A new bottom-up emissions estimation approach for aircraft sources in support of air quality modelling for community-scale assessments around airports.

Transportation infrastructure (including roadway traffic, ports, and airports) is critical to the nation's economy. With a growing economy, aircraft activity is expected to grow across the world. In the US, airport-related emissions, while generally small, are not an insignificant source of air pollution and related adverse health effects. However, currently there is a lack of tools that can easily be applied to study near-source pollution and explore the benefits of improvements to air quality and exposures. Screening-level air quality modelling is a useful tool for examining urban-scale air quality impacts of airport operations. Spatially-resolved aircraft emissions are needed for the screening-level modelling. In order to create spatially-resolved aircraft emissions, we developed a bottom-up emissions estimation methodology that includes data from a global chorded inventory dataset from the aviation environmental design tool (AEDT). The initial implementation of this method was performed for Los Angeles International Airport (LAX). This paper describes a new emissions estimation methodology for aircraft emissions in support of community-scale assessments of air quality around airports and presents an illustration of its application at the Los Angeles International Airport during the LAX 2011/2012 Air Quality Source Apportionment Study.

The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS): Integration of Low-Cost Sensors and Reference Grade Monitoring in a Complex Metropolitan Area. Part 1: Overview of the Project.

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 (PM2.5), 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 PM2.5 concentrations (gravimetric) across all sites ranged from 7.92 to 9.34 µg/m3. Mean PM2.5 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 PM2.5 and BC concentrations when the winds originate near known source locations. Initial data analysis indicated that the observed PM2.5 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.

Advice and Frequently Asked Questions (FAQs) for Citizen-Science Environmental Health Assessments.

Citizen science provides quantitative results to support environmental health assessments (EHAs), but standardized approaches do not currently exist to translate findings into actionable solutions. The emergence of low-cost portable sensor technologies and proliferation of publicly available datasets provides unparalleled access to supporting evidence; yet data collection, analysis, interpretation, visualization, and communication are subjective approaches that must be tailored to a decision-making audience capable of improving environmental health. A decade of collaborative efforts and two citizen science projects contributed to three lessons learned and a set of frequently asked questions (FAQs) that address the complexities of environmental health and interpersonal relations often encountered in citizen science EHAs. Each project followed a structured step-by-step process in order to compare and contrast methods and approaches. These lessons and FAQs provide advice to translate citizen science research into actionable solutions in the context of a diverse range of environmental health issues and local stakeholders.

Associations between socio-demographic characteristics and chemical concentrations contributing to cumulative exposures in the United States.

Association rule mining (ARM) has been widely used to identify associations between various entities in many fields. Although some studies have utilized it to analyze the relationship between chemicals and human health effects, fewer have used this technique to identify and quantify associations between environmental and social stressors. Socio-demographic variables were generated based on U.S. Census tract-level income, race/ethnicity population percentage, education level, and age information from the 2010-2014, 5-Year Summary files in the American Community Survey (ACS) database, and chemical variables were generated by utilizing the 2011 National-Scale Air Toxics Assessment (NATA) census tract-level air pollutant exposure concentration data. Six mobile- and industrial-source pollutants were chosen for analysis, including acetaldehyde, benzene, cyanide, particulate matter components of diesel engine emissions (namely, diesel PM), toluene, and 1,3-butadiene. ARM was then applied to quantify and visualize the associations between the chemical and socio-demographic variables. Census tracts with a high percentage of racial/ethnic minorities and populations with low income tended to have higher estimated chemical exposure concentrations (fourth quartile), especially for diesel PM, 1,3-butadiene, and toluene. In contrast, census tracts with an average population age of 40-50 years, a low percentage of racial/ethnic minorities, and moderate-income levels were more likely to have lower estimated chemical exposure concentrations (first quartile). Unsupervised data mining methods can be used to evaluate potential associations between environmental inequalities and social disparities, while providing support in public health decision-making contexts.

A Citizen Science and Government Collaboration: Developing Tools to Facilitate Community Air Monitoring.

The U.S. Environmental Protection Agency (EPA) is actively involved in supporting citizen science projects and providing communities with information and assistance for conducting their own air pollution monitoring. As part of a Regional Applied Research Effort (RARE) project, EPA's Office of Research and Development (ORD) worked collaboratively with EPA Region 2 and the Ironbound Community Corporation (ICC) in Newark, New Jersey, to develop and test the "Air Sensor Toolbox for Citizen Scientists." In this collaboration, citizen scientists measured local gaseous and particulate air pollution levels by using a customized low-cost sensor pod designed and fabricated by EPA. This citizen science air quality measurement project provided an excellent opportunity for EPA to evaluate and improve the Toolbox resources available to communities. The Air Sensor Toolbox, developed in coordination with the ICC, can serve as a template for communities across the country to use in developing their own air pollution monitoring programs in areas where air pollution is a concern. This pilot project provided an opportunity for a highly motivated citizen science organization and the EPA to work together directly to address environmental concerns within the community. Useful lessons were learned about how to improve coordination between the government and communities and the types of tools and technologies needed for conducting an effective citizen science project that can be applied to future efforts.

