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Air quality modelling as a supplementary assessment method in the framework of the European Air Quality Directive

Air quality modelling as a supplementary assessment method in the framework of the European Air Quality Directive

According to the European Air Quality (AQ) Directive, Member States must report annually their AQ to the European Commission (EC). This report can be based on modelling data if the concentration levels do not exceed the established lower assessment thresholds (LAT), or on combining data from modelling and monitoring systems (supplementary assessment methods), if concentrations levels are below the upper assessment threshold (UAT). For the remaining cases, modelling techniques could be applied to provide additional information. In Portugal, the report has been based on monitored data. However, the AQ assessment for the 5-years period 2006-2010 indicates that modelled data can be used alone or combined with monitored data for almost the entire country. This work presents a methodology that combines air pollutant concentration values from monitored data and from a numerical modelling system to deliver information to the AQ assessment report. The AQ combined data were evaluated using the DELTA tool, developed under the FAIRMODE’s activity, verifying the fulfilment of all the defined quality criteria. Additionally, crossing improved concentration fields with geo-databases of land cover, road-maps and demography, using GIS tools, it is possible to quantify exceeding areas, population exposed to exceeding levels or vegetation areas exposed to pollutant levels higher than the vegetation protection thresholds. This methodology produces improved information, especially for areas where the amount of fixed monitoring stations is sparse or non-existent, allowing to obtain a better and broader overview of the AQ in Portugal using this modelling approach to support AQ reporting to the European Commission.
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The next generation of low-cost personal air quality sensors for quantitative exposure monitoring

The next generation of low-cost personal air quality sensors for quantitative exposure monitoring

This approach, outlined in Dutton et al. (2009), provides an additional assessment of measurement uncertainty, and can be compared to the uncertainties calculated using propaga- tion of error to understand if the propagation has captured most real sources of error. To calculate the ARPD, negative data were removed. In the future, zero replacement, or detec- tion limit replacement for data with negative values, will be considered. The ARPD was then multiplied by the average pooled concentration measurements to get units of concen- tration that could be directly compared with the uncertainty estimates derived through propagation. This approach of us- ing paired M-Pods does not necessarily incorporate error due to convection either, since the pair will generally have very similar airflow effects in both units. This is a limitation that should be studied further in this system.
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Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7

Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7

Within the standard CMAQ photolysis module, clear-sky photolysis rates at specific altitudes, latitudes, and hour an- gles are computed offline with the photolysis rate prepro- cessor JPROC and read into the model as a look-up table. The clear-sky photolysis rates are then interpolated to model grid cells at specified time-steps and also adjusted for the presence of cloud cover. Two new options were included in CMAQv4.7 for computing photochemical rate constants. One option utilizes satellite-derived cloud information to ad- just photolysis rates (Pour-Biazar et al., 2007). Predict- ing the location and amount of cloud cover has historically been one of the most difficult problems in numerical weather prediction and air quality modeling. Using cloud informa- tion derived from satellites should give a better represen- tation of the cloud fields. At present, data from the Geo- stationary Operational Environmental Satellite (GOES) are archived for a limited area and time periods and may be ob- tained from the National Aeronautics and Space Administra- tion (NASA)/National Space Science and Technology Cen- ter’s Satellite Assimilation website (http://satdas.nsstc.nasa. gov/data.html). In addition, the preprocessing software is also available from the NASA website. An updated version of the preprocessing software is being developed, which re- grids satellite data to the CMAQ modeling grid domain us- ing the Spatial Allocator Tool (available from the Commu- nity Modeling and Analysis (CMAS) center, http://www.ie. unc.edu/cempd/projects/mims/spatial/). An incremental test using the satellite data revealed problems in the satellite re- gridding/preprocessing software, which are currently being addressed. Because of this problem, no analysis of this in- crement is presented here.
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Bioindicators of Air Quality Species |  Bioindicadores de espécies de qualidade do ar

Bioindicators of Air Quality Species | Bioindicadores de espécies de qualidade do ar

