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Robustness of Land-Use Regression Models Developed from Mobile Air Pollutant Measurements
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s11869-010-0101-8openalex-snapshot
Particulate air pollution, systemic oxidative stress, inflammation, and atherosclerosis
inferredcites →https://doi.org/10.1016/j.atmosenv.2005.02.034openalex-snapshot
Mobile platform measurements of ultrafine particles and associated pollutant concentrations on freeways and residential streets in Los Angeles
inferredcites →https://doi.org/10.1016/j.atmosenv.2005.09.067openalex-snapshot
Predicting long-term average concentrations of traffic-related air pollutants using GIS-based information
inferredcites →https://doi.org/10.1016/j.atmosenv.2006.11.063openalex-snapshot
The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies
inferredcites →https://doi.org/10.1016/j.atmosenv.2008.05.057openalex-snapshot
A review of land-use regression models to assess spatial variation of outdoor air pollution
inferredcites →https://doi.org/10.1016/j.atmosenv.2009.06.040openalex-snapshot
A prediction-based approach to modelling temporal and spatial variability of traffic-related air pollution in Montreal, Canada
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.2015.01.017openalex-snapshot
Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset
inferredcites →https://doi.org/10.1016/j.atmosenv.2015.02.018openalex-snapshot
Development of a land-use regression model for ultrafine particles in Toronto, Canada
inferredcites →https://doi.org/10.1016/j.envpol.2015.04.011openalex-snapshot
Characterizing the spatial distribution of ambient ultrafine particles in Toronto, Canada: A land use regression model
inferredcites →https://doi.org/10.1016/j.envres.2015.12.016openalex-snapshot
A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach
inferredcites →https://doi.org/10.1016/j.jtrangeo.2013.08.006openalex-snapshot
Land-use and socio-economics as determinants of traffic emissions and individual exposure to air pollution
inferredcites →https://doi.org/10.1016/j.scitotenv.2013.11.064openalex-snapshot
Land use regression models for estimating individual NOx and NO2 exposures in a metropolis with a high density of traffic roads and population
inferredcites →https://doi.org/10.1016/j.scitotenv.2014.11.088openalex-snapshot
Spatial variation of ultrafine particles and black carbon in two cities: Results from a short-term measurement campaign
inferredcites →https://doi.org/10.1016/s0048-9697(00)00429-0openalex-snapshot
A regression-based method for mapping traffic-related air pollution: application and testing in four contrasting urban environments
inferredcites →https://doi.org/10.1021/acs.est.5b01209openalex-snapshot
Land Use Regression Models of On-Road Particulate Air Pollution (Particle Number, Black Carbon, PM 2.5 , Particle Size) Using Mobile Monitoring
inferredcites →https://doi.org/10.1021/acs.est.5b04235openalex-snapshot
Investigating the Use Of Portable Air Pollution Sensors to Capture the Spatial Variability Of Traffic-Related Air Pollution
inferredcites →https://doi.org/10.1021/acs.est.6b01807openalex-snapshot
Developing Street-Level PM2.5 and PM10 Land Use Regression Models in High-Density Hong Kong with Urban Morphological Factors
inferredcites →https://doi.org/10.1021/acs.est.6b03476openalex-snapshot
Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression Model
inferredcites →https://doi.org/10.1021/es0606780openalex-snapshot
Application of Land Use Regression to Estimate Long-Term Concentrations of Traffic-Related Nitrogen Oxides and Fine Particulate Matter
inferredcites →https://doi.org/10.1021/es1023042openalex-snapshot
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/es400039uopenalex-snapshot
A Hybrid Approach to Estimating National Scale Spatiotemporal Variability of PM2.5in the Contiguous United States
inferredcites →https://doi.org/10.1021/es401281ropenalex-snapshot
Modeling the Concentrations of On-Road Air Pollutants in Southern California
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
inferredcites →https://doi.org/10.1021/es4040528openalex-snapshot
