# Litter in French urban areas-part 1: composition, sources, and spatio-temporal variations on urban surfaces

Lauriane Ledieu, Romain Tramoy, David Mabilais, Sophie Ricordel, Zoé Bridant, Eric Bouchet, Clémence Bruttin, Bruno Tassin, Johnny Gasperi

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## To cite this version:

Lauriane Ledieu, Romain Tramoy, David Mabilais, Sophie Ricordel, Zoé Bridant, et al., Litter in French urban areas-part 1: composition, sources, and spatio-temporal variations on urban surfaces. Environmental Science and Pollution Research, 2025, 32 (16), pp.10135-10148. 10.1007/s11356-024-35203-8. hal-04734425

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https://hal.science/hal-04734425v1

Submitted on 10 Sep 2025

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Litter in French urban areas – Part 1: composition, sources and spatio-temporal variations

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on urban surfaces.

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Lauriane Ledieu¹,²*, Romain Tramoy³, David Mabilais¹,², Sophie Ricordel¹,², Zoé Bridant¹,², Eric Bouchet⁴,

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Clémence Bruttin⁵, Bruno Tassin⁶, Johnny Gasperi¹,²

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¹Univ Gustave Eiffel, GERS-LEE, F-44344 Bouguenais, France

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²Institut de Recherche en Sciences et Techniques de la Ville IRSTV, CNRS, 1 rue de la Noë, 44321 Nantes, France

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³LEESU, Univ Paris Est Créteil, Ecole des Ponts, Creteil, France

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⁴Nantes Métropole, 2 cours du Champ-de-Mars, 44000 Nantes, France

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⁵Citeo, 50 boulevard Haussmann, 75009 Paris, France

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⁶LEESU, Ecole des Ponts, Univ Paris Est Creteil, Marne-la-Vallée, France

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*lauriane.ledieu@univ-eiffel.fr

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Abstract

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Urban areas concentrate human activities that generate large amounts of waste. A small fraction is mismanaged and ends up on urban surfaces and eventually in waterways. The fraction reaching waterways is usually estimated using poorly constrained data, while litter density on urban surfaces and its subsequent transfer dynamics are also poorly documented. This study provides new insights on litter composition, sources, and spatio-temporal variations in the urban catchments of Pin Sec and Jules Verne (Nantes, France), which are dominated by industrial/commercial, collective and individual housing areas. Regular litter collection on selected street sections was combined with visual counting of street litter (> 1 cm). In total, 50.8 kg (dry weight) of litter were collected and 232,217 items were counted, mainly smoking and food consumption related items. Litter densities ranged between 40,000 and 452,000 items/km² depending on local activities, item mobility and land use as a driver of road and foot

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26 traffics. A median litter accumulation rate of 7 g/inhabitant/day was estimated, suggesting that 0.6% of
27 the total amount of waste generated in public places are mismanaged. Results over space show that litter
28 is strongly correlated with the land use and that litter accumulation is promoted in green spaces. Results
29 over time show that litter stock is quickly renewed and that items may have long residence time on urban
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32 **Keywords**

33 Brands, litter bins, plastics, accumulation, grass strips, rain amount

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35 **1. INTRODUCTION**

36 The presence of litter in the urban environment is as old as the cities themselves. Scientific studies
37 on the presence of urban litter started in the 1970s (Hall and Ackoff 1972) because it raises sanitary,
38 ecological, logistical and operational challenges, that cities and countries must address. Resulting from
39 the implementation of efficient waste management, street cleaning and increasing awareness about
40 littering, a reduction of litter has been observed in many developed countries (Dillon Consulting 2019;
41 Keep America Beautiful 2021). Nevertheless, consumption continues to grow with increasing uses of
42 plastics, especially single-use plastics (Heinrich Böll Stiftung et al. 2020). In the few recent studies
43 carried out on urban environments, litter is therefore mainly plastic materials (Dillon Consulting 2019;
44 Gholami et al. 2020; Youngblood et al. 2022), constituting considerable source of plastics that can reach
45 waterways. Plastic pollution in the open ocean has for many years received the attention of the scientific
46 community. Then, rivers as major pathways for litter along the land-sea continuum gained interest
47 (Lebreton et al. 2017; Schmidt et al. 2017; Meijer et al. 2021; González-Fernández et al. 2021), but little
48 is known about the contribution of urban areas.

49 In fact, based on a short review, results of the few scientific studies on urban areas demonstrate that
50 litter densities are highly variable (Table 1). On global scales, litter inputs are conventionally related to
population density (Lebreton et al. 2017; Schmidt et al. 2017) but some authors highlighted that this factor is not the best to explain spatial variations of litter (Keep Britain Tidy 2020; Youngblood et al. 2022), especially at small spatial scales (Gholami et al. 2020). Most studies have shown increasing litter inputs with increasing urban flows, road and foot traffics (Burns & McDonnell 2020; Chapman and Bomford 2020; Lockwood et al. 2021; Ballatore et al. 2022). In addition, on local scales, litter densities may vary according to punctual and specific activities (AET Group Inc., 2020; Karimi and Faghri, 2021b) like transport facilities, stores, bar/restaurants, etc. (Ballatore et al. 2022; Xiong et al. 2022) where people spend time waiting or having social and recreational activities (Syberg et al. 2020). Moreover, except studies exploring trends of litter densities in a city some years after a first audit (Dillon Consulting, 2019; Environmental Resources Planning, 2020; Environmental Resources Planning, 2018) or during the COVID-19 pandemic (Chapman and Bomford 2020; Pietz et al. 2021; Cowger et al. 2022), few investigated the temporal variations at weekly or monthly frequencies. To the best of our knowledge, three studies addressed this question and observed either a rapid turnover of litter and constant accumulation rates in urban areas from the Ganges River Basin (Youngblood et al. 2022) and at Californian urban roadsides (Cowger et al. 2022), or seasonal variations of litter inputs in the Dutch city of Purmerend (Ballatore et al. 2022). All of these influencing factors lead to litter densities that are not easily generalized (Schuyler et al. 2021; Youngblood et al. 2022). The variable methods and units used (Table 1) also limit feasible comparisons.

