Long-range transport of organic chemicals in the environment



The long-range transport (LRT) of organic chemicals in the environment is reviewed, with particular focus on the role of environmental fate and transport models and the relationship between model results and field data. Results from generic multimedia box models, spatially resolved multimedia box models, and atmospheric transport models are highlighted, including conceptual investigations of cold-trap effect and global fractionation as well as results for particular chemicals, such as hexachlorocyclohexanes, DDT, polychlorinated biphenyls, perfluoroocctanoic acid, and polybrominated diphenyl ethers. Comparison of model results to field data shows that in many cases environmental fate models provide a good description of the distribution dynamics observed in the field, with deviations between measured and modeled concentrations around a factor of five. Sorption to atmospheric aerosols as a key process influencing the LRT of semivolatile organic chemicals (SOCs) is discussed, and the need for more measurements of the aerosol–air partitioning of SOCs and of the reactivity of particle-bound chemicals is pointed out. Key findings from field campaigns measuring legacy persistent organic pollutants (POPs) as well as new POPs are summarized. Finally, the relationship between science and politics in the field of POPs is addressed. Research into the LRT of organic chemicals has always occurred in interaction with political activities aiming to reduce the emissions of POPs. Since the late 1990s, the Stockholm Convention and the Aarhus Protocol on POPs have formed an important political context for research concerning POPs; the implementation of these international treaties creates a demand for ongoing research into the LRT of organic chemicals.


The environmental long-range transport (LRT) of organic chemicals has received considerable attention in the last 15 years. To a large extent, this has been stimulated by the increasing scientific knowledge about organic chemicals transported to the Arctic that became available in the late 1980s. It was already well known in the 1970s that organochlorine compounds, such as DDT or polychlorinated biphenyls (PCBs), can be transported to remote regions, including the Arctic and Antarctic [1–3]. In the 1980s, however, contamination of the Arctic by an array of different chemicals (SOx and NOx, metals, and organic compounds), including the presence of organochlorine compounds in human breast milk, became a major focus of scientific research [4–7]. This led to a broader understanding about levels of organochlorine compounds in Arctic organisms and to the political awareness and concern that remains an important driver of the current interest in the LRT of organic chemicals.

Independent of this issue regarding organochlorines in the Arctic, LRT of chemicals was already a political concern in the 1970s, which is reflected by the Geneva Convention on Long-Range Transboundary Air Pollution (LRTAP; http://www.unece.org/env/lrtap/welcome.html; referred to hereafter as the LRTAP Convention). The LRTAP Convention was signed in 1979 and entered into force in 1983. The LRT problem primarily addressed by the LRTAP Convention was acidification of lakes in Scandinavia by SOx and NOx released from Central Europe. In the 1990s, the issue regarding LRT of organic chemicals also was addressed within the framework of the LRTAP Convention by the development of the 1998 Protocol on Persistent Organic Pollutants (POPs; referred to hereafter as the Aarhus Protocol on POPs), which entered into force in 2003. This new objective is expressed in the preamble of the Aarhus Protocol on POPs: “Acknowledging that the Arctic ecosystems and especially its indigenous people, who subsist on Arctic fish and mammals, are particularly at risk because of the biomagnification of persistent organic pollutants.” (In a similar way, heavy metals were included in the framework of the LRTAP Convention by a Protocol on Heavy Metals, which also entered into force in 2003.)

Also stimulated by the concern about LRT of persistent organic chemicals, the basis for a second international treaty addressing the LRT of organic chemicals was laid in the 1990s. This is the Stockholm Convention on Persistent Organic Pollutants (http://www.pops.int), the text of which was negotiated in the late 1990s and adopted in 2001. The Stockholm Convention entered into force in 2004. Both legislations, the Aarhus Protocol and the Stockholm Convention, cover a set of 12 chemicals or groups of chemicals as a first set of POPs: The organochlorine pesticides aldrin, chlordane, DDT, dieldrin, endrin, heptachlor, mirex, toxaphene; the industrial chemicals hexachlorobenzene (HCB) and PCBs; and the unwanted byproducts, polychlorinated dibenzodioxins and dibenzofurans. In addition to these 12 chemicals, the Aarhus Protocol also covers hexachlorocyclohexane (HCH), chlordecone, hexabromobiphenyl, and polycyclic aromatic hydrocarbons (PAHs).

Under both legislations, a mechanism exists for the identification and inclusion of additional POPs (Annexes D–F of the Stockholm Convention; a separate document for the Aarhus Protocol [8]). Several compounds are currently under consideration for inclusion in one or both legislations (e.g., perfluorooctanesulfonate [PFOS], pentabromo diphenyl ether, and pentachlorobenzene).

Figure Fig. 1..

Role of indicators and metrics in the assessment of an environmental problem. Indicators link scientific investigations to decision-making processes. Metrics provide specific ways of quantifying an indicator on the basis of individual scientific findings.

In addition to the empirical findings of persistent organic chemicals in the Arctic and the initiative to establish international treaties addressing the LRT of such chemicals, a third important element was developed in the 1990s—namely, new scientific concepts for the evaluation of LRT. To be visible in the political discussion, empirical findings, such as levels of organic compounds in remote regions, need to be expressed in terms of appropriate indicators, such as persistence or potential for LRT (LRTP). Indicators reflect particularly important aspects of scientific results and facilitate the perception and evaluation of these scientific results by nonscientists (politicians and the public). In many cases, an indicator can be quantified by several different metrics. Several authors have proposed metrics that can be used to quantify the LRTP in the form of a single number [9–13].

The discussion of metrics for the evaluation of LRT in turn stimulated new model developments and new field work explicitly targeted to quantifying LRT of organic chemicals. In addition, the Stockholm Convention and the Aarhus Protocol create the need for an improved understanding and characterization of the LRT of organic chemicals and, therefore, have stimulated research intended to support the implementation of these international treaties.

After this brief historical introduction, three main aspects of the LRT of organic chemicals are addressed in the present paper: different metrics of LRT and their meaning; environmental fate and transport models that can be used to investigate the environmental distribution and LRT of organic chemicals, as well as several important processes that influence the model results for LRT; and recently reported field data that show the large-scale distribution of chemicals, as well as the question of how LRT metrics can be derived from such field data.

In an extensive review of the global distribution of chemicals, Ballschmiter [14] focused on individual environmental processes, their mechanisms, time constants, and quantitative description. The recent review by Lohmann et al. [15] focused on emissions, time trends, and geophysical factors driving the global fate of known and emerging POPs. Here, a somewhat different perspective is taken: The regulatory needs under the Stockholm Convention and the Aarhus Protocol are taken as the context, after which models of different complexity and their applications to LRT calculations are presented and agreement between models and field data is discussed.


