Symptoms intuitively associated with wind turbine infrasound.

In many countries, a certain proportion of individuals living in the vicinity of wind power areas have reported symptoms that they have intuitively associated with infrasound from wind turbines. While the reason for these symptoms remains under debate, this is the first study to describe the phenomenon by assessing the prevalence and severity of these wind turbine infrasound related symptoms as well as factors associated with being symptomatic. Four wind power areas in Finland assessed to have the most problems intuitively associated with wind turbine infrasound were selected for the study. The questionnaire was mailed to 4847 adults in four distance zones (≤ 2.5 km, > 2.5-5 km, > 5-10 km, > 10-20 km from the closest wind turbine), and 28% responded. In the closest distance zone, 15% of respondents reported having symptoms that they have intuitively associated with wind turbine infrasound. In the whole study area, the symptom prevalence was 5%. Many of the symptomatic respondents were annoyed by audible wind turbine sound and associated their symptoms also with vibration or electromagnetic field from wind turbines. One third of the symptomatic respondents rated their symptoms severe, and the symptom spectrum was very broad covering several organ systems. In multivariate models, many factors such as proximity to wind turbines, impaired health status, being annoyed by different aspects of wind turbines and considering wind turbines as a health risk were associated with having wind turbine infrasound related symptoms. Although causal relationships cannot be assessed based on a cross-sectional questionnaire study, it can be speculated that interpretations of symptoms are affected by many other factors in addition to actual exposure.

Wood stove use and other determinants of personal and indoor exposures to particulate air pollution and ozone among elderly persons in a Northern Suburb.

A six-month winter-spring study was conducted in a suburb of the northern European city of Kuopio, Finland, to identify and quantify factors determining daily personal exposure and home indoor levels of fine particulate matter (PM2.5 , diameter <2.5 µm) and its light absorption coefficient (PM2.5abs ), a proxy for combustion-derived black carbon. Moreover, determinants of home indoor ozone (O3 ) concentration were examined. Local central site outdoor, home indoor, and personal daily levels of pollutants were monitored in this suburb among 37 elderly residents. Outdoor concentrations of the pollutants were significant determinants of their levels in home indoor air and personal exposures. Natural ventilation in the detached and row houses increased personal exposure to PM2.5 , but not to PM2.5abs , when compared with mechanical ventilation. Only cooking out of the recorded household activities increased indoor PM2.5 . The use of a wood stove room heater or wood-fired sauna stove was associated with elevated concentrations of personal PM2.5 and PM2.5abs , and indoor PM2.5abs . Candle burning increased daily indoor and personal PM2.5abs , and it was also a determinant of indoor ozone level. In conclusion, relatively short-lasting wood and candle burning of a few hours increased residents' daily exposure to potentially hazardous, combustion-derived carbonaceous particulate matter.

Particulates and noise exposure during bicycle, bus and car commuting: A study in three European cities.

BACKGROUND: In order to curb traffic-related air pollution and its impact on the physical environment, contemporary city commuters are encouraged to shift from private car use to active or public transport modes. However, personal exposures to particulate matter (PM), black carbon and noise during commuting may be substantial. Therefore, studies comparing exposures during recommended modes of transport versus car trips are needed. METHODS: We measured personal exposure to various-sized particulates, soot, and noise during commuting by bicycle, bus and car in three European cities: Helsinki in Finland, Rotterdam in the Netherlands and Thessaloniki in Greece using portable monitoring devices. We monitored commonly travelled routes in these cities. RESULTS: The total number of one-way trips yielding data on any of the measured parameters were 84, 72, 94 and 69 for bicycle, bus, closed-window car and open-window car modes, respectively. The highest mean PM2.5 (85µg/m3), PM10 (131µg/m3), black carbon (10.9µg/m3) and noise (75dBA) levels were recorded on the bus, bus (again), open-window car and bicycle modes, respectively, all in Thessaloniki, PM and soot concentrations were generally higher during biking and taking a bus than during a drive in a a car with closed windows. Ratios of bike:car PM10 ranged from 1.1 in Thessaloniki to 2.6 in Helsinki, while bus:car ratios ranged from in 1.0 in Rotterdam to 5.6 in Thessaloniki. Higher noise levels were mostly recorded during bicycle rides. CONCLUSION: Based on our study, active- and public-transport commuters are often at risk of higher air pollution and noise exposure than private car users. This should be taken into account in urban transportation planning.

