Eulerian Modeling of Atmospheric Dispersion Over Portugal: Background Concentrations and Emission Data Preparation

1991 ◽  
pp. 409-417
Author(s):  
C. Borrego ◽  
M. Coutinho ◽  
J. Rua
Keyword(s):  
Atmospheric Dispersion ◽  
Data Preparation ◽  
Background Concentrations ◽  
Emission Data ◽  
Eulerian Modeling

scholarly journals Modelling the dispersion of particle numbers in five European cities

2016 ◽  
Vol 9 (2) ◽  
pp. 451-478 ◽  
Author(s):  
J. Kukkonen ◽  
M. Karl ◽  
M. P. Keuken ◽  
H. A. C. Denier van der Gon ◽  
B. R. Denby ◽  
...  
Keyword(s):  
Particle Number ◽  
Atmospheric Dispersion ◽  
Emission Inventories ◽  
Vehicular Traffic ◽  
Background Concentrations ◽  
Reasonable Accuracy ◽  
Annual Average ◽  
Urban Background ◽  
European Cities ◽  
Regional Background

Abstract. We present an overview of the modelling of particle number concentrations (PNCs) in five major European cities, namely Helsinki, Oslo, London, Rotterdam, and Athens, in 2008. Novel emission inventories of particle numbers have been compiled both on urban and European scales. We used atmospheric dispersion modelling for PNCs in the five target cities and on a European scale, and evaluated the predicted results against available measured concentrations. In all the target cities, the concentrations of particle numbers (PNs) were mostly influenced by the emissions originating from local vehicular traffic. The influence of shipping and harbours was also significant for Helsinki, Oslo, Rotterdam, and Athens, but not for London. The influence of the aviation emissions in Athens was also notable. The regional background concentrations were clearly lower than the contributions originating from urban sources in Helsinki, Oslo, and Athens. The regional background was also lower than urban contributions in traffic environments in London, but higher or approximately equal to urban contributions in Rotterdam. It was numerically evaluated that the influence of coagulation and dry deposition on the predicted PNCs was substantial for the urban background in Oslo. The predicted and measured annual average PNCs in four cities agreed within approximately  ≤  26 % (measured as fractional biases), except for one traffic station in London. This study indicates that it is feasible to model PNCs in major cities within a reasonable accuracy, although major challenges remain in the evaluation of both the emissions and atmospheric transformation of PNCs.

scholarly journals Evaluation of a new inference method for estimating ammonia volatilisation from multiple agronomic plots

2018 ◽  
Vol 15 (11) ◽  
pp. 3439-3460 ◽  
Author(s):  
Benjamin Loubet ◽  
Marco Carozzi ◽  
Polina Voylokov ◽  
Jean-Pierre Cohan ◽  
Robert Trochard ◽  
...  
Keyword(s):  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Agricultural Practices ◽  
Surface Fluxes ◽  
Control Treatment ◽  
Background Concentrations ◽  
Inference Method ◽  
Surface Exchange ◽  
Novel Method ◽  
Ammonia Volatilisation

Abstract. Tropospheric ammonia (NH3) is a threat to the environment and human health and is mainly emitted by agriculture. Ammonia volatilisation following application of nitrogen in the field accounts for more than 40 % of the total NH3 emissions in France. This represents a major loss of nitrogen use efficiency which needs to be reduced by appropriate agricultural practices. In this study we evaluate a novel method to infer NH3 volatilisation from small agronomic plots consisting of multiple treatments with repetition. The method is based on the combination of a set of NH3 diffusion sensors exposed for durations of 3 h to 1 week and a short-range atmospheric dispersion model, used to retrieve the emissions from each plot. The method is evaluated by mimicking NH3 emissions from an ensemble of nine plots with a resistance analogue–compensation point–surface exchange scheme over a yearly meteorological database separated into 28-day periods. A multifactorial simulation scheme is used to test the effects of sensor numbers and heights, plot dimensions, source strengths, and background concentrations on the quality of the inference method. We further demonstrate by theoretical considerations in the case of an isolated plot that inferring emissions with diffusion sensors integrating over daily periods will always lead to underestimations due to correlations between emissions and atmospheric transfer. We evaluated these underestimations as −8 % ± 6 % of the emissions for a typical western European climate. For multiple plots, we find that this method would lead to median underestimations of −16 % with an interquartile [−8–22 %] for two treatments differing by a factor of up to 20 and a control treatment with no emissions. We further evaluate the methodology for varying background concentrations and NH3 emissions patterns and demonstrate the low sensitivity of the method to these factors. The method was also tested in a real case and proved to provide sound evaluations of NH3 losses from surface applied and incorporated slurry. We hence showed that this novel method should be robust and suitable for estimating NH3 emissions from agronomic plots. We believe that the method could be further improved by using Bayesian inference and inferring surface concentrations rather than surface fluxes. Validating against controlled source is also a remaining challenge.

scholarly journals Improving the spatial resolution of air-quality modelling at a European scale – development and evaluation of the Air Quality Re-gridder Model (AQR v1.1)

