Air Pollution and Plant Health: A Research on Tokat Province

Abstract

This study aims to investigate the relationship between air quality and vegetation cover in Tokat province between 2019 and 2022. Using the Google Earth Engine platform, vegetation health was assessed through satellite-based indices EVI (Enhanced Vegetation Index), NDVI (Normalised Difference Vegetation Index), NDMI (Normalised Difference Moisture Index) and NDWI (Normalised Difference Water Index). Air pollution data were analysed through NO2 (Nitrogen Dioxide), CO (Carbon Monoxide) and O3 (Ozone) parameters obtained from Sentinel-5P satellite. The results show that especially NO2 has moderate negative effects on plant health in Tokat province. This research emphasises the necessity of developing environmental sustainability strategies and increasing efforts to combat air pollution in Tokat province. This study demonstrates that modern remote sensing technologies such as Google Earth Engine and Sentinel-5P can be effectively used in environmental monitoring and analyses. The findings provide important contributions to the shaping of environmental policies and management strategies at both regional and national levels. Future studies can develop a more comprehensive understanding of the relationship between air pollution and plant health to protect ecosystem health.

Keywords

• Normalised Difference Vegetation Index
• Normalised Difference Water Index
• Normalised Difference Moisture Index
• Enhanced Vegetation Index.

References

1. [1] Mukhlishahi N., (2022) Pemanfaatan tanah, kompos, dan arang sekam untuk pertumbuhan tanaman Cabai. COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat, 2, 142–148.
2. [2] Rahmanto Y, Rifaini A, Samsugi S and Riskiono S D (2020) Sistem Monitoring pH Air Pada Aquaponik Menggunakan Mikrokontroler Arduino UNO. Jurnal Teknologi Dan Sistem Tertanam, 1, 23–28.
3. [3] Kamali M A, Amiroh K, Widyantara H and Hariyanto M D (2023) Pembuatan smart urban farming berbasis internet of things untuk kelompok tani. Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS), 6, 201–214. https://doi.org/10.33474/jipemas.v6i2.19289.
4. [4] Nurhaliza D V, Novianti I, Rahman K R, Rozak R W A, Nurlela T, Sugiarti Y and Setyani Z T (2023) Dampak Perubahan Iklim Terhadap Ketahanan Pangan dan Gizi di Indonesia Demi Tercapainya Tujuan SDGs. Bulletin Agro Industri, 50, 1–7.
5. [5] Gözükara, G., Altunbaş, S., Şimşek, O., Ozan, S., Buyurgan, K., Maltaş, A., Kaplan, M. (2019). Roka (Eruca vesicaria) yetiştiriciliğinde spektral yansıma ile bitki besin maddesi konsantrasyonu arasındaki ilişkinin belirlenmesi. Mediterranean Agricultural Sciences, 32: 55-62.
6. [6] Indian Network for Climate Change Assessment (INCCA). (2010). Climate Change and India: A 4 × 4 Assessment. A Sectoral and Regional Analysis for 2030s. Available online: https://moef.nic.in/downloads/public-information/fin-rpt-incca.pdf (accessed on 15 February 2023).
7. [7] Ahmad, T., Pandey, A.C., Kumar, A. (2022). Long-term precipitation monitoring and its linkage with flood scenario in changing climate conditions in Kashmir valley. Geocarto International, 37, 5497–5522.
8. [8] Ali, R., Gad, A. (2022). The impact of COVID-19 pandemic on wheat yield in El Sharkia Governorate, Egypt. Egyptian Journal of Remote Sensing and Space Science, 25, 249–256.

