Assessing the impact of weather events at mid-latitudes on the atmospheric transport of chemical pollutants using a 2-dimensional multimedia meteorological model

We investigate the long-range transport potential (LRTP) of five different classes of hypothetical chemical pollutants (volatile, multimedia, semivolatile, particle-associated and hydrophilic) during a low pressure weather event using a novel 2 (x- and z-axis)-Dimensional Multi-Media Meteorological Model (2D4M). The atmosphere (z-axis) is described by three atmospheric layers, where two layers constitute the boundary layer and the third layer the free troposphere. The 2D4M can describe distinct weather events on a regional scale and calculate the LRTP of chemicals as a function of time during these events. Four weather factors are used to model weather events and their influence on the atmospheric transport of chemicals: (1) temperature, (2) wind speed and mixing dynamics of the troposphere, (3) hydroxyl radical concentrations and (4) precipitation. We have modeled the impact of variability in each of these factors on LRTP of pollutants during a front event associated with a low pressure period that interrupts a dominant high pressure system. The physico-chemical properties of the pollutant determine which specific weather factors contribute most to variability in transport potential during the event. Volatile and multimedia chemicals are mainly affected by changing atmospheric mixing conditions, wind speeds and OH radical concentrations, while semivolatile substances are also affected by temperature. Low-vapor-pressure pollutants that are particle-associated, and water-soluble pollutants are most strongly affected by precipitation. Some chemical pollutants are efficiently transported from the boundary layer into the upper troposphere during the modeled low pressure event and are transported by much higher wind speeds than in the boundary layer. Our model experiments show that the transport potential of volatile, multimedia and semivolatile compounds is significantly increased during a front event as a result of efficient tropospheric mixing and fast wind speeds in the upper troposphere, whereas low-volatility and hydrophilic chemicals are largely scavenged from the atmosphere. In future LRTP assessment of chemical contaminants as required by the Stockholm Convention and the convention on long-range transboundary air pollution, it is therefore advised to prioritize volatile, multimedia and semivolatile chemicals that are identified in initial screening.     

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.     

Concentrations and fluxes of organic compounds in the atmosphere of the Swedish west coast

Measurements of organic compounds in air and deposition have been carried out in parallel on the Swedish west coast. In this investigation the importance of long-range transport for the occurrence of organic compounds in deposition has been studied. Air samples were collected using a high volume sampler (HVS) and the deposition was sampled on a 1 m² Teflon-coated horizontal surface with runoff for the precipitation to an adsorbent. The samples were analyzed in order to identify and quantify different semivolatile compounds such as PAH and petrogenic hydrocarbons and chlorinated compounds such as PCB, HCH and HCB. Qualitative differences between the content of organic compounds in air and deposition during periods with varying levels of air pollution and different meteorological conditions have been studied and a comparison with other air pollutants, such as soot, has been carried out. The results of the measurements show that deposition of PAH and other hydrocarbons takes place continuously but the greatest amounts are measured in the deposition in connection with episodes together with heavy precipitation. The highest concentrations of PCB and HCH in the air were obtained during a warm dry period in May and the greatest amounts were deposited in a period in May with heavy precipitation.     

Estimating the Effect of Being Indoors on Total Personal Exposure to Outdoor Air Pollution

A personal air quality model (PAQM) has been developed to estimate the effect of being indoors on total personal exposure to outdoor-generated air pollution. Designed to improve air toxics risk assessment, PAQM accounts for individual hourly activity patterns, indoor-outdoor differences, physical exercise level, and geographic location for up to 56 different population groups. Unique hourly activity profiles are specified for each population group; group members are assigned each hour to one of up to 10 different indoor and outdoor microenvironments. To illustrate PAQM use, we apply it to two example cases: a long-term example representative of situations where pollutant health impact is related to integrated exposure (as in the case of potentially carcinogenic air toxics) and a short-term example representative of situations where health impact is related to acute exposure to peak concentrations (as with ozone).

