Using a dispersion model to estimate emission rates of particulate matter from paved roads

From January 1996 to June 1997, we carried out a series of measurements to estimate emissions of PM₁₀ from paved roads in Riverside County, California. The program involved the measurement of upwind and downwind vertical profiles of PM₁₀, in addition to meteorological variables such as wind speed and vertical turbulent intensity. This information was analyzed using a new dispersion model that incorporates current understanding of micrometeorology and dispersion. The emission rate was inferred by fitting model predictions to measurements. The inferred emission factors ranged from 0.2 g VKT^-1 for freeways to about 3 g VKT^-1 for city roads. The uncertainty in these factors is estimated to be approximately a factor of two since the contributions of paved road PM₁₀ emissions to ambient concentrations were comparable to the uncertainty in the mean value of the measurement. At this stage, our best estimate of emission factor lies between 0.1 and 10 g VKT^-1; there is some indication that it is about 0.1 g VKT^-1 for heavily traveled freeways, and is an order of magnitude higher for older city roads. We found that measured silt loadings were poor predictors of emission factors.The measured emission factors imply that paved road emissions may contribute about 30% to the total PM₁₀ emissions from a high traffic area such as Los Angeles. This suggests that it is necessary to develop methods that are more reliable than the upwind–downwind concentration difference technique.     

Estimation of wind blown dust emissions in Europe and its vicinity

The assessment of the wind blown dust emission for Europe and selected regions of North Africa and Southwest Asia was carried out using a mesoscale model. The mesoscale model was parameterized based on the current literature review. The model provides data on PM₁₀ emission from several dust reservoirs (anthropogenic, agriculture, semi- and natural) with spatial resolution of 10 × 10 km and temporal resolution of 1 h. The spatial variability of PM₁₀ emission depends on soil texture, land cover/land use as well as meteorological conditions. Lands covered with water or permanently wet were excluded from the model. The land covered with vegetation is treated as dust reservoir whose dust emission capacity depends on the type of vegetation and cover. The dust reservoirs are divided into reservoirs with stable and unstable surface. The changes of emission in time depend on meteorological parameters.The wind blown dust emission should be treated as a non-continuous spatio-temporal process. The emissions are estimated with high uncertainty. The estimated PM₁₀ yearly total load emitted by wind from the European territory is highly differentiated in space and time and is equal to 0.74 Tg. The total load of PM₁₀ emitted by wind from North African and Southwest Asian land surface located in the vicinity of European boundaries is assessed as nearly 50% (0.43 Tg) of the total load estimated for the whole Europe.The average yearly PM₁₀ emission factor for Europe was estimated at 0.139 Mg km^?2.The PM₁₀ emission from agricultural areas is estimated at 52% of the total wind blown emission from the domain of the European Union project “Improving and applying methods for the calculation of natural and biogenic emissions and assessment of impacts to the air quality” - NatAir.PM₁₀ emission factor for natural areas of Europe is estimated at 0.021 Mg km^?2. Appropriate factors for agricultural areas and anthropogenic areas are 0.157 Mg km^?2 and 0.118 Mg km^?2, respectively. The latter two factors are probably underestimated due to omitting in the model of other dust emission mechanisms than aeolian erosion.     

Establishment of a database of emission factors for atmospheric pollutants from Chinese coal-fired power plants

