This post summarizes the Nandoni Dam qualitative study and shows how to turn semi-structured interviews into policy-ready evidence without adding new primary data.
Key Takeaways
This post condenses the Nandoni Dam findings and practical steps to convert semi-structured interviews (n=30; 03 May–09 Aug 2025) into policy-ready insights.
- The Nandoni Dam interviews show high awareness of visible plastics (96.7%), low microplastic knowledge (≈10%), and broad willingness to join clean-ups (≈86.7%).
- The study used semi-structured interviews with purposive and snowball sampling until thematic saturation, then analysed responses with descriptive statistics and thematic coding.
- Teams can close evidence gaps by adding prompts or visual aids for low-salience topics, combining community clean-ups with representative sampling, and producing cross-segment analyses for targeted action.
Findings snapshot (quick reference)
| Date / Metric | Value | Context / Note | Source |
|---|---|---|---|
| Published | 9 July 2026 | PLoS ONE article DOI e0353457 | PLoS ONE |
| Data collection | 03 May–09 Aug 2025 | Field interviews at Nandoni Dam | Paper methods |
| Sample | n = 30 (10 CF, 10 RF, 10 FM) | Commercial fishers, recreational fishers, fishmongers | Paper results |
| Awareness (visible plastics) | 96.7% | Most observed bottles, bags, diapers | Fig 2 / Results |
| Microplastics knowledge | ≈10% | Only 3 participants familiar with the term | Results |
| Willingness to act | ≈86.7% | Participants prefer municipality-led campaigns | Results |
How the study worked (plain English)
The study used semi-structured interviews administered in local languages with real-time interpretation when needed.
Semi-structured interviews (15–30 minutes each) were administered in local languages with real-time interpretation where necessary.
- Interview guide covered demographics, fishing background, awareness, impacts and solutions.
- Data were analysed with Excel and SPSS for descriptive figures, and thematic coding followed deductive to inductive steps.
- Responses were anonymised with unique IDs and ethical approval was obtained from the University of Venda (ref: FSEA/24/GES/10).
What this means for qualitative researchers & UX/policy teams
Researchers running small-N environmental studies
The Nandoni findings indicate that visible themes dominate when respondents rely on observable cues, while micro-level risks are under-reported.
Visible themes dominate when respondents rely on observable cues, micro-level risks (microplastics) are under-reported.
That pattern suggests adding prompts or visual aids during interviews to surface low-salience topics.
Use cross-segment comparisons (CF vs RF vs FM) to detect responsibility-shifting patterns, the Nandoni study shows visitors and local residents were blamed differently by groups.
UX & stakeholder engagement teams
High stated willingness to act (≈86.7%) is actionable when interventions reduce friction, such as adding bins or municipal pickup.
High stated willingness to act (≈87%) is actionable, craft interventions that reduce friction (bins, municipal pickup) rather than relying on awareness alone.
Collect and tag quotes by theme and stakeholder in your transcript corpus to build persuasive briefs and behaviour-change microcopy for campaigns.
Policy & municipal analysts
The qualitative data highlight infrastructure gaps, so combine perceptions with periodic litter audits and microplastic sampling to make evidence-first budget requests.
Data show infrastructure gaps (bins, enforcement) are primary barriers.
Design monitoring programs that combine community clean-ups with representative environmental sampling across seasons, the authors recommend seasonal follow-up.
Do more, faster with Evidano
Problem: dispersed interviews and mixed languages → Solution
Evidano is an AI-powered qualitative data analysis platform that ingests audio and transcripts and harmonises local terminology for consistent coding.
Ingest audio and transcript files, Evidano supports transcription with custom dictionaries and PII redaction.
Use the built-in translation and dictionary to harmonise local terms and fishing gear names before coding.
Problem: inconsistent coding across coders → Solution
Evidano produces reproducible hierarchical codes that teams can refine collaboratively to reduce coder inconsistency.
