A year-long near-real-time characterization of non-refractory submicron aerosol (NR-PM1) was conducted at an urban (Atlanta, Georgia, in 2012) and rural (Look Rock, Tennessee, in 2013) site in the southeastern US using the Aerodyne Aerosol Chemical Speciation Monitor (ACSM) collocated with established air-monitoring network measurements. Seasonal variations in organic aerosol (OA) and inorganic aerosol species are attributed to meteorological conditions as well as anthropogenic and biogenic emissions in this region. The highest concentrations of NR-PM1 were observed during winter and fall seasons at the urban site and during spring and summer at the rural site. Across all seasons and at both sites, NR-PM1 was composed largely of OA (up to 76 %) and sulfate (up to 31 %). Six distinct OA sources were resolved by positive matrix factorization applied to the ACSM organic mass spectral data collected from the two sites over the 1 year of near-continuous measurements at each site: hydrocarbon-like OA (HOA), biomass burning OA (BBOA), semi-volatile oxygenated OA (SV-OOA), low-volatility oxygenated OA (LV-OOA), isoprene-derived epoxydiols (IEPOX) OA (IEPOX-OA) and 91Fac (a factor dominated by a distinct ion at m∕z 91 fragment ion previously observed in biogenic influenced areas). LV-OOA was observed throughout the year at both sites and contributed up to 66 % of total OA mass. HOA was observed during the entire year only at the urban site (on average 21 % of OA mass). BBOA (15–33 % of OA mass) was observed during winter and fall, likely dominated by local residential wood burning emission. Although SV-OOA contributes quite significantly ( ∼ 27 %), it was observed only at the urban site during colder seasons. IEPOX-OA was a major component (27–41 %) of OA at both sites, particularly in spring and summer. An ion fragment at m∕z 75 is well correlated with the m∕z 82 ion associated with the aerosol mass spectrum of IEPOX-derived secondary organic aerosol (SOA). The contribution of 91Fac to the total OA mass was significant (on average 22 % of OA mass) at the rural site only during warmer months. Comparison of 91Fac OA time series with SOA tracers measured from filter samples collected at Look Rock suggests that isoprene oxidation through a pathway other than IEPOX SOA chemistry may contribute to its formation. Other biogenic sources could also contribute to 91Fac, but there remains a need to resolve the exact source of this factor based on its significant contribution to rural OA mass.

A study of a Powder River Basin (PRB) coal pile found that fugitive emissions from natural and human activity each produced similar levels of downwind fine + coarse (i.e., smaller than 10 µm, or PM10) particle mass concentrations. Natural impacts were statistically removed from downwind measurements to estimate emission factor Ev for bulldozers working on the pile. The Ev determined here was similar in magnitude to emission factors (EFs) computed using a U.S. Environmental Protection Agency (EPA) formulation for unpaved surfaces at industrial sites, even though the latter was not based on data for coal piles. EF formulations from this study and those in the EPA guidance yield values of similar magnitude but differ in the variables used to compute Ev variations. EPA studies included effects of surface silt fraction and vehicle weight, while the present study captured the influence of coal moisture. Our data indicate that the relationship between PRB coal fugitive dust Ev (expressed as mass of PM10 emitted per minute of bulldozer operation) and coal moisture content Mc (in percent) at the study site is best expressed as Ev =10f(Mc) where f(Mc) is a function of moisture. This function was determined by statistical regression between log10(Ev) and Mc where both Ev and Mc are expressed as daily averages of observations based on 289 hours sampled during 44 days from late June through mid-November of 2012. A methodology is described that estimates Mc based on available meteorological data (precipitation amount and solar radiation flux). An example is given of computed variations in daily Ev for an entire year. This illustrates the sensitivity of the daily average particulate EF to meteorological variability at one location. Finally, a method is suggested for combining the moisture-sensitive formulation for Ev with the EPA formulation to accommodate a larger number of independent variables that influence fugitive emissions. Implications: Fugitive coal dust emission factors (EFs) derived by this study contribute to the small existing knowledge base for a type of pollutant that will become increasingly important as ambient particulate standards become tighter. In areas that are not in attainment with standards, realistic EFs can be used for compliance modeling and can help identify which classes of sources are best targeted to achieve desired air quality levels. Reconciling emission factor formulations that are sensitive to different factors (i.e., those from the EPA and this study) produces a more powerful tool for estimating fugitive emissions at a broad range of sources.

