Effects of improved spatial and temporal modeling of on-road vehicle emissions

Numerous emission and air quality modeling studies have suggested the need to accurately characterize the spatial and temporal variations in on-road vehicle emissions. The purpose of this study was to quantify the impact that using detailed traffic activity data has on emission estimates used to model air quality impacts. The on-road vehicle emissions are estimated by multiplying the vehicle miles traveled (VMT) by the fleet-average emission factors determined by road link and hour of day. Changes in the fraction of VMT from heavy-duty diesel vehicles (HDDVs) can have a significant impact on estimated fleet-average emissions because the emission factors for HDDV nitrogen oxides (NOx) and particulate matter (PM) are much higher than those for light-duty gas vehicles (LDGVs). Through detailed road link-level on-road vehicle emission modeling, this work investigated two scenarios for better characterizing mobile source emissions: (1) improved spatial and temporal variation of vehicle type fractions, and (2) use of Motor Vehicle Emission Simulator (MOVES2010) instead of MOBILE6 exhaust emission factors. Emissions were estimated for the Detroit and Atlanta metropolitan areas for summer and winter episodes. The VMT mix scenario demonstrated the importance of better characterizing HDDV activity by time of day, day of week, and road type. More HDDV activity occurs on restricted access road types on weekdays and at nonpeak times, compared to light-duty vehicles, resulting in 5-15% higher NOx and PM emission rates during the weekdays and 15-40% lower rates on weekend days. Use of MOVES2010 exhaust emission factors resulted in increases of more than 50% in NOx and PM for both HDDVs and LDGVs, relative to MOBILE6. Because LDGV PM emissions have been shown to increase with lower temperatures, the most dramatic increase from MOBILE6 to MOVES2010 emission rates occurred for PM2.5 from LDGVs that increased 500% during colder wintertime conditions found in Detroit, the northernmost city modeled.     

Tunnel study of on-road vehicle emissions and the photochemical potential in Taiwan

Motor vehicle emission factors of carbon monoxide (CO) and non-methane volatile organic compounds (NMVOCs) were calculated inside the Chung-Cheng Tunnel of Kaohsiung in Taiwan. The results were compared with those model predictions from the Mobile Taiwan 2.0 model. Individual concentrations of 21 species of NMVOCs were also determined. Photochemical potential of NMVOCs was evaluated by using the maximum incremental reactivity (MIR). Field data showed that the integrated emission factors of CO and NMVOCs for actual fleet were 6.3 and 1.5 g/veh km, respectively. The error range of these factors may be up to 45%. The predicted values by the Mobile Taiwan 2.0 model closely matched the observed data. Concentrations of isopentane, 2-methylpentane, toluene and m,p-xylene were the dominant species of NMVOCs. The ratio of maximum incremental reactivity to NMVOCs concentration was 3.9, similar to those of the studies in the US Fort McHenry and Tuscarora Tunnel.     

Development of molecular marker source profiles for emissions from on-road gasoline and diesel vehicle fleets

As part of the Gasoline/Diesel PM Split Study, relatively large fleets of gasoline vehicles and diesel vehicles were tested on a chassis dynamometer to develop chemical source profiles for source attribution of atmospheric particulate matter in California's South Coast Air Basin. Gasoline vehicles were tested in cold-start and warm-start conditions, and diesel vehicles were tested through several driving cycles. Tailpipe emissions of particulate matter were analyzed for organic tracer compounds, including hopanes, steranes, and polycyclic aromatic hydrocarbons. Large intervehicle variation was seen in emission rate and composition, and results were averaged to examine the impacts of vehicle ages, weight classes, and driving cycles on the variation. Average profiles, weighted by mass emission rate, had much lower uncertainty than that associated with intervehicle variation. Mass emission rates and elemental carbon/organic carbon (EC/OC) ratios for gasoline vehicle age classes were influenced most by use of cold-start or warm-start driving cycle (factor of 2-7). Individual smoker vehicles had a large range of mass and EC/OC (factors of 40 and 625, respectively). Gasoline vehicle age averages, data on vehicle ages and miles traveled in the area, and several assumptions about smoker contributions were used to create emissions profiles representative of on-road vehicle fleets in the Los Angeles area in 2001. In the representative gasoline fleet profiles, variation was further reduced, with cold-start or warm-start and the representation of smoker vehicles making a difference of approximately a factor of two in mass emission rate and EC/OC. Diesel vehicle profiles were created on the basis of vehicle age, weight class, and driving cycle. Mass emission rate and EC/OC for diesel averages were influenced by vehicle age (factor of 2-5), weight class (factor of 2-7), and driving cycle (factor of 10-20). Absolute and relative emissions of molecular marker compounds showed levels of variation similar to those of mass and EC/OC.     

Fuel consumption, emissions estimation, and emissions cost estimates using global positioning data

The methodology laid out in this paper shows that typical operational data from vehicle fleets monitored by a global positioning system (GPS) can be used to estimate heavy-duty diesel vehicle (HDDV) emissions, thereby enabling waste managers and governing bodies to internalize the responsibility for socioenvironmental costs traditionally absorbed by external parties. Although municipal solid waste (MSW) collection vehicles are the subjects of this particular study, the methodology presented here can be applied to any fleet of vehicles monitored by GPS. This study indicates that MSW collection trucks may be considerably less fuel efficient in the field than published values for HDDV fuel efficiency suggest. The average fuel efficiency of one MSW collection truck was estimated as 0.90 +/- 0.44 km/L (2.12 +/- 1.03 mi/gal). This same truck would generate approximately 42 metric tons of CO2 equivalents/yr, which is comparable to the greenhouse gas emissions of a large sport utility vehicle driving six times the distance, in town, for a year. In terms of the impacts such emissions have, projections for the monetary cost of emissions are available but highly variable. They suggest that the external monetary costs of emissions range between 6 and 39% of the annual fuel costs for the studied MSW collection truck. The results of this study indicate a need for further research into valuation of the hidden, external costs of emissions, borne by local and global socioecological communities. The possible implications of this result include poorly advised fleet procurement decisions and underestimation of MSW collection fleet emissions.     

Global estimation of CO emissions using three sets of satellite data for burned area

Using three sets of satellite data for burned areas together with the tree cover imagery and a biogeochemical component of the Integrated Science Assessment Model (ISAM) the global emissions of CO and associated uncertainties are estimated for the year 2000. The available fuel load (AFL) is calculated using the ISAM biogeochemical model, which accounts for the aboveground and surface fuel removed by land clearing for croplands and pasturelands, as well as the influence on fuel load of various ecosystem processes (such as stomatal conductance, evapotranspiration, plant photosynthesis and respiration, litter production, and soil organic carbon decomposition) and important feedback mechanisms (such as climate and fertilization feedback mechanism). The ISAM estimated global total AFL in the year 2000 was about 687 Pg AFL. All forest ecosystems account for about 90% of the global total AFL. The estimated global CO emissions based on three global burned area satellite data sets (GLOBSCAR, GBA, and Global Fire Emissions Database version 2 (GFEDv2)) for the year 2000 ranges between 320 and 390 Tg CO. Emissions from open fires are highest in tropical Africa, primarily due to forest cutting and burning. The estimated overall uncertainty in global CO emission is about ±65%, with the highest uncertainty occurring in North Africa and Middle East region (±99%). The results of this study suggest that the uncertainties in the calculated emissions stem primarily from the area burned data.     

A comparative analysis of using trip-based versus link-based traffic data for regional mobile source emissions estimation

The current method used for calculating vehicle emissions integrates travel data and associated vehicle emission factors. Travel data from traditional travel demand models are normally link-based (e.g., volumes on roadway segments), while California emission factors are trip-based (i.e., average emission factors over an entire trip), creating a mismatch in the modeling interface. Using dynamic simulation for trip assignment, we present a new modeling framework that consistently provides both trip-based and link-based VMT-speed distributions. Using the Sacramento Metropolitan Area and Kern County in California, we demonstrate the feasibility of this new method and quantify the effects of using trip-based versus link-based travel data on regional peak period emission inventories. The comparison results indicate that for the base scenario in both studied regions, the link-based method generally results in higher emissions than the trip-based method. The sensitivities of the link and trip-based methods to road network variations also appear dissimilar. The link-based emissions are more sensitive to facility-related changes, while the trip-based emissions are more sensitive to demand-related changes. This suggests that greater care may need to be taken to specify the effects of this modeling interface issue within the transportation conformity process and subsequent mobile emissions analysis.     

