Abstract

• A framework to explore urban energy and exposure disparities in cities introduced. • Hourly spatiotemporal LUR models with gradient boosting machine developed. • Local environmental data captured from Chicago's smart cities platforms. • Correlation between spatial disparities of energy burden and exposure to PM2.5 captured. • Households with higher energy burden have less opportunity to use natural ventilation. This research develops a framework for integrated energy and exposure to ambient pollution (iEnEx) assessment addressing energy, health, and equity as an interconnected problem within the built environment. Its focus is to explore spatial disparities of households’ energy burden and their exposure to ambient particulate matters (PM 2.5 as end point) in supporting energy efficiency goals of cities. In this study, energy burden is defined as energy affordability of households for paying energy costs. And natural ventilation (NV) is examined as a catalyst of building energy efficiency to measure tradeoffs between energy burden mitigation and exposure to ambient PM 2.5 pollution. The framework is built upon a five-step workflow using spatiotemporal land use regression (LUR) with gradient boosting machine (GBM) and bringing human behavioral patterns along with array of urban big data from smart cities platforms into the model to improve the explanatory capacities of the exposure model. We tested Chicago, IL as a case study. Findings indicate the effectiveness of the proposed framework in integrating urban energy and human health systems for envisioning urban building energy reduction goals. A web-based interactive platform is designed to communicate the results. iEnEx framework can assist designers, engineers, planners, and policymakers in better understanding these systems from environmental and social lenses in the context of equity within the built environment to ensure future sustainable cities.

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