DENL

AcronymDefinition
DENLDay-Evening-Night Level (road traffic noise calculation)
References in periodicals archive ?
For establishing the mean annoyance as a function of DNL or DENL, it is important to note that the estimated annoyance distribution is non-zero outside the interval [0,100], whereas the actual annoyance scores are restricted to that interval.
We presented a model of the distribution of noise annoyance with the mean varying as a function of the noise exposure; DNL and DENL were used as noise descriptors.
The predictability of the annoyance of the general population exposed to a certain noise level (DNL or DENL) is quantified by the width of the confidence interval at that noise level for the noise and annoyance measure concerned.
The model of annoyance as a function of noise exposure (described by DNL or DENL) was fitted to the data from a large set of field studies in which noise exposure and noise annoyance were determined.
Another, more important elaboration of the present model would be the inclusion of more (exposure) variables as predictors of annoyance, in addition to DNL or DENL (at the most exposed side of a dwelling).
Expectations regarding DENL - DNL on the basis of time patterns.
There is no consistent relation between DNL and DENL. The difference between the two metrics depends on the time pattern of the noise exposure.
Assuming a decreasing pattern of [L.sub.Aeq] as described above, the lowest value of DENL - DNL is equal to -0.06 dB.
This means that railway noise generally does not fullfill the two requirements for a significant value of DENL - DNL [stability of the (hourly) [L.sub.Aeq] until 2200 hr and a sharp decrease at 2200-2300 hr].
Assuming this, the above-mentioned calculations indicate that for road traffic noise DENL - DNL will generally be [is less than] 0.5.
Little can be said about the consequence for the value of DENL - DNL.
On the basis of the expectations derived from the time patterns of the noise level and the available relevant empirical evidence, we conclude that the following equations can be used to transform the DNL of a noise exposure into DENL: