ABSTRACT

Statistical relationships between surface ozone (O₃) concentration, precursor species and meteorological conditions in continental South Africa were examined from data obtained from measurement stations in north-eastern South Africa. Three multivariate statistical methods were applied in the investigation, i.e. multiple linear regression (MLR), principal component analysis (PCA) and –regression (PCR), and generalised additive model (GAM) analysis. The daily maximum 8-h moving average O₃ concentrations were considered in these statistical models (dependent variable). MLR models indicated that meteorology and precursor species concentrations are able to explain ~50% of the variability in daily maximum O₃ levels. MLR analysis revealed that atmospheric carbon monoxide (CO), temperature and relative humidity were the strongest factors affecting the daily O₃ variability. In summer, daily O₃ variances were mostly associated with relative humidity, while winter O₃ levels were mostly linked to temperature and CO. PCA indicated that CO, temperature and relative humidity were not strongly collinear. GAM also identified CO, temperature and relative humidity as the strongest factors affecting the daily variation of O₃. Partial residual plots found that temperature, radiation and nitrogen oxides most likely have a non-linear relationship with O₃,while the relationship with relative humidity and CO is probably linear. An inter-comparison between O₃ levels modelled with the three statistical models compared to measured O₃ concentrations showed that the GAM model offered a slight improvement over the MLR model. These findings emphasise the critical role of regional-scale O₃ precursors coupled with meteorological conditions in daily variances of O₃ levels in continental South Africa.

摘要

根据从南非东北部测量站获得的数据，对南非大陆中表面臭氧（O ₃）浓度，前体物种与气象条件之间的统计关系进行了检验。调查采用了三种多元统计方法，即多元线性回归（MLR），主成分分析（PCA）和–回归（PCR）以及广义加性模型（GAM）分析。 在这些统计模型中考虑了每日最大的8小时移动平均O ₃浓度（因变量）。MLR模型表明，气象和前体物种浓度能够解释每日最大O ₃的〜50％的变异性 水平。MLR分析显示，大气中一氧化碳（CO），温度和相对湿度是影响每日O ₃ 变异性的最强因素。夏季，每日O _3的 变化主要与相对湿度有关，而冬季O _3的 水平主要与温度和CO相关。PCA表明，CO，温度和相对湿度不是很强的共线性。GAM还确定CO，温度和相对湿度是影响O ₃每日变化的最强因素。部分残差图发现温度，辐射和氮氧化物很可能与O ₃呈非线性关系与相对湿度和CO的关系可能是线性的。 使用三种统计模型建模的O ₃水平与测量的O ₃ 浓度之间的相互比较表明，GAM模型比MLR模型略有改进。这些发现强调了 在南非大陆 O ₃水平的日常变化中，区域尺度O ₃前兆与气象条件的关键作用。