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Concentrations of four heavy metals (Cr, Cu, Ni, and Zn) were measured at 1,082 sampling sites in Changhua county of central Taiwan. A hazard zone is defined in the study as a place where the content of each heavy metal exceeds the corresponding control standard. This study examines the use of spatial analysis for identifying multiple soil pollution hotspots in the study area. In a preliminary investigation, kernel density estimation (KDE) was a technique used for hotspot analysis of soil pollution from a set of observed occurrences of hazards. In addition, the study estimates the hazardous probability of each heavy metal using geostatistical techniques such as the sequential indicator simulation (SIS) and indicator kriging (IK). Results show that there are multiple hotspots for these four heavy metals and they are strongly correlated to the locations of industrial plants and irrigation systems in the study area. Moreover, the pollution hotspots detected using the KDE are the almost same to those estimated using IK or SIS. Soil pollution hotspots and polluted sampling densities are clearly defined using the KDE approach based on contaminated point data. Furthermore, the risk of hazards is explored by these techniques such as KDE and geostatistical approaches and the hotspot areas are captured without requiring exhaustive sampling anywhere.

Unfortunately, as a result of industrial activities, improper disposal of wastes, pollution of agricultural soils with heavy metals has become an increasingly serious problem throughout the world [

Geostatistical analysis considers the concentration of a potentially hazard in soil as a regionalized variable in space. Geostatistics was developed as a means to describe spatial patterns of soil pollution by semivariograms and to predict the values of soil attributes at unsampled locations [

Hotspot mapping is used to help identify where soil pollution exists and comes from. Recently, Kernel density estimation (KDE) is one of the methods for analyzing the first order properties of a point event distribution [

The purpose of this study was to propose alternative approaches in searching for pollutant hotspots. The primary objective of the present work was to investigate proposals for delineating soil pollutant hazards. First, KDE identifys the hotspots of soil pollutions based on the hazardous metal sampling data. Then, IK and SIS generate a hazard probability map based on the samples for management. A study case from a field survey is provided to estimate the probability maps for hazard delineation. It is expected that results can give references for identification of hazardous areas.

Kernel density estimation is used to identify the location, spatial extent and intensity of soil pollution hotspots. Moreover, the spatial patterns of hazardous probability for heavy metals are estimated using geostatistical methods. The three methods are used for visualization of hotspots of soil pollutions in the case study. Study area and sampling of heavy metals will be discussed in the following sections.

The study area is in Changhua County, which is a critical agricultural region in Taiwan. Changhua city is in the east area and Lugang town lies to the west. Approximately 106 industrial plants are clustered in study area. Most industrial plants in the study area involve metalwork, electroplating, textile and metal surface treatment industries (

Approximately 1 kg of soil was collected for each sample using a stainless steel spade and a plastic scoop and then stored in a plastic food bag. After air drying at room temperature, 3 g of each soil sample were disaggregated, sieved to 0.85 mm and ground to a fine 0.15 mm powder. Each 3 g milled sample was then digested for 2 h at room temperature with 7 mL HNO_{3} and 21 mL HCl (aqua regia, 1:3) to slowly oxidize organic matter in the soil. Next, the digest was filtered and made up to 100 mL with distilled water [

The general form of a kernel density estimator in a 2-D space, termed KDE in the rest of this paper, is given by [

where _{is}_{is}

To identify the soil pollution hotspots, the KDE package based on ArcGIS software was used in the study.

