assessment of heavy metal fluxes in a forest … · results and discussion conclusions assessment...

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Results and discussion CONCLUSIONS ASSESSMENT OF HEAVY METAL FLUXES IN A FOREST WATERSHED IN BASQUE COUNTRY (NORTHERN SPAIN) USING SWAT MODEL PERAZA-CASTRO 1,2* , M., RUIZ- ROMERA 1 , E., SAUVAGE 3,4 , S., SÁNCHEZ-PÉREZ 3,4 , J.M 1 Department of Chemical and Environmental Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV-EHU), Alameda de Urquijo s/n, 48013 Bilbao, Spain 2 School of Health Technologies, Faculty of Medicine, University of Costa Rica, Rodrigo Facio Campus, San Pedro de Montes de Oca, San José, Costa Rica 3 University of Toulouse, INPT, UPS, Laboratoire Ecologie Fonctionnelle et Environnement (Ecolab), Avenue de l’Agrobiopole, 31326 Castanet Tolosan Cedex, France 4 CNRS, Ecolab, 31326 Castanet Tolosan Cedex, France e-mail: [email protected] Scientific context: Nowadays, the main threat to the water body’s damage comes from non-point sources of pollution, as result of intensive agriculture and urban development (Boskidis et al., 2012). All of these sources, sediment represents the highest volume for weight of material transported to the sea. Metals can be transported in association with the sediment (adsorbed) or in solution (soluble contaminants) (FAO, 1993, Boithias et al., 2012). The fine sediment is an important vector for pollutants transport such as heavy metals (Ankers et al., 2003), since it is a material highly enriched. During flood events, a significant proportion of heavy metal content from bottom sediments may be resuspended and transported to coastal zones, affecting negatively on the environment. In this sense, modeling is useful in assessing the impact of agricultural management and land use changes on water, sediment yield and pollutants as metals without altering the physical environment in the catchments. Objective: To quantify annual particulate metal fluxes associated to suspended particulate matter (SPM) from a small forestry catchment to the Bay of Biscay (Northern Spain) during eleven hydrological years (2001-2012). Study area and data The Oka River is located in the Basque Country-northern Spain, within Urdaibai Biosphere Reserve (declared in 1984 by UNESCO). Drainage basin: 31 km 2 Elevation: 13 m to 605 m Average precipitation: 1205 mm Average discharge: 0.64 m 3 s -1 Vegetation: pasture, pinus, eucalyptus Main bedrock: Calcareous Flysch References Criss, R.E., Winston, W.E., 2008. Do Nash values have value? Discussion and alternate proposals. Hydrol. Process. 22, 2723-2725. Jiao, W., Ouyang, W., Hao, F., Huang, H., Shan, Y., Geng, X., 2014. Combine the soil water assessment tool (SWAT) with sediment geochemistry to evaluate diffuse heavy metal loadings at watershed scale. J. Hazard. Mater. 280, 252-259. Peraza-Castro, M., Ruiz-Romera, E., Montoya-Armenta, L.H., Sánchez-Pérez, J.M., Sauvage, S., 2015. Evaluation of hydrology, suspended sediment and Nickel loads in a small watershed in Basque Country (Northern Spain) using eco-hydrological SWAT model. Ann Limnol - Int J Lim 51, 59-70. Methodology Gauging station Flow (m 3 s 1 ) 10 min Turbidity (NTU) 10 min SPM(mg l -1 ) 10 min Water samples collected manually during eight flood events occurred in 2009-2012 were carried to Chemical and Environmental Engineering Laboratory (UPV/EHU) to determinate particulate metal concentration (Cu, Ni, Pb, Cr, Zn, Al, Fe, Mn). Annual fluxes of particulate metals were estimated using Waling and Webb method (Walling and Webb, 1985): V (m 3 ): annual water discharge, Ci (μg l -1 ): particulate metals concentration, Qi (m 3 s 1 ): instantaneous river water flow and n: number of measures. 0 50 100 150 200 250 Cr yield (kg) Date (Year) Simulated and Observed annual Cr yield Observed Cr yield Simulated Cr yield Calibration Validation R 2 =0.52 VE=0.73 d=0.69 R 2 =0.52 VE=0.53 d=0.69 With SPM and particulate metal concentrations obtained in eight flood events at the outlet, linear regression model were employed to express its relationships. It was found that SPM correlated well with metals (R 2 >0.62). Based on these relationships, the long term observed and simulated metal concentrations could be computed from SPM. Particulate metal annual fluxes Particulate metal concentration (pMe) Regression equations pCu (μg l -1 ) 0.0268*SPM + 1.1826 0.84 pNi (μg l -1 ) 0.1843*SPM 0.6935 0.83 pPb (μg l -1 ) 0.0317*SPM + 3.3149 0.62 pCr (μg l -1 ) 0.0909*SPM + 2.5897 0.92 pZn (μg l -1 ) 0.135*SPM + 11.753 0.70 pAl (mg l -1 ) 0.0988*SPM 0.9557 0.97 pFe (mg l -1 ) 