A web-based screening tool for near-port air quality assessments.

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.

Connecting the Dots: Linking Environmental Justice Indicators to Daily Dose Model Estimates.

Many different quantitative techniques have been developed to either assess Environmental Justice (EJ) issues or estimate exposure and dose for risk assessment. However, very few approaches have been applied to link EJ factors to exposure dose estimate and identify potential impacts of EJ factors on dose-related variables. The purpose of this study is to identify quantitative approaches that incorporate conventional risk assessment (RA) dose modeling and cumulative risk assessment (CRA) considerations of disproportionate environmental exposure. We apply the Average Daily Dose (ADD) model, which has been commonly used in RA, to better understand impacts of EJ indicators upon exposure dose estimates and dose-related variables, termed the Environmental-Justice-Average-Daily-Dose (EJ-ADD) approach. On the U.S. nationwide census tract-level, we defined and quantified two EJ indicators (poverty and race/ethnicity) using an EJ scoring method to examine their relation to census tract-level multi-chemical exposure dose estimates. Pollutant doses for each tract were calculated using the ADD model, and EJ scores were assigned to each tract based on poverty- or race-related population percentages. Single- and multiple-chemical ADD values were matched to the tract-level EJ scores to analyze disproportionate dose relationships and contributing EJ factors. We found that when both EJ indicators were examined simultaneously, ADD for all pollutants generally increased with larger EJ scores. To demonstrate the utility of using EJ-ADD on the local scale, we approximated ADD levels of lead via soil/dust ingestion for simulated communities with different EJ-related scenarios. The local-level simulation indicates a substantial difference in exposure-dose levels between wealthy and EJ communities. The application of the EJ-ADD approach can link EJ factors to exposure dose estimate and identify potential EJ impacts on dose-related variables.

Community, state, and federal approaches to cumulative risk assessment: challenges and opportunities for integration.

Community, state, and federal approaches to conventional and cumulative risk assessment (CRA) were described and compared to assess similarities and differences, and develop recommendations for a consistent CRA approach, acceptable across each level as a rigorous scientific methodology, including partnership formation and solution development as necessary practices. Community, state, and federal examples were described and then summarized based on their adherence to CRA principles of: (1) planning, scoping, and problem formulation; (2) risk analysis and ranking, and (3) risk characterization, interpretation, and management. While each application shared the common goal of protecting human health and the environment, they adopted different approaches to achieve this. For a specific project-level analysis of a particular place or instance, this may be acceptable, but to ensure long-term applicability and transferability to other projects, recommendations for developing a consistent approach to CRA are provided. This approach would draw from best practices, risk assessment and decision analysis sciences, and historical lessons learned to provide results in an understandable and accepted manner by all entities. This approach is intended to provide a common ground around which to develop CRA methods and approaches that can be followed at all levels.

Evaluating socioeconomic and racial differences in traffic-related metrics in the United States using a GIS approach.

Previous studies have reported that lower-income and minority populations are more likely to live near major roads. This study quantifies associations between socioeconomic status, racial/ethnic variables, and traffic-related exposure metrics for the United States. Using geographic information systems (GIS), traffic-related exposure metrics were represented by road and traffic densities at the census tract level. Spearman's correlation coefficients estimated relationships between socio-demographic variables and traffic-related exposure metrics, and ANOVA was performed to test for significant differences in socio-demographic variables for census tracts with low and high traffic-related metrics. For all census tracts in the United States, %Whites, %Blacks, and %Hispanics (percent of tract population) had correlation coefficients greater than 0.38 and 0.16 with road density and traffic density, respectively. Regions and states had correlation coefficients as high as 0.78. Compared with tracts with low road and traffic densities (<25th percentile), tracts with high densities (>75th percentile) had values of %Blacks and %Hispanics that were more than twice as high, 20% greater poverty levels, and one-third fewer White residents. Census tracts that had mid-level values for road and traffic densities had the most affluent characteristics. Results suggest that racial/ethnic and socioeconomic disparities exist on national level with respect to lower-income and minority populations living near high traffic and road density areas.

Near-road multipollutant profiles: associations between volatile organic compounds and a tracer gas surrogate near a busy highway.

