Ambient background estimation of PAHs in urban soils: A case study in Macau, China

Tian Xie, Meie Wang, Yuan Zhang, Changfeng Liu, Fei Lu, Shoukang Ding, Weiping Chen and Suriyanarayanan Sarvajayakesavalu

Ecological Modelling, 2022, vol. 472, issue C

Abstract: The estimation of the ambient background levels of urban soil is critical for the identification of contamination events in urban environmental management. However, the establishment of appropriate methods, especially for contaminants stemming mainly from external sources, e.g., polycyclic aromatic hydrocarbons (PAHs), is appealing but controversial. Macau Special Administrative Region of China has one of the highest population densities worldwide. It has a distinct economic structure with a small proportion of heavy industry, providing a typical example of the ambient background estimation of urban soils. The methods on the ambient background estimation of PAHs were elaborated. The ambient backgrounds of the sum of 16 priority PAHs in Macau soils were 393±687, 200±687, 214±440 and 350±456 ng/g according to the mean±2 times standard deviations (σ), Lepeltier, iterative 2σ, and calculated distribution methods, respectively. There were certain differences in the results among the methods due to the different calculation principles. A mass-balanced regression model with respect to urbanization duration was further employed to derive the ambient backgrounds. The model's parameters were determined to interpret the simulation results. The results were generally in accord with those of the statistical techniques. The uncertainty analysis by bootstrap resampling was also applied to test the credibility of the ambient background estimation. The estimation models proposed could be employed for most regions or areas with little or low diffuse pollution sources.

Keywords: Polycyclic aromatic hydrocarbons (PAHs); Soil ambient background; Macau; Statistical methods; Mass balance model; Urbanization (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:472:y:2022:i:c:s0304380022002113

DOI: 10.1016/j.ecolmodel.2022.110108

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