Byeong-Uk Kim

The influence of model resolution on ozone in industrial volatile organic compound plumes

Abstract Regions with concentrated petrochemical industrial activity (e.g., Houston or Baton Rouge) frequently experience large, localized releases of volatile organic compounds (VOCs). Aircraft measurements suggest these released VOCs create plumes with ozone (O3) production rates 2–5 times higher than typical urban conditions. Modeling studies found that simulating high O3 productions requires superfine (1-km) horizontal grid cell size. Compared with fine modeling (4-km), the superfine resolution increases the peak O3 concentration by as much as 46%. To understand this drastic O3 change, this study quantifies model processes for O3 and “odd oxygen” (Ox) in both resolutions. For the entire plume, the superfine resolution increases the maximum O3 concentration 3% but only decreases the maximum Ox concentration 0.2%. The two grid sizes produce approximately equal Ox mass but by different reaction pathways. Derived sensitivity to oxides of nitrogen (NOx) and VOC emissions suggests resolution-specific sensitivity to NOx and VOC emissions. Different sensitivity to emissions will result in different O3 responses to subsequently encountered emissions (within the city or downwind). Sensitivity of O3 to emission changes also results in different simulated O3 responses to the same control strategies. Sensitivity of O3 to NOx and VOC emission changes is attributed to finer resolved Eulerian grid and finer resolved NOx emissions. Urban NOx concentration gradients are often caused by roadway mobile sources that would not typically be addressed with Plume-in-Grid models. This study shows that grid cell size (an artifact of modeling) influences simulated control strategies and could bias regulatory decisions. Understanding the dynamics of VOC plume dependence on grid size is the first step toward providing more detailed guidance for resolution. These results underscore VOC and NOx resolution interdependencies best addressed by finer resolution. On the basis of these results, the authors suggest a need for quantitative metrics for horizontal grid resolution in future model guidance.

Likelihood of Achieving Air Quality Targets under Model Uncertainties

Regulatory attainment demonstrations in the United States typically apply a bright-line test to predict whether a control strategy is sufficient to attain an air quality standard. Photochemical models are the best tools available to project future pollutant levels and are a critical part of regulatory attainment demonstrations. However, because photochemical models are uncertain and future meteorology is unknowable, future pollutant levels cannot be predicted perfectly and attainment cannot be guaranteed. This paper introduces a computationally efficient methodology for estimating the likelihood that an emission control strategy will achieve an air quality objective in light of uncertainties in photochemical model input parameters (e.g., uncertain emission and reaction rates, deposition velocities, and boundary conditions). The method incorporates Monte Carlo simulations of a reduced form model representing pollutant-precursor response under parametric uncertainty to probabilistically predict the improvement in air quality due to emission control. The method is applied to recent 8-h ozone attainment modeling for Atlanta, Georgia, to assess the likelihood that additional controls would achieve fixed (well-defined) or flexible (due to meteorological variability and uncertain emission trends) targets of air pollution reduction. The results show that in certain instances ranking of the predicted effectiveness of control strategies may differ between probabilistic and deterministic analyses.

Recent increase of surface particulate matter concentrations in the Seoul Metropolitan Area, Korea

Recent changes of surface particulate matter (PM) concentration in the Seoul Metropolitan Area (SMA), South Korea, are puzzling. The long-term trend of surface PM concentration in the SMA declined in the 2000s, but since 2012 its concentrations have tended to incline, which is coincident with frequent severe hazes in South Korea. This increase puts the Korean government’s emission reduction efforts in jeopardy. This study reports that interannual variation of surface PM concentration in South Korea is closely linked with the interannual variations of wind speed. A 12-year (2004–2015) regional air quality simulation was conducted over East Asia (27-km) and over South Korea (9-km) to assess the impact of meteorology under constant anthropogenic emissions. Simulated PM concentrations show a strong negative correlation (i.e. R = −0.86) with regional wind speed, implying that reduced regional ventilation is likely associated with more stagnant conditions that cause severe pollutant episodes in South Korea. We conclude that the current PM concentration trend in South Korea is a combination of long-term decline by emission control efforts and short-term fluctuation of regional wind speed interannual variability. When the meteorology-driven variations are removed, PM concentrations in South Korea have declined continuously even after 2012.

Influence of fossil-fuel power plant emissions on the surface fine particulate matter in the Seoul Capital Area, South Korea

ABSTRACT The South Korean government plans to reduce region-wide annual PM2.5 (particulate matter with an aerodynamic diameter ≤2.5 μm) concentrations in the Seoul Capital Area (SCA) from 2010 levels of 27 µg/m3 to 20 µg/m3 by 2024. At the same time, it is inevitable that emissions from fossil-fuel power plants will continue to increase if electricity generation expands and the generation portfolio remains the same in the future. To estimate incremental PM2.5 contributions due to projected electricity generation growth in South Korea, we utilized an ensemble forecasting member of the Integrated Multidimensional Air Quality System for Korea based on the Community Multi-scale Air Quality model. We performed sensitivity runs with across-the-board emission reductions for all fossil-fuel power plants in South Korea to estimate the contribution of PM2.5 from domestic fossil-fuel power plants. We estimated that fossil-fuel power plants are responsible for 2.4% of the annual PM2.5 national ambient air quality standard in the SCA as of 2010. Based on the electricity generation and the annual contribution of fossil-fuel power plants in 2010, we estimated that annual PM2.5 concentrations may increase by 0.2 µg/m3 per 100 TWhr due to additional electricity generation. With currently available information on future electricity demands, we estimated that the total future contribution of fossil-fuel power plants would be 0.87 µg/m3, which is 12.4% of the target reduction amount of the annual PM2.5 concentration by 2024. We also approximated that the number of premature deaths caused by existing fossil-fuel power plants would be 736 in 2024. Since the proximity of power plants to the SCA and the types of fuel used significantly impact this estimation, further studies are warranted on the impact of physical parameters of plants, such as location and stack height, on PM2.5 concentrations in the SCA due to each precursor. Implications: Improving air quality by reducing fine particle pollution is challenging when fossil-fuel-based electricity production is increasing. We show that an air quality forecasting system based on a photochemical model can be utilized to efficiently estimate PM2.5 contributions from and health impacts of domestic power plants. We derived PM2.5 concentrations per unit amount of electricity production from existing fossil-fuel power plants in South Korea. We assessed the health impacts of existing fossil-fuel power plants and the PM2.5 concentrations per unit electricity production to quantify the significance of existing and future fossil-fuel power plants with respect to the planned PM2.5 reduction target.