An iteratively optimized downscaling method for city-scale air quality forecast emission inventory establishment

• Published in: The Science of the total environment 2024-12, Vol.954, p.176824, Article 176824
• Main Authors: Lu, Chengwei, Zhou, Zihang, Liu, Hefan, Chen, Xi, Tan, Qinwen, Wang, Nan, Yang, Xinyue, Huang, Liqiu, Yang, Fumo
• Format: Article
• Language: English
• Subjects: air quality; basins; China; environment; HR-MEIC; inventories; Iterative optimization; model validation; Numerical air quality forecast; pollution control; system optimization; topography; weather forecasting; WRF-CMAQ
• ISSN: 0048-9697; 1879-1026; 1879-1026
• DOI: 10.1016/j.scitotenv.2024.176824

Air quality models (AQMs) are pivotal in forecasting air quality and shaping pollution control strategies. Nonetheless, the effectiveness of AQMs is often compromised in many cities due to the absence of accurate local emission inventories. To address this gap, this study presents a novel AQM-ready emission inventory generation technique with iterative optimization ability for city-scale applications in China. An efficient emission processing tool was introduced in this study, which utilizes the High-Resolution Multi-resolution Emission Inventory for China (HR-MEIC) as input. Using environmental observations and a region map, the tool can justify emissions of different regions iteratively. With the iterative optimization method, the model performance can be notably improved even without local emissions. The optimization was realized by splitting model-ready emissions into different regions and adjusting the emissions using scale factors calculated with the modeling results and the observations of each region. This methodology was applied to the Eight Cities in the Chengdu Plain (CP8C), located in the western margin of Sichuan Basin with complex topography and meteorological conditions, southwestern China, monthly throughout 2023. Air quality modeling was carried out using Weather Forecast and Research Model (WRF) and the Community Multiscale Air Quality Model (CMAQ). The results showed that the optimization acquired a good performance after five cycles for PM2.5 and NO2, with correlation coefficients (R values) surging from 0.62 and 0.37 to 0.77 and 0.73, respectively, while their normalized mean bias (NMB) substantially decreased from 22.8 % and 100.4 % to 3.6 % and 3.3 %. The underestimation on O3 concentration was also improved by the optimization, although enhancements in O3 modeling remained modest. This technique provides an easy-to-copy method to generate reasonable AQM-ready emission files with open emission data and observation data, which would be beneficial for the cities' air quality forecast in cities without local emission inventories. [Display omitted] •City-scale air quality forecast improved using open-source emissions and observations only.•A downscaling, processing and optimizing tool was programmed and introduced.•Reasonable modeling performance was achieved after the iterative optimization.