In addition to air pollutants index (i.e. PSI), ambient air quality can be described by atmospheric visibility since it can be observed directly by general publics. In this study, atmospheric visibility observation, meteorological parameter monitoring, and aerosol particle sampling were conducted to investigate the influences of physicochemical properties of suspended particles and meteorological parameters on atmospheric visibility. This study further applied receptor model and multiple regression linear analysis to forecast atmospheric visibility and develop strategies for improving urban visual air quality at Taipei region. Results from regular visibility observation indicated that the average visibilities were 10.30, 8.05 and 6.00 km in the directions of Tamsui, Sonshan, and Shindian, respectively. Similar trend of visibility variation was also observed for intensive observation. Further analysis of synoptic chart and regular observation data during the period of January 2007¡VMarch 2008 showed that the lowest atmospheric visibility commonly occurred whenas the weather patterns were in sequence of eastward movement of rainy areas in southern China, southerly airstream, strong northeast monsoon, circus-sluice of high pressure outflow, and weak northeast monsoon. Results from chemical analysis of suspended particles at Taipei region indicated that major water-soluble ionic species were SO42-, NO3-, and NH4+ and followed by Cl-, while major metallic content were Ca and K. Carbonaceous analysis showed that the mass ratio of OC/EC ranged from 1.65 to 1.91 for PM2.5 and from 1.37 to 1.88 for PM2.5-10. Ammonium nitrate, organic carbon, and ammonium sulfate were the major chemical species that influenced atmospheric visibility at Taipei region. In this study, we choose the averaged atmospheric visibility in Sonshan as a dependent variable and PM10, NO2, SO2, O3, relative humidity (RH), wind direction (WD), and wind speed (WS) as independent variables to establish multiple linear regression models for forecasting the atmospheric visibility. Results of statistical analysis indicated that high correlation between forecasted and observed atmospheric visibilities was observed (R=0.7167). Furthermore, atmospheric visibility forecasting models were established for various weather patterns. The accuracies of atmospheric visibility verification (September~December, 2007) and forecasting (January~March, 2008) were 91.80% and 87.97%, respectively. This study further applied SPSS stastistic software to conduct factor analysis for atmospheric visibility. Results from factor analysis of visibility indicated that the top three factors (PM10, NO2, and SO2) accounted for 71.13% of variance. Furthermore, variable correlation analysis showed that atmospheric visibility had positive correlation with wind speed and negative correlation with other variables (PM10, NO2, SO2, O3, RH, and WD). Besides, for the significant levels of £\=0.01 or £\=0.05, all variables were proven to be significantly correlated with atmospheric visibility except O3. At Taipei region, the automobile tail emission was the major emission source causing low visibility, thus the most effective strategy for improving atmospheric visibility was to reduce the mission of automobiles and the formation of secondary aerosols containing ammonium nitrate and ammonium sulfate, which could effectively increase the atmospheric visibility at Taipei region.

Doctor of Philosophy / Department of Biological & Agricultural Engineering / Zifei Liu / The Unmix and Positive Matrix Factorization (PMF) models for source apportionment were applied to evaluate prescribed burning impacts on air quality, identify model advantages, and establish a relationship between visibility and PM₂.₅ sources. Speciated PM₂.