In this work, an assembly of coating structures is generated for coated black carbon (BC) based on two different coating methods with limited tunable parameters. By defining typical parameters, the proposed coating structures show great agreement with the detailed morphologies of coated BC particles in the atmosphere. Our models can accurately reproduce the measured linear backscattering depolarization ratio (LDR). We noticed that BC with spherical coatings (Configuration E) has difficulty estimating large LDRs, while the ability to reproduce a large LDR is improved by making the coating structure slightly nonspherical (Configuration C). Our models also show a better performance in reproducing the laboratory‐measured absorption enhancement (Eabs), mass absorption/scattering cross sections, and absorption Ångström exponent than the simplified models. In addition, sizable uncertainties in the optical properties of coated BC with different coating configurations are found. The uncertainties in the LDR, Eabs, mass absorption cross section, and absorption Ångström exponent caused by the coating configurations can reach approximately 220%, 35%, 90%, and 20%, respectively. Therefore, complex coating structures should be carefully considered. In this work, various coating structures are represented by limited tunable parameters, which is beneficial to the parameterization of the optical properties of BC with a complex coating structure. Our models can also provide tools for exploring the agreement between calculations and measurements.

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Abstract. Mineral dust suspended in the atmosphere has significant effects on radiative balance and climate change. The Chinese Loess Plateau (CLP) is generally considered one of the main sources of Asian dust aerosol. After being lifted by wind, dust particles with various size distributions can be transported over different distances. In this study, an original loess sample was collected from Luochuan, which is centrally located on the CLP, and two samples with different size distributions were obtained afterwards. “Pristine loess” was used to represent dust that only affects source regions, part of pristine loess was milled to finer “milled loess” that can be transported over long distances. Light scattering matrices for these two samples were measured at 532 nm wavelength from 5 to 175∘ angles. Particle size distribution, refractive index, chemical component, and microscopic appearance were also characterized for auxiliary analyses. Experimental results showed that there are obvious discrepancies in angular behaviors of matrix elements for pristine loess and milled loess, and these discrepancies are different from those for other kinds of dust with distinct size distributions. Given that the effective radii of these two loess samples differ by more than 20 times, it is reasonable to conclude that the difference in size distributions plays a major role in leading to different matrices, while differences in refractive index and microstructure have relatively small contributions. Qualitative analyses of numerical simulation results of irregular particles also validate this conclusion. Gaussian spheres may be promising morphological models for simulating the scattering matrix of loess but need further quantitative verification. Finally, synthetic scattering matrices for both pristine loess and milled loess were constructed over 0–180∘, and the previous average scattering matrix for loess dust was updated. This study presents measurement results of Chinese loess dust and an updated average scattering matrix for loess, which are useful for validating existing models, developing more advanced models for optical simulations of loess dust, and helping to improve retrieval accuracy of dust aerosol properties over both source and downwind areas.

The widespread use of renewable energy resources requires more immediate and effective fire alarms as a preventive measure. The fire is usually weak in the initial stages, which is not conducive to detection and identification. This paper validates a solution to resolve that problem by a flame detection algorithm that is more sensitive to small flames. Based on Yolov3, the parallel convolution structure of Inception is used to obtain multi-size image information. In addition, the receptive field of the convolution kernel is increased with the dilated convolution so that each convolution output contains a range of information to avoid information omission of tiny flames. The model accuracy has improved by introducing a Feature Pyramid Network in the feature extraction stage that has enhanced the feature fusion capability of the model. At the same time, a flame detection database for early fire has been established, which contains more than 30 fire scenarios and is suitable for flame detection under various challenging scenes. Experiments validate the proposed method not only improves the performance of the original algorithm but are also advantageous in comparison with other state-of-the-art object detection networks, and its false positives rate reaches 1.2% in the test set.

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