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Surface temperature influences human health directly and alters the biodiversity and productivity of the environment. While previous research has identified that the composition of urban landscapes influences the physical properties of the environment such as surface temperature, a generalizable and flexible framework is needed that can be used to compare cities across time and space. This study employs the Structure of Urban Landscapes (STURLA) classification combined with remote sensing of New York City’s land surface temperature (LST). These are then linked using machine learning and statistical modeling to identify how greenspace and the built environment influence urban surface temperature. Further, changes in urban structure are then connected to changes in LST over time. It was observed that areas with urban units composed of largely the built environment hosted the hottest temperatures while those with vegetation and water were coolest. Likewise, this is reinforced by borough-level spatial differences in both urban structure and heat. Comparison of these relationships over the period between 2008 and 2017 identified changes in surface temperature that are likely due to the changes in the presence of water, low-rise buildings, and pavement across the city. This research reinforces how human alteration of the environment changes LST and offers units of analysis that can be used for research and urban planning. 

Urban air pollution poses a major threat to human health. Understanding where and when urban air pollutant concentrations peak is essential for effective air quality management and sustainable urban development. To this end, we implement a mobile monitoring methodology to determine the spatiotemporal distribution of particulate matter (PM) and black carbon (BC) throughout Philadelphia, Pennsylvania and use hot spot analysis and heatmaps to determine times and locations where pollutant concentrations are highest. Over the course of 12 days between June 27 and July 29, 2019, we measured air pollution concentrations continuously across two 150 mile (241.4 km) long routes. Average daily mean concentrations were 11.55 ± 5.34 μg/m 3 (PM 1 ), 13.48 ± 5.59 μg/m 3 (PM 2.5 ), 16.13 ± 5.80 μg/m 3 (PM 10 ), and 1.56 ± 0.39 μg/m 3 (BC). We find that fine PM size fractions (PM 2.5 ) constitute approximately 84% of PM 10 and that BC comprises 11.6% of observed PM 2.5 . Air pollution hotspots across three size fractions of PM (PM 1 , PM 2.5 , and PM 10 ) and BC had similar distributions throughout Philadelphia, but were most prevalent in the North Delaware, River Wards, and North planning districts. A plurality of detected hotspots found throughout the data collection period (30.19%) occurred between the hours of 8:00 AM–9:00 AM. 

Microbes are abundant inhabitants of the near-surface atmosphere in urban areas. The distribution of microbial communities may benefit or hinder human wellbeing and ecosystem function. Surveys of airborne microbial diversity are uncommon in both natural and built environments and those that investigate diversity are stationary in the city, thus missing continuous exposure to microbes that covary with three-dimensional urban structure. Individuals in cities are generally mobile and would be exposed to diverse urban structures outdoors and within indoor-transit systems in a day. We used mobile monitoring of microbial diversity and geographic information system spatial analysis, across Philadelphia, Pennsylvania, USA in outdoor and indoor-transit (subways and train cars) environments. This study identifies to the role of the three-dimensional urban landscape in structuring atmospheric microbiomes and employs mobile monitoring over ~1,920 kilometers to measure continuous biodiversity. We found more diverse communities outdoors that significantly differ from indoor-transit air in microbial community structure, function, likely source environment, and potentially pathogenic fraction of the community. Variation in the structure of the urban landscape was associated with diversity and function of the near-surface atmospheric microbiome in outdoor samples. 

The rhizosphere has been called “one of the most complex ecosystems on earth” because it is a hotspot for interactions among millions of microbial cells. Many of these are microbes are also participating in a dynamic interplay with host plant tissues, signaling pathways, and metabolites. Historically, breeders have employed aplant‐centric perspective when trying to harness the potential of microbiome‐derived benefits to improve productivity and resilience of economically important plants. This is potentially problematic because: (i) the evolution of the microbes themselves is often ignored, and (ii) it assumes that the fitness of interacting plants and microbes is strictly aligned. In contrast, amicrobe‐centric perspective recognizes that putatively beneficial microbes are still under selection to increase their own fitness, even if there are costs to the host. This can lead to the evolution of sophisticated, potentially subtle, ways for microbes to manipulate the phenotype of their hosts, as well as other microbes in the rhizosphere. We illustrate this idea with a review of cases where rhizosphere microbes have been demonstrated to directly manipulate host root growth, architecture and exudation, host nutrient uptake systems, and host immunity and defense. We also discuss indirect effects, whereby fitness outcomes for the plant are a consequence of ecological interactions between rhizosphere microbes. If these consequences are positive for the plant, they can potentially be misconstrued as traits that have evolved to promote host growth, even if they are a result of selection for unrelated functions. The ubiquity of both direct microbial manipulation of hosts and context‐dependent, variable indirect effects leads us to argue that an evolutionary perspective on rhizosphere microbial ecology will become increasingly important as we continue to engineer microbial communities for crop production.

 

ABSTRACT Humans are inextricably linked to each other and our natural world, and microorganisms lie at the nexus of those interactions. Microorganisms form genetically flexible, taxonomically diverse, and biochemically rich communities, i.e., microbiomes that are integral to the health and development of macroorganisms, societies, and ecosystems. Yet engagement with beneficial microbiomes is dictated by access to public resources, such as nutritious food, clean water and air, safe shelter, social interactions, and effective medicine. In this way, microbiomes have sociopolitical contexts that must be considered. The Microbes and Social Equity (MSE) Working Group connects microbiology with social equity research, education, policy, and practice to understand the interplay of microorganisms, individuals, societies, and ecosystems. Here, we outline opportunities for integrating microbiology and social equity work through broadening education and training; diversifying research topics, methods, and perspectives; and advocating for evidence-based public policy that supports sustainable, equitable, and microbial wealth for all.