A local-to-global emissions inventory of macroplastic pollution

doi:10.21203/rs.3.rs-3376960/v1

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A local-to-global emissions inventory of macroplastic pollution

https://doi.org/10.21203/rs.3.rs-3376960/v1

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Negotiations for a Global Treaty on plastic pollution¹ will shape policy on plastics production, use, and waste management for the coming decades. Parties will require a detailed baseline of waste flows and plastic emission sources at high resolution to enable identification of pollution hotspots and their causes². Nationally aggregated waste management data can be distributed to smaller scale to identify generalised points of plastic accumulation and source phenomena^3-11. However, it is challenging to use this type of spatial allocation to assess the conditions under which emissions take place^12,13. To this, we developed a novel methodology for a global macroplastic waste inventory; creating an explanatory framework that combines conceptual modelling of emission mechanisms with measurable activity data. Using machine learning and probabilistic material flow analysis we identify hotspots worldwide (50,072 municipalities) from five land-based plastic waste emission sources. We estimate global plastic waste emissions at 52.5 million metric tonnes (Mt), of which 57% wt. are open burned. Highest contributions: India (9.7 Mt), Nigeria (3.5 Mt), and Indonesia (3.0 Mt). Uncollected waste is the dominant emissions source across Global South. This detailed evidence baseline can inform Treaty negotiations and help develop national and sub-national waste management action plans and source inventories.

Physical sciences/Engineering

Earth and environmental sciences/Environmental sciences

Plastic pollution is a global challenge requiring immediate action due to its environmental persistence and negative impact on ecosystems¹⁴, infrastructure¹⁵, society and the economy¹⁶. The importance of this burgeoning issue has recently been recognised by the ratification of a United Nations draft resolution to create a legally binding global treaty to end plastic pollution ¹, hereafter the ‘Plastics Treaty’. A global plastic pollution emission inventory has been suggested as critical to the success of The Plastics Treaty¹⁷ and such inventories have already been applied in climate change¹⁸ and air quality^19-21 fields.

Previous efforts to model global plastic waste emissions and movement through environment have demonstrated the scale of the issue, highlighting large macroplastic emissions from countries with extensive coastlines, large populations, and insufficient waste management^3-11. Yet, there is a growing understanding that a much higher (sub-national) resolution is required, which identifies plastic pollution hotspots and accounts for specific local solid waste management, behavioural, cultural, and socio-economic conditions^12,17. We believe that the very concept of ‘emissions’ also requires clarification, due to the complexity of the phenomena (see Methods and Extended Data Fig. 1). We use it here for clarity, rather than the loosely defined terms of ‘leakage’ and ‘mismanaged waste’ described elsewhere²², and we deliberately avoid the term ‘release’ suggested by UNECE²³ which could imply deliberate activity.

Mapping and quantification of plastic waste material flows is hindered by the lack of sufficiently detailed and up-to-date records of waste management practices and quantities at a local level²⁴ which prevents the complete assessment of emissions from human systems²⁵. Though coordinated work is underway to remedy this data paucity²⁴, a measurable baseline is urgently required to inform Plastics Treaty obligations¹⁷. As with greenhouse gas¹⁸ or air quality²⁶ emissions inventories, this baseline would enable a more rational distribution of overseas development assistance (ODA); empower policymakers with scarce resources to develop evidence-based specialised national and sub-national strategies, action plans and targets²⁵; and create a strong evidential basis for the reorganisation of material systems which have been the focus of the Treaty proposals²⁷ and negotiations²⁸. Therefore, we created a macroplastic emission inventory using a novel methodology to quantify emissions for 50,702 municipality level administrations from five land-based sources: (1) uncollected waste; (2) littering; (3) collection system; (4) uncontrolled disposal; and (5) rejects from sorting and reprocessing (Fig. 1). Unmeasured data were predicted using machine learning and flows were mapped using probabilistic material flow analysis (MFA) for the year 2020. See methods section and Supplementary Information for detailed methodology.

