A varying-coefficient regression approach to modeling the effects of wind speed on the dispersion of pollutants View Full Text


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Article Info

DATE

2022-04-23

AUTHORS

Kejun He, Yifan Wang, Wei Su, Hanfang Yang

ABSTRACT

The real-world monitoring system of air pollution ordinarily collects data about pollutant concentration levels at pollution sources and monitors stations in a high-frequency manner. Inspired atmospheric models, the meteorological conditions could play an important role in building up the data-driven model for dispersing atmospheric pollutants from pollution sources to monitor stations. We propose a varying-coefficient model to analyze how emissions of monitor stations are influenced by pollution sources with changing with the wind speed. To estimate the unknown coefficient curves, we use a spline basis to approximate the functions. The asymptotic properties of the proposed method are studied and show the consistency of the estimator. Inference procedures based on a resampling subject bootstrap is developed to construct the conservative confidence bands. A simulation study is carried out to demonstrate the performance of our method. Illustrated by a real-world dataset of environmental sensors collected in Shenyang, China, the proposed varying-coefficient model reveals that the wind speed changes the dispersion mechanism of atmospheric pollutants between monitor stations and pollution sources. More... »

PAGES

433-452

References to SciGraph publications

  • 2005. Functional Data Analysis in NONE
  • 1978. A Practical Guide to Splines in NONE
  • 2017-04-08. Efficient estimation of longitudinal data additive varying coefficient regression models in ACTA MATHEMATICAE APPLICATAE SINICA, ENGLISH SERIES
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    http://scigraph.springernature.com/pub.10.1007/s10651-022-00535-6

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    http://dx.doi.org/10.1007/s10651-022-00535-6

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