The Relationship between Cross-Sectional and Time Series Studies

Abstract

There is a dearth of information on dust emissions from sources that are unique to the U.S. Department of Defense testing and training activities. However, accurate emissions factors are needed for these sources so that military installations can prepare accurate particulate matter (PM) emission inventories. One such source, coarse and fine PM (PM₁₀ and PM_2.5) emissions from artillery backblast testing on improved gun positions, was characterized at the Yuma Proving Ground near Yuma, AZ, in October 2005. Fugitive emissions are created by the shockwave from artillery pieces, which ejects dust from the surface on which the artillery is resting. Other contributions of PM can be attributed to the combustion of the propellants. For a 155–mm howitzer firing a range of propellant charges or zones, amounts of emitted PM₁₀ ranged from ～19 g of PM₁₀ per firing event for a zone 1 charge to 92 g of PM₁₀ per firing event for a zone 5. The corresponding rates for PM_2.5 were ～9 g of PM_2.5 and 49 g of PM_2.5 per firing. The average measured emission rates for PM₁₀ and PM_2.5 appear to scale with the zone charge value. The measurements show that the estimated annual contributions of PM₁₀ (52.2 t) and PM_2.5 (28.5 t) from artillery backblast are insignificant in the context of the 2002 U.S. Environment Protection Agency (EPA) PM emission inventory. Using national–level activity data for artillery fire, the most conservative estimate is that backblast would contribute the equivalent of 5 x 10^–4% and 1.6 x 10^–3% of the annual total PM₁₀ and PM_2.5 fugitive dust contributions, respectively, based on 2002 EPA inventory data.     

Testing Re-entrained Aerosol Kinetic Emissions from Roads : a new approach to infer silt loading on roadways

PM₁₀ and PM_2.5 emissions from roadways are currently estimated using the silt loading on the road surface as a surrogate for the emissions potential of road dust. While the United States Environmental Protection Agency prescribes this method in AP-42, there is considerable cost associated with silt loading measurements; it is feasible to sample only a small portion of a roadway network. A new approach for measuring the concentration of suspendable PM₁₀ above road surfaces has been developed to obtain a more spatially representative estimate of a road's potential to emit dust. The Testing Re-entrained Aerosols Kinetic Emissions from Roads (TRAKER) system uses real-time aerosol sensors mounted on a vehicle to measure the concentration of dust suspended from the road while the vehicle is in motion. When coupled with a Global Positioning System (GPS) instrument, TRAKER can be used to efficiently survey the changes in suspendable particles due to varying road conditions over a large spatial domain.In a recent study on paved roads in Las Vegas, the TRAKER system was compared with collocated silt loading measurements. The TRAKER system was also used to survey the relative amounts of suspendable road dust on approximately 300 miles of paved roads. The system provides a unique perspective on road dust sources and their spatial distribution.Results of this study indicated that the difference of the PM₁₀ concentrations measured behind the tire and on the hood is exponentially related to vehicle speed. This was an interesting finding because current AP-42 road dust emissions estimation methods do not include vehicle speed as a factor in the emissions calculations. The experiment also demonstrated that the distribution of suspendable material on roadways is highly variable and that a large number of samples are needed to represent road dust emissions potential on an urban scale for a variety of road and activity conditions.     

Factors influencing PM10 emissions from road pavement wear

Accelerated pavement wear is one of the major environmental disadvantages of studded tyres in northern regions and results in increased levels of PM₁₀. Measurements of PM₁₀ in a road simulator hall have been used to study the influence of pavement properties, tyre type and vehicle speed on pavement wear. The test set-up included three different pavements (one granite and two quartzite with different aggregate sizes), three different tyre types (studded, non-studded, and summer tyres) and different speeds (30–70 km h^?1). The results show that the granite pavement was more prone to PM₁₀ production compared to the quartzite pavements. Studded winter tyres yield tens of times higher PM₁₀ concentrations compared to non-studded winter tyres. Wear from summer tyres was negligible in comparison. It was also shown that wear is strongly dependent on speed; every 10 km h^?1 increase yielded an increase of the PM₁₀ concentration of 680 μg m^?3 in one of the simulator experiments.     

