Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

doi:10.1007/s11783-023-1721-1

Frontiers of Environmental Science & Engineering

2023, Vol. 17

Issue (10): 121 https://doi.org/10.1007/s11783-023-1721-1

本期目录

Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

Haoyang Xian¹, Pinjing He^1,^2,³, Dongying Lan¹, Yaping Qi¹, Ruiheng Wang¹, Fan Lü^1,^2,³, Hua Zhang^1,^2,³(

), Jisheng Long⁴(

)

¹. Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
². Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
³. Shanghai Engineering Research Center of Multi-source Solid Wastes Co-processing and Energy Utilization, Shanghai 200092, China
⁴. Shanghai SUS Environment Co., Ltd., Shanghai 201703, China

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Abstract：

● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste.

● Feature selection methods were used to improve models’ prediction accuracy.

● The best model predicted C, H, and N contents with accuracy R ² ≥ 0.93, 0.87, 0.97.

● Some suitable models showed insensitivity to spectral noise.

● Under moisture interference, the models still had good prediction performance.

Elemental composition is a key parameter in solid waste treatment and disposal. This study has proposed a method based on infrared spectroscopy and machine learning algorithms that can rapidly predict the elemental composition (C, H, N, S) of solid waste. Both noise and moisture spectral interference that may occur in practical application are investigated. By comparing two feature selection methods and five machine learning algorithms, the most suitable models are selected. Moreover, the impacts of noise and moisture on the models are discussed, with paper, plastic, textiles, wood, and leather as examples of recyclable waste components. The results show that the combination of the feature selection and K-nearest neighbor (KNN) approaches exhibits the best prediction performance and generalization ability. Particularly, the coefficient of determination (R²) of the validation set, cross validation and test set are higher than 0.93, 0.89, and 0.97 for predicting the C, H, and N contents, respectively. Further, KNN is less sensitive to noise. Under moisture interference, the combination of feature selection and support vector regression or partial least-squares regression shows satisfactory results. Therefore, the elemental compositions of solid waste are quickly and accurately predicted under noise and moisture disturbances using infrared spectroscopy and machine learning algorithms.

Key words： Elemental composition Infrared spectroscopy Machine learning Moisture interference Solid waste Spectral noise

收稿日期: 2022-05-28 出版日期: 2023-04-28

Corresponding Author(s): Hua Zhang,Jisheng Long

引用本文:

. [J]. Frontiers of Environmental Science & Engineering, 2023, 17(10): 121.
Haoyang Xian, Pinjing He, Dongying Lan, Yaping Qi, Ruiheng Wang, Fan Lü, Hua Zhang, Jisheng Long. Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning. Front. Environ. Sci. Eng., 2023, 17(10): 121.

链接本文:

https://academic.hep.com.cn/fese/CN/10.1007/s11783-023-1721-1
https://academic.hep.com.cn/fese/CN/Y2023/V17/I10/121

