Assessment of the environmental fate of linear alkylbenzenesulphonates

Fugacity models, as developed previously, are applied to the chemical linear alkylbenzenesulphonate (LAS) to predict its ultimate fate in the environment. The behaviour of LAS is illustrated using unit worlds or evaluative environments at three successive levels of complexity (Levels I, II, and III). LAS is shown to have a short environmental life of two days attributable to rapid biodegradation in water. Reported concentration data for LAS in Rapid Creek, South Dakota, downstream from a municipal sewage treatment plant, are compared to expected concentrations as generated by the QWASI (Quantitative Water Air Sediment Interaction) fugacity model; the model being based solely on the physical, chemical, reactivity and transport properties of LAS and the emission rate of the chemical into the river. The data are found to fit the model when a sediment-water mass transfer coefficient of 7.80 × 10⁻³ m/h and an effective sediment bed depth of 3 mm are used. The generally satisfying agreement between environmental observations and the QWASI fugacity models lends credibility to the use of evaluative models.     

Verification of a source-oriented externally mixed air quality model during a severe photochemical smog episode

The CIT/UCD three-dimensional source-oriented externally mixed air quality model is tested during a severe photochemical smog episode (Los Angeles, 7–9 September 1993) using two different chemical mechanisms that describe the formation of ozone and secondary reaction products. The first chemical mechanism is the secondary organic aerosol mechanism (SOAM) that is based on SAPRC90 with extensions to describe the formation of condensable organic products. The second chemical mechanism is the caltech atmospheric chemistry mechanism (CACM) that is based on SAPRC99 with more detailed treatment of organic oxidation products.The predicted ozone concentrations from the CIT/UCD/SOAM and the CIT/UCD/CACM models agree well with the observations made at most monitoring sites with a mean normalized error of approximately 0.4–0.5. Good agreement is generally found between the predicted and measured NO_x concentrations except during morning rush hours of 6–10 am when NO_x concentrations are under-predicted at most locations. Total VOC concentrations predicted by the two chemical mechanisms agree reasonably well with the observations at three of the four sites where measurements were made. Gas-phase concentrations of phenolic compounds and benzaldehyde predicted by the UCD/CIT/CACM model are higher than the measured concentrations whereas the predicted concentrations of other aromatic compounds approximately agree with the measured values.The fine airborne particulate matter mass concentrations (PM_2.5) predicted by the UCD/CIT/SOAM and UCD/CIT/CACM models are slightly greater than the observed values during evening hours and lower than observed values during morning rush hours. The evening over-predictions are driven by an excess of nitrate, ammonium ion and sulfate. The UCD/CIT/CACM model predicts higher nighttime concentrations of gaseous precursors leading to the formation of particulate nitrate than the UCD/CIT/SOAM model. Elemental carbon and total organic mass are under-predicted by both models during morning rush hour periods. When this latter finding is combined with the NO_x under-predictions that occur at the same time, it suggests a systematic bias in the diesel engine emissions inventory. The mass of particulate total organic carbon is under-predicted by both the UCD/CIT/SOAM and UCD/CIT/CACM models during afternoon hours. Elemental carbon concentrations generally agree with the observations at this time. Both the UCD/CIT/SOAM and UCD/CIT/CACM models predict low concentrations of secondary organic aerosol (SOA) (<3.5 μg m⁻³) indicating that both models could be missing SOA formation pathways. The representation of the aerosol as an internal mixture vs. a source-oriented external mixture did not significantly affect the predicted concentrations during the current study.     

