Use of Project MOHAVE perfluorocarbon tracer data to evaluate source and receptor models

Project MOHAVE was a major monitoring, modeling, and data analysis study whose objectives included the estimation of the contributions of the Mohave Power Project (MPP) and other sources to visibility impairment in the southwestern United States, in particular at Grand Canyon National Park. A major element of Project MOHAVE was the release of perfluorocarbon tracers at MPP and other locations during 50-day summer and 30-day winter intensive study periods. Tracer data (from about 30 locations) were sequestered until several source and receptor models were used to predict tracer concentrations. None of the models was successful in predicting the tracer concentrations; squared correlation coefficients between predicted and measured tracer were all less than 0.2, and most were less than 0.1.     

Interpreting organic solute transport data from a field experiment using physical nonequilibrium models

In a field experiment, two inorganic tracers and five organic solutes were injected into an unconfined sand aquifer. Breakthrough response curves were obtained at several points downgradient of the injection zone. These response curves are analyzed using a model which assumes equilibrium sorption and two models which postulate physical nonequilibrium. The physical nonequilibrium models hypothesize the existence of zones of immobile water, which act as diffusion sources and sinks for the solutes. The physical nonequilibrium models better simulate the sharp breakthrough and extended tailing exhibited by the experimental responses than does the model assuming equilibrium sorption. The reasonableness of parameters obtained from curve-fitting the data is assessed. The two physical nonequilibrium models are compared.     

The accuracy of two- and three-way positive matrix factorization models: Applying simulated multisite data sets

The application of three-way data sets (combined multisite data sets) for source apportionment has become common, but its influence on the performance of receptor modeling techniques has not yet been explored systematically. To study the influence of site-to-site correlations of source contributions and the spatial variability of source profiles on two- and three-way positive matrix factorization (PMF), simulated three-way data sets were constructed and modeled by different applications of PMF (PMF2 for each site individually, PMF2 for data sets combining all sites together, and PMF3 for all sites). In addition, the performance of PMF was evaluated under conditions of collinearity and different source categories at two sites. The results indicated that if the sites were contributed by sources with identical profiles, the site-to-site correlations of source contributions would not influence the PMF2, and the three-way blocks could be used by PMF2. However, the PMF2 using three-way data sets was sensitive to the spatial variability of source profiles. For the three-way model, PMF3 could perform well only when all of the sources exhibited strong site-to-site associations among all sites, and at the same time, the spatial variability of source profiles were sufficiently small. It might due to the algorithm that, for each source, PMF3 produces the same source profile and the same temporal variation in daily contributions among all sites.

Implications:?The application of multisite data sets for source apportionment has become common. However, limited work investigated the accuracy of two- and three-way PMFs when using multisite data sets. If the application of PMFs using multisite data sets were not appropriate, the results would be unreasonable. The unreasonable results would supply confused information for PM control strategies. In this work, simulated multisite data sets were modeled by different applications of PMFs. The effort to assess and compare the performance of two- and three-way PMFs using multisite data sets is very limited. The findings could provide information for multisite source apportionment.     

Generalized gene expression programming models for estimating reference evapotranspiration through cross-station assessment and exogenous data supply

Environmental Science and Pollution Research - Adopting methodologies utilizing exogenous data from ancillary stations for determining crop water requirement is a suitable approach to exempt local...     

Models for the field-based toxicity of copper and zinc salts to wheat in 11 Australian soils and comparison to laboratory-based models

Laboratory-based relationships that model the phytotoxicity of metals using soil properties have been developed. This paper presents the first field-based phytotoxicity relationships. Wheat (Triticum aestivum L.) was grown at 11 Australian field sites at which soil was spiked with copper (Cu) and zinc (Zn) salts. Toxicity was measured as inhibition of plant growth at 8 weeks and grain yield at harvest. The added Cu and Zn EC10 values for both endpoints ranged from approximately 3 to 4760 mg/kg. There were no relationships between field-based 8-week biomass and grain yield toxicity values for either metal. Cu toxicity was best modelled using pH and organic carbon content while Zn toxicity was best modelled using pH and the cation exchange capacity. The best relationships estimated toxicity within a factor of two of measured values. Laboratory-based phytotoxicity relationships could not accurately predict field-based phytotoxicity responses.     