An LUR/BME Framework to Estimate PM2.5 Explained by on Road Mobile and Stationary Sources
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/10.1029/2009jd011878openalex-snapshot
Year‐round measurements of nitrogen oxides and ozone in the tropical North Atlantic marine boundary layer
inferredcites →https://doi.org/10.1038/jes.2012.26openalex-snapshot
A spatiotemporal land-use regression model of winter fine particulate levels in residential neighbourhoods
inferredcites →https://doi.org/10.1038/jes.2014.60openalex-snapshot
Population intervention models to estimate ambient NO2 health effects in children with asthma
inferredcites →https://doi.org/10.1039/b813889copenalex-snapshot
Air pollution exposure assessment methods utilized in epidemiological studies
inferredcites →https://doi.org/10.1080/10473289.2005.10464708openalex-snapshot
Assessing Spatial Variability of Ambient Nitrogen Dioxide in Montréal, Canada, with a Land-Use Regression Model
inferredcites →https://doi.org/10.1080/10473289.2006.10464542openalex-snapshot
A Land Use Regression Model for Predicting Ambient Concentrations of Nitrogen Dioxide in Hamilton, Ontario, Canada
inferredcites →https://doi.org/10.1080/136588197242158openalex-snapshot
Mapping urban air pollution using GIS: a regression-based approach
inferredcites →https://doi.org/10.1080/15287390600883018openalex-snapshot
Modeling the Intraurban Variability of Ambient Traffic Pollution in Toronto, Canada
inferredcites →https://doi.org/10.1097/01.ede.0000041910.49046.9bopenalex-snapshot
Estimating Long-Term Average Particulate Air Pollution Concentrations: Application of Traffic Indicators and Geographic Information Systems
inferredcites →https://doi.org/10.1289/ehp.1002221openalex-snapshot
Postmenopausal Breast Cancer Is Associated with Exposure to Traffic-Related Air Pollution in Montreal, Canada: A Case–Control Study
inferredcites →https://doi.org/10.1289/ehp.1408882openalex-snapshot
Lung Cancer and Exposure to Nitrogen Dioxide and Traffic: A Systematic Review and Meta-Analysis
inferredcites →https://doi.org/10.1289/ehp.7939openalex-snapshot
Exposure Assessment for Atmospheric Ultrafine Particles (UFPs) and Implications in Epidemiologic Research
inferredcites →https://doi.org/10.1371/journal.pone.0100307openalex-snapshot
Traffic-Related Air Pollution and the Onset of Myocardial Infarction: Disclosing Benzene as a Trigger? A Small-Area Case-Crossover Study
inferredcites →https://doi.org/10.5194/acp-14-7173-2014openalex-snapshot
Elucidating multipollutant exposure across a complex metropolitan area by systematic deployment of a mobile laboratory
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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Spatial variations in urban air pollution: impacts of diesel bus traffic and restaurant cooking at small scales
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Mapping the Spatiotemporal Variability of Particulate Matter Pollution in Delhi: Insights from Land Use Regression Modelling
inferred← citeshttps://doi.org/10.1007/s40572-021-00310-yopenalex-snapshot
Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air)
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Advancing Long-Term Outdoor Air Pollution Exposure Assessment for Health Studies: Lessons from a Multinational Research Initiative
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Correlation networks of air particulate matter ($$\hbox {PM}_{2.5}$$): a comparative study
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Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors
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Source characterization and health risk assessment of community-level air pollutant exposures through a rotating-site trailer in the Puget Sound region
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Research on the impact of land use and meteorological factors on the spatial distribution characteristics of PM2.5 concentration
inferred← citeshttps://doi.org/10.1016/j.atmosenv.2018.10.013openalex-snapshot
Urban land use regression models: can temporal deconvolution of traffic pollution measurements extend the urban LUR to suburban areas?
