Ann. N.Y. Acad. Sci. ISSN 0077-8923

A N N A L S O F T H E N E W Y O R K A C A D E M Y O F SC I E N C E S Issue: Annals Reports

The science and policy of critical loads of pollutant deposition to protect ecosystems in New York Timothy J. Sullivan1 and Jerry Jenkins2 1

E&S Environmental Chemistry, Inc., Corvallis, Oregon. 2 Wildlife Conservation Society, Saranac Lake, New York

Address for correspondence: Timothy J. Sullivan, E&S Environmental Chemistry, Inc., P.O. Box 609, Corvallis, OR 97339. [email protected]

We discuss the potential for adopting a critical load (CL) of air pollutant–deposition approach to inform natural resource protection and management in New York. The CL reflects the quantitative exposure to pollutants below which significant harmful effects on specified sensitive elements of the environment do not occur. Here, we discuss how CLs can be used to protect sensitive ecosystems against the harmful effects of atmospheric sulfur and nitrogen deposition and associated soil and water acidification and nutrient enrichment. The CL can be used diagnostically to determine resources at risk and prescriptively to evaluate the effectiveness of regulations and to manage resources. Keywords: critical loads; target loads; atmospheric deposition; acidification; nutrient

Introduction New York possesses outstanding natural resources, many of which are sensitive to damage from air pollutants originating both inside and outside the state borders. Air pollution can damage terrestrial and surface water biotic communities. Resource managers need to predict the amount of damage caused by a given amount of pollution, but quantifying ecological damage is difficult. Here we review one approach, the critical load (CL), which is becoming more widely used in the United States. The CL and the closely related target load (TL) specify the level of pollutant input below which significant ecological harm is not expected to occur.1 The CL has been applied to some air pollutants (such as sulfur (S) and reactive nitrogen (N)) and for some sensitive resources (such as lakes, streams, lichens, and forest soils) that have been well studied. Our focus here is mainly on the use of CLs in the state of New York; nevertheless, this tool has been used extensively in Europe and more recently throughout North America. The premise of the CL approach is that there is a pollutant dose that a natural community is not able to tolerate on a long-term basis without incurring

damage. Pollutant loads below the CL are assumed to cause no damage at the point in time specified in the analysis. This can be at an assumed future steadystate condition or at a designated time in the future. Pollutant loads at or above the CL cause progressive damage. When compared to the load that the community is actually receiving, the CL measures risk to ecosystems caused by pollutant exposure. A community receiving pollution in excess of the CL is in exceedance, presumed to be at increased risk of being damaged. Before the CL approach can be put to use, it must be demonstrated that such a tipping-point pollution level indeed exists and that it can be determined with some degree of certainty. Some natural communities might be sufficiently sensitive that there is no long-term pollutant load that they can tolerate without damage (i.e., CL = 0), especially if they have already been damaged by past pollution. Other communities might be sufficiently complex, with populations responding in different ways and with different thresholds, that they cannot be characterized with a single CL or even with multiple CLs. Even when a load that triggers damage exists, there may be significant problems in determining it, especially when the impacts of pollution occur slowly and are

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Table 1. Steps in the critical load process

1. 2. 3. 4. 5.

Specify air pollutants of interest and ecosystem elements to be protected. Determine relationships between pollutant deposition and biological harm (observed empirical CL). Conduct steady-state and dynamic modeling. Compare CL with ambient pollutant deposition level and calculate exceedance. Adopt management targets.

cumulative. Furthermore, multiple pollutants and other ecosystem stressors can work synergistically or may cancel each other out. It is necessary to define what we mean by significant harm to estimate CL. It may be that we can identify a combination of ecosystem changes beyond some level that can be designated as significant harm even if there is no recognizable tipping point. Should one try to protect 99% of the resource, or is 90% sufficient? There may not be a level of protection that is considered by society to be adequate but that is not zero. Scientific interest in CLs began during the 1980s, mainly in Europe. The Clean Air Act (CAA) of 1970 and its subsequent amendments led to National Ambient Air Quality Standards (NAAQS) in the United States to protect human health. Scientists wanted similar standards to protect natural communities and hoped that CLs would help to provide them.2–5 Progress has been made on both the science and the policy and management aspects of CLs.6–9 The implementation of CL often entails a multistep process (Table 1). The first step is to specify the air pollutant(s) of interest that might cause damage and identify the ecosystem(s) that might be damaged by those pollutants. Often, the impacts on the natural community are varied. Nitrogen deposition on forests, for example, affects soil chemistry, tree and shrub growth, understory plant diversity, litter and wood decay rates, and the structure of lichen and mycorrhizal communities. The second step is to determine how much pollutant produces how much of an impact. This can be done by adding pollutants experimentally or by comparing communities receiving different pollutant loads. CLs determined this way are called empirical CLs. They are determined by observation and can be used as screening tools. In many cases, empirical CLs may be sufficient; in others, a third step, modeling, will be required. We may want to know, for example, the CLs of a thousand Adirondack Mountain lakes, or the load 58

that will keep a forest healthy for the next hundred years. In these cases, a mathematical model is applied that simulates the behavior of the community. Typically, these models simulate the chemistry of a community rather than its biology. To do this, researchers need to find a chemical variable that has been shown to be a good indicator of the amount of biological change defined by land managers or policymakers as significant ecological harm. These variables are called indicators or proxies. After the CL has been determined, the next step is to put it to use in resource management or the evaluation of the success of pollution control policies. This involves calculating and mapping exceedances, and then using them to suggest goals, often called TLs. A TL can be an official management goal, with the intent that it will be maintained through emissions regulations or on-site remediation. It is usually calculated with reference to a particular time period. There are many different kinds of CLs. They can be empirical, based on measurements or observation, or determined using a model. The model can be long-term steady state (time invariant) or tied to a particular point in time (dynamic). The CL can be intended to protect against harm caused by S, N, both, or some other pollutant or stressor. The harm under investigation can be caused by excess nutrient supply, acidification, or toxicity. The focus can be on the protection of a single species or an entire biological community; either can be protected to varying levels using multiple indicators of harm. Therefore, there is not one CL for a given biological community; there is a matrix of CLs. Posch et al.10 argued that the application of linked nutrient and acidity chemical criteria for plant occurrence contributes to an optimal N and S deposition envelope to sustain a prescribed biodiversity goal. The last part of the CL process involves moving from CL to a management target. It is fundamentally a policy decision rather than a scientific issue, and can be a complex one. New York contains many different types of aquatic and terrestrial

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communities. Each one contains different species, and each species responds differently to various pollutants. The policy problem is to determine an attainable level of protection for the species and communities deemed to be of most value, and then to devise management actions needed to achieve it. Thus, there are many CLs for protecting ecosystems in New York. There are biologically based loads to protect trees, lichens, herbs, and fish and chemically based loads to protect against acidification and nitrate leaching. There are steady-state loads and 100-year loads, and empirical loads and modelbased loads. All of these can vary from one watershed to another. The goal of this paper is to describe how these CLs are applied in New York and elsewhere. Our focus is on the ways that CLs can be used to protect sensitive ecosystems and to foster recovery from past damage caused by air pollutants. The primary forum for coordination of research on CL development in the United States has since 2006 been the Critical Loads of Atmospheric Deposition (CLAD) Science Committee of the National Atmospheric Deposition Program (NADP).11 Efforts are underway in association with CLAD to collect needed data and improve methods for CL calculations throughout the United States. A key component of recent and ongoing CLAD efforts is the reduction of uncertainty in CL calculations.11 The CL approach is closely aligned with the concept of total maximum daily load (TMDL), which is applied in New York and throughout the United States for management of water pollution under the Clean Water Act. TMDLs are developed in an effort to restore waters in New York that have been placed on the 303(d) list of those deemed to be impaired by acidity for protection of aquatic life.12 The CL also complements the application of the ecosystem service approach to natural resource management.13 What are the ecological impacts of air pollution? Acidic deposition contains a mixture of N and S compounds. Its effects are both acidifying and fertilizing. The acids acidify groundwater and flush important nutrients like calcium and magnesium out of soils. They also free naturally occurring aluminum ions from the soil and transfer them to drainage water where they can harm plant roots, fish, and other life forms.

