Quality and performance of a PM10 daily forecasting model☆

Stadlober, Ernst; Hörmann, Siegfried; Pfeiler, Brigitte

doi:10.1016/j.atmosenv.2007.10.073

Cited by 108 publications

(75 citation statements)

References 9 publications

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“…It is very likely that in Graz this effect arises from the reduced traffic load (see Hörmann et al, 2005, Stadlober et al, 2008. Additionally, we observed that the characteristics of PM10 changes during the winter season (higher levels than expected in February and March than expected by meteorological conditions).…”

Section: Situation In Graz and In Brno 21 Sites And Database In Grazmentioning

confidence: 49%

“…There are considerably lower PM10 concentrations during the warm season and hence, there is no urgent need for action in that season of the year. Models for Graz based on multiple linear regression were already presented in a previous study (Stadlober, Hörmann, and Pfeiler, 2008). In contrast to many other studies it was possible to show the performance of these models in operational mode based on a threeyear trial period.…”

Section: Introductionmentioning

confidence: 90%

“…Note that all models are fitted on a training data set containing seven cold seasons 2002/2003-2008/2009at Graz-Mitte (six cold seasons 2003/2004-2008/2009 at GrazSüd) utilizing observed values of the covariates as in Stadlober et al (2008). These models were employed for predictions of the test data set (days ranging from November 3, 2009 to March 31, 2010).…”

Section: Situation In Graz and In Brno 21 Sites And Database In Grazmentioning

confidence: 99%

“…The quality of the PM10 forecasts is measured and compared by a quality function introduced in Stadlober et al (2008), which is tailored to the particular needs given by the EU-limit value of 50 µg/m 3 .…”

Section: Generalized Linear Models With Gamma Distributionsmentioning

confidence: 99%

“…One remarkable feature of this simple linear model approach is that the parameters and results can still be interpreted conveniently. In contrast to other rather complex models with numerous input parameters and/or functional relationships generated by a black box mechanism, linear models of this type are still transparent for the user (see Hörmann et al, 2005, Stadlober et al, 2008.…”

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confidence: 99%

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Forecasting of Daily PM10 Concentrations in Brno and Graz by Different Regression Approaches

Stadlober

Hübnerová

Michálek

et al. 2016

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Brno and Graz, the second largest cities of their countries, observe in each winter season PM10 concentrations of daily means which regularly exceed the limit value of 50 µg/m 3 . This is mainly caused by unfavorable dissemination conditions of the ambient air. Hence, partial regulation measures have to be taken in Brno and Graz where specific decisions for certain regulations may be based on the average PM10 concentration of the next day provided that reliable forecasts of these values are available. For several sites in the two cities we establish forecasts of daily PM10 concentrations based on multiple linear regression and generalized linear models utilizing both measured covariates of the present day and meteorological forecasts of the next day. The comparisons, based on different quality measures demonstrate the usefulness of both model approaches as they yield results of similar quality. Our prediction models may support future decisions concerning possible traffic restrictions or other regulations.

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Section: Situation In Graz and In Brno 21 Sites And Database In Grazmentioning

confidence: 49%

Section: Introductionmentioning

confidence: 90%

Section: Situation In Graz and In Brno 21 Sites And Database In Grazmentioning

confidence: 99%

Section: Generalized Linear Models With Gamma Distributionsmentioning

confidence: 99%

mentioning

confidence: 99%

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Forecasting of Daily PM10 Concentrations in Brno and Graz by Different Regression Approaches