Evaluation of the performance of RAM with the Regional Air Pollution Study data base

The RAM air quality simulation model's performance is examined using the Regional Air Pollution Study (RAPS) Level-7 data base. Time series analyses were performed to test the adequacy of RAM in simulating the dynamics of pollutants in the atmosphere. Power spectrum and auto-correlation analyses show that the predicted concentrations do not have the same temporal characteristics as the observations during the winter period.Both paired and unpaired analyses are included to critically examine the model performance. The paired comparisons, including those performance measures suggested by the AMS Woods Hole workshop, indicate a better agreement between predicted and observed data at the rural sites than at the urban sites. When the data are segregated according to wind speed and atmospheric stability class, it is found that there is very little agreement between the predicted and measured concentrations under extreme stability (either very unstable or very stable) and low wind speed (less than 2 ms⁻¹ conditions.The measured and predicted daily maximum concentrations are subjected to the ‘bootstrap’ sampling procedure to develop the distribution of the differences between observed and predicted concentrations. These distributions suggest that the errors are random, and, therefore, RAM cannot be calibrated to improve model performance within the urban area. Further, the analyses suggest that improvement of RAM's performance may be realized through a better characterization of area source emissions within the urban area and the inclusion of other physical processes such as a fumigation algorithm in the model.     

Model simulations of the atmospheric trace metals concentrations and depositions over the Baltic Sea

The results of application of two nested Eulerian atmospheric transport models for investigation of airborne heavy metal pollution are presented. The distribution and deposition over Europe and Baltic Sea region were simulated for Pb, Cd and Zn for 2 two-months periods: June–July 1997 and February–March 1998. The European-wide calculations were made with the ADOM model from the GKSS Research Centre, and the Baltic regional calculations were made with the HILATAR model from the Finnish Meteorological Institute. The one-way 3-D nesting was used: hourly concentrations from the ADOM model were used by the HILATAR as vertically resolved boundary conditions. Input data for both models were taken from the weather forecast model HIRLAM and UBA/TNO emission inventory. This allows interpreting of some diversity in the calculation results in terms of different internal parameterization and spatial resolution of the models. Simulation results were compared with high-resolution atmospheric measurements carried out at four stations in the southern part of the Baltic Sea for the same period. Manifesting quite good agreement with observations, the models missed several high deposition events of Cd observed at coastal station Hel. Study of this phenomenon enabled to build a 2-D probability function for potential location of the unknown Cd source.     

Prediction of the vertical profile of ozone based on ground-level ozone observations and cloud cover

A number of statistical techniques have been used to develop models to predict high-elevation ozone (O3) concentrations for each discrete hour of day as a function of elevation based on ground-level O3 observations. The analyses evaluated the effect of exclusion/inclusion of cloud cover as a variable. It was found that a simple model, using the current maximum ground-level O3 concentration and no effect of cloud cover provided a reasonable prediction of the vertical profile of O3, based on data analyzed from O3 sites located in North Carolina and Tennessee. The simple model provided an approach that estimates the concentration of O3 as a function of elevation (up to 1800 m) based on the statistical results with a +/- 13.5 ppb prediction error, an R2 of 0.56, and an index of agreement, d1, of 0.66. The inclusion of cloud cover resulted in a slight improvement in the model over the simple regression model. The developed models, which consist of two matrices of 24 equations (one for each hour of day for clear to partly cloudy conditions and one for cloudy conditions), can be used to estimate the vertical O3 profile based on the inputs of the current day's 1-hr maximum ground-level O3 concentration and the level of cloud cover.     

Evaluating the use of outputs from comprehensive meteorological models in air quality modeling applications

Currently used dispersion models, such as the AMS/EPA Regulatory Model (AERMOD), process routinely available meteorological observations to construct model inputs. Thus, model estimates of concentrations depend on the availability and quality of meteorological observations, as well as the specification of surface characteristics at the observing site. We can be less reliant on these meteorological observations by using outputs from prognostic models, which are routinely run by the National Oceanic and Atmospheric Administration (NOAA). The forecast fields are available daily over a grid system that covers all of the United States. These model outputs can be readily accessed and used for dispersion applications to construct model inputs with little processing. This study examines the usefulness of these outputs through the relative performance of a dispersion model that has input requirements similar to those of AERMOD. The dispersion model was used to simulate observed tracer concentrations from a Tracer Field Study conducted in Wilmington, California in 2004 using four different sources of inputs: (1) onsite measurements; (2) National Weather Service measurements from a nearby airport; (3) readily available forecast model outputs from the Eta Model; and (4) readily available and more spatially resolved forecast model outputs from the MM5 prognostic model. The comparison of the results from these simulations indicate that comprehensive models, such as MM5 and Eta, have the potential of providing adequate meteorological inputs for currently used short-range dispersion models such as AERMOD.     

