Toropov Andrey A, Toropova Alla P, Roncaglioni Alessandra, Benfenati Emilio, Leszczynska Danuta, Leszczynski Jerzy
Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch Street, Jackson, MS 39217-0510, USA.
Molecules. 2023 Oct 23;28(20):7231. doi: 10.3390/molecules28207231.
Data on Henry's law constants make it possible to systematize geochemical conditions affecting atmosphere status and consequently triggering climate changes. The constants of Henry's law are desired for assessing the processes related to atmospheric contaminations caused by pollutants. The most important are those that are capable of long-term movements over long distances. This ability is closely related to the values of Henry's law constants. Chemical changes in gaseous mixtures affect the fate of atmospheric pollutants and ecology, climate, and human health. Since the number of organic compounds present in the atmosphere is extremely large, it is desirable to develop models suitable for predictions for the large pool of organic molecules that may be present in the atmosphere. Here, we report the development of such a model for Henry's law constants predictions of 29,439 compounds using the CORAL software (2023). The statistical quality of the model is characterized by the value of the coefficient of determination for the training and validation sets of about 0.81 (on average).
亨利定律常数的数据使得系统分析影响大气状态并进而引发气候变化的地球化学条件成为可能。评估由污染物导致的大气污染相关过程需要亨利定律常数。其中最重要的是那些能够在长距离上进行长期迁移的污染物。这种能力与亨利定律常数的值密切相关。气态混合物中的化学变化会影响大气污染物的归宿以及生态、气候和人类健康。由于大气中存在的有机化合物数量极其庞大,因此需要开发适用于预测大气中可能存在的大量有机分子的模型。在此,我们报告使用CORAL软件(2023年)开发的这样一个用于预测29439种化合物亨利定律常数的模型。该模型的统计质量由训练集和验证集的决定系数值表征,平均约为0.81。