Estimations of the Ground-Level NO2 Concentrations Based on the Sentinel-5P NO2 Tropospheric Column Number Density Product
Abstract
:1. Introduction
- How much does the accuracy of surface NO2 modeling improve if the S-5P TCVD NO2 product is assimilated into a model?
- What are the impacts of the meteorological conditions and anthropogenic factors on the NO2 modeling results?
- What are the impacts of the temporal averaging of NO2 retrievals and in situ measurements on the overall accuracy?
- Which machine learning models provide the most accurate NO2 estimations?
- What was the accuracy of the latest S-5P TROPOMI TCVD NO2 product version 2.3.1 released on 16 December 2021 [38]?
2. Materials and Methods
2.1. Study Area
2.2. Materials
2.2.1. NO2 Tropospheric Vertical Column Density (NO2 TVCD) Product Retrieved from Sentinel-5P Satellite TROPOMI Measurements
- The NO2 slant column densities are retrieved from the measured radiance and irradiance spectra using the differential optical absorption spectroscopy (DOAS) method;
- The separation of tropospheric and stratospheric columns, i.e., their conversion to tropospheric and stratospheric slant columns;
2.2.2. In Situ Air Quality Measurements
2.2.3. Meteorological Data
2.2.4. Ancillary Data
2.3. Methods
2.3.1. Modeling of Surface NO2 Mass Concentration
- Linear regression with one independent variable (LM)—NO2 TVCD;
- Multiple linear regression with several independent variables (MLM)—NO2 TVCD, T, P, RAD, WS, PBLH, NIGHTLIGHT, POPULATION, ROADS DENSITY, and ELEVATION;
- Random forests with several independent variables (RF)—NO2 TVCD, T, P, RAD, WS, PBLH, NIGHTLIGHT, POPULATION, ROADS DENSITY, ELEVATION, and CT;
- Radial kernel support vector machine with several independent variables (SVM)—NO2 TVCD, T, P, RAD, WS, PBLH, NIGHTLIGHT, POPULATION, ROADS DENSITY, ELEVATION, and CT.
- Stations measuring surface NO2 closer than 100 m from the road (2 stations; 1024 observations; 2% of all observations).
2.3.2. Validation methodology
- R-squared (R2):
- Mean squared error (MSE):
- Root mean squared error (RMSE):
- Bias:
- Mean absolute error (MAE):
- Mean percentage absolute error (MAPE):
2.3.3. Ranking the Modeling Skills of the Selected Predictors
- Compute the model’s MSE;
- For each variable in the model:
- (a)
- Permute the variable;
- (b)
- Calculate the new model MSE according to the variable permutation;
- (c)
- Take the difference between the model MSE and new model MSE;
- 3.
- Collect the results in a list;
- 4.
- Rank the variables’ importance according to the %IncMSE values, whereby the greater the value, the more important the variable [83].
2.3.4. Determining the Variability of the NO2 TVCD and Surface NO2 with Respect to the Meteorological Conditions and Anthropogenic Factors
- 2 °C for T;
- 250 m above ground for PBLH;
- 2 m/s for WS;
- N, S, E, W, SE, SW, NW, and NE for WD;
- 27 types of atmospheric circulations;
- 20 nW/cm2/sr for NIGHTLIGHT;
- People/km for POPULATION.
3. Results
3.1. Numbers of Observations
3.2. Modeling of Surface NO2 Mass Concentration
3.3. Variables’ Importance
- Those with an impact higher than or equal to 10% on the changes in MSE: S5P, NIGHTLIGHT, PBLH, ROADS, RAD, and POP;
- Those with an impact lower than 10% on the changes in MSE: T, P, WS, ELEVATION, and CT.