We estimate that 52.5 million metric tonnes (Mt) (50.7-54.5 Mt) of macroplastic waste were emitted into the unmanaged system in 2020, representing 20% (wt.) of all the municipal plastic waste generated (263 Mt) on earth. Approximately 43% (wt.) (23 Mt) was unburned ‘debris’, meaning it is no longer subject to any form of management or direct control, and is at risk of transport across land and into the aquatic environment. Though our model output variables are not fully aligned with other plastic pollution models, our debris emission estimate is approximately four times more than Ryberg, et al.¹¹ (5.9 Mt, CI: 2.0-20.4 Mt) for 2015 and 22% less than the combined mass of ‘terrestrial’ and ‘aquatic’ pollution (29 Mt 95% CI: 22-39 Mt) reported Lau, et al.⁹ for 2016.

Five explanations are suggested for this incongruence with other models: 1) In the present model, we used a bottom-up approach, rather than regional and archetypal averages distributed to finer resolution (top-down approach); 2) Our finer resolution accounts for spatial heterogeneity in sub-national waste management data; 3) We modelled emissions from five separate downstream sources, rather than the single homogenous source used in other models^3-8,29 - ‘mismanaged (plastic) waste’²² - an umbrella term that encompasses an array of insufficiencies in waste management¹²; 4) Our definition of ‘emission’ includes waste which is transported from ‘dumpsites’ ²⁴ (defined in Methods), but excludes that retained within them as it is mostly buried³⁰ and poses a low risk of being blown or washed into the unmanaged system³¹. Only the ‘working face’ of these sites contains material at risk of transmission through the action of wind and surface water runoff ³² (Section S.8.9). Conversely, it is self-evident that waste that is uncollected, scattered on land, or accumulated in smaller ‘informal dumps’ has a much higher probability of being mobilised and transported across the terrestrial surface and into the aquatic environment; and 5) We account for the open burning of waste (Section S.8.11) which is not specifically considered in most plastic pollution models^3-8,11,29 and which our results indicate contributes to 57% (30.0 Mt) of all plastic waste emitted, resulting in widespread risk to human health and the environment³³.

It is possible that these important and fundamental differences in definitions may, at least in part, explain the mismatch between the previously estimated mass of plastic inputs to oceans³⁴ and the mass of floating marine plastic³⁵. Nevertheless, while the absolute mass of emissions reported in this work is lower than some previous estimates, it still represents many trillions of emitted plastic items (Section S.11), with the potential to cause harm to individual organisms, habitats and ecosystems.

On an absolute basis, we find that plastic pollution emissions are highest across countries in South and Southeast Asia (Fig. 2A and Fig. 2B), with the largest amount (9.3 Mt) emitted by India, equivalent to one fifth of global emissions. In contrast to previous plastic pollution models which positioned China as the world’s highest plastic polluter^5,8, it is ranked fourth in our results, with emissions of 2.8 Mt; less than Nigeria (3.9 Mt) and Indonesia (3.3 Mt). This lower contribution to plastic emissions from China reflects our use of more up-to-date data³⁶ that shows its substantial progress in adopting waste incineration and controlled landfill³⁷. Conversely, India reports that its dumpsites (uncontrolled land disposal) outnumber sanitary landfills by 10:1³⁸, and despite the claim that there is a national collection coverage of 95%, there is evidence that official statistics do not include rural areas; open burning of uncollected waste; or waste recycled by the informal sector³⁹. This means India’s official waste generation data (~0.12 kg·cap^-1·d^-1) is likely to be underestimated and waste collection overestimated. Our model corrects for flows missing in officially reported statistics, resulting in a waste generation rate for India of 0.55 kg·cap^-1·d^-1, which is similar to, and between other comparable estimates^39-41.

Our data for India indicates a collection coverage of 81%, meaning nearly 55% of the country’s plastic waste emissions (31% wt. debris and 24% wt. open burning) come from the 260 million people (19% of the population) whose waste is uncollected. Most of the remaining emissions (37% wt.) are as a result of open burning on dumpsites, where fires are reported to be common³⁹. Overall, we estimate that 56.6 Mt (51.6-62.7 Mt) of municipal solid waste is open burned in India, of which 5.8 Mt (5.0-6.8 Mt) is plastic. This is within the lower end of the ranges modelled by Chaudhary, et al.³⁹ 74.0 Mt (30–92) and Sharma, et al.⁴⁰ 68 Mt (45-105).

