Supplementary Material for
Modeling the dynamics of DDT in a remote tropical floodplain:
indications of post-ban use?
Annelle Mendez1, Carla A. Ng1*, João Paulo Machado Torres2, Wanderley Bastos3, Christian
Bogdal1,4, George Alexandre dos Reis2, and Konrad Hungerbuehler1
Institute for Chemical and Bioengineering, ETH Zurich, CH-8093 Zürich, Switzerland
Institute of Biophysics, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
3
Department of Biology, Federal University of Rondônia, Porto Velho, Brazil
4
Agroscope, Institute for Sustainability Sciences ISS, CH-8046 Zurich, Switzerland
1
2
Contents
S1
Field Measurements ............................................................................................................................. 2
S1.1
Sample Collection ......................................................................................................................... 2
S1.2
TOC Analysis ................................................................................................................................ 2
S1.3
DDX Extraction, Clean-up and Quantification ............................................................................. 2
S1.3.1
Institute of Biophysics at the Federal University of Rio de Janeiro (IBFRJ) ......................... 2
S1.3.2
Safety and Environmental Technology Laboratory, ETH Zürich .......................................... 3
S1.4
S2
Quality Assurance/Quality Control ............................................................................................... 3
Multimedia Model Development ......................................................................................................... 4
S2.1
Description of Flooding................................................................................................................. 4
S2.2
Water Level and Rainfall............................................................................................................... 5
S2.3
Calculation of Degradation Half-lives in Sediments ..................................................................... 6
S2.4
Model Equations............................................................................................................................ 7
S3
Results ................................................................................................................................................ 13
S3.1
Model........................................................................................................................................... 13
S3.2
Comparison of DDX measurements ............................................................................................ 15
S3.3
Sensitivity Analysis ..................................................................................................................... 15
S1
S1 Field Measurements
S1.1 Sample Collection
Surface soil and sediment samples from the top 5 cm were collected using a metal scoop. All
samples were oven-dried at 30°C, ground, and sieved (1000 μm mesh for soils and 212 μm mesh
for sediments). Samples were stored in glass jars with metal caps, covered with aluminum foil,
and stored at -20°C until analysis.
S1.2 TOC Analysis
Approximately 100 mg of dried, pulverized sample were analyzed in a TOC-L Shimadzu Total
Organic Carbon Analyzer (2011 TOC-L v 1.03.01) with a Solid Sample Module SSM-200.
Inorganic carbonates were removed by in situ acidification with 1M HCl. The total organic
carbon in each sample was calculated as the difference between the amount of total carbon and
total inorganic carbon.
S1.3 DDX Extraction, Clean-up and Quantification
S1.3.1 Institute of Biophysics at the Federal University of Rio de Janeiro (IBFRJ)
All glassware was pre-rinsed three times with acetone. All solvents used were of residue grade.
Approximately 3 g of dried sample were extracted in a Soxhlet apparatus over 8 h with
dichloromethane. PCB-103 and 198 were added as internal standards prior to extraction.
Potential interferents were removed by passing the extract through a glass column with a
desulfurizing agent (Na2SO3-NaOH- Al2O3 mixture, 11% H2O w/w) and eluting with n-hexane
(Japenga et al. 1987). Fractionation was performed using a glass chromatographic column
packed with Florisil at the bottom and anhydrous sodium sulfate on top. The first fraction
containing o,p’-DDE and p,p’-DDE was eluted with n-hexane. The second fraction containing
the remaining analytes was eluted with n-hexane/petroleum ether (1:1). The purified extracts
were concentrated and the recovery standard (2,4,5,6-Tetrachloro-m-xylene) was added prior to
quantification.
The samples were analyzed by gas chromatography equipped with an electron capture detector
(GC/ECD) with a 63Ni electron source (Shimadzu GC-14B and GC-2010). The GC was equipped
with a 60m fused silica capillary column coated with a cross-linked 5% phenyl-/95% dimethylpolysiloxane stationary phase. Samples were injected in splitless mode at an injector
temperature of 300°C. Hydrogen was used as a carrier gas at a flow of 35 ml/min at the septum
and 15 mL/min at the injector. Initially, the oven was kept at 110°C for 1 min, followed by a 15
°C/min increase until reaching a temperature of 170 °C. The temperature was then increased at
7.5 °C /min until 290 °C, and kept at that temperature for 10 minutes. The detector was kept at a
constant temperature of 310 °C during the analysis and nitrogen was used as a makeup gas with a
flow of 35 mL/min.
S2
S1.3.2 Safety and Environmental Technology Laboratory, ETH Zürich
Glassware was washed in a glassware washer with detergent and immersed in an alkaline bath
(5% Potassium Hydroxide) for 12 hours and then heated to 450°C overnight. All glassware was
rinsed twice with acetone before use. All solvents used were of residue grade.
Approximately 5 g of sediment were filled into Soxhlet thimbles and spiked with 13C12-labeled
p,p’-DDT as an internal standard. Activated copper was added to remove sulfur and anhydrous
sodium sulfate to remove any humidity. Soxhlet extraction was carried out for 8 hours using nhexane/dichloromethane (1:1). Extracts were concentrated with a rotary evaporator and then
cleaned-up with silica gel chromatography (neutral silica at the bottom, and acidic on top).
