OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning
@article{Lautenschlager2020OpenLUROA, title={OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning}, author={Florian Lautenschlager and Martin Becker and Konstantin Kobs and Michael Steininger and Padraig Davidson and Anna Krause and Andreas Hotho}, journal={Atmospheric Environment}, year={2020} }
Figures and Tables from this paper
13 Citations
Long-term satellite-based estimates of air quality and premature mortality in Equatorial Asia through deep neural networks
- Environmental ScienceEnvironmental Research Letters
- 2020
Atmospheric pollution of particulate matter (PM) is a major concern for its deleterious effects on human health and climate. Over the past 50 years, Equatorial Asia has experienced significant…
The Neural Network Assisted Land Use Regression
- Environmental ScienceAtmosphere
- 2021
Land Use Regression (LUR) is one of the air quality assessment modelling techniques. Its advantages lie mainly in a much simpler mathematical apparatus, quicker and simpler calculations, and a…
Developing land use regression models for environmental science research using the XLUR tool - More than a one-trick pony
- Computer ScienceEnviron. Model. Softw.
- 2021
A new methodology for source apportionment of gaseous industrial emissions.
- Environmental ScienceJournal of hazardous materials
- 2022
Improvements in air quality in the Netherlands during the corona lockdown based on observations and model simulations
- Environmental ScienceAtmospheric Environment
- 2021
Using a DEA–AutoML Approach to Track SDG Achievements
- Computer Science
- 2020
An integrative method that integrates DEA and AutoML to assess and predict performance in SDGs can accurately predict the projected outputs, which can be used as national goals to transform an inefficient country into an efficient country.
An investigation of the impacts of a successful COVID-19 response and meteorology on air quality in New Zealand
- Environmental ScienceAtmospheric Environment
- 2021
Identifying low-PM2.5 exposure commuting routes for cyclists through modeling with the random forest algorithm based on low-cost sensor measurements in three asian cities
- Environmental Pollution
- 2021
National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment.
- Environmental ScienceEnvironmental science & technology
- 2021
Combined predictor variables (traditional + microscale) in the ML-K models outperformed the well-established approaches and show promise for improving national empirical models.
Assessing the intra-urban variability of nitrogen oxides and ozone across a highly heterogeneous urban area
- Environmental ScienceEnvironmental Monitoring and Assessment
- 2021
Traffic-related emissions as well as the operation of a fossil-fuel power plant were found to be the main contributors to the measured NO2 and NOx levels in the GBA, whereas they acted as sinks for O3 concentrations.
References
SHOWING 1-10 OF 58 REFERENCES
Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis
- Environmental ScienceJournal of the Air & Waste Management Association
- 2015
The implications of this research would suggest that it is possible to produce a model of ambient air quality on a citywide scale using the readily available data and enables a lower-cost method of air pollution model development for practitioners and policy makers.
Air Pollution Mapping Using Nonlinear Land Use Regression Models
- Environmental ScienceICCSA
- 2014
The present research deals with a new development of nonlinear LUR models based on machine learning algorithms and their abilities to model the NO2 pollutant in the urban zone of Geneva.
Pushing the spatio-temporal resolution limit of urban air pollution maps
- Environmental Science2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)
- 2014
This paper analyzes one of the largest spatially resolved UFP data set publicly available today containing over 25 million measurements and achieves a 26% reduction in the root-mean-square error-a standard metric to evaluate the accuracy of air quality models-of pollution maps with semi-daily temporal resolution.
Capturing the sensitivity of land-use regression models to short-term mobile monitoring campaigns using air pollution micro-sensors.
- Environmental ScienceEnvironmental pollution
- 2017
A review and evaluation of intraurban air pollution exposure models
- Environmental ScienceJournal of Exposure Analysis and Environmental Epidemiology
- 2005
Hybrid models appear well suited to overcoming the problem of achieving population representative samples while understanding the role of exposure variation at the individual level, and may help to reduce scientific uncertainties that now impede policy intervention aimed at protecting public health.
The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies
- Environmental Science
- 2007
Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013-2015, using a spatiotemporal land-use random-forest model.
- Environmental ScienceEnvironment international
- 2019
Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches.
- Environmental ScienceAtmospheric environment
- 2017
Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter.
- Environmental ScienceEnvironmental science & technology
- 2007
Land use regression is a promising technique for predicting ambient air pollutant concentrations at high spatial resolution by modeling oxides of nitrogen and fine particulate matter in Vancouver, Canada, using two measures of traffic, supporting the usefulness of this approach for assessing spatial patterns of traffic-related pollution.
Land Use Regression Models for Ultrafine Particles and Black Carbon Based on Short-Term Monitoring Predict Past Spatial Variation.
- Environmental ScienceEnvironmental science & technology
- 2015
Developing and evaluating spatial and spatiotemporal LUR models for UFP and Black Carbon, including their ability to predict past spatial contrasts, found that short-term monitoring campaigns may be an efficient tool to develop Lur models.