Method for evaluating short-term exposure concentration of air pollutants of people

Abstract

The invention discloses a method for evaluating short-term exposure concentration of air pollutants of people. The method mainly comprises the following steps: 1, acquiring the following data: (1) land utilization variable data; (2) traffic road distribution variable data; (3) meteorological factor data; (4) daily air pollutant concentration data of the monitoring station; and 2, constructing a model: according to the data in the step 1, fitting the relationship among the land utilization variable, the traffic road distribution variable, the meteorological factor and the daily air pollutant concentration data of the monitoring station. According to the method, the space-time regression model is utilized to popularize a traditional land utilization regression model from a single consideration space dimension to a time and space dimension; meteorological change grid data with high temporal-spatial resolution is obtained by utilizing satellite remote sensing data; and the short-term exposure level of the air pollutant concentration of the crowd is effectively estimated under the condition of considering the short-term trend and the long-term trend of the air pollutant concentration change.

Description

technical field [0001] The invention belongs to environmental science, relates to environmental risk factors and health, in particular to a method for predicting short-term exposure of crowds to air pollutants. Background technique [0002] Air pollution has become one of the major environmental risk factors affecting the health of people in both developed and developing countries. How to effectively predict the air pollution exposure of the population has become a key link in the assessment of air pollution and population health. In the past ten years, environmental and epidemiological researchers in various countries have proposed a variety of methods for the assessment of population exposure to air pollutant concentrations. Among them, the most widely used methods include: (1) Inverse distance weighting algorithm (Inverse distance weighting) :IDW): consider evaluating the pollutant concentrations of air pollution monitoring stations at a certain spatial distance around t...

AI Technical Summary

Problems solved by technology

However, because the variables that the land use regression model mainly depends on, such as land use and road traffic, have very little variation in the short term, it is only suitable for predicting the long-term exposure level of air pollutants in the population, and cannot effectively estimate the short-term exposure level of air pollutants in the population.

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