Urban wastewater impacts on Urban wastewater impacts on the spatial distribution of the spatial distribution of solutes and microbial solutes and microbial constituents in the Musi River, constituents in the Musi River, IndiaIndia

Presented at American Geophysical Union Spring Meeting, Baltimore, 23 May 2006

Urban wastewater Urban wastewater impacts on the spatial impacts on the spatial distribution of solutes distribution of solutes

and microbial and microbial constituents in the constituents in the Musi River, IndiaMusi River, India

Jeroen H.J. Ensink Jeroen H.J. Ensink - London School of - London School of Hygiene & Tropical Medicine, International Water Hygiene & Tropical Medicine, International Water

Management Inst. (IWMI)Management Inst. (IWMI)

Christopher A. Scott Christopher A. Scott - IWMI, NOAA, - IWMI, NOAA, Univ. ArizonaUniv. Arizona

Sandy CairncrossSandy Cairncross - LSTHM- LSTHM

Pollution a Global Threat Pollution a Global Threat to Health and the to Health and the Environment Environment

Source: UNEP Global Environment Monitoring System (GEMS) Water ProgrammeSource: UNEP Global Environment Monitoring System (GEMS) Water Programme

Wastewater Discharge &Wastewater Discharge &Irrigation ReuseIrrigation Reuse

Wastewater Wastewater BiogeochemistryBiogeochemistry Microbial attenuation Coliform die-off Nematode (hookworm) egg deposition

Heavy metals attenuation Deposition, re-suspension

Nutrient attenuation – plant uptake Dissolved solids concentration

Irrigation diversion, evaporation, return flow

Musi River StudyMusi River Study

Sampling Transects

III – rural (25 – 40 km)

II – periurban (10 – 25 km)

I – urban (0 – 10 km)

River DischargeRiver Discharge

0

100

200

300

400

500

600

700

800

900

1,000

Nov-03 Jan-04 Mar-04 Apr-04 Jun-04 Aug-04 Sep-04 Nov-04 Dec-04 Feb-05

Tho

usan

ds

Dai

ly d

isch

arge

(m

3 d-1)

Irrigation DiversionsIrrigation Diversions

0

10

20

30

40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Flo

w (

Mm

3)

Irrigation abstractions Flow at Amberpet

Coliforms – longitudinal Coliforms – longitudinal datadataDec. 03 – Jan. 05 (red squares = mean value)Dec. 03 – Jan. 05 (red squares = mean value)

0

1

2

3

4

5

6

7

8

9

0 5 10 15 20 25 30 35 40

Distance downstream from Hyderabad (km)

LO

G E

.col

i (CFU

100

ml-1

)

Nematode Eggs in Nematode Eggs in WastewaterWastewater

0

20

40

60

80

100

120

140

I II IIISample Point

Ova

litre-

1

Hookworm

Ascaris

Trichuris

Nematode Prevalence in Nematode Prevalence in FarmersFarmers

0

10

20

30

40

50

60

70

I (n=240) II (n=354) III (n=413)Sample point

Pre

vale

nce

(%)

HookwormAscarisTrichuris

Heavy Metals Loading – Heavy Metals Loading – CadmiumCadmium(note – these data within urban (note – these data within urban area only)area only)

0

2

4

6

8

10

12

0 5 10 15 20 25

Sampling Point

Cd

(m g

/L)

Western edge of Hyderabad

Near Nagole bridge,

downstream of Amberpet

Source: Kumar, V.V.R.; Reddy, U.V.B.; and Sudarshan, V. (2000) “Geochemistry of Soils and Evaluation of Pollution, Patancheru-Bolaram Industrial Area of Medak District, Andhra Pradesh, India,” Environmental Geochemistry, Vol. 3, No. 1 and 2, pp. 19-26.

Heavy Metals Heavy Metals Concentration in Sediment - Concentration in Sediment - II

0.0

0.2

0.4

0.6

0.8

1.0

1.2

-10 -5 0 5 10 15 20 25 30 35

Distance Downstream from Amberpet Bridge (km)

No

rmal

ized

Met

al C

on

cen

trat

ion

(--

-)

Copper Zinc Cadmium Lead

Amberpet NagoleHigh Court Pirzadiguda Mutialguda Koremalla Pillaipalli

Source: Gerwe, Caroline. An Assessment of Heavy Metals Contamination in the Wastewater-Irrigated Area of the Musi River

0.00

0.20

0.40

0.60

0.80

1.00

1.20

-10 -5 0 5 10 15 20 25 30 35

Distance Downstream (km)

No

rma

lize

d M

eta

l C

on

ce

ntr

ati

on

(--

-)

