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Plastic waste mapping and monitoring using geospatial approaches


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Riverine plastic pollution has received worldwide attention due to numerous challenges associates with it. This study is premised on the need to reduce plastic leakage from land-based sources into the ocean. Geospatial technology was used to model plastic leakage in Sungai Pinang, Pulau Pinang. This study proposed a citizen-based approach because the plastic waste project requires public participation. Citizen science application was used for plastic waste tracking along the river stretch. Collected data was then used to analyze plastic waste hotspot at the study area. The hotspot map shows that area has higher plastic waste at the middle of the study area which related to residential and recreational areas. Waste collection route was analyzed using the geographical information system (GIS) tool which is Network Analyst to identify the most efficient route collection from the waste point to Pulau Burung sanitary landfill. This GIS-based research method can be applied to other regions and data on the distribution can be used elsewhere. This paper shows how plastic tracking can be used to obtain information on riverine plastic pollution. Our research demonstrated that a combination of citizen science and a GIS technique can be utilized to improve public participation in raising awareness of marine plastic pollution.
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IOP Conference Series: Earth and Environmental Science
Plastic waste mapping and monitoring using
geospatial approaches
To cite this article: A.N. Zulkifli et al 2022 IOP Conf. Ser.: Earth Environ. Sci. 1064 012008
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11TH IGRSM 2022
IOP Conf. Series: Earth and Environmental Science 1064 (2022) 012008
IOP Publishing
Plastic waste mapping and monitoring using geospatial
A.N. Zulkifli1, H. Z. M. Shafri1, K. Hirose2, N. M. Noor3, K. N. A. Maulud4, M.
Z. Asmawi3, L. Mokraoui5 and Y. Ang1
1 Department of Civil Engineering and Geospatial Information Science Research
Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400
UPM Serdang, Selangor, Malaysia
2 Japan Space Systems, The Kikai Shinko Kaikan building 3F, 3-5-8 Shibakoen,
Minato-ku, Tokyo 105-0011 Japan.
3 Kulliyyah of Architecture & Environmental Design, International Islamic
University Malaysia (IIUM), Gombak Campus, Gombak 53100, Selangor
4 Earth Observation Centre, Institute for Climate Change, Universiti Kebangsaan
Malaysia (UKM), Bangi 43600, Selangor, Malaysia
5 GIS Innovation Sdn Bhd, E-6-17, Sunway Geo Avenue, 2, Jalan Lagoon Selatan,
Bandar Sunway, 47500 Subang Jaya, Selangor.
Abstract. Riverine plastic pollution has received worldwide attention due to numerous
challenges associates with it. This study is premised on the need to reduce plastic leakage from
land-based sources into the ocean. Geospatial technology was used to model plastic leakage in
Sungai Pinang, Pulau Pinang. This study proposed a citizen-based approach because the plastic
waste project requires public participation. Citizen science application was used for plastic waste
tracking along the river stretch. Collected data was then used to analyze plastic waste hotspot at
the study area. The hotspot map shows that area has higher plastic waste at the middle of the
study area which related to residential and recreational areas. Waste collection route was
analyzed using the geographical information system (GIS) tool which is Network Analyst to
identify the most efficient route collection from the waste point to Pulau Burung sanitary landfill.
This GIS-based research method can be applied to other regions and data on the distribution can
be used elsewhere. This paper shows how plastic tracking can be used to obtain information on
riverine plastic pollution. Our research demonstrated that a combination of citizen science and a
GIS technique can be utilized to improve public participation in raising awareness of marine
plastic pollution.
Keywords: Riverine plastic pollution, citizen science, GIS, hotspot, network analyst
1. Introduction
Plastic pollution is one of today’s most outstanding environmental issues. Plastic is durable and
inexpensive to produce; thus, it’s not surprising that plastic is ubiquitously integrated into daily lives.
300 million tons of plastic is produced every year, half of which is used for single-use items [1]. Every
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year, society uses an increasing number of plastic materials and many of them are only used once or
only used for a short period. Plastic has been detected on the shorelines, with more being found near
popular tourist destinations and densely populated areas. Many low disposals of plastic waste find their
way into the natural environment and become widely dispersed into rivers, oceans and coastal regions.
