Coastal and synoptic recirculation affecting air pollutants dispersion: A numerical study

Levy, Ilan; Mahrer, Y.; Dayan, Uri

doi:10.1016/j.atmosenv.2009.01.017

Cited by 51 publications

(28 citation statements)

References 20 publications

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“…For these reasons we utilize a stepwise multivariate approach based on combining covariate rankings at individual stations into a single selection metric. To reduce the computational cost of variable selection initially we use a testing subset of stations, including 10 stations (with varying degrees of mean 24 h mean value daymax/min/mean as above, but using only 08:00 to 16:00 LT nightmax/min/mean as above, but using only preceding night: 08:00 to 16:00 LT diff change from previous day 3daymax/min/mean max/min/mean of previous 3 days 6daymax/min/mean max/min/mean of previous 6 days 1 Mesinger et al (2006), 2 Klein and Hartmann (1993), and 3 Levy et al (2009). pollutant levels) from each of the 10 EPA regions (shown in Figs. 4,5,7,and 8).…”

Section: Meteorological Variable Selectionmentioning

confidence: 99%

“…When air masses are returned to a site with ongoing emissions, the buildup of precursor concentrations may generate exceptionally high pollutant levels. To measure this effect we calculate a daily recirculation potential index (RPI) from surface wind speeds based on the ratio between the vector sum magnitude (L) and scalar sum (S) of wind speeds over the previous 24 h ( Levy et al, 2009):…”

Section: Meteorological Variable Generationmentioning

confidence: 99%

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Investigating the observed sensitivities of air-quality extremes to meteorological drivers via quantile regression

et al. 2015

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Abstract. 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 (O 3 ) and fine particulate matter (PM 2.5 ) 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 O 3 and PM 2.5 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 O 3 levels include temperature and relative humidity in the summer, while winter O 3 levels are most commonly associated with incoming radiation flux. Covariates associated with summer PM 2.5 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 PM 2.5 . We find key differences in covariate sensitivities across regions and quantiles. For example, we find nationally averaged sensitivities of 95th percentile summer O 3 to changes in maximum daily temperature of approximately 0.9 ppb • C −1 , while the sensitivity of 50th percentile summer O 3 (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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Section: Meteorological Variable Selectionmentioning

confidence: 99%

Section: Meteorological Variable Generationmentioning

confidence: 99%

Investigating the observed sensitivities of air-quality extremes to meteorological drivers via quantile regression

et al. 2015

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“…Of course, the lower and upper bounds for S depend on the wind speed. This method has been used by numerous authors, both for in situ measurements (f.i., Venegas and Mazzeo, 1999) and numerical modeling (f.i., Levy et al, 2009).…”

Section: Recirculation and Stagnation Areas In The Grenoble Valleysmentioning

confidence: 99%

The Atmospheric Boundary Layer during Wintertime Persistent Inversions in the Grenoble Valleys

Largeron

Staquet

2016

Front. Earth Sci.

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This study addresses the atmospheric boundary layer dynamics in the Grenoble valleys during persistent inversions, for 5 months during the 2006-2007 winter. During a persistent inversion, the boundary layer contains a layer with a positive vertical temperature gradient over a few days. Temperature data recorded on the valley sidewalls are first used. A bulk measure of the boundary layer stability, based upon the temperature difference between the valley top and the valley bottom, is introduced and a criterion is proposed to detect persistent inversions. We show that this criterion is equivalently expressed in terms of the heat deficit inside the boundary layer. Nine episodes are detected and coincide with the PM 10 -polluted periods of the 2006-2007 winter. Secondly, the five strongest and longest persistent inversions are simulated using the MesoNH model. Focus is made on the stagnation stage of the episode, during which the inversion exhibits a diurnal cycle that does not significantly evolve from day to day. Whatever the episode, the inversion develops from the ground over a height of about 1200 m, with a nighttime temperature strength reaching 20 K. The boundary-layer dynamics within the inversion layer are fully decoupled from the (anticyclonic, weak) synoptic flow, independent from the synoptic-wind direction and similar whatever the episode. This implies that these dynamics are controlled by thermal winds and solely depends upon the geometry of the topography and upon the radiative cooling of the ground. Finally, a 2-day high-resolution simulation is made for the strongest case, representative of any persistent inversion. The flow pattern displays a well-defined spatial structure, with a vertical layering resulting from the superposition of the down-valley winds flowing from the different valleys surrounding Grenoble. This pattern persists all day long over a shallow convective layer of about 50 m forming above the ground during the reduced daytime period. Within this shallow layer, convection triggers a weak up-valley wind. Ventilation and stagnation areas in the surface layer are also computed, providing insights for air quality studies. The main characteristics of these persistent inversions are comparable to the most extreme wintertime inversions recorded in the Grand Canyon, Arizona.

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“…Geophysical variables that control sea and lake breezes (Coriolis parameter f not shown) areas, with that degradation modulated by sea breezes (Hinrichsen 1999;Levy et al 2008Levy et al , 2009.…”

Section: Figmentioning

confidence: 99%

Sea and Lake Breezes: A Review of Numerical Studies

Crosman

Horel

2010

Boundary-Layer Meteorol

278

225

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Numerical studies of sea and lake breezes are reviewed. The modelled dependence of sea-breeze and lake-breeze characteristics on the land surface sensible heat flux, ambient geostrophic wind, atmospheric stability and moisture, water body dimensions, terrain height and slope, Coriolis parameter, surface roughness length, and shoreline curvature is discussed. Consensus results on the influence of these geophysical variables on sea and lake breezes are synthesized as well as current gaps in our understanding. A brief history of numerical modelling, an overview of recent high-resolution simulations, and suggestions for future research related to sea and lake breezes are also presented. The results of this survey are intended to be a resource for numerical modelling, coastal air quality, and wind power studies.

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Coastal and synoptic recirculation affecting air pollutants dispersion: A numerical study

Cited by 51 publications

References 20 publications

Investigating the observed sensitivities of air-quality extremes to meteorological drivers via quantile regression

Investigating the observed sensitivities of air-quality extremes to meteorological drivers via quantile regression

The Atmospheric Boundary Layer during Wintertime Persistent Inversions in the Grenoble Valleys

Sea and Lake Breezes: A Review of Numerical Studies

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