Risks from pollutants remaining in the treated waste water or sludge - - PowerPoint PPT Presentation

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Risks from pollutants remaining in the treated waste water or sludge - - PowerPoint PPT Presentation

Oct 11, 2022 266 likes •545 views

Risks from pollutants remaining in the treated waste water or sludge Microplastics in sludge and non-target analysis of Nordic WWTPs Bert van Bavel, Amy Lusher, Rachel Hurley & Marianne Olsen UWWTD Evaluation and Fitness Check of the WFD and

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Risks from pollutants remaining in the treated waste water or sludge

Microplastics in sludge and non-target analysis of Nordic WWTPs

UWWTD Evaluation and Fitness Check of the WFD and FD Workshop on emerging pollutants Room C, DG Environment Avenue de Beaulieu 5, 1160 Brussels 24 October 2018 Bert van Bavel, Amy Lusher, Rachel Hurley & Marianne Olsen

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  • WWTPs receive microplastics in the influent

from a wide variety of potential sources including:

  • Fibres from (domestic/industrial) textile

washing

  • Road runoff
  • Plastics in personal care products
  • Plastics from industrial effluents
  • WWTPs are capable of trapping a large

proportion of microplastics – up to 99%

  • However, many of these particles are

concentrated into the sludge phase1.

  • This leads to an enrichment of sewage

sludge with microplastic particles

1 Carr et al. 2016; Water Res. vol. 91

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  • Final sludge is often applied to agricultural soils

as a fertiliser.

  • Current estimates suggest that 63 000 – 430

000 tons of microplastic are added to European farmlands each year1.

  • Application of sludge from municipal WWTPs

to agricultural land is likely to represent a major input of MPs to soils.

  • These particles may accumulate in soil or be

transferred through runoff and erosion to aquatic environments.

1 Nizzetto et al. 2016; ES&T vol. 50

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Question: To what extent do WWTPs in Norway contribute to the number of MPs released to the envrionment Objective 1: Characterise MPs in sewage sludge Objective 2: Understand the implications of MP/sludge application

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SLIDE 5
  • Sludge samples collected from 8 WWTPs

across Norway

  • Final (treated) sludge was collected from all

WWTP, except Tomasfjord and Linnes.

  • The selected WWTP cover a range of

wastewater and sludge treatment processes,

  • Samples were collected as 100g samples

taken in triplicate across 3-10 days.

  • These were used to produce a composite

10g samples at NIVA (three replicates).

  • Two periods were sampled at Bekkelaget and

VEAS.

  • These were intended to capture dry and

wet weather conditions

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SLIDE 6

Wet weight Average: 1 946 particles kg-1 (464-5 792) Dry weight Average: 6 077 particles kg-1 (1 701-19 837) It is crucial to standardise results for moisture content.

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SLIDE 7
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SLIDE 8

Potential sources of microplastic to WWTPs

  • Attributing potential sources to microplastic

contamination is complex.

  • Across all WWTP, 37% of MPs were beads. These

may come from personal care products, or have an industrial source.

  • 29% of MPs were fibres, which likely derive from the

washing of synthetic textiles.

  • It is very difficult to identify potential sources of

fragments, as WWTPs may also convert these (fragment them further etc.)

  • A small number of black, rubbery particles may be

derived from car tyre wear.

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SLIDE 9
  • Lower limit: 50 µm
  • Average particle size: 644 µm; D50:

297 µm

  • Particles concentrated in finest size

fraction, indicating a potential underestimation of total microplastic content

  • Largest microplastics were generally

fibres, with small diameters

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SLIDE 10

Considerations when interpreting the data

  • Baseline survey that only presents a snapshot
  • f microplastic abundance in sludge during the

sampling periods.

  • Microplastics in sludge material are likely to be
  • heterogeneous. This data is based on

composite samples from 3-10 days.

  • The study does not account for any temporal

variability associated with seasonal variations, weather influence on inflow etc.

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SLIDE 11

Based on this snapshot:

  • On average, 181 679 012 microplastic

particles captured by one WWTP and transferred into the sludge phase each day

  • On average, 1316 MPs per individual

per day (median: 383)

  • Extrapolated to Norwegian

population:

  • Approx. 6.8 billion microplastics per day
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SLIDE 12

446 bn MPs spread on agricultural soils 27 bn MPs added to green areas 112 bn MPs sent to soil producers

584 bn MPs released into the environment via sewage sludge each year

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SLIDE 13

Monitoring in mussels

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SLIDE 14

Monitoring in mussels

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SLIDE 15
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SLIDE 16

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Contaminants associated with microplastics from sludge

