Estimating Macroplastic Mass Transport from Urban Runoff in a Data-Scarce Watershed: A Case Study from Cordoba, Argentina

Abstract

Urban growth has intensified the generation of solid waste, particularly in densely populated and vulnerable neighborhoods, leading to environmental degradation and public health risks. This study presents a multidisciplinary methodology to estimate the mass of macroplastic litter mobilized from urban surfaces into nearby watercourses during storm events. Focusing on the Villa Páez neighborhood in Cordoba, Argentina—a data-scarce and flood-prone urban basin—the approach integrates socio-environmental surveys, field observations, Google Street View analysis, and hydrologic modeling using EPA SWMM 5.2. Macroplastic accumulation on streets was estimated based on observed waste density, and its transport under varying garbage collection intervals and rainfall intensities was simulated using a conceptual pollutant model. Results indicate that plastic mobilization increases substantially with storm intensity and accumulation duration, with the majority of macroplastic mass transported during high-return-period rainfall events. The study highlights the need for frequent waste collection, improved monitoring in vulnerable urban areas, and scenario-based modeling tools to support more effective waste and stormwater management.

1. Introduction

Plastic pollution is an emerging environmental issue that affects terrestrial, freshwater, and marine ecosystems [1,2]. In particular, the presence of plastic in many aquatic systems has been increasing and has become a global concern [3,4]. Recently, plastic pollution has been included as a “novel entity” within the planetary boundaries framework, highlighting the threat it poses to planetary sustainability [5,6].

Once released into the environment, plastics reach across all ecosystems and ecotypes across the globe [7]. Plastic in water can negatively impact human livelihoods, affect aquatic organisms, and pose a risk to human health [8]. Most plastic pollution is produced on land and transported from land to rivers [8]. Over the past five years, the number of published studies on plastic pollution has increased considerably; however, knowledge gaps remain regarding the spatial origin and sources of this pollution within river basins [8,9,10].

It is estimated that between 19 and 23 million metric tons of macroplastic enter aquatic ecosystems annually [2,11]. Urban water systems are considered one of the main sources of this pollution; however, the relationship between river plastic pollution and the connection between urban and natural water systems is not yet fully understood [12,13]. Human activities and poor waste management are the main factors contributing to plastic leakage in urban water systems [13,14]. The mobilization of these wastes into rivers and seas is influenced by extreme precipitation events, stormwater overflows, hydrological conditions, and other environmental factors [15,16].

One of the main challenges is determining where plastic pollution originates within watersheds, particularly in large rivers [8]. While global studies exist for other types of pollution [17], specific information on plastic pollution remains limited [8]. Additionally, identifying the sources of plastic in river sub-basins and the influence of different human activities on their generation is scarce. The lack of a combined analysis of the origin and sources of plastic pollution complicates the design of effective strategies for its reduction and the achievement of the Sustainable Development Goals (SDGs) [8].

Given that knowledge on the extent and severity of plastic impacts at population, community, and ecosystem levels is limited, further studies in this area are needed. To improve knowledge, there is a need to (a) expand research to quantify sources, sinks, fluxes, and fates of plastics in catchments and transitional waters both independently as a major transport routes to marine ecosystems; (b) improve environmentally relevant dose–response relationships for different organisms and effect pathways; (c) scale up from studies on individual organisms to populations and ecosystems, where individual effects are shown to cause harm; and (d) improve biomonitoring through developing ecologically relevant metrics based on contemporary plastic research [18].

Urban basins with the highest plastic contributions are often located in the most vulnerable areas of cities, where waste collection is deficient and urban infrastructure is overburdened. Rapid urbanization has increased the number of inhabitants in informal settlements, exacerbating problems related to sanitation, transportation, and solid waste management. This underscores the need for comprehensive and multidisciplinary analyses of solid waste management, incorporating community participation, strengthening urban infrastructure, and designing public policies to reduce plastic production, promote recycling, and prevent plastics from reaching water bodies.

The sources of plastic entering freshwater ecosystems are varied and spatially heterogeneous, ranging from diffuse inputs stemming from runoff to point sources such as Wastewater Treatment Works and Combined Sewer Overflows (CSOs) [19]. Surface hydrology plays a fundamental role in the mobilization, transport, and retention dynamics of floating plastics. During peak discharge events, cities with high litter burdens experience a strong increase in plastic flux. However, the relationship between surface hydrology and plastic transport is complex and varies significantly across different river channels [20]. Recent studies indicate that more effort is needed to understand plastic transport mechanisms in rivers, as current mass transport estimates for plastic remain highly uncertain [20]. Studies such as that of Lebreton et al. [21] have developed global models to estimate the input of plastic into the oceans from rivers, considering watershed characteristics such as population density, mismanaged plastic waste production, and average monthly runoff. According to these models, between 1.15 and 2.14 million tons of plastic reach the ocean each year, with more than 74% of emissions occurring between May and October. Additionally, plastic concentrations have been found to correlate with population density and the proportion of urban development in watersheds. Roebroek et al. [22] conducted a global analysis on the impact of floods on plastic mobilization. Using flood maps and global data on mismanaged plastic waste (MPW), they estimated that floods significantly increase the potential for plastic mobilization. Under normal conditions, approximately 0.8 million tons of plastic are mobilized annually worldwide; however, during floods with a 10-year return time, this figure rises to 7.3 million tons per year, representing a 140% increase [23,24].