Plastic carried by water flows down to the sea, allowing plastic waste to enter the oceans and pollute the
Globally, plastic contamination has reached an unprecedented level, with significant implications for
marine life. Malaysia ranks first highest annual per capita plastic utilization rate of 16.78kg per person
and second highest in total waste production in terms of plastic waste compared to China, Indonesia,
Philippine, Thailand and Vietnam [2]. To decide the design cleaning system, cost of clean-up and recycle
methods, it is essential to know the amount and type of plastic accumulated in the ocean. Plastic will
have a significant impact on the economy once it dumped into the ocean. Marine wildlife that harmed
by ingestion or entanglements of plastics affects the health of the ecosystem. They also suffer from
injuries, infections, reduce the ability to swim and internal injuries. Every year, thousands of seabirds,
sea turtles, seals and other marine mammals are killed after ingested or entangled in plastic [3].
According to the United States environmental protection agency, the massive aquatic debris has been
impacted at least 267 species globally, including 86% of sea turtles, 44% of seabirds, and 43% of marine
mammals. This effect occurred as a result of both physical hazards from ingestion and entanglement,
and also toxicological threats from pollutants adhering to and trapped within plastic particles. The risk
of contaminating the entire marine food web will occur if could not overcome plastic pollution in the
ocean. Floating plastics also contribute to the spread of aggressive marine organisms and bacteria that
disrupt the ecology. That massive amount of plastic will forever be embedded in the ocean’s wildlife.
Ninety-nine million tons of uncontrolled plastic waste would end up in the environment by 2030 if no
improvement in waste management [4].
Facilitating higher collection and recycling rates would allow plastics to capture before it starts to
create problems in the natural environment. The challenge is to optimize waste collection and
transportation. One of the most promising approaches is the use of geographic information systems
(GIS). It is one of the most recent innovations that has made a major contribution to the waste
management society in a much shorter period of time [5]. There is an urgent need for geospatial
information to mitigate and raise awareness about plastic footprint as 90% of plastic waste entering the
ocean is estimated to come from rivers [6]. Nowadays, millions of people are equipped with incredibly
accurate geospatial data collectors that can be used to map and retrieve geospatial data on plastic
pollution and to some extent on the litter caused by the products and economic activity of a handful of
the global corporation. The origins and subsequent routes for plastic leakage to the ocean required
understanding to curb plastic waste leakage from the ground. Therefore, a GIS based plastic leakage
concentration investigation is necessary to identify areas that need to minimize plastic leakage. In
addition to efficient data collection, citizen science is a potentially applicable approach in obtaining this
data that suit within the existing observation framework.
This study aims to improve waste management in an urban area at the catchment area to prevent
plastic waste emissions and leakage to the marine environment in the urban area. The objectives have
been listed below:
1) To measure observe plastic waste at the study area using citizen science application.
2) To determine the hotspot location of plastic waste deposits in the study area.
3) To identify the efficient collection route for plastic waste in the study area.
2. Study area
The study area was chosen based on pollution condition and its current plastic waste at the catchment
area in the urban area. Sungai Pinang is one of Malaysia’s most polluted river located at the northeast
of Penang Island (5°24'N 100°19'E). The river’s stretch is approximately 3.1km and its will be observe
from two sides. Although the length of the rivers is only 3.1km, it was full of garbage caused by human
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activities along the river. The river basin flows through the densely populated and highly developed area
of Georgetown, Penang’s capital city. The river’s water quality can be classified as class IV in
accordance with the Interim National Water Quality Standard for Malaysia which is considered to be
“very polluted” based on the analysis of water quality conducted by the Department of Drainage and
Irrigation (DID). 88% of garbage collected along the Sungai Pinang was recyclable waste which is
plastic, paper and rubber.
Figure 1. Location of Sungai Pinang.
3. Methodology
3.1 Data collection
To track the location and magnitude of plastic waste, data were collected using the citizen science app
namely Locus map, which includes a manual to track plastic waste data using smartphone apps during
the field survey.
Figure 2. Process of data collection.
