挥发性有机污染物（VOCs）是污染地块中最常见、最重要的一类污染物,苯系物(BTEX)作为VOCs的代表,具有易挥发、毒性强、化学性质活泼的特点,在气-液-非水相流体（NAPL相）进行多相迁移转化,导致其评估和修复的难度较大。本研究通过选取典型复杂松散多孔介质挥发类有机污染场地,选取苯和乙苯作为典型污染物,通过TMVOC模型进行其多相传输的数值模拟,并针对BTEX多相传输模型参数的有效性进行局部及全局灵敏度分析,以提高模型精度,为我国BTEX污染场地的治理提供参考。研究结果表明：局部灵敏度分析对污染物浓度的敏感参数排序为泄漏速率Q>孔隙度n>渗透率k>分子扩散系数D,选用灵敏度指数较大的3个参数泄漏速率、孔隙度及渗透率分三层通过均匀设计法进行全局灵敏度分析,并对所有参数分别与水相中VOCs的总质量分数(XVOCW)进行回归分析,其参数灵敏度排序为Q>n3>k2>n2>n1>k3,其中对于XVOCW影响显著的参数有泄漏速率Q、砂砾层渗透率k2和粉土层、砂砾层、泥岩层的孔隙度n1、n2、n3,按照参数类别进行整合排序的结果为Q>n>k,与局部灵敏度分析的结果相符。
Volatile organic compounds (VOCs) are the most common and critical pollutants in contaminated sites. As representative VOCs, benzene series (BTEX) are characterized by high volatility, strong toxicity, and chemical reactivity. Their multiphase migration and transformation across gas-liquid-non-aqueous phase liquid (NAPL) interfaces significantly increase the complexity of risk assessment and remediation. Enhancing the precision of BTEX multiphase transport models through parameter sensitivity analysis can provide a reference for the treatment of BTEX contaminated sites in China. This study focuses on a typical contaminated site featuring complex loose porous media and selects benzene and ethylbenzene as target pollutants. Numerical simulations of multiphase transport were conducted using the TMVOC model, followed by local and global sensitivity analyses of key parameters. The local sensitivity analyses ranks the key parameters affecting pollutant concentration as follows: leakage rate (Q) > porosity (n) > permeability (k) > molecular diffusion coefficient (D). The three parameters with large sensitivity index, leakage rate, porosity and permeability, are divided into three layers to conduct global sensitivity analysis through uniform design method, and all parameters are respectively analyzed by regression with the total mass fraction of VOCs in the aqueous phase(XVOCW). The sensitivity of the parameters is ranked as Q>n3>k2>n2>n1>k3, among which the parameters that have a significant impact on XVOCW are leakage rate Q, gravel permeability k2, silt porosity n1, gravel porosity n2 and mudstone porosity n3. The result of integrating and sorting according to the parameter categories is Q>n>k, which is consistent with the result of the local sensitivity analysis.
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