Trends in on-road vehicle emissions and ambient air quality in Atlanta,Georgia, USA,from the late 1990s through 2009

On-road vehicle emissions of carbon monoxide (CO), nitrogen oxides (NO_x), and volatile organic compounds (VOCs) during 1995–2009 in the Atlanta Metropolitan Statistical Area were estimated using the Motor Vehicle Emission Simulator (MOVES) model and data from the National Emissions Inventories and the State of Georgia. Statistically significant downward trends (computed using the nonparametric Theil-Sen method) in annual on-road CO, NO_x, and VOC emissions of 6.1%, 3.3%, and 6.0% per year, respectively, are noted during the 1995–2009 period despite an increase in total vehicle distance traveled. The CO and NO_x emission trends are correlated with statistically significant downward trends in ambient air concentrations of CO and NO_x in Atlanta ranging from 8.0% to 11.8% per year and from 5.8% to 8.7% per year, respectively, during similar time periods. Weather-adjusted summertime ozone concentrations in Atlanta exhibited a statistically significant declining trend of 2.3% per year during 2001–2009. Although this trend coexists with the declining trends in on-road NO_x, VOC, and CO emissions, identifying the cause of the downward trend in ozone is complicated by reductions in multiple precursors from different source sectors.

Implications:Large reductions in on-road vehicle emissions of CO and NO_x in Atlanta from the late 1990s to 2009, despite an increase in total vehicle distance traveled, contributed to a significant improvement in air quality through decreases in ambient air concentrations of CO and NO_x during this time period. Emissions reductions in motor vehicles and other source sectors resulted in these improvements and the observed declining trend in ozone concentrations over the past decade. Although these historical trends cannot be extrapolated to the future because pollutant concentration contributions due to on-road vehicle emissions will likely become an increasingly smaller fraction of the atmospheric total, they provide an indication of the benefits of past control measures.     

Comparison of on-road emissions for B-0, B-10, and B-20 in transit buses

Biodiesels are often marketed as being cleaner than regular diesel for emissions. Emission test results depend on the biodiesel blend, but laboratory tests suggest that biodiesels decrease particulate matter, carbon monoxide, hydrocarbons, and air toxins when compared to regular diesel. Results for the amount of oxides of nitrogen (NOx) have been less conclusive. Tests have also not evaluated the commonly available ranges of biodiesel blends in the laboratory. Additionally, little information is available from on-road studies, so the effectiveness of using biodiesels to reduce actual emissions is unknown. A more complex relationship exists between engine operation and the rate of emission production than is typically evaluated using engine or chassis dynamometer tests. On-road emissions can vary dramatically because emissions are correlated to engine mode. Additionally, activity such as idling, acceleration, deceleration, and operation against a grade can produce higher emissions than more stable engine operating modes. Because these modes are not well captured in a laboratory environment, understanding on-road relationships is critical in evaluating the emissions reductions that may be possible with biodiesels. More tests and quantifications of the effects of different blends on engine and vehicle performance are required to promote widespread use of biodiesel. The objective of this research was to conduct on-road tests to compare the emission impacts of different blends of biodiesel to regular diesel fuel under different operating conditions. The team conducted on-road tests that utilized a portable emissions monitoring system that was used to instrument transit buses. Regular diesel and different blends of biodiesel were evaluated during on-road engine operation by instrumenting three in-use transit buses, from the CyRide system of Ames, Iowa, along an existing transit route.     

On-road remote sensing of liquefied petroleum gas (LPG) vehicle emissions measurement and emission factors estimation

In the present study, the real-world on-road liquefied petroleum gas (LPG) vehicle/taxi emissions of carbon monoxide (CO), hydrocarbon (HC) and nitric oxide (NO) were investigated. A regression analysis approach based on the measured LPG vehicle emission data was also used to estimate the on-road LPG vehicle emission factors of CO, HC and NO with respect to the effects of instantaneous vehicle speed and acceleration/deceleration profiles for local urban driving patterns. The results show that the LPG vehicle model years and driving patterns have a strong correlation to their emission factors. A unique correlation of LPG vehicle emission factors (i.e., g km⁻¹ and g l⁻¹) on different model years for urban driving patterns has been established. Finally, a comparison was made between the average LPG, and petrol [Chan, T.L., Ning, Z., Leung, C.W., Cheung, C.S., Hung, W.T., Dong, G., 2004. On-road remote sensing of petrol vehicle emissions measurement and emission factors estimation in Hong Kong. Atmospheric Environment 38, 2055–2066 and 3541] and diesel [Chan, T.L., Ning, Z., 2005. On-road remote sensing of diesel vehicle emissions measurement and emission factors estimation in Hong Kong. Atmospheric Environment 39, 6843–6856] vehicle emission factors. It has shown that the introduction of the replacement of diesel taxis to LPG taxis has alleviated effectively the urban street air pollution. However, it has demonstrated that proper maintenance on the aged LPG taxis should also be taken into consideration.     