2016 ◽  
Vol 9 (12) ◽  
pp. 4475-4489 ◽  
Author(s):  
Mark R. Theobald ◽  
David Simpson ◽  
Massimo Vieno
Keyword(s):  
Air Quality ◽  
Spatial Variability ◽  
Spatial Resolution ◽  
Atmospheric Chemistry ◽  
High Spatial Resolution ◽  
Atmospheric Dispersion ◽  
Substantial Improvement ◽  
Monitoring Networks ◽  
Concentration Data ◽  
Emission Data

Abstract. Currently, atmospheric chemistry and transport models (ACTMs) used to assess impacts of air quality, applied at a European scale, lack the spatial resolution necessary to simulate fine-scale spatial variability. This spatial variability is especially important for assessing the impacts to human health or ecosystems of short-lived pollutants, such as nitrogen dioxide (NO2) or ammonia (NH3). In order to simulate this spatial variability, the Air Quality Re-gridder (AQR) model has been developed to estimate the spatial distributions (at a spatial resolution of 1  ×  1 km2) of annual mean atmospheric concentrations within the grid squares of an ACTM (in this case with a spatial resolution of 50  ×  50 km2). This is done as a post-processing step by combining the coarse-resolution ACTM concentrations with high-spatial-resolution emission data and simple parameterisations of atmospheric dispersion. The AQR model was tested for two European sub-domains (the Netherlands and central Scotland) and evaluated using NO2 and NH3 concentration data from monitoring networks within each domain. A statistical comparison of the performance of the two models shows that AQR gives a substantial improvement on the predictions of the ACTM, reducing both mean model error (from 61 to 41 % for NO2 and from 42 to 27 % for NH3) and increasing the spatial correlation (r) with the measured concentrations (from 0.0 to 0.39 for NO2 and from 0.74 to 0.84 for NH3). This improvement was greatest for monitoring locations close to pollutant sources. Although the model ideally requires high-spatial-resolution emission data, which are not available for the whole of Europe, the use of a Europe-wide emission dataset with a lower spatial resolution also gave an improvement on the ACTM predictions for the two test domains. The AQR model provides an easy-to-use and robust method to estimate sub-grid variability that can potentially be extended to different timescales and pollutants.

scholarly journals A sub-grid model for improving the spatial resolution of air quality modelling at a European scale

2016 ◽  
Author(s):  
Mark R. Theobald ◽  
David Simpson ◽  
Massimo Vieno
Keyword(s):  
Air Quality ◽  
Spatial Variability ◽  
Spatial Resolution ◽  
Atmospheric Chemistry ◽  
High Spatial Resolution ◽  
Atmospheric Dispersion ◽  
Monitoring Networks ◽  
Concentration Data ◽  
Emission Data ◽  
Grid Model

Abstract. Currently, atmospheric chemistry and transport models (CTMs) used to assess impacts of air quality applied at a European scale lack the spatial resolution necessary to simulate fine-scale spatial variability. This spatial variability is especially important for assessing the impacts to human health or ecosystems of short-lived pollutants, such as nitrogen dioxide (NO2) or ammonia (NH3). In order to simulate this spatial variability, a sub-grid model has been developed to estimate the spatial distributions (at a spatial resolution of 1 × 1 km2) of annual mean atmospheric concentrations within the grid squares of a CTM (in this case with a spatial resolution of 50 × 50 km2). This is done by combining high spatial resolution emission data with simple parameterisations of atmospheric dispersion. The sub-grid model was tested for two European sub-domains (the Netherlands and central Scotland) and evaluated using NO2 and NH3 concentration data from monitoring networks within each domain. A statistical comparison of the performance of the two models shows that the sub-grid model represents a substantial improvement on the predictions of the CTM, reducing both mean model error (from 60 % to 40 % for NO2 and from 42 % to 26 % for NH3 and increasing the spatial correlation (r) with the measured concentrations (from 0.0 to 0.42 for NO2 and from 0.74 to 0.85 for NH3). This improvement was greatest for monitoring locations close to pollutant sources. Although the model ideally requires high spatial resolution emission data, which is not available for the whole of Europe, the use of a Europe-wide emission dataset with a lower spatial resolution also gives an improvement on the CTM predictions for the two test domains. The sub-grid model provides a simple and robust method to estimate sub-grid variability that can potentially be extended to different time scales and pollutants.

Automated Emission Data Registration and Segmentation for IC Analysis

2016 ◽  
Author(s):  
Franco Stellari ◽  
Peilin Song
Keyword(s):  
Data Analysis ◽  
Registration Accuracy ◽  
Emission Data ◽  
Data Registration ◽  
Different Types ◽  
Test Chips

Abstract In this paper, the development of advanced emission data analysis methodologies for IC debugging and characterization is discussed. Techniques for automated layout to emission registration and data segmentations are proposed and demonstrated using both 22 nm and 14 nm SOI test chips. In particular, gate level registration accuracy is leveraged to compare the emission of different types of gates and quickly create variability maps automatically.

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