Pattern Recognition Based Software for Oil Spills Identification by Gas-Chromatography and IR Spectrophotometry

For environmental control purposes, floating oil spills in harbours, off shore areas and their sources must often be identified. Pattern recognition, applied to JR spectrophotometric data (600-2000 cm m 1 range), and to chromatographic data ( n -alkanes) for the spill and various suspected sources such as oil and fuels from ships bunkers and harbour installations, can lead to definite conclusions; particularly after artificial weathering formula are used. The software application provides quick and accurate identification of the pollution source. The identification algorithm has a learning stage in which the user creates a minimal database. This database has a tree structure with classes (fuels, crude, etc.) and members representing samples from already known sources. A sample contains JR and chromatographic data and information of the originating source. A larger database means more knowledge, which conveys a better identification. When the origin of an unknown sample is searched for, the software looks for the best match through the database and displays the results in two lists; sorted by calculated similarity. One list displays the classes in which the unknown sample could be included and the other displays the possible sources. An extra check can be done by visual inspection of the overlapped graphics (unknown sample and each of the identified sources).     

HCB,PCB, PCDD and PCDF emissions from ships

Since current estimates of hexachlorobenzene (HCB), polychlorinated biphenyls (PCB), dioxins (PCDD) and furans (PCDF) from ships are based on a relatively limited and old data set, an update of these emission factors has been outlined as a target towards improved Swedish emission inventories. Consequently, a comprehensive study was undertaken focusing on these emissions from three different ships during December 2003 to March 2004. Analyses were performed on 12 exhaust samples, three fuel oil samples and three lubricating oil samples from a representative selection of diesel engine models, fuel types and during different “real-world” operating conditions.The determined emissions corresponded reasonably well with previous measurements. The data suggest however that previous PCDD/PCDF emission factors are somewhat higher than those measured here. As expected the greatest emissions were observed during main engine start-up periods and for engines using heavier fuel oils. Total emissions for 2002, using revised emission factors, have been calculated based on Swedish sold marine fuels and also for geographical areas of national importance. In terms of their toxic equivalence (WHO-TEQ), the PCDD/PCDF emissions from ships using Swedish fuels are small (0.37–0.85 g TEQ) in comparison to recent estimates for the national total (ca. 45 g TEQ). Emissions from other land-based diesel engines (road vehicles, off-road machinery, military vehicles and locomotives) are estimated to contribute a further 0.18–0.42 g TEQ. Similarly, HCB and PCB emissions from these sources are small compared to 1995 national emission inventories.     

Sources and concentration of nanoparticles (<10 nm diameter) in the urban atmosphere

Whilst limited information on particle size distributions and number concentrations in cities is available, very few data on the very smallest of particles, nanoparticles, have been recorded. Measurements in this study show that road traffic and stationary combustion sources generate a significant number of nanoparticles of diameter <10 nm. Measurements at the roadside (4 m from the kerb) and downwind from the traffic (more than 25 m from the kerb) show that nanoparticles (<10 nm diameter) accounted for more than 36–44% of the total particle number concentrations. Measurements designed to sample the plume of individual vehicles showed that both a diesel- and a petrol-fuelled vehicle generated nanoparticles (<10 nm diameter). The fraction of nanoparticles was even greater in a plume 350 m downwind of a stationary combustion source. On a few occasions, a temporal association between nanoparticles in the size range 3–7 nm and solar radiation was observed in urban background air at times when no other local sources were influential, which suggests that homogeneous nucleation can also be an important source of particles in the urban atmosphere.     

