ars.els-cdn.com · Web viewSupplementary data. MANUSCRIPT TITLE: Organophosphorus flame retardants and plasticizers: Sources, occurrence, toxicity and human exposure. AUTHORS: Gao-Ling

Supplementary dataMANUSCRIPT TITLE: Organophosphorus flame retardants and plasticizers: Sources,

occurrence, toxicity and human exposure

AUTHORS: Gao-Ling Wei, Ding-Qiang Li, Mu-Ning Zhuo, Yi-Shan Liao,

Zhen-Yue Xie, Tai-Long Guo, Jun-Jie Li and Zhi-Quan Liang

ADDRS: Guangdong Key Laboratory of Agricultural Environment

Pollution Integrated Control, Guangdong Institute of Eco-

Environmental and Soil Sciences, Guangzhou 510650, China,

Guangzhou Branch, Chinese Academy of Sciences,

Guangzhou 510075, China and University of Chinese

Academy of Sciences, Beijing 100049, China

NO. OF TABLES: 13

NO. OF PAGES: 55

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Human exposure via dust ingestion

In order to make a preliminary evaluation of the human exposure to organophosphorus

(OPs) flame retardants and plasticizers via dust ingestion, we assume 100% absorption of

intake. Average dust ingestion rates are estimated at 20 and 50 mg/d for adults and toddlers,

respectively, while the corresponding high rates become 50 and 200 mg/d (Jones-Otazo et al.,

2005). The values of body weight (bw) are 70 (or 60 or 50) kg for adults and 12 (or 12.3 or

15) kg for toddlers, respectively. Dust ingestion is assumed to be pro-rata to the time spent in

a microenvironment category (Harrad et al., 2008), thus it is necessary to consider different

time-activity patterns for adults and toddlers in different types of microenvironments. Daily

intake of OPs via dust ingestion is estimated with the following equation (Ali et al., 2013;

Kim et al., 2013):

ΣExposure (ng/kg bw/d) = Σ[(CiFi)*Ir]/Body weight

where Ir is the dust ingestion rate (mg/d), Ci is the concentrations (μg/g) of OPs in dusts from

the microenvironment i, Fi is the percentage of daily time spent in the microenvironment i.

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Table S1Comparison of the concentrations (range; arithmetic mean/median; μg/g) of the total of organophosphates (ΣOP) and the predominant halogenated OPs in dusts from various indoor environments worldwide.

Location (no.)

Description of matrix

ΣOP TCEP TCIPP TDCPP Ref

Various indoor environments

Germany (983/436) a

Home, school and business building

–<0.10-121 2.37/0.61

<0.10-375 1.65/0.52

–(Ingerowski et al.,

2001)

Sweden (15)

Domestic, public, occupational site

22.0-5,500 466/59.0

0.19-94.0 11.3/1.40

0.47-73.0 11.0/2.40

0.20-67.0 9.54/1.10

(Marklund et al., 2003)

USA b

(110)Various indoor environments

– – –<0.03-326

4.43/–(Carignan et al.,

2013)HomeSweden(2)

PVC floor22.0-28.0

25.0/–0.19-0.23

0.23/–0.47-0.93

0.70/–0.39-1.10

0.75/–(Marklund et al.,

2003)Sweden(10)

Private home, floor7.00-79.0 36.9/38.0

nd-33.0 7.60/2.10

0.70-11.0 3.10/1.60

2.20-27.0 12.0/30.0

(Bergh et al., 2011)

Germany(6)

Home, floor0.80-6.00

3.00/–0.14-0.28

0.20/–0.37-0.96

0.74/–<0.08-0.11

<0.08/–(Brommer et al.,

2012)Spain(8)

Home, floor3.91-34.8 18.9/21.5

0.25-9.80 1.74/0.51

0.35-10.3 3.91/3.80

nq-1.10 0.38/0.23

(García et al., 2007)

Spain(5)

Home, floor –0.50-11.0

–/3.103.60-8.50

–/3.600.69-2.10

–/0.85(Cristale and Lacorte,

2013)Belgium(33)

Home, floor1.92-94.7 19.4/13.1

<0.08-2.65 0.49/0.23

0.19-73.7 4.82/1.38

<0.08-6.64 0.57/0.36

(Van de Eede et al., 2011)

Romania Home, floor up to 29.0 <0.02-1.16 <0.02-16.4 <0.02-0.46 (Dirtu et al., 2012)

3

6

28

29

30

7

(47) 8.79/7.00 0.18/0.10 2.50/0.86 0.10/0.06New Zealand(34)

Living room floor –/5.51 –/0.11 –/0.35 –/0.23 (Ali et al., 2012)

New Zealand(16)

Mattress –/2.42 –/0.04 –/0.25 –/0.11 (Ali et al., 2012)

USA(50)

Home, floor 3.27/– –<0.14-5.49

0.57/–<0.15-56.1

1.89/–(Stapleton et al.,

2009)USA b

(29)Bedroom, floor – – –

0.27-18.2 1.40/–

(Carignan et al., 2013)

USA b

(31)Main living area, floor

– – –0.56-30.6

4.21/–(Carignan et al.,

2013)USA(16)

Home, floor –0.61-160

–/5.100.34-120

–/2.100.73-24.0

–/2.80(Dodson et al., 2012)

USA(16)

Home, floor –0.33-110

–/2.700.49-140

–/2.200.92-44.0

–/2.10(Dodson et al., 2012)

Japan(148)

Floor33.9-5,980

–/577<1.30-338

–/5.83<1.12-430

–/8.69<1.18-864

–/2.80(Araki et al., 2014)

Japan(120)

Multi-surface17.4-15,100

–/244<1.30-2,320

–/8.261.30-462

–/25.8<1.18-593

–/10.8(Araki et al., 2014)

Japan(41)

Floor –<1.30-308

–/7.505.40-291

–/18.7<1.18-105

–/4.00(Kanazawa et al.,

2010)

Japan(41)

Multi-surface –<1.30-70.7

–/9.8010.3-462

–/50.95.80-127

–/22.3(Kanazawa et al.,

2010)Japan b

(50)Home, floor – – –

<0.11-56.1 1.88/1.75

(Meeker and Stapleton, 2009)

Philippines Residential area, 0.02-4.30 0.02-1.20 – – (Kim et al., 2013)

4

8

9

(17) floor 0.95/0.53 0.18/0.07Philippines(20)

Municipal dumping area, floor

0.06-0.88 0.36/0.24

0.01-0.14 0.04/0.02

– – (Kim et al., 2013)

Pakistan(31)

Rural area, living room floor

0.48-3.67 1.00/0.97

– – – (Ali et al., 2011)

Pakistan(15)

Urban home, floor0.07-0.90 0.48/0.58

<0.01-0.18 0.04/0.02

<0.02-0.09 0.02/–

<0.005-0.26 0.03/–

(Ali et al., 2013)

Kuwait(15)

Urban home, floor 2.26-147 16.9/6.55

0.28-1.80 0.76/0.71

0.12-7.07 1.96/1.46

0.06-1.56 0.53/0.36

(Ali et al., 2013)

Egypt(20)

Living room, carpet, furniture, electronics

0.04-0.96 0.31/0.19

<0.008-0.13 0.05/0.02

<0.01-0.12 0.05/0.03

<0.009-0.56 0.15/0.07

(Abdallah and Covaci, 2014)

OfficeSweden(1)

Office building, linoleum floor

470 48.0 73.0 67.0(Marklund et al.,

2003)Sweden(10)

Office/mechanical workshop

20.0-1,060 376/270

1.30-260 36.0/6.70

3.40-120 32.0/19.0

3.30-91.0 30.0/17.0


Germany(10)

Office building4.30-32.0

14.0/–<0.08-0.17

0.12/–0.18-9.40

3.00/–<0.08-0.29

0.15/–(Brommer et al.,

2012)

USA b

(30)Office building – – –

0.06-72.0 6.06/–


Egypt(20)

Carpet, foam furniture, electronics

0.04-1.51 0.55/0.22

<0.008-0.13 0.06/0.03

<0.01-0.70 0.12/0.08

<0.009-0.49 0.10/0.05


School buildingSweden(1)

University lobby, corridor and sofas

110 1.60 50.0 5.70(Marklund et al.,

2003)Sweden Library, books and 140 94.0 2.90 0.84 (Marklund et al.,

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10

11

(1) shelves 2003)Hotel

Sweden(1)

Wooden floor, linoleum and wall-to-wall carpet

59.0 3.90 8.90 0.91(Marklund et al.,

2003)

Japan(8)

Hotel 2.90-260 74.2/48.5

0.08-2.30 1.03/1.07

1.00-9.80 3.65/2.60

0.07-18.0 4.52/1.70

(Takigami et al., 2009)

StoreSweden(1)

Radio shop, PVC floor covering

22.0 1.40 2.30 0.59(Marklund et al.,

2003)

Sweden(1)

Textile shop, tiled floor

37.0 0.37 1.40 0.20(Marklund et al.,

2003)

Belgium c

(15)Shop

6.70-136 31.2/16.5

<0.08-5.46 1.17/0.59

0.58-24.4 5.16/2.94

<0.08-56.2 4.61/0.76


Day care centreSweden(1)

PVC floor covering 42.0 0.82 2.50 1.80(Marklund et al.,

2003)Sweden(10)

Playroom area57.0-4,300 2,020/1,700

2.50-150 51.0/30.0

0.80-12.0 4.50/3.10

3.90-150 28.0/9.10


HospitalSweden(1)

Ward, PVC floor covering

140 1.00 5.30 0.56(Marklund et al.,

2003)Sweden(1)

Office, PVC floor covering

220 3.80 2.30 2.10(Marklund et al.,

2003)Public placeSweden Cinema, wall-to- 33.0 0.85 2.40 7.00 (Marklund et al.,

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12

13

(1) wall carpet 2003)Sweden(1)

Public dance hall, wooden floor

130 1.00 1.50 1.10(Marklund et al.,

2003)Pakistan(12)

Mosque hall1.00-2.31 1.58/1.46

– – – (Ali et al., 2011)

Egypt d

(11)Carpet, foam furniture, electronics

0.94-5.24 2.17/1.47

<0.008-0.54 0.28/0.23

<0.01-0.47 0.23/0.23

<0.009-1.62 0.60/0.42


PrisonSweden(1)

Cells and corridors, linoleum floor

5,500 8.20 8.90 53.0(Marklund et al.,

2003)TransportationUSA b

(20)Vehicles – – –

<0.03-326 12.5/–


Sweden(1)

Aircraft 34.0 4.20 2.20 0.86(Marklund et al.,

2003)

Germany(12)

Cars2.70-870 190/37.0

<0.08-5.80 0.95/0.28

140-4.30 3.10/3.20

<0.08-620 130/21.0

(Brommer et al., 2012)

Pakistan(15)

Cars0.19-5.60 2.50/2.30

<0.01-1.52 0.26/0.08

<0.02-2.62 0.59/0.10

<0.005-1.24 0.13/0.03

(Ali et al., 2013)

Kuwait(51)

Cars27.0-185 87.9/63.6

0.01-13.7 3.48/1.77

2.49-134 36.0/30.7

0.60-166 35.8/7.63

(Ali et al., 2013)

Egypt(20)

Cars, fabric and leather seat cover

0.20-3.37 1.01/0.56

<0.008-0.57 0.20/0.13

<0.01-1.43 0.51/0.29

<0.009-0.28 0.09/0.06


Electronic equipment/furnitureSweden Computer screen 5,600 220 370 290 (Marklund et al.,

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14

15

(1) (ng/m2) 2003)Sweden(1)

Computer screen(ng/m2)

5,900 210 220 170(Marklund et al.,

2003)Japan(7)

Computer monitor (ng/m2/h)

– nd ndnd-280

–/nd(Saito et al., 2007)

Japan(8)

Surface of TV set (ng/m2/h)

–nd-13,000

–/1,400nd-1,700

–/420nd (Saito et al., 2007)

USA(26)

Furniture (w/w) – – 0.5-2.2% 1-5%(Stapleton et al.,

2009)a A total of 983 and 436 samples were analyzed for TCEP and TCIPP, respectively.b Geometric mean value.c Stores including an electronics store, a mattress shop, a pharmacy, a furniture store, a second-hand shop, and a carpenter workshop.d Public places including four coffee shops, four restaurants and three supermarkets.The full names for the halogenated OPs are displayed in Table 1. –: data not available. nd: not detected. nq: not quantified.

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16

31

32

33

34

35

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Table S2

Comparison of the concentrations (range; arithmetic mean/median; μg/g) of the predominant non-halogenated OPs among indoor dusts from various microenvironments worldwide.