0.038*SPM + 0.4693 0.92 pMn (μg l -1 ) 0.7004*SPM + 10.168 0.90 0 20 40 60 80 100 Ni yield (kg) Date (Year) Simulated and Observed annual Ni yield Observed Ni yield Simulated Ni yield Calibration R 2 =0.50 VE=0.76 d=0.50 Validation R 2 =0.61 VE=0.81 d=0.87 0 20 40 60 80 100 120 140 160 Pb yield (kg) Date (Year) Simulated and Observed annual Pb yield Observed Pb yield Simulated Pb yield Calibration R 2 =0.50 VE=0.81 d=0.45 Validation R 2 =0.69 VE=0.87 d=0.90 0 200 400 600 800 1000 1200 1400 1600 1800 Mn yield (kg) Date (Year) Simulated and Observed annual Mn yield Observed Mn yield Simulated Mn yield Calibration R 2 =0.48 VE=0.72 d=0.55 Validation R 2 =0.55 VE=0.77 d=0.81 0 20 40 60 80 100 Cu yield (kg) Date (Year) Simulated and Observed annual Cu yield Observed Cu yield Simulated Cu yield Validation R 2 =0.63 VE=0.84 d=0.88 0 100 200 300 400 500 600 Zn yield (kg) Date (Year) Simulated and Observed annual Zn yield Observed Zn yield Simulated Zn yield Validation R 2 =0.68 VE=0.86 d=0.90 Calibration R 2 =0.53 VE=0.80 d=0.63 Exportation order of particulate metal yield: Al>Fe>Mn>Zn>Cr>Pb>Cu>Ni. The years 2008/09-2009/10 produced the highest metal load while 2001/02 generated the lowest. Mean specific (kg km -2 y -1 ) metal yield during total period Cu Cr Ni Pb Zn Fe Mn Al Observed 1.6 4.7 1.6 3.2 12.8 1562 29.8 2682 Simulated 1.5 4.5 1.5 2.9 11.2 1544 29.3 2702 Ni and other metal concentrations follows the SPM trend. In this example, simulated Ni ranged between 0.2-11.1 μg l -1 , with a mean of 0.8 μg l -1 comparable with the observed mean of 0.9 μg l -1 . 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 100000 Fe yield (kg) Date (Year) Simulated and Observed annual Fe yield Observed Fe yield Simulated Fe yield Calibration R 2 =0.53 VE=0.80 d=0.63 Validation R 2 =0.54 VE=0.76 d=0.80 0 20000 40000 60000 80000 100000 120000 140000 160000 180000 Al yield(kg) Date (Year) Simulated and Observed annual Al yield Observed Al yield Simulated Al yield Calibration R 2 =0.52 VE=0.70 d=0.57 Validation R 2 =0.50 VE=0.71 d=0.77 Specific particulate metal fluxes (kg km -2 y -1 ) of other catchments River Author Country Land use Cu Cr Ni Pb Zn Fe Mn Al Mero Palleiro et al., 2014 Spain Agroforestry 0.3 1.6 361 15.4 319 Corbeira Soto-Varela et al., 2014 Spain Agroforestry 0.3 1.6 260 8.3 205 Chester Branch Miller et al., 2003 EEUU Agricultural 0.4 1.8 1 0.7 3.3 1090 79 1550 Montoussé Roussiez et al., 2013 France Agricultural 1.6 4.1 2.6 1.1 7 1692 40 2416 Modelling approach SWAT model was applied to asses the temporal variability of discharge, SPM and Ni during 2001–2012 at daily scale in Oka catchment. The details of calibration and validation can be consulted in our earlier paper Peraza-Castro et al., 2014. To evaluate the model performance between simulated and observed annual metal fluxes the following efficiency criteria were used: coefficient of determination (R 2 ), volumetric efficiency (VE) and index of agreement (d). VE represents the fraction delivered at the proper time; its compliment represents the fractional volumetric mismatch (Criss et al., 2008). d represents the ratio of the mean square error and the potential error (Willmot, 1982). Both ,VE and d, were proposed to overcome the insensitivity of R 2 and Nash-Sutcliffe Efficiency. Calibration R 2 =0.45 VE=0.77 d=0.50 The mean specific annual of particulate metal export was compared with other European and American rivers with similar land use and the results are very similar each other; except for Pb and Zn that are slightly higher, which is attributed to the slight industrial and agricultural activity in the area. Al, Fe and Mn are more similar to the basins with agricultural landuse. These metals are major compounds soil. With a good relationships between metal and SPM measured in field and a satisfactory SPM calibration by SWAT, is possible to compute the long term metal concentrations at daily time step. The statistical criteria indicate a fair and satisfactory annual metal fluxes simulation. Swat model is useful to estimate annual fluxes of pollutants which have punctual measures. Oka catchment showed a high annual metal fluxes variability during period study, which is related with sediment load. Annual simulated metal fluxes (kg) ranged from: 32.5-69 for Cu, 93.2-198 for Cr, 30.7-68.4 for Ni, 57-130 for Pb, 220-494 for Zn, 31525-68464 for Fe, 600-1297 for Mn and 53994-121724 for Al. Conclusions Bay of Biscay 0 5 10 15 20 25 30 0 100 200 300 400 500 600 Ni μg l -1 SPM mg l -1 Date (day) Simulated daily SPM and Ni Simulated SPM Simulated Ni Validation Calibration

