DeepRSM: More accurate “emission-pollutant concentration” response surface model with significant cost reduction

This is not an ideal process in actual policy-making. Simulating one day’s data often takes 1-2 weeks to compute on a supercomputer cluster, so its timeliness is also very poor. More importantly, there may be as many as ten or more types of harmful gas emissions, and so it is often necessary to sample a lot of different emission reduction plans and carry out simulations using the CTM model in order to develop a more accurate approximation of the response surface. However, the CTM model involves a huge amount of calculation (solving partial differential equations for continuous integration of the time dimension), and is very time-consuming and labor-intensive. The algorithm complexity is represented by O(N), where N is the number of samples.ĬTM simulates several different emission reduction schemes randomly sampled The air quality corresponding to the different emission reduction schemes on the response surface is then used to estimate the coefficients of the polynomial function using the least squares method or interpolation. Therefore, to obtain a fine-grained, high-precision emission-pollutant concentration response surface model (RSM) is very necessary. A common method is to use a polynomial function RSM (pfRSM) to randomly sample multiple different emission reduction schemes (corresponding to different emission reductions of different pollutants) and use the CTM model to simulate air quality.

Characterize the relationship between emissions and pollutant concentrationĮnvironmentalists have developed a complex numerical model (CTM) to calculate the final concentration of pollutants in the air (air quality) from emissions. The basic principle is to use numerical functions to simulate the entire chemical reaction process of air pollutants, and then at the same time to simulate the final concentration of pollutants in the air by combining light, geographic, and meteorological information.Ītmospheric Chemical Transport Model (CTM)Īlthough the CTM model can satisfactorily simulate the relationship between air quality and emissions, it is far from enough to produce an accurate simulation of a particular emission reduction plan. For policy makers, what’s most important is to quickly identify the emission reduction plan with the best emission reduction effect and the lowest cost. Pollutant emission reduction is the most important method to curb air pollution, including factory shutdowns, motor vehicle restrictions, and coal use restrictions among residents. However, an intractable problem is that it’s difficult to deduce the relationship between air quality and emission reduction by simply following through with the latter, because the whole chemical process of having various reactant gases emitted into the air, reacting in the atmosphere, and finally generating pollutants is very complex and nonlinear. In the past few decades, the rapid progress of human industries and agriculture, the growth of the population, and harmful gases produced by human activities have caused serious air pollution and are endangering human health. In addition to causing lung diseases and human discomfort, studies have shown that in the past 25 years, the fatality rate associated with air pollution has ranked first among all environmental problems.

Controlair for windows