Geostationary Ocean Color Imager (GOCI) is a suitable data source for AOD retrieval, as it can monitor hourly aerosol changes and make up for the low temporal resolution deficiency of polar orbiting satellite. High temporal resolution aerosol optical depth (AOD) products are very important for the studies of atmospheric environment and climate change. Sun photometers can obtain detailed aerosol optical parameters and then the AOD can be estimated, but due to the large variability of the chemical composition and life cycle of atmospheric aerosols, ground stations are not enough to accurately observe and evaluate the spatial distribution characteristics of atmospheric aerosols on a large spatial scale such as regional or global scale. Aerosol optical depth (AOD) is the most basic optical characteristic of aerosols, and thus it is a key indicator for evaluating aerosol radiative forcing. Atmospheric aerosols have an important impact on the radiation budget of the earth climate system and threaten human health. Atmospheric aerosol is the general term for solid and liquid particles suspended in the atmosphere, mainly come from natural sources such as fine ash and dust lifted by the wind, salt particles, volcanic eruption and from human activities the burning of fossil and non-fossil fuels, transportation, and industrial dust. Moreover, the proposed procedure can be a good option to integrate different models to seek an вЂњoptimalвЂќ result. The experimental results authenticated the effectiveness of the BP-SVM-RF method, which can be a reliable model for the regional landslide susceptibility assessment of the study area. The results illustrated that the accuracy expressed by the area under the receiver operating characteristic curve (AUC) of the BP-SVM-RF integrated model was the highest (0.7898), which was better than that of the BP (0.6929), SVM (0.6582), RF (0.7258), BP-SVM (0.7360), BP-RF (0.7569), and SVM-RF models (0.7298). 80 and 20% of the landslide data were randomly selected as the training and testing samples, respectively, and different landslide susceptibility maps were generated based on the GIS platform. Three basic machine learning models (the back propagation (BP), support vector machine (SVM), and random forest (RF) models) were integrated by an objective function where the weight coefficients among different models were computed by the gray wolf optimization (GWO) algorithm. Our objective is to answer this question: Will the integrated model have higher accuracy compared with individual model? The Lvliang mountains area, a landslide-prone area in China, was taken as the study area, and ten factors were considered in the influencing factors system. The present study aims at exploring the way to integrating different models and comparing the results among integrated and individual models. Integration of different models may improve the performance of landslide susceptibility assessment, but few studies have tested it. This comprehensive analysis revealed details associated with the contributions from local and remote anthropogenic and/or natural sources. The aerosols seem to undergo aging and/or hydration processes as shown by the relationships among AOT, extinction due to the fine mode, spectral curvature, and aerosol fine mode radius. The analysis of the interrelations among aerosol properties (fine mode fraction of AOT, volume concentration, spectral curvature, and size distribution) supports these findings. Sporadic intrusions of coarse particles (dust/ash) from remote sources also enhance extinction. This feature-not evident in the baseline climatology- is consistent with the presence of smoke from regional biomass burning. The relationship between AOT and О± clearly illustrated the major contribution to extinction of finer particles under more turbid conditions.

The thresholds of О± and AOT proposed for four aerosol types allowed a satisfactory classification of aerosols. This apportionment can change under particular conditions, e.g., showing increased fine mode load in August and September. Annual and monthly particle size distributions show predominance of the coarse mode. Atmospheric turbidity increases in August and September (40%). The long-term trend of the monthly median aerosol optical thickness (AOT) is not significant whereas monthly О± medians show a significant negative trend. The baseline climatology indicates that the city has low mean aerosol load (0.11 В± 0.1) and mean Г…ngstrГ¶m exponent (О±) of 1.11 (вђ.4). We present a complete characterization of aerosols in Buenos Aires based on their optical and microphysical properties from a long-term record (19-years) of AERONET V3 observations at the CEILAP-BA site.

Veusz mean and error bar