Case study results illustrate that personal exposure, and thus health risk, attributable to outdoor-generated air pollution is sensitive to indoor-outdoor differences and population mobility. Where health impact is related to long-term integrated exposure (e.g., air toxics), exposure and subsequent risk are likely to be lower than that estimated by previous modeling techniques which do not account for such effects.     

Air quality modeling of interpollutant trading for ozone precursors in an urban area

Emission trading is a market-based approach designed to improve the efficiency and economic viability of emission control programs; emission trading has typically been confined to trades among single pollutants. Interpollutant trading (IPT), as described in this work, allows for trades among emissions of different compounds that affect the same air quality end point, in this work, ambient ozone (O3) concentrations. Because emissions of different compounds impact air quality end points differently, weighting factors or trading ratios (tons of emissions of nitrogen oxides (NO(x)) equivalent to a ton of emissions of volatile organic compounds [VOCs]) must be developed to allow for IPT. In this work, IPT indices based on reductions in O3 concentrations and based on reductions in population exposures to O3 were developed and evaluated using a three-dimensional gridded photochemical model for Austin, TX, a city currently on the cusp of nonattainment with the National Ambient Air Quality Standards for O3 concentrations averaged over 8 hr. Emissions of VOC and NO(x) from area and mobile sources in Austin are larger than emissions from point sources. The analysis indicated that mobile and area sources exhibited similar impacts. Trading ratios based on maximum O3 concentration or population exposure were similar. In contrast, the trading ratios did exhibit significant (more than a factor of two) day-to-day variability. Analysis of the air quality modeling indicated that the daily variability in trading ratios could be attributed to daily variations in both emissions and meteorology.     

Assessing the spatial distribution of nitrogen dioxide in London,Ontario

Land use regression (LUR) models have been widely used to characterize the spatial distribution of urban air pollution and estimate exposure in epidemiologic studies. However, spatial patterns of air pollution vary greatly between cities due to local source type and distribution. London, Ontario, Canada, is a medium-sized city with relatively few and isolated industrial point sources, which allowed the study to focus on the contribution of different transportation sectors to urban air pollution. This study used LUR models to estimate the spatial distribution of nitrogen dioxide (NO₂) and to identify local sources influencing NO₂ concentrations in London, ON. Passive air sampling was conducted at 50 locations throughout London over a 2-week period in May–June 2010. NO₂ concentrations at the monitored locations ranged from 2.8 to 8.9 ppb, with a median of 5.2 ppb. Industrial land use, dwelling density, distance to highway, traffic density, and length of railways were significant predictors of NO₂ concentrations in the final LUR model, which explained 78% of NO₂ variability in London. Traffic and dwelling density explained most of the variation in NO₂ concentrations, which is consistent with LUR models developed in other Canadian cities. We also observed the importance of local characteristics. Specifically, 17% of the variation was explained by distance to highways, which included the impacts of heavily traveled corridors transecting the southern periphery of the city. Two large railway yards and railway lines throughout central areas of the city explained 9% of NO₂ variability. These results confirm the importance of traditional LUR variables and highlight the importance of including a broader array of local sources in LUR modeling. Finally, future analyses will use the model developed in this study to investigate the association between ambient air pollution and cardiovascular disease outcomes, including plaque burden, cholesterol, and hypertension.

Implications: Monitoring and modeling of NO₂ throughout the city of London represents an important step toward assessing air pollution health effects in a mid-sized Canadian city. The study supports the introduction of railways to LUR modeling of NO₂. Railways explained approximately 9% of the variability in ambient NO₂ concentrations in London, which suggests that local sources captured by land-use indicators may contribute to the efficacy of LUR models. These findings provide insights relevant to other medium and smaller sized cities with similar land use and transportation infrastructure. Furthermore, London is a central hub for medical research and treatment in southwestern Ontario, with facilities such as the Robarts Research Institute, London Regional Cancer Program (LRCP), and Stroke Prevention & Atherosclerosis Research Centre (SPARC). The models developed in this study will provide estimates of exposure for future analyses examining air pollution health effects in this data-rich population.     

