Добірка наукової літератури з теми "Environmental noise filtration"

With a high thermal efficiency, high reliability and good fuel economy, diesel engines have been widely used. However, with the increasingly stringent standards regarding non-road diesel engine emissions, diesel engines can hardly satisfy the particle emission requirements through internal purification alone. To reduce the particle emission and noise levels of the non-road diesel engine R180, this paper optimized the original muffler, and endowed the muffler with a particulate matter (PM) filtering function to improve the muffling. This study first proposed stainless steel fiber as the filtering medium as it is inexpensive and accessible; a bench experiment was conducted to verify the particle filtration performance and its effect on the overall engine performance. Then, the structure of the existing muffler in non-road diesel engines R180 was optimized, and the stainless steel fiber filtering was integrated. The internal flow field of the optimized muffler was obtained in the computational fluid dynamics software FLUENT, and the acoustic and filtration performance was studied. The experimental and simulation results indicated that the optimized muffler could achieve both particle filtration and noise reduction.

AbstractBackground:Road traffic noise (RTN) is a risk factor for cardiovascular disease (CVD) and hypertension; however, few studies have looked into its association with blood pressure (BP) and renal function in patients with prior CVD.Aim:This study aimed to explore the effect of residential RTN exposure on BP and renal function in patients with CVD from Plovdiv Province.Materials and methods:We included 217 patients with ischemic heart disease and/or hypertension from three tertiary hospitals in the city of Plovdiv (March – May 2016). Patients’ medical history, medical documentation, and medication regimen were reviewed, and blood pressure and anthropometric measurements were taken. Blood samples were analyzed for creatinine, total cholesterol, and blood glucose. Participants also filled a questionnaire. Glomerular filtration rate was estimated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. All participants were asked about their annoyance by different noise sources at home, and those living in the city of Plovdiv (n = 132) were assigned noise map Ldenand Lnightexposure. The effects of noise exposure on systolic blood pressure (SBP), diastolic blood pressure (DBP), and estimated glomerular filtration rate (eGFR) were explored using mixed linear models.Results:Traffic noise annoyance was associated with higher SBP in the total sample. The other noise indicators were associated with non-significant elevation in SBP and reduction in eGFR. The effect of Ldenwas more pronounced in patients with prior ischemic heart disease/stroke, diabetes, obesity, not taking Ca-channel blockers, and using solid fuel/gas at home. Lnighthad stronger effect among those not taking statins, sleeping in a bedroom with noisy façade, having a living room with quiet façade, and spending more time at home. The increase in Ldenwas associated with a significant decrease in eGFR among men, patients with ischemic heart disease/stroke, and those exposed to lower air pollution. Regarding Lnight, there was significant effect modification by gender, diabetes, obesity, and time spent at home. In some subgroups, the effect of RTN was statistically significant.Conclusions:Given that generic risk factors for poor progression of cardiovascular diseases cannot be controlled sufficiently at individual level, environmental interventions to reduce residential noise exposure might result in some improvement in the management of blood pressure and kidney function in patients with CVD.

Size exclusion chromatography (SEC), which separates molecules based on molecular volume, can be coupled with online inductively coupled plasma mass spectrometry (ICP-MS) to explore size-dependent metal–natural organic matter (NOM) complexation. To make effective use of this analytical dual detector system, the operator should be mindful of quality control measures. Al, Cr, Fe, Se, and Sn all exhibited columnless attenuation, which indicated unintended interactions with system components. Based on signal-to-noise ratio and peak reproducibility between duplicate analyses of environmental samples, consistent peak time and height were observed for Mg, Cl, Mn, Cu, Br, and Pb. Al, V, Fe, Co, Ni, Zn, Se, Cd, Sn, and Sb were less consistent overall, but produced consistent measurements in select samples. Ultrafiltering and centrifuging produced similar peak distributions, but glass fiber filtration produced more high molecular weight (MW) peaks. Storage in glass also produced more high MW peaks than did plastic bottles.