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<table><caption>Table 1: Overview of the different studies investigating litter amount and composition in urban areas. The methods used are indicated as well as the litter densities (converted and expressed in items/km² rounded to the nearest thousand). "n.d." for not defined.</caption><thead><tr><th>References</th><th>Zone</th><th>Method</th><th>Size considered</th><th>Number of categories</th><th>Unit used</th><th>Litter densities (items/km²)</th></tr></thead><tbody><tr><td>Cowger et al., 2022</td><td>Inland Empire (USA)</td><td>Visual counting</td><td>&gt; 1 cm</td><td>112 categories (Trash Taxonomy)</td><td>Items/yr/km kg/yr/km</td><td>/</td></tr><tr><td>Rossi et al. 2023</td><td>Lower Passaic River (USA)</td><td>Litter collection and characterization</td><td>&gt; 2.5 cm</td><td>47 categories</td><td>Items/site g/site cm³/site</td><td>/</td></tr><tr><td>Seco Pon and Becherucci 2012</td><td>Mar del Plata (Argentina)</td><td>Visual counting and characterization</td><td>n.d.</td><td>10 categories</td><td>Items/m²</td><td>14,270,000</td></tr><tr><td>Muñoz-Cadena et al. 2012</td><td>Mexico City (Mexico)</td><td>Visual counting and characterization</td><td>&lt; 25 cm² &gt; 25 cm²</td><td>7 categories</td><td>Total number of items</td><td>/</td></tr><tr><td>Ballatore et al. 2022</td><td>Purmerend (Netherlands)</td><td>Crowdsourced litter observations</td><td>n.d.</td><td>n.d.</td><td>Items observed/1 000 persons</td><td>/</td></tr><tr><td>Dillon Consulting, 2019</td><td>Vancouver (Canada)</td><td>Visual counting and characterization</td><td>&lt; 25 cm² &gt; 25 cm²</td><td>96 categories</td><td>Items/site</td><td>35,000*</td></tr><tr><td>AET Group Inc., 2020</td><td>Toronto (Canada)</td><td>Visual counting and characterization</td><td>&lt; 25 cm² &gt; 25 cm²</td><td>90 categories</td><td>Items/site</td><td>30,000*</td></tr><tr><td>Gholami et al., 2020</td><td>Qazvin (Iran)</td><td>Visual counting and characterization</td><td>n.d.</td><td>19 categories</td><td>Items/100 m</td><td>/</td></tr><tr><td>Youngblood et al., 2022</td><td>Ganges River (India, Bangladesh)</td><td>Litter collection and characterization</td><td>n.d.</td><td>82 categories</td><td>Items/m²</td><td>5,900,000</td></tr><tr><td rowspan="6">Schuyler et al., 2021</td><td>Shanghai (China)</td><td></td><td></td><td></td><td></td><td>360,000</td></tr><tr><td>Mombasa (Kenya)</td><td></td><td></td><td></td><td></td><td>40,000</td></tr><tr><td>Capetown (South Africa)</td><td></td><td></td><td></td><td></td><td>460,000</td></tr><tr><td>Yeongsan (South Korea)</td><td>Visual counting and characterization</td><td>n.d.</td><td>84 categories</td><td>Items/m²</td><td>120,000</td></tr><tr><td>Negombo/Colombo (Sri Lanka)</td><td></td><td></td><td></td><td></td><td>140,000</td></tr><tr><td>Kaohsiung (Taiwan)</td><td></td><td></td><td></td><td></td><td>580,000</td></tr><tr><td></td><td>Haiphong (Vietnam)</td><td></td><td></td><td></td><td></td><td>540,000</td></tr></tbody></table>

<footnote n="73">73 *calculated when the surface of investigated sites provided.</footnote>
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Modelling the contribution of land-based litter to the ocean through rivers is challenging. Jambeck
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et al. (2015) were the first authors to assess global plastic inputs to the ocean using a global model with
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the available data at the moment. However, given the scarcity of scientific information on urban litter,
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statistical data was sparse and coarse-grained resulting in rough extrapolations, namely a littering rate
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of 2%. This rate corresponds to the amount of litter collected along US roads by the NGO Keep America
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Beautiful relative to the total municipal waste production in the country. Although the following global
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models have been calibrated with increasing amounts of field data (Lebreton et al. 2017; Schmidt et al.
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2017; Meijer et al. 2021), they still rely on poorly constrained statistical data, resulting in high
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uncertainties (Roebroek et al. 2022). For example, considering consumer films, bags and bottles, other
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consumer packaging, cutlery, and straws as items subject to littering, Kawecki and Nowack (2019)
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estimated that only 1.2% of plastics from the on-the-go consumption are littered in Switzerland. Further
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research is therefore needed and to our knowledge, no scientific studies have been conducted in France
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on this specific topic.

