Abstract Exposure to air pollution has a significant impact on the health of urban populations, so the improvement of methods that model the concentrations of air pollutants within complex urban areas is important in health studies to adequately asses the exposure of the population. This paper presents several hybrid, high-resolution models to simulate the variability of ambient NO2 concentrations in Seoul, the capital of South Korea. These models combine the Community Multiscale Quality (CMAQ) as a regional photochemical model with a fine scale model of either the California Puff dispersion model (CALPUFF) or the land use regression model (LUR). We compared high-resolution estimates of the spatial NO2 concentration from four different hybrid models, including 1) raw CMAQ-CALPUFF; 2) observation-fused CMAQ-CALPUFF; 3) raw CMAQ-LUR; and 4) observation-fused CMAQ-LUR. We conducted numerical simulations of the NO2 concentrations during the winter season and compared the results with field data obtained from mobile measurements captured from December 2017 to February 2018. The results indicate that observation-fused hybrid models offered improved agreement with the mobile measurements: for the CMAQ-CALPUFF model, statistical bias and error were reduced to about 82% and 57%, respectively by using observation-fused CMAQ. We also found significant differences in the sub-grid variability of the NO2 concentrations for the different hybrid models. The predictions obtained with CMAQ-CALPUFF showed concentrations that were more widely distributed (1.7 and 1.4 times for the 10–90th range, observation-fused case) when compared to the only-CMAQ and CMAQ-LUR predictions, respectively. Our study suggests that a properly evaluated hybrid model can increase the predictive accuracy of air pollutant concentration in complex urban areas to improve exposure assessments in health studies.

中文翻译：

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估计城市内二氧化氮浓度的不同混合建模方法的比较

摘要 空气污染暴露对城市人口的健康有重大影响，因此改进模拟复杂城市区域内空气污染物浓度的方法对于健康研究来说非常重要，以充分评估人口的暴露情况。本文提出了几种混合的高分辨率模型来模拟韩国首都首尔的环境二氧化氮浓度的变化。这些模型将作为区域光化学模型的社区多尺度质量 (CMAQ) 与加利福尼亚粉扑扩散模型 (CALPUFF) 或土地利用回归模型 (LUR) 的精细比例模型相结合。我们比较了来自四种不同混合模型的空间 NO2 浓度的高分辨率估计，包括 1) 原始 CMAQ-CALPUFF；2）观察融合的CMAQ-CALPUFF；3) 原始 CMAQ-LUR；和 4) 观察融合 CMAQ-LUR。我们对冬季的 NO2 浓度进行了数值模拟，并将结果与​​从 2017 年 12 月至 2018 年 2 月捕获的移动测量获得的现场数据进行了比较。结果表明，观测融合混合模型与移动测量提供了更好的一致性：对于通过使用观察融合 CMAQ，CMAQ-CALPUFF 模型，统计偏差和误差分别降低到约 82% 和 57%。我们还发现不同混合模型的 NO2 浓度的子网格变异性存在显着差异。与仅 CMAQ 和 CMAQ-LUR 预测相比，使用 CMAQ-CALPUFF 获得的预测显示浓度分布更广泛（第 10-90 范围的 1.7 倍和 1.4 倍，观察融合情况）。