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f | 1 | { | f | 1 | { |
2 | "author": [ | 2 | "author": [ | ||
3 | "Hu, Yongmin", | 3 | "Hu, Yongmin", | ||
4 | "Morgenroth, Eberhard", | 4 | "Morgenroth, Eberhard", | ||
5 | "Jacquin, C\u00e9line" | 5 | "Jacquin, C\u00e9line" | ||
6 | ], | 6 | ], | ||
7 | "author_email": null, | 7 | "author_email": null, | ||
8 | "citation_publication": "Hu, Y., Morgenroth, E., & Jacquin, C. | 8 | "citation_publication": "Hu, Y., Morgenroth, E., & Jacquin, C. | ||
9 | (2025). Online monitoring of greywater reuse system using | 9 | (2025). Online monitoring of greywater reuse system using | ||
10 | excitation-emission matrix (EEM) and K-PARAFACs. Water Research, 268, | 10 | excitation-emission matrix (EEM) and K-PARAFACs. Water Research, 268, | ||
11 | 122604. https://doi.org/10.1016/j.watres.2024.122604\n", | 11 | 122604. https://doi.org/10.1016/j.watres.2024.122604\n", | ||
12 | "creator_user_id": "064a4293-f097-4005-98d5-65b49b35ccf3", | 12 | "creator_user_id": "064a4293-f097-4005-98d5-65b49b35ccf3", | ||
13 | "doi": "10.25678/000CBS", | 13 | "doi": "10.25678/000CBS", | ||
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21 | "license_id": null, | 21 | "license_id": null, | ||
22 | "license_title": null, | 22 | "license_title": null, | ||
23 | "maintainer": "Hu, Yongmin", | 23 | "maintainer": "Hu, Yongmin", | ||
24 | "maintainer_email": "Morgenroth, Eberhard | 24 | "maintainer_email": "Morgenroth, Eberhard | ||
25 | <Eberhard.Morgenroth@eawag.ch>", | 25 | <Eberhard.Morgenroth@eawag.ch>", | ||
26 | "metadata_created": "2024-10-30T07:53:22.827798", | 26 | "metadata_created": "2024-10-30T07:53:22.827798", | ||
t | 27 | "metadata_modified": "2024-10-30T07:56:47.452414", | t | 27 | "metadata_modified": "2024-10-30T07:56:48.506969", |
28 | "name": | 28 | "name": | ||
29 | onitoring-of-greywater-reuse-system-using-excitation-emission-matrix", | 29 | onitoring-of-greywater-reuse-system-using-excitation-emission-matrix", | ||
30 | "notes": "Code and data associated with the manuscript: Online | 30 | "notes": "Code and data associated with the manuscript: Online | ||
31 | monitoring of greywater reuse system using excitation-emission matrix | 31 | monitoring of greywater reuse system using excitation-emission matrix | ||
32 | (EEM) and K-PARAFACs. \r\nAbstract: \r\nA currently increasing | 32 | (EEM) and K-PARAFACs. \r\nAbstract: \r\nA currently increasing | ||
33 | interest in water reuse is met with the concern about water quality. | 33 | interest in water reuse is met with the concern about water quality. | ||
34 | Excitation-emission matrix (EEM) measurements, which are widely | 34 | Excitation-emission matrix (EEM) measurements, which are widely | ||
35 | implemented in laboratory analysis, emerge as a promising tool for | 35 | implemented in laboratory analysis, emerge as a promising tool for | ||
36 | characterizing both microbial and chemical water qualities in the | 36 | characterizing both microbial and chemical water qualities in the | ||
37 | online monitoring of water reuse systems. However, the robustness of | 37 | online monitoring of water reuse systems. However, the robustness of | ||
38 | EEM measurements has been rarely validated in actual online monitoring | 38 | EEM measurements has been rarely validated in actual online monitoring | ||
39 | campaigns where predictions are made for new samples independent of | 39 | campaigns where predictions are made for new samples independent of | ||
40 | those used to establish EEM analysis models, including the popular | 40 | those used to establish EEM analysis models, including the popular | ||
41 | parallel factor analysis (PARAFAC). In this study, two strategies of | 41 | parallel factor analysis (PARAFAC). In this study, two strategies of | ||
42 | conducting PARAFAC were examined for the online monitoring of a | 42 | conducting PARAFAC were examined for the online monitoring of a | ||
43 | greywater reuse system using two EEM datasets from two monitoring | 43 | greywater reuse system using two EEM datasets from two monitoring | ||
