Abstract
This work studied the impacts of ship emissions at a high temporal resolution on the real-time concentrations of PM2.5, NO2, and SO2 in urban harbors and coastal sea areas, taking the Yangtze River Delta (YRD) as an example. The WRF-Chem model with 3 nested grids and ship emissions derived from an automatic identification system (AIS) were combined to simulate the air quality. The AIS data showed significant temporal fluctuations in ship emissions, with hourly mean fluxes of approximately 1082.41 ± 444.41 and 593.55 ± 404.95 g/h/km2 near ports and in the channel waters of the YRD, respectively. The monthly mean contributions of shipping emissions reached 80.72% (2.15 ppbv) and 81.79% (8.79 ppbv) to ambient SO2 and NO2 in Ningbo Port, and 10.61% (6.96 μg/m3) to PM2.5 in Shanghai Port, respectively, regions with dense ship traffic. The relative differences in the PM2.5, SO2, and NO2 concentrations modeled using monthly and hourly ship emissions accounted for −10–15%, −10–30%, and − 5–30%, respectively. Compared with cruise- and land-based measurements, the simulations using hourly emissions were in much better agreement with the observations than those using monthly emissions and appropriately captured some air pollutant concentration peaks. Simulations during shipping-related periods with hourly ship emissions improved the normalized mean bias (NMBs) from −43.03%, 301.49%, and 223.02% to −27.28%, 90.45%, and 167.52%, respectively, for PM2.5, SO2, and NO2, highlighting the importance of using ship emissions with a fine temporal resolution. Our study showed that ignoring hourly fluctuations in ship emissions during air quality modeling leads to considerable uncertainties, especially in coastal urban areas and harbors with high ship activities. These results imply that data with a high temporal resolution, such as hourly ship emissions, are necessary to understand the realistic impacts of shipping traffic and to implement more precise control policies to improve coastal air quality.
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