Applied Ecosystem Services, LLC

Preparing For Change

Sun, 05 Mar 2023 00:00:00 +0000

Climate warming, unpredictable weather, and other factors that you cannot control could harm your business’s profitable sustainability.

Understanding environmental science and regulatory permits and compliance provide you with the knowledge and tools to quickly adapt to these changes.

Acting now is especially important because the future is uncertain and the present is constantly changing. Avoiding environmental permit compliance actions is much better than resolving them after they appear.

This commentary explains environmental science as it affects compliance with regulatory permit conditions and helps you defend against litigation alleging your operation adversely effects the natural environment.

Environmental regulatory science is the science applied to regulations implementing compliance with federal laws such as the Clean Water Act, Comprehensive Environmental Response and Liability Act, Endangered Species Act, National Environmental Protection Act and all the others. When your business or operation requires an environmental permit and compliance with permit conditions you need to understand how the regulators make decisions because you are directly affected by their decisions.

Environmental science is applied ecology, how plants and animals interact with their local environments, aquatic and terrestrial. These interactions are influenced by geography, terrain, geology, chemistry, and biology and observations and measurements are analyzed using models, primarily statistical, and interpreted using established ecological theories. Not only is environmental science complex but it’s not taught well.

There is always too little data available to regulators, primarily because most data are collected by permit holders complying with their permit conditions and these might require only one to four samples each year.

Environmental data differ from other types of data, such as business, financial, and economic. For example, chemical concentrations can never be negative and toxic metals and organic compounds are commonly present in very low concentrations, and often in such low concentrations that they cannot be detected or quantified. Biological data are counts, not continuous values, and their absence from observation or counting does not mean they are not present at that location, only that they were not observed or heard. These, and other differences mean that the models used to analyze them must be suitable for the data constraints.

Another factor in environmental regulatory science is the different needs of regulators and the regulated public.

Environmental regulations are supposed to address requirements of the laws and statutes to which they apply. Agency staffers don’t need to be chemists, biologists, or other field scientists because the agency has manuals that specify the specific steps and methods applied to making permitting and compliance decisions. Agency staffers, like all employees, act to maintain their employment so they focus on documenting compliance with the steps and actions specific to their jobs. How their actions and decisions affect your business or job is not their concern.

On the other hand, you are concerned with sustainability of your job, operation, or business. Costs, time, and effort are always limited and you don’t know all the details, agency politics, and personal agendas that influenced regulatory decisions.

The results of this conflict of interests can result in poor communications between you, the permit holder, and regulatory agency staff.

Sustainable Development Metrics

Sun, 26 Feb 2023 00:00:00 +0000

How to measure sustainability comes up frequently in conversations among mining professionals. Questions asked include what protocol or algorithm should be used, and what measures should to be included. A lot of serious thought has been given this subject by experienced and insightful environmental managers. Yet there is still discomfort that the lists of measures or the procedure to be used may not be “correct.”

Sport analogies may help you understand a solution process in which you can have full confidence. Consider fly fishing: you don’t use the same fly for cutthroat trout in a small, high gradient stream as you would for brown trout in the lower reaches of a different river. When you’re fishing makes a difference, too. Most fishermen ask at a local shop before they wade into the stream what the fish currently seem to like to hit. Or, consider golfing (perhaps a sport to which most miners can better relate): why carry irons from #3 through #9, or different wedges, or fairway woods? Because each club has characteristics best suited to distance from the goal (the cup), position on the fairway, obstacles between the ball and the goal, and our abilities to optimally use a particular club. Different locations, goals, and local environments in these analogies dictate how we approach what we want to accomplish.

The same approach can be used to effectively measure sustainability. Rather than deciding in advance what specific measures (metrics) to use in any specific situation, ask the potentially affected stakeholders, local populations, regulators, and governments what factors are important to them for the project under consideration at the current time and location. Think of it as gaining expert insight, advice, or suggestions that help you reach your goal.

To refine your approach and focus discussions on the most suitable measurements, divide the discussion into consideration of factors in each of the economic, natural, and societal environments. Everyone has concerns in each environmental category, but their priorities of relative importance vary widely. If you apply a process that allows you to quantify these local values and beliefs, then your results are even more robust and can be used over time to re-assess how you are doing.

