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INTRODUCTION: We investigated whether markers of airway and systemic inflammation, as well as heart rate variability (HRV) in asthmatics, change in response to fluctuations in ambient particulate matter (PM) in the coarse [PM with aerodynamic diameter 2.5-10 microm (PM(2.5-10))] and fine (PM(2.5)) size range. METHODS: Twelve adult asthmatics, living within a 30-mile radius of an atmospheric monitoring site in Chapel Hill, North Carolina, were followed over a 12-week period. Daily PM(2.5-10) and PM(2.5) concentrations were measured separately for each 24-hr period. Each subject had nine clinic visits, at which spirometric measures and peripheral blood samples for analysis of lipids, inflammatory cells, and coagulation-associated proteins were obtained. We also assessed HRV [SDNN24HR (standard deviation of all normal-to-normal intervals in a 24-hr recording), ASDNN5 (mean of the standard deviation in all 5-min segments of a 24-hr recording)] with four consecutive 24-hr ambulatory electrocardiogram measurements. Linear mixed models with a spatial covariance matrix structure and a 1-day lag were used to assess potential associations between PM levels and cardiopulmonary end points. RESULTS: For a 1-microg/m(3) increase in coarse PM, SDNN24HR, and ASDNN5 decreased 3.36% (p = 0.02), and 0.77%, (p = 0.05) respectively. With a 1-microg/m(3) increase in coarse PM, circulating eosinophils increased 0.16% (p = 0.01), triglycerides increased 4.8% (p = 0.02), and very low-density lipoprotein increased 1.15% (p = 0.01). No significant associations were found with fine PM, and none with lung function. CONCLUSION: These data suggest that small temporal increases in ambient coarse PM are sufficient to affect important cardiopulmonary and lipid parameters in adults with asthma. Coarse PM may have underappreciated health effects in susceptible populations. [ABSTRACT FROM AUTHOR]

A combined epidemiological-exposure panel study was conducted during the summer of 1998 in Baltimore, Maryland. The objectives of the exposure analysis component of the 28-day study were to investigate the statistical relationships between particulate matter (PM) and related co-pollutants from numerous spatial boundaries associated with an elderly population, provide daily mass concentrations needed for the epidemiological assessment, and perform an extensive personal exposure assessment. Repeated 24-h integrated PM[SUB2.5] (n=394) and PM[SUB10] (n=170) data collections corresponding to stationary residential central indoor, individual apartment, residential outdoor and ambient monitoring were obtained using the same sampling methodology. An additional 325 PM[SUB2.5] personal air samples were collected from a pool of 21 elderly ( 65 + years of age) subjects. These subjects were residents of the 18-story retirement facility where residential monitoring was conducted. Mean daily central indoor and residential apartment concentrations were approximately 10 μg/ m[SUP3]. Outdoor and ambient PM[SUB2.5] concentrations averaged 22 μg/m[SUP3] with a daily range of 6.7-59.3 μg/m[SUP3]. The slope of the central indoor/outdoor PM[SUB2.5] mass relationship was 0.38. The average daily ratio of PM[SUB2.5]/PM[SUB10] mass concentrations across the measurement sites ranged from 0.73 to 0.92. Both the central indoor and mean apartment PM[SUB2.5] mass concentrations were highly correlated with the outdoor variables (r>0.94). The lack of traditionally recognized indoor sources of PM present within the facility might have accounted for the high degree of correlation observed between the variables. Results associated with the personal monitoring effort are discussed in depth in Part 2 of this article. [ABSTRACT FROM AUTHOR]

An integrated epidemiological-exposure panel study was conducted during the summer of 1998 which focused upon establishing relationships between potential human exposures to particulate matter (PM) and related co-pollutants with detectable health effects. The study design incorporated repeated individual 24-h integrated PM[SUB2.5] personal exposure monitoring. A total of 325 PM[SUB2.5] personal exposure samples were obtained during a 28-day study period using a subject pool of 21 elderly (65 + years of age) residents of an 18- story retirement facility near Baltimore, Maryland. Each sample represented a unique 24-h breathing zone measurement of PM[SUB2.5] mass concentration. PM[SUB2.5] and PM[SUB10] mass concentrations collected from the apartments of the subjects as well as residential and ambient sites were compared to individual and mean PM[SUB2.5] personal exposures. Daily PM[SUB2.5] personal exposure concentrations ranged from 2.4 to 47.8 μg/m[SUP3] with an overall individual study mean of 12.9 μg/m[SUP3]. Mean PM[SUB2.5] personal exposures were determined to be highly correlated to those representing the central indoor (r=0.90) and ambient sites (r=0.89). Subjects reported spending an average of 92% of each day within the confines of the retirement center. Based upon measured and modeled exposures, a mean PM[SUB2.5] personal cloud of 3.1 μg/m[SUP3] was estimated. Data collected from these participants may be unique with respect to the general elderly population due to the communal lifestyle within the facility and reported low frequency of exposure to sources of PM. [ABSTRACT FROM AUTHOR]

