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首页> 外文期刊>Air quality and climate change >Dispersion modelling using one-minute meteorological data versus standard one-hour average meteorological dataset for assessment of transient odour impacts
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Dispersion modelling using one-minute meteorological data versus standard one-hour average meteorological dataset for assessment of transient odour impacts

机译：分散建模使用一分钟气象数据与标准一小时平均气象数据集进行瞬态气味影响

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摘要

Dispersion modelling of odorous emissions using meteorological data, averaged over a one-hour period, is the standard industry approach in Australia and New Zealand for modelling of air quality impacts. Odour dispersion may, in certain micrometeorological conditions, result in odour events that may be transient in nature lasting only a few minutes. Modelling using hourly averaged meteorological data in such circumstances has the potential to miss these shorter-term odour events. Dispersion modelling using one-minute data requires significant amount of available data space and extensive modelling timeframes. This paper provides a discussion on whether the complications associated with the resource and time demands of one-minute model runs may be offset by greater precision, through a comparison of predicted impacts derived using one-hour and one-minute average meteorological datasets. CALPUFF was selected as the appropriate atmospheric dispersion model, as it is the only atmospheric dispersion model, commonly used in Australia, capable of modelling emissions at a time resolution of less than an hour. A one-minute averaging period was chosen, as it is the finest resolution of meteorological data collected by the Bureau of Meteorology. The predicted odour concentrations at the nearest sensitive receptors were assessed against criteria established by the NSW legislative framework and odour risk assessment guidance available to Victoria. By considering percentiles provided by both NSW and Victoria legislation, this paper compared the prediction of both high-intensity-low-frequency and low-intensity-high-frequency odour events. The results of the modelling presented in this paper indicated that there were occasions on which shorter term odour events were missed when modelling using hourly averaged meteorological dataset. While greater precision of the predictions in the setting presented in this paper did not result in a different assessment outcome, there is potential that in a different setting the modelling using one-hour average results may underestimate impact on certain sensitive receptors. Greater precision of predictions however should be carefully weighed against the size of modelling files and time consumption associated with modelling one-minute data.
机译：使用气象数据的异常排放分散建模，平均在一个小时内，是澳大利亚和新西兰的标准行业方法，用于建模空气质量影响。在某些微观气象条件下，气味分散体可能导致异味事件，这可能在自然中持续几分钟。在这种情况下使用每小时平均气象数据建模有可能错过这些较短的气味事件。使用一分钟数据的分散建模需要大量的可用数据空间和广泛的建模时间帧。本文介绍了与使用一小时和一小时平均气象数据集导出的预测影响的比较，可以抵消与一分钟模型运行的资源和时间需求相关的并发症的讨论。 Calpuff被选为适当的大气分散模型，因为它是澳大利亚常用的唯一用于澳大利亚的大气分散模型，能够以不到一小时的时间分辨率建模排放。选择一分钟的平均期，因为它是气象局收集的气象数据的最佳分辨率。最近的敏感受体的预测气味浓度被评估了根据南威尔士州立法框架和不适用于维多利亚可用的气味风险评估指导的标准。通过考虑NSW和Victoria立法提供的百分比，本文比较了高强度低频和低强度高频气味事件的预测。本文呈现的建模结果表明，在使用每小时平均气象数据集时，有时会错过短期气味事件。虽然本文所呈现的设置中预测的更高精度并未导致不同的评估结果，但是潜在的是，在不同的设置中，使用单小时平均结果的建模可能会对某些敏感受体进行影响。然而，应更高的预测精度应仔细称重与建模文件和与建模一分钟数据相关的时间消耗的大小。

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