Empirical revision of noise mapping

Makarewicz, Rufin; Galuszka, Michal

doi:10.1016/j.apacoust.2010.10.012

Cited by 17 publications

(8 citation statements)

References 5 publications

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“…Apoint estimation of noise indicators has been already performed [2,3,4]. It should be noted that the probability of point estimation of aparameter being equal to the actual value of the estimated parameter is close to zero.…”

Section: Introductionmentioning

confidence: 98%

Bootstrap Confidence Intervals for Noise Indicators

Stępień¹

2016

Acta Acustica united with Acustica

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Five non-classical models of interval estimation based on the bootstrap resampling method are compared in this paper.The non-classical models do not have to assume belonging of random sample to normal distribution. Theoretical basis of the proposed models is presented and an example of calculation process which makes possible determining confidence intervals of the expected value of long-term noise indicators L DEN and L N .The statistical analysis wascarried out for 95% interval widths obtained by using each of these models. The inference of their usefulness wasperformed on the basis of results of non-parametric statistical tests at significance level α = 0.05. The data used to illustrate the proposed solutions and carry out the analysis were results of continuous monitoring of trafficnoise recorded in 2009 in one of the main arteries of Krakow,Poland.

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Section: Introductionmentioning

confidence: 98%

Bootstrap Confidence Intervals for Noise Indicators

Stępień¹

2016

Acta Acustica united with Acustica

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“…For annual L DEN estimation, researchers have shown that sampling random days during the year gives better precision and representativeness of year equivalent levels than other techniques such as sampling consecutive days, only workdays or only weekends, or random full weeks [27]. Moreover, several authors use random sampling as a basis for comparison of optimizations or improvements proposed in sampling techniques for long-term level estimation [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Annual traffic noise levels estimation based on temporal stratification

Quintero

Balastegui

Garbí

2018

Journal of Environmental Management

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This paper proposes a temporal sampling strategy that increases the accuracy of long-term noise level estimation and allows to establish the estimation error according to the number of sampled days. Days of the week are stratified into working days and weekend days. This research shows how to use measurements of L on working days to estimate the corresponding values for weekend days. This is possible because working days have higher noise levels and less variability than weekend days. The improvement in accuracy allows for a reduction in the number of required sampled days compared to taking samples randomly, which would help to reduce the uncertainty in environmental noise assessment. As a reference, to obtain a 90% confidence interval of ±1 dB for L, the proposed sampling strategy reduces the required measurement days by more than 38%. For L, the reduction is close to 18% of the total number of days. The proposed strategy could be adapted to different environments by simply changing a few parameters.

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“…13 Sampling spans can also be applied to shorten measurement duration; both long-term (complete day measurements) and short-term (few minute measurements) strategies are possible. Though statistical relations can be used to assess the uncertainty on the estimation of L den , 14 it is more current to evaluate the validity of sampling strategies from measurements, to tackle in the mean time the problem of the representativeness of samples. A method to derive the value of L den from the L day is proposed in Zambon et al, 2006.…”

Section: Introductionmentioning

confidence: 99%

Sampling approaches to predict urban street noise levels using fixed and temporary microphones

et al. 2011

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Requirements for static (prediction of L(den) and diurnal averaged noise pattern) and dynamic (prediction of 15 min and 60 min evolution of L(Aeq) and statistical levels L(A90,)L(A50) and L(A10)) noise level monitoring are investigated in this paper. Noise levels are measured for 72 consecutive days at 5 neighboring streets in an inner-city noise measurement network in Gent, Flanders, Belgium. We present a method to make predictions based on a fixed monitoring station, combined with short-term sampling at temporary stations. It is shown that relying on a fixed station improves the estimation of L(den) at other locations, and allows for the reduction of the number of samples needed and their duration; L(den) is estimated with an error that does not exceed 1.5 dB(A) to 3.4 dB(A) according to the location, for 90% of the 3 × 15 min samples. Also the diurnal averaged noise pattern can be estimated with a good accuracy in this way. It was shown that there is an optimal location for the fixed station which can be found by short-term measurements only. Short-term level predictions were shown to be more difficult; 7 day samples were needed to build models able to estimate the evolution of L(Aeq,60min) with a RMSE ranging between 1.4 dB(A) and 3.7 dB(A). These higher values can be explained by the very pronounced short-term variations appearing in typical streets, which are not correlated between locations. On the other hand, moderately accurate predictions can be achieved, even based on short-term sampling (a 3 × 15 minute sampling duration seems to be sufficient for many of the accuracy goals set related to static and dynamic monitoring). Finally, the method proposed also allows for the prediction of the evolution of statistical indicators.

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Empirical revision of noise mapping

Cited by 17 publications

References 5 publications

Bootstrap Confidence Intervals for Noise Indicators

Bootstrap Confidence Intervals for Noise Indicators

Annual traffic noise levels estimation based on temporal stratification

Sampling approaches to predict urban street noise levels using fixed and temporary microphones

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