Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method
Reads0
Chats0
TLDR
In this paper, a machine learning approach is used to estimate surface NO2 concentrations over Germany using satellite data and several meteorological parameters, which is validated against ground-based in situ air quality monitoring network measurements and regional chemical transport model (CTM) simulations.Abstract:
In this paper, we present the estimation of surface NO2 concentrations over Germany using a machine learning approach. TROPOMI satellite observations of tropospheric NO2 vertical column densities (VCDs) and several meteorological parameters are used to train the neural network model for the prediction of surface NO2 concentrations. The neural network model is validated against ground-based in situ air quality monitoring network measurements and regional chemical transport model (CTM) simulations. Neural network estimation of surface NO2 concentrations show good agreement with in situ monitor data with Pearson correlation coefficient (R) of 0.80. The results also show that the machine learning approach is performing better than regional CTM simulations in predicting surface NO2 concentrations. We also performed a sensitivity analysis for each input parameter of the neural network model. The validated neural network model is then used to estimate surface NO2 concentrations over Germany from 2018 to 2020. Estimated surface NO2 concentrations are used to investigate the spatio-temporal characteristics, such as seasonal and weekly variations of NO2 in Germany. The estimated surface NO2 concentrations provide comprehensive information of NO2 spatial distribution which is very useful for exposure estimation. We estimated the annual average NO2 exposure for 2018, 2019 and 2020 is 15.53, 15.24 and 13.27 µµg/m3, respectively. While the annual average NO2 concentration of 2018, 2019 and 2020 is only 12.79, 12.60 and 11.15 µµg/m3. In addition, we used the surface NO2 data set to investigate the impacts of the coronavirus disease 2019 (COVID-19) pandemic on ambient NO2 levels in Germany. In general, 10–30% lower surface NO2 concentrations are observed in 2020 compared to 2018 and 2019, indicating the significant impacts of a series of restriction measures to reduce the spread of the virus.read more
Citations
More filters
Journal ArticleDOI
Atmospheric NO2 Distribution Characteristics and Influencing Factors in Yangtze River Economic Belt: Analysis of the NO2 Product of TROPOMI/Sentinel-5P
TL;DR: Based on the data derived from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 satellite (2017~present), spatial autocorrelation analysis, standard deviation ellipse (SDE), and geodetectors were used to systematically analyze the spatial-temporal evolution and driving factors of tropospheric NO2 vertical column density (NO2 VCD) in the Yangtze River Economic Belt (YREB) from 2019 to 2020 as discussed by the authors.
Journal ArticleDOI
Long-term exposure and health risk assessment from air pollution: impact of regional scale mobility
TL;DR: In this paper , the authors proposed a methodology to perform an indirect and retrospective health risk assessment of all-cause mortality associated with long-term exposure to particulate matter less than 2.5 microns (PM2.5 ), nitrogen dioxide (NO 2 ) and ozone (O 3 ) in a typical Monday to Friday working week.
Journal ArticleDOI
Air Pollution Patterns Mapping of SO2, NO2, and CO Derived from TROPOMI over Central-East Europe
TL;DR: In this paper , the authors investigated the impact of war on air pollution concentration levels in Central and Eastern Europe in 2018-2022 using TROPOMI-S5 satellite data, which were compared with measurements from ground stations in Poland.
Journal ArticleDOI
A Simple and Effective Random Forest Refit to Map the Spatial Distribution of NO2 Concentrations
Yufeng Chi,Yuzhu Zhan +1 more
TL;DR: Zhang et al. as discussed by the authors proposed a random forest-random pixel ID (RF-RID) method, which could reduce local anomalies in the simulation of NO2 spatial distribution and significantly improve prediction accuracy in rural areas.
