Showing papers by "Yelva Roustan published in 2019"
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International Institute for Applied Systems Analysis1, University of Cyprus2, Arizona State University3, United Nations4, University of Hong Kong5, The Chinese University of Hong Kong6, Hong Kong University of Science and Technology7, Katholieke Universiteit Leuven8, University of Toronto9, Purdue University10, University of São Paulo11, University of Victoria12, University of Hamburg13, ParisTech14, University of Mainz15, University of Gothenburg16, University of Reading17, City University of New York18, San Jose State University19, National Cheng Kung University20, University of North Carolina at Chapel Hill21, Ben-Gurion University of the Negev22, Monash University23, Banaras Hindu University24
TL;DR: The Digital Synthetic City (DSC) tool as discussed by the authors uses crowdsourcing methods and sampling within city Testbeds from around the world to generate UCPs at any desired scale meeting the fit-forpurpose goal of WUDAPT.
Abstract: The WUDAPT (World Urban Database and Access Portal Tools project goal is to capture consistent information on urban form and function for cities worldwide that can support urban weather, climate, hydrology and air quality modeling. These data are provided as urban canopy parameters (UCPs) as used by weather, climate and air quality models to simulate the effects of urban surfaces on the overlying atmosphere. Information is stored with different levels of detail (LOD). With higher LOD greater spatial precision is provided. At the lowest LOD, Local Climate Zones (LCZ) with nominal UCP ranges is provided (order 100 m or more). To describe the spatial heterogeneity present in cities with great specificity at different urban scales we introduce the Digital Synthetic City (DSC) tool to generate UCPs at any desired scale meeting the fit-for-purpose goal of WUDAPT. 3D building and road elements of entire city landscapes are simulated based on readily available data. Comparisons with real-world urban data are very encouraging. It is customized (C-DSC) to incorporate each city's unique building morphologies based on unique types, variations and spatial distribution of building typologies, architecture features, construction materials and distribution of green and pervious surfaces. The C-DSC uses crowdsourcing methods and sampling within city Testbeds from around the world. UCP data can be computed from synthetic images at selected grid sizes and stored such that the coded string provides UCP values for individual grid cells.
49 citations
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Complutense University of Madrid1, Norwegian Institute for Air Research2, Swedish Meteorological and Hydrological Institute3, Carnegie Mellon University4, University of Paris5, Netherlands Organisation for Applied Scientific Research6, ENEA7, Barcelona Supercomputing Center8, Norwegian Meteorological Institute9, Chalmers University of Technology10, École des ponts ParisTech11, Free University of Berlin12, University of Tromsø13
TL;DR: In this paper, the authors compared six atmospheric chemistry transport models (CHIMERE, CMAQ, EMEP MSC-W, LOTOS-EUROS, MATCH and MINNI) within the framework of the EURODELTA-Trends model intercomparison.
