Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression

Long-term exposure to low ambient air pollution concentrations and mortality among 28 million people: results from seven large European cohorts within the ELAPSE project

(2022) Massimo Stafoggia et al.   Lancet Planetary Health

Long-Term Exposure to Fine Particle Elemental Components and Natural and Cause-Specific Mortality—a Pooled Analysis of Eight European Cohorts within the ELAPSE Project

(2021) Jie Chen et al.   ENVIRONMENTAL HEALTH PERSPECTIVES

Modelling nationwide spatial variation of ultrafine particles based on mobile monitoring

(2021) Jules Kerckhoffs et al.   ENVIRONMENT INTERNATIONAL

Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018)

(2020) Melanie S. Hammer et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Evaluation of different methods and data sources to optimise modelling of NO2 at a global scale

(2020) Meng Lu et al.   ENVIRONMENT INTERNATIONAL

Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: A machine learning approach

(2020) Riyang Liu et al.   ENVIRONMENT INTERNATIONAL

An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States

(2020) Weeberb J. Requia et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Development of Europe-Wide Models for Particle Elemental Composition Using Supervised Linear Regression and Random Forest

(2020) Jie Chen et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Performance of Prediction Algorithms for Modeling Outdoor Air Pollution Spatial Surfaces

(2019) Jules Kerckhoffs et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors

(2019) Aaron van Donkelaar et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide

(2019) Jie Chen et al.   ENVIRONMENT INTERNATIONAL

An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution

(2019) Qian Di et al.   ENVIRONMENT INTERNATIONAL

Predicting Fine-Scale Daily NO2 for 2005–2016 Incorporating OMI Satellite Data Across Switzerland

(2019) Kees de Hoogh et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Modelling daily PM 2.5 concentrations at high spatio-temporal resolution across Switzerland

(2018) Kees de Hoogh et al.   ENVIRONMENTAL POLLUTION

Satellite-based high-resolution PM 2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression model

(2018) Qingqing He et al.   ENVIRONMENTAL POLLUTION

Satellite-Based Estimates of Daily NO2 Exposure in China Using Hybrid Random Forest and Spatiotemporal Kriging Model

(2018) Yu Zhan et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Estimating daily and intra-daily PM10 and PM2.5 in Israel using a spatio-temporal hybrid modeling approach

(2018) Alexandra Shtein et al.   ATMOSPHERIC ENVIRONMENT

Spatial PM2.5, NO2, O3 and BC models for Western Europe – Evaluation of spatiotemporal stability

(2018) Kees de Hoogh et al.   ENVIRONMENT INTERNATIONAL

Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

(2018) Tomislav Hengl et al.   PeerJ

Global Land Use Regression Model for Nitrogen Dioxide Air Pollution

(2017) Andrew Larkin et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

(2017) Marvin N. Wright et al.   Journal of Statistical Software

Air Pollution and Mortality in the Medicare Population

(2017) Qian Di et al.   NEW ENGLAND JOURNAL OF MEDICINE

Google Earth Engine: Planetary-scale geospatial analysis for everyone

(2017) Noel Gorelick et al.   REMOTE SENSING OF ENVIRONMENT

Back-extrapolated and year-specific NO2 land use regression models for Great Britain - Do they yield different exposure assessment?

(2016) John Gulliver et al.   ENVIRONMENT INTERNATIONAL

A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD

(2016) Yang Bai et al.   Remote Sensing

National Spatiotemporal Exposure Surface for NO2: Monthly Scaling of a Satellite-Derived Land-Use Regression, 2000–2010

(2015) Matthew J. Bechle et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

High-Resolution Satellite-Derived PM2.5 from Optimal Estimation and Geographically Weighted Regression over North America

(2015) Aaron van Donkelaar et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

GWmodel: AnRPackage for Exploring Spatial Heterogeneity Using Geographically Weighted Models

(2015) Isabella Gollini et al.   Journal of Statistical Software

A national satellite-based land-use regression model for air pollution exposure assessment in Australia

(2014) Luke D. Knibbs et al.   ENVIRONMENTAL RESEARCH

A geographically and temporally weighted autoregressive model with application to housing prices

(2014) Bo Wu et al.   INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE

Development and Back-Extrapolation of NO2 Land Use Regression Models for Historic Exposure Assessment in Great Britain

(2013) John Gulliver et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Spatial variation of PM2.5, PM10, PM2.5 absorbance and PMcoarse concentrations between and within 20 European study areas and the relationship with NO2 – Results of the ESCAPE project

(2012) Marloes Eeftens et al.   ATMOSPHERIC ENVIRONMENT

Variation of NO2 and NOx concentrations between and within 36 European study areas: Results from the ESCAPE study

(2012) Josef Cyrys et al.   ATMOSPHERIC ENVIRONMENT

An integrated model study for Europe and North America using the Danish Eulerian Hemispheric Model with focus on intercontinental transport of air pollution

(2012) J. Brandt et al.   ATMOSPHERIC ENVIRONMENT

Temporal stability of land use regression models for traffic-related air pollution

(2012) Rongrong Wang et al.   ATMOSPHERIC ENVIRONMENT

Development of Land Use Regression Models for PM2.5, PM2.5 Absorbance, PM10 and PMcoarse in 20 European Study Areas; Results of the ESCAPE Project

(2012) Marloes Eeftens et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Application of machine learning methods to spatial interpolation of environmental variables

(2011) Jin Li et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices

(2010) Bo Huang et al.   INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE