Boiler briquette coal versus raw coal: Part I--Stack gas emissions

Stack gas emissions were characterized for a steam-generating boiler commonly used in China. The boiler was tested when fired with a newly formulated boiler briquette coal (BB-coal) and when fired with conventional raw coal (R-coal). The stack gas emissions were analyzed to determine emission rates and emission factors and to develop chemical source profiles. A dilution source sampling system was used to collect PM on both Teflon membrane filters and quartz fiber filters. The Teflon filters were analyzed gravimetrically for PM10 and PM2.5 mass concentrations and by X-ray fluorescence (XRF) for trace elements. The quartz fiber filters were analyzed for organic carbon (OC) and elemental carbon (EC) using a thermal/optical reflectance technique. Sulfur dioxide was measured using the standard wet chemistry method. Carbon monoxide was measured using an Orsat combustion analyzer. The emission rates of the R-coal combustion (in kg/hr), determined using the measured stack gas concentrations and the stack gas emission rates, were 0.74 for PM10, 0.38 for PM2.5, 20.7 for SO2, and 6.8 for CO, while those of the BB-coal combustion were 0.95 for PM10, 0.30 for PM2.5, 7.5 for SO2, and 5.3 for CO. The fuel-mass-based emission factors (in g/kg) of the R-coal, determined using the emission rates and the fuel burn rates, were 1.68 for PM10, 0.87 for PM2.5, 46.7 for SO2, and 15 for CO, while those of the BB-coal were 2.51 for PM10, 0.79 for PM2.5, 19.9 for SO2, and 14 for CO. The task-based emission factors (in g/ton steam generated) of the R-coal, determined using the fuel-mass-based emission factors and the coal/steam conversion factors, were 0.23 for PM10, 0.12 for PM2.5, 6.4 for SO2, and 2.0 for CO, while those of the BB-coal were 0.30 for PM10, 0.094 for PM2.5, 2.4 for SO2, and 1.7 for CO. PM10 and PM2.5 elemental compositions are also presented for both types of coal tested in the study.     

Chemical characterisation of PM episodes in NE Spain

The chemical composition of ambient particulate matter (PM) varies widely as a function of its main emission sources and of the chemical reactions which take place in the atmosphere. The aim of this study is to obtain the chemical profile of PM10 and PM2.5 during peak PM episodes, thus identifying the main emission sources and/or atmospheric processes which originate the PM episodes. To this end, cluster analysis was applied to a set of PM10 and PM2.5 data collected throughout 2001 in two urban and industrialised areas in NE Spain. As a result of this analysis, five clusters were identified for each site, and the interpretation of their chemical profiles lead to the identification of five types of peak PM episodes for each site: industrial, traffic and regional re-circulation episodes at both sites, plus crustal episodes in Barcelona, and peak traffic and industrial episodes (T+I) in Tarragona. Traffic episodes are characterised by daily means of 23 and 10 microg/m3 of OM+EC in Barcelona and Tarragona in PM10. Levels of secondary inorganic aerosols reach average daily means of 19 and 11 microg/m3 in Barcelona and Tarragona in PM10 during industrial episodes. High levels of sulphate (>5 microg/m3) and ozone (up to 77 microg/m3 daily mean) are good tracers of regional re-circulation episodes. During crustal episodes daily means of crustal elements reach up to 34 microg/m3 in Barcelona. Special attention has been drawn to the composition of the mineral matter during the different PM episodes.     

Development of an empirical model to estimate real-world fine particulate matter emission factors: the traffic air quality model

The purpose of the study was to quantify the impact of traffic conditions, such as free flow and congestion, on local air quality. The Borman Expressway (I-80/94) in Northwest Indiana is considered a test bed for this research because of the high volume of class 9 truck traffic traveling on it, as well as the existing and continuing installation of the Intelligent Transportation System (ITS) to improve traffic management along the highway stretch. An empirical traffic air quality (TAQ) model was developed to estimate the fine particulate matter (PM2.5) emission factors (grams per kilometer) based solely on the measured traffic parameters, namely, average speed, average acceleration, and class 9 truck density. The TAQ model has shown better predictions that matched the measured emission factor values more than the U.S. Environmental Protection Agency (EPA)-PART5 model. During congestion (defined as flow-speeds < 50 km/hr [30 mi/hr]), the TAQ model, on average, overpredicted the measured values only by a factor of 1.2, in comparison to a fourfold underprediction using the EPA-PART5 model. On the other hand, during free flow (defined as flow-speeds > 80 km/hr [50 mi/hr]), the TAQ model was conservative in that it overpredicted the measured values by 1.5-fold.     

