Nuclear and coal power use in the United States are projected to decline over the coming decades. Here, we explore how simultaneous phase-outs of these energy sources could affect air pollution and distributional health risk with existing grid infrastructure. We develop an energy grid dispatch model to estimate the emissions of CO2, NOx and SO2 from each U.S. electricity generating unit. We couple the emissions from this model with a chemical transport model to calculate impacts on ground-level ozone and fine particulate matter (PM2.5). Our yearlong scenario removing nuclear power results in compensation by coal, gas and oil, leading to increased emissions that impact climate and air quality nationwide. We estimate that changes in PM2.5 and ozone lead to an additional 9,200 yearly mortalities, and that changes in CO2 emissions over that period lead to an order of magnitude higher mortalities throughout the 21st century. Together, air quality and climate impacts incur between \$80.7-\$126.1 billion of annual costs. In a scenario where nuclear and coal power are shut down simultaneously, air quality impacts due to PM2.5 are larger and those due to ozone are smaller, because of more reliance on high emitting gas and oil, and climate impacts are substantially smaller than that of nuclear power shutdowns. With current reliance on non-coal fossil fuels, closures of nuclear and coal plants shift the distribution of health risks, exemplifying the importance of multi-system analysis and unit-level regulations when making energy decisions.
Title: Continental Physical Oceanography and Climatic Effects on Human Lives and Infection Diseases The author watches mechanism shifts caused by climatic influences and active era of seismic energy. Thermal power is stored and released through factors of ocean temperature and evaporation. Those affect human lives and virus vectors in the fields of ocean-atmosphere interaction and seismic oceanography. My presentation includes such research topics: Inundations of max assumed tsunamis and storm surges affect human risks in seashore mega cities SST dipole-effects to global crop production through stomata closing Advection effects from SST anomalies by high potential evaporation causing dry air and losses of human lives as well as houses by forest fires Ocean Impacts to Infection Diseases through Seasonal Climate change: New Applicable fields from Geo-Health linked to continental oceanography comparing to the traditional micro-and-genetic approaches. Ocean impacts to propagation of infection diseases through seasonal climate change such as fundamental air temperature and precipitation for plants and animals Climatic influences on vectors such as mosquito (like malaria, dengue fever, etc.) through blood, virus from breath (COVID-19), and bacteria from mouth, along with the potential risks by fatal viruses of Avian (Bird) Influenza and Classical Swine Fever etc. New recognized other important factors of mega cities by continental bird’s fly-routes, wild animals multiplication and increased travel flows of global human-lives Ecological transitions of deforestation and afforestation in low land or wet-land for wild birds and animals. Satellite sensing and photosynthesis-model mapping for vegetation growth in country-scale, continental, and global watching, by monitoring microbiological diseases through insect habitats, bird’s passages and animal movements. Intensive approaches using data assimilation and synthesizing among meteorological, geophysical, biological, and hydrological factors Related Divisions: Ocean Sciences, Geo-Health, Science and Society, Natural Hazards, Hydrology, BioGeosciences
Access to reliable and sustainable electricity is essential for meeting the needs of society, such as communications, clean water, healthcare, and heating and cooling, both today and into the future. Shifts in extreme weather and a changing climate are challenging traditional and renewable power grids, as evidenced by widespread outages from events like hurricanes and heat waves, which are increasing in intensity and frequency and have the propensity to harm infrastructure and diminish generation capacity. Understanding the changing climate allows utilities to be more resilient in proactively producing or distributing energy. Earth observations (EOs) provide actionable data for monitoring such change, but better collaboration between scientists and end users is needed to ensure data is accessible and relevant to decision-making. Utilizing a capacity building approach, this NASA-funded initiative aims to promote broader utilization of NASA EOs within the energy sector by transferring knowledge and bridging the gap between scientists and end users. To the untrained user, satellite data can be onerous to find and challenging to apply. To address these concerns, we engaged the U.S. Department of Energy and stakeholders across the sector to solicit input on the greatest challenges and opportunities utilities face relevant to resiliency and the usage of EOs. In response, and through an iterative process with end users, we compiled relevant NASA EOs into a user-friendly Esri StoryMap® and developed the first energy-focused NASA ARSET training, both publicly available, followed by broad outreach. The StoryMap® aims to reduce the burden of accessing and using EOs by including only the most applicable data with a focus on terrestrial variables, such as soil moisture and land surface temperature, along with tutorials and use cases. The ARSET training provides an in-depth look at using NASA products to support a more climate resilient energy sector and presents real-world, illustrative examples of the ways in which EOs can be used to better understand the impact of extreme events. This talk will report on the successes and challenges of this capacity building initiative, highlight components of the StoryMap® and ARSET training, and share lessons learned in facilitating increased uptake and use of EOs by the energy sector.
Water availability depends on water quantity and quality. Geogenic contaminants, including non-metals, metals, and metalloids from geologic sources, are among the most prevalent contaminants limiting water availability in the U.S. and globally. Typical geologic materials have geogenic concentrations such that dissolution of very small fractions can cause concentrations exceeding drinking water, ecological, and other water use thresholds. Geogenic contaminants often occur in groundwater due to subsurface water-rock interactions, but their distribution and concentration can also be affected by human activities such as mining, energy production, irrigation, and pumping practices. Many hydrogeologic and biogeochemical factors contribute to causing geogenic contamination that limits water availability. However, sociodemographic features, including drinking water source and missing water quality information, are often overlooked when evaluating, determining, and ranking the merit and benefit of research. Sociodemographic features, data gaps resulting from historical data collection disparities, social vulnerability indices, socioeconomic status, and infrastructure condition/age are examples of environmental justice (EJ) factors. To avoid perpetuating knowledge gaps while setting research priorities, EJ factors can be considered when developing ranking schemes to prioritize water availability research activities. The U.S. Geological Survey (USGS) is working to quantitatively incorporate and prioritize EJ factors in ranking regional-scale, geogenic-related water availability research priorities. USGS ranking schemes incorporate typical physical and geochemical factors such as existing data, climate variables, and water use. Missing and sociodemographic information will also be incorporated to begin addressing EJ inequities. EJ factors include, for example, sparse information about water quality in lower income and minority areas, and unknowns about water quality in areas of substantial cultural or subsistence hunting, fishing, or gathering. By considering both EJ and hydrogeological/biogeochemical factors, decision makers will have a more diverse, interdisciplinary toolbox to increase equity and reduce bias in prioritizing future water availability studies.
Volatile organic compounds (VOCs) play a crucial role in influencing the air quality of the urban atmospheres, especially in nitrogen oxide (NOx) dominated regions like India. Additionally, the associated direct health risks necessitate the identification of VOC sources and their contribution to the VOC budget of a region. This study presents the seasonal variability of VOCs measured using a Proton Transfer Reaction Quadrupole Mass Spectrometer (PTR-QMS) during the year 2019 over the metropolitan Pune region. Also, the VOC sources have been identified using the US EPA PMF 5.0 model for different seasons. Toluene (summer: 2 μg/mΛ3, monsoon: 1.11 μg/mΛ3, winter: 7 μg/mΛ3), o-Xylene (summer: 1.41 μg/mΛ3, monsoon: 1.24 μg/mΛ3, winter: 5.67 μg/mΛ3), Acetaldehyde (summer: 2.80 μg/mΛ3, monsoon: 1.84 μg/mΛ3;, winter: 5.10 μg/mΛ3) and Acetone (summer: 5.95 μg/mΛ3, monsoon: 2.58 μg/mΛ3, winter: 6.21 μg/mΛ3) were found to be the prominent anthropogenic source-based emissions apart from Methanol (summer: 8.68 μg/mΛ3, monsoon: 5.24 μg/mΛ3, winter: 7.72 μg/mΛ3) which mostly has a biogenic source. While most of the identified sources (vehicular emissions, biomass burning, biogenic emissions, photochemical secondary products) are common throughout the year, their contribution to the total measured VOCs across seasons has varied considerably (9-17%, 8-12%, 5-14%, 19-27% respectively). Of the resolved factors, ozone formation potential (OFP) was found to be highest for photochemical secondary products (26%) during all the seasons, followed by vehicular emissions (23%) and background emissions (17%). The sources identified in this work are in agreement with a recent bottom to top emission inventory developed for the region which shows vehicular emissions as a dominant VOC source. This work highlights the role of meteorology in varying the VOCs concentration over the Pune region and eventually the local air quality.
