Human Health Risk Prediction Method of Regional Atmospheric Environmental Pollution Sources Based on PMF and PCA Analysis under Artificial Intelligence Cloud Model

Abstract

In order to solve the problem that atmospheric particulate matter has become the primary pollutant with serious harm and complex sources in recent years, this paper proposes an accurate identification method of pollution sources based on a receptor model to obtain the contribution rate of each pollution source category. This method takes the 75-day measured environmental receptor data of an area under the artificial intelligence cloud model as the basic data, uses the normrnd () function to expand the receptor data, and uses the positive definite matrix factor analysis (PMF) and principal component analysis (PCA) models to verify the rationality of the data expansion. The results are as follows: the number of extended simulated receptor component spectra has a certain effect on the PCA analysis results, but the effect is smaller than the extended range. All relative errors are less than 14%, and the relative error is the smallest when the six simulated receptor component spectra are expanded, that is, the PCA analysis results of the expanded data are most consistent with the measured data; the number of expanded simulated receptor component spectra has a certain influence on the PMF analysis results. But the relative error is less than 40%. When extending the spectrum of six simulated receptor components, the relative error is the smallest, that is, the PMF analysis results of the extended data are most consistent with the measured data. It is proven that this method provides a more direct basis for the targeted treatment of pollution sources that are more harmful to human health.

Retracted: Human Health Risk Prediction Method of Regional Atmospheric Environmental Pollution Sources Based on PMF and PCA Analysis under Artificial Intelligence Cloud Model

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”