Introduction
Materials and Methods
Study Area
-
• Steel processing factory, producing 26,000 tons per year of corrugated iron sheet;
-
• Textile factory, producing 22 million textiles per year, in garment production and dyeing;
-
• Tannery (not operating in 2013), soaking 1000 sheep skins and 3200 goat skins per day;
-
• Meat processing factory, dressing maximally 200 cattle per day.
Sample Collection, Preservation and Analysis
Factories effluent and river water sampling
Hydrology Measurements
Statistical Techniques
Factory | Steel | Textile | Tannery | Meat processing | Brewery | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Campaign (n = 8) | C1 | C2 | C1 | C2 | C1 | C2 | C1 | C2 | C1 | C2 | |
EC (µS/cm) | Median | 5730 | 3800 | 932 | 760 | 710 | 4470 | 1480 | 1590 | 920 | 1130 |
Mean | 14,400 | 4000 | 920 | 800 | 2200 | 5200 | 920 | 1200 | 2100 | 1800 | |
Maximum | 78,000 | 7460 | 1190 | 1010 | 10,570 | 12,280 | 1170 | 1740 | 7100 | 3070 | |
Minimum | 1430 | 620 | 730 | 480 | 450 | 800 | 560 | 740 | 720 | 1,070 | |
Standard error | 920 | 790 | 54 | 63 | 1240 | 1500 | 77 | 116 | 731 | 247 | |
pH | Median | 6.1 | 5.5 | 10.3 | 8.2 | 7.8 | 7.4 | 8.2 | 7.2 | 11.1 | 11.2 |
Maximum | 6.1 | 10.9 | 10.2 | 8.8 | 7.8 | 8.1 | 8.2 | 8.2 | 11.8 | 11.4 | |
Minimum | 0.4 | 2.2 | 7.5 | 7.7 | 7.4 | 5.6 | 6.7 | 7.1 | 5.2 | 6.9 | |
Standard error | 0.7 | 1.1 | 0.4 | 0.1 | 0.0 | 0.4 | 0.4 | 0.1 | 0.7 | 1.1 | |
Cr | Median (µg/L) | 89 | 17 | 4.1 | 3.1 | 6.1 | 26,800 | 2.2 | 9 | 10 | 40 |
Mean (µg/L) | 150 | 32 | 4.1 | 45 | 22 | 33,270 | 2.1 | 60 | 8 | 36 | |
Maximum (µg/L) | 485 | 85 | 4.9 | 297 | 131 | 64,600 | 2.1 | 215 | 16 | 77 | |
Minimum (µg/L) | 2.1 | 1.1 | 2.2 | 2.1 | 2.3 | 813 | 2.3 | 1.1 | 2.1 | 2.9 | |
Standard error (µg/L) | 60 | 11 | 0.7 | 36 | 17 | 7,850 | 0 | 34 | 2 | 8 | |
USEPA guidelinea (µg/L) | 1300 | 1300 | N.A.b | N.A. | 12,000 | 12,000 | N.A. | N.A. | N.A. | N.A. | |
EMoI guidelinec (µg/L) | 1000 | 1000 | 1000 | 1000 | 2000 | 2000 | N.A. | N.A. | N.A. | N.A. | |
Mean effluent (L/s) | 1.7 | 2.2 | 15.4 | 16.5 | 6.8 | 8.4 | 11 | 8.8 | 8.2 | 21 | |
Loadings (g/day) | 11 | 4 | 3 | 4 | 2.5 | 18,500 | 1.1 | 6 | 4 | 40 | |
Cu | Median (µg/L) | 65.2 | 99 | 14 | 6.9 | 11 | 15 | 9.1 | 3.1 | 25 | 26 |
Mean (µg/L) | 125 | 137 | 58 | 13 | 125 | 22 | 31 | 6.8 | 111 | 43 | |
Maximum (µg/L) | 440 | 340 | 290 | 50 | 290 | 85 | 160 | 20 | 290 | 200 | |
Minimum (µg/L) | 8.5 | 0.1 | 3.5 | 0.1 | 8.1 | 0.1 | 2.5 | 0.1 | 4.9 | 1.4 | |
Standard error (µg/L) | 45 | 54 | 34 | 6 | 51 | 0 | 10 | 20 | 3 | 47 | |
USEPA guideline (µg/L) | 1300 | 1300 | N.A. | N.A. | N.A. | N.A. | N.A. | N.A. | N.A. | N.A. | |
EMoI guideline (µg/L) | 2000 | 2000 | 2000 | 2000 | N.A. | N.A. | N.A. | N.A. | N.A. | N.A. | |
Mean effluent (L/s) | 1.7 | 2.2 | 15.4 | 16.5 | 6.8 | 8.4 | 11 | 8.8 | 8.2 | 21 | |
Loadings (g/day) | 6 | 20 | 22 | 9 | 6.3 | 10 | 5 | 3 | 17 | 29 | |
