3D Photochemical Dispersion Models for Secondary Air Pollutant Study: from Input Data Preparation to Model Performance Evaluation

Q3 Agricultural and Biological Sciences
et.al Thongchai Kanabkaew
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引用次数: 0

Abstract

Secondary air pollutants are formed through series of complex reactions occurred in the atmosphere under favorable meteorological conditions. Commonly known species are ozone and secondary aerosols, secondary inorganic and organic aerosols. Through its complexity, varieties of three dimensional (3D) photochemical dispersion models have been developed globally to simulate formations and dispersions of the secondary as well as the primary pollutants in the atmosphere. CMAQ and CAMx are the state of the science models under “one atmospheric approach”, noncommercial and open-source software. These characteristics are attracted and challenged for future air quality management via model simulations. Applications of CMAQ and CAMx are normally based on several steps: preparation of emission input, modeling set-up and model performance evaluation. This article presents the systematic procedures involved with the use of 3D photochemical models starting from theoretical principles for formation of secondary air pollutants, types of photochemical models and its associated physical and chemical modules, input data preparation including reviews on available emission inventories and lastly statistical methods for model performance evaluation.
二次空气污染物研究的三维光化学扩散模型:从输入数据准备到模型性能评估
大气二次污染物是在有利的气象条件下,由大气中发生的一系列复杂反应形成的。常见的种类有臭氧和次生气溶胶,次生无机气溶胶和有机气溶胶。由于其复杂性,全球已经开发了多种三维(3D)光化学扩散模型来模拟大气中二次和一次污染物的形成和扩散。CMAQ和CAMx是“一种大气方法”下的科学模型,非商业性和开源软件。这些特点为未来的空气质量管理模式模拟所吸引和挑战。CMAQ和CAMx的应用通常基于几个步骤:准备排放输入,建模设置和模型性能评估。本文介绍了使用三维光化学模型的系统程序,从二次空气污染物形成的理论原理、光化学模型的类型及其相关的物理和化学模块、输入数据的准备(包括对现有排放清单的审查)和模型性能评估的统计方法开始。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Asia-Pacific Journal of Science and Technology
Asia-Pacific Journal of Science and Technology Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
0.90
自引率
0.00%
发文量
0
审稿时长
8 weeks
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