9. [9] Penuelas J, Pinol J, Ogaya R, Filella I. (1997). Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 18: 2869-2875.
10. [10] Bell GE, Martin DL, Wiese SG, Dobson DD, Smith MW, Stone ML, Solie JB. (2002). Vehicle-mounted optical sensing: an objective means for evaluating turf quality. Crop Science, 42: 197-201.
11. [11] McFeeters, SK. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17, 1425–1432.
12. [12] Gao, B. (1996). NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.
13. [13] Schultz, M., Clevers, J.G.P.W., Carter, S., Verbesselt, J., Avitabile, V., Quang, H.V., Herold, M. (2016). Performance of vegetation indices from Landsat time series in deforestation monitoring. International Journal of Applied Earth Observations, 52, 318–327.
14. [14] Huete, A. (2014). Vegetation Indices. In Encyclopedia of Remote Sensing. Encyclopedia of Earth Sciences Series; Njoku, E.G., Ed.; Springer: New York, NY, USA.
15. [15] Huang, C., Goward, S.N., Masek, J.G., Gao, F., Vermote, E.F., Thomas, N., Schleeweis, K., Kennedy, R.E., Zhu, Z., Eidenshink, J.C., et al. (2009). Development of time series stacks of Landsat images for reconstructing forest disturbance history. International Journal of Digital Earth, 2, 195–218.
16. [16] Kazemi Garajeh, M., Blaschke, T., Hossein Haghi, V., Weng, Q., Valizadeh Kamran, K., Li, Z. (2022). A comparison between Sentinel-2 and Landsat 8 OLI satellite images for soil salinity distribution mapping using a deep learning convolutional neural network. Canadian Journal of Remote Sensing, 48, 452–468.
17. [17] Lin, X., Wu, S., Chen, B., Lin, Z., Yan, Z., Chen, X., Yin, G., You, D., Wen, J., Liu, Q. (2022). Estimating 10-m land surface albedo from Sentinel-2 satellite observations using a direct estimation approach with Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 194, 1–20.
18. [18] Naboureh, A., Bian, J., Lei, G., Li, A. (2021). A review of land use/land cover change mapping in the China-Central Asia-West Asia economic corridor countries. Big Earth Data, 5, 237–257.
19. [19] Loyola, D.G., Gimeno García, S., Lutz, R., Argyrouli, A., Romahn, F., Spurr, R.J., Pedergnana, M., Doicu, A., Molina García, V., Schüssler, O. (2018). The operational cloud retrieval algorithms from TROPOMI on board Sentinel-5 Precursor. Atmospheric Measurement Techniques, 11, 409–427.
20. [20] Vîrghileanu, M., Săvulescu, I., Mihai, B.-A., Nistor, C., Dobre, R. (2020). Nitrogen Dioxide (NO2) Pollution monitoring with Sentinel-5P satellite imagery over Europe during the coronavirus pandemic outbreak. Remote Sensing, 12, 3575.
21. [21] Zhang XX, Wu PF, Chen B. (2010). Relationship between vegetation greenness and urban heat island effect in Beijing City, China. Procedia Environmental Science, 2: 1438-1450.
22. [22] Nega W, Hailu BT, Fetene A. (2019). An assessment of the vegetation cover change impact on rainfall and land surface temperature using remote sensing in a subtropical climate, Ethiopia. Remote Sensing Applications: Society and Environment, 100266.
23. [23] Tamiminia H, Salehi B, Mahdianpari M, Quackenbush L, Adeli S, Brisco B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 152–170.
24. [24] Butz, A., et al. (2012). TROPOMI aboard Sentinel-5 Precursor: Prospective performance of CH4 retrievals for aerosol and cirrus loaded atmospheres. Remote Sensing of Environment, 120, 267–276.
25. [25] Ialongo, I., Virta, H., Eskes, H., Hovila, J., Douros, J. (2020). Comparison of TROPOMI/Sentinel-5 Precursor NO2 observations with ground-based measurements in Helsinki. Atmospheric Measurement Techniques, 13, 205–218.
26. [26] Soleimany, A., Grubliauskas, R., Šerevičienė, V. (2020). Application of satellite data and GIS services for studying air pollutants in Lithuania (case study: Kaunas city). Air Quality Atmosphere & Health, 14, 411–429.
27. [27] 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 Geosciences.
28. [28] Omrani, H., Omrani, B., Parmentier, B., Helbich, M. (2020). Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France. Data Brief, 28, 105089.
29. [29] Gorelick N. et al. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.
30. [30] Yılmaz, B., Demirel, M. ve Balçık, F. (2022). Yanmış alanların Sentinel-2 MSI ve Landsat-8 OLI ile tespiti ve analizi: Çanakkale/Gelibolu orman yangını. Doğal Afetler ve Çevre Dergisi, 8(1), 76-86.
31. [31] Phuong, D.T.K., Nhut, M.C., Tri, N.D. (2021). Air Pollution Assessment Using RS and GIS in Ho Chi Minh City, Vietnam: A Case Study of Period 2015–2019 for SO2 and NO2. IOP Conference Series: Earth and Environmental Science, 652, 012004.
32. [32] Bugdayci, I., Ugurlu, O., Kunt, F. (2023). Spatial Analysis of SO2, PM10, CO, NO2, and O3 Pollutants: The Case of Konya Province, Turkey. Atmosphere, 14, 462.