Field measurements and data investigations were conducted for developing an emission factor database for inventories of atmospheric pollutants from Chinese coal-fired power plants. Gaseous pollutants and particulate matter (PM) of different size fractions were measured using a gas analyzer and an electric low-pressure impactor (ELPI), respectively, for ten units in eight coal-fired power plants across the country. Combining results of field tests and literature surveys, emission factors with 95% confidence intervals (CIs) were calculated by boiler type, fuel quality, and emission control devices using bootstrap and Monte Carlo simulations. The emission factor of uncontrolled SO₂ from pulverized combustion (PC) boilers burning bituminous or anthracite coal was estimated to be 18.0S kg t^?1 (i.e., 18.0 × the percentage sulfur content of coal, S) with a 95% CI of 17.2S–18.5S. NO_X emission factors for pulverized-coal boilers ranged from 4.0 to 11.2 kg t^?1, with uncertainties of 14–45% for different unit types. The emission factors of uncontrolled PM_2.5, PM₁₀, and total PM emitted by PC boilers were estimated to be 0.4A (where A is the percentage ash content of coal), 1.5A and 6.9A kg t^?1, respectively, with 95% CIs of 0.3A–0.5A, 1.1A–1.9A and 5.8A–7.9A. The analogous PM values for emissions with electrostatic precipitator (ESP) controls were 0.032A (95% CI: 0.021A–0.046A), 0.065A (0.039A–0.092A) and 0.094A (0.0656A–0.132A) kg t^?1, and 0.0147A (0.0092–0.0225A), 0.0210A (0.0129A–0.0317A), and 0.0231A (0.0142A–0.0348A) for those with both ESP and wet flue-gas desulfurization (wet-FGD). SO₂ and NO_X emission factors for Chinese power plants were smaller than those of U.S. EPA AP-42 database, due mainly to lower heating values of coals in China. PM emission factors for units with ESP, however, were generally larger than AP-42 values, because of poorer removal efficiencies of Chinese dust collectors. For units with advanced emission control technologies, more field measurements are needed to reduce emission factor uncertainties.     

Direct emission factor for N2O from rice–winter wheat rotation systems in southeast China

A field experiment was conducted in a rice–winter wheat rotation agroecosystem to quantify the direct emission of N₂O for synthetic N fertilizer and crop residue application in the 2002–2003 annual cycle. There was an increase in N₂O emission accompanying synthetic N fertilizer application. Fertilizer-induced emission factor for N₂O (FIE) averaged 1.08% for the rice season, 1.49% for the winter wheat season and 1.26% for the whole annual rotation cycle. The annual background emission of N₂O totaled 4.81 kg N₂O–N ha⁻¹, consisting of 1.24 kg N₂O–N ha⁻¹ for rice, 3.11 kg N₂O–N ha⁻¹ for wheat seasons. When crop residue and synthetic N fertilizer were both applied in the fields, crop residue-induced emission factor for N₂O (RIE) was estimated as well. When crop residue was retained at the rate of 2.25 and 4.50 t ha⁻¹ for each season, the RIE averaged 0.64% and 0.27% for the whole annual rotation cycle, respectively. Based on available multi-year data of N₂O emissions over the whole rice–wheat rotation cycle at 3 sites in southeast China, the FIE averaged 1.02% for the rice season, 1.65% for the wheat season. On the whole annual cycle, the FIE for N₂O ranged from 1.05% to 1.45%, with an average of 1.25%. Annual background emission of N₂O averaged 4.25 kg ha⁻¹, ranging from 3.62 to 4.87 kg ha⁻¹. It is estimated that annual N₂O emission in paddy rice-based agroecosystem amounts to 169 Gg N₂O–N in China, accounting for 26–60% of the reported estimates of total emission from croplands in China.     

Characterization of emissions from portable household combustion devices: particle size distributions,emission rates and factors,and potential exposures

A series of source tests were conducted to characterize emissions of particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO₂), methane (CH₄), and total hydrocarbon (THC ) from five types of portable combustion devices. Tested combustion devices included a kerosene lamp, an oil lamp, a kerosene space heater, a portable gas range, and four unscented candles. All tests were conducted either in a well-mixed chamber or a well-mixed room, which enables us to determine emission rates and emission factors using a single-compartment mass balance model. Particle mass concentrations and number concentrations were measured using a nephelometric particle monitor and an eight-channel optical particle counter, respectively. Real-time CO concentrations were measured with an electrochemical sensor CO monitor. CO₂, CH₄, and THC were measured using a GC-FID technique. The results indicate that all particles emitted during steady burning in each of the tested devices were smaller than 1.0 μm in diameter with the vast majority in the range between 0.1 and 0.3 μm. The PM mass emission rates and emission factors for the tested devices ranged from 5.6±0.1 to 142.3±40.8 mg h⁻¹ and from 0.35±0.06 to 9.04±4.0 mg g⁻¹, respectively. The CO emission rates and emission factors ranged from 4.7±3.0 to 226.7±100 mg h⁻¹ and from 0.25±0.12 to 1.56±0.7 mg g⁻¹, respectively. The CO₂ emission rates and emission factors ranged from 5500±700 to 210,000±90,000 mg h⁻¹ and from 387±45 to 1689±640 mg g⁻¹, respectively. The contributions of CH₄ and THC to emission inventories are expected to be insignificant due both to the small emission factors and to the relatively small quantity of fuel consumed by these portable devices. An exposure scenario analysis indicates that every-day use of the kerosene lamp in a village house can generate fine PM exposures easily exceeding the US promulgated NAAQS for PM_2.5.     

