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| Titel |
Air quality resolution for health impact assessment: influence of regional characteristics |
| VerfasserIn |
T. M. Thompson, R. K. Saari, N. E. Selin |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 14, no. 2 ; Nr. 14, no. 2 (2014-01-28), S.969-978 |
| Datensatznummer |
250118315
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| Publikation (Nr.) |
 copernicus.org/acp-14-969-2014.pdf |
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| Zusammenfassung |
| We evaluate how regional characteristics of population and background
pollution might impact the selection of optimal air quality model resolution
when calculating the human health impacts of changes to air quality. Using
an approach consistent with air quality policy evaluation, we use a regional
chemical transport model (CAMx) and a health benefit mapping program
(BenMAP) to calculate the human health impacts associated with changes in
ozone and fine particulate matter resulting from an emission reduction
scenario. We evaluate this same scenario at 36, 12 and 4 km resolution for
nine regions in the eastern US representing varied characteristics. We find
that the human health benefits associated with changes in ozone
concentrations are sensitive to resolution. This finding is especially
strong in urban areas where we estimate that benefits calculated using
coarse resolution results are on average two times greater than benefits
calculated using finer scale results. In three urban areas we analyzed,
results calculated using 36 km resolution modeling fell outside the
uncertainty range of results calculated using finer scale modeling. In rural
areas the influence of resolution is less pronounced with only an 8%
increase in the estimated health impacts when using 36 km resolution over
finer scales. In contrast, health benefits associated with changes in
PM2.5 concentrations were not sensitive to resolution and did not
follow a pattern based on any regional characteristics evaluated. The
largest difference between the health impacts estimated using 36 km modeling
results and either 12 or 4 km results was at most ±10% in any region.
Several regions showed increases in estimated benefits as resolution
increased (opposite the impact seen with ozone modeling), while some regions
showed decreases in estimated benefits as resolution increased. In both
cases, the dominant contribution was from secondary PM. Additionally, we
found that the health impacts calculated using several individual
concentration–response functions varied by a larger amount than the impacts
calculated using results modeled at different resolutions. Given that
changes in PM2.5 dominate the human health impacts, and given the
uncertainty associated with human health response to changes in air
pollution, we conclude that, when estimating the human health benefits
associated with decreases in ozone and PM2.5 together, the benefits
calculated at 36 km resolution agree, within errors, with the benefits
calculated using fine (12 km or finer) resolution modeling when using the
current methodology for assessing policy decisions. |
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