利用Aura/OMI月均甲醛对流层垂直柱浓度数据对2005年—2016年中国广东省和江苏省的大气甲醛的时空变化规律、不同排放源的前体物的潜在贡献进行了分析。甲醛在广东省主要集中在珠三角地区，在江苏省分布则相对较为均匀。2005年—2010年，随着经济发展，在两省都发现了大面积的甲醛增加趋势；相应的，在2011年—2016年，由于减排等治理措施的实施，在两省都有大面积的甲醛减少现象。广东省的甲醛主要呈现出春季、秋季高于冬季再高于夏季的特征；而江苏地区夏季甲醛浓度远高于其他季节，并与光照强度的季节性特征较为一致。此外，由于广东省甲醛分布的均一性较差，因此区域性因素（如地形等）对广东省甲醛分布可能有着较大的影响。各个排放源中，工业源、交通源对珠三角潜在贡献可能较大；自然源对梅州等林地覆盖地区可能有相对较高的影响；在江苏省，各个排放源对甲醛的贡献相对均衡，但夏季较为频繁的生物质燃烧可能对夏季高值甲醛有着相对较高的贡献。

High-level tropospheric formaldehyde (HCHO) columns were observed in Guangdong and Jiangsu Provinces in China by using the long-term records (2005-2016) of Aura OMI measurements. Given the socioeconomic status of Guangdong and Jiangsu Provinces, research concerning the widespread air pollution issues throughout these regions was imperative. Hence, the spatiotemporal variations in HCHO and the potential contributors were analyzed accordingly. We evaluated the spatiotemporal characteristics of HCHO columns in province-and prefecture-level cites to investigate the region of interest in various spatial resolutions. Hence, administrative shapefiles were used to clip the HCHO distributions to represent various sample regions. The spatial resolution of the OMI monthly gridded HCHO columns was 0.25°×0.25°, but some ancillary data have finer resolutions than that of the level-3 gridded HCHO columns. Accordingly, the inverse distance-weighted interpolation algorithm and KD-Tree method were adopted for re-sampling fine spatial resolutions into 0.25°×0.25°. The linear least square regression adopted in this study was the approach used to infer the potential trends exhibited in the HCHO images. The trends were fitted pixel by pixel, and the regional trends were calculated via zonal averaging of the administrative regions selected in the previously mentioned shapefiles. The Spearman correlation coefficients were employed to evaluate the similarity of the HCHO distribution and multitude sources (e.g., isoprene and anthropogenic VOCs). In Guangdong Province, HCHO columns concentrated in the Pearl River Delta (PRD), but an increasingly widespread distribution was observed in Jiangsu Province. An upward trend was found in both provinces from 2005 to 2010 that may be closely related to the rapid economy development in that period. Meanwhile, from 2011 to 2016, an overall downward trend was observed in these two regions, given the enacted multitude controls. With regard to seasonality, the highest HCHO columns were observed in spring and autumn, whereas the HCHO during summertime had the lowest concentration in Guangdong. The HCHO seasonal patterns corresponded to the seasonal patterns of sunlight intensity. Among all source sectors, industrial and transportation may contribute the most in the PRD, but all sectors may also contribute in the same order of magnitude in Jiangsu. In addition, biogenic sources may have a comparatively large contribution in the Northeastern Guangdong, and the biomass burning in summertime possibly played a substantial role in the summertime HCHO concentration in Jiangsu Province. Although isoprene plays significant role in HCHO concentration in global scale, the anthropogenic impact cannot be neglected as well. According to previous studies, anthropogenic sources have more than 40% influence. In addition, in comparison with isoprene, the Spearman correlation coefficients were high for anthropogenic VOCs with HCHO columns. Given the relatively large amount of emissions, industrial sources may be influential for more than 40%. Biomass burning sources cannot be ignored in woodlands, such as in Northeastern Guangdong. Transportation sources exhibited the most similar Spearman correlation coefficients in Guangdong, especially in PRD. Therefore, even a downward trend of transportation emissions was observed in PRD. They may account for a large amount of HCHO concentration. After a comparison, power-based emissions only took a small fraction of approximately 1% for Guangdong and Jiangsu.