Advancing Citizen Science Stormwater Runoff Sampling: An Interdisciplinary Framework for Enhanced Data Collection, Emerging Contaminant Detection, and Technological Integration

Abstract

Citizen science programs for stormwater runoff monitoring have become increasingly valuable for understanding urban water quality at spatial and temporal scales unattainable through traditional regulatory monitoring alone. However, current sampling protocols face significant operational constraints: labor-intensive manual data collection, time-consuming data entry processes, logistical delays in sample transmission to analytical laboratories, and limited capacity to adapt to emerging contaminants of concern such as microplastics, per- and polyfluoroalkyl substances (PFAS), tire-derived chemicals, and zoonotic pathogens. This article presents an interdisciplinary framework integrating recent advances in mobile technology, sensor development, autonomous systems, data management platforms, and quality assurance protocols to address these challenges. We examine opportunities to enhance efficiency through weather-resistant mobile devices, explore novel sensor technologies for in-situ and field-portable analysis, evaluate autonomous sample collection and transport systems including unmanned aerial vehicles (UAVs), and propose modular platform architectures that enable rapid integration of emerging analytical capabilities. Drawing from environmental science, information technology, robotics, analytical chemistry, and participatory research methodologies, this framework aims to reduce sampling time, improve data quality, enhance volunteer engagement, and expand the scope of citizen science monitoring to address contemporary and future environmental health priorities. The proposed approach maintains scientific rigor while increasing accessibility, scalability, and responsiveness to evolving environmental challenges in urban watersheds.

Introduction

Stormwater runoff represents one of the most significant threats to surface water quality in urbanized watersheds. Unlike point-source pollution from industrial or municipal discharge pipes, stormwater carries diverse contaminants from diffuse sources across the landscape—including nutrients, sediments, pathogens, heavy metals, hydrocarbons, and an expanding array of emerging contaminants [1]. The episodic and spatially heterogeneous nature of stormwater pollution creates substantial monitoring challenges for traditional regulatory agencies operating with limited personnel and fixed sampling locations [2].

Citizen science has emerged as a powerful complementary approach, leveraging community volunteers to expand the spatial coverage and temporal frequency of environmental monitoring [3], [4]. Programs such as the Izaak Walton League's Save Our Streams, the Alliance for Aquatic Resource Monitoring's volunteer monitoring network, and municipal stormwater outreach initiatives have demonstrated that trained volunteers can collect scientifically defensible water quality data [5]. These programs generate valuable datasets while simultaneously increasing environmental literacy and stewardship among participants [6].

Despite these successes, citizen science stormwater monitoring programs face several operational bottlenecks that limit their effectiveness and scope. First, data collection remains predominantly manual, with volunteers recording observations on waterproof paper datasheets or clipboards, which must later be transcribed into digital databases—a process prone to transcription errors and time delays [7]. Second, sample collection requires careful labeling, chain-of-custody documentation, preservation (often requiring ice), and physical transport to analytical laboratories, creating logistical challenges particularly in regions with limited laboratory access [8]. Third, multiple stakeholder groups—such as water quality teams, plastic pollution monitors, and biodiversity surveyors—often traverse the same sampling locations independently, representing an inefficient use of volunteer time and effort [9].

Furthermore, the analytical scope of most citizen science water monitoring programs has not kept pace with emerging environmental concerns. Microplastics contamination in freshwater systems has gained recognition as a pervasive problem requiring monitoring at unprecedented spatial scales [10]. PFAS compounds, sometimes called "forever chemicals," are now detected in water bodies worldwide, with growing evidence of ecological and human health impacts [11]. Tire-derived chemicals, particularly 6PPD-quinone implicated in coho salmon mortality, represent newly recognized toxicants entering aquatic systems through stormwater [12]. Even zoonotic pathogens such as avian influenza viruses can be transported through stormwater networks, creating potential surveillance needs [13].

Addressing these challenges requires an interdisciplinary approach that integrates advances from multiple fields: environmental science provides the theoretical framework and analytical methods; information technology enables efficient data capture and management; sensor engineering delivers field-portable analytical capabilities; robotics and automation offer novel collection and transport mechanisms; and participatory research methodologies ensure protocols remain accessible to non-specialist volunteers while maintaining scientific rigor.

This article presents a comprehensive framework for advancing citizen science stormwater monitoring through technological and methodological innovation. We identify specific opportunities to reduce the time burden of sample collection, data entry, and laboratory logistics; explore technologies that can expand analytical capabilities to address emerging contaminants; and propose platform architectures that enable flexible integration of new sensors, data types, and quality assurance protocols as monitoring needs evolve.

Integration of Disciplines

Environmental Science and Water Quality Monitoring

The foundational discipline for stormwater monitoring is environmental science, which provides established protocols for sample collection, preservation, and analysis [14]. Standard parameters monitored in stormwater include physical characteristics (temperature, turbidity, conductivity), nutrients (nitrate, nitrite, phosphate, ammonia), dissolved oxygen, pH, and bacterial indicators such as Escherichia coli or enterococci [15]. These parameters reflect the ecological health of receiving waters and potential risks to human uses such as recreation or drinking water supply.

Traditional monitoring follows established quality assurance project plans (QAPPs) that specify sampling locations, frequencies, methods, and acceptance criteria [16]. For citizen science programs, simplified protocols must balance scientific rigor with volunteer accessibility. This typically involves selecting robust methods that are less sensitive to minor procedural variations while implementing training programs and quality control measures such as duplicate samples, field blanks, and split samples analyzed by reference laboratories [17].

The temporal dynamics of stormwater present unique monitoring challenges. Contaminant concentrations can vary by orders of magnitude during a single storm event, with the "first flush" phenomenon delivering particularly high pollutant loads in the initial runoff volume [18]. Capturing these dynamics ideally requires event-based sampling with high temporal resolution—an intensive undertaking that highlights the need for larger volunteer networks and more efficient collection methods.