Location (no.) a TEP TPP TNBP TIBP TMPP TPHP TBOEP TEHP EHDPP Ref

Various indoor environmentsSweden (15)

– –0.07-2.20 0.53/0.35

– –0.85-110 11.4/3.10

14.0-5,300 419/31.0

0.06-13.0 1.03/0.16

–(Marklund et al.,

2003)HomeSweden(2)

– –0.21-0.61

0.41/–– –

0.85-0.99 0.92/–

18.0-25.0 21.5/–

0.06-0.070.07/–


2003)Sweden(10)

nd –nd-1.70

0.60/0.300.40-3.60 1.40/1.10

0.10-4.20 1.60/1.20

–0.60-30.0 8.50/4.00

nd-2.20 –(Bergh et al.,

2011)Germany(6)

– –<0.03-0.25

0.13/––

<0.04-0.24 0.09/–

0.18-1.30 0.38/–

<0.06-2.80 0.73/–

– –(Brommer et al.,

2012)Spain(8)

– –0.07-0.65 0.25/0.23

nq-0.27 0.21/0.22

–0.29-9.50 2.60/1.85

1.18-18.5 9.88/9.35

– –(García et al.,

2007)Spain(5)

– –0.05-0.08

–/0.070.09-0.22

–/0.100.35-0.71

–/0.490.80-4.70

–/1.10nd

0.30-0.86 –/0.69

–(Cristale and

Lacorte, 2013)Belgium(33)

<0.05 –0.03-2.70 0.25/0.13

0.70-15.6 4.20/2.99

<0.04-5.07 0.44/0.24

0.04-29.8 2.02/0.50

<0.36-67.6 6.58/2.03

– –(Van de Eede et

al., 2011)Romania(47)

<0.01-0.42 0.03/0.02

–<0.02-0.38 0.07/0.05

<0.10-2.39 0.40/0.39

<0.05-5.50 1.00/0.50

<0.02-22.6 1.60/0.50

<0.05-21.0 2.70/1.50

– –(Dirtu et al.,

2012)

New Zealand

– – –/0.08 – –/0.12 –/0.60 –/4.02 – – (Ali et al., 2012)

9

18

36

37

38

19

(34)New Zealand(16)

– – –/0.07 – –/0.16 –/0.24 –/1.55 – – (Ali et al., 2012)

USA(50)

– – – – –<0.15-1,800

7.36/–– – –

(Stapleton et al., 2009)

USA(16)

<0.02-0.41–/0.03

–<0.08-1.80

–/0.03<0.08-0.18

–/0.080.33-4.40

–/1.00–

2.30-68.0 –/12.0

<0.20-3.70–/<0.20

0.18-3.00 –/0.61

(Dodson et al., 2012)

USA(16)

<0.02-0.25 –<0.08-1.80

–/<0.08<0.08-0.12

–/<0.080.18-10.0

–/0.68–

0.79-170 –/11.0

<0.20-0.34–/<0.20

0.14-1.50 –/0.56

(Dodson et al., 2012)

Japan(148)

<0.52-2.80 <0.49-1.13<0.73-133

–/1.03– <4.00-59.8

1.60-245 –/4.51

6.24-5,890 –/508

<1.34-51.0 –/2.07

–(Araki et al.,

2014)Japan(120)

<0.52-3.31 <0.49<0.73-42.8

–/1.15– <4.00-193

<1.60-889 –/11.5

5.29-14,100 –/111

<1.34-73.1 –/1.47

–(Araki et al.,

2014)Japan(41)

<0.52-2.10 –<0.73-15.6

–/1.40– <4.00-13.9

<1.60-78.4 –/5.40

61.8-5,890 –/1,570

<1.34-16.2 –/4.30

–(Kanazawa et al.,

2010)Japan(41)

<0.73-2.10 –<0.73-2.7

–/1.10– <4.00-102

<1.60-175 –/14.3

5.90-749 –/164

<1.34-6.6 –/2.10

–(Kanazawa et al.,

2010)Japan b

(50)– – – – –

<0.17-1,800 7.40/5.47

– – –(Meeker and

Stapleton, 2009)Philippines(17)

– –<0.006-0.08

0.02/0.02–

<0.003-0.25 0.04/0.02

0.008-2.10 0.35/0.09

–0.004-0.970.21/0.14

0.008-0.77 0.18/0.11

(Kim et al., 2013)

Philippines(20)

– –<0.006-0.28

0.04/0.02–

<0.003-0.14 0.03/0.009

0.01-0.44 0.13/0.07

–0.004-0.37 0.07/0.04

0.008-0.56 0.09/0.03

(Kim et al., 2013)

Pakistan(31)

– –<0.02-0.08 0.02/0.01

<0.40-2.18 0.71/0.68

–<0.002-0.63

0.11/0.09<0.02-2.85 0.05/0.03

– – (Ali et al., 2011)

Pakistan <0.005- <0.005-0.12 <0.02-0.02 <0.02-0.04 – <0.002-0.33 <0.02-0.15 <0.005-0.05 <0.002-0.36 (Ali et al., 2013)

10

20

21

(15) 0.006 0.009/– 0.003/– 0.03/0.03 0.16/0.18 0.03/0.02 0.02/0.02 0.09/0.07Kuwait(15)

<0.005-0.39 0.06/0.02

<0.005-0.08 0.01/–

0.02-0.80 0.12/0.06

<0.02-0.96 0.11/0.05

–0.04-6.89 1.08/0.43

0.03-140 10.7/0.86

<0.005-0.34 0.12/0.07

0.08-11.0 0.93/0.19

(Ali et al., 2013)

Egypt(20)

– –<0.01-0.03 0.02/0.02

<0.02-0.03 0.03/0.02

–0.008-0.30 0.10/0.07

<0.003-0.31 0.09/0.02

–<0.002-0.10

0.05/0.04(Abdallah and Covaci, 2014)

OfficeSweden(1)

– – 0.35 – – 6.80 270 0.43 –(Marklund et al.,

2003)Sweden(10)

nd-0.10 0.70/0.10

–nd-3.20

0.70/0.200.50-9.20 2.40/1.30

0.90-32.0 8.80/5.30

–4.50-960 250/87.0

nd-0.30 0.10/–

–(Bergh et al.,

2011)Germany(10)

– –<0.03-0.41

0.22/––

<0.04-1.90 0.37/–

0.47-4.80 2.50/–

2.90-13.0 7.00/–


2012)Egypt(20)

– –<0.01-0.05 0.03/0.02

<0.02-0.03 0.03/0.03

–0.01-0.34 0.09/0.07

<0.003-1.24 0.26/0.14

–<0.002-0.07



– – 0.32 – – 4.90 50.0 0.36 –(Marklund et al.,

2003)Sweden(1)

– – 0.59 – – 24.0 16.0 0.09 –(Marklund et al.,

2003)HotelSweden(1)

– – 0.13 – – 1.70 42.0 0.22 –(Marklund et al.,

2003)Japan(8)

<0.02-0.09 0.04/0.02

–0.14-1.60 0.66/0.53

–0.09-7.50 2.87/1.95

0.11-2.60 1.30/1.17

1.40-230 58.9/28.9

0.01-0.90 0.22/0.12

–(Takigami et al.,

2009)StoreSweden – – 1.80 – – 0.93 14.0 0.14 – (Marklund et al.,

11

22

23

(1) 2003)Sweden(1)

– – 0.41 – – 3.10 31.0 0.22 –(Marklund et al.,

2003)Belgium(15)

<0.05-0.37 0.13/0.06

–0.05-6.01 0.63/0.21

0.67-4.40 1.45/1.04

<0.04-12.5 1.53/0.20

0.15-34.2 4.70/1.97

<0.20-55.7 11.8/3.61

– –(Van de Eede et

al., 2011)Day care centreSweden(1)

– – 0.20 – – 4.50 31.0 0.13 –(Marklund et al.,

2003)Sweden(10)

nd-4.70 0.70/0.20

–0.10-6.20 2.00/1.20

0.50-1.50 0.90/0.70

0.30-17.0 3.50/1.90

–31.0-4,100 1,900/1,600

nd-0.70 0.20/0.10

–(Bergh et al.,

2011)HospitalSweden(1)

– – 0.07 – – 2.00 210 0.18 –(Marklund et al.,

2003)Sweden(1)

– – 0.18 – – 2.20 120 0.16 –(Marklund et al.,

2003)Public placeSweden(1)

– – 0.14 – – 1.10 21.0 0.11 –(Marklund et al.,

2003)Sweden(1)

– – 0.48 – – 3.30 120 0.19 –(Marklund et al.,

2003)Pakistan(12

– – –0.58-1.62 1.22/1.23

–0.01-0.19 0.01/0.01

<0.02-0.34 0.10/0.07

– – (Ali et al., 2011)

Egypt(11)

– –<0.01-0.26 0.11/0.07

<0.02-0.23 0.14/0.13

–0.12-2.34 0.96/0.63

<0.003-1.03 0.31/0.07

–<0.002-0.07


PrisonSweden – – 0.35 – – 110 5300 13.0 – (Marklund et al.,

12

24

25

(1) 2003)TransportationSweden(1)

– – 2.20 – 4.40 – 18.0 0.14 –(Marklund et al.,

2003)Germany(12)

– –<0.03-0.63 0.11/0.02

–<0.04-150

24.0/–0.50-11.0 3.00/2.30

<0.06-74.0 21.0/7.50


2012)Pakistan(15)

<0.005-0.006

<0.005<0.02-0.25 0.01/0.02

<0.02-0.11 0.04/0.03

–0.002-4.80 0.67/0.25

<0.02-1.53 0.13/0.02

<0.005-0.33 0.05/0.01

0.002-0.44 0.07/0.04

(Ali et al., 2013)

Kuwait (51)

<0.005-0.06

0.01/0.005

<0.005-0.41

0.09/0.08

0.02-9.83 1.69/0.73

0.02-1.77 0.80/0.95

–<0.002-74.2

2.17/1.760.02- 12.5 5.27/4.47

0.005-1.31 0.21/1.35

0.002-4.29 0.88/0.52

(Ali et al., 2013)

Egypt(20)

– –<0.01-0.26 0.07/0.06

<0.02-0.10 0.05/0.05

–0.03-1.87 0.39/0.14

<0.003-0.780.28/0.19

–<0.002-0.20


Electronic equipmentSweden c

(1)– – 30.0 – – 3,300 940 <0.05 –


Sweden c

(1)– – 70.0 – – 4,000 170 <0.05 –


Japan d

(7)– – nd –

nd-5,900–/nd

nd-20,700 –/690

nd – –(Saito et al.,

2007)Japan d

(8)– –

nd-170–/nd

– ndnd-6,700

–/330nd-320

–/nd– –

(Saito et al., 2007)

Sweden e

(18)– – – – – 10% – – –

(Carlsson et al., 2000)

a The arrangement and detailed descriptions of the matrices are in accordance with Table S1 without specification.b Geometric mean value.

13

26

39

40

27

c Concentrations in ng/m2 for the dust samples collected from computer screens d Concentrations in ng/m2/h for the dust samples collected from the covers of computer (no. 7) and TV set (no. 8).e Concentrations in w/w for the dust samples collected from computer video display units.The full names for the non-halogenated OPs are summarized in Table 1. –: data not available. nd: not detected. nq: not quantified.

14

28

41

42

43

44

29

Table S3

Comparison of the concentrations (range; arithmetic mean/median; ng/m3) of ΣOP and the predominant halogenated OPs in indoor air from various indoor environments around the world.

Location (no.)

