超越确定性空气质量建模:AERMOD中的排放输入的概率选方法
Zachery I Emerson1, Tanvir R Khan1
1Newberry, Florida, United States, National Council for Air and Stream Improvement (NCASI).
Integrated environmental assessment and management
|July 24, 2025
概括
这项研究引入了一个概率空气质量模型,该模型集成了排放变化,提供了比传统决定性方法更现实的污染物度估计. 新的框架大大减少了工业环境中二氧化 (NO2) 的过高估计.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 计算机建模 计算建模
背景情况:
- 传统的空气分散模型经常通过使用最大排放率来高估污染物度.
- 这种高估并没有反映出典型的工业运行条件,尤其是可变排放源.
- 需要更现实的,数据驱动的建模方法.
研究的目的:
- 开发和演示一种新的概率模型框架,用于估计工业空气污染物度.
- 为了将排放率的变化纳入空气分散模型.
- 将概率模型结果与传统的决定性方法进行比较.
主要方法:
- 开发了一个概率框架,将蒙特卡洛查与AERMOD结合起来.
- 该框架被应用于模拟来自虚拟 kraft 纸厂的氧化 (NOx) 排放.
- 用数据驱动方法推导出排放率,并纳入了可变性.
主要成果:
- 使用最大排放率的基线AERMOD模拟预测了环境中最高的二氧化 (NO2) 度 (最坏的情况下).
- 概率框架,包括排放变化,产生了较低的估计环境NO2度.
- 结果突出了排放变化对模拟污染物水平的重大影响.
结论:
- 概率空气分散模型为传统的确定性方法提供了更灵活,更准确的替代方案.
- 整合排放变化导致对工业污染物度的评估更为现实.
- 开发的框架可以通过包括额外的变化源来增强,并应用于其他污染物.
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