Creating a sound emissions inventory using fast-responsemonitoring equipment and inverse modeling techniques:Mexicali/Imperial Valley as a case study

摘要

Emissions inventories are a key component of any air quality improvement program. In Mexico,rninformation on source strengths and chemical composition is still scarce and, in many ways,rnuncertain. This is a problem since decisions on control strategies and airshed managementrnpolicies are being made based on a poor understanding of the main sources of pollution. We arernexploring combining novel monitoring and modeling techniques to construct more accuraternemissions inventories. As a case study, we are using the international airshed shared by Mexicornand the United States in the region of the Mexicali and Imperial Valleys. This paper presents thernconceptual framework on which we are planning to support our methodology.rnThe methodology includes the use of a mobile laboratory equipped with fast response monitorsrnfor a variety of gas-phase and aerosol species, and an inverse modeling technique coupled with arnchemistry-transport air quality model. The mobile laboratory is equipped with tunable laserrndifferential absorption spectroscopy (TILDAS) instruments, an aerosol mass spectrometerrn(AMS), and commercial non-dispersive infrared (NDIR) and condensation particle counterrndevices, among other commercial instruments. With this, measurements of gas-phase and finernparticulate matter emissions, all ratioed to NDIR plume excess CO2 measurements can bernobtained. The mobile lab operation modes include: on-road fleet characterization, selectedrnvehicle class on-road chase, stationary mode, and urban core mapping of pollutant distributions.rnThese modes provide direct information to bottom-up emissions inventory constructionrnprocesses, as well as high spatial and temporal resolution maps for inverse model determinationrnof emission profiles (“top-down” approach). The modeling platform consists of the Models-3rnsuite (SMOKE, MM5 and CMAQ), with the air quality model extended with fast, directrnsensitivity analysis and chemical four-dimensional data assimilation capabilities (inversernmodeling module based on ridge regression).