Big-But-Biased Data Analytics for Air Quality

Air pollution is one of the big concerns for smart cities. The problem of applying big data analytics to sampling bias in the context of urban air quality is studied in this paper. A nonparametric estimator that incorporates kernel density estimation is used. When ignoring the biasing weight functio...

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Veröffentlicht in:Electronics (Basel) Jg. 9; H. 9; S. 1551
Hauptverfasser: Borrajo, Laura, Cao, Ricardo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.09.2020
Schlagworte:
Air pollution
Air quality
Algorithms
Big Data
Data analysis
Datasets
Distribution functions
Machine learning
Mathematical analysis
Methods
Nitrogen dioxide
Outdoor air quality
Ozone
Pollutants
Population
Random variables
Smart cities
Smartphones
Surveillance
Weighting functions
Spain
ISSN:2079-9292, 2079-9292
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Zusammenfassung:Air pollution is one of the big concerns for smart cities. The problem of applying big data analytics to sampling bias in the context of urban air quality is studied in this paper. A nonparametric estimator that incorporates kernel density estimation is used. When ignoring the biasing weight function, a small-sized simple random sample of the real population is assumed to be additionally observed. The general parameter considered is the mean of a transformation of the random variable of interest. A new bootstrap algorithm is used to approximate the mean squared error of the new estimator. Its minimization leads to an automatic bandwidth selector. The method is applied to a real data set concerning the levels of different pollutants in the urban air of the city of A Coruña (Galicia, NW Spain). Estimations for the mean and the cumulative distribution function of the level of ozone and nitrogen dioxide when the temperature is greater than or equal to 30 ∘C based on 15 years of biased data are obtained.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics9091551