Inventory of aerosol and sulphur dioxide emissions from India. Part II—biomass combustion

A spatially resolved biomass burning data set, and related emissions of sulphur dioxide and aerosol chemical constituents was constructed for India, for 1996–1997 and extrapolated to the INDOEX period (1998–1999). Sources include biofuels (wood, crop waste and dung-cake) and forest fires (accidental, shifting cultivation and controlled burning). Particulate matter (PM) emission factors were compiled from studies of Indian cooking stoves and from literature for open burning. Black carbon (BC) and organic matter (OM) emissions were estimated from these, accounting for combustion temperatures in cooking stoves. Sulphur dioxide emission factors were based on fuel sulphur content and reported literature measurements. Biofuels accounted 93% of total biomass consumption (577 MT yr⁻¹), with forest fires contributing only 7%. The national average biofuel mix was 56 : 21 : 23% of fuelwood, crop waste and dung-cake, respectively. Compared to fossil fuels, biomass combustion was a minor source of SO₂ (7% of total), with higher emissions from dung-cake because of its higher sulphur content. PM_2.5 emissions of 2.04 Tg yr⁻¹ with an “inorganic fraction” of 0.86 Tg yr⁻¹ were estimated. Biomass combustion was the major source of carbonaceous aerosols, accounting 0.25 Tg yr⁻¹ of BC (72% of total) and 0.94 Tg yr⁻¹ of OM (76% of total). Among biomass, fuelwood and crop waste were primary contributors to BC emissions, while dung-cake and forest fires were primary contributors to OM emissions. Northern and the east-coast India had high densities of biomass consumption and related emissions. Measurements of emission factors of SO₂, size resolved aerosols and their chemical constituents for Indian cooking stoves are needed to refine the present estimates.     

Comparison of measured and model-calculated real-world traffic emissions

The quality of an emission calculation model based on emission factors measured on roller test stands and statistical traffic data was evaluated using source strengths and emission factors calculated from real-world exhaust gas concentration differences measured upwind and downwind of a motorway in southwest Germany. Gaseous and particulate emissions were taken into account. Detailed traffic census data were taken during the measurements. The results were compared with findings of similar studies.The main conclusion is the underestimation of CO and NO_x source strengths by the model. On the average, it amounts to 23% in case of CO and 17% for NO_x. The latter underestimation results from an undervaluation by 22% of NO_x emission factors of heavy-duty vehicles (HDVs). There are significant differences between source strengths on working days and weekends because of the different traffic split between light-duty vehicles (LDVs) and HDVs. The mean emission factors of all vehicles from measurements are 1.08 g km⁻¹ veh⁻¹ for NO_x and 2.62 g km⁻¹ veh⁻¹ for CO. The model calculations give 0.92 g km⁻¹ veh⁻¹ for NO_x and 2.14 g km⁻¹ veh⁻¹ for CO.The source strengths of 21 non-methane hydrocarbon (NMHC) compounds quantified are underestimated by the model. The ratio between the measured and model-calculated emissions ranges from 1.3 to 2.1 for BTX and up to 21 for 16 other NMHCs. The reason for the differences is the insufficient knowledge of NMHC emissions of road traffic.Particulate matter emissions are dominated by ultra-fine particles in the 10–40 nm range. As far as aerosols larger than 29 nm are concerned, 1.80×10¹⁴ particles km⁻¹ veh⁻¹ are determined for all vehicles, 1.22×10¹⁴ particles km⁻¹ veh⁻¹ and an aerosol volume of 0.03 cm³ km⁻¹ veh⁻¹ are measured for LDVs, and for HDVs 7.79×10¹⁴ particles km⁻¹ veh⁻¹ and 0.41 cm³ km⁻¹ veh⁻¹ are calculated. Traffic-induced turbulence has been identified to have a decisive influence on exhaust gas dispersion near the source.     