The ongoing coronavirus disease 2019 (COVID-19) pandemic has caused more than 150 million cases of infection to date and poses a serious threat to global public health. In this work, global COVID-19 data were used to examine the dynamical variations from the perspectives of immunity and contact of 85 countries across the five climate regions: tropical, arid, temperate, cold, and polar. A new approach is proposed to obtain the transmission rates based on the COVID-19 data between the countries with the same climate region over the Northern Hemisphere (NH) and Southern Hemisphere (SH). Our results suggest that the COVID-19 pandemic will persist over a long period of time or enter into regular circulation in multiple periods of 1-2 years. Moreover, based on the simulated results by the COVID-19 data, it is found that the temperate and cold climate regions have higher infection rates than the tropical and arid climate regions, which indicates that climate may modulate the transmission of COVID-19. The role of the climate on the COVID-19 variations should be concluded with more data and more cautions. The non-pharmaceutical interventions still play the key role in controlling and prevention this global pandemic.
An assessment was carried out of the relationship between the Corona Virus (covid-19) and the spread across Iraq of various heavy metal contaminations. At the onset, all the confirmed, recovered and death cases of covid-19 virus in Iraq up to the date of May, 2nd, 2020 were collected and compared with the top three infected countries in the world (USA, Spain and Italy). On the other hand, numerous heavy metal contaminations in different Iraqi cities have been summarized and associated with allowable upper and lower worldwide standard limits. In addition, the study introduced a hierarchical predictive approach for the relationship between confirmed and death cases of covid-19 viruses with heavy metal contamination in various Iraqi cities. It was concluded that all the studied Iraqi cities have heavy metal contamination for different chemical elements exceeding the allowable standard limits. In addition, it was shown that the extreme contents of Copper (Cu), Nickel (Ni), Lead (Pb), and Zinc (Zn) are concentrated in Al-Qadisiyah, Al-Sulaimaniyah, Erbil and Baghdad cities with limits of 160 µg / g, 240.9 µg / g, 378 µg / g and 1080 µg / g respectively. Based on the hierarchical prediction approach, a linear positive relationship between both confirmed and death covid-19 cases with different heavy metal contamination was obtained with a maximum coefficient of determination (R2) of 0.97.
This paper represents the first national-level (United States) estimate of the economic impacts of vibriosis cases as exacerbated by climate change. Vibriosis is an illness contracted through foodborne and waterborne exposures to various Vibrio species (e.g., non-V. cholerae O1 and O139 serotypes) found in estuarine and marine environments, including within aquatic life such as shellfish and finfish. Objectives The objective of this study was to project climate-induced changes in vibriosis related to sea surface temperatures (SSTs) and associated economic impacts in the U.S. Methods Our analysis constructed three logistic regression models by Vibrio species, using vibriosis data sourced from the Cholera and Other Vibrio Illness Surveillance (COVIS) system and using historical SSTs. We relied on previous estimates of the cost-per-case of vibriosis to estimate future total annual medical costs, lost income from productivity loss, and mortality-related indirect costs throughout the U.S. We separately report results for V. parahaemolyticus, V. vulnificus, V. alginolyticus, and “V. spp” given the different associated health burden of each. Results By 2090, increases in SST are estimated to result in a 51 percent increase in cases annually relative to the baseline era (1995) under Representative Concentration Pathway (RCP) 4.5 and a 108 percent increase under RCP8.5. The cost of these illnesses is projected to reach over $5.2 billion annually under RCP4.5 and $7.3 billion annually under RCP8.5, relative to $2.2 billion in the baseline (2018 dollars), equivalent to 140 percent and 234 percent increases respectively. Discussion Vibriosis incidence is likely to increase in the U.S. under moderate and unmitigated climate change scenarios through increases in SST, resulting in a substantial burden of morbidity and mortality, and costing millions of dollars. These costs are mostly attributable to deaths, primarily from exposure to V. vulnificus. Evidence suggests that other factors, including sea surface salinity, may contribute to further increases in vibriosis cases in some regions of the U.S. and should be investigated.
