Estimation of the particle and gas scavenging contributions to wet deposition of organic and inorganic nitrogen

Atmospheric deposition of nitrogen species represents an additional nutrient source to natural environments, and can alter the nitrogen cycle by increasing nutrient levels beyond the requirements of organisms. In Tampa Bay, atmospheric deposition of dissolved inorganic nitrogen species (DIN) has been found to be the second largest nitrogen source, but little is known about dissolved organic nitrogen species (DON). The research goal was to improve the dry and wet deposition estimates by inclusion of the DON contribution. In the atmospheric chemistry field a standard method to measure DON in atmospheric samples has not been agreed upon. This research proposes the use of the ultraviolet (UV)-photolysis method and presents the optimal settings for its application on atmospheric samples. Using a factorial design scheme, experiments on surrogate nitrogen compounds, typically found in the atmosphere, indicated that DON can be xviii measured with no biases if optimal settings are fixed to be solution pH 2 with a 24-hr irradiance period. DIN species (NH4 +, NO2 -, NO3 -) and DON concentrations were determined in fine (PM2.5) and coarse particles (PM10-2.5) as well as in rainwater samples collected at Tampa Bay. The estimates of wet deposition fluxes for NH4 +, NO3 - and DON were 1.40, 3.18 and 0.34 kg-N ha-1yr-1, respectively. Hourly-measured gas concentrations and 24-hr integrated PM10 concentrations were used in conjunction with a below-cloud scavenging model to explain DIN and DON concentration in rainwater samples. Scavenging of aerosol-phase DON contributed only 0.9 ± 0.2% to rainwater DON concentrations, and therefore gas scavenging should be responsible for 99%. These results confirmed the existence of negative biases in the dry and wet deposition fluxes over Tampa Bay. There is increasing interest in simulating wet deposition fluxes, and the proposed below-cloud scavenging model offers a new computational approach to the problem. It integrates the typical gas and particle collection functions and the concept of the deposition-weighted average concentrations. The model uses mass balance to describe the time-dependent cumulative contribution of all droplets in the rain spectrum to the rainwater concentration, giving predictions closer to experimental values and better estimations than those reported in the literature for similar cases