Analytical Chemistry and Emerging Contaminants

Advances in analytical chemistry have simultaneously expanded our understanding of environmental contaminants and created new monitoring challenges. Microplastics analysis requires specialized techniques including density separation, filtration, visual sorting, and spectroscopic identification using Fourier-transform infrared (FTIR) or Raman spectroscopy [19]. While laboratory-based analysis remains the gold standard, field-portable methods are emerging that could enable preliminary assessment during collection events.

PFAS analysis presents particular challenges due to the ubiquity of fluorinated compounds in laboratory and field equipment, requiring meticulous protocols to avoid contamination [20]. Traditional PFAS quantification relies on liquid chromatography-tandem mass spectrometry (LC-MS/MS), which requires specialized facilities. However, recent development of fluorescence-based screening methods and immunoassays may enable field-deployable PFAS detection, albeit with reduced sensitivity and specificity [21].

Tire-derived chemicals including 6PPD and its transformation product 6PPD-quinone have only recently been characterized in environmental samples [22]. Current analytical methods rely on LC-MS/MS or gas chromatography-mass spectrometry (GC-MS), requiring laboratory analysis. The rapid emergence of this contaminant class illustrates the need for monitoring platforms that can readily incorporate new analytes as research identifies additional compounds of concern.

Environmental DNA (eDNA) analysis represents another frontier in water quality assessment, enabling detection of species presence through genetic material shed into water [23]. For stormwater applications, eDNA could identify invasive species, track wildlife populations, or surveil for zoonotic pathogens. Sample collection is relatively simple—filtration or precipitation of water samples—but genetic analysis requires molecular biology laboratory facilities or access to portable PCR devices.

Information Technology and Mobile Data Systems

Mobile technology has transformed field data collection across numerous scientific disciplines [24]. Weather-resistant tablets and smartphones equipped with custom data entry applications can eliminate transcription steps, reduce errors through data validation rules, automatically capture metadata (GPS coordinates, timestamps), and enable real-time data transmission to central databases [25]. For citizen science applications, well-designed mobile interfaces can guide volunteers through sampling protocols with contextualized instructions, photographs, and decision trees that improve data quality [26].

Cloud-based data platforms provide centralized storage, automated quality control checks, data visualization tools, and application programming interfaces (APIs) that enable integration with other systems [27]. Modern platforms support multiple data types—numeric measurements, photographs, spatial data, categorical observations—and can implement role-based access controls that protect data integrity while allowing appropriate sharing among stakeholders [28].

Database architecture choices significantly impact platform extensibility. Relational databases with normalized schemas provide robust data integrity but may require schema modifications to incorporate new data types [29]. Document-oriented or schema-flexible databases offer greater adaptability to changing data structures but require careful implementation to maintain data quality [30]. Hybrid approaches using relational databases for core, stable data types and document storage for variable or emerging data structures may offer optimal balance.

Sensor Technology and Internet of Things

The proliferation of low-cost environmental sensors and Internet of Things (IoT) technologies creates opportunities for enhanced monitoring [31]. Water quality sensors including pH, conductivity, turbidity, dissolved oxygen, and optical dissolved organic matter can now be obtained at price points accessible to citizen science programs [32]. While accuracy and precision may not match research-grade instruments, appropriate calibration and quality control can yield scientifically useful data [33].

Integrating sensors with mobile devices requires consideration of connectivity protocols (Bluetooth, USB, Wi-Fi), power requirements, calibration procedures, and data formatting [34]. Standardized protocols such as the Open Geospatial Consortium's SensorThings API facilitate interoperability between sensors and data platforms [35]. For citizen science applications, sensor systems must be sufficiently intuitive that volunteers can operate them successfully with minimal training—an interface design challenge requiring user-centered development approaches [36].

Emerging sensor technologies particularly relevant to stormwater monitoring include portable spectroscopy devices for contaminant identification, particle counters and imaging systems for microplastics characterization, and electrochemical sensors for trace metal detection [37]. While many such technologies remain in research stages, the trajectory suggests field-portable analysis capabilities will continue expanding.

Robotics, Automation, and Unmanned Systems

Unmanned aerial vehicles (UAVs), commonly known as drones, have found applications across environmental monitoring including aerial imagery, thermal sensing, and atmospheric sampling [38]. For stormwater monitoring, UAVs could potentially collect water samples from locations difficult or dangerous for volunteers to access, such as steep-banked channels, flooded areas, or contaminated sites [39]. Sample collection mechanisms might include dipping containers, deploying absorbent materials, or using vacuum sampling systems.

However, UAV-based water sampling faces significant technical and regulatory challenges. Payload capacity limits sample volume and equipment weight; precise positioning near water surfaces requires sophisticated control systems; sample integrity during flight must be ensured; and regulatory frameworks governing UAV operations vary by jurisdiction [40]. Nevertheless, proof-of-concept demonstrations suggest UAV sampling is technically feasible and may become practical as drone technology advances and costs decrease [41].

Autonomous surface vehicles (ASVs) represent an alternative approach for water sample collection, particularly in ponds, lakes, or calm streams [42]. These watercraft can navigate to specified coordinates, collect samples at multiple depths, and measure in-situ parameters using onboard sensors. While ASVs are currently expensive and require substantial technical expertise, simplified designs optimized for citizen science applications may emerge.

Automated sample processing represents another opportunity for efficiency gains. Laboratory-on-a-chip devices can perform sample preparation, separation, and analysis in miniaturized formats [43]. While most such technologies remain research tools, continuing development may eventually deliver field-deployable systems capable of analyzing multiple parameters from small sample volumes.