Urban atmospheric pollution: personal exposure versus fixed monitoring station measurements

We initiated the PETER (pedestrian environmental traffic pollutant exposure research) project to investigate pedestrians' exposure to traffic related atmospheric pollutants, based on data obtained with the collaboration of selected categories of pedestrian urban workers. We investigated relations between roadside personal exposure levels of volatile aromatic hydrocarbons (including benzene) and particulate matter <10 microm (PM10) among traffic police (n = 126) and parking wardens (n = 50) working in downtown Bologna, Italy. Data were collected from workshifts throughout four 1-week periods in different seasons of 2000-2001. For benzene and PM10, comparisons were made with measurements by fixed monitoring stations, and influence of localized traffic intensity and meteorological parameters was examined. Roadside personal exposure to benzene correlated more strongly with other volatile aromatic hydrocarbons (toluene, xylenes and ethylbenzene) than with PM10. Benzene and PM10 personal exposure levels were higher than fixed monitoring station values (both p<0.0001). At multivariate analysis, benzene and PM10 data from fixed monitoring stations both correlated with meteorological variables, and were also influenced by localized traffic intensity. Plausibly because of the downtown canyon-like streets, weather conditions (during a period of drought) only marginally affected benzene personal exposure, and moderately affected PM10 personal exposure. These findings reinforce the concept that urban atmospheric pollution data from fixed air monitoring stations cannot automatically be taken as indications of roadside exposures.     

A Preliminary Study of Modeling the Air Pollution Effects fromTraffic Engineering Alternatives

Three separate mathematical models were combined to calculate the changes in carbon monoxide (CO) concentrations that might result from traffic engineering changes. The three models used were: (1) The Dynamic Highway Transportation model (DHTM) which relates traffic flow patterns to physical parameters and traffic signal characteristics of a network; (2) an emission model that predicts CO emissions from traffic flow parameters such as number of stops, idling time, etc; and (3) the APRAC-1A urban diffusion model which calculates CO concentrations from source distributions and meteorological factors. The composite model was applied to traffic in downtown Chicago for a specific set of meteorological conditions. Results are compared for two traffic signal control schemes. In those blocks where concentrations were highest, the model indicates a 20% reduction is possible through improved traffic signal controls. The model should be useful for testing other traffic control measures.     

Model for forecasting expressway fine particulate matter and carbon monoxide concentration: application of regression and neural network models

The Borman Expressway is a heavily traveled 16-mi segment of the Interstate 80/94 freeway through Northwestern Indiana. The Lake and Porter counties through which this expressway passes are designated as particulate matter < 2.5 microm (PM2.5) and ozone 8-hr standard nonattainment areas. The Purdue University air quality group has been collecting PM2.5, carbon monoxide (CO), wind speed, wind direction, pressure, and temperature data since September 1999. In this work, regression and neural network models were developed for forecasting hourly PM2.5 and CO concentrations. Time series of PM2.5 and CO concentrations, traffic data, and meteorological parameters were used for developing the neural network and regression models. The models were compared using a number of statistical quality indicators. Both models had reasonable accuracy in predicting hourly PM2.5 concentration with coefficient of determination -0.80, root mean square error (RMSE) <4 microg/m3, and index of agreement (IA) > 0.90. For CO prediction, both models showed moderate forecasting performance with a coefficient of determination -0.55, RMSE < 0.50 ppm, and IA -0.85. These models are computationally less cumbersome and require less number of predictors as compared with the deterministic models. The availability of real time PM2.5 and CO forecasts will help highway managers to identify air pollution episodic events beforehand and to determine mitigation strategies.     

