scholarly journals Development and application of a high resolution hybrid modelling system for the evaluation of urban air quality

2016 ◽  
Vol 141 ◽  
pp. 297-311 ◽  
Author(s):  
N. Pepe ◽  
G. Pirovano ◽  
G. Lonati ◽  
A. Balzarini ◽  
A. Toppetti ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Urban Air Quality ◽  
Urban Air ◽  
Hybrid Modelling ◽  
Modelling System

High Resolution Urban Air Quality Modeling by Coupling CFD and Mesoscale Models: a Review

2019 ◽  
Vol 55 (4) ◽  
pp. 539-556 ◽  
Author(s):  
Rakesh Kadaverugu ◽  
Asheesh Sharma ◽  
Chandrasekhar Matli ◽  
Rajesh Biniwale
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Air Quality Modeling ◽  
Urban Air Quality ◽  
Urban Air ◽  
Mesoscale Models ◽  
Quality Modeling

Networks of Air Quality Sensors and Their Use for High-resolution Mapping of Urban Air Quality

2020 ◽  
Author(s):  
Philipp Schneider ◽  
Nuria Castell ◽  
Paul Hamer ◽  
Sam-Erik Walker ◽  
Alena Bartonova
Keyword(s):  
Sensor Networks ◽  
Air Quality ◽  
High Resolution ◽  
Air Pollutants ◽  
Low Cost ◽  
Urban Air Quality ◽  
Urban Air ◽  
Sensor Systems ◽  
Air Quality Model ◽  
Quality Model

<p>One of the most promising applications of low-cost sensor systems for air quality is the possibility to deploy them in relatively dense networks and to use this information for mapping urban air quality at unprecedented spatial detail. More and more such dense sensor networks are being set up worldwide, particularly for relatively inexpensive nephelometers that provide PM<sub>2.5</sub> observations with often quite reasonable accuracy. However, air pollutants typically exhibit significant spatial variability in urban areas, so using data from sensor networks alone tends to result in maps with unrealistic spatial patterns, unless the network density is extremely high. One solution is to use the output from an air quality model as an a priori field and as such to use the combined knowledge of both model and sensor network to provide improved maps of urban air quality. Here we present our latest work on combining the observations from low-cost sensor systems with data from urban-scale air quality models, with the goal of providing realistic, high-resolution, and up-to-date maps of urban air quality.</p><p>In previous years we have used a geostatistical approach for mapping air quality (Schneider et al., 2017), exploiting both low-cost sensors and model information. The system has now been upgraded to a data assimilation approach that integrates the observations from a heterogeneous sensor network into an urban-scale air quality model while considering the sensor-specific uncertainties. The approach further ensures that the spatial representativity of each observation is automatically derived as a combination of a model climatology and a function of distance. We demonstrate the methodology using examples from Oslo and other cities in Norway. Initial results indicate that the method is robust and provides realistic spatial patterns of air quality for the main air pollutants that were evaluated, even in areas where only limited observations are available. Conversely, the model output is constrained by the sensor data, thus adding value to both input datasets.</p><p>While several challenging issues remain, modern air quality sensor systems have reached a maturity level at which some of them can provide an intra-sensor consistency and robustness that makes it feasible to use networks of such systems as a data source for mapping urban air quality at high spatial resolution. We present our current approach for mapping urban air quality with the help of low-cost sensor networks and demonstrate both that it can provide realistic results and that the uncertainty of each individual sensor system can be taken into account in a robust and meaningful manner.</p><p> </p><p>Schneider, P., Castell N., Vogt M., Dauge F. R., Lahoz W. A., and Bartonova A., 2017. Mapping urban air quality in near real-time using observations from low-cost sensors and model information. Environment international, 106, 234-247.</p>

scholarly journals A very high-resolution assessment and modelling of urban air quality

2019 ◽  
Author(s):  
Tobias Wolf ◽  
Lasse H. Pettersson ◽  
Igor Esau
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
High Resolution ◽  
Landscape Heterogeneity ◽  
Urban Air Quality ◽  
City Centre ◽  
Urban Air ◽  
Large Eddy Simulation Model ◽  
The City ◽  
Very High