Participatory Research and Community Engagement

Technology alone cannot ensure successful citizen science programs; careful attention to participant motivation, training, support, and recognition is essential [44]. Participatory research frameworks emphasize involving community members in all stages of research including question formulation, protocol design, data interpretation, and communication of findings [45]. This approach enhances relevance to local concerns, builds trust between scientists and communities, and empowers participants as co-producers of knowledge rather than merely data collectors [46].

For stormwater monitoring specifically, connecting observations to local waterways participants care about increases motivation and retention [47]. Providing feedback on data quality and how collected data contribute to environmental management decisions reinforces the value of participation [48]. When introducing new technologies, involving volunteers in pilot testing and protocol refinement improves usability and buy-in [49].

The digital divide remains a consideration when deploying mobile technologies. Not all potential volunteers have smartphones or tablets, and cellular data coverage may be limited in some sampling locations [50]. Programs must either provide devices to volunteers, ensure protocols can be completed offline with later synchronization, or maintain alternative data entry options to avoid excluding participants [51].

Methodology and Approach

System Requirements Analysis

Developing an enhanced citizen science stormwater monitoring framework requires systematic analysis of stakeholder needs, operational constraints, and performance requirements. Key stakeholders include volunteer participants, program coordinators, analytical laboratories, data users (researchers, environmental managers, policymakers), and funding organizations. Each group has distinct priorities that the system must address [52].

For volunteers, priorities include minimizing time burden, providing clear instructions, ensuring physical safety, and making participation meaningful. Analysis of existing citizen science programs suggests sampling events should typically require no more than 1-2 hours including travel, setup, sampling, and documentation [53]. Protocols must be comprehensible with reasonable training (typically 2-4 hours initial training plus refreshers) and not require specialized scientific backgrounds [54].

Program coordinators require systems that ensure data quality, manage volunteer communications and scheduling, track quality control samples, handle equipment maintenance and calibration, and generate reports for stakeholders [55]. Reducing administrative burden through automation enables coordinators to support larger volunteer networks.

Analytical laboratories need clear sample identification, proper preservation, chain-of-custody documentation, and advance notice of sample arrivals to manage workflow [56]. Delays between collection and analysis degrade sample integrity, particularly for microbiological parameters and volatile compounds. Technologies that reduce this delay or enable field analysis address a critical constraint.

Data users require metadata-rich, quality-controlled datasets with transparent documentation of methods, access through standard formats and APIs, and confidence in data provenance [57]. For regulatory applications, data must meet specific quality assurance criteria established by agencies such as the U.S. Environmental Protection Agency [58].

Mobile Data Collection Platform Design

Weather-resistant tablets represent the most mature technology for improving citizen science data collection. Modern rugged tablets meet IP67 or IP68 ingress protection ratings, indicating waterproof and dustproof construction suitable for field use [59]. Battery life typically exceeds eight hours, sufficient for extended sampling events. Sunlight-readable displays and glove-compatible touchscreens enable operation in diverse conditions.

Data collection applications should implement several key features. First, pre-populated site information (location coordinates, site identifiers, historical data) reduces data entry and prevents errors. Second, conditional logic guides users through appropriate protocols based on site characteristics or weather conditions. Third, data validation rules enforce constraints (e.g., pH between 0-14, required fields) and flag unusual values for confirmation [60]. Fourth, offline functionality enables data collection without cellular connectivity, with automatic synchronization when network access is restored [61]. Fifth, integrated media capture allows volunteers to photograph site conditions, unusual observations, or quality issues, providing visual documentation that enhances data interpretation [62].

For programs with multiple data collection types (water quality, macroinvertebrates, riparian habitat, plastic pollution), integrated platforms prevent data fragmentation. A single application can support multiple protocols with shared site information and metadata, reducing redundancy when different teams sample the same locations [63].

Implementation of mobile platforms requires consideration of software development approaches. Custom development provides maximum flexibility but requires substantial programming expertise and ongoing maintenance [64]. Commercial environmental data collection platforms such as Fulcrum, Survey123, or Kobo Toolbox offer configurable solutions with reduced development burden but less customization [65]. The optimal choice depends on program size, technical capacity, and specific requirements.

Sensor Integration and Field-Portable Analysis

Integrating water quality sensors with mobile data collection systems creates several advantages: immediate data review enables detection of equipment malfunctions or anomalous conditions warranting re-sampling; automated data transfer eliminates transcription errors; and continuous or high-frequency measurements capture temporal dynamics missed by grab samples [66].

Multi-parameter sondes—instruments combining multiple sensors in a single housing—provide pH, conductivity, temperature, dissolved oxygen, turbidity, and sometimes additional parameters such as chlorophyll or blue-green algae [67]. High-end research sondes cost thousands of dollars, but simplified versions designed for education or citizen science cost hundreds to low thousands and may offer acceptable accuracy for many applications [68].

Sensor protocols must address calibration, which requires reference standards and periodic verification. For citizen science programs, centralized calibration by program staff before distributing equipment to volunteers ensures consistency [69]. Some sensors include automatic calibration checks or built-in diagnostics that alert users to potential problems.

Emerging field-portable analysis technologies relevant to stormwater monitoring include:

• Portable spectrophotometers: Handheld devices for colorimetric analysis of nutrients (nitrate, phosphate, ammonia) using reagent kits. Results are available within minutes but require carrying chemical reagents and proper disposal [70].
• Fluorescence-based sensors: Detect dissolved organic matter, petroleum hydrocarbons, or specific compounds through characteristic fluorescence signatures. Some portable sensors integrate with smartphones [71].
• Electrochemical sensors: Measure specific ions (nitrate, ammonium, heavy metals) using ion-selective electrodes. Accuracy depends on calibration and can be affected by interferences [72].
• Microfluidic devices: Miniaturized lab-on-a-chip systems that perform automated sample processing and analysis. Mostly still in research stages but progressing toward commercial availability [73].
• Portable microscopy: Smartphone-attachable microscopes enable field identification of microorganisms or microplastics. Image analysis software can assist identification [74].

