scholarly journals A regional air quality forecasting system over Europe: the MACC-II daily ensemble production

2015 ◽  
Vol 8 (3) ◽  
pp. 2739-2806 ◽  
Author(s):  
V. Marécal ◽  
V.-H. Peuch ◽  
C. Andersson ◽  
S. Andersson ◽  
J. Arteta ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Composition ◽  
Quality Data ◽  
Model Ensemble ◽  
The Past ◽  
Forecasting System ◽  
The Individual ◽  
Statistical Indicators ◽  
Air Quality Data

Abstract. This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. The paper gives an overall picture of its status at the end of MACC-II (summer 2014). This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs and PAN + PAN precursors) over 8 vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performances of the system are assessed daily, weekly and 3 monthly (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the median ensemble to forecast regional ozone pollution events. The time period of this case study is also used to illustrate that the median ensemble generally outperforms each of the individual models and that it is still robust even if two of the seven models are missing. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10 and show an overall improvement over time. The change of the skills of the ensemble over the past two summers for ozone and the past two winters for PM10 are discussed in the paper. While the evolution of the ozone scores is not significant, there are improvements of PM10 over the past two winters that can be at least partly attributed to new developments on aerosols in the seven individual models. Nevertheless, the year to year changes in the models and ensemble skills are also linked to the variability of the meteorological conditions and of the set of observations used to calculate the statistical indicators. In parallel, a scientific analysis of the results of the seven models and of the ensemble is also done over the Mediterranean area because of the specificity of its meteorology and emissions. The system is robust in terms of the production availability. Major efforts have been done in MACC-II towards the operationalisation of all its components. Foreseen developments and research for improving its performances are discussed in the conclusion.

scholarly journals A regional air quality forecasting system over Europe: the MACC-II daily ensemble production

2015 ◽  
Vol 8 (9) ◽  
pp. 2777-2813 ◽  
Author(s):  
V. Marécal ◽  
V.-H. Peuch ◽  
C. Andersson ◽  
S. Andersson ◽  
J. Arteta ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Composition ◽  
Quality Data ◽  
Model Ensemble ◽  
Gross Error ◽  
Forecasting System ◽  
The Individual ◽  
Statistical Indicators ◽  
Air Quality Data ◽  
Monitoring Stations

Abstract. This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in the MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The paper gives an overall picture of its status at the end of MACC-II (summer 2014) and analyses the performance of the multi-model ensemble. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs (non-methane volatile organic compounds) and PAN+PAN precursors) over eight vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performance of the system is assessed daily, weekly and every 3 months (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the ensemble median to forecast regional ozone pollution events. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10. The statistical indicators for ozone in summer 2014 show that the ensemble median gives on average the best performances compared to the seven models. There is very little degradation of the scores with the forecast day but there is a marked diurnal cycle, similarly to the individual models, that can be related partly to the prescribed diurnal variations of anthropogenic emissions in the models. During summer 2014, the diurnal ozone maximum is underestimated by the ensemble median by about 4 μg m−3 on average. Locally, during the studied ozone episodes, the maxima from the ensemble median are often lower than observations by 30–50 μg m−3. Overall, ozone scores are generally good with average values for the normalised indicators of 0.14 for the modified normalised mean bias and of 0.30 for the fractional gross error. Tests have also shown that the ensemble median is robust to reduction of ensemble size by one, that is, if predictions are unavailable from one model. Scores are also discussed for PM10 for winter 2013–1014. There is an underestimation of most models leading the ensemble median to a mean bias of −4.5 μg m−3. The ensemble median fractional gross error is larger for PM10 (~ 0.52) than for ozone and the correlation is lower (~ 0.35 for PM10 and ~ 0.54 for ozone). This is related to a larger spread of the seven model scores for PM10 than for ozone linked to different levels of complexity of aerosol representation in the individual models. In parallel, a scientific analysis of the results of the seven models and of the ensemble is also done over the Mediterranean area because of the specificity of its meteorology and emissions. The system is robust in terms of the production availability. Major efforts have been done in MACC-II towards the operationalisation of all its components. Foreseen developments and research for improving its performances are discussed in the conclusion.

