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Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression Model
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Indoor environment : airborne particles and settled dust
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A review of land-use regression models to assess spatial variation of outdoor air pollution
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Using advanced dispersion models and mobile monitoring to characterize spatial patterns of ultrafine particles in an urban area
inferredcites →https://doi.org/10.1016/j.atmosenv.2012.01.058openalex-snapshot
Spatial distribution of ultrafine particles in urban settings: A land use regression model
inferredcites →https://doi.org/10.1016/j.atmosenv.2015.01.015openalex-snapshot
Total and size-resolved particle number and black carbon concentrations in urban areas near Schiphol airport (the Netherlands)
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Development of a land-use regression model for ultrafine particles in Toronto, Canada
inferredcites →https://doi.org/10.1016/j.envpol.2012.12.032openalex-snapshot
Improving estimates of air pollution exposure through ubiquitous sensing technologies
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Characterizing the spatial distribution of ambient ultrafine particles in Toronto, Canada: A land use regression model
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Near roadway air pollution across a spatially extensive road and cycling network
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A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach
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Spatial variation of ultrafine particles and black carbon in two cities: Results from a short-term measurement campaign
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Land Use Regression Models of On-Road Particulate Air Pollution (Particle Number, Black Carbon, PM 2.5 , Particle Size) Using Mobile Monitoring
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Land Use Regression Model for Ultrafine Particles in Amsterdam
inferredcites →https://doi.org/10.1021/es304495sopenalex-snapshot
A Land Use Regression Model for Ultrafine Particles in Vancouver, Canada
inferredcites →https://doi.org/10.1021/es401489hopenalex-snapshot
Spatiotemporal Land Use Regression Models of Fine, Ultrafine, and Black Carbon Particulate Matter in New Delhi, India
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An Hourly Regression Model for Ultrafine Particles in a Near-Highway Urban Area
inferredcites →https://doi.org/10.1021/es5001566openalex-snapshot
Emissions from an International Airport Increase Particle Number Concentrations 4-fold at 10 km Downwind
inferredcites →https://doi.org/10.1021/es505791gopenalex-snapshot
Land Use Regression Models for Ultrafine Particles and Black Carbon Based on Short-Term Monitoring Predict Past Spatial Variation
inferredcites →https://doi.org/10.1021/es803068eopenalex-snapshot
Mobile Monitoring of Particle Light Absorption Coefficient in an Urban Area as a Basis for Land Use Regression
inferredcites →https://doi.org/10.1093/ije/dyq128openalex-snapshot
Cohort Profile: The Amsterdam Born Children and their Development (ABCD) Study
inferredcites →https://doi.org/10.1161/cir.0b013e3181dbece1openalex-snapshot
Particulate Matter Air Pollution and Cardiovascular Disease
inferredcites →https://doi.org/10.1289/ehp.1003369openalex-snapshot
Black Carbon as an Additional Indicator of the Adverse Health Effects of Airborne Particles Compared with PM10 and PM2.5
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Potential Role of Ultrafine Particles in Associations between Airborne Particle Mass and Cardiovascular Health
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Post-processing Method to Reduce Noise while Preserving High Time Resolution in Aethalometer Real-time Black Carbon Data
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Design of a mobile aerosol research laboratory and data processing tools for effective stationary and mobile field measurements
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Mobile air monitoring data-processing strategies and effects on spatial air pollution trends
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Spatial and Spatiotemporal Modeling of Intra-Urban Ultrafine Particles: A Comparison of Linear, Nonlinear, Regularized, and Machine Learning Methods
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Europe-Wide Spatial Models for Ultrafine Particles Based on Mobile Monitoring
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Mobile Monitoring and Land Use Regression Modeling of Ultrafine Particle Spatial Distribution in Beijing, China, during August 2022–July 2023
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Land Use Regression Models for Ultrafine Particles in Six European Areas
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Aviation-Related Impacts on Ultrafine Particle Number Concentrations Outside and Inside Residences near an Airport
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Machine Learning Models for Ultrafine Particles in Copenhagen, Denmark
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Impact of Roadside Mobile Monitoring Design on Epidemiologic Inference – A Case Study of Ultrafine Particles and Cognitive Function
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Development and Application of Europe-Wide Outdoor Air Pollution Exposure Models for Epidemiological Studies of Mortality Effects
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Mapping Air Pollution with Mobile Monitoring
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Comparison of NO2 and BC Predictions Estimated Using Google Street View-Based and Conventional European-Wide LUR Models in Copenhagen, Denmark
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Mapping Mobility: Utilizing Local-Knowledge-Derived Activity Space to Estimate Exposure to Ambient Air Pollution among Individuals Experiencing Unsheltered Homelessness
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Assessing and Validating the Ability of Machine Learning to Handle Unrefined Particle Air Pollution Mobile Monitoring Data Randomly, Spatially, and Spatiotemporally
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Black Carbon Concentration Estimation with Mobile-Based Measurements in a Complex Urban Environment
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Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: impacts of COVID-19 pandemic lockdown
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Analysis of mobile monitoring data from the microAeth® MA200 for measuring changes in black carbon on the roadside in Augsburg
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Démarche intégrée d’évaluation de l’exposition individuelle aux particules ultrafines dans le cadre des études épidémiologiques
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