Forest and vegetation fires, used as tools for agriculture and deforestation, are a major source of air pollutants and can cause serious air quality issues in many parts of Asia. Actions to reduce fire may offer considerable, yet largely unrecognised, options for rapid improvements in air quality. In this study, we used a combination of regional and global air quality models and observations to examine the impact of forest and vegetation fires on air quality degradation and public health in Southeast Asia (including Mainland Southeast Asia and south-eastern China). We found that eliminating fire could substantially improve regional air quality across Southeast Asia by reducing the population exposure to fine particulate matter (PM2.5) concentrations by 7% and surface ozone concentrations by 5%. These reductions in PM2.5 exposures would yield a considerable public health benefit across the region; averting 59,000 (95% uncertainty interval (95UI): 55,200-62,900) premature deaths annually. Analysis of subnational infant mortality rate data and PM2.5 exposure suggested that PM2.5 from fires disproportionately impacts poorer populations across Southeast Asia. We identified two key regions in northern Laos and western Myanmar where particularly high levels of poverty coincide with exposure to relatively high levels of PM2.5 from fires. Our results show that reducing forest and vegetation fires should be a public health priority for the Southeast Asia region.
Mosquitoes are known vectors for disease transmission that cause over one million deaths globally each year. The majority of natural mosquito habitats are areas containing standing water such as ponds, lakes, and marshes. These habitats are challenging to detect using conventional ground-based technology on a macro scale. Contemporary approaches, such as drones, UAVs, and other aerial imaging technology are costly when implemented. Multispectral imaging technology such as Lidar is most accurate on a finer spatial scale whereas the proposed convolutional neural network(CNN) approach can be applied for disease risk mapping and further guide preventative efforts on a more global scale. By assessing the performance of autonomous mosquito habitat detection technology, the transmission of mosquito borne diseases can be prevented in a cost-effective manner. This approach aims to identify the spatiotemporal distribution of mosquito habitats in extensive areas that are difficult to survey using ground-based technology by employing computer vision on satellite imagery. The research presents an evaluation and the results of 3 different CNN models to determine their accuracy of predicting large-scale mosquito habitats. For this approach, a dataset was constructed utilizing Google Earth satellite imagery containing a variety of geographical features in residential neighborhoods as well as cities across the world. Larger land cover variables such as ponds/lakes, inlets, and rivers were utilized to classify mosquito habitats while minute sites such as puddles, footprints, and additional human-produced mosquito habitats were omitted for higher accuracy on a larger scale. Using the dataset, multiple CNN networks were trained and evaluated for accuracy of habitat prediction. Utilizing a CNN-based approach on readily available satellite imagery is cost-effective and scalable, unlike most aerial imaging technology. Testing revealed that YOLOv4 obtained greater accuracy in mosquito habitat detection than YOLOR or YOLOv5 for identifying large-scale mosquito habitats. YOLOv4 is found to be a viable method for global mosquito habitat detection and surveillance.
Machine-learning algorithms are becoming popular techniques to predict ambient air PM2.5 concentrations at high spatial resolutions (1x1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to predict 24h-averaged PM2.5 concentrations (mean PM2.5). Over Mexico, none has been developed to predict subdaily peak levels, such as the maximum daily one-hour concentration (max PM2.5). We present a new modeling approach based on extreme gradient boosting (XGBoost) and inverse-distance weighting that uses AOD data, meteorology, and land-use variables to predict mean and max PM2.5 in Central Mexico (including the Mexico City Metropolitan Area) from 2004 through 2019. Our models for mean and max PM2.5 exhibited good performance, with overall cross-validated mean absolute errors (MAE) of 3.68 and 9.21 μg/m3 , respectively, compared to mean absolute deviations from the median (MAD) of 8.55 and 15.64 μg/m3. We also investigated applications of our mean PM2.5 predictions that can aid local authorities in air-quality management and public-health surveillance, such as the co-occurrence of high PM2.5 and heat, compliance with local air-quality standards, and the relationship of PM2.5 exposure with social marginalization.