Science and policy of critical loads

The largest source of S in the atmosphere is the combustion of coal in the generation of electricity. Human-caused N in the atmosphere comes mainly from fossil fuel combustion and agriculture (animal manure and fertilizers). Nitrogen is emitted mainly as nitrogen oxides from boilers, motor vehicles, and other engines, and as a mixture of ammonia and nitrogen oxides from farms and livestock operations. The various N compounds in air pollution are nutrients. They are taken up by plants, stored in biomass, set free by decay, and then cycled again by new growth. When N is scarce, the cycling is tight and most N is retained within the ecosystem. When N becomes abundant, the cycling is looser and excess N leaks into groundwater and migrates to streams and lakes. When excess N is added via air pollution, plants take it up, and the N content of plant tissue rises. Membrane calcium may be lost, often leading to an increased susceptibility of plant foliage to cold damage. Efficient N uptake becomes less important, leading to a decrease in fine-root biomass, which results in a decline in the diversity of mycorrhizal communities. Once N has accumulated in the ecosystem, the structure of the community begins to change. Nitrogen-demanding species, often nonnative, may outcompete the native low-N species.14 Rare species and species adapted to low N availability are lost and the overall number of species declines.15–17 Nitrogen-rich plants are more nutrient rich and therefore attractive to pathogens and herbivores; plant mortality may increase. How well can we quantify the impacts of S and N? To identify the threshold for harm caused by S and N deposition, we must quantify the relationship between pollutants and effects by determining dose– response functions. This may be done in one of two ways: by gradient studies and by experimental studies. Both are observational and are often applied to calculation of empirical CLs. Gradient studies compare natural communities that receive different levels of pollutant deposition but that are otherwise similar. For example, Aber et al.18 determined the response of forests to N fertilization by plotting the spring-season N concentrations in lakes and streams, a measure of N leakage, in the northeastern United States against the wet N deposition load. The results showed that at low loads

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the watersheds retain most of the deposited N, and at high loads they lose more of the deposited N to drainage water. The data showed a classic threshold response, with N leakage rising sharply at deposition loads above around 7–8 kg/ha-year. Another gradient study by Fenn et al.19 examined the response of the lichen communities in California forests to N input. They found that at pristine sites, the lichen communities were dominated by low-N (oliogotrophic) species. As N deposition increased, the communities shifted toward eutrophic species. The great strength of gradient studies is that they represent actual results from natural communities. The sampling can be done over a large area, and the impacts can be measured with relative confidence. Their greatest weakness is that they suggest, but do not demonstrate, causal relationships. The communities being compared will always differ in attributes other than the pollutant load. These differences may cause variable impacts too. In the most difficult case to evaluate, the relationship between deposition and impact could be caused by something correlated with deposition, and not by the deposition at all. Most importantly, such studies do not necessarily tell us about long-term effects. In a study that used both empirical data and biological modeling, Thomas et al.20 used data from 20,000 U.S. Forest Service plots from 19 states to fit growth and survival models for 23 tree species in eastern forests. Many species showed increased growth at low N-deposition loads. However, for some species, like quaking aspen, growth decreased at higher loads. For others, like black cherry, added growth came at the cost of reduced survival. Still others showed reduced growth with increasing load, or no change at all. This kind of result, in which the responses are species specific, shows why it can be difficult to define an overall CL for the forest. Rather, there are different CLs for different species. Land managers may choose to use the lowest CL as protective of the entire ecosystem. Loads of 10 kg/ha N per year appear to be beneficial to fir, tulip, and cherry, tolerated well by paper birch, and damaging to quaking aspen, scarlet oak, and red pine.20 Nevertheless, the power of adding modeling to empirical data is clear: the modelers were able to obtain important results that could not have been obtained through experiment or direct observation alone. These are often the kinds of results that are needed 60