The pollutants Divided may be into primary and secondary, primary Are those released directly from emission sources, like the sulfur dioxide (SO2), hydrogen sulfide (H2S), Nitrogen oxides (NOx), an ammonia (NH3), carbon monoxide (CO), carbon dioxide (CO2) and methane (CH4). The secondaries are those formed in the atmosphere through the Chemical areações between primary pollutants, such as hydrogen peroxide (H2O2), sulfuric acid (H2SO4), nitric acid (HNO3), trioxide Sulphur (SO3), Nitrates OS (NO3), OS sulfates (SO42), ozone (O3) and nitrate peroxyacetyl - PAN - (CH3 = OO2NO2), being the last two are among the most harmful the People and vegetation (FREEDMAN 1995 apud PEDROSA , 2007).
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The next generation of low-cost personal air quality sensors for quantitative exposure monitoring

The next generation of low-cost personal air quality sensors for quantitative exposure monitoring

effect, coupled with generally lower CO levels in the warmer months due to better atmo- spheric mixing and improved motor vehicle combustion (Neff et al., 1997), resulted in flatter and noisier calibration curves than previously seen. To minimize this effect, some portions of the calibration data set were removed for April and May user study calibra- tions. O 3 and NO 2 had slightly worse calibration fits than during the winter calibrations,

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Effect of the Air Filtration System Replacement on Embryo Quality in the Assisted Reproduction Laboratory

Effect of the Air Filtration System Replacement on Embryo Quality in the Assisted Reproduction Laboratory

As there are no studies associating congenital diseases with embryonic contamination in assisted reproduction techniques, it is difficult to evaluate the comprehensiveness of contamination in public health. Even though it cannot be corroborated by studies in humans, there is evidence of gestational infections that impair the reproductive tract and cause malformation in bovine fetuses. 42 The first ob- served consequence was a reduction in the formation of viable embryos for uterine transfer. The embryos may not survive the first cleavages, may present teratogenicity, or simply fail to implant in the uterus. Syndromes that compro- mise fetal health may also occur, bringing the possibility of increased stillbirths, prematurity, or birth of small concepts for gestational age, which was described in studies with cattle in which assisted fertilization was widely used. 42 Although negative associations with air environment have been reported, little is known about the relevance of envi- ronmental microorganisms within human reproduction lab- oratories. In fact, most of the microorganisms isolated from a clean room environment are human commensals, and are probably irrelevant to reproductive cultures. In this regard, the use of measures such as the addition of antibiotics in culture media raises concerns as it may also cause damage and cytotoxic effects. With the reduction in microorganism density achieved through the construction of a clean room, further preventive measures are improved, such as low antibiotic levels needed within the culture medium. 43
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Optimization of a photochemical mechanism for Brazilian urban air quality simulation.

Optimization of a photochemical mechanism for Brazilian urban air quality simulation.

OPTIMIZATION OF A PHOTOCHEMICAL MECHANISM FOR BRAZILIAN URBAN AIR QUALITY SIMULATION. A photochemical reaction mechanism for the description of air quality in Brazilian urban regions is described and evaluated by comparison with chamber experiments. The mechanism was developed for use in ozone modeling and application of control strategies. The oxidation of ethanol and methyl-ter-butyl-ether is also considered. Using this chemical model, a trajectory simulation of Brazil Avenue, Rio de Janeiro, was performed. The model predicts that ozone should reach a maximum of 22.4 ppb at 14:57 h. This value is in good agreement with the experimental measurements of 22.5 ppb for 14:00 h and 22.3 ppb for 15:00 h.
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Evaluating the capability of regional-scale air quality models to capture the vertical distribution of pollutants

Evaluating the capability of regional-scale air quality models to capture the vertical distribution of pollutants