Air Quality Modeling of Interpollutant Trading for Ozone Precursors in an Urban Area

Abstract

Emission trading is a market‐based approach designed to improve the efficiency and economic viability of emission control programs; emission trading has typically been confined to trades among single pollutants. Interpollutant trading (IPT), as described in this work, allows for trades among emissions of different compounds that affect the same air quality end point, in this work, ambient ozone (O₃) concentrations. Because emissions of different compounds impact air quality end points differently, weighting factors or trading ratios (tons of emissions of nitrogen oxides (NO_x) equivalent to a ton of emissions of volatile organic compounds [VOCs]) must be developed to allow for IPT. In this work, IPT indices based on reductions in O₃ concentrations and based on reductions in population exposures to O₃ were developed and evaluated using a three‐dimensional gridded photochemical model for Austin, TX, a city currently on the cusp of nonattainment with the National Ambient Air Quality Standards for O₃ concentrations averaged over 8 hr. Emissions of VOC and NO_x from area and mobile sources in Austin are larger than emissions from point sources. The analysis indicated that mobile and area sources exhibited similar impacts. Trading ratios based on maximum O₃ concentration or population exposure were similar. In contrast, the trading ratios did exhibit significant (more than a factor of two) day‐to‐day variability. Analysis of the air quality modeling indicated that the daily variability in trading ratios could be attributed to daily variations in both emissions and meteorology.     

A Comparative Validation of the RAM and PTMTP Models for Short-Term SO2 Concentrations in Two Urban Areas

A comprehensive and comparative model validation of two EPA models for short-term SO₂ concentrations was performed. The two models tested were RAM (Urban version) and PTMTP (Terrain version). Both are multiple source, multiple receptor gaussian plume models, recommended in the EPA Guideline On Air Quality Models. ¹ The principal difference between the two models is in their use of empirical dispersion coefficients. It was because of the potential for markedly different predicted maximum SO₂ concentrations, and the absence of any testing data on the RAM model, that the validation analysis was undertaken. The current study utilized a full year of air quality data from monitoring sites in two Indiana cities, Michigan City and Indianapolis. Cumulative frequency distributions for each site and model were prepared and comparisons made. The results indicate that the RAM (Urban) model was highly inaccurate in predicting maximum short-term SO₂ concentrations. The PTMTP model, although conservative in its estimates, produces results which more closely resemble the distribution of observed SO₂ concentrations. The body of information presented in this paper is directed to environmental scientists responsible for air quality modeling, and to those persons who set policy on the use of models in air quality studies.     

Chemical Stabilization of Mixed Organic and Metal Compounds: EPA SITE Program Demonstration of the Silicate Technology Corporation Process

In November 1990, the Silicate Technology Corporation's (STC) proprietary process for treating soil contaminated with toxic semivolatile organic and inorganic contaminants was evaluated in a Superfund Innovative Technology Evaluation (SITE) field demonstration at the Selma Pressure Treating (SPT) wood preserving site in Selma, California. The SPT site was contaminated principally with pentachlorophenol (PCP) and arsenic, as well as lesser amounts of chromium and copper. Because of their importance when selecting a remedy for the site, PCP and arsenic were identified as critical analytes to evaluate the effectiveness of treatment.

Evaluation of STC's treatment process was based on contaminant mobility, measured by numerous leaching tests, and structural integrity of the solidified material, measured by physical, engineering, and morphological tests. An economic analysis was also performed, using cost information supplied by STC and supplemented by information generated during the demonstration.

Conclusions drawn from this SITE demonstration evaluation are: (1) the STC process can chemically stabilize contaminated soils similar to those at the Selma site that contain both semivolatile organic and inorganic contaminants; (2) PCP was successfully treated as demonstrated by total waste analysis; (3) heavy metals such as arsenic can be immobilized successfully based on various leach-test criteria; (4) the short-term physical stability of the treated waste was good, with unconfined compressive strengths (UCS) well above landfill solidification standards; (5) treatment resulted in a volume increase of 59 to 75 percent (68 percent average) and a slight increase in bulk density; and (6) the process is expected to cost approximately $190 to $360 per cubic yard when it is used to treat 15,000 cubic yards of waste similar to that found at the STC demonstration site, assuming that on-site, in-place disposal is performed.     

