Sulfur dioxide dispersion and source contribution to receptors of downtown Patras, Greece

Background The development of the city of Patras, including harbour relocation, in conjunction with the protection of the regional ecosystems, requires air quality assessment and management. For this reason, a model applicable in the Patras area is necessary and valuable. The goal of this study was to validate a model suitable for predicting the dispersion of sulfur dioxide (SO₂), based on particular activity, topography and weather conditions. Methods We used the US-EPA ISCLT3 integral dispersion model to predict SO₂ concentrations for Patras, Greece. We assumed that the major contribution to Patras air pollution came from central heating, harbour and traffic. We calculated traffic emissions using COPERTIII. Results and Discussion Assigning suitable values of the mixing height, the model predicted the local and spatial distribution of the mean monthly SO₂ concentrations in downtown Patras, as well computed the contribution of the SO₂ emissions originating from each particular source at each receptor location on a seasonal and annual basis. The comparison between predictions and measurements shows that the model performance for estimating the SO₂ concentrations and period pattern is satisfactory. Conclusion The mixing height was the critical parameter for calibrating the model. Model validation promises satisfactory predictions for SO₂ pollution levels on monthly basis. Recommendations and Outlook The model could be used in predicting SO₂ concentrations and source contribution for several downtown Patras receptors using pertinent meteorological and emission information. It could be also extended to predict the dispersion of other primary air pollutants. The calibrated model predictions could be used to fill gaps in monitoring data, saving money and time, and help in assess and manage air quality as Patras develops.     

Controls on hourly variations in urban background air pollutant concentrations

Average 21st century concentrations of urban air pollutants linked to cardiorespiratory disease are not declining, and commonly exceed legal limits. Even below such limits, health effects are being observed and may be related to transient daytime peaks in pollutant concentrations. With this in mind, we analyse >52,000 hourly urban background readings of PM₁₀ and pollutant gases throughout 2007 at a European town with legal annual average concentrations of common pollutants, but with a documented air pollution-related cardiorespiratory health problem, and demonstrate the hourly variations in PM₁₀, SO₂, NO_x, CO and O₃. Back-trajectory analysis was applied to track the arrival of exotic PM₁₀ intrusions, the main controls on air pollutants were identified, and the typical hourly pattern on ambient concentrations during 2007 was profiled. Emphasis was placed on “worst case” data (>90th percentile), when health effects are likely to be greatest. The data show marked daytime variations in pollutants result from rush-hour traffic-related pollution spikes, midday industrial SO₂ maxima, and afternoon O₃ peaks. African dust intrusions enhance PM₁₀ levels at whatever hour, whereas European PM incursions produce pronounced evening peaks due to their transport direction (across an industrial traffic corridor). Transient peak profiling moves us closer to the reality of personal outdoor exposure to inhalable pollutants in a given urban area. We argue that such an approach to monitoring data potentially offers more to air pollution health effect studies than using only 24 h or annual averages.     

Correlating Emissions with Time and Temperature to Predict Worst-Case Emissions from Open Liquid Area Sources

Abstract

The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates using hourly meteorological data. However, emission rates are typically held constant, based on one measured value. Using constant emission rates can be especially inaccurate for open liquid area sources, like wastewater treatment plant units, which have greater emissions during warmer weather, when volatilization and biological activity increase. If emission rates for a wastewater odor study are measured on a cooler day and input directly into a dispersion model as constant values, odor impact will likely be underestimated. Unfortunately, because of project schedules, not all emissions sampling from open liquid area sources can be conducted under worst-case summertime conditions. To address this problem, this paper presents a method of varying emission rates based on temperature and time of the day to predict worst-case emissions. Emissions are varied as a linear function of temperature, according to Henry’s law, and a tenth order polynomial function of time. Equation coefficients are developed for a specific area source using concentration and temperature measurements, captured over a multiday period using a data-logging monitor. As a test case, time/temperature concentration correlation coefficients were estimated from field measurements of hydrogen sulfide (H₂S) at the Rowlett Creek Wastewater Treatment Plant in Garland, TX. The correlations were then used to scale a flux chamber emission rate measurement according to hourly readings of time and temperature, to create an hourly emission rate file for input to the dispersion model ISCST3. ISCST3 was then used to predict hourly atmospheric concentrations of H₂S. With emission rates varying hourly, ISCST3 predicted 384 acres of odor impact, compared with 103 acres for constant emissions. Because field sampling had been conducted on relatively cool days (85–90 °F), the constant emission rate underestimated odor impact significantly (by 73%).     

