Microbial source tracking of fecal contamination in Laguna Lake, Philippines using the library-dependent method, rep-PCR
| Autor: | Laurice Beatrice Raphaelle O Dela Peña, Mae Ashley G Nacario, Kevin Labrador, Windell L. Rivera, Nicole R. Bolo |
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| Rok vydání: | 2021 |
| Předmět: |
Microbiology (medical)
Pollution Veterinary medicine Swine Philippines media_common.quotation_subject Sewage Indicator bacteria Polymerase Chain Reaction laguna lake Feces Water Quality Tributary Animals Source tracking Waste Management and Disposal Water Science and Technology media_common geography geography.geographical_feature_category business.industry Water Pollution Public Health Environmental and Occupational Health Contamination Fecal coliform Lakes Infectious Diseases rep-pcr Environmental science Cattle dna fingerprinting Water quality microbial source tracking Public aspects of medicine RA1-1270 Water Microbiology business random forest Environmental Monitoring |
| Zdroj: | Journal of Water and Health, Vol 19, Iss 5, Pp 762-774 (2021) |
| ISSN: | 1996-7829 1477-8920 |
| DOI: | 10.2166/wh.2021.119 |
| Popis: | Laguna Lake is an economically important resource in the Philippines, with reports of declining water quality due to fecal pollution. Currently, monitoring methods rely on counting fecal indicator bacteria, which does not supply information on potential sources of contamination. In this study, we predicted sources of Escherichia coli in lake stations and tributaries by establishing a fecal source library composed of rep-PCR DNA fingerprints of human, cattle, swine, poultry, and sewage samples (n = 1,408). We also evaluated three statistical methods for predicting fecal contamination sources in surface waters. Random forest (RF) outperformed k-nearest neighbors and discriminant analysis of principal components in terms of average rates of correct classification in two- (84.85%), three- (82.45%), and five-way (74.77%) categorical splits. Overall, RF exhibited the most balanced prediction, which is crucial for disproportionate libraries. Source tracking of environmental isolates (n = 332) revealed the dominance of sewage (47.59%) followed by human sources (29.22%), poultry (12.65%), swine (7.23%), and cattle (3.31%) using RF. This study demonstrates the promising utility of a library-dependent method in augmenting current monitoring systems for source attribution of fecal contamination in Laguna Lake. This is also the first known report of microbial source tracking using rep-PCR conducted in surface waters of the Laguna Lake watershed. HIGHLIGHTS DNA fingerprinting of E. coli, coupled with machine learning algorithms, can be used to discriminate fecal pollution sources in Laguna Lake, Philippines.; The majority of E. coli isolates can be attributed to sewage contamination, followed by human and agricultural sources.; Source-tracking methods can empower local agencies responsible for water quality management to minimize public health and economic risks. |
| Databáze: | OpenAIRE |
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