ArticleACS ES&T water2025
Low-Cost Optical Sensing for Nitrate and Phosphate: Advancing Community Freshwater Monitoring.
Article in ACS ES&T water, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Analytical Validation of Low-Cost Optical Sensors for Freshwater Monitoring: A Scoping Review of Current Gaps and a Proposed Framework.Sensors (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-resolution water quality data are fundamental to achieving the objectives of the European Water Framework Directive (WFD) and the United Nations Sustainable Development Goal 6 (SDG). While community-based monitoring projects provide valuable data, concerns remain regarding the accuracy and reliability of those data. This study addresses these challenges by developing an affordable, reliable, and open-source optical sensor for monitoring nitrate and phosphate concentrations in freshwater environments. The sensor was developed to support citizen scientists and community monitoring and consists of a 3D-printed case made of polylactic acid, a light-emitting diode (LED), and a commercial ambient light detector managed by a Raspberry Pi Zero W. Data can be stored offline or transmitted in real time via Wi-Fi. The analytical performance was evaluated in laboratory and operational conditions using standard and natural river samples, performing a comparative analysis with a laboratory-based spectrophotometer. Results showed that the sensor provides accurate and repeatable measurements with a significant improvement over conventional colorimetric methods. The sensor technology follows open science principles as the 3D design, operating software, and user guidelines are freely available online to support further advancements and its application in community-based water quality monitoring.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.