The Community model for near-PORT applications (C-PORT) is a screening tool with an intended purpose of calculating differences in annual averaged concentration patterns and relative contributions of various source categories over the spatial domain within about 10 km of the port. C-PORT can inform decision-makers and concerned citizens about local air quality due to mobile source emissions related to commercial port activities. It allows users to visualize and evaluate different planning scenarios, helping them identify the best alternatives for making long-term decisions that protect community health and sustainability. The web-based, easy-to-use interface currently includes data from 21 seaports primarily in the Southeastern U.S., and has a map-based interface based on Google Maps. The tool was developed to visualize and assess changes in air quality due to changes in emissions and/or meteorology in order to analyze development scenarios, and is not intended to support or replace any regulatory models or programs.

This work describes a methodology for modeling the impact of traffic-generated air pollutants in an urban area. This methodology presented here utilizes road network geometry, traffic volume, temporal allocation factors, fleet mixes, and emission factors to provide critical modeling inputs. These inputs, assembled from a variety of sources, are combined with meteorological inputs to generate link-based emissions for use in dispersion modeling to estimate pollutant concentration levels due to traffic. A case study implementing this methodology for a large health study is presented, including a sensitivity analysis of the modeling results reinforcing the importance of model inputs and identify those having greater relative impact, such as fleet mix. In addition, an example use of local measurements of fleet activity to supplement model inputs is described, and its impacts to the model outputs are discussed. We conclude that with detailed model inputs supported by local traffic measurements and meteorology, it is possible to capture the spatial and temporal patterns needed to accurately estimate exposure from traffic-related pollutants.

Spatially and temporally resolved air quality characterization is critical for community-scale exposure studies and for developing future air quality mitigation strategies. Monitoring-based assessments can characterize local air quality when enough monitors are deployed. However, modeling plays a vital role in furthering the understanding of the relative contributions of emissions sources impacting the community. In this study, we combine dispersion modeling and measurements from the Kansas City TRansportation local-scale Air Quality Study (KC-TRAQS) and use data fusion methods to characterize air quality. The KC-TRAQS study produced a rich dataset using both traditional and emerging measurement technologies. We used dispersion modeling to support field study design and analysis. In the study design phase, the presumptive placement of fixed monitoring sites and mobile monitoring routes have been corroborated using a research screening tool C-PORT to assess the spatial and temporal coverage relative to the entire study area extent. In the analysis phase, dispersion modeling was used in combination with observations to help interpret the KC-TRAQS data. We extended this work to use data fusion methods to combine observations from stationary, mobile measurements, and dispersion model estimates.

Increased electrification of the transportation sector promises to both decrease emissions from the largest sector source of greenhouse gases as well as improve health through better air quality. The objective of this study is to evaluate the distribution of air quality health benefits of Medium- and Heavy-Duty Electric Vehicle (MHDEV) policies in New York City (NYC), quantifying differences across neighborhoods (census tracts) and population subgroups (race, ethnicity). We ran an integrated model for a 2040 baseline/business-as-usual scenario and for two policy scenarios simulating different rates of MHDEV adoption, also for 2040. Changes in air quality (fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂)) and health and economic benefits were calculated for the two policy scenarios. We used a modified version of our previously developed ZIP Code-Level Air Pollution Policy Assessment tool which integrates two reduced form models: the Community Air Quality Tools and the Co-Benefits Risk Assessment Health Impacts Screening and Mapping Tool. Our air quality modeling employed a novel receptor network to capture sharp gradients due to medium- and heavy-duty vehicle (MHDV) emissions in the densely populated NYC region. We find that electrification of MHDVs would have substantial air pollution and health benefits for NYC, with full on-road electrification of MHDVs saving $2.4 billion in health costs in 2040, including the prevention of 248 deaths and 173 childhood asthma emergency departments visits. We find that NO₂ benefits contributed between 85% and 97% of the total health benefits, emphasizing its importance for on-road health studies. We find that most health benefits would occur in neighborhoods with the highest percentage of historically marginalized populations, including substantial benefits to Hispanic populations.