Historical trends in the mass and chemical species concentrations of coarse particulate matter in the Los Angeles Basin and relation to sources and air quality regulations

To assess the impact of past, current and proposed air quality regulations on coarse particulate matter (CPM), the concentrations of CPM mass and its chemical constituents were examined in the Los Angeles Basin from 1986 to 2009 using PM data acquired from peer-reviewed journals and regulatory agency database. PM10 mass levels decreased by approximately half from 1988 to 2009 at the three sampling sites examined- located in downtown Los Angeles, Long Beach and Riverside. Annual CPM mass concentrations were calculated from the difference between daily PM10 and PM2.5 from 1999 to 2009. High CPM episodes driven by high wind speed/stagnant condition caused year-to-year fluctuations in the 99th/98th percentile CPM levels. The reductions of average CPM levels were lower than those of PM10 in the same period, therefore the decrease of PM10 level was mainly driven by reductions in the emission levels of PM2.5 (or fine) particles, as demonstrated by the higher annual reduction of average PM2.5 (0.92 microg/m3) compared with CPM (0.39 microg/m3) from 1999 to 2009 in downtown Los Angeles despite their comparable concentrations. This is further confirmed by the significant decrease of Ni, Cr, V and EC in the coarse fraction after 1995. On the other hand, the levels of several inorganic ions (sulfate, chloride and to a lesser extent nitrate) remained comparable. From 1995 to 2008, levels of Cu, a tracer of brake wear, either remained similar or decreased at a smaller rate compared with elements of combustion origins. This differential reduction of CPM components suggests that past and current regulations may have been more effective in reducing fugitive dust (Al, Fe and Si) and combustion emissions (Ni, Cr, V, and EC) rather than CPM from vehicular abrasion (Cu) and inorganic ions (NO3(-), SO4(2-) and Cl(-)) in urban areas. Implications: Limited information is currently available to provide the scientific basis for understanding the sources and physical and chemical variations of CPM, and their relations to air quality regulations and adverse health effects. This study investigates the historical trends of CPM mass and its chemical components in the Los Angeles Basin to advance our understanding on the impact of past and current air quality regulations on the coarse fraction of PM. The results of this study will aid policy makers to design more targeted regulations to control CPM sources to ensure substantial protection of public health from CPM exposure. Supplemental Materials: Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of the Air & Waste Management Association for (1) details of the sampling sites and (2) the daily concentrations of high CPM/PM10 episodes.     

Determinants of perceived air pollution annoyance and association between annoyance scores and air pollution (PM2.5, NO2) concentrations in the European EXPOLIS study

Apart from its traditionally considered objective impacts on health, air pollution can also have perceived effects, such as annoyance. The psychological effects of air pollution may often be more important to well-being than the biophysical effects. Health effects of perceived annoyance from air pollution are so far unknown. More knowledge of air pollution annoyance levels, determinants and also associations with different air pollution components is needed. In the European air pollution exposure study, EXPOLIS, the air pollution annoyance as perceived at home, workplace and in traffic were surveyed among other study objectives. Overall 1736 randomly drawn 25–55-yr-old subjects participated in six cities (Athens, Basel, Milan, Oxford, Prague and Helsinki). Levels and predictors of individual perceived annoyances from air pollution were assessed. Instead of the usual air pollution concentrations at fixed monitoring sites, this paper compares the measured microenvironment concentrations and personal exposures of PM_2.5 and NO₂ to the perceived annoyance levels. A considerable proportion of the adults surveyed was annoyed by air pollution. Female gender, self-reported respiratory symptoms, downtown living and self-reported sensitivity to air pollution were directly associated with high air pollution annoyance score while in traffic, but smoking status, age or education level were not significantly associated. Population level annoyance averages correlated with the city average exposure levels of PM_2.5 and NO₂. A high correlation was observed between the personal 48-h PM_2.5 exposure and perceived annoyance at home as well as between the mean annoyance at work and both the average work indoor PM_2.5 and the personal work time PM_2.5 exposure. With the other significant determinants (gender, city code, home location) and home outdoor levels the model explained 14% (PM_2.5) and 19% (NO₂) of the variation in perceived air pollution annoyance in traffic. Compared to Helsinki, in Basel and Prague the adult participants were more annoyed by air pollution while in traffic even after taking the current home outdoor PM_2.5 and NO₂ levels into account.     