Errors in coarse particulate matter mass concentrations and spatiotemporal characteristics when using subtraction estimation methods

In studies of coarse particulate matter (PM_10-2.5), mass concentrations are often estimated through the subtraction of PM_2.5 from collocated PM₁₀ tapered element oscillating microbalance (TEOM) measurements. Though all field instruments have yet to be updated, the Filter Dynamic Measurement System (FDMS) was introduced to account for the loss of semivolatile material from heated TEOM filters. To assess errors in PM_10-2.5 estimation when using the possible combinations of PM₁₀ and PM_2.5 TEOM units with and without FDMS, data from three monitoring sites of the Colorado Coarse Rural–Urban Sources and Health (CCRUSH) study were used to simulate four possible subtraction methods for estimating PM_10-2.5 mass concentrations. Assuming all mass is accounted for using collocated TEOMs with FDMS, the three other subtraction methods were assessed for biases in absolute mass concentration, temporal variability, spatial correlation, and homogeneity. Results show collocated units without FDMS closely estimate actual PM_10-2.5 mass and spatial characteristics due to the very low semivolatile PM_10-2.5 concentrations in Colorado. Estimation using either a PM_2.5 or PM₁₀ monitor without FDMS introduced absolute biases of 2.4 µg/m³ (25%) to –2.3 µg/m³ (–24%), respectively. Such errors are directly related to the unmeasured semivolatile mass and alter measures of spatiotemporal variability and homogeneity, all of which have implications for the regulatory and epidemiology communities concerned about PM_10-2.5. Two monitoring sites operated by the state of Colorado were considered for inclusion in the CCRUSH acute health effects study, but concentrations were biased due to sampling with an FDMS-equipped PM_2.5 TEOM and PM₁₀ TEOM not corrected for semivolatile mass loss. A regression-based model was developed for removing the error in these measurements by estimating the semivolatile concentration of PM_2.5 from total PM_2.5 concentrations. By estimating nonvolatile PM_2.5 concentrations from this relationship, PM_10-2.5 was calculated as the difference between nonvolatile PM₁₀ and PM_2.5 concentrations.

Implications: Errors in the estimation of PM_10-2.5 concentrations using subtraction methods were shown to be related to the unmeasured semivolatile mass when using certain combinations of TEOM instruments. For the northeastern Colorado region, the absolute bias associated with this error significantly affects mean and 95th percentile values, which would affect assessment of compliance if PM_10-2.5 is regulated in the future. Estimating PM_10-2.5 mass concentrations using nonvolatile mass concentrations from collocated PM₁₀ and PM_2.5 TEOM monitors closely estimates the total PM_10-2.5 mass concentrations. A corrective model that removes the described error was developed and applied to data from two sites in Denver.

Supplemental Materials: Supplemental materials are available for this paper. Go to the publisher's online edition of the Journal of the Air & Waste Management Association.     

Three methods for quantifying proximity of air sampling sites to spatially resolved emissions of semi-volatile organic contaminants

Passive air samplers have made it possible to measure long-term average air concentrations of semi-volatile organic contaminants (SVOCs) at a large number of sampling sites. In order to use the results of such measurement networks in the derivation of empirical measures of long-range transport, a method is required that quantitatively expresses the proximity of air sampling sites to spatially distributed emissions. We propose three increasingly sophisticated tiers for quantifying proximity to emissions. The ‘static’ method assumes that a sampling site is only influenced by emission taking place in the same 1° of latitude by 1° of longitude cell in which it is located. The ‘dispersion’ method additionally accounts for the influence of emissions in neighboring cells by adding the emissions into each cell weighted by the distance between the cell’s center and the center of the cell containing the sampling site. The ‘air-shed’ method quantifies proximity to emissions by combining the emissions in each cell with the probability that air arriving at the sampling site passed through each cell. The probability is calculated for each sampling site by aggregating a large number of air mass back-trajectories. These new proximity gauges were contrasted against the remoteness index RI, which is derived from global atmospheric tracer transport modeling. The four methods were used to quantify the proximity of the sampling sites of the Global Atmospheric Passive Sampling (GAPS) study to global Polycyclic Aromatic Hydrocarbon (PAH) emissions. The proximity gauges produce markedly different results primarily for sites located near steep gradients in population, such as occur in coastal areas or at the feet of mountain ranges. The dispersion method produces quite similar results to the air-shed method using drastically less computational power and input data, but application of the air-shed method may be necessary where winds are strongly directional.     