At the Philadelphia airport, the models tend to slightly un- derestimate (5 to 10 ppb) ozone mixing ratios for August 2006 (Fig. 16) above 4 km, while there is an overestima- tion of 6 to 20 ppb below 1000 m. The models driven by the BCs provided by GEMS and, to a lesser extent, AURAMS replicate very well the observed profile between 1.5 and 3 km, while DEHM is biased high. GEMS profiles are biased low above 4 km, but are in agreement with MOZAIC obser- vations from the ground up to 1.5 km. The models driven by the GEMS BCs (the two instances of CMAQ and CAMx) show some differences within the first 3 km, though they cluster around the corresponding boundary value in the up- per levels. The bias at these altitudes is possibly influenced by the GEMS BCs. The spread among the models is likely driven by photochemistry, the effect of which is stronger in Philadelphia than in Portland, being that the east coast has higher NOx emissions than the western areas (e.g. see Appel et al., 2012). Ground-level ozone mixing ratios sim- ulated from models driven by GEMS range between 45 and 65 ppb, consistent with the notion that local production en- hances summer ozone mixing ratios in polluted areas more than the large-scale background mixing ratios. This result is also shared by DEHM, which exhibits the highest surface mixing ratios, although it had the lowest boundary mixing ratio values. Profiles of ozone mixing ratios measured by ozonesondes for August were available from two rural sites (Fig. 11) within the Philadelphia domain, and are reported in Fig. 16. Measurements at the site STN487 (∼370 km north- east of the airport, close to the coast) were collected daily for August at 18:00 GMT, while 12 h measurements from the STN420 (∼160 km south-west of the airport) were col- lected between 04:00 GMT and 20:00 GMT. Profiles from STN420 are similar to those of the MOZAIC in the first 1000 m but diverge from the modelled mixing ratios more than the MOZAIC above 1000 m. The profiles from this ru- ral ozonesonde station therefore suggest that the GEMS BCs may be biased low at this site (also found by Schere et al., 2012). Ozone mixing ratios from station STN487 also sup- port this inference, as they are also close to the MOZAIC values in the upper levels. Finally, ozonesonde profiles from both sites confirm that the modelled ozone mixing ratios are biased high in the first 1000 m.
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Identification and analysis of source contributions to the air quality in the Amsterdam Region

Identification and analysis of source contributions to the air quality in the Amsterdam Region

The WRF model (Weather Research and Forecasting), from the National Center for Atmospheric Research (NCAR) [9], version 3.5., is a next generation mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. CAMx (Comprehensive Air Quality Model with Extensions) [10] is a 3D chemistry-transport model suited for the simulations of the emission, dispersion, chemical reactions, and removal of pollutants in the troposphere based on the integration of the continuity equation for each chemical species on a system of nested three-dimensional grids. The gas-phase photochemistry is resolved through the Carbon Bond (CB05 or CB6) or the SAPRC99 chemical mechanism. CAMx includes a source apportionment (SA) or attribution capability that chemically apportions ozone to boundary conditions and emissions. This approach estimates the contributions from multiple source areas, categories, and pollutant types to the spatial and temporal distribution of ozone in a single model run. It uses multiple reactive tracers to track the contribution of O3 and precursors (NOx and VOC) to model estimated O3 [10], [11]. This is undertaken to identify the dominant source types contributing to the ozone levels. It also allows quantifying the contribution of boundary and initial conditions and investigating whether ozone formation is NOx or VOC limited. The OSAT reactive tracers are adjusted first for O3 destruction, which results in proportional reductions in O3 tracers, then O3 production.
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THE QUALITY OF AIR IN HOSPITAL ENVIRONMENTS CLIMATIZED AND ITS INFLUENCE IN THE OCCURRENCE OF INFECTIONS

THE QUALITY OF AIR IN HOSPITAL ENVIRONMENTS CLIMATIZED AND ITS INFLUENCE IN THE OCCURRENCE OF INFECTIONS

SUMMARY: Climatized environment is defined as the environment where temperature and humidity are controlled. We have made a review of literature, from 1990 to 2001, through data base MEDLINE, LILACS and Ministry of Health – Brazil. The aim of this study was to analyze the air quality in climatized environment and the last as a risk factor for hospital infection – HI. Twenty-three articles where analyzed and gathered by the focused theme; patterns and principles for maintaining the air quality; air quality and isolation of microorganism; air quality and occurrence of infection. The standard of quality quotes: ventilation, maintenance and cleanness of climatization systems. Aspergillus, Legionella, Acinetobacter, Clostridium, Nocardia, among others where found in air conditioned devices and the first three ones being responsable for booms of HI.
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ARTIGO_Adjustment of Mathematical Models and the Quality of Drying the Pulped Coffee at Different Air Conditions

