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Updated: Apr 6, 2026

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses
Published on: October 21, 2016
Avian Conservation Areas as a Proxy for Contaminated Soil Remediation
Wei-Chih Lin1, Yu-Pin Lin2, Johnathen Anthony3
1Department of Bioenvironmental Systems Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan. b97602046@ntu.edu.tw.
Abstract:
Remediation prioritization frequently falls short of systematically evaluating the underlying ecological value of different sites. This study presents a novel approach to delineating sites that are both contaminated by any of eight heavy metals and have high habitat value to high-priority species. The conservation priority of each planning site herein was based on the projected distributions of eight protected bird species, simulated using 900 outputs of species distribution models (SDMs) and the subsequent application of a systematic conservation tool. The distributions of heavy metal concentrations were generated using a geostatistical joint-simulation approach. The uncertainties in the heavy metal distributions were quantified in terms of variability among 1000 realization sets. Finally, a novel remediation decision-making approach was presented for delineating contaminated sites in need of remediation based on the spatial uncertainties of multiple realizations and the priorities of conservation areas. The results thus obtained demonstrate that up to 42% of areas of high conservation priority are also contaminated by one or more of the heavy metal contaminants of interest. Moreover, as the proportion of the land for proposed remediated increased, the projected area of the pollution-free habitat also increased. Overall uncertainty, in terms of the false positive contamination rate, also increased. These results indicate that the proposed decision-making approach successfully accounted for the intrinsic trade-offs among a high number of pollution-free habitats, low false positive rates and robustness of expected decision outcomes.
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