₅ data were from the Flint Hills (FH) rural and the Kansas City (KC) urban sites. At the FH site, the Unmix model identified five sources: nitrate/agricultural, sulfate/industrial, crustal/soil, smoke, and secondary organic aerosol (SOA); while the PMF model identified the copper source in addition. The smoke source from PMF result includes both primary and secondary aerosols from prescribed burning when the smoke source in Unmix result only includes primary burning aerosols. The secondary smoke aerosols at the FH site were combined with secondary aerosols from other origins and formed the SOA source in Unmix result. Comparative analysis of the modeling results estimated the SOA to be 2.3 to 2.7 times of the primary aerosols in burning season. At the KC site, both receptor models derived seven-source solutions: nitrate/agricultural, sulfate/industrial, crustal/soil, smoke, traffic/SOA, heavy-duty diesel vehicle (HDDV), and calcium. The smoke source at the KC site carries an exceedingly organic carbon to elemental carbon (OC/EC) ratio, which is more than five times higher than in FH smoke source. The PMF results at KC site tend to classify more SOA from nitrate/agricultural and sulfate/industrial sources into traffic/SOA source. In the burning season, the smoke source from both sites showed a relatively high correlation when KC is under west and southwest wind, suggesting that part of the smoke originated PM₂.₅ at the urban site could be from the upwind burning activities. The Tobit modeling recognized the nitrate/agricultural as the leading visibility degradation impact factor at both sites. The latter chapter conducted a review of life cycle assessment (LCA) on carbon footprint (CF) of beef production. The objectives were to evaluate CF range in raising systems from different countries, identify the leading CF contributor and dominant source of uncertainty, and summarize LCA inventory defined in cattle production systems. Most existing beef LCA studies followed a “cradle to farm gate” approach. The CF in 3-phase systems ranged from 16 to 29.5 kg CO2e kg⁻¹ carcass weight. The 2-phase raising system reported a slightly lower CF than the 3-phase system (18.9 to 26.9 kg CO2e kg⁻¹ carcass weight), but no significant differences were observed. The grass-fed system in the US has the highest CF, but the CF of grass-fed systems in the European Union (EU) is 40% less than them in the US. This is because more than half of cattle farms in EU produce both beef and milk, and the CF burden was partaken by the dairy production. Cow-calf phase contributed the most CF in 3-phase raising system, while enteric fermentation was the major contributor. Feed production contributed the most in the feedlot phase if forages were applied rather than concentrates. The leading uncertainty sources reported was land use change and disparate dressing percentage. To improve the LCA accuracy, more research is needed in collecting reliable LCA inventory data such as raising period and feed intake efficiency.

In this Dissertation, systematic work has been carried out to study the road-deposited sediment and its adsorbed pollutants from a stormwater pollution perspective. Solid-phase concentration, surface load, source apportionment, risk assessment, and desorption dynamics of polycyclic aromatic hydrocarbons and/or heavy metals in road-deposited sediments (RDS) were investigated. In order to provide data to assist potential strategies of stormwater pollution mitigation and integrated catchment management to minimise the adverse impacts of RDS adsorbed pollutants on stormwawter quality, the following specific topics were addressed. ⑴ The influences of traffic load and antecedent dry-weather period on pollution level and ecological risk of heavy metals in RDS were analyzed. ⑵ The build-up dynamics and chemical fractionation of metals were determined. ⑶ The potential source contributions and risk assessment of polycyclic aromatic hydrocarbons in size-fractionated RDS were firstly determined by a Principal component analysis - Multiple linear regression receptor model. ⑷ The qualitative and quantitative source apportionments of polycyclic aromatic hydrocarbons were subsequently investigated through a combined qualitative Molecular Diagnostic Ratio and quantitative Positive Matrix Factorization source apportionment with an extended data set. ⑸ The exposure risk of polycyclic aromatic hydrocarbons were evaluated and calculated by incremental lifetime cancer risk models. ⑹ Then, a novel ecological risk assessment approach to the RDS adsorbed toxic substances was developed, which was explored exclusively for the study of RDS for a water pollution aspect. ⑺ Finally, the effects of rainwater, major wastewater constituents of dissolved organic matter and surfactant on the leaching of heavy metals from RDS were carried out.

