@article{5663afbdfd894f4093783143c12ad40a,
title = "Data-Driven Air Quality Characterization for Urban Environments: A Case Study",
keywords = "air pollution autoregressive processes environmental monitoring (geophysics) environmental science computing geophysics computing learning (artificial intelligence) neural nets time series data-driven air quality characterization urban environments case study economic impact social impact poor air quality towns cities real-time air quality levels human health local authority maintained monitoring stations resultant datasets missing labels computational data-driven mechanisms machine learning-based method air quality index environmental monitoring data air quality estimation framework time series prediction standard machine-learning-based predictive algorithms data sparsity nonlinear autoregressive neural network monitoring sites Air quality Meteorology Atmospheric modeling Predictive models Urban areas Neural networks Monitoring Air quality estimation machine learning prediction neural network, 2020",
author = "Y. Zhou and S. De and G. Ewa and C. Perera and K. Moessner",
year = "2018",
month = dec,
day = "3",
doi = "10.1109/ACCESS.2018.2884647",
language = "English",
volume = "6",
pages = "77996--78006",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
}