Open burning rather than intact items (debris) is the predominant emission type across all United Nations sub-regions except for sub-Saharan Africa, where debris emissions (7.6 Mt) are slightly higher than open burning emissions (6.0 Mt) (Fig. 2D). This result is driven by census and survey activity data from 44 countries that indicate lower levels of open burning in the rural areas of low-income countries (LICs), of which there are many in the sub-Saharan Africa region (Fig. S24D and F).

Approximately 69% (36.1 Mt) of the world’s plastic waste emissions come from 20 countries, of which four are LICs, nine are LMCs, and seven are UMCs. Despite high-income countries (HICs) having higher plastic waste generation rates (0.17 kg·cap^-1·d^-1), none are ranked in the top 80 polluters, because most have 100% collection coverage and controlled disposal. Additionally, our modelling accounts for the mitigating impact of street sweeping activity on emissions, which is greater in HICs. We acknowledge that we may have underestimated plastic waste emissions from some HICs because we deliberately excluded plastic waste exports from our analysis. As explained in Section S2, plastic waste exports from OECD countries to the Global South have plummeted to less than 1.3 Mt·y^-1(42), contributing approximately 0.03 Mt·y^-1 of emissions. Although this might affect some individual country results, the overall effect would be negligible in comparison to other sources.

Countries in low- and middle-income groups have much lower plastic waste generation (0.04-0.10 kg·cap^-1·d^-1), however in contrast to HICs, a large proportion of it is either uncollected (11-56% wt.) or disposed of in dumpsites (19-55% wt.). The nine countries which make up the Southern Asia region emit a similar order of magnitude of plastic waste (15.2 Mt) as the 51 countries in Sub-Saharan Africa (13.6 Mt) (Fig. 2C-E), with Nigeria contributing to approximately one third (3.9 Mt) of the Sub-Saharan African burden. Urban areas (cities, towns, and semi-densely populated areas) account for most emissions in all regions (Fig. 2C) because of low rural populations (Section S.7.1) and much lower plastic waste generation. However, we acknowledge that significant data gaps on solid waste management in rural communities exist and future efforts to address plastic pollution must include these often overlooked communities⁴³.

Flexible plastic debris has a higher probability of being emitted into the environment in the Global South compared to rigid debris (ratio 55:45), driven by its propensity for mobilisation under the action of wind and surface water (Fig. 2E). In the Global North (for example, North America) the opposite is true (ratio 33:67), because rigid plastics are more prevalent in the waste, and because emissions are driven by littering rather than meteorological forcing.

The contrast between absolute plastic waste emissions from the Global North and the Global South is stark. However, on a per capita basis, insufficiencies in local and national waste management systems are more apparent (Fig. 3A and Extended Data Fig. 2-5). For example, China, the world’s fourth largest absolute emitter, is one the least polluting UMCs (Fig. 3C), ranked 156 on a per capita basis (1.96 kg·cap^-1·y^-1), and India, the world’s largest absolute emitter is ranked 125 on a per capita basis (6.69 kg·cap^-1·y^-1). Conversely, Russia, the world’s fifth largest emitter on an absolute basis, also has some of the highest emissions of all the UMCs on a per capita basis (11.73 kg·cap^-1·y^-1), because despite its comparatively modest population in that income group, it is reported to have very low levels of controlled disposal^45,46. Many countries in Sub-Saharan Africa which show low absolute plastic emissions are hotspots on a per capita basis (Extended Data Fig. 4). Given the anticipated population boom in the region⁴⁷, it is conceivable that with an average emission rate of 12.26 kg·cap^-1·y^-1, sub-Saharan Africa will become the world’s largest absolute source of plastic pollution within the next few decades.

Municipal scale probability distributions indicate substantial uncertainty within municipalities for some of our model outputs (Fig. 3B). For example, plastic emissions for Agra (India), range from 0.16-33.89 kg·cap^-1·y^-1 (median 4.23 kg·cap^-1·y^-1) and Maracaibo (Venezuela), ranges from 0.03-16.43 kg·cap^-1·y^-1 (median 0.34 kg·cap^-1·y^-1). The large ranges within many municipalities and relatively high kurtosis, for example Shenzhen (45.3) and Maracaibo (18.2) are a consequence of our conservative application of probability density functions for many of the model’s input data which have propagated through to the results.

