Is All Environmental Data Created Equally?
Monitoring environmental conditions requires the collection of extensive data determined by the specific phenomena being studied. Typically, environmental data is collected over time, allowing researchers to track trends, identify issues, and develop solutions to problems. Policymakers rely on robust environmental statistics to create evidence-based policies, set targets, evaluate program effectiveness, and make informed decisions on environmental management and conservation. To support these activities, organizations such as the Environmental Protection Agency (EPA) and the National Oceanic and Atmospheric Administration (NOAA) provide environmental data management and analytic services. Assessing societal impact on the environment involves gathering data about pollution levels, resource extraction, and land use changes to quantify the environmental footprint of human activities and industries to guide sustainability efforts. Environmental data collection provides the foundational knowledge needed to understand, protect, and sustainably manage the natural environment.
Environmental Data and the Elements of Research
Before researching and modeling an environmental issue important decisions must be made regarding data collection. For example, consider an environmental problem like the spread of Tropical Race 4 (TR4), a fungal disease that impacts Cavendish bananas. Key questions include: What kind of data will be collected? How will the data be collected? How will the data be analyzed to model the spread of the disease? How will the resulting models be used to aid decision-making regarding treatments, preventative measures, and conservation efforts? To answer these questions effectively, it is essential to understand the different types of environmental data.
The Diverse Landscape of Environmental Data Resources
When considering data collection, the initial focus often turns to common measurements such as temperature or distance. This is quantitative data, and it consists of things that can be either measured or counted. Quantitative data includes continuous variables (like height or weight) and discrete variables (like number of eggs in a nest or the number of berries in a bush). Mathematical operations can be performed on this type of quantitative data, so it is possible to calculate averages or model trends with functions. Alternatively, data can be qualitative, or categorical, in nature. Categorical data describes attributes or qualities and can be either nominal (like colors or types of animals, with no inherent order) or ordinal (such as satisfaction levels or education levels, which have a defined order). While categorical data can be represented numerically, it cannot be analyzed via mathematical operations.
Factors That Influence Environmental Data Quality
Accurate environmental data collection involves valid and reliable methodologies such as field measurements, remote sensing, or automated sensors. Field measurements are the classic approach to collecting environmental data, involving professionals in the field gathering data using specialized equipment. Measuring water quality, conducting forestry surveys, and wildlife monitoring are examples of activities that involve collecting field data. Remote sensing involves the collection of data via airplanes, drones, or satellites, which capture data from above like urban sprawl, agricultural land use, and ocean temperatures. Automated sensor networks, augmented with AI monitoring, continuously collect data on a wide range of parameters such as air quality, seismic activity, and wildlife migrations without the daily operation of researchers in the field. Regardless of the advancements of AI, human intervention is still required to make sense of the terabytes of data that are collected. Citizen Science projects often enlist the public to help collect and sort environmental data, involving activities such as monitoring bird populations or recording water clarity in local streams. Finally, researchers often attach tiny sensors to animals to track their movements, health, and environmental interactions. This data can provide insights into animal behavior, habitat use, and migration patterns.
The cornerstones of rigorous research are transparency and documentation. Transparency allows other researchers to perform replication studies under similar conditions to verify results. When methodologies are clearly documented, researchers are held accountable for their work. This helps prevent misconduct and ensures that findings are based on sound scientific principles. Transparent methodologies build trust with the public and the scientific community. When the process is open and clear, stakeholders are more likely to believe and act on the findings. Clear documentation of methods, limitations, and potential biases allows others to identify weaknesses and suggest improvements. The peer review process acts as a quality control mechanism, ensuring that research meets the standards of the scientific community before publication. Having independent experts review the work adds credibility to the findings and helps identify errors or methodological flaws that the original researchers may have overlooked.
Making Informed Decisions with Environmental Data
Let’s imagine that an individual works for Pura Vida Bananas Company (a hypothetical company), which operates several banana plantations in Costa Rica. They are very concerned about the spread of Tropical Race 4 (TR4) to their crop since Cavendish bananas, a monoculture, are highly susceptible to infection from fungal disease. To effectively monitor the health of the banana crop and watch for signs of Tropical Race 4 (TR4), they would need to collect a range of data. To begin, they would want to monitor key environmental variables such as soil moisture and pH levels since TR4 is known to thrive in specific soil conditions. Soil moisture would be a quantitative variable since it is measured as either a percentage of water content or volumetric water content. Soil pH represents the hydrogen ion concentration in the soil and can be considered a quantitative variable. However, in this context, it can be treated as a categorical variable since the soil pH is being classified as acidic (pH < 7), neutral (pH = 7), or alkaline/basic (pH > 7).
