Multi-scale concurrent modeling of air pollutants and greenhouse gases over greater Boston. Part 1: Methodology and meteorological evaluation

Air pollution and climate change are two major environmental concerns that exert adverse impacts on human health and ecosystem, especially in densely populated urban centers. Chemical transport modeling is a powerful tool to track the formation, transport, and removal of air pollutants and greenhouse gases (GHGs) to support policy making. An advanced online-couple model, Weather Research and Forecasting model coupled with Chemistry and Greenhouse Gases (WRF-Chem-GHG) is applied over a triple-nested domain in Greater Boston during representative months. The accuracy of meteorological prediction is essential for air pollution prediction as it plays an important role in transport and dispersion. In this Part I paper, a comprehensive evaluation is carried out to assess the model performance of some important meteorological parameters against weather stations or reanalysis data, as well as aerosol-cloud-radiation variables against satellite retrievals. The sensitivity of meteorological parameters to grid resolutions is also examined. The model generally reproduces most of the meteorological parameters with similar bias trends. Temperature at 2-m (T2) and planetary boundary layer height (PBLH) are constantly underpredicted while relative humidity at 2-m (RH2), wind speed and wind direction at 10-m (WS10 and WD10) are mostly overpredicted. Most meteorological parameters show better performance at finer grid resolutions, indicating the benefits of applying fine grid spacing in capturing the urban-scale meteorological variations. The sensitivity simulations with different land surface models (LSMs) and planetary boundary layer (PBL) schemes reveal their impacts on the PBLH and boundary-layer meteorological parameters, emphasizing the importance of choosing proper physical configurations. • First concurrent modeling of air pollutants and CO 2 in Greater Boston at 1-km resolution. • Most meteorological parameters are better captured at finer grid resolutions. • MYNN3 performs the best for meteorological predictions among tested PBL schemes.