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| Titel |
Investigating the observed sensitivities of air-quality extremes to meteorological drivers via quantile regression |
| VerfasserIn |
W. C. Porter, C. L. Heald, D. Cooley, B. Russell |
| 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 ; 15, no. 18 ; Nr. 15, no. 18 (2015-09-21), S.10349-10366 |
| Datensatznummer |
250120040
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| Publikation (Nr.) |
 copernicus.org/acp-15-10349-2015.pdf |
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| Zusammenfassung |
| Air pollution variability is strongly dependent on meteorology. However,
quantifying the impacts of changes in regional climatology on pollution
extremes can be difficult due to the many non-linear and competing
meteorological influences on the production, transport, and removal of
pollutant species. Furthermore, observed pollutant levels at many sites show
sensitivities at the extremes that differ from those of the overall mean,
indicating relationships that would be poorly characterized by simple linear
regressions. To address this challenge, we apply quantile regression to
observed daily ozone (O3) and fine particulate matter (PM2.5)
levels and reanalysis meteorological fields in the USA over the
past decade to specifically identify the meteorological sensitivities of
higher pollutant levels. From an initial set of over 1700 possible
meteorological indicators (including 28 meteorological variables with 63
different temporal options), we generate reduced sets of O3 and
PM2.5 indicators for both summer and winter months, analyzing pollutant
sensitivities to each for response quantiles ranging from 2 to 98 %. Primary
covariates connected to high-quantile O3 levels include temperature and
relative humidity in the summer, while winter O3 levels are most
commonly associated with incoming radiation flux. Covariates associated with
summer PM2.5 include temperature, wind speed, and tropospheric
stability at many locations, while stability, humidity, and planetary
boundary layer height are the key covariates most frequently associated with
winter PM2.5. We find key differences in covariate sensitivities across
regions and quantiles. For example, we find nationally averaged
sensitivities of 95th percentile summer O3 to changes in maximum
daily temperature of approximately 0.9 ppb °C−1, while
the sensitivity of 50th percentile summer O3 (the annual median)
is only 0.6 ppb °C−1. This gap points to differing
sensitivities within various percentiles of the pollutant distribution,
highlighting the need for statistical tools capable of identifying
meteorological impacts across the entire response spectrum. |
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