Estimation of chemical oxygen demand in different water systems by near-infrared spectroscopy
To monitor environmental water pollution effectively and meet human water needs, it is crucial to develop a fast, simple, and accurate method for monitoring chemical oxygen demand (COD) in various water systems. In this study, COD prediction models for different water systems were developed by combi...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Ecotoxicology and Environmental Safety |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651322008041 |
| _version_ | 1811297304521474048 |
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| author | Xueqin Han Danping Xie Han Song Jinfang Ma Yongxin Zhou Jiaze Chen Yanyan Yang Furong Huang |
| author_facet | Xueqin Han Danping Xie Han Song Jinfang Ma Yongxin Zhou Jiaze Chen Yanyan Yang Furong Huang |
| author_sort | Xueqin Han |
| collection | DOAJ |
| description | To monitor environmental water pollution effectively and meet human water needs, it is crucial to develop a fast, simple, and accurate method for monitoring chemical oxygen demand (COD) in various water systems. In this study, COD prediction models for different water systems were developed by combining near-infrared (NIR) spectroscopy with partial least squares regression (PLSR). Samples of wastewater, surface water, and seawater were collected from Guangzhou, Guangdong Province, China. Three pretreatment methods were used to preprocess the spectra in order to improve the accuracy and minimalism of the model. We investigate the performance of two variable selection algorithms, namely, binary gray wolf optimization (BGWO) and competitive adaptive reweighting sampling (CARS). The results show that both BGWO and CARS improved the performance of the model in terms of higher accuracy and less wavelength input; both of the combined model performances were better than that of PLSR alone, and CARS-PLSR achieved the best results. Using CARS-PLSR, surface water, wastewater, and seawater model inputs were reduced by 96 %, 96 %, and 82 % as compared to the PLSR results, respectively, and the testing sets R2 reached 0.860, 0.815, and 0.692, respectively. The spectral variable selection algorithm could identify the important spectral variables between COD content and NIR spectra in three water systems, thereby improving the accuracy and simplicity of the PLSR model for COD prediction. Our results have important practical value for predicting COD content in different water systems by NIR spectroscopy. |
| first_indexed | 2024-04-13T06:02:15Z |
| format | Article |
| id | doaj.art-43944f76d52c48038d9a0006d191a90d |
| institution | Directory Open Access Journal |
| issn | 0147-6513 |
| language | English |
| last_indexed | 2024-04-13T06:02:15Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecotoxicology and Environmental Safety |
| spelling | doaj.art-43944f76d52c48038d9a0006d191a90d2022-12-22T02:59:23ZengElsevierEcotoxicology and Environmental Safety0147-65132022-09-01243113964Estimation of chemical oxygen demand in different water systems by near-infrared spectroscopyXueqin Han0Danping Xie1Han Song2Jinfang Ma3Yongxin Zhou4Jiaze Chen5Yanyan Yang6Furong Huang7Opto-electronic Department of Jinan University, Guangzhou 510632, ChinaSouth China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, ChinaOpto-electronic Department of Jinan University, Guangzhou 510632, ChinaOpto-electronic Department of Jinan University, Guangzhou 510632, ChinaOpto-electronic Department of Jinan University, Guangzhou 510632, ChinaOpto-electronic Department of Jinan University, Guangzhou 510632, ChinaSouth China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China; Corresponding authors.Opto-electronic Department of Jinan University, Guangzhou 510632, China; Corresponding authors.To monitor environmental water pollution effectively and meet human water needs, it is crucial to develop a fast, simple, and accurate method for monitoring chemical oxygen demand (COD) in various water systems. In this study, COD prediction models for different water systems were developed by combining near-infrared (NIR) spectroscopy with partial least squares regression (PLSR). Samples of wastewater, surface water, and seawater were collected from Guangzhou, Guangdong Province, China. Three pretreatment methods were used to preprocess the spectra in order to improve the accuracy and minimalism of the model. We investigate the performance of two variable selection algorithms, namely, binary gray wolf optimization (BGWO) and competitive adaptive reweighting sampling (CARS). The results show that both BGWO and CARS improved the performance of the model in terms of higher accuracy and less wavelength input; both of the combined model performances were better than that of PLSR alone, and CARS-PLSR achieved the best results. Using CARS-PLSR, surface water, wastewater, and seawater model inputs were reduced by 96 %, 96 %, and 82 % as compared to the PLSR results, respectively, and the testing sets R2 reached 0.860, 0.815, and 0.692, respectively. The spectral variable selection algorithm could identify the important spectral variables between COD content and NIR spectra in three water systems, thereby improving the accuracy and simplicity of the PLSR model for COD prediction. Our results have important practical value for predicting COD content in different water systems by NIR spectroscopy.http://www.sciencedirect.com/science/article/pii/S0147651322008041Near-infrared spectroscopyWater systemsChemical oxygen demandSpectral variable selection |
| spellingShingle | Xueqin Han Danping Xie Han Song Jinfang Ma Yongxin Zhou Jiaze Chen Yanyan Yang Furong Huang Estimation of chemical oxygen demand in different water systems by near-infrared spectroscopy Ecotoxicology and Environmental Safety Near-infrared spectroscopy Water systems Chemical oxygen demand Spectral variable selection |
| title | Estimation of chemical oxygen demand in different water systems by near-infrared spectroscopy |
| title_full | Estimation of chemical oxygen demand in different water systems by near-infrared spectroscopy |
| title_fullStr | Estimation of chemical oxygen demand in different water systems by near-infrared spectroscopy |
| title_full_unstemmed | Estimation of chemical oxygen demand in different water systems by near-infrared spectroscopy |
| title_short | Estimation of chemical oxygen demand in different water systems by near-infrared spectroscopy |
| title_sort | estimation of chemical oxygen demand in different water systems by near infrared spectroscopy |
| topic | Near-infrared spectroscopy Water systems Chemical oxygen demand Spectral variable selection |
| url | http://www.sciencedirect.com/science/article/pii/S0147651322008041 |
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