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ESTHER KRUPP
01- A
FILM SERIES +
EVENT GRAPHICS
In partnership with Katra Film Series, brand identity for a new film series was developed.
01- A
FILM SERIES +
EVENT GRAPHICS
In partnership with Katra Film Series, brand identity for a new film series was developed.
01- A
FILM SERIES +
EVENT GRAPHICS
In partnership with Katra Film Series, brand identity for a new film series was developed.
FIELD TEST
FIELD LAB
DESIGN SYSTEM: AI + DIGITAL + PRODUCT + DATA
EXHIBIT DESIGN: 2D + 3D
01 - A
GRASSROOTS
ENVIRONMENTAL REPORTING SYSTEM
In partnership with Brown University as a Masters of Design Engineering thesis, I developed an environmental reporting system focusing on methane and water pollution. Utilizing a "Citizen Science" methodology, the project combined digital and physical tools to empower community members to collect and share environmental data.
BROWN
UNIVERSITY
ENGINEERING
In partnership with Brown University’s Engineering Department, I contributed to innovative projects that merged technical expertise with human-centered design principles. Rooted in interdisciplinary collaboration, my work involved advanced research environmental monitoring systems. By leveraging the department's focus on emerging technologies, I developed scalable solutions addressing global challenges, such as methane detection and water pollution, while fostering community engagement through accessible design methodologies.
RHODE ISLAND
SCHOOL OF DESIGN INDUSTRIAL DESIGN
In collaboration with the Rhode Island School of Design, I applied design-thinking principles to develop solutions for environmental challenges. Combining RISD's emphasis on human-centered design and interdisciplinary creativity, my work focused on creating scalable systems for methane detection and water pollution monitoring. Utilizing both digital and physical tools to empower communities, I blended rigorous research with artistic exploration to address pressing global issues.
MASTERS OF ARTS
DESIGN
ENGINEERING
As part of the Master of Arts in Design Engineering program, I tackled environmental challenges by integrating engineering techniques with human-centered design. My thesis project focused on developing scalable systems for methane detection and water pollution monitoring, leveraging AI-driven analytics and citizen science methodologies. I combined rigorous technical research with innovative design strategies to empower communities and drive sustainable social change.
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
02 - B
THE INITIAL FOCUS: METHANE POLLUTION
The project’s origins lie in addressing methane pollution, one of the most potent greenhouse gases contributing to climate change. Inspired by advancements in satellite-based methane detection, the initial focus centered on harnessing this technology to identify and track emissions from industrial and agricultural sources. By combining real-time data with community-led efforts, the system aimed to bridge the gap between advanced scientific tools and on-the-ground ecoactivism. This focus served as a proving ground for the methodology, demonstrating how localized engagement and cutting-edge technology can work together to tackle complex environmental issues effectively.
SOURCES OF METHANE POLLUTION
01 - D
THE ROLE OF
SATELLITES
Satellites played a pivotal role in this project, providing a high-tech foundation for monitoring methane emissions on a global scale. These advanced systems offered precise, real-time data on emission hotspots, enabling targeted interventions and informing grassroots efforts. By translating satellite data into accessible, actionable insights, the project bridged the gap between cutting-edge technology and community advocacy. This integration empowered local stakeholders to identify pollution sources, track changes over time, and advocate for policy reforms with a robust, evidence-based approach.
PHOTO CREDIT: ENVIRONMENTAL DEFENSE FUND
DIFFICULT TO EXPLORE AREAS
01 - E
OUTCOME:
SCALABILITY
TO OTHER POLLUTANTS
The system’s success in addressing methane pollution revealed its potential for broader environmental applications. Designed with scalability in mind, the framework can be adapted to monitor and combat various pollutants, such as water contaminants, air toxins, and soil degradation. By leveraging the same combination of advanced data collection tools and grassroots ecoactivism, the system empowers communities to tackle localized environmental challenges across diverse contexts. This adaptability ensures the framework remains relevant and impactful, addressing emerging issues in environmental health and sustainability while fostering global collaboration for a cleaner future.
