Study on the Influencing Factors and Prediction of Air Quality in California Based on Multiple Linear Regression and Gaussian Process Regression Models

Jiandong Shan

2024

Abstract

In current environmental science and public health research, air pollution is a key issue in the process of global industrialization and urbanization, and thus systematic studies on air quality are of great importance. This study is devoted to analyzing the historical air quality data in California from 1980 to 2022, using multiple linear regression and Gaussian process regression models to thoroughly investigate the impacts of major pollutants on air quality and their interactions, and to forecast air quality for the next three years. The research identifies an overall improving trend in air quality, particularly marked by a significant short-term enhancement during the COVID-19 pandemic due to reduced human activities. However, as economic activities resume, future air quality may face emerging challenges. Additionally, the significant influence of interactive effects among pollutants reveals the complexity of air quality management. The findings of this study provide robust data support and a theoretical basis for formulating scientific environmental policies and improving air quality, emphasizing the necessity for adaptive strategies and proactive monitoring to ensure sustainable air and environmental health.

Paper Citation

in Harvard Style

Shan J. (2024). Study on the Influencing Factors and Prediction of Air Quality in California Based on Multiple Linear Regression and Gaussian Process Regression Models. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 40-46. DOI: 10.5220/0012818600004547

in Bibtex Style

@conference{icdse24,
author={Jiandong Shan},
title={Study on the Influencing Factors and Prediction of Air Quality in California Based on Multiple Linear Regression and Gaussian Process Regression Models},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={40-46},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012818600004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Study on the Influencing Factors and Prediction of Air Quality in California Based on Multiple Linear Regression and Gaussian Process Regression Models
SN - 978-989-758-690-3
AU - Shan J.
PY - 2024
SP - 40
EP - 46
DO - 10.5220/0012818600004547
PB - SciTePress