The trade-off between field-portable and laboratory analysis involves accuracy, precision, detection limits, cost per sample, and parameter breadth. Laboratory analysis typically offers superior performance but requires sample preservation and transport. An optimal strategy might employ field screening to identify samples warranting detailed laboratory analysis, reducing costs while maintaining comprehensive coverage [75].

Unmanned Systems for Sample Collection and Transport

UAV-based water sampling systems consist of several components: the aerial platform (multirotor or fixed-wing aircraft), sampling mechanism, sample storage, and control systems [76]. Multirotor drones offer vertical takeoff/landing and precise hovering, critical for positioning above sampling locations. Payload capacity typically ranges from 500g to 5kg depending on aircraft size, determining the number and volume of samples collectible in a single flight [77].

Sampling mechanisms include:

• Dip sampling: Lowering a container on a tether to the water surface. Simple but requires precise altitude control [78].
• Pump systems: Using electric pumps to draw water through tubing into storage containers. Enables subsurface sampling but adds weight and complexity [79].
• Absorbent materials: Deploying sorbent materials to collect dissolved or particulate contaminants, then retrieving for analysis. Useful for certain analytes like hydrocarbons [80].
• Automated collection: Mechanical systems that deploy pre-sterilized collection containers, submerge them, and seal them before retrieval. Most complex but offers best contamination control [81].

Key technical challenges include maintaining sample integrity (avoiding contamination from drone components or atmospheric deposition), documenting precise collection locations, operating safely near water (risk of equipment loss if drone malfunctions), and complying with aviation regulations [82]. In the United States, Federal Aviation Administration regulations require UAV operators to maintain visual line of sight, avoid flight over people, and obtain proper certification [83]. Some jurisdictions have additional restrictions on flights over water bodies.

Despite challenges, UAV sampling offers distinct advantages for inaccessible or hazardous locations, rapid response to contamination events, and sampling during high-flow conditions unsafe for volunteers. Proof-of-concept studies have demonstrated successful collection of water samples for bacterial, nutrient, and chemical analysis [84]. As drone technology matures and costs decrease, UAV sampling may transition from specialized applications to routine use in citizen science programs.

Automated ground transport represents an alternative approach. Imagine volunteers collecting samples in standardized containers that are picked up by autonomous delivery vehicles or courier services with proper handling protocols for environmental samples. This could reduce the burden on volunteers to transport samples to laboratories while ensuring timely delivery. However, this approach requires coordination with logistics providers and may face cost constraints [85].

Modular Platform Architecture for Emerging Capabilities

The rapid evolution of both environmental priorities and analytical technologies necessitates flexible system architectures that can incorporate new capabilities without complete redesign. A modular platform approach treats individual components (data collection interfaces, sensor integrations, analytical methods, data management systems) as discrete modules with standardized interfaces [86].

At the data model level, this suggests a core schema capturing universal metadata (sample location, date/time, collector, weather conditions, chain of custody) with extensible structures for parameter-specific data [87]. For example, microplastics observations might include particle counts by size class and polymer type, while eDNA samples record target species and genetic detection methods. Using hierarchical data models or key-value stores enables adding new data types without modifying existing structures [88].

For sensor integration, standardized communication protocols (e.g., Open Geospatial Consortium SensorThings API, MQTT for IoT devices) enable sensors from different manufacturers to interface with data collection platforms [89]. Sensor metadata should follow standard vocabularies (e.g., CF Standard Names for environmental parameters) to facilitate data interoperability [90].

Quality assurance modules should be configurable to accommodate different parameter requirements. For example, microbiological samples require analysis within specified holding times (typically 6-8 hours for bacteria), while samples for metals analysis can be held longer if properly preserved [91]. The system should track holding times and alert coordinators of samples approaching limits.

Data sharing and visualization modules benefit from modular design that allows customization for different user audiences. Scientists may want access to raw data with quality flags and metadata; policymakers may need summary reports and trend analyses; community members might prefer interactive maps and simplified visualizations [92]. APIs enable third-party developers to create custom interfaces for specific needs [93].

Protocols for Emerging Contaminants

Developing citizen science protocols for emerging contaminants requires balancing scientific rigor with volunteer accessibility. We outline conceptual protocols for several priority contaminant classes.

Microplastics

Microplastics sampling typically involves collecting water volume (often 1-5 liters) and filtering through mesh of defined size (commonly 333 μm for larger microplastics, 63 μm for smaller particles) [94]. For citizen science applications, volunteers could collect samples in pre-cleaned containers, with laboratory staff performing filtration, or use field-portable filtration systems with volunteers recording visual particle counts [95]. Advanced characterization using spectroscopy would occur in laboratories, but preliminary abundance estimates could be recorded in the field.

Sample collection must avoid contamination from synthetic clothing (fleece jackets shed fibers), plastic sampling equipment, or atmospheric deposition [96]. Protocol training should emphasize contamination controls such as wearing cotton or wool clothing, rinsing equipment with filtered water, and covering samples during transport.

PFAS

PFAS sampling requires careful contamination control since fluorinated compounds are present in many water-resistant materials, non-stick coatings, and even some laboratory supplies [97]. Protocols specify using HDPE or polypropylene bottles (some PFAS compounds can sorb to glass), avoiding materials like Teflon, and wearing clothing without water-resistant treatments [98]. Collection is otherwise straightforward: grab samples are collected, preserved by chilling, and transported to analytical laboratories.