Analytes were eluted with n-hexane. The eluate was reduced in volume with a rotary evaporator
first and then with a gentle stream of nitrogen. The concentrated extracts were transferred to GC
vials, and 13C6-labeled hexachlorobenzene was added as a recovery standard.
The purified samples were analyzed with a gas chromatograph (HRGC HP 5890 II, Hewlett
Packard, Palo Alto, CA, USA) coupled to an electron ionization high-resolution mass
spectrometer (Thermo Finnigan MAT95, Bremen, Germany) (GC-EI/HRMS). The GC was
equipped with an autosampler (A200S CTC Analytics, Zwingen, Switzerland). Samples were
injected in splitless mode (split closed for 1 min.) at an injector temperature of 240 °C. The GC
was equipped with a fused silica capillary column (30 m, 0.25 mm inner diameter) coated with
0.25 µm of crosslinked 5% phenyl- / 95% dimethyl-polysiloxane stationary phase (RTx-5ms,
Restek Bellefonte, PA, USA). Helium was used as a carrier gas at a constant pressure of 150
kPa. The temperature program started at 110 °C, was held for 1 min, increased to 140°C at
10°C/min, then increased to 226 °C at 2.2°C/min, and finally increased to 300°C at 20°C/min.
The transfer line temperature remained constant at 290°C. The ion source was operated at 180
°C, the electron energy was 70 eV, and the HRMS was tuned to a mass resolution of at least
10,000.
For all compounds the two most abundant signals of the fragment ion clusters were recorded,
including [M-CCl3]+ for DDT (native DDT: m/z 235.0076/237.0046 u/e; 13C12-labeled DDT:
247.0478/249.0449 u/e), [M-CHCl2]+ for DDD (235.0076/237.0046 u/e), and [M-CCl2]+ for
DDE (245.9997571/247.996807 u/e).
S1.4 Quality Assurance/Quality Control
All samples were analyzed in duplicate or triplicate, when the sample amount was sufficient. A
blank was analyzed in parallel to each batch of 4-5 samples. For the blanks, Soxhlet extractions
were carried out with thimbles containing copper and sodium sulfate only, and the blank extracts
were processed exactly as the field sample extracts. The limit of detection for each target analyte
was calculated as two times the standard deviation of two analytical blanks, considering a typical
sample amount of 5 g.
Table S1 Limits of detection (ng/g)
IBFRJ
ETH Zürich
o,p’-DDD
0.31
0.14
p,p’-DDD
1.9
0.88
o,p’-DDE
0.22
0.20
S3
p,p’-DDE
1.4
1.18
o,p’-DDT
0.67
0.42
p,p’-DDT
4.2
2.64
Recovery of the isotope labeled internal standard was 130% for soil samples and 86% for
sediment samples analyzed at ETH Zürich. Recovery of the internal standards ranged from 70110% for the samples analyzed at IBFRJ.
S2 Multimedia Model Development
S2.1 Description of Flooding
Fig. S1 Map of Puruzinho from the Program for the Estimation of Deforestation in the Brazilian Amazon (PRODES
2014). The lake flows from the Puruzinho River into the Madeira River and is additionally fed by two tributaries:
Igarapé Onça and Igarapé Crato.
It is assumed that at the minimum water level, the shape of the water compartment is a rectangle
with an area calculated from the PRODES map, shown in Fig. S1. Since the floodplain soil is
sloped, the cross-section of the water body above the minimum water level is modeled as an
isosceles trapezoid, whose legs represent the floodplain. The width of the flooded floodplain is
calculated by assuming that the difference between the maximum water level and the current
water level is proportional to the difference between the maximum floodplain width and the
width of the currently flooded soil (Fig. S2). Thus, based on the daily water level measurements,
the cross-sectional area of the water body is calculated and multiplied by the length of the lake to
obtain the daily water volume. The width of the aerated floodplain soil is then the difference
between the maximum floodplain width and the currently flooded width. The widths of the
aerated and flooded components of the floodplain soil can then be multiplied by the length of the
lake to calculate the variable areas of the flooded and aerated floodplain soils.
S4
Fig. S2 Diagram showing a cross-section of the lake. At time t, if the water level is above the minimum, a fraction
of the floodplain width is flooded, while the rest is aerated. When the maximum water level is reached, the entire
floodplain is flooded.
S2.2 Water Level and Rainfall
The water levels measured in Puruzinho from 2011-2013 are shown in Fig. S3. A characteristic
lag is observed between the rainfall and the water level, suggesting that water level changes are
driven by regional precipitation. This delay describes a long-lasting flood pulse of low
amplitude, typical of shallow lakes in South American lowlands (Junk and Piedade 2011). A
comparison of water volumes calculated with and without the contribution of direct precipitation
to the daily water levels revealed that the influence of direct precipitation on the overall change
in water volume is insignificant, accounting for less than 0.1 percent of the volume change.
Tributary discharge into Lake Puruzinho results from the collection of rain over a larger drainage
area. Thus, while direct, local precipitation does not contribute significantly to the change in the
volume of Lake Puruzinho, regional precipitation drives the floodplain dynamics.
Fig. S3 Rainfall data (10-day average) from the municipality of Humaitá (blue) and daily water level data from Lake
Puruzinho (green).