A study of fugitive dust emissions from a pile of crushed coal revealed that, in addition to dust being emitted into the atmosphere during periods of pile-management (human) activity, it is also emitted during periods without human activity. This "natural" emission in itself is not surprising given past work on wind erosion of particulate matter from aggregate piles. However, hourly downwind measurements of fine particle (PM10) mass concentrations at two sites revealed that excessive dust was present in the air even when wind speeds were below the erosion threshold estimated from nearby wind speed measurements and regulatory guidance on coal pile aerodynamic characteristics. During periods of natural emissions with higher wind speeds, downwind concentrations were strongly associated with mu(2)-the squared excess of 1-min maximum wind speed above the erosion threshold-consistent with previous work on wind erosion potential. However, 88% of hourly concentrations coincided with lower winds for which wind speed was not a good predictor of airborne dust levels. Evidence was found that natural low-wind PM10 concentrations varied significantly with relative humidity, air temperature, and turbulence parameters (sigma(u) and sigma(w)). Smoke from coal combustion was ruled out as a significant factor in PM10 levels, but statistical evidence along with visual observation suggests that microscale turbulent airflows, including dust devils, were a significant source of PM10 during low wind speeds over the pile. The localized behavior of the turbulence makes it very difficult to develop a strong statistical model of natural downwind concentrations on the basis of off-pile meteorological measurements.

Dry fly ash disposal involves dropping ash from a truck and the movement of a heavy grader or similar vehicle across the ash surface. These operations are known to produce fugitive particulate emissions that are not readily quantifiable using standard emission measurement techniques. However, there are numerous situations--such as applying for a source air permit--that require these emissions be quantified. Engineers traditionally use emission factors (EFs) derived from measurements of related processes to estimate fly ash disposal emissions. This study near a dry fly ash disposal site using state-of-the-art particulate monitoring equipment examines for the first time fugitive emissions specific to fly ash handling at an active disposal site. The study measured hourly airborne mass concentrations for particles smaller than 2.5 μm (PM2.5) and 10 μm (PM10) along with meteorological conditions and atmospheric turbidity at high temporal resolution to characterize and quantify fugitive fly ash emissions. Fugitive fly ash transport and dispersion were computed using the on-site meteorological data and a regulatory air pollutant dispersion model (AERMOD). Model outputs coupled with ambient measurements yielded fugitive fly ash EFs that averaged 96 g Mg−1 (of ash processed) for the PMc fraction (=PM10 - PM2.5) and 18 g Mg−1 for PM2.5. Median EFs were much lower due to the strongly skewed shape of the derived EF distributions. Fugitive EFs from nearby unpaved roads were also characterized. Our primary finding is that EFs for dry fly ash disposal are considerably less than EFs derived using US Environmental Protection Agency AP-42 Emissions Handbook formulations for generic aggregate materials. This appears to be due to a large difference (a factor of 10+) between fugitive vehicular EFs estimated using the AP-42 formulation for vehicles driving on industrial roads (in this case, heavy slow-moving grading equipment) and EFs derived by the current study. Implications Fugitive fly ash emission factors (EFs) derived by this study contribute to the small existing knowledge base for a type of pollutant that will become increasingly important as ambient particulate standards become tighter. In areas that are not in attainment with standards, realistic EFs can be used for compliance modeling and can help identify which classes of sources are best targeted to achieve desired air quality levels. In addition, understanding the natural variability in fugitive fly ash emissions can suggest methods that are most likely to be successful in controlling fugitive emissions related to dry fly ash storage.

We evaluate the simulations of SO2 and sulfate using the Community Multiscale Air Quality model (CMAQ) version 4.6 with the observations over the United States in 2002. MM5 was used for meteorological simulations. While the general seasonal cycles of SO2 and sulfate are simulated well by the model, we find significant systematic biases in the summer. The model low bias in sulfate is considerably more severe than the model bias in SO2. Both ACM and RADM schemes are used in the model to test the sensitivities of simulated sulfate to cloud processing. We carry out detailed modeling analysis and diagnostics for July 2002. Compared to satellite observations of cloud liquid water path, CMAQ cloud modules greatly overestimates convective (sub-grid) precipitating clouds, leading to large overestimation of sulfate wet scavenging. Limiting convective precipitating cloud fraction in the cloud modules to <10% and hence significantly reducing wet scavenging lead to much improved agreement between simulated and observed sulfate. The average lifetime of sulfate in the model increases from 1–2 days to 3–4 days for July. We show that a potential model problem of excessive wet scavenging of sulfate does not necessarily lead to apparent problems in model simulations of sulfate wet deposition rate compared to surface observations. In general, there is still a lack of direct observational constraints from air quality monitoring measurements on model simulated cloud processing of SO2 and sulfate.