This research characterizes associations between multiple pollutants in the near-road environment attributed to a roadway line source. It also examines the use of a tracer gas as a surrogate of mobile source pollutants. Air samples were collected in summa canisters along a 300 m transect normal to a highway in Raleigh, North Carolina for five sampling periods spanning four days. Samples were subsequently measured for volatile organic compounds (VOCs) using an electron capture gas chromatograph. Sulfur hexafluoride (SF6) was released from a finite line source adjacent to the roadway for two of the sampling periods, collected in the canisters and measured with the VOCs. Associations between each VOC, and between VOCs and the tracer, were quantified with Pearson correlation coefficients to assess the consistency of the multi-pollutant dispersion profiles, and assess the tracer as a potential surrogate for mobile source pollutants. As expected, benzene, toluene, ethylbenzene, and m,p- and o-xylenes (collectively, BTEX) show strong correlations between each other; further BTEX shows a strong correlation to SF6. Between 26 VOCs, correlation coefficients were greater than 0.8, and 14 VOCs had coefficients greater than 0.6 with the tracer gas. Even under non-downwind conditions, chemical concentrations had significant correlations with distance. Results indicate that certain VOCs are representative of a larger multi-pollutant mixture, and many VOCs are well-correlated with the tracer gas.

The Environmental Protection Agency's Community-Focused Exposure and Risk Screening Tool (C-FERST) and its potential use for environmental justice efforts.

OBJECTIVES: Our primary objective was to provide higher quality, more accessible science to address challenges of characterizing local-scale exposures and risks for enhanced community-based assessments and environmental decision-making. METHODS: After identifying community needs, priority environmental issues, and current tools, we designed and populated the Community-Focused Exposure and Risk Screening Tool (C-FERST) in collaboration with stakeholders, following a set of defined principles, and considered it in the context of environmental justice. RESULTS: C-FERST is a geographic information system and resource access Web tool under development for supporting multimedia community assessments. Community-level exposure and risk research is being conducted to address specific local issues through case studies. CONCLUSIONS: C-FERST can be applied to support environmental justice efforts. It incorporates research to develop community-level data and modeled estimates for priority environmental issues, and other relevant information identified by communities. Initial case studies are under way to refine and test the tool to expand its applicability and transferability. Opportunities exist for scientists to address the many research needs in characterizing local cumulative exposures and risks and for community partners to apply and refine C-FERST.

Assessment and application of national environmental databases and mapping tools at the local level to two community case studies.

Communities are concerned over pollution levels and seek methods to systematically identify and prioritize the environmental stressors in their communities. Geographic information system (GIS) maps of environmental information can be useful tools for communities in their assessment of environmental-pollution-related risks. Databases and mapping tools that supply community-level estimates of ambient concentrations of hazardous pollutants, risk, and potential health impacts can provide relevant information for communities to understand, identify, and prioritize potential exposures and risk from multiple sources. An assessment of existing databases and mapping tools was conducted as part of this study to explore the utility of publicly available databases, and three of these databases were selected for use in a community-level GIS mapping application. Queried data from the U.S. EPA's National-Scale Air Toxics Assessment, Air Quality System, and National Emissions Inventory were mapped at the appropriate spatial and temporal resolutions for identifying risks of exposure to air pollutants in two communities. The maps combine monitored and model-simulated pollutant and health risk estimates, along with local survey results, to assist communities with the identification of potential exposure sources and pollution hot spots. Findings from this case study analysis will provide information to advance the development of new tools to assist communities with environmental risk assessments and hazard prioritization.

Tools available to communities for conducting cumulative exposure and risk assessments.

This paper summarizes and assesses over 70 tools that could aid with gathering information and taking action on environmental issues related to community-based cumulative risk assessments (CBCRA). Information on tool use, development and research needs, was gathered from websites, documents, and CBCRA program participants and researchers, including 25 project officers who work directly with community groups. The tools were assessed on the basis of information provided by project officers, community members, CBCRA researchers, and by case study applications. Tables summarize key environmental issues and tool features: (1) a listing of CBCRA-related environmental issues of concern to communities; (2) web-based tools that map environmental information; (3) step-by-step guidance documents; (4) databases of environmental information; and (5) computer models that simulate human exposure to chemical stressors. All tools described here are publicly available, with the focus being on tools developed by the US Environmental Protection Agency. These tables provide sources of information to promote risk identification and prioritization beyond risk perception approaches, and could be used by CBCRA participants and researchers. The purpose of this overview is twofold: (1) To present a comprehensive, though not exhaustive, summary of numerous tools that could aid with performing CBCRAs; and (2) To use this toolset as a sample of the current state of CBCRA tools to critically examine their utility and guide research for the development of new and improved tools.