In Latin America, and in Argentina in particular, studies have analyzed plastic transport from the one sub-basin of the Riachuelo-Matanza in Buenos Aires, Argentina. Based on the Comprehensive Environmental Sanitation Plan (PISA), these studies used the EPA SWMM model to simulate surface runoff generation and transport, allowing for the estimation of pollutant loads associated with runoff [23]. These findings highlight the importance of further studying plastic pollution in urban water systems and its connection with hydrological processes to develop effective mitigation strategies and sustainable management approaches [23].

The objective of this study is to propose a methodology for analyzing the generation, accumulation, and transport of macroplastic waste (>5 mm [25]) in urban watersheds, focusing on data-scarce regions that are representative of many cities worldwide, taking the city of Cordoba, Argentina, as a case study. This work seeks to contribute to the expansion of studies on macroplastic litter by using direct and indirect methods to define the effects of garbage in urban watersheds and its impact not only on runoff but also on environmental degradation. Owing to the interdisciplinary nature of the problem, an integral and multidisciplinary analysis is proposed, involving socio-environmental studies, citizen participation, satellite imagery, and hydrological models of contaminant transport.

2. Materials and Methods

Due to inadequate management by responsible organizations, much of this waste is transported by waterways, ultimately reaching lakes or the ocean. An integrated approach to plastics transported by watercourses and riverside populations enables better-informed management decisions that consider the broader environmental problem and provide sustainable responses. Characterizing and understanding the social conditions of urban neighborhoods and settlements, especially those located along riverbanks, is essential for linking the type of plastic waste that is discarded on the streets or deposited in these areas. A methodology that improves the environmental management of urban watersheds by clarifying the dynamics of macroplastic waste accumulation and transport during runoff events will allow for more targeted interventions and more sustainable urban planning.

2.1. Approach

To address the problem, a multidisciplinary study was conducted, integrating socio-environmental analyses, image processing, hydrological modeling, and macroplastic litter transport modeling. As shown in Figure 1, the adopted methodology follows a structured approach. First, previous socio-environmental studies were reviewed to establish baseline knowledge, define the research area, and conduct in-depth field studies to assess the neighborhood’s waste situation. Image analysis was then employed to identify waste accumulation points and estimate their volume, providing a spatial assessment of plastic waste distribution. Based on these results, a plastic litter transport model was developed alongside a hydrological model to quantify the movement of plastic waste within the watershed. By integrating these methodologies, this study offers a comprehensive approach to understanding plastic pollution in the area, highlighting the interaction between social vulnerability, environmental degradation, and waste mismanagement, which collectively impact water quality and urban resilience.

2.2. Socio-Environmental Studies

Understanding the socio-environmental context of urban watersheds is essential to addressing the issue of plastic pollution and its impact on water systems. This section presents previous research and surveys conducted in the study area, which provide key insights into environmental concerns, waste management practices, and community vulnerabilities.

In 2018, within the framework of collaboration between the National University of Cordoba (UNC) and the Alberdi People’s Network, the Faculty of Social Sciences conducted a study titled Socio-environmental Diagnosis of Barrio Villa Páez as part of the course Theory, Spaces, and Intervention Strategies [23]. This study mapped environmental problems in the neighborhood based on residents’ reports, identifying key issues such as solid waste accumulation, noise pollution, and flooding. Building upon these findings, in 2021, a new socio-environmental assessment was carried out under the University Extension and Student Social Commitment (CSE) initiative Impact on Health of Climate Change and Water Pollution in Neighborhoods Adjacent to the Suquia River (Resolution of the Honorable Board of Directors of the National University of Cordoba: RHCD-2022-681-E-UNC-DEC#FCM). This initiative, organized by the Semiology Chair of the Internal Medicine Hospital Unit No. 2 at San Roque Hospital (Faculty of Medical Sciences, UNC), in collaboration with the Chairs of Fluid Mechanics and Sanitary Engineering (Faculty of Exact, Physical and Natural Sciences) and the Chairs of Epistemology of Social Sciences and Social Work and Theory, Spaces, and Intervention Strategies IV (Faculty of Social Sciences), aimed to deepen the understanding of environmental risks and their health impacts. The study employed an observational, prospective, cross-sectional, and descriptive approach. A multi-stage random sample of approximately 2000 households was selected based on 2010 Census data [24]. The entire research team adhered to international [26] and local [27] standards that regulate research on human subjects. Prior authorization was obtained from the president of the Villa Paez Neighborhood Center, Susana Luna, to survey the neighborhood and conduct the survey of residents. This work was approved by the Institutional Committee for Ethical Evaluation of Health Research of the National Hospital of Clinics of the Faculty of Medical Sciences, UNC. Each participant gave their consent to participate by agreeing to answer the survey [28].

2.3. Study Area

The proposed methodology is applied in a specific case where socio-environmental studies, resident involvement studies, and previous runoff calculations exist, although studies on waste remain scarce. This study was conducted in the city of Cordoba, Argentina, specifically in the Villa Paez sector, where citizen science projects are developed [21]. The study area is located at the downstream end of a 5 km² watershed that flows into the Suquia River, which runs southwest–northeast (Figure 2). Cordoba, the second most populated city in Argentina, has historically developed around the Suquia River. Despite its importance, the Suquia River suffers from severe environmental degradation due to human activity. High levels of pollution affect both water quality and the populations that depend on it. Instead of serving as a valuable green corridor, many stretches of the river show significant socio-environmental deterioration, evidenced by signs prohibiting bathing within the city limits. Plastic pollution is among the main pollutants in the river. Poor management of plastic leads to its accumulation in the watershed, either through direct disposal into the river or transport during runoff events. This issue has spurred community efforts, such as a 2020 initiative led by young volunteers who organized river clean-up and restoration activities.