Standardized estimates of fugitive emissions resulting from bulk materials handling are subject to many potential uncertainties based on the material of interest, the specifics of operational handling, and local geography and meteorology. In 2011 EPRI undertook the first of 3 phases of a field monitoring study at a power plant that investigated fugitive emissions of PM2.5 and PM10-2.5 (“PMc” for short) from a large dry storage coal fly ash pile. The results incorporated ambient measurements from May to October of 2011, statistical analyses of meteorological data, use of dispersion modeling to calculate emission factors, and a comparison to AP-42 approaches. Specifically, hourly PM2.5 and PM10 data from beta attenuation monitors (BAMs) was combined with high frequency measurements of light scattering (bscat) to make measurements of background concentrations as well as two sites downwind of a dry fly ash pile at a large coal-fired power plant. Activities monitored on the dry stack included hauling, dumping, and grading. In addition, an unpaved road exists along the base of the dry stack on top of a berm to stabilize the stack. This road is a source of vehicle-generated fugitive dust, and methods were developed to separate out the contribution from the fly ash emissions signal. The results suggest PM2.5 and PMc emission factors for both fly ash and road dust that are considerably lower than those based on AP-42 methods. Planned future work includes similar studies of coal and limestone/gypsum materials.

The relative roles of natural and anthropogenic sources in determining ozone and fine particle concentrations over the continental United States (U.S.) are investigated using an expanded emissions inventory of natural sources and an updated version of the Community Multiscale Air Quality (CMAQ) model. Various 12-month CMAQ simulations for the year 2002 using different sets of input emissions data are combined to delineate the contributions of background pollutants (i.e., model boundary conditions), natural emissions, anthropogenic emissions, as well as the specific impacts of lightning and wildfires. Results are compared with observations and previous air quality model simulations. Wildfires and lightning are both identified as contributing significantly to ozone levels with lightning NO(x) adding as much as 25-30 ppbV (or up to about 50%) to surface 8-h average natural O(3) mixing ratios in the southeastern U.S. Simulated wildfire emissions added more than 50 ppbV (in some cases >90%) to 8-h natural O(3) at several locations in the west. Modeling also indicates that natural emissions (including biogenic, oceanic, geogenic and fires) contributed ≤ 40% to the annual average of total simulated fine particle mass over the eastern two-thirds of the U.S. and >40% across most of the western U.S. Biogenic emissions are the dominant source of particulate mass over the entire U.S. and wildfire emissions are secondary. Averaged over the entire modeling domain, background and natural ozone are dominant with anthropogenically derived ozone contributing up to a third of the total only during summer. Background contributions to fine particle levels are relatively insignificant in comparison. Model results are also contrasted with the U.S. Environmental Protection Agency (EPA) default values for natural light scattering particle concentrations to be used for regional haze regulatory decision-making. Regional differences in EPA guidance are not supported by the modeling and EPA uncertainty estimates for default values are far smaller than the modeled variability in natural particle concentrations.

The first part of the comment requests information on the effect of anthropogenic emissions on the model response to the introduced organic sulfur chemistry. The overall effect on atmospheric sulfur dioxide and sulfate is illustrated in Fig. 1 which plots the change in SO2 and sulfate relative to a model simulation based on the original sulfur chemistry. Large relative increases in both species occur over the oceans because of the organic sulfur chemistry and the very low levels that occur in those locations in the absence of such chemistry. Inland over the eastern United States (where most