Despite the wide uncertainty within each municipality, there are very large differences between many of them, enough to differentiate most challenging locations from the least (Fig. 3B). For example, median plastic emissions for Hamburg (Germany) are estimated at 0.03 kg·cap^-1·y^-1(0.003-0.132) compared to Mogadishu (Somalia) that has almost 600 times more (13.78 kg·cap^-1·y^-1, range 0.72-72.52). Such large differences demonstrate that significant reductions in plastic emissions are feasible, reiterating the importance of measuring sound solid waste management activity data. Ongoing efforts to gather more reliable municipal-scale information²⁴ for SDG 11.6.1 will gradually improve the accuracy of our model; however, much more comprehensive measurement and monitoring is required to improve accuracy of flows that are rarely measured, and which have been populated here using our conceptual sub-models.

Uncollected waste is the largest contributor to plastic pollution in the Global South, accounting for 69% (36.1 Mt) of all plastic waste emissions and 84% (18.9 Mt) of all debris emissions. On a per capita basis, uncollected waste represents 69%, 67% and 80% of emissions in UMCs, LMCs and LICs respectively (Fig. 4B). Approximately 57% (20.4 Mt) of emissions from uncollected waste come from LMCs where the mean collection coverage is 74% (Fig. 4A). Uncollected waste in LMCs accounts for 39% of total global emissions, and 51% (11.6 Mt) of debris emissions. As far as we are aware, none of the other global plastic pollution models^3-8,11,29 has explicitly highlighted uncollected waste as the main source of plastic pollution, instead grouping it within the ‘mismanaged waste’ category or in one case⁹, together with disposal site debris emissions. Here, we show that plastic waste emissions from uncontrolled land disposal sites (dumpsites), whilst important, contribute 24% (12.7 Mt) of total plastic waste emissions, of which 97% (wt.) is open burned. This means that just 0.3 Mt is emitted from land disposal sites as debris, substantially less than has previously been inferred⁹.

In HICs, where collection coverage is nearly 100%, uncollected waste is the source of 14% (0.03 Mt) of plastic waste emissions, just 0.07% of the Global emissions burden. Instead, littering (defined in Table S3)is the largest source of debris emissions in HICs, accounting for 32% (0.08 Mt) of all plastic pollution in the Global North; 30% of per capita emissions (0.07 kg·cap·y^-1) (Fig. 4B). Of this, 0.3 Mt takes place in Northern America and 0.3 Mt in the Rest of Europe region (Fig. 4C); representing 0.09 kg·cap·y^-1and 0.07 kg·cap·y^-1respectively (Fig. 4D). The behavioural nature of littering⁴⁸ contrasts with the underlying drivers of other emission sources, especially those in the Global South. This is because, though littering is negatively correlated with waste receptacle provision⁴⁹, it is largely driven by the decisions of individuals⁴⁸. By contrast, the 1.5 billion individuals whose waste is uncollected in the Global South have little choice but to self-manage (defined in Section S.4.1) it because it arises on their own premises and in much larger quantities compared to littering.

As with the Global South, most emissions from disposal sites in HICs (98% wt.) are from open burning, meaning that plastic debris emissions are comparatively negligible (0.0015 Mt). All open burning at disposal sites in HICs takes place in Kuwait (0.07 Mt) and Saudi Arabia (0.03 Mt), where despite the relative affluence, dumpsites persist^50,51. The very large uncertainty around the median disposal site emissions of plastic waste from open burning in Kuwait (median 0.05 Mt; range 0-0.7 Mt) and Saudi Arabia (median 0.02 Mt; range 0-0.1 Mt) reflects the abject paucity of robust data to evidence the phenomenon (Section S.8.11.3).

The mismanagement of rejects from plastics sorting and reprocessing (recycling system) in both the Global North and South results in 1.0 Mt of plastic waste emissions to the environment. These emissions have often been the focus of attention, particularly in relation to the transboundary trade (exports) in waste plastics⁵². However, here we show that the emissions burden from recycling macroplastic rejects is comparatively exiguous.

