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Green infrastructures have been pointed out as innovative solutions to deal with current and future challenges related to air pollution and climate change. Although the potential of green infrastructures, such as green walls and green roofs, to mitigate air pollution has been documented, evidence at a local scale is still limited. This work aims to increase knowledge about the potentialities of green infrastructures in improving local air quality, focusing on particulate matter, nitrogen dioxide and ozone pollutants, and by using a local-scale computational fluid dynamics model. The ENVI-met model was applied to a particular hour of a summer day over a built-up environment centred on a main avenue in the city of Lisbon (Portugal). The dimensions of the computational domain are 618 m × 594 m × 143 m, and it contains 184 buildings, with the tallest building being 56 m. In addition to the baseline simulation, modelling was also done considering the application of green walls and green roofs to specific buildings located near the main avenue, together with a green corridor. The overall results show no disturbances exerted by green walls on the turbulent flow dynamics and on the air quality levels when compared to the baseline scenario (without green walls). The integrated scenario, which includes green walls, green roofs and a green corridor, will lead to potential local benefits of green infrastructures on O3 concentrations, followed by variable impacts on NO2 and particulate matter concentrations. [ABSTRACT FROM AUTHOR]

Air pollution causes damage and imposes risks on human health, especially in cities, where the pollutant load is a major concern, although the extent of these effects is still largely unknown. Thus, taking the busiest road traffic area in Portugal as a local case study (600 m × 600 m domain, 4 m2 spatial resolution), the objective of this work was to investigate two health risk methodologies (linear and nonlinear), which were applied for estimating short-term health impacts related to daily variations of high-resolution ambient nitrogen dioxide (NO2) concentrations modelled for winter and summer periods. Both approaches are based on the same general equation and health input metrics, differing only in the relative risk calculation. Health outcomes, translated into the total number of cases and subsequent damage costs, were compared, and their associated uncertainties and challenges for health impact modelling were addressed. Overall, for the winter and summer periods, health outcomes considering the whole simulation domain were lower using the nonlinear methodology (less 27% and 28%, respectively). Spatially, these differences are more noticeable in locations with higher NO2 and population values, where the highest health estimates were obtained. When the daily NO2 exposure was less than 6 µg.m−3, a fact that occurred in 95% of the domain cells and in both periods, relatively small differences between approaches were found. Analysing the seasonality effect, total health impacts derived from the linear and nonlinear applications were greater in summer (around 18% in both approaches). This happens due to the magnitude and spatial variability of NO2, as the other health input metrics remained constant. This exploratory research in local scale health impact assessment (HIA) demonstrated that the use of refined input data could contribute to more accurate health estimates and that the nonlinear approach is probably the most suitable for characterising air pollution episodes, thus providing important support in HIA. [ABSTRACT FROM AUTHOR]

Pearl River Delta (PRD), located in south-eastern coast of mainland China, is one of the regions affected by heavy particulate matter (PM) levels. Notwithstanding the potential use of meteorological and air quality modelling to characterize the air pollution problems, little attention has been paid to meteorological model configuration and its impact on air quality modelling applications over the region. Aiming to find the most suitable set of parameterization schemes of the Advanced Research Weather Research and Forecasting (WRF-ARW) model for air quality modelling applications over the PRD region, a performance experiment was performed. Three tests, with different combinations of parameterization schemes, were created and evaluated. For the best configuration modelling setup, meteorological simulations for a winter (i.e. January) and summer (i.e. July) periods are provided. The meteorological model showed a clockwise deviation for the wind direction and tends to overestimate the temperature and wind speed. It is expected that the present work could reduce the meteorological and air quality modelling uncertainty over the PRD region. [ABSTRACT FROM AUTHOR]