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This study aims at improving the estimations of litter accumulation rates in urban areas by (i)
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quantifying litter densities, (ii) characterizing their composition and sources, as well as (iii)
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understanding their spatio-temporal variability. Two urban catchments representative of contrasting land
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uses east of Nantes Metropole (France) were chosen for this application: Jules Verne and Pin Sec. They
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represent a good opportunity to answer to the ambition listed because the studied catchments are located
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in the sixth most populated city in France (Nantes, 325,000 inhabitants).

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## 2. MATERIALS AND METHODS

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### 2.1. Experimental sites

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Two contiguous urban catchments, Jules Verne and Pin Sec, located at the north-east of Nantes (France) and covering a total surface area of 1.05 km² (Figure 1a) were surveyed. These catchments are representative of contrasting land uses with 36% of commercial/industrial areas, 26% of individual housing areas and 13% of collective housing areas (Figure 1b). These catchments also have 24% of their
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surface area covered by green spaces (Figure 1b) of different forms (hedgerows, shrubs, grass strips and green squares with trees, Figure S1).

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<figure><img src="image_3.png" /><figcaption>**Fig. 1** A) Location of Pin Sec and Jules Verne urban catchments at Nantes agglomeration (France). B) Land uses and specific activities of Pin Sec (outlined in dark green) and Jules Verne (outlined in red) catchments. Buildings were taken from the BD-Topo®, roadways were provided by Nantes Métropole, green spaces were taken from the detailed campaign Fluxsap 2010 (Mestayer et al., 2011; no significant change of the green spaces occurred since 2010 in these areas)</figcaption></figure>

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Jules Verne catchment has a lower population density (3,443 inhabitants/km²) than Pin Sec (8,064 inhabitants/km²) but higher pedestrian and road traffics. Besides, JV-1 and JV-4 (Figure 2) constitute a main roadway where road traffic ranges between 6,500 and 18,000 vehicles/day (Nantes Métropole 2020). In addition, the tram stops and the supermarket present in the area generate a lot of traffic.

As part of the Nantes Urban Environment Observatory (ONEVU, Ruban et al., 2007) and the National Observation Service (SNO) Observil, rain amounts are recorded at a 5 min frequency at 3 rain gauges (3039, Précis Mécanique®) covering the area. Their mean values are used for interpretation.

In both catchments, municipal services usually clean the streets every week on Monday, Wednesday and Friday. The cleaning operations are conducted by street sweepers and walking agents collecting the items using litter pliers, but these operations are not conducted homogeneously in every street. Sweeping is only carried out on the roadway in some streets, whereas on the roadway and the sidewalk in others. Some streets are also considered as private where no sweeping is performed. In addition, these frequencies can be adjusted depending on the cleanliness of the street.

## 2.2. Characterization of litter composition and sources

All items above 1 cm were collected manually at 6 specific street sections: i.e. 3 sections in the Pin Sec catchment, labelled from P1 to P3, and 3 sections in the Jules Verne catchment, labelled from J1 to J3 (Figure 2). The sections were 50 m long and their width depend on the street as the roadway, the sidewalk, and the green space if present, were considered at both sides of the street. Sections were chosen to be representative of different land uses, and to allow an easy and safe collection of the items. The land cover and the surface area of each street section were deferred using the QGIS software (Table S1). The total area surveyed corresponds to 0.006 km² (Figure 2).
<figure><img src="image_4.png" /><figcaption>Fig. 2 Map of the street sections (represented by turquoise rectangles and named in blue) investigated for litter collection campaigns C1 to C15, and of the streets (represented in grey and named in black) investigated for the campaigns of visual counting of litter VC1 to VC10, in the Pin Sec and Jules Verne catchments (outlined in green and red, respectively)</figcaption></figure>

After an initial collection campaign (C0) to clean the sections, 15 campaigns, labelled from C1 to C15, were carried out approximately once a week between February and June 2022 (Table S2). Campaigns were achieved over half a weekly day, mainly on Mondays to collect the remaining litter after the street cleaning by the municipal services.

The items were then characterized at the laboratory and weighted according to their material category: plastic, rubber, paper/cardboard, metal, glass, manufactured wood and textile. In addition, the brand of each item, if visually identifiable, was recorded to provide further detail on the sources of litter. The brands recorded were evaluated per campaign and were then merged according to their industrial groups.
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In addition, the items collected during the campaigns C0 to C10 (8,989 items, 67% of the collected litter) were characterized according to their use and associate to an economic sector using the J-list from the MSFD TG ML classification (Fleet et al., 2021; Table S3). This characterization was carried out per campaign, not per section.

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## 2.3. Quantification of litter densities

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### a. Litter densities at small spatio-temporal scales

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The number and the mass of items collected at each section (Figure 2) during the campaigns C1 to C15 were used to express litter densities by count and by mass. To discuss the spatial variations of litter densities, characteristics like the land use, the surface area, the number of litter bins, the presence of parking spaces and the type of green space were reported for each section (Table S1). Four types of green spaces were identified: hedgerows, shrubs, grass strips and green squares with trees (Figure S1). The type of green space and the number of litter bins were deferred using Google Street View and validated on field. To discuss the temporal variations of litter densities, both the number of days and the rain amount since the last campaign were reported during the campaigns (Table S2).