44 | periods for model establishment and model testing respectively. With | 44 | periods for model establishment and model testing respectively. With | ||
45 | the first strategy that is commonly used in laboratory analyses, an | 45 | the first strategy that is commonly used in laboratory analyses, an | ||
46 | entire EEM datasets from one period was used to establish one PARAFAC | 46 | entire EEM datasets from one period was used to establish one PARAFAC | ||
47 | model, and the maximum fluorescence intensity (Fmax) of a PARAFAC | 47 | model, and the maximum fluorescence intensity (Fmax) of a PARAFAC | ||
48 | component was used to predict total cell count (TCC) in another | 48 | component was used to predict total cell count (TCC) in another | ||
49 | period. However, under the disturbance of dissolved organic matter | 49 | period. However, under the disturbance of dissolved organic matter | ||
50 | (DOM) fluorescence in the background, Fmax gave unreliable predictions | 50 | (DOM) fluorescence in the background, Fmax gave unreliable predictions | ||
51 | in model testing. To address this problem, a second and novel strategy | 51 | in model testing. To address this problem, a second and novel strategy | ||
52 | was proposed using an EEM clustering and PARAFAC component shift | 52 | was proposed using an EEM clustering and PARAFAC component shift | ||
53 | mining technique. This unsupervised algorithm, named K-PARAFACs, | 53 | mining technique. This unsupervised algorithm, named K-PARAFACs, | ||
54 | automatically groups EEMs into K clusters and on each cluster | 54 | automatically groups EEMs into K clusters and on each cluster | ||
55 | establishes a cluster-specific PARAFAC model with distinct component | 55 | establishes a cluster-specific PARAFAC model with distinct component | ||
56 | shapes. With this method, multiple PARAFAC models were established on | 56 | shapes. With this method, multiple PARAFAC models were established on | ||
57 | one EEM dataset, with each model representing samples with certain TCC | 57 | one EEM dataset, with each model representing samples with certain TCC | ||
58 | ranges and DOM compositions. In model testing, these cluster-specific | 58 | ranges and DOM compositions. In model testing, these cluster-specific | ||
59 | PARAFAC models served as EEM classifiers. A new sample was not | 59 | PARAFAC models served as EEM classifiers. A new sample was not | ||
60 | characterized by Fmax but by the cluster-specific model that best | 60 | characterized by Fmax but by the cluster-specific model that best | ||
61 | fitted the EEM signal of the sample with the least numerical error. | 61 | fitted the EEM signal of the sample with the least numerical error. | ||
62 | The proposed strategy demonstrates its robustness by successfully | 62 | The proposed strategy demonstrates its robustness by successfully | ||
63 | predicting the TCC trend in test datasets. Our findings suggest that | 63 | predicting the TCC trend in test datasets. Our findings suggest that | ||
64 | K-PARAFACs is a promising tool that enables robust qualitative | 64 | K-PARAFACs is a promising tool that enables robust qualitative | ||
65 | monitoring of water reuse systems with background DOM variability.", | 65 | monitoring of water reuse systems with background DOM variability.", | ||
66 | "num_resources": 4, | 66 | "num_resources": 4, | ||
67 | "num_tags": 7, | 67 | "num_tags": 7, | ||
68 | "open_data": "true", | 68 | "open_data": "true", | ||
69 | "organization": { | 69 | "organization": { | ||
70 | "approval_status": "approved", | 70 | "approval_status": "approved", | ||
71 | "created": "2019-09-18T14:11:48.780355", | 71 | "created": "2019-09-18T14:11:48.780355", | ||
72 | "description": "We are focused on innovative research on treatment | 72 | "description": "We are focused on innovative research on treatment | ||
73 | of water and wastewater. There is a main focus on membrane technology | 73 | of water and wastewater. There is a main focus on membrane technology | ||