Source characterization of ambient fine particles at multiple sites in the Seattle area

To identify major PM_2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) sources with a particular emphasis on the ship engine emissions from a major port, integrated 24 h PM_2.5 speciation data collected between 2000 and 2005 at five United State Environmental Protection Agency's Speciation Trends Network monitoring sites in Seattle, WA were analyzed. Seven to ten PM_2.5 sources were identified through the application of positive matrix factorization (PMF). Secondary particles (12–26% for secondary nitrate; 17–20% for secondary sulfate) and gasoline vehicle emissions (13–31%) made the largest contributions to the PM_2.5 mass concentrations at all of the monitoring sites except for the residential Lake Forest site, where wood smoke contributed the most PM_2.5 mass (31%). Other identified sources include diesel vehicle emissions, airborne soil, residual oil combustion, sea salt, aged sea salt, metal processing, and cement kiln. Residual oil combustion sources identified at multiple monitoring sites point clearly to the Port of Seattle suggesting ship emissions as the source of oil combustion particles. In addition, the relationship between sulfate concentrations and the oil combustion emissions indicated contributions of ship emissions to the local sulfate concentrations. The analysis of spatial variability of PM_2.5 sources shows that the spatial distributions of several PM_2.5 sources were heterogeneous within a given air shed.     

Effect of SME biodiesel blends on PM2.5 emission from a heavy-duty engine

To explore the effect of biodiesel and sulfur content on PM_2.5 emissions, engine dynamometer tests were performed on a Euro II engine to compare the PM_2.5 emissions from four fuels: two petroleum diesel fuels with sulfur contents of 50 and 100 ppm respectively, and two B20 fuels in which soy methyl ester (SME) biodiesel was added to each of the above mentioned petroleum diesel fuels (v/v: 80%/20% for petroleum diesel and SME respectively). Gaseous pollutants and PM_2.5 emissions were sampled with an AVL AMA4000 and Model 130 High-Flow Impactor (MSP Corp). Measurements were made of the PM_2.5 mass, organic carbon (OC), elemental carbon (EC) and the water-soluble ion distribution. The results showed that PM_2.5 emissions decreased with lower sulfur content or blending with SME biodiesel, and the decrease would be more by applying both two methods together. Particles of approximately 0.13 μm contributed 48–83% of PM_2.5 emissions. The impact of sulfur content on this percentage was different for low and high engine speed. The majority of PM_2.5 was comprised of OC and EC, and the carbon emission rate had the same trend as PM_2.5. Since the EC abatement of B20 was larger than OC, the OC/EC ratio of B20 was always larger than that of petroleum diesel. For petroleum diesel, the OC/EC increased with sulfur content, which was not the case for B20. The SO₄^2? had highest emission rate in the water-soluble ions of PM.     

Long-term variation of PM2.5 levels and composition at rural, urban, and roadside sites in Hong Kong: Increasing impact of regional air pollution

