scholarly journals A multi-pollutant and multi-sectorial approach to screen the consistency of emission inventories

2021 ◽  
Author(s):  
Philippe Thunis ◽  
Alain Clappier ◽  
Enrico Pisoni ◽  
Bertrand Bessagnet ◽  
Jeroen Kuenen ◽  
...  
Keyword(s):  
Large Scale ◽  
Geographical Area ◽  
Screening Method ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Screening Approach ◽  
Large Geographical Area ◽  
The Way

Abstract. Some studies show that significant uncertainties affect emission inventories, which may impeach conclusions based on air quality model results. These uncertainties result from the need to compile a wide variety of information to estimate an emission inventory. In this work, we propose and discus a screening method to compare two emission inventories, with the overall goal of improving the quality of emission inventories by feeding back the results of the screening to inventory compilers who can check the inconsistencies found and where applicable resolve errors. The method targets three different aspects: 1) the total emissions assigned to a series of large geographical area, countries in our application; 2) the way these country total emissions are shared in terms of sector of activity and 3) the way inventories spatially distribute emissions from countries to smaller areas, cities in our application. The first step of the screening approach consists in sorting the data and keep only emission contributions that are relevant enough. In a second step, the method identifies, among those significant differences, the most important ones that are evidence of methodological divergence and/or errors that can be found and resolved in at least one of the inventories. The approach has been used to compare two versions of the CAMS-REG European scale inventory over 150 cities in Europe for selected activity sectors. Among the 4500 screened pollutant-sectors, about 450 were kept as relevant among which 46 showed inconsistencies. The analysis indicated that these inconsistencies were almost equally arising from large scale reporting and spatial distribution differences. They mostly affect SO2 and PM coarse emissions from the industrial and residential sectors. The screening approach is general and can be used for other types of applications related to emission inventories.

scholarly journals Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

2017 ◽  
Author(s):  
Jianlin Hu ◽  
Xun Li ◽  
Lin Huang ◽  
Qi Ying ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Health Effects ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Model Performance ◽  
Observation Data ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1 h. The study demonstrates that ensemble predictions by combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories and the results are publicly available for future health effects studies.

scholarly journals Inferring long-distance connectivity shaped by air-mass movement for improved experimental design in aerobiology

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maria Choufany ◽  
Davide Martinetti ◽  
Samuel Soubeyrand ◽  
Cindy E. Morris
Keyword(s):  
Large Scale ◽  
Mass Movement ◽  
Geographical Area ◽  
Small World ◽  
Long Distance ◽  
Air Mass ◽  
Key Factor ◽  
Large Geographical Area ◽  
Spatio Temporal ◽  
Small World Property

AbstractThe collection and analysis of air samples for the study of microbial airborne communities or the detection of airborne pathogens is one of the few insights that we can grasp of a continuously moving flux of microorganisms from their sources to their sinks through the atmosphere. For large-scale studies, a comprehensive sampling of the atmosphere is beyond the scopes of any reasonable experimental setting, making the choice of the sampling locations and dates a key factor for the representativeness of the collected data. In this work we present a new method for revealing the main patterns of air-mass connectivity over a large geographical area using the formalism of spatio-temporal networks, that are particularly suitable for representing complex patterns of connection. We use the coastline of the Mediterranean basin as an example. We reveal a temporal pattern of connectivity over the study area with regions that act as strong sources or strong receptors according to the season of the year. The comparison of the two seasonal networks has also allowed us to propose a new methodology for comparing spatial weighted networks that is inspired from the small-world property of non-spatial networks.

