The potential impacts of climate change on violent conflict are high on the agenda of scholars and policy makers. This article reviews existing literature to clarify the relationship between climate change and conflict risk, focusing on the roles of temperature and precipitation. While some debate remains, substantial evidence shows that climate change increases conflict risk under specific conditions. We examine four key pathways through which climate affects conflict: (i) economic shocks, (ii), agricultural decline, (iii) natural resources competition, and (iv) migration. Key gaps include limited long-term data, insufficient integrated studies, and the inadequate understanding of causal mechanisms, necessitating transdisciplinary research that addresses social vulnerability and underlying pathways.

: To assess household air pollution levels in urban Chicago households and examine how socioeconomic factors influence these levels. : We deployed wireless air monitoring devices to 244 households in a diverse population in Chicago to continuously record household fine particulate matter (PM) concentration. We calculated hourly average PM concentration in a 24-hour cycle. Four factors-race, household income, area deprivation, and exposure to smoking-were considered in this study. : A total of 93085 h of exposure data were recorded. The average household PM concentration was 43.8 μg m. We observed a significant difference in the average household PM concentrations between Black/African American and non-Black/African American households (46.3 versus 31.6 μg m), between high-income and low-income households (18.2 versus 52.5 μg m), and between smoking and non-smoking households (69.7 versus 29.0 μg m). However, no significant difference was observed between households in less and more deprived areas (43.7 versus 43.0 μg m). : Household air pollution levels in Chicago households are much higher than the recommended level, challenging the hypothesis that household air quality is adequate for populations in high income nations. Our results indicate that it is the personal characteristics of participants, rather than the macro environments, that lead to observed differences in household air pollution.

Local social and ecological contexts influence the experience of poverty and inequality in a number of ways that include shaping livelihood opportunities and determining the available infrastructure, services and environmental resources, as well as people's capacity to use them. The metrics used to define poverty and inequality function to guide local and international development policy but how these interact with the local ecological contexts is not well explored. We use a social-ecological systems (SES) lens to empirically examine how context relates to various measures of human well-being at a national scale in Ghana. Using a novel dataset constructed from the 100% Ghanian Census, we examine poverty and inequality at a fine population level across and within multiple dimensions of well-being. First, we describe how well-being varies within different Ghanian SES contexts. Second, we ask whether monetary consumption acts a good indicator for well-being across these contexts. Third, we examine measures of inequality in various metrics across SES types. We find consumption distributions differ across SES types and are markedly distinct from regional distributions based on political boundaries. Rates of improved well-being are positively correlated with consumption levels in all SES types, but correlations are weaker in less-developed contexts like, rangelands and wildlands. Finally, while consumption inequality is quite consistent across SES types, inequality in other measures of living standards (housing, water, sanitation, etc) increases dramatically in SES types as population density and infrastructural development decreases. We advocate that SES types should be recognized as distinct contexts in which actions to mitigate poverty and inequality should better incorporate the challenges unique to each.

Identifying factors that influence moderate-to-vigorous intensity physical activity (MVPA) and sedentary time in metabolic and bariatric surgery (MBS) patients is necessary to inform the development of interventions. Weather/environmental factors may be especially important considering rapid climate change and the vulnerability of people with obesity to heat and pollution. Our study aimed to examine the associations of weather (maximal, average and Wet Bulb Globe Temperatures), and air pollution indices (air quality index [AQI]) with daily physical activity (PA) of both light (LPA) and MVPA and sedentary time before and after MBS.

. Wildfire smoke events are increasing in frequency and intensity due to climate change. Children are especially vulnerable to health effects even at moderate smoke levels. However, it is unclear how parents respond to Air Quality Indices (AQIs) frequently used by agencies to communicate air pollution health risks. . In an experiment (3 × 2 × 2 factorial design), 2,100 parents were randomly assigned to view one of twelve adapted AQI infographics that varied by visual (table, line, gauge), index type (AQI [0-500], AQHI [1-11+]), and risk level (moderate, high). Participants were told to imagine encountering the infographic in a short-term exposure scenario. They reported worry about wildfire smoke, intentions to take risk-mitigating actions (e.g., air purifier use), and support for various exposure reduction policies. Subsequently, participants were told to imagine encountering the same infographic daily during a school week in a long-term exposure scenario and again reported worry, action intentions, and policy support. . Parents' responses significantly differentiated between risk levels that both pose a threat to children's health; worry and action intentions were much higher in the high-risk group than the moderate-risk group in both short-exposure (F = 748.68 p<.001; F = 411.59, p<.001) and long-exposure scenarios (F = 470.51, p<.001; F = 212.01, p<.001). However, in the short-exposure scenario, when shown the AQHI [1-11+] with either the line or gauge visuals, parents' action intentions were more similar between moderate- and high-risk level groups (3-way interaction, F = 6.03, p = .002). . These results suggest some index formats such as the AQHI-rather than the AQI-may better attune parents to moderate levels of wildfire smoke being dangerous to children's health. Our research offers insights for agencies and officials seeking to improve current public education efforts during wildfire smoke events and speaks to the critical need to educate parents and help them act short-term and long-term to protect children's health.