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Because we observed large discrepancies in the presence of litter between the roadway, the sidewalk and the green space, the spatial distribution of items within the street sections was evaluated in further details. During the campaigns C8 to C15, the items collected (5,275 items, 43% of the total collected items) within these different types of surfaces were counted separately. The surface area of roadway, sidewalk and green space in each section was deferred using the QGIS software.

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### b. Litter densities at large spatio-temporal scales

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In order to assess the spatio-temporal representativeness of the 6 selected street sections, litter densities by count were additionally evaluated by a visual counting of items. All items above 1 cm were quantified using a manual counter on 23 streets: 11 streets in the Pin Sec catchment, labelled from PS-1 to PS-11, and 12 streets in the Jules Verne catchment, labelled from JV-1 to JV-12 (Figure 2). Most of the streets in the investigated area were selected to represent contrasting land uses, number of litter bins and type of green spaces (Table S1). Observations were performed on both sides of the street,

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including the roadway, the sidewalk, and the green space if present, for a cumulated surveyed area of 0.14 km² (Figure 2). Parking spaces alongside roadways were included in the counts, but not parking spaces separated from the roadways by sidewalks and/or grass strips. If some cars were parked, only the items around them were included. Characteristics like the land use, the surface area, the number of litter bins, the presence of parking spaces and the type of green space were reported for each street (Table S1) using the QGIS software and Google Street View.

Campaigns of visual counting were conducted once a month, between October 2021 and October 2022. Two campaigns could not be achieved (in March and July 2022), so a total of ten campaigns, labelled from VC1 to VC10, were carried out (Table S4). Campaigns were achieved on a weekly day. Both the number of days and the rain amount since the last campaign were reported during the campaigns (Table S4).

## 2.4. Statistical analysis

In order to assess the factors influencing the spatial variations of litter, correlations between the characteristics of the streets/sections (land use, presence of parking spaces, number of litter bins and type of green space) and the densities of litter (by count and by mass) were evaluated using the Spearman correlation coefficient R. These correlations were considered significant if *p*-values were equal to or lower than 0.05, and representative if *R* was equal to or greater than 0.5. The Wilcoxon-Mann-Whitney test was used to compare the distribution of litter densities between land uses (i.e. collective housing, individual housing and commercial/industrial area) and type of surfaces within the streets (i.e. roadway, sidewalk and green space). Clusters were considered as significantly different if *p*-values were equal to or lower than 0.05. These statistical parameters were calculated using the “tidyverse” package (Wickham et al. 2019) in RStudio version 2022.7.1.554 (RStudio Team 2022). Both statistical analyses are non-parametric as our data do not show a normal distribution.

Temporal variations were also explored by calculating the Spearman correlation coefficient between the characteristics of the campaigns (if that was during holidays, number of days integrated by each campaign and rain amounts) and the densities of litter (by count and by mass).
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## 2.5. Calculation of accumulation rates and mismanaged fraction

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Litter monitoring integrates diverse processes of varying intensities over time: littering (deliberate or accidental) and potential collection/remobilization of items through various vectors (municipal cleaning services, cleanup activities, wind and stormwaters). By collecting the items every 6 to 14 days, campaigns C1 to C15 are representative of the short-term litter accumulation integrating all these processes. Moreover, the collection of litter makes it possible to assess the renewal of items in mass. The results of these campaigns were therefore used to estimate accumulation rates. These rates were expressed for each section and in total, in kg/km²/day, by dividing the mass of items collected by the area surveyed and the number of days integrated by the campaign. The total accumulation rate per campaign was then extrapolated to the total surface area of the catchments (i.e. 1.05 km²) and reported to the number of inhabitants (i.e. 5 048 inhabitants) in order to express it in g/inhabitant/day.

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In order to assess the fraction of mismanaged items in the Pin Sec and Jules Verne catchments, the accumulation rates were compared with the amounts of municipal solid waste annually produced by the inhabitants of these catchments. In Nantes Métropole, the amount of municipal solid waste annually produced per inhabitant equals to 397 kg/inhabitant/yr (considering glass, household and recyclable waste collected door-to-door by municipal services and at the waste collection sites; Nantes Métropole, 2022).

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# 3. RESULTS

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## 3.1. Litter composition and sources

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All categories of materials were observed during the litter collection campaigns. In total, plastic represented the highest proportion by count (62%). Paper/cardboard was the second prevalent material (26%), followed by metal (5%) and glass (4%, Figure 3a).

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The 8,989 items characterized belong to 83 different categories (Table S5) over the 201 categories listed in the J-list (Table S3). Specific items featuring in the top ten most abundant categories account for 84% of the total number of items categorized. Cigarette butts were the most abundant (34%),
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followed by paper fragments (15%), plastic wrappers (10%), fragments of non-foamed plastic (8%), other paper items (4%), glass bottles (4%), paper cups (3%), cardboard boxes (2%), paper cigarette packets (2%) and metal drink cans (1%, Figure 3b).

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<figure><img src="image_5.png" /><figcaption>B Top 10 (by count)
n = 8,989</figcaption></figure>

<figure><img src="image_6.png" /></figure>

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Fig. 3 A) Percentages of the different categories of materials sampled per campaigns and in total (Tot.) during the litter collection campaigns. Number of items are indicated below the campaign label. B) The top ten specific items by count sampled during the campaigns C0 to C10 (n = 8,989) and classified according to the J-list (Fleet et al., 2021). Colors correspond to the categories of materials and percentages of each type of items are indicated

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The economic sector could be associated to 5,170 items (58% of the items categorized according to the J-list type code, Figure 4a). Smoking-related activities were consistently the prevalent economic producer of litter (41%) considering that cigarette butts were the most abundant items by count. Food consumption related items ranked second (16%). Building and construction, medical, personal hygiene and care, recreation related and clothing represented 1%, 0.4%, 0.2% and 0.1% of the items collected, respectively (Figure 4a).

