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New IUCN Report paves the way for a standardised methodology to measure plastic leakage

On 27 August 2019 at the World Water Week in Sweden, IUCN launched a report that identified numerous gaps and opportunities for developing a standard methodology to measure the extent of the plastic pollution crisis. The urgently needed methodology will provide decision makers with improved data collection and analysis on plastic waste management at the global, regional and national levels.

Photo: © By Nguyen Quang Ngoc Tonkin/Shutterstock

As recognised during the Third United Nations Environment Assembly (UNEA-3, Nairobi, 2017), there is currently no standard methodology to measure the extent of the plastic problem. Current methodologies do not allow us to better understand the impact of plastic leakage along the value chain, ultimately preventing us from measuring the extent of the plastic pollution problem.

Apart from providing a comprehensive overview of all 19 existing and emerging plastic footprint methodologies for the first time ever, the publication, Review of plastic footprint methodologies: Laying the foundation for the development of a standardised plastic footprint measurement tool also includes a glossary of key terms related to plastics and environmental footprints. This enables the modelling, field and business communities to speak a common language.

The methodologies reviewed in the report identify the abundance and distribution, types and sources, as well as pathways and sinks of plastic pollution at different scales.

There are two types of methodologies: the first comprises methodologies that identify plastic waste streams and recycling rates at the national or business level; the second comprises methodologies that focus on pathway modelling to measure plastic leakage into waterways and oceans, from either mismanaged waste or in the form of microplastics.

An analysis of the review concludes that plastic footprint methodologies are lacking in several ways.

For example, there is a lack of consensus on the physical and socio-economic drivers of plastic leakage and a lack of specific data to establish key parameters of leakage models. Knowledge about the fate of plastic in the environment (e.g. degradation rate) is also absent.

Also, there is a lack of data to conduct impact assessments and to embed plastic impacts within Life Cycle Assessment (LCA) frameworks. Current LCAs do not account for plastic as a pollutant - they assume that 100% collection of waste streams go to landfill, incineration or recycling.

As a result, existing methodologies are not polymer specific. Nor do they allow us to compare the different types of impacts between plastic pollution and other potential environmental impacts. This makes it difficult to design effective models to assess macro and micro plastic leakages and to tackle plastic pollution at source. It also prevents us from understanding the impacts and opportunities related to plastic usage.

“ The report underlines the critical need to adopt a holistic, all-encompassing approach to measuring the impact of plastic pollution, one that assesses the entire value chain of plastic products and their entire life cycle ,” says  IUCN Global Marine and Polar Programme Director, Minna Epps.  “ Based on the key findings of the report, we are currently working with UN Environment to develop a best-in-class plastic hotspot methodology that can provide key stakeholders with data and analysis needed to inform their decision-making on reducing plastic leakage.”

In line with IUCN’s effort to  close the plastic tap ,  two new publications will be released next month. Both publications stem from the IUCN Baltic Solutions to Plastic Pollution project funded by the Swedish Postcode Foundation.

The Marine Plastic Footprint – Towards a science-based metric for measuring plastic leakage and increasing the materiality and circularity of plastic , comprises a plastic footprint methodology that aims to help companies set priorities for developing circular economy approaches for tackling plastic pollution. It highlights a clear plastic leakage model by having the first comprehensive set of equations and generic data to calculate leakage for plastic sources such as plastic wastes, textile fabrics, tyre dust, micro beads from cosmetics and fishing nets.

Another publication,  Plasticus Mare Balticum – A synthesis of integrated research analysis and policy papers , compiles several reports from the IUCN Baltic Sea project. These include reports on private sector surveys on circular economy, estimates of plastic leakage into the Baltic Sea and impacts of plastic pollution on selected endangered species. It also provides an overview of policies on marine litter in riparian Baltic countries, and includes research results from studies carried out in Canada, Sweden and Norway, highlighting how plastic can interfere with ice formation and melting, and how it influences the earth’s ability to reflect sun’s rays into space.

View the new report, Review of plastic footprint methodologies : Laying the foundation for the development of a standardised plastic footprint measurement tool , here.

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About IUCN’s Close the Plastic Tap Programme

IUCN’s programme of work on plastics has focused principally on seeking solutions to close the plastic tap and tackle plastic pollution at its source. This involves the mobilisation of a wide range of stakeholders including governments, industries and society. It also involves enhancing our understanding of the problem through research and the compilation of the latest science and data on the issue.

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Methodology to address potential impacts of plastic emissions in life cycle assessment

• NON-TOXIC IMPACT CATEGORIES ASSOCIATED WITH EMISSIONS TO AIR, WATER, SOIL
• Open access
• Published: 22 March 2022
• Volume 27 , pages 469–491, ( 2022 )

Cite this article

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• Daniel Maga   ORCID: orcid.org/0000-0001-9426-0660 1 ,
• Christina Galafton 1 ,
• Jan Blömer   ORCID: orcid.org/0000-0001-8270-6450 1 ,
• Nils Thonemann   ORCID: orcid.org/0000-0001-5966-2656 1 , 2 ,
• Aybüke Özdamar   ORCID: orcid.org/0000-0003-1998-2836 1 &
• Jürgen Bertling 1  

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A Correction to this article was published on 01 March 2022

This article has been updated

Products made of plastic often appear to have lower environmental impacts than alternatives. However, present life cycle assessments (LCA) do not consider possible risks caused by the emission of plastics into the environment. Following the precautionary principle, we propose characterization factors (CFs) for plastic emissions allowing to calculate impacts of plastic pollution measured in plastic pollution equivalents, based on plastics’ residence time in the environment.

Methods and materials

The method addresses the definition and quantification of plastic emissions in LCA and estimates their fate in the environment based on their persistence. According to our approach, the fate is mainly influenced by the environmental compartment the plastic is initially emitted to, its redistribution to other compartments, and its degradation speed. The latter depends on the polymer type’s specific surface degradation rate (SSDR), the emission’s shape, and its characteristic length. The SSDRs are derived from an extensive literature review. Since the data quality of the SSDR and redistribution rates varies, an uncertainty assessment is carried out based on the pedigree matrix approach. To quantify the fate factor (FF), we calculate the area below the degradation curve of an emission and call it residence time \({\tau }_{R}\) .

Results and discussion

The results of our research include degradation measurements (SSDRs) retrieved from literature, a surface-driven degradation model, redistribution patterns, FFs based on the residence time, and an uncertainty analysis of the suggested FFs. Depending on the applied time horizon, the values of the FFs range from near zero to values greater than 1000 for different polymer types, size classes, shapes, and initial compartments. Based on the comparison of the compartment-specific FFs with the total compartment-weighted FFs, the polymer types can be grouped into six clusters. The proposed FFs can be used as CFs which can be further developed by integrating the probability of the exposure of humans or organisms to the plastic emission (exposure factor) and for the impacts of plastics on species (effect factor).

Conclusions

The proposed methodology is intended to support (plastic) product designers, for example, regarding materials’ choice, and can serve as a first proxy to estimate potential risks caused by plastic emissions. Besides, the FFs can be used to develop new CFs, which can be linked to one or more existing impact categories, such as human toxicity or ecotoxicity, or new impact categories addressing, for example, potential risks caused by entanglement.

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1 Background and aim

Life cycle assessment (LCA) is a methodology used to estimate potential environmental impacts of products and processes, such as global warming caused by greenhouse gas emissions. LCA studies often estimate environmental impacts of plastic products to be lower than those of alternatives, e.g., due to light-weight design or a lower resource input (Amienyo et al. 2013 ; Humbert et al. 2009 ; Saleh 2016 ). One reason that the LCA studies come to this conclusion might be that plastic emissions caused by loss of plastics, e.g., by abrasion, aging, fragmentation, or littering (Sonnemann and Valdivia 2017 ), are currently not well considered by an appropriate impact category.

Plastics can be found in nearly all ecosystems worldwide (Li et al. 2020 ). Even though the impacts are not fully understood, there is a clear consensus between politicians, industry, and consumers that plastic should not be released to the environment (Nielsen et al. 2020 ). It is assumed that plastics in the environment can threaten biodiversity and ecosystem services, such as fish reproduction, which negatively influence the economy, such as the fishery or tourism industry (Burns and Boxall 2018 ). There is also initial evidence that microplastics might cause inflammatory bowel diseases in humans (Yan et al. 2022 ). In order to address these effects, LCA should take into account emissions of plastics into the environment and their potential impacts (Schwarz et al. 2019 ; Sonnemann and Valdivia 2017 ; Woods et al. 2016 ).

In order to assess the potential risks caused by plastic emissions, two steps are needed: first, the definition and quantification of plastic emissions, and second, the estimation of their fate in the environment. Current research focuses on the leakage of plastics, which covers both micro- and macroplastics, e.g., Siegfried et al. ( 2017 ), Civancik-Uslu et al. ( 2019 ), Unice et al. ( 2019 ), Peano et al. ( 2020 ), Chitaka and von Blottnitz ( 2021 ), Stefanini et al. ( 2021 ), and Amadei et al. ( 2022 ). However, this knowledge has only partially been linked to life cycle inventory (LCI) flows, and corresponding characterization factors (CFs) for plastic emissions are lacking.

Besides information regarding leakage amounts, suitable elementary flows and corresponding CFs need to be defined to incorporate plastic emissions into LCA. A CF describes the extent to which the elementary flow contributes to an impact category, e.g., global warming or a prospective new impact category. In this paper, we develop the basis for a new impact category “plastic pollution” which addresses potential hazards caused by plastic emissions. According to the framework proposed by Udo de Haes et al. ( 2002 ), CFs are defined as follows:

The fate factor (FF) refers to the transmission and distribution of a plastic item between the environmental compartments and its degradation within the environmental compartments. Plastics initially released into one compartment (e.g., a river) might be transmitted to another compartment (e.g., the ocean), where they degrade. Since degradation rates differ between the compartments, it is necessary to know the compartment where most of the degradation takes place.

The exposure factor addresses the probability of exposure of humans or animals to the plastic emission, e.g., by ingestion, inhalation, and entanglement.

The effect factor represents the sensitivity of a species to the pollutant.

The severity factor varies with the expected severity of the damage. A severity factor is to be specified when calculating an endpoint indicator. Therefore, the severity factor is given in parenthesis in the formula for calculating the CF.

A first attempt to calculate FFs was made in the conceptual framework SecµPlast of Croxatto Vega et al. ( 2021 ), in which the formation of secondary microplastic due to photooxidation in LCA is tackled. The exposure of organisms to microplastics has been investigated for selected species (e.g., Cole et al. 2011 ). Although exposure and effects caused by the intake and accumulation of microplastics such as starvation, disturbances of the reproduction and in energy metabolism, or changes in liver physiology (Anbumani and Kakkar 2018 ), as well as toxic reactions to specific polymers (e.g., Gagné 2017 ) or additives (Gallo et al. 2018 ) have been described, the consideration of the effects of different polymers and shapes at the normal concentration found in the environment remains challenging. Woods et al. ( 2016 ) structurally investigated research needs to quantify marine ecological impacts. Within the MariLCA project, this work is currently expanded to cover other environmental compartments such as soil, freshwater, or air (Boulay et al. 2021 ; Woods et al. 2021 ). A first effect factor was proposed by Woods et al. ( 2019 ) and extended by Høiberg et al. ( 2022 ), which conveys the risk of entanglement of organisms with a critical size compared to the size of the plastic item. Even less knowledge is available concerning the severity of the effects caused by plastic emissions. In another recent study, Lavoie et al. ( 2021 ) developed an effect factor for micro- and nano-sized plastics regarding different aquatic species. They found effect differences between polystyrene and other polymers when analyzing many aquatic species (Lavoie et al. 2021 ). Saling et al. ( 2020 ) have proposed a life cycle impact assessment (LCIA) approach to address the potential ecological impacts of microplastics in oceans. Although this approach links dose-dependent experiments, in particular effect concentrations (EC50) and lethal concentrations (LD50), to different polymers and shapes of microplastics, there is still limited knowledge concerning adverse impacts in the environment and the consequences of increasing bioaccumulation of hydrophobic organic compounds into organisms (ECHA 2019 ; Koelmans 2015 ). Another attempt to cover the impacts of direct microplastic emissions to freshwater was conducted by Salieri et al. ( 2021 ), who couple degradation models with the USEtox framework to calculate CFs for a limited number of polymer types. In this article, we focus on plastic emissions (the loss of plastic items into the ecosphere) and their fate in the environment, particularly their residence time, dependent on the emission’s degradation in the environment. Usually the risk potential of substances is addressed by well-established classes of substances such as persistent, bioaccumulative and toxic (PBT) or very persistent and very bioaccumulative (vPvB) substances. Persistent substances that are not bioaccumulative or toxic have not been characterized as risky. However, the potential risk of plastics seems to be mainly driven by their extreme persistence (ECHA 2019 ). Hence, following the precautionary principle, we propose a CF that focuses on plastics’ residence time in the environment and does not consider the exposure and effect of plastic emissions. Accordingly, at this stage, we assume that the exposure and the effect factor in Eq. ( 1 ) both equal 1 and the CF is solely dependent on the FF. However, in the future, this FF can be supplemented by exposure and effect factors to develop CFs linked to one or more existing impact categories such as human toxicity or ecotoxicity or as a starting point for new impact categories focusing, for example, on physical impacts on biota, invasive species, or potential impacts on cultural heritage.

The emission’s persistence in the environment is dependent on its degradation. In literature, the term degradation is used for changes in chemical or physical properties, depolymerization, mass loss, or mineralization (Chamas et al. 2020 ). In general, degradation refers to the reduction of mass through mineralization, measurable by the production of CO 2 (or CH 4 under anoxic conditions), or the consumption of O 2 . However, many environmental degradation studies typically measure the loss of mass, since direct measurements of gas production is not possible. In order to consider these studies, we also consider mass loss as measure for degradation although this might lead to overestimated degradation rates because mass loss may include fragmentation. The term degradation addresses both, biodegradation as characterized by the three processes biodeterioration, biofragmentation, and assimilation (Lucas et al. 2008 ) as well as other forms of degradation such as photodegradation which is usually mainly driven by oxidation and hydrolysis. Saling et al. ( 2020 ) highlighted that weight loss is associated with (bio)degradation and fragmentation. The process of fragmentation is driven by UV radiation, thermal oxidation, mechanical weathering and occurs along with chemical degradation.

2 Methods and materials

As a prerequisite to integrate the plastic-related elementary flows into LCIA, a convention for defining plastic emission types is proposed (cf. “Sect.  2.1 ”). Then, FFs are calculated per plastic emission type based on the following elements:

Redistribution patterns and the final compartment share of plastic emissions (cf. “Sect.  2.2 ”);

Degradation rates for each plastic emission type and environmental compartment (cf. “Sect.  2.3 ”).

The FF of a plastic emission (characterized by a particular polymer type, size, and shape and initially released into a particular environmental compartment) is determined by the final compartment share after a possible redistribution and the expected degradation times in those compartments compared to a reference degradation time, which is set to 1 year in our methodology. We chose this reference time since plastics degrade at different rates in different environmental compartments. Moreover, by choosing a reference time we avoid uncertainties related to the measurement of the degradation of a certain plastic. The resulting FF is expressed as kg plastic pollution equivalent (PPe) per kg plastic emitted. The FF allows for a comparison of various plastic emissions and enables assessing emissions to different compartments by considering the different degradation rates.