On-road remote sensing measurements and fuel-based motor vehicle emission inventory in Hangzhou,China

Motor vehicles are one of the largest sources of air pollutants worldwide. Despite their importance, motor vehicle emissions are inadequately understood and quantified, esp. in developing countries. In this study, the real-world emissions of carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxide (NO) were measured using an on-road remote sensing system at five sites in Hangzhou, China in 2004 and 2005. Average emission factors of CO, HC and NO_x for petrol vehicles of different model year, technology class and vehicle type were calculated in grams of pollutant per unit of fuel use (g l⁻¹) from approximately 32,260 petrol vehicles. Because the availability of data used in traditional on-road mobile source estimation methodologies is limited in China, fuel-based approach was implemented to estimate motor vehicle emissions using fuel sales as a measure of vehicle activity, and exhaust emissions factors from remote sensing measurements. The fuel-based exhaust emission inventories were also compared with the results from the recent international vehicle emission (IVE) model. Results show that petrol vehicle fleet in Hangzhou has significantly high CO emissions, relatively high HC and low NO_x, with the average emission factors of 193.07±15.63, 9.51±2.40 and 5.53±0.48 g l⁻¹, respectively. For year 2005 petrol vehicles exhaust emissions contributed with 182,013±16,936, 9107±2255 and 5050±480 metric ton yr⁻¹ of CO, HC and NO_x, respectively. The inventories are 45.5% higher, 6.6% higher and 53.7% lower for CO, HC and NO_x, respectively, than the estimates using IVE travel-based model. In addition, a number of insights about the emission distributions and formation mechanisms have been obtained from an in-depth analysis of these results.     

Effects of biodiesel on emissions of regulated air pollutants and polycyclic aromatic hydrocarbons under engine durability testing

An 80,000-km durability test was performed on two engines using diesel and biodiesel (methyl ester of waste cooking oil) as fuel in order to examine emissions resulting from the use of biodiesel. The test biodiesel (B20) was blended with 80% diesel and 20% methyl ester derived from waste cooking oil. Emissions of regulated air pollutants, including CO, HC, NO_x, particulate matter (PM) and polycyclic aromatic hydrocarbons (PAHs) were measured at 20,000-km intervals. The identical-model engines were installed on a standard dynamometer equipped with a dilution tunnel used to measure the pollutants. To simulate real-world driving conditions, emission measurements were made in accordance with the United States Environmental Protection Agency (USEPA) FTP transient cycle guidelines. At 0 km of the durability test, HC, CO and PM emission levels were lower for the B20 engine than those for diesel. After running for 20,000 km and longer, they were higher. However, the deterioration coefficients for these regulated air pollutants were not statistically higher than 1.0, implying that the emission factors do not increase significantly after 80,000 km of driving. Total (gaseous+particulate phase) PAH emission levels for both B20 and diesel decreased as the driving mileage accumulated. However, for the engine using B20 fuel, particulate PAH emissions increased as engine mileage increased. The average total PAH emission factors were 1097 and 1437 μg bhp h⁻¹ for B20 and diesel, respectively. For B20, the benzo[a]pyrene equivalence emission factors were 0.77, 0.24, 0.20, 7.48, 5.43 and 14.1 μg bhp h⁻¹ for 2-, 3-, 4-, 5-, 6-ringed and total PAHs. Results show that B20 use can reduce both PAH emission and its corresponding carcinogenic potency.     

Gaseous emissions from outdoor concrete yards used by livestock

Measurements of ammonia (NH₃), nitrous oxide (N₂O) and methane (CH₄) were made from 11 outdoor concrete yards used by livestock. Measurements of NH₃ emission were made using the equilibrium concentration technique while closed chambers were used to measure N₂O and CH₄ emissions. Outdoor yards used by livestock proved to be an important source of NH₃ emission. Greatest emission rates were measured from dairy cow feeding yards, with a mean of 690 mg NH₃-N m⁻² h⁻¹. Smaller emission rates were measured from sheep handling areas, dairy cow collecting yards, beef feeding yards and a pig loading area, with respective mean emission rates of 440, 280, 220 and 140 mg NH₃-N m⁻² h⁻¹. Emission rates of N₂O and CH₄ were much smaller and for CH₄, in particular, emission rates were influenced greatly by the presence or absence of dung on the measurement area.     