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For smoking and food-consumption related items, 194 brands were recorded for which the top three industrial groups and the top ten brands are represented in Figures 4b and 4c, respectively. Among the smoking related items (n = 744), 27 different brands were identified. However, the top ten accounted for 91% of the items for which the brand could be identified by count. Marlboro is the most abundant
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brand (36%) of tobacco products, followed by Winston (23%) and Philip Morris (8%, Figure 4c). The

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Philip Morris International group, which owns the Marlboro and Philip Morris brands, thus leads the top

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three groups (Figure 4b).

### (A) Economic sectors (n = 8,989)

<figure><img src="image_7.png" /></figure>

### (B) Top 3 groups

#### Smoking related items
(n = 744)

<figure><img src="image_8.png" /></figure>

#### Food-consumption related items
(n = 1,075)

<figure><img src="image_9.png" /></figure>

### (C) Top 10 brands

<figure><img src="image_10.png" /></figure>

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<caption>**Fig. 4** A) Boxplots representing the variability (first quartile, third quartile and median; vertical bars: 1.5 times the interquartile range, red crosses: mean percentages) of item percentages by count according to their economic sectors. B) Top three groups and C) top ten brands recorded for smoking related and food-consumption related items. Brands were colored according to the podium of the top three groups</caption>
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Brands were identified for 69% of the food-consumption related items. With 167 different brands recorded, the diversity was high and the top ten accounted for only 40% of all items. This sector therefore represents 79% of all brands recorded. Kinder (8%; sweet wrappers), Heineken (8%; drinks), Haribo (4%; sweet wrappers) and Cristaline (4%; bottled water) were the most prevalent brands. In the top ten, 3 brands corresponded to sweet wrappers, 5 brands corresponded to drinks and the last 2 brands corresponded to a fast food restaurant and to the supermarket present in the Jules Verne catchment area (Figure 4c).

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## 3.2. Litter densities

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A total of 12,330 items were collected during the litter collection campaigns C1 to C15, representing a dry mass of 50.8 kg, which is 4.1 g/item. These amounts correspond to a median litter density of 108,000 items/km² by count and 286 kg/km² by mass (Table S2). In the manuscript, litter densities per km² are expressed rounded to the nearest thousand and raw data are provided in the supplementary material. Litter densities were highly variable depending on the section considered, ranging from 60,000 (P1) to 165,000 items/km² by count (P2, Table S6). During all campaigns, items were found at each section. Excluding C0, litter densities varied from 75,000 (C6, April 2022) to 220,000 items/km² (C2, February 2022) by count (Figure 5, Table S2).

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A total of 232,217 items were counted during the visual counting campaigns VC1 to VC10. This amount represents a median litter density of 164,000 items/km² (Table S4), which is higher than the median litter density quantified at the street sections. According to the streets, litter densities spanned two orders of magnitude ranging from 40,000 (JV-11) to 452,000 items/km² (JV-3, Table S7). According to the campaigns, litter densities ranged from 91,000 (VC2, November 2021) to 223,000 items/km² (VC9, August 2022, Figure 5, Table S4).
<figure><img src="image_11.png" /><figcaption>Fig. 5 Litter densities by count according to the number of days integrated during all campaigns (excluding C0). The Spearman correlation coefficient R and the associated p-value are indicated. Points are represented with different diameter according to the cumulative rain amount fallen during the integrated period of each campaign</figcaption></figure>

### 3.3. Factors influencing the spatio-temporal variations of litter densities and composition

At the scale of the experimental sites, considering the results of all campaigns (C1 to C15 and VC1 to VC10), litter densities by count were significantly different according to land uses. Commercial/industrial areas exhibited the highest densities (median litter density equals to 171,000 items/km²), followed by collective housing areas (median litter density equals to 137,000 items/km²), and individual housing areas exhibited the lowest (median litter density equals to 80,000 items/km², Figure 6a). Moreover, the median litter densities by count per street or street sections exhibited a significant positive correlation with the number of litter bins (*p*-value = 0.006, R = 0.5).

Locally in the commercial/industrial areas, hot spots (> 250,000 items/km²) were observed on the main roadways (JV-1 and JV-4) as well as on both sides of the tram stops (JV-2 and JV-3 in Figure 2, Table S7). In housing areas, such hot spots appeared on streets with large parking spaces over their
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whole length (PS-5 and PS-7, Table S7) but the presence of parking spaces is not significantly correlated
with the litter densities for every street.

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Focusing on the spatial distribution of items within street sections, litter densities by count exhibited significant differences between green spaces, sidewalks and roadways. In fact, sidewalks exhibited much higher litter densities (median litter density equals to 113,000 items/km²) than roadways (median litter density equals to 53 000 items/km²) and green spaces exhibited the highest (median litter density equals to 162,000 items/km²), but also the most variable litter densities by count (Figure 6b). This is explained by the different types of vegetation in green spaces, with significant increase of litter densities from hedgerows to grass strips (Figure 6c).

