As addressing climate changes become a pressing issue in landscape architecture, the importance of landscape performance (LAP) became an important topic. An essential part of LAP is accessing data. Some data are easily accessible in the landscape architecture field, but some are not, such as air quality data. When such data are available in the landscape architecture field, they are often not of high enough quality, regarding scale, adequation, and precision. Also, there are sometimes financial barriers to getting the data. The research team explores an alternative way of collecting longitudinal air quality data to improve LAP measurement, using the Arduino-based cheaply made smart sensors installed on-site over time. The research team conducted experiments in nine comparison sites, collected and analyzed air quality data, including temperature, humidity, equivalent carbon dioxide (eCO2), volatile organic compounds (TVOCs), and fine particulate matter (PM2.5). The result shows that compared to publicly available data, longitudinal data collected by smart sensors are more accurate, dense, and frequent. This study investigates the strengths and capacities of using smart sensors for longitudinal air quality data tracking and offers an alternative way of providing data evidence for sustainable design to mitigate some climate changes issues.