Mohammad Omary

A method for estimating urban background concentrations in support of hybrid air pollution modeling for environmental health studies.

Exposure studies rely on detailed characterization of air quality, either from sparsely located routine ambient monitors or from central monitoring sites that may lack spatial representativeness. Alternatively, some studies use models of various complexities to characterize local-scale air quality, but often with poor representation of background concentrations. A hybrid approach that addresses this drawback combines a regional-scale model to provide background concentrations and a local-scale model to assess impacts of local sources. However, this approach may double-count sources in the study regions. To address these limitations, we carefully define the background concentration as the concentration that would be measured if local sources were not present, and to estimate these background concentrations we developed a novel technique that combines space-time ordinary kriging (STOK) of observations with outputs from a detailed chemistry-transport model with local sources zeroed out. We applied this technique to support an exposure study in Detroit, Michigan, for several pollutants (including NOx and PM2.5), and evaluated the estimated hybrid concentrations (calculated by combining the background estimates that addresses this issue of double counting with local-scale dispersion model estimates) using observations. Our results demonstrate the strength of this approach specifically by eliminating the problem of double-counting reported in previous hybrid modeling approaches leading to improved estimates of background concentrations, and further highlight the relative importance of NOx vs. PM2.5 in their relative contributions to total concentrations. While a key limitation of this approach is the requirement for another detailed model simulation to avoid double-counting, STOK improves the overall characterization of background concentrations at very fine spatial scales.

Creating locally-resolved mobile-source emissions inputs for air quality modeling in support of an exposure study in Detroit, Michigan, USA.

This work describes a methodology for modeling the impact of traffic-generated air pollutants in an urban area. This methodology presented here utilizes road network geometry, traffic volume, temporal allocation factors, fleet mixes, and emission factors to provide critical modeling inputs. These inputs, assembled from a variety of sources, are combined with meteorological inputs to generate link-based emissions for use in dispersion modeling to estimate pollutant concentration levels due to traffic. A case study implementing this methodology for a large health study is presented, including a sensitivity analysis of the modeling results reinforcing the importance of model inputs and identify those having greater relative impact, such as fleet mix. In addition, an example use of local measurements of fleet activity to supplement model inputs is described, and its impacts to the model outputs are discussed. We conclude that with detailed model inputs supported by local traffic measurements and meteorology, it is possible to capture the spatial and temporal patterns needed to accurately estimate exposure from traffic-related pollutants.

Carbon reductions and health co-benefits from US residential energy efficiency measures

The United States (US) Clean Power Plan established state-specific carbon dioxide (CO2) emissions reduction goals for fossil fuel-fired electricity generating units (EGUs). States may achieve these goals through multiple mechanisms, including measures that can achieve equivalent CO2 reductions such as residential energy efficiency, which will have important co-benefits. Here, we develop state-resolution simulations of the economic, health, and climate benefits of increased residential insulation, considering EGUs and residential combustion. Increasing insulation to International Energy Conservation Code 2012 levels for all single-family homes in the US in 2013 would lead to annual reductions of 80 million tons of CO2 from EGUs, with annual co-benefits including 30 million tons of CO2 from residential combustion and 320 premature deaths associated with criteria pollutant emissions from both EGUs and residential combustion sources. Monetized climate and health co-benefits average

Finely Resolved On-Road PM2.5 and Estimated Premature Mortality in Central North Carolina

49 per ton of CO2 reduced from EGUs (range across states:

Modeling the Air Quality and Public Health Benefits of Increased Residential Insulation in the United States

12–

Future Year Air Quality Change Due to Growth in Aircraft Emissions and Changes in Climate

390). State-specific co-benefit estimates can inform development of optimal Clean Power Plan implementation strategies.

Projecting wildfire emissions over the south-eastern United States to mid-century

To quantify the on-road PM2.5 -related premature mortality at a national scale, previous approaches to estimate concentrations at a 12-km × 12-km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on-road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on-road PM2.5 -related premature mortality in central North Carolina with two different concentration estimation approaches-(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36-km × 36-km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space-time interpolation technique to provide annual average PM2.5 concentrations at a Census-block level (∼105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM2.5 -related premature mortality than CMAQ. The major difference is from the primary on-road PM2.5 where the hybrid approach estimated 2.5 times more primary on-road PM2.5 -related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on-road PM2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM2.5 and suggesting that previous studies may have underestimated premature mortality due to PM2.5 from traffic-related emissions.

