While there is a growing interest in employing planning and design tactics to improve air quality issues, landscape architects often rely on coarse data that may not reflect hyperlocal conditions. This paper explores how landscape architects might assemble their own sensor network to collect data on hyperlocal air quality patterns, how this data might vary spatially and temporally across a site, and how findings from this exercise might inform the site analysis process. To explore these issues, we prototyped an affordable and mobile sensor network that could be publicly deployed to capture air quality variation at the block scale. We tested the system by building units and collecting data across the University of California, Davis campus for 30 days. By analyzing the resulting data, the sensor system is capable of identifying spatial and temporal hotspots. More generally, when deployed at a larger scale, such a system could be used to improve our understanding of hyperlocal air quality patterns for planning and design.