Measurement of emission and deposition patterns of ammonia from urine in grass swards

Currently, legislation is being considered to reduce NH₃ emissions in the UK. The major sources of NH₃ and their relative contributions are well known, however, the processes that control the rates of emission are still poorly defined. A series of wind-tunnel experiments has been carried out to determine the effects of various management practices on NH₃ losses. The tunnels were modified to enable NH₃ emission and subsequent deposition to the adjacent swards in the field to be measured. The wind-tunnels were used to examine the effects of herbage length, cutting and N status on rates of NH₃ fluxes, which together with the prevailing environmental conditions affected the rates of NH₃ emission and deposition. Results showed that between 20 and 60% of the NH₃ emitted was deposited within 2 m. Compensation points of between 1.0 and 2.3 μg m⁻³ were calculated for the grass sward.     

Methane emissions from an anaerobic swine lagoon

Gaseous methane (CH₄) emissions from a swine waste holding lagoon were determined periodically during the year. Micrometeorological techniques were used in order that emission rates from the lagoon were measured under ambient conditions with little disturbance to the natural environment. During the cold winter measurement period, CH₄ fluxes were linearly related to lagoon water temperature below 22°C (r=0.87). During warmer measurement periods, both water and air temperatures and windspeed affected emissions rates. In general, flux rates followed a diurnal pattern with greater fluxes during the day when both temperature and windspeed were greatest. Mathematical models using air and water temperature and windspeed factors could explain 47 to 75% of the variation in fluxes. Daily emission rates ranged from 1 to 500 kg CH₄ ha⁻¹ d⁻¹. The average flux for the year was 52.3 kg CH₄ ha⁻¹ d⁻¹ which corresponded to about 5.6 kg CH₄ animal⁻¹ yr⁻¹ from the primary lagoon.     

Broadband UV spectroscopy system used for monitoring of SO2 and NO emissions from thermal power plants

A gas monitoring system based on broadband absorption spectroscopic techniques in the ultraviolet region is described and tested. The system was employed in real-time continuous concentration measurements of sulfur dioxide (SO₂) and nitric oxide (NO) from a 220-ton h^?1 circulating fluidized bed (CFB) boiler in Shandong province, China. The emission coefficients (per kg of coal and per kWh of electricity) and the total emission of the two pollutant gases were evaluated. The measurement results showed that the emission concentrations of SO₂ and NO from the CFB boiler fluctuated in the range of 750–1300 mg m^?3 and 100–220 mg m^?3, respectively. Compared with the specified emission standards of air pollutants from thermal power plants in China, the values were generally higher for SO₂ and lower for NO. The relatively high emission concentrations of SO₂ were found to mainly depend on the sulfur content of the fuel and the poor desulfurization efficiency. This study indicates that the broadband UV spectroscopy system is suitable for industrial emission monitoring and pollution control.     

Decadal reductions of traffic emissions on a transit route in Austria – results of the Tauerntunnel experiment 1997

Real-world emissions of a traffic fleet on a transit route in Austria were determined in the Tauerntunnel experiment in October 1997. The total number of vehicles and the average speed was nearly the same on both measuring days (465 vehicles 30 min⁻¹ and 76 km h⁻¹ on the workday, 477 and 78 km h⁻¹ on Sunday). The average workday fleet contained 17.6% heavy-duty vehicles (HDV) and the average Sunday fleet 2.8% HDV resulting in up to four times higher emission rates per vehicle per km on the workday than on Sunday for most of the regulated components (CO₂, CO, NO_x, SO₂, and particulate matter-PM10). Emission rates of NMVOC accounted for 200 mg vehicle⁻¹ km⁻¹ on both days. The relative contributions of light-duty vehicles (LDV) and HDV to the total emissions indicated that aldehydes, BTEX (benzene, toluene, ethylbenzene, xylenes), and alkanes are mainly produced by LDV, while HDV dominated emissions of CO, NO_x, SO₂, and PM10. Emissions of NO_x caused by HDV were 16,100 mg vehicle⁻¹ km⁻¹ (as NO₂). Produced by LDV they were much lower at 360 mg vehicle⁻¹ km⁻¹. Comparing the emission rates to the results that were obtained by the 1988 experiment at the same place significant changes in the emission levels of hydrocarbons and CO, which accounted 1997 to only 10% of the levels in 1988, were noticed. However, the decrease of PM has been modest leading to values of 80 and 60% of the levels in 1988 on the workday and on Sunday, respectively. Emission rates of NO_x determined on the workday in 1997 were 3130 mg vehicle⁻¹ km⁻¹ and even higher than in 1988 (2630 mg vehicle⁻¹ km⁻¹), presumable due to the increase of the HD-traffic.     