inferred← citeshttps://doi.org/10.1016/j.buildenv.2017.11.043openalex-snapshot
Identifying critical building morphological design factors of street-level air pollution dispersion in high-density built environment using mobile monitoring
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High spatiotemporal characterization of on-road PM2.5 concentrations in high-density urban areas using mobile monitoring
inferred← citeshttps://doi.org/10.1016/j.buildenv.2025.112979openalex-snapshot
Combined effects of urban morphology on land surface temperature and PM2.5 concentration across fine-scale urban blocks in Hangzhou, China
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How multi-sensory experience shapes place-based human sentiments: A hyperlocal analysis using environmental sensing and social media data in the Bronx, New York City
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Application of land use regression model to assess outdoor air pollution exposure: A review
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Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea
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Hyperlocal variation of nitrogen dioxide, black carbon, and ultrafine particles measured with Google Street View cars in Amsterdam and Copenhagen
inferred← citeshttps://doi.org/10.1016/j.envint.2023.108106openalex-snapshot
Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models
inferred← citeshttps://doi.org/10.1016/j.envint.2024.108430openalex-snapshot
A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023
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Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment
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Air pollution mapping and variability over five European cities
inferred← citeshttps://doi.org/10.1016/j.envint.2025.109582openalex-snapshot
Mobile monitoring of air pollution − a position paper on use cases, good practices, challenges, and opportunities
inferred← citeshttps://doi.org/10.1016/j.envpol.2020.115951openalex-snapshot
Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance
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A three-dimensional LUR framework for PM2.5 exposure assessment based on mobile unmanned aerial vehicle monitoring
inferred← citeshttps://doi.org/10.1016/j.envpol.2024.124353openalex-snapshot
High-resolution spatial and spatiotemporal modelling of air pollution using fixed site and mobile monitoring in a Canadian city
inferred← citeshttps://doi.org/10.1016/j.envpol.2025.125689openalex-snapshot
Assessing the role of spatial aggregation schemes with varying campaign durations of mobile measurements on land use regression models for estimating nitrogen dioxide
inferred← citeshttps://doi.org/10.1016/j.envres.2017.08.040openalex-snapshot
Robustness of intra urban land-use regression models for ultrafine particles and black carbon based on mobile monitoring
inferred← citeshttps://doi.org/10.1016/j.envres.2021.111010openalex-snapshot
Characterizing long-term NO2 concentration surfaces across a large metropolitan area through spatiotemporal land use regression modelling of mobile measurements
inferred← citeshttps://doi.org/10.1016/j.envres.2023.115451openalex-snapshot
Exposure assessment for air pollution epidemiology: A scoping review of emerging monitoring platforms and designs
inferred← citeshttps://doi.org/10.1016/j.envres.2023.117457openalex-snapshot
Mobile monitoring of air pollutants; performance evaluation of a mixed-model land use regression framework in relation to the number of drive days.
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A spatio-temporal land use regression model to assess street-level exposure to black carbon
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Quantification of long-term accumulation of inhaled ultrafine particles via human olfactory-brain pathway due to environmental emissions – a pilot study
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Investigating the influence of urban land use and landscape pattern on PM2.5 spatial variation using mobile monitoring and WUDAPT
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2021.148540openalex-snapshot
Development of season-dependent land use regression models to estimate BC and PM1 exposure
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Linking of built environment inequalities with air quality: A case study
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Spatial prediction of on-road air pollution using long-term mobile monitoring: Insights from Delhi
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Predicting Traffic-Related Air Pollution Using Feature Extraction from Built Environment Images
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Improving Air Pollution Predictions of Long-Term Exposure Using Short-Term Mobile and Stationary Monitoring in Two US Metropolitan Regions
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Using Street View Imagery to Predict Street-Level Particulate Air Pollution
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Spatial and Spatiotemporal Variability of Regional Background Ultrafine Particle Concentrations in the Netherlands
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High-Spatial-Resolution Estimates of Ultrafine Particle Concentrations across the Continental United States
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Evaluating the Meteorological Effects on the Urban Form–Air Quality Relationship Using Mobile Monitoring
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Mixed-Effects Modeling Framework for Amsterdam and Copenhagen for Outdoor NO2 Concentrations Using Measurements Sampled with Google Street View Cars
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Characterization of Annual Average Traffic-Related Air Pollution Concentrations in the Greater Seattle Area from a Year-Long Mobile Monitoring Campaign
