Litter origins, accumulation rates, and hierarchical composition on urban roadsides of the Inland Empire, California

The Inland Empire includes San Bernardino County and Riverside County, California, United States (figure 1). The Inland Empire was chosen because most researchers were based there. The topography of the area includes mountains and valley regions, and major land uses are natural vegetation (>90%), developed (2%–5%), and agricultural (1%–4%) areas (Agriculture and Natural Resources U of C 2021). The region has a mean population density of 60 people km⁻² (Census 2021a). The climate is Mediterranean with 50 cm of average annual precipitation, primarily falling as rain in winter and dry summers. Wind is typically low intensity (mean daily 8.3 km h⁻¹) and moves north to south. In the fall and spring, when this study was conducted, strong winds and rain were generally rare, which led us to hypothesize that the primary mode of litter transport would be human transport. The Inland Empire has a robust waste management system that includes municipal and private street-side collection of three recovery streams (landfill, recycling, and yard waste), street sweeping, litter capture devices in storm drains, and frequent manual cleanups.

Zoom In Zoom Out Reset image size

Eighteen researchers each surveyed a unique ∼100–1000 m length of roadside for litter 1–3 times per week for 2–4 weeks in Fall 2018, Fall 2019, or Spring 2020 in the following Inland Empire cities: Riverside, Moreno Valley, Loma Linda, San Dimas, and Palm Desert using a standardized methodology (supplemental information, figure 1 available online at stacks.iop.org/ERL/17/015007/mmedia). Sites were selected based on convenience due to the crowdsourced nature of the surveying. Thus, our observations were not necessarily generalizable to the entire Inland Empire area. Differences in survey duration occurred due to the differences in the amount of time that the researchers could provide.

Before beginning surveys, all surveyors were trained in person during two 1 h sessions and joined a group called Our Clean Community in Litterati, a crowdsourcing litter cellphone app (Litterati 2021) for data collection. Although we used the Litterati application in this study, Open Litter Map (Lynch 2018) or geolocated images on any phone could produce the same results.

During the surveys, both sides of the road were surveyed at each site, including the curb, sidewalk, and sidewalk margin. Private property was not entered. Site length depended on litter abundance, and researchers were instructed to survey until approximately 100–1000 pieces of litter were observed during their first survey. This strategy was used to ensure a low likelihood of non-detect. Each researcher recorded the litter observations and any changes to the methodology. To maintain safety, researchers generally stayed on the sidewalk out of the road. Observations indicated that most litter was concentrated near the sidewalk curb and along the sidewalk margin. All litter (>1 cm length) was recorded and removed from the study location during each survey. Researchers used Litterati to photograph litter and record the material, item, and brand type. The cellphone app recorded location, date, and time simultaneously with each image (figure 2). Digital images of each object were taken within 1 m of where the object was found (figure 2(A)) while ensuring branding marks were visible in the image (figure 2(C)). Receipts were flattened so that all information on the receipt was viewable in the image (figure 2(A)). If multiple objects of the same type or fragments from a single object were found, those objects were logged together in one image. If researchers found any bags full of litter, they reported them as a bag full of trash. However, if a loose pile of litter was found, it was deconstructed and each object was reported individually. Large (too large for a single person to carry) or hazardous (e.g. chemicals, sanitary products) objects were left at the site and only photographed on first observation. Images were retaken if blurry. Material and item types were categorized in Litterati (figure 2(C)) using the most up-to-date version of the Trash Taxonomy classes (Hapich et al 2020) (supplemental information). Using the Trash Taxonomy, our classes were compatible with classes used in other studies (Intergovernmental Oceanographic Commission 2009, Lippiatt et al 2013, Institute for Environment and Sustainability 2014). When a litter class did not exist in the Trash Taxonomy, a new class was created to describe the litter. Brands were recorded when found on an object.

Zoom In Zoom Out Reset image size

Post survey, each researcher delineated their study area on a map using Google Maps (figure 2(B)). Litter location accuracy was calculated as the mean distance from all points recorded outside the study area to the surveyed boundary resulting in ±7 m (figure 2(B)). Each researcher cleaned their data by removing duplicate or inaccurate images, correcting incorrect tags, noting when multiple objects were in one image, and identifying any data points that were not for this study. In total, 18 locations were surveyed, and 146 receipts were found.