scholarly journals Ensemble Forecasts of Air Quality in Eastern China – Part 2. Evaluation of the MarcoPolo-Panda Prediction System, Version 1

2018 ◽  
Author(s):  
Anna Katinka Petersen ◽  
Guy P. Brasseur ◽  
Idir Bouarar ◽  
Johannes Flemming ◽  
Michael Gauss ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Observational Data ◽  
Prediction System ◽  
Model Ensemble ◽  
Individual Model ◽  
Forecasting System ◽  
The Individual ◽  
Pollution Events ◽  
Statistical Indicators

Abstract. An operational multi-model forecasting system for air quality has been developed to provide air quality services for urban areas of China. The initial forecasting system included seven state-of-the-art computational models developed and executed in Europe and China (CHIMERE, IFS, EMEP MSC-W, WRF-Chem-MPIM, WRF-Chem-SMS, LOTOS-EUROS and SILAMtest). Several other models joined the prediction system recently, but are not considered in the present analysis. In addition to the individual models, a simple multi-model ensemble was constructed by deriving statistical quantities such as the median and the mean of the predicted concentrations. The prediction system provides daily forecasts and observational data of surface ozone, nitrogen dioxides and particulate matter for the 37 largest urban agglomerations in China (population higher than 3 million in 2010). These individual forecasts as well as the multi-model ensemble predictions for the next 72 hours are displayed as hourly outputs on a publicly accessible web site (www.marcopolo-panda.eu). In this paper, the performance of the predictions system (individual models and the multi-model ensemble) for the first operational year (April 2016 until June 2017) has been analysed through statistical indicators using the surface observational data reported at Chinese national monitoring stations. This evaluation aims to investigate a) the seasonal behavior, b) the geographical distribution and c) diurnal variations of the ensemble and model skills. Statistical indicators show that the ensemble product usually provides the best performance compared to the individual model forecasts. The ensemble product is robust even if occasionally some individual model results are missing. Overall and in spite of some discrepancies, the air quality forecasting system is well suited for the prediction of air pollution events and has the ability to provide alert warning (binary prediction) of air pollution events if bias corrections are applied to improve the ozone predictions.

scholarly journals Citizens or Consumers? Air Quality Sensor Users and Their Involvement in Sensor.Community. Results from Qualitative Case Study

2021 ◽  
Vol 13 (20) ◽  
pp. 11406
Author(s):  
Michał Wróblewski ◽  
Joanna Suchomska ◽  
Katarzyna Tamborska
Keyword(s):  
Air Quality ◽  
Local Community ◽  
Environmental Problem ◽  
Local Level ◽  
Health And Safety ◽  
Quality Data ◽  
Different Types ◽  
Air Quality Data

This article presents the results of the qualitative research conducted on Polish users of the Sensor.Community network. Different types of motivation behind the decision to engage in the collection of air quality data are discussed. Users’ motives have been found to result predominantly from the concern for the health and safety of their loved ones, as well as the need to control air quality (and ultimately the quality of life) in their immediate environment (home and neighbourhood). Users do not display civic behaviour such as working for the local community. Three factors have been proposed to explain this status quo. First, the motives related to health and safety, as opposed to motives behind seeking a resolution to an environmental problem at the local level, may contribute to the solidification of individualistic attitudes. Second, Sensor.Community is organised in a way that does not promote a greater involvement from the network organisers in the development of the initiative and retention of users. Instead, the network focuses predominantly on the technical aspects of operation. Third, users have no sense of agency as, in our opinion, they remain largely unaware of the value of the data they collect.

scholarly journals Air Quality Data for Catania: Analysis and Investigation Case Study 2010–2011

2014 ◽  
Vol 45 ◽  
pp. 681-690 ◽  
Author(s):  
Rosario Lanzafame ◽  
Pier Francesco Scandura ◽  
Fabio Famoso ◽  
Pietro Monforte ◽  
Carmelo Oliveri
Keyword(s):  
Air Quality ◽  
Quality Data ◽  
Air Quality Data

scholarly journals COVID-19 and the Improvement of the Global Air Quality: The Bright Side of a Pandemic

