Mapping Refrigerant Gases in the New York City Skyline

A review of bottom-up and top-down emission estimates of hydrofluorocarbons (HFCs) in different parts of the world

Hydrofluorocarbons (HFCs) are widespread alternatives for the ozone-depleting substances chlorofluorocarbons and hydrochlorofluorocarbons. They are used mainly as refrigerants or as foam-blowing agents. HFCs do not deplete the ozone layer, but they are very potent greenhouse gases, already contributing to global warming. Since 2019 HFCs are regulated under the Kigali Amendment to the Montreal Protocol, which demands reliable emission estimates to monitor the phase-down. Quantification of emissions is performed with two methods: bottom-up from product inventories or data on chemical sales; or top-down, inferred from atmospheric measurements by inverse modelling or interspecies correlation. Here, we review and compare the two methods and give an overview of HFC emissions from different parts of the world. Emission estimates reported by the different methods vary considerably. HFC emissions of developed countries (Annex I) are reported to the United Nations Framework Convention on Climate Change. These bottom-up estimates add up to only half of global emissions estimated from atmospheric data. Several studies with regional top-down estimates have shown that this gap is not owed to large-scale underreporting of emissions from developed countries, but mostly due to emissions from developing countries (non-Annex I). China accounts for a large fraction of the emissions causing the gap, but not entirely. Bottom-up and top-down estimations of emissions from other developing countries that could identify other large emitters are largely unavailable. Especially South America, West-, Central- and East-Africa, India, the Arabian Peninsula and Northern Australia are not well covered by measurement stations that could provide atmospheric data for top-down estimates.

The Urban Observatory: A Multi-Modal Imaging Platform for the Study of Dynamics in Complex Urban Systems

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.

Pixel-Wise Classification of High-Resolution Ground-Based Urban Hyperspectral Images with Convolutional Neural Networks

Using ground-based, remote hyperspectral images from 0.4–1.0 micron in ∼850 spectral channels—acquired with the Urban Observatory facility in New York City—we evaluate the use of one-dimensional Convolutional Neural Networks (CNNs) for pixel-level classification and segmentation of built and natural materials in urban environments. We find that a multi-class model trained on hand-labeled pixels containing Sky, Clouds, Vegetation, Water, Building facades, Windows, Roads, Cars, and Metal structures yields an accuracy of 90–97% for three different scenes. We assess the transferability of this model by training on one scene and testing to another with significantly different illumination conditions and/or different content. This results in a significant (∼45%) decrease in the model precision and recall as does training on all scenes at once and testing on the individual scenes. These results suggest that while CNNs are powerful tools for pixel-level classification of very high-resolution spectral data of urban environments, retraining between scenes may be necessary. Furthermore, we test the dependence of the model on several instrument- and data-specific parameters including reduced spectral resolution (down to 15 spectral channels) and number of available training instances. The results are strongly class-dependent; however, we find that the classification of natural materials is particularly robust, especially the Vegetation class with a precision and recall >94% for all scenes and model transfers and >90% with only a single training instance.

Standoff chemical plume detection in turbulent atmospheric conditions with a swept-wavelength external cavity quantum cascade laser

Rapid and sensitive standoff measurement techniques are needed for detection of trace chemicals in outdoor plume releases, for example from industrial emissions, unintended chemical leaks or spills, burning of biomass materials, or chemical warfare attacks. Here, we present results from 235 m standoff detection of transient plumes for 5 gas-phase chemicals: Freon 152a (1,1-difluoroethane), Freon 134a (1,1,1,2-tetrafluoroethane), methanol (CH₃OH), nitrous oxide (N₂O), and ammonia (NH₃). A swept-wavelength external cavity quantum cascade laser (ECQCL) measures infrared absorption spectra over the range 955-1195 cm^-1 (8.37- 10.47 µm), from which chemical concentrations are determined via spectral fits. The fast 400 Hz scan rate of the swept-ECQCL enables measurement above the turbulence time-scales, reducing noise and allowing plume fluctuations to be measured. For high-speed plume detection, noise-equivalent column densities of 1-2 ppm*m are demonstrated with 2.5 ms time resolution, improving to 100-400 ppb*m with 100 ms averaging.

IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities

Ambient gas detection and measurement had become essential in diverse fields and applications, from preventing accidents, avoiding equipment malfunction, to air pollution warnings and granting the correct gas mixture to patients in hospitals. Gas leakage can reach large proportions, affecting entire neighborhoods or even cities, causing enormous environmental impacts. This paper elaborates on a deep review of the state of the art on gas-sensing technologies, analyzing the opportunities and main characteristics of the transducers, as well as towards their integration through the Internet of Things (IoT) paradigm. This should ease the information collecting and sharing processes, granting better experiences to users, and avoiding major losses and expenses. The most promising wireless-based solutions for ambient gas monitoring are analyzed and discussed, open research topics are identified, and lessons learned are shared to conclude the study.

Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer

Despite decades of research seeking to derive the urban energy budget, the dynamics of thermal exchange in the densely constructed environment is not yet well understood. Using New York City as a study site, we present a novel hybrid experimental-computational approach for a better understanding of the radiative heat transfer in complex urban environments. The aim of this work is to contribute to the calculation of the urban energy budget, particularly the stored energy. We will focus our attention on surface thermal radiation. Improved understanding of urban thermodynamics incorporating the interaction of various bodies, particularly in high rise cities, will have implications on energy conservation at the building scale, and for human health and comfort at the urban scale. The platform presented is based on longwave hyperspectral imaging of nearly 100 blocks of Manhattan, in addition to a geospatial radiosity model that describes the collective radiative heat exchange between multiple buildings. Despite assumptions in surface emissivity and thermal conductivity of buildings walls, the close comparison of temperatures derived from measurements and computations is promising. Results imply that the presented geospatial thermodynamic model of urban structures can enable accurate and high resolution analysis of instantaneous urban surface temperatures.