Smart noise pollution monitoring system

The source of most noise worldwide is mainly caused by machines and transportation means, including motor vehicles such as cars, buses, trains, aircrafts and so on. The excessive noise, called noise pollution, may harm the activity or balance of human or animal life. Noise pollution can cause annoyance, aggression and sleep disturbances. Chronic exposure to noise may cause noise-induced hearing loss, tinnitus and contribute to cardiovascular problems such as hypertension as well as increased incidence of coronary artery disease. Such may bring about deterioration in the wellbeing of people and increase the number of days of incapacity for work. This paper is an attempt to analyze the impact of transport infrastructure modernisations on the noise pollution in the city of Szczecin. The main objective of this paper was to compare the level of traffic noise in the areas surrounding streets: Powstancow Wielkopolskich, Mieszka I and Aleja Piastow Streets and crossroads of the streets: Taczaka-Lukasinskiego as well as Taczaka-Derdowskiego before and after the modernizations. The comparison of obtained results suggest that in some cases the modernization hasn’t influenced on noise levels. In some, it improved the acoustic situation but hasn’t reduced the noise to keep acceptable levels. The results emphasizethe thesis that some accepted methods of streets and crosswords modernization are sometimes ineffective in the fight against noise pollution. Conclusions: Modernization of intersections in Szczecin improved traffic flow but had a little impact on the noise levels. Modernisations that improve the traffic flow can cause even increment in noise pollution. It should be taken into consideration possible benefits of used methods of city traffic modernization related not only to traffic improvements but also to noise pollution reduction. We suggest computer aided stimulations and acoustic specialist advices prior to any restructures of city traffic. To minimize the noise pollution, comprehensive solutions are needed.

Acoustic contamination assessment during the transition between the COVID-19 restrictions and reactivation: A exploratory analysis in Guayaquil

Noise is an unwanted and unpleasant sound that occurs in the wrong place at the wrong time. It can impair hearing, increase stress levels and reduce concentration and work efficiency. In recent years, noise pollution has been rising rapidly. This study aims to assess the extent of noise pollution and its health effects on residents of the Pepsicola area in Kathmandu Valley. A descriptive cross-sectional study design was employed, using a non-probability sampling technique. Sound pressure levels were measured during peak hours using a sound level meter (SL-4010), and data were collected through semi-structured questionnaires administered to 50 respondents across residential, commercial, industrial, traffic and school zones. The findings revealed that average noise levels were highest in industrial areas and lowest in residential zones during both morning and evening periods. Over 60% of respondents demonstrated limited awareness of noise pollution, while 70% reported experiencing significant health effects, including headaches, hypertension and emotional instability. Some of the recorded noise levels exceeded the limits set by both the World Health Organization (WHO) and the Government of Nepal (GoN), indicating a serious public health concern. To mitigate these effects, the study recommends measures such as roadside tree plantations, stricter enforcement of existing noise regulations and the implementation of effective noise reduction strategies.

This study delves into the impact of ship noise on seafarer well-being, emphasizing fatigue—a significant contributor to maritime accidents due to human error. The investigation, centered around the hypothesis that IMO ship construction standards may not adequately minimize noise levels in seafarer cabins, seeks to establish whether these levels are sufficient to ensure seafarer security and prevent sleep disturbances. According to current IMO regulations, noise levels are set at 55 dB for vessels under 10,000 gross tonnage and 60 dB for those over 10,000, yet WHO guidelines recommend a maximum of 40 dB in bedrooms to avoid sleep disruption. A comprehensive survey involving 221 cadets demonstrates that 79.6% of participants experience sleep disturbances, work disruptions, and stress due to noise, indicating that the present noise standards are insufficient. This paper argues that reducing noise levels in individual cabins to below 40 dB is critical for enhancing seafarer health and safety and could significantly reduce human error-related maritime accidents. The findings advocate for more stringent noise control measures and regulatory reforms to bridge the knowledge gaps and improve labor protection in the maritime industry.