Machine learning and deep learning models have become essential in the recent fast development of artificial intelligence in many sectors of the society. It is now widely acknowledge that the development of these models has an environmental cost that has been analyzed in many studies. Several online and software tools have been developed to track energy consumption while training machine learning models. In this paper, we propose a comprehensive introduction and comparison of these tools for AI practitioners wishing to start estimating the environmental impact of their work. We review the specific vocabulary, the technical requirements for each tool. We compare the energy consumption estimated by each tool on two deep neural networks for image processing and on different types of servers. From these experiments, we provide some advice for better choosing the right tool and infrastructure.

Understanding drivers of water quality in local watersheds is the first step for implementing targeted restoration practices. Nutrient inventories can inform water quality management decisions by identifying shifts in nitrogen (N) and phosphorus (P) balances over space and time while also keeping track of the likely urban and agricultural point and nonpoint sources of pollution. The Chesapeake Bay Program's Chesapeake Assessment Scenario Tool (CAST) provides N and P balance data for counties throughout the Chesapeake Bay watershed, and these data were leveraged to create a detailed nutrient inventory for all the counties in the watershed from 1985-2019. This study focuses on three primary watershed nutrient balance components-agricultural surplus, atmospheric deposition, and point source loads-which are thought to be the leading anthropogenic drivers of nutrient loading trends across the watershed. All inputs, outputs, and derived metrics (n=53) like agricultural surplus and nutrient use efficiency, were subjected to short- and long-term trend analyses to discern how sources of pollution to surface water have changed over time. Across the watershed from 1985-2019, downward trends in atmospheric deposition were ubiquitous. Though there are varying effects, long-term declines in agricultural surplus were observed, likely because nutrients are being managed more efficiently. Multiple counties' point source loads declined, primarily associated with upgrades at major cities that discharge treated wastewater directly to tidal waters. Despite all of these positive developments, recent increases in agricultural surpluses from 2009-2019 highlight that water quality gains may soon be reversed in many agricultural areas of the basin. Besides tracking progress and jurisdictional influence on pollution sources, the nutrient inventory can be used for retrospective water quality analysis to highlight drivers of past improvement/degradation of water quality trends and for decision makers to develop and track their near- and long-term watershed restoration strategies.

Predicting rain from large-scale environmental variables remains a challenging problem for climate models and it is unclear how well numerical methods can predict the true characteristics of rainfall without smaller (storm) scale information. This study explores the ability of three statistical and machine learning methods to predict 3-hourly rain occurrence and intensity at 0.5° resolution over the tropical Pacific Ocean using rain observations the Global Precipitation Measurement (GPM) satellite radar and large-scale environmental profiles of temperature and moisture from the MERRA-2 reanalysis. We also separated the rain into different types (deep convective, stratiform, and shallow convective) because of their varying kinematic and thermodynamic structures that might respond to the large-scale environment in different ways. Our expectation was that the popular machine learning methods (i.e., the neural network and random forest) would outperform a standard statistical method (a generalized linear model) because of their more flexible structures, especially in predicting the highly skewed distribution of rain rates for each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are common problems in climate models. One implication of this study is that machine learning tools must be carefully assessed and are not necessarily applicable to solving all big data problems. Another implication is that traditional climate model approaches are not sufficient to predict extreme rain events and that other avenues need to be pursued.

