Characteristics of aerosol types from AERONET sunphotometer measurements

By using observations from the Aerosol Robotic Network (AERONET), aerosol types are classified according to dominant size mode and radiation absorptivity as determined by fine-mode fraction (FMF) and single-scattering albedo (SSA), respectively. The aerosol type from anthropogenic sources is significantly different with regard to location and season, while dust aerosol is observed persistently over North Africa and the Arabian Peninsula. For four reference locations where different aerosol types are observed, time series and optical properties for each aerosol type are investigated. The results show that aerosol types are strongly affected by their sources and partly affected by relative humidity. The analysis and methodology of this study can be used to compare aerosol classification results from satellite and chemical transport models, as well as to analyze aerosol characteristics on a global scale over land for which satellite observations need to be improved.     

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.     

Assessment of the nested grid model estimates for driving regional visibility models in the southwestern United States

The Nested Grid Model (NGM) is a primitive-equation meteorological model that is routinely exercised over North America for forecasting purposes by the National Meteorological Center. While prognostic meteorological models are being increasingly used to drive air quality models, their use in conducting annual simulations requires significant resources. NGM estimates of wind fields and other meteorological variables provide an attractive alternative since they are typically archived and readily available for an entire year. Preliminary evaluation of NGM winds during the summer of 1992 for application to the region surrounding the Grand Canyon National Park showed serious shortcomings. The NGM winds along the borders between California, Arizona and Mexico tend to be northwesterly with a speed of about 6 m/sec, while the observed flow is predominantly southerly at about 2-5 m/sec. The mesoscale effect of a thermal low pressure area over the highly heated Southern California and western Arizona deserts does not appear to be represented by the NGM because of its coarse resolution and the use of sparse observations in that region. Tracer simulations and statistical evaluation against special high resolution observations of winds in the southwest United States clearly demonstrate the northwest bias in NGM winds and its adverse effect on predictions of an air quality model. The "enhanced" NGM winds, in which selected wind observations are incorporated in the NGM winds using a diagnostic meteorological model provide additional confirmation on the primary cause of the northwest bias. This study has demonstrated that in situations where limited resources prevent the use of prognostic meteorological models, previously archived coarse resolution wind fields in which additional observations are incorporated to correct known biases provide an attractive option.     

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.     

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.     

Assessment of the Nested Grid Model Estimates for Driving Regional Visibility Models in the Southwestern United States

ABSTRACT

The Nested Grid Model (NGM) is a primitive-equation meteorological model that is routinely exercised over North America for forecasting purposes by the National Meteorological Center. While prognostic meteorological models are being increasingly used to drive air quality models, their use in conducting annual simulations requires significant resources. NGM estimates of wind fields and other meteorological variables provide an attractive alternative since they are typically archived and readily available for an entire year. Preliminary evaluation of NGM winds during the summer of 1992 for application to the region surrounding the Grand Canyon National Park showed serious shortcomings. The NGM winds along the borders between California, Arizona and Mexico tend to be northwesterly with a speed of about 6 m/sec, while the observed flow is predominantly southerly at about 2-5 m/sec. The mesoscale effect of a thermal low pressure area over the highly heated Southern California and western Arizona deserts does not appear to be represented by the NGM because of its coarse resolution and the use of sparse observations in that region. Tracer simulations and statistical evaluation against special high resolution observations of winds in the southwest United States clearly demonstrate the northwest bias in NGM winds and its adverse effect on predictions of an air quality model. The “enhanced” NGM winds, in which selected wind observations are incorporated in the NGM winds using a diagnostic meteorological model provide additional confirmation on the primary cause of the northwest bias. This study has demonstrated that in situations where limited resources prevent the use of prognostic meteorological models, previously archived coarse resolution wind fields in which additional observations are incorporated to correct known biases provide an attractive option.     