Tab.1

Fig.1

Fig.2

Element	Model		Validation		Test		Validation CV		Test CV
Element	Algorithm	Feature selection	R²	RMSE	R²	RMSE	R²	RMSE	R²	RMSE
C	RF	None	0.88	4.21	0.90	4.52	0.89	4.22	0.93	3.71
		SPA	0.88	4.17	0.88	4.78	0.88	4.83	0.91	4.26
		CARS	0.86	4.43	0.88	4.81	0.87	4.67	0.89	4.61
	SVR	None	0.89	3.97	0.85	5.52	0.86	5.01	0.87	5.13
		SPA	0.79	5.60	0.77	6.78	0.78	6.23	0.79	6.61
		CARS	0.84	4.81	0.83	6.19	0.82	5.67	0.83	5.92
	PLSR	None	0.80	5.25	0.72	7.59	0.74	6.61	0.79	6.46
		SPA	0.61	5.46	0.63	8.67	0.61	8.08	0.63	8.68
		CARS	0.77	5.60	0.78	6.74	0.73	6.66	0.79	6.63
	XGBOOST	None	0.89	3.91	0.88	4.90	0.88	4.41	0.92	4.01
		SPA	0.90	3.80	0.89	4.64	0.89	4.26	0.92	4.04
		CARS	0.89	4.01	0.88	4.92	0.87	4.50	0.90	4.43
	KNN	None	0.93	3.11	0.91	4.27	0.92	3.52	0.91	4.09
		SPA	0.96	2.39	0.93	3.79	0.94	2.95	0.94	3.45
		CARS	0.95	2.56	0.92	3.82	0.93	3.28	0.94	3.31
H	RF	None	0.84	0.82	0.77	1.19	0.82	0.93	0.85	0.94
		SPA	0.84	0.81	0.82	1.04	0.87	0.90	0.85	0.96
		CARS	0.85	0.78	0.84	0.97	0.84	0.89	0.87	0.87
	SVR	None	0.94	0.49	0.83	1.01	0.84	0.90	0.84	0.99
		SPA	0.88	0.71	0.82	1.05	0.81	0.94	0.84	0.98
		CARS	0.93	0.53	0.84	0.99	0.86	0.86	0.86	0.93
	PLSR	None	0.77	0.95	0.59	1.56	0.67	1.27	0.69	1.37
		SPA	0.63	1.19	0.60	1.56	0.63	1.36	0.59	1.58
		CARS	0.78	0.92	0.73	1.29	0.70	1.22	0.74	1.25
	XGBOOST	None	0.88	0.70	0.76	1.20	0.85	0.86	0.84	0.98
		SPA	0.89	0.67	0.84	0.98	0.87	0.80	0.85	0.95
		CARS	0.88	0.71	0.82	1.04	0.87	0.80	0.80	1.10
	KNN	None	0.91	0.59	0.87	0.85	0.89	0.72	0.85	0.87
		SPA	0.96	0.41	0.83	0.98	0.90	0.70	0.83	0.97
		CARS	0.96	0.42	0.97	0.70	0.91	0.66	0.89	0.63
N	RF	None	0.87	1.73	0.90	1.55	0.90	1.63	0.92	1.37
		SPA	0.89	1.59	0.92	1.40	0.91	1.56	0.94	1.16
		CARS	0.90	1.56	0.92	1.45	0.91	1.56	0.94	1.17
	SVR	None	0.97	0.91	0.93	1.29	0.93	1.32	0.97	0.92
		SPA	0.85	1.88	0.92	1.32	0.91	1.59	0.92	1.30
		CARS	0.88	1.63	0.94	1.14	0.93	1.39	0.94	1.13
	PLSR	None	0.93	1.37	0.81	2.06	0.89	1.63	0.82	2.07
		SPA	0.74	2.56	0.80	2.25	0.78	2.40	0.80	2.26
		CARS	0.89	1.69	0.87	1.85	0.89	1.64	0.89	1.70
	XGBOOST	None	0.94	1.19	0.88	1.68	0.92	1.47	0.91	1.43
		SPA	0.95	1.02	0.92	1.36	0.94	1.27	0.95	1.09
		CARS	0.94	1.24	0.90	1.61	0.93	1.29	0.93	1.34
	KNN	None	0.98	0.51	0.98	0.61	0.97	0.84	0.98	0.52
		SPA	0.99	0.44	0.97	0.78	0.98	0.75	0.98	0.56
		CARS	0.98	0.56	0.97	0.70	0.98	0.68	0.98	0.63
S	RF	None	0.89	0.24	0.79	0.30	0.84	0.28	0.79	0.30
		SPA	0.86	0.27	0.76	0.32	0.82	0.29	0.78	0.31
		CARS	0.89	0.23	0.73	0.34	0.84	0.28	0.76	0.32
	SVR	None	0.92	0.20	0.86	0.24	0.89	0.23	0.87	0.24
		SPA	0.91	0.21	0.79	0.29	0.87	0.25	0.79	0.30
		CARS	0.94	0.18	0.80	0.29	0.88	0.24	0.81	0.28
	PLSR	None	0.92	0.20	0.54	0.41	0.84	0.28	0.66	0.36
		SPA	0.84	0.29	0.72	0.35	0.79	0.32	0.72	0.35
		CARS	0.91	0.21	0.76	0.31	0.86	0.26	0.79	0.30
	XGBOOST	None	0.90	0.23	0.77	0.31	0.85	0.26	0.79	0.31
		SPA	0.88	0.24	0.76	0.32	0.85	0.27	0.79	0.30
		CARS	0.84	0.26	0.79	0.51	0.86	0.26	0.76	0.31
	KNN	None	0.95	0.15	0.83	0.26	0.89	0.23	0.84	0.25
		SPA	0.92	0.20	0.78	0.30	0.87	0.25	0.81	0.29
		CARS	0.93	0.18	0.77	0.31	0.89	0.23	0.78	0.30

