Interannual analysis of noise levels and spectral trends in the marine soundscape of Kongsfjorden, Svalbard

Fourteen years of spectral fluxes derived from collocated Atmospheric Infrared Sounder (AIRS) and Clouds and the Earth's Radiant Energy System (CERES) observations are used in conjunction with AIRS retrievals to examine the trends of zonal mean spectral outgoing longwave radiation (OLR) and greenhouse efficiency (GHE) in the Arctic. AIRS retrieved profiles are fed into a radiative transfer model to generate synthetic clear‐sky spectral OLR. Trends are derived from the simulated clear‐sky spectral OLR and GHE and then compared with their counterparts derived from collocated observations. Spectral trends in different seasons are distinctively different. March and September exhibit positive trends in spectral OLR over the far‐IR dirty window and mid‐IR window region for most of the Arctic. In contrast, spectral OLR trends in July are negative over the far‐IR dirty window and can be positive or negative in the mid‐IR window depending on the latitude. Sensitivity studies reveal that surface temperature contributes much more than atmospheric temperature and humidity to the spectral OLR and GHE trends, while the contributions from the latter two are also discernible over many spectral regions (e.g., trends in the far‐IR dirty window in March). The largest increase of spectral GHE is seen north of 80°N in March across the water vapor v2 band and far‐IR. When the secular fractional change of spectral OLR is less than that of surface spectral emission, an increase of spectral GHE can be expected. Spectral trend analyses reveal more information than broadband trend analyses alone.

Interannual Analysis of Noise Levels and Spectral Trends in the Marine Soundscape of Kongsfjorden, Svalbard

Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics

De Novo Synthesis of Biopaint Using Transformed Bacteria: Analysis of Spectral Intensity Trends and Comparison to Commercial Paint

Abstract. We evaluate Greenland Ice Sheet (GrIS) surface reflectance and albedo trends using the newly released Collection 6 (C6) MODIS (Moderate Resolution Imaging Spectroradiometer) products over the period 2001–2016. We find that the correction of MODIS sensor degradation provided in the new C6 data products reduces the magnitude of the surface reflectance and albedo decline trends obtained from previous MODIS data (i.e., Collection 5, C5). Collection 5 and 6 data product analysis over GrIS is characterized by surface (i.e., wet vs. dry) and elevation (i.e., 500–2000 m, 2000 m and greater) conditions over the summer season from 1 June to 31 August. Notably, the visible-wavelength declining reflectance trends identified in several bands of MODIS C5 data from previous studies are only slightly detected at reduced magnitude in the C6 versions over the dry snow area. Declining albedo in the wet snow and ice area remains over the MODIS record in the C6 product, albeit at a lower magnitude than obtained using C5 data. Further analyses of C6 spectral reflectance trends show both reflectance increases and decreases in select bands and regions, suggesting that several competing processes are contributing to Greenland Ice Sheet albedo change. Investigators using MODIS data for other ocean, atmosphere and/or land analyses are urged to consider similar re-examinations of trends previously established using C5 data.

Effect of topographic correction on forest change detection using spectral trend analysis of Landsat pixel-based composites

The study of marine soundscapes is becoming widespread and the amount of data collected is increasing rapidly. Data owners (typically academia, industry, government, and defense) are negotiating data sharing and generating potential for data syntheses, comparative studies, analyses of trends, and large-scale and long-term acoustic ecology research. A problem is the lack of standards and commonly agreed protocols for the recording of marine soundscapes, data analysis, and reporting that make a synthesis and comparison of results difficult. We provide a brief overview of the components in a marine soundscape, the hard- and software tools for recording and analyzing marine soundscapes, and common reporting formats.

Speech production in the presence of noise results in the Lombard effect, which is known to have a serious impact on speech system performance. In this study, Lombard speech produced under different types and levels of noise is analyzed in terms of duration, energy histogram, and spectral tilt. Acoustic-phonetic differences are shown to exist between different ldquoflavorsrdquo of Lombard speech based on analysis of trends from a Gaussian mixture model (GMM)-based Lombard speech type classifier. For the first time, the dependence of Lombard speech on noise type and noise level is established for the purposes of speech processing systems. Also, the impact of the different flavors of Lombard effect on speech system performance is shown with respect to an in-set/out-of-set speaker recognition task. System performance is shown to degrade from an equal error rate (EER) of 7.0% under matched neutral training and testing conditions, to an average EER of 26.92% when trained with neutral and tested with Lombard effect speech. Furthermore, improvement in the performance of in-set/out-of-set speaker recognition is demonstrated by adapting neutral speaker models with Lombard speech data of limited duration. Improved average EERs of 4.75% and 12.37% were achieved for matched and mismatched adaptation and testing conditions, respectively. At the highest noise levels, an EER as low as 1.78% was obtained by adapting neutral speaker models with Lombard speech of limited duration. The study therefore illustrates the impact of Lombard effect on speaker recognition, and effective methods to improve system performance for speaker recognition when train/test conditions are mismatched for neutral versus Lombard effect speech.

