Abstract Conducting high-resolution air quality analysis by applying microscale dispersion models at the regional scale poses a formidable computing challenge, because a huge number of receptors and the extensive network of roadway links (emission sources) must be processed. As a way to minimize computation cost without undermining estimation precision, this study proposes an innovative link screening methodology, using a supervised machine learning random forest (RF) classification algorithm, that eliminates links with zero or negligible concentration contributions from modeled link-receptor combinations. The study uses 79,328 receptor-link pairs randomly selected from the Atlanta Metropolitan area to train and test the model. The final link screening model employs six variables, including link attributes, urban variables, and meteorological conditions. The RF classifier successfully identifies the small portion of links that contribute more than 95% of concentrations that are estimated by the same model using every link-receptor pair. The efficiency and precision of the smaller dispersion model runs developed using the RF classifier (the ‘reduced-link’ model) are compared to the dispersion modeling without the link-screening process (the ‘whole-link’ model) for downtown Atlanta and northwest Atlanta. Results show that AERMOD run-times for reduced-link models are only 0.2%–1.1% of the times required for whole-link models, because far fewer links are handled during the AERMOD simulation (0.1%–0.6% of links in the whole-link model). The correlation between estimates of the two models ranges from 95% to 97%, depending upon the density of the road network, link activity, link emission rates, meteorology, etc.

中文翻译：

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区域级近路空气质量分析道路连接筛选模型的开发：以颗粒物为例

摘要 通过在区域尺度上应用微尺度扩散模型进行高分辨率空气质量分析带来了巨大的计算挑战，因为必须处理大量的受体和广泛的道路连接（排放源）网络。作为在不影响估计精度的情况下最小化计算成本的一种方法，本研究提出了一种创新的链接筛选方法，使用监督机器学习随机森林 (RF) 分类算法，从建模的链接受体组合中消除具有零或可忽略不计浓度贡献的链接。该研究使用从亚特兰大都会区随机选择的 79,328 个受体链接对来训练和测试模型。最终的链路筛选模型采用六个变量，包括链路属性、城市变量、和气象条件。RF 分类器成功地识别出贡献超过 95% 浓度的一小部分链接，这些链接使用每个链接 - 受体对由同一模型估计。将使用 RF 分类器（“减少链路”模型）开发的较小色散模型运行的效率和精度与亚特兰大市中心和西北部没有链路筛选过程的色散建模（“全链路”模型）进行比较亚特兰大。结果表明，减少链接模型的 AERMOD 运行时间仅为整个链接模型所需时间的 0.2%–1.1%，因为 AERMOD 仿真期间处理的链接要少得多（整个链接的 0.1%–0.6% -链接模型）。两个模型的估计值之间的相关性在 95% 到 97% 之间，具体取决于道路网络的密度，