Villa Paez is a neighborhood located in the northwestern part of the city’s pericentral area, covering 0.57 km², with a population density of 9469.61 inhabitants per km², based on data from the 2010 census. As of the time of writing, neighborhood-level data from the 2022 census have not yet been published [29]. Located south of a bend in the Suquia River, this area experiences two types of flooding: one due to the lack of capacity to convey excess water through the streets, and the other due to the Suquia River overflowing onto the floodplains because of more frequent rainfall events.

Long-term residents report recurring flood events, with risks ranging from minor inundations to severe flooding during extreme events. The neighborhood’s vulnerability is exacerbated by infrastructure limitations, high population density, and inadequate management, factors that increase its exposure to hydrological risks.

The survey included internationally recognized indicators to assess the impacts of climate change, exposure to meteorological events, and community vulnerabilities [25]. It was validated through a qualitative pilot test, evaluating internal reliability, content validity, and construct feasibility. Nine interdisciplinary professionals assessed aspects such as clarity, question relevance, response coding, and interpretation [24].

In September 2022, trained student assistants conducted surveys, ensuring standardized interactions with respondents. The survey consisted mainly of closed-ended and short-answer questions, with open-ended sections for specific details. It was divided into four domains:

• Identification data and comorbidities
• Household exposure to climate-related events and proximity to the Suquia River
• Health and meteorological events
• Waterborne diseases and related health concerns

Surveys were completed in paper or digital format, with all collected data anonymized for confidentiality. The time required, simplicity, pleasantness of the format, brevity and clarity of the questions, recording, coding, and interpretation of the results were also evaluated [28].

Regarding the management of urban solid waste, five questions were asked. These five questions were chosen to address key aspects of waste management practices and their impact on the environment, particularly in relation to disruptions caused by natural events or local practices. Here is how each question contributes to the research:

Whether or not they had municipal waste collection at their home: This question helps establish the baseline for waste management services available to respondents. If waste collection is not available, it highlights the potential for unmanaged waste to accumulate, which may contribute to environmental pollution.

Whether or not they had differentiated collections at their home: Differentiated waste collection (e.g., separating recyclables, organic waste, etc.) is an essential aspect of sustainable waste management. This question provides insight into the respondents’ practices and understanding of recycling and environmental responsibility, which is crucial for assessing the effectiveness of waste management systems in the area.

Frequency of municipal waste collection: Understanding the frequency of waste collection is important to assess the effectiveness of municipal services. Infrequent collection may lead to waste accumulation, which can result in health hazards and environmental degradation. This question helps identify whether services are adequate to manage the volume of waste generated.

Have they ever stopped collecting garbage for more than a week due to a flood, storm, or heat or cold wave: This question examines the vulnerability of waste management systems to extreme weather events. Disruptions to waste collection caused by natural events are critical to understanding how climate change and extreme weather affect waste management infrastructure and public health.

Are there garbage accumulation areas near their home: Identifying the presence of accumulation areas near homes helps assess the local waste management infrastructure and any potential environmental risks posed by unmanaged waste. It also reflects on the effectiveness of municipal systems and community involvement in waste management practices.

Together, these questions aim to provide a comprehensive view of the local waste management situation, its effectiveness, and its challenges, especially in the face of natural events. This information is vital for understanding waste management practices.

2.4. Image Analysis

2.4.1. Garbage in the Street

Although official data indicate that waste collection coverage exceeds 90% throughout the neighborhood, local residents reported frequent and sustained garbage accumulation on the streets. To validate these accounts, a combination of remote and field observation methods was applied. First, the Google Street View tool was used to virtually inspect the neighborhood, using images from 2022 to identify locations with visible waste. Observed items included plastic wrappers, bottles, garbage bags, cardboard, and construction debris. This preliminary visual inspection allowed for the identification of accumulation hotspots and a rough estimation of waste volume and composition.

To corroborate these remote observations, direct field visits were conducted between April and May 2022 by researchers and students participating in citizen science projects coordinated by university faculty. During these visits, systematic inspections were carried out on foot, recording the types and quantities of waste observed. Notably, an average of one plastic bag (measuring 90 × 60 × 20 cm) filled with plastic waste was documented approximately every 100 m of street length (see Figure 3). The estimated number of antecedent dry days before the field visits was 7, based on local meteorological data. This dry period is relevant, as it aligns with one of the pollutant accumulation scenarios used in the hydrological model (see Section 3).

2.4.2. Landfills and Dumping Points

In addition to street-level littering, the neighborhood also contains several informal waste disposal sites. These include open-air dumps, characterized by the uncontrolled deposition of urban waste without treatment or containment measures. Based on household surveys, field observations, and analysis of Google Street View and satellite imagery, two open dumps and eight informal dumping points were identified across the neighborhood. These sites are associated with informal waste recovery systems, where materials are sorted in public spaces or nearby homes, and non-valuable items are discarded in unauthorized locations. This practice, common in many metropolitan areas in Argentina, contributes to the proliferation of small-scale landfills and environmental degradation.