Description of matrix ΣOP TCEP TCIPP TDCPP Ref

Various indoor environments

Sweden(30)

Private homes, workplaces, stores, health care facilities and transportation

9.00-2,320 378/102

1.00-870 75.8/11.5

1.00-2,300 273/31.0

–(Staaf and Östman,

2005)

Sweden(17)

Domestic, public and occupational environments

36.0-950 293/160

0.40-730 129/8.90

10.0-570 118/69.0

<0.20-150 36.1/6.00

(Marklund et al., 2005a)

Home

USA(16)

Multistorey apartments –<2.00-8.30 4.40/3.30

3.40-172 26.0/8.30

nd (Bergh et al., 2010)

Germany(50)

Home –up to 6,000

52.0/–– –

(Ingerowski et al., 2001)

Sweden(1)

Living room 230 0.40 210 <0.50(Marklund et al.,

2005a)Sweden(1)

Bedroom 160 3.00 38.0 <0.50(Marklund et al.,

2005a)

Sweden(10)

Apartment and private house, ordinary household equipment

22.0-190 89.0/78.0

1.00-115 16.6/4.00

7.00-160 36.9/27.0


2005)

Sweden(10)

Private home25.0-149 70.0/58.5

nd-28.0 8.30/4.80

2.40-64.0 15.0/5.60

nd-17.0 3.00/–


15

30

45

46

47

31

Japan a

(25)Summer, tatami mat, carpet, wood and tile floor

–4.96-62.9

20.0/–– – (Takeshi et al., 2006)

Japan a

(21)Winter, wood, tile, tatami mat and carpet floor

–3.85-23.0

9.25/–– – (Takeshi et al., 2006)

Japan(18)

Living roomnd-1,507

–/11.4nd-136 –/1.30

nd-1,260 –/1.90

nd-0.60 (Saito et al., 2007)

Japan(41)

Residential detached house –<12.6-297

–/15.515.5-2,660

–/89.2<11.5-61.4

–/<11.5(Kanazawa et al.,

2010)

Japan(6)

Home450-1,120 763/735

– – – (Otake et al., 2001)

OfficeSweden(4)

Office building 101/– 11.0/– 44.4/– – (Carlsson et al., 1997)

Sweden(3)

Lecture room/office –3.00-12.06.33/4.00

91.0-850517/610

–(Björklund et al.,

2004)Sweden(1)

Linoleum floor, new photocopier

950 730 160 35.0(Marklund et al.,

2005a)Sweden(3)

Computer, desk, chair with polyurethane foam upholstery

65.0-922 380/154

6.00-870 300/25.0

41.0-120 72.0/55.0


2005)Sweden(10)

Office/mechanical workshop108-460 222/234

nd-140 21.0/10.0

16.0-240 110/100

nd-73.0 24.0/28.0


Switzerland(4)

Office7.37-169 68.8/49.4

6.10-56.0 28.8/26.5

nd-130 –(Hartmann et al.,

2004)Norway(2)

Office building 2,410-36,000 2.70-23.0 10.0-21.0 <0.40-7.10 (Green et al., 2008)

Finland b Office/coffee room 50.0-150 <3.00-200 <9.00-40.0 <40.0 (Mäkinen et al., 2009)

16

32

33

(4) 90.0/– 8.00/– 20.0/–Japan(14)

Office building0.90-260

–/20.1nd-42.1 –/3.30

nd-57.6 –/6.00

nd-8.70 (Saito et al., 2007)

China(90)

Common office furniture, not adjacent to industrial activity or main traffic roads

5.00-148 45.5/17.2

1.03-13.4 4.91/3.11

0.83-81.0 24.2/7.76

0.04-14.3 2.25/0.63

(Yang et al., 2014)


School building 213/– 104/– 41.6/– – (Carlsson et al., 1997)

Sweden(1)

Laboratory, linoleum floor 36.0 0.70 31.0 <0.30(Marklund et al.,

2005a)Sweden(1)

University lobby, corridor with sofas

470 2.00 440 1.70(Marklund et al.,

2005a)Sweden(1)

Library, bookshelves, computer

640 590 40.0 <0.70(Marklund et al.,

2005a)Finland c

(3)Computer classroom

50.0-280 90.0/–

70.0-140 100/–

130-200 160/–

<40.0 (Mäkinen et al., 2009)

HotelSweden(1)

Wooden floor, bed, armchair 81.0 2.20 69.0 <0.60(Marklund et al.,

2005a)Day care centerSweden(4)

Day care center 233/– 144/– 52.9/– – (Carlsson et al., 1997)

Sweden(1)

PVC floor covering 96.0 2.50 28.0 59.0(Marklund et al.,

2005a)

Sweden Playroom area 110-580 7.80-230 1.30-72.0 nd-30.0 (Bergh et al., 2011)

17

34

35

(10) 282/224 47.0/25.0 19.0/8.40 6.70/–Health care facility/hospitalSweden(3)

Health care facility, sparsely furnished

60.0-767 380/347

9.00-350 132/35.0

26.0-750 305/140


2005)Sweden(1)

Hospital ward, PVC floor covering, two beds

550 320 69.0 150(Marklund et al.,

2005a)StoreSwitzerland(5)

Electronic store4.30-40.0 25.7/26.5

2.20-22.0 11.8/8.20

nd –(Hartmann et al.,

2004)Switzerland(2)

Furniture store74.1-84.1

79.1/–6.30-11.9

9.10/–46.0-57.0

51.1/––

(Hartmann et al., 2004)

Sweden(1)

Radio shop, PVC floor covering

58.0 29.0 10.0 <0.40(Marklund et al.,

2005a)Sweden(1)

Textile shop, tiled floor 70.0 3.40 32.0 <0.20(Marklund et al.,

2005a)Sweden(1)

Furniture shop, wall-to-wall carpet, tiled floor

160 11.0 73.0 0.80(Marklund et al.,

2005a)Sweden(4)

Electronic equipment, bicycles, clothes and carpets

26.0-113 57.7/34.0

11.0-56.0 33.5/33.5

1.00-96.0 30.5/12.5


2005)Norway(2)

Sporting-goods shop 2,450-47,100 3.50-5.80 27.0-49.0 <0.40-18.0 (Green et al., 2008)

PrisonSweden(1)

Corridor, linoleum floor, no furniture

670 17.0 570 6.00(Marklund et al.,

2005a)Public recreation place

Switzerland Theater 136 36.0 63.0 – (Hartmann et al.,

18

36

37

(1) 2004)Sweden(1)

Public dance hall, between dining area and dance floor

110 16.0 79.0 <0.20(Marklund et al.,

2005a)Sweden(1)

Bowling alley, wooden floor, between alleys

570 460 93.0 <0.40(Marklund et al.,

2005a)Factory/workshopSweden(2)

Plastics factory, concrete floor, extruding plastics

65.0-73.0 69.0/–

3.80-8.90 6.35/–

27.0-32.0 29.5/–

<0.50-0.40(Marklund et al.,

2005a)Sweden d

(3)Workshop

44.0-238 112/55.0

3.00-29.0 14.0/10.0

12.0-22.0 18.7/22.0


2005)Finland c

(6)Circuit board factory

50.0-150 90.0/–

<2.00<5.00-20.0

10.0/–<20.0 (Mäkinen et al., 2009)

Finland e

(4)Circuit board factory

130-280 180/–

<5.00-70.0 10.0/–

<7.00-70.0 10.0/–

<60.0 (Mäkinen et al., 2009)

Finland c

(7)

Furniture, upholstering, sewing, seaming, bundling fabrics

210-1,520 710/–

<3.00-70.0 20.0/–

<10.0 <40.0 (Mäkinen et al., 2009)

Finland e

(2)

Furniture, upholstering, sewing, seaming, bundling fabrics

120-280 190/–

<3.00-10.0 5.00/–

40.0-130 80.0/–

<70.0 (Mäkinen et al., 2009)

Electronics recycling plantSweden(12)

Dismantling hall –15.0-36.0

25.0/–14.3-27.6

20.7/–– (Sjödin et al., 2001)

Finland c

(4)Dismantling and sorting electronics

100-350 210/–

<3.00-200 50.0/–

10.0-30.0 20.0/–

<40.0-40.0 30.0/–

(Mäkinen et al., 2009)

Finland e Dismantling and sorting 1,100-2,580 290-1,100 30.0-120 <60.0 (Mäkinen et al., 2009)

19

38

39

(6) electronics 1,570/– 450/– 60.0/–

Finland c

(2)

Service of the crushing process, dismantling and sorting electronics

330-370 350/–

<3.00-20.0 6.00/–

<10.0 <40.0 (Mäkinen et al., 2009)

Finland e

(6)

Service of the crushing process, dismantling and sorting electronics

70.0-11,900 1,910/–

<3.00-520 40.0/–

<20.0-510 40.0/–

<30.0-450 90.0/–


Vehicle

Switzerland(4)

Car12.8-270 133/124

nd-9.40 5.28/5.85

nd-260 118/107

–(Hartmann et al.,

2004)

Sweden(4)

Car, bus, subway car393-2,320

1,710/2,050nd-320 13.0/–

330-2,300 1,610/1,900

nd-5.00 1.25/–

(Staaf and Östman, 2005)

Garage

Sweden(3)

Car, bus, subway car9.00-354 148/81.0

nd-320 111/12.0

5.00-64.0 25.7/8.00

nd(Staaf and Östman,

2005)

Patch samples (ng/cm2) f

Finland(3)

Circuit board factory0.90-2.10

1.50/–<0.05-1.40

0.30/–<0.10 <0.60 (Mäkinen et al., 2009)

Finland(3)

Furniture workshop1.10-8.30

3.70/–<0.05-0.30

0.10/–<0.10 <0.60 (Mäkinen et al., 2009)

Finland(5)

Electronics recycling plant2.40-11.0

5.70/–0.20-1.50

0.40/–<0.10-1.30

0.20/–<0.60 (Mäkinen et al., 2009)

Finland(6)

Electronics recycling plant1.00-169

15.0/–<0.05-1.20

0.20/–<0.10 <0.60 (Mäkinen et al., 2009)

20

40

41

Hand wash sample (μg/hands) f

Finland(2)

Circuit board factory1.30-9.80

3.50/–<0.001 <0.004

<0.01-0.69 0.07/–


Finland(2)

Furniture workshop27.0-43.0

34.0/–<0.001 <0.004 <0.01 (Mäkinen et al., 2009)

a Concentrations refer to 10th-90th range and geometric mean.b Geometric average concentrations for indoor air from three offices (a circuit board factory, a furniture workshop and an electronics dismantling facility) and one coffee room of an electronics dismantling facility.c Average concentrations for stationary air samples in geometric mean.d Workshops including a bakery, a newspaper printing press and an electronics dismantling facility.e Average concentrations for personal air samples in geometric mean.f Average concentrations in geometric mean.The full names for the halogenated OPs are obtained in Table 1. –: data not available. nd: not detected.

21

42

48

49

50

51

52

53

54

55

43

Table S4Comparison of the concentrations (range; arithmetic mean/median; ng/m3) of the predominant non-halogenated OPs in air from various indoor environments worldwide.

Location (no.) a TEP TPP TNBP TIBP TMPP TPHP TBOEP TEHP EHDPP Ref

Various indoor environmentsSweden(30)

1.00-220 16.4/4.00

–1.00-172 19.5/6.50

– – – – – –(Staaf and

Östman, 2005)Sweden(17)

–<0.10-8.40 3.02/2.70

<0.20-120 19.7/7.80

– –<0.10-23.0 7.05/6.30

<0.30-55.0 7.03/1.70

<0.20-14.0 5.33/3.05


2005a)HomeUSA(16)

2.20-36.0 12.0/7.00

nd4.60-21.0 11.0/11.0

4.40-34.0 12.0/12.0

– – nd – –(Bergh et al.,

2010)Sweden(1)

– 0.20 14.0 – – 8.80 0.60 <0.50 –(Marklund et al.,

2005a)Sweden(1)

– 1.40 120 – – <0.30 <0.40 <0.50 –(Marklund et al.,

2005a)Sweden(10)

1.00-21.0 5.25/3.00

–5.00-80.0 30.1/20.0

– – – – – –(Staaf and


3.20-16.0 8.70/7.30

–3.50-45.0 16.0/9.10

3.00-66.0 18.0/13.0

–nd-0.80 0.20/–

– – –(Bergh et al.,

2011)Japan b

(25)– –

13.6-79.0 34.6/–

– – – – – –(Takeshi et al.,

2006)Japan b

(21)– –

7.98-178 41.1/–

– – – – – –(Takeshi et al.,

2006)Japan nd-58.2 – nd-30.6 – – nd-5.4 nd-13.7 – – (Saito et al.,

22

44

56

57

58

45

(18) –/2.40 –/4.00 –/1.80 2007)Japan(41)

18.1-511 –/62.3

<4.80-17.5–/<4.80

<7.10-121 –/27.1

– –<15.6-32.1

–/<15.6<11.8-159

–/23.0– –

(Kanazawa et al., 2010)

Office

Sweden(4)

– – 18.0/– 25.0/– – 0.70/– 2.20/– <0.50/– –(Carlsson et al.,

1997)

Sweden(3)

nd-13.0 nd-2.004.00-6.004.67/4.00

nd-16.0 –3.00-5.004.00/4.00

– – –(Björklund et al.,

2004)Sweden(1)

– <0.20 8.20 – – 7.10 <0.20 14.0 –(Marklund et al.,

2005a)Sweden(3)

1.00-2.00 1.67/2.00

–3.00-16.0 5.33/6.00

– – – – – –(Staaf and


0.70-91.0 15.0/6.50

nd-2.70 0.60/nd

nd-100 21.0/2.30

4.40-13.0 7.80/7.30

– – – – –(Bergh et al.,

2011)Switzerland(4)