Journal ArticleDOI
Estimation of NO<sub>2</sub> emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning technique
F. Khosrawi,Omaira García,Jason Blake Cohen,Thomas Blumenstock,İbrahim GÜNAYDIN>,Richard Logan,Junshang Mu,John Graham,Jörg Bahm,hkyogh,Jonny Robinson,Hector Berlioz,Kraniotis, G. V.,AMNH Mammalogy,Alexandra Crampton +14 more
TL;DR: In this article , the authors presented a comprehensive method to estimate average tropospheric NO2 emission strengths derived from 4-year (May 2018-June 2022) TROPOspheric Monitoring Instrument (TROPOMI) observations by combining a wind-assigned anomaly approach and a machine learning (ML) method, the so-called gradient descent algorithm.
References
More filters
A Description of the Advanced Research WRF Version 3
C. Skamarock,B. Klemp,Jimy Dudhia,O. Gill,Dale Barker,G. Duda,Xiang-Yu Huang,Wei Wang,G. Powers +8 more
TL;DR: The Technical Note series provides an outlet for a variety of NCAR manuscripts that contribute in specialized ways to the body of scientific knowledge but which are not suitable for journal, monograph, or book publication.
Journal ArticleDOI
The ERA5 global reanalysis
Hans Hersbach,Bill Bell,Paul Berrisford,Shoji Hirahara,András Horányi,Joaquín Muñoz-Sabater,Julien Nicolas,Carole Peubey,Raluca Radu,Dinand Schepers,Adrian Simmons,Cornel Soci,Saleh Abdalla,Xavier Abellan,Gianpaolo Balsamo,Peter Bechtold,Gionata Biavati,Jean Bidlot,Massimo Bonavita,Giovanna de Chiara,Per Dahlgren,Dick Dee,Michail Diamantakis,Rossana Dragani,Johannes Flemming,Richard G. Forbes,Manuel Fuentes,Alan J. Geer,Leo Haimberger,Sean Healy,Robin J. Hogan,Elías Hólm,Marta Janisková,Sarah Keeley,Patrick Laloyaux,Philippe Lopez,Cristina Lupu,Gabor Radnoti,Patricia de Rosnay,Iryna Rozum,Freja Vamborg,Sebastien Villaume,Jean-Noël Thépaut +42 more
Journal ArticleDOI
The Shuttle Radar Topography Mission
Tom G. Farr,Paul A. Rosen,Edward R. Caro,Robert E. Crippen,Riley M. Duren,Scott Hensley,M. Kobrick,Mimi Paller,Ernesto Rodriguez,L. Roth,David Seal,S. Shaffer,Joanne Shimada,Jeffrey W. Umland,Marian Werner,Michael E. Oskin,Douglas W. Burbank,Douglas Alsdorf +17 more
TL;DR: The Shuttle Radar Topography Mission produced the most complete, highest-resolution digital elevation model of the Earth, using dual radar antennas to acquire interferometric radar data, processed to digital topographic data at 1 arc sec resolution.
Journal ArticleDOI
SCIAMACHY: Mission Objectives and Measurement Modes
Heinrich Bovensmann,John P. Burrows,Michael Buchwitz,Johannes Frerick,Stefan Noel,Vladimir Rozanov,Kelly Chance,Albert P. H. Goede +7 more
TL;DR: SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chartography) is a spectrometer designed to measure sunlight transmitted, reflected, and scattered by the earth's atmosphere or surface in the ultraviolet, visible, and near-infrared wavelength region (240-2380 nm) at moderate spectral resolution (0.2-1.5 nm, λ/Δλ ≈ 1000-10
Journal ArticleDOI
The ozone monitoring instrument
Pieternel F. Levelt,G. H. J. van den Oord,Marcel Dobber,A. Malkki,Hubregt J. Visser,Johan de Vries,Piet Stammes,J.O.V. Lundell,Heikki Saari +8 more
TL;DR: The Ozone Monitoring Instrument is a ultraviolet/visible nadir solar backscatter spectrometer, which provides nearly global coverage in one day with a spatial resolution of 13 km/spl times/24 km and will enable detection of air pollution on urban scale resolution.