Testing the capability of the chemistry transport model LOTOS-EUROS to forecast PM10 levels in the Netherlands

Since particulate matter has a direct and adverse impact on public health, a good air quality forecast is important. Several European countries presently use statistical forecasting models, which have their limitations, especially for PM10. An alternative approach is to use a chemistry transport model. Here, the ability of the chemical transport model LOTOS-EUROS to forecast PM10 concentrations in the Netherlands was investigated. LOTOS-EUROS models several PM10 components individually. For sulphate, nitrate and ammonium aerosol the evaluation against observations shows that the modelled annual mean concentrations are within 20% of the measured concentration and that the temporal correlation is reasonably good (R > 0.6). For sea salt the model tended to overestimate the measured concentrations. For elemental carbon the correspondence with black smoke observations was reasonable. However, total PM10 is seriously underestimated, due to unmodelled components (secondary organic aerosols, mineral dust) and missing sources. Therefore, a simple bias correction for four seasons was derived based on the years 2004–2006. The model was compared with the Dutch operational statistical model PROPART and ground-level observations. With bias correction, LOTOS-EUROS performed better than PROPART regarding the timing of events. The major flaw of LOTOS-EUROS was that high values (>50 μg m^?3) were still underestimated. Another advantage of LOTOS-EUROS over the statistical model was the more detailed information in space and time, which facilitates communication of the forecast to the general public.     

Partitioning of nitrate and ammonium between the gas and particulate phases during the 1997 IMADA-AVER study in Mexico City

The partitioning of nitrate and ammonium between the gas and particulate phases is studied combining available equilibrium models and measurements taken in Mexico City during the 1997 IMADA-AVER field campaign. Based on this analysis, there are no significant differences in model predictions, but some discrepancies exist between predictions and observations. The inclusion of crustal elements in the modeling framework improves agreement of model predictions for particulate nitrate against measurements by approximately 5%. Although some equilibrium aerosol models do not explicitly treat crustal elements, these species can be treated as equivalent concentrations of sodium. Atmospheric equilibrium models predict daily average PM_2.5 nitrate concentrations within 20% of the IMADA-AVER measurements at the MER site. Six-hour average PM_2.5 nitrate concentrations are predicted within 30–50% on average except for the afternoon sampling periods (12:00–18:00 h). Investigating the possible sources of these discrepancies, it appears that a dynamic instead of an equilibrium approach is more suitable in reproducing aerosol behavior during these afternoon periods. By applying the Multicomponent Aerosol Dynamic Model (MADM), model performance in predicting concentrations of particulate nitrate significantly improves during the afternoon periods.     

A synoptic climatological approach to forecast concentrations of sulfur dioxide and nitrogen oxides in Hong Kong

The objective of this study is to develop an automated synoptic climatological procedure to forecast high air pollution concentrations in the most polluted synoptic categories. The procedure is able to identify air masses historically associated with high air pollution concentrations. The arrival of air mass can be predicted 24 or 48 h in advance with the use of the weather forecast data. The development and statistical basis of the procedure are discussed, and an analysis of the procedure's ability to forecast weather conditions associated with high air pollution concentrations is presented. In addition, the dataset of 24 weather variables from 1993 to 1995 is used to validate the procedure. The procedure predicts that 70.3 and 83.3% of total high and severe SO2 concentration days fall into the identified most polluted categories, and the corresponding figures for NOx are 47.8 and 73.7%. The agreement between observed and predicted values is generally good. The prediction models can explain about 58 and 45% of total variance for NOx and SO2 with RMSEs of 42.5 and 16.5 microg m(-3), respectively. They are smaller than 1 SD of the observations.     