Long-term study of air pollution plays a decisive role in formulating and refining pollution control strategies. In this study, two 12-month measurements of PM_2.5 mass and speciation were conducted in 00/01 and 04/05 to determine long-term trend and spatial variations of PM_2.5 mass and chemical composition in Hong Kong. This study covered three sites with different land-use characteristics, namely roadside, urban, and rural environments. The highest annual average PM_2.5 concentration was observed at the roadside site (58.0±2.0 μg m⁻³ (average±2σ) in 00/01 and 53.0±2.7 μg m⁻³ in 04/05), followed by the urban site (33.9±2.5 μg m⁻³ in 00/01 and 39.0±2.0 μg m⁻³ in 04/05), and the rural site (23.7±1.9 μg m⁻³ in 00/01 and 28.4±2.4 μg m⁻³ in 04/05). The lowest PM_2.5 level measured at the rural site was still higher than the United States’ annual average National Ambient Air Quality Standard of 15 μg m⁻³. As expected, seasonal variations of PM_2.5 mass concentration at the three sites were similar: higher in autumn/winter and lower in summer. Comparing PM_2.5 data in 04/05 with those collected in 00/01, a reduction in PM_2.5 mass concentration at the roadside (8.7%) but an increase at the urban (15%) and rural (20%) sites were observed. The reduction of PM_2.5 at the roadside was attributed to the decrease of carbonaceous aerosols (organic carbon and elemental carbon) (>30%), indicating the effective control of motor vehicle emissions over the period. On the other hand, the sulfate concentration at the three sites was consistent regardless of different land-use characteristics in both studies. The lack of spatial variation of sulfate concentrations in PM_2.5 implied its origin of regional contribution. Moreover, over 36% growth in sulfate concentration was found from 00/01 to 04/05, suggesting a significant increase in regional sulfate pollution over the years. More quantitative techniques such as receptor models and chemical transport models are required to assess the temporal variations of source contributions to ambient PM_2.5 mass and chemical speciation in Hong Kong.     

Particulate Emission Rates for Unpaved Shoulders along a Paved Road

ABSTRACT

This paper reports the first empirical estimate of particle emissions from unpaved shoulders along paved roads.¹ Its objectives are to develop and demonstrate an emission rate measurement methodology that can be applied in different areas; identify the mechanisms that suspend dust from unpaved shoulders and the observables related to this suspension process; and quantify PM₁₀ mass emissions in the form of an emission rate. To achieve these objectives, fast-response observations from nephelometers and a sonic anemometer were used to characterize shortlived dust plumes generated by passing vehicles. In addition, detailed soil surface measurements determined the mechanical properties of the shoulder surfaces.

Large traffic-induced turbulence events that led to significant dust entrainment were almost exclusively caused by “large” vehicles such as trucks, semis, and vehicles pulling trailers, all traveling 50-65 mph. PM₁₀ emission rates for these large, fast-traveling vehicles were determined to be 8 ± 4 grams per vehicle kilometer traveled under dry conditions. Emissions due to smaller vehicles such as cars, vans, and sport utility vehicles were negligible for normal on-road driving. These results indicate that the majority of PM₁₀ emissions from unpaved shoulders is caused by relatively few vehicles.     

Seasonal and spatial trends in the sources of fine particle organic carbon in Israel,Jordan, and Palestine

A study of carbonaceous particulate matter (PM) was conducted in the Middle East at sites in Israel, Jordan, and Palestine. The sources and seasonal variation of organic carbon, as well as the contribution to fine aerosol (PM_2.5) mass, were determined. Of the 11 sites studied, Nablus had the highest contribution of organic carbon (OC), 29%, and elemental carbon (EC), 19%, to total PM_2.5 mass. The lowest concentrations of PM_2.5 mass, OC, and EC were measured at southern desert sites, located in Aqaba, Eilat, and Rachma. The OC contribution to PM_2.5 mass at these sites ranged between 9.4% and 16%, with mean annual PM_2.5 mass concentrations ranging from 21 to 25 ug m^?3. These sites were also observed to have the highest OC to EC ratios (4.1–5.0), indicative of smaller contributions from primary combustion sources and/or a higher contribution of secondary organic aerosol. Biomass burning and vehicular emissions were found to be important sources of carbonaceous PM in this region at the non-southern desert sites, which together accounted for 30%–55% of the fine particle organic carbon at these sites. The fraction of measured OC unapportioned to primary sources (1.4 μgC m^?3 to 4.9 μgC m^?3; 30%–74%), which has been shown to be largely from secondary organic aerosol, is relatively constant at the sites examined in this study. This suggests that secondary organic aerosol is important in the Middle East during all seasons of the year.     

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.     