Spatial Modeling for Air Pollution Monitoring Network Design: Example of Residential Woodsmoke

Abstract

The purpose of this paper is to demonstrate how to develop an air pollution monitoring network to characterize small-area spatial contrasts in ambient air pollution concentrations. Using residential woodburning emissions as our case study, this paper reports on the first three stages of a four-stage protocol to measure, estimate, and validate ambient residential woodsmoke emissions in Vancouver, British Columbia. The first step is to develop an initial winter nighttime woodsmoke emissions surface using inverse-distance weighting of emissions information from consumer woodburning surveys and property assessment data. Second, fireplace density and a compound topo-graphic index based on hydrological flow regimes are used to enhance the emissions surface. Third, the spatial variation of the surface is used in a location-allocation algorithm to design a network of samplers for the woodsmoke tracer compound levoglucosan and fine particulate matter. Measurements at these network sites are then used in the fourth stage of the protocol (not presented here): a mobile sampling campaign aimed at developing a high-resolution surface of woodsmoke concentrations for exposure assignment in health effects studies. Overall the results show that relatively simple data inputs and spatial analysis can be effective in capturing the spatial variability of ambient air pollution emissions and concentrations.     

Spatial modeling for air pollution monitoring network design: example of residential woodsmoke

The purpose of this paper is to demonstrate how to develop an air pollution monitoring network to characterize small-area spatial contrasts in ambient air pollution concentrations. Using residential woodburning emissions as our case study, this paper reports on the first three stages of a four-stage protocol to measure, estimate, and validate ambient residential woodsmoke emissions in Vancouver, British Columbia. The first step is to develop an initial winter nighttime woodsmoke emissions surface using inverse-distance weighting of emissions information from consumer woodburning surveys and property assessment data. Second, fireplace density and a compound topographic index based on hydrological flow regimes are used to enhance the emissions surface. Third, the spatial variation of the surface is used in a location-allocation algorithm to design a network of samplers for the woodsmoke tracer compound levoglucosan and fine particulate matter. Measurements at these network sites are then used in the fourth stage of the protocol (not presented here): a mobile sampling campaign aimed at developing a high-resolution surface of woodsmoke concentrations for exposure assignment in health effects studies. Overall the results show that relatively simple data inputs and spatial analysis can be effective in capturing the spatial variability of ambient air pollution emissions and concentrations.     

Assessment of contribution of SO2 and NO2 from different sources in Jamshedpur region, India

Contribution of pollution from different types of sources in Jamshedpur, the steel city of India, has been estimated in winter 1993 using two approaches in order to delineate and prioritize air quality management strategies for the development of region in an environmental friendly manner. The first approach mainly aims at preparation of a comprehensive emission inventory and estimation of spatial distribution of pollution loads in terms of SO₂ and NO₂ from different types of industrial, domestic and vehicular sources in the region. The results indicate that industrial sources account for 77% and 68% of the total emissions of SO₂ and NO₂, respectively, in the region, whereas vehicular emissions contributed to about 28% of the total NO₂ emissions. In the second approach, contribution of these sources to ambient air quality levels to which the people are exposed to, was assessed through air pollution dispersion modelling. Ambient concentration levels of SO₂ and NO₂ have been predicted in winter season using the ISCST3 model. The analysis indicates that emissions from industrial sources are responsible for more than 50% of the total SO₂ and NO₂ concentration levels. Vehicular activities contributed to about 40% of NO₂ pollution and domestic fuel combustion contributed to about 38% of SO₂ pollution. Predicted 24-h concentrations were compared with measured concentrations at 11 ambient air monitoring stations and good agreement was noted between the two values. In-depth zone-wise analysis of the above indicates that for effective air quality management, industrial source emissions should be given highest priority, followed by vehicular and domestic sources in Jamshedpur region.     

