![\"\"](\"https:\/\/nilu.com\/wp-content\/uploads\/2024\/04\/nem-model-nilu-figure2.png\")
<\/a>\u201cSome contaminants we find in the Arctic originate locally. Yet most have been transported to the Arctic by air and ocean currents from regions further south. Hence, we need a global perspective to understand where the contaminants come from and how they get there,\u201d says senior scientist Ingjerd Sunde Krogseth from NILU.<\/p>\n
This is particularly important from a regulatory perspective. To enforce scientifically sound regulatory measures, it is vital to know the sources of the contaminants. Will regulatory measures need to be implemented on a national scale, a continental scale \u2013 or a global scale?<\/p>\n
Also relevant is how the rapidly changing climate impacts contaminant transport to the Arctic environment and accumulation in Arctic organisms. This is something mechanistic models can help us to address.<\/p>\n
What lies behind the model name?<\/h2>\n
\u201cNEM is a so-called mechanistic multimedia environmental fate and bioaccumulation model,\u201d says Krogseth. \u201cIt aims to simulate an organic contaminant\u2019s journey through both the physical environment and food webs.\u201d<\/p>\n
Krogseth explains that \u201cmechanistic\u201d means that the model is based on our theoretical understanding of the contaminants and how they behave in the environment \u2013 in other words the underlying mechanisms. \u201cMultimedia\u201d signals that the models account for contaminant distribution and fate in the whole environment.<\/p>\n
\u201cThis is important,\u201d Krogseth states, \u201cbecause the contaminants we work with are present in all media. We can\u2019t look at just air or just water to get the big picture.\u201d<\/p>\n
NEM is also a global model. It can simulate the whole process between global emissions of contaminants and the resulting ecosystem exposure in the Norwegian Arctic with both spatial and temporal resolution.<\/p>\n
\u201cLastly, it is called nested because the model for the physical environment can be run in a nested way,\u201d Krogseth explains. \u201cThus, we can first run a large model domain such as the whole globe with a coarse spatial resolution. Then, we can zoom in to any area of interest \u2013 in our case often the Arctic \u2013 for a more detailed analysis.\u201d<\/p>\n
How does NEM work?<\/h2>\n![\"\"](\"https:\/\/nilu.com\/wp-content\/uploads\/2024\/04\/nem-modellen-english.png\")
<\/a>The Nested Exposure Model: NEM consists of a global module for the physical environment and a bioaccumulation module for selected key species in Norwegian marine areas. Model input (blue arrows) is used together with a set of mathematical equations to predict chemical concentrations in both the physical environment and biota (pink arrows). In the recently published paper, we focused on the geographical area indicated in the pink frame. Illustration: NILU.<\/figcaption><\/figure>\nSimulating the whole journey between global emissions and ecosystem exposure, including variation in time and space, requires a lot of input into NEM. The input can be grouped into three categories:<\/p>\n
\n- Information on the size, time, place, and mode of entry of the chemical emissions<\/li>\n
- Physical-chemical properties and degradation rates of the chemical itself<\/li>\n
- Various information about the physical environment and biota<\/li>\n<\/ol>\n
The model\u2019s backbone is made of mathematical equations. They describe how a chemical will partition in the environment and in organisms, for example between air and water and organic matter. This is based on properties of the chemical and of the environment\/organisms, such as temperature and lipid content. Information on environmental transport rates, eg, air and ocean currents and feeding rates, is also used to calculate how the chemical moves in both the physical environment and the food web.<\/p>\n
Based on these equations and the input data, the model can predict contaminant concentrations in air, water, or soil, as well as in different species. It can also predict contaminant distribution and transport, uptake, and elimination rates. This information can be used to understand for example which prey species seals mainly get their contaminants from.<\/p>\n
Cross-disciplinary collaboration<\/h2>\n![\"\"](\"https:\/\/nilu.com\/wp-content\/uploads\/2024\/04\/nem-model-nilu-figure3.png\")
<\/a>Fraction (in percent) of total estimated PCB-153 concentrations in herring muscle tissue in January 2020 estimated to originate from historical or ongoing primary emissions in the EU (member countries as of 1973). Adapted from Krogseth et al 2023 Environ Sci Process Impacts 25:1986-2000, CC BY 3.0 DEED<\/a><\/figcaption><\/figure>\nAll in all, getting the input data right is sometimes the most time-consuming part.<\/p>\n
Krogseth explains that the bioaccumulation module includes a lot of species-dependent information. Examples are seasonal lipid-content of species, body weight curves for an organism\u2019s whole lifetime, dietary composition including age of prey species, and details about reproduction.<\/p>\n
