A New Cumulative Air Toxics Risk Assessment for Mobile Sources Introducing Stochastic Human Health and Deterministic Ecological Methods

Abstract

Air toxics emitted from mobile and stationary sources continue to pose substantial risks to both human and ecological health, particularly in urban areas and environmentally sensitive regions. However, many existing risk assessment frameworks rely on deterministic assumptions, narrowly focus on select exposure pathways, and often overlook ecological impacts. This dissertation addresses these limitations by developing and applying both deterministic and stochastic methodologies to assess air toxics risk across diverse sources, spatial scales, and exposure scenarios.

One of the key objectives of this research is to introduce a new stochastic risk assessment method for mobile source air toxics (MSATs) that provides a more detailed and comprehensive characterization of health risks. Structured as a manuscript-based thesis, it comprises four peer-reviewed journal articles, each presenting a novel methodology demonstrated through real-world case studies. This structure ensures a logical progression from foundational risk assessment methodologies to more comprehensive, multi-source, multi-pathway, and probabilistic approaches, providing a holistic and integrated evaluation of air toxics risk.

The first article presents a new deterministic human health risk assessment methodology for MSATs. It integrates emissions modeling using MOVES, receptor-specific air dispersion modeling with AERMOD, and multi-pathway exposure estimation across inhalation and ingestion routes. In the Saint Paul, Minnesota case study, benzo(a)pyrene cancer risks exceeded the target threshold at two urban receptors: 4.59 × 10⁻⁵ (45.9 in 1 million) at the Saint Paul–Ramsey Health Center and 2.02 × 10⁻⁵ (20.2 in 1 million) at the Anderson Office Building. Ingestion contributed significantly to total exposure. These findings highlight the importance of cumulative risk analysis and the need for probabilistic methods to capture inter-individual variability.

The second article extends the methodology to assess cumulative, multi-pathway human health risks from stationary sources across Kuwait. It evaluates emissions from glycol dehydration units, wastewater treatment plants, and oil and gas operations using a national emissions inventory and dispersion and deposition modeling across three air quality zones. While most risk levels were within regulatory thresholds, cancer risk at the Ahmadi Hospital site exceeded the 1 × 10⁻⁵ target threshold for adults, with benzo(a)pyrene—the cancer risk driver—contributing a risk of 1.09 × 10⁻⁵ (10.9 in 1 million). This case study demonstrates the value of geographically resolved modeling for informing national-scale mitigation strategies.

The third article introduces the Ecological Health Assessment Methodology (EHAM), addressing a previously underexplored dimension of air toxics risk. EHAM combines fate and transport modeling, habitat-specific food web development, and bioaccumulation analysis to characterize ecological risks from air toxics. Applied across Kuwait’s air quality zones, EHAM revealed elevated risks in the coastal region, particularly for carnivorous shorebirds, with an ecological screening quotient of 3.12 × 10³ driven by the biomagnification of benzo(a)pyrene. By contrast, risks were negligible in the inland and production zones. The EHAM supports both screening-level and comparative ecological risk assessments and enables ecological evaluation at the national scale, extending beyond localized industrial sources to encompass broader emission landscapes.

The fourth article builds on the earlier methodologies and develops the first stochastic human health risk assessment methodology specifically tailored to MSATs. Using Monte Carlo simulation, it quantifies variability in key exposure parameters such as body weight, inhalation rate, exposure duration, and dietary intake. In the Saint Paul case study, the model produced full cancer risk distributions for adults and children. Computed cancer risks ranged from 5.47 × 10⁻⁴ to 4.98 × 10⁻² (95th percentile) for adults, and from 2.95 × 10⁻⁴ to 2.28 × 10⁻² (95th percentile) for children. To contextualize, the upper bound of 4.98 × 10⁻² for adults is comparable to the lifetime odds of dying from all preventable causes of death (1 in 19). The model identified high-exposure scenarios and vulnerable subpopulations often obscured in deterministic assessments.

Together, these contributions form an integrated risk architecture that advances emissions estimation, dispersion modeling, exposure characterization, and probabilistic risk quantification for human and ecological receptors. The methodologies developed in this research provide a comprehensive foundation for air quality engineers, public health scientists, and environmental regulators.

By improving the representation of variability, cumulative exposure, and pathway integration, this work strengthens the credibility, transparency, and practical utility of air toxics risk assessment to support informed environmental decision-making.