The IK estimates the probability that the concentration of a pollutant exceeds a specific control value at a given location [

If the concentration of heavy metal [ _{c}_{c}

The hazardous probability that exceeds _{c}

This ordinary indicator kriging estimator is:

where _{α}_{c}_{α}_{α}_{α}_{c}

where _{i}_{α}_{β}_{c}^{th}^{th}_{i}_{α}_{0}; _{c}^{th}_{0};

In sequential indicator simulation, modeling of the

Define a random path that visits each location of the domain once, in which all nodes {_{i}

At the first visited nodes (_{1}):

Model, using either a parametric or nonparametric approach, the local ccdf of _{1}) conditional on _{α}

Generate, via the Monte Carlo drawing relation, a simulated value ^{(}^{l}^{)} (_{1}) from this ccdf _{Z}_{1} ; _{1} | (

At the ^{th} node _{i}

Model the local ccdf of _{i}

Generate a simulated value ^{(}^{l}^{)} (_{i}

Repeat step 3 until all

The probability of soil heavy metal at _{c}_{c}

where

The hazardous probability that heavy metal concentrations exceed control standards at any of the unsampled sites is determined by geostatistical methods (

The results demonstrate that the hotspots of hazard probability for Cr and Cu are similar. The spatial patterns of hazard probability also reveal hotspots of Cr near industrial plants and irrigation systems in the study area. The Cu hotspots are located in the central and east-northern parts of the study area in the vicinity of industrial plants and irrigation systems. The hotspots of Ni are distributed throughout the study area, except for the south-western part; and the areas with high concentrations of Zn are close to industrial plants and irrigation systems in the north-western part. Furthermore, all probability maps show that the multiple hotspots of hazard probability are close to industrial plants and irrigation systems in the study area.

_{c}^{2}) However, density values for heavy metal Ni are the lowest among the four heavy metals. For long-term pollution monitoring, the Ni pollution sampling points could be increased primarily. Based on these results, the KDE method is an effective approach to make sure of sampling density in delineating heavy metal pollutions for further monitoring.

These techniques such as KDE, IK and SIS are commonly used in exploratory spatial analyses and pattern resolution for soil pollution visualization of heavy metals. All three visualization methods that we used to explore the soil pollution intensity patterns showed similar results (

This study utilizes KDE and geostatistical techniques with 1,082 samples to delineate hazardous zones and quantify the risk of multiple pollutants in a contaminated area. Various methodologies show generally consistent results, but differences exist. The results demonstrate that KDE is an alternative means of determining hazardous hotspots of soil pollutants only using hazardous point data in the preliminary investigation. The polluted sampling density could be detected by using KDE with SIS delineation. Moreover, the geostatistical models are approaches for identifying the risk of hazard delineation and are highly promising for use in evaluating the susceptibility of heavy metals without surveying soil concentrations over an entire study area. All proposed methods can be extended to show that soil pollution is closely related to pollution sources such as industrial factories and the irrigation system in the study area. According to the spatial maps, model assessment of soil pollution hotspots enables remediation planners to help identify hazardous pollution areas. Integrating KDE and geostatistical methods, the KDE method is an effective approach to determine sampling density when delineating heavy metal pollutions by geostatistical methods. The information of spatial sampling density and hotspot pattern could be useful for long-term monitoring and assessment.

The authors wish to thank the anonymous reviewers for their valuable comments and suggestions. The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract NSC-97-2628-H-002-026-MY3, NSC 89-2621-B- 002-004 and NSC 87-2621-P-002-012.

The study area and sampling sites.

The kernel density maps (stretched to min–max range) of (a) Cr (b) Cu (c) Ni (d) Zn.

The probability maps of (a) Cr (b) Cu (c) Ni (d) Zn using indicator kriging based on 1,082 samples.

The probability maps of (a) Cr (b) Cu (c) Ni (d) Zn in 1000 realizations using sequential indicator simulation based on 1,082 samples.

Descriptive statistics of heavy metals for 1,082 samples.

Min (mg/kg) | Median (mg/kg) | Max (mg/kg) | Average (mg/kg) | SD (mg/kg) | Control standards (mg/kg) | Number of observances over control standards | |
---|---|---|---|---|---|---|---|

Cr | 22.6 | 119.0 | 3,070.0 | 194.0 | 212.5 | 250 | 286 |

Cu | 11.0 | 116.0 | 3,810.0 | 194.7 | 222.7 | 200 | 395 |

Ni | 21.3 | 189.2 | 4,020.0 | 271.3 | 259.0 | 200 | 622 |

Zn | 60.5 | 337.0 | 7,850.0 | 526.4 | 549.6 | 600 | 336 |

Min: minimum; Max: maximum; SD: standard deviation.