PBDEs BPA OP NP PS PCBs PAHs DDT/DDE/DDD Cd, Cr, Cu, Pb, Al, Zn, Fe Dioxins? Bacteria Vibrio etc. (Kirstein et al. 2016) Microorganisms

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SLIDE 17

Nordic Council of Ministeries Non- Target Screening

30.10.2018 Forfatternavn 17

Sample ident. Location Site Water (m3/year) Population FO-1-Eff Torshavn UA 11 (Sersjantviken) 11 600 DK-1-Eff Aarhus Marselisborg WWTP 10 Million 202 000 FI-1-Eff Helsinki Viikinmäki 101 Million 800 000 SE-1-Eff Stockholm Henriksdal WWTP 90 Million 1 000 000 GL-1-Eff Nuuk Kakillarnat n.a. 5 000 IS-1-Eff Reykjavik Klettagardar WWT 1 Million 100 000 NO-1-Eff Oslo VEAS 107 Million 700 000

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SLIDE 18

Number CAS number EU number Name of priority substance 1 15972-60-8 240-110-8 Alachlor 2 120-12-7 204-371-1 Anthracene 3 1912-24-9 217-617-8 Atrazine 4 71-43-2 200-753-7 Benzene 32534-81-9 not applicable BDE 28, 47, 99, 100, 153 and 154 6 7440-43-9 231-152-8 Cadmium and its compounds 7 85535-84-8 287-476-5 Chloroalkanes, C10-13 iv 8 470-90-6 207-432-0 Chlorfenvinphos Chlorpyrifos (Chlorpyrifos ethyl) 10 107-06-2 203-458-1 1,2-Dichloroethane 11 75-09-2 200-838-9 Dichloromethane 12 117-81-7 204-211-0 Di(2-ethylhexyl)phthalate (DEHP) 13 330-54-1 206-354-4 Diuron 14 115-29-7 204-079-4 Endosulfan 15 206-44-0 205-912-4 Fluoranthenevi 16 118-74-1 204-273-9 Hexachlorobenzene 17 87-68-3 201-765-5 Hexachlorobutadiene 18 608-73-1 210-158-9 Hexachlorocyclohexane 19 34123-59-6 251-835-4 Isoproturon 20 7439-92-1 231-100-4 Lead and its compounds 21 7439-97-6 231-106-7 Mercury and its compounds 22 91-20-3 202-049-5 Naphthalene 23 7440-02-0 231-111-4 Nickel and its compounds 25154-52-3 246-672-0 Nonylphenols 104-40-5 203-199-4 (4-nonylphenol) 1806-26-4 217-302-5 Octylphenols 140-66-9 not applicable (4-(1,1',3,3'-tetramethylbutyl)- phenol) 26 608-93-5 210-172-5 Pentachlorobenzene 27 87-86-5 201-778-6 Pentachlorophenol 28 not applicable not applicable Polyaromatic hydrocarbons 50-32-8 200-028-5 (Benzo(a)pyrene) 205-99-2 205-911-9 (Benzo(b)fluoranthene) 191-24-2 205-883-8 (Benzo(g,h,i)perylene) 207-08-9 205-916-6 (Benzo(k)fluoranthene) 193-39-5 205-893-2 (Indeno(1,2,3-cd)pyrene) 29 122-34-9 204-535-2 Simazine not applicable not applicable Tributyltin compounds 36643-28-4 not applicable (Tributyltin-cation) 31 12002-48-1 234-413-4 Trichlorobenzenes 32 67-66-3 200-663-8 Trichloromethane (chloroform) 33 1582-09-8 216-428-8 Trifluralin 25 30 9 2921-88-2 220-864-4 24

GC-MS LC-MS Alcohols Alkaloids, Amino acids, Fatty acids, Phenolics steroids POLARITY PCBs PBDEs CPs PAHs Dioxins etc. Metabolites, Organic acids Ionic species, e.g. PFOS, PFOA etc.

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SLIDE 19

Feature detection Non Target

Mass Domain Time Domain

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SLIDE 20
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SLIDE 21

Features Identified

126 352 6241 15031 2000 4000 6000 8000 10000 12000 14000 16000 Level 2 Level 3 Level 4 Level 5

Number of Identified Features

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SLIDE 22

1786 3007 5563 7383 4624 3571 1935 1000 2000 3000 4000 5000 6000 7000 8000 Number of Features

