Using Lidar Technology and the STILT Model to Assess Air Pollution and Improve Estimates of Greenhouse Gas Emissions in Cities

Abstract

Simulation of the planetary boundary layer (PBL) is key for forecasting air quality and estimating greenhouse gas (GHG) emissions in cities. Here, we conducted the first long-term and continuous study of PBL heights (PBLHs) in Boston MA, using a compact lidar (light detection and ranging) instrument. We developed an image recognition algorithm to estimate PBLHs from the lidar measurements and evaluated simulations of the PBL from eight numerical weather prediction (NWP) model versions, which showed different systematic errors and variability in simulating the PBLHs. The NWP model with the best overall agreement for the fully developed PBL had R2= 0.72 and bias of only 0.128 kilometers (km). However, this model predicted a number of anomalously high carbon dioxide concentrations at ground stations, because it occasionally underestimated PBLH to a significant extent. We also developed a novel method that combines lidar data, vertical footprints from the Stochastic Time-Inverted Lagrangian Transport (STILT) model, and a high-resolution anthropogenic carbon dioxide (CO2) emissions inventory to detect, identify, and estimate transboundary air pollution within the nocturnal residual layer (RL). Using our novel method, we identified 42 transboundary air pollution episodes within the nocturnal RL in Boston during a 5-month study period in 2014. A quantitative index for transboundary air pollution was obtained as mean pressure-weighted column CO2 enhancements (∆CO2) contributions in Boston from northeastern states, as simulated by a high-resolution model. Results from this work were used to evaluate the performance of models used to estimate air pollution and GHG fluxes. Such evaluation is critical to track progress on emission reduction targets and guide city, state, and regional policies.