An Air Pollution Control Method Based on Air Pollution Transmission Model

Abstract

The invention discloses an air pollution control method based on an air pollution transmission mode, which includes the following steps: (1) according to meteorological data and air pollution data, for each air monitoring station at each moment, construct its and surrounding air monitoring stations based on Sampling and simulation of air pollution transportation modeling to obtain air pollution transportation examples; (2) According to the air pollution transportation examples, the breadth search algorithm is used to connect continuous air pollution transportation instances in time and space to form a large number of air pollution transmission networks (3) Mining frequent subgraphs of a large number of air pollution transmission networks, extracting frequently occurring pollution transmission paths, and obtaining air pollution transmission patterns; (4) Adopting corresponding air control measures according to the air pollution transmission patterns. The air pollution control method can adopt corresponding air control means according to the air pollution transmission mode, and improves the air control effect.

Description

technical field [0001] The invention belongs to the field of air pollution control, and in particular relates to an air pollution control method based on an air pollution transmission mode. Background technique [0002] In recent years, air pollution has become a global concern due to its serious impact on many aspects of modern society, such as public health, environment and sustainable development. One of the first prerequisites for mitigating air pollution is understanding how pollutants travel over large spatial scales, allowing experts to determine the origin and evolution of pollution. However, capturing such transport processes remains highly challenging due to uncertain pollutant transport due to complex and dynamic wind fields. With the advancement of data sensing and management technologies, many data-driven solutions for monitoring, analyzing and predicting air pollution have been proposed based on the data collected by large-scale and widely distributed weather ...

AI Technical Summary

Problems solved by technology

While identifying potential sources of pollution in a given area to some extent, HYSPLIT fails to account for: a) the inherent uncertainty in dynamic pollutant transport, and b) the complex interplay between multiple cities

In the field of data science, many methods based on Bayesian learning and Granger causality have been proposed, but these methods cannot capture the continuous spread of air pollution across regions.

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