AIR QUALITY IN THE 21ST CENTURY -- CONTENTS -- PREFACE -- PARAMETERS CONTROLLING AMBIENT AIRBENZENE CONCENTRATIONS AND HUMANEXPOSURE IN A MEDIUM SIZED SOUTHEASTERNEUROPEAN CITY -- ABSTRACT -- 1. INTRODUCTION -- 2. MATERIALS AND METHODS -- 2.1. General Information -- 2.2. Measurements Campaign Design -- 2.2.1. Ambient air Measurements -- 2.2.2. Personal Exposure Measurements -- 2.3. Instruments and Apparatus -- 2.4. Quality Assurance and Quality Control -- 3. AMBIENT AIR CONCENTRATIONS -- 3.1. Environmental Data -- 3.1.1 Traffic Data -- 3.1.2. Meteorological Data -- 3.1.3. Urban Ambient Air Benzene Concentrations -- 3.2. Parameters Rising Ambient air Benzene Levels in Local Scale -- 3.2.1. Traffic Density -- 3.2.1.1. General Information -- 3.2.1.2. Data -- 3.2.1.3. Modelling Techniques -- 3.2.1.3.1. DET Model -- 3.2.1.3.2. ANN Model -- 3.2.1.4. Models Evaluation and Discussion -- 3.2.2. Street Canyon Effect -- 3.2.3. Filling Station Proximity -- 3.2.3.1. General Information -- 3.2.3.2. Measurements Results -- 3.2.3.2.1. Passive Sampling Results -- 3.2.3.2.2. Active Sampling Results -- 3.2.3.2.3. Traffic Data -- 3.2.3.2.4. Meteorological Data -- 3.2.3.3. Interpretation Using Models -- 3.2.3.3.1. Model Formulations -- 3.2.3.3.2. Evaluation and Optimization of CALINE 4 -- 3.2.3.4. Contribution of Filling Stations to the Observed Benzene Concentrations -- 4. PERSONAL EXPOSURE RESULTS -- 4.1. General -- 4.2. Control Group -- 4.3. Policemen -- 4.4. Filling Station Employees -- 4.4.1. Introduction -- 4.4.2. Methodology -- 4.4.3. Artificial Neural Network (ANN) Modeling Development -- 4.4.4. Measurements Results -- 4.4.4.1. Environmental Data-Amount of Gasoline Traded -- 4.4.4.2. Personal Exposure Results -- 4.4.5. Artificial Neural Network Modelling Results -- 4.4.6. Relative Importance of the Parameters Constituting the Exposure Pattern.

4.5. Taxi Drivers -- 4.6. Active Sampling Personal Exposure -- 5. CANCER RISK ASSESSMENT -- 6. THE EFFECT OF CHANGES IN TRAFFIC FLOW PATTERNS ONEMISSIONS BY THE MEANS OF ENVIRONMENTAL POLICY -- 7. CONCLUSIONS -- 8. REFERENCES -- METEOROLOGICAL ASPECTS OF AIR QUALITY -- ABSTRACT -- 1. INTRODUCTION -- 2. THE ATMOSPHERIC BOUNDARY LAYER -- 3. MESOSCALE MODELS FOR AIR QUALITY FORECASTS -- 3.1. Case Study and Model Set up -- 3.2. Results -- 4. DIFFICULTIES OF THE STABLE BOUNDARY LAYER -- -Turbulence -- -Longwave Radiative Transfer -- -Soil And Vegetation -- -Elevated Nighttime Wind Maximum -- -Gravity Waves -- - Interactions -- 5. DISPERSION AND MEANDERING IN STABLE BOUNDARY LAYERS -- 5.2. Intermittency in Stable Boundary Layers -- A) Single Station Observations: The Wageningen Field -- B) Spatial Development -- C) Wavelet Analysis -- D) A Climatology of Intermittency of Nighttime Turbulence. -- 6. THE STABLE BOUNDARY-LAYER HEIGHT -- 6.1. Background -- 6.2. Observations -- a) CASES-99 -- b) Sodankylä -- c)Cabauw -- d)SHEBA -- e) SABLES98 -- 6.3. USING DIMENSIONAL ANALYSIS FOR DERIVATION AN EQUATIONFOR THE STABLE BOUNDARY LAYER HEIGHT -- A) Three Dimensionless Groups -- b) Verification -- C) On the Relevance of the Coriolis Parameter F: Two Dimensionless Groups Only -- 6.4. An Alternative Theoretically Based Formulation for the StableBoundary Layer Height -- 6.4.1. Background -- 6.4.3. Results -- 6.5. CONCLUSIONS -- 7. CLOSURE -- ACKNOWLEDGEMENTS -- REFERENCES -- A QUANTITATIVE COMPARISON OF ANGSTROM'STURBIDITY PARAMETERS (α,β) RETRIEVED INDIFFERENT SPECTRAL RANGES BASED ONSPECTRAL SOLAR EXTINCTION MEASUREMENTS -- ABSTRACT -- INTRODUCTION -- EXPERIMENTAL MEASUREMENTS -- THEORETICAL BACKGROUND AND METHODOLOGY -- Definitions -- The Angstrom Parameters -- RESULTS -- Comparisons of Angstrom α Values -- Comparisons of Angstrom β Values -- CONCLUSION.

