Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning

to related objects

Abstract NO 2 O 3 ∘ spatial resolution and daily intervals. NO 2 O 3 R 2 = 0.68 –0.88 and RMSE = 4.77 –8.67 µ g m - 3; R 2 = 0.74 –0.92 and RMSE = 8.53 –13.2 µ g m - 3, respectively). The trained MLP model performed worse than the trained GBT model for both NO 2 O 3 R 2 = 0.46 –0.82 and R 2 = 0.42 –0.9, respectively). NO 2 O 3 NO 2 O 3 NO 2 O 3 NO 2 O 3 NO 2 O 3 NO 2 O 3 NO 2 23 ± 5.3  %) and meteorology-normalized near-surface O 3 1 ± 4.6  %) over 10 major German metropolitan areas when compared to 2019. Finally, our O 3 R 2 = 0.87 –0.94), whereas our NO 2 R 2 = 0.32 –0.64).

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning ; volume:23 ; number:17 ; year:2023 ; pages:10267-10285 ; extent:19
Atmospheric chemistry and physics ; 23, Heft 17 (2023), 10267-10285 (gesamt 19)

Creator: Balamurugan, Vigneshkumar
Chen, Jia
Wenzel, Adrian
Keutsch, Frank N.

DOI: 10.5194/acp-23-10267-2023

URN: urn:nbn:de:101:1-2023092104315213654555

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:45 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Balamurugan, Vigneshkumar
Chen, Jia
Wenzel, Adrian
Keutsch, Frank N.

Other Objects (12)

Spatio-temporal modeling of air pollutant concentrations in Germany using machine learning

Spatio-temporal modeling of air pollutant concentrations in Germany using machine learning

A comparison of statistical and machine learning models for spatio-temporal prediction of ambient air pollutant concentrations in Scotland

A comparison of statistical and machine learning models for spatio-temporal prediction of ambient air pollutant concentrations in Scotland

Machine learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

Machine learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

Machine-learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

Machine-learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

PREDICTION OF POLLUTANT CONCENTRATIONS BY METEOROLOGICAL DATA USING MACHINE LEARNING ALGORITHMS

PREDICTION OF POLLUTANT CONCENTRATIONS BY METEOROLOGICAL DATA USING MACHINE LEARNING ALGORITHMS

Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

Correction to: Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

Correction to: Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

Associations of air pollutant concentrations with longitudinal kidney function changes in patients with chronic kidney disease

Associations of air pollutant concentrations with longitudinal kidney function changes in patients with chronic kidney disease

Integrated unmanned aerial vehicle platform with sensing and sampling systems for the measurement of air pollutant concentrations

Integrated unmanned aerial vehicle platform with sensing and sampling systems for the measurement of air pollutant concentrations

Stormwater Suspended Solids and Pollutant Concentrations in an Urban Stream

Stormwater Suspended Solids and Pollutant Concentrations in an Urban Stream

Exploring Deep Learning for Air Pollutant Emission Estimation

Exploring Deep Learning for Air Pollutant Emission Estimation

Identifying a suitable model for predicting hourly pollutant concentrations by using low-cost microstation data and machine learning

Identifying a suitable model for predicting hourly pollutant concentrations by using low-cost microstation data and machine learning

Spatio-temporal modeling of air pollutant concentrations in Germany using machine learning

Spatio-temporal modeling of air pollutant concentrations in Germany using machine learning

A comparison of statistical and machine learning models for spatio-temporal prediction of ambient air pollutant concentrations in Scotland

A comparison of statistical and machine learning models for spatio-temporal prediction of ambient air pollutant concentrations in Scotland

Machine learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

Machine learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

Machine-learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

Machine-learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

PREDICTION OF POLLUTANT CONCENTRATIONS BY METEOROLOGICAL DATA USING MACHINE LEARNING ALGORITHMS

PREDICTION OF POLLUTANT CONCENTRATIONS BY METEOROLOGICAL DATA USING MACHINE LEARNING ALGORITHMS

Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

Correction to: Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

Correction to: Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

Associations of air pollutant concentrations with longitudinal kidney function changes in patients with chronic kidney disease

Associations of air pollutant concentrations with longitudinal kidney function changes in patients with chronic kidney disease

Integrated unmanned aerial vehicle platform with sensing and sampling systems for the measurement of air pollutant concentrations

Integrated unmanned aerial vehicle platform with sensing and sampling systems for the measurement of air pollutant concentrations

Stormwater Suspended Solids and Pollutant Concentrations in an Urban Stream

Stormwater Suspended Solids and Pollutant Concentrations in an Urban Stream

Exploring Deep Learning for Air Pollutant Emission Estimation

Exploring Deep Learning for Air Pollutant Emission Estimation

Identifying a suitable model for predicting hourly pollutant concentrations by using low-cost microstation data and machine learning

Identifying a suitable model for predicting hourly pollutant concentrations by using low-cost microstation data and machine learning

Spatio-temporal modeling of air pollutant concentrations in Germany using machine learning

Spatio-temporal modeling of air pollutant concentrations in Germany using machine learning

A comparison of statistical and machine learning models for spatio-temporal prediction of ambient air pollutant concentrations in Scotland

A comparison of statistical and machine learning models for spatio-temporal prediction of ambient air pollutant concentrations in Scotland

Machine learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

Machine learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

Machine-learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

Machine-learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

PREDICTION OF POLLUTANT CONCENTRATIONS BY METEOROLOGICAL DATA USING MACHINE LEARNING ALGORITHMS

PREDICTION OF POLLUTANT CONCENTRATIONS BY METEOROLOGICAL DATA USING MACHINE LEARNING ALGORITHMS

Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

Correction to: Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

Correction to: Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

Associations of air pollutant concentrations with longitudinal kidney function changes in patients with chronic kidney disease

Associations of air pollutant concentrations with longitudinal kidney function changes in patients with chronic kidney disease

Integrated unmanned aerial vehicle platform with sensing and sampling systems for the measurement of air pollutant concentrations

Integrated unmanned aerial vehicle platform with sensing and sampling systems for the measurement of air pollutant concentrations

Stormwater Suspended Solids and Pollutant Concentrations in an Urban Stream

Stormwater Suspended Solids and Pollutant Concentrations in an Urban Stream

Exploring Deep Learning for Air Pollutant Emission Estimation

Exploring Deep Learning for Air Pollutant Emission Estimation

Identifying a suitable model for predicting hourly pollutant concentrations by using low-cost microstation data and machine learning

Identifying a suitable model for predicting hourly pollutant concentrations by using low-cost microstation data and machine learning

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.