Zn | Median (µg/L) | 60,040 | 155,750 | 120 | 110 | 90 | 280 | 110 | 140 | 150 | 210 |
Mean (µg/L) | 170,000 | 172,600 | 200 | 230 | 980 | 390 | 160 | 150 | 210 | 220 | |
Maximum (µg/L) | 662,700 | 450,700 | 7190 | 640 | 7190 | 1250 | 180 | 330 | 720 | 440 | |
Minimum (µg/L) | 14,100 | 14,150 | 26 | 29 | 26 | 130 | 25 | 44 | 20 | 68 | |
Standard error (µg/L) | 87,800 | 50,110 | 76 | 85 | 887 | 0 | 125 | 43 | 33 | 76 | |
USEPA guideline (µg/L) | 3500 | 3500 | N.A. | N.A. | N.A. | N.A. | N.A. | N.A. | N.A. | N.A. | |
EMoI guideline (µg/L) | 5,000 | 5,000 | 5,000 | 5,000 | N.A. | N.A. | N.A. | N.A. | N.A. | N.A. | |
Mean effluent (L/s) | 1.7 | 2.2 | 15.4 | 16.5 | 6.8 | 8.4 | 11 | 8.8 | 8.2 | 21 | |
Loadings (g/day) | 4950 | 17,300 | 207 | 160 | 54 | 210 | 47 | 100 | 114 | 280 | |
Pb | Median (µg/L) | 5.1 | 8.2 | 2.9 | 1.1 | 2.1 | 2.1 | 2.9 | 1.1 | 5.9 | 1.1 |
Mean (µg/L) | 16 | 22 | 4.1 | 1.7 | 3.1 | 130 | 3.2 | 2.1 | 4.9 | 2.1 | |
Maximum (µg/L) | 43 | 66 | 7.1 | 4.1 | 3.9 | 1670 | 4.1 | 2.9 | 8.1 | 2.9 | |
Minimum (µg/L) | 2.1 | 0.6 | 2.1 | 0.6 | 2.1 | 0.6 | 2.1 | 0.6 | 2.1 | 0.6 | |
Standard error (µg/L) | 5.7 | 9.5 | 0.7 | 0.7 | 0.3 | 0.0 | 233 | 0.2 | 0.2 | 0.7 | |
USEPA guideline (µg/L) | 120 | 120 | N.A. | N.A. | N.A. | N.A. | N.A. | N.A. | N.A. | N.A. | |
EMoI guideline (µg/L) | 500 | 500 | 500 | 500 | N.A. | N.A. | N.A. | N.A. | N.A. | N.A. | |
Mean effluent (L/s) | 1.7 | 2.2 | 15.4 | 16.5 | 6.8 | 8.4 | 11 | 8.8 | 8.2 | 21 | |
Loadings (g/day) | 1 | 1.3 | 3 | 1 | 1 | 4 | 1 | 0.6 | 3 | 2 |
Station | LD1 | LD2 (M.z. steel) | LD3 (M.z. textile) | LD4 (M.z. tannery) | LD5 (M.z. meat proc.) | WD1 | WD2 (M.z. Brewery) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Campaigns (n = 8) | C1 | C2 | C1 | C2 | C1 | C2 | C1 | C2 | C1 | C2 | C1 | C2 | C1 | C2 | |
EC (µS/cm) | Median | 620 | 530 | 570 | 460 | 750 | 550 | 750 | 980 | 760 | 850 | 430 | 340 | 680 | 1240 |
Mean | 540 | 490 | 540 | 420 | 700 | 550 | 740 | 1050 | 770 | 850 | 400 | 350 | 700 | 1280 | |
Maximum | 718 | 685 | 617 | 574 | 1080 | 650 | 1010 | 1480 | 1110 | 1260 | 480 | 470 | 990 | 2850 | |
Minimum | 200 | 150 | 280 | 180 | 520 | 400 | 420 | 710 | 440 | 290 | 290 | 240 | 430 | 570 | |
Standard error | 66 | 65 | 38 | 43 | 62 | 32 | 66 | 105 | 69 | 113 | 24 | 27 | 71 | 241 | |
pH | Median | 7.5 | 8.0 | 8.1 | 8.3 | 8.3 | 8.1 | 7.8 | 7.9 | 7.6 | 7.6 | 8.1 | 8.4 | 6.3 | 9.5 |
Maximum | 8.3 | 8.2 | 8.5 | 8.7 | 8.8 | 8.5 | 8.2 | 7.9 | 8.5 | 7.9 | 8.5 | 8.7 | 9.5 | 11.2 | |
Minimum | 7.3 | 7.2 | 7.2 | 7.6 | 7.9 | 7.6 | 7.1 | 7.4 | 7.4 | 7.3 | 6.4 | 8.0 | 4.4 | 6.9 | |
Standard error | 0.8 | 0.13 | 0.8 | 0.13 | 0.89 | 0.13 | 0.83 | 0.07 | 0.84 | 0.09 | 0.83 | 0.1 | 0.74 | 0.58 | |
Cr | Median (µg/L) | 3.9 | 2.1 | 12 | 6.1 | 7.9 | 51 | 9.1 | 2660 | 8.9 | 280 | 2.1 | 2.1 | 7.1 | 38 |