Construction of electric vehicle driving cycle for studying electric vehicle energy consumption and equivalent emissions

Environmental Science and Pollution Research - This paper proposes an effective and scientific method for the construction of a representative driving cycle for electric vehicles (EV) and takes it...     

用空气加热器减少轻型汽油车低温冷启动时HC、CO 排放量的研究

按照《轻型汽车污染物排放限值及测量方法(中国Ⅲ、Ⅳ阶段)》(GB 18352.3-2005)的测试规范,在一辆轻型汽油车上使用空气加热器进行发动机进气预热,并测定了车辆低温冷启动时的HC、CO排放量.结果表明,发动机启动后24 s左右,HC的瞬态排放量达到最大值,HC的排放主要集中在启动后的前150 s左右;发动机启动...     

Monitoring odorous sulfur emissions using self-organizing maps for handling ion mobility spectrometry data

Possibilities for monitoring emissions of reduced sulfur compounds in pulp and paper mills were investigated using ion mobility spectrometry (IMS) and a self-organizing map (SOM) algorithm. The reduced sulfur compounds measured were hydrogen sulfide (H2S), dimethyl sulfide (DMS), and methyl mercaptan (MM). Attention was paid to momentary concentrations because there is no monitoring device able to measure peak concentrations of reduced sulfur compounds under field conditions. These methods were evaluated by measuring the reduced sulfur compounds first in the laboratory and then at a process monitoring site at a pulp factory. The aim was to find out whether it would be possible to use the laboratory measurements to recognize the same reduced sulfur compounds at the monitoring site. Data collection was followed by analysis using the SOM algorithm and Sammon's mapping. The results showed that the IMS spectra of reduced sulfur compounds and their mixtures can be distinguished from each other by computationally intelligent methods and that the spectra from the process monitoring site corresponded with the laboratory measurements to a certain extent.     

Interinstrument comparison of remote-sensing devices and a new method for calculating on-road nitrogen oxides emissions and validation of vehicle-specific power

Emissions of nitrogen oxides (NOx) by vehicles in real driving environments are only partially understood. This has been brought to the attention of the world with recent revelations of the cheating of the type of approval tests exposed in the dieselgate scandal. Remote-sensing devices offer investigators an opportunity to directly measure in situ real driving emissions of tens of thousands of vehicles. Remote-sensing NO₂ measurements are not as widely available as would be desirable. The aim of this study is to improve the ability of investigators to estimate the NO₂ emissions and to improve the confidence of the total NOx results calculated from standard remote-sensing device (RSD) measurements. The accuracy of the RSD speed and acceleration module was also validated using state-of-the-art onboard global positioning system (GPS) tracking. Two RSDs used in roadside vehicle emissions surveys were tested side by side under off-carriageway conditions away from transient pollution sources to ascertain the consistency of their measurements. The speed correlation was consistent across the range of measurements at 95% confidence and the acceleration correlation was consistent at 95% confidence intervals for all but the most extreme acceleration cases. VSP was consistent at 95% confidence across all measurements except for those at VSP ≥ 15 kW t^?1, which show a small underestimate. The controlled distribution gas nitric oxide measurements follow a normal distribution with 2σ equal to 18.9% of the mean, compared to 15% observed during factory calibration indicative of additional error introduced into the system. Systematic errors of +84 ppm were observed but within the tolerance of the control gas. Interinstrument correlation was performed, with the relationship between the FEAT and the RSD4600 being linear with a gradient of 0.93 and an R² of 0.85, indicating good correlation. A new method to calculate NOx emissions using fractional NO₂ combined with NO measurements made by the RSD4600 was constructed, validated, and shown to be more accurate than previous methods.

