Air Control Toolbox (ACT_v1.0): a flexible surrogate model to explore mitigation scenarios in air quality forecasts

Abstract

Abstract. We introduce the first toolbox that allows exploring the benefit of air pollution mitigation scenarios in the every-day air quality forecasts through a web interface. Chemistry-transport models (CTMs) are required to forecast air pollution episodes and assess the benefit that shall be expected from mitigation strategies. However, their complexity prohibits offering a high level of flexibility in the tested emission reductions. The Air Control Toolbox (ACT) introduces an innovative automated calibration method to cope with this limitation. It consists of a surrogate model trained on a limited set of sensitivity scenarios to allow exploring any combination of mitigation measures. As such, we take the best of the physical and chemical complexity of CTMs, operated on high-performance computers for the every-day forecast, but we approximate a simplified response function that can be operated through a website to emulate the sensitivity of the atmospheric system to anthropogenic emission changes for a given day and location. The numerical experimental plan to design the structure of the surrogate model is detailed by increasing level of complexity. The structure of the surrogate model ultimately selected is a quadrivariate polynomial of first order for residential heating emissions and second order for agriculture, industry and traffic emissions with three interaction terms. It is calibrated against 12 sensitivity CTM simulations, at each grid point and every day for PM10, PM2.5, O3 (both as daily mean and daily maximum) and NO2. The validation study demonstrates that we can keep relative errors below 2 % at 95 % of the grid points and days for all pollutants. The selected approach makes ACT the first air quality surrogate model capable to capture non-linearities in atmospheric chemistry response. Existing air quality surrogate models generally rely on a linearity assumption over a given range of emission reductions, which often limits their applicability to annual indicators. Such a structure makes ACT especially relevant to understand the main drivers of air pollution episode analysis. This feature is a strong asset of this innovative tool which makes it also relevant for source apportionment and chemical regime analysis. This breakthrough was only possible by assuming uniform and constant emission reductions for the four targeted activity sectors. This version of the tool is therefore not suited to investigate short-term mitigation measures or spatially varying emission reductions.