Determining gaseous emission factors and driver's particle exposures during traffic congestion by vehicle-following measurement techniques

Vehicle gaseous emissions (NO, CO, CO2, and hydrocarbon [HC]) and driver's particle exposures (particulate matter < 1 microm [PM1], < 2.5 microm [PM2.5], and < 10 microm [PM10]) were measured using a mobile laboratory to follow a wide variety of vehicles during very heavy traffic congestion in Macao, Special Administrative Region, People's Republic of China, an urban area having one of the highest population densities in the world. The measurements were taken with high time resolution so that fluctuations in the emissions can be seen readily during vehicle acceleration, cruising, deceleration, and idling. The tests were conducted in close proximity to the vehicles, with the inlet of a five-gas analyzer mounted on the front bumper of the mobile laboratory, and the distance between the vehicles was usually within several meters. To measure the driver's particle exposures, the inlets of the particle analyzers were mounted at the height of the driver's breathing position in the mobile laboratory, with the driver's window open. A total of 178 and 113 vehicles were followed individually to determine the gaseous emission factor and the driver's particle exposures, respectively, for motorcycle, passenger car, taxi, truck, and bus. The gaseous emission factors were used to model the roadside air quality, and good correlations between the modeled and monitored CO, NO2, and nitrogen oxide (NO(x)) verified the reliability of the experiments. Compared with petrol passenger cars and petrol trucks, diesel taxies and diesel trucks emitted less CO but more NO(x). The impact of urban canyons is shown to cause a significant increase in the PM1 peak. The background concentrations contributed a significant amount of the driver's particle exposures.     

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.     

Spatial and temporal variations of PM(2.5) concentration and composition throughout an urban area with high freeway density-the Greater Cincinnati study

The PM(2.5) concentration and its elemental composition were measured in the Cincinnati metropolitan area, which is characterized by intense highway traffic. The spatial and temporal variations were investigated for various chemical elements that contributed to the PM(2.5) fraction during a 1-year-long measurement campaign (December 2001-November 2002). The ambient aerosol monitoring was performed in 11 locations around the city during nine measurement cycles. During each cycle, four Harvard-type impactors were operating in parallel in specific locations to explore various factors affecting the PM(2.5) elemental concentrations. The sampling was performed during business days, thus assuring traffic uniformity. The 24-h PM(2.5) samples were collected on Teflon and quartz filters. Teflon filters were analyzed by X-ray fluorescence (XRF) analysis while quartz filters were analyzed by thermal-optical transmittance (TOT) analysis. In addition to PM(2.5) measurements, particle size-selective sampling was performed in two cycles using micro-orifice uniform deposit impactor; the collected fractionated deposits were analyzed by XRF. It was found that PM(2.5) concentration ranged from 6.70 to 48.3 mug m(-3) and had low spatial variation (median coefficient of variation, CV=11.3%). The elemental concentrations demonstrated high spatial variation, with the median CV ranged from 38.2% for Fe to 68.7% for Ni. For traffic-related trace metals, the highest concentration was detected in the city center site, which was close to a major highway. The particle size selective measurement revealed that mass concentration of the trace metals, such as Zn, Pb, Ni, as well as that of sulfur reach their peak values in the particle size range of 0.32-1.0 mum. Meteorological parameters and traffic intensity were not found to have a significant influence on the PM(2.5) elemental concentrations.     

Evaluations of the chemical mass balance method for determining contributions of gasoline and diesel exhaust to ambient carbonaceous aerosols

