The role of climate and emission changes in future air quality over southern Canada and northern Mexico

International audiencePotential impacts of global climate and emissions changes on regional air quality over southern (western and eastern) Canada and northern Mexico are examined by comparing future summers' (i.e., 2049?2051) average regional O3 and PM2.5 concentrations with historic concentrations (i.e., 2000?2002 summers). Air quality modeling was conducted using CMAQ and meteorology downscaled from the GISS-GCM using MM5. Emissions for North America are found using US EPA, Mexican and Canadian inventories and projected emissions following CAIR and IPCC A1B emissions scenario. Higher temperatures for all sub-regions and regional changes in mixing height, insolation and precipitation are forecast in the 2049?2051 period. Future emissions are calculated to be lower over both Canadian sub-regions, but higher over northern Mexico. Global climate change, alone, is predicted to affect PM2.5 concentrations more than O3: M8hO3 concentrations are estimated to be slightly different in all examined sub-regions while PM2.5 concentrations are estimated to be higher over both Canadian sub-regions (8% over western and 3% over eastern) but 11% lower over northern Mexico. Climate change combined with the projected emissions lead to greater change in pollutant concentrations: M8hO3 concentrations are simulated to be 6% lower over western Canada and 8% lower over eastern Canada while PM2.5 concentrations are simulated to be 5% lower over western Canada and 11% lower over eastern Canada. Although future emissions over northern Mexico are projected higher, pollutant concentrations are simulated to be lower due to US emissions reductions. Global climate change combined with the projected emissions will decrease M8hO3 4% and PM2.5 17% over northern Mexico

Quantification of the impact of climate uncertainty on regional air quality

Uncertainties in calculated impacts of climate forecasts on future regional air quality are investigated using downscaled MM5 meteorological fields from the NASA GISS and MIT IGSM global models and the CMAQ model in 2050 in the continental US. Differences between three future scenarios: high-extreme, low-extreme and base case, are used for quantifying effects of climate uncertainty on regional air quality. GISS, with the IPCC A1B scenario, is used for the base case simulations. IGSM results, in the form of probabilistic distributions, are used to perturb the base case climate to provide the high- and low-extreme scenarios. Impacts of the extreme climate scenarios on concentrations of summertime fourth-highest daily maximum 8-h average ozone are predicted to be up to 10 ppbV (about one-seventh of the current US ozone standard of 75 ppbV) in urban areas of the Northeast, Midwest and Texas due to impacts of meteorological changes, especially temperature and humidity, on the photochemistry of tropospheric ozone formation and increases in biogenic VOC emissions, though the differences in average peak ozone concentrations are about 1–2 ppbV on a regional basis. Differences between the extreme and base scenarios in annualized PM2.5 levels are very location dependent and predicted to range between −1.0 and +1.5 μg m−3. Future annualized PM2.5 is less sensitive to the extreme climate scenarios than summertime peak ozone since precipitation scavenging is only slightly affected by the extreme climate scenarios examined. Relative abundances of biogenic VOC and anthropogenic NOx lead to the areas that are most responsive to climate change. Overall, planned controls for decreasing regional ozone and PM2.5 levels will continue to be effective in the future under the extreme climate scenarios. However, the impact of climate uncertainties may be substantial in some urban areas and should be included in assessing future regional air quality and emission control requirements.United States. Environmental Protection Agency (Science To Achieve Results (STAR) grant No. RD83096001)United States. Environmental Protection Agency (Science To Achieve Results (STAR) grant No. RD82897602)United States. Environmental Protection Agency (Science To Achieve Results (STAR) grant No. RD83107601)East Tennessee State Universit

Quantification of impact of climate uncertainty on regional air quality

International audienceImpacts of uncertain climate forecasts on future regional air quality are investigated using downscaled MM5 meteorological fields from the NASA GISS and MIT IGSM global climate models and the CMAQ model in 2050 in the continental US. Three future climate scenarios: high-extreme, low-extreme and base, are developed for regional air quality simulations. GISS, with the IPCC A1B scenario, is used for the base case. IGSM results, in the form of probabilistic distributions, are used to perturb the base case climate to provide 0.5th and 99.5th percentile climate scenarios. Impacts of the extreme climate scenarios on concentrations of summertime fourth-highest daily maximum 8-h average ozone are predicted to be up to 10 ppbv (about one-eighth of the current NAAQS of ozone) in some urban areas, though average differences in ozone concentrations are about 1?2 ppbv on a regional basis. Differences between the extreme and base scenarios in annualized PM2.5 levels are very location dependent and predicted to range between ?1.0 and +1.5 ?g m?3. Future annualized PM2.5 is less sensitive to the extreme climate scenarios than summertime peak ozone since precipitation scavenging is only slightly affected by the extreme climate scenarios examined. Relative abundances of biogenic VOC and anthropogenic NOx lead to the areas that are most responsive to climate change. Such areas may find that climate change can significantly offset air quality improvements from emissions reductions, particularly during the most severe episodes

Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture

The objective of this work is to detect Alzheimer’s disease using Magnetic Resonance Imaging. For this, we use a three-dimensional densenet-121 architecture. With the use of only freely available tools, we obtain good results: a deep neural network showing metrics of 87% accuracy, 87% sensitivity (micro-average), 88% specificity (micro-average), and 92% AUROC (micro-average) for the task of classifying five different classes (disease stages). The use of tools available for free means that this work can be replicated in developing countries.UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ingeniería::Centro de Investigaciones en Tecnologías de Información y Comunicación (CITIC)UCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ciencias de la Computación e Informátic

Geographic and Racial Variation in Premature Mortality in the U.S.: Analyzing the Disparities

Life expectancy at birth, estimated from United States period life tables, has been shown to vary systematically and widely by region and race. We use the same tables to estimate the probability of survival from birth to age 70 (S70), a measure of mortality more sensitive to disparities and more reliably calculated for small populations, to describe the variation and identify its sources in greater detail to assess the patterns of this variation. Examination of the unadjusted probability of S70 for each US county with a sufficient population of whites and blacks reveals large geographic differences for each race-sex group. For example, white males born in the ten percent healthiest counties have a 77 percent probability of survival to age 70, but only a 61 percent chance if born in the ten percent least healthy counties. Similar geographical disparities face white women and blacks of each sex. Moreover, within each county, large differences in S70 prevail between blacks and whites, on average 17 percentage points for men and 12 percentage points for women. In linear regressions for each race-sex group, nearly all of the geographic variation is accounted for by a common set of 22 socio-economic and environmental variables, selected for previously suspected impact on mortality; R2 ranges from 0.86 for white males to 0.72 for black females. Analysis of black-white survival chances within each county reveals that the same variables account for most of the race gap in S70 as well. When actual white male values for each explanatory variable are substituted for black in the black male prediction equation to assess the role explanatory variables play in the black-white survival difference, residual black-white differences at the county level shrink markedly to a mean of −2.4% (+/−2.4); for women the mean difference is −3.7% (+/−2.3)

Internet of Things for Sustainable Human Health

The sustainable health IoT has the strong potential to bring tremendous improvements in human health and well-being through sensing, and monitoring of health impacts across the whole spectrum of climate change. The sustainable health IoT enables development of a systems approach in the area of human health and ecosystem. It allows integration of broader health sub-areas in a bigger archetype for improving sustainability in health in the realm of social, economic, and environmental sectors. This integration provides a powerful health IoT framework for sustainable health and community goals in the wake of changing climate. In this chapter, a detailed description of climate-related health impacts on human health is provided. The sensing, communications, and monitoring technologies are discussed. The impact of key environmental and human health factors on the development of new IoT technologies also analyzed

Internet of Things for Environmental Sustainability and Climate Change

Our world is vulnerable to climate change risks such as glacier retreat, rising temperatures, more variable and intense weather events (e.g., floods, droughts, and frosts), deteriorating mountain ecosystems, soil degradation, and increasing water scarcity. However, there are big gaps in our understanding of changes in regional climate and how these changes will impact human and natural systems, making it difficult to anticipate, plan, and adapt to the coming changes. The IoT paradigm in this area can enhance our understanding of regional climate by using technology solutions, while providing the dynamic climate elements based on integrated environmental sensing and communications that is necessary to support climate change impacts assessments in each of the related areas (e.g., environmental quality and monitoring, sustainable energy, agricultural systems, cultural preservation, and sustainable mining). In the IoT in Environmental Sustainability and Climate Change chapter, a framework for informed creation, interpretation and use of climate change projections and for continued innovations in climate and environmental science driven by key societal and economic stakeholders is presented. In addition, the IoT cyberinfrastructure to support the development of continued innovations in climate and environmental science is discussed