Validation and optimization of the ATMO-Street air quality model chain by means of a large-scale citizen-science dataset

2022 ◽  
pp. 118946
Author(s):  
H. Hooyberghs ◽  
S. De Craemer ◽  
W. Lefebvre ◽  
S. Vranckx ◽  
B. Maiheu ◽  
...  
Keyword(s):  
Air Quality ◽  
Citizen Science ◽  
Large Scale ◽  
Air Quality Model ◽  
Quality Model ◽  
Model Chain

scholarly journals Inverse modeling of Texas NO<sub>x</sub> emissions using space-based and ground-based NO<sub>2</sub> observations

2013 ◽  
Vol 13 (21) ◽  
pp. 11005-11018 ◽  
Author(s):  
W. Tang ◽  
D. S. Cohan ◽  
L. N. Lamsal ◽  
X. Xiao ◽  
W. Zhou
Keyword(s):  
Inverse Modeling ◽  
Direct Method ◽  
A Priori ◽  
Model Performance ◽  
Ground Level ◽  
Nox Emissions ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Ground Level Ozone

Abstract. Inverse modeling of nitrogen oxide (NOx) emissions using satellite-based NO2 observations has become more prevalent in recent years, but has rarely been applied to regulatory modeling at regional scales. In this study, OMI satellite observations of NO2 column densities are used to conduct inverse modeling of NOx emission inventories for two Texas State Implementation Plan (SIP) modeling episodes. Addition of lightning, aircraft, and soil NOx emissions to the regulatory inventory narrowed but did not close the gap between modeled and satellite-observed NO2 over rural regions. Satellite-based top-down emission inventories are created with the regional Comprehensive Air Quality Model with extensions (CAMx) using two techniques: the direct scaling method and discrete Kalman filter (DKF) with decoupled direct method (DDM) sensitivity analysis. The simulations with satellite-inverted inventories are compared to the modeling results using the a priori inventory as well as an inventory created by a ground-level NO2-based DKF inversion. The DKF inversions yield conflicting results: the satellite-based inversion scales up the a priori NOx emissions in most regions by factors of 1.02 to 1.84, leading to 3–55% increase in modeled NO2 column densities and 1–7 ppb increase in ground 8 h ozone concentrations, while the ground-based inversion indicates the a priori NOx emissions should be scaled by factors of 0.34 to 0.57 in each region. However, none of the inversions improve the model performance in simulating aircraft-observed NO2 or ground-level ozone (O3) concentrations.

scholarly journals Inverse modeling of Texas NO<sub>x</sub> emissions using space-based and ground-based NO<sub>2</sub> observations

2013 ◽  
Vol 13 (7) ◽  
pp. 17479-17517
Author(s):  
W. Tang ◽  
D. Cohan ◽  
L. N. Lamsal ◽  
X. Xiao ◽  
W. Zhou
Keyword(s):  
Inverse Modeling ◽  
Direct Method ◽  
A Priori ◽  
Model Performance ◽  
Ground Level ◽  
Nox Emissions ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Ground Level Ozone

Abstract. Inverse modeling of nitrogen oxide (NOx) emissions using satellite-based NO2 observations has become more prevalent in recent years, but has rarely been applied to regulatory modeling at regional scales. In this study, OMI satellite observations of NO2 column densities are used to conduct inverse modeling of NOx emission inventories for two Texas State Implementation Plan (SIP) modeling episodes. Addition of lightning, aircraft, and soil NOx emissions to the regulatory inventory narrowed but did not close the gap between modeled and satellite observed NO2 over rural regions. Satellite-based top-down emission inventories are created with the regional Comprehensive Air Quality Model with extensions (CAMx) using two techniques: the direct scaling method and discrete Kalman filter (DKF) with Decoupled Direct Method (DDM) sensitivity analysis. The simulations with satellite-inverted inventories are compared to the modeling results using the a priori inventory as well as an inventory created by a ground-level NO2 based DKF inversion. The DKF inversions yield conflicting results: the satellite-based inversion scales up the a priori NOx emissions in most regions by factors of 1.02 to 1.84, leading to 3–55% increase in modeled NO2 column densities and 1–7 ppb increase in ground 8 h ozone concentrations, while the ground-based inversion indicates the a priori NOx emissions should be scaled by factors of 0.34 to 0.57 in each region. However, none of the inversions improve the model performance in simulating aircraft-observed NO2 or ground-level ozone (O3) concentrations.