Since degradation rates vary in different compartments, ideally, various environmental compartments should be considered, such as the marine environment (eulitoral, pelagic, benthic), freshwater systems, marine and river sediment, soils, and air. However, since the knowledge about the transfer rates and degradation rates of plastic items in different compartments is currently very limited, we propose to consider plastic emissions exclusively into the initial compartments fresh or marine water, soil, and air, and redistribution only to the final compartments marine water, marine and river sediment, and soil, as a first step.

The requirements regarding the definition of elementary flows and initial compartments and the materials these requirements are based upon are outlined in “Sect.  2.1 .” “Sect.  2.2 ” explains the patterns underlying plastic redistribution in the ecosphere based on polymer-specific and generic research regarding transport processes in the environment. The calculation of the residence time is dependent on polymer and compartment specific degradation rates extracted from a comprehensive literature review and equations developed by the authors to describe the persistence of plastic emissions in the environment. Details regarding the materials used and the formulas derived can be found in “Sect.  2.3 .” “Sect.  2.4 ” explains how specific surface degradation rates are calculated based on the degradation model. Finally, “Sect.  2.5 ” illustrates the data quality assessment applied to the literature-based data and its implications for further use.

2.1 Defining elementary flows and initial compartments

Elementary flows are used to describe a plastic flow from the technosphere into an environmental compartment of the ecosphere. For each elementary flow, it is necessary to quantify the initial release rate of the corresponding plastic. Following the definition of Edelen et al. ( 2017 ), the initial release is the quantity of plastic emissions that leaves the technosphere and enters the ecosphere. Examples of plastic losses that directly enter the ecosphere are lost fishing nets in the sea, the burying of mulch films, or other plastic applications in an open environment, such as geotextiles which are not removed. However, the challenge is that the boundaries between the technosphere (e.g., streets or sewage systems) and the ecosphere (e.g., soil or freshwater) are often unclear (Maga et al. 2020 ). In this paper, we differentiate between technical flows like microbeads in wastewater, environmental flows addressing the initial release, such as microplastics directly emitted to agricultural soil, and redistribution flows which occur between different environmental compartments. Figure  1 presents the distinction between these three flow types and the boundaries between the technosphere and the ecosphere made in this paper. It is visualized with a simplified model that shows the possible pathways of plastic emission from the point of loss to sinks. Redistribution flows that are assumed to have a higher probability are displayed in bold, those with a smaller likelihood are thinner.

Simplified model to address plastic flows between technosphere and ecosphere

The initial compartment is the environmental compartment where a plastic item is first emitted from the technosphere. For example, during a picnic in the park, plastic cutlery might be emitted onto urban soil. Another example is the emission of plastic microbeads, which can be found in some cosmetics. They most likely reach a wastewater treatment plant as part of the wastewater, where they are either retained and later partially emitted onto agricultural soil as part of sewage sludge or pass the treatment process and are emitted to freshwater. Since degradation speed and transport processes between environmental compartments are country-specific, e.g., dependent on soil and water temperatures or the ratio of water to land, region-specific elementary flows should be defined and characterized, where possible.

Besides the initial compartment, the polymer type, shape, and size of plastic emissions influence transport characteristics and degradation speed. Therefore, these attributes need to be included in the definition of elementary flows. The size, shape, and material type are crucial parameters concerning potential effects (de Ruijter et al. 2020 ). Regarding the shape, the emission can be characterized as a film, a fiber, or a nearly spherical pellet or particle. To simplify, larger plastic items such as bags or cutlery are considered formed film in this publication. The characteristic length refers to the emission’s diameter (fiber, particle) or thickness (film). As shown in “Sect.  2.3 ,” the shape and characteristic length of an emission and the environmental compartment to which the emission is finally redistributed strongly influence its degradation time. Therefore, these attributes should be part of the definition of the elementary flow. Ogonowski et al. ( 2016 ) have shown for Daphnia magna that plastic items’ shape is relevant for exposure and potential impacts. Likewise, Ziajahromi et al. ( 2017 ) found differences in effects on the water flea Ceriodaphnia dubia between beads and fibers. Although the knowledge about the influence of the shape of plastic emissions on adverse impacts in organisms is limited to date, the shape might also be relevant to address the potential effects of plastic emissions more in detail in the future.

The naming of the elementary flow, therefore, should include first the region, second the material type (including specification, e.g., rigid vs. foam), third the shape of the plastic emission (film, fiber, or particle), fourth the characteristic length of the emitted plastic item. Fifth, according to Edelen et al. ( 2018 ), the initial environmental compartment into which the plastic is emitted should be part of the definition.

In order to cover the majority of possible plastic emissions, we propose to differentiate between the following three ranges of characteristic lengths as a proxy: < 0.1 mm, 0.1–1 mm, and > 1 mm. These size classes address the range of the characteristic length of films, fibers, and particles which are typically released to the environment either as microplastic or as macroplastic emissions. As a conservative assumption, to not overestimate the degradation rate, each residence time (cf. “Sect.  3 ”) is calculated using the maximum characteristic length of the respective class. For the class of the largest emissions, a characteristic length of 10 mm is used. When calculating a specific well-known plastic emission’s FF, the exact characteristic length should be used instead (cf. “Sect.  2.3 ”).

Some frequently used LCA processes, such as a transport process via truck, result in various plastic emissions such as tire wear and abrasion of road markings. These plastic emissions should be treated as different flows. Besides, if a plastic emission consists of various polymers on a single-particle level (e.g., tire wear consists of natural rubber and synthetic rubber), it should be treated as one flow. In this case, degradation data should be used that reflects the degradation behavior of this specific emission type. If degradation rates are unavailable for this specific emission type, the degradation rate of the slowest degrading polymer type of the complex should be used as a conservative estimation.

Various approaches to estimate the initial release of macro- and microplastics exist (Maga et al. 2020 ), e.g., Kawecki and Nowack ( 2019 ) for Switzerland and Peano et al. ( 2020 ) and Boucher et al. ( 2020 ) for other countries.

2.2 Estimation of redistribution between environmental compartments

In order to estimate the redistribution of an emission between different environmental compartments, as presented in Fig.  1 , several research papers addressing the fate of plastics were analyzed. For some plastic emissions, specific data could be extracted. For instance, according to the research conducted by Unice et al. ( 2019 ) about the Seine watershed (France), tire wear particles initially accumulate on the road and are washed off by rain equally into the road runoff (water) and onto the surrounding soil. Considering the sewage system and partial re-emittance of tire wear particles as part of the sludge, only 24% of the particles are accurately managed. The rest is emitted to and redistributed in the environment with a final compartment share of 56% on soil, 13–16% in river sediment, 2–5% in the ocean, and 2% in air. However, we assume that the 2% emitted to air do not remain in air but are redistributed to soil and water, as presented in Table 1 .

For other plastic emissions, data concerning redistribution and compartment shares are unavailable. In these cases, assumptions are made based on parameters suggested in the literature, which affect the behavior of plastic emissions in the environment, such as:

Environmental compartment initially emitted to (e.g., Kawecki and Nowack 2019 );

Density of the plastic (e.g., Kowalski et al. 2016 ; Nizzetto et al. 2016 ; Horton and Dixon 2018 );

The emission’s size and shapes (e.g., Chubarenko et al. 2016 ; Fazey and Ryan 2016 ; Kowalski et al. 2016 ).

Regarding the environmental compartment the plastic is initially emitted to, there is a chance that plastic items (mostly macroplastics) emitted to terrestrial environments are transported to water on the surface, e.g., by wind and rain-based erosion (Jambeck et al. 2015 ). The amount of plastic transferred from land into water bodies, which in most cases finally ends up in the ocean, highly depends on local conditions such as the local waste management system, climate conditions, and the proximity of the plastic waste emission to water bodies. According to the model of Jambeck et al. ( 2015 ), macroplastics initially emitted onto soil as mismanaged waste are partly redistributed to the ocean via inland waterways, wastewater outflows, wind, or tides if the plastic is emitted within 50 km of a coast. Accordingly, macroplastics emitted further away from the coast do not end up in the ocean. Although this rough estimation was only made for macroplastics, we assume the same for microplastics. No indication for other redistribution mechanisms (e.g., subsurface redistribution) from soil to other compartments could be found. Hurley and Nizzetto ( 2018 ) concluded that soil systems could store microplastics. Likewise, Fauser et al. ( 1999 ) found little downward movement for tire wear particles initially emitted onto soil, as most particles were found in the upper 1 cm of the soil, and 30 times less, only 2 cm further into the ground. This suggests that there is a very small probability of microplastics reaching groundwater through vertical movement. Bioturbation, the physical displacement of solutes and solids in soils caused by the activities of organisms, particularly by burrowing activities of earthworms, can technically lead to a downward transport in soil (Huerta Lwanga et al. 2016 ). However, since no quantifiable and reliable data are available for transfer rates from soil to groundwater, we neglect these mechanisms and only assume an average redistribution rate from soil to marine water of 27.5% (Jambeck et al. 2015 suggested a range of 15–40%), applied to the respective coastal population share of the analyzed region. Furthermore, we assume that once plastic items reach the ocean, the same redistribution patterns apply to plastics directly emitted into the sea.

Although the shape and characteristic length of the emission might play a role in the redistribution from soil to other compartments, there is no universal information about their influence. Therefore, soil redistribution rates to different compartments are assumed to be independent of the shape, characteristic length, emission material type, and density.

On the other hand, in water bodies, the plastic emission’s density plays a more prominent role, especially concerning the vertical movement of the emission and its proneness to sediment (Chubarenko et al. 2016 ; Kowalski et al. 2016 ). Depending on the density of the plastic ρ p compared to the density of the water ρ w , the plastic item might float (ρ p  < ρ w ), stay in the water column (ρ p \(\approx\) ρ w ), or sink to the sediment (ρ p  > ρ w ). When Sanchez-Vidal et al. ( 2018 ) analyzed 29 sediment samples collected from southern European seas, most fibers found were polymers with higher densities, such as polyester, acrylic, and polyamide. We assume that plastic emissions with a density greater than or equal to that of water ultimately sink and become part of the respective water body’s sediment (river or marine sediment). Emissions with a density smaller than that of water float and therefore remain in the water. We assume that initial plastic emissions to freshwater bodies, such as rivers, with a density below that of freshwater, are transported on the water surface and ultimately reach the sea. Therefore, we do not consider freshwater as a final compartment. Although it might be possible for plastic items with a density below that of freshwater, which are emitted into a water body without connection to a flowing water body, to remain in that freshwater body, we assume this neglectable when determining generic distribution rates.

Although, polymers with lower densities than water can also sink and, ultimately, sediment, for example, through turbulence, (bio-)fouling, or heteroaggregation with suspended solids (Besseling et al. 2017 ), we neglect these mechanisms, because defouling might occur, which might resuspend the emission, creating a loop of sinking and suspending over time (Ye and Andrady 1991 ).

According to Fazey and Ryan ( 2016 ) and Chubarenko et al. ( 2016 ), smaller plastic emissions and those with a high surface area tend to sink faster as they are more susceptible to biofouling due to their surface area-to-mass ratio. On the other hand, water turbulence, e.g., by wind or currents, increases the vertical movement, especially of microplastics in the water (Kooi et al. 2016 ; Lebreton et al. 2018 ). Since clear assignments are impossible, the redistribution rates from fresh and marine water to the river or marine sediment do not consider the emissions’ size and shape.

Like Kawecki and Nowack ( 2019 ) and Peano et al. ( 2020 ), we assume that all plastics emitted to air are deposited onto soil or water, with compartment shares dependent on the water to land surface ratio. They are further redistributed in the same way as plastics directly emitted into these compartments.

Since we assume that transport velocities between the compartments are relatively high compared to the degradation times (especially transport by air and in running waters), any degradation occurring during the redistribution is not considered. For example, water within the river Rhine only takes a few weeks to travel from its source at Tomasee (Switzerland) to its delta at Hoek van Holland in the Netherlands. Due to a lack of available data, a possible recollection from environmental compartments into the technosphere, e.g., by beach cleanups, is not considered. Most assumptions presented in “Sect.  2.3 ” are generic and can be applied in the same way globally and to specific countries. The redistribution of items emitted to soil and air, however, is country-specific because the water to land surface ratio differs per country. The FF presented in SM3 are given for Germany as an example. They may be adapted to suit other regions.

2.3 Calculation of degradation rates, total lifetimes, and residence times

In order to determine the degradation rates of different polymers, a comprehensive literature review was conducted. As presented in Fig.  2 , 146 research papers were identified from peer-reviewed journals accessible via the search engines Web of Science, ScienceDirect, and Google Scholar based on keywords such as “plastic,” “fate,” “degradation,” “depolymerization,” “mineralization,” “mass loss,” and “impact.” Following a snowball sampling approach, research papers quoted in the identified publications were also considered. For polymers for which no sufficient data could be obtained via the described method, research papers were searched applying the same approach, adding into the search term that specific polymer.

Methodological approach of the literature review

Research papers that did not disclose all necessary information, e.g., regarding the shape and characteristic length of the investigated plastic item, were excluded. Besides, only studies were taken into account where degradation measurements were based on either weight loss, biochemical oxygen demand, or the amount of CO 2 formed during the degradation. Studies examining material property changes, such as tensile strength or crystallinity, were left out as there is no available correlation to material loss. Only for polymers for which no data are available concerning the described measurement methods, studies were taken into account that measured viscosity at higher temperatures and relied on Arrhenius projection to deduct degradation speed at temperatures found in nature. The few degradation studies focusing on other environmental compartments and pure laboratory studies under artificial conditions were ruled out.

The data found pertain to many different polymer types, including both fossil-based and biobased polymers. Some fossil-based polymer types are commonly assumed not to be biodegradable (ASTM D7611 standard codes 01–06) and are referred to in this study as conventional fossil-based polymers. Nevertheless, as explained further below, we do assume a slow degradation of these polymers. We refer to polymer types of ASTM D7611 code 07 (other) as either biodegradable fossil-based polymers or biobased polymers, depending on their source of material.

For some polymers and compartments, several data sets from one or more publications were available. If any of these data sets did not indicate any degradation, but others for the same polymer and compartment did, those without degradation measurements were excluded, which was considered a measurement error or an insufficient accuracy of the measurement device. For the same reason, data sets that did not indicate any degradation, but were the only data sets available for the respective polymer and compartment, were set to an SSDR of 0.001 µm per year, as further calculations would be impossible with an SSDR of 0 µm per year. The chosen value is slightly lower than the lowest SSDR measured that was unequal to zero to not overestimate degradation for those polymers and compartments.

Since data availability for various environmental compartments is limited, we only differentiate between degradation in soil, river and marine sediment, as well as marine water at this stage of work. As investigated by Lott et al. ( 2020 , 2021 ) for polyhydroxyalkanoate copolymer (PHA) in eulitoral, pelagic, and benthic habitats of the Mediterranean Sea and Southeast Asia (Pulau, Bangkam Sulawesi), there are relevant differences in degradation rates. Degradation was observed to be faster in the benthic zone compared to the pelagic zone. The region, however, had the greatest influence on the degradation time. Degradation rates of PHA films in SE Asia were observed to be higher than in the Mediterranean Sea. While degradation rates for different regions are generally not available yet, we distinguish between the marine water body and marine and river sediment.

