Characterizing Air Quality in Urban Areas with Mobile Measurement and High Resolution Open Spatial Data: Comparison of Different Machine-Learning Approaches Using a Visual Interface

Air quality is one of the most important topics in our urban life, as it is of great significance for human health and urban planning. However, accurate assessment and prediction of air quality in urban areas are difficult. In major cities, typically only a limited number of air quality monitoring stations are available, and inferring air quality in the un-sampled areas throughout the city is challenging. On the other hand, air quality varies in the urban areas non-linearly; it is highly spatially dependent and considerably influenced by multiple factors, such as building distribution, traffic situation and land uses.

[1]  Suhardi,et al.  Smart city dashboard for integrating various data of sensor networks , 2013, International Conference on ICT for Smart Society.

[2]  Yu Zheng,et al.  U-Air: when urban air quality inference meets big data , 2013, KDD.

[3]  P. Sammulal,et al.  Techniques for Machine Learning based Spatial Data Analysis: Research Directions , 2017 .

[4]  Gang Xie,et al.  Air Quality Prediction: Big Data and Machine Learning Approaches , 2018 .

[5]  Bin Jiang,et al.  Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges , 2015, ArXiv.

[6]  Marcela Rivera,et al.  Effect of the number of measurement sites on land use regression models in estimating local air pollution , 2012 .

[7]  Monica Billger,et al.  Land use Regression as Method to Model Air Pollution. Previous Results for Gothenburg/Sweden☆ , 2015 .

[8]  Jing Wei,et al.  Impact of Land-Use and Land-Cover Change on urban air quality in representative cities of China , 2016 .

[9]  Nazmul Sohel,et al.  Estimation of sulfur dioxide air pollution concentrations with a spatial autoregressive model , 2013 .

[10]  J. Gulliver,et al.  A review of land-use regression models to assess spatial variation of outdoor air pollution , 2008 .

[11]  Lothar Thiele,et al.  Deriving high-resolution urban air pollution maps using mobile sensor nodes , 2015 .

[12]  David Morley,et al.  A land use regression variable generation, modelling and prediction tool for air pollution exposure assessment , 2018, Environmental Modelling & Software.

[13]  Doug Brugge,et al.  An hourly regression model for ultrafine particles in a near-highway urban area. , 2014, Environmental science & technology.

[14]  Marianne Hatzopoulou,et al.  A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach. , 2016, Environmental research.

[15]  Steve Hankey,et al.  Land Use Regression Models of On-Road Particulate Air Pollution (Particle Number, Black Carbon, PM2.5, Particle Size) Using Mobile Monitoring. , 2015, Environmental science & technology.

[16]  Guangjie Han,et al.  RAQ–A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems , 2016, Sensors.

[17]  José Luis Ambite,et al.  Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution , 2017, SIGSPATIAL/GIS.

[18]  María Bermúdez-Edo,et al.  Spatio-Temporal Analysis for Smart City Data , 2018, WWW.

[19]  Bernard De Baets,et al.  Development and evaluation of land use regression models for black carbon based on bicycle and pedestrian measurements in the urban environment , 2018, Environ. Model. Softw..

[20]  Nor Badrul Anuar,et al.  The role of big data in smart city , 2016, Int. J. Inf. Manag..

[21]  Michael Schultz,et al.  Open land cover from OpenStreetMap and remote sensing , 2017, Int. J. Appl. Earth Obs. Geoinformation.