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<figure><img src="image_12.png" /></figure>

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Fig. 6 Spatial variability (first quartile, third quartile and median; vertical bars: 1.5 times the interquartile range, red crosses: mean percentages) of litter densities according to A) the land use of the streets and sections investigated (color code corresponds to the land use map in the Figure 1b), B) the street surfaces where they were collected, and C) the type of green spaces where they were collected during the campaigns of litter collection C8 to C15. The number n of observations is indicated. If the clusters were significantly different, p-values were indicated
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Only the number of days integrated by each campaign explain the observed temporal variations, with a significant positive correlation with litter densities by count (Figure 5). No significant correlation was found with holidays or rain amounts.

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### 3.4. Accumulation rates and mismanaged fraction

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Based on the results of the campaigns C1 to C15, the accumulation rates ranged from 15 to 149 kg/km²/day (min-max, median value equals to 36 kg/km²/day) and from 3 to 31 g/inhabitant/day (min-max, median value equals to 7 g/inhabitant/day, Table S2). At the scale of both catchments, this represents a median annual mass of mismanaged items equals to 12.9 t/yr. Compared to the total amount of waste potentially generated per inhabitant in the Pin Sec and Jules Verne catchments, the mass of mismanaged waste therefore represents a median percentage equals to 0.6%.

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## 4. DISCUSSION

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### 4.1. Litter composition and sources

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The predominance of plastic followed by paper items by count observed in this study is consistent with other surveys on urban litter (Dillon Consulting 2019; Cowger et al. 2022). However, high inputs of plastic are not specific to these areas as this predominance is also observed in roadside ditches (Environmental Resources Planning, 2018; Karimi and Faghri, 2021a; Ledieu et al., 2022a; Vinci Autoroutes, 2021). In the urban area investigated, smoking and food-consumption related items are the most abundant (>60%, Figure 4a). In contrast, the results of a study carried out in the roadside ditches from a highway in Nantes demonstrated that these economic sectors represented only a minor fraction (21%, Ledieu et al., 2022a; Tramoy et al., 2022b). In such a context, unidentified plastic debris were actually the most abundant items (50%). This difference can be explained by more fragmentation of items with road traffic and by the absence of pedestrians on the Nantes highway while they are a potential additional source of litter in urban areas (Burns & McDonnell 2020). Besides, the litter composition observed in this study is similar to that observed in urban areas in the Netherlands (Ballatore et al. 2022), the USA (Cowger et al. 2022; Rossi et al. 2023) or in the Ganges River Basin (Youngblood

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et al. 2022). In fact, cigarette butts are items conventionally littered (Rath et al. 2012; Schultz et al. 2013). For example, they ranked 1st in the top ten items in Great Britain (Keep Britain Tidy 2020) and in the USA (Keep America Beautiful 2021). In addition, the top three observed in this study is the same as the one observed by the association of cities for urban cleanliness (AVPU; Guillaume, 2017). The French urban area investigated in this study therefore appear to be representative of the general trends in the composition of urban litter.

The integrity of items in urban areas provides a good opportunity to characterize litter sources and to adapt the mitigation strategies according to these statements. It shows that awareness actions and regulation policies (e.g. banning plastic filters, Green et al., 2023) are still urgently required for cigarette butts. One of the litter mitigation strategies adopted at European level was the EU single-use plastics directive (Directive (EU) 2019/904). Nevertheless, this directive only restricts the sale of a few types of plastic items (cotton buds, cutlery, plates, straws and stirrers, polystyrene food and beverage containers) and is not sufficient to curb the presence of plastic litter (Kiessling et al. 2023), considering that plastic crisps/sweet wrappers ranked 3rd in the top ten items (Figure 3b). In addition, Extended Producer Responsibility (EPR) programs have now been established to shift responsibility back to the companies responsible for placing the product on the market, but more field data is needed to identify the companies to target. In this study, as in most litter surveys, brands leading the international market like Coca Cola or Marlboro appeared in the top brands (AET Group Inc. 2020; Environmental Resources Planning 2020; Keep Britain Tidy 2020; Ballatore et al. 2022; Cowger et al. 2024). Moreover, Philip Morris International ranking 1st for smoking related items is consistent considering that this group was the largest tobacco producer in 2021 (https://fr.statista.com/statistiques/667455/principales-entreprises-de-tabac-europeennes-classees-par-valeur-de-marche/; accessed 10 April 2023). Spatial variations between countries also appeared according to the citizen habits and the consumption market, considering that most items are food-consumption related. For example, the prevalent beer brand was Heineken in this study as well as in Purmerend (Netherlands, Ballatore et al., 2022), while in Texas, it was Budweiser (USA, Environmental Resources Planning, 2020). At the present time, comparisons are limited because very few studies report the brands of the items and compare them with their sales in litter surveys. In
any way, considering that 4 brands of the top ten brands observed in this study (Coca Cola, Cristaline, Heineken and Haribo, Figure 4c) are partners of the French association Gestes Propres (Gestes Propres, 2021), it can be concluded that the companies responsible for most of the litter have a major role to play in tackling the plastic pollution for which they are responsible (Cowger et al. 2024).

