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Urban wastewater impacts on the spatial distribution of solutes and microbial constituents in the Musi River, India. Presented at American Geophysical Union Spring Meeting, Baltimore, 23 May 2006. - PowerPoint PPT Presentation
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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
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