Studying lunar dust is vital to the exploration of the Moon and other airless planetary bodies. The Ultraviolet and Visible Spectrometer (UVS) on board the LADEE spacecraft conducted a series of Almost Limb activities to look for dust near the dawn terminator region. During these activities the instrument stared at a fixed point in the zodiacal background off the moon's limb while the spacecraft moved in retrograde orbit from the sunlit to the unlit side of the Moon. The spectra obtained from these activities probe altitudes within a few kilometers of the Moon's surface, a region whose dust populations were not well constrained by previous remote-sensing observations from orbiting spacecraft. Filtering these spectra to remove a varying instrumental signal enables constraints to be placed on potential signals from a dust atmosphere. These filtered spectra are compared with those predicted for dust atmospheres with various exponential scale heights and particle size distributions to yield upper limits on the dust number density for these potential populations. For a differential size distribution proportional to s^-3 (where s is the particle size) and a scale height of 1 km, we obtain an upper limit on the number density of dust particles at the moon's surface of 142 m^-3. The primary resource data for the project is available on the Atmosphere node of the Planetary data science Repository. The "Almost Limb" Observation data from these reource files is converted to .sav files. These .sav files contain the value of several vairables during the course of the observation. We are interested in time, latitude, longitude of the observed point and the wavelength and brighntess change recorded during the two minutes of the observation. All the variables above are stored in .txt files. The brighntess observation is then filtered to remove outliers and reduce variations in the data, details of this process are discussed in the manuscript. This processed data is again stored in a .txt file. A model is created to generate the brightness from an expected dust spectrum, this model derives a predicted data set (txt files) which is then compared to our processed data from the observations. The comparison of two gives us an upper limit on dust density which is the result of the manuscript.