scholarly journals Urban Air Quality Model Inter-Comparison Study (UMICS) for Improvement of PM2.5 Simulation in Greater Tokyo Area of Japan

2018 ◽  
Vol 12 (2) ◽  
pp. 139-152 ◽  
Author(s):  
Hikari Shimadera ◽  
Hiroshi Hayami ◽  
Satoru Chatani ◽  
Tazuko Morikawa ◽  
Yu Morino ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Air Quality ◽  
Urban Air ◽  
Comparison Study ◽  
Air Quality Model ◽  
Quality Model

Sensitivity of a Complex Urban Air Quality Model to Input Data

1981 ◽  
Vol 20 (9) ◽  
pp. 1020-1040 ◽  
Author(s):  
Christian Seigneur ◽  
Thomas W. Tesche ◽  
Philip M. Roth ◽  
Larry E. Reid
Keyword(s):  
Air Quality ◽  
Input Data ◽  
Urban Air Quality ◽  
Urban Air ◽  
Air Quality Model ◽  
Quality Model

A polynomial approximation of the traffic contributions for kriging-based interpolation of urban air quality model

2018 ◽  
Vol 105 ◽  
pp. 132-152 ◽  
Author(s):  
Maxime Beauchamp ◽  
Laure Malherbe ◽  
Chantal de Fouquet ◽  
Laurent Létinois ◽  
Frédéric Tognet
Keyword(s):  
Air Quality ◽  
Polynomial Approximation ◽  
Urban Air Quality ◽  
Urban Air ◽  
Air Quality Model ◽  
Quality Model

Urban emission inventory optimisation using sensor data, an urban air quality model and inversion techniques

2019 ◽  
Vol 66 (4) ◽  
pp. 252
Author(s):  
Martin Seaton ◽  
Ian Leslie ◽  
Amy Stidworthy ◽  
Rod Jones ◽  
Jo Dicks ◽  
...  
Keyword(s):  
Air Quality ◽  
Emission Inventory ◽  
Urban Air Quality ◽  
Sensor Data ◽  
Urban Air ◽  
Air Quality Model ◽  
Quality Model ◽  
Inversion Techniques ◽  
And Inversion

Estimates of Uncertainty in Urban Air Quality Model Predictions

2005 ◽  
pp. 483-491
Author(s):  
Sotiris Vardoulakis ◽  
Bernard Fisher ◽  
Norbert Gonzalez-Flesca ◽  
Koulis Pericleous
Keyword(s):  
Air Quality ◽  
Urban Air Quality ◽  
Urban Air ◽  
Air Quality Model ◽  
Quality Model ◽  
Model Predictions

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>

Urban emission inventory optimisation using sensor data, an urban air quality model and inversion techniques

2019 ◽  
Vol 66 (4) ◽  
pp. 252 ◽  
Author(s):  
David Carruthers ◽  
Amy Stidworthy ◽  
Daniel Clarke ◽  
Jo Dicks ◽  
Rod Jones ◽  
...  
Keyword(s):  
Air Quality ◽  
Emission Inventory ◽  
Urban Air Quality ◽  
Sensor Data ◽  
Urban Air ◽  
Air Quality Model ◽  
Quality Model ◽  
Inversion Techniques ◽  
And Inversion

scholarly journals Urban Air Quality Impacts of Distributed Generation

2003 ◽  
Author(s):  
Marc Medrano ◽  
Jack Brouwer ◽  
G. S. Samuelsen ◽  
Marc Carreras ◽  
Donald Dabdub
Keyword(s):  
Air Quality ◽  
Distributed Generation ◽  
Atmospheric Chemistry ◽  
Base Case ◽  
Urban Air ◽  
Air Quality Model ◽  
Quality Model ◽  
Case Scenario ◽  
Temporal Perspective ◽  
Base Case Scenario

Distributed Energy Resources (DER) have the potential to meet a significant portion of increased power demands of the future. DER applications can potentially provide benefits in electrical reliability and power quality, in addition to reducing total energy costs in combined cooling, heating and power (CHP) applications. However, the shift from a central generation paradigm to distributed generation results in different emissions characteristics and profiles from both a spatial and temporal perspective. Distributed generation is characterized by many sparsely distributed stationary sources within an urban air-shed compared to central generation where emissions sources are much larger, but typically located outside the air-shed in more remote locations. As a result, high market adoption of fuel-driven (non-renewable) distributed generation (DG) technologies, such as reciprocating engines and microturbines, may influence the air quality within a region. The present paper estimates air quality impacts for a representative distributed generation scenario in the South Coast Air Basin (SoCAB) of California. Simulations are based on the year 2010 with comparison to a base case scenario with no DG emissions. The DG scenarios are developed for a reasonable percentage of power met by DG, representative spatial distribution and temporal operation, and a mix of DG technologies and emissions factors. The resultant emissions inventory for each DG scenario is then provided as input to a three-dimensional air quality model including detailed atmospheric chemistry and transport for simulation of the SoCAB. Preliminary air quality results suggest that there will be an air quality impact, that the impacts will not be uniform throughout the air-shed, and that individual criteria pollutant concentrations may either rise or fall with the introduction of DG.

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