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.

Beginning in March 1999 at Georgia Institute of Technology, the ASACA (Assessment of Spatial Aerosol Composition in Atlanta) program has provided PM₂. ₅ concentration and speciation using particle concentration monitoring in and around metropolitan Atlanta. Since 1999, three of the ASACA sites have collected PM₂. ₅ in an urban setting: Fort McPherson (FT, SW), South Dekalb (SD, SE), and Tucker (TU, NE). In January 2007, TU was retired and Fire Station 8 (FS8, NE) was employed as the new urban site. Starting in 2002, PM₂. ₅ concentrations have also been characterized at a rural site, Fort Yargo (YG). Water-soluble ionic species and carbonaceous species concentrations are collected daily on filters using a three-channel particulate composition monitor (PCM). From 1999 to 2008, average PM₂. ₅ concentrations range from 12.9 µg/m3 at YG to 15.4 µg/m3 at TU. Sulfate and organic matter are the main components of Atlanta PM, contributing around 26% and 31% respectively to PM mass. Overall ASACA data quality increased from around 5 in 1999 to a value of 9 in 2005. Seasonal PM data quality appears to be significantly affected by volatility of secondary aerosol species during warm months because ionic data quality regularly decreases in the summer. PM is more sensitive to total sulfate concentration than nitrate and ammonia year-round.

The following thesis titled, Atlanta Automotive Particulate Matter Exposure and Evaluation, presents data obtained as a part of a joint project with Emory University, Rollin's School of Public Health. The Atlanta Commuters Exposure (ACE) Study uses both real-time and time-integrated sampling techniques for ambient aerosol concentrations. The ACE study is unique in that it will correlate the ambient aerosol concentrations with the concurrent health measurements. The primary objective of this thesis is to measure the concentration, size distribution and the chemical composition of PM2.5 inside the vehicle cabin for several commuters. The vehicles followed a scripted route along roadways in the Atlanta metropolitan region during periods of peak traffic volume, while the compact air sampling package of both real-time and time-integrated instruments recorded data. Real-time measurements for Particulate Matter (PM) were made using compact Optical Particle Counters (OPC), a Condensation Particle Counter, and a MicroAethalometer. The time-integrated measurements for Elemental Carbon (EC), Organic Carbon (OC), Water Soluble Organic Carbon (WSOC), particulate elemental concentrations, and speciated organics required filter collection methods. Thus a compact air-sampling package was created to combine both sets of real-time and time-integrated instruments. The following results are presented for the first four commutes. The framework for analyzing and presenting results is developed, and will be used for future commutes.

Both long-term and short-term exposure to fine particulate matter (PM2.5) has been shown to increase the rate of respiratory and cardiovascular illness, premature death, and hospital admissions from respiratory causes. It is important to understand what contributes to ambient PM2.5 level to establish effective regulation, and air quality model can provide guidance based on the best scientific understanding available. However, PM2.5 simulations in air quality models have often found performance less than desirable, particularly for organic carbon levels. Here, some of major shortcomings of current air quality model will be addressed and improved by using CMAQ, receptor models, and regression analysis. Detailed source apportionment of PM2.5 performed using the CMAQ-tracer method suggests that wood combustion and mobile sources are the largest sources of PM2.5, followed by meat cooking and industrial processes. Biases in emission estimates are investigated using tracer species, such as organic molecular markers and trace metals that are used in receptor models. Comparison of simulated and observed tracer species shows some consistent discrepancies, which enables us to quantify biases in emissions and improve CMAQ simulations. Secondary organic aerosol (SOA) is another topic that is investigated. CMAQ studies on organic aerosol usually underestimate organic carbon with larger than a 50% bias. Formation of aged aerosol from multigenerational semi-volatile organic carbon is added to CMAQ, significantly improving performance of organic aerosol simulations.

The rapid rising anthropogenic emissions driven by economic growth over China documented by satellite observations and bottom-up inventories have led to severely degraded air quality, and also have been suggested to be linked to the recent upward trends of tropospheric O₃ over the regions downwind of China. Multi-scale modeling analyses facilitated by ground-level, aircraft and satellite observations have been conducted to understand the atmospheric chemistry over China. Analyses using a 1-D photochemical model constrained by measurements at Beijing in August of 2007 suggest that reactive aromatic VOCs are the major source (~75%) of peroxy acetyl nitrate (PAN). Detailed radical budget analyses reveal the very fast ROₓ (OH + HO₂ + RO₂) production, recycling and destruction driven by VOC oxidation and heterogeneous processes. Photoenhanced aerosol surface uptake of NO₂ is found to be the predominant source of nitrous acid (HONO) during daytime (~70%). 3-D regional modeling analyses of tropospheric vertical column densities of glyoxal (CHOCHO) from SCIAMACHY show that anthropogenic emissions of aromatic VOCs are substantially underestimated (by a factor of 5 - 6, regionally varied) over China. Such an underestimation is the main cause of a large missing source of CHOCHO over the region in current global models, and could also partly explain the underestimation of organic aerosols in previous modeling studies.

