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Pure Mathematics 2025
基于两阶段自适应Lasso方法的乌鲁木齐市PM2.5浓度建模研究
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Abstract:
细颗粒物浓度是评估空气质量的重要指标之一，精确预测PM2.5浓度仍是国内外研究的挑战，也是政府和学术界关注的重点。文章以2014年1月1日至2024年9月30日的乌鲁木齐市PM2.5日平均浓度为研究对象，对其线性回归模型应用两阶段自适应Lasso多变点检测与估计方法检测并估计回归系数的多变点，建立了更精准的分段线性回归模型来刻画乌鲁木齐市PM2.5日平均浓度。
The concentration of fine particulate matter (PM2.5) is one of the key indicators for assessing air quality. Accurate prediction of PM2.5 concentration remains a challenge in both domestic and international research and is a major focus of attention for governments and scholars. This study takes the daily average PM2.5 concentrations in Urumqi City from January 1, 2014 to September 30, 2024 as the research object. A two-stage adaptive Lasso multiple change-point detection and estimation method is applied to detect and estimate multiple change points in the regression coefficients of the linear regression City daily average PM2.5 concentrations in Urumqi City.
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