| Literature DB >> 32531878 |
Matthew Mamera1, Johan J van Tol1, Makhosazana P Aghoghovwia1, Gabriel T Mapetere2.
Abstract
Most people in rural areas in South Africa (SA) rely on untreated drinking groundwater sources and pit latrine sanitations. A minimum basic sanitation facility should enable safe and appropriate removal of human waste, and although pit latrines provide this, they are still contamination concerns. Pit latrine sludge in SA is mostly emptied and disposed off-site as waste or buried in-situ. Despite having knowledge of potential sludge benefits, most communities in SA are reluctant to use it. This research captured social perceptions regarding latrine sludge management in Monontsha village in the Free State Province of SA through key informant interviews and questionnaires. A key informant interview and questionnaire was done in Monontsha, SA. Eighty participants, representing 5% of all households, were selected. Water samples from four boreholes and four rivers were analyzed for faecal coliforms and E.coli bacteria. On average, five people in a household were sharing a pit latrine. Eighty-three percent disposed filled pit latrines while 17% resorted to closing the filled latrines. Outbreaks of diarrhoea (69%) and cholera (14%) were common. Sixty percent were willing to use treated faecal sludge in agriculture. The binary logistic regression model indicated that predictor variables significantly (p ˂ 0.05) described water quality, faecal sludge management, sludge application in agriculture and biochar adaption. Most drinking water sources in the study had detections ˂ 1 CFU/100 mL. It is therefore imperative to use both qualitative surveys and analytical data. Awareness can go a long way to motivate individuals to adopt to a new change.Entities:
Keywords: bacteria pollution; biochar; faecal sludge; pit latrine; water quality
Mesh:
Substances:
Year: 2020 PMID: 32531878 PMCID: PMC7312825 DOI: 10.3390/ijerph17114128
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1(a) and (b). Map showing the study location.
Figure 2Map showing sites for the sampled water sources.
Figure 3(a,c,g,h) Borehole drinking water sources and (b,d–f) river sources.
Description and unit of variables used in the binomial logistic model.
| Dependent Variables | Variable Description | Expected Effect |
|---|---|---|
| Y * | Quality of drinking water (0 = Poor 1 = Good) | Determined by explanatory variables |
| Y ** | Community faecal sludge management in VIP/UN-IP Latrines (0 = Empty 1 = Construct new pit) | |
| Y *** | Sewage sludge application in agricultural production (0 = Willing 1 = Not willing) | |
| Y **** | Biochar adaption in sludge treatment (0 = Yes 1 = No) | |
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| Gender (X1) | Gender of the participant (0 = Male 1 = Female) | |
| Age (X2) | Age of participant (Years) | |
| Household size (X3) | Number of household occupancy | |
| Education (X4) | Participant education level (0 = None 1 = Primary 2 = Secondary 3 = Tertiary) | |
| Employment (X5) | Participant employment status (0 = Employed 1 = Self-employed 2= Unemployed) | |
| Income (X6) | Average monthly household income (Measured in SA Rand, ZAR) | |
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| Drinking water source (X7) | Source used (0 = Selected 1 = Not selected) | |
| Household water source (X8) | Source used (0 = Selected 1 = Not selected) | |
| Regularity (X9) | Water supply/ flow (0 = Regular 1 = Not regular 2 = Unreliable) | |
| Sanitation type (X10) | Household sanitation type (0 = VIP 1 = UN-IP latrine) | |
| Latrine users (X11) | Number of household members using pit latrine (Head count) | |
| Sludge filling rate (X12) | Period a pit latrine is used by a household (Measured in years) | |
| Sludge draining (X13) | Pit latrine sludge disposal (0 = Participant 1 = Community 2 = Municipality 3 = Private contractor) | |
| Equipment (X14) | Access to disposal equipment (0 = Yes 1 = No) | |
| Diseases (X15) | Perceived outbreaks in the community (0 = Cholera 1 = Dysentery 2 = Diarrhea 3 = No outbreaks) | |
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| Crops (X16) | Major crops grown (0 = Selected 1 = Not selected) | |
| Fertilizer (X17) | Use of fertilizer in cropping practices (0 = Yes 1 = No) | |
| Manure (X18) | Use of animal manure (0 = Yes 1 = No) | |
| Yield (X19) | Estimate crop yield (0 = Low 1 = Medium 2 = High) | |
| Human manure (X20) | Awareness of the use of faecal sludge (0 = Yes 1 = No) | |
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| Biochar use in latrines (X21) | Willingness to use in pit latrine sludge treatments (0 = Yes 1 = No) | |
| Purchasing (X22) | Willingness to buy Biochar (0 = Yes 1 = No) | |
| Wood ash (X23) | Use of ash in pit latrines (0 = Yes 1 = No) | |
| Groundwater contamination (X24) | Awareness on pit latrine water pollution (0 = Yes 1 = No) | |
| Pit latrine sludge treatment (X25) | Material added in latrine to reduce groundwater contamination (0 = Nothing 1 = Detergent) | |
| Detergent price (X26) | Cost of material applied in pit latrines per month (Measure in SA ZAR) | |
| β1…βn | Coefficients of independent variables X1…Xn | |
| α | Intercept | |
Socio-economic predictor variables characteristic in the study.
