Knowledge of hygroscopic properties is essential to prediction of the role of aerosol in cloud formation and lung deposition. Our objective was to introduce a new approach to classify and predict the hygroscopic growth factors (Gfs) of specific atmospheric sub-micrometre particle types in a mixed aerosol based on measurements of the ensemble hygroscopic growth factors and particle number size distribution (PNSD). Based on a non-linear regression model between aerosol source contributions from PMF applied to the PNSD data set and the measured Gf values (at 90% relative humidity) of ambient aerosols, the estimated mean Gf values for secondary inorganic, mixed secondary, nucleation, urban background, fresh, and aged traffic-generated particle classes at a diameter of 110 nm were found to be 1.51, 1.34, 1.12, 1.33, 1.09 and 1.10, respectively. It is found possible to impute (fill) missing HTDMA data sets using a Random Forest regression on PNSD and meteorological conditions.

吸湿性的知识对于预测气雾剂在云形成和肺部沉积中的作用至关重要。我们的目标是基于对整体吸湿性生长因子和颗粒大小分布（PNSD）的测量，引入一种新方法来分类和预测混合气溶胶中特定大气亚微米颗粒类型的吸湿性生长因子（Gfs）。基于应用于PNSD数据集的PMF气溶胶来源贡献与环境气溶胶的实测Gf值（在90％相对湿度下）的非线性回归模型，估算出次生无机，混合次生，成核的平均Gf值，在城市背景下，发现直径为110 nm的新鲜和老化的交通产生的颗粒类别分别为1.51、1.34、1.12、1.33、1.09和1.10。