In recent years, the rapid development of economy and industry in Xiamen Bay causes serious environmental problems, particularly poor air quality and visibility impairment. There are no large-scale industrial emission sources in Kinmen Island, however, its ambient air quality is always the poorest in Taiwan. Moreover, ambient air quality monitoring data showed that PM10 concentrations varied in daytime and at nighttime. Consequently, this study tired to ascertain the potential causes for this phenomenon. This study selected ten particulate matter (PM) sampling sites at Xiamen Bay, including five sites at Kinmen Island and five sites at metro Xiamen. Particulate matter sampling was conducted in daytime (8:00-17:00) and at nighttime (17:00-8:00), which included regular and intensive sampling. Regular sampling was conducted to collect PM10 with high-volume samplers three times a month from April 2009 to April 2010, while intensive sampling was conducted to collect fine (PM2.5) and coarse (PM2.5-10) particles with dichotomous samplers and particle size distribution with a MOUDI at site B2 for consecutive 5 days in the spring and winter of 2009~2010. After sampling, the physicochemical properties of PM, including mass concentrations, particle size distribution, water- soluble ionic species, metallic elements, and carbonaceous contents were further analyzed. The level of atmospheric PM is affected by meteorological condition, thus PM10 concentrations in winter and fall was much higher than those in spring and summer. Results from backward trajectories showed that the concentrations of PM10 blown from the north were generally higher than those from the south. Furthermore, t-test analysis indicated that PM10 concentrations in daytime and at nighttime at site B3 were significantly different (p-value<0.05). During the intensive sampling periods, PM10 concentrations were mainly affected by coarse particles compared to fine particles. The highest concentration for fine and coarse particle modes occurred at the size ranges of 0.32~0.56 £gm and 3.2~5.6 £gm, respectively. The most abundant water-soluble ionic species of PM10 were secondary inorganic aerosols (SO42-, NO3-, and NH4+) which accounted for 85% of total ions. The daytime and nighttime PM10 concentration ratios (D/N) for Mg, K, Ca, Cr, Mn, Fe, Zn, Al, Cu, As, and V were in the same order of magnitude, however, the D/N ratios of Cd, Pb, Ni, and Ti in spring and summer varied higher than an order of magnitude, indicating that the emission sources of PM were different in daytime and at nighttime. Correlation analysis of OC and EC showed that OC and EC at nighttime had a higher correlation than those in daytime, while OC and EC had a higher correlation in Kinmen Island than those in metro Xiamen, indicating carbonaceous sources must be different in summer and winter at Xiamen Bay. Enrichment factor analysis revealed that ceramic industry, stone processing, and cement industry had higher correlation with PM10 concentration than utility power plants. Crustal dusts consisted of road dusts, farmland dusts, and constructive dusts, while biomass burning was not a negligible sources. Results obtained from PCA and CMB receptor modeling showed that major sources of PM in Xiamen Bay were secondary inorganic aerosols, fuel and biomass burning, marine aerosols, vehicular exhansts, and soil dusts. Besides, stone processing, cement industry, ceramic industry, and utility power plants had the highest contribution in winter. Their contributions in daytime and at nighttime were 38% and 45%, respectively.

Les objectifs principaux de cette étude étaient d'acquérir une meilleure connaissance des niveaux d'exposition aux particules fines PM₂.₅ de leur composition chimique et de leurs sources, dans trois villes situées sur la façade littorale de la région du Nord-Pas-de-Calais. Les particules fines ont été collectées dans le cadre de deux campagnes d'échantillonnage menées entre novembre 2010 et avril 2011 (campagne "hiver" à Dunkerque et Boulogne sur Mer ; campagne "printemps" à Dunkerque et Saint Omer). La composition chimique des PM₂.₅ a été déterminée suite à la quantification d'éléments majeurs, d'éléments traces, d'ions hydrololubles et du carbone total. Pour les deux périodes considérées, les concentrations et la composition chimique en PM₂.₅ évoluent en suivant les mêmes tendances sur chacun des sites. L'influence de sources locales a été mise en évidence en comparant l'évolution temporelle et les roses de concentration de certains éléments majeurs et éléments traces, d'un site à l'autre. Cette exploitation a permis de proposer des rapports spécifiques entre éléments, qui peuvent être utilisés comme traceurs de certaines sources anthropiques. Enfin, l'application d'un modèle source-récepteur, basé sur la factorisation matricielle non-négative (NMF), a permis d'identifier les sources principales des PM₂.₅, d'évaluer leur contribution et leur part relative au niveau de chacun des sites étudiés. / The main objectives of this study were to acquire a better knowledge on the exposure level to fine PM₂.₅ particles and on their chemical composition and sources, in three cities located on the littoral facade of the Nord-Pas-de-Calais region. The particles were collected following two sampling campaigns held between November 2010 and April 2011 ("winter" campaign in Dunkerque and Boulogne sur Mer ; "Spring" campaign in Dunkerque and Saint-Omer). The chemical composition of the collected PM₂.₅ was determined through the quantification of major elements, trace elements, water soluble ions and total carbon. For the two considered sampling periods, PM₂.₅ concentrations and chemical composition trends followed similar tendencies at each site. Local sources influence was evidenced throughout a comparison of the chronological evolution and concentration roses of some major and trace elements between the sites. This analysis allowed the suggestion of specific elemental ratios, which can be used as tracers of some anthropogenic sources. Finally, the use of a source-receptor model, based on a non-negative matrix factorization (NMF) method, allowed the identification of the main PM₂.₅ sources as well as the evaluation of their relative contributions in each of the studied sites.