A wintertime PM2.5 episode at the Fresno,CA, supersite

A winter PM_2.5 episode that achieved a maximum 24-h average of 138 μg m⁻³ at the Fresno Supersite in California's San Joaquin Valley between 2 and 12 January, 2000 is examined using 5-min to 1-h continuous measurements of mass, nitrate, black carbon, particle-bound PAH, and meteorological measurements. Every day PM_2.5 sampling showed that many episodes, including this one, are missed by commonly applied sixth-day monitoring, even though quarterly averages and numbers of US air quality standard exceedances are adequately estimated. Simultaneous measurements at satellite sites show that the Fresno Supersite represented PM_2.5 within the city, and that half or more of the urban concentrations were present at distant, non-urban locations unaffected by local sources. Most of the primary particles accumulated during early morning and nighttime, decreasing when surface temperatures increased and the shallow radiation inversion coupled to a valleywide layer. When this coupling occurred, nitrate levels increased rapidly over a 10–30 min period as black carbon and gaseous concentrations dropped. This is consistent with a conceptual model in which secondary aerosol forms above the surface layer and is effectively decoupled from the surface for all but the late-morning and early afternoon period. Primary pollutants, such as organic and black carbon, accumulate within the shallow surface layer in urban areas where wood burning and vehicle exhaust emissions are high. Such a model would explain why earlier studies find nitrate concentrations to be nearly the same among widely separated sites in urban areas, as winds aloft of 1 to 6 m s⁻¹ could easily disperse the elevated aerosol throughout the valley.     

A highly resolved temporal and spatial air pollutant emission inventory for the Pearl River Delta region, China and its uncertainty assessment

A highly resolved temporal and spatial Pearl River Delta (PRD) regional emission inventory for the year 2006 was developed with the use of best available domestic emission factors and activity data. The inventory covers major emission sources in the region and a bottom–up approach was adopted to compile the inventory for those sources where possible. The results show that the estimates for SO₂, NO_x, CO, PM₁₀, PM_2.5 and VOC emissions in the PRD region for the year 2006 are 711.4 kt, 891.9 kt, 3840.6 kt, 418.4 kt, 204.6 kt, and 1180.1 kt, respectively. About 91.4% of SO₂ emissions were from power plant and industrial sources, and 87.2% of NO_x emissions were from power plant and mobile sources. The industrial, mobile and power plant sources are major contributors to PM₁₀ and PM_2.5 emissions, accounting for 97.7% of the total PM₁₀ and 97.2% of PM_2.5 emissions, respectively. Mobile, biogenic and VOC product-related sources are responsible for 90.5% of the total VOC emissions. The emissions are spatially allocated onto grid cells with a resolution of 3 km × 3 km, showing that anthropogenic air pollutant emissions are mainly distributed over PRD central-southern city cluster areas. The preliminary temporal profiles were established for the power plant, industrial and on-road mobile sources. There is relatively low uncertainty in SO₂ emission estimates with a range of −16% to +21% from power plant sources, medium to high uncertainty for the NO_x emissions, and high uncertainties in the VOC, PM_2.5, PM₁₀ and CO emissions.     

Characteristics of vertical profiles and sources of PM2.5, PM10 and carbonaceous species in Beijing

