Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

Chan, Ka Lok; Khorsandi, Ehsan; Liu, Song; Baier, F. W.; Valks, Pieter

doi:10.3390/rs13050969

Cited by 52 publications

(27 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For Romania, which was one of our study regions, [24] reported that annual SO 2 from power plants decreased between 2005 and 2015, while NO 2 emissions were more or less stable during that time period. TROPOMI led to a new area of top-down NO 2 emission monitoring from space [25,26], as well as bringing advances for surface NO 2 concentration estimates (e.g., [27]). For SO 2 , despite the increased sensitivity of TROPOMI, in 2018/2019 only the largest SO 2 emitters in Europe, e.g., the Polish Bełchatów coal power plant, were visible from space, due to the installation of flue gas desulfurization systems in the European Union [28].…”

Section: Introductionmentioning

confidence: 99%

SAMIRA-SAtellite Based Monitoring Initiative for Regional Air Quality

et al. 2021

View full text Add to dashboard Cite

The satellite based monitoring initiative for regional air quality (SAMIRA) initiative was set up to demonstrate the exploitation of existing satellite data for monitoring regional and urban scale air quality. The project was carried out between May 2016 and December 2019 and focused on aerosol optical depth (AOD), particulate matter (PM), nitrogen dioxide (NO2), and sulfur dioxide (SO2). SAMIRA was built around several research tasks: 1. The spinning enhanced visible and infrared imager (SEVIRI) AOD optimal estimation algorithm was improved and geographically extended from Poland to Romania, the Czech Republic and Southern Norway. A near real-time retrieval was implemented and is currently operational. Correlation coefficients of 0.61 and 0.62 were found between SEVIRI AOD and ground-based sun-photometer for Romania and Poland, respectively. 2. A retrieval for ground-level concentrations of PM2.5 was implemented using the SEVIRI AOD in combination with WRF-Chem output. For representative sites a correlation of 0.56 and 0.49 between satellite-based PM2.5 and in situ PM2.5 was found for Poland and the Czech Republic, respectively. 3. An operational algorithm for data fusion was extended to make use of various satellite-based air quality products (NO2, SO2, AOD, PM2.5 and PM10). For the Czech Republic inclusion of satellite data improved mapping of NO2 in rural areas and on an annual basis in urban background areas. It slightly improved mapping of rural and urban background SO2. The use of satellites based AOD or PM2.5 improved mapping results for PM2.5 and PM10. 4. A geostatistical downscaling algorithm for satellite-based air quality products was developed to bridge the gap towards urban-scale applications. Initial testing using synthetic data was followed by applying the algorithm to OMI NO2 data with a direct comparison against high-resolution TROPOMI NO2 as a reference, thus allowing for a quantitative assessment of the algorithm performance and demonstrating significant accuracy improvements after downscaling. We can conclude that SAMIRA demonstrated the added value of using satellite data for regional- and urban-scale air quality monitoring.

show abstract

Section: Introductionmentioning

confidence: 99%

SAMIRA-SAtellite Based Monitoring Initiative for Regional Air Quality

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The results of this study nonetheless indicate that the TROPOMI NO 2 dataset provides sufficiently high data availability and thus very valuable spatiotemporal information on NO 2 pollution for urban areas in Norway during spring, summer, and fall. During these periods, the TROPOMI NO 2 data have significant potential even in a challenging environment such as Norway for, e.g., local-scale NO 2 mapping and monitoring, including applications relevant for human exposure such as surface-level NO 2 mapping, which converts the column-based retrievals from TROPOMI or similar satellite instruments to surface NO 2 concentrations based on additional information obtained from either chemistry transport models [18] or statistical relationships between satellite-based column information and surface monitoring stations [19][20][21].…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Patterns in Data Availability of the Sentinel-5P NO2 Product over Urban Areas in Norway

et al. 2021

View full text Add to dashboard Cite

Due to its comparatively high spatial resolution and its daily repeat frequency, the tropospheric nitrogen dioxide product provided by the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor platform has attracted significant attention for its potential for urban-scale monitoring of air quality. However, the exploitation of such data in, for example, operational assimilation of local-scale dispersion models is often complicated by substantial data gaps due to cloud cover or other retrieval limitations. These challenges are particularly prominent in high-latitude regions where significant cloud cover and high solar zenith angles are often prevalent. Using the example of Norway as a representative case for a high-latitude region, we here evaluate the spatiotemporal patterns in the availability of valid data from the operational TROPOMI tropospheric nitrogen dioxide (NO2) product over five urban areas (Oslo, Bergen, Trondheim, Stavanger, and Kristiansand) and a 2.5 year period from July 2018 through November 2020. Our results indicate that even for relatively clean environments such as small Norwegian cities, distinct spatial patterns of tropospheric NO2 are visible in long-term average datasets from TROPOMI. However, the availability of valid data on a daily level is limited by both cloud cover and solar zenith angle (during the winter months), causing the fraction of valid retrievals in each study site to vary from 20% to 50% on average. A temporal analysis shows that for our study sites and the selected period, the fraction of valid pixels in each domain shows a clear seasonal cycle reaching a maximum of 50% to 75% in the summer months and 0% to 20% in winter. The seasonal cycle in data availability shows the inverse behavior of NO2 pollution in Norway, which typically has its peak in the winter months. However, outside of the mid-winter period we find the TROPOMI NO2 product to provide sufficient data availability for detailed mapping and monitoring of NO2 pollution in the major urban areas in Norway and see potential for the use of the data in local-scale data assimilation and emission inversions applications.

show abstract

“…Machine learning (ML) is gaining traction as an alternative modeling tool to complement CTM in Earth system science fields [20][21][22][23][24][25][26] . Because photo-chemical processes have a significant impact on ozone, ML models are trained using a wide range of meteorological variables, many of which drive photo-chemical processes [27][28][29][30][31][32][33] .…”

Section: Introductionmentioning

confidence: 99%

Importance of ozone precursor’s measurements in modelling urban surface ozone variability using machine learning model

Balamurugan

2022

Preprint

View full text Add to dashboard Cite

Surface ozone (O3) is primarily formed through complex photo-chemical reactions in the atmosphere, which are non-linearly dependent on precursors (NOX and VOC). Exploring the potential of machine learning (ML) in modeling surface ozone has received little attention, particularly when it comes to the inclusion of limited available ozone precursors information in the ML model. The ML model with past O3 , meteorology (relative humidity, temperature, boundary layer height, wind direction), season type and in-situ NO information explains 87 % (R2 = 0.87) of the ozone variability over Munich, a German metropolitan area. The ML model trained for the urban measurement station in Munich can also explain the ozone variability of the other three stations in the same city, with R2 = 0.88, 0.91, 0.63. While the same model robustly explains the ozone variability of two other German cities’ (Berlin and Hamburg) measurement stations, with R2 ranges from 0.72 to 0.84, giving confidence to use the ML model trained for one location to other locations with sparse ozone measurements. In all cases, including coarse CAMS model O3 simulations in the ML model slightly improves the ML model’s performance in predicting surface ozone.

show abstract

Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

Cited by 52 publications

References 64 publications

SAMIRA-SAtellite Based Monitoring Initiative for Regional Air Quality

SAMIRA-SAtellite Based Monitoring Initiative for Regional Air Quality

Spatiotemporal Patterns in Data Availability of the Sentinel-5P NO2 Product over Urban Areas in Norway

Importance of ozone precursor’s measurements in modelling urban surface ozone variability using machine learning model

Contact Info

Product

Resources

About