Ozone Predictions in Atlanta, Georgia: Analysis of the 1999 Ozone Season

Cardelino, C. A.; Chang, Michael E.; John, J. St.; Murphey, B. B.; Cordle, Jeff; Ballagas, Rafael; Patterson, Lynda; Powell, K.; Stogner, Jim; Zimmer-Dauphinee, Susan

doi:10.1080/10473289.2001.10464342

Cited by 19 publications

(9 citation statements)

References 19 publications

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“…In parallel with the development of remote‐sensed global observation of tropospheric content, various chemistry transport models (CTMs) have been developed over the last 20 years in order to understand and forecast air pollution. These CTMs cover different spatial scales ranging from continental (for example, for Europe [ Zlateve et al , 1992; Builtjes , 1992; Hass et al , 1997; Schmidt et al , 2001; Simpson et al , 2003]) to urban (for example, for Los Angeles [ Lu et al , 1997a, 1997b]; for Milan [ Silibello et al , 1998]; for Atlanta [ Cardelino et al , 2001]; for Paris [ Vautard et al , 2001]). These models require accurate input data, but most of these (emissions, initial and boundary conditions, meteorology) remain uncertain (several references about uncertainties may be found in the work of Beekmann and Derognat [2003]).…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of SCIAMACHY nitrogen dioxide observations, in situ measurements and air quality modeling results over Western Europe

et al. 2007

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[1] The Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) satellite spectrometer provides detailed information on the nitrogen dioxide (NO 2 ) content in the planetary boundary layer. NO 2 tropospheric column retrievals of SCIAMACHY and its predecessor Global Ozone Monitoring Experiment are characterized by errors of the order of 40%. We present here a new SCIAMACHY tropospheric retrieval data set for the year 2003. The cloud free satellite observations are compared to surface measurements and simulations over western Europe performed with the regional air-quality model CHIMERE. The model has a resolution of 50 km similar to the satellite observations. For these comparisons, averaging kernels are applied to the collocated model profiles to remove the dependency of the comparison on a priori NO 2 profile information used in the retrieval. The consistency of both SCIAMACHY and CHIMERE outputs over sites where surface measurements are available allows us to be confident in evaluation of the model over large areas not covered by surface observations. CHIMERE underestimates surface NO 2 concentrations for urban and suburban stations which we mainly attribute to the low representativeness of point observations. No such bias is found for rural locations. The yearly average SCIAMACHY and CHIMERE spatial NO 2 distributions show a high degree of quantitative agreement over rural and urban sites: a bias of 5% (relative to the retrievals) and a correlation coefficient of 0.87 (n = 2003). On a seasonal basis, biases are smaller than 20% and correlation coefficients are larger than 0.75. Spatial correlations between both the model and satellite columns and the European Monitoring and Evaluation Program (EMEP) emission inventory are high in summer (r = 0.74, n = 1779) and low in winter (r = 0.48, n = 1078), related to seasonal changes in lifetime and transport. On the other hand, CHIMERE and SCIAMACHY columns are mutually consistent in summer (r = 0.82) and in winter (r = 0.79). This shows that CHIMERE simulates the transport and chemical processes with a reasonable accuracy. The NO 2 columns show a high daily variability. The daily NO 2 pollution plumes observed by SCIAMACHY are often well described by CHIMERE both in extent and in location. This result demonstrates the capabilities of a satellite instrument such as SCIAMACHY to monitor the NO 2 concentrations over large areas on a daily basis. It provides evidence that present and future satellite missions, in combination with CTM and surface data, will contribute to improve quantitative air quality analyses at a continental scale.

