Assessing Ozone-Related Health Impacts under a Changing Climate

Climate change may increase the frequency and intensity of ozone episodes in future summers in the United States. However, only recently have models become available that can assess the impact of climate change on O(3) concentrations and health effects at regional and local scales that are relevant to adaptive planning. We developed and applied an integrated modeling framework to assess potential O(3)-related health impacts in future decades under a changing climate. The National Aeronautics and Space Administration–Goddard Institute for Space Studies global climate model at 4° × 5° resolution was linked to the Penn State/National Center for Atmospheric Research Mesoscale Model 5 and the Community Multiscale Air Quality atmospheric chemistry model at 36 km horizontal grid resolution to simulate hourly regional meteorology and O(3) in five summers of the 2050s decade across the 31-county New York metropolitan region. We assessed changes in O(3)-related impacts on summer mortality resulting from climate change alone and with climate change superimposed on changes in O(3) precursor emissions and population growth. Considering climate change alone, there was a median 4.5% increase in O(3)-related acute mortality across the 31 counties. Incorporating O(3) precursor emission increases along with climate change yielded similar results. When population growth was factored into the projections, absolute impacts increased substantially. Counties with the highest percent increases in projected O(3) mortality spread beyond the urban core into less densely populated suburban counties. This modeling framework provides a potentially useful new tool for assessing the health risks of climate change

Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite-observed aerosol optical depth

Abstract. Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM2.5). We use a forward geophysical approach to derive ground-level PM2.5 distributions from satellite AOD at 1 km2 resolution for 2011 over the northeastern US by applying relationships between surface PM2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 12×12 km2 horizontal resolution). Seasonal average satellite-derived PM2.5 reveals more spatial detail and best captures observed surface PM2.5 levels during summer. At the daily scale, however, satellite-derived PM2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly to uncertainties in the relationship between surface PM2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM2.5 monitors, we show that uncertainties in modeled PM2.5∕AOD can explain more than 70 % of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM2.5 evaluated at PM2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM2.5∕AOD relationships. Overall, we estimate that uncertainties in the modeled PM2.5∕AOD lead to an error of 11 µg m−3 in daily satellite-derived PM2.5, and uncertainties in satellite AOD lead to an error of 8 µg m−3. Using multi-platform ground, airborne, and radiosonde measurements, we show that uncertainties of modeled PM2.5∕AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model representation of relative humidity and aerosol vertical profile shape contributes some systematic biases. The parameterization of aerosol optical properties, which determines the mass extinction efficiency, also contributes to random uncertainty, with the size distribution being the largest source of uncertainty and hygroscopicity of inorganic salt the second largest. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM2.5

Comparison of multiple PM2.5 exposure products for estimating health benefits of emission controls over New York State, USA

Ambient exposure to fine particulate matter (PM2.5) is one of the top global health concerns. We estimate the PM2.5-related health benefits of emission reduction over New York State (NYS) from 2002 to 2012 using seven publicly available PM2.5 products that include information from ground-based observations, remote sensing and chemical transport models. While these PM2.5 products differ in spatial patterns, they show consistent decreases in PM2.5 by 28%–37% from 2002 to 2012. We evaluate these products using two sets of independent ground-based observations from the New York City Community Air Quality Survey (NYCCAS) Program for an urban area, and the Saint Regis Mohawk Tribe Air Quality Program for a remote area. Inclusion of satellite remote sensing improves the representativeness of surface PM2.5 in the remote area. Of the satellite-based products, only the statistical land use regression approach captures some of the spatial variability across New York City measured by NYCCAS. We estimate the PM2.5-related mortality burden by applying an integrated exposure-response function to the different PM2.5 products. The multi-product mean PM2.5-related mortality burden over NYS decreased by 5660 deaths (67%) from 8410 (95% confidence interval (CI): 4570–12 400) deaths in 2002 to 2750 (CI: 700–5790) deaths in 2012. We estimate a 28% uncertainty in the state-level PM2.5 mortality burden due to the choice of PM2.5 products, but such uncertainty is much smaller than the uncertainty (130%) associated with the exposure-response function

Analysis of long-term observations of NOx and CO in megacities and application to constraining emissions inventories

Long-term atmospheric NOx/CO enhancement ratios in megacities provide evaluations of emission inventories. A fuel-based emission inventory approach that diverges from conventional bottom-up inventory methods explains 1970–2015 trends in NOx/CO enhancement ratios in Los Angeles. Combining this comparison with similar measurements in other U.S. cities demonstrates that motor vehicle emissions controls were largely responsible for U.S. urban NOx/CO trends in the past half century. Differing NOx/CO enhancement ratio trends in U.S. and European cities over the past 25 years highlights alternative strategies for mitigating transportation emissions, reflecting Europe's increased use of light-duty diesel vehicles and correspondingly slower decreases in NOx emissions compared to the U.S. A global inventory widely used by global chemistry models fails to capture these long-term trends and regional differences in U.S. and Europe megacity NOx/CO enhancement ratios, possibly contributing to these models' inability to accurately reproduce observed long-term changes in tropospheric ozone

Development and use of a Fast Response, Nitric Oxide Detector for Air Quality Monntoring and Eddy Correlation Flux Measurements

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Simulated Effects of Climate Change on Summertime Nitrogen Deposition in the Eastern US

It is anticipated that climate change may impact regional-scale air quality and atmospheric deposition in the coming decades. To simulate the effects of climate change on nitrogen (N) deposition across numerous watersheds in the eastern US, we applied the NASA Goddard Institute for Space Studies General Circulation Model (GISS-GCM), Fifth Generation Pennsylvania State University/National Center for Atmospheric Research Mesoscale Model (MM5), Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system, and the US Environmental Protection Agency Community Multiscale Air Quality (CMAQ) Model. Keeping chemical initial and boundary conditions, land use, and anthropogenic area and point source emissions fixed, this modeling system was applied over five summers (June–August) from 1993 to 1997 and five summers from 2053 to 2057. Over these eastern US watersheds, the modeling system estimated 3–14% increases in summertime N deposition as a result of climate change. This increase is primarily due to the direct effects of climate change on atmospheric conditions and chemistry. Wet N deposition is predicted to increase as a result of increased precipitation, while dry N deposition is predicted to increase as higher surface temperatures favor gas-phase nitric acid to particulate nitrate. The simulated increase suggests that additional reductions in N oxides and/or ammonia may be needed to fully realize the anticipated benefits of planned reduction strategies, including the Clean Air Interstate Rule (CAIR)