Wind turbine audibility and noise annoyance in a national U.S. survey: Individual perception and influencing factors.

With results from a nationwide survey sponsored by the U.S. Department of Energy, factors that affect outdoor audibility and noise annoyance of wind turbines were evaluated. Wind turbine and summer daytime median background sound levels were estimated for 1043 respondents. Wind turbine sound level was the most robust predictor of audibility yet only a weak, albeit significant, predictor of noise annoyance. For each 1 dB increase in wind turbine sound level (L1h-max), the odds of hearing a wind turbine on one's property increased by 31% [odds ratio (OR): 1.31; 95% CI (confidence interval): 1.25-1.38] and the odds of moving to the next level of annoyance increased by 9% (OR: 1.09; 95% CI: 1.02-1.16). While audibility was overwhelmingly dependent on turbine sound level, noise annoyance was best explained by visual disapproval (OR: 11.0; 95% CI: 4.8-25.4). The final models correctly predict audibility and annoyance level for 80% and 62% of individuals, respectively. The results demonstrate that among community members not receiving personal benefits from wind projects, the Community Tolerance Level of wind turbine noise for the U.S. aligns with the international average, further supporting observations that communities are less tolerant of wind turbine noise than other common environmental noise sources at equivalent A-weighted sound levels.

Monitoring annoyance and stress effects of wind turbines on nearby residents: A comparison of U.S. and European samples.

As wind turbines and the number of wind projects scale throughout the world, a growing number of individuals might be affected by these structures. For some people, wind turbine sounds and their effects on the landscape can be annoying and could even prompt stress reactions. This comparative study analyzed a combined sample of survey respondents from the U.S., Germany and Switzerland. It utilized a newly developed assessment scale (AS-Scale) to reliably characterize these stress-impacted individuals living within populations near turbines. Findings indicate low prevalence of annoyance, stress symptoms and coping strategies. Noise annoyance stress (NAS-Scale) was negatively correlated with the perceptions of a lack of fairness of the wind project's planning and development process, among other subjective variables. Objective indicators, such as the distance from the nearest turbine and sound pressure level modeled for each respondent, were not found to be correlated to noise annoyance. Similar result patterns were found across the European and U.S. samples.

Changing vessel routes could significantly reduce the cost of future offshore wind projects.

With the recent emphasis on offshore wind energy Coastal and Marine Spatial Planning (CMSP) has become one of the main frameworks used to plan and manage the increasingly complex web of ocean and coastal uses. As wind development becomes more prevalent, existing users of the ocean space, such as commercial shippers, will be compelled to share their historically open-access waters with these projects. Here, we demonstrate the utility of using cost-effectiveness analysis (CEA) to support siting decisions within a CMSP framework. In this study, we assume that large-scale offshore wind development will take place in the US Mid-Atlantic within the next decades. We then evaluate whether building projects nearshore or far from shore would be more cost-effective. Building projects nearshore is assumed to require rerouting of the commercial vessel traffic traveling between the US Mid-Atlantic ports by an average of 18.5 km per trip. We focus on less than 1500 transits by large deep-draft vessels. We estimate that over 29 years of the study, commercial shippers would incur an additional $0.2 billion (in 2012$) in direct and indirect costs. Building wind projects closer to shore where vessels used to transit would generate approximately $13.4 billion (in 2012$) in savings. Considering the large cost savings, modifying areas where vessels transit needs to be included in the portfolio of policies used to support the growth of the offshore wind industry in the US.

Improving spatial representation of global ship emissions inventories.

Ship activity patterns depicted by the International Comprehensive Ocean-Atmosphere Data Set (ICOADS), the Automated Mutual-Assistance Vessel Rescue System (AMVER) data set, and their combination demonstrate different spatial and statistical sampling biases. These differences could significantly affect the accuracy of ship emissions inventories and atmospheric modeling. We demonstrate (using ICOADS) a method to improve global-proxy representativeness by trimming over-reporting vessels that mitigates sampling bias, augment the sample data set, and account for ship heterogeneity. Apparent underreporting to ICOADS and AMVER by ships near coastlines, perhaps engaged in coastwise (short sea) shipping especially in Europe, indicates that bottom-up regional inventories may be more representative locally. Primarily due to the long time series available publicly for ICOADS data, the improved ICOADS data set may be the most appropriate global ship traffic proxy identified to date to be used for a top-down approach. More generally, these three spatial proxies can be used together to perform uncertainty analyses of ship air-emissions impacts on a global scale (http://coast.cms.udel.edu/GlobalShipEmissions/).

Modeling energy use and emissions from North American shipping: application of the ship traffic, energy, and environment model.

The waterway network ship traffic, energy, and environment model (STEEM) is applied to geographically characterize energy use and emissions for interport ship movement for North America, including the United States, Canada, and Mexico. STEEM advances existing approaches by (i) estimating emissions for large regions on the basis of nearly complete data describing historical ship movements, attributes, and operating profiles of individual ships, (ii) solving distances on an empirical waterway network for each pair of ports considering ship draft and width constraints, and (iii) allocating emissions on the basis of the most probable routes. We estimate that the 172 000 ship voyages to and from North American ports in 2002 consumed about 47 million metric tonnes of heavy fuel oil and emitted about 2.4 million metric tonnes of SO2. Comparison with port and regional studies shows good agreement in total estimates and better spatial precision than current top-down methods. In quantifying limitations of top-down approaches that assume existing proxies for ship traffic density are spatially representative across larger domains, we find that International Comprehensive Ocean-Atmosphere Data Set (ICOADS) proxy data are spatially biased, especially at small scales. Emissions estimated by STEEM for ships within 200 nautical miles of the coastal areas of the United States are about 5 times the emissions estimated in previous studies using cargo as a proxy.