S5
S2.3 Calculation of Degradation Half-lives in Sediments
The majority of reported degradation rates for DDT and its transformation products are for nonflooded soils. Almost no measurements are available for degradation rates in sediments under
tropical conditions, and those available for temperate conditions span large ranges. Therefore we
used data on studies comparing degradation rates in flooded and non-flooded tropical and subtropical soils in order to estimate DDT degradation rates in sediments. DDT dissipation half-lives
in flooded and non-flooded soils derived from microcosm and field studies carried out in tropical
regions (Castro and Yoshida 1971; Samuel and Pillai 1988; Xu et al. 1994), assuming first order
kinetics, are summarized in Table S2. It can be observed that the dissipation half-life is shorter
under flooded than under non-flooded conditions. Thus, in the model we used a ratio to describe
how much faster degradation was under flooded conditions in the sediment and the flooded
floodplain compared to the terra-firme soil, which is not flooded. This ratio was calculated by
taking the geometric mean of the ratio of dissipation half-lives in flooded vs. non-flooded soil
(Table S2).
The dissipation half-lives reported represented not only losses through degradation but also
through volatilization and leaching. The latter has been observed to be negligible in field DDT
dissipation studies in the tropics (Enan 1988; Espinosa‐González et al. 1994; Hussain et al. 1988;
Samuel and Pillai 1988; Tayaputch 1988; Zayed et al. 1994), and is irrelevant in microcosm
studies. However, not enough data to discretize the contribution of different loss processes to
dissipation, and hence calculate the degradation half-life, were available for all studies.
Therefore, to account for potentially over-estimating degradation rates by using the dissipation
half-life as a proxy of the degradation half-life, the assumption that degradation in sediments is
the same as in soils (1025 days) was also included in the calculation of the geometric mean of the
degradation ratio between non-flooded and flooded soils.
Table S2 Derivation of ratio between degradation half-life in the sediment and degradation half-life in the soil.
Half-life
flooded (d)
5.9
Half-life
non-flooded (d)
202
Ratio
non-flooded/flooded
35
18
202
11
56
202
3.6
140
202
1.5
17
209
12
36
167
4.7
11
108
9.5
China
33
437
13
Xu et al. 1994
Assumption
1025
1025
1
Schenker et al. 2008
Location
Philippines
India
Geometric Mean
6
S6
Reference
Castro and Yoshida 1971
Samuel and Pillai 1988
S2.4 Model Equations
A detailed description of models using the fugacity approach is presented in Mackay 2001. In
brief, the environment is divided into a number of well-mixed compartments (boxes) with
homogeneous environmental properties linked by inter-compartmental transfer processes. The
concentration of a chemical in a compartment is the product of the fugacity and the bulk fugacity
capacity (Z) of that compartment. Z values describe equilibrium partitioning between phases.
Fugacity, which is the partial pressure of a chemical in a phase, describes the escaping tendency
of a chemical from a particular phase. The transfer rate of a chemical from one phase to another
is driven by the fugacity difference between these phases (Mackay 2001). Based on these
concepts, the change in the amount (M) of chemical over time (t) can be described as:
𝑑𝑀
𝑀
=𝐸+𝐷∙
𝑑𝑡
𝑉∙𝑍
where M, V, E, and Z are vectors with the masses, volumes, emissions, and bulk fugacity
capacities of each compartment, respectively. For DDT, the emissions term refers to direct
emissions from IRS. For DDE and DDD, the emissions term refers to their formation from the
degradation of DDT, described by a D-value (see Table S5). D is a matrix in which the rows and
columns represent the compartments, the diagonal contains the permanent losses from each
compartment, and the off-diagonal terms represent inter-compartmental processes. Each of these
processes is described by a first-order rate constant or by a mass transfer coefficient. The
parameters and equations used to derive fugacity capacities and D-values are shown in Tables
S3, S4, and S5. Except for the organic carbon content and the dimensions (given in Table S4),
the flooded floodplain soil was parameterized in the same way as the sediment, while the aerated
floodplain was parameterized in the same way as the terra-firme soil. Therefore, the chemical
properties, and the equations for the fugacity capacities and D-values assigned to the terra-firme
soil and sediment also apply to the aerated and flooded floodplain, respectively (however, the
organic carbon contents and the dimensions specific to the flooded and aerated floodplains are
used in the calculation of Z and D-values).
Table S3 Chemical-specific parameters
Parameter
log Kow
log Kaw
t1/2a
Description
log octanol-water partition coefficient
log air-water partition coefficient
degradation half-life in air [d-1]
p,p’-DDT
6.41
-3.31
1.43
p,p’-DDE
1.05
-2.77
0.660
p,p’-DDD
0.612
-3.74
1.13
t1/2s
t1/2sed
degradation half-life in soil [d]
degradation half-life in sediment [d]
1025
171
828
138
916
153
ffa
ffaer
fraction of formation in the air
fraction of formation under aerobic
conditions in soil and sediments
fraction of formation under anaerobic
conditions in sediments
fraction of DDT in the active
ingredient
molecular weight
–
–
0.9
0.7
–
0.3
Reference
Schenker et al. 2009
Schenker et al. 2009
USEPA 2014, Bahm and
Kahlil 2004
Schenker et al. 2009
Xu et al. 1994, Castro and
Yoshida 1971, Samuel
and Pillai 1988
Schenker et al. 2007
own assumption
–
0.3
0.7
own assumption
0.75
–
–
ASTDR 2002
354
319
321
Kuo-Ching Ma et al. 2006
ffana
fDDT
Mw
S7
Table S4 Parameters used to derive fugacity capacities and D-values. In the values column, wet refers to December
to May, while dry refers to June to November. For parameters with a higher temporal resolution than seasonal, the
median value (in italics) followed by the range is reported.