Near-road air pollutant concentrations of CO and PM2.5: A comparison of MOBILE6.2/CALINE4 and generalized additive models

The contribution of vehicular traffic to air pollutant concentrations is often difficult to establish. This paper utilizes both time-series and simulation models to estimate vehicle contributions to pollutant levels near roadways. The time-series model used generalized additive models (GAMs) and fitted pollutant observations to traffic counts and meteorological variables. A one year period (2004) was analyzed on a seasonal basis using hourly measurements of carbon monoxide (CO) and particulate matter less than 2.5 μm in diameter (PM_2.5) monitored near a major highway in Detroit, Michigan, along with hourly traffic counts and local meteorological data. Traffic counts showed statistically significant and approximately linear relationships with CO concentrations in fall, and piecewise linear relationships in spring, summer and winter. The same period was simulated using emission and dispersion models (Motor Vehicle Emissions Factor Model/MOBILE6.2; California Line Source Dispersion Model/CALINE4). CO emissions derived from the GAM were similar, on average, to those estimated by MOBILE6.2. The same analyses for PM_2.5 showed that GAM emission estimates were much higher (by 4–5 times) than the dispersion model results, and that the traffic-PM_2.5 relationship varied seasonally. This analysis suggests that the simulation model performed reasonably well for CO, but it significantly underestimated PM_2.5 concentrations, a likely result of underestimating PM_2.5 emission factors. Comparisons between statistical and simulation models can help identify model deficiencies and improve estimates of vehicle emissions and near-road air quality.     

Source apportionment of particulate matter (PM2.5) in an urban area using dispersion,receptor and inverse modelling

Air pollution emission inventories are the basis for air quality assessment and management strategies. The quality of the inventories is of great importance since these data are essential for air pollution impact assessments using dispersion models. In this study, the quality of the emission inventory for fine particulates (PM_2.5) is assessed: first, using the calculated source contributions from a receptor model; second, using source apportionment from a dispersion model; and third, by applying a simple inverse modelling technique which utilises multiple linear regression of the dispersion model source contributions together with the observed PM_2.5 concentrations. For the receptor modelling the chemical composition of PM_2.5 filter samples from a measurement campaign performed between January 2004 and April 2005 are analysed. Positive matrix factorisation is applied as the receptor model to detect and quantify the various source contributions. For the same observational period and site, dispersion model calculations using the Air Quality Management system, AirQUIS, are performed. The results identify significant differences between the dispersion and receptor model source apportionment, particularly for wood burning and traffic induced suspension. For wood burning the receptor model calculations are lower, by a factor of 0.54, but for the traffic induced suspension they are higher, by a factor of 7.1. Inverse modelling, based on regression of the dispersion model source contributions and the PM_2.5 concentrations, indicates similar discrepancies in the emissions inventory. In order to assess if the differences found at the one site are generally applicable throughout Oslo, the individual source category emissions are rescaled according to the receptor modelling results. These adjusted PM_2.5 concentrations are compared with measurements at four independent stations to evaluate the updated inventory. Statistical analysis shows improvement in the estimated concentrations for PM_2.5 at all sites. Similarly, inverse modelling is applied at these independent sites and this confirms the validity of the receptor model results.     

Which meteorological conditions produce worst-case odors from area sources?

Two competing meteorological factors influence atmospheric concentrations of pollutants from open liquid area sources such as wastewater treatment plant units: temperature and stability. High temperatures in summer produce greater emissions from liquid area sources because of increased compound volatility; however, these emissions tend to disperse more readily because of frequent occurrence of unstable conditions. An opposite scenario occurs in winter, with lesser emissions due to lower temperatures, but also frequently less dispersion, due to stable atmospheric conditions. The primary objective of this modeling study was thus to determine whether higher atmospheric concentrations from open liquid area sources occur more frequently in summer, when emissions are greater but so is dispersion, or in winter, when emissions are lesser but so is dispersion. The study utilized a rectangular clarifier emitting hydrogen sulfide as a sample open liquid area source. Dispersion modeling runs were conducted using ISCST3 and AERMOD, encompassing 5 yr of hourly meteorological data divided by season. Emission rates were varied hourly on the basis of a curve-fit developed from previously collected field data. Model output for each season was used to determine (1) maximum 2-min average concentrations, (2) the number of odor events (2-min average concentrations greater than odor detection thresholds), and (3) areas of impact. On the basis of these 3 types of output, it was found that the worst-case odors were associated with summer, considering impacts of meteorology upon both emissions and dispersion. Not accounting for the impact of meteorology on emissions (using a constant worst-case emission rate) caused concentrations to be overpredicted compared with a variable emission rate case. The highest concentrations occurred during stability classes D, E, and F, as anticipated. A comparison of ISCST3 and AERMOD found that for the area source modeled, ISCST3 predicted higher concentrations and more odor events for all seasons.     

