The resolution of the module that is used to model dust emissions can cause the results to differ from the observations. This problem can easily be solved by changing the resolution of your dust module.
We found that the parameter Ev (sediment transport) underestimates the modelled dust emission on vegetated surfaces when wind speeds are similar. This behavior has not been identified previously.
Mineral dust has a significant impact on atmospheric dynamics and weather. It can alter the Earth’s surface temperature, affect ocean circulation and change its chemistry (Cai et.al., 2018). The dust can have a negative impact on human health, by increasing surface PM2.5 levels.
Figure 3 shows that the simulation performance compared against observations from AERONET's network and satellite remote-sensing is improved for the offline Dust Module containing the updated Source Function and the global annual offline strength of 2000 Tg yr-1. Figure 3 shows that the improved simulation performance is due to the higher spatial resolution of the offline emission map, as well as the fact the simulated DOD directly relates to measured z0m and z0s and R and E. The online version uses values for u*/Uh.
Nevertheless, the online version of the model still overestimates annual dust emissions mainly due to the fact that it smoothes meteorological fields down to coarse resolution and ignores vegetation sheltering effects. This artificial dependence on the model resolution is overcome through the introduction of prognostic climateological albedo layers in the dust module, and their use as inputs to the model.
Whether mining or agriculture, dust is not good for the environment. Dust contaminates the air we breathe, harms local fauna and flora, and can affect ecosystems far away. In addition, dust emissions can harm a company's image with regulators and community members as it is often seen as an indication that the company doesn't care about environmental sustainability or workplace safety.
When a company takes steps to avoid the emission of particulates with the use of atomized mist, it shows that they are committed to protecting worker health and the environment. In turn, it is likely that they will have a more positive image with regulators and the community, and ultimately save on legal fees and fines.
In order to prevent fugitive emissions, sources that generate more than 1/4 acre of dust are required to submit a Fugitive Dust Control Plan (FDCP) to the Utah Division of Air Quality (UDAQ). The opacity limits for visible dust are 20% on-site and 10% at property boundaries.
PM10 (particles of dust smaller than ten microns) are airborne particles that can cause various health effects. They include respiratory disease, heart attacks and other cardiovascular diseases, asthma and bronchitis. It can also affect mental well-being, cognitive function and even pregnancy.
PM10 consists of smoke, soot and metals as well as salts, acids, dusts, and salts emitted when loose soils are disturbed or natural surfaces are disturbed. It is usually carried by wind and can be transported over long distances.
Respirable crystalline silica is particularly dangerous. The particles are so small that they bypass the nose, throat and lungs and reach deep into the lungs. They can cause severe illness over time. Symptoms can include itchy, watery eyes and coughing. Children, the elderly and people with lung and heart disease are at high risk. Outdoor workers and exercise enthusiasts also pose a higher risk. Dust outbreaks may also cause bacterial infections.
In addition to reducing air quality, fugitive dust emissions can damage infrastructure (including buildings and vehicles), reduce crop yields and impact tourism. This reduces the number of leaves and fruits that can be used to feed wild animals. The result is a reduction in animal growth and health. Dust also contributes to glacier melt by scattering sunlight, and may pollute rivers with salts that are damaging to aquatic ecosystems.
Evaluating the performance of dust models is complicated by the fact that model results depend on a range of factors including meteorology, land use, vegetation and soil moisture. If the model is set to a high resolution in the meteorology section and this is not corrected, the dust emission thresholds for vegetated areas may be much lower than what has been observed.
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