74 | and adsorption, but also other technologies are investigated, e.g. | 74 | and adsorption, but also other technologies are investigated, e.g. | ||
75 | biological treatment.", | 75 | biological treatment.", | ||
76 | "id": "3dc19a61-a20a-4455-8243-913ea6d734b6", | 76 | "id": "3dc19a61-a20a-4455-8243-913ea6d734b6", | ||
77 | "image_url": | 77 | "image_url": | ||
78 | issenstransfer/Kompetenzzentrum_Trinkwasser/birs_revital_600x300.jpg", | 78 | issenstransfer/Kompetenzzentrum_Trinkwasser/birs_revital_600x300.jpg", | ||
79 | "is_organization": true, | 79 | "is_organization": true, | ||
80 | "name": "drinking-water", | 80 | "name": "drinking-water", | ||
81 | "state": "active", | 81 | "state": "active", | ||
82 | "title": "Drinking Water", | 82 | "title": "Drinking Water", | ||
83 | "type": "organization" | 83 | "type": "organization" | ||
84 | }, | 84 | }, | ||
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207 | { | 207 | { | ||
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224 | "name": "Microbial quality", | 224 | "name": "Microbial quality", | ||
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227 | }, | 227 | }, | ||
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232 | "state": "active", | 232 | "state": "active", | ||
233 | "vocabulary_id": null | 233 | "vocabulary_id": null | ||
234 | }, | 234 | }, | ||
235 | { | 235 | { | ||
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237 | "id": "18dd04d5-bcbb-409f-944d-defbcfabfe99", | 237 | "id": "18dd04d5-bcbb-409f-944d-defbcfabfe99", | ||
238 | "name": "PARAFAC", | 238 | "name": "PARAFAC", | ||
239 | "state": "active", | 239 | "state": "active", | ||
240 | "vocabulary_id": null | 240 | "vocabulary_id": null | ||
241 | }, | 241 | }, | ||
242 | { | 242 | { | ||
243 | "display_name": "UV absorbance", | 243 | "display_name": "UV absorbance", | ||
244 | "id": "23d04160-37cd-4236-857e-be7005974eb0", | 244 | "id": "23d04160-37cd-4236-857e-be7005974eb0", | ||
245 | "name": "UV absorbance", | 245 | "name": "UV absorbance", | ||
246 | "state": "active", | 246 | "state": "active", | ||
247 | "vocabulary_id": null | 247 | "vocabulary_id": null | ||
248 | }, | 248 | }, | ||
249 | { | 249 | { | ||
250 | "display_name": "Water reuse", | 250 | "display_name": "Water reuse", | ||
251 | "id": "5b6b61bd-0df3-4756-ae49-06ccbc0b976b", | 251 | "id": "5b6b61bd-0df3-4756-ae49-06ccbc0b976b", | ||
252 | "name": "Water reuse", | 252 | "name": "Water reuse", | ||
253 | "state": "active", | 253 | "state": "active", | ||
254 | "vocabulary_id": null | 254 | "vocabulary_id": null | ||
255 | } | 255 | } | ||
256 | ], | 256 | ], | ||
257 | "tags_string": "Water reuse,Online monitoring,Microbial | 257 | "tags_string": "Water reuse,Online monitoring,Microbial | ||
258 | quality,PARAFAC,Dissolved organic matter,Excitation emission matrix,UV | 258 | quality,PARAFAC,Dissolved organic matter,Excitation emission matrix,UV | ||
259 | absorbance", | 259 | absorbance", | ||
260 | "taxa": [], | 260 | "taxa": [], | ||
261 | "taxa_generic": [], | 261 | "taxa_generic": [], | ||
262 | "timerange": [ | 262 | "timerange": [ | ||
263 | "2022-01 TO 2022-02", | 263 | "2022-01 TO 2022-02", | ||
264 | "2022-09 TO 2022-10" | 264 | "2022-09 TO 2022-10" | ||
265 | ], | 265 | ], | ||
266 | "title": "Data for: Online monitoring of greywater reuse system | 266 | "title": "Data for: Online monitoring of greywater reuse system | ||
267 | using excitation-emission matrix (EEM) and K-PARAFACs", | 267 | using excitation-emission matrix (EEM) and K-PARAFACs", | ||
268 | "type": "dataset", | 268 | "type": "dataset", | ||
269 | "url": "https://doi.org/10.25678/000CBS/", | 269 | "url": "https://doi.org/10.25678/000CBS/", | ||
270 | "variables": [ | 270 | "variables": [ | ||
271 | "fluorescence", | 271 | "fluorescence", | ||
272 | "dissolved_organic_carbon", | 272 | "dissolved_organic_carbon", | ||
273 | "flow_cytometric_cell_counts" | 273 | "flow_cytometric_cell_counts" | ||
274 | ], | 274 | ], | ||
275 | "version": null | 275 | "version": null | ||
276 | } | 276 | } |