Chromium Arsenic Molybdenum Nickel

Heavy Metals Heavy Metals Concentration in Sediment - Concentration in Sediment - IIII

Source: Gerwe, Caroline. An Assessment of Heavy Metals Contamination in the Wastewater-Irrigated Area of the Musi River

Dissolved NitrogenDissolved Nitrogen

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35 40

Distance downstream from Hyderabad (km)

Disso

lved

nitro

gen

conc

entrat

ion

(mg

l-1)

Dissolved OxygenDissolved Oxygen

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

0 5 10 15 20 25 30 35 40

Distance downstream from Hyderabad (km)

DO

con

cent

ration

(m

g l-1

)

Total Dissolved SolidsTotal Dissolved Solids

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 5 10 15 20 25 30 35 40

Distance downstream from Hyderabad (km)

Salin

ity

conc

entrat

ion

(dS

m-1)

TDS Conceptual ModelTDS Conceptual Model

Hyderabad

Urban drainage

Rural drainage

Irrigated fields

W ater quality sampling points

Irrigation divers ion

Urban dra inage

Rura l dra inage

Return flows

Q in,C in

Q i,C i

Qd,Cd

Qout,Cout

Qu,Cu

Qr,Cr

E

E

I II III IV V VI VIIIVII

E E E E E

E Evaporation

Qbd,CbdQbu,Cbu

TDS Seasonal VariationTDS Seasonal Variation

800

1000

1200

1400

1600

1800

2000

2200

0 10 20 30 40Distance from Hyderabad (km)

TD

S (

mg

l-1)

Maximum, April Minimum, August Annual mean

City Rural Threshold sensitivity for rice = 2010 mgl-1

ConclusionsConclusions Spatial processes critical to

understand microbial and dissolved constituent behavior

Hydraulic infrastructure (weirs) and irrigation operations have a critical impact on spatial and temporal contaminant distribution

Loading Dilution Uptake

Retention Return flow Concentration

Loading Dilution Uptake

Retention Return flow Concentration

Thank Thank youyou

Corroborating Results - Corroborating Results - MexicoMexico

Head-Tail Water Quality Trends, Tula Irrigation District 003, Mexico

0

200

400

600

800

1000

1200

1400

1600

1800

Head Middle Tail

EC

(m

icro

mho

s)

0

2

4

6

8

10

12

Nut

rient

s, 0

.1*B

OD

(m

g/L)

EC

Nitrogen (dry)

Nitrogen (rains)

0.1*BOD (rains)

0.1*BOD (dry)

Phosphates (rains)

Phosphates (dry)

3

4

5

6

7

8

9

10

11

12

0 1500 3000 4500 6000 7500 9000 10500 12000

Distance (m)

TP

(mg/

L)

1000

1050

1100

1150

1200

1250

1300

1350

1400

0 1500 3000 4500 6000 7500 9000 10500 12000

Distance (m)

Con

duct

ivity

(m

hos)

Guanajuato River, Total Phosphorus with Distance from City

Guanajuato River, Conductivity with Distance from City

Millennium Development Millennium Development GoalsGoals Reduce by half the proportion of

people without sustainable access to safe drinking water

But… sobering demographics 880 million additional global population by

2015, virtually all in developing countries After 2015, all worldwide growth in population

will take place in developing country cities Inadequate resources (financial or

water)

The Costs of WW The Costs of WW TreatmentTreatment 73% of urban wastewater in India is

untreated, requiring Rs. 2,92,500 crore (US$ 65 billion) or ten times greater than what the government proposes to invest (Infrastructure Development Finance Corp., 2003).

Technology only part of the cost; land may prove to be the ultimate sticker shock

Mexico City WW Mexico City WW Sources/FateSources/FateSource/Fate Flow

(m3/s) Comments

Wastewater generated in Mexico City

45 194 l/s/cap. At 70% return rate, water supply is 260 l/s/cap.

Primary treatment for irrigating parks/green areas within Mexico City

10 Could irrigate upto 10,000 ha of land, but may be used to maintain wetlands and “floating gardens.”

Primary and secondary treatment for Texcoco Lake Reclamation

1.0 ~ 1.5 Reclamation of sodic soils, reforestation, and Nabor Carillo Lake.

Tertiary treatment for animals and/or groundwater injection, Texcoco Lake

0.05 Sedimentation, flocculation, filtration (sand, activated carbon), chlorination.

Untreated wastewater 34 Discharged to Tula Irrigation District (Hidalgo State) through a network of tunnels, one > 60 km.

Field vs Market (E.coli) in Field vs Market (E.coli) in PakistanPakistan

0

1,000

2,000

3,000

4,000

5,000

6,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

E.c

oli c

ount

s pe

r 10

0 gr

am o

f pro

duce

Field Market