Neighboring northern Kaohsiung with a dense population of petrochemical and petroleum industrial complexes included China Petroleum Company (CPC) refinery plant, Renwu and Dazher petrochemical industrial plants. In recent years, although many scholars have conducted regional studies, but are still limited by the lack of relevant information evidences (such as odorous matters identification and VOCs fingerprint database), while unable to clearly identify the causes of poor ambient air quality. By sampling and analyzing VOCs, we will be able to understand the major sources of VOCs in northern Kaohsiung and their contribution, and to provide the air quality management and control countermeasures for local environmental protection administration. In this study, we sampled and analyzed the speciation of VOCs and sulfur-containing odorous matters (SOMs) in the CPC refinery plants, Renwu and Dazher petrochemical complexes simultaneously with stack sampling. The sampling of VOCs and SOMs were conducted on January 7th, 14th, and 19th, 2011 (dry season) and May 6th, 13rd, and 23rd, 2011 (wet season). We established the emission source database, investigated the characteristics of VOC fingerprints, and estimate the emission factor of each stack. It helps us understand the temporal and spatial distribution of VOCs and ascertain major sources and their contribution of VOCs. Major VOCs emitted from the stacks of the CPC refinery plant were toluene and acetone. It showed that petroleum refinery processes had similar VOCs characteristics and fingerprints. The fingerprints of stack emissions at Renwu and Dashe industrial complexes varied with their processes. Hydrogen sulfide was the major sulfur-containing odorous matter in all petrochemical plants. Compared to other petrochemical complexes, Renwu industrial complex emitted a variety of SOMs species as well as relatively high concentrations of sulfur-containing odorous matters. The petrochemical industrial complexes in the industrial ambient of VOCs analysis results showed that isobutane, butane, isopentane, pentane, propane of alkanes, propene of alkenes, toluene, ethylbenzene, xylene, styrene of aromatics, 2-Butanone (MEK), acetone, of carbonyls are major species of VOCs. In addition, ethene+acetylene+ethane (C2), 1,2-dichloroethane, chloromethane, dichloromethane, MTBE were also occasionally found. Sulfur-containing odorous matter (SOMs) analytical results showed that major odorous matters included hydrogen sulfide, methanethiol, dimethyl sulfide, and carbon disulfide. The highest hydrogen sulfide concentration went up to 5.5 ppbv. In this study, the species of VOCs were divided into alkanes, alkenes, aromatics, carbonyls, and others. The temporal and spatial distribution of various types of VOCs strongly correlated with near-surface wind direction. The most obvious contaminants were alkanes, aromatics, and carbonyls of the dispersion to the downwind. Generally, the ambient air surrounding the petrochemical industrial complexes was influenced by various pollutants in the case of high wind speeds. It showed that stack emission and fugitive sources had an important contribution to ambient air quality. TSOMs and hydrogen sulfide emitting mainly from local sources resulted in high concentration of TSOMs and hydrogen sulfide surrounding the petrochemical industrial complex. Principal component analysis (PCA) results showed that the surrounding areas of petrochemical industrial complexes, regardless of dry or wet seasons, were mainly influenced by the process emissions and solvent evaporation. The impact of traffic emission sources ranked the second. Chemical mass balance receptor modeling showed that stack emissions from the CPC refinery plants contributed about 48 %, while fugitive emission sources and mobile sources contributed about 30 % and 11%, respectively. The stack emissions from Renwu industrial complex contributed about 75 %, while fugitive emission sources and mobile sources contributed about 17 % and 5 %, respectively. The stack emissions from Dazher industrial complex contributed about 68 %, while fugitive emission sources and mobile sources contributed about 21 % and 2 %, respectively.

