李梅, 杨冬偶, 何望君. 大气扩散模型AERMOD与CALPUFF对比研究及展望[J]. 武汉大学学报 ( 信息科学版), 2020, 45(8): 1245-1254. DOI: 10.13203/j.whugis20200110
引用本文: 李梅, 杨冬偶, 何望君. 大气扩散模型AERMOD与CALPUFF对比研究及展望[J]. 武汉大学学报 ( 信息科学版), 2020, 45(8): 1245-1254. DOI: 10.13203/j.whugis20200110
LI Mei, YANG Dong'ou, HE Wangjun. Comparison and Perspectives on Theories and Simulation Results of Gas Dispersion Models AERMOD and CALPUFF[J]. Geomatics and Information Science of Wuhan University, 2020, 45(8): 1245-1254. DOI: 10.13203/j.whugis20200110
Citation: LI Mei, YANG Dong'ou, HE Wangjun. Comparison and Perspectives on Theories and Simulation Results of Gas Dispersion Models AERMOD and CALPUFF[J]. Geomatics and Information Science of Wuhan University, 2020, 45(8): 1245-1254. DOI: 10.13203/j.whugis20200110

大气扩散模型AERMOD与CALPUFF对比研究及展望

Comparison and Perspectives on Theories and Simulation Results of Gas Dispersion Models AERMOD and CALPUFF

  • 摘要: 近年来,大气扩散模型及其计算机应用集成技术成为环境评价、防灾减灾和应急管理的重要研究内容。应急管理领域对气体扩散的时空分辨率和计算速度要求较高。在实际应用中,由于各种大气扩散模型的适用条件不同、参数复杂,如何结合自身需求选择合适的模型成了应急响应辅助分析的首要问题。针对两个常见大气扩散模型AERMOD和CALPUFF进行分析和对比研究。首先,介绍了两个模型的原理;然后,设计了4组对比实验,利用实验结果分析了两个模型之间的异同;最后,对两个模型各自适用的情景进行了分析。研究结果表明,CALPUFF更适用于面向应急管理的高时空分辨率计算要求。

     

    Abstract:
      Objectives  In recent years, atmospheric dispersion models have become important research contents in environmental assessment, disaster prevention and mitigation, and emergency management. At present, mainstream environmental quality regulations models include the AERMOD model and CALPUFF model recommended by the U.S. Environmental Protection Agency (EPA). These models are widely used in environmental assessment and atmospheric environmental quality assessment, but rarely used in toxic gas dispersion simulation and decision support in emergency response. The objective of this article is to compare the differences between the two models and give ideas for future development of expanding the atmospheric dispersion model to the field of emergency response.
      Methods   First, the basic theories of the two models are introduced and analyzed. Then, four comparative experiments are designed and carried out. Experiment 1 is designed to observe whether the pollutants existing in the original wind direction will accumulate to the next moment and affect the new concentration distribution after the sudden change of wind direction in the two models. Experiment 2 is designed to observe the processing capacity of the two models for static wind. Experiment 3 is designed to observe the results of the two models in two different dispersion scales. Experiment 4 is designed to compare the calculation speed of the two models after greatly increasing the spatial resolution.
      Results   Experiment 1 shows that the simulation results of the CALPUFF model for sudden changes in wind direction are more reasonable than the AERMOD model. Experiment 2 shows that the simulation results of CALPUFF for special wind fields, such as breeze, are more reasonable than AERMOD. Experiment 3 shows that AERMOD is not suitable for cases where the experimental range exceeds 50 km, and within 50 km, the simulation results of both models are acceptable. Experiment 4 shows that after greatly improving the mesh resolution, the time‐consuming increase in AERMOD is not large, while the time‐consuming of CALPUFF increases significantly. Considering the calculation accuracy, three‐dimensional terrain and meteorological parameters, the CALPUFF Gaussian puff model has more advantages. At present, the integration of GIS and CALPUFF is more common and the application range is wider, but its disadvantage is that the calculation speed is slower than AERMOD.
      Conclusions   When the demand for calculation speed is low, the AERMOD model should be given priority. And when the requirements for spatial‐temporal resolution and calculation accuracy are relatively high, the CALPUFF model should be given priority. Under normal circumstances, emergency response simulation is preferred to use CALPUFF. Existing atmospheric dispersion models perform well in air pollution prediction and environmental assessment. However, the research of the models in the application of emergency management is not deep enough. Future improvements include higher calculation accuracy, higher running efficiency, more theoretical support for complex application scenarios, and the integration of multiple models to form a dedicated model library and model service chain for emergency management.

     

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