Statistical analysis of primary and secondary atmospheric formaldehyde
Statistical analysis of primary and secondary atmospheric formaldehyde
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DOI：
10.1016/s1352-2310(02)00558-7
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发表时间：
2002-10
影响因子：
5
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中科院分区：
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回归模型加上时间序列数据被用来分析主要和次要来源的甲醛（HCHO）浓度的贡献，确定通过统计类比的主要（一氧化碳，CO）和次要（臭氧，O3）化合物同时测量在休斯敦，得克萨斯州。鉴于数据的复杂性和大气浓度的快速波动，时间序列分析证实了统计分析方法的必要性。甲醛与CO或O3的自相关函数（ACF）和偏自相关函数（PACF）均呈正相关。用于区分初级和次级贡献的回归模型包括三种化合物（一个滞后单位时间，5分钟）对当前甲醛浓度的简单线性回归，导致次级形成与初级排放的比率为1.7。第二个更稳健的模型利用自相关误差过程来近似线性回归的真实性质;该模型还将1.7的次要与主要贡献之比作为10个模型模拟的平均值。从误差过程模型来看，一个滞后时间单位对于CO预测HCHO最重要，而同时测量（滞后0）对于O3预测HCHO最重要。O3和HCHO浓度不影响结果。
Regression models coupled with time series data were used to analyze the contribution of primary and secondary sources to formaldehyde (HCHO) concentrations, as determined by statistical analogy to primary (carbon monoxide, CO) and secondary (ozone, O3) compounds measured simultaneously in Houston, TX. Time series analyses substantiated the need for statistical methods of analysis, given the complexity of the data and the rapid fluctuations that occur in atmospheric concentrations. A positive relationship was found for both the auto-correlation function (ACF) and partial auto-correlation function (PACF) of HCHO with either CO or O3. Regression models used to distinguish primary and secondary contributions included a simple linear regression of the three compounds (one lag unit of time, 5min) on current HCHO concentrations, resulting in a ratio of secondary formation to primary emission of 1.7. A second, more robust model utilized auto-correlated error processes to approximate the true nature of the linear regression; this model also indicates the ratio of secondary to primary contribution at 1.7 as the mean of ten model simulations. From the error processes model, one lag unit of time was most significant for CO predicting HCHO, while simultaneous measurements (lag 0) were most significant for O3predicting HCHO. Outlying O3and HCHO concentrations were shown not to affect the results.