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Page 1: ASSESSMENT OF HEAVY METAL FLUXES IN A FOREST … · Results and discussion CONCLUSIONS ASSESSMENT OF HEAVY METAL FLUXES IN A FOREST WATERSHED IN BASQUE COUNTRY (NORTHERN SPAIN) USING

Results and discussion

CONCLUSIONS

ASSESSMENT OF HEAVY METAL FLUXES IN A FOREST WATERSHED IN BASQUE COUNTRY

(NORTHERN SPAIN) USING SWAT MODEL PERAZA-CASTRO1,2*, M., RUIZ- ROMERA1, E., SAUVAGE3,4, S., SÁNCHEZ-PÉREZ3,4, J.M

1 Department of Chemical and Environmental Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV-EHU), Alameda de Urquijo s/n,

48013 Bilbao, Spain 2 School of Health Technologies, Faculty of Medicine, University of Costa Rica, Rodrigo Facio Campus, San Pedro de Montes de Oca, San José, Costa Rica

3 University of Toulouse, INPT, UPS, Laboratoire Ecologie Fonctionnelle et Environnement (Ecolab), Avenue de l’Agrobiopole, 31326 Castanet Tolosan Cedex, France 4 CNRS, Ecolab, 31326 Castanet Tolosan Cedex, France

e-mail: [email protected]

Scientific context: Nowadays, the main threat to the water body’s damage comes from non-point sources of pollution, as result of intensive agriculture and urban development (Boskidis et al., 2012). All of these sources, sediment represents the highest volume for weight of material transported to the sea. Metals can be transported in association with the sediment (adsorbed) or in solution (soluble contaminants) (FAO, 1993, Boithias et al., 2012). The fine sediment is an important vector for pollutants transport such as heavy metals (Ankers et al., 2003), since it is a material highly enriched. During flood events, a significant proportion of heavy metal content from bottom sediments may be resuspended and transported to coastal zones, affecting negatively on the environment. In this sense, modeling is useful in assessing the impact of agricultural management and land use changes on water, sediment yield and pollutants as metals without altering the physical environment in the catchments.

Objective: To quantify annual particulate metal fluxes associated to suspended particulate matter (SPM) from a small forestry catchment to the Bay of Biscay (Northern Spain) during eleven hydrological years (2001-2012).

