Predicting Groundwater Vulnerability to Geogenic Fluoride Risk: A Screening Method for Malawi and an Opportunity for National Policy Redefinition
Abstract
:1. Introduction
2. Materials and Methods
2.1. Stage 1: Groundwater Fluoride Data Collation
2.2. Stage 2: Geological Data Collation and Digitisation
2.3. Stage 3: Development of Statistical Relationships and Extrapolations
2.4. Stage 4: Development of National Groundwater Risk Maps and Statistics
- Functional
- Partly functional but in need of repair
- Not functional
- No longer exists/abandoned
2.5. Stage 5: Policy Review and Implications
3. Results
3.1. Groundwater Data
3.2. Geological Data
3.3. Statistical Relationships and Extrapolations
3.4. National Groundwater Risk Map and Statistics
4. Discussion
4.1. Discussion of Results
4.2. Policy Review and Implications
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Methodology Development | ||
---|---|---|
Stage 1 | Groundwater Fluoride Data Collation | Collate master data set of groundwater fluoride concentrations and hot springs for Malawi |
Stage 2 | Geological Data Collation and Digitisation | Produce Digital Geological Map of Malawi for use in statistical and spatial analyses with groundwater data |
Stage 3 | Development of Statistical Relationships and Extrapolations | Determine geogenic fluoride risk classifications by calculating fluoride-lithology statistical relationships and extrapolating where data are absent |
Stage 4 | Development of National Groundwater Risk Maps and Statistics | Produce national and catchment-level geogenic fluoride groundwater risk maps. Estimate number of water points and people at risk from groundwater fluoride. |
Stage 5 | Policy Review and Implications | Investigate the Malawian standard documents for fluoride in drinking water relative to the WHO and SDGs |
Data Set | n | Fluoride Concentration (mg/L) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
<1.5 | >1.5 | 1.5–6 | >6 | Min. | Max. | Mean | Median | Std. Dev. | ||
Groundwater F− | 1126 | 86.77% | 13.23% | 13.14% | 0.09% | 0.02 | 10.63 | 0.97 | 0.80 | 0.95 |
Hot Springs | 28 | 0.00% | 100% | 46.43% | 53.57% | 2.21 | 20 | 6.38 | 5.88 | 3.89 |
Classification | Likelihood of Groundwater Fluoride > 1.5 mg/L |
---|---|
Excessive Geogenic Fluoride (hot spring) | 100% |
Elevated Geogenic Fluoride | >60% |
Moderate-high Geogenic Fluoride | 17–60% |
Moderate-low Geogenic Fluoride | 10–17% |
Low Geogenic Fluoride | <10% |
Insufficient Data for classification | Unknown |
District | No. of Water Points | No. of Users | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Elevated Geological Fluoride | Moderate-Low Geological Fluoride | Low Geological Fluoride | Insufficient Data | Total | Elevated Geological Fluoride | Moderate-Low Geological Fluoride | Low Geological Fluoride | Insufficient Data | Total | |||||
Balaka | 0 | 0.0% | 1108 | 73.0% | 410 | 27.0% | 0 | 0.0% | 1518 | 0 | 319,481 | 146,633 | 0 | 466,114 |
Blantyre | 2 | 0.1% | 2119 | 75.5% | 687 | 24.5% | 0 | 0.0% | 2808 | 480 | 456,009 | 164,958 | 0 | 621,447 |
Chikwawa | 22 | 0.9% | 1867 | 74.4% | 562 | 22.4% | 59 | 2.4% | 2510 | 5875 | 542,936 | 192,636 | 20,106 | 761,553 |
Chiradzulu | 0 | 0.0% | 1125 | 67.0% | 554 | 33.0% | 0 | 0.0% | 1679 | 0 | 306,588 | 156,072 | 0 | 462,660 |
Chitipa | 167 | 12.3% | 989 | 72.9% | 143 | 10.5% | 58 | 4.3% | 1357 | 35,470 | 156,417 | 35,376 | 4153 | 231,416 |
Dedza | 12 | 0.7% | 1074 | 66.4% | 532 | 32.9% | 0 | 0.0% | 1618 | 4987 | 392,938 | 178,075 | 0 | 576,000 |
Dowa | 70 | 3.8% | 1564 | 85.2% | 202 | 11.0% | 0 | 0.0% | 1836 | 24,452 | 524,419 | 78,486 | 0 | 627,357 |
Karonga | 10 | 0.5% | 1591 | 83.8% | 43 | 2.3% | 255 | 13.4% | 1899 | 1040 | 256,355 | 11,181 | 45,318 | 313,894 |
