Abstract
High-resolution air quality simulations are often performed using different nested domains and resolutions. In this study, the variability of nitrogen dioxide (NO2) concentrations estimated from two nested domains focused on Portugal (D2 and D3), with 5 and 1 km horizontal grid resolutions, respectively, was investigated by applying the WRF-Chem model for the year 2015. The main goal and innovative aspect of this study is the simulation of a whole year with high resolutions to analyse the spatial variability under the simulation grids in conjunction with detailed land cover (LC) data specifically processed for these high-resolution domains. The model evaluation was focused on Portuguese air quality monitoring stations taking into consideration the station typology. As main results, it should be noted that (i) D3 urban LC categories enhanced pollution hotspots; (ii) generally, modelled NO2 was underestimated, except for rural stations; (iii) differences between D2 and D3 estimates were small; (iv) higher resolution did not impact model performance; and (v) hourly D2 estimates presented an acceptable quality level for policy support. These modelled values are based on a detailed LC classification (100 m horizontal resolution) and coarse spatial resolution (approximately 10 km) emission inventory, the latter suitable for portraying background air pollution problems. Thus, if the goal is to characterise urban/local-scale pollution patterns, the use of high grid resolution could be advantageous, as long as the input data are properly represented.
Highlights
The degradation of air quality around the world, especially in cities, is a consequence of the exponential population growth, intensification of anthropic activities, and lack of urban planning
Air quality guidelines for long-term exposure, mainly to fine particulate matter (PM2.5). These exceedances representing outdoor air pollution are the cause of millions of premature deaths worldwide per year (4.2 million in 2016), being that 91% of these deaths occurred in low- and middle-income countries [1]
Among the main pollution sources, the road traffic sector is responsible for a large proportion of urban air pollution, primarily with regard to nitrogen dioxide (NO2 ) levels, accounting for 39% of NO2 emissions in Europe [3,4]
Summary
The degradation of air quality around the world, especially in cities, is a consequence of the exponential population growth, intensification of anthropic activities, and lack of urban planning. According to the World Health Organisation (WHO), in 2019, 99% of the population was living in areas where pollutant concentrations exceeded the 2005 WHO air quality guidelines for long-term exposure, mainly to fine particulate matter (PM2.5) These exceedances representing outdoor air pollution are the cause of millions of premature deaths worldwide per year (4.2 million in 2016), being that 91% of these deaths occurred in low- and middle-income countries [1]. In the case of traffic-related NO2 , there is a large spatial variability due to the occurrence of different atmospheric circulation patterns and, rapidly falling concentrations with the distance from the road [4,5] In this context, the use of specific modelling tools for assessing air quality from global to local scales using different temporal and grid resolutions is crucial to understand the processes and sources leading to air pollution, as well as to build a basis for policies defining air quality improvement strategies [6,7,8,9]. The increasing option by online models that integrate the parallel computation of meteorology and chemistry represents an additional value to evaluate potential feedbacks, including, for example, direct aerosol effects on the absorption and scattering of solar radiation and the impact of local weather patterns on chemical reactions [10,11,12]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
