Research Article
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Year 2024, Volume: 11 Issue: 3, 130 - 146, 28.09.2024
https://doi.org/10.30897/ijegeo.1339560

Abstract

References

  • Achakulwisut, P., Brauer, M., Hystad, P., Anenberg, S. C. (2019). Global, national, and urban burdens of paediatric asthma incidence attributable to ambient NO 2 pollution: estimates from global datasets. The Lancet Planetary Health, 3(4), e166–e178. doi.org/10.1016/S2542-5196(19)30046-4
  • Aghlmand, M., Kalkan, K., Onur, M. İ., Öztürk, G., Ulutak, E. (2021). Google Earth Engine ile Arazi Kullanımı Haritalarının Üretimi. Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 10(1), 38–47. doi.org/10.28948/ngumuh.795977
  • Alqurashi, A. F., Kumar, L. (2013). Investigating the Use of Remote Sensing and GIS Techniques to Detect Land Use and Land Cover Change: A Review. Advances in Remote Sensing, 02(02), 193–204. doi.org/10.4236/ars.2013.22022
  • Arslan, O., Akyürek, Ö. (2018). Spatial Modelling of Air Pollution from PM10 and SO2 concentrations during Winter Season in Marmara Region (2013-2014). International Journal of Environment and Geoinformatics, 5(1), 1-16. doi.org/10.30897/ ijegeo.412391
  • Burnett, R., Chen, H., Szyszkowicz, M., Fann, N., Hubbell, B., Pope III, C. A., Apte, J. S., Brauer, M., Cohen, A., Weichenthal, S. (2018). Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proceedings of the National Academy of Sciences, 115(38), 9592–9597. doi.org/10.1073/pnas.1803222115
  • Cakmak, N., Yilmaz, O. S., Sanli, F. B. (2023). Spatio-temporal Analysis of Pollutant Gases using Sentinel-5P TROPOMI Data on the Google Earth Engine during the COVID-19 Pandemic in the Marmara Region , Türkiye. E-ZBORNIK, 13(25), 1–14. doi.org/10.47960/2232-9080.2023.25.13.1
  • Dong, J., Xiao, X., Menarguez, M. A., Zhang, G., Qin, Y., Thau, D., Biradar, C., Moore, B. (2016). Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sensing of Environment, 185, 142–154. doi.org/10.1016/ j.rse.2016.02.016
  • Engel-Cox, J. A., Hoff, R. M., Haymet, A. D. J. (2004). Recommendations on the use of satellite remote-sensing data for urban air quality. Journal of the Air and Waste Management Association, 54(11), 1360–1371. doi.org/10.1080/10473289.2004.10471005
  • Engin Duran, H., Pelin Özkan, S. (2015). Trade openness, Urban concentration and city-size growth in Turkey. Regional Science Inquiry, 7(1), 35–46.
  • Grimm, N. B., Foster, D., Groffman, P., Grove, J. M., Hopkinson, C. S., Nadelhoffer, K. J., Pataki, D. E., Peters, D. P. C. (2008). The changing landscape: Ecosystem responses to urbanization and pollution across climatic and societal gradients. In Frontiers in Ecology and the Environment (Vol. 6, Issue 5, pp. 264–272). doi.org/10.1890/070147
  • Guo, X., Zhang, Z., Cai, Z., Wang, L., Gu, Z., Xu, Y., Zhao, J. (2022). Analysis of the Spatial–Temporal Distribution Characteristics of NO2 and Their Influencing Factors in the Yangtze River Delta Based on Sentinel-5P Satellite Data. Atmosphere, 13(11). doi.org/10.3390/atmos13111923
  • Gupta, P., Christopher, S. A., Wang, J., Gehrig, R., Lee, Y., Kumar, N. (2006). Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmospheric Environment, 40(30), 5880–5892. doi.org/10.1016/j.atmosenv.2006.03.016
  • He, Y., Wang, C., Chen, F., Jia, H., Liang, D., Yang, A. (2019). Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm. Remote Sensing, 11(5), 535. doi.org/10.3390/rs11050535
  • Huang, G., Sun, K. (2020). Non-negligible impacts of clean air regulations on the reduction of tropospheric NO2 over East China during the COVID-19 pandemic observed by OMI and TROPOMI. Science of the Total Environment, 745, 141023. doi.org/10.1016/j.scitotenv.2020.141023
  • Kang, Y., Choi, H., Im, J., Park, S., Shin, M., Song, C. K., Kim, S. (2021). Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia. Environmental Pollution, 288(2), 117711. doi.org/10.1016/j.envpol.2021.117711
  • Kaplan, G., Yigit Avdan, Z. (2020). Space-Borne Air Pollution Observation From Sentinel-5P Tropomi: Relationship Between Pollutants, Geographical and Demographic Data. International Journal of Engineering and Geosciences, 2, 130–137. doi.org/10.26833/ijeg.644089
  • Kumar, L., Mutanga, O. (2018). Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10), 1–15. doi.org/10.3390/rs10101509
