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Assessing background air pollutant concentrations for modelling studies:evaluation of addition equations under Irish conditions

机译:评估背景空气污染物模拟研究浓度:在爱尔兰病症下的加法方程评价

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Values adopted for background pollutant concentrations can have a significant effect on the accuracy of the final results in air quality modelling studies. In the absence of a reliable method of combining modelled and background concentrations it has been common practice to sum the percentiles or annual means of each contribution to obtain a value for comparison with limit values. This is often not appropriate as in many cases the meteorological conditions producing high concentrations from the source do not correspond to those resulting in high background concentrations. The validity of a number of equations derived in the UK to add background NO_2 and PM_(10) concentrations to modelled stack contributions has been examined for Irish conditions. The equations allow a total percentile concentration to be predicted at a given receptor based on an annual mean background concentration and hourly modelled concentrations. A theoretical point source was modelled using the point source Gaussian plume equation and corresponding meteorological data, and the addition equations applied using monitored background NO_2 and PM_(10) data. The equations were also tested for a line source, modelled using the General Finite Line Source Model (GFLSM). Baseline values were calculated by addition of the relevant hourly or daily background concentration to the modelled concentrations to produce a full year of total hourly or daily concentrations. Percentiles and annual mean values, and corresponding 95% confidence limits were calculated directly from this data set. Percentile concentrations predicted by each of the equations were compared to the baselinevalues. It was found that all equations produced values outside the confidence limits, indicative of systematic variation. Certain methods, however, provide an improvement to commonly applied addition methods and may be useful for screening purposes. Results for the line source were poorer than those for the point source. Further research in this area is required to develop more accurate methods for the addition of background concentrations to modelled contributions, particularly in the case of line sources.
机译:用于背景污染物浓度的值可以对空气质量建模研究中最终结果的准确性产生显着影响。在没有可靠的方法组合建模和背景浓度的方法中,这是常见的做法,总结每个贡献的百分比或年度手段,以获得与限制值进行比较的值。这通常是不合适的,因为在许多情况下,产生来自源的高浓度的气象条件不对应于导致背景浓度高的那些。为爱尔兰条件检查了英国中衍生的许多方程式的有效性,以添加背景NO_2和PM_(10)浓度以进行建模的堆栈贡献。等式允许基于年平均背景浓度和每小时建模的浓度在给定受体中预测总百分位浓度。使用点源高斯羽毛方程和相应的气象数据建模理论点源,以及使用受监视的背景No_2和PM_(10)数据应用的加法方程。还测试了使用通用有限线源模型(GFLSM)建模的线源的线源。通过向建模浓度添加相关的每月或日背景浓度来计算基线值,以产生总每小时或每日浓度的全年。百分比和年均值,并直接从此数据集计算了相应的95%置信范围。将每个方程预测的百分位浓度与基线值进行比较。发现所有方程都产生了置信范围之外的值,指示系统变化。然而,某些方法提供了对常用添加方法的改进,并且可用于筛选目的。线路源的结果比点来源差。在这方面需要进一步的研究来开发用于添加背景浓度为蓝本的贡献,特别是在线路源的情况下更准确的方法。

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