Estimates of daily air pollution concentrations with complete spatial and temporal coverage are important for supporting epidemiologic studies and health impact assessments. While numerous approaches have been developed for modeling air pollution, they typically only consider each pollutant separately. We describe a spatial multipollutant data fusion model that combines monitoring measurements and chemical transport model simulations that leverages dependence between pollutants to improve spatial prediction. For the contiguous United States, we created a data product of daily concentration for 12 pollutants (CO, NOx, NO, SO, O, PM, and PM species EC, OC, NO, NH, SO) during the period 2005 to 2014. Out-of-sample prediction showed good performance, particularly for daily PM species EC (R = 0.64), OC (R = 0.75), NH (R = 0.84), NO (R2 = 0.73), and SO (R = 0.80). By employing the integrated nested Laplace approximation (INLA) for Bayesian inference, our approach also provides model-based prediction error estimates. The daily data product at 12km spatial resolution will be publicly available immediately upon publication. To our knowledge this is the first publicly available data product for major PM species and several gases at this spatial and temporal resolution.

The brick kiln industrial sector in South Asia accounts for large amounts of short-lived climate forcer (SLCF) emissions, namely black carbon (BC), organic carbon (OC), and sulfur dioxide (SO; the precursor to atmospheric sulfate [SO]). These SLCFs are air pollutants and have important impacts on both human health and the Arctic, a region currently experiencing more than double the rate of warming relative to the global average. Using previously derived Arctic equilibrium temperature response factors, we estimate the contribution to Arctic temperature impacts from previously reported emissions of BC, OC, and SO from four prevalent South Asian brick kiln types (Bull's Trench [BTK], Down Draught [DDK], Vertical Shaft [VSBK], and Zig-zag). Net annual BC (115 gigagrams [Gg]), OC (17 Gg), and SO (350 Gg) baseline emissions from all four South Asian kiln types resulted in 3.36 milliKelvin (mK) of Arctic surface warming. Given these baseline emissions and Arctic temperature responses, we estimate the current and maximum potential emission and temperature mitigation considering two kiln type conversions. Assuming no change in brick production, baseline emissions have been reduced by 17% when considering current BTK to Zig-zag conversions and have the potential to decrease by 82% given a 100% future conversion rate. This results in a 25% and 119% reduction in Arctic warming, respectively. Replacing DDKs with VSBKs increases baseline SLCF emissions by 28% based on current conversions and has the potential to increase by 131%. This conversion still reduces baseline warming by 31% and 149%, respectively. These results show that brick kiln conversions can have different impacts on local air quality and Arctic climate. When considering brick kiln emissions mitigation options, regional and/or local policy action should consider several factors, including local air quality, worker health and safety, cost, quality of bricks, as well as global climate impacts.

Ongoing water quality degradation tied to nitrogen and phosphorus pollution results in significant economic damages by diminishing the recreational value of surface water and compromising fisheries. Progress in decreasing nitrogen and phosphorus pollution to surface water over the past two decades has been slow. Limited resources need to be leveraged efficiently and effectively to prioritize watersheds for restoration. Leveraging recent nitrogen and phosphorus inventories for the years 2002, 2007, and 2012, we extracted relevant flux and demand terms to help identify US subbasins that are likely contributing a disproportionate amount of point and non-point source nutrient pollution to surface water by exploring the mean spatial distribution of terrestrial anthropogenic surplus, agricultural surplus, agricultural nutrient use efficiency, and point source loads. A small proportion of the landscape, <25% of subbasin area of the United States, contains 50% of anthropogenic and agriculture nitrogen and phosphorus surplus while only 2% of landscape contributes >50% of point source loads into surface water. Point source loads are mainly concentrated in urban areas across the country with point source loading rates often exceeding >10.0 kg N ha yr and >1.0 kg P ha yr. However, the ability for future upgrades to wastewater treatment plant infrastructure alone is unlikely to drive further improvement in water quality, outside of local water ways, since point source loads only account for ~4% of anthropogenic N and P surplus. As such, further progress in boosting nutrient use efficiency in agricultural production, usually lowest in areas of intensive livestock production, would likely contribute to the biggest gains to water quality restoration goals. This analysis and the corresponding database integrate multiple streams of information to highlight areas where N and P are being managed inefficiently to give decision makers a succinct platform to identify likely areas and sources of water quality degradation.