In August 2003 during the anticipated month of the 2008 Beijing Summer Olympic Games, we simultaneously collected PM₁₀ and PM_2.5 samples at 8, 100, 200 and 325 m heights up a meteorological tower and in an urban and a suburban site in Beijing. The samples were analysed for organic carbon (OC) and elemental carbon (EC) contents. Particulate matter (PM) and carbonaceous species pollution in the Beijing region were serious and widespread with 86% of PM_2.5 samples exceeding the daily National Ambient Air Quality Standard of the USA (65 μg m⁻³) and the overall daily average PM₁₀ concentrations of the three surface sites exceeding the Class II National Air Quality Standard of China (150 μg m⁻³). The maximum daily PM_2.5 and PM₁₀ concentrations reached 178.7 and 368.1 μg m⁻³, respectively, while those of OC and EC reached 22.2 and 9.1 μg m⁻³ in PM_2.5 and 30.0 and 13.0 μg m⁻³ in PM₁₀, respectively. PM, especially PM_2.5, OC and EC showed complex vertical distributions and distinct layered structures up the meteorological tower with elevated levels extending to the 100, 200 and 300 m heights. Meteorological evidence suggested that there exist fine atmospheric layers over urban Beijing. These layers were featured by strong temperature inversions close to the surface (<50 m) and more stable conditions aloft. They enhanced the accumulation of pollutants and probably caused the complex vertical distributions of PM and carbonaceous species over urban Beijing. The built-up of PM was accompanied by transport of industrial emissions from the southwest direction of the city. Emissions from road traffic and construction activities as well as secondary organic carbon (SOC) are important sources of PM. High OC/EC ratios (range of 1.8–5.1 for PM_2.5 and 2.0–4.3 for PM₁₀) were found, especially in the higher levels of the meteorological tower suggesting there were substantial productions of SOC in summer Beijing. SOC is estimated to account for at least 33.8% and 28.1% of OC in PM_2.5 and PM₁₀, respectively, with higher percentages at the higher levels of the tower.     

Estimation and validation of PM2.5/PM10 exhaust and non-exhaust emission factors for practical street pollution modelling

In order to carry out efficient traffic and air quality management, validated models and PM emission estimates are needed. This paper compares current available emission factor estimates for PM₁₀ and PM_2.5 from emission databases and different emission models, and validates these against eight high quality street pollution measurements in Denmark, Sweden, Germany, Finland and Austria.The data sets show large variation of the PM concentration and emission factors with season and with location. Consistently at all roads the PM₁₀ and PM_2.5 emission factors are lower in the summer month than the rest of the year. For example, PM₁₀ emission factors are in average 5–45% lower during the month 6–10 compared to the annual average.The range of observed total emission factors (including non-exhaust emissions) for the different sites during summer conditions are 80–130 mg km⁻¹ for PM₁₀, 30–60 mg km⁻¹ for PM_2.5 and 20–50 mg km⁻¹ for the exhaust emissions.We present two different strategies regarding modelling of PM emissions: (1) For Nordic conditions with strong seasonal variations due to studded tyres and the use of sand/salt as anti-skid treatment a time varying emission model is needed. An empirical model accounting for these Nordic conditions was previously developed in Sweden. (2) For other roads with a less pronounced seasonal variation (e.g. in Denmark, Germany, Austria) methods using a constant emission factor maybe appropriate. Two models are presented here.Further, we apply the different emission models to data sets outside the original countries. For example, we apply the “Swedish” model for two streets without studded tyre usage and the “German” model for Nordic data sets. The “Swedish” empirical model performs best for streets with studded tyre use, but was not able to improve the correlation versus measurements in comparison to using constant emission factors for the Danish side. The “German” method performed well for the streets without clear seasonal variation and reproduces the summer conditions for streets with pronounced seasonal variation. However, the seasonal variation of PM emission factors can be important even for countries not using studded tyres, e.g. in areas with cold weather and snow events using sand and de-icing materials. Here a constant emission factor probably will under-estimate the 90-percentiles and therefore a time varying emission model need to be used or developed for such areas.All emission factor models consistently indicate that a large part (about 50–85% depending on the location) of the total PM₁₀ emissions originates from non-exhaust emissions. This implies that reduction measures for the exhaust part of the vehicle emissions will only have a limited effect on ambient PM₁₀ levels.     

