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

Abstract

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.

Measurement network for urban noise assessment: Comparison of mobile measurements and spatial interpolation approaches

“…The question of the density in the fixed stations installed required to do this correction cannot be answered in the framework of this study, whose network was too small to carry such analysis. The procedure will indeed gain in efficiency if stations located further can be used to capture temporal noise variations over the network; this can be expected since urban noise levels are highly correlated [34]. Further research will investigate this point.…”

“…First elements of response can be found in [32] [33], which showed how mobile measurements should be operated and processed, and in [34], which showed how temporary and fixed noise stations can be combined to estimate daily noise patterns.…”

Evolution of building façade road traffic noise levels in Flanders

“…This was recently confirmed with urban long-term measurements. 11 The uncertainty produced by this choice is evaluated in detail further in this paper.…”

“…These errors hold for extrapolating to day-evening-night equivalent levels L den 3 in the area considered in ref. 11. Note that in the current study, the aim is not to extrapolate to longer-term levels, but to look at the evolution in road traffic noise exposure over time by using a limited measurement duration.…”

Dynamic noise mapping: A map-based interpolation between noise measurements with high temporal resolution

“…A suitable aggregation period depends on the traffic intensity, but 10 to 15 minutes is usually chosen as an appropriate monitoring period. Sequential monitoring over such time intervals results in low standard errors between measurement periods for continuous traffic flows, and still includes enough temporal resolution to capture typical (diurnal) activity patterns [22]. Finally, one should also consider the latency in calculating updated maps.…”