Evaluating the performance of regional-scale photochemical modeling systems: Part I—meteorological predictions

In this study, the concept of scale analysis is applied to evaluate two state-of-science meteorological models, namely MM5 and RAMS3b, currently being used to drive regional-scale air quality models. To this end, seasonal time series of observations and predictions for temperature, water vapor, and wind speed 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 method of spectral decomposition can help provide additional insight into the models’ performance. The results indicate that both meteorological models under-represent the variance of fluctuations on the intra-day time scale. Correlations between model predictions and observations for temperature and wind speed are insignificant on the intra-day time scale, 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 longer-term components. This better model performance on longer time scales suggests that current regional-scale models are most skillful for characterizing average patterns over extended periods. The implications of these results to using meteorological models to drive photochemical models are discussed.     

The impact of spatial correlation and incommensurability on model evaluation

Standard evaluations of air quality models rely heavily on a direct comparison of monitoring data matched with the model output for the grid cell containing the monitor's location. While such techniques may be adequate for some applications, conclusions are limited by such factors as the sparseness of the available observations (limiting the number of grid cells at which the model can be evaluated) and the incommensurability between volume-averages and pointwise observations. We examine several sets of simulations to illustrate the effect of incommensurability in a variety of cases distinguished by the type and extent of spatial correlation present. Block kriging, a statistical method which can be used to address the issue, is then demonstrated using the simulations. Lastly, we apply this method to actual data and discuss the practical importance of understanding the impact of spatial correlation structure and incommensurability.     

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.     

CityDelta: A model intercomparison study to explore the impact of emission reductions in European cities in 2010

This paper gives an overview of the set up, methodology and the obtained results of the CityDelta (phase 1 and 2) project. In the context of the Clean Air For Europe programme of the European Commission, the CityDelta project was designed to evaluate the impact of emission-reduction strategies on air quality at the European continental scale and in European cities. Ozone and particulate matter (PM) are the main components that have been studied. To achieve this goal, a model intercomparison study was organized with the participation of more than 20 modelling groups with a large number of modelling configurations. Two following main topics can be identified in the project. First, in order to evaluate their strengths and weaknesses, the participating models were evaluated against observations in a control year (1999). An accompanying paper will discuss in detail this evaluation aspect for four European cities. The second topic is the actual evaluation of the impact of emission reductions on levels of ozone and PM, with particular attention to the differences between large-scale and fine-scale models. An accompanying paper will discuss this point in detail. In this overview paper the main input to the intercomparison is described as well as the use of the ensemble approach. Finally, attention is given to the policy relevant issue on how to implement the urban air quality signal into large-scale air quality models through the use of functional relationships.     

Using continuous PM2.5 monitoring data to report an air quality index

As stated in 40 CFR 58, Appendix G (2000), statistical linear regression models can be applied to relate PM2.5 continuous monitoring (CM) measurements with federal reference method (FRM) measurements, collocated or otherwise, for the purpose of reporting the air quality index (AQI). The CM measurements can then be transformed via the model to remove any bias relative to FRM measurements. The resulting FRM-like modeled measurements may be used to provide more timely reporting of a metropolitan statistical area's (MSA's) AQI. Of considerable importance is the quality of the model used to relate the CM and FRM measurements. The use of a poor model could result in misleading AQI reporting in the form of incorrectly claiming either good or bad air quality. This paper describes a measure of adequacy for deciding whether a statistical linear regression model that relates FRM and continuous PM2.5 measurements is sufficient for use in AQI reporting. The approach is the U.S. Environmental Protection Agency's (EPA's) data quality objectives (DQO) process, a seven-step strategic planning approach to determine the most appropriate data type, quality, quantity, and synthesis for a given activity. The chosen measure of model adequacy is r2, the square of the correlation coefficient between FRM measurements and their modeled counterparts. The paper concludes by developing regression models that meet this desired level of adequacy for the MSAs of Greensboro/Winston-Salem/High Point, NC; and Davenport/Moline/Rock Island, IA/IL. In both cases, a log transformation of the data appeared most appropriate. For the data from the Greensboro/Winston-Salem/High Point MSA, a simple linear regression model of the FRM and CM measurements had an r2 of 0.96, based on 227 paired observations. For the data from the Davenport/Moline/Rock Island MSA, due to seasonal differences between CM and FRM measurements, the simple linear regression model had to be expanded to include a temperature dependency, resulting in an r2 of 0.86, based on 214 paired observations.     

