Towards more realistic modelling of tropospheric NO2 with WRF-Chem using fine-tuned temporal emission profiles

NO₂ is an important air pollutant and has been recognized for its hazardous impact on human health. Although routine in-situ measurements of NO₂ are available in many regions of the earth, models for regional chemistry and transport (RCT) are often used to predict trace gas concentrations where no direct measurements are available. An important aspect of realistic NO₂ modelling is to use accurate NO_x emissions with high temporal resolution. The standard practice is to use a monthly or yearly resolved emission inventory in combination with sector-specific hourly emission weights (“temporal profiles”) in order to simulate diurnal, weekly, and seasonal emission patterns. Temporal profiles are typically derived from empirical data, e.g. car counts on highways, and have been known to improve RCT simulations significantly. Nonetheless, in comparison against in-situ measurements, simulated NO₂ concentrations are usually too low at daytime and too high at nighttime, with relative deviations of up to 50 %. This hints towards faulty temporal emission profiles.

We present a novel method to determine improved temporal emission profiles for NO_x emissions in a WRF-Chem simulation for May 2019 in central Europe. The temporal profiles are determined in an iterative procedure that consists of running the simulation, comparing the simulated NO_x concentrations to in-situ reference measurements, and adjusting the hourly temporal profiles to compensate deviations between simulation and reference values. In a subsequent intercomparison of model results with observational datasets (surface concentrations from in-situ measurements, tropospheric vertical column densities from the TROPOMI satellite instrument, and concentration profiles from MAX-DOAS retrievals), we validate our simulation results. In particular, the typical NO₂ underestimation at noontime is resolved and the monthly average of simulated vertical column densities deviates less than 7% from the TROPOMI reference data.