Participatory Science Approaches for Water Quality Monitoring in Bihar, India Through International School‐College‐University Partnerships

Abstract

Provision of safely managed drinking water is a key pillar in achieving public health. Particularly in regions where water quality data may be limited, participatory science approaches offer the potential to bridge data gaps with allied benefits such as community engagement and knowledge exchange. Here, we explore the suitability of participatory science approaches for monitoring water quality in the Indian state of Bihar. A simplified methodology for water sample collection was adapted, with local input, for participatory science approaches. Volunteer participants, ranging from secondary school students to professionals, collected ∼600 water samples, following training. Samples were analyzed for arsenic (As), uranium (U), iron (Fe), manganese (Mn), fluoride (F ⁻ ), and nitrate (NO ₃ ⁻ ). Comparisons were made between samples collected via this participatory science approach and samples obtained via conventional approaches in a geographically similar sampling frame. The comparisons revealed that the exceedance of contaminants against reference guideline values were broadly similar (<10%–15%) between approaches. However, more nuanced differences were observed when comparing cumulative distribution curves of concentration or geochemical associations. There was no statistically significant difference for As distributions arising from participatory science versus conventional sampling, but there were statistically significant differences for other parameters. Differences were plausibly because of the spatial heterogeneity of groundwater chemistry, sample handling/preservation and/or sites. Optimized selection of participating institutes' locations could improve the spatial representativeness of future participatory science initiatives. Results show that integrating participatory science approaches for water quality monitoring offers benefits of knowledge exchange and higher resolution data sets, particularly in data‐scarce regions.