Model study with UAM-V in the Milan area (I) during PIPAPO: simulations with changed emissions compared to ground and airborne measurements

Baertsch-Ritter, N; Prévôt, Andrê S. H.; Dommen, Josef; Aksoyoğlu, Şebnem; Keller, Johannes

doi:10.1016/s1352-2310(03)00508-9

Cited by 38 publications

(20 citation statements)

References 19 publications

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“…Blue colors indicate the locations where ozone formation is VOC sensitive and red colors show the NO x sensitive areas. Under the conditions studied, the plume is in the VOC sensitive regime as found also by Baertsch‐Ritter et al [2003a]. The next step is to see how the particulate species are affected by the reduction of ozone precursor emissions.…”

Section: Resultsmentioning

confidence: 63%

“…Input data such as initial and boundary conditions and emissions were provided by the LOOP project community and are described elsewhere [ Baertsch‐Ritter et al , 2003a]. An NH 3 emission inventory based on land use data was added to the LOOP emission inventory [ Martilli et al , 2002].…”

Section: Model Descriptionmentioning

confidence: 99%

“… Putaud et al [2002] investigated particulate matter chemical composition using the aerosol measurements carried out during the field experiment. There are also studies to investigate the photooxidant formation by means of three‐dimensional model simulations [ Dosio et al , 2002; Baertsch‐Ritter et al , 2003a, 2003b; Martilli et al , 2002]. However, these modeling studies were mainly focused on the gas‐phase chemistry.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of formation and distribution of secondary aerosols in the Milan area (Italy)

Aksoyoğlu¹

2004

J. Geophys. Res.

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, and SOC (secondary organic carbon) were calculated for the particle size below 2.5 mm and compared with the data available from a field experiment, which took place in May-June 1998. Model results for the inorganic aerosols were comparable to the measurements at an urban and a rural station. Sensitivity studies with reduced anthropogenic NO x and volatile organic carbon (VOC) emissions showed that SOC behaves in the same way as ozone, i.e., decreases with reduced VOC emissions and increases with reduced NO x emissions in the plume where ozone production is predicted to be VOC sensitive. Sensitivity of secondary aerosol formation to NH 3 and NO x emissions was studied by reducing these emissions. Varying NH 3 emissions led to an almost linear change in secondary aerosol mass at sites with low NH 3 . At ammonia-rich sites, on the other hand, availability of nitrate became important for the further formation of secondary aerosols. Monoterpene emissions were predicted to contribute about 25% of the secondary organic aerosols produced in the northern part of the model domain, which is mostly a forested area.

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Section: Resultsmentioning

confidence: 63%

Section: Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling of formation and distribution of secondary aerosols in the Milan area (Italy)

Aksoyoğlu¹

2004

J. Geophys. Res.

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“…At the end of these second day, also the concentrations of all species are saved and these values are used to start the third day of CIT simulations (15 March 2000). The first day was devoted to a spin-up period in order to minimize the influence of assumed initial conditions [29,50]. Inflow (Table 9).…”

Section: Initial and Boundary Conditionsmentioning

confidence: 99%

The impact on tropospheric ozone formation on the implementation of a program for mobile emissions control: a case study in São Paulo, Brazil

et al. 2007

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The main sources of reactive hydrocarbons (RHC) and nitrogen oxides (NO x ), ozone precursors, in the Metropolitan Area of São Paulo (MASP) in the southeast of Brazil are emissions from vehicles fleets. Ambient surface ozone and particulate matter concentrations are air quality problem in the MASP. This study examined the impact that implementing a control program for mobile emissions has on ozone concentrations, An episode of high surface ozone concentrations occurring in the MASP during the March 13-15, 2000 period was used as a case study that was modeled for photochemical oxidants using the California Institute of Technology/Carnegie Mellon University three-dimensional photochemical model. Different scenarios were analyzed in relationship to the implementation of the Programa Nacional de Controle de Poluição por Veículos Automotores (PROCONVE, National Program to Control Motor Vehicle Pollution). Scenario 1 assumed that all vehicles were operating within PROCONVE guidelines. Scenarios 2 and 3 considered hypothetical situations in which the PROCONVE was not implemented. Scenario 2 set the premise that vehicles were using pre-1989 technology, whereas scenario 3 allowed for technological advances. A base case scenario, in which the official emission inventory for the year 2000 was employed, was also analyzed. The CIT model results show agreement with most measurements collected during 13-15 March 2000 modeling episode. Mean normalized bias for ozone, CO, RHC and NO x are approximately 9.0, 6.0, −8.3, 13.0%, respectively. Tropospheric ozone concentrations predicted for scenario 2 were higher than those predicted for scenarios 1, 3 and base case. This study makes a significant contribution to the evaluation of air quality improvement and provides data for use in evaluating the economic costs of implementing a program of motor vehicle pollution control aimed at protecting human health.

