Spatial and temporal determinants of A-weighted and frequency specific sound levels-An elastic net approach

Noise in operating theatres, is it safe?

INTRODUCTION

Noise-Induced Hearing Loss (NIHL) is a condition caused by repeated exposure to loud noise, with operating theatre personnel potentially at risk. The aims of this study were to establish the typical noise levels in orthopaedic theatres and to compare these to The Control of Noise at Work Regulations 2005.

MATERIALS AND METHODS

We measured the average noise levels in 40 trauma and orthopaedic surgeries in a single centre. We used the Decibel X app to take measurements, then performed corrections to ascertain noise levels at the surgeon's ear (Leq). The daily noise exposure level for theatre staff for each procedure (LEP, d) and the LEP, d over an average 8-hour working day when performing different groups of procedures were calculated. Data were analysed using descriptive statistics, ANOVA, t-test and the Pearson coefficient of correlation.

RESULTS

The LEP, d lower action value (80 dBA) as set by the Health and Safety Executive (HSE) was met by performing a single revision total knee replacement or a right open ankle debridement. Assuming three procedures are conducted per list, lists consisting of joint replacements (82 dBA) or medium elective procedures (81 dBA) exceed this lower limit. Additionally, lists comprising large and medium bone fractures would be within 1 dB of the limit (79 dBA and 79 dBA, respectively). Soft tissue (74 dBA), arthroscopic (73 dBA), and small bone fracture (71 dBA) procedures had the lowest LEP, d. The greatest contributors to noise levels were surgical instruments. The number of people in the room made a significant difference to noise levels (p = 0.032).

CONCLUSIONS

We have established the baseline noise levels in various orthopaedic procedures. Measures should be taken to meet UK regulations. Further research should determine suitable measures for protection from hearing damage for theatre staff and evaluate the risks high noise levels pose to patients.

Integrating spatial-temporal soundscape mapping with landscape indicators for effective conservation management and planning of a protected area

Protected areas (PAs) possess generous biodiversity, making them great potential for human and wildlife well-being. Nevertheless, rising anthropogenic sounds may pose a serious challenge and threat to the habitats. Therefore, understanding the acoustic environments of PAs and implementing proper conservation strategies are essential for maintaining species richness within the territory. In this study, we investigate the spatial-temporal variations of soundscape distribution in the Dashanbao Protected Area (DPA) of China, ultimately discussing the planning and management strategies. Firstly, to systematically analyse the spatial-temporal soundscape distribution of the reserve, we generated single and multi-acoustic source maps by classifying geographical, biological, and anthropogenic sounds. In the region, we installed 35 recording points and collected sounds using the synchronic recording method. Secondly, we conducted Spearman correlation analyses to examine the relationships between the sound sources and i) temporal variations, ii) landscape feature indicators. Thirdly, we identified the dominant sound sources in the region and their conflict areas through the cross-analysis module of Grass Geographic Information Systems (GIS). Finally, we provided sound control strategies by discussing landscape indicators and land-use management policies. The results show that even though there is conservation planning in the DPA, anthropogenic sounds dominate in certain parts of the reserve depending on diurnal and seasonal cycles. This reveals deficiencies in the DPA's current planning concerning the soundscape and highlights the effectiveness of spatial-temporal mapping. Additionally, our correlation analyses demonstrate that landscape feature indicators can represent how sound environment is affected by landscape. The patch diversity (PD), landscape shape index (LSI), Shannon's Diversity Index (SHDI), woodland, shrubland, and water distance (WD) were identified as the primary predictors for both biological and anthropogenic sounds. None of the indicators exhibited a significant positive or negative correlation with geological sounds. Consequently, to enhance and conserve the acoustic quality of the region, spatial-temporal mapping with landscape indicators can be employed in the management and planning processes.

Long-term measurement study of urban environmental low frequency noise

BACKGROUND

Environmental low frequency noise (LFN < 125 Hz), ubiquitous in urban areas, is an understudied area of exposure science and an overlooked threat to population health. Environmental noise has historically been measured and regulated by A-weighted decibel (dBA) metrics, which more heavily weight frequencies between 2000 and 5000 Hz. Limited research has been conducted to measure and characterize the LFN components of urban environmental noise.

OBJECTIVES

We characterized LFN noise at two urban sites in Greater Boston, Massachusetts (USA) using dBA and full spectrum noise measurements with aims to (1.) analyze spatio-temporal differences in the two datasets; (2.) compare and contrast LFN metrics with dBA noise metrics in the two sites; and (3.) assess meteorological covariate contributions to LFN in the dataset.

METHODS

We measured A- and C-weighted, and flat, unweighted noise levels and 1/3-octave band continuously for 5 months using sound level meters sampling at f = 1 Hz and we recorded sound samples at 44.1 kHz. Our measurement sites were located in two urban, densely populated communities, burdened by close proximity to bus, rail, and aircraft routes.