Stadlober

Hübnerová

Michálek

et al. 2016

AJS

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Time Series Forecasting of Air Quality Based On Regional Numerical Modeling in Hong Kong

et al. 2018

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Based on prevailing numerical forecasting models (Community Multiscale Air Quality [CMAQ] model , Comprehensive Air Quality Model with Extensions, and Nested Air Quality Prediction Modeling System) and observations from monitoring stations in Hong Kong, we employ a set of autoregressive integrated moving average (ARIMA) models with numerical forecasts (ARIMAX) to improve the forecast of air pollutants including PM2.5, NO2, and O3. The results show significant improvements in multiple evaluation metrics for daily (1–3 days) and hourly (1–72 hr) forecast. Forecasts on daily 1‐hr and 8‐hr maximum O3 are also improved. For instance, compared with CMAQ, applying CMAQ‐ARIMA reduces average root‐mean‐square errors (RMSEs) at all stations for daily average PM2.5, NO2, and O3 in the next 3 days by 14.3–21.0%, 41.2–46.3%, and 47.8–49.7%, respectively. For hourly forecasts in the next 72 hr, reductions in RMSEs brought by ARIMAX using CMAQ are 18.2% for PM2.5, 32.1% for NO2, and 36.7% for O3. Large improvements in RMSEs are achieved for nonrural PM2.5 and rural NO2 using ARIMAX with three numerical models. Dynamic hourly forecast shows that ARIMAX can be applied for forecast of 7‐ to 72‐hr PM2.5, 4‐ to 72‐hr NO2, and 4‐ to 6‐hr O3. Besides applying ARIMAX for NO2, we recommend a mixed forecast strategy to ARIMAX for normal values of PM2.5 and O3 and employ numerical models for outputs above 75th percentile of historical observations. Our hybrid ARIMAX method can combine the advantage of ARIMA and numerical modeling to assist real‐time air quality forecasting efficiently and consistently.

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Analysis of PM₁₀ air pollution in Brno based on generalized linear model with strongly rank‐deficient design matrix

et al. 2009

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SUMMARYAn analysis of air pollution by suspended particulate matter (PM 10 ) in Brno, the second largest urban agglomeration of the Czech Republic, based on generalized linear model (GLM) is presented. Average daily concentrations coming from PM 10 monitoring for the period 1998-2005 have been processed. The measured meteorological factors: air temperature and humidity, direction and wind speed were considered as covariates along with some additional seasonal factors.Three standard and six GLMs with strongly rank-deficient design matrix have been applied. The rank deficiency is due to overparameterization which allows one more precise modeling involving, among others, identification of significant air pollution sources (PSs).From each of them the parameter estimates were obtained using both standard estimation procedure and a new sparse parameter estimation technique based on a four-step modification of the basis pursuit algorithm originally suggested for time-scale analysis of digital signals.As the standard estimation algorithms often fail due to numerical instability caused by strong overparameterization, we have applied this new computationally intensive approach allowing us to reliably identify nearly zero parameters in the model and thus to find numerically stable sparse solutions.The goal of the analysis was to identify the model and algorithm yielding most precise 1-day forecasts of the level of pollution by PM 10 with regard to the meteorological and seasonal covariates.

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Quality and performance of a PM10 daily forecasting model☆

Cited by 108 publications

References 9 publications

Forecasting of Daily PM10 Concentrations in Brno and Graz by Different Regression Approaches

Forecasting of Daily PM10 Concentrations in Brno and Graz by Different Regression Approaches

Time Series Forecasting of Air Quality Based On Regional Numerical Modeling in Hong Kong

Analysis of PM₁₀ air pollution in Brno based on generalized linear model with strongly rank‐deficient design matrix

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Quality and performance of a PM10 daily forecasting model☆

Cited by 108 publications

References 9 publications

Forecasting of Daily PM10 Concentrations in Brno and Graz by Different Regression Approaches

Forecasting of Daily PM10 Concentrations in Brno and Graz by Different Regression Approaches

Time Series Forecasting of Air Quality Based On Regional Numerical Modeling in Hong Kong

Analysis of PM10 air pollution in Brno based on generalized linear model with strongly rank‐deficient design matrix

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Analysis of PM₁₀ air pollution in Brno based on generalized linear model with strongly rank‐deficient design matrix