“Sustainability” is a linguistic variable that has no consensus definition. It is no longer adequate to describe it as a process because we can quantify it if we take the appropriate approach and measurements. As a concept it has become important enough in successfully permitting, operating, and closing mines that it deserves to be evaluated in a process that is both technically sound and legally defensible.

Streamlining NEPA Compliance

Sun, 19 Feb 2023 00:00:00 +0000

When looking at streamlining the NEPA (National Environmental Policy Act) process without reducing quality everything is open to reconsideration. Quantifying subjectivity and reordering tasks can dramatically reduce the time to produce a technically sound and legally defensible EIS and Record of Decision (ROD), but there is more that can be done.

Data collection and numerical modeling offer opportunities to increase EIS quality while decreasing the time involved. The data are used to characterize existing environments and predict alternative future environments.

Existing environments characterized are economic, natural, and societal. These are the baselines against which changes resulting from the various project alternatives will be compared. Because alternatives are inexact forecasts of future environments, as long as the baselines are reasonable they serve the intended purpose. Where quantitative values are appropriate (where measurements are meaningful) there could be variability on time scales from hours to decades or longer. Since it is impossible to capture all this variability we can take few measurements and base our characterization on them. Rarely do we gain more insight or useful information by collecting data for a year or more. When quantitative values are not appropriate (e.g., recreational opportunities, aesthetics, quality of life, environmental justice) we can assign qualitative terms (some, many, high, good) on relative scales.

Forecasting alternative future environments is highly qualitative. Mine life is generally at least 10 years, and can easily be 100 years before the site is mined out, every facility closed, and the entire area reclaimed. A lot will happen in that time that cannot be predicted and accounted for in the descriptions of the affected environments. Therefore, it is technically sound to describe these alternative future environments relative to the existing environment. Future components can be described as smaller, steeper, drier, or fewer than currently exists. Making informed qualitative comparisons is also less likely to be challenged on specific numeric values. Describing the anticipated effects of the project alternatives qualitatively can be done quickly, and include a range of technical expert opinions, compared with trying to develop quantitative predictions based on models or Best Professional Judgment.

While the above may initially appear to be inadequate, they fit the purpose of NEPA, particularly an EIS. The EIS is not used to determine whether the project is approved, but to select the alternative that would avoid, minimize, or adequately mitigate for any undesired negative impacts on the environments. The subsequent operating permit is only one of many regulatory constraints on the operator. Compliance with those permit stipulations and conditions, plus any revisions justified when each is renewed, provide adequate adjustments to the assumptions of the alternatives made before the project became operational.

Since the EIS is used to make an informed decision based on forecasts projected decades into the future, a high degree of quantitative accuracy cannot be justified for the time and expense involved. Adjustments to changing environments over the project life are made with renewed permits and demonstration by the operator of compliance with those conditions.

Storing Expensive and Valuable Environmental Chemistry Data

Sun, 12 Feb 2023 00:00:00 +0000

Environmental chemistry data are expensive to obtain and valuable and need proper care in storage so they retain their value and return your investment in them. Expenses start with permit application preparation and baseline collections and continue through monitoring programs, analyses, and reporting.

The proper storage of environmental data is in an appropriately designed database, but many organizations use spreadsheets instead because they are readily available and easy for individuals to learn and use. Proper structure and formatting of spreadsheets reduces duplication, facilitates organizing and finding data, and promotes transfer to statistical analysis software.

There are only a few required data attributes (the column headings): collection location, collection date, constituent name, measured quantity, and a quantification flag. Other information can be in additional columns but are not required for regulatory reporting and operational insights analyses. The rows in the spreadsheet contain all the information for a single set of related attributes.

Data format needs to be consistent. Dates, for example, should use the International Standards Organization (ISO) format of YYYY-MM-DD (e.g., 201304-01 for April 1, 2013). This ensures data will correctly sort regardless of when or where it was entered. It also quickly identifies duplicate entries. Censored data (those values whose concentrations – the signals – cannot be distinguished from noise in in the analytical process) are reported in various formats by laboratories; most often using the less-than (<) symbol with the laboratory’s reporting limit for that sample. This means the quantity column is treated at text by the spreadsheet and those cells cannot be used for computations. This is where the quantification flag mentioned above is used to fix this problem.

