et al.. High-altitude and long-range transport of aerosols causing regional severe haze during extreme dust storms explains why afforestation does not prevent storms.
AbstractClimate change is predicted to induce more extreme events such as storms, heat waves, drought and floods. Dust storms are frequently occurring in northern China. Those storms degrade air quality by decreasing visibility and inducing cardiovascular and respiratory diseases. To control dust storms, the Chinese government has launched a large-scale afforestation program by planting trees in arid areas, but the effectiveness of this program is still uncertain because the trajectories and altitudes of dust transport are poorly known. In particular, afforestation would be effective only if dust transport occurs at low altitudes. To test this hypothesis, we analyzed the extreme dust storm from May 2 to 7, 2017, which resulted in record-breaking dust loads over northern China. For that, we used dust RGB-composite data from the Himawari-8 satellite and the cloud-aerosol lidar, moderate-resolution imaging spectroradiometer data, and surface monitoring data. The source regions of the dust storms were identified using the hybrid single-particle Lagrangian integrated trajectory model and infrared pathfinder satellite observation. Contrary to our hypothesis, results show that dust is transported at high altitude of 1.0-6.5 km over long distances from northwestern China. This finding explains why the afforestation has not been effective to prevent this storm. Results also disclose the highest particulate matter (PM) concentrations of 447.3 μg/m 3 for PM 2.5 and 1842.0 μg/m 3 for PM 10 during the dust storm. Those levels highly exceed Chinese ambient air quality standards of 75 μg/m 3 for PM 2.5 and 150 μg/m 3 for PM 10 .
A moderate precipitation event occurring in northern Xinjiang, a region with a continental climate with little rainfall, and in leeward slope areas influenced by topography is important but rarely studied. In this study, the performance of lightning data assimilation is evaluated in the short-term forecasting of a moderate precipitation event along the western margin of the Junggar Basin and eastern Jayer Mountain. Pseudo-water vapor observations driven by lightning data are assimilated in both single and cycling analysis experiments of the Weather Research and Forecast (WRF) three-dimensional variational (3DVAR) system. Lightning data assimilation yields a larger increment in the relative humidity in the analysis field at the observed lightning locations, and the largest increment is obtained in the cycling analysis experiment. Due to the increase in water vapor content in the analysis field, more suitable thermal and dynamic conditions for moderate precipitation are obtained on the leeward slope, and the ice-phase and raindrop particle contents increase in the forecast field. Lightning data assimilation significantly improves the short-term leeward slope moderate precipitation prediction along the western margin of the Junggar Basin and provides the best forecast skill in cycling analysis experiments.
In order to study characteristics of loads on a cutting drum of a continuous miner, a mathematic model of random loads on the drum is set up in the paper by means of defining probability distributions of three-direction loads, loads and their variations on a cutting drum are given by building a computer simulation program when it cuts pure coal, rock and hard parcel. Results show that swing resistance is the greatest, forward resistance is the second, axial resistance is the least; a cutting resistance torque increases with the increase of broken media strength; loads cut stone layer are much larger than pure coal; range of load change and load fluctuations are big as cutting hard parcel, these conclusions are correspond with the actual situation.
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