Data were archived by Litterati and made accessible through their data portal (Litterati 2021). Data included images, user input labels, time stamps, latitude, and longitude coordinates of every piece of litter (supplemental information, figure 2). Further refinement of the complete dataset resulted in two datasets structured for our specific study objectives. The first dataset (Receipt dataset) includes 146 receipts found at all 18 of the survey locations. Receipt observations were extracted from the total dataset by searching the tags for the keyword 'receipt'. The second dataset (Monitoring site dataset) focused on data from survey locations with comparable quality control procedures (see section 2.3) to compare litter accumulation and composition across sites.

We calculated transport metrics for receipts, including time and distance traveled and corresponding precipitation, wind, and human transport metrics. Of the 146 receipts, 72 had both time and location information for the sale transaction, 85 had at least sale location information, and 75 had at least sale time information. If a receipt had an address, travel distances were calculated as Haversine distance (straight line) from the latitude and longitude coordinates where the object was found to the address listed. Haversine distance was divided by the time between when the receipt was created and when it was found to determine the transport rate (m d⁻¹). We compiled wind (mean daily wind direction (0°–360°), mean daily wind speed (m s⁻¹)) and precipitation (mm, total daily) data for the entire observation period from a weather station (KRAL) near the center of our study region using the Midwestern Regional Climate Center's cli-MATE application (MRCC 2021). If a receipt had a timestamp and sale location on it, mean daily wind directions were vector averaged with daily wind speed for the receipt's potential duration in the environment to estimate the vector mean wind direction the receipt could have experienced. The receipt transport direction was calculated as the straight direction from the address on the receipt to where the receipt was found. Receipts with sale addresses were collected from 16 September 2018 to 18 November 2019, and all were found in Riverside and San Bernadino counties. Data on human trip distances from 01 January 2019 to 31 December 2019 in San Bernardino and Riverside Counties were acquired from the Bureau of Transportation Statistics (Bureau of Transportation Statistics 2021). Trip data was only available from 01 January 2019 onward, but we think this represents the temporal domain of the receipt dataset. The Bureau of Transportation created the binned human trip distance data by using smartphone tracking and aggregating them to daily and county levels (e.g. 100 human trips occurred between 1 and 10 km in San Bernadino Country on 01 February 2019). Trips were defined as movements with a stay of longer than 10 min. We made the data continuous by randomly sampling the distance range bins with uniform probability distributions ranging from the smallest to largest value for the bin.

All survey sites included in the monitoring site dataset needed to have data cleaned by the researcher (described in section 2.1) and trash removed from the entire site at least once during the study to ensure high quality and comparable data. Only seven of the 18 locations met these criteria, and the others were excluded (figure S2). One site was unable to be fully cleaned during the first 2 d of the study, so the data for those first 2 d were omitted from the monitoring site dataset. A technical malfunction disrupted data logging at one site during one survey, but the litter was manually tallied outside Litterati and added to the database without a timestamp or latitude and longitude information.

One site was surveyed on two separate occasions to test the hypothesis that COVID-19 stay at home orders would decrease litter accumulation. First, a year before the COVID-19 pandemic and second, during the 2020 stay-at-home orders issued by California Governor Gavin Newsome effective 19 March 2020. The activity of walking around the neighborhood (essential for conducting this study) was permitted by the stay-at-home order. The stay-at-home order continued throughout the second survey period.

Socio-geographical information was compiled for each site. Demographic data were extracted for each location by merging Census tracts with the 2015 Census tract planning database (Census 2021b). Population density per Census tract was calculated by dividing the population in the tract by the tract area. Population density in the study area was an average of 920 people km⁻², while the Inland Empire was 60 people km⁻². Monitoring sites were only comprised of urban land use areas (primarily residential and mixed commercial-residential). As a result, our sites were biased toward urban high population density regions of the Inland Empire. Road length was calculated in Google Earth by tracing the centerline of the road in the monitoring site. Road widths ranged from 10 to 25 m, and road lengths ranged from 121 to 483 m.

Litter accumulation rates (# d⁻¹ km⁻¹) were calculated for each survey by dividing the number of litter observed by the total number of days since the previous survey and by the length of the surveyed road. Litter mass accumulation (kg d⁻¹ km⁻¹) was computed by conducting a literature review to find the average mass of each litter object (Wijzer 2015, Ballatore et al 2021, Ocean Conservancy 2021). When a reasonable estimate for an object's mass was unavailable, we used the suggested 82.5 g estimate (Wijzer 2015). The mass conversion table was applied to every object (supplemental information).

