Wood Burning, Biomass, Air Pollution, and Climate Change

This experiment examines how framing power plant emissions in terms of air pollution or climate change, and in terms of health or environmental impacts, influences perceived benefits and costs of policies to reduce emissions and intentions to take political action that supports such policies. A moderated-mediation model reveals that focusing on air pollution, instead of climate change, has a positive significant indirect influence on intended political action through the serial mediators of perceived benefits and costs. Political ideology moderates the association between perceived benefits and political action. No framing effects are observed in the comparison between health and environmental impacts.

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Abstract 36: Mapping and Multilevel Modeling of Climate Change and Air Pollution With Risk of Stroke in the United States and China: Findings From the Drexel-SARI Low Carbon and Healthy City Study

Stroke ◽

10.1161/str.47.suppl_1.36 ◽

2016 ◽

Vol 47 (suppl_1) ◽

Author(s):

Longjian Liu ◽

Hui Liu ◽

Xuan Yang ◽

Feng Jia ◽

Mingquan Wang

Keyword(s):

Climate Change ◽

Air Pollution ◽

Air Quality ◽

Pollution Index ◽

The United States ◽

Potential Interaction ◽

Low Carbon ◽

Multilevel Regression ◽

Healthy City ◽

Nationally Representative

Introduction and Hypothesis: Stroke is a leading cause of death and the major cause of disability in the world. However, few studies applied multilevel regression techniques to explore the association of stroke risk with climate change and air pollution. In the study, we aimed to test the hypothesis that the disproportionately distributed stroke rates across the counties and cities within a country are significantly associated with air pollution and temperature. Methods: We used data from U.S. 1118 counties in 49 states, which had estimated measures of particulate matter (PM)2.5 for the years 2010-2013, and data from China 120 cities in 32 provinces (including 4 municipalities), which had measures of Air Pollution Index (API) for the years 2012-2013. We assessed the association between air quality and prevalence of stroke using spatial mapping, autocorrelation and multilevel regression models. Results: Findings from the U.S. show that the highest average PM2.5 level was in July (10.2 μg/m3) and the lowest in October (7.63 μg/m3) for the years 2010-2013. Annual average PM2.5 levels were significantly different across the 1118 counties, and were significantly associated with stroke rates. Multilevel regression analysis indicated that the prevalence of stroke significantly increased by 1.19% for every 10 μg/m3 increase of PM2.5 (p<0.001). Significant variability in PM2.5 by states was observed (p=0.019). More than 70% of the variation in stroke rates existed across the counties (p=0.017) and 18.7% existed across the states (p=0.047). In China, the highest API was observed in the month of December, with a result of 75.76 in 2012 and 97.51 in 2013. The lowest API was observed in July, with a result of 51.21 in 2012, and 54.23 in 2013. Prevalence of stroke was significantly higher in cities with higher API concentrations. The associations between air quality and risk of stroke were significantly mediated by temperatures. Conclusions: The study, using nationally representative data, is one of the first studies to address a positive and complex association between air quality and prevalence of stroke, and a potential interaction effect of temperatures on the air - stroke association.

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