The ability to detect very low concentrations of chemicals varies by time (methods and instruments are constantly improving), laboratory, specific instruments, chemist, sample matrix, sample size and dilution, and other factors. There are multiple names and definitions for the censored (unknown value) data but a good general term is ’reporting limit.’ When you receive laboratory results that show censored data by having the concentration number preceded by a less-than symbol (<), enter only the number (the reporting limit) in the quantity column and use the quantification flag column to indicate it is a censored value. This flag is a binary, True/False, indicator. For consistency with statistical software analyzing your data enter a value of 0 (zero) when the concentration is quantified (uncensored) and a value of 1 (one) when the concentration is below the reporting limit (censored). To make it easy for everyone to remember these flag values name the column with a reminder; for example, ’BRLeq1’ which means ’Below Reporting Limit equals 1’.

As your environmental chemistry data increase you will find different reporting limits for the same constituent. Enter each one with the number provided by the analytical laboratory. There are several ways of calculating and plotting summary statistics and comparing groups with multiple reporting limits. Depending on the concerns to be addressed, the highest reporting limit alone can be used.

These guidelines are a few of the ways to preserve the value of your environmental chemistry data and facilitate the analyses which release the maximum amount of information they contain.

Endangered Species Act: Critical Habitats

Sun, 05 Feb 2023 00:00:00 +0000

Natural resource operators are directly affected by habitat preservation requirements for species listed under the ESA and state equivalents. One possible explanation is that environmental decision-makers do not have sufficient information, ecological training, or appropriate analytical tools so they fall back on the precautionary principle and declare that all actual and potential habitat for the species be left untouched for population sustainability. This is both unnecessary and wasteful as there are robust statistical and spatio-temporal models that can inform technically sound and legally defensible decisions, even with limited data.

Species sustainability decisions require knowledge of population abundance, distribution, and habitat use. This knowledge often comes from the likelihood of the species being present but not observed in a habitat. The typical methods for getting useful knowledge do not provide answers. For many species presence or absence in a habitat plays a major role in determining whether an operation is approved. Species presence/absence and counts estimate population abundance and distribution and whether a specific habitat must be preserved.

As an example: the Oregon spotted frog. It inhabits marshes with extensive shallow water, abundant emergent or floating vegetation, and available food resources. Marsh size 4 hectares (9 acres) or larger support sustainable populations. The problem is detecting the frog during a visit to a marsh possibly inhabited by the species.

Surveys for spotted frogs observed them only 80% of the time when they are actually present. At one marsh, no spotted frogs were observed during a 2-hour visit. How does this information determine presence/absence and, by extrapolation, the frog’s overall abundance and distribution?

The usual approach is null hypothesis significance testing (NHST). The null hypothesis is that the frog is absent; we want to determine the probability of not observing the frog if the null hypothesis is true. The probability (p) threshold is 0.05. If p < 0.05 the null hypothesis is rejected and the alternate hypothesis (the frog is present) is accepted. This alternate hypothesis is not tested or confirmed, only assumed to be true.

In this example, the p-value = 1.0; the probability no frog was observed if it was absent. With such a high p-value rejecting the null hypothesis that the frog was absent fails and the frog is assumed to have been present but not observed. The alternative null hypothesis is that the frog was present. In this case, the probability is 0.2 that it was not observed while actually present. The NHST p-value of 0.02 means null hypothesis is rejected and the frog recorded as being absent when it possibly was actually present.

The NHST approach can record the frog as present or absent depending on which null hypothesis is used. This is because it asks, “What are the probabilities of observing the data given that the various hypothesis are true?” No wonder there is hesitation in making decisions. The better approach asks, “What are the probabilities of the hypotheses being true given the observed data?” This approach is based on existing knowledge as explained by Thomas Bayes, an eighteenth-century English minister. This is the likelihood of an event given another event. Since the Oregon spotted frog was observed in 80% of past surveys we have a basis for determining the probability of observing a frog on any given visit to a marsh.

For the spotted frog example, the likelihood that the frog was present but not seen (0.2) and the likelihood the frog was absent (1.0) are weighted by their respective prior probabilities. Further computation of probabilities determine that the probability of frogs being present in this example marsh when they’re not observed is 17%.

Comparing the two probabilities of the frog’s presence with the NHST conflicting results strongly suggests the Bayesian approach better supports operational, policy, and regulatory decisions. In this case, with only a 17% likelihood of frogs using this marsh it might not be considered critical habitat for spotted frog population sustainability.

