Reconciliation and interpretation of Big Bend National Park particulate sulfur source apportionment: results from the Big Bend Regional Aerosol and Visibility Observational Study--part I

The Big Bend Regional Aerosol and Visibility Observational (BRAVO) study was an intensive monitoring study from July through October 1999 followed by extensive assessments to determine the causes and sources of haze in Big Bend National Park, located in Southwestern Texas. Particulate sulfate compounds are the largest contributor of haze at Big Bend, and chemical transport models (CTMs) and receptor models were used to apportion the sulfate concentrations at Big Bend to North American source regions and the Carbón power plants, located 225 km southeast of Big Bend in Mexico. Initial source attribution methods had contributions that varied by a factor of > or =2. The evaluation and comparison of methods identified opposing biases between the CTMs and receptor models, indicating that the ensemble of results bounds the true source attribution results. The reconciliation of these differences led to the development of a hybrid receptor model merging the CTM results and air quality data, which allowed a nearly daily source apportionment of the sulfate at Big Bend during the BRAVO study. The best estimates from the reconciliation process resulted in sulfur dioxide (SO2) emissions from U.S. and Mexican sources contributing approximately 55% and 38%, respectively, of sulfate at Big Bend. The distribution among U.S. source regions was Texas, 16%; the Eastern United States, 30%; and the Western United States, 9%. The Carbón facilities contributed 19%, making them the largest single contributing facility. Sources in Mexico contributed to the sulfate at Big Bend on most days, whereas contributions from Texas and Eastern U.S. sources were episodic, with their largest contributions during Big Bend sulfate episodes. On the 20% of the days with the highest sulfate concentrations, U.S. and Mexican sources contributed approximately 71% and 26% of the sulfate, respectively. However, on the 20% of days with the lowest sulfate concentrations, Mexico contributed 48% compared with 40% for the United States.     

Translocation trisomy 21 in CVS not found in embryoblast: Three different cell lines in CVS,amnion- and placental culture

Chorionic villus samples from a healthy pregnant female were obtained for first-trimester prenatal diagnosis. A translocation trisomy 21 was diagnosed. A consecutive amniocentesis revealed a normal male karyotype. At term a healthy boy was born. Cytogenetic analysis from cord blood showed a regular karyotype of 46,XY, whereas in term placenta a pathological karyotype of 47,XY,+mar was found.     

Emissions Variability Processor (EMVAP): Design,evaluation, and application

Emissions of pollutants such as SO₂ and NOx from external combustion sources can vary widely depending on fuel sulfur content, load, and transient conditions such as startup, shutdown, and maintenance/malfunction. While monitoring will automatically reflect variability from both emissions and meteorological influences, dispersion modeling has been typically conducted with a single constant peak emission rate. To respond to the need to account for emissions variability in addressing probabilistic 1-hr ambient air quality standards for SO₂ and NO₂, we have developed a statistical technique, the Emissions Variability Processor (EMVAP), which can account for emissions variability in dispersion modeling through Monte Carlo sampling from a specified frequency distribution of emission rates. Based upon initial AERMOD modeling of from 1 to 5 years of actual meteorological conditions, EMVAP is used as a postprocessor to AERMOD to simulate hundreds or even thousands of years of concentration predictions. This procedure uses emissions varied hourly with a Monte Carlo sampling process that is based upon the user-specified emissions distribution, from which a probabilistic estimate can be obtained of the controlling concentration. EMVAP can also accommodate an advanced Tier 2 NO₂ modeling technique that uses a varying ambient ratio method approach to determine the fraction of total oxides of nitrogen that are in the form of nitrogen dioxide. For the case of the 1-hr National Ambient Air Quality Standards (NAAQS, established for SO₂ and NO₂), a “critical value” can be defined as the highest hourly emission rate that would be simulated to satisfy the standard using air dispersion models assuming constant emissions throughout the simulation. The critical value can be used as the starting point for a procedure like EMVAP that evaluates the impact of emissions variability and uses this information to determine an appropriate value to use for a longer term (e.g., 30-day) average emission rate that would still provide protection for the NAAQS under consideration. This paper reports on the design of EMVAP and its evaluation on several field databases that demonstrate that EMVAP produces a suitably modest overestimation of design concentrations. We also provide an example of an EMVAP application that involves a case in which a new emission limitation needs to be considered for a hypothetical emission unit that has infrequent higher-than-normal SO₂ emissions.

