Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 13 of 13 for “"land use regression"”.
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Estimating schoolchildren's pollutant dose of NO₂ while walking to school in Auckland using GIS-based spatial analysis and land use regression model
… is still challenging at population scales because of the difficulty in predicting the local variation of air pollutants and modelling students' commute routes from home to school. Privacy concerns force the use of simulated home addresses that may introduce bias to the estimated dose/exposure. …
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Inequities in Air Pollution Exposure in the U.S.: An Exploration of Disparity Metrics Across Geographic and Temporal Scales
… population demographics from the U.S. Census and land-use regression 𝑃𝑀₂.₅ concentration estimates from the Center for Air, Climate, and Energy Solutions (CACES), we compare the calculations of absolute and relative exposure disparities at different geographic scales and changing demographic …
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Modification by sex and atopy of the effect of chemical air pollutions and biological air pollutants including pollen and fungal spores on children respiratory health in the Western Cape, Cape South Africa
Asthma continues to be a leading cause of morbidity and mortality among children in SubSaharan Africa. Ongoing under-diagnosis and treatment in low-and middle-income countries, together exposure to airborne pollutants and allergens contribute to this disease burden. A cross-sectional study that …
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Ambient air pollution and cardiorespiratory outcomes amongst adult residing in four informal settlements in the Western Province of South Africa
… the Western Cape, South Africa. The study made use of Land Use Regression to estimate each participant’s exposure to particulate matter of aerodynamic diameter of 2.5µm (PM2.5) and nitrogen dioxide (NO2). A questionnaire was adapted from the European Community Respiratory Health Survey and …
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Multiscale Spatial and Temporal Modelling of Fine Particulate Matter (PM2.5) from Wildfire Smoke Using Remote Sensing and Statistical Methods
… for each recent year over the study region: Land Use Regression (LUR), Linear Mixed Effect (LME), and Artificial Neural Network (ANN). Predictor variables were MODIS AOD images, ground PM2.5 measurements, and ancillary land use and meteorological data. LUR models were used to predict PM2.5 in …
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Mobile Monitoring of Air Quality in the Washington DC Region
… spatial patterns of air quality. The end-use of the monitoring campaign described here is as an input for Land Use Regression (LUR) models. 3. Collect unconventional data to characterize the built environment, e.g., videos, sound, etc., that could be employed to improve the LUR models …
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The association between ambient air pollution and cardiorespiratory health outcomes of adults residing in informal settlements of the Western Cape in a 12 month period
… symptoms and relevant covariates. Land use regression modelling in R was used to estimate the annual average PM2.5 and NO2 exposure at each household. Cardio-respiratory outcomes included wheezing in the last 12 months, doctor diagnosed asthma, asthma symptoms score (calculated as …
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Using Mobile Monitoring and Vehicle Emissions to Develop and Validate Machine Learning Empirical Models of Particulate Air Pollution
… contaminants and airborne pathogens. Although land use regression has long been used to assess exposure to air pollution, researchers are increasingly using machine learning algorithms to quantify the concentration of harmful pollutants—for this study black carbon (BC) and particle number (PN). …
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Four essays on statistical modelling of environmental data
… deals with geostatistical, time series, and regression analytical approaches for modelling spatio-temporal processes, using air quality data in the applications. The work is structured into four essays the abstracts of which are given in the following. The first essay is titled 'Spatial …
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Leveraging Street View and Remote Sensing Imagery to Enhance Air Quality Modeling through Computer Vision and Machine Learning
… monitors, and these estimates are commonly used for exposure and health effects studies. Traditional land use regression [LUR] models are one of the cost-effective empirical air quality models. LUR typically relies on fixed-site measurements, GIS-derived variables with limited spatial …
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Exposición a contaminación atmosférica en la cohorte de embarazadas INMA-Asturias
… 67 puntos mediante captadores pasivos. Mediante Land Use Regression se determinó el mejor modelo espacial para predecir exposición en los domicilios. Estas predicciones se corrigieron por estacionalidad. Se construyeron indicadores de exposición para cada mujer y se generó un mapa de exposición …
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A prospective cohort study on ambient air pollution, airborne pollen (and fungal spores) and respiratory morbidities including childhood asthma in adolescents from the Western Cape Province
… were computed for each child’s address using a land-use regression model. Daily PM10 levels obtained from a stationary monitor near two of the study areas were used for the panel study. Airborne pollen and fungal spore measurements were obtained directly from a stationary monitor placed in each …
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New Opportunities in Crowd-Sourced Monitoring and Non-government Data Mining for Developing Urban Air Quality Models in the US
… (e.g., machine learning [ML]) could be used to improve air quality models (i.e., land use regression [LUR]) at local, regional, and national levels for refined exposure assessment. LUR models are commonly used for predicting air pollution concentrations at locations without monitoring …