Model for forecasting expressway fine particulate matter and carbon monoxide concentration: application of regression and neural network models

The Borman Expressway is a heavily traveled 16-mi segment of the Interstate 80/94 freeway through Northwestern Indiana. The Lake and Porter counties through which this expressway passes are designated as particulate matter < 2.5 microm (PM2.5) and ozone 8-hr standard nonattainment areas. The Purdue University air quality group has been collecting PM2.5, carbon monoxide (CO), wind speed, wind direction, pressure, and temperature data since September 1999. In this work, regression and neural network models were developed for forecasting hourly PM2.5 and CO concentrations. Time series of PM2.5 and CO concentrations, traffic data, and meteorological parameters were used for developing the neural network and regression models. The models were compared using a number of statistical quality indicators. Both models had reasonable accuracy in predicting hourly PM2.5 concentration with coefficient of determination -0.80, root mean square error (RMSE) <4 microg/m3, and index of agreement (IA) > 0.90. For CO prediction, both models showed moderate forecasting performance with a coefficient of determination -0.55, RMSE < 0.50 ppm, and IA -0.85. These models are computationally less cumbersome and require less number of predictors as compared with the deterministic models. The availability of real time PM2.5 and CO forecasts will help highway managers to identify air pollution episodic events beforehand and to determine mitigation strategies.     

Quaternary adsorption equilibria from aqueous systems onto decolourizing activated carbon

An experimental study has been conducted to obtain the adsorption isotherms of four typical pollutants from quaternary aqueous systems onto decolourizing activated carbon. The four materials investigated are: Phenol, 1,4-dihydroxybenzene, 4-amino-l-naphthalene sulfonic acid-sodium salt and Benzoic acid. The study has concentrated on the dilute region of concentrations which range from 10 to 165 ppm (mg/L) at an operating temperature of 30 °C.

The quaternary adsorption equilibria have been modeled using the extended Langmuir predictive model and the ideal adsorbed solution (IAS) theory. In employing these models for the prediction of multicomponent adsorption equilibria, the single-solute isotherms are needed. These isotherms have been fitted to Langmuir, Freundlich, and Dubinin models and the resulting model parameters, which are needed for the prediction of multicomponent adsorption equilibria, are reported. Predictions obtained from the extended Langmuir predictive model and the ideal adsorbed solution (IAS) model are in agreement, however, they deviate to an appreciable extent from experimental observations.     

Evaluation of three regional air quality models

This paper summarizes the results of a thorough assessment of existing regional air quality models. Forty-two candidate models were reviewed and three repsesentative models were selected for rigorous and comprehensive assessment. The underlying scientific theories used in the models were evaluated, revealing many limitations. For example, the techniques used in the preparation of meteorological fields that drive the models give insufficient consideration to the physical basis of the relevant atmospheric processes. The primary operational evaluation of each of the models was performed by comparing calculated values with observations from the EPRI Sulfate Regional Experiment (SURE). Both short-term (6-h averages) and long-term (annual averages) comparisons reveal poor correlations for both SO₂ and SO²⁻₄ for the three models evaluated ranging from 0.05 to 0.32 for 3- to 6-h SO₂ concentration to 0.03 to 0.59 for 24-4 and monthly averages; in some cases, the correlations are negative. The results also show that calculated concentrations are generally characterized by high biases for 3- to 6-h concentration predictions. Biases tend to be somewhat smaller for monthly averages. All three models underpredicted wet deposition with average normalized residuals of approximately 0.2 for ENAMAP-2, and 0.5 for RTM-II and ACID.     

Characterization of model error in a simulation of fine particulate matter exposure distributions of the working age population in Helsinki, Finland

Exposure models are needed for comparison of scenarios resulting from alternative policy options. The reliability of models used for such purposes should be quantified by comparing model outputs in a real situation with the corresponding observed exposures. Measurement errors affect the observations, but if the distribution of these errors for single observations is known, the bias caused for the population statistics can be corrected. The current paper does this and calculates model errors for a probabilistic simulation of 48-hr fine particulate matter (PM2.5) exposures. Direct and nested microenvironment-based models are compared. The direct model requires knowledge on the distribution of the indoor concentrations, whereas the nested model calculates indoor concentrations from ambient levels, using infiltration factors and indoor sources. The model error in the mean exposure level was <0.5 microg m(-3) for both models. Relative errors in the estimated population mean were +1% and -5% for the direct and nested models, respectively. Relative errors in the estimated SD were -9% and -23%, respectively. The magnitude of these errors and the errors calculated for population percentiles indicate that the model errors would not drive general conclusions derived from these models, supporting the use of the models as a tool for evaluation of potential exposure reductions in alternative policy scenarios.     

