Emissions from transportation sources can impact local air quality and contribute to adverse health effects. The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS), conducted over a 1-year period, researched emissions source characterization in the Argentine, Turner, and Armourdale, Kansas (KS) neighborhoods and the broader southeast Kansas City, KS area. This area is characterized as a near-source environment with impacts from large railyard operations, major roadways, and commercial and industrial facilities. The spatial and meteorological effects of particulate matter less than 2.5 µm (PM_2.5), and black carbon (BC) pollutants on potential population exposures were evaluated at multiple sites using a combination of regulatory grade methods and instrumentation, low-cost sensors, citizen science, and mobile monitoring. The initial analysis of a subset of these data showed that mean reference grade PM_2.5 concentrations (gravimetric) across all sites ranged from 7.92 to 9.34 µg/m³. Mean PM_2.5 concentrations from low-cost sensors ranged from 3.30 to 5.94 µg/m³ (raw, uncorrected data). Pollution wind rose plots suggest that the sites are impacted by higher PM_2.5 and BC concentrations when the winds originate near known source locations. Initial data analysis indicated that the observed PM_2.5 and BC concentrations are driven by multiple air pollutant sources and meteorological effects. The KC-TRAQS overview and preliminary data analysis presented will provide a framework for forthcoming papers that will further characterize emission source attributions and estimate near-source exposures. This information will ultimately inform and clarify the extent and impact of air pollutants in the Kansas City area.

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.

Data is needed to understand local air quality, reduce exposure, and mitigate the negative impacts on human health. Measuring local air quality often requires a hybrid monitoring approach consisting of the national air monitoring network and one or more networks of air sensors. However, it can be challenging to combine this data to produce a consistent picture of air quality, largely because sensor data is produced in a variety of formats. Users may have difficulty reformatting, performing basic quality control steps, and using the data for their intended purpose. We developed an R-Shiny application that allows users to import text-based air sensor data, describe the format, perform basic quality control, and export the data to standard formats through a user-friendly interface. Format information can be saved to speed up the processing of additional sensors of the same type. This tool can be used by air quality professionals (e.g., state, local, Tribal air agency staff, consultants, researchers) to more efficiently work with data and perform further analysis in the Air Sensor Network Analysis Tool (ASNAT), Google Earth or Geographic Information System (GIS) programs, the Real Time Geospatial Data Viewer (RETIGO), or other applications they already use for air quality analysis and management.

Poor air quality can harm human health and the environment. Air quality data is needed to understand and reduce exposure to air pollution. Air sensor data can sup-plement national air monitoring network data to better understand localized air qual-ity and trends. However, these sensors can have limitations, biases, and inaccuracies that must first be controlled to generate data of adequate quality. Analyzing sensor data requires not only a background knowledge of air quality but also often requires extensive data analysis skills which may require new skills or time that burdens many air agencies (e.g., small states, local). To address these issues, an R-Shiny application has been developed to assist air quality professionals in 1) understanding air sensor data quality through comparison with nearby ambient air reference monitors, 2) ap-plying basic quality assurance (QA) and quality control (QC), and 3) understanding local air quality conditions. This tool provides agencies with the ability to more quickly analyze and utilize air sensor data for a variety of purposes while increasing the re-producibility of analyses. This paper highlights a case study using the tool to explore sensor performance during Canadian wildfire smoke impacts in the midwestern United States during June of 2023.

Data is needed to understand local air quality, reduce exposure, and mitigate the neg-ative impacts on human health. Measuring local air quality often requires a hybrid monitoring approach consisting of the national air monitoring network and one or more networks of air sensors. However, it can be challenging to combine this data to produce a consistent picture of air quality largely because sensor data is produced in a variety of formats. Users may have difficulty reformatting, performing basic quality control steps, and using the data for their intended purpose. We developed an R-Shiny application that allows users to import text-based air sensor data, describe the format, do basic quality control, and export the data to standard formats through a us-er-friendly interface. Format information can be saved to speed up processing of addi-tional sensors of the same type. This tool can be used by air quality professionals (e.g., state, local, tribal air agency staff, consultants, researchers) to more efficiently work with data and do further analysis in the Air Sensor Network Analysis Tool (ASNAT), Google Earth or Geographic Information System (GIS) programs, the Real Time Geo-spatial Data Viewer (RETIGO), or other applications they already use for air quality analysis and management.