Inversing NOx emissions based on an optimization model that combines a source-receptor relationship correction matrix and monitoring data: A case study in Linyi, China

Abstract

Inversion based on observational data and the source-receptor relationship (SRR) simulated by an air quality model is an effective means to estimate NO_x emissions. However, the SRR bias induced by the inherent uncertainty of the simulated model leads to potential errors in the inversed emissions, but this is seldom considered in NO_x inversion. In this study, we constructed an inversion model based on the SRR correction (IMSC) by introducing a correction matrix, combined with joint regularization scheme and genetic algorithm. We innovated the dynamic acquisition method of center-restricted parameters for the correction matrix, combined this with other parts of the IMSC, attaining the multi-month and multi-region pollutant emission inversion. Hypothetical examples demonstrated that the IMSC effectively corrected the SRR and accurately estimated the NO_x emissions. The IMSC was used to estimate Linyi county-level NO_x emissions for January, April, July and October (typical months) during 2020 and 2021. An inversion model without SRR correction (IMWSC) was developed for comparison with the IMSC. Results showed that the IMSC more accurately, robustly, and reasonably estimated NO_x emissions. Compared to the IMWSC, the IMSC improved the mean correlation between NO_x emissions and NO₂ observational concentrations by 25.0%, enhancing the correlation between NO_x emissions and NO₂ column concentrations by 111.3%. The similar NO_x emission change ratios (σ_average = 5.1%) between the typical months in 2021 and 2020 among the different counties indicated a more robust performance of the IMSC than the IMWSC (σ_average = 55.2%). In addition, the NO_x emissions inversed by the IMSC also showed better consistency with social activity levels (i.e., electricity consumption). The typical monthly Linyi's county-level NO_x emission characterization was also studied. NO_x emissions were lower in April, July, and October 2021 than the same period in 2020 due to COVID-19 and pollution controls. This study provides strategies for swiftly and accurately estimating pollutant emissions.