Indicators link a body of scientific knowledge, such as field data showing the spatial distribution of a chemical, to a concern about a problem (Fig. 1) [16]. Such a concern is based on a value judgment stating that a certain situation is undesirable (and why); when such a concern is expressed in the political discussion, this creates an issue.

A concern often mentioned in scientific investigations of LRT is contamination of pristine ecosystems. In the context of LRT of chemicals, however, not only contamination of pristine ecosystems and resulting toxic effects in rare species are an issue: In all cases when chemicals are transported to remote regions, humans are also exposed to these chemicals. In particular, humans living in the Arctic suffer from high exposure to many SOCs released in temperate or tropical regions [17]. This means that an imbalance exists between benefits and burdens from the use of these chemicals, and this needs to be settled as an issue of environmental justice, not just as an ecological problem.

A given indicator can be quantified in different ways; the concrete quantity used to calculate numerical values of an indicator is called a metric. Fenner et al. [18] provide an overview of currently used LRT metrics and distinguish between transport-oriented and target-oriented metrics. Transport-oriented metrics describe the movement of a chemical away from a source region, whereas target-oriented metrics focus on the fraction of a chemical that is transported to a remote region and deposited there. Examples of transport-oriented metrics are the spatial range [9], the characteristic travel distance (CTD) [10,11], and the pollutant-specific spatial scale [12,19]. Examples of target-oriented metrics are the Arctic Contamination Potential (ACP) [13] and the transfer efficiency [20].

In particular, the spatial range, the CTD, and the pollutant-specific spatial scale have been compared extensively [12,19,21,22]. The CTD is the point at which a chemical's concentration as a function of distance from the source has dropped to 1/e ≈ 0.37 of the concentration at the point of release. The spatial range is the 95th percentile of the curve showing the concentration as a function of distance from the source (i.e., it is the distance that contains 95% of the area under the concentration curve). The pollutant-specific spatial scale is the box length of a spatially not-resolved multimedia box model chosen such that under steady-state conditions, 50% of the chemical released to the model domain are transported out of the model domain by wind and water flows and 50% are degraded or physically removed (e.g., by sediment burial) within the model domain. These three metrics are equivalent for open models, such as a wind tunnel model or a regional model. In global models, which are closed by their very nature, a decrease to 1/e may not be observed for persistent chemicals that are homogeneously distributed, such as HCB [23]. The spatial range, which can be calculated for any shape of the concentration function, is the LRT metric of choice in this case. (Strictly speaking, even global environmental fate models are not completely closed, because transport occurs to the stratosphere and the deep ocean, which normally are not included in such models. These physical removal processes are represented in the models by loss processes like chemical degradation.)

The purpose of the ACP is to characterize POP contamination of the Arctic; this metric is defined as the amount of chemical present in the surface media in the Arctic region at a certain time divided by the overall amount of chemical released globally until this time [13,24]. The ACP is mostly calculated with the Globo-POP model [25]. Wania [24] determined the ACP for a set of hypothetical chemicals with a broad range of chemical properties and suggested a chemical classification in terms of “single hoppers,” “multiple hoppers,” “swimmers,” and “flyers,” according to the way in which the chemicals are transported to the Arctic (see also next section). Another target-oriented metric is the fraction of a chemical that remains in a remote region, which is calculated as the cumulative net mass flow of a chemical into a target region that occurs in a certain period of time divided by the amount of the chemical that was released during the same period of time [26].

To facilitate the estimation of LRT and persistence of organic chemicals, the Organization for Economic Cooperation and Development (OECD) Screening Tool for Overall Persistence (Pov) and LRTP has recently been developed [27]; this model is described in more detail in the next section. To describe transfer-oriented and target-oriented aspects of LRT, the OECD Tool calculates both CTD and transfer efficiency. For generic assessments (mostly rankings) of larger sets of chemicals, CTD and/or transfer efficiency calculated with the OECD Tool are suitable LRTP metrics. For more detailed assessments of the LRT of a chemical, more highly resolved models normally are used, and the LRT can then be quantified in terms of a more specific metric, such as the ACP, the fraction of a chemical that remains in a remote region, or the transfer efficiency between a source region and a target region. The ongoing development and comparison of different LRT metrics is important, because it helps to find the most informative way to express the type and extent of LRT by a single number and to convey the scientific understanding of LRT to policy makers.


Conceptually, two different approaches to LRT modeling exist, and these approaches require two different types of models. First, a generic value of the LRTP is determined to compare a chemical to many other chemicals (i.e., to rank large sets of chemicals with respect to LRTP and to identify the chemicals with the highest LRTP). Examples of such large sets of chemicals are the Canadian Domestic Substances List [28] (http://www.ec.gc.ca/CEPARegistry/subs_list/) or the list of approximately 70,000 industrial chemicals that have to be registered in the European Union under REACH (Registration, Evaluation and Authorization of Chemicals) [29] (http://ecbwbiu5.jrc.it/). Multimedia box models that can be used for this purpose have been discussed by Fenner et al. [18]; an exemplary model of this type is the OECD Pov and LRTP Tool. The second approach is that for a particular chemical or a small set of chemicals, the mass fluxes between a source region and a target region shall be described in a realistic fashion. This task requires models with a sufficiently high spatial and temporal resolution; currently used models include multiregional multimedia box models and highly resolved atmospheric transport models.

An important conceptual difference exists between box models and atmospheric transport models. Box models solve the multimedia mass-balance equations for a particular chemical but generally do not set up mass balances for air, water, aerosol particles, and so on. Atmospheric transport models, in contrast, are based on equations from fluid dynamics describing the circulation of air, and chemicals are then linked to these air mass flows.

Influence of atmospheric degradation and deposition on LRT model results

The LRT results obtained with environmental fate models strongly depend on a chemical's effective residence time in air, ta,eff, which reflects the combined effect of degradation, physical removal, and net deposition. Chemical property data needed to estimate degradation in air and deposition from air to surface media are the rate constant for reaction with OH radicals in air, kOH; the aerosol–air partition coefficient, Kp (to quantify the effect of wet and dry particle deposition); and the Henry's law constant (to quantify the effect of rain washout and gaseous air–water exchange) [30]. In addition to degradation and deposition, potential revolatilization from surface media influences ta,eff. The extent of revolatilization depends, among others, on the air-surface partition coefficients of a chemical (air–water, air–soil, air–vegetation, and air–snow partition coefficients), on the chemical's lifetime in the surface media [31], and on the air-surface equilibrium status (i.e., whether the surface is already close to equilibrium with the air above or whether net deposition of airborne chemical is still occurring). Particularly relevant for the global cycling of persistent SOCs is air-seawater exchange [32–35]; because of its large surface area and volume, seawater is an important reservoir of SOCs that is closely coupled to the atmosphere.