Implications: Synchronized remote-sensing measurements of NO were taken using two different remote-sensing devices in an off-road study. It was found that the measurements taken by both instruments were well correlated. Fractional NO₂ measurements from a prior study, measurable on only one device, were used to create new NO_x emission factors for the device that could not be measured by the second device. These estimates were validated against direct measurement of total NO_x emission factors and shown to be an improvement on previous methodologies. Validation of vehicle-specific power was performed with good correlation observed.     

Model and algorithm for bi-fuel vehicle routing problem to reduce GHG emissions

Because of the harmful effects of greenhouse gas (GHG) emitted by petroleum-based fuels, the adoption of alternative green fuels such as biodiesel and compressed natural gas (CNG) is an inevitable trend in the transportation sector. However, the transition to alternative fuel vehicle (AFV) fleets is not easy and, particularly at the beginning of the transition period, drivers may be forced to travel long distances to reach alternative fueling stations (AFSs). In this paper, the utilization of bi-fuel vehicles is proposed as an operational approach. We present a mathematical model to address vehicle routing problem (VRP) with bi-fuel vehicles and show that the utilization of bi-fuel vehicles can lead to a significant reduction in GHG emissions. Moreover, a simulated annealing algorithm is adopted to solve large instances of this problem. The performance of the proposed algorithm is evaluated on some random instances.     

A robust method for collecting and processing the on-road instantaneous data of fuel consumption and speed for motorcycles

ABSTRACT

Using the laboratory-based fuel consumption models for predicting real-world fuel consumption requires the measurement of data under certain conditions to obtain high accuracy of predicted result. Therefore, it is necessary to develop a logging device for measuring the real-time fuel consumption and speed of vehicle on the road. This article presents a study on developing the on-board data logging device to collect real-world data of fuel consumption and speed for motorcycles with the update rate of 1 Hz. The instantaneous speed of the motorcycle was determined based on the rotational speed of the wheel and the wheel radius. Another module was used to determine the instantaneous fuel consumption rate (FR) though measuring the duration injection pulse. The relationship between the duration injection pulse and the injected amount of fuel was established with high correlation coefficient of 0.997. In addition, a filter was designed to remove noise in the dataset collected using the data logging device. The random errors in the speed and the FR profiles were detected and replaced, the percentage of these errors is 1.8% and 2.4%, respectively. The developed method is a precise one for transient fuel consumption and speed measurement. In chassis dynamometer test, the average deviation between steady speed measured by the chassis and the data logging device is only approximately 0.35%. At transient state, the biggest deviation between these two datasets is less than 3.5%. The average FR at steady speed measured by the developed method is slightly different from the one measured by the carbon balance method. The difference is 0.9%, 2.5%, and 0.25% at the speeds of 30 km/h, 50 km/h, and 70 km/h, respectively. Following the transient test cycle, the fuel consumption measured by the developed method is 4.35% lower than that determined by the carbon balance method.

Implications: A robust method for collecting and processing the on-road instantaneous data of fuel consumption and speed was developed for motorcycles. The proposed method can record well the real-world driving data for motorcycles, including the fuel consumption and speed, with the update rate of 1 Hz. The filter was designed to minimize noise while maintaining data integrity of the collected dataset, the percentage of errors in the the speed and the FR profiles is 1.8% and 2.4%, respectively. The proposed method, therefore, can be used as effective tools for future studies relating to the fuel consumption and emission of motorcycles on the road.     

Assessment of emissions data for the Toronto region using aircraft-based measurements and an air quality model

An aircraft-based measurement campaign was conducted during the summer of 1996 in the vicinity of Toronto, Canada. The objective of the campaign was to assess the errors in a particular emission inventory used by three-dimensional air quality models. Measurements of NO₂ and hydrocarbons were made both upwind and downwind of Toronto, on days with strong synoptic-scale flow from a west to northerly direction. The chemical composition of the background airmass on these days was typical of unpolluted continental air. Measurements have been compared with the output from an on-line air quality model (MC2-AQ) run at 5 km resolution and suggest that emissions of NO_x from Toronto are well described in the emission database, though evidence that NO_x emissions are underestimated for suburban regions surrounding Toronto was found. In general, no significant underestimation of hydrocarbon emissions was found, though emissions of the model propane species, which includes acetylene and benzene, was underestimated by at least a factor of two.     