WATER & AIR QUALITY MONITORING
Building on the system’s scalable framework, the project expanded to include water and air quality monitoring, addressing critical environmental concerns faced by many communities. Utilizing a blend of IoT-enabled sensors, AI-driven analysis, and citizen science methodologies, the system facilitated the collection of both qualitative and quantitative data. Local participants were trained to measure pollutants like particulate matter, heavy metals, and nitrates, enabling real-time tracking of environmental health. These efforts empowered communities to advocate for cleaner water sources and healthier air while providing policymakers with actionable data to inform environmental regulations. This focus exemplifies how technology and grassroots engagement can drive impactful change.
BIODIVERSITY
LOSS
Expanding the system’s capabilities, the project addressed biodiversity loss by integrating tools and methodologies to monitor ecosystems under threat. Leveraging AI-powered image recognition, acoustic sensors, and community-reported observations, the framework tracked changes in wildlife populations, habitat health, and invasive species. Citizen scientists played a crucial role in gathering local data, creating a dynamic feedback loop between grassroots efforts and scientific analysis. This approach not only highlighted the interconnectedness of environmental issues but also empowered communities to advocate for conservation policies, habitat restoration, and sustainable land use practices.
PUBLIC
HEALTH
Building on the project’s core principles, the system was adapted to monitor environmental factors directly impacting public health. By integrating air and water quality data with health metrics, the framework provided valuable insights into the links between pollution and community well-being. Using real-time data from sensors, citizen-reported health concerns, and AI-driven analysis, the system identified areas with elevated risks of respiratory issues, waterborne diseases, and other pollution-related health problems. Empowering local communities to track and report environmental hazards, this approach facilitated targeted interventions and informed public health policies, creating healthier, safer living environments.
01 - F
OUTCOME:
DESIGN FOR
SCALABILITY
The project’s success in addressing multiple environmental issues demonstrated the importance of designing with scalability in mind. By creating a flexible, modular framework, the system can be easily adapted to different regions, pollutants, and community needs. Whether expanding to monitor new environmental factors or integrating with other data collection networks, the design prioritizes ease of use, low-cost implementation, and rapid deployment. This scalability ensures the system can grow with evolving environmental challenges, empowering communities worldwide to take proactive steps toward sustainability and resilience, while fostering cross-sector collaboration.
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
MODULAR
SENSOR KITS:
To enhance the adaptability and accessibility of the system, modular sensor kits were developed to empower communities with the tools needed for environmental monitoring. These kits include easy-to-use, cost-effective sensors for measuring air and water quality, as well as biodiversity indicators, which can be customized based on local needs and environmental concerns. Designed for both urban and rural settings, the kits enable citizens to collect reliable data and participate in monitoring efforts without requiring specialized technical knowledge. This modular approach ensures that the system can be scaled to diverse environments, empowering a wide range of communities to take an role in environmental stewardship.
AI MODEL
FLEXIBILITY:
A key component of the system’s scalability is its AI model flexibility, which allows for the seamless integration of various environmental data sources and monitoring needs. The AI models are designed to be adaptable, able to process different types of data, such as air and water quality measurements, biodiversity indicators, and pollution-related health metrics. This flexibility enables the system to evolve as new environmental challenges emerge, continuously improving its predictive capabilities and providing real-time, actionable insights. The AI model can be tailored to specific regional conditions, making it a powerful tool for diverse communities to address their unique environmental concerns.
OPEN DATA
PLATFORM
The project’s success is further amplified by its open data platform, which ensures that all collected environmental data is accessible, transparent, and usable by a broad range of stakeholders. By making data freely available to the public, policymakers, researchers, and advocacy groups, the platform fosters collaboration and drives informed decision-making. The open-source nature of the platform encourages innovation, enabling others to build upon the system, share insights, and contribute to ongoing environmental monitoring efforts. This open data approach not only empowers communities to take action but also promotes global cooperation.