Dimensions
Variable Description
ltot
length of the lake
wts
width of terra-firme soil
wfsmax
maximum floodplain width
ww
width of the lake at maximum water
level
hts
depth of the terra-firme soil
hsed
depth of the sediment
hfs
depth of the floodplain
hw
height of the water column
ha
height of the air compartment above
the terra-firme soil
Environmental parameters
Variable Description
focsp
fraction of organic carbon in
suspended particles in water
focsed
fraction of organic carbon in
sediment solids
focts
fraction of organic carbon in terrafirme soil solids
focfs
fraction of organic carbon in
floodplain soil
fomq
fraction of organic matter in aerosols
ρom
ρmm
ρq
ρsp
ρsosed
ρsots
cq
csp
density of organic matter
density of mineral matter
density of aerosols
density of suspended particles in
water
density of sediment solids
density of terra-firme soil solids
concentration of aerosols in the wet
season
concentration of suspended particles
Unit
m
m
m
m
Value
4.742∙103
60
16
589.5
Ref.
PRODES 2014
PRODES 2014
this work
this work
m
m
m
m
m
0.1
0.02
0.02-0.1
6.4, 1.08-11.8
eq. (1)
Mackay 1991
Mackay 2001
this work*
this work
McKone et al. 1997
Unit
–
Value
0.05-0.2
Ref.
Moreira-Turcq et al. 2004
–
0.02-0.04
this work, Almeida 2006
–
0.028
this work
–
0.054
this work*
–
0.3-0.8
kg m-3
kg m-3
kg m-3
kg m-3
1000
2500
1400
eq. (2)
Graham et al. 2003,
Artaxo et al. 2002
Mackay 2001
Mackay 2001
Rissler et al. 2006
kg m-3
kg m-3
kg m-3
eq. (3)
eq. (4)
0.006 (dry),
4.7∙105 (wet)
0.018 (dry),
0.006 (wet)
0.8
0.2
kg m-3
volume fraction of water in sediment m3 m-3
volume fraction of air in terra-firme
m3 m-3
soil
vwts
volume fraction of water in terram3 m-3
firme soil
vsorun
volume fraction of solids in runoff
m3 m-3
Mass transfer coefficients (U) and advective parameters
Variable Description
Unit
da
molecular diffusivity in air
m2d-1
vwsed
vats
this work*, Almeida 2006
Mackay et al.1996
Mackay et al.1991
0.3
Mackay et al. 1991
2∙10-4
Lal 1995
Value
eq. (5)
Ref.
Schwarzenbach et al.
2005
Schwarzenbach et al.
2005
Mackay 2001
McLachlan et al. 2002
Mackay 2001
Mackay 2001
dw
molecular diffusivity in water
m2d-1
eq. (6)
Uas
Ussoa
Usaa
Uswa
air-side air-soil diffusion
soil-side solid phase air-soil diffusion
soil-side air phase air-soil diffusion
soil-side water phase air-soil
m3m-2d-1
m3m-2d-1
m3m-2d-1
m3m-2d-1
24
1.09∙10-5
eq. (7)
eq. (8)
S8
Artaxo et al. 2002
Urain
diffusion
water-side air-water diffusion
water-side sediment-water diffusion
air-side air-water diffusion
sediment-side water phase sedimentwater diffusion
sediment-side solid phase sedimentwater diffusion
rain rate
Usorun
Uwrun
soil solids runoff
soil water runoff
m3m-2d-1
m3m-2d-1
eq. (10)
eq. (11)
Uleach
Usobur
Usedbur
soil water leaching
soil solids burial
sediment solids burial
m3m-2d-1
m3m-2d-1
m3m-2d-1
eq. (12)
5.47∙10-7
1.30∙10-6
Usedres
Uspdep
uspdep
sediment solids resuspension
suspended particle deposition
suspended particle deposition
velocity
aerosol deposition velocity
wind velocity
m3m-2d-1
m3m-2d-1
md-1
eq. (13)
eq. (14)
1
md-1
md-1
259.2
1.62∙105, 1.46∙1051.76∙105
4.47∙104, 0-4.27∙106
Mackay 2001
INMET 2012
Value
2∙10-5
25
Ref.