Indoor air quality is an important health factor as we spend more than 80% of our time indoors. The primary type of indoor pollutant is particulate matter, high levels of which increase respiratory disease risk. Therefore, air purifiers are a common choice for addressing indoor air pollution. Compared with traditional filtration purifiers, negative ion air purifiers (NIAPs) have gained popularity due to their energy efficiency and lack of noise. Although some studies have shown that negative ions may offset the cardiorespiratory benefits of air purifiers, the underlying mechanism is still unclear. In this study, we conducted a full-scale experiment using an in vitro airway model connected to a breathing simulator to mimic inhalation. The model was constructed using computed tomography scans of human airways and 3D-printing technology. We then quantified the effects of NIAPs on the administered dose of 0.5–2.5 μm particles in the small airway. Compared with the filtration purifier, the NIAP had a better dilution effect after a 1-h exposure and the cumulative administered dose to the small airway was reduced by 20%. In addition, increasing the negative ion concentration helped reduce the small airway exposure risk. NIAPs were found to be an energy-efficient air purification intervention that can effectively reduce the small airway particle exposure when a sufficient negative ion concentration is maintained.

A computational method for matched filtration with analytical profile of the blurred digital image of the investigated objects on digital frames has been developed. Such «blurred» objects can be the result of an involuntary shift of a fixed camera, an incorrect choice of the mode of guiding the telescope (diurnal or object tracking) or a failure of the diurnal tracking. This computational method is based on the analytical selection of the typical form of the object’s image, as well as on the choice of special parameters for the transfer function of the matched filter for the blurred digital image, which makes it possible to evaluate the required parameters of the blurred digital image. In addition, determining the number of Gaussians of the object’s image makes it possible to perform the most accurate assessment of the initial approximation of the parameters of their shape. Thus, matched filtration makes it possible to highlight the investigated objects with a blurred image of a typical shape against the background of substrate noise. Using the computational method of matched filtration makes it possible to improve the segmentation of images of reference objects on the frame and reduce the number of false detections. The developed computational method for matched filtration with analytical profile of the blurred digital image of the investigated objects on the frames was tested in practice as part of the research of the CoLiTec project. It was implemented in the intraframe processing unit of the Lemur software for the operational automated detection of new and observation of known objects with a weak brightness. Owing to the Lemur software using and the proposed computational method introduced into it, more than 500,000 measurements of the various investigated objects were successfully processed and identified.

This study presents the results of a review of publications conducted by researchers in a variety of climates on the implementation of ‘green roofs’ and their impact on the urban environment. Features of green roofs in urban areas have been characterized by a particular emphasis on: Filtration of air pollutants and oxygen production, reduction of rainwater volume discharged from roof surfaces, reduction of so-called ‘urban heat islands’, as well as improvements to roof surface insulation (including noise reduction properties). The review of the publications confirmed the necessity to conduct research to determine the coefficients of the impact of green roofs on the environment in the city centers of Central and Eastern Europe. The results presented by different authors (most often based on a single case study) differ significantly from each other, which does not allow us to choose universal coefficients for all the parameters of the green roof’s impact on the environment. The work also includes analysis of structural recommendations for the future model green roof study, which will enable pilot research into the influence of green roofs on the environment in urban agglomerations and proposes different kinds of plants for different kinds of roofs, respectively.

