Uncertainty assessment of heavy metal soil contamination mapping using spatiotemporal sequential indicator simulation...

Area of Science:

• Environmental Science
• Geostatistics
• Soil Science

Background:

• Accurate mapping of heavy metal soil contamination is crucial for site classification, especially with in situ uncertainty.
• Existing methods like geostatistical space-time kriging (STK) and sequential indicator simulation (SIS) have limitations in handling multi-temporal data.
• Uncertainty assessment in soil contamination relies on quantifying exceedance probabilities.

Purpose of the Study:

• To develop and apply spatiotemporal sequential indicator simulation (STSIS) for assimilating multi-temporal data in heavy metal soil contamination mapping.
• To assess mapping uncertainty, including single and multi-location uncertainties over different time scales.
• To compare the performance of STSIS against traditional STK and SIS techniques.

Main Methods:

• Developed spatiotemporal sequential indicator simulation (STSIS) using additive (STSIS_A) and non-separable (STSIS_NS) space-time semivariogram models.
• Utilized multi-temporal soil copper (Cu) concentration data from 2010-2014 in the Qingshan district, Wuhan City, China.
• Generated multiple STSIS realizations to assess various mapping uncertainties.

Main Results:

• STSIS successfully assimilated multi-temporal data for mapping heavy metal distributions and assessing associated uncertainties.
• The method allowed for detailed assessment of single-location and multi-location uncertainties across annual and multi-year periods.
• STSIS demonstrated superior performance compared to both STK and SIS techniques in the analysis of contaminated soils.

Conclusions:

• Spatiotemporal sequential indicator simulation (STSIS) offers a robust approach for integrating multi-temporal data in soil contamination studies.
• STSIS provides more accurate mapping and uncertainty quantification for contaminated sites than conventional geostatistical methods.
• The developed STSIS models are effective tools for managing and assessing risks associated with heavy metal pollution in soils.