Particulate matter emissions from on-road vehicles in a freeway tunnel study

Particulate matter, including coarse particles (PM_2.5–10, aerodynamic diameter of particle between 2.5 and 10 μm) and fine particles (PM_2.5, aerodynamic diameter of particle lower than 2.5 μm) and their compositions, including elemental carbon, organic carbon, and 11 water-soluble ionic species, and elements, were measured in a tunnel study. A comparison of the six-hour average of light-duty vehicle (LDV) flow of the two sampling periods showed that the peak hours over the weekend were higher than those on weekdays. However, the flow of heavy-duty vehicles (HDVs) on the weekdays was significant higher than that during the weekend in this study. EC and OC content were 49% for PM_2.5–10 and 47% for PM_2.5 in the tunnel center. EC content was higher than OC content in PM_2.5–10, but EC was about 2.3 times OC for PM_2.5. Sulfate, nitrate, ammonium were the main species for PM_2.5–10 and PM_2.5. The element contents of Na, Al, Ca, Fe and K were over 0.8 μg m^?3 in PM_2.5–10 and PM_2.5. In addition, the concentrations of S, Ba, Pb, and Zn were higher than 0.1 μg m^?3 for PM_2.5–10 and PM_2.5. The emission factors of PM_2.5–10 and PM_2.5 were 18 ± 6.5 and 39 ± 11 mg km^?1-vehicle, respectively. The emission factors of EC/OC were 3.6/2.7 mg km^?1-vehicle for PM_2.5–10 and 15/4.7 mg km^?1-vehicle for PM_2.5 Furthermore, the emission factors of water-soluble ions were 0.028(Mg²⁺)–0.81(SO₄^2?) and 0.027(NO₂^?)–0.97(SO₄^2?) mg km^?1-vehicle for PM_2.5–10 and PM_2.5, respectively. Elemental emission factors were 0.003(V)–1.6(Fe) and 0.001(Cd)–1.05(Na) mg km^?1-vehicle for PM_2.5–10 and PM_2.5, respectively.     

Measurements of the physical properties of particles in the urban atmosphere

Measurements of the physical properties of particles in the atmosphere of a UK urban area have been made, including particle number count by condensation nucleus counters with different lower particle size cut-offs; particle size distributions using a Scanning Mobility Particle Sizer; total particle Fuchs surface area using an epiphaniometer and particle mass using Tapered Element Oscillating Micro-balance (TEOM) instruments with size selective (PM₁₀ and PM_2.5) inlets. Mean particle number counts at three sites range from 2.86×10⁴ to 9.60×10⁴ cm^-3. A traffic-influenced location showed a substantially higher ratio of particle number to PM₁₀ mass than a nearby background location despite being some 70 m from the roadway. Operating two condensation nucleus counters in tandem to determine particles in the 3–7 nm size range by difference showed signficant numbers of particles in this range, apparently related to homogeneous nucleation processes. Measurements with the Scanning Mobility Particle Sizer showed a clear difference between roadside size distributions and those at a nearby background location with an additional mode in the roadside samples below 10 nm diameter. Particle number counts were found to show a significant linear correlation with PM₁₀ mass (r²=0.44; n=44 for 24 h data at an urban background location), although during one period of high pollution a curvilinear relationship was found. Measurements of the diurnal variation in PM₁₀ mass, particle number count and Fuchs surface area show the same general pattern of behaviour of the three variables, explicable in terms of vehicle emission source strength and atmospheric dispersion, although the surface area growth was out of phase with the particle number and mass. It appears that particle number gives the clearest indication of recent road traffic emissions.     

Quantification of self pollution from two diesel school buses using three independent methods

We monitored two Seattle school buses to quantify the buses’ self pollution using the dual tracers (DT), lead vehicle (LV), and chemical mass balance (CMB) methods. Each bus drove along a residential route simulating stops, with windows closed or open. Particulate matter (PM) and its constituents were monitored in the bus and from a LV. We collected source samples from the tailpipe and crankcase emissions using an on-board dilution tunnel. Concentrations of PM₁, ultrafine particle counts, elemental and organic carbon (EC/OC) were higher on the bus than the LV. The DT method estimated that the tailpipe and the crankcase emissions contributed 1.1 and 6.8 μg m^?3 of PM_2.5 inside the bus, respectively, with significantly higher crankcase self pollution (SP) when windows were closed. Approximately two-thirds of in-cabin PM_2.5 originated from background sources. Using the LV approach, SP estimates from the EC and the active personal DataRAM (pDR) measurements correlated well with the DT estimates for tailpipe and crankcase emissions, respectively, although both measurements need further calibration for accurate quantification. CMB results overestimated SP from the DT method but confirmed crankcase emissions as the major SP source. We confirmed buses’ SP using three independent methods and quantified crankcase emissions as the dominant contributor.     

