Zhang Xueying, Symanski Elaine, Paduch Hannah Renee, Kloog Itai, Wang Yuxuan, Liu Yang
Res Sq. 2025 Sep 9:rs.3.rs-7557276. doi: 10.21203/rs.3.rs-7557276/v1.
Accurate meteorological inputs are essential for air pollution dispersion modeling. Traditionally, dispersion models rely on observational meteorological data collected from weather stations at fixed locations. However, the sparse distribution of weather stations limits the ability to capture fine-scale meteorological variability, particularly in areas far from weather stations. In this study, we developed a novel framework for generating American Meteorological Society/Environmental Protection Agency (EPA) Regulatory Model (AERMOD) compatible surface meteorology data (.sfc) using the High-Resolution Rapid Refresh (HRRR) dataset, which provides predicted meteorological variables at a 3-km spatial resolution and an hourly temporal resolution. We followed the AERMOD Model Formulation document to create three scenarios of surface meteorology data, including two exploratory scenarios by setting HRRR meteorology parameter ranges and recalculating key parameters based on whether an hour filled in convective or stable planetary boundary layer status. We then applied these HRRR-derived meteorology data in the Research LINE source (R-LINE) dispersion model to predict traffic-related nitrogen dioxide (NO ) concentrations at 443 Air Quality System monitoring sites across the United States (U.S.) in the year 2019. For comparison, we also ran R-LINE using observational-based surface meteorology data preprocessed with AERMET. NO concentrations predicted by R-LINE were compared against NO measurement data by the meteorology inputs (three HRRR scenarios versus weather station data) using simple linear regression coefficient of determination (R ) and Index of Agreement (IOA). The three HRRR scenarios yielded a higher R on average (0.26) than did the observational data (R = 0.16), suggesting over 60% increase in explained variance. In simple linear regression analyses stratified by distance between NO sites and weather stations as well as by traffic magnitude around NO sites, HRRR data generally outperformed observational data. Site-specific IOA analyses further showed that, compared to observational meteorology data, HRRR inputs performed better across most of the continental U.S. but not as well in urban areas. Overall, our findings demonstrate that HRRR data has the potential to be utilized in air pollution dispersion modeling and that it has superior predictive ability in locations that lack nearby weather stations.
准确的气象输入对于空气污染扩散模型至关重要。传统上，扩散模型依赖于从固定地点的气象站收集的观测气象数据。然而，气象站的稀疏分布限制了捕捉精细尺度气象变化的能力，特别是在远离气象站的地区。在本研究中，我们开发了一种新颖的框架，使用高分辨率快速刷新（HRRR）数据集生成与美国气象学会/环境保护局（EPA）监管模型（AERMOD）兼容的地面气象数据（.sfc），该数据集以3公里的空间分辨率和每小时的时间分辨率提供预测的气象变量。我们遵循AERMOD模型公式文档创建了三种地面气象数据场景，包括两种探索性场景，通过设置HRRR气象参数范围并根据一小时处于对流或稳定行星边界层状态重新计算关键参数。然后，我们将这些源自HRRR的气象数据应用于研究线源（R-LINE）扩散模型，以预测2019年美国443个空气质量系统监测站点与交通相关的二氧化氮（NO ）浓度。为了进行比较，我们还使用经过AERMET预处理的基于观测的地面气象数据运行R-LINE。使用简单线性回归决定系数（R ）和一致性指数（IOA），将R-LINE预测的NO 浓度与气象输入（三种HRRR场景与气象站数据）的NO 测量数据进行比较。三种HRRR场景的平均R （0.26）高于观测数据（R = 0.16），表明解释方差增加了60%以上。在按NO 站点与气象站之间的距离以及NO 站点周围的交通量分层的简单线性回归分析中，HRRR数据通常优于观测数据。特定站点的IOA分析进一步表明，与观测气象数据相比，HRRR输入在美国大陆大部分地区表现更好，但在城市地区表现不佳。总体而言，我们的研究结果表明，HRRR数据有潜力用于空气污染扩散建模，并且在缺乏附近气象站的地点具有卓越的预测能力。
