**2. Methods**

*Spatial Variability in Environmental Science - Patterns, Processes, and Analyses*

impervious area fractions, building heights, and canyon aspect ratios.

a more streamlined approach of interpreting UHI intensity on the basis of measurement techniques, locations, and urban morphological characterizations such as

The causes of UHI effects are related to fundamental differences in the surface energy balance between urban and rural areas. The 3D structure of, and man-made materials in, urban areas cause albedo changes during daytime and "radiation trapping" at night [6, 9–13], causing stronger heat admission during daytime, and slower radiative heat losses at night. In addition, anthropogenic heat from the human population and its energy use in urban areas significantly enhances the UHI effect [14–22]. While rural areas convert a substantial fraction of daytime incoming net radiation into latent heat fluxes, the dominance of impervious areas and an associated lower vegetation density in urban areas compared with their rural surroundings causes a redistribution of incoming net radiation into urban heat storage and sensible heat fluxes. Increased sensible heat fluxes increase the daytime UHI intensity, while high heat storage fluxes exacerbate nighttime UHI intensities when stored heat is returned into the atmosphere [23–28]. Detailed numerical studies such as by Ryu and Baik [29] have shown that impervious surface area, a proxy for energy balance flux changes, is likely the dominant factor determining daytime UHI intensity, while anthropogenic heat releases may dominate nighttime UHI intensity. Both these factors interact with the 3D structure of the urban fabric and the prevailing meteorological conditions. This can cause daytime cool islands as man-made (impervious) surfaces store heat and can shade road "canyons"; and maximum nighttime heat islands as stored heat together with anthropogenic heat are released back into shallower nighttime surface air layers. The results also concur with higher net radiation levels under high pressure conditions in summer, and the associated lack of turbulent heat transport under low wind speeds in urban areas as

To investigate these phenomena, researchers have used both stationary and mobile air temperature measurements extensively. While early studies often used only a few weather station locations [30, 31], or limited mobile traverses [32–34], newer studies have profited from now widely available, small form factor, accurate, and cost-effective electronic temperature sensors deployed in either stationary or mobile fashion. However, the correct deployment and interpretation of such sensors and their data still requires careful consideration, such as of radiation shielding and sensor response time aspects. In comparison, a hand-operated sling psychrometer provides a highly accurate, battery-independent low-key tool that can be operated by any lay person and can be immediately ready at the required time. Sling psychrometers provide dry-bulb and wet-bulb temperatures, and thus serve to provide both air temperature and humidity. They have been used in the past for UHI "spot" measurements [35–37], supplementing weather station and mobile data, and are ideally suited as "hands-on" data collection tools in undergraduate student research

This chapter describes a semester-long student project to determine the UHI intensity of a mid-size metropolitan area in east Texas, the Bryan/College Station (BCS) metro area, home of Texas A&M University. As part of a spring semester course on environmental atmospheric science, students were tasked to maintain regular air temperature measurements near the places they lived in town, then turn in a writing assignment at the end of the semester. During the following summer and fall semesters, the first author maintained two of the measurement sites and also carried out a mobile measurement study using her private automobile. Here, we discuss selected results from the measurements in context of past UHI studies. We also introduce an ongoing project of integrating these measurement results with

**26**

projects [38].

remotely sensed land cover data.

summarized by Arnfield [1].