La composition chimique des particules submicroniques (PM₁) a été suivie pendant plus d'un an ( juillet 2013- septembre 2014), à résolution temporelle fine (< 30 min.), à l'aide d'un analyseur ACSM pour la fraction non-réfractaire (organiques, sulfates, nitrates, ammoniums et chlorures) et d'un aethalomètre (carbone suie), complétés par une observation micro-météorologique. Une campagne intensive (juillet 2014) a enrichi le jeu de données avec le suivi de composés organiques volatils par analyse PTR-ToFMS. Le site de mesure est de type urbain de fond, sous l'influence d'une large zone industrielle et portuaire. La composition chimique des aérosols a été analysée de manière globale, saisonnière et selon 4 secteurs de vent. L'étude de la conversion SO₂-SO₄ dans le secteur industriel a montré que ce processus est favorisé à humidité relative élevée (> 70%), faible turbulence verticale (σw : 0-0.5 m sˉ¹) et faible vitesse de vent (0-2 m sˉ¹). À l'aide d'un modèle source récepteur PMF (Positive Matrix Factorization), trois sources primaires d'espèces organiques, liées au trafic, à la combustion de biomasse et à la cuisson domestique, ont été identifiées, ainsi qu'une source secondaire. Les analyses PMF saisonnière et par secteur, avec et sans contraintes, ont permis d'identifier 2 facteurs supplémentaires en secteurs marin et industriel. Quelques cas (brises de mer, épisodes de pollution et passages de bateaux) ont été étudiés, permettant dans le dernier cas d'extraire un spectre de masse moyen lié aux émissions des navires, ShOA (Ship-like Organic Aerosol). Ce facteur contribue en moyenne pour seulement 0.5% à la fraction organique particulaire mais jusqu'à plus de 90% sur de courtes périodes. / The chemical composition of submicron particles (PM₁) was monitored for over one year (July 2013-September 2014), at high temporal resolution (< 30 min), using an Aerosol Chemical Speciation Monitor (ACSM) for the non-refractory fraction (NR-PM₁ : organic, sulfate, nitrate, ammonium and chloride) and an aethalometer for black carbon (BC), together with micrometeorology parameters. An intensive campaign (July 2014) completed the data set including the monitoring of volatile organic compounds by PTR-TOFMS. The chosen site has an urban background typology, under the influence of a large area with industrial and harbor activities. The chemical composition of aerosols was analyzed globally, seasonally and using four wind sectors. A study of the SO₂-to-SO₄ conversion in the industrial sector has shown that this process is favored at high relative humidity (> 70%), low vertical turbulence (σw : 0-0.5 m sˉ¹) and low wind speed (0-2 m sˉ¹). Using PMF (Positive Matrix factorization) source receptor modeling, three primary sources of organic species, relatied to traffic, combustion of biomass and domestic cooking, have been identified, as well as a secondary source. The seasonal and sector PMF analyses, with and without constraints, helped to identify two additional factors in the marine and industrial sectors. Some specific events (sea breezes, high pollution events and nearby ship movements) were studied, allowing to extract an average mass spectrum associated with ship emissions for the latter, ShOA (Ship-like organic aerosol). This factor only contributes to 0.5% of the particulate organic fraction on average but up to more than 90% over short periods.

