| Literature DB >> 29436334 |
Abu Mohd Naser1,2, Eilidh M Higgins2, Shaila Arman1, Ayse Ercumen3, Sania Ashraf1, Kishor K Das1, Mahbubur Rahman1, Stephen P Luby4, Leanne Unicomb1.
Abstract
We assessed the ability of sodium dichloroisocyanurate (NaDCC) to provide adequate chlorine residual when used to treat groundwater with variable iron concentration. We randomly selected 654 tube wells from nine subdistricts in central Bangladesh to measure groundwater iron concentration and corresponding residual-free chlorine after treating 10 L of groundwater with a 33-mg-NaDCC tablet. We assessed geographical variations of iron concentration using the Kruskal-Wallis test and examined the relationships between the iron concentrations and chlorine residual by quantile regression. We also assessed whether user-reported iron taste in water and staining of storage vessels can capture the presence of iron greater than 3 mg/L (the World Health Organization threshold). The median iron concentration among measured wells was 0.91 (interquartile range [IQR]: 0.36-2.01) mg/L and free residual chlorine was 1.3 (IQR: 0.6-1.7) mg/L. The groundwater iron content varied even within small geographical regions. The median free residual chlorine decreased by 0.29 mg/L (95% confidence interval: 0.27, 0.33, P < 0.001) for every 1 mg/L increase in iron concentration. Owner-reported iron staining of the storage vessel had a sensitivity of 92%, specificity of 75%, positive predictive value of 41%, and negative predictive value of 98% for detecting > 3 mg/L iron in water. Similar findings were observed for user-reported iron taste in water. Our findings reconfirm that chlorination of groundwater that contains iron may result in low-level or no residual. User reports of no iron taste or no staining of storage containers can be used to identify low-iron tube wells suitable for chlorination. Furthermore, research is needed to develop a color-graded visual scale for iron staining that corresponds to different iron concentrations in water.Entities:
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Year: 2018 PMID: 29436334 PMCID: PMC5928807 DOI: 10.4269/ajtmh.16-0954
Source DB: PubMed Journal: Am J Trop Med Hyg ISSN: 0002-9637 Impact factor: 2.345
Figure 1.Study sites for the small- and large-scale iron study.
Figure 2.Study organizational diagram and sampling strategies.
Iron concentration in tubewell water and residual-free chlorine levels in treated water 30 minutes after adding a 33-mg-sodium dichloroisocyanurate tablet to 10 L of water in the small- and large-scale study
| Study | Subdistrict | Iron concentration | Residual-free chlorine | ||
|---|---|---|---|---|---|
| Median (IQR) | Greater than 3 mg/L, | Median (IQR) | Less than 0.2 mg/L, | ||
| Small-scale study | Kaligonj ( | 1.4 (0.7–2.1) | 10 (21) | 1.1 (0.3–1.6) | 11 (23) |
| Kapasia ( | 1.2 (0.52–2.9) | 28 (23) | 1.4 (0.5–1.6) | 21 (18) | |
| Sreepur ( | 1.12 (0.46–2.1) | 18 (19) | 1.4 (0.77–1.7) | 11 (11) | |
| Large-scale study | Gafaorgaon ( | 0.31 (0.17–0.62) | 3 (4) | 1.4 (0.92–1.7) | 8 (80) |
| Nandail ( | 0.61 (0.27–2.25) | 15 (23) | 1 (0.25–1.6) | 14 (21) | |
| Muktagacha ( | 0.81 (0.42–1.47) | 4 (7) | 1.4 (1–1.7) | 5 (8) | |
| Trishal ( | 1 (0.46–1.77) | 7 (12) | 1.3 (0.9–1.6) | 8 (13) | |
| Gouripur ( | 0.73 (0.25–1.62) | 7 (13) | 1.8 (0.87–2.1) | 4 (7) | |
| Mirzapur ( | 1.94 (0.6–5) | 25 (38) | 0.9 (0.07–1.6) | 21 (32) | |
| Both studies | Total ( | 0.91 (0.36–2) | 117 (18) | 1.3 (0.6–1.7) | 103 (16) |
IQR = interquartile range.
Figure 3.Variation of the median well water iron concentration across unions.
