Topic
Air pollutant concentrations
About: Air pollutant concentrations is a research topic. Over the lifetime, 1652 publications have been published within this topic receiving 36138 citations.
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TL;DR: Ass associations with surrounding green and air pollution generally remained, but attenuated, and joint odds ratios (JOR) of combined exposure to air pollution, rail-traffic noise and decreased surrounding green were higher than the odds ratios of single-exposure models.
30 citations
TL;DR: In this paper, the authors presented an urban environmental quality health index based on noise levels and air pollutant concentrations, which is calculated through a multi-criteria combination of noise and air pollutants concentrations, where trade-off is allowed.
30 citations
TL;DR: Estimates showed that the most affected residential areas were central districts in the city center from domestic heating emissions due to meteorological condition and demographic reasons, and air quality modeling is a great tool for assisting policy makers how to decrease emissions and improve air quality.
30 citations
TL;DR: In this article, the effects of traffic, road network, employment and social-demographic characteristics on air pollutant emissions at the level of traffic analysis zone (TAZ) were investigated.
30 citations
01 Oct 2013
TL;DR: A meta-modelling approach based on neural network evaluation is proposed to improve the estimated spatial distribution of the pollutant concentrations and provides more reliable results of the estimation and offers better predictions of ozone concentrations than those obtained by using the TAPM-CTM model only, when compared to the measurement data collected at monitoring stations.
Abstract: Continuous measurements of the air pollutant concentrations at monitoring stations serve as a reliable basis for air quality regulations. Their availability is however limited only at locations of interest. In most situations, the spatial distribution beyond these locations still remains uncertain as it is highly influenced by other factors such as emission sources, meteorological effects, dispersion and topographical conditions. To overcome this issue, a larger number of monitoring stations could be installed, but it would involve a high investment cost. An alternative solution is via the use of a deterministic air quality model (DAQM), which is mostly adopted by regulatory authorities for prediction in the temporal and spatial domain as well as for policy scenario development. Nevertheless, the results obtained from a model are subject to some uncertainties and it requires, in general, a significant computation time. In this work, a meta-modelling approach based on neural network evaluation is proposed to improve the estimated spatial distribution of the pollutant concentrations. From a dispersion model, it is suggested that the spatially-distributed pollutant levels (i.e. ozone, in this study) across a region under consideration is a function of the grid coordinates, topographical information, solar radiation and the pollutant's precursor emission. Initially, for training the model, the input-output relationship is extracted from a photochemical dispersion model called The Air Pollution Model and Chemical Transport Model (TAPM-CTM), and some of those input-output data are correlated with the ambient measurements collected at monitoring stations. Here, improved radial basis function networks, incorporating a proposed technique for selection of the network centres, will be developed and trained by using the data obtained and the forward selection approach. The methodology is then applied to estimate the ozone concentrations in the Sydney basin, Australia. Once executed, apart from the advantage of inexpensive computation, it provides more reliable results of the estimation and offers better predictions of ozone concentrations than those obtained by using the TAPM-CTM model only, when compared to the measurement data collected at monitoring stations.
29 citations