Using Multiple Criteria for Fingerprinting Unknown Oil Samples Having Very Similar Chemical Composition

This paper describes a case study in which a multi-criterion approach was used to fingerprinting and identifying mystery oil samples. Three unknown oil samples were received from Quebec on March 28, 2001 for chemical analysis. The main purpose of this analysis was to determine the nature and the type of the products, detailed hydrocarbon composition of the samples, and whether these samples came from the same source. The samples were analyzed by gas chromatography with a flame ionization detector (GC-FID) and by gas chromatography coupled with mass spectrometry (GC-MS). Hydrocarbon distribution patterns of unknown oils were recognized. Multiple suites of analytes were quantified and compared. A variety of diagnostic ratios of “source-specific marker” compounds for interpreting chemical data were further determined and analyzed. The chemical fingerprinting results reveal the following: (1) These three oils are most likely a hydraulic-fluid type oil. (2) These three oils are very “pure”, largely composed of saturated hydrocarbons with the total aromatics being only 4–10% of the TPH. (3) The oils are a mixture of two different hydraulic fluids. There is no clear sign indicating they had been weathered. (4) The PAH concentrations are extremely low (<10 μg/g oil) in the oil samples, while the biomarker concentration are unusually high (4700–5500 μg/g oil). (5) Three major unknown compounds in the oil samples were positively identified. They are antioxidant compounds added to oils. (6) Samples 2996 and 2997 are identical and come from the same source. (7) The sample 2998 has group hydrocarbon compositions (including the GC traces, TPH, and total saturates) very similar to samples 2996 and 2997. But, it is not identical in chemical composition to samples 2996 and 2997, and they do not come from the same source.     

Receptor modeling of source apportionment of Hong Kong aerosols and the implication of urban and regional contribution

Understanding the spatial–temporal variations of source apportionment of PM_2.5 is critical to the effective control of particulate pollution. In this study, two one-year studies of PM_2.5 composition were conducted at three contrasting sites in Hong Kong from November 2000 to October 2001, and from November 2004 to October 2005, respectively. A receptor model, principal component analysis (PCA) with absolute principal component scores (APCS) technique, was applied to the PM_2.5 data for the identification and quantification of pollution sources at the rural, urban and roadside sites. The receptor modeling results identified that the major sources of PM_2.5 in Hong Kong were vehicular emissions/road erosion, secondary sulfate, residual oil combustion, soil suspension and sea salt regardless of sampling sites and sampling periods. The secondary sulfate aerosols made the most significant contribution to the PM_2.5 composition at the rural (HT) (44 ± 3%, mean ± 1σ standard error) and urban (TW) (28 ± 2%) sites, followed by vehicular emission (20 ± 3% for HT and 23 ± 4% for TW) and residual oil combustion (17 ± 2% for HT and 19 ± 1% for TW). However, at the roadside site (MK), vehicular emissions especially diesel vehicle emissions were the major source of PM_2.5 composition (33 ± 1% for diesel vehicle plus 18 ± 2% for other vehicles), followed by secondary sulfate aerosols (24 ± 1%). We found that the contribution of residual oil combustion at both urban and rural sites was much higher than that at the roadside site (2 ± 0.4%), perhaps due to the marine vessel activities of the container terminal near the urban site and close distance of pathway for the marine vessels to the rural site. The large contribution of secondary sulfate aerosols at all the three sites reflected the wide influence of regional pollution. With regard to the temporal trend, the contributions of vehicular emission and secondary sulfate to PM_2.5 showed higher autumn and winter values and lower summer levels at all the sites, particularly for the background site, suggesting that the seasonal variation of source apportionment in Hong Kong was mainly affected by the synoptic meteorological conditions and the long-range transport. Analysis of annual patterns indicated that the contribution of vehicular emission at the roadside was significantly reduced from 2000/01 to 2004/05 (p < 0.05, two-tail), especially the diesel vehicular emission (p < 0.001, two-tail). This is likely attributed to the implementation of the vehicular emission control programs with the tightening of diesel fuel contents and vehicular emission standards over these years by the Hong Kong government. In contrast, the contribution of secondary sulfate was remarkably increased from 2001 to 2005 (p < 0.001, two-tail), indicating a significant growth in regional sulfate pollution over the years.     