Abstract: . The wet deposition of nitrogen and sulfur in Europe for the
period 1990–2010 was estimated by six atmospheric chemistry transport models
(CHIMERE, CMAQ, EMEP MSC-W, LOTOS-EUROS, MATCH and MINNI) within the
framework of the EURODELTA-Trends model intercomparison. The simulated wet
deposition and its trends for two 11-year periods (1990–2000 and
2000–2010) were evaluated using data from observations from the EMEP
European monitoring network. For annual wet deposition of oxidised nitrogen
(WNOx), model bias was within 30 % of the average of the observations for
most models. There was a tendency for most models to underestimate annual wet
deposition of reduced nitrogen (WNHx), although the model bias was within 40 %
of the average of the observations. Model bias for WNHx was inversely
correlated with model bias for atmospheric concentrations of NH 3 + NH 4 + , suggesting that an underestimation of wet deposition partially
contributed to an overestimation of atmospheric concentrations. Model bias
was also within about 40 % of the average of the observations for the
annual wet deposition of sulfur (WSOx) for most models. Decreasing trends in WNOx were observed at most sites for both 11-year
periods, with larger trends, on average, for the second period. The models
also estimated predominantly decreasing trends at the monitoring sites and
all but one of the models estimated larger trends, on average, for the second
period. Decreasing trends were also observed at most sites for WNHx, although
larger trends, on average, were observed for the first period. This pattern
was not reproduced by the models, which estimated smaller decreasing trends,
on average, than those observed or even small increasing trends. The largest
observed trends were for WSOx, with decreasing trends at more than 80 %
of the sites. On average, the observed trends were larger for the first
period. All models were able to reproduce this pattern, although some models
underestimated the trends (by up to a factor of 4) and others
overestimated them (by up to 40 %), on average. These biases in modelled
trends were directly related to the tendency of the models to under- or
overestimate annual wet deposition and were smaller for the relative trends
(expressed as % yr −1 relative to the deposition at the start of the
period). The fact that model biases were fairly constant throughout the time series
makes it possible to improve the predictions of wet deposition for future
scenarios by adjusting the model estimates using a bias correction calculated
from past observations. An analysis of the contributions of various factors
to the modelled trends suggests that the predominantly decreasing trends in
wet deposition are mostly due to reductions in emissions of the precursors
NOx , NH3 and SOx . However, changes
in meteorology (e.g. precipitation) and other (non-linear) interactions
partially offset the decreasing trends due to emission reductions during the
first period but not the second. This suggests that the emission reduction
measures had a relatively larger effect on wet deposition during the second
period, at least for the sites with observations.
34 citations
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University of Paris1, Carnegie Learning2, Norwegian Institute for Air Research3, Swedish Meteorological and Hydrological Institute4, Chalmers University of Technology5, Netherlands Organisation for Applied Scientific Research6, ENEA7, Norwegian Meteorological Institute8, École des ponts ParisTech9, Barcelona Supercomputing Center10, University of Tromsø11
TL;DR: In this article, several chemical transport models (CTMs) were applied for the 1990-2010 period to investigate air quality changes in Europe as well as the capability of the models to reproduce observed long-term air quality trends.
Abstract: . In the framework of the EURODELTA-Trends (EDT) modeling
experiment, several chemical transport models (CTMs) were applied for the
1990–2010 period to investigate air quality changes in Europe as well as
the capability of the models to reproduce observed long-term air quality
trends. Five CTMs have provided modeled air quality data for 21 continuous years in Europe using emission scenarios prepared by the International Institute for Applied Systems Analysis/Greenhouse Gas – Air Pollution Interactions and Synergies (IIASA/GAINS)
and corresponding year-by-year meteorology derived from ERA-Interim global
reanalysis. For this study, long-term observations of particle sulfate
( SO 4 2 - ), total nitrate ( TNO3 ), total ammonium ( TNHx ) as well as sulfur dioxide ( SO2 ) and nitrogen dioxide ( NO2 ) for multiple sites in Europe were used to evaluate the model results. The trend analysis was performed for the full 21 years (referred to as PT) but also for two 11-year subperiods: 1990–2000 (referred to as P1) and 2000–2010 (referred to as P2). The experiment revealed that the models were able to reproduce the faster
decline in observed SO2 concentrations during the first decade, i.e., 1990–2000, with a 64 %–76 % mean relative reduction in SO2 concentrations indicated by the EDT experiment (range of all the models) versus an 82 % mean relative reduction in observed concentrations. During the second decade (P2), the models estimated a mean relative reduction in SO2 concentrations of about 34 %–54 %, which was also in line with that
observed (47 %). Comparisons of observed and modeled NO2 trends
revealed a mean relative decrease of 25 % and between 19 % and 23 % (range of
all the models) during the P1 period, and 12 % and between 22 % and 26 %
(range of all the models) during the P2 period, respectively. Comparisons of observed and modeled trends in SO 4 2 - concentrations
during the P1 period indicated that the models were able to reproduce the
observed trends at most of the sites, with a 42 %–54 % mean relative
reduction indicated by the EDT experiment (range of all models) versus a
57 % mean relative reduction in observed concentrations and with good
performance also during the P2 and PT periods, even though all the models
overpredicted the number of statistically significant decreasing trends
during the P2 period. Moreover, especially during the P1 period, both
modeled and observational data indicated smaller reductions in
SO 4 2 - concentrations compared with their gas-phase precursor (i.e.,
SO2 ), which could be mainly attributed to increased oxidant levels and pH-dependent cloud chemistry. An analysis of the trends in TNO3 concentrations indicated a 28 %–39 % and 29 % mean relative reduction in TNO3 concentrations for the full period for model data (range of all the models) and observations,
respectively. Further analysis of the trends in modeled HNO3 and
particle nitrate ( NO 3 - ) concentrations revealed that the relative
reduction in HNO3 was larger than that for NO 3 - during the P1 period, which was mainly attributed to an increased availability of
“free ammonia”. By contrast, trends in modeled HNO3 and
NO 3 - concentrations were more comparable during the P2 period.