to inform landscape-scale management decision making.9 In experimental studies, different loads of a pollutant are applied and the impacts measured. For example, in the 1980s, there was much debate about whether N might be reducing the cold tolerance of red spruce in the mountains of New England. Schaberg et al.21 established sample plots in Vermont and treated them for 12 years with four different loads of N. They then resurveyed them and measured membrane calcium, electrolyte leakage, and cold tolerance. The relationship between N deposition and winter mortality was clearly demonstrated. Trees growing on N-treated plots showed greater winter injury, and individual treated trees showed decreased physiological integrity and cold tolerance (Fig. 1). Experiments like this are valuable because they establish cause–effect relationships in a way that gradient studies cannot. However, they are often not designed to detect threshold effects, especially in situations where the threshold occurs at a very low dose, and so they can be less useful for determining CLs. In New York and other eastern states, high air pollution makes it difficult to tease out the threshold levels at which ecosystem effects are initiated. The inability of many earlier experimental studies to identify the lowest loads at which impacts begin to occur made it difficult to use such studies to identify response thresholds. Conversely, the large experimental loads that many researchers used provided clear proof of damage and identified loads that caused unequivocal harm. North America now has a large, ecologically diverse literature on the biological impacts of N and S deposition. Recent examples include studies on lichens in the Pacific Northwest,22 desert vegetation,16 sugar maple in New York,23 alpine plants in Colorado,24 grassland species in the Midwest,25,26 and many others. Such studies compose an important part of the foundation of CL assessment. Pardo et al.27 provided a review and comparison of the CLs derived from these studies. How are CLs determined? Empirical CLs The simplest way to determine CLs is by examining dose–response functions from empirical studies. In Aber et al.’s28 study of N leakage, spring NO3 − concentrations tended to rise suddenly at N deposition

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Science and policy of critical loads

Figure 1. Frost tolerance (left panel) and winter injury (right panel) as a function of nitrogen added and total nitrogen load. From Ref. 21.

loads of around 7 or 8 kg/ha-year, and this was taken as the threshold. In Fenn et al.’s19 lichen study, tissue N levels increased gradually. They found that lichens from pristine sites had tissue N concentrations of about 1%. This corresponded to a deposition load of 3.1 kg/ha-year in their gradient study, and they selected this as the CL. If we are working from experimental data with only a few treatment levels, we will not have a clear dose–response curve and may have trouble identifying the threshold at which impacts begin. However, we still may be able to discern an interval that brackets the CL and a good upper bound. Studies like that of Thomas et al.,20 in which some species benefit and some are harmed by the deposition input, do not lead to a clear CL. However, they still may indicate thresholds for changes in species abundance and diversity, or at which accompanying ecosystem services are no longer provided. If these changes are deemed sufficiently important, the thresholds at which they occur can be used to estimate an overall CL for the ecosystem. Finally, if we have a system in which a chemical variable like soil base saturation or the concentration of aluminum in surface water is a good predictor of biological response, we can model the relationship between deposition and soil or water chemistry and use it to determine the CL. This approach essentially substitutes a single chemical proxy for an ensemble of species responses. Examples of CLs for nitrogen fertilization Because N is an essential nutrient, N deposition has multiple direct effects on plants. Pardo et al.27 assembled approximately 50 studies from 3200 field

sites in the continental United States and used them to estimate empirical CLs for different types of receptors in 15 different ecoregions. The studies they reviewed included lichens and shrubs in arctic tundra, bryophytes in bogs, grasses in prairies, mycorrhizal fungi in Pacific Coast forests, and invasive grass species in deserts. The results are preliminary—most ecosystems have had only a few studies conducted, and many of these had large margins of error—but they still indicate the applicability of the empirical approach. The results showed considerable variation. Considering that the study sites range from the Arctic to central Florida, this is hardly surprising. But they also showed a noteworthy consistency. All six biomes showed impacts from N fertilization at loads in the range of 2–10 kg/ha-year. Lichens and mycorrhizae appear to be among the most sensitive life forms and often respond at loads less than 5 kg/ha-year. Impacts on herbs, for which there are fewer studies, are either more variable or just less well known. The upper map in Figure 2 shows the spatial distribution of the CL estimates for forested biomes. The hatching indicates the approximate level of reliability, based on the number of studies conducted and how well they corroborate each other. The upper limits of the loads vary considerably. Their lower limits are more consistent; excepting the Mediterranean California biome, they lie between 0 and 5 kg/ha-year. In contrast, the lower map on Figure 2 shows the estimates for herbs and shrubs. There are fewer studies, and so the reliability is lower. The whole eastern temperate forest is listed as “judgment only,” meaning that there are no direct studies and the