Quantifying road dust resuspension in urban environment by Multilinear Engine: A comparison with PMF2

Atmospheric PM pollution from traffic comprises not only direct emissions but also non-exhaust emissions because resuspension of road dust that can produce high human exposure to heavy metals, metalloids, and mineral matter. A key task for establishing mitigation or preventive measures is estimating the contribution of road dust resuspension to the atmospheric PM mixture. Several source apportionment studies, applying receptor modeling at urban background sites, have shown the difficulty in identifying a road dust source separately from other mineral sources or vehicular exhausts. The Multilinear Engine (ME-2) is a computer program that can solve the Positive Matrix Factorization (PMF) problem. ME-2 uses a programming language permitting the solution to be guided toward some possible targets that can be derived from a priori knowledge of sources (chemical profile, ratios, etc.). This feature makes it especially suitable for source apportionment studies where partial knowledge of the sources is available.In the present study ME-2 was applied to data from an urban background site of Barcelona (Spain) to quantify the contribution of road dust resuspension to PM₁₀ and PM_2.5 concentrations. Given that recently the emission profile of local resuspended road dust was obtained (Amato, F., Pandolfi, M., Viana, M., Querol, X., Alastuey, A., Moreno, T., 2009. Spatial and chemical patterns of PM₁₀ in road dust deposited in urban environment. Atmospheric Environment 43 (9), 1650–1659), such a priori information was introduced in the model as auxiliary terms of the object function to be minimized by the implementation of the so-called “pulling equations”.ME-2 permitted to enhance the basic PMF solution (obtained by PMF2) identifying, beside the seven sources of PMF2, the road dust source which accounted for 6.9 μg m^?3 (17%) in PM₁₀, 2.2 μg m^?3 (8%) of PM_2.5 and 0.3 μg m^?3 (2%) of PM₁. This reveals that resuspension was responsible of the 37%, 15% and 3% of total traffic emissions respectively in PM₁₀, PM_2.5 and PM₁. Therefore the overall traffic contribution resulted in 18 μg m^?3 (46%) in PM₁₀, 14 μg m^?3 (51%) in PM_2.5 and 8 μg m^?3 (48%) in PM₁. In PMF2 this mass explained by road dust resuspension was redistributed among the rest of sources, increasing mostly the mineral, secondary nitrate and aged sea salt contributions.     

Commuter exposure to particulate matter in public transportation modes in Hong Kong

This study examined commuter’s exposure to respirable suspended particulate matters while commuting in public transportation modes. The survey was conducted between October 1999 and January 2000 in Hong Kong. A total of eight public transportation modes, that are bus, tram, public light bus, taxi, ferry, Kowloon–Canton Railway, Mass Transit Railway and Light Rail Transit, were selected in the study. They were grouped into four categories: (T1) railway transport; (T2) non-air-conditioned roadway transport; (T3) air-conditioned roadway transport and (T4) marine transport. Both PM₁₀ and PM_2.5 levels were investigated. The results indicate that the particulate level is greatly affected by the mode of transport as well as the ventilation system of the transport. The overall average PM₁₀ concentration level in T2 (147 μg m⁻³) is the highest and is followed by T4 (81 μg m⁻³) and T3 (65 μg m⁻³). The PM₁₀ level in T1 (50 μg m⁻³) is the lowest. Notably, the commuter exposure in tram (175 μg m⁻³) is the highest among all the monitored commuting modes. Commuting modes such as railway and air-conditioned vehicle are recommended as a substitute for non-air-conditioned vehicle. The PM_2.5 to PM₁₀ ratio in transports ranged from 63% to 78%. Higher PM_2.5 to PM₁₀ ratio is found in vehicles with air-conditioning system. For the double deck vehicle, higher PM₁₀ level has resulted in the lower deck. The average upper-deck to lower-deck PM₁₀ ratio is 0.836, 0.751 and 0.738 in air-conditioned bus, non-air-conditioned bus and non-air-conditioned tram, respectively. Typical concentration profiles in different transports are also presented.     