Modeling Relationships between Indoor and Outdoor Air Quality

ABSTRACT

Information about the ratio between indoor and outdoor concentrations (IO ratios) of air pollutants is a crucial component in human exposure assessment. The present study examines the relationship between indoor and outdoor concentrations as influenced by the combined effect of time patterns in outdoor concentrations, ventilation rate, and indoor emissions. Two different mathematical approaches are used to evaluate IO ratios. The first approach involves a dynamic mass balance model that calculates distributions of transient IO ratios. The second approach assumes a linear relationship between indoor and outdoor concentrations. We use ozone and benzene as examples in various modeling exercises. The modeled IO ratio distributions are compared with the results obtained from linear fits through plots of indoor versus outdoor concentrations.     

Numerical simulation of air pollution in urban areas: Model development

A three-dimensional, grid-based numerical air pollution model for the estimation of air pollutant concentrations in an urban area is developed. Based on the continuity equation, the modeling system incorporates the combined influences of advective transport, turbulent diffusion, chemical transformation, source emissions and surface removal of air contaminants. Recent developments in plume rise and plume penetration processes, objective wind field analysis procedures and numerical solution techniques incorporated into the model are described.     

Industrial sources influence air concentrations of hydrogen sulfide and sulfur dioxide in rural areas of western Canada

A survey of monthly average concentrations of sulfur dioxide (SO2) and hydrogen sulfide (H2S) at rural locations in western Canada (provinces of Alberta, British Columbia, and Saskatchewan) was conducted in 2001-2002, as part of an epidemiological study of the effects of oil and gas industry emissions on the health of cattle. Repeated measurements were obtained at some months and locations. We aimed to develop statistical models of the effect of oil and gas infrastructure on air concentrations. The regulatory authorities supplied the information on location of the different oil and gas facilities during the study period and, for Alberta, provided data on H2S content of wells and flaring volumes. Linear mixed effects models were used to relate observed concentrations to proximity and type of oil and gas infrastructure. Low concentrations were recorded; the monthly geometric mean was 0.1-0.2 ppb for H2S, and 0.3-1.3 ppb for SO2. Substantial variability between repeated measurements was observed. The precision of the measurement method was 0.005 ppb for both contaminants. There were seasonal trends in the concentrations, but the spatial variability was greater. This was explained, in part, by proximity to oil/gas/bitumen wells and (for SO2) gas plants. Wells within 2 km of monitoring stations had the greatest impact on measured concentrations. For H2S, 8% of between-location variability was explained by proximity to industrial sources of emissions; for SO2 this proportion was 18%. In Alberta, proximity to sour gas wells and flares was associated with elevated H2S concentrations; however, the estimate of the effect of sour gas wells in the immediate vicinity of monitoring stations was unstable. Our study was unable to control for all possible sources of the contaminants. However, the results suggest that oil and gas extraction activities contribute to air pollution in rural areas of western Canada.     