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Prediction of Short-Term Ultrafine Particle Exposures Using Real-Time Street-Level Images Paired with Air Quality Measurements
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National Exposure Models for Source-Specific Primary Particulate Matter Concentrations Using Aerosol Mass Spectrometry Data
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Impact of Mobile Monitoring Network Design on Air Pollution Exposure Assessment Models
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Key Themes, Trends, and Drivers of Mobile Ambient Air Quality Monitoring: A Systematic Review and Meta-Analysis
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Characterizing Ultrafine Particle Mobile Monitoring Data for Epidemiology
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Mapping Source-Specific Air Pollution Exposures Using Positive Matrix Factorization Applied to Multipollutant Mobile Monitoring in Seattle, WA
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Europe-Wide Spatial Models for Ultrafine Particles Based on Mobile Monitoring
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Refining Air Pollution Exposure Estimates: A Comparison of Citywide and Neighborhood Land Use Regression Models in Toronto
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Aviation-Related Impacts on Ultrafine Particle Number Concentrations Outside and Inside Residences near an Airport
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Restaurant Impacts on Outdoor Air Quality: Elevated Organic Aerosol Mass from Restaurant Cooking with Neighborhood-Scale Plume Extents
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Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression
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Land-Use Regression Modeling of Source-Resolved Fine Particulate Matter Components from Mobile Sampling
inferred← citeshttps://doi.org/10.1021/acs.est.9b02086openalex-snapshot
Urban Ultrafine Particle Exposure Assessment with Land-Use Regression: Influence of Sampling Strategy
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Cost-Effective Mapping of Hyperlocal Air Pollution Using Large-Scale Mobile Monitoring and Land-Use Machine Learning
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Pedestrian exposure to black carbon and PM2.5 emissions in urban hot spots: new findings using mobile measurement techniques and flexible Bayesian regression models
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Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment
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Influence of on-road mobile monitoring design on ultrafine particle exposure models and cognitive health inferences
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Advancing environmental exposure assessment science to benefit society
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Adapting Public Annotated Data Sets and Low-Quality Dash Cameras for Spatiotemporal Estimation of Traffic-Related Air Pollution: A Transfer-Learning Approach
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Design and evaluation of mobile monitoring campaigns for air pollution exposure assessment in epidemiologic cohorts
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Protecting the patches from the footprints: examining the land use factors associated with forest patches in Atewa range forest reserve
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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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Mapping Air Pollution with Mobile Monitoring
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Modelling Cyclists’ Multi-Exposure to Air and Noise Pollution with Low-Cost Sensors—The Case of Paris
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Understanding NO2 Concentration Dynamics within Tema Metropolitan Area of Ghana Using Generalized Linear Model
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Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies
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Spatial Patterns in Rush-Hour vs. Work-Week Diesel-Related Pollution across a Downtown Core
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A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets
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A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods
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Influence of Regional PM2.5 Sources on Air Quality: A Network-Based Spatiotemporal Analysis in Northern Thailand
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Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics
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Mapping and Statistical Analysis of NO2 Concentration for Local Government Air Quality Regulation
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Improving Correlations between Land Use and Air Pollutant Concentrations Using Wavelet Analysis: Insights from a Low-cost Sensor Network
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Bayesian Effect Selection for Additive Quantile Regression with an Analysis to Air Pollution Thresholds
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High-resolution air quality maps for Bucharest using a mixed-effects modeling framework
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To what extent is the description of streets important in estimating local air quality: a case study over Paris
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Response to anonymous referees
inferred← citeshttps://doi.org/10.5194/egusphere-2024-1043-rc1openalex-snapshot
Comment on egusphere-2024-1043
inferred← citeshttps://doi.org/10.5194/egusphere-2024-1043-rc2openalex-snapshot
Comment on egusphere-2024-1043
Editorial review for independent work. ICSAC — the Institute for Complexity
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