Mackay et al. 1991
INMET 2012
Uwa
Uwsed
Uaw
Usedww
Usedsow
uqdep
uwind
m3m-2d-1
m3m-2d-1
m3m-2d-1
m3m-2d-1
0.72
0.24
72
eq. (9)
Mackay et al. 1991
Mackay et al. 1991
Mackay et al. 1991
Mackay 2001
m3m-2d-1
8.64∙10-6
Thibodeaux et al. 2010
m3m-2d-1
5.00∙10-4, 0-7.44∙10-2
INMET 2012, INMET
2015
Mackay et al. 1992
Lal et al. 1995, Mackay et
al.1991
Mackay 1991
MacLeod et al. 2011
Smith 2003, MoreiraTurcq et al. 2004
Ruiz et al. 2001
Mackay 2001
Mackay 2001
Gw
volumetric flow rate of water
m3d-1
Others
Variable Description
Unit
Q
scavenging ratio
–
T
temperature
°C
*Estimated as explained in Methods section in main text
The equations associated with Table S3 are:
0.8
(1) ℎ𝑎 = 0.22 ∙ (√𝐴𝑡𝑜𝑡 )
(2) 𝜌sp = 2 ∙ 𝑓ocsp ∙ 𝜌om + (1 − 2 ∙ 𝑓ocsp ∙ 𝑟ocsp ) ∙ 𝜌mm
(3) 𝜌sosed = 2 ∙ 𝑓ocsed ∙ 𝜌om + (1 − 2 ∙ 𝑓ocsed ) ∙ 𝜌mm
(4) 𝜌sots = 2 ∙ 𝑓octs ∙ 𝜌om + (1 − 2 ∙ 𝑓octs ) ∙ 𝜌mm
(5) 𝑑a = 1.55/𝑀𝑤 0.65 ∙ 36/100 ∙ 24
(6) 𝑑w = 2.7 ∙ 10−4 /𝑀𝑤 0.71 ∙ 36/100 ∙ 24
(7) 𝑈saa = 𝑑a ∙ 𝑣ats 0.33 /(𝑣wts + 𝑣ats ) ∙ 2/ℎts
(8) 𝑈swa = 𝑑w ∙ 𝑣wts 0.33 /(𝑣wts + 𝑣ats ) ∙ 2/ℎts
(9) 𝑈sedw = 𝑑w ∙ 𝑣wsed 0.33 ∙ 2/ℎsed
(10) 𝑈spdep = 𝑐sp ∙ 𝑢𝑠𝑝𝑑𝑒𝑝 /𝜌sp
S9
this work*
(11) 𝑈sedres = ( 𝑈spdep ∙ 𝜌sp − 𝑈𝑠𝑒𝑑𝑏𝑢𝑟 ∗ 𝜌sosed )/𝜌sosed
(12) 𝑈tsrun = 0.4 ∙ 𝑈𝑟𝑎𝑖𝑛
(13) 𝑈tsrun = 0.4 ∙ 𝑈𝑟𝑎𝑖𝑛 ∙ 𝑣𝑠𝑜𝑟𝑢𝑛
(14) 𝑈leach = 0.4 ∙ 𝑈𝑟𝑎𝑖𝑛
Table S5 Equations for calculating the fugacity capacities
Fugacity Capacity (mol m-3 Pa-1)
Pure Phase
air
water
aerosol
suspended particles in water
solids in sediment
solids in terra-firme soil
Bulk Phase
air
water
sediment
terra-firme soil
𝑍𝑝𝑎 = 1/(𝑅 ∙ 𝑇)
𝑍𝑝𝑤 = 𝑍𝑝𝑎 /𝐾𝑎𝑤
𝑍𝑝𝑞 = 𝑍𝑝𝑎 ∙ 1.22 ∙ 𝐾𝑜𝑤 /𝐾𝑎𝑤 ∙ 𝑓𝑜𝑚𝑞 ∙ 𝜌𝑞 /1000
𝑍𝑝𝑠𝑝 = 𝑍𝑝𝑤 ∙ 0.35 ∙ 𝐾𝑜𝑤 ∙ 𝑓𝑜𝑐𝑠𝑝 ∙ 𝜌𝑠𝑝 /1000
𝑍𝑝𝑠𝑜𝑠𝑒𝑑 = 𝑍𝑝𝑤 ∙ 0.35 ∙ 𝐾𝑜𝑤 ∙ 𝑓𝑜𝑐𝑠𝑒𝑑 ∙ 𝜌𝑠𝑜𝑠𝑒𝑑 /1000
𝑍𝑝𝑠𝑜𝑡𝑠 = 𝑍𝑝𝑤 ∙ 0.35 ∙ 𝐾𝑜𝑤 ∙ 𝑓𝑜𝑐𝑡𝑠 ∙ 𝜌𝑠𝑜𝑡𝑠 /1000
𝑍𝑎 = (1 − 𝑐𝑞 /𝜌𝑞 ) ∙ 𝑍𝑝𝑞 + 𝑐𝑞 /𝜌𝑞 ∙ 𝑍𝑝𝑞
𝑍𝑤 = (1 − 𝑐𝑠𝑝 /𝜌𝑠𝑝 ) ∙ 𝑍𝑝𝑤 + 𝑐𝑠𝑝 /𝜌𝑠𝑝 ∙ 𝑍𝑝𝑠𝑝
𝑍𝑠𝑒𝑑 = (1 − 𝑣𝑤𝑠𝑒𝑑 ) ∙ 𝑍𝑝𝑠𝑜𝑠𝑒𝑑 + 𝑣𝑤𝑠𝑒𝑑 ∙ 𝑍𝑝𝑤
𝑍𝑡𝑠 = (1 − 𝑣𝑤𝑡𝑠 ) ∙ 𝑍𝑝𝑠𝑜𝑠𝑒𝑑 + 𝑣𝑤𝑠𝑒𝑑 ∙ 𝑍𝑝𝑤
S10
Table S6 Equations for calculating D-values
Process
Intermedia Exchange
Air-Water
diffusion