Self-reported health in the vicinity of five wind power production areas in Finland.

In many countries, some people living in the vicinity of wind power production areas report having symptoms that they intuitively associate with wind turbines. Recently public discussions have focused especially on wind turbine infrasound. However, scientific evidence supporting an association is lacking. The aim of this study was to assess the association between exposure to wind turbines and the prevalence of self-reported symptoms, diseases and medications. A cross-sectional questionnaire study (n = 2,828) was conducted in the vicinity of five wind power production areas in Finland in 2015-2016. Each area had 3-16 turbines with a nominal power of 2.4-3.3 MW. The response rate was 50% (n = 1,411). Continuous and categorised (≤ 2.5, > 2.5-5, > 5-10 km) distance between the respondents' home and the closest wind turbine was used to represent exposure to wind turbines. Wind turbine sound pressure level outdoors could be reliably modelled only for the closest distance zone where the yearly average was 34 dB and maximum 43 dB. The data on symptoms (headache, nausea, dizziness, tinnitus, ear fullness, arrhythmia, fatigue, difficulties in falling asleep, waking up too early, anxiety, stress), diseases (hypertension, heart insufficiency, diabetes), and medications (analgesics for headache, joint/muscle pain and other pain, and medication for sleep disturbance, anxiety and depression, and hypertension) was obtained from the questionnaire. Logistic regression analyses were adjusted for age, sex, marital status, education, work situation, smoking, alcohol consumption, physical activity, body mass index, and hearing problems. Annoyance and sleep disturbance due to wind turbine noise were inversely associated with the distance to the closest wind turbine. The prevalence of symptoms, diseases and medications was essentially the same in all distance categories. In multivariate regression modelling, the odds ratio estimates were generally close to unity and statistically non-significant. Beyond annoyance and sleep disturbance, there were no consistent associations between exposure to wind turbines and self-reported health problems. The results do not support the hypothesis that broadband sound or infrasound from wind turbines could cause the proposed health problems.

Spatial variation of PM elemental composition between and within 20 European study areas--Results of the ESCAPE project.

An increasing number of epidemiological studies suggest that adverse health effects of air pollution may be related to particulate matter (PM) composition, particularly trace metals. However, we lack comprehensive data on the spatial distribution of these elements. We measured PM2.5 and PM10 in twenty study areas across Europe in three seasonal two-week periods over a year using Harvard impactors and standardized protocols. In each area, we selected street (ST), urban (UB) and regional background (RB) sites (totaling 20) to characterize local spatial variability. Elemental composition was determined by energy-dispersive X-ray fluorescence analysis of all PM2.5 and PM10 filters. We selected a priori eight (Cu, Fe, K, Ni, S, Si, V, Zn) well-detected elements of health interest, which also roughly represented different sources including traffic, industry, ports, and wood burning. PM elemental composition varied greatly across Europe, indicating different regional influences. Average street to urban background ratios ranged from 0.90 (V) to 1.60 (Cu) for PM2.5 and from 0.93 (V) to 2.28 (Cu) for PM10. Our selected PM elements were variably correlated with the main pollutants (PM2.5, PM10, PM2.5 absorbance, NO2 and NOx) across Europe: in general, Cu and Fe in all size fractions were highly correlated (Pearson correlations above 0.75); Si and Zn in the coarse fractions were modestly correlated (between 0.5 and 0.75); and the remaining elements in the various size fractions had lower correlations (around 0.5 or below). This variability in correlation demonstrated the distinctly different spatial distributions of most of the elements. Variability of PM10_Cu and Fe was mostly due to within-study area differences (67% and 64% of overall variance, respectively) versus between-study area and exceeded that of most other traffic-related pollutants, including NO2 and soot, signaling the importance of non-tailpipe (e.g., brake wear) emissions in PM.