Cities are home to more than half of the world’s population, and this figure is set to continue to rise amidst ongoing global urbanization trends. Against this backdrop, urban development is increasingly confronted with multifaceted challenges. These range from public health emergencies, exemplified by the COVID-19 global pandemic, to the environmental hazards driven by climate change, including extreme heat waves and more frequent severe storms. Confronted with these substantial risks, the urgency of devising and implementing strategies for sustainable and resilient urban development has become paramount. Given this context, the work presented in this thesis aims to advance understanding of some critical urban sustainability challenges, and to develop models, tools, and sensing systems that can support progress towards a more sustainable and resilient urban future.
The first part of the thesis focuses on the role and usage of urban parks during a global public health emergency. Urban parks became critical for maintaining the well-being of urban residents during the COVID-19 global pandemic. To examine the impact of COVID-19 on urban park usage, New York City (NYC) was selected as a case study, and SafeGraph mobility data, which was collected from a large sample of mobile phone users, was used to assess the change in park visits and travel distance to a park based on park type, the income level of the visitor’s census block group (visitor CBG) and that of the park census block group (park CBG). All analyses were adjusted for the impact of temperature on park visitation, and the research work was focused primarily on park visits made by NYC residents.
Overall, for the eight most popular park types in NYC, namely – Community Park, Flagship Park, Jointly Operated Playground, Nature Area, Neighborhood Park, Playground, Recreation Field/Courts and Triangle/Plaza – visits dropped by 49.2% from 2019 to 2020. The peak reduction in visits occurred in April 2020. Visits to all park types, excluding Nature Areas, decreased from March to December 2020 as compared to 2019. Parks located in higher-income CBGs tended to have lower reductions in visits, with this pattern being primarily driven by visits to large parks, including Flagship Parks, Community Parks and Nature Areas. All types of parks saw significant decreases in distance traveled to visit the park, with the exception of the Jointly Operated Playground, Playground, and Nature Area park types. Visitors originating from lower-income CBGs traveled shorter distances to parks and had less reduction in travel distances compared to those from higher-income CBGs. Furthermore, both before and during the pandemic, people tended to travel a greater distance to parks located in high-income CBGs compared to those in low-income CBGs. Finally, multiple types of parks proved crucial destinations for NYC residents during the pandemic.
These included Nature Areas to which the visits remained stable, along with Recreation Field/Courts which had relatively small decreases in visits especially for lower-income communities. Results from this particular research study can support future park planning by shedding light on the different users of certain park types before and during a global crisis, where access to green spaces can help alleviate the human well-being consequences associated with mitigating the crisis, including the type of “lockdown” or limited mobility policies implemented in 2020 during the COVID-19 global pandemic.
The second part of the thesis investigates the role of urban greening and other land surface features in influencing the urban heat island effect in NYC. The urban heat island (UHI) effect describes the phenomenon whereby cities are generally warmer than surrounding rural areas. UHI effects can exacerbate extreme heat events, leading to an increase in heat-related illness and mortality. Here, the runoff coefficient was used as a numerical surrogate for urban greening, with lower runoff coefficients being associated with higher fractions of urban greening. Using a high-resolution landcover GIS dataset developed for New York City (NYC), which classified the city into more than 13 million land patches, the runoff coefficient of land use across the entire city was mapped down to a resolution of 30m×30m, along with five other variables including surface albedo, distance to water bodies, land surface elevation, building density and building height.
Daytime land surface temperature (LST) in summer was used as a surrogate for the UHI effect in NYC, and the work investigated the relationship between the runoff coefficient and LST. The work also examined the relationship between LST and the variables of surface albedo, distance to a water body, land surface elevation, building density and building height. Results indicate that runoff coefficient can explain a large portion of variability related to urban LST, with lower runoff coefficients (more greenery) being associated with lower LST. Use of the five other variables improves the predictability of LST, although the influence each variable has on LST varies with urban setting and context. The research work presented in this part of the thesis also shows the disproportionately higher exposure to urban heat in lower-income communities in NYC. The findings can be used to develop strategies to mitigate UHI effects in NYC and other cities around the world.
In the third part of the thesis, a wireless environmental sensing system is developed for monitoring urban green spaces, with demonstrated application for stormwater management. The monitoring of urban green spaces, including monitoring of soil conditions and soil health, is crucial for sustainable urban development and ecological resilience. Leveraging advances in wireless environmental sensing, a LoRaWAN-based system capable of measuring air temperature/humidity, soil temperature and moisture, and soil moisture dynamics is designed and deployed across seven diverse urban green spaces for a full year at Columbia University’s Morningside Campus in New York City.
The data collected by this sensing network reveals notable variations in soil moisture across the seven monitored sites, which are influenced by a combination of vegetation type, soil conditions, and physical settings. Monitored lawns consistently showed higher soil moisture levels due to their slower draining soil type, underlying concrete structures, and lower canopy rainfall interception and transpiration loss, whereas one monitored tree pit site with a more rapidly draining soil type showed significantly lower soil moisture throughout the study period, despite having comparable physical settings with another monitored site. Seasonal trends indicated lower summer moisture in some monitored areas due to increased evaporation and transpiration under high temperatures, while others areas maintained higher soil moisture as a result of frequent irrigations. Models were developed to quantify soil moisture response to rainfall events. It was found that the increase in soil moisture at each monitored site was highly dependent on the rainfall depth and the initial soil moisture. Overall, the results show that a range of diverse green spaces can help retain and drain storms up to certain sizes of 30-50mm.
However, proactively designed soil drainage systems are needed to handle extreme storm events above 50mm. The study highlights the effectiveness of LoRaWAN technology in urban environmental monitoring and provides valuable insights into how different urban green spaces can contribute to stormwater management. The findings presented in this portion of the thesis demonstrate the instrumental role that monitoring, data analysis and modeling can play in helping city planners and environmental managers optimize urban green spaces for ecological benefits and enhance urban resilience, including in the face of stressors such as climate change.
Overall, with its data-driven, evidence-based insights, this work contributes to the understanding of the multifaceted urban sustainability challenges in a changing environment, including public health emergencies such as the COVID-19 global pandemic, and climate change induced environmental hazards such as extreme heat events and more frequent severe storms. Alongside deepening understanding, the developed quantitative models and sensing technologies presented in this thesis offer practical solutions to support urban development towards a more sustainable and resilient future.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/tm5f-5t86 |
| Date | January 2023 |
| Creators | Zhao, Haokai |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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