Advantages of Continuous Monitoring of Hourly PM2.5 Component Concentrations in Japan for Model Validation and Source Sensitivity Analyses
Satoru Chatani, Syuichi Itahashi, Kazuyo Yamaji
Abstract
Continuous monitoring of hourly PM 2.5 component concentrations has been performed in Japan. The objective of this study was to evaluate the advantages of continuous monitoring to obtain data that can be useful for regional air quality simulations. Inclusion of transboundary transport in the simulations improved the correlation between the observed and simulated hourly concentrations of SO 4 2− , SO 3 − , secondary organic aerosols (SOA), and metals in PM 2.5 . Black carbon was an exception, suggesting the overestimation of emissions in upwind countries. Including volcanic and dust emissions also improved the correlations between the observed and simulated hourly concentrations of SO 4 2− and metals, respectively. However, despite the good correlation achieved by including transboundary transport, it also resulted in overestimated NO 3 2− and SOA concentrations in western Japan during the winter. Further improvements are necessary, such as balancing with SO 4 2− and the dry deposition of gaseous HNO 3 for NO 3 − , and new treatment of the partitioning and aging of semivolatile organic aerosols, which have been incorporated into recent models for SOA. The differences in model performance with regard to simulating metal concentrations suggest imbalances in the speciation profiles used for countries other than Japan. Further, comparing the observed and simulated hourly concentrations helped identify the key processes driving air quality. This revealed evening peaks in black carbon concentrations, owing to the relatively stable atmosphere; and early morning peaks in NO 3 − concentration, owing to the low temperature and high humidity through thermodynamic equilibrium. This study demonstrated that continuous monitoring of hourly variations in PM 2.5 composition is valuable for understanding the roles of the emission sources and for improving future models, both of which contribute to deriving effective PM 2.5 suppression strategies.