ARTIGO_Adjustment of Mathematical Models and the Quality of Drying the Pulped Coffee at Different Air Conditions

mathematical model to fit the experimental drying data conducted with different air humidity (40%, 50% and 60%), temperatures (23, 40 and 60 °C) and the quality of the coffee. The cherries coffee were separated and standardized in the processes of washing, mechanical and manual separation. Then, approx. 85 kg of coffee cherries were pulped and taken directly to the yard. The washed coffee was completed dried in a mechanical dryer and yard. The results showed that the different conditions of the ambient air significantly influenced the processes of drying. The water content of the hygroscopic equilibrium of pulped coffee is directly proportional to the water activity and relative humidity (RH), decreasing with increasing temperature, for the same value of equilibrium. The Oswin model was best represented by the hygroscopicity of the pulped coffee, while the Midilli model shows the best fit to describe the drying curves of the washed coffee. The effective diffusion coefficient increases with increasing temperature of the drying air and reducing of RH, being described by the Arrhenius equation. Electrical conductivity, potassium leaching, total titratable acidity and grease acidity increase with increasing drying temperature regardless of the type of processing. Reducing sugars, total sugars and the sensorial quality was negatively affected with increasing drying temperature regardless of the type of processing. The drying at 60 °C/40% RH negatively affected the coffee quality.
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Influences of the 2010 Eyjafjallajökull volcanic plume on air quality in the northern Alpine region

Influences of the 2010 Eyjafjallajökull volcanic plume on air quality in the northern Alpine region

Signatures of the volcanic plume are hardly visible in the satellite images from 18 April (Fig. 4, upper panels). This is due to the increasing influence of the low pressure system south of the Alps hampering the direct observation of the vol- canic plume over Tyrol and the northern Alpine area. The further approach of the cyclonic system induced southerly winds across the Alps contrasting the north-easterly winds the day before (Fig. 4, middle right panel). Some foehn- like impact on the air flow on 18 April (subsidence in the North of the Alps, blue areas in Fig. 4) is suggested by the banded structure of vertical velocity and short-term southerly winds at ZSH during noon. However, the foehn did not break through to e.g. the Inn valley bottom. However, the associ- ated cyclonic system rapidly crossed the Alps and lost con- trol over the air flow in the area of interest. The remaining volcanic material over the southern Benelux area and neigh- bouring France again moved slowly in a south-easterly di- rection, but satellite images largely lost track of the material due to cloudiness and the overall lower particle concentra- tions. A weak “ash” signature was found over France on 18 April (not shown) which was still present in the area on 19 April and moving to the South-East. Although observa- tional conditions over Germany improved again on 19 April, the ash load of the atmosphere was no longer visible from space. This was mainly due to stronger downward mixing to the ground and thus dilution, as it will be discussed later. Westerly winds dominated in the following days when ash remnants over Europe were no longer detected by passive satellite instruments.
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Exploring the links between urban stucture and air quality

Exploring the links between urban stucture and air quality

In response to environmental sustainability issues, urban planners have focused their attention on the types of urban structure that will best serve our growing cities. In this thesis, by urban structure is understood not only the morphologic structures of the city, represented by its key-structures (road and rail networks, ports and airports, telecommunications and social infrastructures), but also the way how residential, industrial, services and recreational land uses are distributed throughout the city. As Newton [1997] so well wrote, the city is a villain, a victim and a white knight with respect to air quality. A villain since its transport, residences and industries consume enormous amounts of energy, emitting enormous quantities of air pollutants and therefore contributing significantly to urban air pollution. A victim because its residents and image are affected negatively by the atmospheric pollution, which reduces the quality of life and health as well as the attractiveness of the city to tourists and potential new business and residents. But the city can also be a white knight since changes in its structure and development may lead to a substantial reduction of traffic, energy consumption and air pollutants levels.
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Impact of external industrial sources on the regional and local air quality of Mexico Megacity