Number of Features

Denmark Finland Norway Greenland Faroe Island Island Sweden

The number of aligned features per country

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SLIDE 23

Identified Features

4 4 5 5 7 38 243 243 1097 1 10 100 1000 10000 100% 75% 50%

Number of Identified Features

Level 2 Level 3 Level 4

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SLIDE 24

1 126 Level 2 1415 Level 5 No Id

  • 1

746 Level 3/4

Iminostilbene 2--2 2--3
  • 0.0096 0.007594 0.001812
0.96142 0.977747 5.414167 Carbamazepine 8--8 8--19
  • 0.0118 0.012322 0.003408 0.967817 0.990947 5.148764
EDDP 2--2 2--4
  • 0.0127 0.009197
0.00486 0.870806 0.936894 5.0777 Citalopram 2--2 2--7
  • 0.0049 0.008689 0.004882 0.769167 0.919138 5.013305
1011-Dihydro-1011 3--3 3--13
  • 0.0153 0.009662 0.000782 0.900109 0.989684 4.994792
Oxazepam 2--2 2--15
  • 0.0118 0.002843 0.002828 0.786866 0.951676 4.973542
D617 3--3 3--5
  • 0.0153
0.01144 0.005002 0.808444 0.94301 4.961454 Venlafaxine 3--3 3--11
  • 0.0155 0.007517 0.001447
0.80046 0.989519 4.954979 Triamterene 2--2 2--6
  • 0.0121
0.00951 0.003359 0.80496 0.925238 4.935198 Iminostilbene 2--2 2--11
  • 0.0116 0.009013 0.003819 0.786707 0.959966 4.886673
Sitagliptin 6--6 6--28
  • 0.0216 0.009152 0.003643 0.928727
0.99489 4.878617 Trospium 2--2 2--3
  • 0.02 0.015267 0.008193 0.861633 0.931792 4.838425
Telmisartan 4--4 4--9
  • 0.0218 0.016792 0.003352 0.942233
0.98092 4.833153 Diphenhydramine2--2 2--11
  • 0.0124 0.009193 0.000275
0.68 0.960186 4.800186 Amisulpride 4--4 4--20
  • 0.0195 0.015381 0.004573 0.946148 0.984975 4.781123
Bupropion 4--5 4--10 0.004 0.009923 0.006477 0.86717 0.974892 4.754062 Carbamazepine 5--6 5--10
  • 0.0118 0.012052 0.000933 0.892843 0.968736 4.739078
Cocaine 2--2 2--16
  • 0.0167 0.012822 0.004756 0.662266 0.961933 4.549199
Carbamazepine-10 4--5 4--18
  • 0.0138 0.010651 0.000862 0.873633 0.980494 4.508126
Sulfapyridine 4--5 4--7
  • 0.0165 0.010657 0.005398 0.736388 0.994805 4.478193
Valsartan 18--26 18--27
  • 0.0207 0.011566 0.006632 0.976819 0.993419 4.413238
Cis-Zeatin 14--19 14--29
  • 0.01471 0.015829 0.010893 0.927778 0.983871
4.39345 Azithromycin 7--11 7--22
  • 0.0092 0.007914 0.004783 0.949264 0.987392 4.382256
Bisoprolol 11--20 11--31
  • 0.0024 0.007732 0.004019 0.987241 0.983309
4.36605 Climbazol 6--8 6--14
  • 0.0149 0.009139 0.004276 0.753206 0.981126 4.359332
Irbesartan 10--15 10--16
  • 0.0213 0.010492 0.005117 0.959101 0.985245 4.330146
Clarithromycin 2--2 2--26
  • 0.0328 0.006087 0.002535 0.619689 0.978594 4.328283
Crotamiton 6--10 6--9
  • 0.0087 0.010575 0.009805 0.877245 0.967736 4.323981
Celiprolol 9--11 9--31
  • 0.0226 0.015361 0.009074 0.880467 0.968066 4.265533
Metoprolol 9--15 9--16
  • 0.0173 0.009708 0.004145 0.980698 0.992809 4.265507
Carbamazepine-10 3--6 3--3
  • 0.01685 0.013928 0.003402 0.998301
0.97817 4.231471 Mirtazapine 3--4 3--57
  • 0.0138 0.011405 0.002801 0.793292 0.979869 4.198161
Tiamulin 3--5 3--10 0.0109 0.010338 0.00514 0.942207 0.957721 4.173928 DEET 2--10 2--1
  • 0.0087
0.00565 0.000607 0.970065 0.887151 4.163216 Methadone 2--3 2--3
  • 0.0115 0.011027 0.008457 0.714886 0.861099 4.154985
1,3-bis(2,6-dimeth6--10 6--19
  • 0.01618 0.009488 0.005133 0.939395 0.993052 4.128447
Cyheptamide 3--6 3--9 0.003641 0.002581 0.002187 0.90514 0.888973 4.104113 1-Phenylpyrazolid4--6 4--80