Global atmospheric emission inventory of polycyclic aromatic hydrocarbons (PAHs) for 2004

The global atmospheric emissions of the 16 polycyclic aromatic hydrocarbons (PAHs) listed as the US EPA priority pollutants were estimated using reported emission activity and emission factor data for the reference year 2004. A database for emission factors was compiled, and their geometric means and frequency distributions applied for emission calculation and uncertainty analysis, respectively. The results for 37 countries were compared with other PAH emission inventories. It was estimated that the total global atmospheric emission of these 16 PAHs in 2004 was 520 giga grams per year (Gg y^?1) with biofuel (56.7%), wildfire (17.0%) and consumer product usage (6.9%) as the major sources, and China (114 Gg y^?1), India (90 Gg y^?1) and United States (32 Gg y^?1) were the top three countries with the highest PAH emissions. The PAH sources in the individual countries varied remarkably. For example, biofuel burning was the dominant PAH source in India, wildfire emissions were the dominant PAH source in Brazil, while consumer products were the major PAH emission source in the United States. In China, in addition to biomass combustion, coke ovens were a significant source of PAHs. Globally, benzo(a)pyrene accounted for 0.05% to 2.08% of the total PAH emission, with developing countries accounting for the higher percentages. The PAH emission density varied dramatically from 0.0013 kg km^?2 y in the Falkland Islands to 360 kg km^?2 y in Singapore with a global mean value of 3.98 kg km^?2 y. The atmospheric emission of PAHs was positively correlated to the country's gross domestic product and negatively correlated with average income. Finally, a linear bivariate regression model was developed to explain the global PAH emission data.     

Source apportionment of ambient PM2.5 at five spanish centres of the european community respiratory health survey (ECRHS II)

Fine particulate matter (PM_2.5) was sampled at 5 Spanish locations during the European Community Respiratory Health Survey II (ECRHS II). In an attempt to identify and quantify PM_2.5 sources, source contribution analysis by principal component analysis (PCA) was performed on five datasets containing elemental composition of PM_2.5 analysed by ED-XRF. A total of 4–5 factors were identified at each site, three of them being common to all sites (interpreted as traffic, mineral and secondary aerosols) whereas industrial sources were site-specific. Sea-salt was identified as independent source at all coastal locations except for Barcelona (where it was clustered with secondary aerosols). Despite their typically dominant coarse grain-size distribution, mineral and marine aerosols were clearly observed in PM_2.5. Multi-linear regression analysis (MLRA) was applied to the data, showing that traffic was the main source of PM_2.5 at the five sites (39–53% of PM_2.5, 5.1–12.0 μg m⁻³), while regional-scale secondary aerosols accounted for 14–34% of PM_2.5 (2.6–4.5 μg m⁻³), mineral matter for 13–31% (2.4–4.6 μg m⁻³) and sea-salt made up 3–7% of the PM_2.5 mass (0.4–1.3 μg m⁻³). Consequently, despite regional and climatic variability throughout Spain, the same four main PM_2.5 emission sources were identified at all the study sites and the differences between the relative contributions of each of these sources varied at most 20%. This would corroborate PM_2.5 as a useful parameter for health studies and environmental policy-making, owing to the fact that it is not as subject to the influence of micro-sitting as other parameters such as PM₁₀. African dust inputs were observed in the mineral source, adding on average 4–11 μg m⁻³ to the PM_2.5 daily mean during dust outbreaks. On average, levels of Al, Si, Ti and Fe during African episodes were higher by a factor of 2–8 with respect to non-African days, whereas levels of local pollutants (absorption coefficient, S, Pb, Cl) showed smaller variations (factor of 0.5–2).     

Size distribution and anthropogenic sources apportionment of airborne trace metals in Kanazawa,Japan

Aerosol samples were collected from Kanazawa, Japan to examine the size distribution of 12 elements and to identify the major sources of anthropogenic elements. Key emission sources were identified and, concentrations contributed from individual sources were estimated as well. Concentrations of elements V, Ca, Cd, Fe, Ba, Mg, Mn, Pb, Sr, Zn, Co and Cu in aerosols were determined with ICP-MS. The results showed that Ca, Mg, Sr, Mn, Co and Fe were mainly associated with coarse particles (>2.1 μm), primarily from natural sources. In contrast, the elements Zn, Ba, Cd, V, Pb and Cu dominated in fine aerosol particles (<2.1 μm), implying that the anthropogenic origin is the dominant source. Results of the factor analysis on elements with high EF_Crust values (>10) showed that emissions from waste combustion in incinerators, oil combustion (involving waste oil burning and oil combustion in both incinerators and electricity generation plants), as well as coal combustion in electricity generation plants were major contributors of anthropogenic metals in the ambient atmosphere in Kanazawa. Quantitatively estimated sum of mean concentrations of anthropogenic elements from the key sources were in good agreement with the observed values. Results of this study elucidate the need for making pollution control strategy in this area.     