Indicator variogram models for heavy metals.

Threshold (mg/kg) | Model | C_{0} |
C_{0}+C |
R (m) | RSS | ^{2} | |
---|---|---|---|---|---|---|---|

Cr | 250 | Exp. | 0.0237 | 0.1874 | 120 | 2.52E-04 | 0.859 |

Cu | 200 | Exp. | 0.0251 | 0.2202 | 135 | 3.08E-04 | 0.904 |

Ni | 200 | Exp. | 0.0206 | 0.2352 | 249 | 3.39E-03 | 0.723 |

Zn | 600 | Exp. | 0.0221 | 0.2042 | 147 | 6.05E-04 | 0.808 |

Exp.: Exponential model; C_{0}: Nugget; C_{0}+C: Sill; R: Range; RSS: Residual Sum of Squares; ^{2}: Coefficient of determination

Indicator variogram models for the 25th, 50th, and 75th percentiles of heavy metals in 1,082 samples.

Heavy metal | Model | Parameters | RSS | ^{2} | |||
---|---|---|---|---|---|---|---|

C_{0} |
C_{0}+C |
R (m) | |||||

Cr | 25% | Exp. | 0.020 | 0.184 | 216 | 1.730E-03 | 0.722 |

50% | Exp. | 0.026 | 0.247 | 171 | 1.202E-03 | 0.807 | |

75% | Exp. | 0.025 | 0.190 | 120 | 2.075E-04 | 0.852 | |

Cu | 25% | Exp. | 0.017 | 0.184 | 240 | 2.008E-03 | 0.737 |

50% | Exp. | 0.025 | 0.247 | 186 | 7.016E-04 | 0.899 | |

75% | Exp. | 0.024 | 0.190 | 108 | 5.293E-04 | 0.663 | |

Ni | 25% | Exp. | 0.015 | 0.179 | 222 | 2.614E-03 | 0.634 |

50% | Exp. | 0.022 | 0.237 | 228 | 3.608E-03 | 0.671 | |

75% | Exp. | 0.018 | 0.183 | 159 | 5.723E-04 | 0.805 | |

Zn | 25% | Exp. | 0.024 | 0.190 | 222 | 1.464E-03 | 0.768 |

50% | Exp. | 0.028 | 0.250 | 171 | 3.795E-04 | 0.936 | |

75% | Exp. | 0.021 | 0.189 | 144 | 8.077E-03 | 0.710 |

Exp.: Exponential model; C_{0}: Nugget; C_{0}+C: Sill; R: Range; RSS: Residual Sum of Squares; ^{2} : Coefficient of determination.

Polluted sampling density value based on SIS probability criteria.

Critical probability(_{c} |
Number of grid which value is over _{c} |
Density value (L/m^{2}) | ||
---|---|---|---|---|

Mean | Range | |||

Cr | 0.6 | 591 | 0.00028 | 0.00066 |

0.7 | 467 | 0.00031 | 0.00066 | |

0.8 | 373 | 0.00033 | 0.00066 | |

0.9 | 310 | 0.00034 | 0.00066 | |

Cu | 0.6 | 851 | 0.00029 | 0.00082 |

0.7 | 643 | 0.00032 | 0.00081 | |

0.8 | 505 | 0.00034 | 0.00080 | |

0.9 | 403 | 0.00036 | 0.00080 | |

Ni | 0.6 | 2,157 | 0.00023 | 0.00071 |

0.7 | 1,554 | 0.00027 | 0.00070 | |

0.8 | 1,099 | 0.00030 | 0.00067 | |

0.9 | 773 | 0.00032 | 0.00067 | |

Zn | 0.6 | 709 | 0.00028 | 0.00079 |

0.7 | 560 | 0.00030 | 0.00079 | |

0.8 | 453 | 0.00032 | 0.00079 | |

0.9 | 379 | 0.00033 | 0.00079 |