## 4.2. Factors influencing litter spatial distribution

The litter densities quantified in this study are in the same range as those quantified in urban areas as different as Yeongsan in South Korea or Negombo in Sri Lanka (Schuyler et al. 2021). Nevertheless, the results of this study, as well as those of the studies on urban litter reported in the Table 1 show high spatial variations, spanning one and three orders of magnitude, respectively. Absolute litter densities are therefore difficult to compare. However, the results of this study confirm that commercial sites are the most littered areas (Keep Britain Tidy 2020; Gholami et al. 2020; Pietz et al. 2021; Youngblood et al. 2022; Xiong et al. 2022), where litter densities may be more than twice those of individual housing areas. On local scales, hotspots have also been observed around transport facilities, parking spaces and stores. These trends support that litter densities are a function of urban flows (Ballatore et al. 2022), and mainly foot traffic as suggested by Rossi et al. (2023). Fast food restaurants play a key role as well because of the on-the-go consumption they generate (Basilico et al. 2020; Gholami et al. 2020; Keep America Beautiful 2021; Lockwood et al. 2021). This is supported by the presence of the Burger King brand in the top ten brands in this study (Figure 4c). Brands such as McDonald (Environmental Resources Planning 2020; Keep Britain Tidy 2020; Ballatore et al. 2022) or Tim Hortons (AET Group Inc. 2020) are actually prevalent in most litter surveys. The variety of such local activities in commercial areas leads to high variations of litter densities (Figure 6a), but the visual counting of litter carried out at the street scale in this study did not allow to depict these variations in detail. Finally, as in Toronto (Canada), litter densities are positively correlated with the number of litter bins on each street but it is difficult to state whether the presence of litter bins causes the presence of litter or whether litter bins are merely distributed to meet increasing demand (AET Group Inc. 2020). However, this variable is rarely considered in studies on urban litter. These local trends however suggest
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that the transport of items in urban areas may be limited, favoring their accumulation on urban surfaces (Cowger et al. 2022), especially as hotspots typically have high densities of cigarette butts.

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The spatial distribution of litter in urban areas is mainly discussed according to its potential sources (Lockwood et al. 2021; Ballatore et al. 2022; Xiong et al. 2022), while it is also influenced by its temporary storage in accumulation zones. This study highlights a clear role of green spaces. A visible example of such areas is given in Figure S2. Green spaces create strong litter accumulation zones for two reasons. Firstly, as already observed along rivers, vegetation has a good trapping capacity (Ivar do Sul et al., 2014; Ledieu et al., 2022b; Liro et al., 2022), and items related to food consumption that are made of soft plastics, for example, can be easily transported by the wind towards the green spaces, where they remain trapped (Khoeriyah and Sembiring 2023). Secondly, litter is more difficult to remove from these areas than on sidewalks or roadways, because of low surface runoff (e.g. Gholami et al., 2020; Mellink et al., 2022) and limited access to municipal mechanized cleaning equipment. Therefore, the current urban greening may challenge future cleanliness management practices. Factors that influence litter accumulation in natural environments, such as the presence of vegetation are therefore also valid in urban areas. The type of green areas also appears to be important, with higher accumulation in grass strips and shrubs, than in dense hedgerows (Figure 6c). However, this tendency can result from an easier identification of items in grass strips and shrubs and/or to the fact that the latter are mainly used to cover public spaces whereas dense hedgerows are mainly used to separate public from private spaces.

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The spatial distribution of litter is therefore mainly related to the land use and this factor may also influence litter management and people's sensitivity to littering. Besides people are more likely to litter in previously littered areas (Schultz et al., 2013) and some citizens may clean up their neighborhood (AET Group Inc. 2020). Supermarkets also hire private companies to clean their parking spaces, as litter can reduce the attractiveness of a business and thus cause economic losses (Karimi and Faghri, 2021b). Given these variable practices, some spaces at the interface between different city departments (e.g. traffic and transportation, cleanliness, cleaning, sanitation and green spaces services) or at the interface between spaces under the responsibility of different actors (private vs. public) actually are abandoned-

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like areas – also from a research point of view – where no cleaning is regularly carried out. Land use therefore drives the spatial distribution of litter from its generation to its potential removal.

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### 4.3. Factors influencing litter temporal dynamics

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The results of the litter collection campaigns confirm the quick renewal of litter highlighted by Youngblood et al. (2022) and Cowger et al. (2022), but not constant accumulation rates. In fact, these rates showed temporal variations of an order of magnitude from one week to another (Table S2), suggesting that many inputs and outputs occurred at different intensities over time. It is reasonable to assume that litter inputs are a function of urban flows over time too. For example, high levels of litter have been observed in summer (Table S4; Lockwood et al., 2021; Stocker, 2020), likely as a result of increased outdoor activity (Ballatore et al., 2022). Each urban area is therefore susceptible to have its own temporal evolution, even more so as cleaning operations are not conducted homogeneously in each street. Besides, none of the temporal factors examined in this study were sufficient to elucidate the temporal variations observed at the scale of the experimental site. In addition, litter removal by municipal cleaning services or stormwater is actually a punctual and incomplete process. Considering the significant correlation between litter densities and the number of days integrated by each campaign, this leads to a progressive accumulation of litter, even though rain events have occurred.

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Items may therefore have different residence times on urban surfaces, depending on their spatial distribution. The residence time of items is, however, difficult to estimate because of the multiple processes integrated during litter monitoring. One process observed during this study that can have a considerable impact on the temporal variations of litter densities is that items accumulated in green spaces can be fragmented during the grass cutting without prior litter removal. For example, in section P2, the litter density quadrupled from one week to the next, mainly due to grass cutting. This may raise an issue for urban soils by potentially enriching them in microplastics in the long term (Basilico et al. 2020).