Mean (µg/L) | 3 | 440 | 11 | 380 | 6.9 | 230 | 9 | 6880 | 11 | 4280 | 3.1 | 37 | 7.9 | 30 | |
Maximum (µg/L) | 21 | 2690 | 44 | 2160 | 25 | 1130 | 15 | 25,900 | 16 | 18,250 | 4.9 | 154 | 13 | 73 | |
Minimum (µg/L) | 1.9 | 1.1 | 2.1 | 0.7 | 2.1 | 0.7 | 1.9 | 206 | 2.1 | 26 | 2.1 | 1.2 | 2.1 | 2.1 | |
Standard error (µg/L) | 4.1 | 330 | 5.1 | 260 | 3.1 | 140 | 8.9 | 3360 | 6.1 | 2580 | 0.1 | 22 | 1.1 | 9.1 | |
Mean river flows (L/s) | 98 | 184 |
120
|
240
|
135
|
277
|
138
|
287
| 142 | 296 | 360 | 1320 | 360 | 1,320 | |
Loadings (g/day) | 34 | 32 |
124
|
124
|
93
|
1220
|
110
|
66,000
|
110
|
7260
|
62
|
228
|
218
|
4330
| |
Cu | Median (µg/L) | 23 | 0.4 | 17 | 14 | 63 | 41 | 10 | 21 | 14 | 27 | 8 | 0.2 | 13 | 33 |
Mean (µg/L) | 80 | 300 | 83 | 270 | 100 | 160 | 41 | 85 | 65 | 190 | 51 | 34 | 73 | 350 | |
Maximum (µg/L) | 303 | 1900 | 248 | 1540 | 250 | 830 | 250 | 360 | 270 | 1180 | 270 | 150 | 270 | 2450 | |
Minimum (µg/L) | 3.1 | 0.1 | 6.9 | 0.1 | 4.1 | 0.1 | 2.9 | 0.1 | 3.1 | 0.1 | 2.1 | 0.1 | 3.1 | 0.1 | |
Standard error (µg/L) | 37 | 240 | 36 | 190 | 37 | 100 | 30 | 45 | 33 | 140 | 33 | 22 | 35 | 300 | |
Mean river flows (L/s) | 98 | 180 |
120
|
240
|
130
|
280
|
140
|
290
| 140 | 300 | 360 | 1,320 | 360 | 1,320 | |
Loadings (g/day) | 195 | 6 |
176
|
290
|
735
|
980
|
119
|
521
|
172
|
691
| 249 | 23 |
404
|
3,760
| |
Zn | Median (µg/L) | 72 | 110 | 95 | 520 | 71 | 187 | 30 | 205 | 81 | 214 | 41 | 137 | 106 | 194 |
Mean (µg/L) | 77 | 110 | 109 | 886 | 91 | 525 | 52 | 384 | 127 | 528 | 67 | 151 | 194 | 175 | |
Maximum (µg/L) | 126 | 3310 | 367 | 2780 | 218 | 1600 | 131 | 1050 | 611 | 2120 | 143 | 338 | 855 | 278 | |
Minimum (µg/L) | 26 | 16 | 29 | 9.1 | 54 | 34 | 15 | 67 | 15 | 25 | 8.9 | 12 | 14 | 46 | |
Standard error (µg/L) | 15 | 402 | 37 | 365 | 21 | 209 | 17 | 127 | 65 | 250 | 19 | 45 | 92 | 29 | |
Mean river flows (L/s) | 98 | 184 |
120
|
240
|
135
|
277
|
138
|
287
| 142 | 296 | 360 | 1320 | 360 | 1320 | |
Loadings (g/day) | 610 | 1750 |
985
|
10,800
|
828
|
4480
|
358
|
5080
|
994
|
5470
| 1280 | 15,630 |
3300
|
22,130
| |
Pb | Median (µg/L) | 2.1 | 1.1 | 2.9 | 1.1 | 2.9 | 3.1 | 3.9 | 5.1 | 3.1 | 0.8 | 2.1 | 1.1 | 3.9 | 1.1 |
Mean (µg/L) | 1.1 | 11 | 1.1 | 9.9 | 1.1 | 8.1 | 0.4 | 128 | 1.1 | 7.9 | 3.1 | 2.1 | 2.1 | 1.1 | |
Maximum (µg/L) | 4.9 | 70 | 6.1 | 60 | 4.9 | 34 | 4.1 | 980 | 4.1 | 44 | 3.9 | 7.1 | 4.9 | 5.1 | |
Minimum (µg/L) | 2.1 | 1.1 | 2.1 | 1.1 | 1.9 | 1.1 | 2.1 | 0.6 | 2.1 | 0.6 | 2.1 | 1.1 | 2.1 | 1.1 | |
Standard error (µg/L) | 0.4 | 8 | 0.7 | 7 | 0.4 | 3.9 | 4.1 | 121 | 0.4 | 5.1 | 0.3 | 0.7 | 0.2 | 0.4 | |
Mean river flows (L/s) | 98 | 184 |
120
|
240
|
135
|
277
|
138
|
287
| 142 | 296 | 360 | 1320 | 360 | 1320 | |
Loadings (g/day) | 17 | 16 |
31
|
21
|
35
|
72
|
48
|
124
|
37
|
20
| 62 | 114 |
124
|
114
|
Metals Mass Transport Loadings
Quality Assurance
Results
Discharges of the Leyole and Worka rivers
