@ARTICLE{8555540, 
author={Y. {Zhou} and S. {De} and G. {Ewa} and C. {Perera} and K. {Moessner}}, 
journal={IEEE Access}, 
title={Data-Driven Air Quality Characterization for Urban Environments: A Case Study}, 
year={2018}, 
volume={6}, 
number={}, 
pages={77996-78006}, 
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;air pollution;machine learning prediction;neural network}, 
doi={10.1109/ACCESS.2018.2884647}, 
ISSN={2169-3536}, 
month={},}