Harmonizing low-cost and regulatory air quality monitoring networks with interpretable semi-supervised learning: Reducing exposure misclassification in underrepresented communities.

• Tác giả: Michael L Grieneisen, Tan Mi, Die Tang, Fumo Yang, Mingle Yang, Yu Zhan, Xi Zheng
• Ngôn ngữ: eng
• Ký hiệu phân loại: 636.681 Swans
• Thông tin xuất bản: Netherlands : Journal of hazardous materials , 2025
• Bộ sưu tập: NCBI
• ID: 695266

Tóm tắt

Low-cost sensors (LCSs) can address gaps in regulatory air quality monitoring station (AQMS) distribution, but they face data quality issues and spatial misalignment challenges when calibrating large-scale LCS networks against AQMS networks. This study proposed a semi-supervised learning model that uses data augmentation via chained imputation (CI-DA) to address the spatial misalignment problem by synthesizing pseudo-LCS data, thereby enhancing the use of LCS in PM