Hydrocarbon emission fingerprints from contemporary vehicle/engine technologies with conventional and new fuels

The present paper presents results from the analysis of 29 individual C₂–C₉ hydrocarbons (HCs) specified in the European Commission Ozone Directive. The 29 HCs are measured in exhaust from common, contemporary vehicle/engine/fuel technologies for which very little or no data is available in the literature. The obtained HC emission fingerprints are compared with fingerprints deriving from technologies that are being phased out in Europe. Based on the total of 138 emission tests, thirteen type-specific fingerprints are extracted (Mean ± SD percentage contributions from individual HCs to the total mass of the 29 HCs), essential for receptor modelling source apportionment. The different types represent exhaust from Euro3 and Euro4 light-duty (LD) diesel and petrol-vehicles, Euro3 heavy-duty (HD) diesel exhaust, and exhaust from 2-stroke preEuro, Euro1 and Euro2 mopeds. The fuels comprise liquefied petroleum gas, petrol/ethanol blends (0–85% ethanol), and mineral diesel in various blends (0–100%) with fatty acid methyl esters, rapeseed methyl esters palm oil methyl esters, soybean oil methyl or sunflower oil methyl esters. Type-specific tracer compounds (markers) are identified for the various vehicle/engine/fuel technologies.An important finding is an insignificant effect on the HC fingerprints of varying the test driving cycle, indicating that combining HC fingerprints from different emission studies for receptor modelling purposes would be a robust approach.The obtained results are discussed in the context of atmospheric ozone formation and health implications from emissions (mg km^?1 for LD and mopeds and mg kW h^?1 for HD, all normalised to fuel consumption: mg dm^?3 fuel) of the harmful HCs, benzene and 1,3-butadiene.Another important finding is a strong linear correlation of the regulated “total” hydrocarbon emissions (tot-HC) with the ozone formation potential of the 29 HCs (ΣPO₃ = (1.66 ± 0.04) × tot-RH; r² = 0.93). Tot-HC is routinely monitored in emission control laboratories, whereas C₂–C₉ are not. The revealed strong correlations broadens the usability of data from vehicle emission control laboratories and facilitates the comparison of the ozone formation potential of HCs in exhaust from of old and new vehicle/engine/fuel technologies.     

Source apportionment of time and size resolved ambient particulate matter measured with a rotating DRUM impactor

Ambient particulate chemical composition data acquired from samples collected using a three-stage Davis Rotating-drum Universal-size-cut Monitoring (DRUM) impactor in Detroit, MI, between February and April 2002 were analyzed through the application of a three-way factor analysis model. PM_2.5 (particulate matter ⩽2.5 μm in aerodynamic diameter) was collected by a DRUM impactor with 3-h time resolution and three size modes (2.5 μm>D_p>1.15 μm, 1.15 μm>D_p>0.34 μm and 0.34 μm>D_p>0.1 μm). A novel three-way factor analysis model was applied to these data where the source profiles are a three-way array of size, composition and source while the contributions are a matrix of sample by source. Nine factors were identified: road salt, industrial (Fe+Zn), cloud processed sulfate, two types of metal works, road dust, local sulfate source, sulfur with dust, and homogeneously formed sulfate. Road salt had high concentrations of Na and Cl. Mixed industrial emissions are characterized by Fe and Zn. The cloud processed sulfate had a high concentration of S in the intermediate size mode. The first metal works represented by Fe in all three size modes and by Zn, Ti, Cu, and Mn. The second included a high concentration of small size particle sulfur with intermediate size Fe, Zn, Al, Si, and Ca. Road dust contained Na, Al, Si, S, K, and Fe in the large size mode. The local and homogeneous sulfate factors show high concentrations of S in the smallest size mode, but different time series behavior in their contributions. Sulfur with dust is characterized by S and a mix of Na, Mg, Al, Si, K, Ca, Ti, and Fe from the medium and large size modes. This study shows that the utilization of time and size resolved DRUM data can assist in the identification of sources and atmospheric processes leading to the observed ambient concentrations.     