2.5. Hydrological Model and Macroplastic Transport

The hydrological model implemented was previously developed by Lopez [30]. To simulate infiltration processes in the basin’s permeable sub-area, the SCS-CN model was used due to its parameter simplicity, as it depends mainly on the curve number value. The average CN-SCS values for the city of Cordoba are approximately 82 for streets, 74 for parks, and 79 for residences [31]. Therefore, it was decided to use these values according to the type of soil, the cover, and the conditions of the ground surface, distinguishing between parks (CN = 74) and other areas [30].

The challenges of quantifying urban flooding in Villa Paez stem from multiple methodological obstacles. Urban catchments often lack comprehensive historical hydrological data, making precise flood characterization difficult. The heterogeneous nature of flooding processes complicates consistent measurement, while precipitation records remain incomplete. Additionally, significant land-use changes over decades alter runoff dynamics, further compromising the reliability of traditional hydrological modeling approaches. These limitations necessitate innovative research methodologies, such as integrating citizen science, satellite imagery, and field observations to develop more comprehensive urban flood [30]. To evaluate the behavior of the urban hydrology model, a participatory monitoring program was implemented in a vulnerable community in Cordoba, Argentina. Due to the lack of official hydrological data, local residents collaborated in collecting water level observations within the urban catchment [32]. The EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single events or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads [33]. The model was validated using two major recorded flood events, showing a good agreement between simulated and observed water levels, as shown in Figure 4. Additionally, the results were compared with survey responses from approximately 200 households, confirming the model’s accuracy in mapping high-risk flood-prone areas. The findings were shared with local authorities to enhance flood risk management strategies [34].

2.5.1. Design Storms

The study basin lacks historical rainfall information. For this reason, and given the presence of a meteorological station belonging to the National Meteorological Service, “Cordoba Observatory” (31°25′15″ S; 64°11′54″ O; 425 msnm), located 3 km from the neighborhood, its historical rainfall records were used to determine the Intensity–Duration–Frequency (IDF) within the agreement developed by INA-CIRSA (National Water Institute Sub-management Center of the Semiarid Region)—FCEFyN-UNC (Faculty of Exact, Physical and Natural Sciences—National University of Cordoba) and SMN (National Meteorological Service), as shown in the Figure 5. This information is used to determine the rainfall for different recurrences times [35].

Below, the IDF curves presented by INA-CIRSA, FCEFyN-UNC, and SMN (2024) [35] are used to obtain the rainfall intensity:

$$\begin{gathered} \ln i_{d,T}=A\ast {\mathsf{\varnothing }}_T-B\ast {\delta }_d+C \\ {\mathsf{\varnothing }}_T=2.5844588\ast {\left(\ln T\right)}^{3/8}-2.252573 \\ {\delta }_d={\left(\ln d\right)}^{5/3} \end{gathered}$$

(1)

where

• i = Precipitation intensity in mm/h
• T = recurrence period in years
• d = duration of the storm in minutes
• A = 0.381
• B = 0.154
• C = 5.054

2.5.2. EPA SWMM—Transport of Contaminants

In the Villa Paez neighborhood, the stormwater infrastructure is sparse, with a limited network of conduits. Notably, the few existing stormwater pipes have diameters exceeding 400 mm or equivalent sections (Figure 6), which are sufficiently wide to allow the passage of large macroplastic. Furthermore, due to the lack of regular waste collection and the limited presence of stormwater inlets, a significant portion of plastic litter is transported directly overland during rainfall events.

The EPA SWMM 5.2 software includes tools that allow modeling accumulation and entrainment processes, but they must be supported by calibration data to generate realistic results [36]. EPA SWMM 5.2 software is not specifically designed to simulate the behavior of large macroplastic items (e.g., plastic bags, bottles); it allows the definition of user-specified pollutants whose surface accumulation and transport can be modeled under rainfall-runoff conditions. In this study, macroplastic litter was conceptually represented as a user-defined pollutant to analyze relative changes in accumulation and transport under different waste collection and precipitation scenarios. This approach, while simplified, follows methodologies used in recent studies (e.g., Bagheri et al. [37]; van Emmerik & Schwarz [38]) that apply sediment-based modeling frameworks to explore macroplastic dynamics in urban environments.

Field visits carried out through citizen science projects revealed that, on average, one consortium garbage bag (90 × 60 × 20 cm) containing macroplastic litter is found every 100 m of street. Two accumulation scenarios were simulated: scenario 1 assumed 7 days without garbage collection, with one bag per 100 m; and scenario 2 assumed 30 days without collection, with four bags per 100 m.

In both scenarios, plastic was assumed to represent 11% of total waste, based on a municipal waste composition study conducted by CORMECOR S.A. (Intermunicipal Corporation for Urban Solid Waste Management of Greater Cordoba). The specific weight of plastic was taken as 86.5 kg/m³ [39]. These values were used to estimate the mass of plastic accumulated per unit area (C₁) in each subcatchment, considering uniform land use across the Villa Páez neighborhood.

The buildup process was modeled using the potential function, which assumes that the accumulated pollutant mass B increases over time, raised to a given power, up to a defined maximum value. This relationship is described by the following expression:

B = Min (C₁, C₂ tC₃,)

(2)

where C₁ = buildup (kg/ha); t = buildup time interval (days); C₂ = buildup rate constant [kg/(ha days)]; and C₃ = buildup time exponent, dimensionless.