Study area and data

The Oka River is located in the Basque Country-northern Spain, within Urdaibai Biosphere Reserve (declared in 1984 by UNESCO). Drainage basin: 31 km2 Elevation: 13 m to 605 m Average precipitation: 1205 mm Average discharge: 0.64 m3 s-1

Vegetation: pasture, pinus, eucalyptus Main bedrock: Calcareous Flysch

References ♦ Criss, R.E., Winston, W.E., 2008. Do Nash values have value? Discussion and alternate proposals. Hydrol. Process. 22, 2723-2725. ♦ Jiao, W., Ouyang, W., Hao, F., Huang, H., Shan, Y., Geng, X., 2014. Combine the soil water assessment tool (SWAT) with sediment

geochemistry to evaluate diffuse heavy metal loadings at watershed scale. J. Hazard. Mater. 280, 252-259. ♦ Peraza-Castro, M., Ruiz-Romera, E., Montoya-Armenta, L.H., Sánchez-Pérez, J.M., Sauvage, S., 2015. Evaluation of hydrology, suspended

sediment and Nickel loads in a small watershed in Basque Country (Northern Spain) using eco-hydrological SWAT model. Ann Limnol - Int J Lim 51, 59-70.

Methodology

♦ Gauging station Flow (m3 s1) 10 min Turbidity (NTU) 10 min SPM(mg l-1) 10 min ♦ Water samples collected manually during

eight flood events occurred in 2009-2012 were carried to Chemical and Environmental Engineering Laboratory (UPV/EHU) to determinate particulate metal concentration (Cu, Ni, Pb, Cr, Zn, Al, Fe, Mn).

♦ Annual fluxes of particulate metals were estimated using Waling and Webb method (Walling and Webb, 1985):

V (m3): annual water discharge, Ci (µg l-1): particulate metals concentration, Qi (m3 s1): instantaneous river water flow and n: number of measures.

0

50

100

150

200

250

Cr

yiel

d (

kg)

Date (Year)

Simulated and Observed annual Cr yield

Observed Cr yield Simulated Cr yield

Calibration Validation

R2=0.52 VE=0.73 d=0.69

R2=0.52 VE=0.53 d=0.69

♦ With SPM and particulate metal concentrations obtained in eight flood events at the outlet, linear regression model were employed to express its relationships.

♦ It was found that SPM correlated well with metals (R2>0.62).

♦ Based on these relationships, the long term observed and simulated metal concentrations could be computed from SPM.

Particulate metal annual fluxes

Particulate metal concentration (pMe)

Regression equations R²

pCu (µg l-1) 0.0268*SPM + 1.1826 0.84

pNi (µg l-1) 0.1843*SPM0.6935 0.83

pPb (µg l-1) 0.0317*SPM + 3.3149 0.62

pCr (µg l-1) 0.0909*SPM + 2.5897 0.92

pZn (µg l-1) 0.135*SPM + 11.753 0.70

pAl (mg l-1) 0.0988*SPM0.9557 0.97

pFe (mg l-1) 0.038*SPM + 0.4693 0.92

pMn (µg l-1) 0.7004*SPM + 10.168 0.90

0

20

40

60

80

100

Ni y

ield

(kg

)

Date (Year)

Simulated and Observed annual Ni yield

Observed Ni yield Simulated Ni yield

Calibration R2=0.50 VE=0.76 d=0.50

Validation R2=0.61 VE=0.81 d=0.87

0

20

40

60

80

100

120

140

160

Pb

yie

ld (

kg)

Date (Year)

Simulated and Observed annual Pb yield

Observed Pb yield Simulated Pb yield

Calibration R2=0.50 VE=0.81 d=0.45

Validation R2=0.69 VE=0.87 d=0.90

0

200

400

600

800

1000

1200

1400

1600

1800

Mn

yie

ld (

kg)

Date (Year)

Simulated and Observed annual Mn yield

Observed Mn yield Simulated Mn yield

Calibration R2=0.48 VE=0.72 d=0.55

Validation R2=0.55 VE=0.77 d=0.81

0

20

40

60

80

100

Cu

yie

ld (

kg)

Date (Year)

Simulated and Observed annual Cu yield

Observed Cu yield Simulated Cu yield

Validation R2=0.63 VE=0.84 d=0.88

0

100

200

300

400

500

600

Zn y

ield

(kg

)

Date (Year)

Simulated and Observed annual Zn yield

Observed Zn yield Simulated Zn yield

Validation R2=0.68 VE=0.86 d=0.90

Calibration R2=0.53 VE=0.80 d=0.63

♦ Exportation order of particulate metal yield: Al>Fe>Mn>Zn>Cr>Pb>Cu>Ni. ♦ The years 2008/09-2009/10 produced the highest metal load while 2001/02 generated the lowest.