A hybrid modeling approach to resolve pollutant concentrations in an urban area

A modeling tool that can resolve contributions from individual sources to the urban environment is critical for air-toxics exposure assessments. Air toxics are often chemically reactive and may have background concentrations originated from distant sources. Grid models are the best-suited tools to handle the regional features of these chemicals. However, these models are not designed to resolve pollutant concentrations on local scales. Moreover, for many species of interest, having reaction time scales that are longer than the travel time across an urban area, chemical reactions can be ignored in describing local dispersion from strong individual sources making Lagrangian and plume-dispersion models practical. In this study, we test the feasibility of developing an urban hybrid simulation system. In this combination, the Community Multi-scale Air Quality model (CMAQ) provides the regional background concentrations and urban-scale photochemistry, and local models such as Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT) and AMS/EPA Regulatory Model (AERMOD) provide the more spatially resolved concentrations due to local emission sources. In the initial application, the HYSPLIT, AERMOD, and CMAQ models are used in combination to calculate high-resolution benzene concentrations in the Houston area. The study period is from 18 August to 4 September of 2000. The Mesoscale Model 5 (MM5) is used to create meteorological fields with a horizontal resolution of 1×1 km². In another variation to this approach, multiple HYSPLIT simulations are used to create a concentration ensemble to estimate the contribution to the concentration variability from point sources. HYSPLIT simulations are used to model two sources of concentration variability; one due to variability created by different particle trajectory pathways in the turbulent atmosphere and the other due to different flow regimes that might be introduced when using gridded data to represent meteorological data fields. The ensemble mean concentrations determined by HYSPLIT plus the concentrations estimated by AERMOD are added to the CMAQ calculated background to estimate the total mean benzene concentration. These estimated hourly mean concentrations are also compared with available field measurements.     

Evaluation of an approach for the characterization of reactive and available pools of 20 potentially toxic elements in soils: part II--solid-solution partition relationships and ion activity in soil solutions

To assess environmental risks related to contaminants in soil it is essential to predict the available pool of inorganic contaminants at regional scales, accounting for differences between soils from variable geologic and climatic origins. An approach composed of a well-accepted soil extraction procedure (0.01 M CaCl(2)) and empirical Freundlich-type models in combination with mechanistically based models which to date have been used only in temperate regions was applied to 136 soils from a South European area and evaluated for its possible general use in risk assessment. Empirical models based on reactive element pools and soil properties (pH, organic carbon, clay, total Al, Fe and Mn) provided good estimations of available concentrations for a broad range of contaminants including As, Ba, Cd, Co, Cu, Hg, Mo, Ni, Pb, Sb, Se and Zn (r(2): 0.46-0.89). The variation of the pools of total Al in soils expressed the sorptive capacity of aluminosilicates and Al oxides at the surfaces and edges of clay minerals better than the actual variability of clay contents. The approach has led to recommendations for further research with particular emphasis on the impact of clay on the solubility of As and Sb, on the mechanisms controlling Cr and U availability and on differences in binding properties of soil organic matter from different climatic regions. This study showed that such approach may be included with a good degree of certainty for first step risk assessment procedures to identify potential risk areas for leaching and uptake of inorganic contaminants in different environmental settings.     

AERCOARE: An overwater meteorological preprocessor for AERMOD

AERCOARE is a meteorological data preprocessor for the American Meteorological Society and U.S Environmental Protection Agency (EPA) Regulatory Model (AERMOD). AERCOARE includes algorithms developed during the Coupled-Ocean Atmosphere Response Experiment (COARE) to predict surface energy fluxes and stability from routine overwater measurements. The COARE algorithm is described and the implementation in AERCOARE is presented. Model performance for the combined AERCOARE-AERMOD modeling approach was evaluated against tracer measurements from four overwater field studies. Relatively better model performance was found when lateral turbulence measurements were available and when several key input variables to AERMOD were constrained. Namely, requiring the mixed layer height to be greater than 25 m and not allowing the Monin Obukhov length to be less than 5 m improved model performance in low wind speed stable conditions. Several options for low wind speed dispersion in AERMOD also affected the model performance results. Model performance for the combined AERCOARE-AERMOD modeling approach was found to be comparable to the current EPA regulatory Offshore Coastal Model (OCD) for the same tracer studies. AERCOARE-AERMOD predictions were also compared to simulations using the California Puff-Advection Model (CALPUFF) that also includes the COARE algorithm. Many model performance measures were found to be similar, but CALPUFF had significantly less scatter and better performance for one of the four field studies. For many offshore regulatory applications, the combined AERCOARE-AERMOD modeling approach was found to be a viable alternative to OCD the currently recommended model.

