Low-cost calibration method for PM2.5 monitoring nodes

Abstract

The invention provides a low-cost calibration method for PM2.5 monitoring nodes. The method includes the following steps that the nodes are deployed near an air quality inspection station, and training samples consistent in time and space are obtained; models are built to show the relationship between a number read at each node and a PM 2.5 true value; training data is preprocessed, wherein parts of characteristics are standardized and a training sample set and a testing sample set are determined by means of a set-aside method; for a linear invariant model, a three-layered back-propagation neural network is adopted to train a multiple linear regression module on the training sample set, and verification of accuracy of the models is completed on the testing sample set; for a linear variable model, in the time interval, the training samples are fitted simply by means of the least square method to obtain a linear parameter, in different time periods, linear parameters, average values of node readings and average values of the sensitive characteristics data serve as new training samples, post-pruning-strategy-based CART regression tree training is adopted on the new training samples, and verification of reliability of the models is completed on the testing sample set; an off-line model which is verified to be accurate is written to a node program.

Description

technical field [0001] The invention relates to a low-cost PM2.5 monitoring node calibration method, in particular to obtain reliable time-space consistent data samples, and select appropriate machine learning models for data samples of indoor environment and outdoor environment. Background technique [0002] The main components of the air quality index (AQI) include fine particulate matter (PM2.5), inhalable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO) and other pollution The measured concentration value of the substance. Among them, fine particulate matter (PM2.5) refers to particulate matter with a diameter less than or equal to 2.5 microns. Fine particles have a large area, strong activity, are easy to be accompanied by toxic and harmful substances, and have a long residence time in the atmosphere and a long transportation distance, because the smaller the diameter, the deeper the part entering the respiratory...

AI Technical Summary

Problems solved by technology

However, this method of estimating based on historical data has an important flaw: it cannot make timely responses to changes in PM2.5 concentration within the interval

Defects in 1) can be solved by deploying a large number of PM2.5 monitoring nodes in 2), but the low accuracy of node readings has become a problem that cannot be ignored

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