The extracted data sets contain information regarding the investigated plastics (polymers, additives), the shape and characteristic length of the research subject, the degradation test compartment, whether the degradation conditions could be considered natural, measured degradation, and the time span of the experiment. Data extracted from research papers that did not indicate having examined plastics with additional additives to enhance biodegradation were marked as “no enhancing additives.” Nevertheless, it can be assumed that these plastics include typical additives such as plasticizers, antistatic agents, flame-retardants, or UV stabilizers that are indifferent to or even reduce the degradability. However, the knowledge about types and quantities of additives might be relevant for future integration of more realistic degradation rates and additional ecotoxicity impact assessments.

As a result, 38 studies concerning 172 data sets for various polymer types and the environmental compartments marine water, marine sediment, river sediment, and soil under natural or near-natural conditions and without additional additives to influence degradation were used for calculating degradation rates. Data for degradation in soil include values measured during experiments with compost under near-natural conditions. Many studies, especially concerning conventional fossil-based polymers, did not last long enough to reach a significant degradation. In these cases, the value at the last measuring point is taken. In cases where experiments lasted long enough to reach a degradation of more than 50%, the value at the measuring point closest to 50% degradation is taken.

As mentioned before, the residence times depend on (1) the polymer type, (2) the shape of the plastic item, (3) the initial size of the investigated item, and (4) the environmental compartment where the degradation takes place. Following Chamas et al. ( 2020 ), three assumptions are made:

The degradation mainly happens in the top layer at the surface of the emitted item (Fig.  3 ) (cf. Ohtake et al. 1998 ).

A specific surface degradation rate (SSDR) v d can be defined that depends on the type of plastic and the environmental compartment the degradation takes place.

v d is assumed to be constant during the entire time of degradation.

Surface degradation

Naturally, these assumptions are simplifications: some polymers will show degradation in the bulk material or might be eroded in part by mechanical influences. However, also following Chamas et al. ( 2020 ), we assume that surface degradation is the factor that ultimately determines the amount of time needed for a plastic emission to vanish.

The initial plastic item is eroded from the overall outer surface. Therefore, the characteristic length d t reduces over time t by twice the degradation rate v d , irrespective of the item’s shape (Fig.  3 ). Only for hollow items (e.g., closed bottles), this might not be true initially. However, it can be assumed that these items fracture quickly.

Films are considered to degrade at the main surfaces A (from both sides) only, fibers are considered to be cylinders whose length L does not reduce significantly over time, and particles are regarded as spheres. As illustrated in Fig.  4 and Eq. ( 2 ), the characteristic length d t at a point in time t refers to the film’s thickness or the fiber or particle’s diameter, respectively.

Schematic illustration of surfaces and characteristic length d of different shapes (film, fiber, and particle)

From this equation, the total lifetime \({\tau }_{L}\) of the emission until the polymer is completely degraded can directly be calculated by setting d t  = 0:

The total lifetime only depends on the initial size and the specific surface degradation rate and is independent of the shape of the emission due to our degradation model chosen. Nevertheless, the persistence of a specific plastic emission depends not only on this total lifetime but also on the temporal degradation behavior. This temporal degradation behavior depends on the shape because even with a constant SSDR, the velocity of mass degradation varies over time due to a change of the surface-to-volume ratio. Hence, we introduce the residence time \({\tau }_{R}\) as measure for the persistence of plastic emissions in the environment (see below). The degrading volume V t of a particular plastic emission at time t is calculated depending on its shape:

Here \(A\) is the surface area of the foil and \(L\) the length of the fiber. In general, the volume can be calculated by a constant and the characteristic length to the power of a :

where a equals 1 for infinite films, 2 for fibers, and 3 for particles, respectively. Particles are idealized as spheres. However, assuming particles as cubes would lead to the same result. Nevertheless, irregular shapes of three-dimensional items may be approximately reflected by using a different constant or power in Eq. ( 5 ). As long as the constant and power a in Eq. ( 5 ) stay approximately constant during the item’s degradation, they cancel out in the following calculation. Combining Eqs. ( 2 ) and ( 5 ) leads to the emission’s remaining volume V t at a particular time concerning the initial volume \({V}_{0}\) .

Assuming a constant density, the same relation holds for the masses of the emission (cf. Fig.  5 ):

Degradation of emission with same total lifetime ( τ L  = 200 years) but different shape and different degradation curves (functions see Eq. ( 10 ))

The environmental impact according to our approach can be calculated by integrating the curve function of the actual dimensionless fraction of the remaining mass: the dimension of this quantity is time and it will be called residence time \({\tau }_{R}\) (explanation see below):

Hence, the residence time \({\tau }_{R}\) of a film, fiber, or particle is:

As shown in Fig.  5 , the velocity of mass degradation of films is constant, resulting in a linear reduction of mass, while for fibers and particles, the degradation is faster in the beginning and slower at the end. This results in a higher residence time \({\tau }_{R}\) for films than for particles at similar life time Eq. ( 9 ).

Figure  6 shows the residence time \({\tau }_{R}\) of a particle. In this case, the residence time is 75 years. In contrast, the point in time at which 50% of the mass is degraded (half-lifetime) of the particle would be slightly lower with 61.9 years. The name residence time was chosen, because during the total degeneration process mass will continuously vanish and the average age of this leaving mass is equal to the residence time introduced. Furthermore, the residence time can be interpreted as that time a non-degradable emission would have to stay in the environment to give the same impact: in Fig.  6 , the gray box has the same area as the integral below the degradation curve.

Residence time of a particle: the area of the box and the area below the degradation curve are equal

The consideration of time horizons \({\tau }_{H}\) is a common practice in LCIA (Hauschild and Huijbregts 2015 ). When calculating FFs (“Sect.  3.3 ” and supplementary material), time horizons of 100, 500, and 1000 years are applied. Both a 100- and 500-year time horizon are commonly used for other environmental impact assessments, e.g., global warming potential. The longer time horizons allow for greater differentiation between hardly degradable polymers. By selecting a short time horizon, e.g., 100 years, FFs will only differ when emissions degrade faster than 100 years while polymers with life times of, e.g., 500 years, 10,000 years, or 50,000 years will all obtain an residence time close to 100 years. This might encourage politicians and the industry to focus on fast degradable polymers if resulting emissions into the environment are hardly avoidable. When applying a time horizon, the integration is performed until the time horizon is reached and Eq. ( 8 ) becomes:

This means, as illustrated in Fig.  7 , by applying a time horizon, only the area inside the time horizon is considered (blue area), while everything after the time horizon is omitted (gray area).

Calculation of the residence time of a particle for a time horizon given. The area of the light blue box and the blue area are equal

The ratio of \(\tau\) R and \(\tau\) H ranges between 0 and 1 and measures the “occupation” of the time horizon. However, the results need to be interpreted cautiously: the residence time in a time horizon is always smaller than the residence time without a time horizon. Especially durable polymers like PVC with residence times greater than 1000 years without considering a time horizon might appear more favorable than they are when interpreting the residence time with a short time horizon (e.g., 100 years). Consequently, the residence time must always be interpreted relative to the time horizon considered. If the value of the residence time is close to the value of the time horizon, very little degradation occurs during this time horizon. The equations obtained ( 8 and 10 ) hold for the degradation models chosen. However, the approach can be easily adapted to other degradation mechanisms (e.g., Junker et al. 2016 ) as long as the degradation curves are known.

Another derivation of the residence time and the FF as measure of the environmental impact is shown in Fig.  8 . Assuming a constant yearly flow \(\dot{m}\) there is only a partly degeneration in the first year. After 1 year, the remaining amount is transferred to the second year and a fresh inflow will appear (and so on). The curve will have the same shape as in the calculations before. When summing up all yearly amounts by integrating the curve Eq. ( 9 ), the hold-up M , i.e., the total mass accumulated in the environment due to this emission, is calculated. The degeneration of this hold-up balances the emission flow in the steady state case. Dividing this hold-up by the emission flow a residence time is calculated, which is equal to the residence time before.

Visualization of the residence time approach and the reference material

This shows vividly the equivalence of mass-flow and residence time as stated before. One unit of an emission A with a specific residence time results in the same hold-up as an emission of two units of an equally sized emission B with half the residence time of emission A. This holds for diluted, widely spreaded emissions. It is not suitable to calculate effects of local or temporal concentration hot spots.

With the residence time approach, it is straightforward to calculate the fate of an emission that is divided and transferred to different final environmental compartments. The mass of the initial release is distributed according to the transfer factors \({T}_{i,j}\) and for each environmental compartment, residence times are calculated separately. The transfer factors are the fractions of an emission i transferred to compartments j . The overall residence time is calculated as a weighted sum of the individual ones.

In Fig.  9 , an emission is divided into two compartments (30/70%) with total lifetimes of 200 and 100 years, respectively. The residence time of the entire emission is the weighted sum of the individual ones (50 and 25 years, respectively). From the residence time (32.5 years), a total lifetime of 130 years of the emission in a hypothetical compartment can be calculated by Eq. ( 9 ).

Residence time for a plastic emission (particle) that is redistributed to two environmental compartments

2.4 Calculation of specific surface degradation rates

The degradation model described is used to evaluate experimental results in the literature.

Experimental studies on degradation usually report the loss of mass Δ m relative to the initial mass m 0 during a period. In order to calculate the specific surface degradation rate v d (SSDR) of a plastic emission, Eq. ( 7 ) can be rearranged:

This yields the SSDR of the experimental mass loss Δ m / m 0 during a period t and the given initial characteristic length d 0 and shape (to determine the power a ). SSDRs are calculated for each polymer type and each compartment. The SSDR values displayed in the supplementing material are based on experimental data extracted from the research papers analyzed, as explained at the beginning of this section. For polymers for which data are insufficient to calculate SSDRs, values are estimated according to the following assumptions, which have to be confirmed or improved in the future:

Where data for only one of the environmental compartments are available, degradation is assumed to be comparable in the other three, and the same value is utilized for all four compartments.

Where data are available for river or marine sediment but not the other type of sediment, the same value is utilized for both types.

Where data are available for marine water and soil, but not river and marine sediment, the lower degradation rate of the two compartments, marine water and soil, is used for both sediment types as a conservative estimate not to overestimate the sediment’s degradation.

For polystyrene (PS) and polyvinyl chloride (PVC), no data are available to calculate SSDR in any compartment. SSDR of PS and PVC are estimated to aim towards 0 (cf. Chamas et al. 2020 based on information published by Otake et al. 1995 , obtained by a measurement method otherwise irrelevant to our research: through observation by phase contrast microscope and scanning electron microscopy or mere estimation). For further calculations, the SSDR for these polymers is set to 0.001 µm per year.

2.5 Data quality and uncertainty analysis

The procedure to select data for calculating FFs is based on a data quality assessment. For example, for some polymer types and environmental compartments, several studies with different degradation rates were available. The assessment of the data quality enabled the decision for the most accurate degradation rate to be used. In order to indicate the quality of the data provided in this paper, we adapted the pedigree matrix approach, which was first introduced by Funtowicz and Ravetz ( 1990 ) and was adapted by Weidema and Wesnæs ( 1996 ) for life cycle inventory data, and applied it to our input data. The pedigree matrix approach allows to assess data quality and translation to uncertainty values although a small sample size for most of the SSDRs is given. If the number of SSDRs increases or uncertainty values for SSDR measurements are provided, these values should be used instead. Since no pedigree matrix exists for degradation rates and transfer coefficients between environmental compartments, we altered the initial categories to suit the research purpose. Like Laner et al. ( 2016 ), we distinguish between experimental data and expert judgments. Scores indicate good data quality (data quality indicator score (DQIS) = 1) up to low data quality (DQIS = 4).

The quality of experimental data is affected by its reliability, completeness, temporal and geographical correlation as well as the measurement method used. For the geographical correlation, Germany is set as a reference, to match the country of the redistribution patterns, as described in “Sect.  2.3 .” The quality of expert judgments depends on the foundation of the judgment, for example, on an (empirical) database, the experts’ qualification, and the transparency of the procedure by which the judgment was obtained (Laner et al. 2016 ).

Based on these DQIS, uncertainty scores are calculated for each input data set: transfer coefficients and degradation rates as explained in SM1 . The modeler sets the size and shape of a plastic emission for each elementary flow; therefore, these parameters are assumed not to induce additional uncertainty. However, this could be the case in terms of measurement uncertainties. Nonetheless, the uncertainty induced by size and shape is assumed to be small compared to the uncertainty induced by transfer coefficients and degradation rates. For transfer coefficients, expert judgment is used to apply relevant information from existing publications to our research case and Germany’s environmental conditions. For degradation rates, where more than one data set per polymer and compartment were found, the data set with the lower uncertainty score is used for further calculations. The geometric average of SSDRs is used when more than one data set had the same lower uncertainty score. The uncertainty calculation of the FFs is done using Monte Carlo simulation based on the geometric standard deviation (GSD) and median, taking log-normal distributions for the estimates of SSDR and redistribution (Limpert et al. 2001 ).

In order to calculate the confidence interval of 68%, the FFs need to be multiplied by the GSD for the upper limit and divided by it for the lower limit. To calculate the confidence interval of 98%, multiply or divide the given FFs by GSD 2 . The Python script for the FFs’ uncertainty calculation is given in SM4  for reproducibility and traceability reasons.

While one of the key contributions of this paper is the development of a new methodology to include plastic-related elementary flows in LCA, the following chapter presents the data found in literature regarding redistribution patterns and degradation rates, which were used to calculate FFs for a vast number of elementary flows. The list of FFs provided in SM3 is non-exhaustive. Following our approach, LCA modelers are able to calculate FFs for their specific plastic emission regarding polymer type, size, shape, and initial compartment. Besides, the methodology can be applied to different areas or countries. Because certain elements, e.g., redistribution and the data quality assessment of the degradation rates, require a regional focus, the results are given for Germany as an exemplary region. The modeler might choose a different regional focus and therefore adjust the FFs based on the equations presented in this paper.

3.1 Compartment shares and redistribution

Table 1 provides an overview of the final distribution of plastic emissions between environmental compartments due to their redistribution. For natural and synthetic rubber, data on initial release and redistribution are based on Unice et al. ( 2019 ), who analyzed the fate of tire wear particles in the Seine watershed (France). Redistribution rates are suggested for the other polymers based on the initial environmental compartment and the polymer’s density, as described in “Sect.  2.2 .” Redistribution rates from fresh and marine water to the other compartments are generic. As explained in “Sect.  2.1 ,” redistribution rates from soil and air are given for Germany. The shares of coastal populations of other countries can, e.g., be based on the CIA World Factbook and the geographic information system (GIS) data provided by Jambeck et al. ( 2015 ).

Based on a redistribution rate from soil to marine water of 27.5% and a coastal population share of 11% in Germany, 97% of plastics emitted to soil will remain in soil; the rest might sink to the river sediment (2.7%) or be redistributed to the ocean and remain in the marine water (3%) or sink to the marine sediment (0.3%) (Unice et al. 2019 ), dependent on the polymer’s density.