– –up to 8.10 4.68/5.30

– nd-0.370.93-3.10 2.18/2.35

nd-1.20nd-0.60

0.24/0.17–


Norway(2)

– – 8.20-16.0 <0.01-3.60 –2,300-36,000

10.0-51.0 – <0.20-0.42(Green et al.,

2008)Finland c

(4)– – <5.00 – <4.00 – <30.0 <2.00 –


Japan(14)

0.44-8.80 –/3.20

nd-0.860.46-21.7

–/6.60– – nd-0.60

nd-118 –/0.97

– –(Saito et al.,

2007)China(90)

–<0.01-0.13 0.02/<0.01

0.01-90.3 9.64/0.47

–0.03-0.20 0.11/0.09

0.25-10.2 2.09/1.41

0.02-5.47 0.81/0.27

0.29-2.58 1.12/0.84

0.06-0.75 0.35/0.22

(Yang et al., 2014)

School buildingSweden – – 37.9/– 26.3/– – 0.65/– 2.43/– <0.50/– – (Carlsson et al.,

23

46

47

(12) 1997)Sweden(1)

– 2.80 0.50 – – 0.90 <0.30 <0.20 –(Marklund et al.,

2005a)Sweden(1)

– <0.40 4.20 – – 18.0 <0.80 <0.70 –(Marklund et al.,

2005a)Sweden(1)

– 0.70 7.80 – – <0.40 <0.80 <0.80 –(Marklund et al.,

2005a)Finland c

(3)– – <5.00 – <4.00 <6.00 <30.0

<1.00-7.00 3.00/–

–(Mäkinen et al.,

2009)HotelSweden(1)

– <0.30 5.10 – – 2.30 <0.70 2.60 –(Marklund et al.,

2005a)Day care center

Sweden(4)

– – 13.0/– 7.60/– – <0.50/– 5.90/– 10.0/– –(Carlsson et al.,

1997)

Sweden(1)

– 0.60 3.70 – – 1.10 1.00 <0.30 –(Marklund et al.,

2005a)Sweden(10)

0.80-20.0 5.70/1.70

nd-0.90 0.10/–

3.70-320 61.0/18.0

nd-63.0 5.70/12.0

– – – – –(Bergh et al.,

2011)Health care facilitySweden(3)

7.00-13.0 10.0/10.0

–1.00-2.00 1.67/2.00

– – – – – –(Staaf and


– 4.80 5.40 – – 0.70 1.40 <0.20 –(Marklund et al.,

2005a)StoreSwitzerland – – 1.70-17.0 – nd-0.21 0.19-5.70 nd nd-2.80 – (Hartmann et al.,

24

48

49

(5) 11.1/13.0 1.97/1.40 0.76/0.24 2004)Switzerland(2)

– –14.0-17.0

15.5/–– nd

0.56-1.10 0.83/–

nd-2.500.62-1.20

0.91/––


Sweden(1)

– 2.60 3.60 – – 13.0 <0.5 <0.40 –(Marklund et al.,

2005a)Sweden(1)

– <0.10 31.0 – – 1.80 1.70 <0.30 –(Marklund et al.,

2005a)Sweden(1)

– 1.70 68.0 – – 2.00 0.60 <0.30 –(Marklund et al.,

2005a)Sweden(4)

1.00-19.0 7.50/5.00

–5.00-172 48.5/8.50

– – – – – –(Staaf and

Östman, 2005)Norway(2)

– – 9.80-14.0 5.90-7.90 –2,400-47,000

8.00-55.0 – <0.20(Green et al.,

2008)PrisonSweden(1)

– <0.40 20.0 – – <0.40 55.0 3.50 –(Marklund et al.,

2005a)Public recreation placeSwitzerland(1)

– – 29.0 – 2.10 3.40 nd 3.40 –(Hartmann et al.,

2004)Sweden(1)

– 5.60 12.0 – – <0.10 1.80 <0.20 –(Marklund et al.,

2005a)Sweden(1)

– 3.00 <0.20 – – 6.60 3.30 <0.40 –(Marklund et al.,

2005a)Factory/workshopSweden(2)

–4.40-8.40

6.40/–3.80-7.80

5.80/–– –

6.30-23.0 14.6/–

1.70-3.20 2.45/–

<0.50-1.20 –(Marklund et al.,

2005a)

25

50

51

Sweden(3)

1.00-23.0 9.00/3.00

–3.00-28.0 9.33/6.00

– – – – – –(Staaf and

Östman, 2005)Finland c

(6)– –

<7.00-60.0 10.0/–

– <2.009.00-90.0

30.0/–<20.0 <1.00 –


Finland d

(4)– –

<7.00-40.0 10.0/–

–<3.00-8.00

4.00/–30.0-100

50.0/–<50.0 <4.00 –


Finland c

(7)– –

110-1,200 560/–

– <4.00<3.00-530

9.00/–<30.0 <1.00 –


Finland d

(2)– –

40.0-60.0 50.0/–

–<4.00-7.00

4.00/–<10.0 <60.0 <4.00 –


Electronics recycling plantSweden(12)

– –9.00-18.0

14.0/–– –

12.0-40.0 19.0/–

20.0-36.0 29.0/–

– –(Sjödin et al.,

2001)Finland c

(4)– – <5.00 – <4.00

<5.00-10.0 20.0/–

<30.0 <2.00 –(Mäkinen et al.,

2009)Finland d

(6)– – <8.00 –

60.0-180 100/–

490-1,700 760/–

<50.0<2.00-70.0

6.00/––


Finland c

(2)– –

<9.00-100 20.0/–

– <4.00480-660

560/–<40.0 <3.00 –


Finland d

(6)– – <9.00 –

<3.00-8,100 110/–

<4.00-10,300 850/–

<60.0<2.00-90.0

10.0/––


China e

(4)– – – – –

28,600-72,600 46,500/42,500

– – – (Bi et al., 2010)

VehicleSwitzerland(4)

– –2.50-14.0 8.68/9.10

– nd0.36-0.90 0.68/0.74

nd nd –(Hartmann et al.,

2004)

26

52

53

Sweden(4)

nd-220 69.5/29.0

–5.00-15.0 8.25/2.00

– –nd-3.00 0.75/–

–nd-18.0 4.50/–

–(Staaf and

Östman, 2005)

Garage

Sweden(3)

nd-6.00 2.33/1.00

–2.00-4.00 2.67/2.00

– –nd-1.00 0.33–

–nd-2.00 0.67/–

–(Staaf and

Östman, 2005)Exhaust gasJapan(4)

– – – –258-659 405/353

– – – –(Takimoto et al.,

1999)Patch samples (ng/cm2) f

Finland(3)

– <0.08 <0.07 – <0.06 – <0.50 <0.03 –(Mäkinen et al.,

2009)Finland(3)

– <0.080.10-7.40

1.60/–– <0.06 – <0.50 <0.03 –


Finland(5)

–1.00-9.30

3.10/–<0.07 –

<0.06-0.60 0.09/–

– <0.50<0.03-1.50

0.20/––


Finland(6)

–<0.08-160

6.70/–<0.07 –

<0.06-2.90 0.30/–

– <0.50 <0.03 –(Mäkinen et al.,

2009)Hand samples (μg/hands) f

Finland(2)

–1.30-8.70

3.30/–<0.002 –

up to 0.35 0.02/–

– <0.01 <0.001 –(Mäkinen et al.,

2009)Finland(2)

–22.0-25.0

24.0/–<0.002-17.0

0.12/––

0.61-4.10 1.60/–

– <0.010.17-0.48

0.29/––


a The arrangement and detailed descriptions of the matrices are in agreement with Table S3 without specification.b Concentrations refer to 10th-90th range and geometric mean.c Average concentrations for stationary air samples in geometric mean.d Average concentrations for personal air samples in geometric mean.

27

54

59

60

61

62

55

e Samples from workshops for recycling of printed circuit boards.f Average concentrations in geometric mean.The full names for the halogenated OPs are listed in Table 1. –: data not available. nd: not detected.

28

56

63

64

65

57

Table S5Comparison of the concentrations (range; arithmetic mean/median; pg/m3) of ΣOP and the major halogenated OPs in outdoor air from different regions around the world.

Location (no.)Description of

matrixΣOP TCEP TCIPP TDCPP Ref

Urban area

Japan (25) aBack yard of the houses, summer

–4,570-58,400

14,300/–– – (Takeshi et al., 2006)

Japan (21) aBack yard of the houses, winter

–2,600-11,700

6,590/–– – (Takeshi et al., 2006)

Japan (8)Verandas and

below the eavesnd-8,300 – nd-3,100 – (Saito et al., 2007)

Norway (6) Near main roads1,370-20,300 6,970/3,120

510-6,200 2,390/1,450

240-3,700 1,320/490

<40.0-72.047.0/<40.0

(Green et al., 2008)

USA (27) Urban sites270-4,450

1,500/1,390180/120 530/410 120/79.0 (Salamova et al., 2014b)

USA (22) Urban sites110-8,100

2,100/1,310120/110 850/320 520/110 (Salamova et al., 2014b)

Remote area

Finland (1)Background air

from remote area13,000 2.00 810 20.0 (Marklund et al., 2005c)

Norway (7) Remote areas119-1,100 410/230

<200-270 124/<200

<200-330 133/<200

<40.0-250 124/<200


Sturgeon Point, USA (16)

Rural and remote sites

30.0-1,100 34.0/21.0

130/150 170/72.0 28.0/28.0 (Salamova et al., 2014b)

Sleeping Bear Dunes, Rural and remote 22.0-27.0 11.0/8.00 25.0/27.0 nd (Salamova et al., 2014b)

29

58

66

67

68

59

USA (16) sites 12.0/11.0Eagle Harbor, USA (26)

Rural and remote sites

13.0-740 170/100

6.00/6.00 32.0/29.0 52.0/32.0 (Salamova et al., 2014b)

European Arctic (34)Atmospheric

particle33.0-1,450

426/3344.00-63.0 19.0/15.0

10.0-186 62.0/57.0

2.30-294 59.0/10.0

(Salamova et al., 2014a)

Sea areaGerman North Sea (20)

Marine atmosphere

109-1420 513/370

6.00-16342.9/30.5

38.0-1,200332/271

nd-78.06.05/–

(Möller et al., 2011)

The West Pacific, Ocean (11)

Marine aerosols –0.90-130

30.0/–0.20-9.00

1.20/–0.75-830

130/–(Cheng et al., 2013)

Southern Ocean near Antarctica (20)

Marine aerosols –0.70-40.0

6.00/–0.10-4.00

0.89/–0.80-78.0

11.0/–(Cheng et al., 2013)

The Pacific, Indian, Arctic, Southern Ocean (30)

Marine aerosols110-2,900 700/550

20.0-2,000 280/190

20.0-620 250/210

nd-780 80.0/40.0

(Mo ̈ller et al., 2012)

Arctic Ocean (6) Marine aerosols220-1,400 690/540

170-590 360/290

90.0-530 270/280

nd-7.00 (Mo ̈ller et al., 2012)

Northern Pacific Ocean (3)

Marine aerosols290-630 470/480

160-280 220/200

100-270 180/170

5.00-8.00 6.00/5.00


Sea of Japan (2) Marine aerosols450-2,900

1,680/–240-1,960

1,100/–130-620

380/–20.0-50.0

30.0/–(Mo ̈ller et al., 2012)

Indian Ocean (15) Marine aerosols110-1,200 630/590

46.0-570 210/180

37.0-550 300/220

nd-220 58.0/37.0


Southern Ocean (1) Marine aerosols 270 74.0 55.0 80.0 (Mo ̈ller et al., 2012)

30

60

61

East Indian Archipelago, Philippine Sea (3)

Marine aerosols120-1,700 680/270

19.0-160 74.0/43.0

22.0-410 160/43.0

49.0-78.0 300/80.0


Mediterranean Sea (19)

Marine aerosols414-5,110

2,240/1,89069.7-854 300/210

126-2,340 963/953

nd-460 79.7/44.0

(Castro-Jimenez et al., 2014)

Black Sea (4) Marine aerosols1,720-6,170 2,970/2,010

308-2,420 928/492

539-2,720 1,230/820

nd-96.9 60.3/72.1


a Concentration refers to 10th-90th range and geometric mean.The full names for the halogenated OPs are obtained in Table 1. –: data not available. nd: not detected.

31

62

69

70

63

Table S6Comparison of the concentrations (range; arithmetic mean/median; pg/m3) of the predominant non-halogenated OPs in outdoor air from different regions worldwide.

