Deep Learning-Based Prediction of Particulate Matter and Application of Air Quality Health Impact Indicators in Industrial Complexes
Year: 2025
Type: article
Abstract: Particulate matter (PM) has been classified by the International Agency for Research on Cancer under the WHO as a Group 1 carcinogen, posing a serious environmental health risk. Outdoor workers in industrial complexes face particularly high exposure compared to the general population. This study developed a deep learning–based prediction model to manage fine particulate matter exposure among industrial complex outdoor workers and evaluated the applicability of the Air Quality Health Index (AQHI) as a health impact assessment tool. Hourly air quality data collected from the Yeosu National Industrial Complex between 2023 and 2024 (PM2.5, PM10, O3, NO2, CO, SO2) were used to construct LSTM- and GRU-based prediction models. Model performance was assessed through input sequence length optimization and k-step-ahead forecasting to evaluate reliability across prediction horizons. Results showed that PM2.5 achieved optimal performance with 6–24 h input sequences, while PM10 reached its best performance with 336 h input sequences. In k-step-ahead forecasting, one-hour predictions yielded the highest accuracy, with reliable ranges up to 2 h for PM2.5 and 1 h for PM10 based on an R2 threshold of 0.7. Predicted AQHI exhibited patterns consistent with observed AQHI, confirming its potential as a health-oriented evaluation index. Furthermore, by introducing the k-step-ahead approach, this study mitigated error accumulation inherent in recursive multi-step forecasting and applied the Hong Kong AQHI methodology, thereby contributing to health risk assessment for outdoor workers in industrial complexes.
Source: 대한환경공학회지
Authors: Jiwon Seo, Jeongeon Oh, Hyeongseok Lee, Minsu Son, Seungcheol Na, Seongjun Kim, Yong-Gyun Park
Institution: Chonnam National University
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
Funder: Ministry of Environment
OpenAlex ID: W7083295483