Import a codebook or let Evidano generate initial themes from the interview corpus, then refine with human review.
Evidano produces hierarchical codes and subcodes for reproducible thematic structures.
Problem: need segment comparisons (CF vs RF vs FM) → Solution
Evidano enables cross-segment frequency analysis and co-occurrence networks to surface stakeholder-clustered concerns.
Run cross-segment frequency analysis and co-occurrence networks to surface which concerns cluster by stakeholder group, then export charts and tables for briefs.
Problem: stakeholders want quick visuals → Solution
Evidano creates one-click visualisations that turn qualitative patterns into shareable graphics for meetings and funding pitches.
One-click visualizations (word clouds, co-occurrence, hierarchical code maps) turn qualitative patterns from Nandoni-like studies into shareable graphics.
Security & trust
Evidano uses encrypted storage and proprietary models tuned for qualitative research and does not use user data to train third-party models.
Evidano uses encrypted storage and proprietary LLMs tuned for qualitative research, user data is not used to train third-party models, see Evidano for enterprise options.
Checklist: reproduce a Nandoni-style analysis in 7 steps
This checklist outlines the seven reproducible steps to run a Nandoni-style qualitative analysis from collection to export.
- 1) Collect interviews (audio plus short metadata tags: stakeholder type, date, location).
- 2) Upload to Evidano and run transcription (enable custom dictionary for local terms).
- 3) Translate where needed and harmonise vocabulary (Evidano auto-suggests mappings).
- 4) Auto-generate themes, then review and merge into a small codebook (3–8 top themes).
- 5) Tag stakeholder segment and run cross-segment frequency and co-occurrence analyses.
- 6) Extract representative quotes per theme and stakeholder and assemble a one-page policy brief.
- 7) Export visuals and a reproducible CSV for further statistical checks or seasonal tracking.
Ethics & limits (research note)
The Nandoni study followed university ethics approval and verbal informed consent, and researchers should maintain consent and anonymisation when working with human subjects.
The Nandoni study followed university ethics approval and verbal informed consent (ref: FSEA/24/GES/10).
When working with human subjects, maintain consent, anonymisation, and avoid re-identification in quotes. This post focuses on research workflow, not clinical or diagnostic claims.
Wrapping up & next steps
If you run qualitative analysis of freshwater plastic pollution, combine targeted interview prompts with an AI-assisted pipeline to speed conversion from transcripts to policy-ready insight.
Start by reviewing the full study on PLoS ONE.
Then try a project on Evidano to import transcripts, harmonise codebooks, and produce cross-segment visuals quickly, or Try Evidano for free.
FAQ: qualitative analysis of freshwater plastic pollution
What were the key findings of the Nandoni Dam study?
The Nandoni Dam study found high awareness of visible plastics (96.7%), low microplastic knowledge (≈10%), and about 86.7% willingness to act.
Researchers interviewed 30 stakeholders between 03 May and 09 Aug 2025 and reported that most observed bottles, bags, and diapers, while only three participants recognised the term microplastics.
How were participants sampled and interviewed?
The study used purposive and snowball sampling and conducted semi-structured interviews in local languages with real-time interpretation.
Interviews lasted 15–30 minutes, the guide covered demographics, fishing background, awareness, impacts and solutions, and sampling continued until thematic saturation within groups.
What analysis methods were used on the interview data?
The study combined descriptive statistics (Excel and SPSS) with deductive-to-inductive thematic coding for qualitative analysis.
Responses were anonymised with unique IDs and ethical approval was obtained from the University of Venda (ref: FSEA/24/GES/10).
How can teams act on the study findings?
Teams should prioritise low-friction interventions like bins and municipal pickup, add prompts to surface microplastics, and pair clean-ups with representative environmental sampling.
Cross-segment analyses (CF vs RF vs FM) and tagged quotes by stakeholder can inform targeted education and monitoring programs.