Emissions of volatile fatty acids from feed at dairy facilities

Recent studies suggest that dairy operations may be a major source of non-methane volatile organic compounds in dairy-intensive regions such as Central California, with short chain carboxylic acids (volatile fatty acids or VFAs) as the major components. Emissions of four VFAs (acetic acid, propanoic acid, butanoic acid and hexanoic acid) were measured from two feed sources (silage and total mixed rations (TMR)) at six Central California Dairies over a fifteen-month period. Measurements were made using a combination of flux chambers, solid phase micro-extraction fibers coupled to gas chromatography mass spectrometry (SPME/GC–MS) and infra-red photoaccoustic detection (IR-PAD for acetic acid only). The relationship between acetic acid emissions, source surface temperature and four sample composition factors (acetic acid content, ammonia-nitrogen content, water content and pH) was also investigated. As observed previously, acetic acid dominates the VFA emissions. Fluxes measured by IR-PAD were systematically lower than SPME/GC–MS measurements by a factor of two. High signals in field blanks prevented emissions from animal waste sources (flush lane, bedding, open lot) from being quantified. Acetic acid emissions from feed sources are positively correlated with surface temperature and acetic acid content. The measurements were used to derive a relationship between surface temperature, acetic acid content and the acetic acid flux. The equation derived from SPME/GC–MS measurements predicts estimated annual average acetic acid emissions of (0.7 + 1/?0.4) g m^?2 h^?1 from silage and (0.2 + 0.3/?0.1) g m^?2 h^?1 from TMR using annually averaged acetic acid content and meteorological data. However, during the summer months, fluxes may be several times higher than these values.     

Heavy metal distribution in dust from elementary schools in Hermosillo,Sonora, México

The city of Hermosillo, Sonora in northern Mexico was investigated for its heavy metals content. Samples of sedimented dust in roofs from 25 elementary schools were analyzed for their contents of Ni, Cr, Zn, Cd, Co, Ba, V, Pb, Fe and Cu after digestion with nitric acid. The results of the analysis were used to determine spatial distribution and magnitude of heavy metals pollution. The results of this study reveal that heavy metals distribution is different in two areas of the city. The southern area contains higher concentrations of heavy metals than the northcentral area. The mean level of Cd in exterior dust is 5.65 mg kg⁻¹ in the southern area whereas the mean level of Cd is 2.83 mg kg⁻¹ in the northcentral area. Elevated concentrations of Zn (2012 mg kg⁻¹), Pb (101.88 mg kg⁻¹), Cr (38.13 mg kg⁻¹) and Cd (28.38 mg kg⁻¹) in roof dust were found in samples located near industrial areas. Principal component analysis (PCA) was applied to the data matrix to evaluate the analytical results and to identify the possible pollution sources of metals. PCA shows two main sources: (1) Pb, Cd, Cr and Zn are mainly derived from industrial sources, combined with traffic sources; (2) Fe, Co and Ba are mainly derived from natural sources. V and Ni are highly correlated and possibly related to fuel combustion processes. Enrichment factors were calculated, which in turn further confirms the source identification. Ba and Co are dominantly crustal. Anthropogenically added Cd, Pb, Zn and Cr show maximum enrichment relative to the upper continental crustal component. The distribution of the heavy metals in dust does not seem to be controlled only by the topography of the city, but also by the location of the emission sources.     

