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首页> 外文期刊>Atmospheric environment >Coupling of the Weather Research and Forecasting Model with AERMOD for pollutant dispersion modeling. A case study for PM10 dispersion over Pune, India
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Coupling of the Weather Research and Forecasting Model with AERMOD for pollutant dispersion modeling. A case study for PM10 dispersion over Pune, India

机译：气象研究和预报模型与AERMOD的耦合，用于污染物扩散模型。印度浦那PM10扩散的案例研究

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摘要

The prediction of spatial variation of the concentration of a pollutant governed by various sources and sinks is a complex problem. Gaussian air pollutant dispersion models such as AERMOD of the United States Environmental Protection Agency (USEPA) can be used for this purpose. AERMOD requires steady and horizontally homogeneous hourly surface and upper air meteorological observations. However, observations with such frequency are not easily available for most locations in India. To overcome this limitation, the planetary boundary layer and surface layer parameters required by AERMOD were computed using the Weather Research and Forecasting (WRF) Model (version 2.1.1) developed by the National Center for Atmospheric Research (NCAR). We have developed a preprocessor for offline coupling of WRF with AERMOD. Using this system, the dispersion of respirable particulate matter (RSPM/PM10) over Pune, India has been simulated. Data from the emissions inventory development and field-monitoring campaign (13-17 April 2005) conducted under the Pune Air Quality Management Program of the Ministry of Environment and Forests (MoEF), India and USEPA, have been used to drive and validate AERMOD. Comparison between the simulated and observed temperature and wind fields shows that WRF is capable of generating reliable meteorological inputs for AERMOD. The comparison of observed and simulated concentrations of PM10 shows that the model generally underestimates the concentrations over the city. However, data from this single case study would not be sufficient to conclude on suitability of regionally averaged meteorological parameters for driving Gaussian models like AERMOD and additional simulations with different WRF parameterizations along with an improved pollutant source data will be required for enhancing the reliability of the WRF-AERMOD modeling system.
机译：预测由各种来源和汇所控制的污染物浓度的空间变化是一个复杂的问题。高斯空气污染物扩散模型，例如美国环境保护局（USEPA）的AERMOD，可以用于此目的。 AERMOD需要稳定且水平均匀的每小时地表和高空气象观测。但是，这种频率的观测资料在印度的大多数地方都不容易获得。为了克服此限制，使用美国国家大气研究中心（NCAR）开发的天气研究和预报（WRF）模型（2.1.1版）计算了AERMOD所需的行星边界层和表层参数。我们已经开发了预处理器，用于WRF与AERMOD的离线耦合。使用该系统，模拟了可呼吸性颗粒物（RSPM / PM10）在印度浦那的扩散。来自环境和森林部（MoEF），印度和USEPA的浦那空气质量管理计划下进行的排放清单开发和现场监测活动（2005年4月13日至17日）的数据已用于驱动和验证AERMOD。在模拟和观察到的温度和风场之间的比较表明，WRF能够为AERMOD生成可靠的气象输入。对观察到的和模拟的PM10浓度的比较表明，该模型通常低估了整个城市的浓度。然而，来自该单个案例研究的数据不足以得出区域平均气象参数是否适合于驱动AERMOD之类的高斯模型的结论，并且将需要采用不同WRF参数化的附加模拟以及改进的污染源数据来增强该方法的可靠性。 WRF-AERMOD建模系统。

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