SystematicReview of Microorganism Removal Performanceby Physiochemical Water Treatment Technologies

Access to safe drinking water is crucial for public health necessitating the use of effective water treatment processes. We conducted a systematic literature review on microorganism removal by physical treatment processes used in drinking water treatment systems with the aim of providing current summary data to update the World Health Organization’s Guidelines for Drinking Water Quality (GDWQ) and to reflect on the data available for comparison of treatment technologies. We reviewed peer-reviewed articles reporting original data that were published between 1997 and March 2022 on the following physical treatment technologies: roughing filters, storage reservoirs, bank filtration, conventional and high-rate clarification, dissolved air flotation, lime softening, granular media filtration, slow sand filtration, precoat filtration, membrane filtration, granular activated carbon, ceramic membrane filtration, and soil aquifer treatment. The literature search was conducted in several databases including Web of Science and PubMed. Data from 165 articles were included in the analysis and used to calculate Log Reduction Values (LRVs) for each technology by microbial contaminant type (bacteria, virus, or protozoa). The quantity and quality of data ranged widely for each technology. We found granular media, membranes (microfiltration (MF), ultrafiltration (UF), and reverse osmosis (RO)), and precoat filtration to remove the most protozoa with average LRVs of 3.0 (95% CI 2.8–3.3), 5.7 (95% CI 5.4–6.0), and 4.4 (95% CI 4.1–4.7), respectively. Bacteria was removed most effectively by membrane filtration (MF, UF, RO) with average LRVs of 4.5 (95% CI 3.9–5.1) and moderately by dissolved air flotation, lime softening, and soil aquifer treatment with average LRVs of 2.7, 2.6, and 2.4 respectively. Viruses were removed most effectively by reverse osmosis membrane filtration with an average LRV of 4.9 (95% CI 4.0–5.7). This data provides valuable information on pathogen reduction and areas of needed research. The variation in results underscores the importance of further consideration when selecting technologies to use and the need for standardized reporting in both lab and field studies. It is important to consider variables in water quality and technology operation that may impact treatment effectiveness when selecting treatment options for use. The findings contribute to ongoing efforts to revise the WHO’s GDWQ, offering updated insights into LRVs for different water treatment technologies.

Publication Details

Journal
Figshare
Published
2025-03-27
DOI
https://doi.org/10.1021/acs.est.4c03459.s001
Primary Topic
Fecal contamination and water quality
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SystematicReview of Microorganism Removal Performanceby Physiochemical Water Treatment Technologies

Figshare
Fecal contamination and water quality
article

SystematicReview of Microorganism Removal Performanceby Physiochemical Water Treatment Technologies

article en

Abstract

Access to safe drinking water is crucial for public health necessitating the use of effective water treatment processes. We conducted a systematic literature review on microorganism removal by physical treatment processes used in drinking water treatment systems with the aim of providing current summary data to update the World Health Organization’s Guidelines for Drinking Water Quality (GDWQ) and to reflect on the data available for comparison of treatment technologies. We reviewed peer-reviewed articles reporting original data that were published between 1997 and March 2022 on the following physical treatment technologies: roughing filters, storage reservoirs, bank filtration, conventional and high-rate clarification, dissolved air flotation, lime softening, granular media filtration, slow sand filtration, precoat filtration, membrane filtration, granular activated carbon, ceramic membrane filtration, and soil aquifer treatment. The literature search was conducted in several databases including Web of Science and PubMed. Data from 165 articles were included in the analysis and used to calculate Log Reduction Values (LRVs) for each technology by microbial contaminant type (bacteria, virus, or protozoa). The quantity and quality of data ranged widely for each technology. We found granular media, membranes (microfiltration (MF), ultrafiltration (UF), and reverse osmosis (RO)), and precoat filtration to remove the most protozoa with average LRVs of 3.0 (95% CI 2.8–3.3), 5.7 (95% CI 5.4–6.0), and 4.4 (95% CI 4.1–4.7), respectively. Bacteria was removed most effectively by membrane filtration (MF, UF, RO) with average LRVs of 4.5 (95% CI 3.9–5.1) and moderately by dissolved air flotation, lime softening, and soil aquifer treatment with average LRVs of 2.7, 2.6, and 2.4 respectively. Viruses were removed most effectively by reverse osmosis membrane filtration with an average LRV of 4.9 (95% CI 4.0–5.7). This data provides valuable information on pathogen reduction and areas of needed research. The variation in results underscores the importance of further consideration when selecting technologies to use and the need for standardized reporting in both lab and field studies. It is important to consider variables in water quality and technology operation that may impact treatment effectiveness when selecting treatment options for use. The findings contribute to ongoing efforts to revise the WHO’s GDWQ, offering updated insights into LRVs for different water treatment technologies.

Figshare
Clean water and sanitation
Openalex Percentile: Top 62%
Fecal contamination and water quality
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.