State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
Beijing Key Laboratory of Urban Atmospheric Volatile Organic Compounds Pollution Control and Application, Beijing Municipal Research Institute of Environment Protection, Beijing, 100037, China.
Chemosphere. 2022 May;295:133756. doi: 10.1016/j.chemosphere.2022.133756. Epub 2022 Feb 8.
Quantifying the driving effect of each factor on atmospheric secondary pollutants is crucial for pollution prevention. We aim to establish a simple and accessible method to identify ozone (O) and particulate matter (PM) concentration trends induced by emissions and meteorology. The method comprises five main steps, which involve matrix construction and mutual calculations, and the whole process is demonstrated and verified by employing long-term monitoring data. With regard to the case study, O and PM concentration variance between the target and base year are respectively -4.74 and 0.20 μg/m under same meteorological conditions, among which the contribution of the emissions driver and meteorological driver are respectively -5.81 and 1.07 μg/m for O and respectively 0.55 and -0.35 μg/m for PM. Additionally, 84.45% of O variance is attributable to the emissions driver in terms of relative importance, which is 52.88% for PM. The meteorological driver is further separated into atmospheric secondary reaction and regional transport. The results reveal that ongoing prevention policy for O is effective; however, it needs to be further optimized for PM.
量化每个因素对大气二次污染物的驱动作用对于污染防治至关重要。我们旨在建立一种简单易用的方法，以识别排放和气象因素引起的臭氧（O）和颗粒物（PM）浓度趋势。该方法包括五个主要步骤，涉及矩阵构建和相互计算，整个过程通过使用长期监测数据进行了演示和验证。就案例研究而言，在相同气象条件下，目标年和基准年的 O 和 PM 浓度方差分别为-4.74 和 0.20 μg/m，其中排放驱动因素和气象驱动因素的贡献分别为-5.81 和 1.07 μg/m 用于 O，分别为 0.55 和-0.35 μg/m 用于 PM。此外，O 方差的 84.45%归因于排放驱动因素，而 PM 为 52.88%。气象驱动因素进一步分为大气二次反应和区域传输。结果表明，目前针对 O 的预防政策是有效的；然而，对于 PM，还需要进一步优化。