Field screening for PFAS using immunoassay kits or fluorescence-based methods is emerging but currently offers limited sensitivity and specificity compared to LC-MS/MS [99]. As these technologies mature, preliminary field screening could identify hotspots warranting detailed laboratory analysis.

Tire-Derived Chemicals

6PPD-quinone and other tire-derived chemicals enter stormwater from roadways, with highest concentrations during initial rainfall after dry periods [100]. Sampling protocols resemble standard water quality grab samples: collect water in amber glass or suitable plastic bottles, preserve by acidification and chilling, and transport to laboratories for GC-MS or LC-MS/MS analysis [101]. Targeting sampling locations downstream of roadways and during storm events maximizes detection probability.

Currently, no field-portable analytical methods exist for tire-derived chemicals, but their importance for salmon-bearing streams in particular may drive development of rapid screening tools [102].

Environmental DNA for Pathogen Surveillance

eDNA collection for pathogen surveillance (e.g., avian influenza) involves filtering water volume (typically 0.5-2 liters) through sterilized membrane filters (0.2-0.45 μm pore size) or precipitating DNA using specialized buffers [103]. Filters or precipitated samples are preserved in nucleic acid stabilization solution and frozen or chilled until analysis.

Sample collection is feasible for trained volunteers using pre-assembled sterile filtration units [104]. However, genetic analysis requires laboratory capabilities including DNA extraction, PCR amplification, and sequencing. Portable PCR devices are becoming available and might eventually enable field-based eDNA analysis, though this remains challenging for most citizen science programs [105].

Quality Assurance and Data Validation

Maintaining data quality is paramount for citizen science programs seeking to generate scientifically defensible results. Quality assurance encompasses training, standard operating procedures, equipment maintenance and calibration, quality control samples, and data validation [106].

Training protocols should include classroom instruction, hands-on practice, and competency evaluation before volunteers conduct independent sampling [107]. Regular refresher training addresses drift in protocol adherence and introduces protocol updates. Written standard operating procedures with photographs provide reference materials volunteers can consult during sampling.

Quality control samples include field blanks (contaminant-free water opened at sampling sites to detect atmospheric contamination), equipment blanks (contaminant-free water processed through sampling equipment), field replicates (multiple samples collected from the same location), and split samples (portions of samples sent to different laboratories) [108]. The proportion of quality control samples typically ranges from 5-10% of total samples, with higher proportions during program startup or when introducing new protocols [109].

Data validation involves range checks (identifying values outside physically possible or likely ranges), temporal consistency checks (sudden implausible changes between consecutive samples), spatial consistency checks (values inconsistent with nearby sites), and comparison with historical data [110]. Automated validation rules in data management systems can flag suspicious values for review by program coordinators or expert scientists [111].

For programs generating data intended for regulatory use, formal quality assurance project plans following EPA guidance document requirements provide documented protocols and acceptance criteria [112]. While this level of rigor may exceed needs for programs focused on education or preliminary screening, the framework provides useful structure for any program seeking high-quality data.

Case Studies and Implementation Examples

Urban Stormwater Monitoring Network: Seattle, Washington

The Seattle-area Green/Duwamish Watershed Toxics Assessment examined stormwater contributions to contamination of the Duwamish River, a designated Superfund site [113]. Citizen science volunteers augmented professional sampling by collecting grab samples from stormwater outfalls throughout the watershed. Initial protocols used paper datasheets and manual sample labeling, requiring approximately 90 minutes per site including documentation time.

Program coordinators implemented weather-resistant tablets with a custom application developed using Esri Survey123 platform [114]. The digital workflow pre-populated site identifiers and GPS coordinates, guided volunteers through sampling steps with photographs, and generated QR code labels for sample bottles that encode collection metadata. Implementation reduced per-site time to approximately 45 minutes while eliminating transcription errors. Data uploaded automatically when volunteers returned to areas with cellular coverage, enabling near-real-time quality review.

The program integrated pH, conductivity, and temperature sensors connected to tablets via Bluetooth. Sensor readings appeared automatically in the data collection form, and out-of-range values triggered confirmation prompts. This integration detected several instances of sensor malfunction and one case of accidental sampling from a groundwater seep rather than stormwater outfall, demonstrating quality control value.

Sample transport remained a bottleneck. Volunteers delivered samples to the program coordinator's office, who compiled them for weekly delivery to the analytical laboratory approximately 45 miles away. To address this, the program established a partnership with a medical courier service that already operated daily routes between Seattle and the laboratory's location. Volunteers placed samples in designated coolers at fire stations participating in the program, and the courier picked them up on regular routes. This reduced the average time from collection to analysis from 3-4 days to less than 24 hours, improving data quality particularly for bacterial indicators [115].

Agricultural Watershed Nutrient Monitoring: Iowa Soybean Association

The Iowa Soybean Association initiated the Water Quality Initiative to monitor nutrient losses from agricultural watersheds and evaluate conservation practice effectiveness [116]. With hundreds of small streams across Iowa's agricultural landscape, comprehensive monitoring required extensive volunteer networks. Multiple organizations including farm groups, conservation districts, and watershed coalitions conducted sampling, often sampling overlapping areas with different focuses (nutrients, sediment, pesticides).

To improve efficiency and coordination, the initiative developed an integrated mobile platform that accommodated multiple protocols [117]. Volunteer teams equipped with rugged tablets could conduct water quality sampling, riparian habitat assessment, and soil conservation practice inventories using different forms within a single application. Shared site information meant that when multiple teams visited the same locations, redundant data entry was minimized.

The platform integrated with a laboratory information management system (LIMS) at the analytical facility. Sample metadata transferred automatically to the LIMS, eliminating manual entry by laboratory staff and ensuring accurate matching between physical samples and metadata. When results became available, they automatically populated the environmental database, making data accessible to farmers and conservation planners within days of sampling [118].