REVIEWED BY -- REFERENCES -- SIMULTANEOUS EVALUATION OF ODOR EPISODESAND AIR QUALITY. METHODOLOGY TO IDENTIFYAIR POLLUTANTS AND THEIR ORIGIN COMBININGCHEMICAL ANALYSIS (TD-GC/MS),SOCIAL PARTICIPATION, ANDMATHEMATICAL SIMULATIONS TECHNIQUES -- ABSTRACT -- 1. INTRODUCTION -- 2. ODOR IMPACT AND AIR QUALITY ASSESSMENT ANDMANAGEMENT FOR URBAN AREAS -- 3. METEOROLOGICAL ANALYSIS -- 4. SOCIAL PARTICIPATION -- 4.1. Model of Social Participation for Controlling Odor Episodes and AirQuality: Identifying Chemical Compounds and their Sources -- 4.1.1. Participation Levels -- 4.1.1.1. Sensory and Occurrence Data -- 4.1.2. Annoyance Level Evaluation -- 5. IMPACT MAPS -- 6. CHEMICAL CONTROL -- 6.1. Air Sampler Development -- 6.2. Analytical Methodology -- 6.2.1. Multi-sorbent Tubes -- 6.2.2. Analytical Instrumentation -- 6.3. Concentration Maps -- 6.4. Odor Maps -- 6.5. Air Quality Determination -- 6.5.1. Outdoor Air -- 6.5.2. Indoor Air -- 6.6. Identification of Recently Identified Pollutants and UnderstudiedCompounds -- 7. CASE STUDIES -- 7.1. Back-Trajectory Application -- 7.2. Application of Integrated Methodology (Social Participation, ChemicalControl and Numerical Modeling) -- 7.2.1. Description of the Study Area -- 7.2.2. Meteorological Analysis -- 7.2.3. Impact Maps -- 7.2.4. Social Control (Social Participation) -- 7.2.5. Chemical Control -- 7.2.5.1. Qualitative VOC Determination -- 7.2.5.2. Quantitative VOC Determination -- 7.2.5.3. Concentration Maps -- 8. CONCLUSIONS -- REFERENCES -- LICHEN BIOMONITORING OF AIR POLLUTION:ISSUES FOR APPLICATIONSIN COMPLEX ENVIRONMENTS -- ABSTRACT -- INTRODUCTION -- LICHEN DIVERSITY VALUE (LDV) METHOD -- MONITORING IN A VARIABLE ENVIRONMENT -- OBJECTIVE AND APPROACH -- WORKED EXAMPLE I. POLLUTION-RELATED SIGNAL ANDCLIMATE-RELATED NOISE IN LICHEN BIOMONITORING ATREGIONAL SCALE: A CASE-STUDY FROM LIGURIA (NW ITALY).