NO2 and SO2dispersion modeling and relative roles of emission sources over Map Ta Phut industrial area,Thailand

Map Ta Phut industrial area (MA) is the largest industrial complex in Thailand. There has been concern about many air pollutants over this area. Air quality management for the area is known to be difficult, due to lack of understanding of how emissions from different sources or sectors (e.g., industrial, power plant, transportation, and residential) contribute to air quality degradation in the area. In this study, a dispersion study of NO₂ and SO₂ was conducted using the AERMOD model. The area-specific emission inventories of NO_x and SO₂ were prepared, including both stack and nonstack sources, and divided into 11 emission groups. Annual simulations were performed for the year 2006. Modeled concentrations were evaluated with observations. Underestimation of both pollutants was found, and stack emission estimates were scaled to improve the modeled results before quantifying relative roles of individual emission groups to ambient concentration over four selected impacted areas (two are residential and the others are highly industrialized). Two concentration measures (i.e., annual average area-wide concentration or AC, and area-wide robust highest concentration or AR) were used to aggregately represent mean and high-end concentrations for each individual area, respectively. For AC-NO₂, on-road mobile emissions were found to be the largest contributor in the two residential areas (36–38% of total AC-NO₂), while petrochemical-industry emissions play the most important role in the two industrialized areas (34–51%). For AR-NO₂, biomass burning has the most influence in all impacted areas (>90%) except for one residential area where on-road mobile is the largest (75%). For AC-SO₂, the petrochemical industry contributes most in all impacted areas (38–56%). For AR-SO₂, the results vary. Since the petrochemical industry was often identified as the major contributor despite not being the largest emitter, air quality workers should pay special attention to this emission group when managing air quality for the MA.

Implications: Effective air quality management in Map Ta Phut Industrial Area, Thailand requires better understanding of how emissions from various sources contribute to the degradation of ambient air quality. Based on the dispersion study here, petrochemical industry was generally identified as the major contributor to ambient NO₂ and SO₂. By accounting for all stack and non-stack sources, on-road mobile emissions were found to be important in some particular areas.     

Correlating emissions with time and temperature to predict worst-case emissions from open liquid area sources

The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates using hourly meteorological data. However, emission rates are typically held constant, based on one measured value. Using constant emission rates can be especially inaccurate for open liquid area sources, like wastewater treatment plant units, which have greater emissions during warmer weather, when volatilization and biological activity increase. If emission rates for a wastewater odor study are measured on a cooler day and input directly into a dispersion model as constant values, odor impact will likely be underestimated. Unfortunately, because of project schedules, not all emissions sampling from open liquid area sources can be conducted under worst-case summertime conditions. To address this problem, this paper presents a method of varying emission rates based on temperature and time of the day to predict worst-case emissions. Emissions are varied as a linear function of temperature, according to Henry's law, and a tenth order polynomial function of time. Equation coefficients are developed for a specific area source using concentration and temperature measurements, captured over a multiday period using a data-logging monitor. As a test case, time/temperature concentration correlation coefficients were estimated from field measurements of hydrogen sulfide (H2S) at the Rowlett Creek Wastewater Treatment Plant in Garland, TX. The correlations were then used to scale a flux chamber emission rate measurement according to hourly readings of time and temperature, to create an hourly emission rate file for input to the dispersion model ISCST3. ISCST3 was then used to predict hourly atmospheric concentrations of H2S. With emission rates varying hourly, ISCST3 predicted 384 acres of odor impact, compared with 103 acres for constant emissions. Because field sampling had been conducted on relatively cool days (85-90 degrees F), the constant emission rate underestimated odor impact significantly (by 73%).     