The US. Department of Energy Gasoline/Diesel PM Split Study was conducted to assess the sources of uncertainties in using an organic compound-based chemical mass balance receptor model to quantify the relative contributions of emissions from gasoline (or spark ignition [SI]) and diesel (or compression ignition [CI]) engines to ambient concentrations of fine particulate matter (PM2.5) in California's South Coast Air Basin (SOCAB). In this study, several groups worked cooperatively on source and ambient sample collection and quality assurance aspects of the study but worked independently to perform chemical analysis and source apportionment. Ambient sampling included daily 24-hr PM2.5 samples at two air quality-monitoring stations, several regional urban locations, and along freeway routes and surface streets with varying proportions of automobile and truck traffic. Diesel exhaust was the dominant source of total carbon (TC) and elemental carbon (EC) at the Azusa and downtown Los Angeles, CA, monitoring sites, but samples from the central part of the air basin showed nearly equal apportionments of CI and SI. CI apportionments to TC were mainly dependent on EC, which was sensitive to the analytical method used. Weekday contributions of CI exhaust were higher for Interagency Monitoring of Protected Visual Environments (IMPROVE; 41+/-3.7%) than Speciation Trends Network (32+/-2.4%). EC had little effect on SI apportionment. SI apportionments were most sensitive to higher molecular weight polycyclic aromatic hydrocarbons (indeno[123-cd]pyrene, benzo(ghi)perylene, and coronene) and several steranes and hopanes, which were associated mainly with high emitters. Apportionments were also sensitive to choice of source profiles. CI contributions varied from 30% to 60% of TC when using individual source profiles rather than the composites used in the final apportionments. The apportionment of SI vehicles varied from 1% to 12% of TC depending on the specific profile that was used. Up to 70% of organic carbon (OC) in the ambient samples collected at the two fixed monitoring sites could not be apportioned to directly emitted PM emissions.     

Regional Monitoring of Ambient Air Carbon Monoxide in Fairbanks,Alaska

A method employing the timed fill of Mylar bags was used to obtain average carbon monoxide concentration values for ten locations in the Fairbanks, Alaska, area. The method is shown to be accurate, reliable, and inexpensive. The correlation coefficient between the bag sampling method and a continuous carbon monoxide analyzer was 0.945; correlation of carbon monoxide data from several locations to a reference in downtown Fairbanks showed a general decrease with distance from the reference analyzer. Analysis of data from Fairbanks shows that carbon monoxide concentrations during the winter months may have to be reduced 50-75% to achieve the State and National Ambient Air Quality Standards; the high levels of carbon monoxide are the result of emissions from automobile traffic during periods of severe inversion in the Fairbanks basin.     

Effects of instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, Georgia

Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO2, CO, NOx, and O3) and fine particulate matter ([PM2.5] PM2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7-40% of the temporal variation in the daily pollutant measures and was largest for the PM2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO2, CO, NOx, and EC). Population-weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O3 to 70% of the temporal variation for SO2 and EC. Wind     

PM10 and PM2.5 source apportionment in the Barcelona Metropolitan area, Catalonia, Spain

Levels of total suspended particles, PM10, PM2.5 and PM1 were continuously monitored at an urban kerbside in the Metropolitan area of Barcelona from June 1999 to June 2000. The results show that hourly levels of PM2.5 and PM1 are consistent with the daily cycle of gaseous pollutants emitted by traffic, whereas TSP and PM10 do not follow the same trend, at least in the diurnal period. The PM2.5/PM10 ratio is dependent on the traffic emissions, whereas additional contribution sources for the >10 μm fraction must be taken into account in the diurnal period. Different PM10 and PM2.5 source apportionment techniques were compared. A methodology based on the chemical determination of 83% of both PM10 and PM2.5 masses allowed us to quantify the marine (4% in PM10 and <1% in PM2.5), crustal (26% in PM10 and 8% in PM2.5) and anthropogenic (54% in PM10 and 73% in PM2.5) loads. Peaks of crustal contribution to PM10 (up to 44% of the PM10 mass) were recorded under Saharan air mass intrusions. A different seasonal trend was observed for levels of sulphate and nitrate, probably as a consequence of the different thermodynamic behaviour of these PM species and the higher summer oxidation rate of SO₂.     

Particulate air pollution from combustion and construction in coastal and urban areas of China