Location (no.) a TEP TPP TNBP TIBP TPHP TBOEP TEHP RefUrban area

Japan (25) b – –6,000-33,000

13,700/–– – – – (Takeshi et al., 2006)

Japan (21) b – –6,000-24,100

9,270/–– – – – (Takeshi et al., 2006)

Japan (8) nd-1,400 – nd-1,700 – – nd-1,100 – (Saito et al., 2007)

Norway (6) – –300-3,700 1,270/570

320-4,400 1,250/410

<50.0-1,000132/<70.0

<100-340133/200

– (Green et al., 2008)

USA (27) – – 250/180 – 140/110 320/260 41.0/42.0 (Salamova et al., 2014b)

USA (22) – – 150/130 – 200/180 330/230 66.0/57.0 (Salamova et al., 2014b)

Remote area

Finland (1) – – 280 – 12,000 - – (Marklund et al., 2005c)

Norway (7) – – <200<10.0-230 149/150

<50.0<100-150 64.3/<100


Sturgeon Point, USA (16)

– – 34.0/32.0 – 43.0/34.0 76.0/77.0 9.00/8.00 (Salamova et al., 2014b)

Sleeping Bear Dunes, USA (16)

– – 34.0/28.0 – 42.0/44.0 67.0/58.0 5.00/9.00(Salamova et al.,

2014b)

Eagle Harbor, – – 180/61.0 – 55.0/31.0 68.0/51.0 9.00/9.00 (Salamova et al., 2014b)

32

64

71

72

73

65

USA (26)

European Arctic (34) – –5.60-1,000174/56.0

–1.10-52.020.0/17.0

47.0-208100/63.0

1.00-42.012.0/9.00

(Salamova et al., 2014a)

Sea areaGerman North Sea (20)

– –nd-150

44.7/35.0nd-150

29.1/22.04.00-29034.4/16.5

nd-80.018.1/6.50

nd-31.05.95/2.50

(Möller et al., 2011)

West Pacific Ocean (11)

– – – – –nd-95.019.0/–

– (Cheng et al., 2013)

Southern Ocean near Antarctica (20)

– – – – –nd-20.02.00/–

– (Cheng et al., 2013)

Pacific, Indian, Arctic, Southern Ocean (30)

– –nd-80.0

20.0/10.0nd-100

30.0/20.0nd-160

30.0/20.0nd-80.0

30.0/30.0nd-90.0

20.0/8.00(Mo ̈ller et al.,

2012)

Arctic Ocean (6) – –nd-40.0

20.0/10.020.0-40.0 20.0/30.0

10.0-60.0 20.0/20.0

nd-11.0nd-6.00

3.00/1.00(Mo ̈ller et al., 2012)

Northern Pacific Ocean (3)

– –6.00-14.0 11.0/13.0

nd-21.0 14.0/21.0

9.00-20.0 20.0/20.0

nd-20.0 8.00/7.00

1.00-10.0 5.00/3.00


Sea of Japan (2) – –10.0-33.0

22.0/–11.0-63.0

37.0/–25.0-97.0

61.0/–15.0-81.0

48.0/–5.00-38.0

22.0/–(Mo ̈ller et al., 2012)

Indian Ocean (15)

– –7.00-75.0 26.0/21.0

7.00-96.0 29.0/23.0

nd-74.0 26.0/18.0

nd-44.0 32.0/32.0

4.00-51.0 19.0/14.0


Southern Ocean (1) – – 14.0 16.0 19.0 nd 7.00(Mo ̈ller et al.,

2012)

33

66

67

Philippine Sea (3) – –10.0-33.0 18.0/12.0

10.0-23.0 15.0/12.0

nd-155 88.0/17.0

nd-77.06.00-92.0 37.0/12.0


Mediterranean Sea (19)

– –56.5-599 295/304

4.20-644 237/171

nd-79.5 25.3/21.2

–55.8-307 149/121


Black Sea (4) – –202-369 306/327

66.5-191 143/158

2.70-40.1 28.5/35.6

–36.3-191 149/184


a The arrangement and detailed descriptions of the matrices are in line with Table S5 without specification.b Concentration refers to 10th-90th range and geometric mean.The full names for the non-halogenated OPs are obtained in Table 1. –: data not available. nd: not detected.

34

68

74

75

76

69

Table S7Comparison of the concentrations (range; arithmetic mean/median; ng/L) of ΣOP and the predominant halogenated OPs in waters and precipations available around the world.

Location (no.) Description of matrix ΣOP TCEP TCIPP TDCPP RefSewage treatment plant (STP) influent waste water Germany(1)

Municipal STP 38,900 21,100 – –(Fries and Püttmann,

2001)Germany(3)

Municipal STP17,200-41,200 29,200/29,200

853-1,120 986/983

– –(Fries and Püttmann,

2003)Germany(1)

Industrial STP 4,810 568 – –(Fries and Püttmann,

2003)Germany(5)

Population equivalents: 300,000

– –240-1,000 512/470

– (Bester, 2005)

Germany(18)

Population equivalents: 1,100,000

–up to 640

253/–460-5,800

1,550/–up to 250

103/–(Meyer and Bester,

2004)Sweden(9)

Population equivalents: 3,400-775,000

17,000-69,000 36,900/32,000

90.0-1,000 468/420

1,100-18,000 3,960/2,500

210-450 311/310

(Marklund et al., 2005b)

Spain(11)


–81.0-720450/570

293-6,500 960/910

9.10-81.0 25.0/26.0

(Rodil et al., 2012)

Norway(3)


–2,000-2,5002,170/2,000

1,860-2,900 2,320/2,200

630-820 727/730


Sewage treatment plant effluent waste waterGermany(1)

Municipal STP 35,800 33,800 – –(Fries and Püttmann,

2001)Germany(3)

Municipal STP 2,030-7,510 3,930/2,250

214-557 352/286


2003)

35

70

77

78

79

71

Germany(1)

Industrial STP 397 nd – –(Fries and Püttmann,

2003)Germany(5)

Population equivalents: 300,000

– –230-610 380/330

– (Bester, 2005)

Germany(11)

Effluent – 5.00-130 50.0-400 20.0-120(Andresen et al.,

2004)Germany(18)

Population equivalents: 1,100,000

–up to 470

357/–680-6,600

2,270/–up to 310

137/–(Meyer and Bester,

2004)Austria(5)

Population equivalents <10,000

–43.0-1,600 391/74.0

350-1,000 560/460

23.0-260 85.0/53.0

(Martínez-Carballo et al., 2007)

Austria(7)


–80.0-150 110/100

310-960 730/580

27.0-160 81.0/74.0


Austria(4)

Population equivalents >1,000,000

–<8.80-140 67.0/61.0

270-1,400 733/630

19.0-1,400 387/65.0


Spain(11)


–81.0-810 380/382

100-4,800 810/812

9.20-81.0 44.0/46.0

(Rodil et al., 2012)

Sweden(3)

Municipal and industrial STP

–<500-36,000

22,700/32,000<500-4,000 2,330/3,000

<500-3,000 1,330/1,000

(Paxéus, 1996)

Sweden(8)


7,900-39,000 9,790/6,750

350-890 504/465

1,500-24,000 4,690/2,000

130-340 225/210


Western Europe(13)

Population equivalents: 3,400-1,600,000

– 200/– 600/– –(Reemtsma et al.,

2006)Norway(3)


–1,600-2,2001,800/1,600

1,700-2,100 1,900/1,900

86.0-740 499/670


South Korea(7)

Industrial and Municipal STP

–92.0-2,620

537/–– – (Kim et al., 2007)

36

72

73

Raw water from sea-based solid waste disposal site

Japan(12)

Waste disposal site23,400-157,000 59,400/28,300

4,230-87,400 24,800/8,260

11,300-48,200

2,3500/16,200

680-6,180 2,290/1,180

(Kawagoshi et al., 1999)

Japan(12)

Surrounding sea50.0-8,750 2,420/1,570

up to 4,640 1,400/930

up to 3,060 872/510

up to 200 140/150


River waterJapan(–)

River and sea water – 14.0-347 16.0-176 –(Ishikawa et al.,

1985)USA(139)

Stream waterup to 540

–/100up to 160

–/100– – (Kolpin et al., 2002)

Germany(51)

Rhine, Elbe, Main, Oder, Nidda and Schwarzbach

River, river shores

519-1,870 932/665

17.0-220 84.6/36.0


2001)

Germany(14)

Oder River190-2,820

1,280/1,260nd-1,240 317/220


2003)Germany(26)

Ruhr River – 13.0-130 20.0-200 27.0-57.0(Andresen et al.,

2004)Germany(5)

Rhine River – – 80.0-100 13.0-36.0 (Andresen et al., 2004)

Germany(1)

Lippe River – – 100 17.0(Andresen et al.,

2004)USA(18)

Stream water –48.0-700

–/195–

100-400 –/200

(Haggard et al., 2006)

South Korea(8)

The Youngsan, the Nakdong and Han River

–14.0-81.0

42.0/–– – (Kim et al., 2007)

37

74

75

Austria(4)

Danube, Schwechat and Liesing River

141-922 438/345

13.0-130 50.8/30.0

33.0-170 89.0/76.5

<3.00-19 13.7/15.0


Italy(2)

Tiber River – nd-7.00 54.0-117 –(Bacaloni et al.,

2007)Spain(28)

Along the Mero river basin –up to 56.0 6.80/7.20

up to 720 65.0/69.0

nd (Rodil et al., 2012)

Germany(16)

Elbe, Weser, Ems, Rhine, Meuse and Scheldt River

58.3-1,090 402/381

3.29-69.9 22.9/18.7

24.3-570 146/128

5.30-67.0 22.5/18.9

(Bollmann et al., 2012)

Germany(53)

Elbe River85.4-511 227/214

–31.0-305 94.4/65.2

–(Bollmann et al.,

2012)UK(13)

Aire River113-26,300 6,030/2,760

119-316167/181

113-26,100 6,040/–2,500

62.0-14974.3/74.0

(Cristale et al., 2013b)

Spain(32)

Arga, Nolón and Besòs River, source to mouth

up to 7,200 1,490/110

<1.50-330 85.0/16.0

<7.20-1,800 445/69.0

<5.30-200 91.7/100

(Cristale et al., 2013a)

Marine waterMediterranean coast (6)

Coastal waternd-1,250 339/175

nd-400 117/–

– –(Barceló et al.,

1990)North Sea(15)

Marine water – –0.90-7.90 3.78/3.10

– (Weigel et al., 2005)

German Bight, North Sea (14)

Offshore water6.90-37.016.6/14.7

1.00-6.00 2.76/2.80

5.00-28.1 12.2/10.8

0.90-2.80 1.61/1.20

(Andresen et al., 2007)

Germany Bight(18)

Marine water from German Bight

5.00-50.0 – 3.00-28.0 –(Bollmann et al.,

2012)Pearl River Delta, China (23)

Coastal water, dry season2,040-3,120 2,620/2,750

400-640 504/495

470-1,150 761/750

– (Wang et al., 2014)

38

76

77

Pearl River Delta, China(23)

Coastal water, wet season 1,080-2,500 1,520/1,360

220-1,160 421/320

150-570 328/295

– (Wang et al., 2014)

China (13)

Seawater from Yellow Sea and East China Sea

91.9-1390 440/342

21.0-618 134/55.2

15.8-170 88.9/84.1

24.0-378 115/88.5

(Hu et al., 2014)

Lake waterGermany a

(42)Urban lentic surface water –

9.00-66.0 –/23.0

27.0-175 –/85.0

–(Regnery and

Püttmann, 2010a)Germany a

(41)Urban lentic surface water –

14.0-184 –/61.0

52.0-379 –/126

–(Regnery and

Püttmann, 2010a)Central Italy b

(13)Volcanic lakes, surrounded by houses and bath resorts

55.0-1,530 562/326

nd-27.010.5/9.00

6.00-62.024.0/19.0

5.00-67876.7/20.0

(Bacaloni et al., 2008)

Germany a

(2)Remote lentic surface water –

<3.00-15.0 –/3.00

<4.00-46.0 –/14.0

–(Regnery and



<3.00-9.00 –/3.00

<4.00-32.0 –/7.00

–(Regnery and



<3.00-27.0 –/3.00

<4.00-115 –/18.0

–(Regnery and

Püttmann, 2010a)Germany(2)

Montan lake – 6.00/– 31.0/– –(Regnery and


Montane reservoir, close to a road and a campground

– 33.0/– 312/– –(Regnery and Püttmann,

2010a)Central Italy b

(13)Volcanic lakes, unspoiled

environment33.0-134 61.5/48.0

nd-5.000.38/nd

nq-5.002.08/2.00

nd-23.05.23/2.00


Drinking water sourceUSA Source water for drinking- – up to 120 – up to 110 (Stackelberg et al.,