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
02 - G
COMMUNITY-DRIVEN:
CITIZEN SCIENCE AS
AN UNIVERSAL METHOD
At the heart of the project is the integration of citizen science as a universal methodology for environmental monitoring and advocacy. By engaging local communities in data collection and analysis, the project empowers individuals to become active participants in scientific discovery and environmental protection. This approach democratizes environmental science, allowing people from diverse backgrounds to contribute to research, raise awareness, and influence policy. The universal applicability of citizen science encourages global collaboration, making it a powerful tool for tackling pressing environmental issues, from air and water quality to biodiversity loss, and fostering a sense of ownership and responsibility within communities.
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
02 - H
COMMUNITY-DRIVEN:
ADVOCACY ALIGNMENT ACROSS ISSUES
The project emphasizes the power of community-driven advocacy, aligning local efforts across a wide range of environmental and public health issues. By uniting diverse community stakeholders around shared goals, the system creates synergies between various forms of activism — whether focused on pollution, biodiversity, or climate justice. This alignment fosters a collaborative approach, where data collected on one issue can inform and strengthen advocacy on others, amplifying the collective impact. Through workshops, partnerships with local organizations, and targeted outreach, the project empowers communities to advocate for systemic change, ensuring that their voices are heard across multiple environmental sectors and policy arenas.
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
MULTIPLE ISSUES TO ADDRESS
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
02 - C
MARKET RESEARCH:
METHODOLOGY
FOR POLLUTION MONITORING DEVICES
The development of the environmental reporting system was informed by comprehensive market research into existing pollution monitoring devices. This research incorporated both qualitative and quantitative methods, such as interviews with industry experts, surveys of potential users, and analysis of current market trends. Technical programs like SPSS and Tableau were used for data analysis to identify patterns in consumer preferences and gaps in existing technologies. Additionally, feedback from pilot studies was gathered through platforms like Google Forms and Qualtrics, allowing for rapid iteration of the device design. By combining research insights with design thinking tools, the system was developed to be both cost-effective and adaptable, offering a scalable solution to community-driven pollution monitoring.
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
HAND-HELD METHANE DEVICES
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
02 - C
ETHNOGRAPHIC RESEARCH:
ADVOCACY & ACTIVISM
Ethnographic research was integral to understanding the dynamics of local advocacy and activism in environmental issues. By engaging directly with community groups, environmental NGOs, and activists, the project gathered in-depth insights into their needs, motivations, and challenges. This research, conducted through participant observation, interviews, and focus groups, highlighted how communities mobilize around environmental justice and the tools they need to strengthen their advocacy efforts. Ethnographic methods such as storytelling and mapping were used to capture the lived experiences of individuals directly affected by pollution, ensuring their voices were central to the design process. This research informed the development of tailored, community-driven solutions and fostered deeper collaboration between grassroots organizations and technological innovations in environmental monitoring.
ALTERNATIVE ECO-ACTIVISM GROUPS
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
02 - C
DATA COLLECTION:
BLENDING QUANTITATIVE & QUALITATIVE APPROACHES
A key aspect of the project’s success was the integration of both quantitative and qualitative data collection methods, ensuring a comprehensive understanding of environmental challenges. Quantitative data, such as pollution levels, air and water quality metrics, and sensor readings, were gathered using IoT-enabled devices and monitored through real-time tracking systems. Simultaneously, qualitative data was collected through community interviews, field observations, and open-ended surveys, capturing local knowledge, personal experiences, and social dynamics that shaped environmental perceptions. Tools like ArcGIS for spatial data analysis, alongside platforms like NVivo for qualitative coding, allowed the project team to cross-reference scientific data with community narratives. This blended approach not only provided a holistic view of environmental issues but also ensured that the insights were actionable, relevant, and grounded in both scientific evidence and local experience.