The purpose of our study was to create a macroplastic pollution inventory method for baselining and monitoring emissions at local-scale where on-the-ground actions can be applied. Such an emissions inventory, explaining the mechanisms for emission from the waste management and societal systems, could form a basis for a more detailed and comprehensive assessment of possible interventions. Once macroplastics have entered the environment they are technically and economically challenging to remove⁵³, and over time will inevitably fragment into innumerable microplastics⁵⁴ making clean-up efforts even more challenging. Minimising plastic pollution at source by preventing the emission event in the first place, must be a priority of the Plastics Treaty¹⁷, and our first-of-a-kind insight indicates that tackling uncollected waste would have a bigger impact than mitigating all other land-based macroplastic sources combined. Importantly, we already have a large global workforce of informal recyclers, entrepreneurs who our model shows collect more than 49.8 Mt (46.9-53.6 Mt) of waste plastics annually, much of which would otherwise be mismanaged.

It is self-evident that interventions to reduce uncollected plastic waste would either focus on upstream material reduction to reduce waste generation and/or substantial improvement of waste collection services and infrastructure and our emissions inventory sets a detailed basis for this. As highlighted elsewhere^9,55, mitigating plastic waste emissions will require a multi-sectoral approach that includes addressing insufficiencies across the lifecycle including redesign of product systems, source reduction and improving recycling systems worldwide. The plausibility of timely and at-scale deployment of such interventions needs to be carefully re-assessed in the context of our new results.

The large mass of waste which is burned in open uncontrolled fires has not formed a central part of discussions at Plastics Treaty negotiations^27,28. Yet, according to our model, more plastic waste is burned than is emitted as debris worldwide, releasing a cocktail of potentially hazardous substances and climate forcing emissions, which may have a substantial impact on human health and ecological systems³³. An unintended consequence of interventions to mitigate the release of debris could result in an increase in emissions from open burning and vice versa⁵⁶. Therefore, we propose that the inclusion of this phenomenon is a critical component of the forthcoming negotiations: clearly, choosing between two major forms of plastic pollution should not be an option.

We acknowledge that there is a dearth of robust, quality controlled, and verifiable waste management activity data. We have tediously screened, assessed, harmonised, and corrected relevant data, incorporating uncertainty using a probabilistic approach. We have designed a conceptual framework that allows the model’s input data and structure to be continuously updated. As additional quality-controlled locally obtained measurements from across the waste and resources system become available, and our understanding of release mechanism improves, the model’s precision and accuracy can be ameliorated.

As with international climate change agreements⁵⁷, signatories to the Plastics Treaty will require a method to calculate and baseline their plastic waste emissions so that they can compare them with others. Our emissions inventory enables them to carry out these estimates at high resolution by conceptualising the mechanisms of emission; providing insights into the nature, extent, and causes of plastic pollution; and therefore, enabling development of evidence-based national and sub-national action plans to eliminate plastic in our environment.

Data and code availability statement

All data relating to this study are available in the main text and Supplementary Information. Model results, inputs, and R code are available at ⁶⁵ (Dryad repository link – to be updated).

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We created a macroplastic emission inventory using a novel methodology to quantify emissions from land-based sources for 50,702 municipality level administrations⁵⁸ (see Supplementary Information for detailed method). We define plastic emissions as material that has moved from the managed or mismanaged systems (where waste is subject to form of control, however basic), to the unmanaged system (the environment) with no control. For example, open dumpsites, defined here as structures that contain concentrations of (usually formally) collected waste with only basic control to prevent its interaction with the environment, are a form of control, because most of the buried waste is unlikely to undergo further movement into the environment.

Material was mapped through 81 downstream (after-use phase) processes to simulate the flow of land-based macroplastics through globally diverse waste management systems (Fig. 1 and Section S.4). Emissions of debris (physical particles >5 mm) and open burning (combustion in open uncontrolled fires) from municipal solid waste (defined in Section S2) were quantified for flexible and rigid plastics (format). Activity data (the intensity of waste and resources recovery management activity) were obtained from four global^59-62 and two national^36,63 waste management databases. These were checked for errors, harmonized to a consistent basis, and corrected if necessary, creating the first comprehensively quality controlled city-level solid waste management database with worldwide coverage.

