Prediction of ozone levels in London using the MM5–CMAQ modelling system

Sokhi, Ranjeet S.; José, Roberto San; Kitwiroon, Nutthida; Fragkou, E.; Pérez, J. L.; Middleton, D. R.

doi:10.1016/j.envsoft.2004.07.016

Cited by 82 publications

(25 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CTMs, such as GEOS-Chem (Bey et al 2001), MOZART (Brasseur et al 1998) and CMAQ (Byun and Schere 2006) have been applied to estimate ground-level ozone at city level (Lei et al 2007, Sokhi et al 2006), country level (Liu, Zhang, et al 2010, Tong and Mauzerall 2006), continent level (Fusco and Logan 2003, Pfister et al 2008) or beyond. Due to limitations of both computational capacity and the spatial resolution of emission inventories, ozone estimation from CTM is usually not spatially resolved enough to assess exposure at local scale.…”

Section: Introductionmentioning

confidence: 99%

A hybrid model for spatially and temporally resolved ozone exposures in the continental United States

Rowland

Koutrakis

et al. 2016

Journal of the Air & Waste Management Association

113

View full text Add to dashboard Cite

Ground-level ozone is an important atmospheric oxidant, which exhibits considerable spatial and temporal variability in its concentration level. Existing modeling approaches for ground-level ozone include chemical transport models, land-use regression, Kriging, and data fusion of chemical transport models with monitoring data. Each of these methods has both strengths and weaknesses. Combining those complementary approaches could improve model performance. Meanwhile, satellite-based total column ozone, combined with ozone vertical profile, is another potential input. We propose a hybrid model that integrates the above variables to achieve spatially and temporally resolved exposure assessments for ground-level ozone. We used a neural network for its capacity to model interactions and nonlinearity. Convolutional layers, which use convolution kernels to aggregate nearby information, were added to the neural network to account for spatial and temporal autocorrelation. We trained the model with AQS 8-hour daily maximum ozone in the continental United States from 2000 to 2012 and tested it with left out monitoring sites. Cross-validated R2 on the left out monitoring sites ranged from 0.74 to 0.80 (mean 0.76) for predictions on 1 km×1 km grid cells, which indicates good model performance. Model performance remains good even at low ozone concentrations. The prediction results facilitate epidemiological studies to assess the health effect of ozone in the long term and the short term.

show abstract

Section: Introductionmentioning

confidence: 99%

A hybrid model for spatially and temporally resolved ozone exposures in the continental United States

Rowland

Koutrakis

et al. 2016

Journal of the Air & Waste Management Association

113

View full text Add to dashboard Cite

show abstract

“…Grid resolutions finer than 1 km are typically not used, because of the excessive computational resource requirements, and sometimes because the parameterizations in a particular model may not be adequate for grid cell sizes lower than 1 km. Examples of some CMAQ and CAMx nested grid applications with the finest grid resolution at 1 km are provided in [23][24][25][26][27]. However, even a resolution of 1 km is inadequate to resolve sub-grid scale features, such as plumes from elevated point sources, whose initial dimensions are of the order of a few tens of meters.…”

Section: Nested Grid Modelingmentioning

confidence: 99%

Sub-Grid Scale Plume Modeling

2011

View full text Add to dashboard Cite

Multi-pollutant chemical transport models (CTMs) are being routinely used to predict the impacts of emission controls on the concentrations and deposition of primary and secondary pollutants. While these models have a fairly comprehensive treatment of the governing atmospheric processes, they are unable to correctly represent processes that occur at very fine scales, such as the near-source transport and chemistry of emissions from elevated point sources, because of their relatively coarse horizontal resolution. Several different approaches have been used to address this limitation, such as using fine grids, adaptive grids, hybrid modeling, or an embedded sub-grid scale plume model, i.e., plumein-grid (PinG) modeling. In this paper, we first discuss the relative merits of these various approaches used to resolve sub-grid scale effects in grid models, and then focus on PinG modeling which has been very effective in addressing the problems listed above. We start with a history and review of PinG modeling from its initial applications for ozone modeling in the Urban Airshed Model (UAM) in the early 1980s using a relatively simple plume model, to more sophisticated and state-of-the-science plume models, that include a full treatment of gas-phase, aerosol, and cloud chemistry, embedded in contemporary models such as CMAQ, CAMx, and WRF-Chem. We present examples of some typical results from PinG modeling for a variety of applications, discuss the implications of PinG on model predictions of source attribution, and discuss possible future developments and applications for PinG modeling. OPEN ACCESSAtmosphere 2011, 2 390

show abstract

“…MM5 is widely used and tested in the scientific community (see e.g. Colle et al, 2003;Gilliam et al, 2006) which includes European groups that use this model to derive meteorological input fields for their atmospheric chemistry models (Jakobs et al, 1995;Sokhi et al, 2006). The model can be run with several combinations of physical parameterisations, depending on purpose and grid resolution.…”

Section: Meteorological Fieldsmentioning

confidence: 99%

The aerosol distribution in Europe derived with the Community Multiscale Air Quality (CMAQ) model: comparison to near surface in situ and sunphotometer measurements

Matthias¹

2008

Atmos. Chem. Phys.

View full text Add to dashboard Cite

Abstract. The aerosol distribution in Europe was simulated with the Community Multiscale Air Quality (CMAQ) model system version 4.5 for the years 2000 and 2001. The results were compared with daily averages of PM 10 measurements taken in the framework of EMEP and with aerosol optical depth (AOD) values measured within AERONET. The modelled total aerosol mass is typically about 30-60% lower than the corresponding measurements. However a comparison of the chemical composition of the aerosol revealed a considerably better agreement between the modelled and the measured aerosol components for ammonium, nitrate and sulfate, which are on average only 15-20% underestimated. Sligthly worse agreement was determined for sea salt, that was only avaliable at two sites. The largest discrepancies result from the aerosol mass which was not chemically specified by the measurements. The agreement between measurements and model is better in winter than in summer. The modelled organic aerosol mass is higher in summer than in winter but it is significantly underestimated by the model. This could be one of the main reasons for the discrepancies between measurements and model results. The other is that primary coarse particles are underestimated in the emissions. The probability distribution function of the PM 10 measurements follows a log-normal distribution at most sites. The model is only able to reproduce this distribution function at non-coastal low altitude stations. The AOD derived from the model results is 20-70% lower than the values observed within AERONET. This is mainly attributed to the missing aerosol mass in the model. The day-to-day variability of the AOD and the logCorrespondence to: V. Matthias (volker.matthias@gkss.de) normal distribution functions are quite well reproduced by the model. The seasonality on the other hand is underestimated by the model results because better agreement is achieved in winter.

show abstract

Prediction of ozone levels in London using the MM5–CMAQ modelling system

Cited by 82 publications

References 8 publications

A hybrid model for spatially and temporally resolved ozone exposures in the continental United States

A hybrid model for spatially and temporally resolved ozone exposures in the continental United States

Sub-Grid Scale Plume Modeling

The aerosol distribution in Europe derived with the Community Multiscale Air Quality (CMAQ) model: comparison to near surface in situ and sunphotometer measurements

Contact Info

Product

Resources

About