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Section: Introductionmentioning

confidence: 99%

Intercomparison of SCIAMACHY nitrogen dioxide observations, in situ measurements and air quality modeling results over Western Europe

et al. 2007

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“…Several chemistry‐transport models (CTMs) have been developed in order to better understand the laws driving the chemistry and transport of air pollutants and to forecast exceedances of their information and alert thresholds. Such models cover spatial scales from regional (e.g., for Los Angeles, Lu et al [1997a, 1997b]; for Atlanta, Cardelino et al [2001]; for Milan, Silibello et al [1998]; and for Paris, Vautard et al [2001]) to continental (for Europe, Schmidt et al [2001], Hass et al [1997], and Mosca et al [1998]). They require an array of variables as input data (emissions, initial and boundary conditions, meteorology…) that are in most cases quite uncertain and, in some, difficult to collect in real time.…”

Section: Introductionmentioning

confidence: 99%

Three‐dimensional ozone analyses and their use for short‐term ozone forecasts

Blond¹

2004

J. Geophys. Res.

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[1] A statistical interpolation method is evaluated for routine production of ozone threedimensional fields over western Europe. These fields are used for initializing short-term ozone forecasts issued from a chemistry-transport model. We mainly address two questions: (1) To what extent can the use of surface ozone observation data improve the description of ozone fields relative to raw simulations? (2) Does the use of ozone analysis improve short-term forecasts of the troposphere's chemical composition? The method consists of combining ozone simulations with surface ozone measurements. The resulting analyses are compared with independent observations in a statistical way over a long period of time (four consecutive summers). The improvement of the root-mean-square (RMS) error of the analyses relative to the raw simulations is $30%. The short-term (1-2 days in advance) ozone forecasts are improved on average (by $1 ppb of RMS error) if ozone analyses are used for initialization. The improvement is almost lost after a lead time of 36 hours. However, in cases where a model error propagates throughout the model domain, the improvement can be much larger ($10 ppb). We analyze one such case.

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“…First of all, neural networks are the most frequently used to produce forecasts of PM 10 concentration (Paschalidou et al 2011;Carnevale et al 2011;Kukkonen et al 2003). Therefore, multiple linear regression modeling is frequently used as in the studies of Stadlober et al (2008), Cordelino et al (2001) and Paschalidou et al (2009). Moreover, Chaloulakou et al (2003) and Grivas and Chaloulakou (2006) compared the performance of neural networks and multiple regression model to forecast the daily average of PM 10 .…”

Section: Introductionmentioning

confidence: 98%

Analysis and prediction of $$\mathrm{PM}_{10}$$ PM 10 concentration levels in Tunisia using statistical learning approaches

2016

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Over the past years, the health impact of airborne particulate matter PM 10 has become a very topical subject. Thereby, a lot of research effort in the environmental sciences goes towards the modeling and the prediction of ambient PM 10 concentrations. In this paper, we are interested in the statistical classification of the daily mean PM 10 concentration in Tunisia according to the authority regulation. We consider two monitoring stations: a big industrial station and a traffic station. The main goal of this work is to determine the pertinent predictors of PM 10 concentration within a nonlinear multiclass framework. To do this, we used two popular statistical learning methods; the support vector machines (SVM) and the random forests (RF). The statistical results obtained on the real datasets, show that RF outperform SVM for the purpose of variable selection even with a reduced number of observations compared to the number of explicative variables. It was also demonstrated that the PM 10 concentration measured yesterday is the most relevant predictor of its present-day value. Moreover, we found that the more delayed values of PM 10 concentration may be crucial to get an accurate prediction.

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Ozone Predictions in Atlanta, Georgia: Analysis of the 1999 Ozone Season

Cited by 19 publications

References 19 publications

Intercomparison of SCIAMACHY nitrogen dioxide observations, in situ measurements and air quality modeling results over Western Europe

Intercomparison of SCIAMACHY nitrogen dioxide observations, in situ measurements and air quality modeling results over Western Europe

Three‐dimensional ozone analyses and their use for short‐term ozone forecasts

Analysis and prediction of $$\mathrm{PM}_{10}$$ PM 10 concentration levels in Tunisia using statistical learning approaches

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