wet gaseous deposition
wet aerosol deposition
dry aerosol deposition
Air-soil
diffusion
D-value (mol Pa-1 d-1)
−1
𝐷dif_aw = (1/(𝑈𝑎𝑤 ∙ 𝐴𝑤 ∙ 𝑍𝑝𝑎 ) + 1/(𝑈𝑤𝑎 ∙ 𝐴𝑤 ∙ 𝑍𝑝𝑤 ))
𝐷𝑤𝑔𝑑𝑒𝑝_𝑎𝑤 = 𝐴𝑤 ∙ 𝑈𝑟𝑎𝑖𝑛 ∙ 𝑍𝑝𝑤
𝐷𝑤𝑔𝑑𝑒𝑝_𝑎𝑤 = 𝐴𝑤 ∙ 𝑈𝑟𝑎𝑖𝑛 ∙ 𝑄 ∙ 𝑐𝑞 /𝜌𝑞 ∙ 𝑍𝑝𝑞
𝐷𝑤𝑔𝑑𝑒𝑝_𝑎𝑤 = 𝐴𝑤 ∙ 𝑢𝑞𝑑𝑒𝑝 ∙ 𝑐𝑞 /𝜌𝑞 ∙ 𝑍𝑝𝑞
𝐷dif_as = (1/(𝑈𝑤𝑠𝑒𝑑 ∙ 𝐴𝑠 ∙ 𝑍𝑝𝑎 ) + 1/(𝑈𝑠𝑤𝑎 ∙ 𝐴𝑠 ∙ 𝑍𝑝𝑤 + 𝑈𝑠𝑎𝑎 ∙ 𝐴𝑡𝑠 ∙ 𝑍𝑝𝑎 + 𝑈𝑠𝑠𝑜𝑎 ∙ 𝐴𝑡𝑠 ∙
−1
wet gaseous deposition
wet aerosol deposition
dry aerosol deposition
Water-Air
diffusion
Water-Sediment
diffusion
𝑍𝑝𝑠𝑜𝑡𝑠 ))
𝐷𝑤𝑔𝑑𝑒𝑝_𝑎𝑠 = 𝐴𝑡𝑠 ∙ 𝑈𝑟𝑎𝑖𝑛 ∙ 𝑍𝑝𝑤
𝐷𝑤𝑔𝑑𝑒𝑝_𝑎𝑠 = 𝐴𝑡𝑠 ∙ 𝑈𝑟𝑎𝑖𝑛 ∙ 𝑄 ∙ 𝑐𝑞 /𝜌𝑞 ∙ 𝑍𝑝𝑞
𝐷𝑤𝑔𝑑𝑒𝑝_𝑎𝑠 = 𝐴𝑡𝑠 ∙ 𝑢𝑞𝑑𝑒𝑝 ∙ 𝑐𝑞 /𝜌𝑞 ∙ 𝑍𝑝𝑞
𝐷dif_wa = 𝐷difaw
𝐷dif_wsed = (1/(𝑈𝑎𝑠 ∙ 𝐴𝑠𝑒𝑑 ∙ 𝑍𝑝𝑤 ) + 1/(𝑈𝑠𝑒𝑑𝑤𝑤 ∙ 𝐴𝑠𝑒𝑑 ∙ 𝑍𝑝𝑤 + 𝑈𝑠𝑒𝑑𝑠𝑜𝑤 ∙ 𝐴𝑠𝑒𝑑 ∙
−1
𝑍𝑝𝑠𝑜𝑠𝑒𝑑 ))
𝐷𝑑𝑒𝑝_𝑤𝑠𝑒𝑑 = 𝑈𝑠𝑝𝑑𝑒𝑝 ∙ 𝐴𝑠𝑒𝑑 ∙ 𝑍𝑝𝑠𝑝
particle deposition
Sediment-Water
diffusion
𝐷dif_sedw = 𝐷dif_wsed
resuspension
𝐷𝑟𝑒𝑠_𝑠𝑒𝑑𝑤 = 𝑈𝑠𝑒𝑑𝑟𝑒𝑠 ∙ 𝐴𝑠𝑒𝑑 ∙ 𝑍𝑝𝑠𝑜𝑠𝑒𝑑
Soil-Air
𝐷difsa= 𝐷dif_as
diffusion
Soil-Water
solids runoff
𝐷𝑠𝑜𝑟𝑢𝑛 = 𝑈𝑠𝑜𝑟𝑢𝑛 ∙ 𝐴𝑡𝑠 ∙ 𝑍𝑝𝑠𝑜𝑡𝑠
water runoff
𝐷𝑤𝑟𝑢𝑛 = 𝑈𝑤𝑟𝑢𝑛 ∙ 𝐴𝑡𝑠 ∙ 𝑍𝑝𝑤
Losses from the system
Air
𝐷dega = ln(2)/𝑡1/2𝑎 ∙ 𝑉𝑎 ∙ 𝑍𝑝𝑎
Degradation
Advection
𝐷adva = 𝑢wind ∗ (ℎa ∙ (wts + ww ) + (𝐴𝑥wmax − 𝐴𝑥w )) ∙ 𝑍a
Water
𝐷degw = ln(2)/𝑡1/2𝑠 ∙ 𝑉𝑤 ∙ 𝑍𝑤
Degradation
Advection
𝐷advw = 𝐺𝑤 ∙ 𝑍𝑤
Sediment
𝐷degsed = ln(2)/𝑡1/2𝑠𝑒𝑑 ∙ 𝑉𝑠𝑒𝑑 ∙ 𝑍𝑠𝑒𝑑
Degradation
Burial
𝐷𝑏𝑢𝑟𝑠𝑒𝑑 = 𝑈𝑠𝑒𝑑𝑏𝑢𝑟 ∙ 𝐴𝑠𝑒𝑑 ∙ 𝑍𝑝𝑠𝑜𝑠𝑒𝑑
Soil
𝐷degts = ln(2)/𝑡1/2𝑠 ∙ 𝑉𝑠 ∙ 𝑍𝑡𝑠
Degradation
Burial
𝐷𝑏𝑢𝑟𝑡𝑠 = 𝑈𝑠𝑜𝑏𝑢𝑟 ∙ 𝐴𝑡𝑠 ∙ 𝑍𝑝𝑠𝑜𝑡𝑠
Leaching
𝐷𝑙𝑒𝑎𝑐ℎ = 𝑈𝑙𝑒𝑎𝑐ℎ ∙ 𝐴𝑡𝑠 ∙ 𝑍𝑝𝑤
Formation (applicable to DDE and DDD)
𝐷afor = 𝐷𝑑𝑒𝑔𝑎𝐷𝐷𝑇 ∙ 𝑓𝑓𝑎
Air
𝐷wfor = 𝐷𝑑𝑒𝑔𝑤𝐷𝐷𝑇 ∙ 𝑓𝑓𝑎𝑒𝑟
Water
𝐷sedfor = 𝐷𝑑𝑒𝑔𝑠𝑒𝑑𝐷𝐷𝑇 ∙ 𝑓𝑓𝑎𝑛𝑎
Sediment (anaerobic)
𝐷sedfor = 𝐷𝑑𝑒𝑔𝑠𝑒𝑑𝐷𝐷𝑇 ∙ 𝑓𝑓𝑎𝑒𝑟
Sediment (aerobic)
𝐷tsfor = 𝐷𝑑𝑒𝑔𝑡𝑠𝐷𝐷𝑇 ∙ 𝑓𝑓𝑎𝑒𝑟
Soil
S11
Table S7 Uncertainty distributions of parameters included in Monte Carlo simulations. The range is given for
triangular and uniform distributions, while the standard deviation (σ) is given for normal distributions and for the
log-transformed values when the distribution is log-normal. For normal, log-normal, and triangular distributions, the