MCEQLS Approach in Multi-Criteria Evaluation of Quality of Learning Repositories

2013 ◽  
pp. 96-117 ◽  
Author(s):  
Eugenijus Kurilovas
Keyword(s):  
User Interfaces ◽  
Large Scale ◽  
Optimization Methods ◽  
Multiple Criteria ◽  
Quality Model ◽  
Multi Criteria Evaluation ◽  
Quality Of Learning ◽  
Learning Software ◽  
Evaluation Of Learning

This chapter analyzes the quality of XML learning object repositories. Special attention is paid to the models and methods to evaluate the quality of learning repositories. Multiple criteria decision analysis and optimization methods are explored to be applied for evaluating the quality of learning repositories. This chapter also presents the results of several large-scale projects co-funded by EU research programs that have been implemented in the area of learning repositories. Learning repositories’ technological quality model (system of criteria) and novel comprehensive model for evaluating the quality of user interfaces of learning repositories are presented in more detail. The general MCEQLS (Multiple Criteria Evaluation of Learning Software) approach is presented in this chapter. It is shown that the MCEQLS approach is suitable for evaluating the quality of learning repositories. The author believes that research results presented in the chapter will be useful for all educational stakeholder groups interested in developing learning repositories.

284 Acute neurology in aneurin bevan university health board

2018 ◽  
Vol 89 (10) ◽  
pp. A43.1-A43
Author(s):  
Joe Anderson
Keyword(s):  
Geographical Area ◽  
Service Evaluation ◽  
Undergraduate Teaching ◽  
Admission Diagnosis ◽  
Health Board ◽  
Acute Medicine ◽  
Routine Work ◽  
Neurology Clinic ◽  
Large Geographical Area

Aneurin Bevan University Health Board (ABUHB) provides for a population of 6 40 000 in South East Wales, in addition to South Powys. The large geographical area contains 3 sites with acute unselected medical intake as well as 3 other hospitals. The neurology department, comprising 6 consultants (1 stroke/neurology), 1 associate specialist and 1 trainee, is based in Newport. Service evaluation (2013) revealed that 1 in 6 neurology clinic appointments were used to see patients discharged from acute medicine, with a median waiting time of 14 weeks. In 2015 the ‘Neurologist of the Week’ service was launched; 5 consultants participate on a weekly rota. Routine work is cancelled and replaced with a 10 DCC (~40 hours) acute neurology week. This includes 3 acute clinics (each 3 patients), daily MAU round for the largest site, and a triage and advice service for primary and secondary care. Visits to other hospital sites are made when needed. The service has led to significant improvements in quality of care, neurology training and undergraduate teaching and is highly valued by colleagues. Repeated evaluations show ~55% of acute clinic patients are discharged, with ~40% of appointments preventing or shortening an admission. Diagnosis is significantly changed in ~40% of consultations.

scholarly journals Mechanism reduction for the formation of secondary organic aerosol for integration into a 3-dimensional regional Air Quality Model: <i>α</i>-pinene oxidation system

2008 ◽  
Vol 8 (4) ◽  
pp. 13301-13354
Author(s):  
A. G. Xia ◽  
D. V. Michelangeli ◽  
P. A. Makar
Keyword(s):  
Screening Method ◽  
Oxidation Mechanism ◽  
Principal Component ◽  
Organic Aerosol ◽  
General Mechanism ◽  
Air Quality Model ◽  
Quality Model ◽  
Sensitivity Matrix ◽  
Mechanism Reduction ◽  
Wide Range

Abstract. A detailed α-pinene oxidation mechanism was reduced systematically through the successive application of five mechanism reduction techniques. The resulting reduced mechanism preserves the ozone- and organic aerosol-forming properties of the original mechanism, while using less species. The methodologies employed included a directed relation graph method with error propagation (DRGEP, which removed a large number of redundant species and reactions), principal component analysis of the rate sensitivity matrix (PCA, used to remove unnecessary reactions), the quasi-steady-state approximation (QSSA, used to remove some QSS species), an iterative screening method (ISSA, which removes redundant species and reactions simultaneously), and a new lumping approach dependant on the hydrocarbon to NOx ratio (which reduced the number of species in mechanism subsets for specific hydrocarbon to NOx ranges). This multistage methodology results in a reduction ratio of 2.5 for the number of both species and reactions compared with the full mechanism. The simplified mechanism reproduces the important gas and aerosol phase species (the latter are examined in detail by individual condensing species as well as in classes according to four functional groups: PANs, nitrates, organic peroxides, and organic acids). The total SOA mass is also well represented in the condensed mechanism, to within 16% of the detailed mechanism under a wide range of conditions. The methodology described here is general, and may be used in general mechanism reduction problems.

