Knowledge-guided machine learning reveals pivotal drivers for gas-to-particle conversion of atmospheric nitrate.

Particulate nitrate, a key component of fine particles, forms through the intricate gas-to-particle conversion process. This process is regulated by the gas-to-particle conversion coefficient of nitrate (ε(NO)). The mechanism between ε(NO) and its drivers is highly complex and nonlinear, and can be characterized by machine learning methods.

Corrigendum: OpenCL-accelerated first-principles calculations of all-electron quantum perturbations on HPC resources.

[This corrects the article DOI: 10.3389/fchem.2023.

OpenCL-accelerated first-principles calculations of all-electron quantum perturbations on HPC resources.

We have proposed, for the first time, an OpenCL implementation for the all-electron density-functional perturbation theory (DFPT) calculations in FHI-aims, which can effectively compute all its time-consuming simulation stages, i.e., the real-space integration of the response density, the Poisson solver for the calculation of the electrostatic potential, and the response Hamiltonian matrix, by utilizing various heterogeneous accelerators.

Targeting Atmospheric Oxidants Can Better Reduce Sulfate Aerosol in China: HO Aqueous Oxidation Pathway Dominates Sulfate Formation in Haze.

Particulate sulfate is one of the most important components in the atmosphere. The observation of rapid sulfate aerosol production during haze events provoked scientific interest in the multiphase oxidation of SO in aqueous aerosol particles. Diverse oxidation pathways can be enhanced or suppressed under different aerosol acidity levels and high ionic strength conditions of atmospheric aerosol.

Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM pollution.

PM pollution is a complex process mainly affected by emission sources and meteorological conditions. However, it is hard to accurately assess the effects of emission sources and meteorological conditions on the variation of PM concentrations in the complex atmospheric environment. In this study, the Random Forest model with Shapley Additive exPlanations (RF-SHAP) and Partial Dependence Plot (RF-PDP) was combined with Positive Matrix Factorization (PMF) to evaluate the impacts of various factors on PM pollution.

Machine learning and theoretical analysis release the non-linear relationship among ozone, secondary organic aerosol and volatile organic compounds.

Fine particulate matter (PM) and ozone (O) pollutions are prevalent air quality issues in China. Volatile organic compounds (VOCs) have significant impact on the formation of O and secondary organic aerosols (SOA) contributing PM. Herein, we investigated 54 VOCs, O and SOA in Tianjin from June 2017 to May 2019 to explore the non-linear relationship among O, SOA and VOCs.

Texture classification by texton: statistical versus binary.

Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods.

Correlating linactant efficiency and self-assembly: structural basis of line activity in molecular monolayers.

Surfactants exhibit characteristic phenomena, including the reduction of interfacial free energy, self-assembly into aggregates, and even the formation of lyotropic liquid crystalline phases at high concentrations. Our research has shown that a semifluorinated phosphonic acid can act as the two-dimensional analogue of a surfactant-a linactant-by reducing the line tension between hydrocarbon-rich and fluorocarbon-rich phases in a Langmuir monolayer. This linactant can also self-assemble into nanoscale clusters in a monolayer.