Abstract Rainfall is a precious water resource, especially for Shenzhen with scarce local water resources. Therefore, an effective rainfall prediction model is essential for improvement of water supply efficiency and water resources planning in Shenzhen. In this study, a deep learning model based on zero sum game (ZSG) was proposed to predict ten-day rainfall, the regular models were constructed for comparison, and the cross-validation was performed to further compare the generalization ability of the models. Meanwhile, the sliding window mechanism, differential evolution genetic algorithm, and discrete wavelet transform were developed to solve the problem of data non-stationarity, local optimal solutions, and noise filtration, respectively. The k-means clustering algorithm was used to discover the potential laws of the dataset to provide reference for sliding window. Mean square error (MSE), Nash–Sutcliffe efficiency coefficient (NSE) and mean absolute error (MAE) were applied for model evaluation. The results indicated that ZSG could better optimize the parameter adjustment process of models, and improved generalization ability of models. The generalization ability of the bidirectional model was superior to that of the unidirectional model. The ZSG-based models showed stronger superiority compared with regular models, and provided the lowest MSE (1.29%), NSE (21.75%), and MAE (7.5%) in the ten-day rainfall prediction.

This work presents a novel metal-supported bilayer lipid membrane (BLM) biosensor built on tyrosinase to quantitate phenol. The detection strategy is based on the enzyme–analyte initial association and not the commonly adopted monitoring of the redox cascade reactions; such an approach has not been proposed in the literature to date and offers many advantages for environmental monitoring with regard to sensitivity, selectivity, reliability and assay simplicity. The phenol sensor developed herein showed good analytical and operational characteristics: the detection limit (signal-to-noise ratio = 3) was 1.24 pg/mL and the sensitivity was 33.45 nA per pg/mL phenol concentration. The shelf life of the tyrosinase sensor was 12 h and the lifetime (in consecutive assays) was 8 h. The sensor was reversible with bathing at pH 8.5 and could be used for eight assay runs in consecutive assays. The validation in real water samples showed that the sensor could reliably detect 2.5 ppb phenol in tap and river water and 6.1 ppb phenol in lake water, without sample pretreatment. The prospects and applicability of the proposed biosensor and the underlying technology are also discussed.

Introduction. In this paper, there are presented results of investigations on the development and validation of the method for the determination of dicamba residues in import foodstuff (soybeans). The identification and quantitative determination of dicamba are performed by capillary gas-liquid chromatography with mass-selective detection (ionization-electron impact). Material and methods. The stages of sample preparation include four steps as extraction with acidified acetonitrile, followed by the filtration and evaporation; freezing the sample with filtration and evaporation; dissolving the dry residue in a mixture of acetone: water (1:20); purification by repeated redistribution in a system of immiscible solvents under varying the pH of the aqueous medium (pH 9-10: dichloromethane, hexane, pH 2: hexane-tert-butyl methyl ether). The chromatographic measurement was preceded by the derivatization of the acid to the corresponding methyl ester by the treatment with a solution of diazomethane in diethyl ether Results. The lower limit of the quantitative evaluation of dicamba in samples of soybean beans is of 0.01 mg/kg, the signal-to-noise ratio at the detection limit accounts of 20:1. The completeness of extraction of dicamba, established on the basis of analysis of model samples with the introduction of a substance at four points within the defined range, amounted to 85-95%. The average quadratic deviation of the repetition varies in the range of 3.3-4.9%. Discussion. The use of diethyl ether containing dibutylhydroxytoluene (6 ppm) as a stabilizer, as well as the interfering effect of phthalates, led to the formation of poorly resolved peaks of the methyl ester of dicamba, dibutylhydroxytoluene, and dibutyl phthalate. The combination of the use of different methods of data collection (in the scanning mode and in the mode of recording individual ions) made it possible to identify these components. Replacement of the used diethyl ether, as well as variation of chromatographic conditions, for separation of dicamba and phthalate peaks, made it possible to achieve the necessary selectivity of detection of the analyte.