The available experimental data for kOH have been compiled by Klopffer and Wagner [36]; this compilation shows that for most semivolatile organic compounds, kOH has not been measured. Therefore, estimation methods, such as the Atmospheric Oxidation Program Software (AOPWIN) [37] (http://www.epa.gov/oppt/exposure/pubs/episuite.htm), have to be used for SOCs. The AOPWIN software estimates the rate constant of a molecule based on contributions from all relevant functional groups of the molecule [38–40]. The group contributions used in AOPWIN, however, have been derived from a set of small molecules, and whether the resulting values can be applied to complex molecules, such as many SOCs, is not clear [40,41]. Therefore, the AOPWIN results obtained for SOCs are fraught with considerable uncertainty and may systematically overestimate compound reactivity. In other words, a first problem for the calculation of ta,eff of SOCs is that kOH of the fraction of SOCs in the gas phase is uncertain.

A second problem for the calculation of ta,eff of SOCs is the following: To calculate the chemical's fraction sorbed to aerosols and, on this basis, the removal of the chemical by wet and dry particle deposition, the aerosol–air partition coefficient, Kp, is required. Values of Kp often are derived from the octanol–air partition coefficient, KOA, in a relationship such as Kp = cKOA [42]. This approach is limited to aerosols whose sorptive capacity is determined by organic matter. For an average aerosol, as it is assumed in generic LRTP models, this is the case [43]. In more highly resolved models, however, it is desirable to take aerosols consisting of mineral components and elemental carbon into account, because these aerosols may dominate the aerosol–air partitioning of SOCs in certain regions. For such aerosols, however, Kp cannot be derived from KOA, as is further discussed below.

A third problem is that the extent to which the particle-bound fraction, Φ, of an SOC is subject to degradation is unknown. It has been proposed that the particle-bound fraction is shielded against OH radicals and light and, therefore, is not significantly degraded [44–48]. Reactivity of organic components contained in atmospheric aerosols, however, has been observed in general [49,50]. Accordingly, it must be concluded that the half-life of SOCs bound to aerosol particles is largely unknown and that experimental investigations of the reactivity of SOCs on aerosol particles are highly desirable.

Finally, the effect of deposition to vegetation and to ice and snow on the LRT of SOCs also has been investigated [10, 51-56]. Vegetation (particularly forest canopies) has a large surface area that can filter SOCs out of the air, and measured air–vegetation deposition velocities are higher than those for other surfaces [57,58]. The capacity of a forest canopy to store and degrade SOCs, however, is limited [59]. Deposition of SOCs to forest canopies may reduce the LRT of SOCs with KOA values between 107 and 1011 [51,59], but the overall importance of deposition to forest canopies to the LRT and global fate of chemicals is limited [60,61].

The effect of snow on the environmental fate of chemicals is complex [54,56] and can only be touched on here. Efficient scavenging of chemicals from air by snowfall is a factor that potentially reduces the LRT of airborne chemicals [55]. After deposition to a snowpack, different processes take place, depending on the properties of the chemical and of the snowpack [56,62]. Relatively volatile chemicals, such as lighter PCBs, revolatilize to a significant extent. Involatile and water insoluble chemicals, such as heavy PCBs and polybrominated diphenyl ethers (PBDEs), are associated to particles in the snowpack and are released to soils during or after snowmelt; chemicals with relatively high water solubility and affinity to the ice surface, such as α-HCH, are released with meltwater. An interesting effect of large, snow-covered areas on the LRT of relatively volatile compounds (lighter PCBs and HCB) may be that a repulsive effect of the snow cover in high latitudes prevents the chemicals from deposition and reduces their northward transport [56].

Generic multimedia box models for LRTP screening

Several generic LRTP models were developed in the 1990s, including ChemRange [9,63], TaPL3 [11], and ELPOS [64]. In addition, LRT calculations also were performed with already-existing box models, such as CalTOX [65] (http://eetd.lbl.gov/ied/era/) and SimpleBox [66]. These generic LRTP models assume homogeneous environmental conditions and are based on average values of temperature, wind speed, precipitation, and so on. They yield LRT metrics as output, derived from air–water partition coefficient (KAW) and KOW values, and degradation half-lives (soil, water, and air) as chemical-specific input.

Because LRTP models such as ChemRange, TaPL3, and so on are generic, the question of how they can be evaluated and tested for validity is of particular interest. This question can be addressed by comparisons of model results to field data and by model intercomparisons. Shen et al. [67] measured 17 organochlorine compounds along a south–north transect from Central America to the Arctic and derived profiles of chemical concentrations in passive samplers as a function of latitude. From these profiles, they derived empirical travel distances (ETDs), which range from less than 500 km for heptachlor up to 13,000 km for pentachlorobenzene. In addition, they calculated CTDs and spatial ranges with three generic LRTP models (TaPL3, ELPOS, and ChemRange) and then compared results from these models to the empirical data. The three models were completely consistent in terms of ranking the chemicals, and the model results were in very good agreement with rankings according to the ETDs and also with the numerical values of the ETDs. This demonstrates that LRTP metrics also can be derived from field data and that the generic LRTP models yield results that describe the empirical LRTP results for organochlorine chemicals well.

Muir et al. [68] performed a similar analysis for five current-use pesticides (CUPs; atrazine/desethylatrazine, alachlor, disulfoton, dacthal, and diazinon). In their case, the empirical data are concentrations of the pesticides measured in the water column of Canadian lakes from 40°N to more than 70°N. However, whereas Shen et al. [67] had concentrations in air for the full range from 30°N to 80°N, the CUP data have a gap between 55°N and 70°N and only a few data points north of 70°N, which means that their ETDs are more uncertain. In the comparison with results from the three generic LRTP models, Muir et al. [68] found that all three models yield LRT results that are considerably below the values derived from the field data. To understand this discrepancy, it is helpful to consider the processes determining the effective atmospheric residence time of SOCs, ta,eff—namely, degradation in air and net deposition to surface media. For the CUPs investigated by Muir et al., both degradation and deposition likely are overestimated by the three generic LRTP models: degradation because the kOH values used are too high (applies to disulfoton, dacthal, and diazinon), and deposition because rain washout of chemicals with low Henry's law constant is overestimated (applies to atrazine, desethylatrazine, and alachlor).