After initially being emitted into the air, 2.4% of the plastics will be deposited onto fresh water and 97.6% onto soil, based on a surface share of 2.4% water and 97.6% infrastructure or vegetation (both considered soil) in Germany (Statistisches Bundesamt 2017 ).

3.2 Specific surface degradation rates

The SSDR of fossil-based plastics, natural and synthetic rubber, and biobased plastics in the four compartments, river sediment, marine water and sediment, and soil, are displayed in Fig.  10 . Beige dots and triangles display degradation rates in marine sediment, dark green dots and triangles degradation rates in marine water, light blue dots and triangles represent degradation rates in marine sediment, and brown dots and triangles degradation rates in soil (including compost). Dots indicate values found in literature and triangles represent expert estimates.

Specific surface degradation rates of different polymers in different environmental compartments in µm per year on a logarithmic scale (data can be found in SM2 )

Figure  10 displays all degradation rates found during the literature review, independent of the data quality. As can be seen in Fig.  10 , very few data are currently available for conventional fossil-based plastics. Only Yabannavar and Bartha ( 1994 ) and Boyandin et al. ( 2013 ) conducted experiments in soil with polyolefins (PE and PP), and no data are available to calculate degradation rates of conventional fossil-based plastics in marine and river sediment. Most studies investigating conventional fossil-based plastics were conducted in marine water.

For some kinds of PE, degradation rates vary very little. For example, for HDPE, Sudhakar et al. ( 2007 ) and Artham et al. ( 2009 ) conducted experiments in marine water at the Bay of Bengal. The corresponding degradation rates are 11.4–12.0 µm per year. Likewise, for PE, the variation among the data is very small (0.0–0.6 µm per year in studies conducted by three different research groups: Yabannavar and Bartha ( 1994 ); Rutkowska et al. ( 2002a ); Boyandin et al. ( 2013 )). On the other hand, Sudhakar et al. ( 2007 ) and Artham et al. ( 2009 ) conducted the same experiments as for HDPE also for LDPE, but their results on LPDE differ substantially (14.4–38.0 µm per year). The higher SSDR of LDPE compared to HDPE can be explained due to more amorphous zones. Besides, highly crystalline PE is highly stable against degradation. This tendency, however, is not reflected by all data sets. Similarly, Eich et al. ( 2020 ), who also investigated the degradation of LDPE, found no degradation in the marine water of the Mediterranean Sea. In summary, it can be stated that there exist huge differences for different kinds of PE and even the degradation speed of the same kind of PE (LPDE) varies clearly between different studies.

In the case of PP, degradation rates range from 0.2 µm per year (Resmeriță et al. 2018 ) to 7.6 µm per year (Sudhakar et al. 2007 ) and for PU from 0 to 193 µm per year (both Rutkowska et al. 2002b ). The reported low degradation rates might be related to the limited bioavailability of the plastic’s molecules to microorganisms. For example, according to Gilan et al. ( 2004 ), the low degradation rates observed for PE might be since most bacterial surfaces are hydrophilic and PE is hydrophobic. In practice, other nutrition/energy sources might be more easily available to microorganisms than those contained in plastics. Besides, the amount of species of microorganisms that can degrade plastics is limited. For example, according to Kumar Sen and Raut ( 2015 ), only 19 genera of bacteria and 12 fungal genera are known to degrade LDPE.

Compared to the conventional fossil-based polymers, more data are available for biodegradable fossil-based and biobased polymers. Similar to conventional fossil-based polymers, more degradation data are available for marine water than for soil. Nevertheless, soil degradation rates are available for PBAT, PBS, PHA, PHB, PHBV, PLA(-blends), and starch-blends. Besides, some data are available concerning degradation in freshwater (for PBS, PBSA, PCL, PES, PEA, PHB, and PLA-blends), in river sediment (for PLA-blends), and in marine sediment (for PBSe and PBSeT). Degradation rates are available for most biobased polymers (except PBAT) for at least two different compartments. As might be expected, degradation seems to be faster for polymers that are commonly characterized as biodegradable or compostable. Nevertheless, in total, there is a smooth gradient from high to low degradability.

Concerning conventional fossil-based polymer types, data are insufficient to either support or contradict the conclusion of Andrady ( 2011 ) that degradation is faster on land than in aquatic systems, e.g., due to greater exposure to degradative forces such as UV light. For biodegradable fossil-based and biobased polymer types, the extracted data from the literature supports that hypothesis for all cases where data are sufficient to calculate both rates.

Besides differences in degradation rates related to the polymer type, the residence time \({\tau }_{R}\) of plastic degradation depends on the shape (film, fiber, particle) and size (characteristic length < 0.1 mm, 0.1–1 mm, and > 1 mm) as expressed in Eq. ( 10 ) and visualized in Fig.  11 . Although only moderate changes of the input variables degradation rate, characteristic length, and shape are considered, the average lifetimes span several orders of magnitude: particles always degrade faster than fibers and films of the same characteristic length, although the initial characteristic length’s influence is more dominant than the shape of the emission. Especially for low degradation rates, the average lifetime exponentially increases, which leads to less reliable experimental results for such polymers in reasonable experiment times.

Residence times \({{\varvec{\tau}}}_{{\varvec{R}}}\) depending on surface degradation rates (SSDR), the shape, and characteristic length of plastic emissions

3.3 Calculation of fate factors

According to the methodology described in “Sects.  2.3 and 2.4 ,” FFs were calculated for Germany for different time horizons: 100, 500, and 1000 years. Examples of typical plastic emissions are presented in Table 2 . The complete list of FFs is given in SM3 . SM3 also provides the FFs without applying any time horizon and the corresponding GSD.

In general, the FF represents the extent of the persistency of a plastic emission compared to a reference emission with an residence time of 1 year. Conventional fossil-based plastics with a characteristic length of 1 mm and more result in very long residence times of more than 200 years up to 1000 years. This is the case, for example, for yogurt cups (PS), picnic cutlery (PS), plastic caps (PE), microbeads from cosmetics (PE), PET bottles but also for pellet losses (e.g., PVC). No significant degradation occurs when the FF for the different time horizons is close to the respective time horizon. This applies to yogurt cups (PS), picnic cutlery (PS), microbeads (PE), and pellet losses (PVC).

In products with a characteristic length less than 1 mm, such as plastic bags (LDPE, PLA), shorter residence times are estimated. Surprisingly, considering a time horizon of 500 and 1000 years, the residence time of LDPE bags emitted to soil was calculated to be shorter than for PLA bags also emitted to soil. In contrast, with a time horizon of 100 years, the residence time of PLA bags (3 years) is slightly shorter than that of LDPE bags (4 years). This is because 97% of the PLA bag degrades very fast in soil and only 3% is redistributed to river and marine sediment where PLA shows no significant degradation. That means plastic emissions that degrade significantly faster in one compartment receive a comparably higher FF with an increasing time horizon than plastic emissions with a similar degradation speed in all compartments. Therefore, to allow for a deeper interpretation of the results, compartment-specific residence times should be analyzed.

Very short residence times of less than 4 years (FF < 4) were calculated for tire wear, balloons (NR/SBR), abrasion from drinking and sewage pipes (PP), and very fine plastic particles such as those released by a lawn trimmer (PA) string abrasion. With a time horizon of 100 years, hardly degradable plastic emissions receive an FF close to 100. Compared to a time horizon of 500 years, conventional fossil-based plastics with a characteristic length of 1 mm or more are characterized similarly, all with the highest FF. One exception is the abrasion of PET fibers from washing with an residence time of 233 years with a time horizon of 500 years. When changing the time horizon to 1000 years, littered plastic caps (PE) are assigned an FF of 419 years and the littered PET bottle of 358 years. Accordingly, it can be concluded that a larger time horizon allows for more differentiation.

4 Discussion

Applying the methodology results in elementary flows and corresponding FFs that can be integrated into LCA. Depending on certain modeling choices (e.g., the time horizon) and the share of compartment-specific FFs compared to the total FFs, differences among polymer types become more or less apparent (cf. “Sect.  4.1 ”). While this paper is the first to present FFs for plastic-related elementary flows, there are still some challenges regarding the application to LCA (cf. “Sect. 4.2 ”).

4.1 Underlying patterns of plastic emissions’ fate in the environment

The FFs are more than pure degradation data of a polymer type, because size, shape, initial compartment, and redistribution are considered, too. When not applying a time horizon and combining Eqs. ( 4 ) and ( 9 ), it can be concluded that the characteristic length influences the fate linearly while the SSDR enters the equation inversely proportionally (Fig.  11 ). At all times, the remaining mass of the emission is smaller for particles and fibers than for films due to a faster degradation in the beginning (Fig.  5 ). Some polymers show very different degradation rates in different compartments (e.g., PLA). Consequently, under some circumstances conventional fossil-based polymers degrade faster than biodegradable fossil-based polymers (cf. Table 2 , examples 1 and 10).

When applying a time horizon, the patterns are even more complex Eq. ( 11 ): if there is no appreciable degradation of an emission within a time horizon, i.e., the residence time is close to the time horizon, the time horizon is “saturated” and changes of the degradation parameters (SSDR and initial size) do not alter the value of \({\tau }_{A}\) significantly (e.g., Table 2 , examples 12 and 13). Looking at the example of the plastic bags again (Table 2 , examples 1 and 10), it can be noted that the PLA bag degrades faster than the one made of LDPE when applying a time horizon of 100 years, while it degrades much slower when applying a 500 or 1000 year time horizon, due to a very fast degradation of the major fraction in soil and a very slow degradation of the minor fraction distributed to marine and river sediment.

In this publication, we were able to develop FFs for Germany for 24 polymers. In order to find similarities and differences in polymers’ behavior in the environment, we clustered them. For the clustering, we compare the compartment-specific FFs with the total FF (see SM3 ). The polymer types are grouped into six clusters (types A to F in Fig.  12 ), independent of the shape and size of the emission. In Fig.  12 , it is shown in which compartment degradation happens when a polymer is emitted to a specific compartment. For instance, when emitting a type A polymer to soil, degradation mainly occurs in soil and partly in marine water, but no degradation is observed in the sediment compartments. Types A and B are polymers with a density lower than water. When emitted to soil or air, most of their mass is found in soil after the redistribution. When emitted to fresh or marine water, types A and B emissions float on the water surface and are ultimately transported to marine water. Type A polymers (HDPE, LDPE, PEA, PES, PP) degrade equally fast in all final compartments. Therefore, the contribution of the FFs for the different environmental compartments is equal to the final compartment share of the emitted plastic mass. Type B polymers (PE) differ from type A polymers in higher SSDRs in soil than in all other compartments, resulting in a lower degradation time in soil. For emissions to fresh and marine water, the fate is similar to type A polymers. For emissions to soil and air, however, the overall fate of type B polymers is dominated by the high persistency in marine water, despite the bigger compartment share of the mass in soil. The polymers of types C to F have a higher density than water and thus sink through the water column and ultimately reach either the river or the marine sediment when emitted to water. This results in a degradation in marine and river sediment, as well as soil, but no degradation in marine water for polymers of types C to F. SSDRs of type C polymers (NR/SBR, PA, PBAT, PBSA, PC, PCL, PET, PHB, PHBV, PS, PU, PVC, starch-blend) are in the same order of magnitude in all final compartments and the redistribution drives the degradation pattern. The share of compartment-specific FFs to the total FF for types D to F polymers is explained by the ratio of the SSDR in soil to the SSDRs in the sediments. The greater the ratio, the less soil degradation plays a role. PBS, PBSe, and PBSeT are type D polymers (SSDR in soil is approximately ten times higher than the SSDR in the other compartments), PHA is the type E polymer (SSDR in soil is approximately 100 times higher than the SSDR in the other compartments), and PLA(-blend) for a type F polymer (SSDR in soil is 70,000 times higher than the SSDR in the other compartments). In the future, it might be possible that a different clustering is necessary. For instance, new classes could be introduced when considering other polymers. In addition, other polymers could be assigned to the already defined classes for which no FF has yet been determined due to lack of data.

Clustering of the different degradation behavior of plastic emissions in different compartments

4.2 Application of the fate factors to LCA

The proposed methodology links FFs to the residence time of plastics in the environment and addresses how many years it takes for the plastic emission to degrade in the final compartments. We assume that the risk potential of plastic in the environment is correlated to its persistence in nature.

The prerequisite for applying the proposed methodology to life cycle analysis is properly modeling technical flows, combined with accurate initial release rates. As mentioned before, the most precise option is to measure or calculate specific initial release rates for the investigated products in their entire life cycle. If the precise modeling of the product-specific initial release is not possible, less precise data for estimating initial release rates for other countries can be taken, e.g., from Peano et al. ( 2020 ). When determining the initial release of plastics emitted by a product, the corresponding elementary flows should be named according to the convention described in “Sect.  2.2 ,” including the precise characteristic length instead of the size classes, if available. If the characteristic length is not available, the proposed elementary flows can be used as proxies. However, it needs to be taken into account that we used the upper end of the range of the characteristic length for the calculations, leading to relatively long residence times.

In order to apply the FFs to a specific region, the redistribution rates (cf. Table 1 ) can be adapted to region-specific conditions. Following these steps, regionalized and emission-specific FFs can be determined.

The proposed methodology is intended to support (plastic) product designers, for example, to support materials’ choice. In particular, products with higher littering rates or those where abrasion leads to the release of microplastics should be assessed by conventional LCIA categories and analyzed regarding plastic emissions. However, the proposed FFs can also be used to evaluate products ex-ante, such as giving additional advice to producers on which packaging solution performs better from a plastic emission point of view.

4.3 Limitations of the methodology

The concept presented in this article is limited on several accounts due to the varying certainty of the applied parameters. Data on polymers’ degradability are scarce for some polymers, particularly for conventional fossil-based polymers such as PET or PP, as presented in Fig.  10 . One reason for the limited data availability is the need for long experiment durations for slowly degrading polymers and the associated high costs for degradability tests that are close to reality. Besides, there is a lack of reliable field test methods and standards for assessing and certifying biodegradation (Lott et al. 2020 ). Test methods differ, for example, in trial duration, the method to measure degradation (e.g., weight loss vs. CO 2 emission), climatic and environmental conditions. Norms addressing degradation measurements were mainly developed exclusively for biobased plastics focusing on compostability in industrial compost (e.g., EN 13,432, ISO 17088, EN14995, ISO 18606, ASTM D6400, AS 4736) or garden compost (AS 5810, NF T 51–800). Only the DIN EN norm 17,033 was developed to measure the biodegradability of mulch films in agricultural soils and horticulture (EN 17033:2018  2018 ).

Following the proposed methodology to calculate residence times of plastic emissions, we probably estimate relatively low residence times since it is scientifically challenging to differentiate between (bio)degradation and fragmentation processes as primary drivers for the observed mass losses of plastics in the environment. Additionally, slowly degrading polymers tend to have too short calculated resident times compared to polymers with faster degradation speed since initial losses of better degradable monomers, small molecules, or additives can lead to underestimated degradation rates. Most studies on the degradation of conventional fossil-based plastics only lasted long enough to reach a minimal degradation. Long-term experiments are needed to measure the degradation to substantial values (> 10%, ideally to ≥ 50%) in different environmental compartments. Besides, because microplastics found in the environment and those used in laboratory experiments differ (Phuong et al. 2016 ), laboratory results are not transferable to the field and reduce the amount of usable data. If no data were available for the degradation of a polymer in a particular environmental compartment, degradation data from another environmental compartment are used as approximate values, leading to higher data uncertainty.

