Influence of air quality model resolution on uncertainty associated with health impacts
We use regional air quality modeling to evaluate the impact of model resolution on uncertainty associated with the human health benefits resulting from proposed air quality regulations. Using a regional photochemical model (CAMx), we ran a modeling episode with meteorological inputs simulating condi...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Copernicus GmbH
2013
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| Online Access: | http://hdl.handle.net/1721.1/77950 https://orcid.org/0000-0002-6396-5622 |
| _version_ | 1811091053786169344 |
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| author | Thompson, Tammy M. Selin, Noelle Eckley |
| author2 | Massachusetts Institute of Technology. Center for Global Change Science |
| author_facet | Massachusetts Institute of Technology. Center for Global Change Science Thompson, Tammy M. Selin, Noelle Eckley |
| author_sort | Thompson, Tammy M. |
| collection | MIT |
| description | We use regional air quality modeling to evaluate the impact of model resolution on uncertainty associated with the human health benefits resulting from proposed air quality regulations. Using a regional photochemical model (CAMx), we ran a modeling episode with meteorological inputs simulating conditions as they occurred during August through September 2006 (a period representative of conditions leading to high ozone), and two emissions inventories (a 2006 base case and a 2018 proposed control scenario, both for Houston, Texas) at 36, 12, 4 and 2 km resolution. The base case model performance was evaluated for each resolution against daily maximum 8-h averaged ozone measured at monitoring stations. Results from each resolution were more similar to each other than they were to measured values. Population-weighted ozone concentrations were calculated for each resolution and applied to concentration response functions (with 95% confidence intervals) to estimate the health impacts of modeled ozone reduction from the base case to the control scenario. We found that estimated avoided mortalities were not significantly different between the 2, 4 and 12 km resolution runs, but the 36 km resolution may over-predict some potential health impacts. Given the cost/benefit analysis requirements motivated by Executive Order 12866 as it applies to the Clean Air Act, the uncertainty associated with human health impacts and therefore the results reported in this study, we conclude that health impacts calculated from population weighted ozone concentrations obtained using regional photochemical models at 36 km resolution fall within the range of values obtained using fine (12 km or finer) resolution modeling. However, in some cases, 36 km resolution may not be fine enough to statistically replicate the results achieved using 2, 4 or 12 km resolution. On average, when modeling at 36 km resolution, an estimated 5 deaths per week during the May through September ozone season are avoided because of ozone reductions resulting from the proposed emissions reductions (95% confidence interval was 2–8). When modeling at 2, 4 or 12 km finer scale resolution, on average 4 deaths are avoided due to the same reductions (95% confidence interval was 1–7). Study results show that ozone modeling at a resolution finer than 12 km is unlikely to reduce uncertainty in benefits analysis for this specific region. We suggest that 12 km resolution may be appropriate for uncertainty analyses of health impacts due to ozone control scenarios, in areas with similar chemistry, meteorology and population density, but that resolution requirements should be assessed on a case-by-case basis and revised as confidence intervals for concentration-response functions are updated. |
| first_indexed | 2024-09-23T14:56:24Z |
| format | Article |
| id | mit-1721.1/77950 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:56:24Z |
| publishDate | 2013 |
| publisher | Copernicus GmbH |
| record_format | dspace |
| spelling | mit-1721.1/779502022-09-29T11:31:21Z Influence of air quality model resolution on uncertainty associated with health impacts Thompson, Tammy M. Selin, Noelle Eckley Massachusetts Institute of Technology. Center for Global Change Science Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Change Thompson, Tammy M. Selin, Noelle Eckley We use regional air quality modeling to evaluate the impact of model resolution on uncertainty associated with the human health benefits resulting from proposed air quality regulations. Using a regional photochemical model (CAMx), we ran a modeling episode with meteorological inputs simulating conditions as they occurred during August through September 2006 (a period representative of conditions leading to high ozone), and two emissions inventories (a 2006 base case and a 2018 proposed control scenario, both for Houston, Texas) at 36, 12, 4 and 2 km resolution. The base case model performance was evaluated for each resolution against daily maximum 8-h averaged ozone measured at monitoring stations. Results from each resolution were more similar to each other than they were to measured values. Population-weighted ozone concentrations were calculated for each resolution and applied to concentration response functions (with 95% confidence intervals) to estimate the health impacts of modeled ozone reduction from the base case to the control scenario. We found that estimated avoided mortalities were not significantly different between the 2, 4 and 12 km resolution runs, but the 36 km resolution may over-predict some potential health impacts. Given the cost/benefit analysis requirements motivated by Executive Order 12866 as it applies to the Clean Air Act, the uncertainty associated with human health impacts and therefore the results reported in this study, we conclude that health impacts calculated from population weighted ozone concentrations obtained using regional photochemical models at 36 km resolution fall within the range of values obtained using fine (12 km or finer) resolution modeling. However, in some cases, 36 km resolution may not be fine enough to statistically replicate the results achieved using 2, 4 or 12 km resolution. On average, when modeling at 36 km resolution, an estimated 5 deaths per week during the May through September ozone season are avoided because of ozone reductions resulting from the proposed emissions reductions (95% confidence interval was 2–8). When modeling at 2, 4 or 12 km finer scale resolution, on average 4 deaths are avoided due to the same reductions (95% confidence interval was 1–7). Study results show that ozone modeling at a resolution finer than 12 km is unlikely to reduce uncertainty in benefits analysis for this specific region. We suggest that 12 km resolution may be appropriate for uncertainty analyses of health impacts due to ozone control scenarios, in areas with similar chemistry, meteorology and population density, but that resolution requirements should be assessed on a case-by-case basis and revised as confidence intervals for concentration-response functions are updated. Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Change United States. Environmental Protection Agency. STAR Program (Grant R834279) 2013-03-20T15:32:26Z 2013-03-20T15:32:26Z 2012-10 2012-09 Article http://purl.org/eprint/type/JournalArticle 1680-7324 1680-7316 http://hdl.handle.net/1721.1/77950 Thompson, T. M., and N. E. Selin. “Influence of Air Quality Model Resolution on Uncertainty Associated with Health Impacts.” Atmospheric Chemistry and Physics 12.20 (2012): 9753–9762. https://orcid.org/0000-0002-6396-5622 en_US http://dx.doi.org/10.5194/acp-12-9753-2012 Atmospheric Chemistry and Physics Creative Commons Attribution 3.0 http://creativecommons.org/licenses/by/3.0/ application/pdf Copernicus GmbH Copernicus |
| spellingShingle | Thompson, Tammy M. Selin, Noelle Eckley Influence of air quality model resolution on uncertainty associated with health impacts |
| title | Influence of air quality model resolution on uncertainty associated with health impacts |
| title_full | Influence of air quality model resolution on uncertainty associated with health impacts |
| title_fullStr | Influence of air quality model resolution on uncertainty associated with health impacts |
| title_full_unstemmed | Influence of air quality model resolution on uncertainty associated with health impacts |
| title_short | Influence of air quality model resolution on uncertainty associated with health impacts |
| title_sort | influence of air quality model resolution on uncertainty associated with health impacts |
| url | http://hdl.handle.net/1721.1/77950 https://orcid.org/0000-0002-6396-5622 |
| work_keys_str_mv | AT thompsontammym influenceofairqualitymodelresolutiononuncertaintyassociatedwithhealthimpacts AT selinnoelleeckley influenceofairqualitymodelresolutiononuncertaintyassociatedwithhealthimpacts |