| Variables | Response | (%) | Total (%) |
|---|---|---|---|
| Water quality | Good | 58.7 | 100 |
| Poor | 41.3 | ||
| Sludge management | Dispose | 83.4 | 100 |
| Reconstruct latrine | 16.6 | ||
| Sludge use in agriculture | Willing | 60 | 100 |
| Not willing | 40 | ||
| Biochar adaption | Yes | 73.8 | 100 |
| No | 26.2 |
Socio-economic characteristics of the households in the study.
| Variables | Total (%) | 1st Group | 2nd Group | |
|---|---|---|---|---|
| Gender (%) | Male | 36.3 | 69.8 | 30.2 |
| Female | 63.8 | 68.6 | 31.4 | |
| Age (Mean-49, Minimum-18, Maximum-80) | Below 30 | 21.3 | 69.12 | 30.89 |
| 31–54 | 40 | 72.66 | 27.35 | |
| Above 55 | 38.8 | 65.32 | 34.68 | |
| Average household sizes | 4.6 | 5 | 4.7 | |
| Participant highest education level (%) | None | 2.5 | 87.5 | 12.5 |
| Primary | 15 | 66.67 | 33.33 | |
| Secondary | 77.5 | 69.76 | 30.24 | |
| Tertiary | 5 | 56.25 | 43.75 | |
| Participant employment status | Employed | 11.3 | 52.78 | 47.22 |
| Self-employed | 16.3 | 67.31 | 32.69 | |
| Unemployed | 72.5 | 55.33 | 44.67 | |
| Estimated monthly household income (%) | Below ZAR 1200 | 41.3 | 68.94 | 31.06 |
| ZAR 1200–4500 | 45 | 67.80 | 32.20 | |
| Above 4500 | 13.8 | 77.27 | 22.73 | |
Frequencies of the significant variables on the water and sanitation in Monontsha village.
| Variable | Frequencies (%) | |
|---|---|---|
|
| Tap water | 4.2 |
| Rainwater harvesting | 8.5 | |
| Municipal tank | 61 | |
| River | 8.5 | |
| Borehole | 17.8 | |
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| 2–3 | 33.3 |
| 4–6 | 58.8 | |
| 7–14 | 8.2 | |
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| Municipality | 51.25 |
| Private contractor | 48.75 | |
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| Yes | 2 |
| No | 98 | |
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| Cholera | 14 |
| Diarrhea | 69 | |
| No outbreak | 17 | |
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| Vegetables | 55.5 |
| Field crops | 44.5 | |
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| Yes | 71.3 |
| No | 28.8 | |
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| Yes | 82.5 |
| No | 17.5 | |
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| Yes | 7.5 |
| No | 92.5 | |
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| Yes | 12.5 |
| No | 87.5 | |
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| Not treating | 77.5 |
| Detergents | 22.5 | |
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| ˂ ZAR 50 | 12.5 |
| ZAR 51–100 | 7.5 | |
| ˃ ZAR 100 | 2.5 | |
Binary logistic regression of factors influencing water quality, faecal sludge management, sludge application in agriculture and adoption of biochar within a community.
