Environment

Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continents

Wagner, Felix
Nachtigall, Florian
Franken, Lukas
Milojevic-Dupont, Nikola
Pereira, Rafael H. M.
Koch, Nicolas
Runge, Jakob
Gonzalez, Marta
Creutzig, Felix
2023

Climate change mitigation in urban mobility requires policies reconfiguring urban form to increase accessibility and facilitate low-carbon modes of transport. However, current policy research has insufficiently assessed urban form effects on car travel at three levels: (1) Causality -- Can causality be established beyond theoretical and correlation-based analyses? (2) Generalizability -- Do relationships hold across different cities and world regions? (3) Context specificity -- How do relationships vary across neighborhoods of a city? Here, we address all three gaps via causal graph...

Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach

Xu, Yanyan
Jiang, Shan
Li, Ruiqi
Zhang, Jiang
Zhao, Jinhua
Abbar, Sofiane
González, Marta C.
2019

Air pollution imposes significant environmental and health risks worldwide and is expected to deteriorate in the coming decade as cities expand. Measuring population exposure to air pollution is crucial to quantifying risks to public health. In this work, we introduce a big data analytics framework to model residents' stay and commuters' travel exposure to outdoor PM2.5 and evaluate their environmental justice, with Beijing as an example. Using mobile phone and census data, we first infer travel demand of the population to derive residents' stay activities in each analysis zone, and then...

Spatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks

Albert, Adrian
Kaur, Jasleen
Strano, Emanuele
Gonzalez, Marta
2019

Accurately forecasting urban development and its environmental and climate impacts critically depends on realistic models of the spatial structure of the built environment, and of its dependence on key factors such as population and economic development. Scenario simulation and sensitivity analysis, i.e., predicting how changes in underlying factors at a given location affect urbanization outcomes at other locations, is currently not achievable at a large scale with traditional urban growth models, which are either too simplistic, or depend on detailed locally-collected socioeconomic data...

Modeling Urban Air Quality Using Taxis as Sensors

Noulas, Anastasios
Acikmese, Yasin
LI, Charles QC
Patel, Milan Y.
Babul, Shazia Ayn
Cohen, Ronald C.
Lambiotte, Renaud
Gonzalez, Marta C.
2025

Monitoring urban air quality with high spatiotemporal resolution continues to pose significant challenges. We investigate the use of taxi fleets as mobile sensing platforms, analyzing over 100 million PM2.5 readings from more than 3,000 vehicles across six major U.S. cities during one year. Our findings show that taxis provide fine-grained, street-level air quality insights while ensuring city-wide coverage. We further explore urban air quality modeling using traffic congestion, built environment, and human mobility data to predict pollution variability. Our results highlight geography-...

Mobile phone location data for disasters: A review from natural hazards and epidemics

Yabe, Takahiro
Jones, Nicholas K. W.
Rao, P. Suresh C.
Gonzalez, Marta C.
Ukkusuri, Satish V.
2022

Rapid urbanization and climate change trends, intertwined with complex interactions of various social, economic, and political factors, have resulted in an increase in the frequency and intensity of disaster events. While regions around the world face urgent demands to prepare for, respond to, and to recover from such disasters, large-scale location data collected from mobile phone devices have opened up novel approaches to tackle these challenges. Mobile phone location data have enabled us to observe, estimate, and model human mobility dynamics at an unprecedented spatio-temporal...

Global scale coupling of pyromes and fire regimes

Pais, Cristobal
Gonzalez-Olabarria, Jose Ramon
Moudio, Pelagie Elimbi
Garcia-Gonzalo, Jordi
González, Marta C.
Shen, Zuo-Jun Max
2023

Different interpretations of the fire regime concept have limited the capacity to allocate specific fire regimes worldwide. To solve this limitation, in this study, we present a framework to frame contemporary fire regimes spatially on a global scale. We process historical wildfire records between 2000 and 2018 across the six continents. We uncover 15 global pyromes with clear differences in fire-related metrics, such as frequency and size. The pyromes were further divided into 62 regimes based on spatial aggregation patterns. This spatial framing of contemporary fire regimes allows for an...

Fire spread simulations using Cell2Fire on synthetic and real landscapes

Kim, Minho
Pais, Cristobal
Gonzalez, Marta C.
2025

Fire spread models (FSMs) are used to reproduce fire behavior and can simulate fire propagation over landscapes. As wildfires have emerged into a global phenomenon with far-reaching impacts on the natural and built environments, FSM simulations provide crucial information to better understand and predict fire behavior in various landscapes. In this study, we tested Cell2Fire, a recently developed cellular automata-based FSM, against benchmarking models used in the U.S., Canada, and Chile. We experimented on synthetically generated landscapes (homogeneous and heterogeneous mix of fuels),...

EEZ Mobility: A Toolf or Modeling Equitable Installation of Electric Vehicle Charging Stations

Clark, Callie
Ozturk, Ayse Tugba
Hong, Preston
Gonzalez, Marta C.
Moura, Scott J.
2022

Public electric vehicle (EV) chargers are unevenly distributed in California with respect to income, race and education-levels. This creates inequitable access to electric mobility especially for low-income communities of color, which. are less likely to have access to home charging stations. These vulnerable communities are also more likely to be located in areas with poor air quality and would therefore benefit from EV adoption. Currently programs exist in California that fund incentives for public EV chargers in “Disadvantaged Communities” but the process for identifying these...

Dimension reduction approach for understanding resource-flow resilience to climate change

Salgado, Ariel
He, Yiyi
Radke, John
Ganguly, Auroop Ratan
Gonzalez, Marta C.
2024

Networked dynamics are essential for assessing the resilience of lifeline infrastructures. The dimension-reduction approach was designed as an efficient way to map the high-dimensional dynamics to a low-dimensional representation capturing system-level behavior while taking into consideration network structure. However, its application to socio-technical systems has not been considered yet. Here, we extend the dimension-reduction approach to resource-flow dynamics in multiplex networks. We apply it to the San Francisco fuel transportation network, considering the flow between refineries,...

DeepAir: deep learning and satellite imagery to estimate high-resolution at scale

Guo, Wenxuan
Hu, Zhaoping
Jin, Ling
Xu, Yanyan
Gonzalez, Marta C
2025

Air pollution, specifically PM
2.5
, has become a significant global concern owing to its detrimental impacts on public health. Even so, the high-resolution monitoring of air pollution is still a challenge on a global scale. To cope with this, machine learning (ML) techniques have been utilized to infer the concentration of air pollutants at a fine scale. In this study, we propose
DeepAir
, a learning framework for estimating PM
2.5
concentrations at a fine scale with sparsely distributed observations.
DeepAir
integrates a pre-trained convolutional neural...