ImplicationsEmissions of pollutants from combustion sources can vary widely depending on fuel sulfur content, load, and transient conditions such as startup and shutdown. While monitoring will automatically reflect this variability on measured concentrations, dispersion modeling is typically conducted with a single peak emission rate assumed to occur continuously. To realistically account for emissions variability in addressing probabilistic 1-hr ambient air quality standards for SO₂ and NO₂, the authors have developed a statistical technique, the Emissions Variability Processor (EMVAP), which can account for emissions variability in dispersion modeling through Monte Carlo sampling from a specified frequency distribution of emission rates.     

Evaluation of low wind modeling approaches for two tall-stack databases

The performance of the AERMOD air dispersion model under low wind speed conditions, especially for applications with only one level of meteorological data and no direct turbulence measurements or vertical temperature gradient observations, is the focus of this study. The analysis documented in this paper addresses evaluations for low wind conditions involving tall stack releases for which multiple years of concurrent emissions, meteorological data, and monitoring data are available. AERMOD was tested on two field-study databases involving several SO₂ monitors and hourly emissions data that had sub-hourly meteorological data (e.g., 10-min averages) available using several technical options: default mode, with various low wind speed beta options, and using the available sub-hourly meteorological data. These field study databases included (1) Mercer County, a North Dakota database featuring five SO₂ monitors within 10 km of the Dakota Gasification Company’s plant and the Antelope Valley Station power plant in an area of both flat and elevated terrain, and (2) a flat-terrain setting database with four SO₂ monitors within 6 km of the Gibson Generating Station in southwest Indiana. Both sites featured regionally representative 10-m meteorological databases, with no significant terrain obstacles between the meteorological site and the emission sources. The low wind beta options show improvement in model performance helping to reduce some of the overprediction biases currently present in AERMOD when run with regulatory default options. The overall findings with the low wind speed testing on these tall stack field-study databases indicate that AERMOD low wind speed options have a minor effect for flat terrain locations, but can have a significant effect for elevated terrain locations. The performance of AERMOD using low wind speed options leads to improved consistency of meteorological conditions associated with the highest observed and predicted concentration events. The available sub-hourly modeling results using the Sub-Hourly AERMOD Run Procedure (SHARP) are relatively unbiased and show that this alternative approach should be seriously considered to address situations dominated by low-wind meander conditions.

Implications: AERMOD was evaluated with two tall stack databases (in North Dakota and Indiana) in areas of both flat and elevated terrain. AERMOD cases included the regulatory default mode, low wind speed beta options, and use of the Sub-Hourly AERMOD Run Procedure (SHARP). The low wind beta options show improvement in model performance (especially in higher terrain areas), helping to reduce some of the overprediction biases currently present in regulatory default AERMOD. The SHARP results are relatively unbiased and show that this approach should be seriously considered to address situations dominated by low-wind meander conditions.     

Microscale air quality impacts of distributed power generation facilities

The electric system is experiencing rapid growth in the adoption of a mix of distributed renewable and fossil fuel sources, along with increasing amounts of off-grid generation. New operational regimes may have unforeseen consequences for air quality. A three-dimensional microscale chemical transport model (CTM) driven by an urban wind model was used to assess gaseous air pollutant and particulate matter (PM) impacts within ~10 km of fossil-fueled distributed power generation (DG) facilities during the early afternoon of a typical summer day in Houston, TX. Three types of DG scenarios were considered in the presence of motor vehicle emissions and a realistic urban canopy: (1) a 25-MW natural gas turbine operating at steady state in either simple cycle or combined heating and power (CHP) mode; (2) a 25-MW simple cycle gas turbine undergoing a cold startup with either moderate or enhanced formaldehyde emissions; and (3) a data center generating 10 MW of emergency power with either diesel or natural gas-fired backup generators (BUGs) without pollution controls. Simulations of criteria pollutants (NO₂, CO, O₃, PM) and the toxic pollutant, formaldehyde (HCHO), were conducted assuming a 2-hr operational time period. In all cases, NO_x titration dominated ozone production near the source. The turbine scenarios did not result in ambient concentration enhancements significantly exceeding 1 ppbv for gaseous pollutants or over 1 µg/m³ for PM after 2 hr of emission, assuming realistic plume rise. In the case of the datacenter with diesel BUGs, ambient NO₂ concentrations were enhanced by 10–50 ppbv within 2 km downwind of the source, while maximum PM impacts in the immediate vicinity of the datacenter were less than 5 µg/m³.

