Improved characterization of ambient PM_2.5 mass concentration and chemical speciation is a topic of interest in air quality and climate sciences. Over the past decades, considerable efforts have been made to improve ground-level PM_2.5 using remotely sensed data. Here we present two new approaches for estimating atmospheric PM_2.5 and chemical composition based on the High Spectral Resolution Lidar (HSRL)-retrieved aerosol extinction values and types and Creating Aerosol Types from Chemistry (CATCH)-derived aerosol chemical composition. The first methodology (CMAQ-HSRL-CH) improves EPA's Community Multiscale Air Quality (CMAQ) predictions by applying variable scaling factors derived using remotely-sensed information about aerosol vertical distribution and types and the CATCH algorithm. The second methodology (HSRL-CH) does not require regional model runs and can provide atmospheric PM_2.5 mass concentration and chemical speciation using only the remotely sensed data and the CATCH algorithm. The resulting PM_2.5 concentrations and chemical speciation derived for NASA DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) Baltimore-Washington, D.C. Corridor (BWC) Campaign are compared to surface measurements from EPA's Air Quality Systems (AQS) network. The analysis shows that the CMAQ-HSRL-CH method leads to considerable improvement of CMAQ's predicted PM_2.5 concentrations (R² value increased from 0.37 to 0.63, the root mean square error (RMSE) was reduced from 11.9 to 7.2 μg m⁻³, and the normalized mean bias (NMB) was lowered from −46.0 to 4.6%). The HSRL-CH method showed statistics (R² = 0.75, RMSE = 8.6 μgm⁻³, and NMB = 24.0%), which were better than the CMAQ prediction of PM_2.5 alone and analogous to CMAQ-HSRL-CH. In addition to mass concentration, HSRL-CH can also provide aerosol chemical composition without specific model simulations. We expect that the HSRL-CH method will be able to make reliable estimates of PM_2.5 concentration and chemical composition where HSRL data are available.

改善环境 PM _2.5质量浓度和化学形态的表征是空气质量和气候科学领域的一个热门话题。在过去的几十年里，人们为利用遥感数据改善地面 PM _2.5做出了巨大的努力。在这里，我们提出了两种基于高光谱分辨率激光雷达 (HSRL) 检索的气溶胶消光值和类型以及从化学 (CATCH) 衍生的气溶胶化学成分创建气溶胶类型来估计大气 PM _2.5和化学成分的新方法。第一种方法 (CMAQ-HSRL-CH) 通过应用利用有关气溶胶垂直分布和类型的遥感信息以及 CATCH 算法得出的可变缩放因子，改进了 EPA 的社区多尺度空气质量 (CMAQ) 预测。第二种方法 (HSRL-CH) 不需要运行区域模型，仅使用遥感数据和 CATCH 算法即可提供大气 PM _2.5质量浓度和化学形态。将 NASA DISCOVER-AQ（从与空气质量相关的柱和垂直解析观测中获取表面状况信息）巴尔的摩-华盛顿特区走廊 (BWC) 活动得出的 PM _2.5浓度和化学形态与 EPA 空气质量的表面测量结果进行比较系统（AQS）网络。分析表明，CMAQ-HSRL-CH方法使CMAQ预测的PM _2.5浓度得到显着改善（R ²值从0.37增加到0.63，均方根误差（RMSE）从11.9减少到7.2 μg m ⁻³ ，归一化平均偏差 (NMB) 从 -46.0 降低至 4.6%）。 HSRL-CH方法显示统计数据（R ² = 0.75，RMSE = 8.6 μgm ^-3 ，NMB = 24.0%），优于单独的PM _2.5的CMAQ预测并且与CMAQ-HSRL-CH类似。除了质量浓度之外，HSRL-CH还可以提供气溶胶化学成分，无需特定的模型模拟。我们预计 HSRL-CH 方法将能够在 HSRL 数据可用的情况下对 PM _2.5浓度和化学成分做出可靠的估计。