39

78

79

(12) water treatment 95.0/– 102/– 2007)Germany(4)

Montane reservoir, drinking water supply

– 18.5/– 45.0/– –(Regnery and Püttmann,

2010a)

Central Italy b

(13)

Volcanic lakes, affected by agricultural and tourism

activities

22.0-2,110 401/167

nd-64.012.5/nd

2.00-27.011.8/10.0

nd-35.012.4/7.00


South Korea(2)

Lake water near intake points for water supply of

Seoul and Gwangju– 14.0-25.0 – - (Kim et al., 2007)

Drinking waterUSA(4)

Finished water from drinking-water treatment

–70.0-99.086.0/87.5

–150-250190/181

(Stackelberg et al., 2004)

USA(12)


–up to 50.0

4.00/––

up to 70.0 12.0/–


South Korea(2)


– <10.0 – – (Kim et al., 2007)

Spain(24)

Metropolitan area, private homes

–up to 47.0 7.20/<7.30

29.0-200 57.0/62.0

nd (Rodil et al., 2012)

China(39)

Tap water, inland/coastal and developed/less

developed cities

85.1-325 165/–

14.4-83.212.5/–

14.4-83.233.4/–

1.50-3.502.50/–

(Li et al., 2014)

Ground waterGermany (45)

Water from wells –1.00-754

–/50.0– –

(Fries and Püttmann, 2001)

Germany (76)

Water from wells154-1,770 747/620

nd-312 150/108


2003)

40

80

81

Italy(9)

Water from wells –nd-8.000.89/–

nd-12.02.33/–

nd(Bacaloni et al.,

2008)Rain waterGermany (1)

Rain water from urban area

1,390 79.0 – –(Fries and Püttmann,

2001)Germany(1)


1,430 121 – –(Fries and Püttmann,

2003)Germany(2)

Roof runoff from urban area

869-1,500 80.0-148 – –(Fries and Püttmann,

2003)Germany(26)


–up to338–/73.0

46.0-2,660 –/743

up to 32.0–/7.00

(Regnery and Püttmann, 2009)

Germany (90)


–10.0-485

–/71.032.0-3,560

–/403<1.00-532

–/5.00(Regnery and

Püttmann, 2010b)Germany (42)

Storm water holding tank, urban area

–33.0-275

–/77.016.0-5,790

–/880<1.00-73.0


Püttmann, 2010b)Italy(2)

Rome, urban area1,440-1,650

1,550/–149-161

155/–633-739

686/–360-448

404/–(Bacaloni et al.,

2008)Germany(27)

Rain water from sparsely populated area

–up to 390

53.7/–up to 1150

139/–up to 53.0


Püttmann, 2009)Germany(29)

Rain water from rural area

–11.0-390

–/40.0<1.00-497

–/16.0<1.00-497


Püttmann, 2010b)Germany (48)

Rain water from small village

–<2.00-127

–/12.05.00-1,210

–/134<1.00-87.0


Püttmann, 2010b)Germany(10)

Storm water holding tank, small village

–23.0-131

–/78.0197-4,850

–/410<1.00-36.0


Püttmann, 2010b)Italy From a parking site near 234 19.0 28.0 108 (Bacaloni et al.,

41

82

83

(1) Martignano Lake 2008)Ireland(-)

Rain water from seashore in remote area

– 1.00-21.0 1.00-4.50 –(Laniewski et al.,

1998)Snow (ng/kg)Poland and Sweden (-)

Snow 1,000-4,500 – – –(Laniewski et al.,

1998)Finland(7)

Snow from remote areas130-26,000 9,300/430

7.00-39.0 20.0/12.0

70.0-210 130/120

4.00-230 40.0/10.0

(Marklund et al., 2005c)

Finland(3)

Snow from airport13,000-26,000 21,300/25,000

29.0-39.0 350/370

100-210 140/120

4.00-20.0 8.00/5.00


Finland(3)

Snow from road intersection150-430 330/400

7.00-10.0 9.00/8.00

110-170 140/130

8.00-230 80.0/10.0


Finland(1)

Snow from a forested area 130 7.00 70.0 30.0(Marklund et al.,

2005c)Germany d

(42)Snow from sparsely

populated area–

up to 488–/42.0

up to 385–/73.0

up to 113–/23.0


Finland e

(1)Deposition from Pallas 1,300 550 510 –


a Concentration range for 5th-95th range.b Concentration ranges for mean values.c Concentration ranges refer to median levels.d Concentration in ng/L for deposition flux.e Concentration in ng/m2/d for deposition flux.The full names for the halogenated OPs are obtained in Table 1. –: data not available. nd: not detected. nq: not quantified.

42

84

80

81

82

83

84

85

85

Table S8

Comparison of the concentrations (range; arithmetic mean/median; ng/L) of the predominant non-halogenated OPs in waters and precipations from different regions around the world.

Location (no.) a TEP TPP TMPP TNBP TIBP TBOEP TPHP TEHP Ref

Sewage treatment plant (STP) influent waste waterGermany(1)

– – – 5,030 – 12,800 – –(Fries and


– – –13,000-19,900 15,400/13,300

–2,920-27,400 12,800/8,170

– –(Fries and


– – – 2,340 – 1,900 – –(Fries and


– – –up to 5,500

887/–up to 2,200

1,150/–1,800-8,000

3,800/–up to 290

114/––

(Meyer and Bester, 2004)

Sweden(9)

– – –6,600-52,000 18,900/13,000

5,200-35,000 12,800/9,400

6,600-52,000 18,900/13,000

76.0-290 178/180


2005b)Spain(11)

up to 82.0 25.0/57.0

– –290-3,800 810/820

up to 10,000 600/580

63.0-8,300 860/910

– – (Rodil et al., 2012)

Norway(3)

– – –160-1,800 747/280

210-410 297/270

5,600-9,200 7,930/9,000

3,100-14,000 8,330/7,900


Sewage treatment plant effluent waste waterGermany(1)

– – – 1,490 – 542 – –(Fries and


– – –50.0-1,120

622/694–

775-7,180 2,960/913

– –(Fries and

Püttmann, 2003)Germany – – – 235 – 162 – – (Fries and

43

86

86

87

88

87

(1) Püttmann, 2003)Germany(11)

– – – – <6.30-2,000 <10.0-500 10.0-30.0 –(Andresen et al.,

2004)Germany(18)

– – –up to 2,300

380/–up to 290

133/–65.0-1,200

427/–up to 250

53.0/––

(Meyer and Bester, 2004)

Austria(5)

34.0-110 71.0/62.0

– nd-55.0<11.0-310 146/190

–180-2,700 916/270

<7.00-170 –(Martínez-Carballo

et al., 2007)Austria(7)

56.0-210 121/110

– –nd-810 292/220

–13.0-5,400

967/130<7.00-87.0 26.0/19.0

–(Martínez-Carballo

et al., 2007)Austria(4)

22.0-120 60.0/49.0

– –<11.0-420 160/108

–17.0-2,900

794/12914.0-30.0 22.0/23.0

–(Martínez-Carballo

et al., 2007)Spain(3)

up to 73.0 18.0/–

– –72.0-810 420/570

up to 480 78.0/80.0

46.0-4,700 630/720

– – (Rodil et al., 2012)

Sweden(3)

– – –nd-3,000

1,670/2,000–

nd-28,000 15,700/19,000

nd-3,000 – (Paxéus, 1996)

Sweden(8)

– – –360-6,100

2,800/2,700–

3,100-30,000 9,790/6,750

41.0-130 71.0/58.0


2005b)Norway(3)

– – –270-1,300 643/360

250-310 277/270

1,600-3,300 2,700/3,200

1,700-3,500 2,700/2,900


Raw water from sea-based solid waste disposal siteJapan(12)

1,330-10,600 5,770/5,380

– –230-1,490 693/530

–850-6,260 2,280/129

up to 110 25.0/–

–(Kawagoshi et al.,

1999)Japan(12)

10.0-1,000 258/110

– –up to 150 51.8/40.0

– – – –(Kawagoshi et al.,

1999)River waterJapan – – 67.0-259 5.00-36.0 – 13.0-31.0 – – (Ishikawa et al.,

44

88

89

(–) 1985)USA(139)

– – – – – –up to 220

–/40– (Kolpin et al., 2002)

Germany(51)

– – –100-1,510 485/218

–103-663 378/386

– –(Fries and


– – –69.0-1,040

510/417–

121-952 478/420

– –(Fries and


– – – 13.0-130 50.0-160 10.0-200 <10.0-80.0 –(Andresen et al.,

2004)Germany(5)

– – – 30.0-120 30.0-50.0 80.0-140 – –(Andresen et al.,

2004)Germany(1)

– – – 30.0 100 130 – –(Andresen et al.,

2004)USA(18)

– – –31.0-560

–/100– nd

9.00-63.0 –/34.0

–(Haggard et al.,

2006)Austria(4)

13.0-51.0 31.8/31.5

– nd20.0-110 71.8/78.5

–24.0-500 179/96.0

<4.40-10.0 7.67/7.00

nd(Martínez-Carballo

et al., 2007)Italy(2)

27.0-45.0 – – 82.0-114 98.0-137 87.0-323 11.0-165 –(Bacaloni et al.,

2007)Spain(7)

up to 56.0 7.10/–

– –10.0-380 63.0/65.0

nd nd – – (Rodil et al., 2012)

Germany(16)

5.16-84.5 42.0/42.9

– –<3.80-84.0 31.6/16.1

7.50-62.8 25.5/25.7

<3.90-103 41.2/38.8

<3.68-10.3 1.11/<3.68

–(Bollmann et al.,

2012)Germany(53)

10.0-179 26.3/19.6

– –7.50-62.8 25.5/25.7

–<5.85-146 31.9/27.0

– –(Bollmann et al.,

2012)UK(13)

– – – – – –6.30-22.013.1/15.1

–(Cristale et al.,

2013b)

45

90

91

Spain(32)

– –<4.20-9.20 6.80/6.30

<1.20-370 92.0/53.0

<1.90-1,200 113/11.0

<44.0-4,600 1,450/1,700

<1.60-35.0 11.6/7.40

<0.80-4.00 2.00/1.70


Japan b

(77)– –

80.0-1,840 720/–

– – – – – (Cho et al., 1994)

Marine waterMediterranean coast (6)

– – –nd-300

70.2/50.0nd-900 152/–

– – – (Barceló et al., 1990)

German Bight (14)

– – –up to 2.40

0.47/––

up to 2.000.14/–

up to 3.450.47/–

–(Andresen et al.,

2007)Germany(18)

0.70-7.00 – – – 0.50-5.00 <1.95-6.00 – –(Bollmann et al.,

2012)Lake waterGermany c

(42)– – –

9.00-122 –/32.0

<7.00-82.0 –/10.0

<30.0-30.0 –/<30.0

– –(Regnery and

Püttmann, 2010a)Germany c

(41)– – –

<4.00-83.0 –/17.0

<7.00-62.0 –/8.00

<30.0-652 –/53.0

– –(Regnery and

Püttmann, 2010a)Italy d

(13)nq-42.0

13.2/13.0nq-486

98.0/13.01.00-12.05.46/5.00

nq-784146/42.0

1.00-380141/96.0

9.00-12741.1/23.0

nq-16.06.00/4.00

–(Bacaloni et al.,

2008)Germany c

(2)– – –

<4.00-23.0–/4.00

<7.00-56.0–/<7.00

– – –(Regnery and


(20)– – –

<4.00- 33.0 –/<4.00

<7.00-101 –/7.00



(20)– – –

<4.00-29.0 –/5.00

<7.00-77.0 –/9.00



(2)– – – <4.00 <7.00 <30.0 – –

(Regnery and Püttmann, 2010a)

46

92

93

Germany(4)

– – – 4.50/– <7.00 <30.0 – –(Regnery and


– – – 7.00/– 11.0/– 31.0/– – –(Regnery and

Püttmann, 2010a)Italy d

(13)1.00-5.002.38/2.00

1.00-5.002.15/2.00

nq-7.002.77/3.00

4.00-19.08.92/8.00

3.00-21.09.00/7.00

8.00-54.024.8/17.0

nq-8.003.00/3.00


2008)Drinking water sourceUSA(12)

– – –up to 140

48.0/––

up to 570 35.7/–

up to 80.0 49.0/–

–(Stackelberg et al.,

2007)Italy d

(13)1.00-27.05.62/2.00

2.00-951120/13.0

nq-17.06.15/4.00

3.00-636113/17.0

1.00-35167.8/22.0

8.00-11541.5/26.0

nq-21.08.38/6.00


2008)Drinking waterUSA(4)

– – –80.0-10087.0/84.0

–170-350225/190

nd –(Stackelberg et al.,

2004)USA(12)