Natural emissions for regional modeling of background ozone and particulate matter and impacts on emissions control strategies

Natural emissions adopted in current regional air quality modeling are updated to better describe natural background ozone and PM concentrations for North America. The revised natural emissions include organosulfur from the ocean, NO from lightning, sea salt, biogenic secondary organic aerosol (SOA) precursors, and pre-industrial levels of background methane. The model algorithm for SOA formation was also revised. Natural background ozone concentrations increase by up to 4 ppb in annual average over the southeastern US and Gulf of Mexico due to added NO from lightning while the revised biogenic emissions produced less ozone in the central and western US. Natural PM_2.5 concentrations generally increased with the revised natural emissions. Future year (2018) simulations were conducted for several anthropogenic emission reduction scenarios to assess the impact of the revised natural emissions on anthropogenic emission control strategies. Overall, the revised natural emissions did not significantly alter the ozone responses to the emissions reductions in 2018. With revised natural emissions, ozone concentrations were slightly less sensitive to reducing NOx in the southeastern US than with the current natural emissions due to higher NO from lightning. The revised natural emissions have little impact on modeled PM_2.5 responses to anthropogenic emission reductions. However, there are substantial uncertainties in current representations of natural sources in air quality models and we recommend that further study is needed to refine these representations.     

Simulating industrial emissions using atmospheric dispersion modeling system: model performance and source emission factors

In this paper, the Gaussian Atmospheric Dispersion Modeling System (ADMS4) was coupled with field observations of surface meteorology and concentrations of several air quality indicators (nitrogen oxides (NOx), carbon monoxide (CO), fine particulate matter (PM10) and sulfur dioxide (SO2)) to test the applicability of source emission factors set by the European Environment Agency (EEA) and the United States Environmental Protection Agency (USEPA) at an industrial complex. Best emission factors and data groupings based on receptor location, type of terrain and wind speed, were relied upon to examine model performance using statistical analyses of simulated and observed data. The model performance was deemed satisfactory for several scenarios when receptors were located at downwind sites with index of agreement 'd' values reaching 0.58, fractional bias 'FB' and geometric mean bias 'MG' values approaching 0 and 1, respectively, and normalized mean square error 'NMSE' values as low as 2.17. However, median ratios of predicted to observed concentrations 'Cp/Co' at variable downstream distances were 0.01, 0.36, 0.76 and 0.19 for NOx, CO, PM10 and SO2, respectively, and the fraction of predictions within a factor of two of observations 'FAC2' values were lower than 0.5, indicating that the model could not adequately replicate all observed variations in emittant concentrations. Also, the model was found to be significantly sensitive to the input emission factor bringing into light the deficiency in regulatory compliance modeling which often uses internationally reported emission factors without testing their applicability.     

Evaluation of a unified regional air-quality modeling system (AURAMS) using PrAIRie2005 field study data: The effects of emissions data accuracy on particle sulphate predictions

The effects of the accuracy of major-point source emissions input data on the predictions of a regional air-quality model (AURAMS) were investigated through a series of scenario simulations. The model domain and time period were chosen to correspond to that of PrAIRie2005, an air-quality field study with airborne and ground-based mobile measurement platforms that took place between August 12th and September 7th, 2005, over the city of Edmonton, Alberta, Canada. The emissions data from standard sources for three coal-fired power-plants located west (typically upwind) of the city were compared to the continuous emissions monitoring system (CEMS) taking place at the time of the study – the latter showed that the original emissions inventory data considerably overestimated NO_x, SO₂, and primary particulate emissions during the study period. Further field investigation (stack sampling) in the fall of 2006 showed that the measured primary particle size distribution and chemical speciation for the emissions were strikingly different from the distribution and speciation originally used in the model. The measured emissions were used to scale existing emissions data in accord with the CEMS and in-stack measurements.The effects of these improvements to the emissions data were examined sequentially in nested AURAMS simulations (finest horizontal resolution 3 km), and were compared to airborne aerosol mass spectrometer (Aerodyne AMS) measurements of particle sulphate, and particle distributions from an airborne passive cavity aerosol spectrometer probe (PCASP). The emissions of SO₂ had the greatest impact on predicted PM₁ sulphate, while the primary particle size distribution and chemical speciation had a smaller role. The revised emissions data greatly improved the comparisons between observations and model values, though over-predictions of fine-mode sulphate still occur near the power-plants, with the use of the revised emissions data. The modified emissions also had a significant impact on the larger particles of the particulate matter, with more primary PM in sizes greater than 1 μm diameter than had previously been estimated, and higher large particle concentrations close to the power-plants.