In our implementation, the model parameters were assigned as follows: the maximum possible accumulation (C₁) was estimated based on the calculated mass of plastic per hectare; the buildup rate constant (C₂) was then derived by dividing C₁ by the length of the dry period prior to rainfall (7 or 30 days, depending on the scenario); and the time exponent (C₃) was fixed at a value of 1, following the methodology adopted by [23].

To estimate the initial macroplastic accumulation (C₁) used in the model, field observations revealed that approximately one standard-sized plastic bag (90 × 60 × 20 cm) filled with mixed waste was found every 100 m of street under regular waste collection conditions. For the scenario representing a 7-day collection interval, this value was taken directly. For the 30-day accumulation scenario, the amount of waste observed increased to four bags per 100 m, reflecting prolonged accumulation in the absence of collection.

Table 1. Macroplastic accumulation parameters used in the hydrological model for the 7-day and 30-day waste collection scenarios.

Scenario	Dry Period (days)	Plastic Bags per 100 m	Plastic Mass Per Bag (kg)	Plastic per 100 m (kg)	C1 (kg/ha)	C2 (kg/ha)	C3
Scenario 1	7	1	1.05	1.05	4.11	0.587	1
Scenario 2	30	4	1.05	4.20	16.44	0.548	1

summarizes the accumulation parameters for both scenarios.

Although several illegal dumping sites and informal waste disposal points were identified throughout the Villa Paez catchment during field visits and analysis of satellite and Street View imagery, the present study focused exclusively on the accumulation and mobilization of macroplastic litter on streets and impervious surfaces. This decision was made to align with the capabilities of the EPA SWMM model, which simulates pollutant buildup and washoff primarily from surface runoff, rather than modeling fixed-point pollutant sources. However, the presence of dumping sites highlights the need for further research into alternative or complementary modeling approaches that can capture the influence of these concentrated sources on plastic pollution dynamics.

Pollutant washoff (or drag) due to runoff was modeled using the Exponential Washoff function, where the rate of pollutant removal W (expressed in mass per hour) is proportional to the runoff flow rate q, raised to a power, and to the accumulated pollutant mass B:

W = C₁ × qC₂ × B

(3)

where W = rate of washoff (mg = h); C₁= washoff coefficient (mm⁻¹); C₂ = washoff exponent (unitless); q = runoff rate per unit area (mm = h); and B = pollutant buildup (kg) [37].

According to sediment transport theory, values of the C₂ exponent typically range from 1.1 to 2.6, with most values near 2 [10]. In this study, a value of 2 was adopted, based on this range and supported by prior applications in similar urban hydrological contexts.

Determining the value for the washoff coefficient C₁, however, was more challenging due to the lack of site-specific monitoring data. To address this limitation, we followed the approach of Penza [23], who estimated C₁ = 0.001 in a similar urban context. This value was adopted under the assumption that, given the high vulnerability of the basin to runoff and surface wash, extreme rainfall events could result in up to 95% of the accumulated street plastic being mobilized by overland flow. No sensitivity analysis was conducted in this initial implementation, which we recognize as a limitation of the study. Future work should incorporate field data and sensitivity testing to refine this coefficient and better capture the variability of plastic washoff in different hydrological and land-use conditions.

While the Exponential Washoff function in EPA SWMM was originally developed for the transport of fine particulate matter and sediments, several recent studies have adopted sediment transport theory to simulate macroplastic dynamics in urban and fluvial systems. Although macroplastics—such as plastic litter or garbage bags—differ in size, buoyancy, and shape from traditional sediments, their mobilization is influenced by similar hydrological drivers such as runoff intensity, surface roughness, and flow connectivity. Given the current lack of standardized models for macroplastic transport, especially in urban contexts, sediment-based approaches provide a conceptual and practical framework for estimating plastic mobilization and accumulation. For example, the Plastic Pathfinder model conceptualizes the terrestrial transport of macroplastics using force–balance approaches inspired by sediment transport equations [20,40]. Similarly, the INCA-Macroplastics model applies sediment-like thresholds to simulate detachment, accumulation, and remobilization of plastics in river catchments [41]. Huthoff et al. [42] also utilized a simplified hydrodynamic approach derived from sediment transport theory to estimate macroplastic retention in floodplain areas. These studies support the relevance of applying sediment transport theory as a conceptual and operational framework for understanding macroplastic dynamics.

Street Sweeping and BMP treatment (Best Management Practices)

Street Sweeping and BMP treatment were not considered in this analysis, as the focus was limited to pollutant accumulation and washoff processes under typical urban drainage conditions.

3. Results

The results of the socio-environmental surveys were used to fit the input data for the plastic transport model. For the modeling of the scenarios, a rainfall duration of 120 min was used. This approach yielded results for each simulated scenario, allowing for an analysis of the basin’s response to different conditions and an estimation of plastic accumulation and washoff in the area based on these conditions.

3.1. Survey Results

A total of 222 residents responded to the survey concerning household waste management services in the Villa Paez neighborhood. The survey results are found in

Table 2. Summary of survey responses related to waste collection and accumulation in the Villa Paez neighborhood (n = 222).