The program piloted deployment of continuous nutrient sensors at selected sites to capture storm-driven loading dynamics. While too expensive for widespread volunteer deployment, data from these sensor sites helped volunteers understand the temporal variability their grab samples might miss. Researchers are exploring development of lower-cost optical nitrate sensors that might eventually become accessible for broader volunteer use [119].

Coastal Microplastics Assessment: Surfrider Foundation Blue Water Task Force

The Surfrider Foundation's Blue Water Task Force conducts bacterial water quality monitoring at beaches nationwide [120]. A California chapter expanded their focus to include microplastics in stormwater outflows to coastal waters. Initial protocols required volunteers to filter large water volumes (20-50 liters) through fine mesh, a time-consuming process taking 60-90 minutes per sample even with battery-powered pumps [121].

To accelerate sampling, the program adopted a stratified approach. For routine monitoring, volunteers collected smaller volumes (5 liters) and performed visual counts of large microplastics (>1 mm) in the field using standardized counting chambers and portable microscopy [122]. These data provided rapid assessment of relative microplastics abundance across sites and over time. A subset of samples underwent comprehensive laboratory analysis including FTIR identification of polymer types, validating field observations and characterizing smaller particles.

The program developed an image analysis protocol where volunteers photographed microplastics in counting chambers using smartphones with standardized microscope attachments [123]. Images uploaded to a cloud platform where machine learning algorithms performed preliminary particle counts and classifications. Volunteers reviewed algorithm results, correcting errors and building training datasets that improved algorithm performance. This "human-in-the-loop" approach combined volunteer engagement with computational efficiency [124].

For samples requiring laboratory analysis, the program piloted UAV-based collection at sites with difficult access (eroding bluffs, restricted areas). A custom multirotor drone with a 2.5 kg payload carried sterile sampling containers. The system successfully collected samples from six difficult-access locations, though operational challenges included wind near coastal bluffs and regulatory coordination with authorities managing restricted areas [125]. While UAV sampling remained specialized rather than routine, it demonstrated feasibility for addressing monitoring gaps.

Emerging Contaminant Rapid Response: Chesapeake Bay PFAS Screening

Following detection of PFAS contamination in several Chesapeake Bay tributaries, the Chesapeake Bay Program sought to rapidly screen the watershed to identify potential hotspots [126]. Traditional laboratory analysis using LC-MS/MS would cost several hundred dollars per sample, prohibitive for broad screening of hundreds of potential sites.

The program deployed field-deployable immunoassay kits that provide semi-quantitative PFAS screening at approximately one-tenth the cost of laboratory analysis [127]. Citizen science volunteers collected water samples and performed field screening following manufacturer protocols. Samples exceeding threshold concentrations (indicating potential contamination) were preserved and sent to laboratories for confirmatory analysis including specific PFAS compound identification.

This two-tiered approach enabled screening 250 sites with confirmatory analysis of 45 samples showing elevated field screening results. The strategy identified eight previously unknown PFAS contamination sources while maintaining project budget constraints [128]. Data collected on weather-resistant tablets included photographs of potential contamination sources (industrial facilities, fire training areas, wastewater treatment plants) that helped investigators prioritize follow-up investigations.

The Chesapeake Bay case illustrates how emerging field-screening technologies, even if less sensitive than laboratory methods, can enable broader spatial coverage and more rapid response when integrated with strategic confirmatory analysis [129].

Discussion

Technology Adoption and Implementation Challenges

While the technologies and methods discussed offer substantial potential for enhancing citizen science stormwater monitoring, practical implementation faces several challenges. Cost represents an immediate barrier: weather-resistant tablets cost 500 to 2000-10,000+ [130]. Programs operating with limited budgets must carefully prioritize investments.

A phased implementation strategy helps manage costs. Initial adoption of mobile data collection platforms provides high return on investment through time savings and data quality improvements, with tablets potentially shared among volunteers rather than each person owning a device [131]. Sensor integration can begin with simpler parameters (temperature, pH, conductivity) using lower-cost instruments before expanding to sophisticated multi-parameter sondes. UAV sampling remains specialized for high-priority applications rather than routine deployment.

Training requirements increase with technological complexity. While most volunteers can learn tablet-based data entry with minimal instruction (typically 15-30 minutes), operating water quality sensors requires understanding calibration, maintenance, and interpretation of readings (typically 1-2 hours initial training) [132]. UAV operation requires substantial training, certification in many jurisdictions, and ongoing practice to maintain proficiency, suggesting most programs would designate specific trained operators rather than broadly distributing this capability [133].

Technology dependence introduces potential points of failure. Tablets may malfunction, run out of battery power, or lack cellular connectivity; sensors may drift out of calibration or suffer damage; drones may experience mechanical failures. Robust programs maintain backup protocols (paper datasheets as contingency, backup tablets, spare sensors) and preventive maintenance schedules [134].

The pace of technological change creates sustainability challenges. Mobile devices and sensors eventually become obsolete, requiring replacement. Software platforms require ongoing maintenance and updates. Programs should evaluate whether technologies are based on open standards and have active user communities, increasing likelihood of long-term support [135].

Data Quality Considerations

Technology does not automatically ensure data quality; indeed, sophisticated equipment incorrectly used can generate misleading results. For sensor-based measurements, calibration frequency and procedures critically affect accuracy [136]. Multi-parameter sondes typically require calibration before each use or at minimum weekly, using certified reference standards. Programs must establish clear protocols for calibration, documentation, and verification.

Lower-cost sensors often have reduced accuracy and precision compared to research-grade instruments. For example, an inexpensive pH sensor might have accuracy of ±0.2 pH units compared to ±0.01 units for a laboratory meter [137]. Users must understand measurement uncertainty and interpret data accordingly. For many citizen science applications—identifying sites with potential problems, tracking broad trends, educational purposes—moderate accuracy suffices. For regulatory applications or research, higher-grade instrumentation may be necessary [138].

