Evaluation of Dynamic Vertical Column Height Approach for Converting Sentinel-5P Data into Surface Pollutant Concentrations in Surabaya City
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Abstract
Urban air quality monitoring requires methods that are both comprehensive and cost-effective. Sentinel-5P TROPOMI satellite data are freely available; however, their units (mol/m²) cannot directly represent surface pollutant concentrations (μg/m³) without the Vertical Column Height (VCH) parameter, which is commonly assumed to be constant. This assumption contradicts the dynamic nature of the atmosphere. This study demonstrates that VCH is inherently dynamic through the integration of Sentinel-5P data with in-situ air quality sensor observations in Surabaya City. Daily VCH values were calculated for SO₂, CO, and NO₂ at the Kebonsari and Wonorejo monitoring stations throughout 2024 by pairing Vertical Column Density (VCD) data (retrieved via Google Earth Engine) with surface pollutant concentrations (obtained from the Surabaya Environmental Agency) based on matching dates and coordinates. The results show that VCH varies significantly on both daily and monthly scales, indicating that a static VCH assumption is not representative. The correlation between VCH and in-situ concentrations is negative, with the following order of strength: CO (r = −0.7989; R² = 0.6383) > NO₂ (r = −0.5913; R² = 0.3496) > SO₂ (r = −0.3818; R² = 0.1457), consistent with the atmospheric lifetime of each pollutant. Additional analysis shows that MODIS surface temperature shows weak positive correlations with VCH for all pollutants: SO₂ (r = 0.2903; R² = 0.0843) > CO (r = 0.2464; R² = 0.0607) > NO₂ (r = 0.0033; R² ≈ 0.0000), indicating that surface temperature alone is insufficient to explain vertical pollutant distribution. A dynamic VCH approach is therefore necessary for more accurate satellite-to-surface pollutant conversion, and future models require integration of more comprehensive meteorological parameters
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References
[1] B. Mamtimin and F. X. Meixner, “Air pollution and meteorological processes in the growing dryland city of Urumqi (Xinjiang, China),” Science of The Total Environment, vol. 409, no. 7, pp. 1277–1290, 2011, doi: https://doi.org/10.1016/j.scitotenv.2010.12.010.
[2] A. D. Syafei, A. Fujiwara, and J. Zhang, “Spatial and Temporal Factors of Air Quality in Surabaya City: An Analysis based on a Multilevel Model,” Procedia Soc. Behav. Sci., vol. 138, pp. 612–622, Jul. 2014, doi: 10.1016/j.sbspro.2014.07.246.
[3] M. Santoso et al., “Assessment of Urban Air Quality in Indonesia,” Aerosol Air Qual. Res., vol. 20, pp. 2142–2158, 2020, doi: 10.4209/aaqr.2019.09.0451.
[4] C. Morillas, S. Alvarez, J. C. M. Pires, A. J. Garcia, and S. Martinez, “Impact of the implementation of Madrid’s low emission zone on NO2 concentration using Sentinel-5P/TROPOMI data,” Atmos. Environ., vol. 320, p. 120326, 2024, doi: https://doi.org/10.1016/j.atmosenv.2024.120326.
[5] A. Cofano, F. Cigna, L. S. Amato, M. S. de Cumis, and D. Tapete, “Exploiting sentinel‐5p tropomi and ground sensor data for the detection of volcanic so2 plumes and activity in 2018–2021 at Stromboli, Italy,” Sensors, vol. 21, no. 21, Nov. 2021, doi: 10.3390/s21216991.
[6] N. Theys et al., “Sulfur dioxide vertical column DOAS retrievals from the Ozone Monitoring Instrument: Global observations and comparison to ground-based and satellite data,” J. Geophys. Res., vol. 120, no. 6, pp. 2470–2491, 2015, doi: 10.1002/2014JD022657.
[7] M. A. Rahman et al., “Weather radar detection of planetary boundary layer and smoke layer top of peatland fire in Central Kalimantan, Indonesia,” Sci. Rep., vol. 11, no. 1, Dec. 2021, doi: 10.1038/s41598-020-79486-6.
[8] L. H. Yang et al., “Tropospheric NO2vertical profiles over South Korea and their relation to oxidant chemistry: implications for geostationary satellite retrievals and the observation of NO2diurnal variation from space,” Atmos. Chem. Phys., vol. 23, no. 4, pp. 2465–2481, Feb. 2023, doi: 10.5194/acp-23-2465-2023.
[9] T. Verhoelst et al., “Ground-based validation of the Copernicus Sentinel-5P TROPOMI NO2 measurements with the NDACC ZSL-DOAS, MAX-DOAS and Pandonia global networks,” Atmos. Meas. Tech., vol. 14, no. 1, pp. 481–510, Jan. 2021, doi: 10.5194/amt-14-481-2021.
[10] D. L. Goldberg, S. C. Anenberg, D. Griffin, C. A. McLinden, Z. Lu, and D. G. Streets, “Disentangling the Impact of the COVID-19 Lockdowns on Urban NO2 From Natural Variability,” Geophys. Res. Lett., vol. 47, no. 17, Sep. 2020, doi: 10.1029/2020GL089269.
[11] D. Jacob, INTRODUCTION TO ATMOSPHERIC CHEMISTRY: SUPPLEMENTAL QUESTIONS AND PROBLEMS, 11th ed. Princeton University Press, 1999.
[12] P. W. Atkins and J. De Paula, Physical chemistry : thermodynamics, structure, and change, 10th ed. 2014.
[13] H. Akoglu, “User’s guide to correlation coefficients,” Sep. 01, 2018, Emergency Medicine Association of Turkey. doi: 10.1016/j.tjem.2018.08.001.