A self-tuning adaptive trend extraction method for process monitoring and diagnosis

One of the major issues in processing permanent downhole gauge (PDG) data is that there exist too many transients over a reasonable time period, say six months. A formula has been proposed to predict the transients that may be detected or missed. Reasonable prediction has been achieved via the formula. Noise usually exists in data recorded by permanent downhole gauges. Denoising is thus one of the most important steps in PDG data processing. In order to denoise the data, data noise level must be estimated beforehand. Unfortunately, the data noise level is typically case-dependent, therefore, it is impossible to identify a universal value for the level that may be used for all the application scenarios. One appropriate approach to estimate the noise level is to first best fit the data, subtract predicted pressure response from recorded values, and then calculate the noise level based on the difference1. We proposed to apply nonlinear regression via Polytope method2 for best-fitting PDG data to determine the noise level. It is found that the new approach is superior to the least square error (LSE) linear regression as used by Khong1, because the bottomhole wellbore pressure response in a well should be treated as a nonlinear function of time over majority of the well production/injection/shut-in period. Unless very small range of the data (say 2 hours) is considered, linear pressure response with time is not anticipated. Furthermore, with nonlinear regression through the Polytope approach, there is no strong restriction in data quantity and data density, hence, automatic detection of data noise level can be implemented. Other improvements to the PDG data processing procedure, such as trend analysis, steamlinization of data preprocessing, step outlier removal, and so on, will also be discussed.

Acoustic noise assessment is a crucial problem in areas in which transportation means, such as motorway, railway, airport, etc., are present. Dwelled areas, in fact, represent a sensible point, that is affected by several externalities, among which, acoustic noise is very important. In this paper, the techniques known as Time Series Analysis (TSA), are used to analyze datasets of noise level produced by transport systems. This approach is based on the analysis of trend and seasonality of the series, and on the implementation of a function of the time that can provide predictions for future time periods. According to the choice and to the input of each model, the forecast horizon can vary from few days further to any time period in the future. Two techniques will be presented: one is based on a Deterministic Decomposition (DD-TSA), able to predict at any future time period; the second is based on a stochastic approach, and adopt the so called SARIMA (Seasonal AutoRegressive Integrated Moving Average) models, to provide prediction on a short time range. Both techniques will be applied to a road traffic noise dataset and to an airport noise levels time series. Results will show that the typology of transportation system does not affect the prediction performances of both the DD-TSA and the SARIMA techniques, even though the time basis of the data is different, being daily for traffic noise and hourly for airport.

We employed cryogenic X-ray Raman spectroscopy to investigate the early-stage decomposition of the high explosive molecule hexanitrohexaazaisowurtzitane (CL-20). By systematically varying the radiation dose under cryogenic conditions, we induced the decomposition of the molecule using ionizing radiation and observed the evolution of spectral features at the carbon, nitrogen, and oxygen K edges. Through extensive first-principles calculations, we identified key intermediates in the early stages of the decomposition process, resulting from C-C and C-N bond cleavage which leads to the opening of the internal cage structure. A detailed analysis of spectral trends and fingerprints provided evidence supporting N-NO2 homolytic cleavage as the primary initial decomposition pathway. The combination of advanced core-level spectroscopy methods and state-of-the-art theoretical calculations enabled a comprehensive characterization of the molecular changes induced by controlled radiation dose exposures. Our findings establish a benchmark for understanding the decomposition chemistry of high-explosive materials, offering important insights into their stability and reactivity under extreme conditions.