Automated data collection can introduce artifacts if sensor malfunctions are not detected. Fouling (accumulation of biological or chemical deposits on sensors) causes drift over time, particularly for optical sensors [139]. Automated validation algorithms help flag suspicious data, but expert review remains important. Parallel collection of grab samples analyzed by certified laboratories provides ground-truthing for sensor-based measurements [140].

For emerging contaminants, limited availability of certified reference materials and inter-laboratory comparison studies complicates quality assurance [141]. Field-screening methods may lack standardization, and performance characteristics (detection limits, false positive/negative rates) may not be thoroughly documented. Programs should clearly communicate data limitations and validation status when reporting results.

Equity and Inclusion Considerations

Technology-enhanced monitoring programs risk exacerbating existing inequities in citizen science participation. Communities with lower socioeconomic status may have less access to smartphones or tablets, lower cellular data coverage, and less familiarity with technology [142]. Programs must intentionally address these barriers to ensure diverse participation.

Strategies include providing equipment to volunteers rather than requiring personal devices, ensuring applications function offline, offering multiple languages and literacy-appropriate interfaces, and maintaining technology-free participation options [143]. Community-based participatory approaches that involve target communities in protocol design help ensure accessibility and cultural appropriateness [144].

Monitoring priorities may differ between communities. While regulatory agencies might prioritize parameters like dissolved oxygen and nutrients, residents of communities with environmental justice concerns may prioritize contaminants with direct health implications like lead or PFAS [145]. Flexible platforms that accommodate community-identified priorities alongside standard parameters recognize diverse stakeholder needs.

Data access and interpretation present additional equity considerations. If data are only available through sophisticated online platforms requiring technical expertise to interpret, communities most affected by water quality problems may be effectively excluded from using information they helped collect [146]. User-friendly visualization tools, community data workshops, and partnerships with community organizations help ensure data accessibility and utility [147].

Integration with Professional Monitoring Networks

Citizen science stormwater monitoring can complement but not replace professional monitoring conducted by regulatory agencies and research institutions [148]. Professional monitoring provides quality-assured data meeting regulatory standards, uses certified analytical methods, and focuses on sites selected through statistically rigorous designs. Citizen science offers broader spatial coverage, higher temporal frequency, community engagement, and flexibility to rapidly investigate emerging issues [149].

Optimal integration involves clearly defining roles and relationships. Citizen science data might be used for screening and prioritization, identifying sites warranting detailed professional investigation [150]. Professional monitoring might provide validation for citizen science methods through parallel sampling and analysis. Both programs might share data through common platforms, creating more comprehensive watershed understanding than either could achieve independently [151].

Some jurisdictions are developing frameworks for incorporating citizen science data into regulatory decision-making. The European Water Framework Directive includes provisions for citizen science contributions [152]. In the United States, some states accept volunteer monitoring data for certain purposes if quality assurance requirements are met [153]. These frameworks incentivize citizen science programs to implement rigorous quality assurance while providing pathways for data to inform environmental management.

Adaptability to Future Environmental Challenges

The emergence of previously unknown or unmonitored contaminants will continue as industrial chemicals evolve, analytical capabilities improve, and environmental understanding advances [154]. Tire-derived chemicals exemplify this pattern: 6PPD has been used in tires for decades, but its environmental transformation product and toxicity only recently became known [155]. Modular monitoring platforms that can readily incorporate new analytes and methods provide resilience to evolving priorities.

Climate change will intensify stormwater monitoring challenges through increased precipitation intensity, longer droughts followed by extreme storms, and shifting timing of runoff events [156]. More dynamic hydrological conditions require more intensive monitoring to characterize pollutant loading. Citizen science networks with efficient protocols and rapid deployment capabilities can help address increased monitoring demands [157].

Emerging analytical technologies will continue expanding field-portable capabilities. Miniaturized mass spectrometers, advanced biosensors, and lab-on-a-chip devices currently in research stages may become commercially available at price points accessible to citizen science programs [158]. Platform architectures designed for extensibility can integrate these technologies as they mature.

The COVID-19 pandemic demonstrated how environmental monitoring networks can be repurposed for disease surveillance. Wastewater monitoring for SARS-CoV-2 showed that water sampling networks can track pathogen prevalence [159]. Stormwater monitoring networks with appropriate biosafety protocols and eDNA capabilities could potentially contribute to surveillance for zoonotic diseases, vector-borne pathogens, or antimicrobial resistance genes [160].

Economic Considerations and Cost-Benefit Analysis

Evaluating cost-effectiveness requires comparing not only direct financial costs but also volunteer time (an in-kind contribution with economic value), data quality improvements, and expanded monitoring capabilities [161]. A comprehensive economic analysis might proceed as follows.

Consider a baseline scenario: 50 volunteers conducting monthly stormwater sampling at 25 sites using traditional methods (paper datasheets, manual sample processing, volunteer-delivered samples to laboratory). Each sampling event requires approximately 2 hours volunteer time including travel, sampling, and documentation. Data transcription requires approximately 15 minutes per sample by program staff. Annual costs include laboratory analysis (~15,000), coordinator salary (~$30,000 for 0.5 FTE), and supplies (~$2,000). Total annual budget: $47,000. Volunteer time contribution: 600 hours/year (valued at ~15,000 in-kind).

