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Dust emissions refer to the spatial displacement of dust particles from wind forcing, which is a key component of dust circulation. It plays an important role in the energy, hydrological, and carbon cycles of the Earth's systems. However, most dust emission schemes only consider natural dust, neglecting anthropogenic dust induced by human activities, which led to large uncertainties in quantitative estimations of dust emissions in numerical modeling. To fully consider the mechanisms of anthropogenic dust emissions, both indirect and direct anthropogenic dust emission schemes were constructed and developed in the study. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) retrievals were used to constrain the simulations at global scale. The results showed that the schemes reasonably reproduced the spatio-temporal distributions of anthropogenic dust from 2007 to 2010. The high centers of anthropogenic dust emission flux appeared in India, eastern China, North America, and Africa range from 0.9 to 11 μg m−2 s−1. Compared with natural dust emissions, indirect anthropogenic dust emissions have indistinctive seasonal variation, with differences less than 3.2 μg m−2 s−1. Pasturelands contribute higher anthropogenic dust emissions than croplands, with emissions of approximately 6.8 μg m−2 s−1, accounting for 60 % of indirect anthropogenic dust emissions. Moreover, average anthropogenic dust emissions in urban areas have a value of 13.5 μg m−2 s−1, which is higher than those in rural areas (7.9 μg m−2 s−1). This study demonstrates that the environmental problems caused by anthropogenic dust in urban areas cannot be ignored.

Cellular automata (CA) are considered to be effective models to simulate the behavior of oil spills for overcoming the difficulty of obtaining parameters in numerical models of oil spills. Besides, CA models are convenient to combine geographic information system (GIS) to display the simulation results. This paper presents a new oil spill simulation based on logistic-regression CA model, which easily obtain the weights of the impact factors. The model also can simulate the dynamic changes of oil spill using only a few inputs, such as the initial image, impact factors, and their weights. It was applied to simulate the oil spill in DeepSpill project using five factors, the distance factor, wind, current, temperature, and salinity. Experiments showed that the simulation results are consistent with the verification image with the total accuracy and Kappa coefficient of simulation results as high as 96.8% and 0.834 respectively. We also study the influence of sampling ratio on simulation results. The accuracy improves with the increasing ratio. However, the performances improve only slightly when the ratio reaches 20%. We also analyze the sensitivity of temperature, salinity, winds, currents, and distance. Experiments showed that the simulation results will only expanse around the original area without considering the current and wind. The simulation results will have big model error without considering distance factor. However, less model error occurs in the simulation results without using temperature and salinity.