Tab.2

Fig.3

Element	Model		Validation		Test		Validation CV		Test CV
Element	Algorithm	Feature selection	R²	RMSE	R²	RMSE	R²	RMSE	R²	RMSE
C	RF	None	0.90	3.21	0.90	3.37	0.90	3.18	0.90	3.31
		SPA	0.90	3.25	0.89	3.46	0.90	3.19	0.89	3.45
		CARS	0.90	3.22	0.89	3.39	0.89	3.18	0.90	3.32
	SVR	None	0.93	2.77	0.92	2.97	0.91	2.87	0.92	2.97
		SPA	0.93	2.76	0.91	3.17	0.91	2.95	0.91	3.12
		CARS	0.93	2.75	0.92	3.04	0.91	2.90	0.92	3.03
	PLSR	None	0.93	2.75	0.91	3.17	0.90	3.08	0.90	3.35
		SPA	0.91	2.98	0.88	3.57	0.89	3.35	0.89	3.53
		CARS	0.91	2.44	0.93	2.78	0.91	3.14	0.87	3.71
	XGBOOST	None	0.91	3.16	0.87	3.76	0.90	3.18	0.88	3.55
		SPA	0.92	2.88	0.88	3.59	0.89	3.22	0.89	3.39
		CARS	0.91	3.14	0.87	3.71	0.89	3.26	0.89	3.52
	KNN	None	0.88	3.53	0.87	3.66	0.88	3.51	0.90	3.35
		SPA	0.90	3.35	0.88	3.58	0.88	3.48	0.88	3.52
		CARS	0.90	3.26	0.88	3.65	0.87	3.56	0.89	3.45
H	RF	None	0.88	0.46	0.88	0.48	0.88	0.45	0.88	0.47
		SPA	0.89	0.46	0.87	0.50	0.88	0.46	0.87	0.49
		CARS	0.87	0.49	0.87	0.50	0.86	0.49	0.86	0.50
	SVR	None	0.87	0.48	0.87	0.48	0.89	0.44	0.87	0.48
		SPA	0.91	0.59	0.87	0.43	0.91	0.39	0.91	0.42
		CARS	0.85	0.50	0.86	0.50	0.88	0.45	0.87	0.50
	PLSR	None	0.92	0.39	0.87	0.49	0.88	0.45	0.89	0.45
		SPA	0.91	0.41	0.86	0.51	0.87	0.48	0.86	0.51
		CARS	0.94	0.33	0.93	0.36	0.92	0.36	0.93	0.36
	XGBOOST	None	0.91	0.41	0.86	0.51	0.89	0.45	0.86	0.51
		SPA	0.90	0.44	0.86	0.51	0.88	0.46	0.86	0.51
		CARS	0.89	0.45	0.83	0.56	0.86	0.49	0.86	0.52
	KNN	None	0.90	0.44	0.81	0.59	0.88	0.46	0.87	0.48
		SPA	0.90	0.43	0.85	0.53	0.88	0.47	0.87	0.51
		CARS	0.90	0.43	0.85	0.59	0.88	0.47	0.86	0.52
N	RF	None	0.95	1.36	0.93	1.56	0.95	1.35	0.94	1.42
		SPA	0.97	1.02	0.93	1.62	0.96	1.13	0.92	1.52
		CARS	0.96	1.26	0.94	1.49	0.96	1.25	0.94	1.42
	SVR	None	0.97	0.95	0.97	0.98	0.98	0.90	0.98	0.91
		SPA	0.99	0.76	0.99	0.78	0.98	0.77	0.98	0.78
		CARS	0.98	0.84	0.98	0.81	0.98	0.84	0.98	0.80
	PLSR	None	0.95	1.10	0.95	1.40	0.95	1.38	0.95	1.33
		SPA	0.91	1.93	0.91	1.85	0.91	1.78	0.91	1.84
		CARS	0.96	1.15	0.96	1.25	0.96	1.17	0.96	1.25
	XGBOOST	None	0.96	1.26	0.92	1.67	0.95	1.28	0.90	1.82
		SPA	0.97	1.00	0.94	1.38	0.97	1.04	0.93	1.64
		CARS	0.96	1.22	0.95	1.41	0.96	1.23	0.95	1.30
	KNN	None	0.98	0.77	0.98	0.93	0.98	0.85	0.97	0.99
		SPA	0.99	0.71	0.98	0.84	0.98	0.77	0.98	0.85
		CARS	0.98	0.85	0.98	0.95	0.98	0.84	0.98	0.82
S	RF	None	0.64	0.32	0.59	0.29	0.56	0.33	0.69	0.27
		SPA	0.48	0.39	0.50	0.33	0.47	0.37	0.60	0.31
		CARS	0.63	0.33	0.60	0.30	0.61	0.32	0.70	0.28
	SVR	None	0.71	0.29	0.62	0.29	0.76	0.24	0.86	0.19
		SPA	0.93	0.14	0.91	0.14	0.92	0.13	0.93	0.13
		CARS	0.89	0.17	0.87	0.17	0.89	0.16	0.94	0.12
	PLSR	None	0.87	0.19	0.89	0.16	0.86	0.18	0.92	0.14
		SPA	0.69	0.30	0.80	0.22	0.76	0.26	0.69	0.28
		CARS	0.89	0.17	0.90	0.16	0.88	0.17	0.90	0.16
	XGBOOST	None	0.61	0.33	0.56	0.31	0.53	0.35	0.54	0.33
		SPA	0.65	0.32	0.64	0.27	0.55	0.35	0.49	0.36
		CARS	0.72	0.29	0.72	0.25	0.63	0.32	0.74	0.26
	KNN	None	0.64	0.26	0.47	0.34	0.55	0.35	0.59	0.27
		SPA	0.59	0.33	0.54	0.29	0.64	0.30	0.45	0.34
		CARS	0.73	0.23	0.59	0.29	0.70	0.26	0.45	0.34