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“…In terms of the Gaussian model for simulating air quality, Islam (1999) used a dispersion model to determine the location of an unknown emission source; Owen et al (2000) calculated the concentrations of NO x for a summer and winter period in London by using an urban scale Gaussian dispersion model (ADMSUrban); Hao et al (2001) applied the Industrial Source Complex Short Term (ISCST-3) model to facilitate the study of emission source contributions to ambient concentrations of CO and NO x in Beijing; Kuhlwein et al (2002) developed a Gaussian multi-source model to calculate pollutant concentrations in the Augsburg area of southern Germany; Krishna et al (2004) applied the ISCST-3 model to examine the assimilative capacity and the dispersion of pollutants in the summer and winter seasons due to industrial sources in the Visakhapatnam bowl area of India. In terms of urban airshed models, Winner and Cass (1999) evaluated the relationship between pollutant emissions and the long-term frequency distribution of O 3 concentrations in Southern California using a photochemical airshed model; Chang and Cardelino (2000) applied an Eulerian 3-D photochemical-transport grid model for generating next-day peak ozone concentration forecasts in Atlanta of USA; Baertsch-Ritter et al (2003) applied the 3-D photochemical Urban Airshed Model with variable grid (UAM-V) to investigate the temporal and spatial dynamics of the photo-oxidant production in the highly polluted Milan area of Italy; Oanh and Zhang (2004) applied the integrated variable-grid urban airshed model/systems applications international mesoscale model (UAM-V/SAIMM), to investigate photochemical pollution in the Bangkok Metropolitan Region of Thailand.…”

Section: Introductionmentioning

confidence: 99%

A gaussian-box modeling approach for urban air quality management in a northern chinese city—I. model development

Cheng

Feng

et al. 2006

Water Air Soil Pollut

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A Gaussian-box modeling approach was presented in this paper to examine the urban air quality due to multiple point-and area-source emissions in the northern Chinese city of Fengnan, which is associated with a deteriorated air quality as a consequence of industrialization and rapid urban growth. A 3-D multibox (3DMB) air quality model was developed to predict air quality due to area-source emissions. It improved upon the conventional box models by allowing consideration of more details in spatial variations of emission sources and meteorological conditions. The modeling domain was divided into various layers within the mixing height, while each layer was associated with a number of sub-boxes. A multi-source and multi-grid Gaussian modeling approach was then applied to predict the air quality in different sub-boxes that are associated with multiple point-source emissions. Thus the Gaussian-box modeling approach could effectively simulate impacts of both area-and point-source emissions but also reflect more details of the spatial variations in source distributions and meteorological conditions. This modeling approach was employed to predict daily average SO 2 , TSP and PM 10 concentrations for each sub-box during the heating and non-heating seasons, respectively. The analysis of the mean normalized error of the modeling results demonstrates the feasibility and applicability of the developed method, and the presented method could provide more useful and scientific bases for developing effective urban air quality control and management strategies.

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Model study with UAM-V in the Milan area (I) during PIPAPO: simulations with changed emissions compared to ground and airborne measurements

Cited by 38 publications

References 19 publications

Modeling of formation and distribution of secondary aerosols in the Milan area (Italy)

Modeling of formation and distribution of secondary aerosols in the Milan area (Italy)

The impact on tropospheric ozone formation on the implementation of a program for mobile emissions control: a case study in São Paulo, Brazil

A gaussian-box modeling approach for urban air quality management in a northern chinese city—I. model development

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