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Figure 2. Spatial distribution of N empirical critical loads for forests (top map) and for shrubs and herbs (bottom map). From Ref. 27.

results are based on comparisons with forests in other regions. Determining CLs from models Most empirical CLs are based on the response of individual species. But there is no practical way to research the responses of all the species in a forest or in a stream or lake. And even if there was, there is no way, short of stopping all emissions, to stop all change and completely protect every species everywhere. We can, however, look for a load that is generally protective of the structure and function of a community or ecosystem. Such a CL would err on the side of caution and protect most species, or protect the more sensitive species, from major changes.

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It would prevent changes from rising to the level where they eliminate species and/or substantially alter ecosystem function and cause broad ecological harm. Several approaches are possible. The mostly widely used involve modeling a chemical indicator or proxy. This approach looks for a chemical variable that is closely linked to the overall ecological condition of the community and then uses a biogeochemical model to describe how that variable changes with deposition. In aquatic systems, acid-neutralizing capacity (ANC) is the common chemical proxy choice. In forest systems, the N content or ratio of the calcium to aluminum concentrations in groundwater and the base saturation of the soil are common choices

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for assessing effects on forest trees. Because these variables influence or correlate with many biological processes, it is often possible to find values for them that mark thresholds for biologically damaging change. In surface waters, diversity drops and fish mortality increases markedly below about ANC = 50 ␮eq/L. In forests, essential mineral nutrients become increasingly scarce below a base saturation of about 20%. Recent research by Sullivan et al.23 demonstrated that sugar maple regeneration in the Adirondack Mountains in New York is nearly absent where upper mineral soil base saturation is less than 12%. These thresholds, which are then used to compute CLs, are called critical values. Proxy modeling is attractive because it replaces many biologically based CLs with a single chemically based one. The chemical response of different watersheds can differ greatly, depending on physiography, geology, and soil conditions. Thus, each modeled watershed will have its own CL, and when the model is run on a region, the result will be an ensemble of CLs. This complicates applications of CL across space. Possible solutions are to select the most sensitive element for protection of some percentage of the resource (e.g., 95% or 99%). The former approach is typically used in empirical studies where few sites are available. The latter can be used in modeling studies across multiple sites or entire regions. Steady-state mass-balance models. The easiest way of modeling CL for acidification is to use a simple mass-balance (SMB) model.29 This approach is widely used in Europe and Canada for calculating CLs to protect forest soils and surface waters against acidification. A watershed is treated as a single box, into and out of which acids and bases flow. The watershed data and the proxy variable are entered, and a single linear equation is solved. For example, McNulty et al.30 used an SMB model to determine critical acid loads for forest soils throughout the United States. They divided the country into 1-km2 grid cells and estimated the runoff, soil properties, and forest types for each, using national databases. They used the soil-solution base cation/aluminum (BC/Al) ratio as their proxy variable and assumed that the critical value for the onset of biological damage was 10 in deciduous forests and 1 in coniferous forests, values that have been used for CL analyses in Europe (see also Ref. 31). The model was then