Daily (24 h) and hourly air quality data at several sites are used to examine the performance of the fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5)-Community Multiscale Air Quality Model (CMAQ) system over a 3-month period in 2003. A coarse (36 km) model grid was expected to provide relatively poor performance for ozone and comparatively better performance for fine particles, especially the more regional sulfate and carbonaceous aerosols. However, results were different from this expectation. Modeling showed significant skill for ozone at several locations but very little skill for particulate species. Modeling did poorly identifying surface wind directions associated with the highest and lowest pollutant exposures at most sites, although results varied widely by location. Model skill appeared to be better for ozone when spatial-temporal (S-T) patterns were examined, due in part to the ability of the model to reproduce much of the temporal variance associated with the diurnal photochemical cycle. At some sites the modeling even performed well in replicating the directional variability of hourly ozone despite relatively low spatial resolution. MM5-CMAQ spatial (directional) representation of 24-h-average particulate data was not good in most cases, but model skill improved somewhat when hourly data were examined. Modeling exhibited skill for sulfate at only one of nine sites using 24-h data averaged by daily resultant wind direction, at two of six sites when hourly data were averaged by direction, and at four of six sites when the combined spatial and temporal variance of sulfate was examined. Results were generally poorer for total carbon aerosol mass and total mass of particulate matter with diameter of less than 2.5 mu m (PM(2.5)). The primary result of this study is that an S-T analysis of pollutant patterns reveals model performance insights that cannot be realized by only examining model error statistics as is typically done for regulatory applications. Use of this S-T analysis technique is recommended for better understanding model performance during longer simulation periods, especially when using grids of finer spatial resolution for applications supporting local air quality management studies. Of course, using this approach will require measuring semicontinuous fine particle data at more sites and for longer periods.

An air quality modeling system—“Models-3/CMAQ”—is studied to determine the relationship between simulated cloud cover and atmospheric sulfur. Sulfur dioxide oxidation to sulfate occurs in the atmosphere through gas-phase reactions and reactions in clouds. The latter heterogeneous reactions can be rapid compared to gas-phase chemistry and a model must correctly simulate cloud cover to avoid serious bias. An evaluation of CMAQ revealed serious problems with diagnosed cloud cover and a bias in simulated sulfate production that was consistent with cloud biases. CMAQ modifications were tested to determine the sensitivity of the sulfur balance to cloud modeling assumptions. The model's vertical layer structure, limits on subgrid-scale cloud base and top heights, and the ability to simulate subgrid-scale convective cloud formation were found to be important. After applying model changes, atmospheric sulfur simulations were less biased and sulfur dioxide and sulfate were in better balance.

Models-3/CMAQ—the air quality modeling system developed by the United States Environmental Protection Agency—simulates atmospheric sulfur chemistry using both homogeneous and heterogeneous reactions. Homogeneous gas-phase oxidation of SO2 proceeds in response to available sunlight. Heterogeneous reactions in clouds oxidize SO2 only when clouds and certain oxidants and/or catalysts are present. The atmospheric balance between SO2 and sulfate affects the lifetimes of both species and influences source-receptor relationships. This paper describes a series of tests that examined the relationship between sulfur balances and cloud cover as simulated in CMAQ using both default and alternate cloudrelated parameterizations.

Site and regional trends in seasonally averaged particle SO4(2-) concentrations were examined for a large portion of the United States using data collected by the CASTNet air monitoring network. Trends were analyzed for overlapping periods of 1988-1999 and 1992-1999. The largest absolute SO4(2-) decreases--approximately -0.4 microg/m3/yr--between 1988 and 1999 occurred in summer for sites in the Ohio River Valley and areas to the east. Generally, the largest SO4(2-) reductions were found for summer, but larger relative reductions often occurred for spring and autumn. Sulfate changes during 1992-1999 were quite different from those found for 1988-1999 and were not entirely consistent with changes in SO2 emissions. In some locations, the 1992-1999 period saw smaller declines in SO4(2-), while in other places seasonal SO4(2-) actually increased. Increases were mostly confined to summer and autumn across the southern and southwestern states. Multivariate analysis of ambient sulfur levels, by region, versus SO2 emissions reveals that annual emissions are associated with more than 80% of the variance in seasonal sulfur (SO2 and SO4(2-)) in more than three-quarters of the cases examined. The weakest associations were found for the southeastern United States.

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTCloud water deposition to Appalachian forestsRichard J. Vong, John T. Sigmon, and Stephen F. MuellerCite this: Environ. Sci. Technol. 1991, 25, 6, 1014–1021Publication Date (Print):June 1, 1991Publication History Published online1 May 2002Published inissue 1 June 1991https://pubs.acs.org/doi/10.1021/es00018a002https://doi.org/10.1021/es00018a002research-articleACS PublicationsRequest reuse permissionsArticle Views153Altmetric-Citations62LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose Get e-Alerts