SOCIALLY-DRIVEN QUALITATIVE APPROACHES
The project’s socially-driven qualitative approaches emphasized the importance of understanding environmental issues through the lens of community experiences and social contexts. This method prioritized narratives from local residents, particularly those in marginalized and frontline communities, to ensure that environmental challenges were framed not just by scientific data, but also by the social, economic, and cultural factors influencing people’s lives. By conducting in-depth interviews, focus groups, and participatory workshops, the research captured diverse perspectives on pollution, health, and activism. Tools such as ethnographic field notes and community mapping were used to illustrate how local knowledge and lived experiences contribute to shaping environmental awareness and advocacy efforts. This informed the design of interventions that were scientifically sound and culturally & socially relevant. solving.
AI INTEGRATION + DATA ANALYTICS
APPROACH
The integration of AI and advanced data analytics was central to enhancing the project’s ability to process and analyze vast amounts of environmental data. Machine learning algorithms were deployed to identify patterns and trends in the data collected from pollution sensors, satellite imagery, and community-reported observations. By combining AI with traditional data analytics tools like Python, R, and TensorFlow, the system was able to not only predict pollution levels and identify pollution hotspots but also recommend interventions for communities and policymakers. This approach enabled real-time data processing and provided predictive insights that improved operational efficiency and resource allocation. This impowered communities to understand environmental issues and translating data into actionable information through intuitive dashboards and visualizations.
DESIGN + PROTOTYPING METHODOLOGIES
The project utilized a robust design and prototyping methodology to create solutions for environmental monitoring. Initial concepts were developed through rapid prototyping techniques, using tools like SketchUp, Rhino, and Adobe XD to visualize and refine both hardware and software components. This approach allowed for continuous feedback and improvement from key stakeholders, including community members, environmental scientists, and local advocacy groups. Prototypes of the pollution monitoring devices were tested in real-world conditions, incorporating user feedback to optimize their usability, durability, and effectiveness. Techniques such as low-fidelity wireframes for app interfaces and 3D printing for sensor components were employed to quickly iterate and test ideas, ensuring that the final designs were both functional and user-friendly.
02 - C
FIELD TEST
FIELD LAB
The final product of this design and research initiative is a comprehensive, community-driven environmental monitoring system that combines cutting-edge technology with grassroots engagement. The system features modular pollution sensors capable of monitoring methane, water quality, air quality, and other environmental indicators in real time. These sensors are paired with an intuitive mobile application and open data platform, enabling users to collect, analyze, and share environmental data with ease. The system offers predictive analytics and actionable insights, helping communities identify pollution hotspots and take proactive measures for mitigation. The platform also fosters collaboration between local advocacy groups, government agencies, and environmental scientists, creating a network for data-sharing and collective action. The final product bridges the gap between science, technology, and social advocacy, empowering communities to actively participate in environmental protection and drive systemic change.
FIELD TEST +
FIELD LAB:
MODULAR
SENSOR KITS
The Field Test + Field Lab phase of the project focused on the deployment and real-world testing of the modular sensor kits, designed to be scalable, flexible, and user-friendly for community-driven environmental monitoring. These sensor kits were tested in various local environments to assess their functionality, accuracy, and ease of use by non-expert community members. Each kit is designed with modular components, allowing users to easily swap out sensors for different environmental metrics, such as methane, water quality, or particulate matter, depending on their specific needs. During the field test, real-time data was collected, and iterative improvements were made based on user feedback to refine both hardware and software. The sensor kits were not only scientifically reliable but also practical for widespread use, empowering communities to take control of their environmental monitoring efforts.
CITIZEN
EMPOWERMENT THROUGH TECHNOLOGY
The Citizen Empowerment through Technology phase of the project focused on leveraging the modular sensor kits and data platform to engage and educate community members in environmental stewardship. By equipping local residents with accessible, easy-to-use technology, the project enabled individuals to actively participate in the collection, analysis, and sharing of environmental data. This empowerment approach transformed everyday citizens into "eco-activists". By democratizing access to environmental data, the project empowered communities to make informed decisions, influence environmental policies, and take actions to address pollution. This shift toward citizen-driven environmental monitoring not only expanded the reach of data collection but also fostered a culture of collective responsibility for safeguarding the planet.
