Molecular contaminants on the surface of nanoparticles (NPs) are critical in determining the environmental safety and health (ESH) impacts of NPs. In order to characterize the surface properties that relate to adsorption and desorption interactions, a method has been developed for studying the dynamic interactions of adsorbing species on NP samples. The results are analyzed using a process simulator to determine fundamental properties such as capacity, affinity, rate expressions, and activation energies of NP interactions with contaminants. The method is illustrated using moisture as a representative model compound and particles of SiO₂, HfO₂, and CeO₂, which are three oxides used in semiconductor manufacturing. The effect of particle size and temperature on the surface properties of porous oxide NPs was investigated. Infrared spectra peaks corresponding to the stretching vibration of water molecules were monitored by in-site Fourier transform infrared (FTIR) spectroscopy. These are related to the moisture concentration on the surface of NPs. A transient multilayer model was developed to represent the fundamental steps in the process. The thermal stability of adsorbed species and the strength of bonding to the surface were evaluated by determining the activation energies of the various steps. The results indicate that the surface interaction parameters are dependent on species, temperature, and particle size. SiO₂ has the highest adsorption capacity and therefore is most prone to the adsorption of moisture and similar contaminants. However, the affinity of the NPs for H₂O retention is highest for CeO₂ and lowest for SiO₂. As temperature decreases, NPs exhibit a higher saturated moisture concentration and are more prone to the adsorption of moisture and similar contaminants. Furthermore, smaller NPs have a higher saturated surface concentration and a slower response to purging and desorption. Factors contributing to the environmental and health impact of NPs (extent of surface coverage, capacity, and activation energy of retention) have been investigated during this study. The second objective of this study is to develop a method to measure and control the contamination in ultra-high-purity (UHP) gas delivery systems. Modern semiconductor manufacturing plants have very stringent specifications for the moisture content at the point-of-use, usually below several parts per billion (ppb). When the gas delivery system gets contaminated, a significant amount of purge time is required for recovery of the background system. Therefore, it is critical for high-volume semiconductor manufacturers to reduce purge gas usage as well as purge time during the dry-down process. A method consisting of experimental research and process simulations is used to compare steady-state purge (SSP) process of constant pressure and flow rate with the pressure-cycle purge (PCP) process of cyclic pressure and flow rate at a controlled interval. The results show that the PCP process has significant advantages over the SSP process under certain conditions. It can reduce the purge time and gas usage when the gas purity at point-of-use is the major concern. The process model is validated by data congruent with the experimental results under various operating conditions and is useful in conducting parametric studies and optimizing the purge process for industrial applications. The effect of key operational parameters, such as start time of PCP process as well as choice of PCP patterns has been studied.