In order to analyze the uncertainty introduced by SSDR and redistribution, GSDs of the FFs calculated for Germany are presented as boxplots in Fig.  13 . In the boxplots, the median represents the middle GSD of polymers’ FFs. Uncertainty represented by the median GSD varies between 1.2 and 3.2. Low uncertainty (with a median GSD lower than 2) is given for most plastic types (22 out of 24). Low uncertainty is due to representative values for SSDR. The uncertainty introduced due to redistribution is the same for all plastic types (GSD = 1.3). FFs of PVC and PS emissions have higher GSDs (higher than 3) as SSDRs for these plastic types rely on expert estimates. Expert estimates go along with the greatest possible uncertainty as defined here. Variability in FFs is represented by the GSD distribution spread and can be quantified by the interquartile range (IQR). The IQR represents the difference between the 25th and 75th percentiles of a distribution. For PET, the IQR is 1.3, which represents the highest variability among all FFs GSDs. The GSDs of PET’s SSDRs are the reason for the variability of the GSD values for the FFs of PET. The GSD of the SSDR in marine and river sediment is 1.37, in marine water 1.25, and in soil 2.87. For the remaining polymers, FFs GSDs are lower (IQR ranges between 0.01 and 0.3).

Geometric standard deviation of FFs for each plastic type

We assume that degradation happens exclusively at the surface of the emitted item. However, some polymers might degrade more volume-driven (bulk degradation) than surface-driven, such as PET and polyamides (Lyu et al. 2005 ; Pickett and Coyle 2013 ). Although the total surface area of the plastic emission (e.g., macro vs. microplastic) might play a role in the redistribution of the emission, our methodology only considers its characteristic length. All other differences like shape and size are neglected, although they might have an impact on the redistribution (e.g., airborne particles, migration in soil).

Another limiting factor is that specific additives were not taken into account in this publication. For example, the general composition of car tire is approximately 40–60 wt% NR/SBR (Wagner et al. 2018 ; Wik and Dave 2009 ); the remains are a mixture of fillers, softeners, vulcanization agents, and additives (Baensch-Baltruschat et al. 2020 ; Wagner et al. 2018 ; Wik and Dave 2009 ). Similarly, Ioakeimidis et al. ( 2016 ) based their degradation measurements on littered PET bottles found in the Aegean Sea, for which no information is available concerning additives used in production. For similar cases, degradation of the emissions is different from pure polymers or a mixture of polymers.

We limit the environmental compartments to freshwater, marine water, soil, river sediment, and marine sediment in the described methodology. However, in the future, when more data become available for redistribution and degradation, additional environmental compartments should be considered. For example, we did not differentiate between different marine compartments other than water and sediment, although the degradation rate can differ in various marine compartments (eulitoral, pelagic, benthic) and climate zones (Lott et al. 2020 ). Differentiation in climate zones is still an issue regarding regionalized FFs. Regionalization should also be applied to the above-mentioned redistribution rates. For example, road runoff treatment is central in determining the number of plastic particles ending up in surface water. Water management differs between countries or regions, resulting in large differences in the amount released to freshwaters. Even within one country, different runoff water treatment systems can co-exist.

5 Conclusions and perspectives

The proposed methodology is a crucial step to consider plastic emissions to the environment in LCA. We proposed FFs and respectively CFs for plastic emissions allowing to calculate impacts of plastic pollution measured in plastic pollution equivalents, based on plastics’ residence time in the environment. We also provided a basis for developing a future impact category addressing potential impacts caused by plastic emissions in LCA. Regarding some aspects of the calculations, assumptions or estimations were made, which call for quality assurance by increasing available information. The methodology consists of several independent elements which can be replaced or improved independently:

Degradation measurements (SSDRs) retrieved from literature,

The degradation model (in our case surface-driven degradation),

Redistribution patterns,

An FF based on the residence time,

Estimation of the data quality by a pedigree matrix and uncertainty analysis.

As pointed out before, the SSDRs derived from literature entail uncertainty due to, e.g., measurement inaccuracies or additives. The retrieved values can be replaced by more accurate or certain ones, once available, especially concerning degradation data the redistribution between different environmental compartments. In the future, these data should focus on those from experiments where degradation measurements are obtained according to the test setup suggested by Lott et al. ( 2020 ). That means, after proving a polymer’s biodegradability in the laboratory, an as good as possible standardized real-life experiment should be conducted, leading to more realistic and comparable data and reducing the impact of artificial conditions on the one hand and exceeded weight loss rates caused by fragmentation on the other hand.

Other more sophisticated approaches can replace the degradation model (surface-driven degradation), e.g., Junker et al. ( 2016 ). Even different approaches for different polymers can be used in one model. The residence time approach shows several advantages when treating time horizons and combining different compartments compared to the more familiar half-lifetime approach. However, the latter one could be used instead, if preferred.

Concluding, the proposed FFs can serve as the first indication of plastic emissions’ fate in the environment. They may be used as a proxy where more detailed information is unavailable to evaluate micro- and macroplastics’ potential aquatic and terrestrial impacts. The proposed methodology is flexible and can be adapted to available data concerning a specific product or process, e.g., the characteristic length or shape of the emission. In order to fully characterize the impact of plastic emissions on ecosystems, biodiversity, and humans, our proposed FFs need to be combined with factors for the probability of the exposure of humans or organisms to the plastic emission (exposure factor) and for the impacts of plastics on species (effect factor). The effect factor should also take into consideration the expected severity of the damage. Coupling our approach with other work on exposure factors, effect factors, and marine litter impact models is possible but might need adjustments (Lavoie et al. 2021 ; Saling et al. 2020 ; Woods et al. 2019 , 2021 ). Besides, each methodological approach only addresses a particular type of impact, for example, physical impacts such as entanglement or chemical impacts such as toxicity. Combined, they are not exhaustive and leave out specific impacts caused by alien species or pathogens transported on plastic emissions.

In addition, the process of releasing chemicals and metabolites during degradation is still not investigated but could be crucial for assessing the effect of plastic emissions (Lambert et al. 2014 ). For example, additional hazards caused by the release of additives processed in the plastics can later be incorporated into this model in a way similar to the USEtox methodology (Rosenbaum et al. 2008 ). In order to address possible toxicological risks caused by the release of additives, fillers, or reinforcement substances and to integrate the suggested model with ecotoxicological data, their types and degree should be included in the elementary flows’ definition if known. Therefore, additional research is needed to yield comprehensive effect factors and develop complete CFs for micro- and macroplastics in marine and terrestrial environments. Finally, although not yet in the public focus, soluble polymers such as used, for example, in detergents, might be harmful, too. Further methodological development is needed for their consideration since they probably behave differently in the environment and parameters such as the characteristic length are difficult to assess.

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Change history

11 april 2022.

A Correction to this paper has been published: https://doi.org/10.1007/s11367-022-02047-8

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Open Access funding enabled and organized by Projekt DEAL. This work was carried out within the project PlastikBudget (grant: 01UP1702A-B), which is part of the funding program “Plastics in the Environment – Sources • Sinks • Solutions” funded by Germany’s Federal Ministry for Education and Research (Bundesministerium für Bildung und Forschung—BMBF). In addition to that, this project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 860720. The authors gratefully acknowledge the funding agencies for their support.

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Maga, D., Galafton, C., Blömer, J. et al. Methodology to address potential impacts of plastic emissions in life cycle assessment. Int J Life Cycle Assess 27 , 469–491 (2022). https://doi.org/10.1007/s11367-022-02040-1

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Analysis of Sampling Methodologies for Noise Pollution Assessment and the Impact on the Population

Guillermo rey gozalo.

1 Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, 5 Poniente 1670, Talca 3460000, Chile

Juan Miguel Barrigón Morillas

2 Departamento de Física Aplicada, Escuela Politécnica, Universidad de Extremadura, Avda. de la Universidad s/n, Cáceres 10003, Spain; se.xenu@nogirrab

Today, noise pollution is an increasing environmental stressor. Noise maps are recognised as the main tool for assessing and managing environmental noise, but their accuracy largely depends on the sampling method used. The sampling methods most commonly used by different researchers (grid, legislative road types and categorisation methods) were analysed and compared using the city of Talca (Chile) as a test case. The results show that the stratification of sound values in road categories has a significantly lower prediction error and a higher capacity for discrimination and prediction than in the legislative road types used by the Ministry of Transport and Telecommunications in Chile. Also, the use of one or another method implies significant differences in the assessment of population exposure to noise pollution. Thus, the selection of a suitable method for performing noise maps through measurements is essential to achieve an accurate assessment of the impact of noise pollution on the population.

1. Introduction

A recent publication by the World Health Organization points out that noise pollution, ranked second among a series of environmental stressors for their public health impact and, contrary to the trend for other environmental stressors which are declining, is actually increasing in Europe [ 1 ].

Noise is known to have auditory and non-auditory health impacts [ 2 ]. Environmental noise causes both psychological and physiological non-auditory health effects and the evidence for the non-auditory effects is growing [ 3 ]. Specifically, road traffic is considered to be the main source of community noise pollution. The most important non-auditory effects of traffic noise are annoyance and sleep disturbance [ 4 , 5 , 6 , 7 ]. Annoyance is a feeling of displeasure that can result in adverse emotions including irritability, stress, fear, and even depression [ 8 , 9 , 10 , 11 , 12 ]; it is associated with health-related quality of life [ 13 , 14 , 15 ].

Nighttime noise exposure directly influences sleep disturbance causing body motility, sleep stage changes, delayed sleep onset latency, and nocturnal awakenings [ 2 , 6 , 16 ]. Sleep disturbances can lead to serious long term health effects and there is increasing evidence from epidemiological studies that indicate long-term noise exposure leads to cardiovascular diseases, obesity or diabetes [ 17 , 18 , 19 , 20 , 21 ].

In considering the adverse effects of noise, the European Commission recognised community noise as an important environmental problem and adopted the European Noise Directive to assess and manage environmental noise [ 22 ]. The Directive focuses on noise mapping that aims to evaluate the number of people exposed to environmental noise. The precision of noise maps is essential to an appropriate identification of affected places and for planning suitable control measurements. In addition, a proper management of noise pollution can lead to benefits in reducing air pollutants because of the relation between them [ 23 , 24 ].

The European Noise Directive has not only been applied to European countries, but has also been used as a reference by non-European countries [ 25 , 26 , 27 , 28 ]. For example, in Chile, where this study was developed, over recent years the government has supported a number of projects initiated to gather knowledge about the acoustic situation in the cities [ 29 ]. As in other countries, different methods or strategies have been used for noise mapping, such as computation methods or studies carried out with “ in situ ” measurements. The use of an appropriate sampling method is important for the precision of noise maps, because even computation methods need to be validated and calibrated using “ in situ ” measurements [ 30 , 31 ].

Nowadays the sampling methods more commonly used in noise mapping are based on systematic random sampling using a regular grid or on the stratification of urban roads [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. There are also studies that carry out a stratification of land use after selecting any of the previous sampling strategies [ 40 , 41 ].

The grid method is the only sampling method that is accepted in an international standard, ISO 1996-2, that represents a verified reference for the measurement of noise levels in urban environments [ 42 ]. The grid method is widely used in many scientific fields because its use guarantees the statistical principle of equal probability and, moreover, a uniform coverage of the area under study. However, the grid method has other drawbacks. The standard says that the source of these problems stems from the existence of a high sound level variability in cases of proximity to the noise sources or the existence of large physical obstacles.

The stratification of urban roads is an increasingly popular method [ 34 , 36 ]. It is based on the generally accepted assumption that road traffic is the most important source of noise in cities, and for most streets it can be considered the main cause of the spatial and temporal variability of that noise. The stratification of urban roads used by a great number of researchers is based on information from the relevant ministries of transport [ 27 , 37 , 38 , 39 , 40 ]. These organisations classify the roads according to their main function and especially according to their design features.

In this context, our research group has been working for some years on the development of a sampling method for “ in situ ” noise measurements. We term this method the categorisation method. On the basis of the concept of street functionality, each stratum defined by the categorisation method presents a sound level variability that is lower than the total sound spatial variability in a city. This has produced significant improvements in both the reduction of the number of sampling points and in the estimation of noise levels in unsampled streets. Its usefulness has mainly been studied in Spanish cities with a wide range of populations: from 2000 to 3,250,000 inhabitants [ 43 , 44 , 45 ]. However, the economic development and urban planning of Chilean cities are different from the European cities analysed with the categorisation method in previous studies. Overall, European cities have typically been developed from a medieval historic centre with a complex street structure. Nowadays, shopping centres and administration centres are located in the historic centre. Chilean cities have a grid street plan in which streets run at right angles to each other, forming a grid. Also, another important difference is the fact that Chilean cities classify their roads according to a legislative procedure, whereas no standard classification exists for the roads in Spanish cities. The applicability of both methods based on roads classification has never been previously compared. In view of the above, the following objectives have been set out in this study:

• Compare the applicability and predictive capacity of two sampling methods—the legislative road classification and the categorisation method—in the assessment of urban noise in a Chilean city.
• Compare both sampling methods in terms of the prediction of exposure levels and the percentage of people annoyed.

Achieving these objectives will facilitate better understanding of the suitability of different noise situation sampling methods in cities. Information about the percentage of the population exposed in a Chilean city will also be provided. Until now this information has not been available in the Chilean cities evaluated. According to the European Noise Directive, the knowledge of the percentage of the population exposed is required for establishing effective preventive and, if necessary, corrective measures.

This study was conducted in the city of Talca (Maule region, Chile). Talca has a population of about 200,000 inhabitants (the population increases during the academic year due to the influx of university students) and is the tenth largest city in the country. The highest percentage of the active population (approximately 55%) works in the service sector, followed by the industrial sector (approximately 36%). This city does not have a historic centre and a high percentage of buildings have only one floor. The mean annual temperature and rainfall are 13 °C and 750 mm, respectively.

Three sampling methods were analysed: the grid method [ 42 ], road types established by the Ministry of Transport and Telecommunications of Chile (MTT) [ 46 ], and the categorisation method [ 45 ]. In order to compare the uncertainties using a similar sampling time the same number of sampling points (52) was selected for each measurement method. The grid method was analysed because it is accepted in an international standard, but its applicability was not compared with the other sampling methods.

2.1. Grid Method

In the grid method, a grid is superimposed over a city map and the measurement points are located at the nodes of the square or at the nearest location when the nodes are inaccessible. The area of Talca is approximately 29 km 2 . A total of 35 squares with 52 sampling points were drawn on the city map using a grid square with 800 m of resolution. A similar square grid resolution has been used in previous studies [ 33 ]. Figure 1 a shows the map of Talca with the grid used for this study.

Sampling methods used in the city of Talca. ( a ) Sampling squares of grid method; ( b ) Ministry of Transport and Telecommunications (MTT) road types; ( c ) and categorisation method.