Also, trends of TNHx concentrations were, in general, underpredicted by all models, with worse performance for the P1 period than for P2. Trends in modeled anthropogenic and biogenic secondary organic aerosol (ASOA and BSOA) concentrations together with the trends in available emissions of
biogenic volatile organic compounds (BVOCs) were also investigated. A strong
decrease in ASOA was indicated by all the models, following the reduction in
anthropogenic non-methane VOC (NMVOC) precursors. Biogenic emission data provided by the
modeling teams indicated a few areas with statistically significant increase
in isoprene emissions and monoterpene emissions during the 1990–2010 period
over Fennoscandia and eastern European regions (i.e., around 14 %–27 %),
which was mainly attributed to the increase of surface temperature. However,
the modeled BSOA concentrations did not linearly follow the increase in
biogenic emissions. Finally, a comprehensive evaluation against positive
matrix factorization (PMF) data, available during the second period (P2) at
various European sites, revealed a systematic underestimation of the
modeled SOA fractions of a factor of 3 to 11, on average, most
likely because of missing SOA precursors and formation pathways, with
reduced biases for the models that accounted for chemical aging of
semi-volatile SOA components in the atmosphere.
25 citations
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TL;DR: A multitemporal methodology to retrieve aerosol type, to map the aerosol concentration, and to quantify mass flow rate from airborne hyperspectral data is described in this paper, which is in a good agreement with in situ stack measurements and modeling.
Abstract: In this paper, we focus on airborne hyperspectral imaging methodology to characterize particulate matter (PM) near industrial emission sources. Two short-term intensive campaigns were carried out in the vicinity of a refinery in the south of France, in September 2015 and February 2016. Different protocols of in situ PM measurements were performed, at stack measurements (flow rate and offline chemical analysis) and online measurement at the refinery border (size distribution, concentration, and chemistry of aerosols). A multitemporal methodology to retrieve aerosol type, to map the aerosol concentration, and to quantify mass flow rate from airborne hyperspectral data is described in this paper. This method applied to the refinery detected plume from the main stack yields a black carbon to sulfate ratio of 10/90 in mass inside the plume, with an average size distribution smaller than 100 nm. These results are in a good agreement with the online analysis of aerosols at the refinery border. The resulting quantitative map with a metric spatial resolution leads to an estimated flow rate of about 1 g/s and is in a good agreement with in situ stack measurements and modeling.
4 citations
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Purdue University1, University College Dublin2, International Institute for Applied Systems Analysis3, University of Cyprus4, University of Hong Kong5, The Chinese University of Hong Kong6, Hong Kong University of Science and Technology7, Katholieke Universiteit Leuven8, University of Toronto9, University of São Paulo10, University of Victoria11, University of Hamburg12, ParisTech13, University of Mainz14, University of Gothenburg15, University of Reading16, University Corporation for Atmospheric Research17, City University of New York18, San Jose State University19, National Cheng Kung University20, University of North Carolina at Chapel Hill21, Ben-Gurion University of the Negev22, Monash University23, Banaras Hindu University24
2 citations