Litter composition was assessed using the Trash Taxonomy. Data were merged to the most up-to-date Trash Taxonomy material, items, and brand alias, and hierarchy tables. Datasets were reconciled to the Trash Taxonomy (Hapich et al 2020) tables using Open Refine (Openine 2021) and reconcile-csv (Open Knowledge Foundation 2021). This procedure makes the data compatible with the 69 other litter surveys incorporated in the Trash Taxonomy. Litter composition was computed for each survey day by dividing the total number of pieces observed for an individual category by the total number of pieces observed.

Statistics were calculated in R with hypothesis tests determined as significant or insignificant using the significance level of 0.05.

2.4.1. Origins and transport processes

2.4.2. Litter accumulation rates

2.4.3. Litter composition

Three plausible mechanisms could transport receipts in the Inland Empire: runoff, wind, and people. The median receipt transport rate was 290 m d⁻¹, and 50% of the data fell between 147 and 1497 m d⁻¹. Although we do not estimate litter transport rates over land due to wind or runoff, we suspect the observed transport rates were much faster than those processes. Nevertheless, we rigorously tested the likelihood of wind and runoff transport. Based on the precipitation data during the receipt's potential duration in the environment, we determined that 68 out of 75 receipts (91%) did not experience any precipitation (figure 3(A)). Using Pearson's product-moment circular correlation, we compared the vector mean wind direction the receipt could have experienced to the mean transport direction (figure 3(B)). No correlation between wind direction and receipt transport direction was observed (correlation −0.1, p-value 0.357). The receipt transport distances followed a similar cumulative distribution function (log-normal) to the human trip distance distribution but were offset to smaller distances (figure 3(C)). Of the 85 receipts with sale locations, the maximum transport distance was 136 km, and the minimum was 27 m (three times as large as the estimated mean location uncertainty (7 m)) (figure 3(C)). Half of the receipts originated from less than 1.67 km away from where they were found. Linear regression fit to log₁₀ transformed data between the paired quantile values revealed that the offset between receipt and human trip distances increased by 1.1 times per unit of human trip distance and had a y-intercept of −0.9 (figure SI 1).

Zoom In Zoom Out Reset image size

Did cleanup or COVID-19 stay-at-home orders affect accumulation rates? The majority of monitoring sites had relatively stable litter accumulation rates throughout the sampling period (figure 4). The total mean annual accumulation rate estimate is 40 349 (33 255–47 865) # km⁻¹ yr⁻¹ or 1170 (917–1447) kg km⁻¹ yr⁻¹ for the survey region. Site mean litter accumulation rates varied over an order of magnitude, ranging from 0.021 to 0.25 # m d⁻¹. Most observations at sites were within half an order of magnitude of one another (figure 5). We did not find a steady drop in the litter accumulation rate at any monitoring site (figure 4). Instead, trends in litter accumulation rates seemed random throughout each survey, and none were significant (figure 4). We observed a small decrease (20%) in the mean litter accumulation rate during COVID-19 stay-at-home orders at Site 7 (0.2, 0.11–0.29 # m d⁻¹) compared to before COVID-19 (0.24, 0.21–0.26 # m d⁻¹), but the means were not significantly different (two sided t test, p = 0.42). Power analysis revealed that a Cohen's d effect size of 0.45 (small-medium effect) amounting to a shift of 37% from the current mean accumulation rate of the full monitoring site dataset (figure 5) would be required to attain a power of 0.8 and p value of 0.05 for future repeat studies.

Zoom In Zoom Out Reset image size

Zoom In Zoom Out Reset image size

The most common material types found in the litter surveys were plastic (mean proportion: 56, 53%–59%) and paper (33, 30%–35%) (figure 6(A)). Food-related items (38, 35%–42%) and tobacco-related items (14, 11%–17%) were the most abundant litter item types (figure 6(B)). Correspondingly the top four brands were all cigarette and food producers: Philip Morris (4, 2%–7%), Mars Incorporated (2, 1%–3%), RJ Reynolds (2, 1%–3%), and Jack in The Box (1, 1–3%) (figure 6(C)). The majority of objects were unbranded (66, 62%–70%), and many brands (13, 11%–15%) were identifiable but could not be merged with the Trash Taxonomy.