Recently in urban areas, road traffic noise is one of the primary sources of noise pollution. Variation in noise level is impacted by the synthesis of traffic and the percentage of heavy vehicles. Presentation to high noise levels may cause serious impact on the health of an individual or community residing near the roadside. Thus, predicting the vehicular traffic noise level is important. The present study aims at the formulation of regression, an artificial neural network (ANN) and an adaptive neuro-fuzzy interface system (ANFIS) model using the data of observed noise levels, traffic volume, and average speed of vehicles for the prediction of L10 and Leq. Measured noise levels are compared to the noise levels predicted by the experimental model. It is observed that the ANFIS approach is more superior when compared to output given by regression and an ANN model. Also, there exists a positive correlation between measured and predicted noise levels. The proposed ANFIS model can be utilized as a tool for traffic direction and planning of new roads in zones of similar land use pattern.

Electric Vehicles (EV) have emerged as an environmentally friendly solution, with much lower emissions and minimal noise during operation. This reduces the excessive noise impact generated by Internal Combustion Engine (ICE). This study aims to identify noise transition on each floor of a building when traffic is dominated by EV. The methods used in this research include the NMPB method, direct measurement of noise emissions from vehicle sound sources, and simulations. Based on simulation results with 100% ICE, the daytime noise level in the building lobby across the entire frequency range is 71.2 dB(A). The noise standard that causes disturbance to humans from road traffic noise is 55 dB(A). This indicates that the existing noise level is uncomfortable and disruptive to office workers during working hours. Subsequently, simulations were conducted for low, medium, and high frequencies. The daytime noise level in the lobby based on traffic simulation with 100% ICE is 61 dB(A) at low frequency, 68.7 dB(A) at medium frequency, and 62.3 dB(A) at high frequency. In the simulation with 50% ICE and 50% EV, the noise levels obtained are 60.4 dB(A) at low frequency, 68.9 dB(A) at medium frequency, and 61.1 dB(A) at high frequency. In the simulation with 100% EV, the noise levels obtained are 56.9 dB(A) at low frequency, 65.6 dB(A) at medium frequency, and 51 dB(A) at high frequency. The significant reduction in environmental noise levels at low and high frequencies when using 100% EV indicates that the transition to EV can significantly reduce noise disturbances in office buildings without additional acoustic treatment and can contribute to reduced building construction costs when traffic is dominated by EV.

The growing mechanization of agriculture has brought about numerous benefits in terms of efficiency and productivity. However, one often overlooked consequence of this technological advancement is the increase in noise pollution caused by farm machinery, particularly tractors. This research presents a comprehensive evaluation of noise levels produced by tractors when used in conjunction with various farm equipment, under different environmental conditions. The primary objective of the study is to analyze how specific environmental factors such as ambient temperature, relative humidity, wind speed, and terrain characteristics influence the intensity and frequency distribution of tractor-generated noise during field operations. Field experiments were conducted using a standardized sound level meter positioned at operator-ear level, capturing both instantaneous and average sound pressure levels during tractor operations. The tractors were tested with multiple implements, including rotavators, cultivators, ploughs, and seed drills, under varying loads and soil types. Each trial was conducted under distinct environmental conditions, including dry, wet, and humid atmospheres, across both flat and uneven terrains. The results revealed significant variation in noise levels depending on the implement type and environmental setting. It was observed that heavier implements and operations on rough terrain produced higher noise emissions, especially under hot and dry weather conditions. Conversely, noise levels were slightly lower during operations in moist and cooler environments. The findings emphasize the importance of integrating environmental considerations into the assessment of tractor noise emissions. The study also highlights the need for the development of noise control strategies in agricultural machinery design and the promotion of safe work practices to minimize auditory health risks for machine operators and nearby workers. This research contributes valuable insights into sustainable and ergonomically conscious farming practices in the context of environmental acoustics.

Noise pollution is an extensive concern for urban areas around the world. Gazipur is one of the significant hubs of the garment industry in Bangladesh. The purpose of the study was to address the highest sources of noise pollution in this City and identify the public perceptions of noise pollution. Noise levels have been measured from 38 locations of the City from 8.00 am to 6.00 pm including industrial, mixed, commercial, residential, and sensitive zones on both working and non-working days. ArcGIS based assessment using IDW spatial interpolation technique was used for the mapping of the measured noise levels across the study area. The highest average noise level (100.9 dB) was observed at Joydebpur Junction in the period of the evening (4.00 pm to 6.00 pm) and the lowest average noise level (47.5 dB) was observed in front of the Banghabandhu Satellite Ground Station in the period of the afternoon (12.00 pm to 2.00 pm). Heavy industrialization, vehicles horn, construction sites, traffic noise, loudspeakers, expired engines, loud talking, and outdoor parties are the extensive reasons for the increase in noise pollution in Gazipur City. Study showed that the noise level in every location was higher than the standard levels. Survey data indicated that city dwellers endure noise pollution headaches, irritable moods, sleeplessness, bad temper, and stress. This study strongly suggests that proper implementation of the existing laws helps to mitigate and reduce the noise pollution situation in the study area.