mean, geometric mean, and peak, respectively, are the values given in Tables S2 and S3.
Distribution
σ or Range
log Kow (p,p’-DDT) normal
0.44
log Kaw (p,p’-DDT) normal
0.10
log Kow (p,p’-DDE) normal
0.43
log Kaw (p,p’-DDE) normal
1.3
log Kow (p,p’-DDD) normal
0.67
log Kaw (p,p’-DDD) normal
1.3
kdega
log-normal
0.50
kdegs
log-normal
0.53
kdegsed
log-normal
0.53
ffa
triangular
0.7-1
ffaer
triangular
0.5-0.9
ffana
triangular
0.1-0.5
fDDT
triangular
0.65-0.85
hfs
uniform
0.02-0.1
focsp
uniform
0.05-0.2
focsed
uniform
0.02-0.04
focts
log-normal
0.42
fomq
uniform
0.3-0.8
ρom
log-normal
0.2
ρmm
log-normal
0.2
ρq
log-normal
0.2
cq
normal
7∙10-6 (dry), 5∙10-5 (wet)
csp
log-normal
0.2 (dry), 0.4 (wet)
vwsed
log-normal
0.05
vats
log-normal
0.05
vwts
log-normal
0.05
Uas
log-normal
0.55
Uwa
log-normal
0.55
Urain
log-normal
daily
Usedbur
log-normal
0.59
uspdep
log-normal
0.55
uwind
log-normal
0.55
*Estimated as explained in Methods section in main text.
S12
Reference
Shen and Wania 2005
Shen and Wania 2005
Shen and Wania 2005
Shen and Wania 2005
Shen and Wania 2005
Shen and Wania 2005
USEPA 2014
Schenker et al. 2009
this work*
this work*
this work*
this work*
ASTDR 2002
this work*
Moreira-Turcq et al. 2003, Mackay 2001
this work, Almeida 2006
this work
Graham et al. 2003, Artaxo et al. 2002
MacLeod et al. 2012
MacLeod et al. 2012
Rissler et al. 2006
Artaxo et al. 2002
own assumption, Almeida 2006
MacLeod et al. 2012
MacLeod et al. 2012
MacLeod et al. 2012
MacLeod et al. 2012
MacLeod et al. 2012
INMET 2012, BDMEP 2015
Smith 2003, Moreira-Turcq et al. 2004
MacLeod et al. 2012
BDMEP 2015
S3 Results
S3.1 Model
Fig. S4 Median p,p’-DDT concentrations in the model compartments (lines) assuming that 100% of the emissions
occur into the air and DDT use in IRS stops after 1998, and median measured concentrations in the soils (circles)
and sediments (squares). The whiskers show the range in the measurements. Concentrations in flooded and aerated
floodplains were similar, so only the flooded floodplain is shown.