Tab.3

Fig.4

1	O AdedejiZ H Wang (2019). Intelligent waste classification system using deep learning convolutional neural network. In: 2nd international conference on sustainable materials processing and manufacturing (SMPM). Sun City, South Africa: Procedia Manufacturing, 607–612
2	H Altun , A Bilgil , B C Fidan . (2007). Treatment of multi-dimensional data to enhance neural network estimators in regression problems. Expert Systems with Applications, 32(2): 599–605 https://doi.org/10.1016/j.eswa.2006.01.054
3	L Breiman . (2001). Random forests. Machine Learning, 45(1): 5–32 https://doi.org/10.1023/A:1010933404324
4	S Chakraborty , B Li , S Deb , S Paul , D C Weindorf , B S Das . (2017). Predicting soil arsenic pools by visible near infrared diffuse reflectance spectroscopy. Geoderma, 296: 30–37 https://doi.org/10.1016/j.geoderma.2017.02.015
5	K Chen , Y Peng , S Lu , B Lin , X Li . (2021). Bagging based ensemble learning approaches for modeling the emission of PCDD/Fs from municipal solid waste incinerators. Chemosphere, 274: 129802 https://doi.org/10.1016/j.chemosphere.2021.129802
6	T Chen, C Guestrin (2016). Assoc Comp M XGBoost: a Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, USA: Association for Computing Machinery, 785–794
7	M Y Chin , C T Lee , K S Woon . (2022). Policy-driven municipal solid waste management assessment using relative quadrant eco-efficiency: a case study in Malaysia. Journal of Environmental Management, 323: 116238 https://doi.org/10.1016/j.jenvman.2022.116238
8	A Demetrious , K Verghese , P Stasinopoulos , E Crossin . (2018). Comparison of alternative methods for managing the residual of material recovery facilities using life cycle assessment. Resources, Conservation and Recycling, 136: 33–45 https://doi.org/10.1016/j.resconrec.2018.03.024
9	M El-Fadel , E Bou-Zeid , W Chahine , B Alayli . (2002). Temporal variation of leachate quality from pre-sorted and baled municipal solid waste with high organic and moisture content. Waste Management (New York, N.Y.), 22(3): 269–282 https://doi.org/10.1016/S0956-053X(01)00040-X
10	X P Feng , H M Chen , Y Chen , C Zhang , X D Liu , H Y Weng , S P Xiao , P C Nie , Y He . (2019). Rapid detection of cadmium and its distribution in Miscanthus sacchariflorus based on visible and near-infrared hyperspectral imaging. Science of the Total Environment, 659: 1021–1031 https://doi.org/10.1016/j.scitotenv.2018.12.458
11	D Garcés , E Díaz , H Sastre , S Ordóñez , J M González-Lafuente . (2016). Evaluation of the potential of different high calorific waste fractions for the preparation of solid recovered fuels. Waste Management (New York, N.Y.), 47: 164–173 https://doi.org/10.1016/j.wasman.2015.08.029
12	M Govindappa , S Tejashree , V Thanuja , B Hemashekhar , C Srinivas , O Nasif , A Pugazhendhi , V B Raghavendra . (2021). Pomegranate fruit fleshy pericarp mediated silver nanoparticles possessing antimicrobial, antibiofilm formation, antioxidant, biocompatibility and anticancer activity. Journal of Drug Delivery Science and Technology, 61: 102289 https://doi.org/10.1016/j.jddst.2020.102289
13	M G Goydaragh , R Taghizadeh-Mehrjardi , A A Jafarzadeh , J Triantafilis , M Lado . (2021). Using environmental variables and Fourier transform infrared spectroscopy to predict soil organic carbon. Catena, 202: 105280 https://doi.org/10.1016/j.catena.2021.105280