To know that the model works as intended, Krogseth explains that they test model results against actual measurements. So far, they have evaluated NEM for PCB-153 in the European atmosphere, in cod and herring in the Norwegian marine areas, and in key species of the Kongsfjorden ecosystem. The model-estimated concentrations are in reasonable agreement with measurements.<\/p>\n
\u201cOne of the strengths of NEM is that once evaluated, it can be used to tease out where the contaminants actually come from, and how the pollutant load may change in the future, given changes in emissions.\u201d<\/p>\n
The model can also be used to evaluate how the contaminant\u2019s behaviour is impacted by properties of the environment or organisms. Right now, the team behind NEM is expanding and evaluating the model to use it to look at the effect of climate change on contaminant dynamics in the Arctic environment and food chain.<\/p>\n
\u201cTo get all this input data right, we are completely dependent on good collaboration across scientific disciplines \u2013 with chemists, geophysicists, biologists, and ecotoxicologists,\u201d says Krogseth.<\/p>\n
The cross-disciplinary collaboration in the Fram Centre is essential to ensure this, for instance through the COPE project and the Fram Centre research programmes CLEAN and SUDARCO. Input from collaborators is vital to create realistic climate scenarios for changes occurring both in the physical environment and in the food webs that Krogseth and her partners can use as input to NEM.<\/p>\n
<\/a>Simulating the whole journey between global emissions and ecosystem exposure, including variation in time and space, requires a lot of input into NEM. The input can be grouped into three categories:<\/p>\n
- \n
- Information on the size, time, place, and mode of entry of the chemical emissions<\/li>\n
- Physical-chemical properties and degradation rates of the chemical itself<\/li>\n
- Various information about the physical environment and biota<\/li>\n<\/ol>\n
The model\u2019s backbone is made of mathematical equations. They describe how a chemical will partition in the environment and in organisms, for example between air and water and organic matter. This is based on properties of the chemical and of the environment\/organisms, such as temperature and lipid content. Information on environmental transport rates, eg, air and ocean currents and feeding rates, is also used to calculate how the chemical moves in both the physical environment and the food web.<\/p>\n
Based on these equations and the input data, the model can predict contaminant concentrations in air, water, or soil, as well as in different species. It can also predict contaminant distribution and transport, uptake, and elimination rates. This information can be used to understand for example which prey species seals mainly get their contaminants from.<\/p>\n
Cross-disciplinary collaboration<\/h2>\n
<\/a>Fraction (in percent) of total estimated PCB-153 concentrations in herring muscle tissue in January 2020 estimated to originate from historical or ongoing primary emissions in the EU (member countries as of 1973). Adapted from Krogseth et al 2023 Environ Sci Process Impacts 25:1986-2000, CC BY 3.0 DEED<\/a><\/figcaption><\/figure>\n All in all, getting the input data right is sometimes the most time-consuming part.<\/p>\n
Krogseth explains that the bioaccumulation module includes a lot of species-dependent information. Examples are seasonal lipid-content of species, body weight curves for an organism\u2019s whole lifetime, dietary composition including age of prey species, and details about reproduction.<\/p>\n
To know that the model works as intended, Krogseth explains that they test model results against actual measurements. So far, they have evaluated NEM for PCB-153 in the European atmosphere, in cod and herring in the Norwegian marine areas, and in key species of the Kongsfjorden ecosystem. The model-estimated concentrations are in reasonable agreement with measurements.<\/p>\n
\u201cOne of the strengths of NEM is that once evaluated, it can be used to tease out where the contaminants actually come from, and how the pollutant load may change in the future, given changes in emissions.\u201d<\/p>\n
The model can also be used to evaluate how the contaminant\u2019s behaviour is impacted by properties of the environment or organisms. Right now, the team behind NEM is expanding and evaluating the model to use it to look at the effect of climate change on contaminant dynamics in the Arctic environment and food chain.<\/p>\n
\u201cTo get all this input data right, we are completely dependent on good collaboration across scientific disciplines \u2013 with chemists, geophysicists, biologists, and ecotoxicologists,\u201d says Krogseth.<\/p>\n
The cross-disciplinary collaboration in the Fram Centre is essential to ensure this, for instance through the COPE project and the Fram Centre research programmes CLEAN and SUDARCO. Input from collaborators is vital to create realistic climate scenarios for changes occurring both in the physical environment and in the food webs that Krogseth and her partners can use as input to NEM.<\/p>\n