With the emergence of Internet-of-Things (IoT) and big data technologies, data-driven fault diagnosis approaches, notably deep learning (DL)-based methods, have shown promising capabilities in achieving high accuracy through end-to-end learning. However, these deterministic neural networks cannot incorporate the prediction uncertainty, which is critical in practical applications with possible out-of-distribution (OOD) data. This present article develops a reliable and intelligent fault diagnosis (IFD) framework based on evidence theory and improved visual geometry group (VGG) neural networks, which can achieve accurate and reliable diagnosis results by incorporating additional estimation of the prediction uncertainty. Specifically, this article treats the predictions of the VGG as subjective opinions by placing a Dirichlet distribution on the category probabilities and collecting the evidence from data during the training process. A specific loss function assisted by evidence theory is adopted for the VGG to obtain improved uncertainty estimations. The proposed method, which incorporates evidential VGG (EVGG) neural networks, as termed here, is verified by a case study of the fault diagnosis of rolling bearings, in the presence of sensing noise and sensor failure. The experimental results illustrate that the proposed method can estimate the prediction uncertainty and avoid overconfidence in fault diagnosis with OOD data. Also, the developed approach is shown to perform robustly under various levels of noise, which indicates a high potential for use in practical applications.

Although noise pollution is critical, its intangibility—lacking visibility, taste, or smell—often leads to it receiving less attention compared to other pollution types like air or water pollution. While the air around us is constantly filled with sounds, we may not always perceive it as noise. As researchers and policymakers increasingly emphasize aspects of quality of life, urban visual pollution has garnered growing attention, particularly in developed countries. Accordingly, the study focuses on noise and visual pollution to give an overview about awareness and current situation in one of the developing countries. Noise and visual pollution significantly impact public health. Noise pollution stemming from sources such as construction, motorized vehicles, and aircraft triggers anxiety, stress, and sleep disturbances. Prolonged exposure is associated with cardiovascular disease. Visual pollution, encompassing unsightly elements in the environment, impairs balance and heightens biological stress responses. Addressing these issues necessitates community awareness and the implementation of effective mitigation strategies based on community perception. The study provides an inclusive overview of noise environmental awareness and visual environmental awareness. Conducted through online questionnaires and result analysis, it aims to comparatively analyze these aspects and formulate targeted mitigation strategies.

Piecewise linear regression is a fundamental challenge in science and engineering. For typical applications where noise level varies in observations, the problem becomes much more challenging. In this paper, we propose a convex optimization based piecewise linear regression method which incorporates variation of the noise level. More precisely, we newly design a convex data-fidelity function as a weighted sum of approximation errors to mitigate effect of the noise level variation. The weights are automatically adjusted to the varying noise level within the framework of convex optimization. Numerical examples show performance improvements by the proposed method.

Due to rapid urbanization and exponential growth in transportation; traffic noise has become a major area of concern. Noise not only disturbs our day-to-day life but also have severe adverse health effects over humans which further may lead to mortality. This paper focuses on the behavior of noise levels of Lucknow city over a decade and establishes its correlation with impact on human health in terms of annoyance and sleep disturbance. Apart from Leq, different noise parameters like L10, L50, L90, Traffic Noise Index (TNI), Noise Pollution Index (NPI), and Noise Climate (NC) have also been analyzed to understand the variation of noise. At all the locations, the noise level has been found exceeding their prescribed standards during day time and night time except at Amausi. Out of nine locations, TNI was found to be exceeding at three locations during day time and NPI exceeding at one location. However, during night time both values of TNI and NPL were observed within the limit at all the locations. From the noise map of the city during day time and night time, among all sampling locations, Charbagh has been found to be worst affected by noise pollution. A strong positive correlation has been observed among the total population, vehicular count, and day and night time noise data, which directly contribute to a higher percentage of sleep disturbance and annoyance among residents. Due to the increase in noise levels over a period of time, almost four times the population get affected by high annoyance and almost double the population get affected by sleep disturbance.

