Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics
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Title
Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics
Authors
Keywords
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Journal
SENSORS
Volume 21, Issue 14, Pages 4717
Publisher
MDPI AG
Online
2021-07-12
DOI
10.3390/s21144717
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Note: Only part of the references are listed.- Environmental and Health Impacts of Air Pollution: A Review
- (2020) Ioannis Manisalidis et al. Frontiers in Public Health
- Monitoring, Mapping, and Modeling Spatial–Temporal Patterns of PM2.5 for Improved Understanding of Air Pollution Dynamics Using Portable Sensing Technologies
- (2020) Ronan Hart et al. International Journal of Environmental Research and Public Health
- Data fusion for air quality mapping using low-cost sensor observations: Feasibility and added-value
- (2020) Alicia Gressent et al. ENVIRONMENT INTERNATIONAL
- Graph-Deep-Learning-Based Inference of Fine-Grained Air Quality From Mobile IoT Sensors
- (2020) Tien Huu Do et al. IEEE Internet of Things Journal
- Deep-MAPS: Machine-Learning-Based Mobile Air Pollution Sensing
- (2020) Jun Song et al. IEEE Internet of Things Journal
- Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities—a case study in Sheffield
- (2019) Said Munir et al. ENVIRONMENTAL MONITORING AND ASSESSMENT
- A review of artificial neural network models for ambient air pollution prediction
- (2019) Sheen Mclean Cabaneros et al. ENVIRONMENTAL MODELLING & SOFTWARE
- Development of a land use regression model for black carbon using mobile monitoring data and its application to pollution-avoiding routing
- (2019) Annelies Van den Hove et al. ENVIRONMENTAL RESEARCH
- Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea
- (2019) Chris C. Lim et al. ENVIRONMENT INTERNATIONAL
- Fine-Scale Spatiotemporal Air Pollution Analysis Using Mobile Monitors on Google Street View Vehicles
- (2019) Yawen Guan et al. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
- Field Test of Several Low-Cost Particulate Matter Sensors in High and Low Concentration Urban Environments
- (2018) Karoline K. Johnson et al. Aerosol and Air Quality Research
- Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?
- (2018) Lidia Morawska et al. ENVIRONMENT INTERNATIONAL
- The Imperial County Community Air Monitoring Network: A Model for Community-based Environmental Monitoring for Public Health Action
- (2017) Paul B. English et al. ENVIRONMENTAL HEALTH PERSPECTIVES
- Robustness of intra urban land-use regression models for ultrafine particles and black carbon based on mobile monitoring
- (2017) Jules Kerckhoffs et al. ENVIRONMENTAL RESEARCH
- Robustness of Land-Use Regression Models Developed from Mobile Air Pollutant Measurements
- (2017) Marianne Hatzopoulou et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data
- (2017) Joshua S. Apte et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- Miniaturized Monitors for Assessment of Exposure to Air Pollutants: A Review
- (2017) et al. International Journal of Environmental Research and Public Health
- Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models
- (2016) Matthew D. Adams et al. JOURNAL OF ENVIRONMENTAL MANAGEMENT
- Mobile monitoring of particulate matter: State of art and perspectives
- (2016) Fernando Gozzi et al. Atmospheric Pollution Research
- Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset
- (2015) Joris Van den Bossche et al. ATMOSPHERIC ENVIRONMENT
- A land use regression model for estimating the NO2 concentration in shanghai, China
- (2015) Xia Meng et al. ENVIRONMENTAL RESEARCH
- A national fine spatial scale land-use regression model for ozone
- (2015) Jules Kerckhoffs et al. ENVIRONMENTAL RESEARCH
- Land Use Regression Models of On-Road Particulate Air Pollution (Particle Number, Black Carbon, PM2.5, Particle Size) Using Mobile Monitoring
- (2015) Steve Hankey et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- Deriving high-resolution urban air pollution maps using mobile sensor nodes
- (2015) David Hasenfratz et al. Pervasive and Mobile Computing
- Response to discussion on: “An integrated statistical approach for evaluating the exceedance of criteria pollutants in the ambient air of megacity Delhi”, Atmospheric Environment
- (2013) Pragati Sharma et al. ATMOSPHERIC ENVIRONMENT
- Identifying pollution sources and predicting urban air quality using ensemble learning methods
- (2013) Kunwar P. Singh et al. ATMOSPHERIC ENVIRONMENT
- Dispersion model evaluation of PM2.5, NOx and SO2 from point and major line sources in Nova Scotia, Canada using AERMOD Gaussian plume air dispersion model
- (2013) Mark D. Gibson et al. Atmospheric Pollution Research
- Mobile measurements and regression modeling of the spatial particulate matter variability in an urban area
- (2012) Hendrik Merbitz et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Kriged and modeled ambient air levels of benzene in an urban environment: an exposure assessment study
- (2011) Kristina W Whitworth et al. Environmental Health
- A land use regression for predicting NO2 and PM10 concentrations in different seasons in Tianjin region, China
- (2010) Li Chen et al. JOURNAL OF ENVIRONMENTAL SCIENCES
- Within-urban variability in ambient air pollution: Comparison of estimation methods
- (2007) Julian D. Marshall et al. ATMOSPHERIC ENVIRONMENT
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