C2–C5 Hydrocarbon measurements in the Netherlands 1981–1991

Measurements of C₂–C₅ hydrocarbons on an hourly basis at the TNO site in Delft from 1982 to 1984 and at Moerdijk over the period 1981–1991 are presented. In combination with meteorological data (wind direction and wind speed) the Delft and Moerdijk series are evaluated to identify source categories, annual variations, background concentrations and trends. The C₂–C₅ hydrocarbon concentrations at Delft and Moerdijk are determined mainly by emission characteristics and meteorological dispersion; the dominant sources are relatively nearby and atmospheric degradation is not of much importance. Under conditions of high wind speed the concentrations measured at Moerdijk in the marine sector are close to the Atlantic background concentrations in winter and somewhat above this in summer. The continental background concentrations are higher than the marine background concentrations by a factor of almost two. The annual variation of acetylene is more pronounced than that of the other hydrocarbons, most likely due to a different seasonal variation in acetylene emissions. The annual variation of propene is smoother, indicating stronger sources in summer than in winter. This feature of propene is observed in continental as well as in marine sectors. The observations show that at Moerdijk C₂–C₄ concentrations measured in Rijnmond sector have decreased considerably since the early 1980s, corresponding with changes in emissions in that area. Averaged over all wind directions the trend of all species is downward, but for acetylene the trend is significant at a 95% confidence interval. The acetylene concentrations show an annual downward trend of 3% during the 1980s, supporting other estimates of decreasing hydrocarbon emissions from traffic over this period at the same rate.     

High time-resolved measurements of organic air toxics in different source regimes

High time-resolved (HTR) measurements can provide significant insight into sources and exposures of air pollution. In this study, an automated instrument was developed and deployed to measure hourly concentrations of 18 gas-phase organic air toxics and 6 volatile organic compounds (VOCs) at three sites in and around Pittsburgh, Pennsylvania. The sites represent different source regimes: a site with substantial mobile-source emissions; a residential site adjacent to a heavily industrialized zone; and an urban background site. Despite the close proximity of the sites (less than 13 km apart), the temporal characteristic of outdoor concentrations varied widely. Most of the compounds measured were characterized by short periods of elevated concentrations or plume events, but the duration, magnitude and composition of these events varied from site to site. The HTR data underscored the strong role of emissions from local sources on exposure to most air toxics. Plume events contributed more than 50% of the study average concentrations for all pollutants except chloroform, 1,2-dichloroethane, and carbon tetrachloride. Wind directional dependence of air toxic concentrations revealed that emissions from large industrial facilities affected concentrations at all of the sites. Diurnal patterns and weekend/weekday variations indicated the effects of the mixing layer, point source emissions patterns, and mobile source air toxics (MSATs) on concentrations. Concentrations of many air toxics were temporally correlated, especially MSATs, indicating that they are likely co-emitted. It was also shown that correlations of the HTR data were greater than lower time resolution data (24-h measurements). This difference was most pronounced for the chlorinated pollutants. The stronger correlations in HTR measurements underscore their value for source apportionment studies.     

PM10 Levels in the Lower Fraser Valley,British Columbia,Canada: An Overview of Spatiotemporal Variations and Meteorological Controls

ABSTRACT

Three years of hourly averaged PM₁₀ (particulate matter less than 10 Lrm in diameter) tapered element oscillating microbalance (TEOM) data from 10 sites in the large coastal valley incorporating Greater Vancouver were used to investigate the spatiotemporal dimensions and air pollution meteorology of particulate pollution. During the period studied, the provincial “acceptable” objective daily concentration of 50 μg m^-3 was exceeded at 7 of the 10 sites. The highest annual, seasonal, and maximum hourly concentrations were recorded at Abbotsford in the central valley. Mean seasonal PM₁₀ concentrations were highest in the wintertime in the western Lower Fraser Valley (LFV) and in the summertime at the central and eastern valley locations. Within the network, interstation correlations of daily average concentrations exceed 0.8 at interstation distances less than 20 km and decrease thereafter. For daily maximum concentrations (hourly), interstation correlations decrease sharply with distance. Meteorological conditions responsible for elevated par-ticulate concentrations in the LFV are associated with (1) short periods (1- to 3-hr duration) of reduced dispersion during summer nights at sites close to primary sources, (2) summer anticyclonic conditions when photochemical pollutant concentrations build up across the entire valley, and (3) occasional wintertime “gap wind” events in the eastern valley.     

