Poster Session: Plenary Session Room
Climate Change and Its Impact on Cardiovascular Disease: A Literature Review of the Imminent Risk Global Warming Poses on Cardiovascular Morbidity and Mortality View Digital Media
Poster Session Lucy Cooke Davies, Zoya Arif, Samuel Odlin
Climate change is increasingly recognized as a significant public health challenge. It poses a risk to many areas of medicine, with profound evidence for its threat against cardiovascular health. In this study we investigate both the cause and effect of these threats and suggest changes healthcare and society can initiate to counteract them. Our methodology involves a comprehensive literature review of select papers chosen from relevance to our topic, key words, language published and date released. The findings demonstrate that while a gradual increase in global temperatures is associated with a rise in cardiovascular mortality, the growing frequency of extreme weather events, such as short, intense heatwaves, has a much more significant impact. Additionally, exposure to air pollutants such as carbon monoxide and nitrogen dioxide is linked to an increased prevalence of respiratory disease, which in turn significantly increases both cardiovascular morbidity and mortality. There are actions which healthcare systems and wider society can take to account for this imminent threat. Some of those we propose include committing to switch to more environmentally friendly medications, implementing more ‘green’ transport options for patients and encouraging intuitive health. In conclusion, climate change threatens to exacerbate the already challenging burden of cardiovascular disease. It is imperative that both healthcare providers and society as a whole implement the necessary adaptations that can mitigate this risk. If not we aren’t going to kill the earth. We are going to change the earth to kill us.
Featured Political Climates and Nonprofit Environmentalism: A Comparative Analysis of State Influence on Environmental Nonprofits in Florida and California
Poster Session Maya Lis
This research analyzes the long-term impact of political viewpoints on the environmental nonprofit sector. The focus is on the differences and similarities among nonprofits operating in politically conservative and politically liberal state environments. Specifically, the study explores how the political climate of a state influences the mission, goals, and support systems of environmental nonprofits. It examines how these organizations are affected by state government policies, particularly in terms of formation, funding, and operational execution. The study compares environmental nonprofits in the conservative state of Florida to those in the liberal state of California. Key questions explored include: How do nonprofits' missions align with or resist the prevailing political ideologies? What are the differences in how nonprofits receive funding in different political climates, particularly regarding public and private sector support? Finally, the study assesses the role of state governments in shaping the strategic goals and operational effectiveness of these organizations. Through interviews, case studies, and a comparative policy analysis across several states, the research provides insights into how political environments affect environmental nonprofits’ missions, strategies, and sustainability, offering valuable recommendations for nonprofit leaders and policymakers.
Shifts in Airborne Tree Pollen Concentration Peaks Amid Climate Change: A Five-year Study in Charleston, SC, United States
Poster Session Atin Adhikari, Arpita Chatterjee, Jayanta Gupta, Sarah Sejoro, Oluwatoyin Ayo Farai, Jing Kersey
Climate change has significantly increased the length of pollen seasons and elevated airborne pollen levels, resulting in earlier and more intense allergy seasons. Coastal areas are particularly vulnerable, yet there is limited understanding of these impacts in such regions. This study focuses on Charleston, South Carolina, one of the states most affected by climate change. Different types of pollen can combine through inhalation or through similar responses to climate change. Understanding this clustering can help us develop better strategies to manage and reduce allergy symptoms. We collected environmental data and tree pollen data for multiple taxa from 2017 to 2021 from federal databases, and the National Allergy Bureau of AAAAI. A time-series cluster analysis was conducted on tree pollen concentrations, excluding the winter months, and we employed Dynamic Time Warping (DTW) to compute the pairwise optimal alignment of pollen counts across the years. Our analysis reveal three prominent clusters. A shift in pollen counts occurred in May during 2019-2021, whereas in 2021, it shifted to June. By analyzing the normalized distances, we found that the pollen counts of 2017 and 2018 were relatively similar, with a normalized distance of 28.5. In contrast, the pollen counts of the more recent year, 2021, differed significantly from 2017, with a normalized distance of 70.9. We observed a sharp increase (nearly two-fold) in pollen concentrations in the post-COVID years for the less dominant groups and the Juniper and Pinaceae families formed the most dominant cluster.
Models for Visualized Spatial Assessment and Forecasting of the Level of Technogenic Impact on the Atmosphere of Regional Territories: Development, Research and Practical Application
Poster Session Olga Ivashchuk, Bagdat Yagaliyeva
The results of the development and application of specialized situational models are presented, allowing for simulation experiments to assess and predict the impact level of various components of emissions from anthropogenic sources into the atmosphere. Two types of models have been developed. The first model enables structural graphical analysis of emission volumes for different types of economic activities in a specific area (region, district, city, etc.) based on enterprise report data and statistics, with visualization and spatial analytics on an interactive map. The second type of model is an artificial neural network integrated with geographic information system tools and an electronic map, which allows for the assessment and forecasting of greenhouse gas dispersion and accumulation results in the studied area based on the following parameters: emission volumes from enterprises, wind speed and direction, as well as the geographical layout of the area of interest to the researcher. The results of using the models for a specific area—the Belgorod region, which is a major agricultural region in Russia—are demonstrated. The simultaneous use of the presented models provides the opportunity to select optimal areas for planting priority agricultural crops or carbon plants, ensuring increased productivity of the area and its economic significance while considering ecological safety. Based on the conducted simulation experiments, practical recommendations have been developed.