Simulating urban-scale air pollutants and their predicting capabilities over the Seoul metropolitan area

Urban-scale air pollutants for sulfur dioxide, nitrogen dioxide, particulate matter with aerodynamic diameter > or = 10 microm, and ozone (O3) were simulated over the Seoul metropolitan area, Korea, during the period of July 2-11, 2002, and their predicting capabilities were discussed. The Air Pollution Model (TAPM) and the highly disaggregated anthropogenic and the biogenic gridded emissions (1 km x 1 km) recently prepared by the Korean Ministry of Environment were applied. Wind fields with observational nudging in the prognostic meteorological model TAPM are optionally adopted to comparatively examine the meteorological impact on the prediction capabilities of urban-scale air pollutants. The result shows that the simulated concentrations of secondary air pollutant largely agree with observed levels with an index of agreement (IOA) of >0.6, whereas IOAs of approximately 0.4 are found for most primary pollutants in the major cities, reflecting the quality of emission data in the urban area. The observationally nudged wind fields with higher IOAs have little effect on the prediction for both primary and secondary air pollutants, implying that the detailed wind field does not consistently improve the urban air pollution model performance if emissions are not well specified. However, the robust highest concentrations are better described toward observations by imposing observational nudging, suggesting the importance of wind fields for the predictions of extreme concentrations such as robust highest concentrations, maximum levels, and >90th percentiles of concentrations for both primary and secondary urban-scale air pollutants.     

Long range transport and gas/particle distribution of polycyclic aromatic hydrocarbons at a remote site in the Mediterranean Sea

Polycyclic aromatic hydrocarbon (PAH) concentrations have been determined for 14 successive days in a remote site of the Mediterranean Sea situated in Corsica, France. Both particulate and gas phases were collected and analyzed. For any receptor site the concentration of adsorbed PAH on particles is determined by three parameters, in order of decreasing importance: the source area, nearby sources and precipitation along the trajectory followed by the particles. For two air masses originating from the same source area, PAH concentrations can be reduced by 60% by particle scavenging during precipitation events. The identification of the source area is in complete agreement with the classification based on the mineral elements. The gas phase concentrations are determined by the source area only; they remain high compared to the concentrations in the industrial zone, thus proving that the gaseous PAH are not strongly degraded by chemical aggressors during transport. Factor analysis clearly shows the different effects involved during transport. The gas/particle ratio is determined essentially by the temperature and molecular weight of the PAH and not by the origin of the emissions. However precipitation influences this ratio to a non-negligible extent through scavenging of the aerosols. For example, the gas/particle ratio, for pyrene, varies from 2 to 4 between two ‘dry’ episodes with a temperature difference of 2.2° C, and from 6 to 13 because of the particle scavenging by rain. These results can be used as a data base and are expected to guide the conception of transport models including the parameters considered in this study.     

Urban tracer dispersion experiments during the second DAPPLE field campaign in London 2004

As part of the DAPPLE programme two large scale urban tracer experiments using multiple simultaneous releases of cyclic perfluoroalkanes from fixed location point sources was performed. The receptor concentrations along with relevant meteorological parameters measured are compared with a three screening dispersion models in order to best predict the decay of pollution sources with respect to distance. It is shown here that the simple dispersion models tested here can provide a reasonable upper bound estimate of the maximum concentrations measured with an empirical model derived from field observations and wind tunnel studies providing the best estimate. An indoor receptor was also used to assess indoor concentrations and their pertinence to commonly used evacuation procedures.     