  • Kural, G., Balkıs, N. Ç., Aksu, A. (2018). Source identification of Polycyclic Aromatic Hydrocarbons (PAHs) in the urban environment of İstanbul. International Journal of Environment and Geoinformatics, 5(1), 53-67. Lelieveld, J., Pozzer, A., Pöschl, U., Fnais, M., Haines, A., Münzel, T. (2020). Loss of life expectancy from air pollution compared to other risk factors: A worldwide perspective. Cardiovascular Research, 116(11), 1910–1917. doi.org/10.1093/cvr/cvaa025 Liu, D., Chen, N., Zhang, X., Wang, C., Du, W. (2020). Annual large-scale urban land mapping based on Landsat time series in Google Earth Engine and OpenStreetMap data: A case study in the middle Yangtze River basin. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 337–351.
  • Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F., Wang, S. (2018). High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sensing of Environment, 209(February), 227–239. doi.org/10.1016/j.rse.2018.02.055
  • Magro, C., Nunes, L., Gonçalves, O., Neng, N., Nogueira, J., Rego, F., Vieira, P. (2021). Atmospheric Trends of CO and CH4 from Extreme Wildfires in Portugal Using Sentinel-5P TROPOMI Level-2 Data. Fire, 4(2), 25. doi.org/10.3390/fire4020025
  • Martin, R. V. (2008). Satellite remote sensing of surface air quality. Atmospheric Environment, 42(34), 7823–7843.
  • Patel, N. N., Angiuli, E., Gamba, P., Gaughan, A., Lisini, G., Stevens, F. R., Tatem, A. J., Trianni, G. (2015). Multitemporal settlement and population mapping from landsatusing google earth engine. International Journal of Applied Earth Observation and Geoinformation, 35(PB), 199–208. doi.org/10.1016/j.jag.2014.09.005
  • Potts, D. A., Marais, E. A., Boesch, H., Pope, R. J., Lee, J., Drysdale, W., Chipperfield, M. P., Kerridge, B., Siddans, R., Moore, D. P., Remedios, J. (2021). Diagnosing air quality changes in the UK during the COVID-19 lockdown using TROPOMI and GEOS-Chem. Environmental Research Letters, 16(5). doi.org/10.1088/1748-9326/abde5d
  • Raja, R. A. A., Anand, V., Kumar, A. S., Maithani, S., Kumar, V. A. (2013). Wavelet Based Post Classification Change Detection Technique for Urban Growth Monitoring. Journal of the Indian Society of Remote Sensing, 41(1), 35–43. doi.org/10.1007/s12524-011-0199-7
  • Saadat, H., Adamowski, J., Bonnell, R., Sharifi, F., Namdar, M., Ale-Ebrahim, S. (2011). Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 66(5), 608–619. doi.org/10.1016/j.isprsjprs.2011.04.001
  • Sünsüli, M., Kalkan, K. (2022). Sentinel-5P uydu görüntüleri ile azot dioksit (NO2) kirliliğinin izlenmesi. Türkiye Uzaktan Algılama Dergisi, 4(1), 1–6.
  • Tømmervik, H., Johansen, B. E., Pedersen, J. P. (1995). Monitoring the effects of air pollution on terrestrial ecosystems in Varanger (Norway) and Nikel-Pechenga (Russia) using remote sensing. Science of the Total Environment, 160, 753–767.
  • Tyagi, S., Garg, N., Paudel, R. (2014). Environmental Degradation: Causes and Consequences. European Researcher, 81(8–2), 1491. doi.org/10.13187/er.2014.81.1491
  • Vos, K., Splinter, K. D., Harley, M. D., Simmons, J. A., Turner, I. L. (2019). CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery. Environmental Modelling and Software, 122(June), 104528. doi.org/10.1016/j.envsoft.2019.104528
  • Wang, S., Hao, J. (2012). Air quality management in China: Issues, challenges, and options. Journal of Environmental Sciences, 24(1), 2–13. doi.org/10.1016/S1001-0742(11)60724-9
  • Wong, B. A., Thomas, C., Halpin, P. (2019). Automating offshore infrastructure extractions using synthetic aperture radar & Google Earth Engine. Remote Sensing of Environment, 233(November 2018), 111412. doi.org/10.1016/j.rse.2019.111412
  • Yilmaz, O. S., Acar, U., Sanli, F. B., Gulgen, F., Ates, A. M. (2023). Mapping burn severity and monitoring CO content in Türkiye’s 2021 Wildfires, using Sentinel-2 and Sentinel-5P satellite data on the GEE platform. Earth Science Informatics, 16(1), 221–240. doi.org/10.1007/s12145-023-00933-9
  • Yuan, F., Sawaya, K. E., Loeffelholz, B. C., Bauer, M. E. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98(2–3), 317–328. doi.org/10.1016/j.rse.2005.08.006
  • URL-1. https://data.tuik.gov.tr (25.04.2022)
  • URL-2 https://www.tripreport.com/cities/ (25.04.2022)
  • URL-3 https://web.archive.org (25.04.2022)
  • Ülker, D., Bayırhan, İ., Mersin, K., Gazioğlu, C. (2021). A comparative CO2 emissions analysis and mitigation strategies of short-sea shipping and road transport in the Marmara Region. Carbon Management, 12(1), 1-12.

