An optical absorbance-based sensor designed to measure the concentration of vital Nitrogen (N), Phosphorus (P) and Potassium (K) nutrients in urban soil was developed. This device was characterized and tested in nine diverse green spaces around New York City’s Morningside Heights neighborhood, including street-tree pits and park spaces. The results show that the sensor can detect at minimum, a 1.4% change in nutrient concentration. Additionally, it was shown that the sensor can operate in various ambient light settings (indoor and outdoor) after calibration. A study of NYC’s green spaces shows that, on average, soil in street-tree pits that supports plant life has 54% more N, 34% more P, and 37% more K than park spaces, respectively. This new sensor technology will enable more detailed monitoring of soil nutrient conditions and thus help promote healthy green spaces in large urban environments.

Plant Spike is an in situ low-cost sensor system that is wireless, miniature, and low powered. It can be seamlessly implanted in subsurface locations across major cities to measure urban soil health. Plant Spike incorporates noncontact soil moisture monitoring, temperature monitoring, light intensity monitoring, advanced power management, and Bluetooth low energy transmit-only communication for transmitting information to a client device. With a novel combination of aggressive power reduction techniques, the system's lifetime is over two years with a 500-mAh battery. By connecting on-board sensors to a single-chip microcontroller, the total component and assembly cost of each module is less than $10. The sensor system has been tested within an urban soil testbed located on Columbia University's Morningside Campus in New York City as well as street tree pits located in Morningside Heights, proving the functionality and robustness of the system. Plant Spike is able to measure temperature and light ranges that are comparable to the fluctuations experienced by soils located within the climate zone of New York City.

Building sector energy consumption represents a significant fraction of the overall energy consumption in urban communities. While there has been increasing focus on the development of smart environments to support energy savings in urban commercial buildings, the development of smart environments for the residential sector has been less common. Instead, urban residential energy-saving efforts have focused on improving a building's physical infrastructure, introducing energy-saving appliances, and motivating residents to adopt energy conservation practices. This paper illustrates that creating an affinity between a building resident's thermal preferences and a building apartment's unregulated thermal environment represents an alternative means of generating an energy-efficient environment for multi-family, residential buildings. Two years of 15-min interval summer data, obtained from smart cyber-physical systems installed in 310 apartments across two New York City buildings, is used to develop data-enabled (D-E) models of resident thermal preference and unregulated apartment temperatures. Both of these models use a linear mixed-effects approach. The alignment of optimal resident-apartment pairs is then formulated as an integer-programming problem to explore the building energy saving (BES) potential associated with minimizing the difference between unregulated apartment temperature and the residents' thermal preference based on summer cooling loads. The work shows that the energy saving potential vary with a building's physical characteristics as well as the variation in residents' thermal preferences. The work further demonstrates that both the unregulated temperature and the resident's preferred temperature in a given apartment are not only affected by outside temperature and external relative humidity, but also strongly affected by the apartment's geographic orientation. For the specific buildings considered, up to 28% cooling energy saving is possible with full alignment. The paper provides all building monitoring data as an open source data set.