Question	Response	Percentage
Do you have municipal waste collection at your home?	Yes (203) respondents	92%
	No (18 respondents)	8%
Do you have differentiated (separated) waste collection?	No	86%
	Yes	8%
Frequency of municipal waste collection	6 days a week	80%
	Less than 6 days a week	15%
	Not specified/other	5%
Has waste collection ever been interrupted for more than a week due to extreme weather?	No	63%
	Yes	24%
	Don’t know	14
Are there garbage accumulation points near your home?	Yes	51%
	No	45%
	Don’t know	4%

Note: The last question was georeferenced. Two main garbage accumulation points were identified: one to the north and one to the south of the neighborhood (see Figure 7).

. The vast majority (92%) reported having municipal waste collection at their homes, while only 8% indicated they did not. These findings are consistent with data from the 2010 national census, which noted that over 90% of households in the area have regular garbage collection.

When asked about differentiated (segregated) waste collection at their residences, 86% of respondents stated that such a service was not available. Only 8% reported having access to differentiated collection, and 6% were unsure. This suggests limited implementation of waste separation practices at the household level.

Regarding the frequency of municipal waste collection, 80% of participants indicated that waste is collected six days a week, while 15% reported a lower frequency. The remaining respondents either did not specify or indicated irregular collection patterns.

Participants were also asked whether there had ever been an interruption in waste collection services lasting more than a week due to extreme weather events such as floods, storms, or heat/cold waves. The majority (63%) responded negatively, whereas 24% acknowledged such interruptions, and 14% were uncertain. These responses suggest that, while waste collection services are generally reliable, extreme weather can occasionally disrupt operations.

Finally, residents were asked whether there were informal waste accumulation sites near their homes. Slightly more than half (51%) reported the presence of such sites, 45% denied their existence, and 4% were unsure. Notably, this question was georeferenced, and responses indicated the presence of two major accumulation points—one in the northern sector of the neighborhood and another in the southern area (Figure 7).

The spatial distribution of reported waste accumulation points has been georeferenced, revealing two critical areas prone to uncontrolled waste disposal. These two areas are open-air dumps. The one to the north of the neighborhood is a street where people dispose garbage, which accumulates and is cleaned by the municipality intermittently. However, the area quickly fills up with new garbage. This situation is a cause for concern for the neighbors and has been reported in local newspapers [43]. The second site, situated to the south of the neighborhood, is an unoccupied lot where individuals also discard waste. In response to this situation, the lot has been enclosed by a wall; however, the problem persists. These results highlight the need for improved waste management infrastructure and climate-resilient collection systems to prevent plastic leakage into the Suquia River.

The survey results highlight key waste management deficiencies that directly influence plastic accumulation and transport within the watershed. The high percentage of residents without access to differentiated waste collection (86%) and the presence of informal dumping sites reinforce the observed accumulation patterns. The georeferenced identification of waste accumulation points corresponds to areas where people habitually dispose of their waste, indicating that these sites serve as primary sources of plastic pollution. Additionally, the reported interruptions in waste collection due to extreme weather events (24% of respondents) indicate that storm-driven plastic mobilization is likely exacerbated by delayed collection services. These findings underscore the need for improved waste management infrastructure, particularly in vulnerable areas where waste accumulation hotspots coincide with increased runoff and plastic transport. The integration of the socio-environmental survey data with the hydrological model improves the precision of plastic pollution assessments and allows for the proposal of specific mitigation strategies to reduce the leakage of plastics into the Suquia River.

3.2. Modeling Results for Scenario 1

Table 3. Accumulation and transport of macroplastics for different return times (Tr) under the 7-Day Waste Collection scenario.

	Return Time (Years)
	2	5	10	25	50	100
Initial Accumulation (kg)	148.79	148.79	148.79	148.79	148.79	148.79
Residual Accumulation (kg)	7.87	7.84	7.83	7.82	7.81	7.80
Surface runoff (kg)	93.67	127.55	140.18	146.57	148.15	148.72
Remaining Accumulation (kg)	62.99	29.09	16.44	10.05	8.46	7.87
Remaining Accumulation %	42.33	19.55	11.05	6.75	5.69	5.29

shows the accumulation and transport of plastics for different return times for 7 days without garbage collection. The initial accumulation is 148.79 kg. The surface runoff, in kg, represents the amount of contaminant that is transported by the flow. This varies from 93.67.kg for a return time of 2 years to 148.72 kg for a return time of 100 years. The remaining accumulation varies from 62.99 kg for a return time of 2 years to 7.87 kg for a return time of 100 years. This indicates that flow for a minor return time can transport lesser amount of plastic.

The percentage variation of remaining accumulation between the return time of 2 years and 10 years is 31.28%, and between 25 years and 100 years, is 1.46%. This indicates that contaminant carryover increases considerably when the recurrence time is increased from 1 year to 10 years. While for recurrence times of 50 to 100 years, the relative increase in plastic carryover is smaller. This is because the plastic carryover capacity for recurrences of 10 years or more already exceeds the speed and depth that allow the plastic to be carried on the road. The small relative increase from 50 to 100 years can be attributed to the residual plastic that remains immobilized during lower recurrence periods.

As illustrated in Figure 8, precipitation is represented in millimeters by the blue color, and the amount of plastic is represented in milligrams per liter by the orange color for outlet node 0 and the gray color for outlet node 1. The locations of outlet nodes 0 and 1 used in the EPA SWMM model are indicated in Figure 2. These data are presented for a return time of two years. The figure reveals that the maximum precipitation occurs 15 min into the simulation, while the maximum transport of plastic is observed at 64 min. The difference between these two points is 49 min. It is also evident that the peak in transport occurs immediately after the initiation of plastic mobilization. The maximum amount of plastic that exits the outlet node 0 is 44.12 milligrams per liter (mg/L), and for outlet node 1, it is 39.98 mg/L. The difference between the two amounts is 4.14 mg/L.