Investigating the Relationship between Urbanization and Air Pollution Using Google Earth Engine Platform: A Case Study of Istanbul

Year 2024, Volume: 11 Issue: 3, 130 - 146, 28.09.2024
https://doi.org/10.30897/ijegeo.1339560

Abstract

Rapid population growth in megacities such as Istanbul has led to various effects, such as industrialization, urbanization, loss of green areas, increasing vehicle traffic, and higher consumption of fossil fuels. These reasons, along with many other environmental factors, contribute to the rise of air pollution in urban life. This study aimed to examine the relationship between urbanization and air pollution in Istanbul. For this purpose, land cover maps covering Istanbul province were produced using Landsat-5 (TM), Landsat-8 (OLI), and Sentinel-2 (MSI) images for the years 1996 to 2021 at three-year intervals on the Google Earth Engine platform. Land cover for classification purposes was divided into five different classes: forest, water surface, urban area, and bare land, and classified using a random forest machine learning algorithm. To examine the impact of this urban area growth on air pollution, in the second step of the study, the column number density values of Sentinel 5P (TROPOMI) data for SO2, NO2, CO, and O3 gases for 2019, 2020, and 2021 were analyzed. The averages of the data from 39 air pollution monitoring stations across Istanbul were also examined. According to this classification, the urban area expanded from 491 km2 in 1996 to 1222 km2 by 2021. Considering the total surface area of Istanbul province, the urban area, which was 9% in 1996, reached 23% by 2021. The TROPOMI values were calculated as follows: the average column number density values for SO2, NO2, CO, and O3 were 0.0003538 mol/m², 0.0339514 mol/m², 0.0000984 mol/m², and 0.1453515 mol/m², respectively. Similarly, the gas concentrations of SO2, NO2, CO, and O3 measured from the ground stations were calculated as 6.603 µ/m3, 786,815 µ/m3, 43.763 µ/m3 and 45.773 µ/m3, respectively. Correlations between urbanization and TROPOMI values revealed a positive correlation of 0.39, 0.02, and 0.80 for SO2, NO2, and CO gases, while a negative correlation of 0.25 was found for O3 gas. The study also examined correlations between TROPOMI and ground station measurements, resulting in positive correlations of 0.55, 0.66, and 0.16 for SO2, NO2, and CO gases, respectively, while a negative correlation of 0.05 was found for O3 gas. Based on these findings, among the air pollutants studied both through TROPOMI and ground station data, the highest correlation was observed for CO gas.