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.     

Air pollution in the Krusne Hory region,Czech Republic during the 1990s

Emissions from the Black Triangle Region were considered to be the major source of air pollution problems in Europe during the 1990s. This discussion reviews the changes in emissions and pollution concentrations in the Krusne Hory Region (Czech Republic) in the winter half of the year during most of the past decade, and describes the relationships with meteorology. Sulfur dioxide (SO₂) is used as the example pollutant. The results show a decrease in pollution concentrations since 1996, as air pollution control and management strategies for important point sources take effect. The winter of 1995–1996 was especially harsh in the number of pollution episodes. Correlations between SO₂ and meteorological parameters are inconsistent. Wind direction provides the best relationship at monitoring stations along the Krusne Hory Plateau, with wind speed and temperature more variable depending on month and location. For the valley stations, higher SO₂ concentrations are strongly related to colder temperatures, higher relative humidities, and lower wind speeds. A case study during the winter of 1995–1996 (November 9–15) illustrated the importance of synoptic high pressure and a low-level inversion in minimizing plume dispersion from point sources. Specific sources of SO₂ affecting each station could thus be identified.     

Reduction of atmospheric emissions due to switching from fuel oil to natural gas at a power plant in a critical area in Central Mexico

ABSTRACT

A case study was conducted to evaluate the SO₂ emission reduction in a power plant in Central Mexico, as a result of the shifting of fuel oil to natural gas. Emissions of criteria pollutants, greenhouse gases, organic and inorganic toxics were estimated based on a 2010 report of hourly fuel oil consumption at the “Francisco Pérez Ríos” power plant in Tula, Mexico. For SO₂, the dispersion of these emissions was assessed with the CALPUFF dispersion model. Emissions reductions of > 99% for SO₂, PM and Pb, as well as reductions >50% for organic and inorganic toxics were observed when simulating the use of natural gas. Maximum annual (993 µg/m³) and monthly average SO₂ concentrations were simulated during the cold-dry period (152–1063 µg/m³), and warm-dry period (239–432 µg/m³). Dispersion model results and those from Mexico City’s air quality forecasting system showed that SO₂ emissions from the power plant affect the north of Mexico City in the cold-dry period. The evaluation of model estimates with 24 hr SO₂ measured concentrations at Tepeji del Rio suggests that the combination of observations and dispersion models are useful in assessing the reduction of SO₂ emissions due to shifting in fuels. Being SO₂ a major precursor of acid rain, high transported sulfate concentrations are of concern and low pH values have been reported in the south of Mexico City, indicating that secondary SO₂ products emitted in the power plant can be transported to Mexico City under specific atmospheric conditions.

Implications: Although the surroundings of a power plant located north of Mexico City receives most of the direct SO₂ impact from fuel oil emissions, the plume is dispersed and advected to the Mexico City metropolitan area, where its secondary products may cause acid rain. The use of cleaner fuels may assure significant SO₂ reductions in the plant emissions and consequent acid rain presence in nearby populated cities and should be compulsory in critical areas to comply with annual emission limits and health standards.     

Wood smoke as a source of particle-phase organic compounds in residential areas

The objective of this study was to investigate the organic composition of wood smoke emissions and ambient air samples in order to determine the wood smoke contribution to the ambient air pollution in the residential areas. From November 2005 to March 2006 particle-phase PM₁₀ samples were collected in the residential town Dettenhausen surrounded by forests near Stuttgart in southern Germany. Samples collected on pre-baked glass fibre filters were extracted using toluene with ultrasonic bath and analysed by gas chromatography mass spectrometry (GC-MS). 21 polycyclic aromatic hydrocarbons (PAH) including 16 USEPA priority pollutants, different organic wood smoke tracers, primarily 21 species of syringol and guaiacol derivatives, levoglucosan and its isomers mannosan, galactosan and dehydroabietic acid were detected and quantified in this study. The concentrations of these compounds were compared with the fingerprints of emissions from hardwood and softwood combustion carried out in test facilities at Universitaet Stuttgart and field investigations at a wood stove during real operation in Dettenhausen. It was observed that the combustion derived PAH was detected in higher concentrations than other PAH in the ambient air PM₁₀ samples. Syringol and its derivatives were found in large amounts in hardwood burning but were not detected in softwood burning emissions. On the other hand, guaiacol and its derivatives were found in both softwood and hardwood burning emissions, but the concentrations were higher in the softwood smoke compared to hardwood smoke. So, these compounds can be used as typical tracer compounds for the different types of wood burning emissions. In ambient air samples both syringol and guaiacol derivatives were found which indicates the wood combustion contribution to the PM load in such residential areas. Levoglucosan was detected in high concentrations in all ambient PM₁₀ samples. A source apportionment modelling, Positive Matrix Factorization (PMF) was implemented to quantify the wood smoke contribution to the ambient PM₁₀ bound organic compounds in the residential area.     

