Sensitivity Analysis Of Parameters In AERMOD Model

Atmospheric Environmental Impact Assessment is an important component of environmental impact assessment system, which is an influential environmental protection system. It provides a vital basis for determining the appropriateness of the proposed site and scale of the construction projects or planning projects by evaluating to what degree these projects impact the regional atmospheric environment after their completion.The basic prediction tool of atmospheric environmental impact is the atmospheric diffusion model, including the box-type model, Gaussian model, Lagrangian/Eulerian model, computational fluid dynamics model, and aerosol dynamics model. Currently, the mainstream international atmospheric dispersion models belong to the Gaussian model. Based on model ISC3, AERMOD (AERMIC Model) model was developed by the AMS/EPA Regulatory Model Improvement Committee AERMIC made up of U.S. Environmental Protection Agency and the American Meteorological Society It is a steady-state plume dispersion model, which can simulate the short term (hourly average, daily average), long-term (annual average) concentrations distribution of the pollutants emitted from the point source, point source, and the body source according to the data of atmospheric diffusion boundary layer. This model takes into account the impact of the building wake, namely plume downwash, which is applicable to rural or urban areas, simple or complex terrain.In order to facilitate the understanding of the operation mechanism of the model, we briefly introduce the algorithm of AERMOD model, particularly focusing on the influence of surface meteorological parameters, high-altitude meteorological parameters and topographic parameters on AERMOD operation results. The parameter sensitivity analysis is divided into two steps:1) to determine if the changes in the parameters will affect AERMOD model operation results. If so, we will further analyze its sensitivity. If not, we will ignore them in the follow-up research;2) to use Oneway ANOVA analysis to further study the sensitivity of these parameters and put them in the order.The results show that, among parameters including time, the universe level radiation, vertical radiation of the universe, the global levels of radiation, vertical radiation, the level of scattered radiation, total cloud amount, low cloud cover, dry bulb temperature, dew point temperature, relative humidity, observatories surface pressure, wind direction, wind speed, wet bulb temperature, wind speed, rainfall, type of precipitation, sea level pressure, cloud base height, horizontal visibility, current weather, precipitation, broadband aerosol optical thickness, snow depth, distance on snow days, hours, rainfall, and many other surface meteorological parameters, only the wind direction, wind speed, site pressure, cloudiness, and the dry bulb temperature will affect the model operation results without considering settlement factors. The parameter sensitivity order is like this:wind speed cloud amount>dry bulb temperature>site pressure.Terrain parameters (reflectivity, Bonn, roughness) have great impact on the result of the operation and therefore when determining the parameter values, we should be science-based and resort to relevant certification authorities. Different land use types also have considerable impact on the diffusion of pollutants concentration. Under the same conditions, different surface pollutant concentrations are ordered like this:wetland> desert shrubs> farmland> grassland> deciduous forest> Cities> coniferous forest> the water, without the magnitude difference.As to the impact of the high-altitude meteorological data on AERMOD model computation results, there is no conclusive evidence between the AERMOD aerological estimates and mesoscale model simulations. Both have their own advantages and disadvantages. Result indicates that it’s not necessary to use the high-altitude mesoscale simulation data, and it depends on the specific situation.This study will help the environmental assessment workers screen and optimize the relevant parameters under the existing meteorological data and surface data, estimate the potential error, so that the atmospheric prediction in the environmental assessment is of more practical significance.