples Harbour (Tyrrhenian Sea, Southern Italy)

The analysis of 90 surficial sediments from three docks of the Naples Harbour (Levante, Granili, and Diaz) permits to compare the distribution modes of heavy metals with grain sizes, total organic carbon content (TOC) and distribution patterns of benthic foraminifera. Foraminiferal density and species richness decrease with the increasing toxic elements concentrations from the Levante to the Diaz dock. Median concentrations of Ni, Pb, Zn, and Hg (medians of 21.43 mg/kg, 270.24 mg/kg, 489.65 mg/kg, and 1.18 mg/kg, respectively) were reported for the Diaz dock where foraminifera are absent, thus suggesting a possible impact of toxic elements on the benthic ecosystem balance. Compared to the unpolluted marine sediments of the Granili dock, the Levante area shows higher heavy metals levels and a quasi-oligotypic benthic assemblage. This is dominated by the tolerant species Ammonia tepida that may be used as bio-indicator of pollution of anthropised marine sediments.

Temporal-spatial distribution and variability of cadmium contamination in soils in Shenyang Zhangshi irrigation area, China

Heavy metal contamination in soils has been of wide concern in China in the last several decades. The heavy metal contamination was caused by sewage irrigation, mining and inappropriate utilization of various agrochemicals and pesticides and so on. The Shenyang Zhangshi irrigation area (SZIA) in China is a representative area of heavy metal contamination of soils resulting from sewage irrigation for about 30 years duration. This study investigated the spatial distribution and temporal variation of soil cadmium contamination in the SZIA. The soil samples were collected from the SZIA in 1990 and 2004; Cd of soils was analyzed and then the spatial distribution and temporal variation of Cd in soils was modelled using kriging methods. The kriging map showed that long-term sewage irrigation had caused serious Cd contamination in topsoil and subsoil. In 2004, the Cd mean concentrations were 1.698 and 0.741 mg/kg, and the maxima 10.150 and 7.567 mg/kg in topsoils (0-20 cm) and subsoils (20-40 cm) respectively. These values are markedly more than the Cd levels in the second grade soil standard in China. In 1990, the Cd means were 1.023 and 0.331 mg/kg, and the maxima 9.400 and 3.156 mg/kg, in topsoils and subsoils respectively. The soil area in 1990 with Cd more than 1.5 mg/kg was 2701 and 206.4 hm(2) in topsoils and subsoils respectively; and in 2004, it was 7592 and 1583 hm(2), respectively. Compared with that in 1990, the mean and maximum concentration of Cd, as well as the soil area with Cd more than 1.5 mg/kg had all increased in 2004, both in topsoils and subsoils.

A method for risk assessment for three contaminated sites in northern Italy

A stepwise human health risk assessment procedure carried out using American Society for Testing and Materials methodology was applied to three contaminated sites located in northern Italy: an uncontrolled landfill, an abandoned industrial site and an industrial waste contaminated land. Two different tiers of analysis were performed by means of the analytical model RBCA Tool Kit and the numerical model API-DSS, respectively. The latter was applied according to both a deterministic and a probabilistic risk analysis. The comparison of the two approaches highlighted the great benefit provided by probabilistic analysis for the risk assessment based on site specific parameters and more complex models, in particular for groundwater exposure route. Finally, the risk-based site specific target levels were compared to the generic regulatory threshold limits fixed by the Italian regulation. The comparison showed the regulatory limits generally too restrictive for non-carcinogenic substances but less protective for carcinogenic chemicals, outlining the need of a site-specific risk assessment especially for carcinogenic substances.

[Public health risk maps using geostatistical methods]

The purpose of this paper was to demonstrate an application of geostatistical methods to public health risk maps through the identification of areas with elevated concentrations of heavy metals. The study focused on the element lead (Pb) from aerial transportation or loading of particles due to soil leaching in an area with major urban and industrial concentration in the Baixada Santista on the coastland of São Paulo State, Brazil. Maps with the spatial distribution of lead were produced using ordinary kriging; subsequently indicative kriging was performed to identify soil sites with contamination levels higher than the maximum acceptable level defined by the Sao Paulo State Environmental Control Agency. The resulting maps showed areas with increased probability of public health risk. The methodology proved to be a promising approach for decision-making related to health public policies and environmental planning.