A comparison of hourly with annual air pollutant emissions: Implications for estimating acute exposure and public health risk

Health risks from air pollutants are evaluated by comparing chronic (i.e., an average over 1 yr or greater) or acute (typically 1-hr) exposure estimates with chemical- and duration-specific reference values or standards. When estimating long-term pollutant concentrations via exposure modeling, facility-level annual average emission rates are readily available as model inputs for most air pollutants. In contrast, there are far fewer facility-level hour-by-hour emission rates available for many of these same pollutants. In this report, we first analyze hour-by-hour emission rates for total reduced sulfur (TRS) compounds from eight kraft pulp mill operations. This data set is used to demonstrate discrepancies between estimating exposure based on a single TRS emission rate that has been calculated as the mean of all operating hours of the year, as opposed to reported hourly emission rates. A similar analysis is then performed using reported hourly emission rates for sulfur dioxide (SO₂) and oxides of nitrogen (NO_x) from three power generating units from a U.S. power plant. Results demonstrate greater variability at kraft pulp mill operations, with ratios of reported hourly to average hourly TRS emissions ranging from less than 1 to greater than 160 during routine facility operations. Thus, if fluctuations in hourly emission rates are not accounted for, over- or underestimates of hourly exposure, and thus acute health risk, may occur. In addition to this analysis, we also demonstrate an additional challenge when assessing health risk based on hourly exposures: the lack of human health reference values based on 1-hr exposures.

Implications: Largely due to the lack of reported hourly emission rate data for many air pollutants, an hourly average emission rate (calculated from an annual emission rate) is often used when modeling the potential for acute health risk. We calculated ratios between reported hourly and hourly average emission rates from pulp and paper mills and a U.S. power plant to demonstrate that if not considered, hourly fluctuations in emissions could result in an over- or underestimation of exposure and risk. We also demonstrate the lack of 1-hr human health reference values meant to be protective of the general population, including children.     

Characterization of Carbonaceous Species of Ambient PM2.5 in Beijing,China

Abstract

One-week integrated fine particulate matter (i.e., particles <2.5 μm in diameter; PM_2.5) samples were collected continuously with a low-flow rate sampler at a downtown site (Chegongzhuang) and a residential site (Tsinghua University) in Beijing between July 1999 and June 2000. The annual average concentrations of organic carbon (OC) and elemental carbon (EC) at the urban site were 23.9 and 8.8 μg m^?3, much higher than those in some cities with serious air pollution. Similar weekly variations of OC and EC concentrations were found for the two sampling sites with higher concentrations in the winter and autumn. The highest weekly variations of OC and EC occurred in the winter, suggesting that combustion sources for space heating were important contributors to carbonaceous particles, along with a significant impact from variable meteorological conditions. High emissions coupled with unfavorable meteorological conditions led to the max weekly carbonaceous concentration the week of November 18–25, 1999. The weekly mass ratios of OC:EC ranged between 2 and 4 for most samples and averaged 2.9, probably suggesting that secondary OC (SOC) is present most weeks. The range of contemporary carbon fraction, based on the C14 analyses of eight samples collected in 2001, is 0.330–0.479. Estimated SOC accounted for ～38% of the total OC at the two sites. Average OC and EC concentrations at Tsinghua University were 25% and 18%, respectively, higher than those at Chegongzhuang, which could be attributed to different local emissions of primary carbonaceous particles and gaseous precursors of SOC, as well as different summer photochemical intensities between the two locations.