Predicting the frequency of extreme air quality events,Environmental Science: Processes & Impacts

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Predicting the frequency of extreme air quality events
Environmental Science: Processes & Impacts ( IF 4.3 ) Pub Date : 2017-11-21 00:00:00 , DOI: 10.1039/c7em00401j
Richard J. C. Brown _{1,

2,

3} , Peter M. Harris _{1,

2,

3}

Affiliation

A new method for predicting the frequency of extreme air quality events is proposed. The method is based on knowing the number of times a pollutant is measured at different concentrations during a period of data collection and fitting this data to a Weibull-type function. Extrapolation of the function to higher concentrations then allows the frequency of extreme events that did not occur during the collection period to be predicted, albeit with an unspecified uncertainty. Prediction of the frequency of events over a given concentration, which was exceeded during the period of data collection, has also been performed assuming Poisson statistics. The assumption of Poisson statistics enables the provision of an uncertainty statement to accompany the prediction. The methods are trialled on a data set of daily average PM₁₀ mass concentrations recorded at Marylebone Road in London between 2007 and 2016, inclusive. Using the method it was predicted that a daily average PM₁₀ mass concentration of over 100 μg m⁻³, corresponding to the highest UK Daily Air Quality Index band, should be expected once in just over three years and this agreed well with real observations, demonstrating the utility of this new technique.

中文翻译：

预测极端空气质量事件的发生频率

提出了一种预测极端空气质量事件发生频率的新方法。该方法基于知道在数据收集期间以不同浓度测量污染物的次数，并将此数据拟合为Weibull型函数。通过将功能外推到更高的浓度，可以预测在收集期间未发生的极端事件的发生频率，尽管不确定性不确定。在给定集中度下发生事件的频率的预测是在假设泊松统计的情况下进行的，在给定集中度下该事件的频率在数据收集期间被超过。泊松统计量的假设可以使不确定性陈述与预测一起提供。这些方法在每天平均PM ₁₀的数据集上试用2007年至2016年（含）在伦敦马里波恩路（Marylebone Road）记录的质量浓度。使用该方法，预计应该在短短三年内达到每天一次的PM ₁₀质量平均浓度超过100μgm ^-3，这相当于英国最高的每日空气质量指数带，这与实际观察结果非常吻合，展示了这项新技术的实用性。

更新日期：2017-11-21

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