Science and policy of critical loads

used, cell by cell, to compute the maximum load that would keep the BC/Al ratio above this value. The result, which was the first U.S. map of CLs, is shown in Figure 3 (top panel). The estimated CLs range from less than 1000 eq/ha-year in the Northeast to over 2000 eq/ha-year in much of the South and West, and over 4000 eq/ha-year in a few places. These estimates suggest that it is mostly in the East, where acidic deposition has been heaviest, that pollutant loads exceed the CL to protect forest soil against acidification. This can be seen by mapping the exceedance (Fig. 3, bottom panel). If this map is correct, forest soil acidification is largely an eastern problem. In only about 17% of the country—parts of the Great Lake states, Florida, the high Appalachians, and much of the Northeast— does acidic deposition exceed the CL of acidity for protecting forest soils. To apply an SMB model to the whole country, the researchers had to make a number of assumptions. They assumed that the weathering rate—a critical quantity for which almost no measurements are available—could be estimated from the clay content and overall acidity of the soil. They assumed that the BC/Al ratio in soil solution was a good proxy for forest health and could be used to separate damaged from undamaged forests. The validity of these assumptions is uncertain. But reasonable approximation is a common computational technique, and often a powerful and useful one. It is possible that the assumptions are sufficiently true to allow the model to give reasonably accurate results. But it is also possible that the true spatial patterns in acid sensitivity are not well captured. Dynamic model simulations allow for incorporation of uncertainty about the input parameters by conducting multiple model calibrations to yield a range of responses. It is more difficult to assess the validity of the massbalance models. Thus, before we accept the results of the SMB model when applied to the United States or to a more geographically limited region, we need to know whether it has been tested and, if so, how well it performed. Several recent publications report tests of SMB model applications. The results were mixed. In a field study, Fenn et al.19 compared measured CL for N leakage from California conifer forests to CL values computed from SMB and dynamic models. They found that the mass-balance model consistently underestimated the CLs.

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Figure 3. Steady-state mass-balance model estimates of critical loads of acidity for protecting forest soils (top map) and their exceedance (bottom map). From Ref. 30.

In a modeling study, Rapp and Bishop32 looked at how well SMB models predicted the acidity of surface waters. They found them to perform well in the acidification phase when deposition was steady or increasing, but poorly in the recovery phase when deposition was decreasing. In a field study, Watmough et al.33 compared the observed health of trees on 110 plots in Ontario,

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Canada, observed over a 13-year period, with CL exceedances computed from a mass-balance model. They found no significant difference in forest health between the plots where the CL was exceeded and those where it was not. Duarte et al.34 compared the observed health of trees on 4064 plots in New York and New England to the CL exceedances computed from a mass-balance

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model. Two of the results are particularly relevant here. The first is that CLs computed from the SMB model depended heavily on the weathering rate, and hence on the soil data used to compute the weathering rate. Thus, uncertainties in the soil data create corresponding uncertainties in the calculated CL. In New York, for example, Duarte et al.34 found that, depending on the soil values they used, the average CL could be as low as 394 eq/ha-year or as high as 2413 eq/ha-year, a sixfold difference. This level of uncertainty makes it difficult to assess whether CLs are being exceeded. The second important finding is that acid loads do affect forest health, but not necessarily for all species, and not in ways that make it easy to identify CLs. Like the results from Thomas et al.20 , Duarte et al.34 suggested individual species responses rather than a community response. SMB models, although widely used to estimate the steady-state CL of acidity, are difficult to test thoroughly because the CL is a long-term steadystate condition. Taken together, the results discussed above suggest considerable uncertainty regarding the validity of SMB estimates of CL for protecting aquatic or terrestrial resources. Perhaps the model results will more closely match observations if we simply wait longer until the ecosystem comes into steady state with the pollutant load. Dynamic models and TLs. SMB models assume current and future steady-state conditions. They cannot calculate watershed condition at a particular time in the future, or back-calculate preindustrial conditions. As a consequence, results are less useful for management than we might desire. Simulating the dynamic aspects of damage and recovery requires a dynamic model that is based on hydrogeochemical processes. Dynamic models can be used to simulate soil or water chemistry or biological response. Some estimate plant species distributions, relative abundance, and biodiversity. Once a dynamic model is initialized, it runs in stepwise fashion. A time interval is assumed to pass, and the chemical values are recalculated. New deposition (and in some cases also climate) values are read from the scenario, another step is taken, and the process repeats. One of the most widely used dynamic watershed models of acid-base chemistry is the model of