MATERIALS AND METHODS -- Sampling Design -- Lichen Sampling -- Data Analysis -- RESULTS -- DISCUSSION -- i) To Define Homogeneous Bioclimatic Regions -- ii) To Identify the Influence of Each Atmospheric Pollutant for EachBioclimatic Region and to Analyse Trends of Biodiversity Indices -- iii) To Elaborate Proper Scales for Interpreting lichen Biomonitoring Data -- WORKED EXAMPLE II. BIOMONITORING OF AIR POLLUTION INFOREST ECOSYSTEMS: PROBLEMS AND PERSPECTIVES -- MATERIALS AND METHODS -- The Study in Tuscany -- The Study in Liguria -- RESULTS -- Casentino National Park -- Liguria -- DISCUSSION -- Casentino National Park -- Liguria -- WORKED EXAMPLE III. NON POLLUTION-RELATED NOISE INLICHEN BIOMONITORING AT WITHIN-SITE SCALE:A CASE STUDY FROM ITALIAN-SLOVENIAN BORDERLINE -- MATERIALS AND METHODS -- Survey Area -- Sampling Strategy -- Non-pollution Related Factors -- Data Analysis -- RESULTS -- Local Epiphytic Vegetation -- Lichen Diversity Variability at within-Plot Scale -- LDV Variability and Non Pollution-related Factors -- DISCUSSION -- LDV variability at within- and between-sites scale: pollution and nonpollution-related predictors -- GENERAL CONCLUSIONS -- REFERENCES -- HYDROCARBON CONTAMINATION ANDENVIRONMENTAL HEALTH QUALITY: AN OVERVIEW -- ABSTRACT -- INTRODUCTION -- CONCEPT OF ENVIRONMENTAL QUALITY -- Definition of the Environment -- Major Components of the Environment -- 1. Soil -- 2. Water -- 3. Atmosphere -- Environmental Impact Assessment -- CONCEPT OF HYDROCARBONS -- PRIMARY SOURCES OF HYDROCARBON CONTAMINANTS ANDENVIRONMENTAL/HEALTH IMPACT -- Crude Oils -- Oil Spill -- Natural Gas -- Gas Flaring -- Produced Water -- Lubricating Oils -- Coal -- SECONDARY SOURCES OF HYDROCARBON CONTAMINANTS ANDENVIRONMENTAL / HEALTH IMPACT -- Oil Refinery -- Petrochemicals / Petrochemical Plants -- Flue Gases / Vehicle Emissions -- i) Carbon Monoxide (CO).

ii) Sulfur Dioxide (SO2) -- iii) Oxides of Nitrogen -- Leaded Gasoline -- Trace Heavy Metals -- Biological Decay of Sewage and Refuse -- Odors -- Biomass / Wood Fuels -- Spent Oil-based Drilling Fluid and Oily Drill Cuttings -- The Greenhouse Gases and Global Warming -- OTHER MINOR SOURCES OF HYDROCARBON CONTAMINATION -- Natural Seeps and Organic Matter -- Shale Oil and Condensates -- CONCLUSION AND RECOMMENDATIONS -- REFERENCES -- BIVARIATE STOCHASTIC VOLATILITY MODELSAPPLIED TO MEXICO CITY OZONE POLLUTIONDATA -- Abstract -- 1. Introduction -- 2. Bivariate Stochastic Volatility Models (BSV) -- 2.1. Model I -- 2.2. Model II -- 2.3. Model III -- 2.4. Model IV -- 2.5. Model V -- 2.6. Model VI -- 3. Bayesian Analysis -- 3.1. Class I -- 3.2. Class II -- 3.3. Class III -- 4. Model Selection -- 5. An Application to theWeekly Ozone Averages in Mexico City -- 5.1. Description of the Data -- 5.2. Application of the Models and Results -- 5.2.1. Model I -- 5.2.2. Model II -- 5.2.3. Model III -- 5.2.4. Model IV -- 5.2.5. Model V -- 5.2.6. Model VI -- 5.2.7. Model Selection -- 6. Conclusion -- Acknowledgements -- References -- OPTIMIZATION APPROACHES FOR AIR QUALITYMONITORING NETWORK DESIGN -- ABSTRACT -- INTRODUCTION -- AQMN-RESULTS -- AIR QUALITY MONITORING SITING CRITERION -- METHODOLOGY DESCRIPTION -- 1. Representation of Spatial-Temporal Patterns -- 2. Detection of Violations over Ambient Standards -- UTILITY FUNCTION APPROACH -- 1. Structure of the Utility Function -- 2. Computational Algorithm of the Utility Function Based on SinglePollutant -- 3. Computational Algorithm of the Utility Function Based on MultiPollutants Network Design -- CASE STUDY -- STUDY AREA OPTIMIZATION MODELING RESULTS DISCUSSION -- 1. Single Pollutant Monitoring Network Optimization Modeling Results -- 2. Multi-Pollutant Monitoring Network Optimization Modeling Results -- REFERENCES.

SENSITIVITY OF LAND USE PARAMETERS ANDPOPULATION ON THE PREDICTION OFCONCENTRATION USINGTHE AERMOD MODEL FOR AN URBAN AREA.