ADVANCING ENVIRONMENTAL NOISE POLLUTION ANALYSIS IN URBAN AREAS BY CONSIDERING THE VARIATION OF POPULATION EXPOSURE IN SPACE AND TIME
- e-GEO, Geography and Regional Planning Research Centre, FCSH, Universidade Nova de Lisboa, Lisbon, Portugal
- IN+, Center for Innovation, Technology and Policy Research, Instituto Superior Técnico, Oeiras, Portugal
Keywords: Environmental noise, Population exposure, Spatial analysis, Dasymetric mapping, Lisbon
Abstract. Ambient noise is a subtle form of pollution in large urban areas, degrading human health and well-being. In Europe, directives require that urban environmental noise be measured and mapped for the main periods of the daily cycle. Subsequent analyses of human exposure to noise in those periods is usually conducted using resident (i.e., nighttime) population from the census and assuming constant densities within the enumeration units. However, population distribution and densities vary considerably from night to day in metropolitan areas, and disregard for that process results in gross misestimation of exposure to ambient noise in the daytime period.
This study considers the spatio-temporal variation of population distribution in assessing exposure to ambient noise in a major urban area, the city of Lisbon, Portugal. Detailed and compatible day- and nighttime population distribution maps were used, developed by means of "intelligent dasymetric mapping". After categorizing noise levels in existing maps in each period, classified according to current legislation, human exposure to ambient noise was assessed with temporally matching population surfaces. Population exposure to noise in 2000 and 2009 was compared and further analyzed in regards to main source of noise, i.e. road traffic vs. aircraft..
Results show that human exposure to noise shifts substantially in time and space, with a significant increase in exposed population from the nighttime to daytime period, especially in the higher noise levels. This is due to the combined effects of the daily variation of noise patterns and population distribution.