The opportunity costs for decision-makers (operational, policy, and regulatory) can be reduced by applying Bayesian methods to calculate species population abundances, distributions, and habitats used because the decisions are based on technically sound and legally defensible analytical methods.

Nonpoint Source Pollution Regulation

Sun, 29 Jan 2023 00:00:00 +0000

Nonpoint source water pollution adversely impacting a beneficial use is regulated by discharge allocations from a Total Maximum Daily Load. Whether a waterbody requires a TMDL depends on how its condition is assessed.

The Clean Water Act, as amended in 1987, specifies using environmental ambient conditions to assess waterbodies, but regulators focus on single chemical ions or compounds based on point source discharge regulations. The limitations of this approach and the benefits of a different approach are explained here.

Understanding the assessment process helps you to avoid, or resolve, concerns about your operations. Load allocations affect your permitted business operations if it discharges storm water over a diffuse area, not from pipe outfalls. For example, precipitation surface flows over crop lands, wood lots and forests, dairies, feedlots and other confined animal feeding operations (CAFOs), or mining operations with exposed soils.

Knowing how receiving waters are, and should be, assessed allows you to better understand the relationship of your operations to adjacent and nearby waterbodies.

Regulators often struggle with waterbody assessments and it’s common for the regulated public who are affected to be dissatisfied with the results.

You need to understand this process because assessments and resulting TMDLs focus on specific chemicals in the water rather than the CWA objective of restoring and maintaining the physical, chemical, and biological integrity of the nation’s waters.

The 1987 revision of the CWA directed the EPA to develop and implement regulatory programs for control of nonpoint sources of pollution based on ambient waterbody conditions. States and tribes must assess water quality status every two years to determine whether corrective TMDLs are needed.

A total maximum daily load, a TMDL, is the quantity of a chemical pollutant determined by the regulator to be the most that can be discharged into the waterbody ecosystem each day without adversely impacting a designated use.

The biannual water quality status assessments are the most important component of the process because everything else depends on their scientific robustness.

Assessments are specific to a designated use in a comparatively homogeneous area and are currently limited to a specific chemical pollutant rather than to ecosystem ambient conditions. More on this point later.

These issues make it difficult to determine a TMDL and effectively allocate discharge limits to point and nonpoint dischargers. Recommendations for increased effectiveness of the waterbody assessment process by incorporating appropriate environmental science and data analyses were developed in 2001 by the National Research Council. Not all states, nor the EPA, have implemented all their recommendations.

The focus of water quality laws was originally on human health, then gradually broadened to include aesthetic uses such as “fishable and swimmable.” The 1972 CWA amendment expanded the goal to “restore and maintain the chemical, physical, and biological integrity of the Nation’s waters.”

Despite the 1987 amendments’ re-focus on ambient conditions regulatory focus has remained on point sources, the discharge of pollutants into receiving waters at specific locations. However, there are pollutants entering waterbodies from diffuse sources and hydrologic alterations that create nonpoint source pollution. Applying point source effluent-based criteria to nonpoint source discharges is highly problematic and ineffective.

There are three major consequences from applying effluent-based, rather than ambient-based, standards to diffuse nonpoint source pollution discharges.

First, regulatory efforts to measure and communicate water quality status are described in terms of permit conditions rather than the receiving water condition. The two are not the same.

Second, effluent standards apply only to point sources. Pollutants from nonpoint sources such as agricultural, silvicultural, and construction activities largely escape oversight because they cannot be measured the same way.

Third, chemical pollutants came to dominate water quality policy while physical and biological determinants of the ambient condition were only infrequently considered.

In non-urban areas the condition of a waterbody depends on more than the loads of specified pollutants from sources discharging under a NPDES permit. For example, changes in the hydrologic regime associated with residential and industrial development activities can destabilize stream banks, increase loads of sediments and nutrients, eliminate key aquatic species, or otherwise change the aquatic ecosystem. Biological, hydrological, and physical changes to a waterbody are included in the CWA’s definition of pollution.

Compliance with CWA Section 303(d) requires states to identify waters not meeting ambient water quality standards in each assessment unit, define the pollutants and pollution factors and their sources, establish total maximum daily loads necessary to achieve those standards, and allocate daily loads to all sources within the assessment area. Degradation factors that do not fit the pollutant definition such as changes of habitat, flow alterations, channelization, and modification or loss of riparian habitat may be considered as reasons for not meeting standards and their effects on water chemistry must be considered in the TMDL development process.