Impact of external industrial sources on the regional and local air quality of Mexico Megacity

ing the MEGAPOLI project. They found that in specific cases the influence of external sources on the air quality can be significant and dominating, even more than mobile source emissions. These studies corroborate earlier findings for sulfur dioxide obtained for the Mexico megacity, since aside from the large number of industries within the basin, the city can be impacted not only by emission sources located in the State of Hi-

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Specific approach for continuous air quality monitoring

Specific approach for continuous air quality monitoring

Rapid industry development as well as increase of traffic volume across the world have resulted in air quality becoming one of the most important factors of everyday life. Air quality monitoring is the necessary factor for proper decision making regarding air pol- lution. An integral part of such investigations is the measurement of wind characteristics, as the wind is the most influential factor in turbulent pollution diffusion into the atmos- phere. The most of the air pollution originates from combustion processes, so it is impor- tant to make quantitative, as well as qualitative analysis, as the sources of pollution can be very distant. In this paper, specific methodology for continuous wind, temperature and air quality data acquisition is presented. Comparison of the measured results is given, as well as the detailed presentation of the characteristics of the acquisition software used. Keywords: air quality, atmospheric conditions, acquisition software, wind data, CO 2 .
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Air Quality Assessment Using Interpolation Technique

Air Quality Assessment Using Interpolation Technique

Air pollution is increasing rapidly in almost all cities around the world due to increase in population. Mumbai city in India is one of the mega cities where air quality is deteriorating at a very rapid rate. Air quality monitoring stations have been installed in the city to regulate air pollution control strategies to reduce the air pollution level. In this paper, air quality assessment has been carried out over the sample region using interpolation techniques. The technique Inverse Distance Weighting (IDW) of Geographical Information System (GIS) has been used to perform interpolation with the help of concentration data on air quality at three locations of Mumbai for the year 2008. The classiication was done for the spatial and temporal variation in air quality levels for Mumbai region. The seasonal and annual variations of air quality levels for SO 2 , NO x and SPM (Suspended Particulate Matter) have been focused in this study. Results show that SPM
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Effects of urban land expansion on the regional meteorology and air quality of eastern China

Effects of urban land expansion on the regional meteorology and air quality of eastern China

We use WRF/Chem v3.5 (Grell et al., 2005) to simulate me- teorological fields and atmospheric chemistry in four hypo- thetical urban land surface expansion scenarios in July for the 5 years from 2008 to 2012. We focus on summertime air quality because of the high ozone and other secondary pollutant levels. The modeling framework is constructed on a single domain of 100 × 100 cells with a 10 km horizontal grid spacing, and covers nine provinces in eastern and cen- tral China (Fig. 1). In this study, the physical options include the Lin microphysics scheme (Lin et al., 1983), RRTM long- wave radiation scheme (Mlawer et al., 1997), Goddard short- wave scheme (Kim and Wang, 2011), MM5 M–O surface layer scheme (Chen and Dudhia, 2001), YSU boundary layer scheme (Hong et al., 2006), New Grell cumulus scheme, and Unified Noah land surface model (Chen and Dudhia, 2001). The chemical options include the RADM2 chemical mecha- nism, MADE/SORGAM aerosol scheme, Madronich F-TUV photolysis scheme, and Megan biogenic emission scheme (Guenther et al., 2006). The 1.0 ◦ × 1.0 ◦ NCEP Final Op- erational Global Analysis data (http://rda.ucar.edu/datasets/ ds083.2/) have been processed to provide the meteorolog- ical initial conditions and boundary conditions. We utilize the modified 2008 IGBP (International Geosphere Biosphere Programme) MODIS 20-category 30 s land-use data, which is available from the WRF website (http://www2.mmm.ucar. edu/wrf/users/), to represent current land-cover conditions. Anthropogenic emission data are from the Multi-resolution Emission Inventory for China (MEIC), developed by Ts- inghua University for the year 2010, which consists of the emission rates for each month from five sectors (agriculture, industry, power plants, residential, and transportation). The MEIC is a unit/technology-based, bottom-up emission model that covers ∼ 700 anthropogenic emission source categories in China. It is an update of the emission inventory developed by the same group (Lei et al., 2011; Zhang et al., 2009). We used the MEIC 2010 data of the corresponding months as input for all simulations of 2008 through 2012, ignoring the year-to-year variation in emissions.
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Understanding the impact of recent advances in isoprene photooxidation on simulations of regional air quality