There were three steps (figure 2) involved in this process which is find plastic waste, take photo and
lastly input dimension. For the first step, the mobile application was opened and plastic tracking was
start around the target area. The next step, a measurement scale was placed near the plastic waste and
photo point was added using the locus map application. The camera application has permission to access
location services. Plastic waste photo can be simply taken without a scale if the plastic waste is
unreachable or far. Lastly, the total height, width and depth of the plastic waste were measured and the
dimension of the plastic waste was input and saved in the description.
3.2 Hotspot map analysis
The plastic hotspot maps were created in several steps, which are briefly summarized below. The exact
steps taken to process the data will vary depending on the options selected, but will follow the same
general procedures as described further. Each data point was recorded with altitude and longitude, as
well as date and time, providing the necessary information for creating the plastic hotspot maps.
3.2.1 Point mapping. Point mapping has become obsolete as GIS software has proliferated and mapping
techniques has become more sophisticated. To import csv file, “Add Delimited Text Layer” was selected
and file that needs to be imported was chosen. The file format selected must be comma separated value
(CSV). Next, the X and Y fields were representing the longitude and latitude respectively with
Open Locus map application
and start tracking. Walk around
study area and find plastic
Place a scale next to the plastic
waste or just take a photo
without a scale if the plastic
waste is unreachable
Measure total height, width and
depth of the plastic waste and
input in the description and
save the data taken.
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EPSG:4326-WGS 84 as the coordinate system. Lastly, the layer was saved as an ESRI shapefile and the
new layer of the data collected was represented.
3.2.2 Kernel density estimation (KDE) map. Heatmap is a useful visualization tool for displaying event
density or occurrence. A heatmap also used in point clustering, where more points in an area have a
higher value than fewer points in the same area. As a result, a concentration of event occurrences can
be seen using heatmap. QGIS is an opensource GIS software that can be used to generate a heatmap
from a set of data points using the Heatmap Plugin. For creating a heatmap, the plugin employs the KDE
algorithm. The color ramp parameter specifies the circular neighborhood around each point in which
that point will have an effect. This value is heavily influenced by the nature of the input data. For this
study, it is assumed that the plastic waste will have an impact up to 5 kilometers from the location.
Another parameter is the kernel shape, which is the function that determines how a point's influences
should be spread out over a given radius. For this calculation, the Heatmap renderer employs the Quartic
3.2.3 Standard deviation ellipse map. Standard deviational ellipse has long been used as a versatile GIS
tool for defining the geographic distribution of relevant features. There are two methods for producing
a standard deviational ellipse, each with variation. The default method is the one proposed by Robert
Yuill [7]. This method does not account for degrees of freedom and does not yield a radius equal to the
standard distance deviation for a random point distribution. The other methods for producing a standard
deviational ellipse is a CrimeStat [8] method, which includes correction for degrees of freedom. A
polygon layer with the standard deviational ellipse is generated with the following attributes: meanx,
meany, majorsd, majorangle, direction and eccentrici. Attributes for weight can be selected. In this
study, the standard deviational ellipse map was used for determining the hotspot location of the study
area from the center of the study area.
3.3 Efficient route collection map analysis
The collection or transport route of the waste is intended to determine the effectiveness of the system
and its costs. The operation involves the removal and transfer of waste from the assembly point to the
landfill. The waste was to be sent to the one available landfill in Pulau Pinang, namely Pulau Burung
Sanitary Landfill Located at Nibong Tebal. The challenge is to achieve optimal waste collection and
transportation operation. The use of spatial modelling tools and GIS for collection and transportation
optimization can bring economic and environmental benefits by reducing travel time, distance, fuel
consumption and emission of pollutants.
3.3.1 Dustbin placement. The distance between two bins should not exceed 500 meters. The distance
between the bins can be calculated based on the amount of garbage that is likely to be received at the
container from the affected area. However, the preferred walking distance for depositing municipal solid
waste into the collection bin is within 75 meters. Therefore, bigger bins are located at a distance of 500
meters between bins at residential, industrial and areas while smaller bins are placed at a distance of 75
meters between bins at recreational area.
Table 1. Size and distance between bins.