Quantile regression random forest models⁶⁴ predicted data for remaining global municipalities (those without measured data) using national and sub-national socioeconomic indicators. Waste management, circular economy, and plastic waste emission characteristics, variables which are not commonly measured or reported, were estimated using data from literature or through the creation of novel conceptual models. These newly developed ‘sub-models’ (Sections S.8.2, S.8.3, S.8.3.4, S.8.5, S.8.5.2, S.8.8, S.8.9, S.8.11.1 and S.9.1.2) used data on human behaviour, material value, socio-economic development, population density, and solid waste management performance; creating an explanatory framework through which to estimate unmeasured system characteristics. The use of “process level sub-models” to describe larger systems has recently been advocated for plastic pollution modelling¹³.

Probabilistic (Monte Carlo simulation) material flow analysis (MFA) mapped flows of municipal solid waste (5,000 iterations) throughout the system (Section S.4), resulting in detailed information on municipal solid waste and plastic waste management for each global municipality. Emissions into the unmanaged system, defined here as uncontained waste which is no longer subject to any form of management or control were estimated for five key sources: (1) uncollected waste; (2) littering; (3) collection system; (4) uncontrolled disposal; and (5) rejects from sorting and reprocessing (Extended Data Fig. 1).

These flows and their associated uncertainty were aggregated to national, regional, and global scale to align with reporting for SDG11.6.1²⁴ and create a multi-resolution global plastic emission inventory. This inventory is the first stage prerequisite for a second terrestrial transport model (not discussed further here), collectively named the ‘Spatio-temporal quantification of plastic pollution origins and transportation’ model (SPOT). Whilst we acknowledge that upstream processes during the production, conversion and use-phases result in an array of emissions from plastics, they are outside the scope of our modelling. To improve comprehension of proportionality, results aggregated above municipal scale are reported as mean and numbers in brackets are the range unless otherwise specified. As there are no datasets with which to validate our model outputs, we took the same approach as Lau, et al.⁹ and carried out global sensitivity analysis to assess the influence of the model inputs and structure on its results (Section S.10).

Methods references

58 GADM. GADM database of global administrative areas. https://gadm.org/ (2012).

59 UN-Habitat. Wastewise cities (WaCT) data portal. https://unh.rwm.global/ (2022).

60 Wasteaware. Wasteaware benchmark indicators. http://wabi.wasteaware.org/ (2022).

61 Kaza, S., Yao, L., Bhada-Tata, P. & Van Woerden, F. What a waste 2.0: a global snapshot of solid waste management to 2050. https://openknowledge.worldbank.org/bitstream/handle/10986/30317/9781464813290.pdf?sequence=12&isAllowed=y (World Bank Publications, Washington, DC, 2018).

62 United Nations Statistics Division (UNSD). UNSD environmental indicators - waste. https://unstats.un.org/unsd/envstats/qindicators (New York, 2020).

63 SIPSN, National waste management information system (Sistem informasi pengelolaan sampah nasional). https://sipsn.menlhk.go.id/sipsn/public/home (2022).

64 Meinshausen, N. Quantile regression forests. J. Machin. Learn. Res. 7, 983-999 (2006).

65 DBPR. Data from: A local-to-global emissions inventory of macroplastic pollution. INSERT DOI when created (Dryad, 2023).

Yes there is potential Competing Interest. Detailed in the Cover Letter, in line with the Nature Research Double-anonymized peer review guidelines.

SIGuide.docx
SI Guide
ST02SPOTSystemofEquations.xlsx
ST_02_SPOT_System_of_equations
ST04SPOTMFAOutputsGlobalRegionalIncome.xlsx
ST_04_MFA_Outputs_Global_Regional_Income
ST01SPOTDataCleaning.xlsx
ST_01_SPOT_Data_Cleaning
ST03SPOTMFAOutputsNational.xlsx
ST_03_SPOT_MFA_Outputs_National
SupplementaryInformation.pdf
Supplementary Information
Extendeddata.docx

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A local-to-global emissions inventory of macroplastic pollution

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Abstract

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Introduction

Global emissions of plastic waste

Plastic emission hotspots outlook

Per capita emission hotspots

Sources of plastic emissions

An inventory to support the Treaty

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References

Methods

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Supplementary Files

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