Retrospective analysis of 2015-2017 wintertime PM2.5 in China: response to emission regulations and the role of meteorology
Retrospective analysis of 2015-2017 wintertime PM2.5 in China: response to emission regulations and the role of meteorology
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2015-2017年我国冬季PM2.5回顾分析：排放法规响应与气象作用
DOI：
10.5194/acp-19-7409-2019
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发表时间：
2019-06-05
影响因子：
6.3
通讯作者：
Chen, Min
中科院分区：
文献类型：
--
作者：
Chen, Dan;Liu, Zhiquan;Chen, Min
为了更好地表征与人为排放相关的气溶胶种类，将网格点统计插值（GSI）和化学天气研究与预报（WRF/Chem）数据同化系统从GOCART气溶胶方案更新为模拟气溶胶相互作用和化学模型（MOSAIC） 4-bin （MOSAIC- 4bin）气溶胶方案。利用更新的三维变分（3DVAR）系统，每小时对来自1600多个站点的冬季（1月）地表PM2.5（空气动力学直径小于2.5 pm的细颗粒物）观测数据进行同化。在对照试验（不同化）中，使用中国2010年多分辨率排放库存（MEIC_2010）模拟的1月份PM2.5浓度在四川盆地、华中地区、长江三角洲和珠江三角洲分别严重高估了98-134、46-101、32-59和19-60 μ g(-3)，表明2010年的排放量不适合2015-2017年。由于近年来实施了严格的排放控制策略。东北地区、新疆地区和能源金三角地区分别被低估11-12、53-96和22-40 μ g / m（-3）。同化实验显著降低了高、低偏差到+/- 5 μ g / m（-3）以内。利用同化试验的观测资料和再分析资料，探讨了年际变化及其驱动因素。排放的作用是用总组合差异（同化实验）减去气象影响（对照实验）得到的。结果表明，2015年1月至2016年1月，华北平原的PM2.5减少了约15 μ g(-3)，但气象发挥了主导作用（贡献了约12 μ g(-3)）。从2016年到2017年1月，NCP的变化是不同的；气象因素导致PM2.5增加约23 μ g(-3)，而排放控制措施导致PM2.5减少8 μ g(-3)，综合影响仍显示该地区PM2.5增加。分析证实，排放控制策略确实得到了实施，两年内的排放量都有所减少。使用数据同化方法，本研究有助于确定排放控制策略可能或可能不会产生立即可见影响的原因。这种方法仍然存在很大的不确定性，特别是不准确的排放输入，并且忽略模式中的气溶胶-气象反馈也会在分析中产生很大的不确定性。
To better characterize anthropogenic emission-relevant aerosol species, the Gridpoint Statistical Interpolation (GSI) and Weather Research and Forecasting with Chemistry (WRF/Chem) data assimilation system was updated from the GOCART aerosol scheme to the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) 4-bin (MOSAIC-4BIN) aerosol scheme. Three years (20152017) of wintertime (January) surface PM2.5 (fine particulate matter with an aerodynamic diameter smaller than 2.5 pm) observations from more than 1600 sites were assimilated hourly using the updated three-dimensional variational (3DVAR) system. In the control experiment (without assimilation) using Multi-resolution Emission Inventory for China 2010 (MEIC_2010) emissions, the modeled January averaged PM2.5 concentrations were severely overestimated in the Sichuan Basin, central China, the Yangtze River Delta and the Pearl River Delta by 98-134, 46-101, 32-59 and 19-60 mu g m(-3), respectively, indicating that the emissions for 2010 are not appropriate for 2015-2017, as strict emission control strategies were implemented in recent years. Meanwhile, underestimations of 11-12, 53-96 and 22-40 mu g m(-3) were observed in northeastern China, Xinjiang and the Energy Golden Triangle, respectively. The assimilation experiment significantly reduced both high and low biases to within +/- 5 mu g m(-3).The observations and the reanalysis data from the assimilation experiment were used to investigate the year-to-year changes and the driving factors. The role of emissions was obtained by subtracting the meteorological impacts (by control experiments) from the total combined differences (by assimilation experiments). The results show a reduction in PM2.5 of approximately 15 mu g m(-3) for the month of January from 2015 to 2016 in the North China Plain (NCP), but meteorology played the dominant role (contributing a reduction of approximately 12 mu g m(-3)). The change (for January) from 2016 to 2017 in NCP was different; meteorology caused an increase in PM2.5 of approximately 23 mu g m(-3), while emission control measures caused a decrease of 8 mu g m(-3), and the combined effects still showed a PM2.5 increase for that region. The analysis confirmed that emission control strategies were indeed implemented and emissions were reduced in both years. Using a data assimilation approach, this study helps identify the reasons why emission control strategies may or may not have an immediately visible impact. There are still large uncertainties in this approach, especially the inaccurate emission inputs, and neglecting aerosol- meteorology feedbacks in the model can generate large uncertainties in the analysis as well.