Metals in the Effluents and Effluents Mixing Zones of the River Waters
Metals in the effluents of the five factories
Metals in the effluent mixing zones of the Leyole and Worka rivers
Discussion
Industrial Development and Pollution Management in the Kombolcha Industrial Zone
Industrial Effluents and Metals Pollution in the Kombolcha Industrial Zone
Factory | Expected effluent composition | Treatment facility |
---|---|---|
Steel processing | toxics: As, CN, Cr, Cd, Cu, Fe, Hg, Pb, Zn; non-toxic: Fe3+, Ca2+, Mg2+, Mn2+. | Retaining ponds |
Textile | Acid and alkaline, disinfectants: C12, H2O2, formalin, phenol | Facultative lagoons |
Tannery | Cr and organic wastes (i.e. Bio- oxidizables (BOD)) | Anaerobic lagoons |
Meat processing | Organic wastes, suspended solids, and BOD, nutrients (P, N) | Anaerobic lagoons |
Brewery | organic wastes, suspended solids, BOD, nutrients (P, N) | No treatment facility |
Factory effluent | Metals | Concentration (µg/L) | Country | Reference |
---|---|---|---|---|
Tannery | Cr | 23,020 | Kenya | Mwinyikione et al. (2006) |
10,820 | Ethiopia | Gebrekidan et al. (2009) | ||
5790 | Nigeria | Emmanuel and Adepeju (2015) | ||
3540 | Ethiopia | Ayalew and Assefa (2014) | ||
264,000 | Uganda | Oguttu et al. (2008) | ||
811,410 | Morocco | Ilou et al. (2014) | ||
95,000 | India | Ganesh et al. (2006) | ||
77,000 | Albania | Floqi et al. (2007) | ||
5, 420,000 | Bangladesh | Hashem et al. (2015) | ||
Pb | 1060–1920 | Nigeria | Akan et al. (2007) | |
2870–3100 | Nigeria | Emmanuel and Adepeju (2015) | ||
760 | Morocco | Ilou et al. (2014) | ||
1970 | Pakistan | Tariq et al. (2006) | ||
Steel processing | Zn | 5520 | Nigeria | Adakole and Abolude (2009) |
2900 | Bangladesh | Ahmed et al. (2012) | ||
168,150 | Romania | Alexa (2013) | ||
498,500 | India | Majumdar et al. (2007) | ||
Textile | Cu | 5140 | Nigeria | Yusuff and Sonibare (2004) |
2200–4500 | Nigeria | Ohioma et al. (2009) | ||
1090 | Pakistan | Sial et al. (2006) | ||
1700 | Pakistan | Manzoor et al. (2006) |
Industrial Pollution Control Policy and Implementation in Ethiopia
Issue | Industrial effluent pollution | |||
---|---|---|---|---|
Pollution regulation and control | ||||
Regulatory structures | Federal level (EEPA), Regional level (REPA), Local level (Kombolcha Bureau of Environmental Protection, Land Administration and Use (EPLAU)) | |||
Regulatory organs | Federal environmental institutions and the Council (Ethiopian Ministry of Environment, Forest and Climate change), Regional environmental institutions, Sectorial environmental institutions | |||
Control and command | Emission standards (limits of effluent quality discharge into water for eight categories of industries includinga (EEPA 2010)): | |||