The assessment process needs to be explained in terms of established biological and ecological theories as well as the appropriateness of statistical models applied to the available data.

The TMDL process depends on observations and measurements, not experiments. Therefore, it is greatly influenced by how water quality standards are determined. Currently, water quality standards are maximum concentrations of specific toxic metal ions or organic chemical compounds thought to be protective of fish, wildlife, livestock, and human uses.

For example, rather than the too-broad concept of fishable and swimmable a better description for a designated use of human contact recreation should protect humans from microbial pathogens while swimming, wading, or boating. A sufficiently detailed designated use might distinguish between beach use, primary water contact recreation, and secondary water contact recreation.

Other designated uses should be defined to protect humans, wildlife, and livestock from harmful substances in water and fish tissues. Specific aquatic life designated uses should be designed to protect fish and shellfish. It’s not only chemicals that need to be considered. Parasites and water temperature can be of greater importance to fish survival, growth, and reproduction than are the chemical constituent concentrations of the water.

While one set of chemical concentration criteria could include acceptable constituent measurements of inorganic and organic molecules, one constituent alone is insufficient as animals living in the water (as well as human fullbody contact) are affected by the entire composition of constituents rather than a single one, or a small subset.

Biological criteria should be used along with physical and chemical criteria to determine whether a assessment unit is meeting its designated use. The most weight should be placed on biological criteria because they are most closely related to the long term and fluctuating ambient conditions than are chemical measurements.

Measurements of pollutants in surface and ground waters, sediments, and soils are highly variable. Analyzing these observational data requires different statistical models than do experimental, financial, and public data. Statistical models in spreadsheets and basic stat courses taught in science and business programs cannot produce valid results because they were designed for non-environmental data. This means that the common frequentist hypothesis-testing models can be used in only very limited situations with environmental data.

With this background it’s time to look at how waterbodies are assessed for compliance with the CWA goal.

One state’s regulator says they use a statistical hypothesis testing approach, a binomial test, to derive a critical number of sample excursions that scales with the number of representative samples to evaluate beneficial use attainment status of waterbodies.

There is much wrong with this sentence.

The frequentist hypothesis testing approach was developed for use in analyzing biological experimental results, comparing the frequency of a desired result in a treatment group to that in a control group. It cannot apply to environmental chemical data because neither one nor a set of constituent concentrations can be assigned to a null or an alternative hypotheses. In addition, the hypothesis testing approach cannot be applied to diffuse pollution elements, such as habitat loss, channel alteration, or biotic community structure and function in the assessment unit.

The binomial test is not a frequentist hypothesis test and cannot provide correct water quality results.

“Binomial” literally means two numbers. A binomial distribution shows the number of successes (heads or tails) in a set of experiments such as flipping a coin. All binomial tests of data ask a simple question, such as success or failure, yes or no, true or false, present or absent.

Chemical concentrations are continuous values, not integer counts. Neither a concentration nor a set of concentrations can be characterized into yes/no, success/failure, or true/false dichotomies.

In addition to the inappropriate application of frequentist hypothesis tests and the binomial distribution the state uses other statistical models that have assumptions not met by water quality data.

Telling you how a regulatory process produces incorrect results is only half of my job. I need also to provide an alternative process because the assessment results can put waterbodies on the TMDL implementation list when they are not impaired based on complete ambient conditions.

Local biota reflect ambient water quality much better than do fluctuating chemical constituent concentrations because the biota are there as water chemistry varies within its natural range.

Fish are not found in every assessment unit, in all reaches of those they do inhabit, and they move around a lot so they’re not suitable indicators of ambient water quality. But, benthic macroinvertebrates, the animals living on the waterbody’s substrate, are found almost everywhere along the river network from the highest first-order streams to the mouth of the river, and in almost all ponds, lakes, and reservoirs. Benthic macroinvertebrates include the juvenile stages of insects, arachnids such as mites, snails, fresh water mussels, worms, and other small animals. Their community composition remains stable because their food resources are stable and characteristic of the reach’s position within the river continuum.

Benthic macroinvertebrates are easily associated one of five principal feeding strategies reflecting the primary type of food resource: shredders, filterers, gatherers, grazers, and predators.