An extensive PM monitoring study was conducted during the 1998 Baltimore PM Epidemiology-Exposure Study of the Elderly. One goal was to investigate the mass concentration comparability between various monitoring instrmnentation located across residential indoor, residential outdoor, and ambient sites. Filter-based (24-h integrated) samplers included Federal Reference Method Monitors (PM[SUB2.5]-FRMs), Personal Environmental Monitors (PEMs), Versatile Air Pollution Samplers (VAPS), and cyclone-based instruments. Tapered element oscillating microbalances (TEOMs) collected real-time data. Measurements were collected on a near-daily basis over a 28-day period during July-August, 1998. The selected monitors had individual sampling completeness percentages ranging from 64% to 100%. Quantitation limits varied from 0.2 to 5.0 μg/m[SUP3]. Results from matched days indicated that mean individual PM[SUB10] and PM[SUB2.5] mass concentrations differed by less than 3 μg/m[SUP3] across the instrumentation and within each respective size fraction. PM[SUB10] and PM[SUB2.5] mass concentration regression coefficients of determination between the monitors often exceeded 0.90 with coarse (PM[SUB10-2.5]) comparisons revealing coefficients typically well below 0.40. Only one of the outdoor collocated PM[SUB2.5] monitors (PEM) provided mass concentration data that were statistically different from that produced by a protoype PM[SUB2.5] FRM sampler. The PEM had a positive mass concentration bias ranging up to 18% relative to the FRM prototype. [ABSTRACT FROM AUTHOR]

The land sector is anticipated to play an important role in achieving U.S. GHG emissions targets by reducing emissions and increasing sequestration from the atmosphere. This study assesses how much different levels of investment could stimulate land-based mitigation activities in the U.S. By applying a dynamic economic model of the land use sectors, with representation of 26 forestry and agricultural mitigation strategies across 11 U.S. regions, the study shows that annual investments of $2.4 billion could deliver abatement of around 80 MtCO 2 e annually. Under an optimal allocation of investments, the forestry sector and the Corn Belt are projected to receive the largest share of funds. Restricting land-based activities eligible for funds significantly reduces overall potential mitigation. For instance, if $24 billion investments are allocated only to agricultural activities, mitigation declines by 48% to 54 MtCO 2 e/yr over the next ten years. Finally, the level of abatement from each policy depends on the timing of implementation as the lowest cost mitigation actions are generally taken by the policy implemented first., (© 2024. The Author(s).)

Understanding greenhouse gas mitigation potential of the U.S. agriculture and forest sectors is critical for evaluating potential pathways to limit global average temperatures from rising more than 2° C. Using the FASOMGHG model, parameterized to reflect varying conditions across shared socioeconomic pathways, we project the greenhouse gas mitigation potential from U.S. agriculture and forestry across a range of carbon price scenarios. Under a moderate price scenario ($20 per ton CO 2 with a 3% annual growth rate), cumulative mitigation potential over 2015-2055 varies substantially across SSPs, from 8.3 to 17.7 GtCO 2 e. Carbon sequestration in forests contributes the majority, 64-71%, of total mitigation across both sectors. We show that under a high income and population growth scenario over 60% of the total projected increase in forest carbon is driven by growth in demand for forest products, while mitigation incentives result in the remainder. This research sheds light on the interactions between alternative socioeconomic narratives and mitigation policy incentives which can help prioritize outreach, investment, and targeted policies for reducing emissions from and storing more carbon in these land use systems.

Stabilizing climate change well below 2 °C and towards 1.5 °C requires comprehensive mitigation of all greenhouse gases (GHG), including both CO 2 and non-CO 2 GHG emissions. Here we incorporate the latest global non-CO 2 emissions and mitigation data into a state-of-the-art integrated assessment model GCAM and examine 90 mitigation scenarios pairing different levels of CO 2 and non-CO 2 GHG abatement pathways. We estimate that when non-CO 2 mitigation contributions are not fully implemented, the timing of net-zero CO 2 must occur about two decades earlier. Conversely, comprehensive GHG abatement that fully integrates non-CO 2 mitigation measures in addition to a net-zero CO 2 commitment can help achieve 1.5 °C stabilization. While decarbonization-driven fuel switching mainly reduces non-CO 2 emissions from fuel extraction and end use, targeted non-CO 2 mitigation measures can significantly reduce fluorinated gas emissions from industrial processes and cooling sectors. Our integrated modeling provides direct insights in how system-wide all GHG mitigation can affect the timing of net-zero CO 2 for 1.5 °C and 2 °C climate change scenarios., (© 2021. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