Prediction of the Vertical Profile of Ozone Based on Ground-Level Ozone Observations and Cloud Cover

Abstract

A number of statistical techniques have been used to develop models to predict high-elevation ozone (O₃) concentrations for each discrete hour of day as a function of elevation based on ground-level O₃ observations. The analyses evaluated the effect of exclusion/inclusion of cloud cover as a variable. It was found that a simple model, using the current maximum ground-level O₃ concentration and no effect of cloud cover provided a reasonable prediction of the vertical profile of O₃, based on data analyzed from O₃ sites located in North Carolina and Tennessee. The simple model provided an approach that estimates the concentration of O₃ as a function of elevation (up to 1800 m) based on the statistical results with a ±13.5 ppb prediction error, an R² of 0.56, and an index of agreement, d ₁, of 0.66. The inclusion of cloud cover resulted in a slight improvement in the model over the simple regression model. The developed models, which consist of two matrices of 24 equations (one for each hour of day for clear to partly cloudy conditions and one for cloudy conditions), can be used to estimate the vertical O₃ profile based on the inputs of the current day’s 1-hr maximum ground-level O₃ concentration and the level of cloud cover.     

A comparison study of three urban air pollution models

The predictions of three urban air pollution models with varying degrees of mathematical and computational complexities are compared against the hourly SO₂ ground-level concentrations observed on 10 winter nights of the RAPS experiment in St. Louis. The emphasis in this study is on the prediction of urban area source concentrations. Statistics for the paired comparison of predictions of each model with the observations are presented. The RAM and the ATDL model with stable diffusion coefficients overestimated the observed night-time concentrations. The results show that the performance of the ATDL model with near-neutral diffusion coefficients is comparable to the more sophisticated 3-D grid numerical model.     

On the interpretation of event and sub-event rainfall chemistry

Variations in precipitation chemistry between and within rain events have been examined in order to identify possible relationships with synoptic, mesoscale and micrometeorological processes. A microprocessor-based acid rain monitor was used to provide high resolution meteorological and rain chemistry data from which two case study events have been selected to illustrate event and sub-event rainfall chemistry characteristics. Event rainfall chemistry is strongly influenced by the history of the prevailing air mass and the synoptic situation. From back trajectories calculated at the 950 mbar level it is clear that air mass history can change markedly within a few hours. These observations emphasise the value of high resolution rainfall chemistry measurements. Pollutant concentrations in rainwater have been shown to fluctuate markedly within the course of individual events as a result of both advective and scavenging processes. Advective effects may result from: (a) air mass discontinuities at frontal zones; and/or (b) variable rainfall interception of the air mass prior to arrival at the site. A simple mathematical model has been developed to describe the scavenging mechanisms and it shows good agreement with field observations. Theoretical considerations suggest that in-cloud processes give rise to most of the observed decline in concentrations.     

Evaluating the performance of regional-scale photochemical modeling systems: Part II—ozone predictions

In this paper, the concept of scale analysis is applied to evaluate ozone predictions from two regional-scale air quality models. To this end, seasonal time series of observations and predictions from the RAMS3b/UAM-V and MM5/MAQSIP (SMRAQ) modeling systems for ozone were spectrally decomposed into fluctuations operating on the intra-day, diurnal, synoptic and longer-term time scales. Traditional model evaluation statistics are also presented to illustrate how the scale analysis approach can help improve our understanding of the models’ performance. The results indicate that UAM-V underestimates the total variance (energy) of the ozone time series when compared with observations, but shows a higher mean value than the observations. On the other hand, MAQSIP is able to better reproduce the average energy and mean concentration of the observations. However, both modeling systems do not capture the amount of variability present on the intra-day time scale primarily due to the grid resolution used in the models. For both modeling systems, the correlations between the predictions and observations are insignificant for the intra-day component, high for the diurnal component because of the inherent diurnal cycle but low for the amplitude of the diurnal component, and highest for the synoptic and baseline components. This better model performance on longer time scales suggests that current regional-scale models are most skillful in characterizing average patterns over extended periods, rather than in predicting concentrations at specific locations, during 1–2 day episodic events. In addition, we discuss the implications of these results to using the model-predicted daily maximum ozone concentrations in the regulatory framework in light of the uncertainties introduced by the models’ poor performance on the intra-day and diurnal time scales.