Mean specific (kg km-2 y-1) metal yield during total period

Cu Cr Ni Pb Zn Fe Mn Al

Observed 1.6 4.7 1.6 3.2 12.8 1562 29.8 2682

Simulated 1.5 4.5 1.5 2.9 11.2 1544 29.3 2702

♦ Ni and other metal concentrations follows the SPM trend. In this example, simulated Ni ranged between 0.2-11.1 µg l-1, with a mean of 0.8 µg l-1 comparable with the observed mean of

0.9 µg l-1 .

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

Fe y

ield

(kg

)

Date (Year)

Simulated and Observed annual Fe yield

Observed Fe yield Simulated Fe yield

Calibration R2=0.53 VE=0.80 d=0.63

Validation R2=0.54 VE=0.76 d=0.80

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

Al y

ield

(kg)

Date (Year)

Simulated and Observed annual Al yield

Observed Al yield Simulated Al yield

Calibration R2=0.52 VE=0.70 d=0.57

Validation R2=0.50 VE=0.71 d=0.77

Specific particulate metal fluxes (kg km-2 y-1) of other catchments

River Author Country Land use Cu Cr Ni Pb Zn Fe Mn Al

Mero Palleiro et al., 2014 Spain Agroforestry 0.3 1.6 361 15.4 319

Corbeira Soto-Varela et al., 2014 Spain Agroforestry 0.3 1.6 260 8.3 205

Chester Branch Miller et al., 2003 EEUU Agricultural 0.4 1.8 1 0.7 3.3 1090 79 1550

Montoussé Roussiez et al., 2013 France Agricultural 1.6 4.1 2.6 1.1 7 1692 40 2416

Modelling approach

♦ SWAT model was applied to asses the temporal variability of discharge, SPM and Ni during 2001–2012 at daily scale in Oka catchment. The details of calibration and validation can be consulted in our earlier paper Peraza-Castro et al., 2014.

♦ To evaluate the model performance between simulated and observed annual metal fluxes the following efficiency criteria were used: coefficient of determination (R2), volumetric efficiency (VE) and index of agreement (d). VE represents the fraction delivered at the proper time; its compliment represents the fractional volumetric mismatch (Criss et al., 2008). d represents the ratio of the mean square error and the potential error (Willmot, 1982). Both ,VE and d, were proposed to overcome the insensitivity of R2 and Nash-Sutcliffe Efficiency.

Calibration R2=0.45 VE=0.77 d=0.50

♦ The mean specific annual of particulate metal export was compared with other European and American

rivers with similar land use and the results are very similar each other; except for Pb and Zn that are slightly higher, which is attributed to the slight industrial and agricultural activity in the area.

♦ Al, Fe and Mn are more similar to the basins with agricultural landuse. These metals are major compounds soil.

♦ With a good relationships between metal and SPM measured in field and a satisfactory SPM calibration by SWAT, is possible to compute the long term metal concentrations at daily time step.

♦ The statistical criteria indicate a fair and satisfactory annual metal fluxes simulation. ♦ Swat model is useful to estimate annual fluxes of pollutants which have punctual measures. ♦ Oka catchment showed a high annual metal fluxes variability during period study, which is related

with sediment load. ♦ Annual simulated metal fluxes (kg) ranged from: 32.5-69 for Cu, 93.2-198 for Cr, 30.7-68.4 for Ni, ♦ 57-130 for Pb, 220-494 for Zn, 31525-68464 for Fe, 600-1297 for Mn and 53994-121724 for Al.

Conclusions

Bay of Biscay

0

5

10

15

20

25

300

100

200

300

400

500

600

Ni µ

g l -1

SPM

mg

l-1

Date (day)

Simulated daily SPM and Ni

Simulated SPM Simulated Ni

Validation Calibration