一种代优化的缩放方法,用于城市规模的空气质量预测,建立排放清单
Chengwei Lu1, Zihang Zhou2, Hefan Liu2
1College of Architecture and Environment, Sichuan University, Chengdu 610065, China; Chengdu Academy of Environmental Sciences, Chengdu 610072, China.
The Science of the total environment
|October 10, 2024
概括
这项研究引入了一种新方法,用于为中国城市创建空气质量模型准备的排放清单. 该技术通过使用环境观测来代优化排放,即便没有本地排放数据,也可以改善空气质量预测.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 计算机建模 计算建模
背景情况:
- 空气质量模型 (AQM) 对于预测和污染控制至关重要,但由于局部排放清单不准确而受到限制.
- 许多城市地区往往无法获得准确的排放数据,这阻碍了有效的空气质量管理.
- 这种限制在地理和气象条件复杂的地区尤为明显.
研究的目的:
- 开发和验证一种新的技术,用于为中国城市提供具有代优化能力的AQM准备的排放库存.
- 通过利用现有的环境观测来提高AQM性能,以应对缺失的当地排放数据的挑战.
- 提供可转移的方法来生成可靠的排放档案,用于空气质量预测.
主要方法:
- 开发了一种高效的排放处理工具,使用中国的高分辨率多分辨率排放清单 (HR-MEIC) 作为输入.
- 实施了一种代优化方法,通过模型结果和环境观测得出的规模因子调整区域排放.
- 将该方法应用于成都平原的八个城市 (CP8C),使用WRF-CMAQ模型在2023年期间进行月度模拟.
主要成果:
- 代优化在五个周期后显著改善了PM2.5和NO2的模型性能,将相关系数 (R) 分别从0.62提高到0.77和0.37提高到0.73.
- 对于PM2.5和NO2的正常化平均偏差 (NMB) 显著下降,分别从22.8%降至3.6%和100.4%降至3.3%.
- 臭氧 (O3) 度低估值减少了,尽管O3建模的改进是适度的.
结论:
- 开发的技术有效地使用开放数据源生成合理的AQM准备的排放文件,改善空气质量预测.
- 代优化即使在数据稀缺的环境中也提高了AQM性能,为缺乏本地排放库存的城市提供了实用解决方案.
- 该方法为改善城市空气质量管理和预测提供了一种有价值,易于复制的方法.
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