Identification of sources affecting fog formation using receptor modeling approaches and inventory estimates of sectoral emissions

Positive matrix factorization (PMF) was used to identify factors affecting fog formation in Kanpur during the ISRO-GBP land campaign-II (LC-II) in December 2004. PMF predicted factors were validated by contrasting the emission strength of sources in the foggy and clear periods, using a combination of potential source contribution function (PSCF) analysis and quantitative emission inventory information. A time series aerosol chemical data set of 29 days and 12 species was decomposed to identify 4-factors: Secondary species, Biomass burning, Dust and Sea salt. PMF predicted particle mass with a satisfactory goodness-of-fit (slope of 0.83 ± 0.17 and R² of 0.8), and strong species within 11–12% relative standard deviation. Mean contributions of anthropogenic factors were significantly higher during the foggy period for secondary species (2.9 ± 0.3) and biomass burning (1.2 ± 0.09) compared to the clear period. Local sources contributing to aerosols that mediated fog events at Kanpur, based on emissions in a 200 km × 200 km area around Kanpur city were thermal power plants and transportation (SO₂) and biofuel combustion (BC and OM). Regional scale sources influencing emissions during the foggy period, in probable source regions identified by PSCF included thermal power plants, transportation, brick kilns and biofuel combustion. While biofuel combustion and transportation are distributed area sources, individual point sources include coal-fired thermal power plants located in Aligarh, Delhi, Ghaziabad, Jhansi, Kanpur, Rae Bareli and Rupnagar and brick kilns located in Allahabad, Agra, Farrukhabad, Ghaziabad, Kanpur, Ludhiana, Lucknow and Rae Bareli. Additionally, in the foggy period, large areas of probable source regions lay outside India, implying the significance of aerosol incursion from outside India.     

Outdoor air pollution in close proximity to a continuous point source

Data are lacking on human exposure to air pollutants occurring in ground-level outdoor environments within a few meters of point sources. To better understand outdoor exposure to tobacco smoke from cigarettes or cigars, and exposure to other types of outdoor point sources, we performed more than 100 controlled outdoor monitoring experiments on a backyard residential patio in which we released pure carbon monoxide (CO) as a tracer gas for continuous time periods lasting 0.5–2 h. The CO was emitted from a single outlet at a fixed per-experiment rate of 120–400 cc min^?1 (～140–450 mg min^?1). We measured CO concentrations every 15 s at up to 36 points around the source along orthogonal axes. The CO sensors were positioned at standing or sitting breathing heights of 2–5 ft (up to 1.5 ft above and below the source) and at horizontal distances of 0.25–2 m. We simultaneously measured real-time air speed, wind direction, relative humidity, and temperature at single points on the patio. The ground-level air speeds on the patio were similar to those we measured during a survey of 26 outdoor patio locations in 5 nearby towns. The CO data exhibited a well-defined proximity effect similar to the indoor proximity effect reported in the literature. Average concentrations were approximately inversely proportional to distance. Average CO levels were approximately proportional to source strength, supporting generalization of our results to different source strengths. For example, we predict a cigarette smoker would cause average fine particle levels of approximately 70–110 μg m^?3 at horizontal distances of 0.25–0.5 m. We also found that average CO concentrations rose significantly as average air speed decreased. We fit a multiplicative regression model to the empirical data that predicts outdoor concentrations as a function of source emission rate, source–receptor distance, air speed and wind direction. The model described the data reasonably well, accounting for ～50% of the log-CO variability in 5-min CO concentrations.     

