Hyperspectral Inversion and Analysis of Zinc Concentration in Urban Soil in the Urumqi City of China

Source:
By:Author(s)

DOI: https://doi.org/10.30564/jees.v5i2.5947

Abstract:

Excessive accumulation of zinc (Zn) in urban soil can lead to environmental pollution and pose a potential threat to human health and the ecosystem. How to quickly and accurately monitor the urban soil zinc content on a large scale in real time and dynamically is crucial. Hyperspectral remote sensing technology provides a new method for rapid and nondestructive soil property detection. The main goal of this study is to find an optimal combination of spectral transformation and a hyperspectral estimation model to predict the Zn content in urban soil. A total of 88 soil samples were collected to obtain the Zn contents and related hyperspectral data, and perform 18 transformations on the original spectral data. Then, select important wavelengths by Pearson's correlation coefficient analysis (PCC) and CARS. Finally, establish a partial least squares regression model (PLSR) and random forest regression model (RFR) with soil Zn content and important wavelengths. The results indicated that the average Zn content of the collected soil samples is 60.88 mg/kg. Pearson's correlation coefficient analysis (PCC) and CARS for the original and transformed wavelengths can effectively improve the correlations between the spectral data and soil Zn content. The number of important wavelengths selected by CARS is less than the important wavelengths selected by PCC. Partial least squares regression model based on first-order differentiation of the reciprocal by CARS (CARS-RTFD-PLSR) is more stable and has the highest prediction ability (R² = 0.937, RMSE = 8.914, MAE = 2.735, RPD = 3.985). The CARS-RTFD-PLSR method can be used as a means of prediction of Zn content in soil in oasis cities. The results of the study can provide technical support for the hyperspectral estimation of the soil Zn content.

References:

[1] Zhao, R.X., 2004. Huan jing wu ran hua xue (Chinese) [Environmental pollution chemistry]. China Industry Press: Beijing. [2] Chen, Y.Z., Wang, F., Wang, G., et al., 2012. Research advances on zinc pollution and remediation of soil system. Fujian Journal of Agricultural Sciences. 27(8), 901-908. [3] Patel, A.K., Ghosh, J.K., Sayyad, S.U., 2022. Fractional abundances study of macronutrients in soil using hyperspectral remote sensing. Geocarto International. 37(2), 474-493. [4] Yang, Y., Cui, Q.F., Jia, P., et al., 2021. Estimating the heavy metal concentrations in topsoil in the Daxigou mining area, China, using multispectral satellite imagery. Scientific Reports. 11, 11718. [5] Wei, L.F., Pu, H.C., Wang, Z.X., et al., 2020. Estimation of soil arsenic content with hyperspectral remote sensing. Sensor. 20, 4056-4071. [6] Tan, K., Wang, H.M., Chen, L.H., et al., 2021. Estimating the distribution trend of soil heavy metals in mining area from HyMap airborne hyperspectral imagery based on ensemble learning. Journal of Hazardous Materials. 401, 1-17. [7] Ye, M., Zhu, L., Li, X.J., et al., 2022. Estimation of the soil arsenic concentration using a geographically weighted XGBoost model based on hyperspectral data. Science of the Total Environment. 858, 159798-159798. [8] Bian, Z.J., Sun, L.N., Tian, K., et al., 2021. Estimation of heavy metals in Tailings and soils using hyperspectral technology: A case study in a Tin Polymetallic mining area. Bulletin of Environmental Contamination and Toxicology. 107, 1022-1031. [9] Zhang, B., Guo, B., Zou, B., et al., 2022. Retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine, Inner Mongolia, China. Environmental Pollution. 300, 118981-118992. [10] Hou, L., Li, X.J., Li, F., 2018. Hyperspectral-based inversion of heavy metal content in the soil of coal mining areas. Journal of Environmental Quality. 48, 57-63. [11] Omran, E.S.E., 2016. Inference model to predict heavy metals of Bahr El Baqar soils, Egypt using spectroscopy and chemometrics technique. Modeling Earth Systems and Environment. 2, 1-17. [12] Yang, H.F., Xu, H., Zhong, X.N., 2022. Prediction of soil heavy metal concentrations in copper tailings area using hyperspectral reflectance. Environmental Earth Sciences. 81, 183-193. [13] Wei, B., Jiang, F., Li, X., et al., 2010. Heavy metal induced ecological risk in the city of Urumqi, NW China. Environmental Monitoring and Assessment. 160, 33-45. [14] Li, J.M., Zhang, Y.T., 2019. Wu lu mu qi bu tong gong neng qu lin dai tu rang zhong jin shu wu ran te zheng fen xi (Chinese) [Characteristics of heavy metal pollution in forest belt soil of different functional zones in Urumqi, Xinjiang]. Journal of Environmental Sciences. 28, 1859-1866. [15] Sidikjan, N., Eziz, M., Li, X., et al., 2022. Spatial distribution, contamination levels, and health risks of trace elements in topsoil along an urbanization gradient in the City of Urumqi, China. Sustainability. 14(19), 12646. [16] Hini, G., Eziz, M., Wang, W., et al., 2020. Spatial distribution, contamination levels, sources, and potential health risk assessment of trace elements in street dusts of Urumqi city, NW China. Human and Ecological Risk Assessment: An International Journal. 26(8), 2112-2128. [17] Yao, X.D., Wang, J., Wang, Y.M., et al., 2022. Wu lu mu qi mou gong ye yuan qu tu rang zhong jin shu qian zai sheng tai feng xian ping jia (Chinese) [Potential ecological risk assessment on heavy metals in the soil of an industrial park in Urumqi, China]. Transactions of Nonferrous Metals Society of China. 12, 160-166. [18] Soil and Sediment-Determination of Aqua Regia Extracts of 12 Metal Elements-Inductively Coupled Plasma Mass Spectrometry [Internet]. Ministry of Environmental Protection of the People's Republic of China; 2016. Available from: https://english.mee.gov.cn/Resources/standards/Soil/Method_Standard4/201607/t20160704_357088.shtml [19] Yuan, Z.R., Wei, L.F., Zhang, Y.X., et al., 2020. Hyperspectral inversion and analysis of heavy metal arsenic content in farmland soil based on optimizing CARS combined with PSO-SVM algorithm. Spectroscopy and Spectral Analysis. 40(2), 567-573. [20] Zhong, X.J., Yang, L., Zhang, D.X., et al., 2022. Effect of different particle sizes on the prediction of soil organic matter content by visible-near infrared spectroscopy. Spectroscopy and Spectral Analysis. 42(8), 2542-2550. [21] Ma, X.M., Zhou, K.F., Wand, J.L., et al., 2022. Optimal bandwidth selection for retrieving Cu content in rock based on hyperspectral remote sensing. Journal of Arid Land. 14(1), 102-114. [22] Samuel, N.A., Anna, F.H., Andreas, A., et al., 2021. Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques. Hydrology and Earth System Sciences. 25, 2739-2758. [23] Michelle, D., Onisimo, M., Riyad, I., 2011. Examining the utility of random forest and AISA Eagle hyperspectral image data to predict Pinus patula age in KwaZulu-Natal, South Africa. Geocarto International. 26(4), 275-289. [24] Rukeya, S., Nijat, K., Abdugheni, A., et al., 2018. Possibility of optimized indices for the assessment of heavy metal contents in soil around an open pit coal mine area. International Journal of Applied Earth Observation and Geoinformation. 73, 14-25. [25] Vohland, M., Joachim, B., Joachim, H., et al., 2011. Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy. Geoderma. 166, 198-205. [26] Wang, Y.Y., Niu, R.Q., Lin, G., et al., 2023. Estimate of soil heavy metal in a mining region using PCC-SVM-RFECV-AdaBoost combined with reflectance spectroscopy. Environmental Geochemistry and Health. Ahead of print. DOI: https://doi.org/10.1007/s10653-023-01488-W [27] Liu, W.W., Li, M.J., Zhang, M.Y., et al., 2020. Hyperspectral inversion of mercury in reed leaves under different levels of soil mercury contamination. Environmental Science and Pollution Research. 27, 22935-22945. [28] Zhou, M., Zou, B., Tu, Y.L., et al., 2022. Spectral response feature bands extracted from near standard soil samples for estimating soil Pb in a mining area. Geocarto International. 37(26), 13248-13267.