scholarly journals <i>E pluribus unum</i>*: ensemble air quality predictions

2013 ◽  
Vol 13 (14) ◽  
pp. 7153-7182 ◽  
Author(s):  
S. Galmarini ◽  
I. Kioutsioukis ◽  
E. Solazzo
Keyword(s):  
Air Quality ◽  
Forecast Error ◽  
Air Quality Model ◽  
Quality Model ◽  
Novel Approach ◽  
Forecasting Method ◽  
Forcing Mechanisms ◽  
Improved Accuracy ◽  
International Initiative

Abstract. In this study we present a novel approach for improving the air quality predictions using an ensemble of air quality models generated in the context of AQMEII (Air Quality Model Evaluation International Initiative). The development of the forecasting method makes use of modelled and observed time series (either spatially aggregated or relative to single monitoring stations) of ozone concentrations over different areas of Europe and North America. The technique considers the underlying forcing mechanisms on ozone by means of spectrally decomposed previsions. With the use of diverse applications, we demonstrate how the approach screens the ensemble members, extracts the best components and generates bias-free forecasts with improved accuracy over the candidate models. Compared to more traditional forecasting methods such as the ensemble median, the approach reduces the forecast error and at the same time it clearly improves the modelled variance. Furthermore, the result is not a mere statistical outcome depended on the quality of the selected members. The few individual cases with degraded performance are also identified and analysed. Finally, we show the extensions of the approach to other pollutants, specifically particulate matter and nitrogen dioxide, and provide a framework for its operational implementation. *One out of many

scholarly journals Examination of Nudging Schemes in the Simulation of Meteorology for Use in Air Quality Experiments: Application in the Great Lakes Region

2019 ◽  
Vol 58 (11) ◽  
pp. 2421-2436 ◽  
Author(s):  
M. Talat Odman ◽  
Andrew T. White ◽  
Kevin Doty ◽  
Richard T. McNider ◽  
Arastoo Pour-Biazar ◽  
...  
Keyword(s):  
Air Quality ◽  
Great Lakes ◽  
Large Scale ◽  
Lake Michigan ◽  
Forecast Errors ◽  
Great Lakes Region ◽  
Air Quality Model ◽  
Quality Model ◽  
Four Dimensional Data Assimilation ◽  
The Impact

AbstractHigh levels of ozone have been observed along the shores of Lake Michigan for the last 40 years. Models continue to struggle in their ability to replicate ozone behavior in the region. In the retrospective way in which models are used in air quality regulation development, nudging or four-dimensional data assimilation (FDDA) of the large-scale environment is important for constraining model forecast errors. Here, paths for incorporating large-scale meteorological conditions but retaining model mesoscale structure are evaluated. For the July 2011 case studied here, iterative FDDA strategies did not improve mesoscale performance in the Great Lakes region in terms of diurnal trends or monthly averaged statistics, with overestimations of nighttime wind speed remaining as an issue. Two vertical nudging strategies were evaluated for their effects on the development of nocturnal low-level jets (LLJ) and their impacts on air quality simulations. Nudging only above the planetary boundary layer, which has been a standard option in many air quality simulations, significantly dampened the amplitude of LLJ relative to nudging only above a height of 2 km. While the LLJ was preserved with nudging only above 2 km, there was some deterioration in wind performance when compared with profiler networks above the jet between 500 m and 2 km. In examining the impact of nudging strategies on air quality performance of the Community Multiscale Air Quality model, it was found that performance was improved for the case of nudging above 2 km. This result may reflect the importance of the LLJ in transport or perhaps a change in mixing in the models.

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