Figure 9 illustrates the relationship between precipitation and total flow at the end of the basin. Precipitation, measured in millimeters, is represented by the blue line, while the total flow, measured in cubic meters per second, is represented by the orange line at outlet node 0 and by the gray line at outlet node 1. The flow reaches the outlets at 65 min from the beginning of the precipitation, coinciding with the mobilization of the plastic. This indicates that the flow commences to transport the contaminant upon its arrival.

3.3. Modeling Results for Scenario 2

Table 4. Accumulation and transport of macroplastics for different return times (Tr) under the 30-Day Waste Collection scenario.

	Tr (Years)
	2	5	10	25	50	100
Initial Accumulation (kg)	595.40	595.40	595.40	595.40	595.40	595.40
Residual Accumulation (kg)	7.74	7.71	7.70	7.69	7.68	7.67
Surface runoff (kg)	374.74	510.26	560.80	586.34	592.66	594.98
Remaining Accumulation (kg)	228.4	92.85	42.30	16.75	10.42	8.09
Remaining Accumulation %	38.36	15.59	7.10	2.81	1.75	1.36

shows the accumulation and transport of plastics for different return times for 30 days without garbage collection. The initial accumulation is 595.40 kg. The surface runoff, in kg, is the amount of contaminant that the flow transports. This varies from 374.74 kg for a return time of 2 years to 594.98 kg for a return time of 100 years. The remaining accumulation varies from 228.4 kg for a return time of 2 years to 1.36 kg for a return time of 100 years. This indicates that flow for a minor return time can transport a lesser amount of plastic. The percentage variation of remaining accumulation between the return time of 2 years and 10 years is 31.26% and between 25 years and 100 years is 1.45%. This indicates that contaminant carryover increases considerably when the recurrence time is increased from 1 year to 10 years. While for recurrence times of 50 to 100 years, the relative increase in plastic carryover is smaller. This is because the plastic carryover capacity for recurrences of 10 years or more already exceeds the speed and depth that allow the plastic to be carried on the road. The small relative increase from 50 to 100 years can be attributed to the residual plastic that remains immobilized during lower recurrence times.

As illustrated in Figure 10, precipitation is represented in millimeters by the blue color, and the amount of plastic is represented in milligrams per liter by the orange color for outlet node 0 and the gray color for outlet node 1. These data are presented for a return time of two years. The figure reveals that the maximum precipitation occurs 15 min into the simulation, while the maximum transport of plastic is observed at 64 min. The difference between these two points is 49 min. It is also evident that the peak in transport occurs immediately after the initiation of plastic mobilization. The maximum amount of plastic that exits the outlet node 0 is 177.58 mg/L, and for outlet node 1, it is 160.93 mg/L. The difference between the two amounts is 16.65 mg/L.

Figure 11 illustrates the relationship between precipitation and total flow at the end of the basin. Precipitation, measured in millimeters, is represented by the blue line, while the total flow, measured in cubic meters per second, is represented by the orange line at the outlet node 0 and by the gray line at the outlet node 1. The flow reaches the outlets at 65 min from the beginning of the precipitation, coinciding with the mobilization of the plastic. This indicates that the flow begins to transport the contaminant upon its arrival.

In Figure 12, it is observed that plastic surface runoff increases considerably when going from a return time of 2 years to 10 years, while the increase for extreme return times, such as 50 and 100 years, is smaller in both scenarios. It is also observed that the frequency of cleaning is important because in scenario 2 a greater amount of dragged garbage is observed.

3.4. Interpretation of Outputs

The results obtained from the simulation appear to be reasonable and consistent with the known behavior of macroplastic transport under varying hydrological conditions. The variation in transported plastic litter across return times reflects expected physical processes, whereby higher-intensity rainfall events generate greater runoff of plastic and velocities, enhancing the capacity to entrain and mobilize larger plastics. Previous studies have shown that macroplastic transport is closely linked to flow energy, with thresholds of velocity and depth needed to initiate movement [25,38]. The observed plateau in transport efficiency for return times above 25 years suggests that the flow conditions exceed the critical thresholds for mobilizing nearly all available litter, aligning with findings by Meijer et al. [11], who noted that macroplastic transport becomes highly efficient under strong runoff conditions. Moreover, the delayed peak in plastic concentration relative to peak rainfall (by approximately 49 min) supports realistic hydrological response times and flow accumulation dynamics, similar to results reported in urban macroplastic modeling efforts [37]. These patterns indicate that the simulation outputs are consistent with field observations and theoretical expectations, validating the use of SWMM for exploring macroplastic accumulation and transport in urban catchments.

4. Discussion

Plastic pollution poses a persistent ecological risk, which is expected to increase due to rising plastic production and the long-term degradation of macroplastics into micro- and nanoplastics [18]. In the Villa Paez neighborhood in Cordoba, Argentina, this risk is compounded by vulnerability to flooding and inefficient waste management. Despite having a six-day-a-week waste collection system, survey results and field visits indicate recurring garbage accumulation in streets and informal dumping points. Storms and flooding events often interrupt waste collection, and the absence of a differentiated waste collection system further exacerbates pollution in public spaces.