Because the primary source of food varies along the river continuum the relative proportions of the five feeding strategies also varies. But, within a limited assessment area the relative proportions remain quite constant throughout the year.

Before determining whether a permitted activity has adverse impacts on a specific designated use it’s necessary to quantify inherent natural variability of functional feeding group compositions. By common acceptance, all values within 95% of that variability range is accepted as normal.

When an assessment based on ambient conditions indicates the need for a remedial TMDL a location-specific approach should be developed that is based on established ecological, hydrological, and geomorphic theories.

What I want you to remember are:

Ambient-based water quality standards include pollution as well as chemical pollutants.
For TMDL assessment units biota provide more technically sound and legally defensible decisions than do chemical data, especially effluent-based standards.
The assessment methods used, and how load allocations are assigned, must be related to the CWA’s objective of ecosystem integrity.

The Fate of Biological Data: Too Little Information

Sun, 22 Jan 2023 00:00:00 +0000

It is widely accepted that raw data need to be converted to information (commonly by statistical analyses) and the results interpreted to form knowledge before informed decisions can be made. With biological data this process is not followed as frequently as it should. Modern spatial analyses and statistical models can provide valuable and useful information that is otherwise lost.

Biological data are counts, presence/absence, proportions, and frequencies. They are not continuous variables with a true zero so the familiar parametric statistics cannot be used. Sometimes the variable of interest is an index such as species richness. Plant and animal data are dependent upon location and time, including the time of day.

Three of the questions that collection and analysis of biologic data can answer are: 1) Why is this plant or animal species (or community of species) found at this location and time? 2) Where else is this species (or community) located? 3) Why is this species (or community) not in this location that seems similar to where we find it?

Answering these questions requires collection of known or likely explanatory variables when the biotic data are gathered. Topographic, geologic, chemical, hydrologic, basin and stream network topology are categories of known or likely variables that influence the biota observed at any location and time.

In addition to modeling the interactions of these explanatory and response variables in a graphical spatial analysis system, statistical models have been developed to perform linear and non-linear regressions on categorical and nominal data mixed with continuous data. Bayesian inference techniques allow robust population estimates from mark and recapture studies of animals.

These analytical techniques provide knowledge and insights not otherwise available to operators, regulators, and other decision-makers. They can be applied to evaluate grazing, logging, mining, energy production (hydroelectric, geothermal, wind) and distribution (electrical transmission lines, oil and gas pipelines), agricultural practices, and reclamation/restoration programs.

Three examples briefly described illustrate the range of possibilities for policy and operational decisions to be better based on science.

Wolves have been re-introduced into the Pacific northwest. They are found around grazing cattle, particularly calves, and around deer and elk herds.

But, are they evenly distributed or clumped? If the latter, what environmental conditions can explain their distribution?

Salmonid recovery efforts in the Columbia and Salmon Rivers assume that if dams are removed and other blocked habitats opened populations of the five will become established. Will conditions in these areas be sufficiently similar to river reaches with established populations to anticipate re-population? Similarly, if a population of resident fish is present in a stream with known water chemistry, why are they not present in a nearby drainage with similar water chemistry?

Cheatgrass has become well established across western rangelands, from valley floors to higher elevations. Yet the coverage is not uniform and may change over time. What explanatory variables are associated with areas of high (or low) cheatgrass coverage? If areal coverage declines (rather than expanding as it has in the past), what conditions might explain this distributional change?

The above do not preclude applying these analytical and statistical models to baseline data for environmental impact assessments or demonstrating compliance with environmental permit conditions. Instead of relying on “best professional judgment”, diversity indices, and similar approaches to explaining biotic data use of robust spatial analyses and statistical models result in interpretations that are technically sound and legally defensible.

Environmental Decision-Making in Uncertain Times

Sun, 15 Jan 2023 00:00:00 +0000

Every business complying with environmental laws can be profitable and sustainable while operating environmentally responsibly.

Environmental aspects may not have the same importance to you as other business aspects, but it’s under your control to avoid issues that can cost time and money better spent elsewhere.

You should take action now to limit your risk of costly or damaging environmental issues such as permit compliance enforcement. Acting now is especially important because the future is uncertain and the present is constantly changing.

Every business required to comply with environmental laws and statutes can be profitable and sustainable while operating environmentally responsibly. This podcast episode explains what to adjust in your environmental program so you can continue to prosper in uncertain futures.