This paper analyzes changes in U.S. energy-intensive, trade-exposed (EITE) manufacturing over the past decade, through the lens of previously proposed climate policy measures. The American Clean Energy and Security Act of 2009 defined measures and thresholds for EITE eligibility and proposed compensatory allowances designed to reduce negative competitive impacts to domestic industry and to prevent emissions leakage. We undertook a retrospective analysis of the 2009 eligibility criteria, using the same methods with more recent data to examine trends over the 2004-2017 period. We find that energy intensity, emissions intensity, output, and emissions have fluctuated with economic conditions, and defining measures and thresholds that remain informative is challenging. Had ACES been enacted as written and not revised, the number of sectors qualifying for rebates would have decreased from 39 to 26, after adjustment for the changes in North American Industry Classification System definitions. Emissions from the eligible sectors fell 26% across the three periods of analysis, while emissions from manufacturing as a whole fell 5%. We decompose the changes in emissions into scale and intensity measures based on a hybrid measure derived from Grossman and Krueger [(1993). Environmental impacts of a North American free trade agreement. In The US-Mexico Free Trade Agreement, PM Garber (ed.). Cambridge, MA: MIT Press] and Kaya and Yokoburi [(1997). Environment, Energy, and Economy: Strategies for Sustainability. Tokyo: United Nations University Press]. As an alternative, we perform the same analyses using the EPA's Greenhouse Gas Reporting Program data. These data, not available when ACES was written, offer annual greenhouse gas estimates for facilities that emit more than 25,000 tons CO 2 e annually. Finally, we draw some recommendations for future policy including (1) using measures that make price level adjustments straightforward or unnecessary, (2) keeping EITE policy focused on a small group of industries to minimize sectoral reclassification problems, (3) identifying industries prone to emissions leakage rather than just changes in output and (4) consider spatial heterogeneity of emissions and trade patterns.

The primary aim of this study was to evaluate cardio-metabolic burden by insurance status for Hispanic/Latino adults in Santa Barbara, CA. HbA1c, body mass index (BMI), and health insurance status were evaluated via community-based screenings. Cardiovascular (CV) risk was assessed using the Framingham Heart Study calculator and compared with a National Health and Nutrition Examination Survey background population. Of 593 participants (73% female, age 47.3 years (SD ± 15.2), 44.5% had no insurance or did not know if they had insurance. 16% had an HbA1c in the diabetes range, comprised of 9% self-reporting diabetes and 7% self-reporting no diabetes. 39% had an HbA1c in the prediabetes range. Elevated HbA1c was associated with significantly higher BMI (p = 0.003) and lack of health insurance (p = 0.031). 10-year CV risk was significantly higher than the general population (p < 0.001). This program identified a high burden of cardio-metabolic disease and uninsurance coverage in Hispanic/Latino adults in Santa Barbara.

Background: Food choices are essential to successful glycemic control for people with diabetes. We compared the impact of three carbohydrate-rich meals on the postprandial glycemic response in adults with type 1 diabetes (T1D). Methods: We performed a randomized crossover study in 12 adults with T1D (age 58.7 ± 14.2 years, baseline hemoglobin A1c 7.5% ± 1.3%) comparing the postprandial glycemic response to three meals using continuous glucose monitoring: (1) "higher protein" pasta containing 10 g protein/serving, (2) regular pasta with 7 g protein/serving, and (3) extra-long grain white rice. All meals contained 42 g carbohydrate; were served with homemade tomato sauce, green salad, and balsamic dressing; and were repeated twice in random order. After their insulin bolus, subjects were observed in clinic for 5 h. Linear mixed effects models were used to assess the glycemic response. Results: Compared with white rice, peak glucose levels were significantly lower for higher protein pasta (-32.6 mg/dL; 95% CI -48.4 to -17.2; P  < 0.001) and regular pasta (-43.2 mg/dL, 95% CI -58.7 to -27.7; P  < 0.001). The difference between the two types of pastas did not reach statistical significance (-11 mg/dL; 95% CI -24.1 to 3.4; P  = 0.17). Total glucose area under the curve was also significantly higher for white rice compared with both pastas ( P  < 0.001 for both comparisons). Conclusions: This exploratory study concluded that different food types of similar macronutrient content (e.g., rice and pasta) generate significantly different postprandial glycemic responses in persons with T1D. These results provide useful insights into the impact of food choices on and optimization of glucose control. Clinical Trial Registry: clinicaltrials.gov NCT03362151.

