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Climate change exacerbates environmental challenges caused by urban growth. This study offers a multifaceted approach to understanding socioenvironmental vulnerability in cities by considering fine-scale spatial distribution of social and physical metrics overlaid with runoff and surface temperature (ST) data. Stormwater runoff depth and surface temperature data for micro-subbasins in Philadelphia were estimated and integrated to create an environmental vulnerability index. Social variables representing poverty, the elderly population, population density, and buildings with basements were used to develop a social vulnerability index. Simple analysis based on medians of combined environmental and social vulnerability indexes identified specific subbasins of the city that are most vulnerable to heat and flooding impacts of climate change. Hotspot analysis of combined social and environmental vulnerability data identified regions of the city that would benefit most from a focus on implementation of new green-blue infrastructures. Results indicated that neighborhoods in southern Philadelphia are most vulnerable to flooding and heat. The outlined approach can be used for any city, and provides city managers with a city-scale visualization for prioritizing areas in immediate need of climate mitigation practices. [ABSTRACT FROM AUTHOR]

Governments around the world are beginning to plan for the effects of climate change. In Philadelphia, Pennsylvania, USA, the city is implementing a variety of green infrastructure practices through the program Green Cities, Clean Waters to meet state and federal stormwater regulations. Though not a current goal of the program, when implemented effectively, a cobenefit of green infrastructure is increased local resilience to potential ecosystem alterations, such as increasing summer temperatures and heavier precipitation, also defined as climate change adaptation. We analyzed the potential of the Green City, Clean Waters plan to increase the city's resilience to the future consequences of climate change. Three future landcover models of Philadelphia were used to analyze climate change adaptation through green infrastructure in the near term, midcentury, and end of century under two climate change scenarios. Green infrastructure was overall found to locally decrease runoff throughout Philadelphia over time. Green infrastructure impact on surface temperature showed mixed results. Impact on runoff and surface temperature differed between types of green infrastructure. As the city is forecasted to grow warmer, wetter, and more urbanized over the century, runoff and local temperatures will increase on average throughout the city, despite the planned growth in green infrastructure. If increased resilience is to keep pace with climate change, the city government will need to expand its green infrastructure plan and consider the cobenefit of climate change adaptation when planning new projects. Additionally, for true climate change resiliency to be achieved, green infrastructure implementation must be connected to citywide greening efforts, accelerate and continue beyond the near term for localities to function as they do today. [ABSTRACT FROM AUTHOR]

Abstract: Local food systems receive increasing attention as a potential solution to problems in the globalized food system, and the promotion of agriculture and urban sustainability. Despite the centrality of geography of place in the study and practice of local food systems, methods of geographical analysis examining local food systems are just beginning to develop. In this paper we argue that spatial research methods are central to the understanding and evaluation of different components of local food systems. We use the city of Philadelphia as an example to explore the socio-spatial structure of the current local food system, and the integration of remote sensing and GIS techniques to estimate land potential for urban food production. [Copyright &y& Elsevier]

• Mobile monitoring measures air pollutants with high spatiotemporal resolution. • PM 2.5 and BC concentrations vary by STURLA class. • PM 2.5 and BC concentrations are correlated with urban structure. • The STURLA methodology can be applied to air pollutant modeling. Understanding relationships between urban structure patterns and air pollutants is key to sustainable urban planning. In this study, we employ a mobile monitoring method to collect PM 2.5 and BC data in the city of Philadelphia, PA during the summer of 2019 and apply the Structure of Urban Landscapes (STURLA) methodology to examine relationships between urban structure and atmospheric pollution. We find that PM 2.5 and BC vary by STURLA class, and some classes exhibit significant difference in pollution concentrations. We also find that the proportions in which STURLA components are present throughout the urban landscape can be used to predict the spatial distribution of urban air pollution. Among frequently sampled STURLA classes, gpl (grass, pavement, and low-rise buildings) hosted the highest PM 2.5 concentrations on average (16.60 ± 4.29 µg/m3), while tgbwp (trees, grass, bare soil, water, pavement) hosted the highest BC concentrations (2.31 ± 1.94 µg/m3). Furthermore, STURLA combined with machine learning modeling was able to correlate PM 2.5 (R2= 0.68, RMSE 2.82 µg/m3) and BC (R2 = 0.64, RMSE 0.75 µg/m3) concentrations with urban landscape composition and interpolate concentrations throughout the city. These results demonstrate the efficacy of the STURLA methodology in modeling relationships between air pollution and urban structure patterns. [ABSTRACT FROM AUTHOR]

This research presents a fully automated framework for runoff estimation, applied to Philadelphia, Pennsylvania, a major urban area. Trends in global urbanization are exacerbating stormwater runoff, making it an increasingly critical challenge in urban areas. Understanding the fine-scale spatial distribution of local flooding is difficult due to the complexity of the urban landscape and lack of measured data, but it is critical for urban management and development. A one-meter resolution Digital Elevation Model (DEM) was used in conjunction with a model developed by using ArcGIS Pro software to create urban micro-subbasins. The DEM was manipulated to account for roof drainage and stormwater infrastructure, such as inlets. The generated micro-subbasins paired with 24-h storm data with a 10-year return period taken from the National Resources Conservation Service (NRCS) for the Philadelphia area was used to estimate runoff. One-meter land-cover and land-use data were used to estimate pervious and impervious areas and the runoff coefficients for each subbasin. Peak runoff discharge and runoff depth for each subbasin were then estimated by the rational and modified rational methods and the NRCS method. The inundation depths from the NRCS method and the modified rational method models were compared and used to generate percent agreement, maximum, and average of inundation maps of Philadelphia. The outcome of this research provides a clear picture of the spatial likelihood of experiencing negative effects of excessive precipitation, useful for stormwater management agencies, city managers, and citizen. [ABSTRACT FROM AUTHOR]

Much of the research regarding traffic crashes considers various geometric roadway features; however, ever-evolving urban watersheds and climate change increasingly impact roadway conditions. Little research has focused on the relationship between high-resolution drainage characteristics and the spatial distribution of crashes. This study incorporated local environmental and drainage risk factors in assessing network safety performance using spatial analysis techniques. Kernel density surfaces and the Local Getis Ord Gi* statistics were used to identify and visualize locations prone to experiencing crashes during wet conditions. Spatial regression modelling was used to link crashes to environmental and traffic risk factors across a citywide network. Proof-of-concept for this framework is demonstrated in the City of Philadelphia, Pennsylvania using publicly available spatial data. The results of this study show a relationship between local drainage, environmental characteristics, and wet crash distribution, providing novel insight into roadway safety during wet conditions. • This study incorporated local environmental and drainage risk factors to assess network safety performance using spatial analysis techniques. • Spatial regression modelling was used to link crashes to environmental and traffic risk factors across a citywide network. • This study links drainage, environmental characteristics, and crashes, providing novel insight into roadway safety during wet conditions. [ABSTRACT FROM AUTHOR]