References

  • Achakulwisut, P., Brauer, M., Hystad, P., Anenberg, S. C. (2019). Global, national, and urban burdens of paediatric asthma incidence attributable to ambient NO 2 pollution: estimates from global datasets. The Lancet Planetary Health, 3(4), e166–e178. doi.org/10.1016/S2542-5196(19)30046-4
  • Aghlmand, M., Kalkan, K., Onur, M. İ., Öztürk, G., Ulutak, E. (2021). Google Earth Engine ile Arazi Kullanımı Haritalarının Üretimi. Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 10(1), 38–47. doi.org/10.28948/ngumuh.795977
  • Alqurashi, A. F., Kumar, L. (2013). Investigating the Use of Remote Sensing and GIS Techniques to Detect Land Use and Land Cover Change: A Review. Advances in Remote Sensing, 02(02), 193–204. doi.org/10.4236/ars.2013.22022
  • Arslan, O., Akyürek, Ö. (2018). Spatial Modelling of Air Pollution from PM10 and SO2 concentrations during Winter Season in Marmara Region (2013-2014). International Journal of Environment and Geoinformatics, 5(1), 1-16. doi.org/10.30897/ ijegeo.412391
  • Burnett, R., Chen, H., Szyszkowicz, M., Fann, N., Hubbell, B., Pope III, C. A., Apte, J. S., Brauer, M., Cohen, A., Weichenthal, S. (2018). Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proceedings of the National Academy of Sciences, 115(38), 9592–9597. doi.org/10.1073/pnas.1803222115
  • Cakmak, N., Yilmaz, O. S., Sanli, F. B. (2023). Spatio-temporal Analysis of Pollutant Gases using Sentinel-5P TROPOMI Data on the Google Earth Engine during the COVID-19 Pandemic in the Marmara Region , Türkiye. E-ZBORNIK, 13(25), 1–14. doi.org/10.47960/2232-9080.2023.25.13.1
  • Dong, J., Xiao, X., Menarguez, M. A., Zhang, G., Qin, Y., Thau, D., Biradar, C., Moore, B. (2016). Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sensing of Environment, 185, 142–154. doi.org/10.1016/ j.rse.2016.02.016
  • Engel-Cox, J. A., Hoff, R. M., Haymet, A. D. J. (2004). Recommendations on the use of satellite remote-sensing data for urban air quality. Journal of the Air and Waste Management Association, 54(11), 1360–1371. doi.org/10.1080/10473289.2004.10471005
  • Engin Duran, H., Pelin Özkan, S. (2015). Trade openness, Urban concentration and city-size growth in Turkey. Regional Science Inquiry, 7(1), 35–46.
  • Grimm, N. B., Foster, D., Groffman, P., Grove, J. M., Hopkinson, C. S., Nadelhoffer, K. J., Pataki, D. E., Peters, D. P. C. (2008). The changing landscape: Ecosystem responses to urbanization and pollution across climatic and societal gradients. In Frontiers in Ecology and the Environment (Vol. 6, Issue 5, pp. 264–272). doi.org/10.1890/070147
  • Guo, X., Zhang, Z., Cai, Z., Wang, L., Gu, Z., Xu, Y., Zhao, J. (2022). Analysis of the Spatial–Temporal Distribution Characteristics of NO2 and Their Influencing Factors in the Yangtze River Delta Based on Sentinel-5P Satellite Data. Atmosphere, 13(11). doi.org/10.3390/atmos13111923
  • Gupta, P., Christopher, S. A., Wang, J., Gehrig, R., Lee, Y., Kumar, N. (2006). Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmospheric Environment, 40(30), 5880–5892. doi.org/10.1016/j.atmosenv.2006.03.016