Modeled Full‐Flight Aircraft Emissions Impacts on Air Quality and Their Sensitivity to Grid Resolution

According to the Residential Energy Consumption Survey (RECS), homes in the United States consume approximately 10 quadrillion BTUs of energy each year, including electricity consumption for cooling, various fuels utilized for space heating, and other end uses. Electricity consumption will influence emissions from power plants, and these along with direct residential fuel combustion will also contribute to emissions of multiple key pollutants, with corresponding air quality and health impacts. We have developed models to quantify the energy savings associated with increased residential insulation and to estimate in monetary terms the environmental and public health benefits. We are considering both retrofits to existing housing and new construction, focusing on the 2012 International Energy Conservation Code (IECC), which specifies R-values and U-factors by climate zone and a number of other structural components and design specifications. We are applying EnergyPlus to a series of template files to estimate energy savings by fuel type and state, for both retrofits and new construction. To determine the emissions reductions related to reduced electricity generation, we used EPA’s AVERT tool. AVERT uses the basic attributes of electricity dispatch modeling to determine the power plants most likely influenced by energy efficiency programs, and provides the direct nitrogen oxide (NOx), sulfur dioxide (SO2), and carbon dioxide (CO2) emissions reductions on a plant-by-plant basis. For residential combustion, we used EPA’s AP-42 database and other resources to quantify direct emissions by fuel type (including natural gas, fuel oil, and wood). To model the health benefits of the criteria pollutant emissions, we linked the emissions reductions due to increased energy efficiency with the Community Multiscale Air Quality (CMAQ) model. We developed a series of simulations using CMAQ v4.7.1 instrumented with the Decoupled Direct Method (DDM), an advanced sensitivity analysis technique that allows us to estimate the influence of individual pollutants from individual sources or regions. We considered direct residential combustion by state, leveraging Census and housing start data to determine spatial patterns of emissions within states, and modeled individual power plants in geographic groupings using a design of experiments that allow us to estimate the impacts for all major power plants on the grid. We focused on fine particulate matter and ozone concentrations, as the key drivers of monetized health impacts. As CMAQ provides concentration estimates by grid cell, we are able to determine total public health benefits in terms of avoided mortality and morbidity as well as the distribution of those benefits for directly modeled facilities and locations. We estimate 19,000 premature deaths per year associated with EGU emissions, with more than half of the EGU-related health impacts attributable to emissions from seven states with significant coal combustion. We also estimate 10,000 premature deaths per year associated with residential combustion emissions, driven by primary PM2.5 emissions. In general, primary PM2.5 health damage functions are an order of magnitude larger than those of secondary PM2.5 precursors. Our findings reinforce the significance of source-specific assessment of air quality and health impacts for developing public health policies.

A Global-Scale Multi-resolution Study of Surface Air Quality Impacts from Commercial Aircraft Emissions

Increased growth in aviation activity in the future is projected to show increased emissions from this sector, and hence approximately proportional increases in concentrations if other factors were unchanging. However, emissions from other anthropogenic sources are generally expected to decrease due to several projected emissions control measures, and changes in climate will also occur. In this study, we evaluated air quality changes due to growth in aviation activities from 2005 to 2025, focusing on 99 major U.S. airports with aircraft activity data during landing and takeoff (LTO) activity developed for a growth scenario in 2025. We also assessed changes in climate based upon IPCC RCP 4.5 projections scenario, and used dynamically downscaled meteorology from the Climate Earth System Model (CESM) to WRF over the continental U.S. We performed six annual simulations at 36-km resolution using the WRF-SMOKE-CMAQ modeling system for 2005 and 2025, with and without aircraft emissions, and with and without changes in future year climate from CESM/WRF. We focused on assessing the incremental changes in O3, NO2 and PM2.5 due to changes in emissions (due to aircraft and non-aviation sources) and meteorology. We see a net increase in annual average PM2.5 due to aviation increase from a factor of 5.5 (2025 vs. 2005) without incorporating change in climate to 5.9 with change in climate. Similarly, the changes in summer season average of daily maximum 8-h O3 due to aviation changes from a factor of 3.1–3.3 with change in climate. Both these changes translate to about a ∼7 % additional increase in the future year that we attribute as the “climate penalty” factor. Detailed analyses of the O3 changes show that the effect of change in climate is more pronounced at higher end of concentrations, where the grid-cells with values exceeding the U.S. NAAQS of 75 ppb see a 60 % increase due to change in climate. The changes in 1-h NO2 due to aircraft increase by a factor of ∼2 in 2025 vs. 2005, with increases around major airports being as high as a factor of 6.

A Multiscale Modeling Study to Assess Impacts of Full-Flight Aircraft Emissions on Upper Troposphere and Surface Air Quality

Wildfires can impair human health because of the toxicity of emitted pollutants, and threaten communities, structures and the integrity of ecosystems sensitive to disturbance. Climate and socioeconomic factors (e.g. population and income growth) are known regional drivers of wildfires. Reflecting changes in these factors in wildfire emissions estimates is thus a critical need in air quality and health risk assessments in the south-eastern United States. We developed such a methodology leveraging published statistical models of annual area burned (AAB) over the US Southeast for 2011–2060, based on county-level socioeconomic and climate projections, to estimate daily wildfire emissions in selected historical and future years. Projected AABs were 7 to 150% lower on average than the historical mean AABs for 1992–2010; projected wildfire fine-particulate emissions were 13 to 62% lower than those based on historical AABs, with a temporal variability driven by the climate system. The greatest differences were in areas of large wildfire impacts from socioeconomic factors, suggesting that historically based (static) wildfire inventories cannot properly represent future air quality responses to changes in these factors. The results also underscore the need to correct biases in the dynamical downscaling of wildfire climate drivers to project the health risks of wildfire emissions more reliably.