Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China.
School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.
Sci Total Environ. 2021 May 15;769:144535. doi: 10.1016/j.scitotenv.2020.144535. Epub 2021 Jan 7.
An accurate characterization of spatial-temporal emission patterns and speciation of volatile organic compounds (VOCs) for multiple chemical mechanisms is important to improving the air quality ensemble modeling. In this study, we developed a 2017-based high-resolution (3 km × 3 km) model-ready emission inventory for Guangdong Province (GD) by updating estimation methods, emission factors, activity data, and allocation profiles. In particular, a full-localized speciation profile dataset mapped to five chemical mechanisms was developed to promote the determination of VOC speciation, and two dynamic approaches based on big data were used to improve the estimation of ship emissions and open fire biomass burning (OFBB). Compared with previous emissions, more VOC emissions were classified as oxygenated volatile organic compound (OVOC) species, and their contributions to the total ozone formation potential (OFP) in the Pearl River Delta (PRD) region increased by 17%. Formaldehyde became the largest OFP species in GD, accounting for 11.6% of the total OFP, indicating that the model-ready emission inventory developed in this study is more reactive. The high spatial-temporal variability of ship sources and OFBB, which were previously underestimated, was also captured by using big data. Ship emissions during typhoon days and holidays decreased by 23-55%. 95% of OFBB emissions were concentrated in 9% of the GD area and 31% of the days in 2017, demonstrating their strong spatial-temporal variability. In addition, this study revealed that GD emissions have changed rapidly in recent years due to the leap-forward control measures implemented, and thus, they needed to be updated regularly. All of these updates led to a 5-17% decrease in the emission uncertainty for most pollutants. The results of this study provide a reference for how to reduce uncertainties in developing model-ready emission inventories.
准确刻画挥发性有机物（VOCs）在多种化学机制下的时空排放特征和化学形态对于改善空气质量集合模拟具有重要意义。本研究通过更新估算方法、排放因子、活动数据和分配方案，构建了一个基于 2017 年的高分辨率（3km×3km）、适用于模式的广东省排放清单。特别地，我们开发了一个完整本地化的、与五个化学机制相对应的物种谱数据集，以促进 VOC 物种的确定，并使用基于大数据的两种动态方法来改进船舶排放和露天生物质燃烧（OFBB）的估算。与以往的排放相比，更多的 VOC 排放被归类为含氧挥发性有机化合物（OVOC）物种，它们对珠江三角洲（PRD）地区总臭氧生成潜势（OFP）的贡献增加了 17%。甲醛成为广东最大的 OFP 物种，占总 OFP 的 11.6%，表明本研究中开发的适用于模式的排放清单具有更高的反应性。利用大数据还可以更好地捕捉到船舶源和 OFBB 的高时空变异性，这些源和 OFBB 此前被低估。台风天气和节假日期间的船舶排放量减少了 23-55%。2017 年，95%的 OFBB 排放量集中在广东 9%的地区和 31%的天数内，表明其具有很强的时空变异性。此外，本研究表明，由于近年来采取了跨越式的控制措施，广东的排放量迅速变化，因此需要定期更新。这些更新使大多数污染物的排放不确定性降低了 5-17%。本研究的结果为如何降低开发适用于模式的排放清单的不确定性提供了参考。
