What is Big Data? Well, it is something exactly like what is sounds---data sets that store an immense amount of information. In recent years, big data has prospered beyond the fields of business analytics and marketing and continued to expand its influence on scientific research and innovation. Among them, environmental science is one field that truly takes a leap in progress with the help of this resource. Researchers and scientists of the field have utilized this new tool to obtain some groundbreaking observations, analysis, and results.
One of the most extensive applications of big data in environmental science is environmental monitoring and protection. Due to the variant and location-specific nature of data gathering in environmental studies, obtaining a comprehensive and multi-perspective analysis on the subjects may be challenging. Big data’s ability to hold a large capacity and process quick, real-time analysis resolves this problem. As a result, scientists are able to track specific patterns and analyze observations easily through these enormous data sets.
A specific application of big data in monitoring climate trends is tracing carbon footprints on a global scale. Carbon dioxide, along with many other greenhouse gases, contributes to the major cause of climate change and global warming. Big data lays out the infrastructure for an adequate monitoring system by locating specific sources of emissions and predicting potential impacts of the pollution. The Woods Hole Carbon Monitoring System is an apt example of how big data can be utilized in these types of systems. The project aims at using satellite-based tools to establish an extensive carbon-monitoring map that efficiently depicts the current status of carbon emissions and the vulnerability of forest carbon storage specifically in the tropical and arctic regions. Big data also upholds the ability to predict the impacts of anthropogenic activities, such as deforestation and overgrazing. For example, Eyes on the Forest, a program that specifically investigates deforestation in Indonesia, can partner with platforms such as Trase to bring the whole picture of the global supply chain, trade flows, and consumer patterns for commodities.
Big data has also been used in endangered species conservation. Programs like Earth Insights tracks the population of endangered species around the world, providing early warnings for ecologists so that conservation efforts can be implemented at an early stage. Through the use of various sensors and captured images, detailed information regarding the target species can be stored as big data and interpreted or studied by the scientists throughout the research. Furthermore, the system can provide insights toward the effectiveness of conservation strategies by monitoring the specific impacts of these strategies on the endangered species, allowing scientists to adjust their plans more effectively.
Another crucial application of big data is in urban planning and development. It allows extensive monitoring of demographics, geographics, and even traffic to design and regulate city development with minimum pollution and other anthropogenic impacts on the environment. Many local or state governments around the world have already implemented some extent of sustainable city planning. For instance, in 2016, researches from Wuhan University in China used satellite sensing to observe patterns of urban development in Shanghai and analyzed specific relations between human activities and urbanization to provide helpful advice for policy-makers and city-designers. Moreover, pioneers in the field have developed applicable programs that further strengthen the potential of big data in efficient urban development. CityScope is a tool developed at MIT that exemplifies the powerful application of big data in city-planning. The platform stimulates the potential impacts of proposed interventions on the local ecosystems and develops models that optimize the desired outcomes. Consequently, data-driven tools can play an essential role in future city development, where efficient speculations can result in a minimal impact of urbanization on the environment.
With greater monitoring and analysis of the problem, big data provides an innovative, alternate route for possible solutions that can effectively mitigate the current environmental crisis---machine learning. Machine learning has emerged in recent years as an extremely popular and efficient tool to perform tasks with enormous datasets that are often beyond the scope of human abilities. It grants computers the ability to “learn” and produce an output that can often predict patterns and provide beneficial analysis to challenging problems such as climate change. Machine learning in environmental science utilizes geospatial data to further enhance monitoring methods and sustainable development. Scholars from Stanford University have conducted a study using machine learning to locate specific concentrated animal feeding operations, or CAFOs, and map out an efficient monitoring system that traces water pollution in nearby areas. The system allows scientists to explicitly observe the direct environmental impacts of agricultural activities on the nearby water sources and encourage policy-makers to develop more sustainable practices that may substantially reduce the risk to public health or the local habitat. With thousands of other studies also applying machine learning in environmental research, the ingenious combination of big data and this new type of AI technology will undoubtedly breed more and more feasible solutions that can save the environment.
Although big data seems like the “panacea for all of the world’s environmental problems”, they do face limitations and potential challenges. First, there are many factors to consider in the evaluation of the data’s variety, accuracy, and consistency. “Big” is used to describe these types of data not because of its size, but because of its complexity. Working with big data can be challenging, especially in the field of environmental science. Researchers may have difficulties obtaining data under consistent conditions due to the high variability of the changing environment, especially in studies for species protection. There are also hardware limitations to support its capacity and concerns regarding the security of the data. Due to its immense size, finding a safe and suitable place for big data storage may be a challenge. Lastly, there is much questioning regarding the lack of full “open access” on a global scale, where scientists and researchers around the world can have access to the datasets. Some programs and projects may privatize the data because they want to keep the fruition of their hard work to themselves.
Nonetheless, big data still proves to be an impactful tool that can be the key to many unsolved problems in the future.