Related references
Note: Only part of the references are listed.Probabilistic Machine Learning with Low-Cost Sensor Networks for Occupational Exposure Assessment and Industrial Hygiene Decision Making
Andrew N. Patton et al.
ANNALS OF WORK EXPOSURES AND HEALTH (2022)
Stationary and portable multipollutant monitors for high-spatiotemporal-resolution air quality studies including online calibration
Colby Buehler et al.
ATMOSPHERIC MEASUREMENT TECHNIQUES (2021)
Moving beyond Fine Particle Mass: High-Spatial Resolution Exposure to Source-Resolved Atmospheric Particle Number and Chemical Mixing State
Qing Ye et al.
ENVIRONMENTAL HEALTH PERSPECTIVES (2020)
Combining forward with recurrent neural networks for hourly air quality prediction in Northwest of China
Zhili Zhao et al.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2020)
Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore
Abhirup Datta et al.
ATMOSPHERIC ENVIRONMENT (2020)
Spatial Correlation of Ultrafine Particle Number and Fine Particle Mass at Urban Scales: Implications for Health Assessment
Provat K. Saha et al.
ENVIRONMENTAL SCIENCE & TECHNOLOGY (2020)
Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques
Fi-John Chang et al.
SCIENCE OF THE TOTAL ENVIRONMENT (2020)
Calibration of Portable Particulate Matter-Monitoring Device using Web Query and Machine Learning
Byoung Gook Loh et al.
SAFETY AND HEALTH AT WORK (2019)
Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea
Chris C. Lim et al.
ENVIRONMENT INTERNATIONAL (2019)
Field and Laboratory Evaluations of the Low-Cost Plantower Particulate Matter Sensor
Misti Levy Zamora et al.
ENVIRONMENTAL SCIENCE & TECHNOLOGY (2019)
Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?
Lidia Morawska et al.
ENVIRONMENT INTERNATIONAL (2018)
Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment
Yu Zhan et al.
ENVIRONMENTAL POLLUTION (2018)
Predicting monthly high-resolution PM2.5 concentrations with random forest model in the North China Plain
Keyong Huang et al.
ENVIRONMENTAL POLLUTION (2018)
Assessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise - Part II
C. Borrego et al.
ATMOSPHERIC ENVIRONMENT (2018)
A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring
Naomi Zimmerman et al.
ATMOSPHERIC MEASUREMENT TECHNIQUES (2018)
High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data
Joshua S. Apte et al.
ENVIRONMENTAL SCIENCE & TECHNOLOGY (2017)
Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015
Aaron J. Cohen et al.
LANCET (2017)
Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches
Cole Brokamp et al.
ATMOSPHERIC ENVIRONMENT (2017)
The next generation of low-cost personal air quality sensors for quantitative exposure monitoring
R. Piedrahita et al.
ATMOSPHERIC MEASUREMENT TECHNIQUES (2014)
The Changing Paradigm of Air Pollution Monitoring
Emily G. Snyder et al.
ENVIRONMENTAL SCIENCE & TECHNOLOGY (2013)
Predicting intra-urban variation in air pollution concentrations with complex spatio-temporal dependencies
Adam A. Szpiro et al.
ENVIRONMETRICS (2010)
Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting et al.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2007)
Share Paper