14	F S Higashikawa, C A Silva, C A Nunes, M A Sánchez-Monedero (2014). Fourier transform infrared spectroscopy and partial least square regression for the prediction of substrate maturity indexes. Science of the Total Environment, 470–471: 536–542 https://doi.org/10.1016/j.scitotenv.2013.09.065
15	C C Holmes , N M Adams . (2003). Likelihood inference in nearest-neighbour classification models. Biometrika, 90(1): 99–112 https://doi.org/10.1093/biomet/90.1.99
16	M M Hoque , M T U Rahman . (2020). Landfill area estimation based on solid waste collection prediction using ANN model and final waste disposal options. Journal of Cleaner Production, 256: 120387 https://doi.org/10.1016/j.jclepro.2020.120387
17	Y C Huang , J Y Chen , Q N Duan , Y J Feng , R Luo , W J Wang , F L Liu , S F Bi , J C Lee . (2022). A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning. Frontiers of Environmental Science and Engineering, 16(3): 38 https://doi.org/10.1007/s11783-021-1472-9
18	Q H Jiang , Y Y Chen , L Guo , T Fei , K Qi . (2016). Estimating soil organic carbon of cropland soil at different levels of soil moisture using VIS-NIR spectroscopy. Remote Sensing (Basel), 8(9): 755 https://doi.org/10.3390/rs8090755
19	L Kandlbauer , K Khodier , D Ninevski , R Sarc . (2021). Sensor-based particle size determination of shredded mixed commercial waste based on two-dimensional images. Waste Management (New York, N.Y.), 120: 784–794 https://doi.org/10.1016/j.wasman.2020.11.003
20	M Kannangara , R Dua , L Ahmadi , F Bensebaa . (2018). Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches. Waste Management (New York, N.Y.), 74: 3–15 https://doi.org/10.1016/j.wasman.2017.11.057
21	N Kardani , A N Zhou , M Nazem , X S Lin . (2021). Modelling of municipal solid waste gasification using an optimised ensemble soft computing model. Fuel, 289: 119903 https://doi.org/10.1016/j.fuel.2020.119903
22	N Karimi , K T W Ng , A Richter . (2021). Prediction of fugitive landfill gas hotspots using a random forest algorithm and Sentinel-2 data. Sustainable Cities and Society, 73: 103097 https://doi.org/10.1016/j.scs.2021.103097
23	G Kaur , D Kaur , S K Kansal , M Garg , M Krishania . (2022). Potential cocoa butter substitute derived from mango seed kernel. Food Chemistry, 372: 131244 https://doi.org/10.1016/j.foodchem.2021.131244
24	H Li , Y Liang , Q Xu , D Cao . (2009). Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta, 648(1): 77–84 https://doi.org/10.1016/j.aca.2009.06.046
25	H Y Li , S Y Jia , Z C Le . (2019a). Quantitative analysis of soil total nitrogen using hyperspectral imaging technology with extreme learning machine. Sensors (Basel), 19(20): 4355 https://doi.org/10.3390/s19204355
26	R Li , M Gong , B W Biney , K Chen , W Xia , H Liu , A Guo . (2022a). Three-stage pretreatment of food waste to improve fuel characteristics and incineration performance with recovery of process by-products. Fuel, 330: 125655 https://doi.org/10.1016/j.fuel.2022.125655
27	X Li , Y Ma , M Zhang , M Zhan , P Wang , X Lin , T Chen , S Lu , J Yan . (2019b). Study on the relationship between waste classification, combustion condition and dioxin emission from waste incineration. Waste Disposal & Sustainable Energy, 1(2): 91–98 https://doi.org/10.1007/s42768-019-00009-9