Natural emissions of methane from geological seepage in Europe

Recent studies have shown that geological emissions of methane are an important greenhouse-gas source. Remarkable amounts of methane, estimated in the order of 40–60 Tg yr^?1, are naturally released into the atmosphere from the Earth's crust through faults and fractured rocks. The main source is natural gas, both microbial and thermogenic, produced in hydrocarbon-prone sedimentary basins and injected into the atmosphere through macro-seeps (onshore and offshore mud volcanoes and other seeps) and microseepage, an invisible but pervasive flux from the soil. This source is now evaluated for Europe on the basis of a literature survey, new field measurements and derived emission factors. The up-scaling criteria recommended by the EMEP/CORINAIR guidelines are applied to the local point and area source data.In Europe, 25 countries host oil and/or natural gas reservoirs and potentially, or actually, emit geological methane. Flux data, however, are available only from 10 countries: the onshore or offshore petroliferous sectors of Denmark, Italy, Greece, Romania, Spain, Switzerland, United Kingdom and Black Sea countries (Bulgaria, Ukraine, Georgia). Azerbaijan, whose emissions due to mud volcanism are known to be relevant, is included in the estimate.The sum of emissions, regional estimates and local measurements, related to macro-seeps leads to a conservative total value of about 2.2 Tg yr^?1. Together with the potential microseepage fluxes from the petroliferous basins, estimated on the basis of the Total Petroleum System concept (around 0.8 Tg yr^?1), the total European seepage is projected to 3 Tg yr^?1. This preliminary figure would represent, in terms of magnitude, the second natural methane source for Europe after wetlands. The estimate will have to be refined by increasing the number of seepage measurements both on lands, where there is high potential for microseepage (e.g., Germany, Hungary, Romania, Ukraine, Belarus, Russia, Georgia) and in coastal marine areas (the North Sea, the Black Sea, offshore Greece and Italy) where emission factors and the extent of the underwater seeping area are not completely known.     

Effects of soil dust episodes and mixed fuel sources on source apportionment of PM10 particles in Kuopio,Finland

A receptor modeling study was carried out in Kuopio, Finland, between January and April 1994. Near the center of town, the daily mean concentrations were measured for PM10, sulphur dioxide, carbon monoxide and Black Smoke. Elemental concentrations of PM10 samples for 38 days were analyzed by ICP-MS. The main sources and their contributions to the measured concentrations of PM10 particles were solved by receptor modeling using a factor analysis-multiple linear regression (FA-MLR) model. Because a dust episode was very strong during two sampling days, the FA analysis was strongly influenced by this episode and did not give main factors. The factor analysis, when the two episode days were omitted, gave credible factors related to the sources in the study area. The four major sources and their estimated contributions to the average PM10 concentration of 27.2 μg m^-3 were: soil and street dust 46–48%, heavy fuel oil burning 12–18%, traffic exhaust 10–14%, wood burning ca. 11% and unidentified sources 15–25%. However, during spring dust episode days, with maximum PM10 concentration of 150 μg m^-3, the main source of PM10 was soil.     

Source apportionment of secondary organic aerosol during a severe photochemical smog episode

The UCD/CIT air quality model was modified to predict source contributions to secondary organic aerosol (SOA) by expanding the Caltech Atmospheric Chemistry Mechanism to separately track source apportionment information through the chemical reaction system as precursor species react to form condensable products. The model was used to predict source contributions to SOA in Los Angeles from catalyst-equipped gasoline vehicles, non-catalyst equipped gasoline vehicles, diesel vehicles, combustion of high sulfur fuel, other anthropogenic sources, biogenic sources, and initial/boundary conditions during the severe photochemical smog episode that occurred on 9 September 1993. Gasoline engines (catalyst+non-catalyst equipped) were found to be the single-largest anthropogenic source of SOA averaged over the entire model domain. The region-wide 24-h average concentration of SOA produced by gasoline engines was predicted to be 0.34 μg m⁻³ with a maximum 24-h average concentration of 1.81 μg m⁻³ downwind of central Los Angeles. The region-wide 24-h average concentration of SOA produced by diesel engines was predicted to be 0.02 μg m⁻³, with a maximum 24-h average concentration of 0.12 μg m⁻³ downwind of central Los Angeles. Biogenic sources are predicted to produce a region-wide 24-h average SOA value of 0.16 μg m⁻³, with a maximum 24-h average concentration of 1.37 μg m⁻³ in the less-heavily populated regions at the northern and southern edges of the air basin (close to the biogenic emissions sources). SOA concentrations associated with anthropogenic sources were weakly diurnal, with slightly lower concentrations during the day as mixing depth increased. SOA concentrations associated with biogenic sources were strongly diurnal, with higher concentrations of aqueous biogenic SOA at night when relative humidity (RH) peaked and little biogenic SOA formation during the day when RH decreased.     