2.2. Road Types Established by the MTT

The Ministry of Transport and Telecommunications of Chile (MTT) classifies urban roads according to their main function and their urban design features. However, in practice, urban characteristics, such as the width of the roads, are more relevant. Five types of roads are differentiated: highway, trunk, service, collector, and local. A similar classification has been used in recent acoustic assessment studies of cities in Chile and in other countries [ 27 , 37 , 38 , 39 , 40 ].

The sampling points were then randomly selected along the total length of each road type taking into account two factors. First, in the types of roads with a greater length (see Figure 2 ), a greater number of sampling points were selected with a minimum of eight sampling points for each road type. Second, equivalent points (those points located on the same section of a street with no important intersection between them) were discarded. For this reason, only one sampling point was selected in the highway road type. Figure 1 b shows the road types and locations of the sampling points: one point in highways, eight in trunk, twelve in service, eight in collector, and twenty-three in local road types.

Length of road types and road categories in Talca.

2.3. Categorisation Method

As previously mentioned, the categorisation method is based on the concept of street functionality, that is to say, the functionality of the streets of the city as a communication path between different parts of the city and between the city and other urban areas. In addition, other variables such as the flow of vehicles, the type of traffic, the average speed, and urban variables may have a clear relationship with functionality [ 47 ]. The streets of Talca were classified according to the definitions proposed in the categorisation method established in previous work [ 48 ].

A strategy similar to the previous method was used to select the sampling points in each road category. Figure 1 c shows the categorisation of different streets in the city and the locations of sampling points: eight points in Category 1, eight in Category 2, ten in Category 3, twelve in Category 4, and fourteen in Category 5.

2.4. Measurement Procedure

The measurements of different methods were carried out simultaneously from March to July 2015 following the ISO 1996-2 guidelines [ 42 ]. The measurements were performed on different working days and the sampling time for each measurement was 15 min. Previous studies [ 36 , 49 ] showed stability of the daily noise levels in the aforementioned months, and also these studies indicated that the main temporal variability of noise levels was among time-intervals within the day. At each sampling point, for each sampling strategy, at least five measurements were randomly selected in the following time-intervals: diurnal (from 07.00 to 19.00), evening (from 19.00 to 23.00), and nocturnal (from 23.00 to 07.00). A type-I sound level meter (2250 Brüel & Kjaer; Nærum, Denmark) was used with tripod and windshield and it was placed at a height of 1.5 m and at 2 m from the curb.

The A-weighted equivalent sound level ( L Aeq ) was used to analyse the results in the present study at different time-intervals of the day. The L Aeq registered in the diurnal period (from 07:00 to 19.00) and evening period (from 19.00 to 23.00) was very similar. For this reason, L Aeq from 7.00 to 23.00 ( L d ) was analysed. The noise descriptor L den was calculated following the guidelines of the European Noise Directive [ 22 ]. Other relevant information (traffic flow, types of vehicles, meteorological conditions, urban variables, etc. ) was also noted.

2.5. Statistical Analysis

In the acoustic assessment in Talca, the applicability of different sampling methods was analysed using the calculated noise descriptors ( L d , L n and L den ) at each sampling point ( P ij ). The subscript “ i ” refers to the point code and the subscript “ j ” refers to the sampling method.

In the grid method there are no assumptions of the location of sampling points in urban roads. However, the location of the sampling points with respect to the traffic noise source was similar in the different sampling methods. For this reason, the sound values registered in the sampling points of the grid method were used to analyse the predictive capacity of the others two sampling methods. The noise value assigned to each square ( S i ) was the median value of the four nodes of the square. For each square, the interquartile range was calculated from these four values. Moreover, the difference in sound levels between adjacent grid points was calculated. This difference should not be greater than 5 dB according to ISO 1996-2 [ 42 ].

For the MTT road types and the categorisation method a similar statistic procedure was carried out. The value assigned to each road type ( R i ) or road category ( C i ) was the average of the sound levels measured at the sampling points ( P ij ). This value was the expected value for all of the other points located in the same road type or road category. The average sound value and its variability will determine whether the stratums formed by road categories or by road types present significant differences. This hypothesis was assessed using the nonparametric tests Kruskal-Wallis and Mann-Whitney U [ 50 , 51 ]. This hypothesis was not tested with an inferential analysis in previous studies that used a legislative road classification [ 27 , 37 , 38 , 39 , 40 ]. The Kruskal-Wallis test was used to compare all the road categories in order to identify any significant differences. When such differences were found, Mann-Whitney U tests were used to compare pairs of road categories. The Mann-Whitney U test evaluates whether two independent samples or observations come from the same distribution. To avoid any errors due to the use of data from the same population rather than randomly selected data, the Holm correction was used [ 52 ].

In contrast to previous statistical tests, the receiver operating characteristics analysis ( ROC ) was used to evaluate the discriminative capacity of the MTT road types and of categorisation method to differentiate the sound values of the sampling points between pairs of strata (stratum i versus stratum j ) [ 45 ]. For the categorisation method and for MTT, the strata are the road categories and road types, respectively. The ROC analysis allows us to establish the upper and lower limits of the sound levels assigned to each stratum, to calculate the sensitivity (capacity to include previously assigned sampling points in the stratum), the non-specificity (proportion of sampling points that were not initially assigned to a certain stratum but that the ROC analysis indicated belonged to that stratum), and the predictive values (proportion of the sampling points that the ROC analysis assigned to a stratum that matched the strata to which they were initially assigned, relative to the total number of sampling points that the ROC analysis determined for the stratum). To do so, the following equations were used:

After studying the functioning of both methods, the predictive capacity of each method was then analysed using the sound values of the sampling points of the other methods as controls [ 53 , 54 ]. The parameter used for this analysis was the prediction error (ε i ), which is the difference between the measured value (control value) and the predicted value. The equations used to calculate the prediction error of the MTT road types (Equation (4)), and categorisation method (Equation (5)), respectively, were as follows:

The subscript “ i ” refers to the sampling point code ( P i ), road type code ( R i ) or road category code ( C i ), and the subscript “ j ” refers to the sampling methods in which the error is not being analysed. Next, the median prediction error obtained for each road category or road type was compared with the null value. For this, the Wilcoxon signed-rank test was applied [ 55 ]. This test determines whether the median of the prediction errors was biased. If the distribution of the prediction errors is unbiased, then a zero value will be obtained for the median.

Prediction errors of the different methods were also compared. To that end, the median absolute error of prediction (|ε i |) was analysed using the Mann-Whitney test [ 51 ]. If there is no significant difference it is assumed that the sampling methods have a similar predictive capacity.

Finally, the population exposed to noise was analysed and the population annoyed by noise was estimated. The demographic data of the geographic information system of the National Statistics Institute of Chile [ 56 ] were used to analyse the population exposed to noise. Noise levels registered in the road categories or road types were assigned to populations that reside in them [ 54 ]. Internationally validated equations were used to estimate the population annoyed by noise. Thus, the percentages of annoyed (% A ) and highly annoyed (% HA ) population were estimated from the L den descriptor with the following equations [ 57 , 58 ]:

With respect to nocturnal noise, the percentages of population with little sleep disturbance (% LSD ), sleep disturbance (% SD ), and those who were highly sleep disturbed (% HSD ) were estimated from L n descriptor using the following equations [ 59 ]:

3.1. Study of the Functioning of Sampling Methods

3.1.1. grid method.

Having calculated the sound values of L d , L n and L den descriptors in the different sampling points, the sound values of the different square grids were calculated. The results are shown in Table 1 . Table 1 shows that the interquartile range of sound values registered in the cells is quite high. Previous studies [ 33 , 48 ] reported high uncertainties in the predictive capacity of the grid squares, due to the high variability of the sound levels among nearby streets with different functionality. Therefore, if the sound differences between adjacent sampling points are analysed, 69%, 49% and 59% are higher than 5 dB for L d , L n and L den descriptors, respectively.

Median (M e ) and interquartile range (IQR) of L d , L n and L den descriptors registered in the square grids.

3.1.2. MTT Road Types

This stratified sampling is based on the hypothesis that different strata—road types in this case—have significant differences in sound values. First, to resolve this hypothesis, a descriptive analysis through a box plot was carried out ( Figure 3 ).

Box plot of L d , L n and L den descriptors registered in each road types.

Figure 3 shows that average values of sound descriptors decrease from trunk to local road type. In highway road types, as previously indicated, only one sampling point was used. In this road type the sound values of 76.4 dB, 70.1 dB and 78.9 dB were registered for the L d , L n and L den descriptors, respectively. Figure 3 also shows the analysis of the variability in mean sound levels. Trunk and service road types have an overlap of interquartile range and local road types have a high variability.

The hypothesis was resolved first by using the Kruskal-Wallis test. This test indicated significant differences ( p -value ≤ 0.001) for all the sound descriptors studied. Thus, the Mann-Whitney U test was then applied to analyse the differences among road type pairs ( Table 2 ).

p -Values with Holm adjustment of pairwise comparisons of road types using Mann-Whitney U test.

As shown in Table 2 , the Mann-Whitney U test found no significant differences ( p -value > 0.05) between trunk and service road types for L d , L n and L den descriptors. Nevertheless, for the remaining pairs of road types, significant differences ( p -value ≤ 0.05) for all sound indicators analysed were found.

In order to corroborate the quality of the previous results and to obtain more information about the MTT road types, the classification capacity of this method was then examined using ROC analysis. The results of this analysis are shown in Figure 4 .

Results of ROC analysis for the different sound descriptors registered in the Ministry of Transport and Telecommunications road types.

From the results shown in Figure 4 , the following can be noted:

• Regarding the ROC sensitivity (%), which is a measure of the capacity to include previously assigned sampling points in the stratum, only the collector road type for L n and L den has values above 80%. The sensitivity has low percentages for the sound descriptors analysed, sometimes even lower than 50%, because of the presence of overlaps among trunk and service road types and the high variability of the local road type.
• Regarding the non-specificity (%), which measures the proportion of sampling points that were not initially assigned to a given stratum, but which the ROC analysis indicates belong to that stratum, only the local road type has values lower than 10% for all the sound descriptors. The collector road type also has high non-specificity values for all the sound descriptors, although it has high sensitivity values for L n and L den .
• Finally, with regard to the predictive values of the different road types (which represent the proportion of the sampling points that the ROC analysis assigned to the stratum that matched the road types to which they were initially assigned, relative to the total number of sampling points that the ROC analysis determined for the stratum) only the local road type has values above 80% for all the sound descriptors. The stratum predicted by the ROC analysis for local road types has a high percentage of sampling points that MTT had initially classified in this road type. However, other sampling points of local road types have high values and these points are classified in other road types according to ROC analysis. Therefore, the local road type has low sensitivity values.

3.1.3. Categorisation Method

The different road categories defined by the method are based on the assumption of having significantly different noise levels. Therefore, like the MTT road types method, a descriptive and inferential analysis was conducted to test this hypothesis. The results of the descriptive analysis are shown in Figure 5 .

Box plot of L d , L n and L den descriptors registered in each road categories.

In the box plot, the interquartile ranges of the different road categories and sound descriptors have no overlaps. Category 5 has the greatest variability but it is considerably lower than that presented by the local road type.

An inferential analysis was then conducted using the Kruskal-Wallis and Mann-Whitney tests. The Kruskal-Wallis test indicates significant differences ( p -value ≤ 0.001) for all the sound descriptors studied. Thus, the Mann-Whitney U test with Holm correction was applied to analyse the differences among road category pairs ( Table 3 ).

p -Values with Holm adjustment of pairwise comparisons of road categories using Mann-Whitney U test.

As shown in Table 3 , the Mann-Whitney U test found significant differences ( p -value ≤ 0.01) among all pairs of road categories studied for all sound descriptors analysed. To corroborate the previous results, as carried out for the previous method, the classification capacity of the categorisation method was studied via ROC analysis. The results of this analysis are shown in Figure 6 .

Results of ROC analysis for the different sound descriptors registered in the road categories.

The results presented in Figure 6 show that the sensitivity of different sound descriptors is higher than 80% for all road categories (except the L n in Category 4), and even for the L den descriptor it is 100%. These high percentages are also obtained for the predictive value and therefore the percentages obtained in non-specificity are very low. They are lower than 5% in all sound descriptors.

These results differ from the previous method and it is therefore essential to compare the predictive capacity of both sampling methods. The results of this comparison are shown in the following section.

3.2. Predictive Capacity Analysis

In analysing the predictive capacity of the sampling methods, the sound values registered at the sampling points of the methods that were not being analysed were used.

To evaluate predictive capacity of the MTT road types, the sampling points chosen for the grid and categorisation method were used to compare the predictions of the MTT road types. All 104 sampling points evaluated in the grids and road categories could be associated with one of the road types (only one point was located in the highway road type, therefore, this road was not analysed). The sound values of these sampling points were compared with the mean value of the road type in which they were located and the prediction error was calculated using the difference between them (Equation (4)). The prediction error was analysed according to the road type where the control sampling point ( P ij ) was located. Table 4 shows the median from the error for the analysed sound descriptors.

Prediction errors (ε) of Ministry of Transport and Telecommunications road types for L d , L n and L den descriptors.

No.: Number; * Significant at p ≤ 0.05; ** Significant at p ≤ 0.01; n.s. Non-significant difference ( p > 0.05).

Prediction errors of MTT road types are mostly lower than the 3 dB considered as suitable for estimations on noise maps. However, according to the Wilcoxon signed-rank test, errors by underestimation in trunk and service road types have significant differences with respect to the null value (except for the L den descriptor in the service road type). These two road types, as noted above, showed no significant difference in the average sound values registered. This fact directly affects the predictive capacity of the method.

The predictive capacity of the categorisation method was then analysed. To this end, using a similar procedure to that described above, the sampling points employed for the grid method and MTT road types were used to compare with the predictions of the road categories. All 104 of the sampling points evaluated in the grids and road types could be associated with one of the road categories. The sound values of these sampling points were compared with the mean value of the road category in which they were located and the prediction error was calculated using the difference between them (Equation (5)). The prediction error was analysed according to the road category where the control sampling point was located ( P ij ). Table 5 shows the median from the error for the sound descriptors analysed.

Prediction errors (ε) of the categorisation method for L d , L n and L den descriptors.

No.: Number; n.s. Non-significant difference ( p > 0.05).

The prediction errors of the categorisation method are lower than 2 dB and have no significant differences with respect to the null value for all road categories and sound descriptors analysed (n.s.). These prediction errors are mostly lower compared with those of the MTT road types. However, to produce a detailed analysis of the differences in the estimation errors of the sampling methods, the median absolute errors of prediction were compared (|ε i |) using the Mann-Whitney test. The results are shown in Table 6 .

Absolute values of prediction errors (|ε|) for L d , L n and L den for road types and road categories and comparison to prediction errors of both methods (Categorisation and Ministry of Transport and Telecommunication (MTT)) using Mann-Whitney U test.

No.: Number; Sig.: Significance; * Significant at p ≤ 0.05; ** Significant at p ≤ 0.01; *** Significant at p ≤ 0.001; n.s. Non-significant difference ( p > 0.05).

