OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning

  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},

Figures and Tables from this paper

Long-term satellite-based estimates of air quality and premature mortality in Equatorial Asia through deep neural networks

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

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

Using a DEA–AutoML Approach to Track SDG Achievements

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.

National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment.

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

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.



Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis

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

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

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.

A review and evaluation of intraurban air pollution exposure models

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.

Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013-2015, using a spatiotemporal land-use random-forest model.

Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter.

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.

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.