As building codes and green building programs require tighter home construction, the need for outdoor air ventilation to improve indoor air quality increases. Major improvements in building envelopes and duct systems have led to decreases in heating and cooling loads causing fewer HVAC system run-time hours, and increasing the probability for air stagnation within homes with poor outdoor air ventilation. ASHRAE Standard 62.2 quantifies the amount of whole-house ventilation required based on the number of occupants and the square footage of conditioned space, but leaves the design of the ventilation system up to the mechanical engineer or HVAC contractor. In 2010, ASHRAE began requiring flow testing for confirmation of outdoor air ventilation rates, yet few municipalities and green building programs have adopted the new standard. Builders in mixed-humid climates are forced to balance the need for outdoor air ventilation with the upfront costs for mechanical ventilation systems, and the potential for increased humidity loads and energy costs associated with mechanical ventilation strategies. One common solution employed in the southeastern United States involves a central fan integrated supply (CFIS) ventilation system controlled with an air-cycler for minimum run-time to meet ASHRAE Standard 62.2. While this system has been tested and proven to meet design ventilation rates, those tests were often conducted on homes constructed by well trained builders receiving strong oversight from building scientists and the design ventilation rates were not always ASHRAE compliant. The following report analyzes whether the CFIS ventilation system with air-cycler controller provides ventilation meeting ASHRAE Standard 62.2 when employed by builders with minimal training and support.

Four receptor models and a chemical transport model were used to quantify the sources of PM2.5 impacting the St. Louis Supersite (STL-SS) between June 2001 and May 2003. The receptor models utilized two independent datasets, one that included ions and trace elements and a second that incorporated 1-in-6 day organic molecular marker data. Since each source apportionment (SA) technique has its own limitations, this work compared the results of five different SA approaches to better understand the biases and limitations of each. The source impacts predicted by these five models were then integrated into an ensemble-trained SA methodology. The ensemble method offered several improvements over the five individual SA techniques. Primarily, the ensemble method calculated source impacts on days when individual models either did not converge to a solution or did not have adequate input data to develop source impact estimates. Additionally, the ensemble method resulted in fewer days on which major emissions sources (e.g., secondary organic carbon and diesel vehicles) were estimated to have either a zero or negative impact on PM2.5 concentrations at the STL-SS. When compared with a traditional chemical mass balance (CMB) approach using measurement-based source profiles (MBSPs), the ensemble method was associated with better fit statistics, including reduced chi-squared values and improved PM2.5 mass reconstruction. A comparison of the different modeling techniques also revealed some of the subjectivities associated with applying specific SA models to the STL-SS dataset. For instance, positive matrix factorization (PMF) results were very sensitive to both the fitting species and number of factors selected for the analysis, whereas source impacts predicted in CMB were sensitive to the selection of source profiles to represent local metals processing emissions. Additionally, the different SA approaches predicted different impacts for the same source on a given day, with correlation coefficients ranging from 0.03 to 0.66 for gasoline vehicle, -0.51 to 0.85 for diesel vehicles, -0.29 to 0.86 for dust, -0.34 to 0.76 for biomass burning, 0.22 to 0.72 for metals processing, and -0.70 to 0.68 for secondary organic carbon. These issues emphasized the value of using several different SA techniques at a given receptor site, either by comparing source impacts predicted by different models or by utilizing an ensemble-trained SA technique.

Ein mobiler Aerosolstandard für ultrafeine Partikel wurde als neue Möglichkeit zur Qualitätskontrolle für innovative Umweltmessungen in Luftgütemessnetzen entwickelt. Die Bestimmung der Anzahl ultrafeiner Partikel in Ergänzung zur Überwachung von Feinstaub PM10 oder PM2.5 eröffnet neue Möglichkeiten, die Luftqualität zukünftig besser beurteilen zu können. Ultrafeine Partikel sind ein zweckmäßiger Indikator, um z. B. die positive Wirkung einer Umweltzone in Luftreinhalteplänen nachzuweisen.