Size of bin (L)
Distance between bins (m)
3.3.2 Network analysis. The Network Analysis tools have been incorporated into the GIS software and
can be accessed by activating the Processing Toolbox. These features allow to compute the lowest cost
paths and service areas based on the network data. The start and end points of the path must be selected
After determining the points, the shortest option was selected in the path type to calculate the route. This
means that the cost represented the distance between the two locations in this case. The shortest route
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was computed. Next, the parameter tab was returned and the Path type was changed to calculate the
option to the fastest path to travel. The new route will calculate the time taken to trace between the two
4. Results and Discussion
4.1 Data collection
This study proposed a simple method for plastic monitoring and sampling, based on citizen science data
collection. Data obtained using the strategy presented were used to quantify plastic distribution at Sungai
Pinang and identify hotspot for future plan. The Locus map mobile application have been developed to
help citizen science collect plastic data in urban and natural water system. This app can be used to map
plastic hotspots in urban water systems over time. Figure 3 (a) below shows the data of plastic waste
collected using mobile citizen science application, namely Locus map and Figure 3 (b) depicts some of
plastic waste photo that have been gathered along Sungai Pinang. The geotag photos consist of
coordinates, date and time of the photo.
Figure 3 (a) Plastic waste data collection using Locus map application. (b). Plastic waste at Sungai
4.2 Hotspot location
Analysis of where plastic waste accumulated is the key component to tackle plastic waste problem.
Identifying hotspots is the first step in determining which areas have high levels of plastic waste for
future planning. Using historical data to guide future actions, hotspot mapping can help anticipate the
amount of plastic garbage that will be generated in the following round. It tends to congregate in specific
areas for reasons related to urbanisation and population density. In this way, it functions as a
fundamental strategy for predicting where plastic will occur, based on the assumption that past patterns
of plastic waste are a useful indicator for future patterns. Hotspot geographic locations “of high plastic
concentration, relative to the distribution of plastic waste across the entire region of interest” are often
used to describe the concentration or clusters of plastic waste.
There are variety of mapping approaches that may be used to discover and investigate plastic waste
patterns. These methods could be as simple as representing each plastic waste as a point and observing
its geographic distribution. Many of these mapping techniques have been subjected to a number of
studies that looked into their applicability for hotspot mapping. These reviews, on the other hand, have
been little more that visual comparisons of each method or activities evaluating their usability.
Importantly, these studies have shown that different techniques of hotspot mapping can give varied
outcomes, but none of them have indicated which technique is the best for predicting future plastic waste
patterns. Considering this, it would be beneficial to determine whether there are any differences in the
efficacy techniques of hotspot mapping to forecast plastic waste patterns. This will aid in selecting the
most appropriate hotspot mapping technique in predicting future plastic waste patterns.
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Figure 4. Hotspot mapping (a) Point mapping, (b) KDE, (c) Standard deviational ellipse (Yuill
method) and (d) Standard deviational ellipse (CrimeStat method).
Figure 4 depicts the hotspot maps generated for each of these techniques for plastic waste data at
Sungai Pinang. Point mapping has become outdated since the ubiquity of GIS software and the rising
sophistication of mapping techniques. Point mapping cannot directly identify hotspot location. It can
only be assumed by observing the spatial pattern of the plastic waste. However, the assumption could
be incorrect as it does not consider other parameter to identify the hotspot location such as the dimension
or volume of the plastic waste. The plastic waste is assumed high at the south east to the middle of the
river stretch by observing the point mapping.
The KDE map revealed the clear hotspot at the study area. The volume of the plastic waste was set
as the weightage to identify hotspot location using Kernel density estimation. The hotspot was found
approximately in the middle of the river stretch. The hotspot is observed to be related to the residential
and recreational areas along the river stretch. In Sungai Pinang, fewer plastic hotspots were discovered
at the factory and forest area. Nevertheless, high volume of plastic waste was discovered at the south
east and north west of the river. Wind speed and Direction may also have an impact on the distribution
of hotspots. However, more investigation is needed to investigate this further.
The ellipse is known as standard deviational ellipse because of the method calculates the standard
deviation of the coordinates from the mean center to define the axes of the ellipse. The ellipse shows if
the feature distribution is elongated and thus has a specific orientation. A standard deviational ellipse is
an ellipse that surrounds 68 percent or at least 95 percent of a point distribution. There are two methods
for calculating a standard deviational ellipse which is Yuill method and CrimeStat method. Standard
deviation can help to understand the spread or dispersion of data. Mapping the trend for a set of
distributional data may reveal a link to specific features. It also used to describe the dispersion of a point
distribution. A mean center is found in the middle of the data distribution. The pink color point at the
Yuill and CrimeStat method of standard deviational ellipse is the mean center of the point distribution.