Fifty-eight years ago Bob Dylan wrote and sang his song “The Times They Are A-Changin’.” The lyrics apply very well to the world in which we now live: a global COVID-19 pandemic, massive economic disruptions, and the changing climate producing weather extremes including severe wildland fires, very high temperatures, torrential precipitation, and drastically decreased surface and ground water supplies. We’ve not before experienced all these changes simultaneously so making decisions affecting our business’s future is challenging.

Environmental aspects of a business may not have the same importance as do market conditions, cost of production, and similar issues. This is understandable because environmental science is not a part of business training programs. Yet, businesses needing any environmental permit to operate too often are unprepared to resolve concerns raised by regulators and others. This lack of in-house environmental science expertise can have serious impacts on the operation. It’s much better, and less expensive, to have an environmental program that helps you avoid environmental issues regardless of how ’normal’ changes.

Environmental regulators’ vested interest is documenting that they enforce regulations that implement applicable laws and statutes; they protect their agency. Environmental permit holders’ vested interest is operating profitably and sustainably; they protect their jobs and businesses. Frequently the two are in conflict when they should have a common interest in sustainable, environmentally-responsible, profitable business operations.

When the applicable science is not understood by the public the result can be administrative appeals, legal challenges that increase time and costs for the permit applicant or holder. When the science is not well understood by regulatory agency staff the result is commonly referred to as paralysis by analysis.

To protect your business you need to understand how regulators and others evaluate environmental aspects of your permit application or permit compliance.

Basically, all environmental regulations ask three questions to assess compliance with the relevant law or statute:

First, does (or will) the permitted activity adversely effect the natural environment? This is forecasting.
Second, how does (or would) the operating activity degrade the natural environment? This is cause and effect.
Third, are there synergistic or cumulative impacts from multiple activities affecting the natural environment? This is spatio-temporal, multi-variable interaction.

Answering these questions requires relevant data properly analyzed. Permit requirements for data collection address the regulator’s needs, not the permit holder’s.

Environmental data never are as abundant or frequent as research data so the analytical models applied are specific to these data limitations yet are robust and provide technically sound and legally defensible results.

The environmental data your operations need are unique. What you do, how you do it, and where you are located are different even from others in your industry. Getting the data you need is not a major expense, but it must also demonstrate your compliance with the applicable federal and state laws and statutes.

When your environmental management program is tuned to your specific needs, and decision makers and stakeholders understand that the results are technically sound and legally defensible, they are provided with answers to their concerns.

Companies invest money collecting baseline and compliance monitoring data beyond the minimal needs of the regulators so proper analyses are needed to produce the return on investment that justifies the effort and expense.

With proper planning, collection efforts, and analyses supporting regulatory science can more than repay their cost by eliminating, or decreasing, decision delays and quantifying the relationships of the permitted operation to the natural environment. With global warming/climate change and societal focus on sustainability, understanding a permitted operation’s relationship to the natural environment is critical information for permit holders and regulators.

Effective Regulation of Water Quality

Sun, 08 Jan 2023 00:00:00 +0000

Current regulation of water quality, based on the statutes they implement, fail to effectively describe the current state of water bodies. There are historical and political reasons for this condition but no excuse to continue as we have for the past almost 60 years. Our understanding of aquatic ecosystems and the development of appropriate statistical models for environmental data provide the means to more effectively regulate water quality to benefit natural ecosystems and human health.

The concept of “health” does not apply to ecosystems, only to individual animals. The water in a secondary treatment ponds at municipal sewage treatment plant is not health for human consumption but is a robust, well-functioning ecosystem for converting sewage into more benign water that can be discharged into a receiving river. Rather than health of ecosystems we should describe them in terms of current condition and desired future condition.

Water quality today is defined by maximum concentration limits of individual chemical constituents. Most often the focus is on toxic metals and organic compounds such as pesticides, but also on multi-component constituents such as total dissolved solids (TDS) and total suspended solids (TSS), visible as turbidity or cloudiness of the water. This is unrealistic, even when there is a list of such constituents because it is very rare for a regulator to consider the entire composition rather than individual components. Consider the human health example of a physician diagnosing a patient’s health based on only temperature or blood pressure.