To determine degradation of CUPs by OH radicals, second-order OH reaction rate constants have to be estimated with AOPWIN (see above), and then these rate constants are multiplied by an average concentration of OH radicals of approximately 7 × 105 molecules/cm3. Both of these components may contribute to the low LRTP results obtained with the LRTP models: OH radical concentrations in northern Canada likely are lower than the average of approximately 7 × 105 molecules/cm3 assumed in the models. In addition, the second-order rate constants obtained with the AOPWIN method may be too high (see above). As pointed out by Klöpffer and Wagner [36], the advent of quantitative structure–activity relationship methods in the 1990s has caused a sharp reduction in experimental studies. This is unfortunate, because further experimental studies are crucial to broaden the empirical basis of methods such as AOPWIN, and measurements of OH reaction rate constants of complex substances are highly desirable.

Rain washout of chemicals with Henry's law constants below 0.01 Pa·m3/mol is overestimated in the generic LRTP models, because continuous rainfall is assumed in the models. In reality, rainfall events are separated by dry periods in most regions of the world. In these dry periods, water-soluble chemicals also can undergo significant LRT [69]. The effect of these transport events is not represented in the current versions of the generic LRTP models, which leads to the underestimation of the LRTP of atrazine and alachlor observed by Muir et al. [68]. The effect of intermittent rain, however, can be approximated in generic LRTP models by introducing an upper bound of the wet deposition velocity that represents the effect of periods without rain [30].

A different approach to validating a generic LRTP model was used by Stroebe et al. [70]. They investigated whether the so-called Junge hypothesis is reproduced by the ChemRange model, a generic global LRTP model. The Junge hypothesis [71] relates a chemical's atmospheric lifetime to the variability in the chemical's concentration that is measured in the air. Chemicals with long lifetimes are relatively homogeneously distributed in the environment, especially in regions distant from the source; therefore, their concentrations exhibit low scatter. This relationship can be expressed as ρ = a · tba, where ρ is the coefficient of variation (standard deviation divided by mean) of a chemical's concentrations measured in the field, ta is the chemical's residence time in air, and a and b are coefficients. Junge reported a = 0.14 years and b = 1, but in general, a and b depend on the type of chemical investigated, the location of the measurements in relationship to the source region, and other factors [70]. A Junge relationship with b = 1 represents the long-term effect of the large-scale distribution dynamics of airborne chemicals. Using the ChemRange model, Stroebe et al. [70] calculated coefficients of variation of chemical concentrations in a region far from the source for a range of volatile chemicals with atmospheric lifetimes from 1 to 104 days. They found that the model reproduces the empirical relationship that Junge [71] derived from field data on atmospheric trace gases (b = 1), which indicates that the treatment of atmospheric LRT in the model correctly reflects the large-scale and long-term distribution dynamics of airborne chemicals.

If the Junge relationship is applied to SOCs, the distinction between the effective atmospheric lifetime and the degradation lifetime in air is essential. As introduced above, the effective residence time in air, ta,eff, reflects the overall removal of a chemical from the air by degradation and net deposition and, therefore, is shorter than or close to the degradation half-life. For SOCs with significant net deposition from air to the surface media, a Junge relationship is only observed if ta,eff is related to p [70].

Finally, it is of interest how different the various generic LRTP models actually are. Fenner et al. [18] compared nine multimedia box models that have been used for Pov and LRTP calculations. They defined a set of 3,175 hypothetical chemicals with half-lives in air from 4 h to one year, half-lives in water from 1 d to 10 years, log KAW from -11 to 2, and log KOW from 1 to 8. With all nine models, Fenner et al. determined the overall persistence, Pov, and LRTP of the 3,175 hypothetical chemicals, ranked the chemicals according to Pov and LRTP, and between any pair of models, calculated rank correlation coefficients (RCCs). For Pov, eight of the nine models show RCCs above 0.9. The rankings in Globo-POP, the ninth model, differ from those in the other eight models, because Globo-POP is the only model with different temperatures in different regions. For LRTP, the models are less consistent, both because some use transfer-oriented and some target-oriented LRTP metrics (but five models using transfer-oriented LRTP metrics still have RCCs above 0.9) and because of different amounts of ocean water and, thereby, different importance of LRT in water. On the basis of this model comparison, the OECD Expert Group for Multimedia Models decided that a consensus model for Pov and LRTP screening should be developed; this model is available from the OECD website as the OECD Pov and LRTP Screening Tool (http://tinyurl.com/66q47j). Comparison of the OECD Tool with the nine models investigated by Fenner et al. yields RCCs as high as those found for these nine models themselves [27]. The OECD Tool has been used for the evaluation of the current POP candidate chemicals under the Stockholm Convention [72,73] (http://www.sust-chem.ethz.ch/downloads).

In conclusion, generic LRTP models are reliable and useful if they are employed within their application domain: for chemicals with a low Henry's law constant, the intermittent rain approach should be implemented. Depending on the region represented, appropriate OH radical concentrations and kOH values should be used, and the chemical properties may have to be adjusted to temperatures lower than 298 K.

Spatially resolved multimedia box models

Two spatially resolved multimedia box models developed in the 1990s are Globo-POP [25] and CliMoChem [74]. These models are global models consisting of well-mixed latitudinal zones. The main purpose of the models is to investigate the south–north migration of chemicals, whereas transport in the east–west direction is assumed to be fast and cannot be resolved in these models. Transport of chemicals in the south–north direction is driven by large-scale eddy diffusion, which leads to exchange of air and water masses between the different latitudinal zones of the models. The eddy diffusion coefficients used in the models are based on empirical data for hemispheric and interhemispheric mixing of trace gases [75,76].

