Applied Ecosystem Services, LLC

When a permitting, compliance, or litigation case involves geochemistry (water, sediments, soils, rocks) using the appropriate statistical analyses of available data-–and communicating the results to non-technical decision-makers or finders of fact clearly and effectively-–are critical. This is particularly true when a client can be penalized by a regulatory agency or sued by an environmental NGO for violating the Clean Water Act (CWA) by exceeding the arbitrary maximum concentration limit for the chemical.

Most people are familiar with statistical hypothesis tests such as the t-test and ANOVA to analyze whether two or more samples (from a parametric distribution) came from the same population. The nonparametric equivalents (Wilcoxon and Kruskal-Wallis tests) are less familiar but equally robust. What is not always clear is that these models are applied to one or more response variables; e.g., chemical concentrations that result from natural or anthropogenic causes. They do not answer the question of why these values were observed.

Ground water pollution is a nationwide concern associated with landfills, hazardous waste disposal sites, mine mills, tailing ponds, power plants, and similar industrial facilities. While regulators might state explicit instructions for ground water sampling and chemical analyses, not all the statistical models are appropriate or capable of separating natural variability from anthropogenic influence. Download the PDF.

The objective of the 1972 Clean Water Act is to restore and maintain the chemical, physical, and biological integrity of waters in the US. Water quality standards define clean; therefore, how standards are set is important for policy and regulatory decisions. Standards based on maximum concentration limits (MCL) of toxic chemicals apply to potable waters but not to aquatic life, wildlife, livestock, human recreation, irrigation, or industrial uses. MCLs provide no knowledge of the physical or biological integrity of the water body.

NEPA, CEQ regulations, and agency directives describe in detail what is to be done in preparing an EA or EIS that is compliant with the law and all regulations. It does not direct staff or external contractors how each requirement is to be met. This white paper presents specific requirements and explains how the APPLIED ECOSYSTEM SERVICES’ quantitative approximate reasoning model, Eikos™ fulfills these requirements so that the results are demonstrably technically sound and legally defensible.

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.

The relationship between a company requiring environmental permits and environmental regulators is equivalent to that of a prospective house buyer and a real estate agent. Until the early 1990s all real estate agents and brokers were required by statute to represent only the seller’s interests; most still are. This means a buyer has to be aware of the agent’s agenda (get more money for the seller and his commission) and act to protect his interests.

Across the western US drought, wildland fires, cheatgrass, Western juniper, Lahontan cutthroat trout, bull trout, salmon, bald eagles, desert tortoise, and sage grouse all affect where and how natural resource companies operate. Project planning and approvals can be greatly facilitated by application of advanced statistical and spatial models to environmental data. Causal relationships between explanatory variables such as habitat, food, and predators to response variables (species numbers and distributions) are explained by linear regression models.

Across the western US drought, wildland fires, cheatgrass, Western juniper, Lahontan cutthroat trout, bull trout, salmon, bald eagles, desert tortoise, and sage grouse all affect where and how natural resource companies operate. Project planning and approvals can be greatly facilitated by application of advanced statistical and spatial models to environmental data. Causal relationships between explanatory variables such as habitat, food, and predators to response variables (species numbers and distributions) may be explained by linear regression models.

Sustainability means different things to different people. It is so subjective that there is no consensus definition of what it means; some have described it as a process rather than as a goal. While there are two broad categories in which sustainability appears in the business world, they are just different ways of expressing the same societal values and beliefs. More importantly, many senior executives around the world expressed a need to better incorporate a meaningful measure of corporate contributions to sustainability, to learn how they can do better, and to change the approaches they have applied in the past1.