School of Environment, Beijing Normal University, Beijing, 100875, PR China; State Environmental Protection Key Laboratory of Odor Pollution Control, Tianjin Academy of Eco-environmental Sciences, Tianjin 300191, PR China.
School of Environment, Beijing Normal University, Beijing, 100875, PR China.
Environ Pollut. 2023 Jan 1;316(Pt 1):120535. doi: 10.1016/j.envpol.2022.120535. Epub 2022 Oct 28.
The assessment of the health risks of volatile organic compounds (VOCs) emitted from landfills via dispersion model is crucial but also challenging because of remarkable variations in their emissions and meteorological conditions. This study used a probabilistic approach for the assessment of the health risks of typical VOCs by combining artificial neural network models for emission rates and a numerical dispersion model enhanced by probability analysis. A total of 8753 rounds of simulation were performed with distributions of waste compositions and the valid hourly meteorological conditions for 1 year. The concentration distributions and ranges of the typical health-risky VOCs after dispersion were analyzed with 95% probability. The individual and cumulative non-carcinogenic risks of the typical VOCs were acceptable with all values less than 1 in the whole study domain. For individual carcinogenic risks, only ethylbenzene, benzene, chloroform, and 1, 2-dichloroethane at extreme concentrations showed minor or moderate risks with a probability of 0.1%-1% and an impact distance of 650-3000 m at specific directions. The cumulative carcinogenic risks were also acceptable at 95% probability in the whole study domain, but exceeded 1 × 10 or even 1 × 10 at some extreme conditions, especially within the landfill area. The vertical patterns of the health risks with height initially increased, and then decreased rapidly, and the peak values were observed around the height of the emission source. The dispersion simulation and health risk assessment of the typical health-risky VOCs enhanced by Monte Carlo can accurately reflect their probabilistic dispersion patterns and health risks to surrounding residents from both spatial and temporal dimensions. With this approach, this study can provide important scientific basis and technical support for the health risk assessment and management of landfills.
评估垃圾填埋场排放的挥发性有机化合物（VOCs）通过扩散模型对健康造成的风险至关重要，但由于其排放和气象条件的显著变化，这也是一项具有挑战性的工作。本研究采用概率方法，通过组合排放率的人工神经网络模型和通过概率分析增强的数值扩散模型，对典型 VOC 的健康风险进行评估。对 1 年的垃圾组成分布和有效小时气象条件进行了 8753 轮模拟。分析了扩散后典型高风险 VOC 的浓度分布和范围，置信度为 95%。在整个研究区域内，所有值均小于 1，因此典型 VOC 的个体和累积非致癌风险是可以接受的。对于个体致癌风险，只有在极端浓度下的乙基苯、苯、氯仿和 1,2-二氯乙烷表现出较小或中等风险，概率为 0.1%-1%，在特定方向上的影响距离为 650-3000 m。在整个研究区域内，累积致癌风险在 95%的概率下也是可以接受的，但在某些极端条件下，特别是在垃圾填埋场内，风险超过了 1×10-6 甚至 1×10-5。健康风险随高度的垂直分布模式最初增加，然后迅速下降，在排放源高度附近观察到峰值。通过蒙特卡罗法增强的典型高风险 VOC 的扩散模拟和健康风险评估，可以准确反映它们的概率扩散模式及其对周围居民的时空健康风险。通过这种方法，本研究可以为垃圾填埋场的健康风险评估和管理提供重要的科学依据和技术支持。