Kasungu | 40 | 2.2% | 1387 | 74.6% | 432 | 23.2% | 0 | 0.0% | 1859 | 9265 | 374,052 | 90,238 | 0 | 473,555 |
Lilongwe | 101 | 2.3% | 3793 | 87.0% | 466 | 10.7% | 0 | 0.0% | 4360 | 42,377 | 1,136,296 | 173,853 | 0 | 1,352,526 |
Machinga | 4 | 0.3% | 1187 | 92.0% | 99 | 7.7% | 0 | 0.0% | 1290 | 1600 | 445,461 | 29,400 | 0 | 476,461 |
Mangochi | 36 | 0.9% | 3293 | 86.5% | 476 | 12.5% | 0 | 0.0% | 3805 | 10,723 | 883,423 | 155,745 | 0 | 1,049,891 |
Mchinji | 10 | 0.5% | 1813 | 97.7% | 32 | 1.7% | 0 | 0.0% | 1855 | 2900 | 466,838 | 6381 | 0 | 476,119 |
Mulanje | 0 | 0.0% | 1525 | 83.4% | 300 | 16.4% | 3 | 0.2% | 1828 | 0 | 357,125 | 58,563 | 110 | 415,798 |
Mwanza | 27 | 4.6% | 0 | 0.0% | 562 | 95.4% | 0 | 0.0% | 589 | 7508 | 0 | 129,432 | 0 | 136,940 |
Mzimba | 416 | 9.6% | 2691 | 62.2% | 1207 | 27.9% | 11 | 0.3% | 4325 | 56,988 | 614,476 | 217,360 | 1542 | 890,366 |
Neno | 0 | 0.0% | 74 | 15.5% | 403 | 84.5% | 0 | 0.0% | 477 | 0 | 22,869 | 130,738 | 0 | 153,607 |
Nkhata Bay | 1 | 0.1% | 314 | 23.1% | 534 | 39.3% | 509 | 37.5% | 1358 | 270 | 41,962 | 73,611 | 99,355 | 215,198 |
Nkhotakota | 76 | 4.9% | 990 | 64.0% | 481 | 31.1% | 0 | 0.0% | 1547 | 10,908 | 220,673 | 90,739 | 0 | 322,320 |
Nsanje | 11 | 0.8% | 1101 | 77.9% | 289 | 20.4% | 13 | 0.9% | 1414 | 2580 | 213,463 | 45,919 | 2170 | 264,132 |
Ntcheu | 9 | 0.7% | 536 | 41.1% | 760 | 58.2% | 0 | 0.0% | 1305 | 5000 | 219,963 | 264,214 | 0 | 489,177 |
Ntchisi | 3 | 0.3% | 873 | 77.0% | 258 | 22.8% | 0 | 0.0% | 1134 | 1780 | 254,517 | 47,766 | 0 | 304,063 |
Phalombe | 15 | 1.3% | 1068 | 92.1% | 77 | 6.6% | 0 | 0.0% | 1160 | 5092 | 249,935 | 15,876 | 0 | 270,903 |
Rumphi | 114 | 11.1% | 703 | 68.6% | 144 | 14.0% | 64 | 6.2% | 1025 | 12,952 | 100,193 | 21,705 | 6207 | 141,057 |
Salima | 1 | 0.1% | 1056 | 80.0% | 263 | 19.9% | 0 | 0.0% | 1320 | 206 | 302,130 | 68,599 | 0 | 370,935 |
Thyolo | 0 | 0.0% | 1749 | 85.6% | 294 | 14.4% | 0 | 0.0% | 2043 | 0 | 446,923 | 62,739 | 0 | 509,662 |
Zomba | 21 | 1.2% | 1667 | 93.6% | 93 | 5.2% | 0 | 0.0% | 1781 | 6510 | 657,837 | 28,999 | 0 | 693,346 |
AUNA * | 0 | 0.0% | 22 | 73.3% | 1 | 3.3% | 7 | 23.3% | 30 | 0 | 5835 | 200 | 2705 | 8740 |
Total | 1168 | 2.3% | 37,279 | 75.0% | 10,304 | 20.7% | 979 | 2.0% | 49,730 | 248,963 | 9,969,114 | 2,675,494 | 181,666 | 13,075,237 |
% of Malawi Population | 1.4% | 56.8% | 15.2% | 1.0% | 74% |
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Addison, M.J.; Rivett, M.O.; Phiri, P.; Mleta, P.; Mblame, E.; Wanangwa, G.; Kalin, R.M. Predicting Groundwater Vulnerability to Geogenic Fluoride Risk: A Screening Method for Malawi and an Opportunity for National Policy Redefinition. Water 2020, 12, 3123. https://doi.org/10.3390/w12113123
Addison MJ, Rivett MO, Phiri P, Mleta P, Mblame E, Wanangwa G, Kalin RM. Predicting Groundwater Vulnerability to Geogenic Fluoride Risk: A Screening Method for Malawi and an Opportunity for National Policy Redefinition. Water. 2020; 12(11):3123. https://doi.org/10.3390/w12113123
Chicago/Turabian StyleAddison, Marc J., Michael O. Rivett, Peaches Phiri, Prince Mleta, Emma Mblame, Gift Wanangwa, and Robert M. Kalin. 2020. "Predicting Groundwater Vulnerability to Geogenic Fluoride Risk: A Screening Method for Malawi and an Opportunity for National Policy Redefinition" Water 12, no. 11: 3123. https://doi.org/10.3390/w12113123
APA StyleAddison, M. J., Rivett, M. O., Phiri, P., Mleta, P., Mblame, E., Wanangwa, G., & Kalin, R. M. (2020). Predicting Groundwater Vulnerability to Geogenic Fluoride Risk: A Screening Method for Malawi and an Opportunity for National Policy Redefinition. Water, 12(11), 3123. https://doi.org/10.3390/w12113123