Being the driest continent in the world, there is a significant reliance on groundwater resources within many communities and industries throughout Australia. Particularly in regional areas with low rainfall and surface runoff resources, the underlying groundwater availability plays a pivotal role in population capacity and economic prosperity. Whilst the importance of groundwater resources is indisputable, many aspects of its real world homeostatic processes, in both macro and micro scales, remain difficult to decipher and explain. Within Australia’s fractured rock aquifer systems, attributed with storage of the largest volume of groundwater resources nationally, there is still only fragmented understandings of several of their principal components and capacities. This is inclusive even of key aquifer characteristics, such as total volume estimations, regeneration sources, and their flow or transportation methods. Improved modeling capabilities and techniques based on prominent and robust hydrogeological principals are continually emerging from advancing technologies, new data sources and forward thinking. However, within the field data retrieval facet of hydrological research a seemingly slower evolution is taking place. A vast quantity of aquifer information is still derived directly from intrusive observation wells. Although the plethora of information these wells can yield in modelling is invaluable, there are some profound limitations that must still be addressed. Wells are costly to establish due to drilling expenses, can only provide single point information, and can also be disruptive to the homeostasis of the system. The self-potential method is an electro-kinetic geophysical method that has recently been re-identified as an immensely promising groundwater technique. It is a fast, passive, inexpensive surface technique which requires no drilling. Uniquely and most importantly however, it is the only geophysical method that is directly sensitive to not only the presence of groundwater, but also the physical flow of groundwater due to its generation of a measurable electrical signal. Previously regarded as a predominately qualitative geophysical tool, contributing factors including advancements in low-cost instrumentation and processing capabilities have meant self-potential surveys can now provide spatially significant quantitative data for a range of groundwater modelling inputs such as permeability. The method has been recurrently reviewed since its early conception in international geophysical literature through to modern times. However, only a small quantity of this peer reviewed research has been conducted within Australia. A lesser extent of published literature therefore deals in particularly with addressing the challenges of both our harsh climate, and surface and geological conditions. With our own unique geological and hydrogeological settings, current and future challenges regarding securement of groundwater resources, and increasingly common practice of industrial geotechnical processes such as fracking, all research and findings are vital contributions to furthering our understanding of potential groundwater applications for self-potential methods on home soil. This research thesis provides analyses of multiple electro-kinetic field research projects. New self-potential datasets have been collected in the Adelaide Hills targeting stimulated fractured-rock aquifers up to 40m below surface - a considerably deep target for the method, particularly within highly conductive Australian geological conditions. Previously collected geophysical datasets from the Adelaide Hills have been reprocessed from two to four-dimensions utulising newly constructed algorithms, then reanalysed with supporting geophysical datasets. And finally, a long term (46 day) self-potential monitoring program was conducted at a commercial-use porous media aquifer to investigate novel techniques in both autonomous groundwater flow presence investigation, and environmental noise filtering methodologies for a given self-potential dataset. This research endeavors to draw further conclusion on the self-potential methods prospective as a value-adding and commercial viability modern geophysical technique in Australian groundwater research. Additionally, employing use of artificial neural networks (machine learning) for the self-potential autonomous detection and environmental noise filtration methods, we highlight the current gap in geophysical literature regarding the combination of these techniques. A light is drawn to the combined techniques immensely promising future of potential applications and contributions within the wider electrical geophysics data automation and filtration space. Much akin to our continual pursuit for mineralisation deposits, Australia is searching deeper than ever before for crucial groundwater supplies as shallower sedimentary aquifers are becoming fully utilised or depleted. As we move forward towards this new era of deepening natural resources, we must further develop both old and new tools which can enhance clarity of understanding within these challenging hydrogeological systems.
Thesis (MPhil) -- University of Adelaide, School of Physical Sciences, 2018

Ultrafine particles (UFP) emitted by traffic have been associated with health risks for people living and working near major roadways. Studies have shown that people living in near-roadway communities experience higher risk of aggravated asthma, respiratory diseases and even childhood leukemia. Sharp concentration gradients of UFP have been reported near major highways with the concentration decreasing rapidly away from the road. Dispersion of UFP downwind of a road depends on many parameters, such as the atmospheric stability and wind speed. Presence of different structures such as noise barriers and vegetation can greatly influence the dispersion and downwind concentrations of UFP. These structures can block the traffic emissions and increase vertical mixing. In addition, vegetation can reduce UFP by deposition processes. Two sets of experiments were conducted in this thesis to investigate the effect of barriers on UFP deposition and dispersion.