Sur un site multi-influencé par des émissions urbaines et industrielles, l'analyse de la pollution aux aérosols, au voisinage des sources, requiert une connaissance multi-échelles de la dynamique atmosphérique. une campagne de mesure a été développée afin d'étudier la variabilité météorologique et micro-météorologique et l'évolution des particules, en particulier, submicroniques, sur une durée d'une année. Des oscillations de la concentration en aérosols, autour de la moyenne régionale, ont été identifiées le long du littoral dunkerquois, et attribuées aux phénomènes météorologiques locaux à proximité des industries. Des méthodes de reconnaissance et d'apprentissage supervisé, faisant appel aux mesures par anémomètre ultrasonique et aux profils verticaux du vent par lidar Doppler, ont été mises en œuvre pour établir la variabilité de phénomènes pertinents dans les événements de pollution de l'air : brise de mer, brouillard, front et tempête. L'analyse d'une base de données de six ans a permis de montrer que l’occurrence annuelle des brises de mer est corrélée à celle du nombre de journées anticycloniques. Par ailleurs, la fréquence annuelle des brouillards pourrait être liée à la concentration annuelle régionale en aérosols. L'analyse des covariances du vent a révélé deux situations contrastées, à faible et à fort flux turbulents. Le brouillard et la brise de mer, de faible flux, génèrent une pollution élevée aux PM₁, et sont le siège d'une forte concentration en aérosols organiques oxygénés (aérosols secondaires). Les situations à fort flux, favorisant les échanges verticaux, sont associées à une forte variabilité des sulfates particulaires. L'observation de longue durée a permis de mettre en évidence la construction d'épisodes de pollution particulaire, au cours de séquences de phénomènes météorologiques locaux, du fait des sources locales, mais aussi par incorporation de la pollution à plus grande échelle. / On a site that is multi-influenced by urban and industrial emissions, the analysis of aerosol pollution, in the vicinity of sources, requires a multi-scale knowledge of atmospheric dynamics. A measurement campaign was developed in order to study the meteorological and micro-meteorological variability and the evolution of particles, in particular, submicronic evolution, during a one-year period.Oscillations of the aerosol concentration around the regional average were identified along the Dunkirk coastline, and were attributed to the local meteorological phenomena close to the industries. Recognition and machine learning methods using measurements by an ultrasonic anemometer and vertical wind profiles by a Doppler lidar, were implemented to define the variability of relevant phenomena in air pollution events : sea breeze, fog, front and storm. A six-years database analysis has highlighted a correlation between the annual sea breeze occurrence and the annual number of anticyclonic days. Furthermore, the annual fog frequency could be connected with the annual regional concentration of aerosols. Analysis of wind covariance revealed two contrasting situations, low-level and high-level turbulent fluxes. The fog and the sea breeze, with low-level fluxes, generate a high PM₁ pollution and are in favor of a high organic oxygenated aerosols concentration (secondary aerosols). High-level fluxes situations, favoring vertical exchanges, are associated with a large variability of sulfate aerosols. The long-term observation, made it possible to highlight the development of episodes of particulate pollution during local weather phenomena, owing to the local emissions, but also by taking into account the larger-scale pollution.

Submitted by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-06-05T14:58:52Z No. of bitstreams: 1 TES_RENATA_DE_PARIS_COMPLETO.pdf: 8873897 bytes, checksum: 43b2a883518fc9ce39978e816042ab5f (MD5) / Made available in DSpace on 2017-06-05T14:58:53Z (GMT). No. of bitstreams: 1 TES_RENATA_DE_PARIS_COMPLETO.pdf: 8873897 bytes, checksum: 43b2a883518fc9ce39978e816042ab5f (MD5) Previous issue date: 2016-10-28 / Conselho Nacional de Pesquisa e Desenvolvimento Cient?fico e Tecnol?gico - CNPq / O uso de conforma??es obtidas por trajet?rias da din?mica molecular nos experimentos de docagem molecular ? a abordagem mais precisa para simular o comportamento de receptores e ligantes em ambientes moleculares. Entretanto, tais simula??es exigem alto custo computacional e a sua completa execu??o pode se tornar uma tarefa impratic?vel devido ao vasto n?mero de informa??es estruturais consideradas para representar a expl?cita flexibilidade de receptores. Al?m disso, o problema ? ainda mais desafiante quando deseja-se utilizar modelos de receptores totalmente flex?veis (Fully-Flexible Receptor - FFR) para realizar a triagem virtual em bibliotecas de ligantes. Este estudo apresenta um m?todo inovador para otimizar a triagem virtual baseada em docagem molecular de modelos FFR por meio da redu??o do n?mero de experimentos de docagem e, da invoca??o escalar de workflows de docagem para m?quinas virtuais de plataformas em nuvem. Para esse prop?sito, o workflow cient?fico basedo em nuvem, chamado e-FReDock, foi desenvolvido para acelerar as simula??es da docagem molecular em larga escala. e-FReDock ? baseado em um m?todo seletivo sem param?tros para executar