Wania and Mackay [77,78] discussed the pathways of different types of chemicals to the Arctic and the possibility that certain chemicals accumulate in polar regions, mainly because lower temperature leads to slower degradation and lower vapor pressure and Henry's law constant or, correspondingly, higher KOA values. This effect is called cold condensation, although it is not a condensation in the strict sense but, rather, a shift in partitioning equilibria from air toward surface media, which is more accurately reflected by the expression “cold-trap effect.” The cold-trap effect is most visible for volatile compounds, such as carbon tetrachloride or HCB, because these compounds have reached a nearly uniform global distribution in the atmosphere. Spatially constant concentrations in air in combination with stronger partitioning into condensed media in colder regions lead to an increase in chemical concentration in the condensed media along a transect from warmer to colder regions (Fig. 2, top). This has been observed in the field, for example, for CCl4 and chlorofluorocarbons [79] and for HCB [80,81]. For chemicals with low vapor pressure (e.g., PCB 180), the temperature dependence of the vapor pressure or KOA is even stronger than that of CCl4 and HCB (the enthalpy of vaporization, which determines the temperature dependence of the vapor pressure, is approximately 30, 60, and 95 kJ/mol for CCl4, HCB, and PCB 180, respectively). For chemicals with low vapor pressure, however, the main amount resides in the surface media, not in the air. For such a chemical, if it is very persistent, a nearly uniform distribution will, theoretically, be reached after very long times (Fig. 2, middle). In this case, the cold-trap effects lead to lower concentrations in air in colder regions (instead of higher concentrations in surface media), making the chemical less mobile. In reality, however, low-volatility chemicals are still far from having a uniform distribution, because their LRT is much slower than that of volatile compounds. The cold-trap effect is then masked by the gradient that is caused by incomplete transport away from the source region (Fig. 2, bottom).

Figure Fig. 2..

Conceptual illustration of the cold-trap effect: (A) persistent volatile compounds, such as CCl4; (B) persistent semivolatile compounds, such as polychlorinated biphenyl (PCB) 180 (scenario with uniform distribution in surface media as the long-term result of long-range transport [LRT]); and (C) persistent semivolatile compounds (scenario with inhomogeneous distribution; point in time before scenario B). Emission of chemicals is assumed to occur in the warm region.

In addition to the cold trap effect, which refers to the environmental distribution of a single chemical, a second question is how different the transport efficiencies of several chemicals in comparison are (e.g., in a suite of PCB homologues). To address this question, the term global fractionation has been introduced. Conceptually, global fractionation refers to different transport efficiencies of chemicals caused by different chemical properties, such as partition coefficients, degradation half-lives, and temperature dependencies of these properties. Ideally, the effect of different chemical properties would be analyzed in a homogeneous environment and with equal emissions of all chemicals at the same location and time. In reality, the environment is highly inhomogeneous—for example, in terms of precipitation, organic matter content of soils, and emissions, which are spatially and temporally diverse and differ from chemical to chemical. Therefore, concentration trends observed in the field are a complex superposition of the effects of release patterns, chemical properties, and varying environmental conditions. To reduce the effect of confounding factors, model investigations are useful, because environmental conditions and release patterns can be controlled in environmental fate models and the relationships between chemical properties and the efficiencies of LRT can be evaluated systematically [82,83].

One aspect of global fractionation that can be directly investigated in the field is how the relative composition of a mixture of chemicals changes as a function of latitude. For PCB concentrations measured in soils along a latitudinal transect from England to Norway, Meijer et al. [84] found an increase in the fraction of trichloro and tetrachloro PCBs, a constant fraction of pentachloro to hexachloro PCBs, and a decrease in the fraction of heptachloro and octachloro PCBs. Both the Globo-POP and CliMoChem models clearly reproduce this fractionation observed in PCB concentrations in soil [83,85]. Lighter PCBs, because of their higher vapor pressure and lower KOA, are more mobile than heavier PCBs and, therefore, are relatively enriched in remote regions. In the future, however, this picture may change for two reasons [82,83]: First, lighter PCBs are less persistent than heavier ones, so in the long-term, the fraction of lighter PCBs will decrease (whereas the heavier ones will form long-lasting reservoirs). Second, the low vapor pressure of heavier PCBs makes their volatilization and mobilization slow, but in the course of decades, substantial amounts will still be released and transported via air (whereas lighter PCBs are quick and reach the Arctic soon after their release, heavier PCBs can catch up in the long-term).

Figure Fig. 3..

Independent pieces of information needed for an environmental fate assessment of a chemical. All four components are uncertain, and the influence of the different uncertainties on the overall assessment needs to be evaluated.

Using the Globo-POP model and the ACP as the model endpoint, Wania [24] has investigated which combinations of chemical properties lead to high ACP values. Volatile chemicals (i.e., “flyers”) are transported quickly and in high amounts via the air (Fig. 2, top). “Swimmers” are chemicals with a low Henry's law constant and a persistence sufficiently high for transport by ocean currents (e.g., HCHs). “Multiple hoppers” are chemicals for which, after transport over a certain distance and deposition to the ground, the distribution between air and surface media is close to equilibrium. Higher temperatures in the summer then cause revolatilization of the chemical that was deposited earlier so that another “hop” of LRT can take place. Even in the case of these chemicals, however, substantial amounts reach remote regions in a single “hop” taking place within days or weeks after release [86]. To what extent revolatilization after deposition (i.e., release from secondary sources) actually contributes to the transport to remote regions needs further investigation. “Single hop” chemicals, finally, have such a high KOA that the soil to which they are deposited after LRT remains undersaturated, and almost no revolatilization takes place. These chemicals have the slowest LRT, but in the long-term, substantial amounts of these chemicals also can undergo LRT.

Spatially revolved multimedia box models also have been used to investigate the large-scale distribution of several individual chemicals. These investigations comprise four main components (Fig. 3): An emission inventory defining the amounts of the chemical released to the environment as well as the spatial and temporal distribution of these releases, the chemical property data (i.e., partition coefficients and degradation half-lives), the model itself as well as the selection and parameterization of environmental processes used in the model; and the field data to which the model results can be compared.

Wania et al. [87] evaluated the Globo-POP model comparing model results and field data for α-HCH. On this basis, Wania and Mackay [88] investigated the global distribution and mass balance of α-HCH using the Globo-POP model, with a particular focus on the period after 1980, when the usage of technical HCH, which is the source of α-HCH to the environment, was strongly reduced in northern temperate countries. They found that in the northern polar region of the model, a long-lived reservoir of α-HCH is formed that accounts for more than 30% of the chemical's global environmental inventory after 1992. The burden of α-HCH in the northern temperate region, in contrast, strongly decreased during the same period. Similarly, a shift has occurred from a large reservoir of α-HCH in cultivated soils before 1984 (>40% of the global inventory) to a more persistent reservoir in surface ocean water (>50% of the global inventory after 1992). This shift toward more long-lived reservoirs in colder regions also is reflected by the evolution of the overall persistence of α-HCH over time. Wania and Mackay calculated the overall persistence of α-HCH as the global amount present in the model, Mtot, divided by global degradation mass flux, Fdeg:Pov = Mtot/Fdeg. Both components, Mtot and Fdeg, are a function of time in this calculation, and Mtot decreases from approximately 100 kt in 1985 to less than 10 kt in 1997. In parallel, Pov increases from approximately eight months to approximately 1.5 years.