This study used a task-based approach to reconstruct employee noise exposures at two large automotive manufacturing plants for the period 1970-1989, utilizing historic noise measurement data, work history records, documented changes in plant operations, focus group discussions, structured interviews with long-tenure employees, and task-based job profiles. Task-based job noise exposure profiles were developed in the 1990s when the plants conducted task-based noise monitoring. Under the assumption that tasks and time-at-task profile within jobs did not change over time, these profiles were applied to historic jobs. By linking historic noise exposure measurements to job tasks, this approach allowed task-based reconstructed noise exposure profiles to capture variability of daily noise exposures. Reconstructed noise exposures, along with task-based noise exposure measurements collected at each plant during the 1990s, were analyzed to examine time trends in workplace noise levels and worker noise exposure. Our analysis of noise exposure trends revealed that noise levels for many jobs declined by ≥3 dBA from 1970 to 1998 as operational and equipment changes occurred in the plants and some noise control measures were implemented, but for some jobs, noise levels increased in the mid- to late 1990s, most likely because of an increase in production at that time. Overall, the percentage of workers exposed to noise levels >90 dBA decreased from 95% in 1970 to 54% in 1998 at one of the plants and decreased from 36% in 1970 to ~5% in 1999 at the other plant. These reductions indicate a degree of success for the hearing conservation program. However, the actual number of employees with noise exposure >90 dBA increased because of a substantial increase in the number of production employees, particularly in jobs with high noise levels, which shows a hearing conservation program challenge that companies face during periods of increased production. Future analysis of hearing levels in these plant populations will help determine whether noise level reduction translates into decreased hearing loss at these plants.

<p>Underwater noise is recognised as a form of marine pollutant and there is evidence that over exposure to excessive levels of noise can have effects on the wellbeing of the marine ecosystem. Consequently, the variation in the ambient sound levels in the deep ocean has been the subject of a number of recent studies, with particular interest in the identification of long-term trends. We describe a statistical method for performing long-term trend analysis and uncertainty evaluation of the estimated trends from deep-ocean noise data. This study has been extended to include  measured data  from four monitoring stations located in the Indian (Cape Leeuwin & Diego Garcia), Pacific (Wake Island) and Southern Atlantic (Ascension Islands) Oceans over periods spanning between 8 to 15 years. The data were obtained from the hydro-acoustic monitoring stations of the Preparatory Commission for the Comprehensive Nuclear Test Ban Treaty Organization (CTBTO). The monitoring stations provide information at a sampling frequency of 250 Hz, leading to very large datasets, and at acoustic frequencies up to 105 Hz.</p><p>The analysis method uses a flexible discrete model that incorporates terms that capture seasonal variations in the data together with a moving-average statistical model to describe the serial correlation of residual deviations. The trend analysis is applied to time series representing daily aggregated statistical levels for four frequency bands to obtain estimates for the change in sound pressure level (SPL) over the examined period with associated coverage intervals. The analysis demonstrates that there are statistically significant changes in the levels of deep-ocean noise over periods exceeding a decade. The main features of the approach include (a) using a functional model  with terms  that represent both long-term and seasonal behaviour of deep-ocean noise, (b) using a statistical model to capture the serial correlation of the residual deviations that are not explained by the functional model, (c) using daily aggregation intervals derived from 1-minute  sound pressure level averages, and (d) applying a non-parametric approach to validate the uncertainties of the trend estimates that avoids the need to make an assumption about the distribution of the residual deviations.</p><p>The obtained results show the long term trends vary differently at the four stations. It was observed that low frequency noise generally dominated the significant trends in these oceans. The relative differences between the various statistical levels are remarkably similar for all the frequency bands. Given the complexity of the acoustic environment, it is difficult to identify the main causes of these trends. Some possible explanations for the observed trends are discussed. It was however observed some stations are subjected to strong seasonal variation with a high degree of correlation with climatic factors such as sea surface temperature, Antarctic ice coverage and wind speed. The same seasonal effects is less pronounced in station located closer to the equator.</p>

Shipping noise has been identified as a threat on underwater ecosystems. In particular, several impacts have been documented on cetaceans, e.g., communication’s masking and stress increasing. To assess the risk related to such anthropogenic noise requires both quantifying the noise levels and estimating the distribution of the cetaceans’ population. However, current methods evaluating the risks related to anthropogenic pressures generally rely on strong expert priors, which may be difficult to define. This presentation aims at introducing a new framework for the comparison of anthropogenic pressure levels maps and cetaceans’ distribution in order to infer the risk of impact and provide management solutions. The methodology, combining simple statistical analyses and a theoretical representation tool was applied to the Bay of Biscay using shipping noise model and fin whale observation from regular surveys for the years 2012 and 2016. Relationships between cetaceans’ distribution and noise levels were investigated and linked to mammals theoretical responses to pressure. A trend analysis between 2012 and 2016 was also proposed to identify the noise hotspots. The results were interpreted in both terms of ecological meaning for fin whale and conservation measures for shipping noise, according to marine policies requirements.