experimentos de docagem ensemble com m?ltiplos ligantes. Como dados de entrada do e-FReDock, aplicou-se seis m?todos de agrupamento para particionar conforma??es com diferentes caracter?sticas estruturais no s?tio de liga??o da cavidade do substrato do receptor, visando identificar grupos de conforma??es favor?veis a interagir com espec?ficos ligantes durante os experimentos de docagem. Os resultados mostram o elevado n?vel de qualidade obtido pelos modelos de receptores totalmente flex?veis reduzidos (Reduced Fully-Flexible Receptor - RFFR) ao final dos experimentos em dois conjuntos de an?lises. O primeiro mostra que e-FReDock ? capaz de preservar a qualidade do modelo FFR entre 84,00% e 94,00%, enquanto a sua dimensionalidade reduz em uma m?dia de 49,68%. O segundo relata que os modelos RFFR resultantes s?o capazes de melhorar os resultados de docagem molecular em 97,00% dos ligantes testados quando comparados com a vers?o r?gida do modelo FFR. / The use of conformations obtained from molecular dynamics trajectories in the molecular docking experiments is the most accurate approach to simulate the behavior of receptors and ligands in molecular environments. However, such simulations are computationally expensive and their execution may become an infeasible task due to the large number of structural information, typically considered to represent the explicit flexibility of receptors. In addition, the computational demand increases when Fully-Flexible Receptor (FFR) models are routinely applied for screening of large compounds libraries. This study presents a novel method to optimize docking-based virtual screening of FFR models by reducing the size of FFR models at docking runtime, and scaling docking workflow invocations out onto virtual machines from cloud platforms. For this purpose, we developed e-FReDock, a cloud-based scientific workflow that assists in faster high-throughput docking simulations of flexible receptors and ligands. e-FReDock is based on a free-parameter selective method to perform ensemble docking experiments with multiple ligands from a clustered FFR model. The e-FReDock input data was generated by applying six clustering methods for partitioning conformations with different features in their substrate-binding cavities, aiming at identifying groups of snapshots with favorable interactions for specific ligands at docking runtime. Experimental results show the high quality Reduced Fully-Flexible Receptor (RFFR) models achieved by e-FReDock in two distinct sets of analyses. The first analysis shows that e-FReDock is able to preserve the quality of the FFR model between 84.00% and 94.00%, while its dimensionality reduces on average 49.68%. The second analysis reports that resulting RFFR models are able to reach better docking results than those obtained from the rigid version of the FFR model in 97.00% of the ligands tested.

If sufficiently persistent, semi-volatile organic contaminants (SVOCs) can travel long distances through the atmosphere from their points of release and become concentrated in cold, remote regions. As air is sampled for SVOCs to establish both their presence and the success of emission reduction efforts, it becomes helpful to determine sampling site proximity to sources and the origin of the sampled air masses. Comparing three increasingly sophisticated methods for quantifying source proximity of sampling locations, it was judged necessary to account for the actual history of the sampled air through construction of an airshed, especially if wind is highly directional and population distribution is very non-uniform. The airshed concept was improved upon by introducing a ‘geodesic’ grid of equally spaced cells, rather than a simple latitude/longitude grid, to avoid distortion near Earth’s poles and to allow for the comparison of airshed shapes. Assuming that a perfectly round airshed reveals no information about sources allows the significance of each cell of an airshed to be judged based on its departure from roundness. Combining air-mass histories with a 2 year-long series of SVOC air concentrations at Little Fox Lake in Canada’s Yukon Territory did not identify distinct source regions for most analytes, although γ-hexachlorocyclohexane appears to originate broadly in north-eastern Russia and/or Alaska. Based on this remoteness from sources, the site is judged to be well suited to monitor changes in the hemispheric background concentrations of SVOCs. A model-based exploration revealed wet-gaseous deposition as the dominant process responsible for cold-trapping SVOCs in mountain soils. Such cold trapping is particularly effective if precipitation rate increases with altitude and if temperature differences along the mountain are large. Considerable sensitivity of the modeled extent of cold-trapping to parameters as diverse as scale, mean temperature, atmospheric particle concentration and time relative to emission maxima is consistent with the wide variety of observed enrichment behaviour. Concentration gradients of polycyclic aromatic hydrocarbons and polychlorinated biphenyls in air and soil measured on four Western Canadian mountains with variable distance from sources revealed source proximity as the main driver of concentrations at both the whole-mountain scale and along individual mountain transects.