The Yuill method does not correct the degree of freedom. The yellow oval shape (Figure 5c) shows
the standard deviational ellipse which does not involve the correction of degree of freedom meanwhile
the red oval shape shows the standard deviational ellipse which involve the correction of degree of
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IOP Publishing
freedom. The mean center of the data distribution is always found in the middle of the standard
deviational ellipse. The standard deviational ellipse which had the freedom correction encircle a lot of
points compared to the Yuill method which did not involve the freedom correction. The CrimeStat
method automatically have the correction of the degree of freedom for calculating the standard
deviation. The figure shows that each technique identified similar areas, but in term of predicting future
spatial patterns of plastic waste, KDE was better at defining hotspots and predicting where future plastic
waste may occur. KDE is quickly becoming the technique of choice for those who generate hotspot map
due to its ability to outperformed others in accurately identifying the location, size, orientation and
spatial distribution of the underlying point data, as well as the visual appeal of the output that it produces.
4.3 Efficient route collection map
One of the most common applications for GIS is calculating the shortest distance between two points.
Collection and transportation are the critical components of solid waste management. This study deals
with optimized path for solid waste collection using GIS. A route is provided using the QGIS software
that connects all of the dustbin locations that has been generated using Google Earth references. The
most efficient and cost-effective route was chosen from among the various alternatives.
Figure 5. Location of the dustbins.
The collection and transportation process accounts for roughly 60 percent to 80 percent of the total
cost of waste management [9]. Inefficient of waste collection and transport will have a significant impact
on management companies by increasing operational cost. Routes should be planned to avoid traffic
jams or acoustic impacts, as well as to reduce fuel costs and CO2 emissions. Any reduction in distance
between collection points may be appealing as it reduces collection time, cost and air pollution emission.
The effective routing of collection trucks is for ensuring improved performance in waste collection.
Routing in waste collection refers to the scheduling and defining of routes for trucks to follow during
the collection process. Typically, there is no systematic or well-organized method for scheduling waste
collection vehicles or trucks. This is usually based on practical experience and intuitive methods, which
results in inefficient and costly practices that have negative consequences for business operations, public
health and the environment. Figure 6 (a) and (b) depicts the shortest and fastest path of waste collection.
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Figure 6 (a) Shortest path of waste collection; (b) Fastest path of waste collection.
Table 2. Shortest route.
Table 3. Fastest route.
Solving a route analysis mean finding the quickest, shortest or even most scenic route depending on
the impedance. If time is an impediment, the best route is the quickest route while the shortest route at
a given time of day and date is the best if the impedance is a time attribute with real-time or historical
traffic. Therefore, the best route is the one with the lowest impedance or lowest cost. The collection
times for the shortest and fastest routes from Sungai Pinang to the Pulau Burung Sanitary Landfill were
2.355 and 1.366 hours respectively with the total distance of 57.854 km for the shortest route while
67.621 km is the distance for the fastest route. The results showed that the shortest path is not necessarily
having the shortest time to travel. The distinction between shortest and fastest route is that shortest route
takes the shortest path between the collection points and reduces travel distance whereas fastest route
takes the fastest path and reduces travel time. As a result, it is more likely to choose a highway with a
higher speed limit over a shorter route with slower speed limits because the time spent on the highway
is shorter.
5. Conclusion and recommendation
The development of GIS and its use throughout the world has contributed a lot in improving waste
management systems. GIS can add value to waste management applications by providing outputs for
decision support and analysis in a wide spectrum of projects such as route planning for waste collection,
site selection exercises for transfer stations, landfills or waste collection points. GIS provides a flexible
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IOP Publishing
platform which integrates and analyses maps and waste management databases. Using QGIS, the most
efficient route has been determined. The collector may use their preferred route either the fastest route
or shortest route. Indicator such as truck travel distances, scheduling and routing are used to assess the
performance of solid waste collection systems. Current GIS approaches have relatively limited street
and traffic constraints, making them difficult to adapt and apply in other jurisdictions. Recommendations
for future research and improved efficiency include a holistic approach to routing problem that considers
street network impedances, constraints and environmental conservation. Routes of waste collection is
good to review every one to two years, depending on the growth of the service area and annexations.
