Modeling black carbon removal by city trees: Implications for urban forest planning

Abstract

Urban forests provide multiple ecosystem services, including particulate matter (PM) air pollution removal. While previous studies have assessed relationships between atmospheric PM concentrations and urban land use and land cover, few studies have modeled PM removal by trees in relation to urban form (e.g., topography, land use, land cover, and proximity to emission sources). Particulate matter is a mixture of particles, including black carbon (BC), a byproduct of incomplete fossil fuel and biomass combustion with strong warming potential and linked to adverse health outcomes. We coupled empirical BC deposition data, collected from urban trees in Denton, Texas, with 226 urban form variables to generate land use regression models of annual and seasonal BC removal. Annual and seasonal models revealed emission source proxies, terrain exposure towards emission sources, and topographic exposure as influential to BC removal by trees. Regression equations were applied at one-meter resolution to estimate the BC removal potential of tree planting across the city. The resultant maps, which show regions of probable high and low BC removal by trees, can be used by arborists, urban foresters, landscape architects, and urban planners to inform urban forest design, planning, and decision-making.