ARTIFICIAL INTELLIGENCEAS A BRIGDE FOR TRUST+TRANSPARENCY
By integrating intelligent chatbots into the environmental monitoring platform, we enabled users to interact with the system in a more personalized, accessible way. These chatbots provided real-time, AI-driven responses to questions about environmental data, sensor readings, and pollution trends, making complex information easier to understand for non-expert users. The chatbots also played a crucial role in validating the data collected by community members, answering queries about data quality and providing reassurance about its accuracy and reliability. Chatbots helped build trust in the data collection process, empowering community members to make informed decisions and take action. Conversational AI facilitated direct communication between users, local advocacy groups, and policymakers, promoting transparency and collaboration.
02 - C
FIELD TEST: DEVICE
Designed for ease of use by community members, were deployed across various environments to assess their accuracy, durability, and effectiveness in detecting pollutants. Feedback from users was collected to refine the devices, ensuring they were both scientifically reliable and practical for everyday use. This phase provided crucial data to fine-tune the sensor technology and interface, enabling the project team to make necessary adjustments for optimal performance in diverse settings. The successful field testing of these devices validated the system’s potential for scalable, grassroots environmental monitoring.
PORTABILITY
These pollution monitoring devices are lightweight and portable, designed to be easily deployed and moved across different locations. Their compact size allows users to carry and set them up quickly in various environments, from urban areas to remote rural locations. This portability makes it convenient for community members to actively participate in environmental monitoring without the need for complex infrastructure or permanent installations. The ease of transport also ensures that the devices can be used for temporary, ad-hoc monitoring in areas with fluctuating pollution levels.
COST-EFFECTIVENESS
The devices are designed to be affordable and cost-effective, making environmental monitoring accessible to a wide range of users, including local communities, non-profit organizations, and grassroots activists. By keeping production costs low without sacrificing functionality, the devices are more affordable compared to traditional high-end pollution monitoring equipment. This cost-effectiveness encourages widespread adoption, allowing for large-scale, community-driven environmental data collection without significant financial barriers.
REAL-TIME DATA
The pollution monitoring devices are equipped with real-time data collection and feedback capabilities, allowing users to receive immediate insights into pollution levels as they occur. This feature empowers communities to act quickly, whether by reporting data to local authorities or taking direct action to address pollution sources. The real-time data can be accessed through mobile apps or online dashboards, ensuring that all stakeholders, from community members to policymakers, stay informed and can make data-driven decisions.
02 - C
FIELD LAB
The Field Lab phase of the project introduced paper testing strips for water quality, designed to provide a simple and accessible method for community members to monitor water sources in their environment. These strips are color-coded to indicate the presence and concentration of key pollutants, such as nitrates, phosphates, and pH levels. When dipped into a water sample, the strips change color, which can then be compared against a color chart to determine water quality. This low-cost, easy-to-use tool allows for rapid, on-site testing, making it ideal for non-expert users in both urban and rural areas. By integrating these paper strips into the environmental monitoring system, the project empowers communities to take immediate action in identifying and addressing water contamination issues.
ACCESSIBILITY
Designed to make environmental testing easy and accessible to everyone, regardless of technical expertise. By using paper testing strips that change color to indicate water quality, this method eliminates the need for expensive or complicated lab equipment. The strips are intuitive and can be used by individuals with minimal training, allowing local communities to actively engage in monitoring their water sources. This accessibility ensures widespread participation in environmental conservation, empowering individuals to track and report water quality independently.
