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Wang Y, Niu R, Lin G, Xiao Y, Ma H, Zhao L. Estimate of soil heavy metal in a mining region using PCC-SVM-RFECV-AdaBoost combined with reflectance spectroscopy. Environ Geochem Health 2023; 45:9103-9121. [PMID: 36869963 DOI: 10.1007/s10653-023-01488-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
Soil contamination with heavy metals is a relatively serious issue in China. Traditional soil heavy metal survey methods cannot meet the demand for rapid and real-time large-scale area soil heavy metal surveys. We chose a typical mining area in Henan Province as the study area, collected 124 soil samples in the field and obtained their soil hyperspectral data indoors using a spectrometer. After different spectral transformations of the soil spectral curves, Pearson correlation coefficients (PCC) between them and the heavy metals Cd, Cr, Cu, and Ni were calculated, and after correlation evaluation, the best spectral transformations for each heavy metal were determined and preselected characteristic wavebands were obtained. Then the support vector machine recursive feature elimination cross-validation (SVM-RFECV) is used to select among the preselected feature wavebands to obtain the final modeled wavebands, and the Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Partial Least Squares (PLS) methods were used to establish the inversion model. The results showed that the PCC-SVM-RFECV can effectively select characteristic wavebands with high contribution to modeling from high-dimensional data. Spectral transformations methods can improve the correlation of spectra with heavy metals. The location and quantity of characteristic wavebands for the four heavy metals were different. The accuracy of AdaBoost was significantly better than that of GBDT, RF, and PLS (i.e., Ni: [Formula: see text]). This study can provide a technical reference for the use of hyperspectral inversion models for large-scale monitoring of soil heavy metal content.
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Affiliation(s)
- Yueyue Wang
- School of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China
| | - Ruiqing Niu
- School of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China.
- Henan Science and Technology Innovation Center of Natural Resources (Application Research of Information Perception Technology), Xinyang, 464000, Henan, China.
| | - Guo Lin
- Department of Atmospheric and Oceanic Science, University of Colorado Boulder, Boulder, 80309, USA
| | - Yingxu Xiao
- School of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China
| | - Hangling Ma
- School of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China
| | - Lingran Zhao
- School of Automation, China University of Geosciences, Wuhan, 430074, China
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Jia M, Jiang F, Evangeliou N, Eckhardt S, Huang X, Ding A, Stohl A. Rapid decline of carbon monoxide emissions in the Fenwei Plain in China during the three-year Action Plan on defending the blue sky. J Environ Manage 2023; 337:117735. [PMID: 36931069 DOI: 10.1016/j.jenvman.2023.117735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/26/2023] [Accepted: 03/10/2023] [Indexed: 06/18/2023]
Abstract
The Fenwei Plain is one of China's most polluted regions, with poor atmospheric dispersion conditions and an outdated energy structure. After implementing multiple policies in recent years, significant reductions in air pollutant concentrations were observed. In this study, based on the Lagrangian-Bayesian inversion framework FLEXINVERT, we constructed a variable resolution inversion system focusing on the Fenwei Plain and inferred the carbon monoxide (CO) emissions using in-situ atmospheric CO observations from April 2014 to March 2020. We analyzed the spatiotemporal variations of the CO emissions and discussed their causes, especially the effect of the "Three-year Action Plan on Defending the Blue Sky" (TAPDBS). Before the policy, CO emissions temporarily increased, and the overall decrease in CO emissions per unit of Gross Domestic Product (GDP) slowed down. When the policy was implemented, CO emission fluxes declined sharply, with an average drop of 28%, accompanied by an even higher 37% decrease of CO emission per GDP. The reasons for the decline in CO emissions in Shanxi, Shaanxi and Henan are diverse. The decrease in energy intensity is the reason for CO emission reduction in Shannxi and Henan province but not in Shanxi province. This research fills the gap in emission information in recent years and confirms that TAPDBS has brought a breakthrough in both economic development and air quality protection in the Fenwei Plain.
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Affiliation(s)
- Mengwei Jia
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
| | - Fei Jiang
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China; Frontiers Science Center for Critical Earth Material Cycling, Nanjing University, Nanjing, 210023, China.
| | - Nikolaos Evangeliou
- NILU - Norwegian Institute for Air Research, Department of Atmospheric and Climate Research, Kjeller, 2007, Norway
| | - Sabine Eckhardt
- NILU - Norwegian Institute for Air Research, Department of Atmospheric and Climate Research, Kjeller, 2007, Norway
| | - Xin Huang
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Aijun Ding
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Andreas Stohl
- Department of Meteorology and Geophysics, University of Vienna, UZA II, Althanstraße 14, Vienna, 1090, Austria
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Chang C, Li X, Duanmu L, Li H, Zhou W. Locating leakage in pipelines based on the adjoint equation of inversion modeling. Heliyon 2023; 9:e17270. [PMID: 37383185 PMCID: PMC10293730 DOI: 10.1016/j.heliyon.2023.e17270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 05/23/2023] [Accepted: 06/13/2023] [Indexed: 06/30/2023] Open
Abstract
This paper presents an adjoint method for locating potential leakage in a single-phase fluid pipeline based on the analytic solution of inversion modeling. By studying the mechanism of pipeline leakage pressure, the adjoint equation based on the governing equation of transient flow is established in the single-liquid phase aspect using inverse adjoint theory and sensitivity analysis method. The inverse transient adjoint equation is primarily derived from the single linear fluid pipeline in the semi-infinite domain. The Laplace method is then used to obtain an analytical solution that determines the location of pipeline leakage. The experimental results indicate that the analytic solution can quickly and accurately judge the leakage location of the pipeline. Furthermore, it presents a new approach to engineering applications, such as gas-liquid two-phase flow complex pipe networks, etc.
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Affiliation(s)
- Chang Chang
- Institute of Building Environment and Facility Engineering, Dalian University of Technology, Dalian 116024, China
| | - Xiangli Li
- Institute of Building Environment and Facility Engineering, Dalian University of Technology, Dalian 116024, China
| | - Lin Duanmu
- Institute of Building Environment and Facility Engineering, Dalian University of Technology, Dalian 116024, China
| | - Hongwei Li
- School of Energy and Power Engineering, Northeast Electric Power University, Jilin 132012, China
| | - Wenbin Zhou
- School of Science and Engineering, University of Dundee, Dundee, DD1 4HN, UK
- Department of Mechanical Engineering, Imperial College London, London, SW7 2AZ, UK
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