Human activity and disturbance are thought to be perceived as a source of risk analogous to predation risk in wildlife. As such, can alter behaviour and habitat use of foraging animals. Through increased exposure to human disturbance, urban wildlife may face an increase in perceived risk during food acquisition. Urban habitats also include novel resources that could result in urban wildlife having to face distinct tradeoffs associated with foraging and patch use. To examine how a successful invasive mammal, the eastern grey squirrel, balances risk and safety under human disturbance, we measured giving‐up densities (GUD) at artificial food patches placed at sites subject to varying levels of urbanisation to investigate how features associated with human disturbance might influence feeding decisions. We found differences in GUDs between ‘safe' and risky' patches were reduced closer to roads under noisy conditions, suggesting that the risk of predation is perceived by squirrels as reduced when disturbance from human activities is highest. There was also a significant effect of the variability in noise levels on patterns of patch exploitation associated with roads, with the larger GUD differences between safe and risky patches found further from roads exacerbated under more variable noise levels, suggesting the consistency of human disturbances also moderated squirrel risk perception while foraging around roads. Our results suggest that human activities can have doubled‐edged impacts on the urban landscape of fear through offering reduced risk from predators whilst increasing foraging costs via noise disturbance.

A new network of 16 broadband seismic stations is operational in the northern Chhattisgarh region to monitor the seismicity arising in the otherwise seismically quiescent zone. Ambient noise is comprehensively analyzed to evaluate station performance and validate the data quality. Power spectral densities are computed for all stations, comparing ambient noise levels against global limits for data from November 2022 to December 2023. Results indicate that noise levels consistently fall within global noise limits. The study emphasizes the pronounced effect of instrumental tilt on the horizontal components of broadband seismometers. Spatial variation of ambient noise levels across various period bands of the noise spectrum highlights the contribution of diverse noise sources such as anthropogenic activities, surface waves, body waves, and barometric effects. Seasonal variations in short-period, microseism, and long-period bands reveal temporal variation of noise levels. Diurnal variation in the short-period band indicates a prominent effect of cultural noise. Furthermore, the proximity of seismic stations to major mining areas induces high noise levels in the very short-period band during blasting activities compared to non-blasting periods. Our study thus explores the variability of the ambient noise environment while validating the robustness and stability of the seismic installation across the study region.

Noise exposure is a significant occupational hazard in the water industry, particularly in pump houses where excessive noise levels pose risks to workers’ health and safety. This study evaluated the compliance of Noise Risk Assessment (NRA) at selected pump houses based on the Industry Code of Practice for Management of Occupational Noise Exposure and Hearing Conservation 2019 (ICOP 2019). The study focused on two pump houses, LV-1 and HT-2. A mixed-methods approach was employed, incorporating qualitative compliance evaluation against ICOP 2019 guideline and quantitative noise mapping using GIS-based software. Noise measurement data were collected from pre-existing reports, and spatial noise distribution was analysed to identify high-risk areas. The results showed that the highest recorded noise levels were with LV-1 reaching up to 92.0 dB(A) in the lower floor area, while HT-2 had localized hotspots with peak noise levels of 87.4 dB(A) near Pump Units 1 to 4 and the air compressor. A positive correlation between relationship between pump capacity and noise levels was found, especially in HT-2 where noise levels increased more rapidly with pump capacity. The findings also showed that both pump houses exceeded permissible exposure limits, with LV-1 exhibiting a more uniform distribution of high noise levels, while HT- 2 had localized noise hotspots. Compliance evaluation revealed that while LV-1 met most requirements, gaps existed in noise mapping and personal monitoring. HT-2, however, showed significant deficiencies in compliance, particularly in personal noise exposure monitoring, calibration documentation, and implementation of noise control measures. The NRA highlighted the need for enhanced noise mitigation strategies, including engineering and administrative controls, to reduce occupational noise exposure at pump houses. Findings also suggested that noise mapping should incorporate dynamic assessment techniques to account for variations in operational noise levels over time.