Scholarlink

<p>Particulate matter (PM) mass closure, also known as mass reconstruction (RM), establishes a balance between gravimetrically measured PM mass (MM) and the sum of its analyzed chemical components. It aims to reduce sampling artifacts, account unmeasured mass, and ensure measurements reliability. However, it is laborious, time consuming and incapable to capture non-linear atmospheric complexity. To address this, we have developed a novel machine learning based Two Step Regression model designed to compute non-linear and linear components sequentially subject to mass closure constraints. Model is developed from speciation dataset measured in coal-mining area of Singrauli—Sonebhadra, India. Its robustness and chemical plausibility have been ensured via step-wise hyper-parameter tuning and explainable AI (SHAP) respectively. The Extreme gradient boosting—Ridge regression (XGB-RR) with intermediate conditional constraint has performed better relative to others with low RMSE, and high R<sup>2</sup>. The optimized XGB-RR model is applied for PM<sub>10</sub> and PM<sub>2.5</sub> dataset at two distinct elevations. The model yields linearly resolved RM having strong correlation (≥ 0.97) with MM and chemically explainable components having maximum deviation (~ 3%) from the actual measurements. The model offers scalable, frequent and interpretable alternative for offline and online source apportionment (SA) studies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development of 2-step regression model for reconstruction of ambient air PM mass: Theoretical, and machine learning approaches

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月3日

查看全文
  • Jay Singh Rajput,
  • Neel Kamal,
  • K. V. George

摘要

Particulate matter (PM) mass closure, also known as mass reconstruction (RM), establishes a balance between gravimetrically measured PM mass (MM) and the sum of its analyzed chemical components. It aims to reduce sampling artifacts, account unmeasured mass, and ensure measurements reliability. However, it is laborious, time consuming and incapable to capture non-linear atmospheric complexity. To address this, we have developed a novel machine learning based Two Step Regression model designed to compute non-linear and linear components sequentially subject to mass closure constraints. Model is developed from speciation dataset measured in coal-mining area of Singrauli—Sonebhadra, India. Its robustness and chemical plausibility have been ensured via step-wise hyper-parameter tuning and explainable AI (SHAP) respectively. The Extreme gradient boosting—Ridge regression (XGB-RR) with intermediate conditional constraint has performed better relative to others with low RMSE, and high R2. The optimized XGB-RR model is applied for PM10 and PM2.5 dataset at two distinct elevations. The model yields linearly resolved RM having strong correlation (≥ 0.97) with MM and chemically explainable components having maximum deviation (~ 3%) from the actual measurements. The model offers scalable, frequent and interpretable alternative for offline and online source apportionment (SA) studies.