Surface water concentrations of the fragrance compound OTNE in Germany--a comparison between data from measurements and models

Emissions of the fragrance compound OTNE (Iso-E-Super) to surface waters have been investigated by means of a combined analytical (measurements) and modeling approach. The compound is an ingredient in many household products and is emitted into surface waters almost exclusively via the wastewater pathway. Measured concentrations of OTNE in surface waters of the Ruhr river catchment basin ranged from 30 to 100 ng L(-1), and were thus in the same order of magnitude as the polycyclic musks AHTN and HHCB. The geo-referenced exposure model GREAT-ER (Geo-referenced Regional Exposure Assessment Tool for European Rivers) was used to simulate OTNE concentrations in the Ruhr river basin. Model results could plausibly explain monitoring data at all sampling sites when considering the discharge conditions during the sampling period and specific local characteristics. According to the model, approximately half of the total OTNE emissions into the Ruhr river basin are transferred from surface water into the atmosphere and the sediment. Volatilization from lakes was identified as the major removal process of the fragrance compound OTNE. To identify possible regional differences, samples from the Danube in Hungary were also analysed. The OTNE concentrations were in the same order of magnitude (29-810 ng L(-1)) as in the Ruhr catchment, but exhibited higher spatial variability.     

Mass transfer of sulfur dioxide into falling drops: A comparison of experimental data with absorption models

Absorption of pollutant gases into water drops is one of the major removal mechanism in clouds, rains and scrubbers. A laboratory system for the absorption of sulfur dioxide into falling water drops was investigated. Monodispersed drops (0.84 mm diameter) were generated via a vibrating orifice and exposed to pollutant gas during their fall time (0.36 s). The drop stream was then quenched and analyses for S⁴⁺ and total S were carried out. For gas phase SO₂ concentrations of 0.95–1.17 ppm liquid phase concentrations for S⁴⁺ of 0.67–1.38 and for total S of 1.01–1.93 μmolℓ⁻¹ were measured. The experimental results were compared to calculations based on several models that describe SO₂-washout. Some models overpredicted and some underpredicted the observed results. This was in part due to the fact that the “string-of-falling-drops” system exhibits laminar-jet properties. Calculations based on an equivalent “laminar-jet” model gave concentrations of 0.62–1.17 μmolℓ⁻¹.     

Sources of organic aerosol: Positive matrix factorization of molecular marker data and comparison of results from different source apportionment models

This paper presents results from positive matrix factorization (PMF) of organic molecular marker data to investigate the sources of organic carbon (OC) in Pittsburgh, Pennsylvania. PMF analysis of 21 different combinations of input species found essentially the same seven factors with distinctive source-class-specific groupings of molecular markers. To link factors with source classes we directly compare PMF factor profiles with actual source profiles. Six of the PMF factors appear related to primary emissions from sources such as motor vehicles, biomass combustion, and food cooking. Each primary factor contributed between 5% and 10% of the annual-average OC with the exception of the cooking related factor which contributed 20% of the OC. However, the contribution of the cooking factor was sensitive to the specific combinations of input species. PMF could not differentiate between gasoline and diesel emissions, but the aggregate contribution of primary emissions from these two source classes is estimated to be less than 10% of the annual-average OC. One factor appears related to secondary organic aerosol based on its substantial contribution to biogenic oxidation products. This secondary factor contributed more than 50% of the summertime average OC. Reasonable agreement was observed between the PMF results and those of a previously published chemical mass balance (CMB) analysis of the same molecular marker dataset. Individual PMF factors are correlated with specific CMB sources, but systematic biases exist between the two estimates. These biases were generally within the uncertainty of the two estimates, but there is also evidence that PMF is not cleanly differentiating between source classes.     