28	Y Li , Z Wang , B Guan . (2022b). Separation and identification of nanoplastics in tap water. Environmental Research, 204: 112134 https://doi.org/10.1016/j.envres.2021.112134
29	W J Lu , W Z Huo , H Gulina , C Pan . (2022). Development of machine learning multi-city model for municipal solid waste generation prediction. Frontiers of Environmental Science and Engineering, 16(9): 119 https://doi.org/10.1007/s11783-022-1551-6
30	S M Lundberg, S I Lee (2017). A unified approach to interpreting model predictions. In: Proceedings of the 31st international conference on neural information processing systems. Long Beach, USA: Curran Associates Inc., 4768–4777
31	A P M Michel , A E Morrison , V L Preston , C T Marx , B C Colson , H K White . (2020). Rapid identification of marine plastic debris via spectroscopic techniques and machine learning classifiers. Environmental Science & Technology, 54(17): 10630–10637 https://doi.org/10.1021/acs.est.0c02099
32	M E S Mirghani , N A Kabbashi , M Z Alam , I Y Qudsieh , M F R Alkatib . (2011). Rapid method for the determination of moisture content in biodiesel using FTIR spectroscopy. Journal of the American Oil Chemists’ Society, 88(12): 1897–1904 https://doi.org/10.1007/s11746-011-1866-0
33	A Nzihou (2020). Handbook on Characterization of Biomass, Biowaste and Related By-Products. Cham, Switzerland: Springer Cham
34	A Paul , L Wander , R Becker , C Goedecke , U Braun . (2019). High-throughput NIR spectroscopic (NIRS) detection of microplastics in soil. Environmental Science and Pollution Research, 26(8): 7364–7374 https://doi.org/10.1007/s11356-018-2180-2
35	T Peršak , B Viltuznik , J Hernavs , S Klancnik . (2020). Vision-based sorting systems for transparent plastic granulate. Applied Sciences, 10(12): 4269 https://doi.org/10.3390/app10124269
36	M H Ren , H J Zhang , Y Fan , H Q Zhou , R Cao , Y Gao , J P Chen . (2021). Suppressing the formation of chlorinated aromatics by inhibitor sodium thiocyanate in solid waste incineration process. Science of the Total Environment, 798: 149154 https://doi.org/10.1016/j.scitotenv.2021.149154
37	M Said , M Amr , Y Sabry , D Khalil , A Wahba . (2021). Plastic sorting based on MEMS FTIR spectral chemometrics sensing. Conference on Optical Sensing and Detection VI, Proceedings of SPIE, 11354: 113540J https://doi.org/10.1117/12.2555876
38	A J Smola , B Schölkopf . (2004). A tutorial on support vector regression. Statistics and Computing, 14(3): 199–222 https://doi.org/10.1023/B:STCO.0000035301.49549.88
39	J Tao , R Liang , J Li , B Yan , G Chen , Z Cheng , W Li , F Lin , L Hou . (2020). Fast characterization of biomass and waste by infrared spectra and machine learning models. Journal of Hazardous Materials, 387: 121723 https://doi.org/10.1016/j.jhazmat.2019.121723
40	C M Vong , W F Ip , C C Chiu , P K Wong . (2015). Imbalanced learning for air pollution by meta-cognitive online sequential extreme learning machine. Cognitive Computation, 7(3): 381–391 https://doi.org/10.1007/s12559-014-9301-0
41	L Wang , R Wang . (2022). Determination of soil pH from Vis-NIR spectroscopy by extreme learning machine and variable selection: a case study in lime concretion black soil. Spectrochimica Acta. Part A: Molecular and Biomolecular Spectroscopy, 283: 121707 https://doi.org/10.1016/j.saa.2022.121707