The One Health concept provides a framework to examine the linkages between human, animal, plant, environmental, and ecosystem health. This framework can be represented as a multi-dimensional matrix tool that can be used to examine and address complex GeoHealth challenges. This matrix tool facilitates comprehensive, systems-based thinking and can include up to four dimensions depending upon users' needs. This presentation will briefly review the One Health matrix tool and apply it, as an example, to examine the global impact of human and animal fecal wastes. Applying the tool reveals linkages between food-borne illnesses, food insecurity, antimicrobial resistance, and climate change. Understanding these linkages is necessary for developing effective and equitable public policies that are needed to achieve many of the United Nations' Sustainable Development Goals.
In 2020, people’s health suffered a great crisis under the dual effects of the COVID-19 pandemic and the extensive, severe wildfire in the western and central United States (U.S.). Parks, including city, national, and cultural parks, offer a unique opportunity for people to maintain their recreation behaviors following the social distancing protocols during the pandemics. However, massive forest wildfires in western and central US, producing harmful toxic gases and smoke, pose significant threats to human health and affect their recreation behaviors and visitations to parks. In this study, we employed the Geographically and Temporally Weighted Regression (GTWR) Models to investigate how COVID-19 and wildfires jointly shaped human visitations to parks, regarding the number of visitors, dwell time, and travel distance from home, during June - September 2020. Our findings indicated that people tended to travel closer from home and spent less time at parks as more COVID-19 cases were reported. However, with the stay-at-home restriction lifted and the reopen of some large national parks, people traveled further distances to those places (e.g., Yellowstone National Park) regardless the peak of pandemics in June 2020. Moreover, we found people intended to decrease the visitations to the parks surrounded by wildfires and shorten the time there. This study provides important insights on people’s responses in recreation and social behaviors when facing multiple serve crises that impact their health and wellbeing, which could support the preparation and mitigation of the health impacts from future pandemics and natural hazards.
Climate change is known to increase the frequency and intensity of hot days (daily maximum temperature ≥ 30°C), both globally and locally. Exposure to extreme heat is associated with numerous adverse human health outcomes. This study estimated the burden of heat-related illness (HRI) attributable to anthropogenic climate change in North Carolina physiographic divisions (Coastal and Piedmont) during the summer months from 2011-2016. Additionally, assuming intermediate and high greenhouse gas emission scenarios, future HRI morbidity burden attributable to climate change was estimated. The association between daily maximum temperature and the rate of HRI was evaluated using the Generalized Additive Model. The rate of HRI assuming natural simulations (i.e., absence of greenhouse gas emissions) and future greenhouse gas emission scenarios were predicted to estimate the HRI attributable to climate change. During 2011-2016, we observed a significant decrease in the rate of HRI assuming natural simulations compared to the observed. About 15% of HRI is attributable to anthropogenic climate change in Coastal (13.40% (IQR: -34.90,95.52)) and Piedmont (16.39% (IQR: -35.18,148.26)) regions. During the future periods, the median rate of HRI was significantly higher (78.65%:Coastal and 65.85%:Piedmont), assuming a higher emission scenario than the intermediate emission scenario. We observed significant associations between anthropogenic climate change and adverse human health outcomes. Our findings indicate the need for evidence-based public health interventions to protect human health from climate-related exposures, like extreme heat, while minimizing greenhouse gas emissions.
Wildfires result in human fatalities not only due to the direct exposure to flames, but also indirectly through smoke inhalation. The Mediterranean basin with its hot and dry summers is a hotspot for such devastating events. The situation has further been aggravated in recent years by climate change as well as a growing and aging population in the region. To assess the health impacts due to short-term exposure to air pollution created by the 2021 summer wildfires in the eastern and central Mediterranean basin, we used a regional-scale chemistry transport model to simulate concentrations of major air pollutants such as fine particulate matter with a diameter less than 2.5 μm (PM2.5), SO2, NO2, and O3 - in a fire and a no-fire scenario. Elevated short-term exposure of the population to air pollutants are associated with excess all-cause mortality using relative risks for individual pollutants from previously published meta-analyses. Our estimates indicate that the short-term exposure to wildfire-caused changes in O3 accounted for 741 (95% CI:556-940) excess deaths in total over the entire region of investigation during the wildfire season between mid-July to early October 2021. This is followed by 270 (95% CI:177-370) excess deaths due to elevated PM2.5 exposure, rendering the health effect of increased O3 from wildfires larger than the effect of increased PM2.5. We show this to be attributed largely to the spatially more widespread impact of wildfires on O3. Our study concludes with a discussion on uncertainties associated with the health impact assessment based on different air pollutants.
