Fine particulate matter (PM) chemical composition has strong and diverse impacts on the planetary environment, climate, and health. These effects are still not well understood due to limited surface observations and uncertainties in chemical model si...
There is a growing need to apply geospatial artificial intelligence analysis to disparate environmental datasets to find solutions that benefit frontline communities. One such critically needed solution is the prediction of health-relevant ambient gr...
Coarse-mode aerosol optical depths (cAODs) are critical for understanding the impact of coarse particle sizes, especially dust aerosols, on climate. Currently, the limited data length and high uncertainty of satellite products diminish the applicabil...
Journal of environmental sciences (China)
39481934
Based on observed meteorological elements, photolysis rates (J-values) and pollutant concentrations, an automated J-values predicting system by machine learning (J-ML) has been developed to reproduce and predict the J-values of OD, NO, HONO, HO, HCHO...
Journal of exposure science & environmental epidemiology
38561475
BACKGROUND: Although PM (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies.
Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for clim...
To monitor health risks associated with vaping, we introduce a multi-spectral optical sensor powered by machine learning for real-time characterization of electronic cigarette aerosols. The sensor can accurately measure the mass of particulate matter...
The linkages between BrC optical properties and chemical composition remain inadequately understood, with quantified chromophores explaining less than 25% of ambient aerosol light absorption. This study characterized 38 typical chromophores in aeroso...
Environmental science and pollution research international
39625623
In the present work, it is the first time an interpretable machine learning model has been developed for the estimation of Particulate Matter 10 µm (PM) concentrations over India using Aerosol Optical Depth (AOD) from two different satellites, i.e. I...
Low-cost monitors for measuring airborne contaminants have gained popularity due to their affordability, portability, and ease of use. However, they often exhibit significant biases compared to high-cost reference instruments. For optimal accuracy, t...