Abstract. Urban air quality is one of the most prominent environmental concerns for a modern city dweller. Accurate monitoring of air quality is difficult due to intrinsic urban landscape heterogeneity and superposition of multiple polluting sources. Existing approaches often do not provide the necessary spatial details and peak concentrations of pollutants, especially at larger distances from measuring stations. A more advanced approach is needed. This study presents a very high-resolution air quality assessment with the large-eddy simulation model PALM. This fully three-dimensional primitive-equation hydro-dynamical model resolves both structural details of the complex urban surface and turbulent eddies larger than 10 m in size. We ran a set of 9 meteorological scenarios in order to assess the dispersion of pollutants in Bergen, a middle-sized Norwegian city embedded in a coastal valley. This set of scenarios represents typically observed conditions with high air pollution from nitrogen dioxide (NO2) and particulate matter (PM2.5). The modelling methodology helped to identify pathways and patterns of air pollution caused by the three main local air pollution sources in the city. These are road vehicle traffic, domestic house heating with wood-burning fireplaces and ships docked in the harbour area next to the city centre. The study produced vulnerability maps, highlighting the most impacted districts for each scenario.

Advances on urban air quality modeling: bias correction approach for estimated annual NO2 levels and macroscopic traffic simulators for scenario planning

2021 ◽  
Author(s):  
Jan Mateu Armengol ◽  
Daniel Rodriguez-Rey ◽  
Jaime Benavides ◽  
Oriol Jorba ◽  
Marc Guevara ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Traffic Flow ◽  
Bias Correction ◽  
Scenario Planning ◽  
Urban Air Quality ◽  
Pollutant Emission ◽  
Urban Air ◽  
Air Quality Forecast ◽  
Modelling System

<p>Awareness of air pollution impacts on public health is quickly increasing, especially in urban areas where legal air quality (AQ) limits are often exceeded. This awareness has driven policymakers to minimize citizens' exposure not only by direct legislative control in emissions (i.e., the application of a Low Emission Zone), but also by applying mobility restrictions to modify traffic patterns, and by the use of forecasted warnings to alert citizens of air pollution episodes. The European AQ directives encourage the use of numerical models to support the design and evaluation of such strategies.</p><p>In this framework, we present a versatile AQ model, CALIOPE-Urban (Benavides et al., 2019), able to address the threefold objectives to (i) compute urban air quality forecast at the street-scale resolution; (ii) to perform reanalysis studies of historical periods using a bias correction methodology that preserves the model spatial variability; and (iii) to simulate the traffic flow response to the application of different traffic restrictions and their effect on urban AQ.</p><p>In this contribution, we discuss two specific applications. On the one hand, CALIOPE-Urban is used to estimate the NO2 levels in the city of Barcelona (Spain) during the entire year of 2019. To do so, we report accurate maps of NO2 levels during the whole year by consistently integrating the AQ model data with publicly available observations from the official monitoring network in Catalonia (XVPCA) available in Barcelona by means of a bias correction method. On the other hand, the macroscopic traffic simulator BCN-VML (Rodriguez-Rey et al. 2021) coupled with CALIOPE-Urban is used to assess the AQ impact of the traffic flow-induced changes after the application of a traffic restriction policy. </p><p><strong>References</strong></p><p>Benavides, J., Snyder, M., Guevara, M., Soret, A., Pérez García-Pando, C., Amato, F., Querol, X., and Jorba, O.: CALIOPE-Urban v1.0: coupling R-LINE with a mesoscale air quality modelling system for urban air quality forecasts over Barcelona city (Spain), Geosci. Model Dev., 12, 2811–2835, https://doi.org/10.5194/gmd-12-2811-2019, 2019.</p><p>Rodriguez-Rey, D., Guevara, M., Linares, MP., Casanovas, J., Salmerón, J., Soret, A., Jorba, O., Tena, C., Pérez García-Pando, C.: A coupled macroscopic traffic and pollutant emission modelling system for Barcelona, Transportation Research Part D, accepted for publication.</p>

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