To compare the predictive capacity of different sampling methods, the road type or road category where the control sampling point ( P ij ) was located was used as reference. Table 6 shows that the errors were higher for MTT road types for all sound descriptors analysed, regardless of road categories or road types taken as a reference. Taking the road category in which the control sampling point was placed as a reference, the error of L n descriptor showed no significant differences between both sampling methods in Category 3 and 4. Taking the road type where the control sampling point was placed as a reference, the errors of both sampling methods in the collector road type showed no significant differences for all sound descriptors. The error of the night level in trunk and service road types and the error of the day, afternoon and night level in the trunk road type revealed no significant differences. Indeed, the differences in errors of both sampling methods are reduced if road types are taken as a reference. However, it is important to keep in mind that this classification had problems of statistical differentiation.

3.3. Calculation of Exposure Level and the Percentage of Annoyance

In the previous section the predictive capacity of sound values was analysed according to the different sampling methods. A sampling method that presents significant uncertainties of prediction will directly influence the calculation of the exposed population. Therefore, the variation in the level of exposed population and the percentage of annoyance depending on the sampling method used were analysed. In this study, the categorisation and MTT road type methods were analysed.

Figure 7 shows the percentage of exposed population according to the L den descriptors registered in different road categories and road types. Depending on the selected method, the results of population exposed to noise can change significantly. According to the MTT road types method, of the populations that reside in the highway, trunk, service and collector road type areas, 10% are exposed to levels higher than 65 dB. These areas whose L den > 65 dB are referred to as black acoustic zones [ 60 ]. However, in the case of the categorisation method, 23% of the population resides in black acoustic zones. Likewise, if the level of noise exposure in the road type and in the road category where a higher percentage of population resides is compared, the local road type population is in an acoustic grey zone (55 ≤ L den ≤ 65), whereas in Category 5 the population is in a white acoustic zone ( L den < 55). Therefore, the differences in the capacity of sound prediction can clearly be misleading in the calculation of the percentage of exposed population.

Population exposed to noise in different road categories and road types.

Finally, we calculated the percentages of annoyed population and percentages of the population who are sleep disturbed by noise using both the MTT road types and the categorisation method. The results are shown in Figure 8 .

Percentages annoyance indicators (percentages of annoyed (% A ) and highly annoyed (% HA ) population; percentages of lowly sleep disturbed (% LSD ), sleep disturbed (% SD ) and highly sleep disturbed (% HSD ) population) obtained from the proposed equations [ 58 , 59 ] for road types and road categories.

The results show that different road types have percentages of annoyance and sleep disturbed by noise higher than those registered in the different road categories. Those road types that register higher noise levels, and therefore higher levels of noise annoyance, are those that had a higher level of sound prediction uncertainty. The trunk and service road type have similar percentages of annoyance to Categories 2 and 3. However, in previous analysis significant problems of differentiation between these two road types were found. Furthermore, the difference in the percentages of annoyance between the local road type and Category 5 should be noted, being those with lower noise levels. These differences were also detected in the analysis of sound exposure.

4. Discussion

The variability of sound values registered in the grid squares of Talca is quite high. This result indicates a low predictive capacity of the grid method to assess the noise exposure. If the interquartile range obtained in the cells is compared with that obtained in the local road type and in Category 5 (the road type and road category with the highest variability of noise levels), more than 50% and 75% of the grids register a greater value, respectively. Indeed, the grid size is quite high; however, as stated above, in this study has been considered relevant to use the same number of sampling points in each measurement method. Following the instructions of the ISO 1996-2 [ 42 ], if intermediate grid points would be added when the sound differences between adjacent grid points were higher than 5 dB, a new sampling would have carried out with a number of similar points. However, as shown in previous studies [ 33 ], the selection of new sampling points does not guarantee a difference between adjacent points lower than 5 dB. Consequently, this method was not used in order to compare the uncertainties between different sampling methods.

Regarding the MTT road types, the results show an overlap of interquartile range of the sound values registered in the trunk and service road types. Also, the local road type has a high sound variability. These results are similar to those obtained in other studies carried out in cities of Chile with legislative road classification [ 38 ]. Consequently, the ROC analysis indicates that this method has a low percentage of sensitivity and predictive capacity and a high percentage of non-specificity. Nevertheless, the sound values in the different road categories of the categorisation method have highly significant statistical differences. The road categories also have a high percentage of sensitivity and predictive capacity and a very low percentage of specificity.

The prediction errors of the categorisation method are lower than those of the MTT method for the different urban roads analysed. These differences in the prediction of sound values involve differences in the estimation of exposure levels and percentage of annoyance. According to the MTT method, 10% of the population is exposed to L den > 65 dB, whereas this is 23% of population according to the categorisation method. Also, as shown in Figure 8 , road types have percentages of annoyance and sleep disturbed by noise higher than those registered by road categories.

Finally, the exposed population and the percentage of annoyance obtained using the categorisation method were compared with the results obtained in other cities. Lee et al. [ 28 ] carried out a recent acoustic study in Seoul (S, Korea) and the percentage obtained from population that exceeds the level of 65 dB for the L d descriptor and the level of 55 dB for L n descriptor were compared with European cities. In Talca 11% of the population (Category 1, 2 and 3) is exposed to average levels at daytime that are higher than 65 dB and to average levels at night that are higher than 55 dB. For both time periods these percentages are higher than those obtained in the cities of Helsinki (Finland) and Berlin (Germany), and are similar to those obtained in cities such as Frankfurt (Germany). However, these percentages are lower than those obtained the cities of Seoul, Copenhagen (Denmark) and Madrid (Spain). In a further acoustic study recently carried out by Braubach et al. [ 15 ] in the cities of Basel (Switzerland), Rotterdam (The Netherlands) and Thessaloniki (Greece), limit values of 64 dB (annoyance by noise), 67.5 dB (major noise problem), and 65 dB (major noise problem) were found using the L den descriptor. The population of Talca residing in Category 1 to Category 4 is exposed to levels greater than 64 and 65 dB for the L den descriptor and for Category 1 to Category 3 the population is exposed to levels higher than 65.5 dB. Therefore, 23% and 14% of the population is exposed to values greater than 64–65 dB and 67.5 dB respectively. These percentages are much higher than those obtained in the cities of Basel, Rotterdam and Thessaloniki.

5. Conclusions

The selection of a suitable sampling method is essential to achieve an accurate assessment of the impact of noise pollution on the population. The grid, MTT road types and categorisation methods were analysed in the city of Talca (Chile). The major conclusions drawn from the results are as follows:

The grid squares have a high variability of sound values. This high variability leads to differences in sound values registered at adjacent points of more than 5 dB in 69%, 49% and 59% for L d , L n and L den descriptors, respectively.

The MTT road types have a low percentage of sensitivity and predictive capacity (except for the collector road type for L n and L den that has values above 80% of sensitivity and for the local road type for all the sound descriptors that has values above 80% of predictive capacity) and a high percentage of non-specificity (except for the local road type for all the sound descriptors that has values lower than 10%). This low discrimination and predictive capacity is caused, among other factors, by the lack of significant differentiation of sound values registered in trunk and service road types and by the high variability of the sound values of the local road type.

Average sound values in the different road categories of the categorisation method have highly significant statistical differences. The road categories also have a high percentage of sensitivity (>75%) and predictive capacity (>80%) and a very low percentage of specificity (<5%). Therefore, the functional stratification of noise levels observed in European cities that were studied previously is also found in Chilean cities. These results suggest a great advance in the validity of the categorisation method because of its application in a Chilean city.

The predictive capacity of the categorisation method is higher than that of the MTT method. This difference in the predictive capacity of sound values involves differences in the estimation of exposure levels and in the percentage of annoyance. Consequently, the categorisation method is more accurate than the MTT method to assess the impact of noise pollution on the population.

Talca is a city affected by noise pollution and also by its related problems of public health of its inhabitants. The percentages of population exposed to daytime and nighttime sound levels that are harmful to health are higher than those obtained in Helsinki and Berlin. Furthermore, the percentage of exposed population to L den > 64 dB is much higher than that obtained in the cities of Basel, Rotterdam and Thessaloniki.

Acknowledgments

This research was supported by the National Commission for Scientific and Technological Research (CONICYT) through the Nacional Fund for Scientific and Technological Development (FONDECYT) for research initiation No. 11140043. The authors thank the collaboration of Gonzalo B. Pacheco-Covili in the data collection for this study.

Author Contributions

Both authors contributed substantially to the conception of the study. Guillermo Rey Gozalo was responsible for the design of the study and the analysis of data in collaboration with Juan Miguel Barrigón Morillas. Interpretation of the results was discussed between both authors. Both authors read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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U.S. Department of Energy Announces $28 Million to Decarbonize Domestic Iron and Steel Production

WASHINGTON, D.C. — In support of President Biden’s Investing in America agenda, the U.S. Department of Energy (DOE) today announced $28 million in funding to 13 projects across 9 states to advance zero-process-emission ironmaking and ultra-low life cycle emissions steelmaking. The transformative technologies funded through this program would be the first to meet both emissions and cost parity goals, meaning the new, transformative concepts must be cost competitive with existing technologies. The teams announced today—managed by DOE’s Advanced Research Projects Agency-Energy (ARPA-E) under the Revolutionizing Ore to Steel to Impact Emissions (ROSIE) program —support the Biden-Harris Administration’s goals to reduce harmful, climate-change fueling emissions and imports of iron and steel products.   “Iron and steel production are among the most difficult industrial sectors to decarbonize, which is why ARPA-E is laser focused on accelerating game-changing technological breakthroughs to lower emissions from these critical sectors,” said ARPA-E Director Evelyn N. Wang. “Today’s announcement will help the nation achieve President Biden’s ambitious clean energy and net-zero goals while also reinforcing America’s global leadership in clean manufacturing for generations to come.” The iron and steel industry accounts for approximately 7% of global greenhouse gas (GHG) emissions and 11% of global carbon dioxide (CO2) emissions. By 2050, global iron and steel demand is projected to rise as much as 40%. This projected growth underscores the importance of lowering emissions from this industry. Current blast furnace technologies—responsible for approximately 70% of global iron and steel GHG emissions—require carbon for heat, chemistry, and structure, making the process particularly difficult to decarbonize. The ROSIE projects selected today seek to revolutionize not just the iron or steelmaking process, but the entire supply chain from ore to final steel production. The following teams will work to develop and demonstrate novel technologies that produce iron-based products from iron-containing ores and alternative feedstocks without process emissions in the ironmaking step:

• Argonne National Laboratory (Lemont, IL) will further develop a microwave-powered hydrogen plasma rotary kiln process for reducing iron ore that would eliminate carbon dioxide emissions from the ironmaking process. Argonne’s method has the potential to reduce carbon dioxide emissions arising from ironmaking by 35% compared to the blast furnace process when using today’s grid and by 88% when using a future low-carbon grid, while also reducing the cost of making hot rolled steel. (Award amount: $3,066,221)
• Blue Origin (Los Angeles, CA) will use an “Ouroboros” system that produces high-purity ferro-silicate pig iron from low-quality iron ores using molten oxide electrolysis (MOE) with zero direct process greenhouse gas emissions. Blue Origin will leverage and transfer the MOE expertise developed for lunar applications toward novel, terrestrial iron making approaches. The approach could reduce greenhouse gas emissions from the terrestrial ironmaking industry and clean up mine tailing storage facilities across the country. (Award amount: $1,109,422)
• Electra (Boulder, CO) will develop a process for producing iron at the temperature of a cup of coffee using unconventional feedstocks and a process involving two electrochemical cell stacks. If successful, the project will produce iron for use in green steel with 80% less greenhouse gas emissions at half the cost of existing traditional fuel-based processes. (Award amount: $2,874,596)
• Form Energy (Somerville, MA) will leverage its patent-pending breakthrough to directly produce iron powders from alkaline iron ore slurries in a first-of-a-kind powder-to-powder process. Using domestically available iron ore feedstocks, the process has the potential to produce greenhouse gas emission-free iron at cost parity with today’s carbon-intensive ironmaking methods. (Award amount: $1,000,000)
• Georgia Institute of Technology (Atlanta, GA) will work on a method to produce net-shaped engineered lattice structures and cellular structures of alloy steels by solid-state direct reduction of extruded structures. Several potential markets for the use of structural steels—where lightweighting and stiffness are most highly valued—include aerospace, military, and civilian aircraft, as well as automotive structural components. (Award amount: $2,843,274)
• Limelight Steel (Oakland, CA) will convert iron ore into iron metal using a laser furnace without emitting carbon dioxide at lower cost than a blast furnace. The process leverages semiconductor laser diodes, which enable new temperature and pressure ranges to reduce high- and low-grade iron ore fines into molten iron metal. Limelight estimates that their technology would reduce energy consumption of steelmaking by 46% and emissions by 81%. (Award amount: $2,910,346)
• Pennsylvania State University (State College, PA)  will develop an efficient, productive, and reliable electrochemical process for the economical reduction of iron ore at temperatures below 600°C without direct greenhouse gas emissions. The approach of a metallic anode protected by a solid metal oxide would overcome many of the challenges of anodic degradation that have hindered historical progress in this area. A host of electrolytes will be investigated while processing mixed Fe(II) and Fe(III) ores and simultaneously addressing ore impurities.  (Award amount: $760,000)
• Phoenix Tailings (Woburn, MA) will utilize an ore-to-iron production process using the arc generated from an air-gapped electrode to electrolyze the molten oxide electrolyte powered by clean electricity. Molten oxide electrolysis is a promising alternative to conventional approaches, but until now has required anode materials that are either consumable or prohibitively expensive. (Award amount: $1,000,000)
• Tufts University (Medford, MA) will develop a method to directly reduce iron ore concentrates with ammonia, eliminating all direct process emissions from the ironmaking step, as well as emissions that result from baking iron ore with clay to make hard pellets. By using low-grade ores, bypassing the pellet-hardening step, and lowering melting costs, this new approach to ammonia-based reduction would reduce the cost of domestic steel while decreasing total steel emissions by greater than 60%. (Award amount: $2,924,514)
• University of Minnesota (Minneapolis, MN)  will work on a fully electrified microwave hydrogen plasma process to replace blast furnace technology. The technology will use blast furnace and direct reduction grade iron ore concentrates, eliminating the emissions associated with the pelletization, sintering, and coke-making steps in the conventional blast furnace process.  (Award amount: $2,820,071)
• University of Nevada (Las Vegas, NV) will develop technology to use electrowinning to convert pulverized iron ore into pure iron that is deposited on a cathode. The goal is to create a laboratory-scale prototype of an impeller-accelerated reactor that maintains the production of one kilogram per hour of over 95% pure iron for 100 hours. (Award amount: $2,102,353)
• University of Utah (Salt Lake City, UT)  will advance a hydrogen-reduction melt-less steelmaking technology. The proposed process has the potential to drastically reduce energy consumption by eliminating several high-energy steps in traditional iron and steelmaking and is conducted at substantially lower temperatures than conventional methods. This approach is projected to decrease energy use by at least 50% in the production of steel mill products and up to 90% in creating near-net-shape steel components. (Award amount: $3,479,082)
• Worcester Polytechnic Institute (Worcester, MA)  will focus on manufacturing technologies for low carbon electrolyzed iron powder to be used in iron-silicon electrical steel. The work could revolutionize iron production by replacing the traditional carbothermic process while significantly reducing energy usage, greenhouse gas emissions, and cost. (Award amount: $1,241,919)

Access project descriptions for the teams announced today on the  ARPA-E website . If successful, novel ironmaking technologies meeting the metrics set forth by ROSIE will enable a reduction of U.S. emissions by over 65 million metric tonnes CO2 emitted annually (approximately 1% of U.S. emissions) and global emissions by over 2.9 gigatonnes annually (5.5% of global emissions).

