Atmospheric reactive nitrogen (Nr), including its reduced form ammonia (NH3) and oxidized forms (NOy), is a critical component of the Earth system that significantly impacts air quality, ecosystem health, and climate. While essential for agriculture, excess Nr is lost to the environment, initiating a cascade of detrimental effects, including the formation of ground-level ozone and fine particulate matter (PM2.5), ecosystem acidification, and biodiversity loss. As emissions of oxidized nitrogen decline, the role of agricultural ammonia in driving these impacts has grown, yet it remains the largest source of uncertainty in the U.S. nitrogen budget due to a lack of direct flux observation.

To address these significant knowledge gaps, we propose a novel Tiered and Machine Learning Assisted Monitoring (TMLAM) framework designed to provide comprehensive and cost-effective NH3 flux data. The framework synergistically combines:

Tier 1) direct, high-fidelity eddy covariance flux measurements at a few "supersites" to develop and evaluate process models;

Tier 2) inferential modeling driven by low-cost sensors to expand spatial coverage into critical regions; and

Tier 3) upscaling using existing air quality monitoring networks and reanalysis data.

A key innovation of the TMLAM framework is a data-driven site selection strategy that moves beyond traditional ecoregions. We hypothesize that coherent regions for NH3 land atmosphere exchange are defined by multivariate environmental drivers, including soil, vegetation, and atmospheric composition. Using unsupervised machine learning (K-means clustering) on principal components of high-resolution environmental data, we identify these "Nitrogen Exchange Similar Areas" (NESAs). This approach allows for strategic placement of Tier 1 sites in highly representative areas and Tier 2 sites at the boundaries between NESAs to characterize model uncertainty. This presentation will detail the TMLAM framework and the ML-based methodology for NESA determination. In addition, we will discuss sensors suitable for each tier and recent advances in inferential modeling for Tier 2 sites. Finally, we will also evaluate and rank potential sites for the future implementation of TMLAM for NH3 or Nr fluxes.