  • He, Y., Wang, C., Chen, F., Jia, H., Liang, D., Yang, A. (2019). Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm. Remote Sensing, 11(5), 535. doi.org/10.3390/rs11050535
  • Huang, G., Sun, K. (2020). Non-negligible impacts of clean air regulations on the reduction of tropospheric NO2 over East China during the COVID-19 pandemic observed by OMI and TROPOMI. Science of the Total Environment, 745, 141023. doi.org/10.1016/j.scitotenv.2020.141023
  • Kang, Y., Choi, H., Im, J., Park, S., Shin, M., Song, C. K., Kim, S. (2021). Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia. Environmental Pollution, 288(2), 117711. doi.org/10.1016/j.envpol.2021.117711
  • Kaplan, G., Yigit Avdan, Z. (2020). Space-Borne Air Pollution Observation From Sentinel-5P Tropomi: Relationship Between Pollutants, Geographical and Demographic Data. International Journal of Engineering and Geosciences, 2, 130–137. doi.org/10.26833/ijeg.644089
  • Kumar, L., Mutanga, O. (2018). Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10), 1–15. doi.org/10.3390/rs10101509
  • Kural, G., Balkıs, N. Ç., Aksu, A. (2018). Source identification of Polycyclic Aromatic Hydrocarbons (PAHs) in the urban environment of İstanbul. International Journal of Environment and Geoinformatics, 5(1), 53-67. Lelieveld, J., Pozzer, A., Pöschl, U., Fnais, M., Haines, A., Münzel, T. (2020). Loss of life expectancy from air pollution compared to other risk factors: A worldwide perspective. Cardiovascular Research, 116(11), 1910–1917. doi.org/10.1093/cvr/cvaa025 Liu, D., Chen, N., Zhang, X., Wang, C., Du, W. (2020). Annual large-scale urban land mapping based on Landsat time series in Google Earth Engine and OpenStreetMap data: A case study in the middle Yangtze River basin. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 337–351.
  • Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F., Wang, S. (2018). High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sensing of Environment, 209(February), 227–239. doi.org/10.1016/j.rse.2018.02.055
  • Magro, C., Nunes, L., Gonçalves, O., Neng, N., Nogueira, J., Rego, F., Vieira, P. (2021). Atmospheric Trends of CO and CH4 from Extreme Wildfires in Portugal Using Sentinel-5P TROPOMI Level-2 Data. Fire, 4(2), 25. doi.org/10.3390/fire4020025
  • Martin, R. V. (2008). Satellite remote sensing of surface air quality. Atmospheric Environment, 42(34), 7823–7843.
  • Patel, N. N., Angiuli, E., Gamba, P., Gaughan, A., Lisini, G., Stevens, F. R., Tatem, A. J., Trianni, G. (2015). Multitemporal settlement and population mapping from landsatusing google earth engine. International Journal of Applied Earth Observation and Geoinformation, 35(PB), 199–208. doi.org/10.1016/j.jag.2014.09.005
  • Potts, D. A., Marais, E. A., Boesch, H., Pope, R. J., Lee, J., Drysdale, W., Chipperfield, M. P., Kerridge, B., Siddans, R., Moore, D. P., Remedios, J. (2021). Diagnosing air quality changes in the UK during the COVID-19 lockdown using TROPOMI and GEOS-Chem. Environmental Research Letters, 16(5). doi.org/10.1088/1748-9326/abde5d
