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 Drainage and Environment, Results of

the Monitoring of Non Point Source Pollution

Viesturs Jansons

Department of Environmental Engineering and Water Management

Latvia University of Agriculture

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Water pollution sources

Point source pollution Non-point source pollution

• hard to determine the nature, extent, transport and fate of contaminants from agriculture

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The contribution of Agriculture to N and P inputs into surface water in EU countries

Country N P

Belgium 66*

Denmark 77 26

France 55 No data

Germany 57 48

Italy 69 31

Sweden 45 No data

UK 89** N and P from the agriculture to air, soil and wter

Source: P.De Clercq et.all. Nutrient Management legislation in European countries. 2001, Wageningen. Netherlands.

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Monitoring of agricultural non-point source pollution , Bērze monitoring site

Small catchment station - 368 ha, Field drainage station - 77 ha,

•Constructed 1966. •In operation (hydrological data) since 1967. •Water quality sampling programme since 1994. •Renovated measurement structure and new equipment installed in 2006.

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Bērze monitoring station 2005

Berze.kmz

Moldova, 3.10 - 6. 10. 2006 6

Berze monitoring siteMeasurement structure V–shape Crump weir

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Berze monitoring site

Measurement equipment - YSI data loggers, powered from solar panels. GSM data transfer with mobile phone from stations to university PC.

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Results: Nutrient concentrations, 1994-2010

11,3 mg/l NO3-N

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Measurements with nitrate zonde

YSI 6000XLM

•Water to

•Conductivity•Dissolved O2

•Nitrate / N•Ammonium /N•ORP

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Impact of the extreme climate conditions on nitrogen run-off (autumn 2006 - winter 2007.)

Dry and hot summer (19.VI.2006)

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Impact of the extreme climate conditions on nitrogen run-off (autumn 2006 - winter 2007.)

Bērze drenu lauks / Berze drainage field

0,00

0,20

0,40

0,60

0,80

1,00

1,20

1,40

1,60

1,80

2006

.11.

0820

06.1

1.09

2006

.11.

10

2006

.11.

11

2006

.11.

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2006

.11.

13

2006

.11.

14

2006

.11.

15

2006

.11.

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2006

.11.

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2006

.11.

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2006

.11.

19

2006

.11.

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2006

.11.

21

0

20

40

60

80

100

120

140

160

180

Caurplūdums / Discharge l / s Nitrāti / Nitrate mg/l

Q, l s-1

N / NO-13 mg l-1

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Impact of the extreme climate conditions on nitrogen run-off (autumn 2006 - winter 2007.)

Content of the mineral nitrogen, mg kg-1 dry soil

2006.g. autumn 2007.g. spring

Field

Soil layer

(cm) NO3- N NH4-N NO3- N NH4-N

0 - 30 21.4 6.6 2.6 4.1

30 - 60 6.8 3.3 6.1 3.1 Silarāji

60 - 90 1.3 2.7 8.2 2.7

0 - 30 2.3 3.6 2.2 3

30 - 60 0.5 3.1 0.8 2.4 Dzelzarāji

60 - 90 0.5 2.9 0.6 2.3

0 - 30 16.6 4.1 7.1 2.9

30 - 60 3.3 3.3 5.7 3.1 Klaipiņi

60 - 90 0.9 3.4 5.1 2.6

0 - 30 11.4 3.9 3.4 3.5

30 - 60 1.7 3.1 3.1 3.2 Puķes

60 - 90 1.3 2.6 2.4 2.7

0 - 30 14 4.1 3.5 3.2

30 - 60 9.6 3.4 5.5 3.4 Vāverītes

60 - 90 1.9 3 4.9 3

0 - 30 34 3.7 5.9 3.7

30 - 60 18.9 3.7 7.9 3.3 Kāpas

60 - 90 7.1 3.2 11.3 3.1

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Nitrogen Run-off / Load L= C Q

0,00

2,00

4,00

6,00

8,00

10,00

12,00

14,00

Janu

ary

Febr

uary

Mar

chA

pril

May

June July

Aug

ust

Sept

embe

rO

ctob

erN

ovem

ber

Dec

embe

rJa

nuar

yFe

brua

ryM

arch

Apr

ilM

ayJu

ne July

Aug

ust

Sept

embe

rO

ctob

erN

ovem

ber

Dec

embe

r

N tot kg/ha year Nitrogen run-off, 2006-2007

Berze catchment

Berze drainage

L dr 2007=37,3 kg N /ha

L dr 1994-2010 = 17,8 kg N /ha

NHF XXVI Riga 2010 14

Water Quality assessment Good water quality (WFD 2015)?

Numeric values of the parameters / standards ?

US EPA method based on percentile selection of data plotted as frequency distribution could be used.

(EU JRC publication: A.C. Cardoso et al; "Criteria for the Identification of Freshwaters Subject to Eutrophication)

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Monitoring sites, drainage field scale

6,2

10,312

3,869

13,225

7,383

4,351

3,166

8,935

1,1

0,16

0,241

1,825

0,01

0,1

1

10

100

0 10 20 30 40 50 60 70 80 90 100

probability %

N-NO3 mg L-1

B?rze drainage field (76.6 ha)Mellup?te drainage field (12.95ha)Vienziemite drainage field (67 ha)GammaBerzeDrGammaMellDrGammaVienzDr

11,2 mg l-1 N-NO3

Probability (Gamma distribution) Curves for the Nitrate Values Evaluation in the Drainage field Run- off

Fair

Good

ExcelentBad

Poor

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Numeric Values for Water Quality Criteria

Ntot (mg L-1) Ptot (mg L-1) Agricultural land Agricultural land

Quality class

Field Drainage

Small catchment

Field Drainage

Small catchment

Excellent 4.5 1.5 0.015 0.025

Good 4.5-5.5 1.5-2.5 0.015-0.020

0.025-0.050

Fair 5.5-10.0 2.5-7.5 0.020-0.075

0.050-0.150

Poor 10.0-12.0 7.5-10.5 0.075-0.135

0.150-0.250

Bad 12.0 10.5 0.135 0.250

Climate change regional models (SMHI) show that in the Northern and Central part of the Baltic region:

•Winter precipitation and temperature could increase, but snow packs will decrease;

•Shorter duration of snow cover, and frequent melting periods may take place;

•Summer temperatures could increase, but rainfall decrease, extreme weather conditions (floods, dry weather) may occur.

Drainage run-off and Climate change

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Conclusions 1. Water quality nutrient criteria may be developed for run-off from farm

land for the field drainage and small catchment.

2. Criteria could be developed taking into account available monitoring information in different geographical scales, and may be adjusted with neighboring EU countries with similar regional climate, soil, and crop management conditions, e.g. Baltic - Nordic countries.

3. Field drainage / small catchments’ water quality assessment should include full scale of water quality classes (excellent poor). Reference values for nutrients concentration (mg L-1) for water quality classification (5 classes according WFD) could be designated using following percentiles: (1) <90% excellent quality, (2) 90 – 75% good quality, (3) 75 – 25% fair quality; (4) 25-10% poor quality; (5). > 10% bad quality.

4. Taking into account climate changes, in future drainage may play more important role in agriculture , but could cause an increase of non-point source pollution from agriculture.

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Thank You for attention! Questions?