I. Ribeiro

Air quality modelling as a supplementary assessment method in the framework of the European Air Quality Directive

According to the European Air Quality (AQ) Directive, member states must annually report their AQ to the European Commission (EC). This report can be based on modelling data if the concentration levels do not exceed the established lower assessment thresholds (LAT), or on combined data from modelling and monitoring systems (supplementary assessment methods) if concentrations levels are below the upper assessment threshold (UAT). This work presents and applies a methodology that combines air pollutant concentration values from monitored data and from a numerical modelling system to deliver AQ information for Portugal in 2010. This methodology produces improved information, especially for areas where the amount of fixed monitoring stations is sparse or non-existent, allowing obtaining a better and broader overview of the AQ in Portugal to support AQ reporting to the European Commission.

Ensemble Techniques to Improve Air Quality Assessment: Focus on O3 and PM

Five air quality models were applied over Portugal for July 2006 with an ensemble purpose. These models were used, with their own meteorology, parameterizations, boundary conditions and chemical mechanisms, but with the same emission data. The validation of the individual models and its ensemble for ozone (O3) and particulate matter was performed using monitoring data from 22 background stations over Portugal. After removing the bias from each model, different ensemble techniques were applied and compared. Besides the median, several weighted ensemble approaches were tested and intercompared: static (SLR) and dynamic (DLR) multiple linear regressions (using less-square optimization method) and the Bayesian Model Averaging (BMA) methodology. The goal of the comparison is to estimate to what extent the ensemble analysis is an improvement with respect to the single model results. The obtained results revealed that no one of the 4 tested ensembles clearly outperforms the others on the basis of statistical parameters and probabilistic analysis (reliability and resolution properties). Nevertheless, statistical results have shown that the application of the weights slightly improves ensemble performance when compared to those obtained from the median ensemble. The same statistical analysis together with the probabilistic measures demonstrates that the SLR and BMA methods are the best performers amongst the assessed methodologies.

Children’s exposure to traffic-related pollution: assessment of CO exposure in a typical school day

This paper evaluates the exposure of four children to carbon monoxide (CO) in two different classrooms during the time spent in school in a typical school day, using a numerical modelling approach. The study is focused on an area of 550 × 550 m 2 centred at a primary school of the city of Aveiro, in central Portugal, which is located close to a road with moderate traffic and has naturally ventilated rooms. Air quality data were measured in the school yard. Traffic emissions were estimated with TREM model, using traffic counts data. Simulations of CO concentrations in the study domain were performed with the computational fluid dynamics model VADIS, considering the influence of buildings and trees over the dispersion. Indoor concentrations were simulated using a mass transfer approach. Results show that the individual exposure of children is spatially dependent, as a consequence of the wind flow and air pollutant dispersion patterns.

Improvement of Ensemble Technique Using Spectral Analysis and Decomposition of Air Pollution Data

The current study proposes a novel approach for the multi-model ensemble to be applied in air pollution forecasting. The methodology is based on decomposition of air pollution time series on different components (short-term, daily fluctuations, synoptic scale, etc.) and calibration of the ensemble for each of these components independently taking into account the performance of individual predictors. Therefore, the same model may have a different contribution for the ensemble at high and low frequency fluctuations. The Kolmogorov-Zurbenko (KZ) low-pass filter is used for the time series decomposition. The Fourier analysis is implemented to determine the contribution of different frequencies to the data variance allowing better understanding of the model performance and to define the ensemble weights. The methodology was tested using a group of four different air quality models that were applied over mainland Portugal for the 2006 year, and for main pollutants like O3 and PM10. The approach implemented in this work was compared with one of the most used ensemble technique showing clear advantages.

Air Quality Modelling and Its Applications

Air quality numerical modelling systems are powerful tools for research and policy-making purposes. They describe mathematically the innumerable physical and chemical processes that characterise the atmosphere, with the aim of estimating the air quality levels over a region, ranging from the entire globe to a street, through a long- or short-term analysis. In this chapter an overview on selected air quality models is provided, with examples of numerical applications in the scope of the assessment of the air quality impacts caused by hypothetical changes on emissions, climate and other conditions.

How does the use of biodiesel affect urban air quality

This study aims to assess the influence of the use of a B20 fuel by road transports on urban air quality. For this purpose emission scenarios were designed and their impacts on air quality assessed using the WRF-EURAD modelling system applied over the Porto urban area. The scenarios consider emissions of CO, NOx, NMVOC, PM10, PM2.5, formaldehyde, acetaldehyde, acrolein and benzene from the use of pure diesel (reference scenario) and the use of a diesel blended with 20%(v/v) of biodiesel (B20 scenario). The results reveal that the use of B20 fuels improves the air quality over the Porto urban area, reducing the concentrations of most of the studied pollutants, with the exception of NO2. However, the concentrations vary in a small amount. Despite carbonyl compound emissions not being considered as individual input for the WRF-EURAD numerical modelling system, the equivalent ozone production estimated from the compounds emission variations indicate that the use of B20 can increase the O3 concentrations over the Porto urban area.