By integrating satellite imagery, Google Street View, and georeferenced survey data, it was possible to map garbage accumulation zones and estimate plastic waste volumes on the streets. The hydrological model applied to the catchment allowed for the simulation of surface flow and plastic transport under different storm scenarios.

Although several informal dumping sites and garbage accumulation points were identified throughout the catchment via field visits and remote imagery, the modeling approach in this study focused exclusively on macroplastic litter accumulated on streets. This decision was based on the need to prioritize the most dynamic and hydrologically connected sources of plastic during rainfall-runoff events. The influence of these larger, often static waste sites on plastic transport represents a relevant research gap and should be addressed in future studies using different modeling strategies or combined observational approaches.

These simulations revealed that plastic transport begins approximately 64 min after rainfall initiation. The maximum transport of contaminants occurs concurrently with the flow’s arrival, at which point the peak of plastic transport also occurs before the peak of flow discharge. This indicates that the peak of transport of plastic occurs before the peak of discharge of flow. This demonstrates that plastic mobilization is initiated under relatively low flow conditions, consistent with findings by Roebroek et al. [22] and Penza [23], who also noted early-stage mobilization of plastics during storm events.

Our modeling shows a significant increase in plastic drag when recurrence intervals are reduced from 10 to 1 year, suggesting that frequent, less intense rainfalls are highly efficient at mobilizing accumulated street litter. Conversely, the relative increase in plastic drag is lower when comparing extreme events (e.g., 50-year vs. 100-year recurrence), likely due to the system already reaching its drag capacity. These results support the conclusion by Roebroek et al. [22] that smaller, frequent floods can have a disproportionately large effect on plastic transport, especially in urban areas with substantial surface litter.

Moreover, our comparison of two accumulation scenarios (7 days vs. 30 days) highlights the critical role of waste collection frequency. The 30-day scenario produced significantly higher plastic runoff, indicating that even modest improvements in cleaning schedules could greatly reduce plastic mobilization during the rainy season. These insights are valuable for cities with limited resources and weak infrastructure, where preventive waste management measures can have outsized impacts.

Although EPA SWMM was not originally designed for macroplastic, this study reinforces its growing application in approximating macroplastic transport, particularly when pipe networks are limited and overland flow dominates the transport mechanism [37]. These findings are especially relevant in data-scarce urban catchments, where scenario modeling can guide more effective waste management and infrastructure planning.

An important consideration for future work—and a limitation of the present study—is the need to calibrate the drag coefficients used in the model. The coefficients were estimated from literature and not derived from site-specific observations or empirical measurements. As mentioned in the conclusion, refining these parameters using field data would improve model reliability and result in drag equations more representative of real-world behavior. Including this aspect in future studies would enhance model performance and provide more accurate predictions for planning purposes.

These results allow us to make decisions regarding the management of plastic waste. Since most plastic is transported during rainy seasons with low recurrence times and Cordoba has a marked wet season, street and garbage dump cleaning should be intensified before the season to reduce the number of pollutants that end up in our river and affect the environment.

5. Conclusions

The study enabled the formulation of a comprehensive methodology for urban areas characterized by high population density and scarcity of data, thereby facilitating a comprehensive understanding of the problem. As a result, an analysis was carried out taking into account the pillars of sustainable development and a study where social vulnerability, the environmental situation, the deterioration of socio-environmental quality, and the deterioration of water bodies interact.

The scarcity of data on urban areas linked to urban solid waste is a very frequent problem. The methodology proposed for analyzing macroplastic waste transport linked to an urban basin involved collecting problem-specific information at the site, conducting surveys and site visits to understand the issue, and using satellite images and Google Street View to determine garbage deposits in the neighborhood. This methodology was an adequate approach to the problem that allowed modeling plastic transport using the hydrological model EPA SWMM. Although field visits and remote sensing revealed the presence of informal dumping grounds and garbage accumulation points outside the main drainage paths, the present study focused on street litter due to its direct interaction with surface runoff. These unmanaged waste deposits represent an additional source of plastics that was not modeled but should be addressed in future research through complementary modeling strategies or site-specific monitoring.

To refine the results obtained in this work, the drag coefficients should be adjusted using an approach based on observations or measurements to estimate the constants and thus generate the drag equation that resembles reality for the model. In instances where reliable data are limited, as evidenced by the study, and Google Street View images are employed, it is recommended to implement a specialized algorithm capable of identifying various types of garbage. This approach enables more precise measurement in extensive area basins and facilitates comprehensive analysis at the street level.

One important aspect to continue working on is the generation of a georeferenced database of citizen contributions. The scarcity of data on urban areas linked to urban solid waste is a problem frequently faced by professionals when conducting studies. In this sense, a database containing information collected by citizens would be of great help to achieve studies that are more representative of reality. For this database to be useful for any professional, it must be easy to use, freely available, and easily accessible, and also have constant maintenance and updating. Little is known about this transport, and it involves and requires all actors in society to reverse the environmental problems linked to plastic pollution. Our rivers are a fundamental part of our environmental and social heritage; therefore, their protection and knowledge are necessary to be able to protect and manage them properly. A river not only conducts the flow of water, but it also conducts the life that develops in it and in its environment. In that paradigm, the Suquia River is the creator of life throughout its course, and its protection must be a priority for the people of Cordoba.