– nd –up to 180

15.0/–– nd – –


Spain(24)

up to 47.0 7.20/7.80

– –10.0-290 57.0/60.0

nd nd – – (Rodil et al., 2012)

China(39)

– – –24.1-151

7.48/––

24.1-151 70.1/–

19.8-84.1 40.0/–

– (Li et al., 2014)

Ground waterGermany (45)

– – –1.00-3,720

–/421–

1.00-2,010–/281

– –(Fries and

Püttmann, 2001)Germany (76)

– – –nd-1,120 449/278

–154-410 273/277

– –(Fries and

Püttmann, 2003)Italy nd-1.00 nd-5.00 nd-29.0 nd-10.0 nd-10.0 nq-53.0 nd-164 – (Bacaloni et al.,

47

94

95

(9) 0.22/– 1.33/– 3.22/– 2.33/– 2.33/– 20.0/18.0 22.3/6.00 2008)Rain waterGermany (1)

– – – 922 – 393 – –(Fries and


– – – 911 – 394 – –(Fries and


– – – 669-907 – 120-448 – –(Fries and

Püttmann, 2003)

Germany(26)

– – –up to 818

–/203up to 1,410

–/244up to 162

–/25.0– –


Germany (90)

– – –<1.00-1,680

–/108<2.00-1,410

–/106<3.00-505

–/21.0– –

(Regnery and Püttmann, 2010b)

Germany (42)

– – –4.00-417

–/57.02.00-1,480

–/117up to 1,620

–/77.0– –


Italy(2)

42.0-50.0 46.0/–

15.0-19.0 17.0/–

3.00-4.00 3.50/–

44.0-48.0 46.0/–

30.0-39.0 34.5/–

109-115 112/–

18.0-20 19.0/–


2008)Germany(27)

– – –up to 253

–/57.0up to 367

–/97.0up to 208

–/7.00– –


Germany(29)

– – –<1.00-458

–/64.0<2.00-424

–/41.0<3.00-242

–/17.0– –


Germany (48)

– – –<1.00-110

–/16.0<2.00-160

–/14.0<3.00-205

–/<3.00– –


Germany(10)

– – –13.0-347

–/13832.0-826

–/359up to 77.0

–/36.0– –


Italy(1)

12.0 2.00 2.00 11.0 6.00 38.0 8.00 –(Bacaloni et al.,

2008)

48

96

97

Japan f

(8)– –

1.00-97.0 30.0/–

– – – – – (Cho et al., 1996)

Japan g

(–)– –

18,500-101,000 61,000/–

– – – – – (Cho et al., 1996)

Japan h

(4)– –

33.0-87.0 59.5/58.9

– – – – –(Takimoto et al.,

1999)Snow (ng/kg)Finland(7)

– –nd-9,900 3,650/430

10.0-25,000 7,170/20.0

–2.00-90.0 20.0/7.00

4.00-830 220/70.0

nd-130 60.0/50.0


Finland(3)

– –260-9,900 3,650/780

2,100-25,000 16,700/23,000

–7.00-90.0 40.0/30.0

120-830 500/540

1.00-100 30.0/8.00


Finland(3)

– – nd10.0-20.0 20.0/10.0

–4.00-10.0 7.00/6.00

4.00-70.0 30.0/7.00

nd-130(Marklund et al.,

2005c)Finland(1)

– – – 20.0 – 2.00 4.00 –(Marklund et al.,

2005c)Germany i

(42)– – –

up to 458–/64.0

up to 458–/100

up to 242–/17.0

– –(Regnery and

Püttmann, 2009)Finland j

(1)– – – 230 – – – –


Oil product (μg/g)Finland(14

– – <0.30-12,000 <0.50-190,000 – – <0.30-8.90 <0.30-4.20(Marklund et al.,

2005c)a The arrangement and detailed descriptions of the matrices are in accordance with Table S7 without specification.b River water collected from Kurose River, Japan.c Concentrations refer to 5th-95th range for lake water from urban areas. d Concentration ranges for mean values.

49

98

89

90

91

92

99

e Concentration ranges for median levels. f Rain water collected from rooftop on a campus in Hiroshima.g Rainfall drop from a greenhouse.h Rain water collected from Kurose river basin.i Concentration in ng/L for snow samples.j Concentration in ng/m2/d for deposition flux.The full names for the non-halogenated OPs are available in Table 1. –: data not available. nd: not detected. nq: not quantified.

50

100

93

94

95

96

97

98

99

101

Table S9

Comparison of the concentrations (range; arithmetic mean/median; ng/g) of ΣOP and the dominant halogenated OPs in sludge, sediment and soil samples from different regions around the world.

Location (no.) Description of matrix ΣOP TCEP TCIPP TDCPP RefSludge from sewage treatment plant

Norway (2)Population equivalents: 45000-290000

3,870-4,810 <9.00 650-944 110-330 (Green et al., 2008)

Sweden (17)Population equivalents: 3400-775000

620-6,900 4,240/4,400

6.60-110 40.9/36.0

61.0-1,900 874/790

3.00-260 79.8/41.0


Spain (5) Primary sludge – nd820-2,900

–/920300-600

–/310(Cristale and

Lacorte, 2013)

Spain (5) Biology sludge – nd600-950

–/81092.0-600

–/110(Cristale and

Lacorte, 2013)

Germany (20) Sludge – –1,000-21,000

5,100/–– (Bester, 2005)

South China (19) Sludge96.7-1,310

420/2666.90-17.1 11.8/11.4

6.30-54.4 20.5/15.9

11.8-64.0 23.4/18.9

(Zeng et al., 2014)

Sediment

Germany (37)Elbe River, highly industrialized areas

–<1.00-41.0

–/7.405.90-311

–/57.0<1.00-13.0

–/7.90(Stachel et al., 2005)

Austria (4) River sediment2.40-1,940

583/196nd-160

<0.61-1,300 472/95.0

nd(Martínez-Carballo

et al., 2007)

Spain (–)River and marine sediments

– 45.9/– 38.0/– –(García-López et al.,

2009a)Spain and USA (–)

River sediments – – 4.0-10 –(García-López et al.,

2009b)

51

102

100

101

102

103

Spain (21) River sediments3.80-824 151/79.0

<2.70-9.70 6.02/5.15

<4.50-365 116/85.0

<1.90-12.0 8.00/8.40


Spain (5) River sediments –6.80-10.0

–/7.2092.0-600

–/3503.80-8.50

–/7.20(Cristale and

Lacorte, 2013)

Norway (8) River sediments486-22,500 4,980/1,920

<63.0-1,600 566/475

63.0-16,000 2,860/655

63.0-870 320/215


Norway (4) In pump pit at landfill7,460-17,900 11,200/9,660

27.0-380 191/179

490-1,300 838/780

1,500-4,100 2,980/3,150


Norway (4)From automobile destruction sites

22,700-33,800 27,000/25,900

2,300-5,500 3,330/2,750

9,500-24,000 14,600/12,50

0

<250-8,800 5,430/5,300


Japan (33)Bottom sediments from waste disposal site

up to 10,900 1,930/1,040

up to 7,400 780/145

up to 1,180 115/10.0

up to 709 52.4/–


Taiwan (5)River and marine sediments

1.00-12.6 7.38/9.00

nd-1.50 1.02/1.20

nd-9.50 4.52/4.70

nd-1.10 0.34/nd

(Chung and Ding, 2009)

China (28) From Taihu Lake3.38-14.3 7.88/7.99

0.62-3.17 1.75/1.66

0.40-2.27 1.41/1.47

<0.30-5.54 1.31/1.03

(Cao et al., 2012)

Soil

Germany (6)From the university campus in Osnabrueck

– 4.96/– 1.23/– <0.07(Mihajlović et al.,

2011)

The full names for the halogenated OPs are displayed in Table 1. –: data not available. nd: not detected.

52

104

103

105

Table S10

Comparison of the concentrations (range; arithmetic mean/median; ng/g) of the major non-halogenated OPs in sludge, sediment and soil samples from different regions around the world.

Location (no.) a TEP TPP TMPP TNBP TIBP TPHP TBOEP TEHP EHDPP Ref

Sludge from sewage treatment plantNorway (2)

– – – 69.5-270 52.0-81.0 13.0-1,100 1,200-2,200 – 462-1,200(Green et al.,

2008)Sweden (17)

– – –39.0-850 371/280

27.0-2,700 550/380

52.0-320 133/120

480-1,900 1,080/990

–320-4,600 1,360/970


Spain (5)

– –82.0-3,600

–/96.0–/56.0

58.0-8,300 –/930

300-500 –/490

80.0-370 –/180

460-1,000 –/480

550-890 –/730

(Cristale and Lacorte, 2013)

Spain (5)

– –72.0-2,100

–/93.060.0-200

–/88.096.0-10,000

–/69010.0-150

–/65.0nd

600-1,150 –/660

190-530 –/390


South China(5)

– – –7.10-805 74.3/17.4

–<4.20-657 109/48.3

25.1-784 150/102

– –(Zeng et al.,

2014)

SedimentGermany (37)

– – –<1.00-23.0

–/4.90– –

<1.00-93.0 –/18.0

– –(Stachel et al.,

2005)

Austria (4)

<2.50-81.0 49.5/–

–nd-39.0 22.7/–

<11.0-50.0 30.7/27.0

–nd-160 82.0/–

2.40-130 46.0/25.9

nd-140 57.5/28.0

–(Martínez-

Carballo et al., 2007)

Spain (–)

– – – – 7.80/– 6.40/– – – –(García-López et al., 2009a)

Spain and – – – 2.80-8.00 – – – – – (García-López

53

106

104

105

106

107

USA (–) et al., 2009b)Spain (21)

– –<6.70-84.0 24.4/14.0

<2.50-13.0 6.82/6.15

<2.50-8.40 4.45/3.30

<2.00-23.0 7.41/4.95

<60.0<2.80-290 35.1/11.0

<15.0-63.0 31.8/29.0


Spain (5)

– –8.00-62.0

–/9.200.92-10.0

–/8.000.30-7.90

–/2.909.50-40.0

–/22.0nd

9.10-500 –/47.0

11.0-70.0 –/50.0


Norway (8)

– – –66.0-480 162/115

62.0-470 134/88.5

<38.0-370 87.5/–

<63.0-3,100 509/101

–140-680 336/245


Norway (4)

– – –760-4,300

2,290/2,040195-1,100 611/575

1,000-5,000 2,800/2,600

540-1,000 698/625

–320-1,500 768/625


Norway (4)

– – –210-880 493/440

<250-230 160/150

900-1,600 1,200/1,150

1,600-2,900 2,200/2,150

–650-1,300 995/1,020


Japan (33)

up to 86.0 22.6/14.0

–up to 2,560

142/–up to 253 31.8/11.0

–up to 130

12.8/––

up to 7,120 513/50.0

–(Kawagoshi et

al., 1999)Taiwan (5)

– – – – –nd-3.10

1.50/1.30– – –

(Chung and Ding, 2009)

China (28)

– – –0.40-2.65 1.08/0.91

–0.40-1.19 0.65/0.57

1.03-5.0 2.00/1.66

– –(Cao et al.,

2012)Germany b (11)

– – –15.0-170 63.4/46.0

– – – – –(Ricking et al.,

2003)SoilGermany (6)

– – – <9.00 – 3.61/– <0.60 – –(Mihajlović et

al., 2011)USA c (9)

– –nd-130,000 30,000/100

– –nd-6,000 1,670/–

– –(David and

Seiber, 1999)Japan d (5)

– –36.0-340151/108

– – – – – –(Cho et al.,

1996)

54

108

109

a The arrangement and detailed descriptions of the matrices are in agreement with Table S9 without specification.b Sediments from Havel and Spree River.c Soil from Air Force Based Top soils around a greenhouse.The full names for the non-halogenated OPs are obtained in Table 1. –: data not available. nd: not detected.

55

110

107

108

109

110

111

111

Table S11Comparison of the concentrations (range; arithmetic mean/median; ng/g lipid weight) of ΣOP and the predominant halogenated OPs in biota samples from different regions.