  • Raja, R. A. A., Anand, V., Kumar, A. S., Maithani, S., Kumar, V. A. (2013). Wavelet Based Post Classification Change Detection Technique for Urban Growth Monitoring. Journal of the Indian Society of Remote Sensing, 41(1), 35–43. doi.org/10.1007/s12524-011-0199-7
  • Saadat, H., Adamowski, J., Bonnell, R., Sharifi, F., Namdar, M., Ale-Ebrahim, S. (2011). Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 66(5), 608–619. doi.org/10.1016/j.isprsjprs.2011.04.001
  • Sünsüli, M., Kalkan, K. (2022). Sentinel-5P uydu görüntüleri ile azot dioksit (NO2) kirliliğinin izlenmesi. Türkiye Uzaktan Algılama Dergisi, 4(1), 1–6.
  • Tømmervik, H., Johansen, B. E., Pedersen, J. P. (1995). Monitoring the effects of air pollution on terrestrial ecosystems in Varanger (Norway) and Nikel-Pechenga (Russia) using remote sensing. Science of the Total Environment, 160, 753–767.
  • Tyagi, S., Garg, N., Paudel, R. (2014). Environmental Degradation: Causes and Consequences. European Researcher, 81(8–2), 1491. doi.org/10.13187/er.2014.81.1491
  • Vos, K., Splinter, K. D., Harley, M. D., Simmons, J. A., Turner, I. L. (2019). CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery. Environmental Modelling and Software, 122(June), 104528. doi.org/10.1016/j.envsoft.2019.104528
  • Wang, S., Hao, J. (2012). Air quality management in China: Issues, challenges, and options. Journal of Environmental Sciences, 24(1), 2–13. doi.org/10.1016/S1001-0742(11)60724-9
  • Wong, B. A., Thomas, C., Halpin, P. (2019). Automating offshore infrastructure extractions using synthetic aperture radar & Google Earth Engine. Remote Sensing of Environment, 233(November 2018), 111412. doi.org/10.1016/j.rse.2019.111412
  • Yilmaz, O. S., Acar, U., Sanli, F. B., Gulgen, F., Ates, A. M. (2023). Mapping burn severity and monitoring CO content in Türkiye’s 2021 Wildfires, using Sentinel-2 and Sentinel-5P satellite data on the GEE platform. Earth Science Informatics, 16(1), 221–240. doi.org/10.1007/s12145-023-00933-9
  • Yuan, F., Sawaya, K. E., Loeffelholz, B. C., Bauer, M. E. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98(2–3), 317–328. doi.org/10.1016/j.rse.2005.08.006
  • URL-1. https://data.tuik.gov.tr (25.04.2022)
  • URL-2 https://www.tripreport.com/cities/ (25.04.2022)
  • URL-3 https://web.archive.org (25.04.2022)
  • Ülker, D., Bayırhan, İ., Mersin, K., Gazioğlu, C. (2021). A comparative CO2 emissions analysis and mitigation strategies of short-sea shipping and road transport in the Marmara Region. Carbon Management, 12(1), 1-12.
There are 37 citations in total.

Details

Primary Language English
Subjects Photogrammetry and Remote Sensing
Journal Section Research Articles
Authors

Duygu Yasan 0000-0001-6979-4388

Uğur Acar 0000-0003-3676-4259

Osman Salih Yılmaz 0000-0003-4632-9349

Early Pub Date September 15, 2024
Publication Date September 28, 2024
Published in Issue Year 2024 Volume: 11 Issue: 3

Cite

APA Yasan, D., Acar, U., & Yılmaz, O. S. (2024). Investigating the Relationship between Urbanization and Air Pollution Using Google Earth Engine Platform: A Case Study of Istanbul. International Journal of Environment and Geoinformatics, 11(3), 130-146. https://doi.org/10.30897/ijegeo.1339560