In recent decades, the carbon sink provided by the U.S. forest sector has offset a sizable portion of domestic greenhouse gas (GHG) emissions. In the future, the magnitude of this sink has important implications not only for projected U.S. net GHG emissions under a reference case but also for the cost of achieving a given mitigation target. The larger the contribution of the forest sector towards reducing net GHG emissions, the less mitigation is needed from other sectors. Conversely, if the forest sector begins to contribute a smaller sink, or even becomes a net source, mitigation requirements from other sectors may need to become more stringent and costlier to achieve economy wide emissions targets. There is acknowledged uncertainty in estimates of the carbon sink provided by the U.S. forest sector, attributable to large ranges in the projections of, among other things, future economic conditions, population growth, policy implementation, and technological advancement. We examined these drivers in the context of an economic model of the agricultural and forestry sectors, to demonstrate the importance of cross-sector interactions on projections of emissions and carbon sequestration. Using this model, we compared detailed scenarios that differ in their assumptions of demand for agriculture and forestry products, trade, rates of (sub)urbanization, and limits on timber harvest on protected lands. We found that a scenario assuming higher demand and more trade for forest products resulted in increased forest growth and larger net GHG sequestration, while a scenario featuring higher agricultural demand, ceteris paribus led to forest land conversion and increased anthropogenic emissions. Importantly, when high demand scenarios are implemented conjunctively, agricultural sector emissions under a high income-growth world with increased livestock-product demand are fully displaced by substantial GHG sequestration from the forest sector with increased forest product demand. This finding highlights the potential limitations of single-sector modeling approaches that ignore important interaction effects between sectors.

Agriculture is the single largest source of anthropogenic non-carbon dioxide (non-CO 2 ) emissions. Reaching the climate target of the Paris Agreement will require significant emission reductions across sectors by 2030 and continued efforts thereafter. Here we show that the economic potential of non-CO 2 emissions reductions from agriculture is up to four times as high as previously estimated. In fact, we find that agriculture could achieve already at a carbon price of 25 $/tCO 2 eq non-CO 2 reductions of around 1 GtCO 2 eq/year by 2030 mainly through the adoption of technical and structural mitigation options. At 100 $/tCO 2 eq agriculture could even provide non-CO 2 reductions of 2.6 GtCO 2 eq/year in 2050 including demand side efforts. Immediate action to favor the widespread adoption of technical options in developed countries together with productivity increases through structural changes in developing countries is needed to move agriculture on track with a 2 °C climate stabilization pathway.

This paper provides a detailed, cross-model analysis and discussion of the implications of carbon tax scenarios on changes in sectoral output, energy production and consumption and the competitiveness of the United States' economy. Our analysis focuses on the broad patterns apparent across models in both qualitative and quantitative terms at the sector level, with a focus on energy-intensive, trade-exposed sectors. We identify how variations in carbon tax trajectories and different options for using the revenue from the tax drive these results.

The Energy Modeling Forum (EMF) 32 study compares a range of coordinated scenarios to explore implications of U.S. climate policy options and technological change on the electric power sector. Harmonized policy scenarios (including mass-based emissions limits and various power-sector-only carbon tax trajectories) across 16 models provide comparative assessments of potential impacts on electric sector investment and generation outcomes, emissions reductions, and economic implications. This paper compares results across these policy alternatives, including a variety of technological and natural gas price assumptions, and summarizes robust findings and areas of disagreement across participating models. Under a wide range of policy, technology, and market assumptions, model results suggest that future coal generation will decline relative to current levels while generation from natural gas, wind, and solar will increase, though the pace and extent of these changes vary by policy scenario, technological assumptions, region, and model. Climate policies can amplify trends already under way and make them less susceptible to future market changes. The model results provide useful insights to a range of stakeholders, but future research focused on intersectoral linkages in emission reductions (e.g., the role of electrification), effects of energy storage, and better coverage of bioenergy with carbon capture and storage (BECCS) can improve insights even further.

Agriculture is one of the sectors that is expected to be most significantly impacted by climate change. There has been considerable interest in assessing these impacts and many recent studies investigating agricultural impacts for individual countries and regions using an array of models. However, the great majority of existing studies explore impacts on a country or region of interest without explicitly accounting for impacts on the rest of the world. This approach can bias the results of impact assessments for agriculture given the importance of global trade in this sector. Due to potential impacts on relative competitiveness, international trade, global supply, and prices, the net impacts of climate change on the agricultural sector in each region depend not only on productivity impacts within that region, but on how climate change impacts agricultural productivity throughout the world. In this study, we apply a global model of agriculture and forestry to evaluate climate change impacts on US agriculture with and without accounting for climate change impacts in the rest of the world. In addition, we examine scenarios where trade is expanded to explore the implications for regional allocation of production, trade volumes, and prices. To our knowledge, this is one of the only attempts to explicitly quantify the relative importance of accounting for global climate change when conducting regional assessments of climate change impacts. The results of our analyses reveal substantial differences in estimated impacts on the US agricultural sector when accounting for global impacts vs. US-only impacts, particularly for commodities where the United States has a smaller share of global production. In addition, we find that freer trade can play an important role in helping to buffer regional productivity shocks.