Using the CliMoChem model, Schenker et al. [89] compared model results for DDT with measured DDT concentrations and estimated future DDT levels caused by DDT usage against malaria. Similar to the results of Wania and Mackay [88] for α-HCH, Schenker et al. [89] found long-lived residues of DDT in Arctic soils in their model.

Wania [90] and Schenker et al. [91] also used the two zonally averaged models to investigate the hypothesis that levels of perfluorooctanoic acid (PFOA) observed in Arctic snow are caused by atmospheric deposition of PFOA formed by degradation of volatile precursors [92]. Schenker et al. [91] investigated airborne distribution, deposition, and transformation to PFOA of fluorotelomer alcohols and perfluorooctane sulfonamidoethanols. The model results are within a factor of two in agreement with PFOA flux estimates derived from PFOA measurements in ice from Greenland [92]. This indicates that transport of both types of volatile precursors to the Arctic and subsequent transformation into PFOA is a possible explanation for PFOA levels observed in Arctic surface media, such as inland ice. For the overall mass flux of PFOA to the Arctic, transport of PFOA itself in ocean water also is relevant. The importance of these two pathways, PFOA in ocean water and PFOA from volatile precursors in air, is the subject of a controversial scientific debate. In absolute terms, the amount of PFOA entering the Arctic via volatile precursors is only a few percent of the amount transported via ocean currents [91]. For inland ecosystems and, perhaps, also oceanic surface water, however, deposition of PFOA formed out of volatile precursors probably is still a relevant source of exposure to PFOA.

The problem that Kp values derived from the KOA are not appropriate for all types of aerosols has been addressed by Götz et al. [93]. They used poly-parameter linear free-energy relationships (pp-LFERs) derived by Goss and Schwarzenbach [94] and by Roth et al. [95] to describe sorption of SOCs to the different components of a fine and a coarse aerosol fraction (organic matter, elemental carbon, sea salt, and silica). On this basis, regionally different concentration and composition of atmospheric aerosols [96,97] can be taken into account in spatially resolved environmental fate models. Götz et al. [93] implemented this approach in the CliMoChem model, and using the LRT of three pesticides (α-HCH, terbuthylazine, and trifluraline) to the Arctic as an example, they compared it to the approach using Kp values derived from the KOA. They found better agreement between model results and pesticide concentrations measured in Arctic air [98] if the pp-LFER description of aerosol–air partitioning is used: Discrepancies are a factor of two to eight (pp-LFER approach) and a factor of 10 to 50 (KOA approach). Because aerosol–air partitioning is a key factor determining the LRT of SOCs, regional differences in aerosol concentration and composition should be taken into account in spatially resolved environmental fate models.

In addition, PBDEs have been investigated with several environmental fate models. The LRT of PBDEs is not yet well understood. Wania and Dugani [99] used four generic LRTP models to estimate the transport distances of nine PBDE congeners (from monobromo to decabromo). In these calculations, constant environmental conditions were assumed, as is typical of generic LRTP models. Breivik et al. [100], in contrast, used the CoZMo-POP model with variable environmental conditions (continuous vs intermittent rain and presence vs absence of vegetation) to estimate CTDs of BDEs 47, 99, and 209. In addition, they investigated measured PBDE concentrations in sediments from lakes between 43°N and 80°N. Particularly difficult to understand is the LRT of deca-BDE (i.e., BDE 209), which sorbs strongly to aerosol particles because of its very high KOA and undergoes only slow reaction with OH radicals but fast direct photolysis. Whereas Wania and Dugani [99] reported relatively low LRTP of BDE 209 because of its fast deposition with aerosol particles, Breivik et al. [100] found in some model scenarios considerably higher CTDs of BDE 209 as well as an ETD of approximately 500 km, which is only by a factor of 1.5 smaller than ETDs derived from the lake sediment data for PCBs. Neither Wania and Dugani [99] nor Breivik et al. [100], however, included direct photolysis of PBDEs, which is an important degradation process in air. Raff and Hites [48] determined photolytic lifetimes of PBDEs from mono- to deca-BDE and investigated the influence of photolytic degradation, degradation by reaction with OH, and deposition with particles on the atmospheric lifetime of PBDEs. For BDE 209, they assumed a fraction of 99.999% in the particle-bound phase in combination with the assumption that photolysis of BDE 209 occurs only in the gas phase. Under these conditions, deposition with aerosol particles is still the most important removal mechanism of BDE 209 from the atmosphere. If, however, only a slow but nonzero photolytic reaction of particle-bound BDE 209 is assumed, then photolysis is the main removal mechanism of BDE 209 in the atmosphere in temperate and tropical regions [101]. Under this assumption, the LRT and deposition mass fluxes of BDE 209 in remote regions are considerably smaller than those in the scenario without photolysis of the particle-bound fraction of BDE 209. The case of BDE 209 clearly demonstrates how important an improved understanding of the reactivity of particle-bound SOCs is.

A global multimedia box model with higher spatial resolution is BETR-Global [102]. In contrast to Globo-POP and CliMoChem, BETR-Global has spatial resolution in both the latitudinal and the longitudinal direction. The model has been designed to bridge the gap between multicompartment models with low spatial resolution, on the one hand, and highly resolved general circulation models, on the other. It consists of 288 grid cells with a size of 15° × 15°. Air flow between the grid cells is based on global reanalysis data from the U.S. National Centers of Environmental Prediction [103].

The BETR-Global model has been used mainly to investigate the global distribution of PCBs. Using the PCB emission inventory by Breivik et al. [104], MacLeod et al. [102] demonstrated that PCB concentrations in air obtained with the model are in good agreement with PCB concentrations measured at 11 long-term monitoring sites of the Integrated Atmospheric Deposition Network around the Laurentian Great Lakes (USA and Canada) and of the European Monitoring and Evaluation Programme (agreement within a factor of 3.2 for 60% of the model-vs-field concentration pairs). On this basis, MacLeod et al. used the model to investigate the influence of changes in atmospheric conditions caused by climate change on the distribution and levels of PCBs. They related changes in PCB concentrations in several regions of the model to changes in the North Atlantic Oscillation (NAO) index. The NAO index is a climate indicator that describes conditions in the atmospheric pressure field between Iceland and the Azores. MacLeod et al. found that the maximum variability in PCB concentrations in air related to NAO variability is a factor of two. More pronounced effects of changing atmospheric conditions on the distribution and levels of SOCs may occur in the future as a consequence of ongoing climate change. The relationship between climate change and pollutant dynamics is receiving increasing attention and is being investigated on various scales from regional to global [105]. In general, the BETR-Global model can be used to investigate source–receptor relationships between regions represented by different grid cells (see next section).

