• 关键词：PM_2.5  正定矩阵因子分析  本地和非本地贡献  空气域  粤港澳大湾区(GBA)

• 基金项目：国家自然科学基金重大研究计划重点支持项目（No.91644221）；香港环境保护署项目（No.CE28/2014（EP），CE15/2016（EP））

• 王怡然
• 华南理工大学环境与能源学院, 广州 510006

• 袁自冰
• 华南理工大学环境与能源学院, 广州 510006

• 赵恺辉
• 华南理工大学环境与能源学院, 广州 510006

• 张舒
• 华南理工大学环境与能源学院, 广州 510006

• 张夏夏
• 华南理工大学环境与能源学院, 广州 510006

• 李文石
• 华南理工大学环境与能源学院, 广州 510006

• 谢岩
• 华南理工大学环境与能源学院, 广州 510006

• 杨雷峰
• 华南理工大学环境与能源学院, 广州 510006

• 刘启汉
• 香港科技大学环境学部, 香港 999077

• 郁建珍
• 香港科技大学环境学部, 香港 999077

• 岳玎利
• 广东省环境监测中心, 广州 510308

• 谭振威
• 澳门地球物理暨气象局, 澳门 999078

• 摘要：粤港澳大湾区（简称"大湾区"）建设是我国新时代重大国家战略之一.虽然大湾区空气质量在我国处于领先地位，但与世界先进湾区相比还有较大差距.制定大湾区PM_2.5精细化防控策略，需要在识别大湾区各城市PM_2.5污染来源的基础上，量化PM_2.5本地和非本地贡献及时空变化规律.基于此，本研究首次在大湾区15个站点同步开展持续一年的PM_2.5采样和组分分析，并将正定矩阵因子分析模型与后向轨迹结合，建立一种定量识别PM_2.5本地与非本地贡献的新方法.通过对大湾区不同季节所属空气域进行划分，厘清大湾区各城市PM_2.5本地与非本地贡献的动态化特征.结果发现，在2015年，大湾区15个站点共解析出9种PM_2.5污染源，分别为机动车、重油、老化海盐、扬尘源、二次硫酸盐、二次硝酸盐、金属冶炼、生物质燃烧和新鲜海盐.其中，二次硫酸盐和机动车是大湾区最主要的两个PM_2.5污染源.不同站点非本地贡献占比为51%~72%，表明外来传输是大湾区 PM_2.5污染的主要来源.内陆和沿海站点污染源的本地与非本地贡献差异较为显著，主要原因是气象条件和排放特征的差异.值得注意的是，2015年大湾区超过一半的时间处于同一个空气域，而有43%的时间处于两个不同空气域.进一步在每个季节划分空气域，发现大湾区处于两个空气域时，秋、冬季节沿海站点易形成单独的空气域，此时非本地贡献较强（68%~72%）；春季内陆站点易形成单独的空气域，此时本地贡献较强（94%）.基于对PM_2.5本地和非本地贡献变化情况的定量识别，能够为大湾区各城市制定动态的PM_2.5排放控制策略提供科学支撑.

• Abstract：Development of Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the national strategies in China. Although at the leading position of China, air quality in the GBA is still far worse than those in other renowned bay areas in the world, e.g. San Francisco, New York and Tokyo. To formulate refined PM_2.5 prevention and control strategies in GBA, it is essential to identify PM_2.5 emission sources in different cities of GBA, and to quantitatively characterize local and non-local contributions and their spatio-temporal variations. In this study, based on the first-ever regionally integrated PM_2.5 speciation dataset simultaneously collected at fifteen stations across the GBA in the entire year of 2015, we developed a novel approach by combining Positive Matrix Factorization source apportionment with an optimized backward trajectory analysis, in an aim to quantify local and non-local contributions to PM_2.5. Local and non-local contributions were further quantified in different air-sheds during different seasons, which provides important implications for city-level dynamic control of PM_2.5 over the GBA. In 2015, nine source factors were identified, including vehicle exhaust, residual oil, aged sea salt, crustal soil, secondary sulfate, secondary nitrate, trace metals, biomass burning and fresh sea salt. Secondary sulfate was the largest contributor to PM_2.5, followed by vehicle exhaust. Non-local contributions accounted for 51%~72% at different sites, suggesting PM_2.5 over the GBA were mainly transported from outside. Significant differences in local and non-local relative contributions existed between inland and coastal areas, which was largely driven by emission and meteorological conditions. We also highlighted that GBA was in a single air-shed for more than half of time in 2015 and split into two air-sheds for 43% of time. Seasonal analysis revealed that in the two-air-shed pattern, non-local sources contributed 68%~72% over coastal stations which formed a separated air-shed in autumn and winter. In comparison, for the inland stations which formed a separated air-shed in spring, local contribution was predominant (94%). Based on the quantitative identification of local and non-local contributions and their seasonal and spatial variations, this study provides scientific guidance in formulating dynamic and region-specific PM_2.5 control measures over the GBA.

• Key words：PM_2.5  positive matrix factorization  local and non-local separation  air-shed  Greater Bay Area(GBA)

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