Understanding the impact of recent advances in isoprene photooxidation on simulations of regional air quality

We use data from the INTEX-NA/ICARTT campaigns in the summer of 2004 (1 July to 15 August) (Singh et al., 2006; Fehsenfeld et al., 2006) to evaluate model perfor- mance and constrain the uncertainties in the isoprene nitrate chemistry. Observations are available from NASA DC-8 and NOAA WP-3D (18 flights on each aircraft). The two aircrafts have different sampling emphasis, with DC-8 targeted at re- gional airmasses over North America and WP-3D aimed at local flows downwind of urban centers and point sources in the northeastern United States (Fig. 3). During INTEX- NA/ICARTT, the total alkyl and multifunctional nitrates (PANs) are measured by thermal dissociation laser induced fluorescence (TD-LIF, Day et al., 2002). The dataset repre- sents the most spatially extensive measurements of PANs over the eastern United States to date. While PANs includes isoprene nitrates (ING), the PANs measurement also in- cludes other organic nitrates as well. As mentioned, these same data have been used in previous studies investigating the formation and fate of isoprene nitrates (Horowitz et al., 2007; Perring et al., 2009a).
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Indoor air quality in primary schools

Indoor air quality in primary schools

The IAQ in school buildings is expected to be a key role player in the assessment of the effects of the children personal exposure to air pollution as children spend at least a third of their time inside school buildings, that is, approximately seven or more hours a day in school (Almeida et al., 2010; EPA, 2010; Pegas et al., 2010; Wheeler et al., 2009; Ramachandran et al., 2005). Poor IAQ can affect scholarly performance and attendance (Daisey et al. 2003; Godoi et al. 2009). Hence, several studies about air quality in schools have recently been published (Goyal & Khare, 2009; Tippayawong et al., 2009; Fraga et al., 2008; Fromme et al., 2007; Hwang et al., 2006). Environmental asthma triggers commonly found in school buildings include respiratory viruses; cockroaches and other pests; mold resulting from excess moisture in the building; dander from animals in the classroom; and dander brought on the clothing from animals at home. Second-hand smoke and dust mites are other known environmental asthma triggers found in schools. Children with asthma may be affected by other pollutants from sources inside schools, such as unvented stoves or heaters and common products including chemicals, cleaning agents, perfumes, pesticides and sprays.
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Air quality over Portugal in 2020

Air quality over Portugal in 2020

To meteorological prediction the WRF model has a large variety of physical parameterizations, which include microphysics, cumulusparameterizationandradiation,land–surfaceandplanetary boundary layer schemes. The parameterizations selection was basedonrecommendationsincludedinWangetal.(2014),aswell as on validation and sensitivity studies previously performed over Portugal(Aquilinaetal.,2005;Carvalhoetal.,2006)andoverthe Iberian Peninsula (Fernandez et al., 2007). Table 1 compiles the parameterizations used in this work. The global meteorological fields from the National Center for Environmental Prediction (NCEP/NOAA,2000),whichprovidefinaloperationalglobaldataon 1°by1°gridswithatemporalresolutionofsixhours,wereusedto supply initial and boundary conditions for the coarse domain (C125), while for the other domains, the initial and boundary conditionscomefromtherespectiveparentdomainandfromthe previous simulated day. The land use data set from USGS24 was usedwithinWRFsimulationsforC125andIP25domains,whilefor thePT05simulationdomainanupgradebasedontheCorineLand Cover2000forPortugal(Martins,2012)wasconsidered.
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