| Variables | Water Quality | Faecal Sludge Management | Sludge Application in Agriculture | Biochar Adoption | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| β | Exp β | β | Exp β | β | Exp β | β | Exp β | ||||||
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| Gender | 1.45 | 0.19 | 4.25 | −0.07 | 0.94 | 0.92 | 0.10 | 0.84 | 1.11 | −0.67 | 0.51 | 0.51 |
| Age | 0.45 | 0.77 | 1.57 | −0.65 | 0.62 | 0.52 | 0.05 | 0.89 | 1.05 | 1.07 | 0.50 | 2.92 | |
| Household size | 2.00 | 0.20 | 7.08 | −4.76 | 0.04 | 0.00 | −0.16 | 0.46 | 0.86 | 0.56 | 0.78 | 1.76 | |
| Education | 8.25 | 1.00 | 1.21 | −1.27 | 1 | 0.28 | 0.28 | 059 | 1.32 | 1.17 | 1.00 | 3.21 | |
| Employment | 0.14 | 0.54 | 1.06 | −0.04 | 0.98 | 0.96 | 0.19 | 0.63 | 1.21 | 0.47 | 0.74 | 1.59 | |
| Income | 0.30 | 0.37 | 1.26 | 8.06 | 0.99 | 33 | 0.23 | 0.51 | 1.26 | −0.84 | 0.56 | 0.43 | |
| Drinking water | |||||||||||||
| 1 Tap water | - | 0.97 | 0.00 | - | 0.92 | 17.5 | - | 0.99 | 0.00 | - | 0.99 | 1.39 | |
| Rain harvesting | −9.89 | 0.99 | 0.00 | 18.4 | 0.99 | 19.7 | −0.05 | 0.81 | 0.63 | −19.3 | 0.99 | 0.00 | |
| Municipal tank | −0.92 | 0.50 | 0.40 | 0.04 | 0.97 | 1.04 | 0.50 | 0.54 | 1.64 | −0.54 | 0.67 | 0.58 | |
| River | −2.61 | 0.14 | 0.07 | −2.54 | 0.26 | 0.08 | 0.20 | 0.79 | 1.23 | −0.46 | 0.73 | 0.64 | |
| Borehole | −3.73 | 0.04 * | 0.24 | 0.04 | 0.001 *** | 1.04 | 0.49 | 0.50 | 1.64 | −0.53 | 0.67 | 0.58 | |
| Household water | −0.92 | 0.54 | 0.40 | −0.06 | 0.97 | 0.94 | 0.27 | 0.75 | 1.31 | −0.19 | 0.89 | 0.83 | |
| Regularity | −0.10 | 1.00 | 0.00 | −15.3 | 0.99 | 0.00 | 0.50 | 0.77 | 1.64 | 17.4 | 0.99 | 60 | |
| Constant | 0.35 | 0.12 | 1.42 | −1.64 | 0.00 | 0.19 | 41.6 | 0.99 | 113 | 2.2 | 0.00 | 9 | |
| Nagelkerke R2 | 0.66 | 0.61 | 0.24 | 0.33 | |||||||||
| Chi-squared | 53.8 | 35.3 | 15.4 | 13.6 | |||||||||
| 0.01 * | 0.006 * | 0.57 | 0.69 | ||||||||||
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| Sanitation type | 9.3 | 1.00 | 0.18 | 0.52 | 0.79 | 1.68 | 1.39 | 0.18 | 4.00 | 19.7 | 0.99 | 51 |
| Latrine users | 0.17 | 0.80 | 1.17 | −1.44 | 0.02 * | 023 | −22.6 | 0.99 | 0.00 | 2.32 | 0.21 | 10.2 | |
| Sludge filling rate | 0.68 | 0.12 | 0.26 | 0.68 | 0.65 | 1.96 | 1.89 | 0.03 * | 6.59 | 19.6 | 0.99 | 54 | |
| Sludge emptying | 0.09 | 0.89 | 1.10 | −1.68 | 0.03 * | 0.19 | −1.66 | 0.01 * | 0.19 | 0.02 | 0.98 | 1.02 | |
| Equipment | −4.31 | 0.99 | 0.00 | 1.94 | 0.02 * | 27.2 | 20.1 | 1 | 51 | 0.98 | 1.00 | 2.66 | |
| Diseases | |||||||||||||
| 1 Cholera | - | 0.99 | 0.00 | - | 0.12 | 0.12 | - | 1 | 0.75 | - | 0.99 | 45 | |
| Diarrhea | 0.43 | 0.59 | 1.54 | −1.50 | 0.01 * | 0.22 | −1.17 | 0.14 | 0.31 | −0.83 | 1.00 | 0.44 | |
| No outbreak | −20.8 | 0.98 | 0.00 | −20.6 | 0.99 | 0.00 | −1.28 | 0.26 | 0.28 | −1.02 | 0.52 | 0.36 | |
| Constant | 1.07 | 1 | 2.91 | −40.2 | 1 | 0.00 | 24.5 | 1 | 42 | 19.7 | 1 | 34 | |
| Nagelkerke R2 | 0.53 | 0.69 | 0.33 | 0.33 | |||||||||
| Chi-squared | 39.8 | 42 | 22.1 | 13.6 | |||||||||
| 0.004 * | 0.04 * | 0.05 * | 0.4 | ||||||||||
|
| Crops | 3.64 | 0.04 * | 2.37 | 1.34 | 0.05 * | 3.82 | −1.60 | 0.03 * | 0.20 | 1.61 | 0.26 | 5.00 |
| Fertilizer | 1.71 | 0.27 | 5.54 | −3.93 | 0.18 | 0.02 | −0.31 | 0.85 | 0.73 | 0.92 | 1.00 | 2.52 | |
| Manure | 0.30 | 0.63 | 1.35 | −0.71 | 0.54 | 0.49 | −1.26 | 0.08 | 0.28 | −1.61 | 0.34 | 0.20 | |
