Accuracy of long-term volunteer water monitoring data: A multiscale analysis from a statewide citizen science program

Internet of Things and citizen science as alternative water quality monitoring approaches and the importance of effective water quality communication

The increasing demand for water and worsening climate change place significant pressure on this vital resource, making its preservation a global priority. Water quality monitoring programs are essential for effectively managing this resource. Current programs rely on traditional monitoring approaches, leading to limitations such as low spatiotemporal resolution and high operational costs. Despite the adoption of novel monitoring approaches that enable better data resolution, the public's comprehension of water quality matters remains low, primarily due to communication process deficiencies. This study explores the advantages and challenges of using Internet of Things (IoT) and citizen science as alternative monitoring approaches, emphasizing the need for enhancing public communication of water quality data. Through a systematic review of studies implemented on-field, we identify and propose strategies to address five key challenges that IoT and citizen science monitoring approaches must overcome to mature into robust sources of water quality information. Additionally, we highlight three fundamental problems affecting the water quality communication process and outline strategies to convey this topic effectively to the public.

Marine waters assessment using improved water quality model incorporating machine learning approaches

In marine ecosystems, both living and non-living organisms depend on "good" water quality. It depends on a number of factors, and one of the most important is the quality of the water. The water quality index (WQI) model is widely used to assess water quality, but existing models have uncertainty issues. To address this, the authors introduced two new WQI models: the weight based weighted quadratic mean (WQM) and unweighted based root mean squared (RMS) models. These models were used to assess water quality in the Bay of Bengal, using seven water quality indicators including salinity (SAL), temperature (TEMP), pH, transparency (TRAN), dissolved oxygen (DOX), total oxidized nitrogen (TON), and molybdate reactive phosphorus (MRP). Both models ranked water quality between "good" and "fair" categories, with no significant difference between the weighted and unweighted models' results. The models showed considerable variation in the computed WQI scores, ranging from 68 to 88 with an average of 75 for WQM and 70 to 76 with an average of 72 for RMS. The models did not have any issues with sub-index or aggregation functions, and both had a high level of sensitivity (R2 = 1) in terms of the spatio-temporal resolution of waterbodies. The study demonstrated that both WQI approaches effectively assessed marine waters, reducing uncertainty and improving the accuracy of the WQI score.

Citizen science for assessing pesticide impacts in agricultural streams

The majority of central European streams are in poor ecological condition. Pesticide inputs from terrestrial habitats present a key threat to sensitive insects in streams. Both standardized stream monitoring data and societal support are needed to conserve and restore freshwater habitats. Citizen science (CS) offers potential to complement international freshwater monitoring while it is often viewed critically due to concerns about data accuracy. Here, we developed a CS program based on the Water Framework Directive that enables citizen scientists to provide data on stream hydromorphology, physicochemical status and benthic macroinvertebrates to apply the trait-based bio-indicator SPEAR_pesticides for pesticide exposure. We compared CS monitoring data with professional data across 28 central German stream sites and could show that both CS and professional monitoring identified a similar average proportion of pesticide-sensitive macroinvertebrate taxa per stream site (20 %). CS data were highly correlated to the professional data for both stream hydromorphology and SPEAR_pesticides (r = 0.72 and 0.76). To assess the extent to which CS macroinvertebrate data can indicate pesticide exposure, we tested the relationship of CS generated SPEAR_pesticides values and measured pesticide concentrations at 21 stream sites, and found a fair correlation similar to professional results. We conclude that given appropriate training and support, citizen scientists can generate valid data on the ecological status and pesticide contamination of streams. By complementing official monitoring, data from well-managed CS programs can advance freshwater science and enhance the implementation of freshwater conservation goals.