In China, the areas that are undergoing rapid urban growth are faced with increasingly more complicated air pollution problems. Sources of air pollution need to be identified and their contributions quantified. In this study, PM2.5 (particulate matter with aerodynamic diameters < or =2.5 microm), PM2.5-10 (particulate matter with aerodynamic diameters 2.5-10 microm), organic carbon (OC), and elemental carbon (EC) concentrations were measured from April to July 2009 at four selected areas in Xiamen (the downtown area, an industrial park, a suburb, and one remote site). The contributions of carbonaceous aerosols to PM2.5 and PM2.5-10 were 20-30% and 10-20%, respectively, indicating that finer particles contained more carbonaceous aerosols. The EC concentrations in PM2.5 at the downtown, industrial, suburb, and remote sites were 2.16 +/- 0.61, 2.05 +/- 0.45, 1.69 +/- 0.54, and 0.65 +/- 0.43 microg m-3, respectively, showing a decrease from the urban and industrial hotspots to the surrounding areas. These data show that carbonaceous aerosols emitted from the combustion of fossil fuels in urban and industrial hotspots influence air quality at the regional scale. Higher levels of PM2.5 and PM2.5-10 were observed at the suburb site compared to the urban and industrial sites. Peak EC concentrations in PM2.5 were observed during the morning and evening rush hours. However, peak PM2.5 levels at the suburb site were observed around noon, which coincides with construction work hours, instead of the morning and evening rush hours when emissions from combustion dominated. These findings indicate that both fuel combustion and construction have exacerbated air pollution in coastal and urban areas in China.     

Urban CO Concentrations and Vehicle Emissions

The effect of the general growth of CO vehicular emissions in urban areas on the CAMP station measurements in downtown areas, where vehicular traffic is saturated is considered. With the assumption that the street-level CO concentration is derived from the sum of an urban background term and a local street-effect term, the urban background CO concentration is computed with a diffusion model by introducing a simple area source distribution. The local street-effect term is taken to be constant at a saturation emission level corresponding to a saturation traffic density when the emission per vehicle-mile and meteorological conditions are fixed. The present analysis indicates that the local street-effect term, AC, has a major role in determining street-level concentrations for pollutants, such as CO, whose air quality standard is based on maximum concentrations with averaging times of 1 hour and 8 hours. The relevance of this analysis to the abatement requirements of the Clean Air Amendments and to the driving cycle adopted is discussed.     

Primary and secondary carbonaceous species in PM2.5 samples in Milan (Italy)

Seasonal elemental carbon (EC) and organic carbon (OC) concentration levels in PM2.5 samples collected in Milan (Italy) are presented and discussed, enriching the world-wide database of carbonaceous species in fine particulate matter (PM). High-volume PM2.5 sampling campaigns were performed from August 2002 through December 2003 in downtown Milan at an urban background site. Compared to worldwide average concentrations, in Milan warm-season OC and both warm- and cold-season EC are relatively low; conversely, cold-season OC concentrations are rather high. Consequently, high values for the OC/EC ratio are observed, especially in the winter period. The relation between OC/EC ratio values and wind direction is investigated, pointing out that the highest ratios are associated to winds blowing from those nearby areas where wood consumption for domestic heating is larger. Information on the OC partitioning between its primary and secondary fraction are derived by means of the EC-tracer method and principal component analysis. In the warm-season, OC is mainly of secondary origin, secondary organic aerosol (SOA) accounting for about 84% of the particulate organic matter and 25–28% of the PM2.5 mass. For the cold season the full application of the EC-tracer method was not possible and the primary organic aerosol deriving from traffic could only be estimated. However, principal component analysis (PCA) suggest a prevailing primary origin for OC, thus raising the attention on space heating emissions, and on wood combustion in particular, for air quality control. The role of traffic emissions on PM2.5 concentration levels, as a primary source, are also assessed: EC and primary organic matter from traffic account for a warm-season 30% and a cold-season 7% of the total carbon in PM2.5, that is for about 10% and 6% of PM2.5 mass, respectively. This latter small primary contribution estimated for the cold-season points out that stationary sources, which were not thought to play a significant role on PM concentration levels, may conversely be as much responsible for ambient particulate pollution.     

Emission factors for high-emitting vehicles based on on-road measurements of individual vehicle exhaust with a mobile measurement platform