Soil-gas diffusivity plays a fundamental role on diffusion-controlled migration of climate impact gases from different terrestrial ecosystems including managed pasture systems. Soil-gas diffusivity has a strong bearing on soil type/texture and soil structure (e.g., density) and typically shows a depth-dependent behavior in subsurface. This study investigated the gas diffusivity in soils sampled from a managed pasture site at Ambewela, Sri Lanka at 0-5 cm depth range along a downgrading transect. The soils were pre-characterized for particle-size distribution, organic matter content, dry density and particle density. Soil-gas diffusivity was measured using one-chamber diffusion apparatus using N2 and O2 as experimental gases. The measured diffusivity, together with selected intact and repacked soil data from literature, were tested against the existing predictive gas diffusivity models. We used a generalized descriptive parametric two-region model to represent bimodal/two-region behaviour of selected soils which was able to statistically outperform the predictive models for both intact and repacked soils and hence demonstrated its applicability to better characterize site-specific greenhouse gas emissions with useful implications for pasture management.
Mosquitoes are major vectors of disease and thus a key public health concern. Some cities have programs to track mosquito abundance and vector competence, but such fieldwork is expensive, time-consuming, and retrospective. We present a comparative analysis of two machine-learning-based regression techniques for forecasting the rate at which mosquito abundance changes and the rate at which mosquitoes test positive for West Nile Virus (WNV) in our AOI, the City of Chicago, three weeks in advance. We selected an initial pool of climatic inputs based on the findings of prior work. Ordinary least squares regression was run on each input individually and then in various groups. A p-value cutoff of 0.05 was used to determine which were best suited for predicting the derivatives of mosquito abundance and WNV positivity rate. Using these inputs, we trained four machine learning models using two types of regression: a Random Forest Regressor (RFR) and Backward Elimination Linear Regression (BELR). We optimized our RFR’s hyperparameters using Randomized Search Cross Validation and further reduced our BELR inputs using a p-value of 0.05. The enhanced vegetation index and temperature, described in various metrics, emerged as common inputs across the four models. In three of the four models, the respective temperature metric was the most important feature while EVI varied between second and last place. Our root mean square error largely resided within the hundredths place or less, but spiked at novel, week-to-week extremes in the testing data. Our methodology and results indicate valuable directions for future research into forecasting mosquito population abundance and vector competence. This work is particularly applicable to public health programs, as our models’ use of open-source, remote sensing data to predict how quickly the mosquito population and their vector competence will change three weeks in advance streamlines disease monitoring and prevention.
Increasing wildfire activity across the Western US poses a significant public health threat. While there is evidence that wildfire smoke is detrimental for respiratory health, the impacts on cardiovascular health remain unclear. This study evaluates the association between fine particulate matter (PM2.5) from wildfire smoke and cardiorespiratory hospital visits in California during the 2004-2009 wildfire seasons. We estimate daily mean wildfire-specific PM2.5 with GEOS-Chem, a global three-dimensional model of atmospheric chemistry, with wildfire emissions from the Global Fire Emissions Database (GFED3). We defined a “smoke event day” as cumulative 0-1-day lag wildfire-specific PM2.5>= 98th percentile of cumulative 0-1 lag day wildfire PM2.5. Associations between exposure and outcomes are estimated using negative binomial regression. Results indicate that smoke event days are associated with a 3.3% (95% CI: [0.4%,6.3%]) increase in visits for all respiratory diseases and a 10.3% (95% CI: [2.3%,19.0%]) increase for asthma specifically. Stratifying by age, we found the largest effect for asthma among children ages 0-5y. We observed no significant association between exposure and overall cardiovascular disease, but stratified analyses revealed increases in visits for all cardiovascular, ischemic heart disease, and heart failure among non-Hispanic white individuals and those older than 65y. Further, we found significant interaction between smoke event days and daily temperature for all cardiovascular disease visits, suggesting that days with high wildfire PM2.5 and high temperatures may pose greater disease risk. These results suggest increases in adverse outcomes from wildfire smoke exposure and indicate the need for improved prevention and adaptations to protect vulnerable populations.