Implications: A new meteorological preprocessor called AERCOARE was developed for offshore source dispersion modeling using the U.S. Environmental Protection Agency (EPA) regulatory model AERMOD. The combined AERCOARE-AERMOD modeling approach allows stakeholders to use the same dispersion model for both offshore and onshore applications. This approach could replace current regulatory practices involving two completely different modeling systems. As improvements and features are added to the dispersion model component, AERMOD, such techniques can now also be applied to offshore air quality permitting.     

Analysis of short-term ozone and PM2.5 measurements: Characteristics and relationships for air sensor messaging

Air quality sensors are becoming increasingly available to the general public, providing individuals and communities with information on fine-scale, local air quality in increments as short as 1 min. Current health studies do not support linking 1-min exposures to adverse health effects; therefore, the potential health implications of such ambient exposures are unclear. The U.S. Environmental Protection Agency (EPA) establishes the National Ambient Air Quality Standards (NAAQS) and Air Quality Index (AQI) on the best science available, which typically uses longer averaging periods (e.g., 8 hr; 24 hr). Another consideration for interpreting sensor data is the variable relationship between pollutant concentrations measured by sensors, which are short-term (1 min to 1 hr), and the longer term averages used in the NAAQS and AQI. In addition, sensors often do not meet federal performance or quality assurance requirements, which introduces uncertainty in the accuracy and interpretation of these readings. This article describes a statistical analysis of data from regulatory monitors and new real-time technology from Village Green benches to inform the interpretation and communication of short-term air sensor data. We investigate the characteristics of this novel data set and the temporal relationships of short-term concentrations to 8-hr average (ozone) and 24-hr average (PM_2.5) concentrations to examine how sensor readings may relate to the NAAQS and AQI categories, and ultimately to inform breakpoints for sensor messages. We consider the empirical distributions of the maximum 8-hr averages (ozone) and 24-hr averages (PM_2.5) given the corresponding short-term concentrations, and provide a probabilistic assessment. The result is a robust, empirical comparison that includes events of interest for air quality exceedances and public health communication. Concentration breakpoints are developed for short-term sensor readings such that, to the extent possible, the related air quality messages that are conveyed to the public are consistent with messages related to the NAAQS and AQI.

Implications: Real-time sensors have the potential to provide important information about fine-scale current air quality and local air quality events. The statistical analysis of short-term regulatory and sensor data, coupled with policy considerations and known health effects experienced over longer averaging times, supports interpretation of such short-term data and efforts to communicate local air quality.     

Effects of Heating Season on Residential Indoor and Outdoor Polycyclic Aromatic Hydrocarbons, Black Carbon, and Particulate Matter in an Urban Birth Cohort

Exposure to air pollutants has been associated with adverse health effects. However, analyses of the effects of season and ambient parameters such as ozone have not been fully conducted. Residential indoor and outdoor air levels of polycyclic aromatic hydrocarbons (PAH), black carbon (measured as absorption coefficient [Abs]), and fine particulate matter <2.5 μm (PM)(2.5) were measured over two-weeks in a cohort of 5-6 year old children (n=334) living in New York City's Northern Manhattan and the Bronx between October 2005 and April 2010. The objectives were to: 1) characterize seasonal changes in indoor and outdoor levels and indoor/outdoor (I/O) ratios of PAH (gas + particulate phase; dichotomized into Σ(8)PAH(semivolatile) (MW 178-206), and Σ(8)PAH(nonvolatile) (MW 228-278)), Abs, and PM(2.5); and 2) assess the relationship between PAH and ozone. Results showed that heating compared to nonheating season was associated with greater Σ(8)PAH(nonvolatile) (p<0.001) and Abs (p<0.05), and lower levels of Σ(8)PAH(semivolatile) (p<0.001). In addition, the heating season was associated with lower I/O ratios of Σ(8)PAH(nonvolatile) and higher I/O ratios of Σ(8)PAH(semivolatile) (p<0.001) compared to the nonheating season. In outdoor air, Σ(8)PAH(nonvolatile) was correlated negatively with community-wide ozone concentration (p<0.001). Seasonal changes in emission sources, air exchanges, meteorological conditions and photochemical/chemical degradation reactions are discussed in relationship to the observed seasonal trends.