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### 4.4. Recommendations on mismanaged fraction estimations
In this study, the median percentage of littered items (i.e. 0.6%) is three times lower than the 2% of littering rate estimated in the USA by Jambeck et al. (2015) and two times lower than the 1.2% calculated by Kawecki and Nowack (2019) for products from the on-the-go consumption in Switzerland. However, the results of this study may be slightly underestimated considering that the spatial variations of litter densities quantified at the section scale were four times lower than those quantified at the street scale. Nevertheless, such approaches raise the question of what kind of data the quantified litter densities should be compared to. In this study as well as in that of Jambeck et al. (2015), amounts of litter were compared with the waste generation. However, in the case of Jules Verne and Pin Sec catchments, this waste production only includes residential areas and economic activities that generate moderate amounts of waste (up to 10 m³/week). Waste from economic activities that generate higher volumes is managed by private companies (Veolia, Paprec, Suez, etc.; personal communication with the cleaning services of Nantes Métropole). Yet, the more littered areas are mainly commercial ones where it is therefore difficult to quantify waste production and leakages. In addition, this waste production does not include the items removed by the municipal cleaning services either in the public bins, or by the street sweepers and walking agents. In contrast, Kawecki and Nowack (2019) based their estimates on the on-the-go consumption using litter typologies. Thus, this approach is based on several assumptions and neglects the accidental litter leakages. Considering these gaps, further research is needed to assess the fraction of mismanaged items that reintroduce the conventional waste management system and to further develop the reflection on how to estimate littering rates.

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## CONCLUSIONS

By combining different approaches, this study demonstrates the advantages and the limitations of both methods, i.e. litter observation and litter collection, and the benefits of combining them. Visual counting at the street scale is very useful for assessing the spatial distribution of litter, identifying hotspots and quantifying the stock of litter on urban surfaces at a given time. In contrast, litter collection on restricted sections is essential to assess the litter typology and masses, and to evaluate their regeneration after removal. The combination of both methods therefore allows to assess the
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representativeness of the selected sections. Considering that the estimation of litter densities in urban areas is traditionally conducted in selected sections, the choice of the sections appears crucial.

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This study provides a considerable dataset describing the composition and spatio-temporal evolution of urban litter in two French urban catchments. The data are consistent with the few litter surveys carried out elsewhere (USA, Canada, Great Britain, Netherlands, etc.). In urban areas, litter is mainly composed of food-consumption and smoking related items. Items are well preserved and often travel short distances between their sources and their accumulation zones, making it easier to identify their source than in other receiving environments (rivers, ocean, ...). The ambitions of the Extended Producer Responsibility (EPR) program could be very helpful to further reduce urban litter, given the typology of items and the brands identified. More awareness actions and regulation policies for producers, especially regarding cigarette butts, may also be helpful. Nevertheless, a deeper analysis of litter generation and sources in order to identify the better ways to reduce litter requires an interdisciplinary approach between environmental, human and social sciences.

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Litter densities are highly variable in space and in time. The spatial distribution of items depends mainly on their input sources and their accumulation zones. Yet, both are mainly driven by the land use, as it influences urban flows, local activities as well as the collection/remobilization and mobility of items. Thus, the litter management strategy should be better adapted to the main sources as well as to the main accumulation zones such as green spaces. However, the results would be different considering that a better management of the accumulation zones would mainly reduce the urban environment contamination, whereas acting on the litter sources appear to be the better way to reduce littering rates considering the quick regeneration of items and their continuous input.

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Urban litter can be a considerable source of plastic litter. This is not because such a typology is specific to these contexts but because of the high density of plastic items being used in urban areas. However, mismanaged fractions of waste are difficult to estimate and to predict because of the large fluctuations of litter densities and the difficulties in quantifying the total waste production in a given area. Further studies are therefore needed to better understand litter variations and to find a better way of estimating mismanaged fractions of waste and their subsequent leakages towards waterways.

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491

492 Acknowledgments

493 Authors are grateful to the Nantes Urban Environment Observatory (ONEVU-IRSTV) for
494 providing the hydrometeorological data. Authors also gratefully thanked the Digital Resources
495 Department of Nantes Métropole for providing the GIS datas, and the road, the cleaning, and sewerage
496 teams of the Erdre et Loire division for their collaboration.

497

498 Statements and Declarations

499 Author Contributions

500 Johnny Gasperi, Bruno Tassin, Romain Tramoy, Clémence Bruttin, Eric Bouchet and Lauriane Ledieu
501 contributed to the study conception and design, and to the data analysis. Material preparation and data
502 collection were performed by Lauriane Ledieu, David Mabilais, Sophie Ricordel and Zoé Bridant. The
503 first draft of the manuscript was written by Lauriane Ledieu and all authors commented on previous
504 versions of the manuscript. All authors read and approved the final manuscript.

505

506 Funding

507 This work was co-funded by the Région des Pays de la Loire (France) and Nantes Métropole, as part of
508 the Plasti-nium Project, and by Citeo, as part of the Stritter project.

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510 Data availability

511 All data are provided in the article. A statement of data availability is therefore not applicable.

512

513 Competing Interests
514 The authors have no relevant financial or non-financial interests to disclose.

515

516 Ethical Approval

517 Not applicable.

518

519 Consent to Participate

520 Not applicable.

521

522 Consent to Publish

523 Not applicable.

524
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