Spectral Derivatives of Optical Depth for Partitioning Aerosol Type and Loading

Stacked long and short-term memory (SLSTM) - assisted terahertz spectroscopy combined with permutation importance for rapid red wine varietal identification

Mislabeling of low-value red wines as high-value ones is common, which seriously undermines consumer rights and interests. However, traditional sensory and chemical analysis methods have limitations, which highlights the need for novel detection techniques. To address above issues, terahertz time-domain spectroscopy (THz-TDS) combined with deep learning (DL) was employed to distinguish different red wine varieties quickly and non-destructively, contributing to correctly identifying red wine labels. Compared with the other models, the stacked long and short-term memory (SLSTM) model based on the first derivative (1-st der) spectra performed the best (Precision: 85.72 %, Recall: 85.61 %, F1-score: 85.59 %, Accuracy: 85.61 %). In addition, feature selection (FS) was used to explore the feasibility of improving model accuracy and reducing prediction time by eliminating redundant frequencies. Compared to full frequency, the 1-st der-SLSTM model based on permutation importance (PI) performed slightly lower (Precision: 84.42 %, Recall: 84.10 %, F1-score: 84.14 %, Accuracy: 84.18 %), but the prediction time was reduced by 2 s. Therefore, different models can be selected based on different detection needs by weighing accuracy and prediction time. In conclusion, the current research demonstrates that the SLSTM-assisted THz-TDS technology provides a novel approach for fast, accurate and non-destructive for fast, accurate and non-destructive discrimination of red wine labels, facilitating the maintenance of market discipline.

Embedded information of aerosol type, hygroscopicity and scattering enhancement factor revealed by the relationship between PM2.5 and aerosol optical depth

Satellite aerosol optical depth (AOD) provides an alternative way to depict the spatial distribution of near-surface PM_2.5. In this study, a mathematical formulation of how PM_2.5 is related to AOD is presented. When simplified to a linear equation, a functional dependence of the slope on the aerosol type, scattering enhancement factor f(RH), and boundary layer height is revealed, while the influence of the vertical aerosol profile is embedded in the intercept. Specifically, we focus on the effects of aerosol properties and employ a new aerosol index (Normalized Gradient Aerosol Index, NGAI) for classifying aerosol subtypes. The combination of AOD difference at shorter wavelengths over longer-wavelength AOD from AERONET data could distinguish and subclassify aerosol types previously indistinguishable by AE (i.e., urban-industrial pollution, U/I, and biomass burning, BB). AOD-PM_2.5 regressions are performed on these aerosol subtypes at various relative humidity (RH) levels. The results suggest that BB aerosols are nearly hydrophobic until the RH exceeds 80 %, while the AOD-PM_2.5 regressions for U/I depend on RH levels. Moreover, the scattering enhancement factor f(RH) can be calculated by taking the ratio of intercepts between dry and humidity conditions, which is proposed and tested for the first time in this study. Our results show an f(RH ≥ 80 %) of ∼2.6 for U/I-dominated aerosols, whereas the value is not over 1.5 for BB aerosols. The f(RH) can be further used to derive the optical hygroscopicity parameter (κ_sca), demonstrating that the NGAI can be used to exploit differences in aerosol hygroscopicity and improve the AOD-PM_2.5 relationship.

Sea Salt Aerosol Identification Based on Multispectral Optical Properties and Its Impact on Radiative Forcing over the Ocean

The ground-based measurement of sea salt (SS) aerosol over the ocean requires the massive utilization of satellite-derived aerosol products. In this study, n-order spectral derivatives of aerosol optical depth (AOD) based on wavelength were examined to characterize SS and other aerosol types in terms of their spectral dependence related to their optical properties such as particle size distributions and complex refractive indices. Based on theoretical simulations from the second simulation of a satellite signal in the solar spectrum (6S) model, AOD spectral derivatives of SS were characterized along with other major types including mineral dust (DS), biomass burning (BB), and anthropogenic pollutants (APs). The approach (normalized derivative aerosol index, NDAI) of partitioning aerosol types with intrinsic values of particle size distribution and complex refractive index from normalized first- and second-order derivatives was applied to the datasets from a moderate resolution imaging spectroradiometer (MODIS) as well as by the ground-based aerosol robotic network (AERONET). The results after implementation from multiple sources of data indicated that the proposed approach could be highly effective for identifying and segregating abundant SS from DS, BB, and AP, across an ocean. Consequently, each aerosol’s shortwave radiative forcing and its efficiency could be further estimated in order to predict its impact on the climate.

Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach

Fine particulate matter (PM_2.5) is associated with various adverse health outcomes and poses serious concerns for public health. However, ground monitoring stations for PM_2.5 measurements are mostly installed in population-dense or urban areas. Thus, satellite retrieved aerosol optical depth (AOD) data, which provide spatial and temporal surrogates of exposure, have become an important tool for PM_2.5 estimates in a study area. In this study, we used AOD estimates of surface PM_2.5 together with meteorological and land use variables to estimate monthly PM_2.5 concentrations at a spatial resolution of 3 km² over Taiwan Island from 2015 to 2019. An ensemble two-stage estimation procedure was proposed, with a generalized additive model (GAM) for temporal-trend removal in the first stage and a random forest model used to assess residual spatiotemporal variations in the second stage. We obtained a model-fitting R² of 0.98 with a root mean square error (RMSE) of 1.40 μg/m³. The leave-one-out cross-validation (LOOCV) R² with seasonal stratification was 0.82, and the RMSE was 3.85 μg/m³, whereas the R² and RMSE obtained by using the pure random forest approach produced R² and RMSE values of 0.74 and 4.60 μg/m³, respectively. The results indicated that the ensemble modeling approach had a higher predictive ability than the pure machine learning method and could provide reliable PM_2.5 estimates over the entire island, which has complex terrain in terms of land use and topography.