RESULTS

We found that (1.) LFN does not follow the same seasonal trends as A-weighted dBA loudness; there are spatial differences in LFN and its very low frequency noise components (VLFN) between two urban sites; (2.) VLFN and LFN are statistically significant drivers of LCeq (nearly independent of frequency) minus LAeq, (LCeq-LAeq) >10 dB, an accepted LFN metric; and (3.) LFN was minimally affected by high wind speeds at either Site.

IMPACT STATEMENT

Environmental low-frequency noise (LFN < 125 Hz), ubiquitous in urban areas, is an understudied area of exposure science and an overlooked risk to population health. We measured environmental noise across the full spectrum of frequencies continuously for five months at two urban sites located in Environmental Justice communities. We found that LFN did not follow the same seasonal trends as A-weighted (dBA) loudness, and we observed spatial differences in LFN and very low frequency noise (VLFN < 20 Hz) at the two sites. Not characterizing LFN and basing noise regulations only on A-weightings, a poor predictor of LFN, may expose populations to LFN levels of concern.

Spatial modelling and inequalities of environmental noise in Accra, Ghana

Noise pollution is a growing environmental health concern in rapidly urbanizing sub-Saharan African (SSA) cities. However, limited city-wide data constitutes a major barrier to investigating health impacts as well as implementing environmental policy in this growing population. As such, in this first of its kind study in West Africa, we measured, modelled and predicted environmental noise across the Greater Accra Metropolitan Area (GAMA) in Ghana, and evaluated inequalities in exposures by socioeconomic factors. Specifically, we measured environmental noise at 146 locations with weekly (n = 136 locations) and yearlong monitoring (n = 10 locations). We combined these data with geospatial and meteorological predictor variables to develop high-resolution land use regression (LUR) models to predict annual average noise levels (LAeq24hr, Lden, Lday, Lnight). The final LUR models were selected with a forward stepwise procedure and performance was evaluated with cross-validation. We spatially joined model predictions with national census data to estimate population levels of, and potential socioeconomic inequalities in, noise levels at the census enumeration-area level. Variables representing road-traffic and vegetation explained the most variation in noise levels at each site. Predicted day-evening-night (Lden) noise levels were highest in the city-center (Accra Metropolis) (median: 64.0 dBA) and near major roads (median: 68.5 dBA). In the Accra Metropolis, almost the entire population lived in areas where predicted Lden and night-time noise (Lnight) surpassed World Health Organization guidelines for road-traffic noise (Lden <53; and Lnight <45). The poorest areas in Accra also had significantly higher median Lden and Lnight compared with the wealthiest ones, with a difference of ∼5 dBA. The models can support environmental epidemiological studies, burden of disease assessments, and policies and interventions that address underlying causes of noise exposure inequalities within Accra.

Community daytime noise pollution and socioeconomic differences in Chicago, IL

Environmental noise may affect hearing and a variety of non-auditory disease processes. There is some evidence that, like other environmental hazards, noise may be differentially distributed across communities based on socioeconomic status. We aimed to a) predict daytime noise pollution levels and b) assess disparities in daytime noise exposure in Chicago, Illinois. We measured 5-minute daytime noise levels (L_{eq, 5-min}) at 75 randomly selected sites in Chicago in March, 2019. Geographically-based variables thought to be associated with noise were obtained, and used to fit a noise land-use regression model to estimate the daytime environmental noise level at the centroid of the census blocks. Demographic and socioeconomic data were obtained from the City of Chicago for the 77 community areas, and associations with daytime noise levels were assessed using spatial autoregressive models. Mean sampled noise level (L_{eq, 5-min}) was 60.6 dBA. The adjusted R² and root mean square error of the noise land use regression model and the validation model were 0.60 and 4.67 dBA and 0.51 and 5.90 dBA, respectively. Nearly 75% of city blocks and 85% of city communities have predicted daytime noise level higher than 55 dBA. Of the socioeconomic variables explored, only community per capita income was associated with mean community predicted noise levels, and was highest for communities with incomes in the 2^nd quartile. Both the noise measurements and land-use regression modeling demonstrate that Chicago has levels of environmental noise likely contributing to the total burden of environmental stressors. Noise is not uniformly distributed across Chicago; it is associated with proximity to roads and public transportation, and is higher among communities with mid-to-low incomes per capita, which highlights how socially and economically disadvantaged communities may be disproportionately impacted by this environmental exposure.

Space-time characterization of community noise and sound sources in Accra, Ghana

Urban noise pollution is an emerging public health concern in growing cities in sub-Saharan Africa (SSA), but the sound environment in SSA cities is understudied. We leveraged a large-scale measurement campaign to characterize the spatial and temporal patterns of measured sound levels and sound sources in Accra, Ghana. We measured sound levels and recorded audio clips at 146 representative locations, involving 7-days (136 locations) and 1-year measurements between 2019 and 2020. We calculated metrics of noise levels and intermittency and analyzed audio recordings using a pre-trained neural network to identify sources. Commercial, business, and industrial areas and areas near major roads had the highest median daily sound levels (LAeq_24hr: 69 dBA and 72 dBA) and the lowest percentage of intermittent sound; the vice-versa was found for peri urban areas. Road-transport sounds dominated the overall sound environment but mixtures of other sound sources, including animals, human speech, and outdoor music, dominated in various locations and at different times. Environmental noise levels in Accra exceeded both international and national health-based guidelines. Detailed information on the acoustical environmental quality (including sound levels and types) in Accra may guide environmental policy formulation and evaluation to improve the health of urban residents.

