AbstractTrees in urban settings have a significant role in regulating urban hydrologic cycles. Urban trees, either as standalone plantings or as part of a tree pit, are an increasingly popular stormwater management tool. Beyond their aesthetic contribution to urban environments, trees are widely accepted as reducing the ambient air temperature. However, there is limited long-term quantitative information regarding the temperature mitigation performed by urban trees through the use of temperature sensors over a large urban area. This study monitored air temperature at locations throughout the city of Camden, New Jersey. Sensors were installed under canopies of trees of different sizes throughout the city using a statistical experimental design. The tree size (small or large) and canopy (intersecting or nonintersecting), along with the street orientation (predominantly north-south or east-west) and time of day (daylight, nighttime, or full-day), were experimental design factors. Sensors attached to poles along the streets or in parking lots served as controls. This study recorded temperatures at 10-min intervals from early August through late November 2017 using logging thermistors mounted in radiation shields about 4 m above the ground surface. Using the maximum daily air temperature at control sites, all temperature data were categorized into three groups of hot, average, and cool days. The groups were analyzed separately using the analysis of variance to test the significance of the categorical variables. During hot days (a maximum temperature larger than 30°C), there was a meaningful statistical difference between recorded mean air temperatures under trees with intersecting canopies and the control sites. A categorical analysis of street orientation for hot and average days showed that during the daytime, east-west streets were hotter than north-south streets, while this trend reversed at night when north-south streets were hotter than east-west streets. For cool days, there were no differences for the studied categorical factors.