Equity and modeling in sustainability science: Examples and opportunities throughout the process.

Equity is core to sustainability, but current interventions to enhance sustainability often fall short in adequately addressing this linkage. Models are important tools for informing action, and their development and use present opportunities to center equity in process and outcomes. This Perspective highlights progress in integrating equity into systems modeling in sustainability science, as well as key challenges, tensions, and future directions. We present a conceptual framework for equity in systems modeling, focused on its distributional, procedural, and recognitional dimensions. We discuss examples of how modelers engage with these different dimensions throughout the modeling process and from across a range of modeling approaches and topics, including water resources, energy systems, air quality, and conservation. Synthesizing across these examples, we identify significant advances in enhancing procedural and recognitional equity by reframing models as tools to explore pluralism in worldviews and knowledge systems; enabling models to better represent distributional inequity through new computational techniques and data sources; investigating the dynamics that can drive inequities by linking different modeling approaches; and developing more nuanced metrics for assessing equity outcomes. We also identify important future directions, such as an increased focus on using models to identify pathways to transform underlying conditions that lead to inequities and move toward desired futures. By looking at examples across the diverse fields within sustainability science, we argue that there are valuable opportunities for mutual learning on how to use models more effectively as tools to support sustainable and equitable futures.

Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results From a Community-Partnered Low-Cost Sensor Deployment.

The Strathcona neighborhood in Vancouver is particularly vulnerable to environmental injustice due to its close proximity to the Port of Vancouver, and a high proportion of Indigenous and low-income households. Furthermore, local sources of air pollutants (e.g., roadways) can contribute to small-scale variations within communities. The aim of this study was to assess hyperlocal air quality patterns (intra-neighborhood variability) and compare them to average Vancouver concentrations (inter-neighborhood variability) to identify possible disparities in air pollution exposure for the Strathcona community. Between April and August 2022, 11 low-cost sensors (LCS) were deployed within the neighborhood to measure PM2.5, NO2, and O3 concentrations. The collected 15-min concentrations were down-averaged to daily concentrations and compared to greater Vancouver region concentrations to quantify the exposures faced by the community relative to the rest of the region. Concentrations were also estimated at every 25 m grid within the neighborhood to quantify the distribution of air pollution within the community. Using population information from census data, cumulative hazard indices (CHIs) were computed for every dissemination block. We found that although PM2.5 concentrations in the neighborhood were lower than regional Vancouver averages, daily NO2 concentrations and summer O3 concentrations were consistently higher. Additionally, although CHIs varied daily, we found that CHIs were consistently higher in areas with high commercial activity. As such, estimating CHI for dissemination blocks was useful in identifying hotspots and potential areas of concern within the neighborhood. This information can collectively assist the community in their advocacy efforts.

Selecting Data Analytic and Modeling Methods to Support Air Pollution and Environmental Justice Investigations: A Critical Review and Guidance Framework.

Given the serious adverse health effects associated with many pollutants, and the inequitable distribution of these effects between socioeconomic groups, air pollution is often a focus of environmental justice (EJ) research. However, EJ analyses that aim to illuminate whether and how air pollution hazards are inequitably distributed may present a unique set of requirements for estimating pollutant concentrations compared to other air quality applications. Here, we perform a scoping review of the range of data analytic and modeling methods applied in past studies of air pollution and environmental injustice and develop a guidance framework for selecting between them given the purpose of analysis, users, and resources available. We include proxy, monitor-based, statistical, and process-based methods. Upon critically synthesizing the literature, we identify four main dimensions to inform method selection: accuracy, interpretability, spatiotemporal features of the method, and usability of the method. We illustrate the guidance framework with case studies from the literature. Future research in this area includes an exploration of increasing data availability, advanced statistical methods, and the importance of science-based policy.

Characterizing Elevated Urban Air Pollutant Spatial Patterns with Mobile Monitoring in Houston, Texas.

Diverse urban air pollution sources contribute to spatially variable atmospheric concentrations, with important public health implications. Mobile monitoring shows promise for understanding spatial pollutant patterns, yet it is unclear whether uncertainties associated with temporally sparse sampling and instrument performance limit our ability to identify locations of elevated pollution. To address this question, we analyze 9 months of repeated weekday daytime on-road mobile measurements of black carbon (BC), particle number (PN), and nitrogen oxide (NO, NO2) concentrations within 24 census tracts across Houston, Texas. We quantify persistently elevated, intermittent, and extreme concentration behaviors at 50 m road segments on surface streets and 90 m segments on highways relative to median statistics across the entire sampling domain. We find elevated concentrations above uncertainty levels (±40%) within portions of every census tract, with median concentration increases ranging from 2 to 3× for NO2, and >9× for NO. In contrast, PN exhibits elevated concentrations of 1.5-2× the domain-wide median and distinct spatial patterns relative to other pollutants. Co-located elevated concentrations of primary combustion tracers (BC and NOx) near 30% of metal recycling and concrete batch plant facilities within our sampled census tracts are comparable to those measured within 200 m of highways. Our results demonstrate how extensive mobile monitoring across multiple census tracts can quantitatively characterize urban air pollution source patterns and are applicable to developing effective source mitigation policies.