Collecting meteorological data such as temperature, humidity, and rainfall amounts are quantitative in nature. These data help provide insights regarding climatic variations that can result in excessive moisture that could promote fungal growth or as an aid in determining irrigation patterns to optimize crop moisture supply as needed. Tracking a variety of qualitative plant health indicators would be crucial for the monitoring program as well. Traditionally, this has been a labor-intensive task involving manual data collection in the field. However, this process can be automated with the implementation of AI-managed cameras that enable real-time monitoring of the banana crop to detect early leaf symptoms such as yellowing, wilting, or brown streaks. Qualitative indicators like signs of rot within the pseudo-stems and roots would still need to be observed directly in the field. Detecting the initial signs of a TR4 infection is critical for disease spread monitoring and establishing quarantine measures.
Red Flags in Environment Data Collection
The potential spread of TR4 fungal infection in banana crops illustrates some of the types of environmental data necessary to address natural challenges. Regardless of the environmental issue at hand, it is crucial to consider all variables—both quantitative and qualitative—to gain a holistic view of the problem. Additionally, collecting environmental data involves several ethical considerations that must be addressed to ensure that research is conducted responsibly. When environmental data collection involves human participants, or impacts local communities, informed consent is crucial. Participants should be fully aware of the purpose of the research, how the data will be used, and any potential risks. Engaging local communities in the research process helps to ensure that their perspectives, needs, and rights are respected. Additionally, safeguarding collected data against unauthorized access, theft, or misuse is also critical.
Data collection activities must also be designed to minimize their impact on the environment. This includes putting plans into place to ensure avoiding disruption to wildlife, ecosystems, and natural habitats. Researchers should consider the long-term sustainability of their data collection methods and employ non-invasive techniques, waste reduction protocols, and energy efficient modalities whenever possible. Finally, researchers must comply with relevant laws and regulations governing environmental data collection. This includes obtaining necessary permits and adhering to established guidelines for ethical research. Ethical considerations in collecting environmental data are multifaceted and crucial for conducting responsible research. Researchers must remain vigilant and proactive in identifying and addressing ethical issues throughout the data collection process.
Researchers must actively acknowledge and address potential bias to maintain the integrity of their environmental research. This involves consciously setting aside personal beliefs and assumptions that could influence data collection, analysis, or interpretation. In environmental research, bias can manifest when researchers unintentionally design studies or analyze data in ways that confirm their initial hypotheses or expectations. Through peer review and independent verification, researchers can reduce bias and enhance the validity of their findings. It is also important to recognize that funding sources can introduce bias if they have vested interests in particular outcomes. Therefore, researchers should disclose funding sources and potential conflicts of interest to ensure transparency and mitigate bias in data interpretation. Furthermore, societal pressures and expectations can shape research agendas and interpretations. Awareness of these influences is essential for researchers to maintain impartiality and focus on objective data collection and analysis.
Conclusion
In 1960, physicist Eugene Wigner wrote a paper entitled “The Unreasonable Effectiveness of Mathematics in the Natural Sciences.” In that paper, Wigner highlighted how surprising it is that seemingly abstract mathematical concepts can be so useful in understanding the natural world. Mathematical models can help us to make sense of the world around us. They demonstrate the relationship between variables and provide tools we can use to make predictions beyond available data. However, the accuracy of a mathematical model is only as good as the data that is used to build it. If data is not collected ethically using valid and reliable methodologies, the model developed can lead to misinterpretation and misinformation. As we progress deeper into the 21st century, our awareness of environmental problems grows exponentially. Future environmental plans and policies hinge on the tools we use for data collection, the quality of that data, and the analyses applied to it—key components of effective data-driven decision-making. Environmental issues naturally elicit emotional responses, but our plans and policies must be grounded in empirical models derived from the natural world, not driven by opinion, hype, or hysteria.
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Written by Bruce Brazell, Associate Professor of Practice of Mathematics