Recreational atmospheric pollution episodes: Inhalable metalliferous particles from firework displays

The use of fireworks creates an unusual and distinctive anthropogenic atmospheric pollution event. We report on aerosol samples collected during Las Fallas in Valencia, a 6-day celebration famous for its firework displays, and add comparative data on firework- and bonfire-contaminated atmospheric aerosol samples collected from elsewhere in Spain (Barcelona, L’Alcora, and Borriana) and during the Guy Fawkes celebrations in London. Specific high-profile official firework events during Las Fallas included the afternoon Mascletà and the nightly aerial displays (especially in the climactic final 2 days of the fiesta) and were accompanied by pollution spikes in suspended particles, NO, SO₂, and the creation and dispersal of an aerosol cloud enriched in a range of metallic elements. Notable metal aerosol concentration increases recorded during Las Fallas were potassium (from 500 to 5900 ng m⁻³), aluminium (as Al₂O₃ from around 600 to 2200 ng m⁻³), titanium (from 200 to 700 ng m⁻³), magnesium (from 100 to 500 ng m⁻³), lead (from 17 to 379 ng m⁻³), barium (from 39 to 322 ng m⁻³), strontium (from 3 to 112 ng m⁻³), copper (from 12 to 71 ng m⁻³), and antimony (from 1 to 52 ng m⁻³). Firework-contaminated aerosols of similarly metalliferous composition were also identified at the other monitoring sites, although different sites show variations attributable to other sources such as bonfires and local industry. Unusual levels of the trace elements Ba, Sr and (to a lesser extent) Cu, always in proportions with Ba dominant, along with strongly enhanced K, Pb, and Sb, are identified as being particularly characteristic of firework aerosols. Although firework-related recreational pollution episodes are transient in nature, they are highly concentrated, contribute significantly to total annual metal emissions, and are on average fine enough to be easily inhaled and a health risk to susceptible individuals.     

Polycyclic aromatic hydrocarbons in atmospheric particles in the metropolitan area of Porto Alegre,Brazil

The aim of the present study is to identify and quantify the main sources of polycyclic aromatic hydrocarbons (PAHs) associated with aerosols (PM₁₀) collected at three different sampling stations: 8° Distrito, CEASA and Charqueadas. The samples were collected between November 2001 and November 2002, and the concentrations of 16 major PAHs were determined according to EPA. The filters containing particulate matter were extracted with dichloromethane in Soxhlet and the extracts were later analysed in a gaseous chromatograph coupled to a mass spectrometer (GS/MS). The average concentrations of PAHs ranged between 0.04 and 2.30 ng m⁻³. The analysis of principal components was applied to the chemical and meteorological variables in order to facilitate the identification of sources of PAHs emission into the atmospheric particulate. The study identified the following sources of PAHs: vehicular emissions, such as diesel oil, petrol, alcohol, and kerosene; industrial emissions, like lubricating oils; emissions from hospital waste burning, and coal burning at power plants.     

Atmospheric emission of polychlorinated biphenyls from multiple industrial thermal processes

In this study, field measurements were conducted to estimate and characterize the atmospheric emission levels and profiles of polychlorinated biphenyls (PCBs) from multiple industrial thermal processes. The emission levels and profiles of PCBs from five types of thermal processes at twenty-three plants were studied and compared with eight processes reported in our previous studies. Correlation analysis was preformed to identify a marker congener for emission of ΣPCB. A significant correlation was observed between congener CB-118 and ΣPCB (R² = 0.65 and p < 0.01), which suggests that CB-118 is a good marker congener for emission of ΣPCB. The profiles of PCBs emitted from the thirteen thermal processes were compared, and this information could be used for studying source–receptor relationships and identifying the specific sources of PCBs. To prioritize the sources for control, the concentrations of PCBs from thirteen industrial thermal sources were compared. The PCB concentrations from secondary zinc smelting and thermal wire reclamation were about one to three order magnitude higher than those of other sources, which suggests that these two sources be given priority in PCB source control. Finally, the atmospheric emission factors of PCBs from the thirteen industrial sources were summarized, and these data will be useful for developing an integrated emission inventory of PCBs.