Definition of yearly emission factor of dust and greenhouse gases through continuous measurements in swine husbandry

The object of this study was to develop an accurate estimation method to evaluate the contribution of the various compartments of swine husbandry to dust and GHG (greenhouse gases, CO₂, CH₄ and N₂O) emission into the atmosphere during one year of observation.A weaning, a gestation, a farrowing and a fattening room in an intensive pig house were observed in three different periods (Autumn–Winter, Springtime and Summer, monitoring at least 60% of each period (20% at the beginning, in the middle and at the end) of each cycle).During monitoring, live weight, average live weight gain, number of animals and its variation, type of feed and feeding time were taken into account to evaluate their influence on PM₁₀, or the fraction of suspended particulate matter with an aerodynamic diameter less than or equal to 10 μm [Emission Inventory Guidebook, 2007. B1100 Particle Emissions from Animal Husbandry Activities. Available from: <http://reports.eea.europa.eu/EMEPCORINAIR5/en/B1100vs1.pdf> (accessed October 2008)] and to define GHG emission.The selected piggery had a ventilation control system using a free running impeller to monitor continuously real-time environmental and management parameters with an accuracy of 5%.PM₁₀ concentration was monitored by a sampler (Haz Dust EPAM 5000), either continuously or through traditional gravimetric technique, and the mean value of dust amount collected on the membranes was utilized as a correction factor to be applied to continuously collected data.PM₁₀ concentration amount incoming from inlets was removed from PM₁₀ emission calculation, to estimate the real contribution of pig house dust pollution into atmosphere.Mean yearly emission factor of PM₁₀ was measured in 2 g d^?1 LU^?1 for the weaning room, 0.09 g d^?1 LU^?1 for the farrowing room, 2.59 g d^?1 LU^?1 for the fattening room and 1.23 g d^?1 LU^?1 for the gestation room. The highest PM₁₀ concentration and emission per LU was recorded in the fattening compartment while the lowest value was recorded in the farrowing room.CO₂, CH₄ and N₂O concentrations were continuously measured in the exhaust ducts using an infrared photoacoustic detector IPD (Brüel & Kjaer, Multi-gas Monitor Type 1302, Multipoint Sampler and Doser Type 1303) sampling data every 15 min, for the 60% of the cycles.Yearly emission factor for CO₂ was measured in 5997 g d^?1 LU^?1 for the weaning room, 1278 g d^?1 LU^?1 for the farrowing room, 13,636 g d^?1 LU^?1 for the fattening room and 8851 g d^?1 LU^?1 for the gestation room.Yearly emission factor for CH₄ was measured in 24.57 g d^?1 LU^?1 for the weaning room, 4.68 g d^?1 LU^?1 for the farrowing room, 189.82 g d^?1 LU^?1 for the fattening room and 132.12 g d^?1 LU^?1 for the gestation room.Yearly emission factor for N₂O was measured in 3.62 g d^?1 LU^?1 for the weaning room, 0.66 g d^?1 LU^?1 for the farrowing room, 3.26 g d^?1 LU^?1 for the fattening room and 2.72 g d^?1 LU^?1 for the gestation room.     

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).     

