Multi-scale concurrent modeling of air pollutants and greenhouse gases over Greater Boston. Part 2: Chemical evaluation and sensitivity simulations

Criteria air pollutants (CAPs) and greenhouse gases (GHGs) are often co-emitted atmospheric chemicals that bear intertwined consequences on human health and the environment. This work evaluates performance of the newly developed air quality model that concurrently simulates CAPs and GHGs over triple-nested domains in Greater Boston. The model satisfactorily reproduces ozone with normalized mean bias (NMBs) within ±15% and normalized mean errors (NMEs) within 25% in most months and reduced NMBs and NMEs at finer grid resolutions. NMBs and NMEs of fine particulate matter (PM 2.5 ) can reach beyond benchmarks due to uncertainties in meteorology, emissions, chemical boundary conditions, and physical schemes for urban processes. Uncertainties in carbon dioxide simulations are mainly due to lack of reliable emission inventory and observations. To identify major sources of uncertainty, sensitivity simulations are carried out to investigate the impacts of planetary boundary layer (PBL) schemes, primary particle emission, urban canopy models (UCMs) and chemical boundary conditions. Compared to Yonsei University (YSU) PBL scheme used in the baseline, using Mellor, Yamada, Nakanishi and Niino Level 3 (MYNN3) reduces biases and errors for most meteorological and chemical parameters in January 2023, but not necessarily in July 2023. Using multilayer UCMs yields better prediction of PM 2.5 diurnal variations at urban sites but worse results at non-urban sites. Adjusting primary particle emissions and chemical boundary conditions improves model performance in January and July 2023. This work demonstrates that addressing uncertainties of UCM-PBL coupling and input data quality would benefit future model simulations at fine scales in urban areas.