Die Sondermessung in Riesa wurde von September 2008 bis August 2009 - die Bestimmung von Dioxinen, Furanen und polychlorierten Biphenylen im Staubniederschlag noch bis Dezember 2009 - durchgeführt. Sie diente zur Überprüfung der berechneten Immissionssituation für Feinstaub <10 μm (PM10) und Stickstoffdioxid (NO2) sowie zur Überprüfung des Einflusses der Elbe-Stahlwerke Feralpi GmbH (ESF) auf die Luftqualität in Riesa. Die auf die Messung folgende Modellierung der Luftqualität im Riesa ergab die höchsten Konzentrationen von PM10 im Gewerbegebiet. Die höchsten NO2-Konzentrationen treten entlang der Hauptverkehrsstraßen auf. Grenzwertüberschreitungen wurden an Straßenabschnitten mit Wohnbebauung nicht festgestellt. Die berechneten Immissionskonzentrationen stimmen gut mit den gemessenen überein. Die Abweichungen betragen jeweils ca. 10 % für den Jahresmittelwert von PM10 bzw. NO2. Sie liegen damit unter den von der EU-Richtlinie (2008/50/EG) geforderten Werten. Bei den Messungen der nachfolgenden Luftschadstoffe wurden keine Überschreitungen bei Grenz- und Zielwerten festgestellt: - PM10 - NO2 - Blei, Cadmium und Arsen im PM10 - Staubniederschlag - Blei, Cadmium und Arsen im Staubniederschlag. Der Einfluss der typischen Emissionen eines Stahlwerkes, wie Blei und Zink, aber auch Cadmium und Eisen, ist jedoch deutlich erkennbar. Bei Dioxinen, Furanen und dioxinähnlichen polychlorierten Biphenylen im Feinstaub PM 10 und im gasförmigen Zustand wird der vom Bund/Länderausschuss für Immissionsschutz (LAI) 2004 emp-fohlene Zielwert für die langfristige Luftreinhalteplanung (150 fg TE/m3) sicher eingehalten. Das Jahresmittel in Riesa lag bei 35 fg TE/m3. Der Zielwert für die Deposition (4 pg TE/(d*m2) dagegen wird in Riesa - wie auch an anderen Orten in Deutschland - überschritten. Die Mittelwerte über die Probenahmezeit von 16 Monaten an den vier Messpunkten lagen zwischen 5,5 und 14,7 pg TE/(d*m2). Die polychlorierten Biphenyle hatten daran einen maßgeblichen Anteil. Als wesentliche Quelle wurde die Verarbeitung des Schrotts, insbesondere die Schredderleichtfraktion und die Nichteisenmetalle, ermittelt. Die höheren Konzentrationen sind auf das unmittelbare Anlagenumfeld beschränkt und nehmen mit zunehmender Entfernung von der Quelle rasch ab. Am Friedrich-Ebert-Platz werden bereits Konzentrationen erreicht, die sich von denen der Vergleichsmessung im städtischen Hintergrund an der Station Chemnitz-Mitte kaum noch unterscheiden.

<p>In this thesis the sink-effect in indoor materials wasstudied using mathematical modelling and experimental studies.The sink-effect is a concept which is commonly used tocharacterise the ability of different indoor materials to sorbcontaminants present in the indoor air. The sorption process ismore or less reversible, i.e. molecules sorbed in materials athigh contaminant concentrations may again be desorbed at lowerconcentrations. Knowledge of the sorption capacity of materialsand the rate at which sorption and desorption takes place is offundamental importance for mathematical simulation of indoorair quality. The aim of this work is to contribute withknowledge about how the sink-effect can be described inmathematical terms and how the interaction parametersdescribing the sorption capacity and sorption/desorptionkinetics can be determined. The work has been of amethodological nature. The procedure has been to set upphysically sound mathematical models of varying complexity andto develop small-scale chamber experiments. Two differentdynamic chamber methods have been used. One is based on amodified standard FLEC-chamber while the other uses a chamberwith two compartments, one on each side of the material. The"twin-compartment" method was designed due to the observationthat the contaminant readily permeated straight through theselected materials, which resulted in uncontrolled radiallosses in the FLEC-chamber. In order to be useful forcomparison between experiments and calculations and parameterfitting, the boundary conditions in the chambers must beprecisely known and controlled. This matter has shown to be themost crucial and difficult problem in the research. A varietyof mathematical models for the sink-effect have been proposed.In some models advanced fluid simulations were used in order totest the influence ofill-defined flow boundary conditions. Theaim of the modelling is to find a formulation with a minimum ofinteraction parameters, which is generally useful, i.e. both insmall-scale laboratory environments and in full-scale like anoffice room. Estimated model parameters are shown to be able toyield a reasonably good fit to experimental data for thesorption process but a less satisfactory fit for the desorptionprocess.</p><p><b>Keywords:</b>sink-effect, sorption, adsorption, diffusion,indoor air quality, volatile organic compounds, VOC,contaminants, building materials</p>