Quantifying natural source mercury emissions from the Ivanhoe Mining District,north-central Nevada,USA

In order to assess the importance of mercury emissions from naturally enriched sources relative to anthropogenic point sources, data must be collected that characterizes mercury emissions from representative areas and quantifies the influence of various environmental parameters that control emissions. With this information, we will be able to scale up natural source emissions to regional areas. In this study in situ mercury emission measurements were used, along with data from laboratory studies and statistical analysis, to scale up mercury emissions for the naturally enriched Ivanhoe Mining District, Nevada. Results from stepwise multi-variate regression analysis indicated that lithology, soil mercury concentration, and distance from the nearest fault were the most important factors controlling mercury flux. Field and lab experiments demonstrated that light and precipitation enhanced mercury emissions from alluvium with background mercury concentrations. Diel mercury emissions followed a Gaussian distribution. The Gaussian distribution was used to calculate an average daily emission for each lithologic unit, which were then used to calculate an average flux for the entire area of 17.1 ng Hg m⁻² h⁻¹. An annual emission of ∼8.7×10⁴ g of mercury to the atmosphere was calculated for the 586 km² area. The bulk of the Hg released into the atmosphere from the district (∼89%) is from naturally enriched non-point sources and ∼11% is emitted from areas of anthropogenic disturbance where mercury was mined. Mercury emissions from this area exceed the natural emission factor applied to mercury rich belts of the world (1.5 ng m⁻² h⁻¹) by an order of magnitude.     

Inventory of aerosol and sulphur dioxide emissions from India: I—Fossil fuel combustion

A comprehensive, spatially resolved (0.25°×0.25°) fossil fuel consumption database and emissions inventory was constructed, for India, for the first time. Emissions of sulphur dioxide and aerosol chemical constituents were estimated for 1996–1997 and extrapolated to the Indian Ocean Experiment (INDOEX) study period (1998–1999). District level consumption of coal/lignite, petroleum and natural gas in power plants, industrial, transportation and domestic sectors was 9411 PJ, with major contributions from coal (54%) followed by diesel (18%). Emission factors for various pollutants were derived using India specific fuel characteristics and information on combustion/air pollution control technologies for the power and industrial sectors. Domestic and transportation emission factors, appropriate for Indian source characteristics, were compiled from literature. SO₂ emissions from fossil fuel combustion for 1996–1997 were 4.0 Tg SO₂ yr⁻¹, with 756 large point sources (e.g. utilities, iron and steel, fertilisers, cement, refineries and petrochemicals and non-ferrous metals), accounting for 62%. PM_2.5 emitted was 0.5 and 2.0 Tg yr⁻¹ for the 100% and the 50% control scenario, respectively, applied to coal burning in the power and industrial sectors. Coal combustion was the major source of PM_2.5 (92%) primarily consisting of fly ash, accounting for 98% of the “inorganic fraction” emissions (difference between PM_2.5 and black carbon+organic matter) of 1.6 Tg yr⁻¹. Black carbon emissions were estimated at 0.1 Tg yr⁻¹, with 58% from diesel transport, and organic matter emissions at 0.3 Tg yr⁻¹, with 48% from brick-kilns. Fossil fuel consumption and emissions peaked at the large point industrial sources and 22 cities, with elevated area fluxes in northern and western India. The spatial resolution of this inventory makes it suitable for regional-scale aerosol-climate studies. These results are compared to previous studies and differences discussed. Measurements of emission factors for Indian sources are needed to further refine these estimates.     

Lidar-assisted measurement of PM10 emissions from agricultural tilling in California's San Joaquin Valley – Part II: emission factors

Emission factors for agricultural operations are needed in order to develop reliable PM₁₀ emissions inventories and air quality models for air basins with significant agricultural land use. A framework was developed to analyze the PM₁₀ vertical profiles collected downwind of tilling operations in the San Joaquin Valley. The methods calculate emission factors on the basis of profile shape and assign quality ratings to each land preparation test. Uncertainties in the calculated emission factors and plume heights were used as one criterion for evaluating the relative quality of the reported emission factor. Other quality ratings were based on the magnitude of the difference in measured up- and downwind concentrations, wind direction, whether the tests were conducted near the edges of the field, and how well the proposed model fit the profile data. The emission factors from different operations were compared taking the quality of the emission factor into account. Plume heights and emission factors for 24 valid test profiles ranged from 2 to 20 m (mean=9.8; SD=3.6; median=9.8) and zero to 800 mg m⁻² (mean=152; SD=240; median=43), respectively. Key environmental properties governing PM₁₀ emission from these operations include relative humidity, soil moisture and vertical temperature gradient. Surprisingly, no discernable relationships were found between implement type or wind speed and the measured emission factors.