Radiative transfer budgets for scattering and absorbing plumes: Measurements and model predictions

The 1981 VISTTA field study characterized the composition and appearance of particle-rich plumes from three different sources. This paper compares the VISTTA observations with the predictions of two plume visibility models. Observations and predictions are analyzed from the perspective of exact solutions to the equations of radiative transfer for a somewhat idealized atmosphere. These solutions, which explicity relate plume/sky contrast to the composition of plume and background and the geometry of sun, plume and observer, are shown to be consistent with the VISTA observations. The simplified relationships are used as the basis for budgeting radiative transfer by the plume and background, and for analyses of the sensitivity of plume appearance to individual variables.The optics predictions of the two models are less accurate for plumes dominated by particle scattering than they are for plumes dominated by NO₂ absorption. Inaccurate prediction of plume particle size distributions can be identified as an important source of error. Inaccurate prediction of background sky radiance is suspected as another.     

Skill and uncertainty of a regional air quality model ensemble

Recently several regional air quality projects were carried out to support the negotiation under the Clean Air For Europe (CAFE) programme by predicting the impact of emission control policies with an ensemble of models. Within these projects, CITYDELTA and EURODELTA, the fate of air quality at the scale of European cities or that of the European continent was studied using several models. In this article we focus on the results of EURODELTA. The predictive skill of the ensemble of models is described for ozone, nitrogen dioxide and secondary inorganic compounds, and the uncertainty in air quality modelling is examined through the model ensemble spread of concentrations.For ozone daily maxima the ensemble spread origin differs from one region to another. In the neighbourhood of cities or in mountainous areas the spread of predicted values does not span the range of observed data, due to poorly resolved emissions or complex-terrain meteorology. By contrast in Atlantic and North Sea coastal areas the spread of predicted values is found to be larger than the observations. This is attributed to large differences in the boundary conditions used in the different models. For NO₂ daily averages the ensemble spread is generally too small compared with observations. This is because models miss highest values occurring in stagnant meteorology in stable boundary layers near cities. For secondary particulate matter compounds the simulated concentration spread is more balanced, observations falling nearly equiprobably within the ensemble, and the spread originates both from meteorology and aerosol chemistry and thermodynamics.     

Detecting crucial dispersal pathways using a virtual ecology approach: A case study of the mirid bug <Emphasis Type="Italic">Stenotus rubrovittatus</Emphasis>

Detecting dispersal pathways is important both for understanding species range expansion and for managing nuisance species. However, direct detection is difficult. Here, we propose detecting these crucial pathways using a virtual ecology approach, simulating species dynamics using models, and virtual observations. As a case study, we developed a dispersal model based on cellular automata for the pest insect Stenotus rubrovittatus and simulated its expansion. We tested models for species expansion based on four landscape parameters as candidate pathways; these are river density, road density, area of paddy fields, and area of abandoned farmland, and validated their accuracy. We found that both road density and abandoned area models had prediction accuracy. The simulation requires simple data only to have predictive power, allowing for fast modeling and swift establishment of management plans.     

Group method of data handling (GMDH) in modelling of growth dynamics of trees irrigated with wastewater

This paper describes the formulation and analysis of growth dynamics models for trees irrigated with wastewater. The models can be used to obtain the characteristics of a species, depending on treatment conditions and climate factors, at all stages of growth. The experiments were carried out at the University of Patras to identify the characteristics (height rate and mortality) of the forest tree Pinus brutia cultivated under different treatment conditions. The growth dynamics models are designed on the basis of the group method of data handling. This principle generates sets of estimation models with different complexity and accuracy. By analysing their structures, qualitative features of the models may be assessed, and general linear models for different treatment cases compiled.     