Location (no.) Description of matrix ΣOP TCEP TCIPP TDCPP RefSweden (72)

Marine herring61.0-200

–/1102.00-3.40

–/2.7042.0-150

–/60.0<1.50-<2.00

(Sundkvist et al., 2010)

Sweden (10)

Marine perch330-490

–/41043.0-69.0

–/56.0140-250

–/190<9.60-<11.0


Sweden (41)

Marine mussels 190-1,600<2.00-55.0

40.2/–130-1,300

986/–<3.40-<8.10


Sweden (5)

Marine eelpout 15,000/– 59.0/– 310/– <8.10(Sundkvist et al.,

2010)Sweden (5)

Marine salmon 34.0/– 1.50/– 23.0/– <1.10(Sundkvist et al.,

2010)Sweden (60)

Freshwater perch, background350-1,000

–/720nd-83.0 –/51.0

220-750 –/535

<9.60-<16.0(Sundkvist et al.,

2010)Sweden(27)

Freshwater perch, close to sources

1,600-11,000 –/1,900

39.0-160 –/51.0

170-770 –/320

49.0-140 –/55.0


Sweden(2)

Freshwater carp, close to sources

1,600/– 23.0/– 110/– 36.0/–(Sundkvist et al.,

2010)Philippines (31)

Demersal fishes230-1,900 784/680

– – – (Kim et al., 2011a)

Philippines (28)

Pelagic fishes110-760 394/370

– – – (Kim et al., 2011a)

PRD, China (14)

Fishes (catfish and grass carp) – 82.7-4,690 62.7-883 nd-251 (Ma et al., 2013)

56

112

112

113

114

113

PRD, China (12)

Domestic birds (chicken and duck)

– 33.7-162 3.89-21.4 nd-43.7 (Ma et al., 2013)

Sweden (286)

Human milk46.0-180

–/99.02.10-8.20

–/4.9022.0-82.0

–/45.01.60-5.30

–/4.30(Sundkvist et al.,

2010)USA (9)

Pine needles<2.50-2,600

654/43.2<2.50-1,950

324/14.6<2.50-863 107/<2.50

<2.50-1,320 223/<19.8

(Aston et al., 1998)

The full names for the halogenated OPs are obtained in Table 1. –: data not available. nd: not detected.

57

114

115

115

Table S12Comparison of the concentrations (range; arithmetic mean/median; ng/g lipid weight) of the main non-halogenated OPs in biota samples from different regions.

Location (no.) TEP TPP TMPP TNBP TIBP TPHP TBOEP TEHP EHDPP Ref

Sweden (72) – –<0.30-<0.40

3.10-7.90 –/3.40

–7.10-34.0

–/16.0<3.00-<4.10

–3.00-7.50

–/4.00(Sundkvist et

al., 2010)

Sweden (10) – –20.0-23.0

–/22.016.0-23.0

–/20.0–

64.0-81.0 –/72.0

<20.0-<22.0

–37.0-39.0


al., 2010)

Sweden (41) – –11.0-110

–/83.414.0-20.0

–/18.4–

18.0-93.0–/72.9

<7.00-<17.0

–14.0-16.0


al., 2010)

Sweden (5) – – –/19.0 –/120 – –/400 <17.0 – –/14,000(Sundkvist et

al., 2010)

Sweden (5) – – –/1.80 –/1.60 – –/4.20 <2.40 – –/1.50(Sundkvist et

al., 2010)

Sweden (60) – –nd-43.0 –/18.0

12.0-36.0 –/23.0

–21.0-180

–/76.0<20.0-<28.0

–8.90-150


al., 2010)

Sweden (27) – –22.0-137

–/24.034.0-4,900

–/81.0–

100-170 –/150

240-1,000 –/270

–160-190

–/160(Sundkvist et

al., 2010)

Sweden (2) – – – –/26 – –/810 –/570 – –(Sundkvist et

al., 2010)Philippines (31)

31.0-300176/–

nd-30.04.10/–

nd-24.05.12/–

nd-280177/–

–nd-91.054.0/–

nd-31.08.05/–

nd-300246/–

nd-94.080.7/–

(Kim et al., 2011a)

Philippines (28)

nd-410146/–

nd-35.06.36/–

nd-45.04.50/–

nd-59077.0/–

–nd-35016.4/–

nd-50.02.58/–

nd-2,00088.1/–

nd-53019.3/–

(Kim et al., 2011a)

Philippines b (5)

nd-231 –/138

nd-2.88 –/1.06

nd-45.4 –/10.4

nd-266 –/111

–23.9-351

–/106nd-14.1 –/6.96

nd-189 –/115

nd-739(Kim et al.,

2011b)

58

116

116

117

118

117

Sweden c (24) – – nd-3.10 – – – – – –(Campone et

al., 2010)PRD, China (14)

nd-8.23 – – 43.9-2,950 – nd-45.7 164-8,840 nd-3.61 nd-1.87(Ma et al.,

2013)PRD, China (12)

nd – – 11.7-281 – nd-209 48.1-266 nd-13.9 nd-21.6(Ma et al.,

2013)

Sweden (286) – –nd-3.70–/0.80

11.0-57.0 –/12.0

–3.10-11.0

–/8.50nd-63.0–/4.70

–3.50-13.0


al., 2010)a The arrangement and detailed descriptions of the matrices are in line with Table S11 without specification.b Fishes including bluetail mullet, coral grouper and flathead grey mullet.c Fish tissues.The full names for the non-halogenated OPs are detailed in Table 1. –: data not available. nd: not detected.

59

118

119

120

121

122

119

Table S13Reference doses (RfD) values (ng/kg bw/d) and estimated exposure (ng/kg bw/d) under different scenarios to organophosphate compounds via

dust ingestion.

CountryExposure scenario

TCEP TCIPP TDCPP TNBP TIBP TMPP TPHP TBOEP EHDPP ΣOP Ref

RfD 22,000 80,000 15,000 24,000 13,000 70,000 15,000 – –(Van de Eede et al., 2011)

Belgium

Toddler Mean (P50) /High (P50) /High (P95)

1.00 /3.70 /19.8

5.60/22.4/92.7

1.50/5.90/9.80

0.50/2.10/6.40

12.2/48.8/143

1.00/3.90/9.60

2.00/8.20/40.7

8.20/33.0/226

–32.0/128/548


Adult (non-working) Mean (P50) /High (P50) /High (P95)

0.10/0.20/1.00

0.50/1.20/4.80

0.10/0.30/0.50

0.10/0.10/0.30

1.00/2.50/7.30

0.10/0.20/0.50

0.20/0.40/2.10

0.70/1.70/11.6

–2.80/6.60/28.1

Adult (working) Mean (P50) /High (P50) /High (P95)

0.10/0.30/1.50

0.50/1.40/10.2

0.30/0.70/7.70

0.05/0.10/0.90

0.70/1.70/5.00

0.10/0.20/2.50

0.10/0.40/5.10

0.70/1.80/23.7

–2.55/6.60/56.6

Germany Toddler Mean (P50)

0.04/0.33

0.13/1.40

0.02/1.50

0.01/0..20

– 0.01/0.21

0.11/0.44

0.27/0.61

– 0.73/5.60

(Brommer et al., 2012)

60

120

123

124

125

121

/High (P50)/High (P95)

/1.70 /4.40 /97.0 /1.10 /25.0 /5.60 /22.0 /160

Adult Mean (P50)/High (P50)/High (P95)

0.24/0.05/0.28

0.79/0.25/2.20

0.10/0.27/17.0

0.05/0.04/0.21

–0.07/0.03/4.60

0.32/0.21/1.60

0.17/0.56/5.40

–2.60/1.60/32.0

New Zealand

Toddler Mean (P5)/High (P50)/High (P95)

0.08/1.36/6.83

0.20/5.48/39.8

0.08/2.93/25.7

0.08/1.23/10.7

–0.17/2.17/7.27

0.08/5.99/23.3

2.66/50.6/145

–3.35/69.8/259

(Ali et al., 2012)Adult

Mean (P5)/High (P50)/High (P95)

0.01/0.06/0.29

0.01/0.23/1.70

0.01/0.13/1.10

0.01/0.05/0.46

–0.01/0.09/0.31

0.01/0.26/1.00

0.18/2.17/6.23

–0.24/2.99/11.1

Romania

Toddler Mean (P5)/High (P50)/High (P95)

0.08/1.68/12.0

0.48/14.4/193

0.006/0.98/4.10

0.0001/0.76/4.53

–0.27/8.93/79.0

0.41/8.75/168

2.56/25.1/306

–3.81/60.6/767

(Dirtu et al., 2012)Adult

Mean (P5)/High (P50)/High (P95)

0.006/0.07/0.52

0.03/0.62/8.28

0.0004/0.04/0.18

0.00001/0.03/0.20

–0.02/0.38/3.40

0.03/0.38/7.23

0.18/1.08/13.1

–0.26/2.60/32.9

USA Toddler Mean (P5)/High (P50)/High (P95)

– 0.58/17.3/62.0

0.44/31.3/759

– – – 2.33/91.2

/10400

– – 3.35/140

/11200

(Stapleton et al., 2009)

61

122

123

Adult Mean (P5)/High (P50)/High (P95)

–0.04/0.74/3.00

0.03/1.34/33.0

– – –0.16/3.91/447

– –0.23/5.99/482

Pakistan

Toddler Mean (min)/High (P50)/High (max)

0.04/0.25/2.92

0.08/0.33/1.42

0.02/0.08/4.25

0.08/0.33/0.37

0.08/0.42/0.67

0.008/1.12/6.00

0.008/2.92/5.50

0.06/0.28/2.42

–0.27/9.58/15.0

(Ali et al., 2013)

Adult Mean (min)/High (P50)/High (max)

0.003/0.01/0.17

0.006/0.02/0.14

0.001/0.004/0.21

0.006/0.01/0.02

0.005/0.02/0.03

0.001/0.05/0.25

0.001/0.13/0.37

0.004/0.01/0.14

–0.02/0.46/0.78

Taxi driver Mean (min)/High (P50)/High (max)

0.007/0.02/0.39

0.01/0.03/0.56

0.004/0.008/0.39

0.01/0.01/0.06

0.01/0.01/0.02

0.001/0.03/0.19

0.001/0.14/1.13

0.01/0.01/0.38

–0.07/0.75/1.58

Kuwait Toddler Mean (min)/High (P50)/High (max)

1.15/11.8/30.0

0.50/24.3/118

0.25/6.00/25.9

0.09/0.97/13.3

0.08/0.90/16.0

0.31/3.17/183

0.18/7.17/115

0.13/14.3/2340

–9.42/109/2450

(Ali et al., 2013)

Adult Mean (min)/High (P50)/High (max)

0.07/0.54/1.64

0.06/1.92/8.86

0.02/0.48/6.03

0.006/0.06/0.84

0.005/0.04/0.66

0.02/0.13/7.52

0.01/0.35/4.94

0.009/0.72/96.8

–0.94/6.39/106

Taxi driver Mean (min)

0.14/0.72

0.56/6.88

0.15/1.71

0.01/0.178

0.01/0.03

0.04/0.10

0.02/0.57

0.02/1.33

– 6.53/16.1

62

124

125

/High (P50)/High (max)

/3.65 /30.4 /33.8 /2.37 /0.49 /5.66 /5.03 /74.8 /113

Egypt

Toddler Mean (min)/High (P50)/High (max)

0.02/0.47/1.90

0.04/0.59/4.10

0.02/1.05/6.50

0.03/0.24/0.59

0.04/0.35/0.53

–0.05/1.25/5.57

0.01/0.30/3.95

0.01/0.43/0.97

0.33/3.30/14.8

(Abdallah and Covaci, 2014)Adult

Mean (min)/High (P50)/High (max)

0.002/0.04/0.13

0.004/0.05/0.24

0.003/0.07/0.45

0.003/0.02/0.05

0.005/0.03/0.04

–0.006/0.09/0.41

0.001/0.04/0.43

0.001/0.03/0.06

0.04/0.24/1.14

Malate

Toddler High (P50)/High (P95)

0.39/5.80

– –0.22/0.66

–0.21/1.93

1.00/16.0

–1.27/6.27

4.69/38.1

(Kim et al., 2013)Adult

High (P50)/High (P95)

0.02/0.27

– –0.01/0.03

–0.01/0.09

0.05/0.73

–0.06/0.28

0.22/1.74

Payatas

Toddler High (P50)/High (P95)

0.19/0.87

– –0.23/0.61

–0.09/1.27

0.80/4.73

–0.39/4.67

2.16/15.2

(Kim et al., 2013)Adult

High (P50)/High (P95)

0.01/0.04

– –0.01/0.03

–0.004/0.06

0.04/0.22

–0.02/0.22

0.10/0.70

Human exposure were estimated based on mean (50 and 20 mg/d for toddler and adult, respectively) and high dust ingestion rates (200 and 50 mg/d for toddler and adult, respectively), and the minnum, P5, P50, P95 and maxium concentrations of OPs. –: data not available.

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ars.els-cdn.com · Web viewSupplementary data. MANUSCRIPT TITLE: Organophosphorus flame retardants and plasticizers: Sources, occurrence, toxicity and human exposure. AUTHORS: Gao-Ling