Zoom In Zoom Out Reset image size

We found that human trips were the primary transport mechanism of the receipts in our study sites. There is an offset between human trip distances and litter transport, which is likely due to the differences in the calculation of human trips (travel path) and receipt transport distances (as the crow flies), and the acquisition and/or deposition of litter within a given trip rather than at the trip endpoints. Receipt addresses were slightly further away than the nearest potential receipt address on average (figure S3). This result may indicate that litter item purchase occurs at convenient locations along personal and work commutes. Additionally, human trip distance distributions could be a good proxy for estimating litter transport distances using regression on paired quantiles (figure S1). Although this research focuses on receipts in the Inland Empire, we expect that these metrics apply to similar litter morphologies and sources on roadsides in the United States with similar human trip distributions, climate, and waste management practices. Future studies should assess other types of litter in different regions to better understand global litter transport and incorporate a social science component to determine the societal drivers of litter accumulation.

Most of the receipts found on roadsides in this study originated less than 1.6 km away. Local action has been proposed as a strategy to combat litter accumulation (Rochman et al 2020). If local actions in the Inland Empire targeted prevention of receipt-like litter from sale locations within 1.6 km to litter hotspots, they would likely account for half of the sources supplying the litter.

We were surprised by the persistence of litter accumulation rates despite our efforts to keep the sites clean and COVID-19 stay-at-home orders. Litter behavior studies have noted that keeping areas clean affects the aptitude of people to litter, but that effect is generally small (Schultz et al 2013). We may not have had a long enough study period to detect the effect of cleanup. Although only a small number of observations (5) were collected during stay-at-home orders, these data (and the prevalence of essential goods in the composition i.e. food items) point toward the idea that litter accumulation may be tied to essential everyday activities, which were continued during the stay-at-home order. Additionally, Seco Pon and Becherucci (2012) found that litter standing stock on roadsides in Argentina was stable throughout the seasons of the year without removing any of the litter, similar to the first observations of standing stock at Site 7, which were both around 200 pieces (figure S2). Litter accumulation may be generally balanced by litter removal at these monitoring sites. Site 4 stood out as having a significantly lower litter accumulation rate than most other sites (figure 5). Site 4 also had the lowest Cal Enviroscreen score (an index of environmental burden) of all the sites (supplemental information, OEHHA 2021). Future work could assess if any of the variables in the Cal Environscreen are strong determinants of litter accumulation.

The stability of the litter accumulation rates indicates that this method may be valid for future investigation into interventions. Since the cleanup did not impact the litter accumulation, this method would be valid to test other prevention strategies without removing the effect from the measurement itself. To further support our approach for regional analysis, the city of Riverside (central to where much of the survey monitoring occurred) estimated a litter accumulation rate of 2855 kg km⁻¹ yr⁻¹ in 2010 with street sweeping data (Riverside City 2021), while our estimate was 1170 (917–1447) kg km⁻¹ yr⁻¹ in 2018–2020, which were within a factor of two. However, future studies should randomize monitoring sites throughout the Inland Empire for a more robust regional inference. Based on power analysis, future repeat studies should expect an intervention capable of producing a shift in the mean accumulation rate greater than ±37% or collect more data to quantify a statistically robust effect at the sites.

Plastic, food items, cigarette products, and brands with high market prevalence were also known to be the most prevalent litter objects in environmental compartments around the world (Roper and Parker 2006, Muñoz-Cadena et al 2012, Ballatore et al 2021, Morales-Caselles et al 2021). In another study in Southern California, Marlboro and Jack-in-the-box were also prevalent brands on beach litter (Moore et al 2001). Solutions have been proposed to decrease single-use plastic product availability and engage corporations through corporate social responsibility initiatives (Landon-Lane 2018). The approach we have developed for measuring the uncertainty in the proportion of objects attributed to manufacturers should assist in developing effective corporate social responsibility strategies. We would like to see the most prevalent manufacturers of litter found at our sites take an active role in decreasing the abundance of their waste. Future studies could employ this monitoring technique to measure their efficacy.

Unmerged and unbranded categories were prevalent, not useful, and resulted from a limitation of our current classification capabilities for litter. Unmerged categories were not in the Trash Taxonomy (Hapich et al 2020) and should be added to the Trash Taxonomy in future work. Most objects were unbranded and therefore nearly untraceable to their producer. This discrepancy hampers corporate social responsibility initiatives for the producers of unbranded products. We advocate for improving material fingerprinting and branding policies that increase the identifiability of manufacturers who created the products (Almroth et al 2021).