Objective analysis of air pollution monitoring network data; spatial interpolation and network density

To obtain meaningful interpretations of the results of fixed air pollution monitoring stations, quantitative information on the spatial representativity of these results is of crucial importance. On the basis of data of the Dutch Air Pollution Monitoring Network a study was undertaken to estimate the absolute and relative errors which are involved in extrapolating the results beyond the actual spatial argument of measurement. In the first place, three interpolation techniques, optimum interpolation, eigenvector interpolation and so called distance-density interpolation, were compared for SO₂, NO, NO₂ and O₃. The differences between the results of these techniques proved to be small. It was further concluded that the interpolation errors and the associated persistence in space and time, as given by mutual correlations, should be specified with respect to pure space- or space-time variability. In the second place, the interpolation errors were generalized to other network densities on the basis of the optimum interpolation scheme and empirical observations. From the resulting relation between interpolation error and network density it was concluded that the SO₂-network of 108 stations over the measurement area of 150 × 220 km² results in relative errors of 20 %, small enough to detect mesoscale transports downwind of major source areas. The effect of sampling error, i.e., the small scale influences of local sources combined with the effect of measurement error, appeared to be of overriding importance in the efficiency of reconstruction of pollutant concentration fields at a given confidence level from monitoring network data.     

A comparison of two stomatal conductance models for ozone flux modelling using data from two Brassica species

In this study we tested and compared a multiplicative stomatal model and a coupled semi-empirical stomatal-photosynthesis model in their ability to predict stomatal conductance to ozone (g_st) using leaf-level data from oilseed rape (Brassica napus L.) and broccoli (Brassica oleracea L. var. italica Plenck). For oilseed rape, the multiplicative model and the coupled model were able to explain 72% and 73% of the observed g_st variance, respectively. For broccoli, the models were able to explain 53% and 51% of the observed g_st variance, respectively. These results support the coupled semi-empirical stomatal-photosynthesis model as a valid alternative to the multiplicative stomatal model for O₃ flux modelling, in terms of predictive performance.     

Source apportionment of exposures to volatile organic compounds. I. Evaluation of receptor models using simulated exposure data

Four receptor-oriented source apportionment models were evaluated by applying them to simulated personal exposure data for select volatile organic compounds (VOCs) that were generated by Monte Carlo sampling from known source contributions and profiles. The exposure sources modeled are environmental tobacco smoke, paint emissions, cleaning and/or pesticide products, gasoline vapors, automobile exhaust, and wastewater treatment plant emissions. The receptor models analyzed are chemical mass balance, principal component analysis/absolute principal component scores, positive matrix factorization (PMF), and graphical ratio analysis for composition estimates/source apportionment by factors with explicit restriction, incorporated in the UNMIX model. All models identified only the major contributors to total exposure concentrations. PMF extracted factor profiles that most closely represented the major sources used to generate the simulated data. None of the models were able to distinguish between sources with similar chemical profiles. Sources that contributed <5% to the average total VOC exposure were not identified.     

Gas/particle partitioning of polychlorinated dibenzo-p-dioxins and dibenzofurans in atmosphere; evaluation of predicting models

The gas/particle partitioning of polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs) was measured at three sites for a year in order to monitor the variation of PCDD/Fs levels and describe their partitioning. The air concentrations of PCDD/Fs ranged from 71 to 1161 fg I-TEQ/m³ and large changes in these levels did not correlate with seasonal changes during this study. Different homolog patterns were observed in the gas/particle phase. High chlorinated dioxin/furans dominated the particle phase while low chlorinated dioxin/furans dominated the gas phase. The high correlation coefficient between log [(PCDD/Fs_vap)(TSP)/(PCDD/Fs_pat)] and 1/T was observed in lower chlorinated dioxin/furans unlike in OCDD/F. The slope of homolog ranged from −0.410 to −1.025 and that of 2,3,7,8-substituted isomers ranged from −0.379 to −0.772 in plots of the log partition coefficient (K_p) versus the log subcooled vapor pressure (P_L°).The octanol/air partition coefficient (K_oa)-based model of PCDD/Fs is more compatible with experimental data than those of the Junge–Pankow model that tends to overestimate results, even though both models include some level of uncertainty. However, both models can underestimate the particle phase of PCDD/Fs, especially when the ambient air temperature is extremely low in winter.     