Implications: Plausible scenarios of distributed fossil generation consistent with the electricity grid’s transformation to a more flexible and modernized system suggest that a substantial amount of deployment would be required to significantly affect air quality on a localized scale. In particular, natural gas turbines typically used in distributed generation may have minor effects. Large banks of diesel backup generators such as those used by data centers, on the other hand, may require pollution controls or conversion to natural gas-fired reciprocal internal combustion engines to decrease nitrogen dioxide pollution.     

Searching for evidence of acid-catalyzed enhancement of secondary organic aerosol formation using ambient aerosol data

Laboratory experiments suggest that strong acids promote formation of enhanced levels of secondary organic aerosol (SOA), and organic aerosols may contribute to the health impacts of fine PM. We report results from examining hourly speciated fine particle data for evidence of ambient aerosol acidity-catalyzed SOA formation, as indicated by larger increases in the concentrations of organic aerosol mass occurring on days and in locations where more acidic aerosol (lower NH₄⁺/SO₄⁼ molar ratios) exists. Data sets from the southeastern U.S. were examined for which hourly acidity of PM_2.5 aerosols could be estimated, and for which hourly organic carbon (OC) content had been measured simultaneously. Within-day organic aerosol changes during selected periods were statistically related to concurrent aerosol acidity levels estimated from nitrate-corrected ammonium-to-sulfate ratios. Data from the Look Rock, TN, TVA/IMPROVE site for mid-July to mid-August 2004 showed average compositions frequently as acidic as NH₄HSO₄, however, no apparent increases in OC levels with increasing aerosol acidity were observed, even when [OC] changes were compared with time-delayed aerosol acidity estimates. SEARCH network data (2003–2004) for rural Centreville, AL (CTR) and Yorkville, GA (YRK) sites were also examined. Warm-season acidity levels were higher at CTR than at YRK, and daytime levels exceeded those at night at both sites. At the YRK site no consistent positive correlations were found between changes in OC or TC levels and aerosol acidity, even with time lags up to 6 h. Aerosol acidity at this site, however, is relatively low due to nearby agricultural sources of NH₃. In contrast, during selected periods from April to October 2004, at CTR, 6-h lagged OC changes were weakly correlated with daytime, nitrate-corrected NH₄⁺/SO₄⁼ molar ratios, but distinguishing this apparent relationship from meteorological effects on measured OC levels is challenging.     

Regional source identification using Lagrangian stochastic particle dispersion and HYSPLIT backward-trajectory models

The main objective of this study was to investigate the capabilities of the receptor-oriented inverse mode Lagrangian Stochastic Particle Dispersion Model (LSPDM) with the 12-km resolution Mesoscale Model 5 (MM5) wind field input for the assessment of source identification from seven regions impacting two receptors located in the eastern United States. The LSPDM analysis was compared with a standard version of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) single-particle backward-trajectory analysis using inputs from MM5 and the Eta Data Assimilation System (EDAS) with horizontal grid resolutions of 12 and 80 km, respectively. The analysis included four 7-day summertime events in 2002; residence times in the modeling domain were computed from the inverse LSPDM runs and HYPSLIT-simulated backward trajectories started from receptor-source heights of 100, 500, 1000, 1500, and 3000 m. Statistics were derived using normalized values of LSPDM- and HYSPLIT-predicted residence times versus Community Multiscale Air Quality model-predicted sulfate concentrations used as baseline information. From 40 cases considered, the LSPDM identified first- and second-ranked emission region influences in 37 cases, whereas HYSPLIT-MM5 (HYSPLIT-EDAS) identified the sources in 21 (16) cases. The LSPDM produced a higher overall correlation coefficient (0.89) compared with HYSPLIT (0.55-0.62). The improvement of using the LSPDM is also seen in the overall normalized root mean square error values of 0.17 for LSPDM compared with 0.30-0.32 for HYSPLIT. The HYSPLIT backward trajectories generally tend to underestimate near-receptor sources because of a lack of stochastic dispersion of the backward trajectories and to overestimate distant sources because of a lack of treatment of dispersion. Additionally, the HYSPLIT backward trajectories showed a lack of consistency in the results obtained from different single vertical levels for starting the backward trajectories. To alleviate problems due to selection of a backward-trajectory starting level within a large complex set of 3-dimensional winds, turbulence, and dispersion, results were averaged from all heights, which yielded uniform improvement against all individual cases.