The first set of experiments was performed in a wind tunnel facility to address UFP deposition to vegetation barriers solely. Two analytical models were proposed to characterize UFP dry deposition to vegetation measured during the wind tunnel experiment. The first model was derived from the filtration theory to explain UFP dry deposition to pine and juniper branches. The model agrees well with the experimental data indicating that pine and juniper branches can be treated as fibrous filters. The fiber diameters of pine derived from the experimental data were also similar to the physical diameters of pine needles; thus, providing further evidence that vegetation can be regarded as fibers. The second model was derived from the continuity equation and can predict the branch-scale dry deposition of UFP using conventional canopy properties such as the drag coefficient and leaf area density. Both models agree with the measurement results to within 20%.

The second set of experiments was done in three near-roadway environments to investigate the effects of barriers on the dispersion and dry deposition of UFP. We used mobile and stationary measurements to obtain the spatial and temporal variability of UFP. Both mobile and stationary measurements indicated that vegetation and noise barriers can reduce downwind UFP concentrations through dispersion and dry deposition by 20-60 %.

In conclusion, the effect of barriers on UFP dispersion and deposition has been characterized in this thesis. Two analytical models were also proposed from the wind tunnel experiments to characterize dry deposition and agreed well with the measurement results. The analytical model could benefit future climate and air quality models.

Dissertation

Abstract Acquiring acoustic slowness data in open & cased hole and a reliable cement bond log in one run without jeopardising data quality or increasing rig time is desired for fast and optimize data acquisition. This paper reviews the steps taken to ensure acoustic slowness and cement bond data acquisition fulfils the objective, while minimising the cost in an offshore challenging environment for formations with variable acoustic velocities that could be masked by strong casing arrivals. Crossed dipole acoustic logging is typically preferred to acquire within open hole environment for best quality signal. However, due to drilling challenges this could not be done in the subject well. Data was acquired in 6in open hole and 7" liner (8.5 in Open hole behind) cased hole section together in one run. Shear slowness in slow formation requires propagation of the low frequency dipole flexural wave whereas compressional slowness acquisition and cement bond evaluation requires high frequency monopole data. An improved understanding of cased-hole acoustic modes allowed developing the ability to transmit acoustic energies at optimal frequencies in order to acquire formation slowness concurrently with cement bond. Acoustic data quality in cased hole is dependent on cement bond quality. Poor bonding or presence of fluid between casing and the formation inserts noise in the data by damping the acoustic signal. Hence, understanding of the cement bond quality is critical in interpreting the cased hole acoustic data. The low amplitude of the compressional first arrival indicated the presence of cement bonded with the casing. Absence of casing ringing signal at the beginning and presence of strong formation signal in the VDL indicated good bonding of cement with formation. Filtration of the cased hole acquired semblances were necessary to remove the casing and fluids noises. Acquired data shows good coherency and continuous compressional and shear slowness's were extracted from the good quality semblances. This integrated strategy to acquire the formation slowness and to evaluate the cement bond quality and top of cement allowed meeting all objectives with one tool in single run. The risk of casing waves that could have masked the formation slowness signal was mitigated by transmitting acoustic energies at optimal frequencies with wider bandwidth followed by the semblance processing. The effects of borehole ovality, tool centralization, or casing centralization on waveform propagation were studied to supplement the interpretation. The first times strategic logging application in PETRONAS allowed time and cost saving and fulfilled all data acquisition plan. Data quality assurance and decision tree allowed drafting a workflow to assure data quality. This solution showed importance of smart planning to maximise advanced tools capabilities to acquire acoustic slowness data and cement evaluation in single run in offshore challenging environment.