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1279
Author(s):  
Hamideh Habibi ◽  
Ripendra Awal ◽  
Ali Fares ◽  
Masoud Ghahremannejad
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Nitrogen Monoxide ◽  
Weather Conditions ◽  
Quality Data ◽  
Global Response ◽  
The Past ◽  
Air Quality Improvement ◽  
Bright Side ◽  
Air Quality Data

The objective of this investigation is to study the impacts of the global response to COVID-19 on air pollution and air quality changes in major cities across the globe over the past few months. Air quality data (NO2, CO, PM2.5, and O3) were downloaded from the World Air Quality Index project for the January 2019–April 2020 period. Results show a significant reduction in the levels of 2020 NO2, CO, and PM2.5 compared to their levels in 2019. These reductions were as high as 63% (Wuhan, China), 61% (Lima, Peru), and 61% (Berlin, Germany), in NO2, CO, and PM2.5 levels, respectively. In contrast, 2020 O3 levels increased substantially, as high as 86% (Milan, Italy), in an apparent response to the decrease in titration by nitrogen monoxide and its derivatives. Significant differences in the weather conditions across the globe do not seem to impact this air quality improvement trend. Will this trend in the reduction in most air pollutants to unprecedented levels continue in the next few weeks or even months? The response to this and other questions will depend on the future global economic and environmental policies.

scholarly journals Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1

2019 ◽  
Vol 12 (3) ◽  
pp. 1241-1266 ◽  
Author(s):  
Anna Katinka Petersen ◽  
Guy P. Brasseur ◽  
Idir Bouarar ◽  
Johannes Flemming ◽  
Michael Gauss ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Observational Data ◽  
Prediction System ◽  
Multimodel Ensemble ◽  
Individual Model ◽  
Forecasting System ◽  
The Individual ◽  
Pollution Events ◽  
Statistical Indicators

Abstract. An operational multimodel forecasting system for air quality has been developed to provide air quality services for urban areas of China. The initial forecasting system included seven state-of-the-art computational models developed and executed in Europe and China (CHIMERE, IFS, EMEP MSC-W, WRF-Chem-MPIM, WRF-Chem-SMS, LOTOS-EUROS, and SILAMtest). Several other models joined the prediction system recently, but are not considered in the present analysis. In addition to the individual models, a simple multimodel ensemble was constructed by deriving statistical quantities such as the median and the mean of the predicted concentrations. The prediction system provides daily forecasts and observational data of surface ozone, nitrogen dioxides, and particulate matter for the 37 largest urban agglomerations in China (population higher than 3 million in 2010). These individual forecasts as well as the multimodel ensemble predictions for the next 72 h are displayed as hourly outputs on a publicly accessible web site (http://www.marcopolo-panda.eu, last access: 27 March 2019). In this paper, the performance of the prediction system (individual models and the multimodel ensemble) for the first operational year (April 2016 until June 2017) has been analyzed through statistical indicators using the surface observational data reported at Chinese national monitoring stations. This evaluation aims to investigate (a) the seasonal behavior, (b) the geographical distribution, and (c) diurnal variations of the ensemble and model skills. Statistical indicators show that the ensemble product usually provides the best performance compared to the individual model forecasts. The ensemble product is robust even if occasionally some individual model results are missing. Overall, and in spite of some discrepancies, the air quality forecasting system is well suited for the prediction of air pollution events and has the ability to provide warning alerts (binary prediction) of air pollution events if bias corrections are applied to improve the ozone predictions.

scholarly journals Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

2021 ◽  
Vol 18 (3) ◽  
pp. 1333
Author(s):  
Ahmad R. Alsaber ◽  
Jiazhu Pan ◽  
Adeeba Al-Hurban 
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Random Forest ◽  
Missing Values ◽  
Imputation Method ◽  
Environmental Data ◽  
Environmental Research ◽  
Quality Data ◽  
Data Set ◽  
Air Quality Data

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

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