Smart Monitoring and Ozone Precursor Analysis in the Port Area of Central Taiwan

This study focuses on volatile organic compound (VOC) emissions at the western terminal of Taichung Port, where 15 advanced air quality sensors were deployed to establish an intelligent monitoring network. Site selection was based on historical pollution hotspots, prevailing wind directions, and the presence of high-emission industrial facilities. The deployment employed a “smart fence” strategy, featuring sensors equipped with wind speed and direction modules to identify pollutant sources and transport dynamics, thereby providing real-time data to support air quality management. To ensure data reliability, sensor calibration and validation were conducted for O₃, NO₂, CO, VOCs, temperature, and humidity. The temperature and humidity sensors demonstrated strong correlations (R² > 0.8) and were effectively corrected using linear regression. O₃ sensors showed high correlation (R² > 0.9) and were successfully adjusted (RMSE reduced to 4.63 ppb).

From September to December 2024, VOC concentrations were further monitored across 13 industrial parks in Taichung City. Results revealed considerable spatial and temporal variability, as well as short-term high concentration events. Parks such as Renhua, Wufeng, Central Taiwan Science Park, and Fengzhou exhibited notably high VOC levels and standard deviations, indicating the presence of occasional emission sources. Many monitoring sites displayed standard deviations 3–5 times greater than the mean, highlighting frequent transient pollution events. It is recommended that local authorities intensify source tracking and real-time control measures in identified hotspots.

Additionally, a Positive Matrix Factorization (PMF) analysis identified six major VOC sources: vehicle emissions, biomass burning, fuel evaporation, industrial emissions, background pollutants, and solvent use. The study’s finding of 28.3% for vehicle emissions in Taichung Port aligns with existing literature, indicating consistency in source profiles. An O₃ prediction model was also developed using data from Dali Traffic Station and the advanced sensors, applying both XGBoost and ANN algorithms. XGBoost demonstrated superior performance (R² up to 0.88). SHAP analysis identified relative humidity, temperature, and NOₓ as the most influential variables. This model supports real-time O₃ prediction and hotspot identification.