Awareness of the human health impacts of exposure to air pollution is growing rapidly. For example, it has become evident that the adverse health effects of air pollution are more pronounced in disadvantaged populations. Policymakers in many jurisdictions have responded to this evidence by enacting initiatives that lead to lower concentrations of air pollutants, such as urban traffic restrictions. In this review, we focus on the interplay between advances in environmental exposure assessment and developments in policy. We highlight recent progress in the granular measurement of air pollutants and individual-level exposures, and how this has enabled focused local policy actions. Finally, we detail an illustrative study designed to link individual-level health-relevant exposures with economic, behavioral, biological, familial, and environmental variables.

Using side-facing observations of the New York City (NYC) skyline, we identify lighting technologies via spectral signatures measured with Visible and Near Infrared (VNIR) hyperspectral imaging. The instrument is a scanning, single slit spectrograph with 872 spectral channels from 0.4–1.0 $\mathsf{\mu }$ m. With a single scan, we are able to clearly match the detected spectral signatures of 13 templates of known lighting types. However, many of the observed lighting spectra do not match those that have been measured in the laboratory. We identify unknown spectra by segmenting our observations and using Template-Activated Partition (TAP) clustering with a variety of underlying unsupervised clustering methods to generate the first empirically-determined spectral catalog of roughly 40 urban lighting types. We show that, given our vantage point, we are able to determine lighting technology use for both interior and exterior lighting. Finally, we find that the total brightness of our scene shows strong peaks at the 570 nm $\mathrm{Na}$ - $\mathrm{II}$ , 595 nm $\mathrm{Na}$ - $\mathrm{II}$ and 818 nm $\mathrm{Na}$ - $\mathrm{I}$ lines that are common in high pressure sodium lamps, which dominate our observations.

We describe an “Urban Observatory” facility designed for the study of complex urban systems via persistent, synoptic, and granular imaging of dynamical processes in cities. An initial deployment of the facility has been demonstrated in New York City and consists of a suite of imaging systems—both broadband and hyperspectral—sensitive to wavelengths from the visible (∼400 nm) to the infrared (∼13 micron) operating at cadences of ∼0.01–30 Hz (characteristically ∼0.1 Hz). Much like an astronomical survey, the facility generates a large imaging catalog from which we have extracted observables (e.g., time-dependent brightnesses, spectra, temperatures, chemical species, etc.), collecting them in a parallel source catalog. We have demonstrated that, in addition to the urban science of cities as systems, these data are applicable to a myriad of domain-specific scientific inquiries related to urban functioning including energy consumption and end use, environmental impacts of cities, and patterns of life and public health. We show that an Urban Observatory facility of this type has the potential to improve both a city’s operations and the quality of life of its inhabitants.

Cities are now home to more than 50% of the world’s population and emit large quantities of pollutants from sources such as fossil fuel combustion and the leakage of refrigerants. We demonstrate the utility of persistent synoptic longwave hyperspectral imaging to study the ongoing leakage of refrigerant gases in New York City, compounds that either deplete the stratosphere ozone or have significant global warming potential. In contrast to current monitoring programs that are based on country-level reporting or aggregate measures of emissions, we present the identification of gaseous plumes with high spatial and temporal granularity in real-time over the skyline of Manhattan. The reported data highlights the emission of chemicals scheduled for phase-out. Our goal is to contribute to better understanding of the composition, sources, concentration, prevalence and patterns of emissions for the purposes of both research and policy.

Understanding the behavior of steel-concrete interaction is of fundamental importance to the study of reinforced concrete structures. In this paper, a technique for the development of a constitutive model for that interaction is presented. In the experimental part of the study, rebar and wedge pull-out specimens were tested. The stress and displacement in the tangential direction near the rebar interface were measured; whereas phase measurement speckle interferometry was used for accurate surface displacement measurement and crack growth detection. A fracture mechanics-based numerical simulation was then used to evaluate the normal stress at the interface using the measured crack length and crack opening displacement. This hybrid numerical-experimental technique was shown to successfully predict the measured normal stress and crack profile of the wedge splitting specimen.

Digital Surface Models (DSMs) have a wide range of applications, including the spatial and temporal analysis of human habitation. Traditionally, DSMs are generated by rasterizing Light Detection and Ranging (LiDAR) point clouds. While LiDAR provides high-resolution details, the acquisition of required data is logistically challenging and costly, leading to limited spatial coverage and temporal frequency. Satellite imagery, such as Synthetic Aperture Radar (SAR), contains information on surface height variations in the scene within the reflected signal. Transforming satellite imagery data into a global DSM is challenging but would be of great value if those challenges were overcome. This study explores the application of a U-Net architecture to generate DSMs by coupling Sentinel-1 SAR and Sentinel-2 optical imagery. The model is trained on surface height data from multiple U.S. cities to produce a normalized DSM (NDSM) and assess its ability to generalize inferences for cities outside the training dataset. The analysis of the results shows that the model performs moderately well when inferring test cities but its performance remains well below that of the training cities. Further examination, through the comparison of height distributions and cross-sectional analysis, reveals that estimation bias is influenced by the input image resolution and the presence of geometric distortion within the SAR image. These findings highlight the need for refinement in preprocessing techniques as well as advanced training approaches and model architecture that can better handle the complexities of urban landscapes encoded in satellite imagery.