Enhanced scenario: Same volunteer network equipped with 10 shared tablets ($5,000 one-time cost), basic multi-parameter sensors ($3,000 one-time cost), mobile data platform (custom development ~$25,000 or subscription service ~$2,000/year), and courier service for sample transport ($3,000/year). Time per sampling event reduced to 1.25 hours through elimination of paper documentation and efficient sensor use. Data transcription eliminated. Sensor data reduces some laboratory analysis needs (e.g., field pH and conductivity measurements accepted rather than laboratory analysis), reducing laboratory costs to ~$12,000/year. Enhanced data quality enables use for regulatory reporting, providing value to local government (~$10,000 equivalent value in avoided consultant costs). Initial year total cost: $55,000 (including one-time equipment). Subsequent years: $47,000. Volunteer time reduced to 375 hours/year ($9,375 in-kind value).

Over five years, the enhanced scenario reduces cumulative volunteer time by 1,125 hours (valued at ~$28,000), eliminates ~600 hours of data transcription by program staff (~$15,000), and generates additional value through regulatory-quality data (~$50,000 over five years). Total five-year net benefit: ~$60,000 considering reduced costs and increased value, despite higher initial investment [162].

This simplified analysis illustrates that while technology requires upfront investment, efficiency gains and enhanced capabilities can provide positive return on investment over program lifespan. Actual economics vary with program scale, volunteer retention, technology choices, and specific requirements [163].

Future Research Directions

Several research needs emerge from this analysis. First, comparative studies evaluating data quality from various citizen science protocols and technologies would help programs select appropriate methods. While individual programs often conduct internal validation, systematic comparisons across technologies, training approaches, and volunteer demographics remain limited [164].

Second, development of standardized protocols for emerging contaminants specifically designed for citizen science would accelerate broader monitoring. Most current methods for microplastics, PFAS, or tire-derived chemicals were developed for professional laboratories. Adaptation or development of simplified field-appropriate protocols requires collaborative research between analytical chemists and citizen science practitioners [165].

Third, human-dimensions research examining volunteer motivation, retention, and learning in technology-enhanced programs would inform program design. Does sophisticated technology increase or decrease volunteer engagement? How does technology mediate the connection between volunteers and the environment they monitor? What training approaches effectively build technology skills among diverse participants [166]?

Fourth, development of machine learning approaches for automated data quality control and pattern recognition could enhance programs' analytical capabilities. For example, algorithms that identify contamination events, detect long-term trends, or flag potential sensor malfunctions could provide decision support for program coordinators [167].

Fifth, research on data integration and synthesis methods would help realize the potential value of large citizen science datasets. How can data from multiple programs with varying protocols be harmonized? What statistical approaches appropriately account for volunteer observer variability? How can citizen science and professional monitoring data be optimally combined [168]?

Conclusion

Citizen science stormwater monitoring stands at a transition point. Traditional approaches relying on manual data collection, paper records, and conventional analytical methods have demonstrated the value of volunteer networks for expanding monitoring coverage and engaging communities in watershed stewardship. However, current methods face constraints—labor intensity, logistical complexity, and limited adaptability to emerging contaminants—that restrict the scope and efficiency of programs.

An interdisciplinary approach integrating environmental science, information technology, sensor engineering, automation, and participatory research methods offers pathways to overcome these constraints. Weather-resistant mobile devices with purpose-designed applications can eliminate data transcription, reduce errors, decrease time requirements, and enhance volunteer experience. Integration of water quality sensors enables immediate data feedback and continuous measurement capabilities. Emerging technologies including UAV-based sampling, field-portable analytical devices, and automated sample transport address logistical bottlenecks. Modular platform architectures allow programs to adapt to evolving monitoring priorities by incorporating new sensors, analytical methods, and data types.

Implementation of these enhanced approaches requires careful attention to multiple considerations. Cost-effectiveness must be evaluated in specific program contexts, with phased implementation strategies helping manage initial investments. Quality assurance protocols must evolve alongside technological capabilities to ensure data meet intended uses. Training programs must build volunteer capacity with new technologies while remaining inclusive and accessible. Partnerships between citizen science programs, analytical laboratories, and professional monitoring networks create synergies that amplify individual program capabilities.

Case studies from diverse geographic and organizational contexts demonstrate feasibility and value of technology-enhanced approaches. Programs have successfully reduced sampling time by 30-50% through mobile data platforms, improved sample turnaround through optimized logistics, expanded analytical scope to emerging contaminants, and piloted innovative collection methods including UAV sampling. These examples provide models that other programs can adapt to local contexts.

Looking forward, citizen science stormwater monitoring will likely become increasingly sophisticated while simultaneously more accessible. Continuing miniaturization and cost reduction of analytical technologies will expand field-portable capabilities. Advances in automation and artificial intelligence will streamline data collection, quality control, and interpretation. Platform standardization and interoperability will facilitate data integration across programs and regions. Yet technology alone does not ensure successful citizen science; maintaining focus on community engagement, volunteer support, meaningful participation in research processes, and equitable access remains essential.

The enhanced framework presented here positions citizen science stormwater monitoring to address contemporary challenges including emerging contaminants, climate-driven hydrological changes, and expanding spatial scales of environmental concern. By reducing the burden of data collection and analysis, these approaches enable volunteer networks to expand coverage, deepen engagement, and contribute more substantively to environmental understanding and management. The convergence of environmental monitoring needs with technological capabilities creates an opportune moment for citizen science to increase its contributions to watershed science and stewardship.

Ultimately, technology serves as a means rather than an end. The fundamental value of citizen science lies in connecting people to the environments they inhabit, building environmental literacy, democratizing scientific investigation, and expanding the collective capacity to understand and protect water resources. Enhanced methods and tools amplify these contributions, enabling citizen scientists to ask more questions, gather more evidence, and participate more fully in the scientific enterprise. As stormwater monitoring programs adopt and adapt the technologies and approaches discussed here, they will continue evolving as vital components of environmental monitoring infrastructure and community environmental engagement.

References