EV and Air Quality
Visualizing the health benefits of fleet electrification in China
View Research Paper in Nature SustainabilityDesign Tools
Project Overview
This project is a collaboration with researchers from MIT City Science Lab and Tsinghua University's School of Environment to create an interactive data visualization platform showcasing the findings from their groundbreaking research published in Nature Sustainability.
As the UIUX lead, I independently designed the web interface and led the visual direction, using Figma for interface design and Rhino with Grasshopper as algorithmic data visualization tools to transform complex scientific data into accessible, engaging visualizations that clearly communicate the significant air quality and health benefits of electric vehicle adoption in China.
Interactive map showing PM2.5 concentration changes across China
Project Video
Watch this video demonstration of the interactive data visualization platform:
Research Background
The research, published in Nature Sustainability, demonstrates how electrifying China's vehicle fleet can deliver significant air quality improvements and health benefits. Key findings include:
- Electrifying 27% of private vehicles and a larger proportion of commercial fleets can reduce annual concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and summer ozone by 2030
- This scenario could prevent approximately 17,456 premature deaths annually nationwide
- The Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta regions would see the most significant health benefits, accounting for about 37% of the total
Visualization of key research findings and health impacts
My Role and Contributions
As the UIUX lead for this project, my responsibilities included:
Information Architecture
Developed a structured approach to presenting complex scientific data in a logical, accessible format for both expert and non-expert audiences.
Algorithmic Data Visualization
Utilized Rhino and Grasshopper to create parametric data visualizations that effectively communicate the spatial and temporal patterns of air quality improvements and health benefits.
Interface Design
Created the complete web interface in Figma, designing intuitive navigation and interaction patterns that guide users through the research findings in a compelling narrative format.
Collaboration with Scientists
Worked closely with environmental researchers to ensure scientific accuracy while making complex data accessible to policymakers and the general public.
Design Process
The design process for this project involved several key phases:
Research and Data Analysis
Collaborated with scientists to understand the research methodology, data structure, and key findings. Identified the most important metrics and relationships to highlight in the visualization.
Initial research documentation and data analysis planning
Conceptualization and Wireframing
Developed multiple visualization concepts in Figma, testing them with researchers to ensure they accurately represented the data while being accessible to non-expert audiences.
Early wireframes exploring different visualization approaches
Algorithmic Visualization Development
Used Rhino and Grasshopper and TouchDesigner to create parametric data models that could generate visualizations directly from the research data, ensuring accuracy while creating visually compelling representations.
Grasshopper parametric modeling for data visualization
Interface Design and Refinement
Finalized the web interface design in Figma, creating a comprehensive design system and interactive prototype to guide implementation.
Site Details
Navigation System
Designed a progressive navigation system that guides users through the research narrative, from introduction to detailed findings. The vertical timeline navigation allows users to track their progress through the content while maintaining context.
Vertical timeline navigation with progress indicators
Regional Comparison Interface
Created an interactive interface for comparing air quality changes across the three key regions of China (Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta). The interface allows users to toggle between different pollutants (PM2.5, NO2, O3) and see their spatial distribution.
Interactive regional comparison showing pollutant distribution
Data Visualization Color System
Developed a carefully calibrated color system to represent pollution concentration changes, with blue tones indicating improvements (reductions) and red tones showing increases. The color scale was designed to be both scientifically accurate and intuitively understandable.
Color system for representing pollution concentration changes
Interactive Scenario Comparison
Designed an interactive tool that allows users to directly compare the "With EVs" and "Without EVs" scenarios, highlighting the differences in air quality and health outcomes. The interface includes animated transitions to help users understand the relationship between the two scenarios.
Interactive tool for comparing EV adoption scenarios
Key Visualization Features
Interactive Regional Maps
Designed interactive maps that visualize air quality changes across different regions of China, allowing users to compare scenarios with and without EV adoption.
The maps use a carefully selected color palette to represent pollution concentration levels, with interactive elements that reveal detailed data for specific regions.
Health Impact Visualizations
Created visualizations that translate abstract health statistics into more tangible representations, showing the number of premature deaths avoided through EV adoption across different regions.
These visualizations include comparative elements that highlight the disproportionate benefits in densely populated urban areas.
Temporal Comparison Tools
Designed interactive tools that allow users to explore how air quality changes over time in different scenarios, with options to view monthly, seasonal, and annual patterns.
These tools help illustrate the delayed time between emissions reductions and air quality improvements due to atmospheric chemistry processes.
Design Innovations
Algorithmic Data Representation
Used Grasshopper's parametric modeling capabilities to create data-driven visualizations that automatically update when the underlying data changes, ensuring accuracy and consistency.
Multi-dimensional Data Visualization
Developed novel ways to represent multiple pollutants (PM2.5, NO2, O3) and their varying impacts across different regions and timeframes in a single, coherent visual system.
Progressive Disclosure Interface
Designed an interface that layers information, allowing users to progressively explore deeper levels of detail according to their interest and expertise.
Comparative Visualization Framework
Created a visual framework that enables direct comparison between different scenarios, making it easy to understand the impact of EV adoption on air quality and health outcomes.
Impact and Outcomes
This project has had significant impact in several areas:
- Policy Influence: The visualizations have been used in presentations to policymakers, helping inform decisions about EV incentives and infrastructure development in China.
- Public Awareness: The accessible format has helped raise public awareness about the health benefits of EV adoption beyond just carbon emission reductions.
- Scientific Communication: The project serves as a model for effective communication of complex environmental research to diverse audiences.
- Cross-disciplinary Collaboration: The successful collaboration between designers and scientists demonstrates the value of interdisciplinary approaches to addressing environmental challenges.
Innovative visualization approach showing the relationship between EV adoption and health outcomes
Conclusion
This project demonstrates how effective data visualization and user experience design can bridge the gap between complex scientific research and public understanding. By making the health benefits of EV adoption more tangible and accessible, the platform helps support informed decision-making by policymakers and the public.
The collaboration between MIT City Science Lab, Tsinghua University, and our design team showcases the power of interdisciplinary approaches to addressing environmental challenges and communicating scientific findings in ways that can drive meaningful action.