[14] A. Schneider, G. Hommel, and M. Blettner, “Lineare regressionsanalyse - Teil 14 der serie zur bewertung wissenschaftlicher publikationen,” Nov. 05, 2010. doi: 10.3238/arztebl.2010.0776.
[15] N. A. Krotkov et al., “Aura OMI observations of regional SO2 and NO2 pollution changes from 2005 to 2015,” Atmos. Chem. Phys., vol. 16, no. 7, pp. 4605–4629, Apr. 2016, doi: 10.5194/acp-16-4605-2016.
[16] N. Theys et al., “Sulfur dioxide retrievals from TROPOMI onboard Sentinel-5 Precursor: Algorithm theoretical basis,” Atmos. Meas. Tech., vol. 10, no. 1, pp. 119–153, Jan. 2017, doi: 10.5194/amt-10-119-2017.
[17] D. P. Edwards et al., “Satellite-observed pollution from Southern Hemisphere biomass burning,” Journal of Geophysical Research Atmospheres, vol. 111, no. 14, Jul. 2006, doi: 10.1029/2005JD006655.
[18] M. Kazemi Garajeh, G. Laneve, H. Rezaei, M. Sadeghnejad, N. Mohamadzadeh, and B. Salmani, “Monitoring Trends of CO, NO2, SO2, and O3 Pollutants Using Time-Series Sentinel-5 Images Based on Google Earth Engine,” Pollutants, vol. 3, no. 2, pp. 255–279, Jun. 2023, doi: 10.3390/pollutants3020019.
[19] S. Shetty, P. Schneider, K. Stebel, P. David Hamer, A. Kylling, and T. Koren Berntsen, “Estimating surface NO2 concentrations over Europe using Sentinel-5P TROPOMI observations and Machine Learning,” Remote Sens. Environ., vol. 312, p. 114321, 2024, doi: https://doi.org/10.1016/j.rse.2024.114321.
[20] V. E. Fioletov, C. A. McLinden, N. Krotkov, and C. Li, “Lifetimes and emissions of SO2 from point sources estimated from OMI,” Geophys. Res. Lett., vol. 42, no. 6, pp. 1969–1976, Mar. 2015, doi: 10.1002/2015GL063148.
[21] T. Holloway, H. Levy, and P. Kasibhatla, “Global distribution of carbon monoxide,” Journal of Geophysical Research Atmospheres, vol. 105, no. D10, pp. 12123–12147, May 2000, doi: 10.1029/1999JD901173.
[22] L. N. Lamsal et al., “Evaluation of OMI operational standard NO2 column retrievals using in situ and surface-based NO2 observations,” Atmos. Chem. Phys., vol. 14, no. 21, pp. 11587–11609, Nov. 2014, doi: 10.5194/acp-14-11587-2014.
[23] D. L. Goldberg et al., “NO 2 concentration differences under clear versus cloudy skies and implications for applications of satellite measurements,” Apr. 15, 2025. doi: 10.5194/egusphere-2025-1350.
[24] L. Canché-Cab, L. San-Pedro, B. Ali, M. Rivero, and M. Escalante, “The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques,” Artif. Intell. Rev., vol. 57, no. 12, Dec. 2024, doi: 10.1007/s10462-024-10962-5.
[25] N. S. Berman, H. J. S. Fernando, E. Pardyjak, F. Yu, A. Mahalov, and A. Grachev, “A study of the turbulent mixing in the atmospheric boundary layer of phoenix, Arizona,” Proceedings of the 1999 3rd ASME/JSME Joint Fluids Engineering Conference, FEDSM’99, San Francisco, California, USA, 18-23 July 1999 (CD-ROM), p. 1, 1999, [Online].
[26] A. P. Cuesta-Mosquera, M. Wahl, J. G. Acosta-López, J. A. García-Reynoso, and B. H. Aristizábal-Zuluaga, “Mixing layer height and slope wind oscillation: Factors that control ambient air SO2 in a tropical mountain city,” Sustain. Cities Soc., vol. 52, p. 101852, 2020, doi: https://doi.org/10.1016/j.scs.2019.101852.
[27] I. Faloona et al., “Sulfur dioxide in the tropical marine boundary layer: Dry deposition and heterogeneous oxidation observed during the pacific atmospheric sulfur experiment,” J. Atmos. Chem., vol. 63, no. 1, pp. 13–32, May 2009, doi: 10.1007/s10874-010-9155-0.
[28] M. N. Deeter et al., “The MOPITT Version 6 product: Algorithm enhancements and validation,” Atmos. Meas. Tech., vol. 7, no. 11, pp. 3623–3632, Nov. 2014, doi: 10.5194/amt-7-3623-2014.
[29] H. M. Worden et al., “Decadal record of satellite carbon monoxide observations,” Atmos. Chem. Phys., vol. 13, no. 2, pp. 837–850, 2013, doi: 10.5194/acp-13-837-2013.
[30] A. Coppalle, V. Delmas, and M. Bobbia, “Variability of Nox and No2 concentrations observed at pedestrian level in the city centre of a medium sized urban area,” Atmos. Environ., vol. 35, no. 31, pp. 5361–5369, 2001, doi: https://doi.org/10.1016/S1352-2310(01)00296-5.
[31] B. N. Duncan et al., “A space-based, high-resolution view of notable changes in urban NOx pollution around the world (2005–2014),” J. Geophys. Res., vol. 121, no. 2, pp. 976–996, Jan. 2016, doi: 10.1002/2015JD024121.
[32] E. J. Good, D. J. Ghent, C. E. Bulgin, and J. J. Remedios, “A spatiotemporal analysis of the relationship between near-surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series,” Journal of Geophysical Research: Atmospheres, vol. 122, no. 17, pp. 9185–9210, Sep. 2017, doi: 10.1002/2017JD026880.