Atmospheric transport models

General circulation models of the atmosphere have increasingly been used to investigate the large-scale distribution of SOCs. These models provide much higher spatial and temporal resolution than multimedia box models, and they can be used to determine source–receptor relationships between different regions and to identify trajectories of chemical transport during certain transport events [106–109].

Semeena and Lammel [107] as well as Leip and Lammel [110] used the atmospheric transport model ECHAM coupled to reservoirs representing the surface media, soil, vegetation, and ocean water. They defined LRT metrics [110] and investigated the environmental distribution dynamics of γ-HCH and DDT [86,107]. Using model versions with and without revolatilization from surface media, they compared multiple- and single-hop transport of the two insecticides to remote regions, including the Arctic. According to their model results, both types of transport contribute to similar extents to the amounts of γ-HCH and DDT in the Arctic. γ-Hexachlorocyclohexane exhibits a slightly higher contribution of multiple-hop transport, because it has a lower Henry's law constant than DDT (favoring wet deposition) and is more volatile than DDT (favoring revolatilization after deposition).

The global distribution of γ-HCH also was investigated by Koziol and Pudykiewicz [106] with an atmospheric transport model to which modules for soil, ocean water, and also ice and snow are coupled [106]. The model was evaluated by comparison of model results to measured concentrations in the Arctic and in southern Quebec (Canada), with deviations of a factor of five or less. Annual concentration averages show better agreement than short-term concentration patterns. For India as a source region, transport of γ-HCH to North America along westerlies and to Africa and Latin America along easterlies was demonstrated.

The POPs model of the Meteorological Synthesizing Centre–East (MSEC-POP) has been used to calculate the distribution of four selected PCB congeners in the northern hemisphere for the year 1996 [108]. The discrepancies between calculated PCB concentrations in air and measurements from several locations from midlatitudes to the Arctic were around a factor of four. The model results show, for example, the contribution of different continental regions (Africa, America, different parts of Asia, different parts of Europe, and Russia) to PCB deposition mass fluxes in the Arctic. For PCBs 28 and 118, transport from Russia to the Arctic is higher than Russia's share in the emissions of these two congeners; for PCBs 153 and 180, transport from northwestern Europe to the Arctic is higher than the share of this region in the emission of PCBs 153 and 180. Holoubek et al. [111] compared results from the MSCE-POP model to concentrations of PCB 153, benzo[a]pyrene, and γ-HCH measured at Kosetice (Czech Republic) and found deviations between model results and measured concentrations of approximately a factor of five and below [111].

Hansen et al. [109] developed a version of the Danish Eulerian Hemispheric Model capable of describing the distribution of SOCs (DEHM-POP) [109]. As a case study, they investigated α-HCH; similar to Koziol and Pudykiewicz [106], they found good agreement between model results and measured concentrations of α-HCH in air for annual concentration averages but less agreement for short-term concentration trends. Hansen et al. [109] further discussed the effect of dynamic snow–air exchange of chemicals on the concentration profiles of α-HCH obtained with the DEHM-POP model [112–114]. The snowpack acts as a highly dynamic reservoir receiving chemical with snowfall and releasing it by evaporization. Inclusion of the snowpack tends to improve the agreement between model results and field data, but some measurement sites still show pronounced discrepancies between model results and α-HCH concentrations measured in air.

Hansen et al. [115] compared the DEHM-POP model with the less highly resolved EVn-BETR model, which is a multimedia box model based on BETR North America [116,117], and found good agreement in many respects. A characteristic difference is that advective transport of airborne chemical out of an open model domain is more pronounced in the atmospheric transport model, because dynamic air flow data are used in the atmospheric transport model, whereas in the multimedia box model, long-term averages of air flows out of and into the model domain are used, which leads to a dampened atmospheric dynamics in the box model.

Atmospheric transport models adjusted for the treatment of SOCs are intended to provide the most accurate description of the environmental distribution dynamics of SOCs. Certainly, with these models valuable insights can be gained that go beyond the results from multimedia box models. The high resolution of these models, however, requires a correspondingly high resolution of emission data, descriptions of environmental media and processes other than those in air, and of chemical property data dependent on environmental conditions. In most cases, these components are not available with the same resolution as that used in the description of the atmospheric dynamics. As in multimedia box models, the overall uncertainty of the model results cannot be smaller than the uncertainty of the most uncertain component shown in Figure 3. In many cases, these components are emission data and/or chemical property data.


In the following paragraphs, key findings from large-scale field measurements are highlighted (for a more detailed overview, see, e.g., Scheringer et al. [118]). Field data provide important empirical information about the LRT of chemicals. The mere detection of a chemical in a remote region, however, cannot necessarily be understood as evidence of LRT. Most important, the levels measured in the field must be put into perspective by consideration of the potential influence of nearby primary sources. For example, PBDEs recently measured in Antarctic sediments and biota are released from technical equipment transported to Antarctica [119], whereas DDT measured in Antarctic penguins is the result of atmospheric LRT and release from DDT stored in glaciers over decades [120].

The global distribution of several typical persistent SOCs, including HCH, HCB, PCBs, PBDEs, polychlorinated naphthalenes, and various pesticides, has been measured in several media, such as ocean water and air [121,122], tree bark [81], butter [123], and soils [124]; recently, concentrations in air have been measured with networks of passive samplers [67,125,126]. These studies provide insight regarding inter-hemispheric distributions and spatial trends from source regions to remote regions, including latitudinal trends. Hexachlorobenzene is almost uniformly distributed at the global level, with concentrations in surface media increasing toward the poles [23,80,81], which is caused by the cold-trap effect.

Table Table 1.. Amounts of polychlorinated biphenyl (PCB) 28 and PCB 180 in global soilsa
 Region IRegion IIRegion III
  1. aData from Table 5 in Meijer et al. [124]. Region I = 90°S–30°N; Region II = 30°N–60°N; Region III = 60°N–90°N.

PCB 28502674396635
PCB 18012021360639316
Total PCBs1,100154,400611,72024

For PCBs in soils, Meijer et al. [124] have reported amounts that are present in different latitudinal bands (region I, 90°S–30°N; region II, 30°N–60°N; and region III, 60°N–90°N) (see Table 5 in Meijer et al. [124]). The main sources of the PCBs are located in region II. Absolute and relative amounts of PCBs 28 and 180 in the soils in the three regions are given in Table 1.