Efforts to reduce the health and ecological burdens of household biomass combustion are underway in Ghana, principally by promoting clean cookstoves and fuels. Recent studies have focused on the sustained use of clean cookstoves, but sometimes household adopt a new cookstove and then end use of that stove. In this study, we introduce a novel framework for understanding and encouraging household transitions to cleaner cooking: clean fuel discontinuance. We leveraged data from the Ghana Randomized Air Pollution and Health Study (GRAPHS) (N = 1412) where pregnant women received either improved biomass (BioLite) or dual burner LPG stoves for free. LPG users were given free LPG refills during GRAPHS. Weekly questionnaires were administered. Stove use monitors tracked a sub-cohort (n = 220) 6 months before and after the fuel subsidy. We examined social and ecological determinants of stove use and discontinuance. Overall intervention stove use adherence was high throughout GRAPHS, with self-reported use at 69% and 86% of participant-weeks for BioLite and LPG arms respectively. Participants used intervention stoves less for meals requiring vigorous stirring. Burns from intervention stoves decreased use among BioLite (RR: 0.96, p = 0.009), but not LPG users. Device breakage was mentioned as an impediment in 18% of free-text responses for LPG users and 1% for BioLite. Tree canopy within a spatial buffer-a plausible proxy for biomass fuels access-was the only variable explaining LPG discontinued stove use in adjusted Cox time-to-event analyses (HR = -0.56, p < 0.001). Future studies should consider the stove use discontinuance framework.

Heat is known to cause illness and death not only at extreme temperatures, but also at moderate levels. Although substantial research has shown how summer time temperature distributions have changed over recent decades in the United States, less is known about how the heat index-a potentially more health-applicable metric of heat-has similarly evolved over this period. Moreover, the extent to which these distributional changes have overlapped with indicators of social vulnerability has not been established, despite the applicability of co-varying climatic and sociodemographic characteristics to heat-related health adaptations. Presented here is an analysis of trends in the median, 95th percentile, and 'warm-tail spread' (i.e., intra-seasonal range between the upper extreme and median) of warm-season (May-September) maximum heat index between 1979and 2018 across the conterminous US. Using40 years of data from the North American Regional Reanalysis dataset, it is shown that most of the US has experienced statistically significant positive trends in summertime heat, and that both the magnitude of trends and the shape of the frequency distributions of these measures vary regionally. Comparisons with data from the Social Vulnerability Index show that the most socially vulnerable counties appear to be warming faster than the least vulnerable, but that opposite patterns hold for trends in warm-tail spread. These findings may be applicable to further studies on climate change, heat adaptations, and environmental justice in the US.

A limitation to understanding drivers of long-term trends in terrestrial nitrogen (N) availability in forests and its subsequent influence on stream nitrate export is a general lack of integrated analyses using long-term data on terrestrial and aquatic N cycling at comparable spatial scales. Here we analyze relationships between stream nitrate concentrations and wood N records (n = 96 trees) across five neighboring headwater catchments in the Blue Ridge physiographic province and within a single catchment in the Appalachian Plateau physiographic province in the eastern United States. Climatic, acidic deposition, and forest disturbance datasets were developed to elucidate the influence of these factors on terrestrial N availability through time. We hypothesized that spatial and temporal variation of terrestrial N availability, for which tree-ring N records serve as a proxy, affects the variation of stream nitrate concentration across space and time. Across space at the Blue Ridge study sites, stream nitrate concentration increased linearly with increasing catchment mean wood N. Over time, stream nitrate concentrations decreased with decreasing wood N in five of the six catchments. Wood N showed a significant negative relationship with disturbance and acidic deposition. Disturbance likely exacerbated N limitation by inducing nitrate leaching and ultimately enhancing vegetative uptake. As observed elsewhere, lower rates of acidic deposition and subsequent deacidification of soils may increase terrestrial N availability. Despite the ephemeral modifications of terrestrial N availability by these two drivers and climate, long-term declines in terrestrial N availability were robust and have likely driven much of the declines in stream nitrate concentration throughout the central Appalachians.

Wetland restoration is a primary management option for removing surplus nitrogen draining from agricultural landscapes. However, wetland capacity to mitigate nitrogen losses at large river-basin scales remains uncertain. This is largely due to a limited number of studies that address the cumulative and dynamic effects of restored wetlands across the landscape on downstream nutrient conditions. We analyzed wetland restoration impacts on modeled nitrate dynamics across 279 subbasins comprising the ∼0.5 million km Upper Mississippi River Basin (UMRB), USA, which covers eight states and houses ∼30 million people. Restoring ∼8,000 km of wetlands will reduce mean annual nitrate loads to the UMRB outlet by 12%, a substantial improvement over existing conditions but markedly less than widely cited estimates. Our lower wetland efficacy estimates are partly attributed to improved representation of processes not considered by preceding empirical studies - namely the potential for nitrate to bypass wetlands (i.e., via subsurface tile drainage) and be stored or transformed within the river network itself. Our novel findings reveal that wetlands mitigate surplus nitrogen basin-wide, yet they may not be as universally effective in tiled landscapes and because of river network processing.