Groundwater iron variation at small geographical region (union-level) and residual-free chlorine levels in treated water in the small-scale study
| Subdistrict | Union | Iron concentration | Residual-free chlorine | |||
|---|---|---|---|---|---|---|
| Median (IQR) | Greater than 3 mg/L, | Intra-cluster correlation coefficient | Median (IQR) | Less than 0.2 mg/L, | ||
| Kaligonj ( | Kaligonj | 1.6 (0.4–2.0) | 2 (17) | 0.04 | 1.3 (0.52–1.64) | 2 |
| Muktarpur | 4.3 (1.1–5.5) | 8 (67) | 0 | 0.14 (0.06–1.05) | 8 | |
| Nagari | 1.3 (0.6–1.9) | 0 | 0.3 | 1.26 (0.81–1.64) | 0 | |
| Tumulia | 1.3 (0.8–1.6) | 0 | 0.08 | 1.13 (0.50–1.75) | 1 | |
| Kapasia ( | Barishaba | 0.4 (0.3–1.1) | 0 | 0 | 1.63 (1.54–1.79) | 0 |
| Chandpur | 1.6 (0.9–2.4) | 2 (17) | 0.24 | 0.26 (0.14–1.07) | 4 | |
| Durgapur | 1.1 (0.5–5) | 4 (33) | 0.25 | 1.41 (0.49–1.57) | 3 | |
| Ghagotia | 1.1 (0.5–1.5) | 0 | 0.28 | 1.46 (0.21–1.61) | 3 | |
| Karihata | 0.6 (0.3–3.7) | 4 (33) | 0.06 | 1.44 (1.08–1.61) | 2 | |
| Rayed | 1.3 (0.6–2.9) | 2 (17) | 0.54 | 1.15 (0.54–1.65) | 3 | |
| Sanmania | 1.4 (0.7–2.7) | 3 (25) | 0.09 | 1.64 (0.86–1.80) | 0 | |
| Singasree | 1.2 (0.8–3.6) | 5 (42) | 0.45 | 1.60 (1.33–1.71) | 1 | |
| Toke | 3.0 (1.3–3.6) | 6 (50) | 0.06 | 0.61 (0.05–1.34) | 4 | |
| Taragaon | 0.9 (0.5–1.7) | 2 (17) | 0.12 | 1.38 (0.89–1.47) | 0 | |
| Sreepur ( | Barami | 1.4 (0.4–2.3) | 0 | 0.12 | 1.56 (0.25–1.65) | 3 |
| Gazipur | 0.6 (0.3–1.1) | 0 | 0.13 | 1.58 (1.32–1.79) | 0 | |
| Gosinga | 1.2 (0.5–1.4) | 0 | 0 | 1.11 (0.67–1.52) | 0 | |
| Kaoraid | 0.9 (0.4–1.9) | 2 (17) | 0.36 | 1.11 (0.50–1.45) | 2 | |
| Mawna | 0.8 (0.2–4.2) | 4 (33) | 0.42 | 1.67 (1.36–1.91) | 0 | |
| Prohladpur | 1.8 (0.6–4.7) | 4 (33) | 0 | 1.31 (0.49–1.69) | 2 | |
| Rajbari | 1.6 (1.0–3.6) | 3 (33) | 0 | 1.38 (0.59–1.74) | 1 | |
| Telihati | 0.9 (0.5–5.0) | 5 (42) | 0.21 | 1.36(0.29–1.68) | 3 | |
Union: administrative unit of a subdistrict; for each union, 12 tube wells were sampled.
Interquartile range.
Twelve tube wells tested in each village.
Predictors of iron concentration in tubewell water and residual-free chlorine in treated water
| Co-efficient | 95% confidence interval | ||
|---|---|---|---|
| Factors affecting iron concentration | |||
| Tubewell depth (feet) | −0.002 | −0.003, −0.001 | < 0.001 |
| Tubewell location (subdistrict) | 0.074 | 0.035, 0.114 | < 0.001 |
| Year round presence of water in the tube well | 0.164 | −0.130, 0.457 | 0.274 |
| Factors affecting residual-free chlorine | |||
| Tubewell water iron concentration (mg/L) | −0.299 | −0.332, −0.265 | < 0.001 |
Quantile regression models.
Binary variable (yes/no).