Estimation of vehicular emission inventories in China from 1980 to 2005

Multi-year inventories of vehicular emissions at a high spatial resolution of 40 km×40 km were established in China using the GIS methodology for the period 1980–2005, based on provincial statistical data from yearbooks regarding vehicles and roads, and on the emission factors for each vehicle category in each province calculated by COPERT III program. Results showed that the emissions of CH₄, CO, CO₂, NMVOC, NO_x, PM₁₀, and SO₂ increased from 5, 1066, 19 893, 169, 174, 26, and 16 thousand tons in 1980 to 377, 36 197, 674 629, 5911, 4539, 983, and 484 thousand tons in 2005 at an annual average rate of 19%, 15%, 15%, 15%, 14%, 16%, and 15%, respectively. Statistical analysis of vehicular emissions and GDP showed that they were well positively correlated, which revealed that increase of pollutant emissions has been accompanying the growth of GDP. Spatial distribution of pollutant emissions was rather unbalanced: over three-quarters of the total emissions concentrated in developed regions of China's southeastern, northern and central areas covering only 35.2% of China's territory, while the remaining emissions were distributed over the southwestern, northwestern and northeastern regions covering as much as 64.8% of the territory. In 2005, the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and the Pearl River Delta covering only 2.3%, 2.2%, and 1.9%, respectively, of the territory, generated about 10%, 19%, and 12%, respectively, of the total emissions. Since 1990, motorcycles have been the major contributors to the CH₄, CO, NMVOC, and PM₁₀ emissions, due to the large population. Heavy-duty vans were the major contributors to the NO_x and SO₂ emissions because of high emission factors. Passenger cars contributed about one third of the emissions of each pollutant. Contributions of vehicle categories to emissions varied from province to province, due to the diversity of vehicle compositions among provinces.     

Physicochemical variations in atmospheric aerosols recorded at sea onboard the Atlantic–Mediterranean 2008 Scholar Ship cruise (Part I): Particle mass concentrations,size ratios,and main chemical components

We report on ambient atmospheric aerosols present at sea during the Atlantic–Mediterranean voyage of Oceanic II (The Scholar Ship) in spring 2008. A record was obtained of hourly PM₁₀, PM_2.5, and PM₁ particle size fraction concentrations and 24-h filter samples for chemical analysis which allowed for comparison between levels of crustal particles, sea spray, total carbon, and secondary inorganic aerosols. On-board monitoring was continuous from the equatorial Atlantic to the Straits of Gibraltar, across the Mediterranean to Istanbul, and back via Lisbon to the English Channel. Initially clean air in the open Atlantic registered PM₁₀ levels <10 μg m^?3 but became progressively polluted by increasingly coarse PM as the ship approached land. Away from major port cities, the main sources of atmospheric contamination identified were dust intrusions from North Africa (NAF), smoke plumes from biomass burning in sub-Saharan Africa and Russia, industrial sulphate clouds and other regional pollution sources transported from Europe, sea spray during rough seas, and plumes emanating from islands. Under dry NAF intrusions PM₁₀ daily mean levels averaged 40–60 μg m^?3 (30–40 μg m^?3 PM_2.5; c. 20 μg m^?3 PM₁), peaking briefly to >120 μg m^?3 (hourly mean) when the ship passed through curtains of higher dust concentrations amassed at the frontal edge of the dust cloud. PM₁/PM₁₀ ratios ranged from very low during desert dust intrusions (0.3–0.4) to very high during anthropogenic pollution plume events (0.8–1).