Fig. S5 Mass of p, p’-DDT in the 6 model compartments under baseline scenario. The mass is shown from 19852015 only in order to illustrate the cyclical pattern.
S13
Fig. S6 Median p,p’-DDE (a) and p,p-DDD (b) concentrations in the model compartments (lines) throughout the
model run from 1960-2014 (assuming that DDT use in IRS stops after 1998) and median measured concentrations in
the soils (circles) and sediments (squares). The uncertainty bands of the model results show the 95% confidence
intervals, while the whiskers show the range in the measurements. Concentrations in flooded and aerated floodplains
were similar, so only the flooded floodplain is shown.
S14
S3.2 Comparison of DDX measurements
Table S8 Results from Wilcoxon rank-sum tests comparing DDX concentrations in different years
2005 house soils vs 2011 house soils
2005 house soils vs 2014 house soils
2011 house soils vs 2014 house soils
2011sediments vs 2011 house soils
2011sediments vs 2014 house soils
p,p’-DDT
p
WS
0.025
50
0.037
32
0. 83
37
0.025
50
0.006
29
p,p’-DDE
p
WS
0.005
49
0.60
51
0. 17
45
0.005
49
0.60
51
p,p’-DDD
p
WS
0.005
49
0.0012 30
0.94
30
0.005
49
0.004
38
S3.3 Sensitivity Analysis
Fig. S7 Spearman rank correlation coefficients for the 5 parameters with the largest contribution to variance to p,p’DDT concentrations in the air 7 years before (a) and after (b) the ban on DDT use for IRS in Brazil.
Air
In pre-ban years, at the peak of the rainy season, when water levels are the highest, aerosol
concentrations and rain make a small contribution to the variance of DDT concentrations in the
air. In all seasons before the ban, DDT concentrations in the air are more strongly negatively
correlated with wind. This correlation decreases after the ban, and instead, DDT in air becomes
increasingly sensitive to the Kow and the degradation half-life in the soil, as the soil is the main
source of DDT after direct emissions cease. The same pattern is observed with the DDT
S15
degradation products. However, the parameter with the largest contribution to the variance of
DDE and DDD concentrations was the air-water partition coefficient (Kaw), with a large positive
r in both pre- and post-ban years.
Water
While emissions are ongoing, there is a high negative rank correlation between Kow and the
settling velocity of suspended particles in the water and DDT concentrations in the water. Kow is
the most influential parameter for DDE and DDD concentrations in the water as well. The Kow
dictates the partitioning of DDX into the organic carbon of solids, including suspended particles
that are afterwards deposited into the sediments. Sediment burial also has a significant negative
correlation with DDX concentrations in the water, particularly for DDE. In addition, DDE
concentrations are positively influenced by the concentration of suspended particles in the water.
Furthermore, the degradation half-life in sediments is also moderately negatively correlated to
the concentrations of DDE and DDD. After the ban, the contribution of Kow to the variance of
DDX concentrations in the water decreases, mostly for DDE and DDT. The degradation half-life
in the terra-firme soil becomes the most influential parameter, with a large negative r, for DDE
and DDT, and the second most influential parameter for DDD (as Kow remains the parameter
with the largest contribution to variance). This is not surprising since most of the DDX
accumulation occurs in the terra-firme soil and this compartment represents the main source of
DDX for the water after the ban. Sediment burial and particle settling velocities have a lower
negative contribution to variance of DDX concentrations in the water after emissions cease.
Fig. S8 Spearman rank correlation coefficients for the 5 parameters with the largest contribution to variance to p,p’DDT concentrations in the water 7 years before (a) and after (b) the ban on DDT use for IRS in Brazil.
S16
Floodplain
Similar rank correlations are observed in the flooded and aerated floodplain soils, due to the
close interactions between these compartments. The degradation half-life in the sediment was the
most important parameter throughout the model run for concentrations of all compounds in the
floodplain, followed by the degradation half-life in terra-firme soils (whose influence increases
after the ban), both with a negative r. In the pre-ban years, the Kow also made a moderate positive
contribution to the variance of DDT concentrations in floodplain soils, while the fractions of
formation made a moderate positive contribution to the variance of DDE and DDD
concentrations.
Fig. S9 Spearman rank correlation coefficients for the 5 parameters with the largest contribution to variance to p,p’DDT concentrations in the floodplain 7 years before (a) and after (b) the ban on DDT use for IRS in Brazil.
Terra-firme soil
In the terra-firme soil, the single highest rank correlation (negative) throughout the simulation is
that between each compounds and its degradation half-life in the soil. The fractions of formation
also made a moderate contribution (positive) to the variance of DDD and DDE concentrations,
particularly before the ban. The sensitivity of DDX concentrations in the terra-firme soil to other
parameters is very low.
S17
Fig. S10 Spearman rank correlation coefficients for the 5 parameters with the largest contribution to variance to
p,p’-DDT concentrations in the terra-firme soil 7 years before (a) and after (b) the ban on DDT use for IRS in Brazil.
S18
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