Fuel-based emission factors for 143 light-duty gasoline vehicles (LDGVs) and 93 heavy-duty diesel trucks (HDDTs) were measured in Wilmington, CA using a zero-emission mobile measurement platform (MMP). The frequency distributions of emission factors of carbon monoxide (CO), nitrogen oxides (NO(x)), and particle mass with aerodynamic diameter below 2.5 microm (PM2.5) varied widely, whereas the average of the individual vehicle emission factors were comparable to those reported in previous tunnel and remote sensing studies as well as the predictions by Emission Factors (EMFAC) 2007 mobile source emission model for Los Angeles County. Variation in emissions due to different driving modes (idle, low- and high-speed acceleration, low- and high-speed cruise) was found to be relatively small in comparison to intervehicle variability and did not appear to interfere with the identification of high emitters, defined as the vehicles whose emissions were more than 5 times the fleet-average values. Using this definition, approximately 5% of the LDGVs and HDDTs measured were high emitters. Among the 143 LDGVs, the average emission factors of NO(x), black carbon (BC), PM2.5, and ultrafine particle (UFP) would be reduced by 34%, 39%, 44%, and 31%, respectively, by removing the highest 5% of emitting vehicles, whereas CO emission factor would be reduced by 50%. The emission distributions of the 93 HDDTs measured were even more skewed: approximately half of the NO(x) and CO fleet-average emission factors and more than 60% of PM2.5, UFP, and BC fleet-average emission factors would be reduced by eliminating the highest-emitting 5% HDDTs. Furthermore, high emissions of BC, PM2.5, and NO(x) tended to cluster among the same vehicles.     

Spatiotemporal Features of Severe Air Pollution in Northern Taiwan (8 pp)

Background, Aims and Scope This research attempted to identify the dominant factors simultaneously affecting the airborne concentrations of five air pollutants with principal component analysis and to determine the meteorologically related parameters that cause severe air-pollution events. According to the definition of subPSI and PSI values through the U.S. EPA, the historical raw data of five criteria air pollutants, SO2, CO, O3, PM10 and NO2, were calculated as daily subPSI values. In addition to the airborne concentrations, this study simultaneous collected the surface meteorological parameters of the Taipei meteorological station, established by the Central Weather Bureau. Methods Principal component analysis was conducted to screen severe air pollution scenarios for five air pollutants: SO2, CO, O3, PM10 and NO2. The concentrations of various air pollutants measured at 17 air-quality stations in northern Taiwan from 1995 to 2001 were transformed into daily subPSI values. The correlation analysis of the five air pollutants and four meteorological parameters (wind speed, temperature, mixing height and ventilation rate) were included in this research. After screening severe air pollution scenarios, this study recognized the synoptic patterns easily causing the severe air-pollution events. Results and Discussion Analytical results showed that the eigenvalues of the first two principal components for SO2, CO, O3, PM10 and NO2 were greater than 1. The first component of five air pollutants explained 64, 64, 67, 76 and 63% of subPSI variance for SO2, CO, O3, PM10 and NO2, respectively. Only the correlation coefficient of NO2 and CO had statistically significant positive values (0.82); other pollutant pairs presented medium (0.4 to 0.7) or low (0 to 0.4) positive values. The correlation coefficients for air pollutants and three meteorological parameters (wind speed, mixing height and ventilation index) were medium or low negative values. In northern Taiwan, spring was most likely induced high concentrations and the component scores of the first component for SO2, CO, PM10 and NO2; summer was the worst season that caused high O3 episodes. Consequently, the analytical results of factor loadings for the first principal component and emission inventory of various sources revealed that mobile sources were dominant factors affecting ambient air quality in northern Taiwan. Conclusion According to the results of principal component analysis for the five air pollutants, the first two of 17 components were cited as major factors and explained 71% of subPSI variance. Based on the inventory of NOx emissions and the isopleth diagram of factor loading for the first component, mobile sources in the southwest Taipei City accounted for the highest factor loading values and emission inventory values. Synoptic analysis and principal component analysis demonstrated that three types of weather patterns (high-pressure recirculation, prefrontal warm sector and the southwesterly wind system) easily caused the severe air-pollution scenarios. In summary, if severe air-pollution days occurred, the average meteorological parameters experienced adverse conditions for diffusing air pollutants; that is, the average values of wind speed, mixing height and ventilation index were lower than 2.1 ms-1, 360 m and 800 m2s-1, respectively. If one of the three synoptic patterns were to occur in combination with adverse meteorological conditions, severe air-pollution events would be developed. Recommendation and Outlook By utilizing synoptic patterns, this work found three weather systems easily caused severe air-pollution events over northern Taiwan. Analytical results showed, respectively, the wind speed and mixing height were less than 2.1 m/s and 360 m during severe air-pollution events.