Abstract: Physical and transition risks across socio-environmental systems are becoming increasingly complex, multi-faceted, compounding, and span unjust societal landscapes. Multi-Sector Dynamics (MSD) explores the existence and extent that human and natural systems co-exist, interact, and co-evolve. To meet this need, we have developed an open-science, visualization platform that harmonizes, combines, overlays, and diagnoses landscapes of risks and inequities across socio-economics, human health, biodiversity, demographics, as well as the natural, managed, and built environmental systems. The platform’s current geographic focus allows for an MSD-inspired perspective that resolves combinatory-risk landscapes across the United States at the county level. Combinatory-risk indices from weighted composites of a variety of indicators are created and based on user specifications to areas-of-concern.

As a visual example – we demonstrate where “hotspots” of environmental risks compound. As separate mappings (Figure 1a), current risks to land, water availability and quality, and exposure to poor air quality exhibit features not discernably co-located. The resultant landscape of combinatory risk (Figure 1b) exhibits discernable, prominent “hotspots” across California, the Mississippi River basin, the Southeast, and Mid-Atlantic states. Concurrently, another combined transition-risk mapping indicates that the lower Mississippi River contains the largest portion of fossil energy employment along with high levels of poverty and unemployment. This highlights a potential connection between contrasting regional effects of a low-carbon energy transition. Other examples will demonstrate similar connections and compounding landscapes. Quantitative metrics will show the profound effect the incorporation of socio-demographics has on the “top 5 list” of states that experience the most severe compounding physical and transition risks, and underscore the importance of the choice in these metrics are for the interpretation and assessment of priorities into deep-dive analysis and actions.

Abstract: Mercury (Hg) is a neurotoxic contaminant that bioaccumulates in the marine food chain. 137 countries are now parties to the Minamata Convention on Mercury, which aims to combat growing risks of Hg pollution to human health and the environment. Atmospheric Hg trends over time serve as important indicators in evaluating the effectiveness of the Minamata Convention. However, there are several challenges associated with interpreting observed atmospheric Hg time series, including: data gaps, few long-term (>10 years) time series, analytical uncertainties in the measurements, meteorological variability, and the representativeness of measurement sites for broader spatial scales. Novel statistical techniques, including generalized additive models, dynamic linear modelling, and meteorological ensembles, have shown potential in recent atmospheric trends studies for overcoming these challenges; however, many of these promising techniques have yet to be applied to Hg time series. Harnessing such state-of-the-art statistical approaches, we analyze atmospheric Hg measurements for 1990–present in a regression-based framework to produce more accurate assessments of trends and their uncertainties. We apply quantile regression to analyze difference in Hg trends from the 5^th–95^thpercentiles. We hypothesize that lower percentile (e.g., 5^th) trends are more indicative of trends in background Hg on the hemispheric scale, whereas higher percentile trends (e.g., 95^th) are more indicative of local or regional emission changes. Indeed, observed and simulated trends from North American sites generally show strongly decreasing 95^th percentile trends and stagnant 5^th percentile trends, illustrating that regional emissions have decreased while hemispheric trends have stayed stagnant or increased. Using companion simulations in the global atmospheric Hg model GEOS-Chem, we analyze the sensitivity of available Hg measurement time series to trends in anthropogenic Hg emissions. The combined model–observation analysis can aid the Minamata Convention effectiveness evaluation in disentangling the anthropogenic contribution to recent Hg trends.

Abstract: Smoke particulate matter emitted by fires in the Amazon Basin poses a threat to human health. Past research on this threat has mainly focused on the health impacts on countries as a whole or has relied on hospital admission data to quantify the health response. Such analyses do not capture the impact on people living in Indigenous territories close to the fires and who often lack access to medical care and may not show up at hospitals. Here we quantify the premature mortality due to smoke exposure of people living in Indigenous territories across the Amazon Basin. We use the atmospheric chemistry transport model GEOS-Chem to simulate PM_2.5 from fires and other sources, and we apply the latest epidemiological data to estimate the effects on public health. We estimate that smoke from fires in South America accounted for ~12,000 premature deaths each year from 2014-2019 across the continent, with about ~230 of these deaths occurring in Indigenous lands. Put another way, smoke exposure accounts for 2 premature deaths per 100,000 people per year across South America, but 4 premature deaths per 100,000 people in the Indigenous territories. However, Bolivia and Brazil are hotspots and deaths in indigenous territories in these countries are 9 and 12 per 100,000 people, respectively. Our analysis shows that smoke PM_2.5 from fires has a detrimental effect on human health across South America, with a disproportionate impact on people living in Indigenous territories.