This study proposed a data preprocessing method for mobile traffic pollution observation based on previous work. The model was validated using 7days' (26 runs) observations of PM2.5 and CO concentrations collected in Shanghai. The data revealed the spatial distributions and temporal variations of PM2.5 and CO concentrations. Results showed that, an objective and comparable air pollutant distribution was characterized by the methods selected to remove the abnormal samples of high values, to correct the pollution background, and to determine the spatio-temporal scale. The high pollutant concentrations along the busy road intersections and their adjacent road sections were attributed to factors including large traffic flows, high proportion of diesel vehicles, frequent congestion and poor air flow. At these locations, PM2.5 and CO concentrations were 1.7~2.8 and 12~20times larger than observations on the clean campus, respectively. The living or production area showed about 3-fold higher PM2.5 concentrations when compared with the campus, while this for CO in the living area was not prominent. The averaged PM2.5 concentration of the whole area had a descending order in early morning, morning, afternoon and noon during a day. The averaged CO concentration was close in early morning and morning, which was greater than noon and afternoon. High humidity and low wind speed were unfavourable to air pollutant diffusion, and led to an accumulation of high pollutant concentrations along arterial roads in early morning.%基于文献建立交通污染移动观测的预处理方法,并通过上海某区域7d 26次PM2.5、CO浓度观测实验进行验证,进而揭示了路边PM2.5、CO浓度的空间分布及其时间变化特征.结果表明,异常高值样本剔除,背景校正及时空尺度选择等方法的有效组合,能增强污染物时空表达的客观性和可比性.交通流量大、柴油车比例高、常发性拥堵、空气流通不畅等因素往往导致繁忙路口及其相连路段PM2.5、CO高浓度集聚,比清洁校园增加1.7~2.8,12~20倍.居住或生产区域的PM2.5浓度高出校园2倍左右,居住小区较校园的CO浓度增幅不明显.一天中PM2.5空间平均浓度呈现清晨>上午>下午>中午,CO则表现为清晨和上午相近,均大于中午和下午.湿度大和风速小不利于污染物扩散,从而造成清晨主干路附近形成污染物的高浓度集聚区.
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