Science and policy of critical loads

acidification of groundwater in catchments (MAGIC).35 Sullivan et al.36,37 used MAGIC to estimate CLs for soils and lakes in the Adirondack Park in New York. To determine the CLs for N and S that would protect aquatic biota and forests, they modeled the watersheds of 97 lakes. For each watershed, they determined the CLs for three different values of lake ANC, two values of soil base saturation, and two values of the BC/Al ratio in groundwater, each over two time periods. The result was a matrix of 1358 different CLs, one for each combination of watershed, critical value, and time period. Figure 4 (top panel) shows the CL of S necessary to maintain or restore lake ANC at or above 50 ␮eq/L in 2100. This value (50 ␮eq/L) is a common critical value for surface waters in the United States, and one that has been shown to afford good biological protection in the Adirondacks. An observed strong correlation between ANC and CL allowed Sullivan et al.36 to estimate CLs for the 1100 lakes larger than 1 ha studied by the Adirondack Lakes Survey in the 1980s. The lakes with high sensitivities, and thus low CLs, are mostly located in the western half of the Adirondack Park, where the soils are the least fertile and the fertility losses from acidic deposition have been most intense. In many places, high-CL and low-CL lakes were right next to one another. This demonstrates how much adjacent watersheds can vary and that generalized maps miss a lot of potentially significant spatial detail. Because of the variation among watersheds, there is no CL that will be universally applicable. Some watersheds have already been acidified to the point that they are projected to fail to recover by the year 2100, even if acidic deposition is cut to zero and held there throughout the duration of the simulation.36,37 Given this variability, modeling results are sometimes presented as the percentage of watersheds that will attain the critical chemical value. For example, in Sullivan et al.’s36 simulations, a load of 670 eq/ha-year will protect half of the lakes in the Adirondack population to ANC of 50 ␮eq/L in the year 2100. Presenting CLs like this, allowing us to say that a certain reduction in deposition will result in a certain percentage gain in protection, is a useful tool as a basis for resource management and for evaluation of the efficacy of emissions controls.

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be compared with results of scenario modeling of preindustrial and future conditions under various levels of assumed future deposition. Using CL estimates to inform resource management might entail several steps.6 The first would be to select the ecological communities and stressors for which we can compute CLs. In New York, we can probably do this most effectively for the acidification of lakes and for N leakage from forests. We are beginning to be able to quantify the effects of soil acidification on forests in New York.23,38 The next step is to compare the calculated loads to the estimated deposition and map the CL exceedance.30 Figure 4 (bottom panel) gives the estimated CL exceedances for over 1000 sampled Adirondack Lakes Survey watersheds calculated by Sullivan et al.,36 based on a critical value of ANC = 50 ␮eq/L. The map is coded by the extent to which ambient deposition exceeds the modeled CL. It suggests that while many Adirondack lakes are protected at ambient levels of S deposition, there are about 600 lakes for which existing deposition is higher than the CL, and nearly 300 in which it is twice the CL or more. One of the most useful aspects of the CL approach is the calculation of exceedance. Knowledge of where and to what extent ambient air pollutant loads exceed levels that are sustainable without causing ecological harm can inform vulnerability assessments, determine the effectiveness of existing regulations, and contribute to management decisions such as whether to pursue remediation strategies. This approach has become part of the forest planning process of the U.S. Forest Service and was used in the U.S. Environmental Protection Agency’s most recent review of the secondary (for protection of the environment as opposed to human health) NAAQS for oxides of S and N.38

Critical loads for sulfur, eq/ha/yr

Critical loads for sulfur, eq/ha/yr < 250 250-500 500-750

750-1,000

> 1,000

Exceedance of the critical load for sulfur > 2X 1.5 – 2.0 1 – 1.5 Not exceeded

Figure 4. Target loads for sulfur deposition in the Adirondack Park to protect lake ANC at 50 µeq/L in the year 2010 (top map) and their exceedance (bottom map). From Ref. 36.

How can CLs be used to assess vulnerability and protect ecosystems? One purpose of the CL approach is to manage resources in order to protect sensitive ecosystems from being damaged in the future and allow already damaged ecosystems to recover. The CL estimates can 66

Summary How much do we know about CLs and how close are we to being able to use them for resource management in New York? We have outlined a process for implementing CLs for resource management in New York. Scientists now have the ability to calculate CLs for many natural communities. The way that these loads are calculated and the certainty we have in them differ greatly. It has not yet been determined if or how the CL approach will ultimately be used to manage sensitive