42	Y Wang , Y Shi , J Zhou , J Zhao , T Maraseni , G Qian . (2021). Implementation effect of municipal solid waste mandatory sorting policy in Shanghai. Journal of Environmental Management, 298: 113512 https://doi.org/10.1016/j.jenvman.2021.113512
43	Z Wang , B Peng , Y Huang , G Sun . (2019). Classification for plastic bottles recycling based on image recognition. Waste Management (New York, N.Y.), 88: 170–181 https://doi.org/10.1016/j.wasman.2019.03.032
44	S Wold , M Sjöström , L Eriksson . (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2): 109–130 https://doi.org/10.1016/S0169-7439(01)00155-1
45	S Xiao , H Dong , Y Geng , X Tian , C Liu , H Li . (2020). Policy impacts on municipal solid waste management in Shanghai: a system dynamics model analysis. Journal of Cleaner Production, 262: 121366 https://doi.org/10.1016/j.jclepro.2020.121366
46	S Xu , M Wang , X Shi , Q Yu , Z Zhang . (2021). Integrating hyperspectral imaging with machine learning techniques for the high-resolution mapping of soil nitrogen fractions in soil profiles. Science of the Total Environment, 754: 142135 https://doi.org/10.1016/j.scitotenv.2020.142135
47	Y Xu , J Liu , Y Sun , S Chen , X Miao . (2023). Fast detection of volatile fatty acids in biogas slurry using NIR spectroscopy combined with feature wavelength selection. Science of the Total Environment, 857: 159282 https://doi.org/10.1016/j.scitotenv.2022.159282
48	B Yan , R Liang , B Li , J Tao , G Chen , Z Cheng , Z Zhu , X Li . (2021). Fast identification and characterization of residual wastes via laser-induced breakdown spectroscopy and machine learning. Resources, Conservation and Recycling, 174: 105851 https://doi.org/10.1016/j.resconrec.2021.105851
49	C Zhang , F Liu , Y He . (2018). Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis. Scientific Reports, 8(1): 2166 https://doi.org/10.1038/s41598-018-20270-y
50	D Q Zhang , S K Tan , R M Gersberg . (2010). Municipal solid waste management in China: status, problems and challenges. Journal of Environmental Management, 91(8): 1623–1633 https://doi.org/10.1016/j.jenvman.2010.03.012
51	Y Zhang , S Kang , S Allen , D Allen , T Gao , M Sillanpää . (2020). Atmospheric microplastics: a review on current status and perspectives. Earth-Science Reviews, 203: 103118 https://doi.org/10.1016/j.earscirev.2020.103118
52	K Zheng , Q Li , J Wang , J Geng , P Cao , T Sui , X Wang , Y Du . (2012). Stability competitive adaptive reweighted sampling (SCARS) and its applications to multivariate calibration of NIR spectra. Chemometrics and Intelligent Laboratory Systems, 112: 48–54 https://doi.org/10.1016/j.chemolab.2012.01.002
53	H Zou , S Huang , M Ren , J Liu , F Evrendilek , W Xie , G Zhang . (2022). Efficiency, by-product valorization, and pollution control of co-pyrolysis of textile dyeing sludge and waste solid adsorbents: their atmosphere, temperature, and blend ratio dependencies. Science of the Total Environment, 819: 152923 https://doi.org/10.1016/j.scitotenv.2022.152923
54	X Zou, J Zhao, M J W Povey, H Mei, H Mao (2010). Variables selection methods in near-infrared spectroscopy. Analytica Chimica Acta, 667(1–2): 14–32 https://doi.org/10.1016/j.aca.2010.03.048

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