Tanning and the production of leather goods; The manufacture of textiles; Extraction of mineral ores, the production of metals and metal products; The manufacture of cement and cement products; Preservation of woods and manufacture of wood products including furniture; The production of pulp, paper and paper products and; The manufacture and formulation of chemical products including pesticides. | ||||
Strengths | Manifestation of Ethiopian Environmental Policy | |||
Formulation of laws and regulation to control industrial pollution (proclamations of the environmental protection organs; Environmental Pollution Control proclamation; the Environmental Impact Assessment (EIA) proclamations; and the Water Resources and Management proclamation) | ||||
Weaknesses | Priority given to development over environmental protection | Lack of regulatory oversight relating to EIA | Reliance on use of effluent limits | Absence of any requirement to monitor or report for compliance of effluent limits |
Source of weaknesses | • Lack of awareness and political commitments to environmental protection• Absence of clear links between development objectives and environmental protection• Foreign investor indifference to environmental protection | • Lack of effective rules and legal enforcement for EIA• Lack of environmental protection awareness by EIA licensing bodies• Absence of political commitment• Lack of communication among EIA regulatory institutions | • Lack of financial and technical resources by concerned institutions• Lack of economic incentive• Limited monitoring infrastructure for effluent receiving environments such as rivers• Lack of clear protection guidelines to effluent mixing environments | • Absence of rules for clear monitoring schemes for industrial pollutants• Limited professional, technical/finance capacity• Absence of technology standards to control pollution by industries• Lack of enforcement to compliance emission guidelines• Lack of transparency (for public use) in monitoring records |
Possible solutions | • Awareness raising of decision makers in environmental protection• Prioritizing sustainable development in policy formulation and guidance | • Reformulating clear rules and strict implementation of EIA legal enforcement• Systematic use of EIA and coordinating the tasks of EIA regulatory institutions (e.g. licensing organization and EEPA) | • Introducing economic incentives schemes i.e. collecting revenue from emission fees, taxes and subsidies• Expanding monitoring infrastructures• Developing effect based water quality guidelines after mixing of effluents in receiving water bodies | • Formulating clear rules for emission monitoring in industries• Developing technology based emission guidelines• Capacity building of emission controlling institutions• Strict follow up of legal enforcements• Public disclosure of available monitoring records• Development of environmental management systems linked with monitoring and reporting |