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natural resources in New York that are threatened by the atmospheric deposition of acid precursors, nutrients, or toxic materials. Ongoing work to reduce the uncertainty inherent in CL calculations and to accommodate the matrix of values that are calculated will be important in making those decisions. Empirical CLs for N have been estimated for many plant communities. Results show that N has biological impacts on almost every type of terrestrial plant community that has been studied, including impacts on biodiversity, and that biological changes often occur before chemical changes can be identified. Impacts on fresh waters in New York are more often caused by S than by N. Modeling studies typically utilize a predetermined critical chemical indicator value to simulate the threshold for biological damage. The most commonly used aquatic critical value, ANC = 50 ␮eq/L, has been shown to provide a good benchmark for effects on aquatic biota in New York. Soil base saturation and soil solution BC/Al ratios are the commonly used indicators for forests; the latter is more uncertain. SMB models have been used in New York and elsewhere throughout the United States to calculate the CL of acidity. They are easy to use but are based on steady-state conditions, which may not be reached for a long time. Only a few studies have attempted to validate them, and those studies have obtained mixed results. Dynamic watershed models have much more detailed representations of watershed chemistry than SMB models and can predict past and future chemistry. They have a demonstrated ability to simulate acidification and to a lesser extent N cycling. Most existing ecosystem models assume that different kinds of stressors act independently. Where stressors act together and catalyze the effects of insects, changing temperature, drought, and interspecific competition, existing techniques for estimating risk may not be adequate. Models such as ForSAFE-Veg that consider the effects of these complicating factors are under development39 and can be used in an effort to elucidate the confounding effects of multiple stressors. Despite their differences and uncertainties, models that have been applied to the state of New York suggest that acidic, and perhaps also nutrient, deposition on many forests and the watersheds of many surface waters is in excess of the CL (especially in the Adirondack Mountains), and that biological dam-

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age is occurring or will occur in the future. Mitigating this damage may require new management approaches. To base these on CL, it will be necessary to simplify and integrate the ensembles of CLs produced by observations and models. This will require deciding what species are to be protected, when change becomes damage, and what fraction of watersheds are to be protected at what level. Acknowledgments This manuscript was commissioned by the New York State Energy Research and Development Authority (NYSERDA), under the supervision of Greg Lampman. This analysis draws upon decades of work by many researchers in the United States and abroad. Their work has created the science of air pollution–effects ecology, and their contribution is gratefully acknowledged. The following individuals reviewed an earlier draft of this paper: T. Blett, D. Burns, J. Hettelingh, W. Jackson, G. Lampman, J. Lynch, S. McNulty, C. O’Dea, and L. Pardo. Conflicts of interest The authors declare no conflicts of interest. References 1. Nilsson, J. & P. Grennfelt. 1988. Critical Loads for Sulphur and Nitrogen. Copenhagen: Nordic Council of Ministers. 2. Hicks, B.B. et al. 1993. A national critical loads framework for atmospheric deposition effects assessment: III. Deposition characterization. Environ. Manage. 17: 343–353. 3. Holdren, G.R. et al. 1993. A national critical loads framework for atmospheric deposition effects assessment: IV. Model selection, applications, and critical loads mapping. Environ. Manage. 17: 355–363. 4. Hunsaker, C.T. et al. 1993. A national critical loads framework for atmospheric deposition effects assessments: II. Defining assessment end points, indicators, and functional subregions. Environ. Manage. 17: 335–341. 5. Strickland, T.C. et al. 1993. A national critical loads framework for atmospheric deposition effects assessment: I. Method summary. Environ. Manage. 17: 329–334. 6. Porter, E. et al. 2005. Protecting resources on federal lands: implications of critical loads for atmospheric deposition on nitrogen and sulfur. Bio. Sci. 55: 603–612. 7. Lovett, G.M. 2013. Critical issues for critical loads. Proc. Nat. Acad. Sci. 110: 808–809. 8. Burns, D.A. et al. 2008. Critical loads as a policy tool for protecting ecosystems from the effects of air pollutants. Front. Ecol. Environ. 6: 156–159. 9. McNulty, S.G. & J.L. Boggs. 2010. A conceptual framework: redefining forest soil’s critical acid loads under a changing climate. Environ. Pollut. 158: 2053–2058.

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