Application of land-use regression models to estimate sound pressure levels and frequency components of road traffic noise in Taichung, Taiwan

Few studies have applied land-use regression to predict road traffic noise exposure, and there are few predictive models for different frequencies. This study aimed to measure 24-h average road traffic noise levels and to analyze the frequency components over one year to establish land-use regression models of noise exposure. Fifty monitoring stations were set up to conduct 3 measurements for A-weighted equivalent sound pressure levels over 24 h (L_eq,24h) and night equivalent sound pressure levels (L_night), as well as octave-band analyses, during the 2013-2014 period. Noise measurements were integrated with land-use types, road and traffic information, meteorological data and geographic information systems to construct land-use regression models. Leave-one-out cross-validation was performed to test the validity of the predictive models. The annual means of L_eq,24h and L_night were 66.4 ± 4.7 A-weighed decibels (dBA) and 62.1 ± 6.0 dBA, respectively. Octave-band frequency analyses revealed that the highest means over 24 h and at night were 61.4 ± 5.3 decibels (dB) and 56.7 ± 6.6 dB (both at 1000 Hz), respectively. The model-explained variance (R²) of the full-frequency noise was 0.83 for L_eq,24h and 0.79 for L_night. The R² values for octave-band-frequency noise ranged from 0.67 to 0.88 for L_eq,24h and 0.65 to 0.85 for L_night, with the highest R² at 250 Hz for L_eq,24h and at 125 Hz for L_night. The differences between the model R² and the leave-one-out cross-validation R² ranged from 5% to 15% for both L_eq,24h and L_night at all frequencies. In the validation, the root mean squared error was 2.09 dBA and 2.80 dBA for the full-frequency L_eq,24 and L_night, respectively, and ranged from 1.89 to 2.62 dB and from 2.51 to 3.28 dB for the octave-band-frequency L_eq,24h and L_night, respectively. This study observed that the annual means of the measured L_eq,24h and L_night in Taichung were both above 60 dBA and had the highest level at 1000 Hz. The developed land-use regression models of L_eq,24 and L_night both had good predictive capacity for the full frequency spectrum and within octave bands and can therefore be applied for epidemiological studies.

Façades, floors and maps - Influence of exposure measurement error on the association between transportation noise and myocardial infarction

BACKGROUND

Epidemiological research on transportation noise uses different exposure assessment strategies based on façade point estimates or regulatory noise maps. The degree of exposure measurement error and subsequent potentially biased risk estimates related to exposure definition is unclear. We aimed to evaluate associations between transportation noise exposure and myocardial infarction (MI) mortality considering: assumptions about residential floor, façade point selection (loudest, quietest, nearest), façade point vs. noise map estimates, and influence of averaging exposure at coarser spatial scales (e.g. in ecological health studies).

METHODS

L_den from the façade points were assigned to >4 million eligible adults in the Swiss National Cohort for the best match residential floor (reference), middle floor, and first floor. For selected floors, the loudest and quietest exposed façades per dwelling, plus the nearest façade point to the residential geocode, were extracted. Exposure was also assigned from 10 × 10 m noise maps, using "buffers" from 50 to 500 m derived from the maps, and by aggregating the maps to larger areas. Associations between road traffic and railway noise and MI mortality were evaluated by multi-pollutant Cox regression models, adjusted for aircraft noise, NO₂ and socio-demographic confounders, following individuals from 2000 to 2008. Bias was calculated to express differences compared to the reference.

RESULTS

Hazard ratios (HRs) for the best match residential floor were 1.05 (1.02-1.07) and 1.03 (1.01-1.05) per IQR (11.3 and 15.0 dB) for road traffic and railway noise, respectively. In most situations, comparing the alternative exposure definitions to this reference resulted in attenuated HRs. For example, assuming everyone resided on the middle or everyone on first floor introduced little bias (%Bias in excess risk: -1.9 to 4.4 road traffic and -4.4 to 10.7 railway noise). Using the noise grids generated a bias of approximately -26% for both sources. Averaging the maps at a coarser spatial scale led to bias from -19.4 to -105.1% for road traffic and 17.6 to -34.3% for railway noise and inflated the confidence intervals such that some HRs were no longer statistically significant.

CONCLUSION

Changes in spatial scale introduced more bias than changes in residential floor. Use of noise maps to represent residential exposure may underestimate noise-induced health effects, in particular for small-scale heterogeneously distributed road traffic noise in urban settings.