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		Urban Environment Research Group Arizona State University
Arizona Single-Layer Urban canopy Model (ASLUM) ASLUM is a state-of-the-art urban land surface model developed by our group since 2009. The model incorporates holistic physics of flow and transport of energy, moisture, and scalars over built terrains. The evolution of the first three generations of the model resolves canyon façade heterogeneity, urban hydrology, irrigation, green roofs, and anthropogenic heat and moisture fluxes. ASLUM (v3.x) with green roofs has been incorporated into the Weather Research & Forecasting (WRF v3.6.1) via our collaboration with the National Center for Atmospheric Research, and released for public weather services in 2015. The latest ASLUM (v4.1) (Li & Wang, 2020, see Publication) features comprehensive parameterization of exchange of carbon dioxide (CO2) in urban environment from biogenic, anthropogenic, and abiotic sources.
Sustainable Urban Mitigation Solutions By urban mitigation, our research endeavor is not focused exclusively on reducing the ambient temperature, aka urban heat island (UHI) mitigation. Instead, we include multiple objectives measured by a compound environmental index (CEI) tailored to specific needs of various stakeholders including, but not limited to, UHI reduction, carbon mitigation, air quality improvement, enhancement of energy use efficiency, and ecosystem services. Engineering solutions for urban mitigation broadly include the use of white/grey (pavement materials), green (urban vegetation), and blue (open water bodies) infrastructure. We seek to optimize these solutions by combining a portfolio of strategies using stakeholder-inspired and data-driven (e.g. machine learning) methods.				Quantifying compound environmental index (CEI): (a) design of urban greening strategies, (b) ML surrogate modeling, and (c) definition of the Jacobian matrix for quantification of environmental response
Urban Networks & Causality Cities are not isolated hotspots on Earth's surface. They are, as Jacobs pointed, 'problems in organized complexity' (The Death and Life of Great American Cities). We probe into how various cities and/or environmental variables (heat, pressure, humidity, wind, pollution, etc.) are intrinsically connected, and how changes made in local cities can causally affect spatially adjacent or even distant cities as connected networks (graphs). Megacities, we believe, can causally mediate the evolution of future urban climate in the face of global changes.
Complex Urban System Research Our decadal long research in urban dynamic guides us to shift the paradigm of urban climate research from process-based to data-driven-system-based framework, by treating built environments as complex dynamic systems and invoking cutting-edge techniques of data science, network analysis, causality, and machine learning. Schematics for complex urban climate research: (a) synthesis of urban database, to drive (b) multi-scale urban climate modeling, and (c) modeling of complex urban system, using machine learning, causal inference, and decision-support tool for urban planning and visualization.