High density air monitoring campaigns provide spatial patterns of pollutant concentrations which are integral in exposure assessment. Such analysis can assist with the determination of links between air quality and health outcomes, however, problems due to missing data can threaten to compromise these studies. This research evaluates four methods; mean value imputation, inverse distance weighting (IDW), inter-species ratios, and regression, to address missing spatial concentration data ranging from one missing data point up to 50% missing data. BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations were measured in Windsor and Sarnia, Ontario in the fall of 2005. Concentrations and inter-species ratios were generally similar between the two cities. Benzene (B) was observed to be higher in Sarnia, whereas toluene (T) and the T/B ratios were higher in Windsor. Using these urban, industrialized cities as case studies, this research demonstrates that using inter-species ratios or regression of the data for which there is complete information, along with one measured concentration (i.e. benzene) to predict for missing concentrations (i.e. TEX) results in good agreement between predicted and measured values. In both cities, the general trend remains that best agreement is observed for the leave-one-out scenario, followed by 10% and 25% missing, and the least agreement for the 50% missing cases. In the absence of any known concentrations IDW can provide reasonable agreement between observed and estimated concentrations for the BTEX species, and was superior over mean value imputation which was not able to preserve the spatial trend. The proposed methods can be used to fill in missing data, while preserving the general characteristics and rank order of the data which are sufficient for epidemiologic studies.

中文翻译：

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评估加拿大安大略省西南部两个相邻城市环境 BTEX 浓度的缺失值方法

高密度空气监测活动提供污染物浓度的空间模式，这是暴露评估中不可或缺的一部分。这种分析可以帮助确定空气质量和健康结果之间的联系，但是，由于数据缺失导致的问题可能会危及这些研究。本研究评估了四种方法；平均值插补、反距离加权 (IDW)、种间比率和回归，以解决从一个缺失数据点到 50% 缺失数据的缺失空间浓度数据。BTEX（苯、甲苯、乙苯和二甲苯）浓度于 2005 年秋季在安大略省的温莎和萨尼亚进行了测量。这两个城市的浓度和种间比率大致相似。在萨尼亚观察到苯 (B) 较高，而温莎的甲苯 (T) 和 T/B 比更高。以这些城市、工业化城市为案例研究，本研究表明，使用具有完整信息的数据的物种间比率或回归，以及一个测量浓度（即苯）来预测缺失浓度（即 TEX）结果预测值与实测值吻合良好。在这两个城市，总的趋势仍然是留一法的一致性最好，其次是 10% 和 25% 的缺失，而 50% 的缺失案例的一致性最低。在没有任何已知浓度的情况下，IDW 可以在 BTEX 物种的观察浓度和估计浓度之间提供合理的一致性，并且优于无法保留空间趋势的平均值插补。