The relative amounts show that lighter PCBs are more broadly distributed than the heavier ones. However, substantial amounts (hundreds of tonnes) of a heavy PCB, such as PCB 180, have been transported away from the sources. In the long-term, this process will continue.

Some data sets, such as PCBs in European soils [84] or in global butter samples [123], show enrichment of lighter PCBs toward the poles (latitudinal fractionation), but in other data sets, this trend is not visible (global soils and biota in Europe) [124,127]. Wania and Su [83] discuss the influence of confounding factors, such as highly variable concentration of organic matter in soils.

In addition to the long-term distribution on the global scale, the contribution of particular episodic transport events to the LRT of organic chemicals also has been determined. An important case is transport from East Asia to North America, which has been investigated by measurements at ground level and in the higher troposphere in western Canada [128] and the western United States [129,130], in combination with measurements in East Asia [129,131], and back-trajectory calculations. The back-trajectory calculations can be performed with a model such as HYSPLIT [132] (http://www.arl.noaa.gov/ready/hysplit4.html) and show the origin of air masses that reached the sampling site at the sampling time as well as how much time the air masses spent in different possible source regions of the chemicals measured. Chemicals transported from Asia to North America are, for example, particulate-phase PAHs [130], organochlorine pesticides [128], and to a lesser extent, fluorinated organic compounds [129]. The back-trajectory calculations indicate that for some chemicals, urban areas in North America are stronger sources than transport from Asia, but for particulate-phase PAHs and selected organochlorine pesticides, the contribution of inflow from Eastern Asia can be significant. For α-HCH, the measurements by Harner et al. [128] confirm results from the atmospheric transport model of Koziol and Pudykiewicz [106]. Beyond the quantification of chemical outflow from source regions and estimation of transport mass flows, measurements of chemicals in source and target regions during transport events provide empirical data that are highly valuable for the evaluation of fate and transport models.

In addition to POPs that have been investigated for several decades, such as PCBs and organochlorine pesticides, more and more other compounds have been detected in the environment during recent years, with some of them in remote regions. This includes several chemicals that have been in use for many years, such as PBDEs, perfluorinated surfactants and their precursors, or the flame retardants Dechlorane Plus and hexabromocyclododecane [133–135]. The term emerging contaminants has been introduced for this problem of chemicals that may have been in the environment for a while but that have not been detected previously. In particular, PBDEs and perfluorinated compounds have been found to be widespread in the environment. Since the late 1980s, PBDEs have been measured in many environmental media, biota, and human tissue in many regions of the world [136–138]. The PBDEs used in the largest amounts are the commercial penta- and decabromo preparations. The pentabromo congeners are more volatile than deca-BDE and undergo LRT, which is illustrated by their detection in the Arctic [138]. The deca-BDE has a very high log KOA (˜15) and is strongly bound to aerosol particles. This reduces its LRTP compared to more volatile compounds, but during episodic transport events taking place in periods without rain, even deca-BDE can be transported over distances of 1,000 km and more [139,140].

In the group of the perfluorinated chemicals (PFCs), PFOS and PFOA are the compounds that have been detected most frequently in biota and environmental media all over the world. They are highly persistent and represent the ultimate degradation products of several other PFCs, such as fluorotelomer alcohols and perfluorosulfonamido ethanols. Both PFOS and PFOA are highly water soluble and end up in ocean water, and in the long-term their LRT is caused by ocean currents. In addition to the LRT of PFOS and PFOA themselves, however, airborne LRT of volatile precursors and their subsequent degradation into PFOS and PFOA is an important mechanism [92,141]. Volatile precursors of PFOS and PFOA have been measured by Jahnke et al. [142] along a transect from Europe to South Africa, by Barber et al. [143] across Europe, and by Stock et al. [144] across North America. The releases and environmental distribution of these various PFCs that eventually are converted into PFOS and PFOA are now key factors determining the environmental and human exposure to PFCs, especially after the significant reductions of direct emissions of PFOA and PFOS in recent years.


A first important result of the research into the LRT of organic chemicals during the last 15 years is that environmental fate models and field studies have increasingly been related to each other. Models have been improved and evaluated by comparison of model results with field data and by model intercomparison studies. In many cases, good agreement exists between model results and field data.

On the model side, there remain different schools of environmental fate models used for LRT investigations. One school is the multimedia box models, originally based on a chemical engineering approach describing environmental media as well as mixed-chemical reactors. The second school is the multimedia models based on atmospheric transport models that have been expanded by a set of reservoirs linked to the dynamic atmosphere and that represent the surface media. As described in this review, these two modeling approaches serve different purposes and can well be complementary to each other. Furthermore, multimedia box models with relatively high spatial resolution and incorporating air flow patterns from atmospheric transport models form a link between the two approaches, so it can be stated that the two approaches are converging.

There remain many open questions in the overall picture regarding environmental LRT of organic chemicals. First, for many chemicals, the overall picture of emissions, chemical properties, environmental distribution, and final removal by environmental loss processes still has considerable gaps: Emissions are incompletely known; chemical properties and, especially, the relationships between chemical properties and environmental conditions are uncertain; and mechanisms of LRT and environmental degradation are incompletely understood. In the case of PCBs, for example, considerable inconsistencies exist between high estimates of environmental losses caused by reaction with OH radicals, on the one hand, and emissions and environmental pools that are too small to feed the loss processes, on the other [47,124,145–147].

A particular problem is the behavior of SOCs sorbed to atmospheric aerosols. Experimental investigations of the degradation processes of SOCs adsorbed on and/or absorbed into various types of aerosol particles are highly desirable. In addition, gas-phase reactions of SOCs with OH radicals need to be studied more extensively. The existence of quantitative structure–activity relationships that work well for certain groups of chemicals does not imply that experimental studies are no longer needed.

Finally, the Stockholm Convention and the Aarhus Protocol on POPs have defined a clear need for continuous support in terms of well-evaluated scientific results about the LRT of chemicals. That these international treaties entered into force a few years ago does not mean that their implementation is a routine process. On the contrary, it requires continuous efforts from scientists in the identification and evaluation of potential POPs, in the evaluation of the effectiveness of mitigation measures, in the monitoring of levels in humans and environmental media, in the identification and characterization of POP emission sources, and in many other aspects. To distribute the benefits and burdens of chemical usage in a fair way between different parts of the world, the LRT of chemicals needs to be reduced as far as possible.


I thank Matthew MacLeod, Urs Schenker, and Fabio Wegmann for helpful comments.