| Yield | −1.83 | 0.27 | 0.16 | 0.85 | 0.69 | 0.43 | −0.30 | 0.83 | 0.74 | −19.4 | 1.00 | 0.00 | |
| Human manure use | 0.29 | 0.63 | 1.35 | 0.72 | 0.59 | 0.49 | −0.53 | 0.46 | 0.59 | −2.02 | 0.05 * | 1.82 | |
| Constant | 0.41 | 0.99 | 1.04 | −13.2 | 1 | 0.00 | 2.22 | 1 | 9.18 | 79.2 | 0.99 | 241 | |
| Nagelkerke R2 | 0.28 | 0.56 | 0.45 | 0.63 | |||||||||
| Chi-squared | 18.8 | 32.1 | 31 | 28.4 | |||||||||
| 0.22 | 0.07 | 0.05* | 0.05 * | ||||||||||
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| Biochar use in latrines | −0.43 | 0.85 | 0.65 | −0.24 | 0.82 | 0.79 | −1.65 | 0.08 | 0.19 | −36.7 | 0.99 | 0.00 |
| Purchasing | −1.31 | 0.05 * | 0.27 | 0.37 | 0.77 | 1.44 | 1.07 | 0.30 | 2.90 | 3.6 | 0.01 * | 95 | |
| Wood ash | −0.74 | 0.68 | 0.48 | −0.33 | 0.81 | 0.72 | 2.26 | 0.05 * | 9.57 | −1.60 | 0.23 | 0.20 | |
| Water pollution | −0.38 | 0.93 | 0.68 | −0.97 | 0.44 | 0.38 | 0.78 | 0.35 | 2.18 | −1.37 | 0.02 * | 0.26 | |
| Pit sludge treatment | −4.51 | 0.82 | 0.00 | −2.21 | 0.03 * | 0.11 | 21.7 | 0.99 | 258 | −19.2 | 0.99 | 0.00 | |
| Detergent price | |||||||||||||
| 1 ˂ ZAR 50 | - | 0.44 | 8.89 | - | 0.001 *** | 0.09 | - | 0.04 * | 0.88 | - | 1.00 | 0.07 | |
| ZAR 51–100 | 8.89 | 0.99 | 13.7 | −1.85 | 0.21 | 0.92 | 23.8 | 0.99 | 21.7 | −2.98 | 0.98 | 0.03 | |
| ˃ ZAR 100 | 9.91 | 0.99 | 15.1 | 0.42 | 0.83 | 1.52 | −0.13 | 0.96 | 0.88 | −22.5 | 0.99 | 0.00 | |
| Constant | 2.69 | 0.1 | 14.7 | 0.04 | 0.99 | 1.04 | −22.1 | 0.99 | 0.00 | 22.7 | 0.99 | 70 | |
| Observations | 80 | 80 | 80 | 80 | |||||||||
| Nagelkerke R2 | 0.19 | 0.23 | 0.34 | 0.51 | |||||||||
| Chi-squared | 11.8 | 11.6 | 23.4 | 22.3 | |||||||||
| 0.16 | 0.17 | 0.003 * | 0.002 * | ||||||||||
β is the model intercept coefficient which is the expected mean value of Y when all predictor variables Xn = 0; Exp (β) is odds ratio which represents the constant effect of a predictor X, on the likelihood that one outcome will occur; * and *** Significance at 0.05 and 0.001 probability level; 1 X is the baseline variable for categorical variables in the models.
Water analysis for faecal coliforms and E. coli bacteria contaminates (CFU/100 mL).
| Site | Water Source |
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|---|---|---|---|---|---|---|---|
| Oct 2019 | Dec 2019 | Feb 2020 | |||||
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| Borehole | ˂1 | ˂1 | ˂1 | ˂1 | - | - |
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| River | 687 * | 687 * | 99 * | 70 * | 1414 * | 980 * |
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| Borehole | ˂1 | ˂1 | ˂1 | ˂1 | 1 | ˂1 |
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| River | 11 * | 11 * | ˃2420 * | ˃2420 * | ˃2420 * | ˃2420 * |
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| River | 1986 * | 50 * | 261 * | 86 * | 1986 * | 1553 * |
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| River | ˃2420 * | ˃2420 * | ˃2420 * | ˃2420 * | ˃2420 * | 1553 * |
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| Borehole | ˂1 | ˂1 | ˂1 | ˂1 | ˂1 | ˂1 |
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| Borehole | ˂1 | ˂1 | 3 * | 2 * | ˂1 | ˂1 |
*-Levels which exceed drinking water national standards (˃1 CFU/100 mL) according to SANS, [34].