Leveraging Diverse Observations of Atmospheric Composition and Model-Based Sensitivity Analysis to Refine Estimates of Air Pollution Emissions and Impacts

Congmeng Lyu

doi:10.17918/00000583

Ground-level ozone, which forms photochemically in the atmosphere from precursor emissions of oxides of nitrogen (NO_x) and volatile organic compounds, is a criteria pollutant that harms human health and public welfare. For a representative summer episode, premature mortality and potential productivity losses (PPLs) of selected crops and trees attributable to ozone exposure have been quantified using ozone fields from the Community Multiscale Air Quality (CMAQ) model. We applied exposure-response models for the increased risk of premature mortality due to long-term exposure to ozone over a theoretical minimum risk exposure level (TMREL) and for the reduced accumulation of vegetative biomass for four crop species and eleven tree species using the W126 metric designed to capture impacts on plants. To elucidate which emissions contributed to these disbenefits, we applied adjoint-based sensitivity analysis, which efficiently estimates sensitivities of concentration-based metrics with respect to numerous emissions parameters simultaneously. The adjoint of CMAQ was applied to the continental U.S. to calculate the influence of spatially-resolved ozone precursor emissions on the annual average, domain-wide daily maximum 8-hour average over the TMREL (elevated MDA8), premature mortality attributable to exposure to ozone above the TMREL, and PPLs. These quantities provide the impact in terms of the percent reduction in precursor emissions. Additionally, locations where similar percent reductions in ozone precursor emissions would impact one or more endpoints greater than average have been identified. NOx emissions were found to contribute most to the three metrics. The distinct spatial patterns of emissions influences on public welfare disbenefits as compared to the elevated MDA8 and premature mortality suggest that the current regulatory averaging time motivates different emissions control strategies than those that could most directly protect public welfare. Recently, oil and natural gas (O&NG) production activities in the Denver-Julesburg Basin have expanded rapidly. Associated nonmethane hydrocarbon (NMHC) emissions contribute to photochemical formation of ground-level ozone and include benzene as well as other hazardous air pollutants. Using positive matrix factorization (PMF) and chemical mass balance (CMB) methods, we estimate how much O&NG activities and other sources contribute to morning NMHC mixing ratios measured from 2013 to mid-2016 at a site in Platteville, CO, in the Denver-Julesburg Basin, and at a contrasting site in downtown Denver. A novel adjoint sensitivity analysis method is then used to estimate corresponding contributions to ozone and ozone-linked mortality in the Denver region. Average 6 - 9 am NMHC mixing ratios in Platteville were seven times higher than those in Denver in 2013 but four times higher in 2016. CMB estimates that O&NG activities contributed to the Platteville (Denver) site an average of 96% (56%) of NMHC on a carbon basis while PMF indicated 92% (33%). Average vehicle-related contributions of NMHC are estimated as 41% by CMB and 53% by PMF in Denver. Estimates of the fractional contribution to potential ozone and ozone-linked mortality from O&NG activities are smaller while those from vehicles are larger than the NMHC contributions. CMB (PMF) indicate that greater than 78% (40%) of annual average benzene in Denver is attributable to vehicle emissions while greater than 75% (67%) of benzene in Platteville is attributable to O&NG activities. The Community Multiscale Air Quality (CMAQ) model calculates the impact of emissions on atmospheric composition, including inorganic aerosols, while considering the transport and reactions of chemical constituents. Adjusting emissions by comparing modeled concentrations with observations is justified when the science processes are well understood as is the case for inorganic species such as ammonia (NH3). The Finite Difference Mass Balance (FDMB) method and four-dimensional variational (4D-Var) data assimilation leverage differences in simulated and actual observations to revise estimates of emissions with spatial specificity. In this study, we evaluate the capability of a CMAQ-based data assimilation system to improve NH3 emissions, which are relatively uncertain given the diversity of emissions processes in the agricultural sector. To do so, the iterative FDMB and a Python-based four-dimensional variational framework (Py4Dvar) are integrated with CMAQ and its adjoint to constrain NH3 emissions with observations from the satellite-based Cross-track Infrared Sounder (CrIS). Observing System Simulation Experiments (OSSEs) are conducted with the CrIS observation operator to evaluate the extent to which emissions are expected to be recovered with the hybrid assimilation framework. The OSSEs conducted with the 2007 modeling platform and 2016 CrIS data on a regional domain in Georgia and the OSSEs conducted with the 2017 modeling platform and 2017 CrIS data on the continental U.S. domain result in promising recovery of the true emissions.

Leveraging Diverse Observations of Atmospheric Composition and Model-Based Sensitivity Analysis to Refine Estimates of Air Pollution Emissions and Impacts

Files and links (1)

Abstract

Metrics

Details

Leveraging Diverse Observations of Atmospheric Composition and Model-Based Sensitivity Analysis to Refine Estimates of Air Pollution Emissions and Impacts

Files and links (1)

Abstract

Metrics

Details

Drexel University Social media