Therefore, it will be aware when the routes have changed to the point where they are no longer efficient.
The use of GIS has made it simple to check on route times and ensure that they are longer due to service.
For effective waste collection and transportation, systematic routing should consider cost savings and
environmental preservation.
The authors wish to acknowledge the support from the Japan Space Systems (JSS) with code grant ZF-
2020-005 in this project. The authors also would like to express sincere gratitude to the reviewers for
providing valuable feedback.
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OpenLitterMap rewards users with Littercoin for producing open data on litter. Open data on the geospatial characteristics of litter provide means of invoking and evaluating responses to plastic pollution. OpenLitterMap currently works as a web app on all devices with native mobile apps in development. The stack includes the integration of the Laravel PHP Framework on the backend; Vue for frontend reactivity; NativeScript-Vue for mobile apps; Bulma for CSS; Leaflet for web-mapping; Turf.js for geospatial analysis; the Ethereum Blockchain for tokenization; Stripe; ChartJS; AWS; and more. Anywhere from a single cigarette butt to the contents of an entire beach or street clean can be logged in a single geotagged photo. Alternatively, a simple index may be used if litter is incalculable. The open data includes an increasing 100+ pre-defined types of litter; 20+ corporate brands; verification status; coordinates; timestamp; phone model; the latest OpenStreetMap address at each location; and the litter presence as a Boolean. To date, 100% of all submitted data (~ 8200 photos, ~ 28,000 litter from over 150 contributors) has been manually verified which is being used to develop machine learning algorithms.
It is well known that Asia generates and receives large quantities of plastic waste. Through a detailed study of plastic waste generation and trade, the management and treatment of plastic waste in Asia were analyzed from the regional perspective. The amounts of plastic waste in municipal solid waste and in industrial solid waste were estimated to be 79 Mt and 42 Mt, respectively, in Asia. The overall treatment and recycling status in Asia are unsatisfactory. Asia imported 74% of the plastic waste in the world in 2016, and China (mainland) imported the most plastic waste until 2017, with 5.8 to 8.3 Mt. In 2017, about half the plastic waste imported by Asia came from other regions, and after subtracting the exported quantity, 98% of the plastic waste was left in Asia for treatment and disposal. The plastic waste imported by Asia declined about 72% in monetary value in 2018. There is still a large gap between the plastic waste quantity imported to Asia and that exported from Asia. China’s ban of plastic waste imports caused import quantities to drop to 52 kt in 2018, simultaneously, exports from the largest exporting countries or regions such as Hong Kong (China), the USA, Japan, and Germany decreased. While Vietnam, Malaysia and some other Asian countries and regions saw significant increases in plastic waste imports from 2016 to 2018. Considering this situation, countries in Asia are starting to strictly limit plastic waste imports from other countries.
On some measures, the global governance of plastic is improving. Curbside recycling and community cleanups are increasing. Companies like Toyota, Walmart, and Procter & Gamble are reducing waste to landfill. And all around the world, as research consolidates and activism intensifies, towns, cities, and legislatures are banning some uses of plastic, such as for grocery bags and as microbeads in consumer products. Yet the amount of plastic flowing into the oceans is on track to double from 2010 to 2025. Why? Partly, the dispersal, durability, and mobility of microplastics make governance extremely hard. At the same time, the difficulty of governing plastic has been rising as production accelerates, consumption globalizes, pollution sources diversify, and international trade obscures responsibility. As pressures and complexities mount, the global governance of plastic – characterized by fragmented authority, weak international institutions, uneven regulations, uncoordinated policies, and business-oriented solutions – is failing to rein in marine plastic pollution. In large part, as this article demonstrates, this governance landscape reflects industry efforts to resist government regulation, deflect accountability, and thwart critics, coupled with industry advocacy of corporate self-regulation and consumer responsibility as principles of governance. These findings confirm the need for more hard-hitting domestic regulation of industry as well as an international plastics treaty to scale up local reforms. This article is available for downloading at