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

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.

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

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-2739-2015 ◽

2015 ◽

Vol 8 (3) ◽

pp. 2739-2806 ◽

Cited By ~ 9

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.

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Assessment & Forecast of Air Quality data based on Neural Network

International Journal of Pharmaceutical Research ◽

10.31838/ijpr/2020.12.01.231 ◽

2020 ◽

Vol 12 (01) ◽

Keyword(s):

Neural Network ◽

Air Quality ◽

Quality Data ◽

Air Quality Data

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Making Air Quality Data Meaningful

Proceedings of the 2020 ACM Designing Interactive Systems Conference ◽

10.1145/3357236.3395517 ◽

2020 ◽

Author(s):

Szu-Yu (Cyn) Liu ◽

Justin Cranshaw ◽

Asta Roseway

Keyword(s):

Air Quality ◽

Quality Data ◽

Air Quality Data

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Feature Selection and Analysis in Air Quality Data

2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) ◽

10.1109/confluence51648.2021.9376882 ◽

2021 ◽

Author(s):

Manish Mahajan ◽

Santosh Kumar ◽

Bhasker Pant ◽

Umesh Kumar Tiwari ◽

Rijwan Khan

Keyword(s):

Feature Selection ◽

Air Quality ◽

Quality Data ◽

Air Quality Data

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18031333 ◽

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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