ARPA-E advances high-potential, high-impact clean energy technologies across a wide range of technical areas that are strategic to America's energy security. Learn more about these efforts and ARPA-E's commitment to ensuring the United States continues to lead the world in developing and deploying advanced clean energy technologies. 

Selection for award negotiations is not a commitment by DOE to issue an award or provide funding. Before funding is issued, DOE and the applicants will undergo a negotiation process, and DOE may cancel negotiations and rescind the selection for any reason during that time.

Press and General Inquiries: 202-287-5440 [email protected]

Evs Project On Air Pollution For Class 11th And 12th

Table of Contents

Acknowledgement:

I would like to take this opportunity to express my heartfelt gratitude to the individuals and organizations who have played a significant role in the completion of this Evs Project on Air Pollution. Their unwavering support, guidance, and contributions have been instrumental in the success of this endeavor.

First and foremost, I would like to extend my sincere appreciation to my teacher [mention teacher’s name] for providing me with valuable insights and guidance throughout the project. Their expertise and encouragement have been invaluable in shaping my understanding of the subject matter and guiding me in the right direction.

I am also grateful to [mention names of experts, environmentalists, or researchers] for their assistance and willingness to share their knowledge and experiences. Their valuable inputs during interviews, discussions, or consultations have enriched the project and provided a deeper understanding of the complexities surrounding air pollution.

I would like to acknowledge the support and cooperation received from [mention names of organizations or institutions]. Their provision of necessary resources, such as research materials, data, and access to facilities, has significantly contributed to the project’s comprehensiveness and credibility.

Furthermore, I extend my thanks to my friends and classmates who have been a constant source of encouragement and support throughout the project. Their feedback and constructive criticism have helped me refine my ideas and strengthen the project’s overall quality.

Lastly, I would like to express my gratitude to my family for their unwavering support and understanding during the project’s duration. Their encouragement, patience, and belief in my abilities have been vital in keeping me motivated and focused.

I acknowledge that this project would not have been possible without the collective effort and support of these individuals and organizations. Their contributions have truly made a difference in shaping this project on air pollution, and I am immensely grateful for their involvement.

Once again, I extend my heartfelt thanks to everyone who has played a role in this project. Your support and guidance have been invaluable, and I am grateful for the opportunity to work on such an important topic under your guidance.

Introduction:

Air pollution is a pressing environmental issue that has garnered significant attention worldwide due to its detrimental effects on human health, ecosystems, and the overall well-being of our planet. The continuous release of pollutants into the atmosphere from various sources has resulted in a degraded air quality that poses severe risks to both the environment and public health.  

The primary objective of this Evs Project on Air Pollution is to shed light on the seriousness of this issue and raise awareness among individuals, communities, and policymakers. By understanding the causes, consequences, and potential solutions related to air pollution, we can take proactive measures to address and mitigate its harmful effects.

Air pollution originates from multiple sources, both natural and human-induced. Natural sources include volcanic eruptions, wildfires, and dust storms, while human activities contribute significantly to the problem. Emissions from industries, power plants, vehicles, and improper waste disposal are among the primary culprits responsible for air pollution.

The consequences of air pollution are far-reaching and impact various aspects of our lives. It adversely affects human health, leading to respiratory problems, cardiovascular diseases, allergies, and even premature death. Additionally, air pollution has severe implications for ecosystems, harming plant and animal life and disrupting delicate ecological balances. Furthermore, it contributes to climate change by influencing the Earth’s radiation balance and exacerbating global warming.

Recognizing the urgency of the situation, it is essential to explore and implement effective measures to combat air pollution. Through this project, we aim to provide insights into the possible solutions and strategies that can be adopted at individual, community, and governmental levels. By promoting sustainable practices, advocating for stricter emission controls, and encouraging the use of clean energy sources, we can make significant progress in reducing air pollution levels and preserving the health of our planet.

By increasing awareness and understanding of air pollution, we can empower individuals to make informed choices and take actions that contribute to a cleaner and healthier future. This project serves as a platform to educate and inspire students, policymakers, and the general public to prioritize and actively engage in efforts to combat air pollution.

In conclusion, this Evs Project on Air Pollution aims to highlight the severity of the problem and emphasize the importance of addressing it promptly. By comprehending the causes and consequences of air pollution and exploring potential solutions, we can pave the way for a sustainable and healthier future for ourselves and future generations.

Evs Project on Air Pollution:

The Evs Project on Air Pollution is a comprehensive study that delves into the multifaceted aspects of air pollution. It encompasses an in-depth analysis of its causes, effects, and potential solutions. By conducting thorough research and gathering relevant data, this project seeks to enhance awareness among individuals, communities, and policymakers regarding the pressing need to tackle air pollution promptly and effectively.

One of the primary objectives of this project is to identify and examine the various causes of air pollution. It explores both natural and anthropogenic factors that contribute to the degradation of air quality. Natural causes include volcanic eruptions, dust storms, and pollen, while anthropogenic causes encompass emissions from industries, transportation, energy generation, and household activities. By understanding the root causes, this project highlights the need for comprehensive strategies to address and mitigate these sources of pollution.

Furthermore, the project investigates the wide-ranging effects of air pollution on the environment, public health, and climate change. It explores the detrimental impacts on ecosystems, including the depletion of biodiversity, disruption of ecological balance, and damage to vegetation. The project also emphasizes the severe health consequences for humans, such as respiratory illnesses, cardiovascular diseases, and impaired lung function. Additionally, it underscores the role of air pollution in exacerbating climate change by contributing to the greenhouse effect and altering weather patterns.

The Evs Project on Air Pollution goes beyond merely identifying the problems associated with air pollution. It aims to present potential solutions and strategies to mitigate this issue effectively. It explores both individual and collective actions that can be taken to reduce air pollution. These may include adopting sustainable transportation alternatives, promoting the use of clean energy sources, implementing stricter emission standards and regulations, advocating for effective waste management practices, and fostering public awareness and education on the importance of clean air.

By increasing awareness through this project, individuals, communities, and policymakers can be motivated to prioritize and take action against air pollution. It emphasizes the need for collaborative efforts involving government initiatives, industry practices, and individual responsibility to achieve substantial progress in addressing this environmental concern.

In conclusion, the Evs Project on Air Pollution aims to provide a comprehensive understanding of the causes, effects, and potential solutions to air pollution. By raising awareness and advocating for effective measures, this project seeks to empower individuals and communities to take proactive steps towards mitigating air pollution and safeguarding the well-being of the environment and future generations.

Examples of Air Pollution:

The section on examples of air pollution provides a detailed exploration of various sources that contribute to the deterioration of air quality. It focuses on highlighting specific instances or case studies related to air pollution, shedding light on their environmental and health impacts.

Industrial emissions are one of the prominent sources of air pollution. Factories and manufacturing facilities release a range of pollutants into the atmosphere, including particulate matter, sulfur dioxide, nitrogen oxides, and volatile organic compounds. These emissions can lead to smog formation, acid rain, and respiratory issues for nearby communities. For example, the industrial region of Norilsk in Russia has experienced severe air pollution due to metal smelting operations, resulting in significant environmental damage and adverse health effects on the local population.

Vehicular pollution is another major contributor to air pollution, particularly in urban areas. Exhaust emissions from automobiles release harmful pollutants like carbon monoxide, nitrogen dioxide, and particulate matter. Cities with high traffic congestion often experience elevated pollution levels and associated health problems. For instance, Delhi, the capital city of India, has witnessed severe air pollution due to the large number of vehicles on its roads, leading to respiratory ailments and reduced air quality indexes.

Indoor air pollution is a lesser-known but significant concern. Activities such as cooking with solid fuels like wood, coal, or biomass release harmful pollutants into indoor environments. This can have adverse effects on the health of individuals, especially women and children who are exposed to these pollutants for extended periods. In rural areas of developing countries, where clean cooking technologies are not readily available, indoor air pollution poses a serious health risk.

Agricultural activities, particularly the use of chemical fertilizers and pesticides, contribute to air pollution as well. The release of ammonia, pesticides, and other chemicals into the air can lead to smog formation and adversely affect air quality. This pollution can have detrimental effects on both human health and ecosystems.

The burning of fossil fuels, including coal, oil, and natural gas, is a significant source of air pollution globally. Power plants, residential heating systems, and industrial processes that rely on fossil fuel combustion emit greenhouse gases, sulfur dioxide, nitrogen oxides, and particulate matter. These pollutants contribute to climate change, smog formation, and respiratory diseases. For instance, the severe air pollution episodes witnessed in Beijing, China, were largely attributed to the burning of coal for heating and industrial purposes.

By examining these specific examples and case studies, the project aims to illustrate the diverse sources and impacts of air pollution. It emphasizes the urgency of addressing these sources through effective policies, technological advancements, and individual actions to safeguard the environment and public health.

Importance of Evs Project on Air Pollution:

The Evs Project on Air Pollution plays a vital role in today’s world by addressing one of the most pressing environmental challenges we face. It holds immense importance as it helps individuals, communities, and policymakers understand the gravity of the air pollution problem and its far-reaching consequences on ecosystems, human health, and climate change. By raising awareness through this project, it serves as a catalyst for inspiring action and driving changes in policies and personal behavior to effectively reduce air pollution.

Firstly, the project educates individuals about the detrimental effects of air pollution on ecosystems. It highlights how air pollutants can harm plant and animal life, disrupt ecological balances, and lead to the loss of biodiversity. By understanding these impacts, individuals gain a deeper appreciation for the interconnectedness of ecosystems and recognize the need to protect and preserve them.

Secondly, the project emphasizes the severe health implications of air pollution on human well-being. It sheds light on how air pollutants, such as particulate matter, ozone, and nitrogen dioxide, can contribute to respiratory problems, cardiovascular diseases, allergies, and even premature death. By creating awareness about these health risks, the project empowers individuals to prioritize their own well-being and take proactive measures to minimize exposure to air pollutants.

Furthermore, the Evs Project on Air Pollution addresses the critical link between air pollution and climate change. It highlights how certain pollutants, such as carbon dioxide and other greenhouse gases, contribute to global warming and the disruption of weather patterns. By understanding this connection, individuals recognize the urgency of reducing air pollution as part of the broader efforts to mitigate climate change and its associated impacts, such as rising sea levels, extreme weather events, and habitat loss.

Additionally, the project plays a crucial role in advocating for changes in policies and regulations. By raising awareness about the adverse effects of air pollution, it prompts individuals to engage with policymakers and demand stricter emission standards, increased investment in renewable energy sources, and sustainable urban planning. This project can contribute to the development and implementation of more effective environmental policies that prioritize air quality and protect public health.

Moreover, the Evs Project on Air Pollution encourages changes in personal behavior and lifestyle choices. By educating individuals about the sources of air pollution and their own contribution to it, the project promotes the adoption of sustainable practices. It inspires individuals to make conscious decisions such as reducing reliance on private vehicles, supporting clean energy alternatives, practicing proper waste management, and promoting indoor air quality.

In conclusion, the Evs Project on Air Pollution is of paramount importance in our world today. By raising awareness about the gravity of the problem, its detrimental effects on ecosystems, human health, and climate change, it inspires action and drives changes in policies and personal behavior to reduce air pollution. This project empowers individuals to make informed choices and actively contribute to creating a cleaner and healthier environment for ourselves and future generations.

How Can We Make It Happen?

This section explores practical steps and measures that can be taken to address air pollution. It discusses the importance of adopting sustainable transportation, promoting renewable energy sources, implementing stricter emission standards, encouraging waste management practices, and raising awareness among the general public. The focus is on individual and collective actions that can contribute to reducing air pollution.

The Three Pillars:

The three pillars of this project are:

Education and Awareness: This pillar emphasizes the need to educate individuals about the causes and impacts of air pollution. It promotes awareness campaigns, workshops, and educational programs to empower people with knowledge and encourage them to take action.

Policy and Regulation: This pillar emphasizes the importance of enacting and enforcing stringent policies and regulations to control air pollution. It discusses the role of government bodies, international agreements, and collaborations in formulating effective policies and implementing pollution control measures.

Technology and Innovation: This pillar focuses on the role of technology and innovation in combating air pollution. It explores advancements in clean energy technologies, air quality monitoring systems, and sustainable practices that can significantly reduce pollution levels.

Conclusion:

The Evs Project on Air Pollution serves as a catalyst for change, promoting awareness, sustainable practices, and policy advocacy to address the urgent issue of air pollution. By delving into the causes, effects, and potential solutions, this project empowers individuals, communities, and governments to take concerted action towards creating a cleaner and healthier future for ourselves and future generations.

Through this project, individuals gain a comprehensive understanding of the causes and effects of air pollution. Armed with knowledge, they can recognize the detrimental impact it has on ecosystems, human health, and climate change. This awareness fuels a sense of responsibility and urgency to take action against air pollution.

The project emphasizes the importance of collective efforts, urging individuals, communities, and governments to work together. By collaborating, sharing knowledge, and implementing sustainable practices, we can effectively combat air pollution. The project highlights the significance of initiatives such as adopting clean transportation alternatives, promoting renewable energy sources, implementing stricter emission regulations, and raising public awareness.

Furthermore, the Evs Project on Air Pollution underlines the importance of policy advocacy. It emphasizes the need for governments to enact and enforce stringent regulations and standards to control air pollution effectively. This includes collaboration on an international level to address transboundary pollution and foster sustainable practices across borders.

Ultimately, the project recognizes the shared responsibility of individuals, communities, and governments to protect our planet. By actively participating in efforts to reduce air pollution, we can contribute to the creation of a healthier environment for ourselves and future generations. It is crucial for us to recognize the interconnectedness of our actions and their impact on the planet.

In conclusion, the Evs Project on Air Pollution serves as a call to action, inspiring individuals, communities, and governments to work collectively towards combatting air pollution. By raising awareness, promoting sustainable practices, advocating for effective policies, and fostering collaboration, we can create a cleaner, healthier, and more sustainable future for all. It is our responsibility to protect and preserve our planet for current and future generations.

Certificate of Completion

This is to certify that I, [Student’s Name], a [Class/Grade Level] student, have successfully completed the project on “Evs Project On Air Pollution For Class 11th And 12th.” The project explores the fundamental principles and key aspects of the chosen topic, providing a comprehensive understanding of its significance and implications.

In this project, I delved into in-depth research and analysis, investigating various facets and relevant theories related to the chosen topic. I demonstrated dedication, diligence, and a high level of sincerity throughout the project’s completion.

Key Achievements:

Thoroughly researched and analyzed Evs Project On Air Pollution For Class 11th And 12th. Examined the historical background and evolution of the subject matter. Explored the contributions of notable figures in the field. Investigated the key theories and principles associated with the topic. Discussed practical applications and real-world implications. Considered critical viewpoints and alternative theories, fostering a well-rounded understanding. This project has significantly enhanced my knowledge and critical thinking skills in the chosen field of study. It reflects my commitment to academic excellence and the pursuit of knowledge.

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