3.4. Changes in NO2 TVCD and Surface NO2 Mass Concentration in Respect to Meteorological Conditions and Other Factors
4. Discussion
5. Conclusions
- There were at least 120 days per year when it was possible to perform model calculations for Poland using TROPOMI observations;
- The results revealed that the machine learning methods (RF and SVM) gave ca. 63% accuracy for hourly estimations of NO2 and 69% accuracy for weekly averages;
- The implementation of meteorological and anthropogenic variables improved the quality of the models. The MLM approach gave 6% (hourly) and 7% (weekly) lower MAPE values than the LM-S5P approach, while the RF and SVM approaches gave 11% (hourly) and 12% (weekly) lower MAPE values than the LM-S5P approach;
- The planetary boundary layer height, solar radiation, nightlights, roads density, and population influenced the estimations the most;
- The air temperature, wind speed, surface pressure, elevation, and type of atmospheric circulation influenced the estimations the least;
- The trends for the surface NO2 and TVCD of NO2 were negative for increases in air temperature, PBLH, and wind speed, while they were positive for increases in population and nightlights;
- The RF model created within the study was better fitted to the actual values than the CAMS ensemble median model.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Surface NO2 Mass Concentration Estimations [µg/m3] | ||||||||
---|---|---|---|---|---|---|---|---|
MEAN | MIN | MAX | SD | 1st Q | 3rd Q | |||
Testing dataset (n = 27,241) | 10.1 | <0.1 | 111.4 | 8.9 | 4.4 | 13.1 | ||
Training dataset (n = 22,678) | 10.4 | <0.1 | 100.6 | 8.6 | 4.4 | 12.9 | ||
METHOD | R2 | MSE | RMSE [µg/m3] | Bias [µg/m3] | MAE [µg/m3] | MAPE [%] | ||
Hourly measurements | LM-S5P | 0.32 | 53.2 | 7.3 | 0.1 | 4.9 | 48.4 | |
MLM | 0.45 | 43.1 | 6.6 | 0.4 | 4.2 | 42.1 | ||
RF | 0.53 | 34.8 | 5.9 | <0.1 | 3.7 | 37.2 | ||
SVM | 0.54 | 37.7 | 6.1 | 1.0 | 3.7 | 36.9 | ||
METHOD | R2 | MSE | RMSE [µg/m3] | Bias [µg/m3] | MAE [µg/m3] | MAPE [%] | ||
Weekly averages | LM-S5P | 0.33 | 38.1 | 6.2 | 0.1 | 4.3 | 42.4 | |
MLM | 0.49 | 29.0 | 5.4 | 0.2 | 3.6 | 35.5 | ||
RF | 0.60 | 23.1 | 4.8 | −0.1 | 3.1 | 30.8 | ||
SVM | 0.59 | 24.3 | 4.9 | 0.8 | 3.2 | 31.2 |
1st Q | 2–3rd Q | 4th Q | |
---|---|---|---|
Hourly measurements | 2.5 | 1.7 | −5.1 |
Weekly averages | 2.2 | 1.3 | −4.0 |
Variable | S5P | T | P | RAD | WS | PBLH | NIGHTLIGHT | POP | ROADS | ELEVATION | INTERCEPT |
---|---|---|---|---|---|---|---|---|---|---|---|
Coefficient hourly | 0.62 (57%) | −0.01 (1%) | −0.03 (3%) | −0.06% (6%) | −0.07 (6%) | −0.08 (7%) | 0.05 (5%) | 0.03 (3%) | 0.06 (6%) | 0.01 (1%) | 0.07 (6%) |
Coefficient weekly | 0.69 (53%) | −0.04 (3%) | −0.04 (3%) | −0.06 (5%) | −0.07 (5%) | −0.10 (8%) | 0.06 (5%) | 0.04 (3%) | 0.08 (6%) | 0.01 (1%) | 0.10 (8%) |
R2 | MAE [μg/m3] | MAPE [%] | |
---|---|---|---|
RF MODEL | 0.54 | 3.9 | 40.0 |
CAMS | 0.46 | 5.4 | 52.7 |
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Grzybowski, P.T.; Markowicz, K.M.; Musiał, J.P. Estimations of the Ground-Level NO2 Concentrations Based on the Sentinel-5P NO2 Tropospheric Column Number Density Product. Remote Sens. 2023, 15, 378. https://doi.org/10.3390/rs15020378
Grzybowski PT, Markowicz KM, Musiał JP. Estimations of the Ground-Level NO2 Concentrations Based on the Sentinel-5P NO2 Tropospheric Column Number Density Product. Remote Sensing. 2023; 15(2):378. https://doi.org/10.3390/rs15020378
Chicago/Turabian StyleGrzybowski, Patryk Tadeusz, Krzysztof Mirosław Markowicz, and Jan Paweł Musiał. 2023. "Estimations of the Ground-Level NO2 Concentrations Based on the Sentinel-5P NO2 Tropospheric Column Number Density Product" Remote Sensing 15, no. 2: 378. https://doi.org/10.3390/rs15020378