AFFORDABILITY
Paper testing strips are a cost-effective solution for water quality monitoring. Unlike high-tech sensors or laboratory testing, these strips are inexpensive to produce and purchase, making them a practical choice for large-scale community-driven monitoring projects. The low cost enables widespread use, particularly in underserved or low-income areas, where access to environmental testing tools might otherwise be limited. This affordability helps to democratize environmental monitoring without significant financial burden.
ON-SITE RESULTS
One of the key characteristics of the Field Lab method is its ability to provide real-time results on-site. The color-changing paper strips offer immediate feedback on water quality, allowing users to quickly assess pollution levels or contamination in water sources. This real-time capability is essential for prompt action, whether it's identifying unsafe water for consumption or flagging pollution sources for further investigation. The ability to instantly view results to make informed decisions in response to water quality issues.
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
01 - C
WEBSITE
The website serves as a comprehensive platform for the Environmental Reporting System, integrating AI-powered chatbots to enhance accessibility and user engagement. It offers real-time environmental data, interactive tools, and educational resources, allowing users to input data from modular sensor kits, view color-coded water quality results, and track pollution trends over time. A data visualization dashboard and chatbot support translate raw data into actionable insights, while community forums and educational content empower users to collaborate, participate in eco-activism, and contribute to sustainable solutions.
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
DESKTOP WEBSITE
01 - D
CHATBOT AI
The chatbot is an AI-powered assistant designed to simplify environmental reporting and engagement. It guides users through data entry, explains test results from modular sensor kits or color-coded water quality strips, and provides personalized insights into pollution trends. Beyond data interpretation, the chatbot answers questions, offers educational resources on environmental conservation, and connects users to local advocacy groups or initiatives. With its intuitive interface and real-time support, the chatbot bridges the gap between complex environmental data and actionable community-driven solutions.
ONLINE ARTICLES
02 - C
COMMUNITY WORKSHOPS & PUBLIC TRAINING SESSIONS
Community workshops and public training sessions are at the heart of the initiative, fostering grassroots involvement and environmental stewardship. These hands-on sessions teach participants how to use modular sensor kits, interpret water quality testing results, and navigate the digital reporting platform. By blending technical education with actionable guidance, workshops empower attendees to collect reliable environmental data and contribute to broader conservation efforts. Public training sessions are designed to be inclusive, welcoming individuals of all expertise levels, and often feature collaborations with local advocacy groups, policymakers, and scientists. Through these gatherings, communities build skills, share knowledge, and establish networks that drive lasting environmental impact.
COMMUNITY WORKSHOPS
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
02 - C
FUTURE STAKEHOLDERS
The success and scalability of the Environmental Reporting System rely on the engagement of diverse future stakeholders. Potential stakeholders include local governments seeking data-driven insights for policy decisions, environmental advocacy groups aiming to bolster grassroots activism, and educators integrating real-world environmental monitoring into their curricula. Corporations and industries focused on sustainability can leverage the system to meet compliance goals and enhance transparency in their operations. Additionally, tech developers and innovators play a crucial role in advancing the system's modularity and AI capabilities. By fostering collaboration among these stakeholders, the platform ensures ongoing innovation, widespread adoption, and a collective commitment to environmental conservation.
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
GOVERNMENT +
POLICY MAKERS
These stakeholders include local, regional, and national government agencies focused on environmental protection, urban planning, and public health. They can use the system to access accurate, real-time data for shaping regulations, tracking compliance, and developing policies to mitigate pollution and improve community health.
COMMUNITY + ADVOCACY GROUPS
Grassroots organizations, non-profits, and citizen science groups play a critical role in implementing the system at the community level. They can use the tools to empower local residents, organize workshops, and advocate for sustainable practices while fostering awareness and activism around environmental issues now and in the future.
ACADEMIC + TECH INNOVATORS
Universities, research institutions, and private-sector technology developers can contribute to refining and expanding the system. Researchers can utilize the platform for data collection and studies, while tech innovators can enhance frameworks. Industries prioritizing environmental compliance use it for monitoring and reporting.
PHOTO CREDIT: ARTIFICIAL INTELLIGENCE
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