Sensitivity of a molecular marker based positive matrix factorization model to the number of receptor observations

To investigate the impact of the number of observations on molecular marker-based positive matrix factorization (MM-PMF) source apportionment models, daily PM_2.5 samples were collected in East St. Louis, IL, from April 2002 through May 2003. The samples were analyzed for daily 24-h average concentrations of elemental and organic carbon, trace elements, and speciated particle-phase organic compounds. A total of 273 sets of observations were used in the model and consisted of all valid sets of observations from the year long data set minus one sixth of the measurements, which were collected every 6th day and were analyzed by different chemical analysis techniques. In addition to the base case of 273 samples, systematic subsets of the data set were analyzed by PMF. These subsets of data included 50% of the observations (135–138 days), 33% of the observations (90–92 days) and 20% of the observations (52–56 days). In addition, model runs were also examined that used 48-h, 72-h, 6-day, and weekly average concentrations as model inputs. All MM-PMF model runs were processed following the same procedures to explore the stability of the source attribution results. Consistent with previous MM-PMF results for East St. Louis, the main sources of organic aerosol were found to be mobile sources, secondary organic aerosols (SOAs), resuspended soil and biomass combustions, as well as an n-alkane dominated point source and other combustion sources. The MM-PMF model was reasonably stable when the number of observations in the input was reduced to ninety, or approximately 33% of observations present in the base case. In these cases, the key factors including resuspended soil, mobile and secondary factors, which accounted for more than 70% of the measured OC concentrations, were stable as defined by a relative standard deviation (RSD) of less than 30%. Similar results were obtained from the smaller data subsets, but resulted in larger uncertainties, with several of these factors yielding RSD of greater than 30%. The three factors with the largest OC contributions were more stable than the other minor factors, even when the number of observations was nominally 50 days. Secondary organic aerosol (SOA) was the most stable factor observed in the model runs. Since it is unclear if these results can be broadly applied to all MM-PMF models, additional studies of this nature are needed to assess the broader applicability of these conclusions. Until such studies are implemented, this paper provides a foundation to design future studies in sampling strategies for source apportionment using MM-PMF.     

Validation of the Gaussian puff models TSTEP and REM-3 using the Kincaid dataset

Two Gaussian short-range atmospheric dispersion models, TSTEP and REM-3, have been validated with the Kincaid dataset of the Model Validation Kit. TSTEP and REM-3 show high scatter compared to the observations. Both models have the tendency to underestimate lower concentratrations and to overestimate higher concentrations, but, within a factor of five, many predictions are in agreement with the observations. A comparison of TSTEP with REM-3 shows that the models are in good agreement with each other, in particular for low concentrations. Both models are sensitive to the choice of the (effective) release height.     

N:P ratios,light limitation,and cyanobacterial dominance in a subtropical lake impacted by non-point source nutrient pollution

A long-term (28-year) data set was used to investigate historical changes in concentrations of phosphorus (P), nitrogen (N), N:P ratios, and Secchi disk transparency in a shallow subtropical lake (Lake Okeechobee, Florida, USA). The aim was to evaluate changes in the risk of N2-fixing cyanobacterial blooms, which have infrequently occurred in the lake's pelagic zone. Predictions regarding bloom risk were based on previously published N:P ratio models. Temporal trends in the biomass of cyanobacteria were evaluated using phytoplankton data collected in 1974, 1989-1992, and 1997-2000. Concentrations of pelagic total P increased from near 50 microg l-1 in the mid-1970s to over 100 microg l-1 in the late 1990s. Coincidentally, the total N:P (mass) ratio decreased from 30:1 to below 15:1, and soluble N:P ratio decreased from 15:1 to near 6:1, in the lake water. Published empirical models predict that current conditions favor cyanobacteria. The observations confirm this prediction: cyanobacteria presently account for 50-80% of total phytoplankton biovolume. The historical decrease in TN:TP ratio in the lake can be attributed to a decreased TN:TP ratio in the inflow water and to a decline in the lake's assimilation of P, relative to N. Coincident with these declines in total and soluble N:P ratios, Secchi disk transparency declined from 0.6 m to near 0.3 m, possibly due to increased mineral turbidity in the lake water. Empirical models predict that under the turbid, low irradiance conditions that prevail in this lake, non-heterocystous cyanobacteria should dominate the phytoplankton. Our observations confirmed this prediction: non-N2-fixing taxa (primarily Oscillatoria and Lyngbya spp.) typically dominated the cyanobacteria community during the last decade. The only exception was a year with very low water levels, when heterocystous N2-fixing Anabaena became dominant. In the near-shore regions of this shallow lake, low N:P ratios potentially favor blooms of N2-fixing cyanobacteria, but their occurrence in the pelagic zone is restricted by low irradiance and lack of stable stratification.