Air Quality Response Modeling for Decision Support
Air Quality Response Modeling for Decision Support
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DOI：
10.3390/atmos2030407
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发表时间：
2011-08
期刊：
影响因子：
2.9
通讯作者：
D. Cohan;S. Napelenok
中科院分区：
文献类型：
--
作者：
D. Cohan;S. Napelenok
空气质量管理依靠光化学模型来预测污染物浓度对排放变化的响应。这种模拟对于臭氧和细颗粒物等随排放变化而非线性变化的二次污染物尤其重要。在光化学模型中探测污染物-排放关系的许多技术已经开发出来，并用于各种决策支持应用。然而，大气响应建模仍然很复杂，因为需要根据可观测数据验证灵敏度结果。这篇手稿回顾了大气响应建模的科学现状，以及为描述灵敏度结果的准确性和不确定性所做的努力。
Air quality management relies on photochemical models to predict the responses of pollutant concentrations to changes in emissions. Such modeling is especially important for secondary pollutants such as ozone and fine particulate matter which vary nonlinearly with changes in emissions. Numerous techniques for probing pollutant-emission relationships within photochemical models have been developed and deployed for a variety of decision support applications. However, atmospheric response modeling remains complicated by the challenge of validating sensitivity results against observable data. This manuscript reviews the state of the science of atmospheric response modeling as well as efforts to characterize the accuracy and uncertainty of sensitivity results.