In aquatic ecosystems every organism found at a specific location and at a specific time is exposed to all the physical and chemical components of the water and not just selected single ones. Basing regulatory decisions on one or a few chemical constituents reveals nothing about the effects of the water on the plants and animals found there. The animals (including freshwater mussels and clams) will move to favorable locations instinctively.

Chemical samples collected for baseline or permit compliance monitoring are snapshots in space and time. The fallacy of this approach was first written by the Greek philosopher Heraclitus (535-547 BCE), “No man ever steps in the same river twice, for it’s not the same river and he’s not the same man.” Taking a single sample and extrapolating to longer times and more places has no scientific support.

Because aquatic animals are mobile and found only where all components of the water body are acceptable to them water quality statutes and regulations should be based on extensive biological data (correctly analyzed) and not on chemical data. (Potable water quality is distinct from general environmental water quality and the latter is the focus of all non-drinking water chemistry.)

There are many examples of Clean Water Act regulatory decisions that are not based on technically sound and legally defensible science and statistics. With climate changing more rapidly and noticeably than in the past, and water resources becoming more unpredictable in the western U.S., changing how water quality is managed has broad benefits politically, economically, environmentally, and societally.

Avoiding Permit Compliance Actions

Sun, 01 Jan 2023 00:00:00 +0000

Every business complying with environmental laws can be profitable and sustainable while operating responsibly. Learning how to go beyond minimal permit compliance requirements to avoid regulatory compliance enforcement actions is as important as are other aspects of your job or operations when you are responsible for environmental permits.

The rapidly warming climate and resulting more frequent and severe weather patterns such as megadroughts, massive wildland fire, severe flooding, hotter summers, and colder winters affect environmental permit holders in unpredictable ways. To avoid permit compliance actions under these uncertain conditions permit holders need to understand the context for environmental regulations.

Congress passes environmental laws such as the Clean Water Act, National Environmental Policy Act, and the Endangered Species Act and directs specific federal agencies to enforce them. For water-related laws the EPA almost always delegates enforcement to Tribes and States.

State legislatures write their versions of the laws into statutes and the state’s environmental regulatory agency creates regulations to comply with the statutes. The regulatory agency’s sole responsibility is demonstrating to the legislature that their permits comply with state statutes and that they enforce permit holder compliance with permit conditions. They do not need to prove harm or adverse impacts to landscapes, fish, wildlife, livestock, or humans because the regulations assume permit conditions ensure no adverse effects on the natural environment.

This assumption is seen in non-environmental regulations in daily life. For example, having lower speed limits in school zones than in the surrounding residential or commercial areas is intended to protect students who might pay no attention to traffic when coming to and going from the school. This means that when a school zone is marked for 20 mph from 7:00 AM to 5:00 PM when schools are in session a law enforcement officer can ticket a car traveling at any speed greater than 20 mph at all times between those even when there’s no child, adult, or other vehicle present. It doesn’t matter that the intent of the speed limit (to protect children outside the school) doesn’t apply. The sign says 20 mph and any exceedance can result in a fine.

Environmental regulations are enforced in the same way. If a point source discharges storm waters that leave more sediment particles then allowed by a permit condition along the receiving water’s banks the regulator can impose a fine for harming ESA-listed fish habitat even if that reach of stream has no such fish known to occupy it at any time in their life cycle.

This is the background for the first principle for avoiding compliance enforcement actions: you’re guilty unless you can prove you’re innocent.

The second principle for avoiding compliance enforcement actions is to accept that permit conditions reflect the regulator’s response to the legislature, not to your business or the local environment in which you operate. Documenting your compliance with all permit conditions is insufficient to effectively defend yourself when the local environment changes and in litigation when your are sued by neighbors, an environmental NGO, or other non-regulatory individuals or groups alleging your operations adversely affect water quality, fish, or wildlife.

To protect yourself in these uncertain future conditions you must move beyond the minimal permit conditions and invest in a program that’s specific to your operation and location; one that provides technically sound and legally defensible documentation that your operation does not harm whatever the regulator or litigant alleges. Think of this as environmental insurance; you hope you never need it but are grateful you have it when it’s needed.

The third principle of avoiding compliance enforcement action is to use the knowledge you gain about the local ecosystem’s dynamics and how your operations interact with it. This allows you to quickly adjust to changing conditions with minimal time, effort, and cost. Your environmental knowledge can be as critical to your operation’s sustainability and profitability as are your knowledge of resources, production, and markets.