Ozone Air Quality Models

Abstract

The field of ozone air quality modeling, or as it is commonly referred to, photochemical air quality modeling, has undergone rapid change in recent years. Improvements in model components, as well as in methods of interpreting model performance, have contributed to this change. Attendant with this rapid change has been a growing need for those developing and using air quality models and policy makers to have a common understanding of the use and role of models in the decision making process. This Critical Review highlights recent advances and continuing problem areas in photochemical air quality modeling. Emphasis is placed on the components and input data for such models, model performance evaluation, and the implications for their use in regulatory decisions.     

Ozone response to precursor controls: comparison of data analysis methods with the predictions of photochemical air quality simulation models

Comparisons were made between the predictions of six photochemical air quality simulation models (PAQSMs) and three indicators of ozone response to emission reductions: the ratios of O₃/NO_z and O₃/NO_y and the extent of reaction. The values of the two indicator ratios and the extent of reaction were computed from the model-predicted mixing ratios of ozone and oxidized nitrogen species and were compared to the changes in peak 1 and 8 h ozone mixing ratios predicted by the PAQSMs. The ozone changes were determined from the ozone levels predicted for base-case emission levels and for reduced emissions of volatile organic compounds (VOCs) and oxides of nitrogen (NO_x). For all simulations, the model-predicted responses of peak 1 and 8 h ozone mixing ratios to VOC or NO_x emission reductions were correlated with the base-case extent of reaction and ratios of O₃/NO_z and O₃/NO_y. Peak ozone values increased following NO_x control in 95% (median over all simulations) of the high-ozone (>80 ppbv hourly mixing ratio in the base-case) grid cells having mean afternoon O₃/NO_z ratios less than 5 : 1, O₃/NO_y less than 4 : 1, or extent less than 0.6. Peak ozone levels decreased in response to NO_x reductions in 95% (median over all simulations) of the grid cells having peak hourly ozone mixing ratios greater than 80 ppbv and where mean afternoon O₃/NO_z exceeded 10 : 1, O₃/NO_y was greater than 8 : 1, or extent exceeded 0.8. Ozone responses varied in grid cells where O₃/NO_z was between 5 : 1 and 10 : 1, O₃/NO_y was between 4 : 1 and 8 : 1, or extent was between 0.6 and 0.8. The responses in such grid cells were affected by ozone responses in upwind grid cells and by the changes in ozone levels along the upwind boundaries of the modeling domains.     

Stock assessment of landlocked ayu Plecoglossus altivelis altivelis in Japan through length-based models

Environmental Science and Pollution Research - Ayu Plecoglossus altivelis altivelis is a key commercially and culturally important freshwater osmeroid in Japan. Its native population is mostly an...     

Box models versus Eulerian models in air pollution modeling

Box models are widely used in air pollution modeling. They allow the use of simple computational tools instead of the simulation of 3D Eulerian grid models, given by a large set of partial differential equations. We investigate here the theoretical justification of such box models. The key point is the comparison with the underlying Eulerian model describing the dispersion of pollutants in the atmosphere. We restrict the study to a vertical monodimensional case for more clarity. The main result is that the nonlinearity of the chemical kinetics, which is a characteristic feature of chemistry, induces the loss of accuracy.     

Forecasting hourly PM<Subscript>10</Subscript> concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management

In the present work, two types of artificial neural network (NN) models using the multilayer perceptron (MLP) and the radial basis function (RBF) techniques, as well as a model based on principal component regression analysis (PCRA), are employed to forecast hourly PM₁₀ concentrations in four urban areas (Larnaca, Limassol, Nicosia and Paphos) in Cyprus. The model development is based on a variety of meteorological and pollutant parameters corresponding to the 2-year period between July 2006 and June 2008, and the model evaluation is achieved through the use of a series of well-established evaluation instruments and methodologies. The evaluation reveals that the MLP NN models display the best forecasting performance with R ² values ranging between 0.65 and 0.76, whereas the RBF NNs and the PCRA models reveal a rather weak performance with R ² values between 0.37-0.43 and 0.33-0.38, respectively. The derived MLP models are also used to forecast Saharan dust episodes with remarkable success (probability of detection ranging between 0.68 and 0.71). On the whole, the analysis shows that the models introduced here could provide local authorities with reliable and precise predictions and alarms about air quality if used on an operational basis.