Mapping urban air quality in near real-time using observations from low-cost sensors and model information.

[1]  W. Cleveland Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .

[2]  W. B. Petersen,et al.  USER'S GUIDE FOR HIWAY-2. A HIGHWAY AIR POLLUTION MODEL , 1980 .

[3]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

[4]  Noel A. C. Cressie,et al.  Statistics for Spatial Data: Cressie/Statistics , 1993 .

[5]  P. Kitanidis Introduction to Geostatistics: Applications in Hydrogeology , 1997 .

[6]  J. Chilès,et al.  Geostatistics: Modeling Spatial Uncertainty , 1999 .

[7]  Timothy C. Coburn,et al.  Geostatistics for Natural Resources Evaluation , 2000, Technometrics.

[8]  R. Reese Geostatistics for Environmental Scientists , 2001 .

[9]  Eugenia Kalnay,et al.  Atmospheric Modeling, Data Assimilation and Predictability , 2002 .

[10]  G. Evensen,et al.  Sequential Data Assimilation Techniques in Oceanography , 2003 .

[11]  Gerard B. M. Heuvelink,et al.  About regression-kriging: From equations to case studies , 2007, Comput. Geosci..

[12]  Bruce Denby,et al.  Comparison of two data assimilation methods for assessing PM10 exceedances on the European scale , 2008 .

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

[14]  Geir Evensen,et al.  Sequential data assimilation , 2009 .

[15]  Marshall Sahlins The Conflicts of the Faculty , 2009, Critical Inquiry.

[16]  Harold McInnes,et al.  Modelling long-term averages of local ambient air pollution in Oslo, Norway: evaluation of nitrogen dioxide, PM10 and PM2.5 , 2009 .

[17]  Geostatistics with Applications in Earth Sciences , 2010 .

[18]  B. Denby,et al.  Spatial mapping of ozone and SO2 trends in Europe. , 2010, The Science of the total environment.

[19]  E. Hand,et al.  Citizen science: People power , 2010, Nature.

[20]  E. Hand Volunteer army catches interstellar dust grains , 2010 .

[21]  Chariton Kouridis,et al.  European Topic Centre on Air polluti on and Climate change Mitigation , 2011 .

[22]  Manuel Aleixandre,et al.  Review of Small Commercial Sensors for Indicative Monitoring of Ambient Gas , 2012 .

[23]  C. Sabel,et al.  Quantifying human exposure to air pollution--moving from static monitoring to spatio-temporally resolved personal exposure assessment. , 2013, The Science of the total environment.

[24]  Patrick Berghmans,et al.  Monitoring PM10 and Ultrafine Particles in Urban Environments Using Mobile Measurements , 2013 .

[25]  Bert Brunekreef,et al.  Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe - The ESCAPE project , 2013 .

[26]  Audrey de Nazelle,et al.  Improving estimates of air pollution exposure through ubiquitous sensing technologies. , 2013, Environmental pollution.

[27]  E. Snyder,et al.  The changing paradigm of air pollution monitoring. , 2013, Environmental science & technology.

[28]  Gb Stewart,et al.  The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks , 2013 .

[29]  Vivien Mallet,et al.  BLUE‐based NO 2 data assimilation at urban scale , 2013 .

[30]  Bernard De Baets,et al.  Cyclist exposure to UFP and BC on urban routes in Antwerp, Belgium , 2014 .

[31]  William Lahoz,et al.  Recent satellite-based trends of tropospheric nitrogen dioxide over large urban agglomerations worldwide , 2014 .

[32]  W. Lahoz,et al.  Data assimilation: making sense of Earth Observation , 2014, Front. Environ. Sci..

[33]  L. Shang,et al.  The next generation of low-cost personal air quality sensors for quantitative exposure monitoring , 2014 .

[34]  Frank de Leeuw,et al.  Air quality status and trends in Europe , 2014 .

[35]  Joris Van den Bossche,et al.  Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset , 2015 .

[36]  L. Spinelle,et al.  Sensors and Actuators B: Chemical Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide (cid:2) , 2022 .

[37]  I. Barmpadimos,et al.  Two-week NO2 maps for the City of Zurich, Switzerland, derived by statistical modelling utilizing data from a routine passive diffusion sampler network , 2015 .

[38]  W. Lahoz,et al.  Mobile technologies and services for environmental monitoring: The Citi-Sense-MOB approach , 2015 .

[39]  Edmund Seto,et al.  Variability in and agreement between modeled and personal continuously measured black carbon levels using novel smartphone and sensor technologies. , 2015, Environmental science & technology.

[40]  L. Morawska,et al.  The rise of low-cost sensing for managing air pollution in cities. , 2015, Environment international.

[41]  Matthieu Plu,et al.  A regional air quality forecasting system over Europe : the MACC-II daily ensemble production , 2015 .

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

[43]  Laurent Francis,et al.  Assessment of air quality microsensors versus reference methods: The EuNetAir joint exercise , 2016 .

[44]  Alexander Baklanov,et al.  Megacities, air quality and climate , 2016 .

[45]  David Hasenfratz,et al.  Statistical modelling of particle number concentration in Zurich at high spatio-temporal resolution utilizing data from a mobile sensor network , 2016 .

[46]  Alena Bartonova,et al.  Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? , 2017, Environment international.