  • 0.00941 0.010627 0.007079 0.794318 0.977621 4.094339
Finasteride 44--94 44--77
  • 0.005 0.017798 0.016074 0.984861 0.983439
4.0839 Ciprofloxacin 3--5 3--6
  • 0.0205 0.017163 0.002851 0.945212 0.985404 4.048616
PEG-9EO 7--15 7--23
  • 0.00523 0.015351 0.004214
0.9876 0.983712 4.031812 lauramine oxide 2--6 2--2
  • 0.01016 0.009959
8.23E-05 0.964419 0.950957 3.974376 4-tert-Butyl-2-[(di 3--6 3--6
  • 0.01241
0.00923 0.003084 0.841308 0.959108 3.945416 PEG-11 17--22 17--116
  • 0.0338 0.018237 0.008337
0.98035 0.971405 3.932955 PEG-9EO 9--15 9--46
  • 0.02223 0.014172 0.004878 0.973711 0.996844 3.932555
Bexarotene 10--169 10--5 0.0062 0.020197 0.016974 0.995747 0.913269 3.904816 Azithromycin 10--11 10--41
  • 0.0422 0.028313 0.010742 0.966293 0.998334 3.900827
Furosemide 3--6 3--13
  • 0.01 0.010653 0.005725 0.925878 0.943809 3.899686
34-Methylenediox3--8 3--5
  • 0.0104
0.00622 0.001851 0.930332 0.981531 3.898663 Aspidospermine 3--6 3--29
  • 0.002 0.011027 0.015092 0.831723 0.981367
3.88809 Citalopram 21--46 21--43
  • 0.0159 0.014612 0.010358 0.992762 0.983787 3.872149
PEG-11 15--22 15--128
  • 0.0298 0.014518 0.006937
0.9802 0.97263 3.86803 2,2-Bis(hydroxyme 3--9 3--11
  • 0.00124
0.00327 0.00222 0.958323 0.999087 3.867709 Pantoprazole 4--11 4--9
  • 0.0046
0.01007 0.002793 0.934932 0.973229 3.855361 PEG-7EO 6--11 6--27
  • 0.02066 0.016493 0.005387 0.961651 0.986587 3.814239
alpha-Ketometopr4--10 4--10
  • 0.01402 0.011577 0.003323
0.97731 0.979085 3.772395 PEG-14 18--27 18--77
  • 0.03515 0.016643 0.009045 0.983641 0.968759
3.761 4-hydroxy omepra3--6 3--8
  • 0.01958 0.013032
0.00575 0.858517 0.996041 3.744558 PEG-15 21--29 21--126
  • 0.03894 0.020598 0.009425 0.985081
0.97513 3.705211 4-(Dipropylamino)5--18 5--7 0.006349 0.013535 0.014594 0.986965 0.905015 3.699379 PEG-11 11--19 11--83
  • 0.0248
0.01749 0.005013 0.957197 0.958231 3.698428 PEG-6 5--13 5--11
  • 0.01687 0.010643 0.005018 0.978093 0.960849 3.675142
PEG-12 14--23 14--70
  • 0.03158 0.017473 0.008367 0.977431
0.95772 3.667851 PEG-10 11--20 11--73
  • 0.02601 0.015216 0.006083 0.972997 0.956845 3.661842
Dibucaine 2--5 2--5
  • 0.0147 0.013543 0.001691 0.857798 0.993934 3.645732
Flecainide 5--12 5--10
  • 0.0229 0.015794 0.007466 0.962765 0.983039 3.622004
3,4,5-Triethoxyben 4--17 4--10
  • 0.0023 0.019149 0.004958 0.979961 0.953045 3.579806
Carbamazepine-10 2--5 2--7
  • 0.0138 0.007971 0.007177 0.840221 0.966435 3.579656
4-OH-Carbamazep4--13 4--8
  • 0.00985
0.02446 0.002667 0.969507 0.943237 3.576544 Losartan 7--19 7--16
  • 0.0205
0.01059 0.010332 0.932851 0.993165 3.546317 Colchicine 26--223 26--38 0.0005 0.021066 0.0157 0.998011 0.963204 3.521615 Atenolol 2--13 2--3
  • 0.0037 0.003075 0.000949 0.966445 0.920092 3.516036
PEG-10 10--20 10--83
  • 0.03101 0.011919 0.006876 0.977271 0.994319
3.50659
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SLIDE 25

Examples of 100%

Cetirizine (antihistamine) Olopatadine (antihistamine) 2,2- Bis(hydroxymeth yl)-1,3- propanediyl dioctanoate (Personal Care Product)

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SLIDE 26

Examples 75%

Disopyramide (antiarrhythmic medication) Bisoprolol (antihypertensive agent)

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SLIDE 27

Examples of 50%

Caffeine Carbamazepine Diphenhydramine