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Fuel-Based Particulate Matter and Gaseous Emission Factors Determined from Vehicles in Pittsburgh, Pa's Squirrel Hill Tunnel

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Fuel-Based Particulate Matter and Gaseous Emission Factors Determined from Vehicles in Pittsburgh, Pa's Squirrel Hill Tunnel FUEL-BASED PARTICULATE MATTER AND GASEOUS EMISSION FACTORS DETERMINED FROM VEHICLES IN PITTSBURGH, PA'S SQUIRREL HILL TUNNEL Andrew Grieshop, Eric Lipsky and Allen Robinson Carnegie Mellon University, Pittsburgh, PA Why a Tunnel Study? In short… • Vehicles are a major source of fine particles Fuel-based Emission Factors PM2.5 Emissions as a Function of Fleet Composition In general, emissions studies in traffic tunnels are and other pollutants: Similarly, the total PM mass and OC and EC emissions from the different vehicle classes can be used to provide a composite emissions profile of the 2.5 – Carbonaceous Aerosol (OC and EC) • Fuel-based EFs were determined (i.e. mg/kg fuel vs. mg/mile or mg/hp-hr) separated. In each case, multiple instruments/methods were used to determine the tunnel and full range of vehicles found in a geographical region. – Ultra-fine particles • Kg fuel determined through a mass balance on the carbon in fuel background concentrations during the sample periods. The results of each are shown here, along with This study was specifically undertaken to provide an – Gases (NOx, CO, VOCs, NH3, SO2, etc.) values from literature and a brief explanation of the methods used. OC/EC analysis was completed automotive traffic source profile for the Pittsburgh Air • Ideal, stoichiometric combustion of octane (gasoline): • Reliable, representative Emission Factors using NIOSH method 5040. Overall car and HDDV EFs are tabulated in the Conclusions. Quality Study (PAQS) EPA Supersite project. Along (EFs) are needed. with taking a small suite of gas-phase measurements, C8H18 + (12.5O2 + 47N2 ) ⎯⎯→ 8CO2 + 9H 2O + 47N2 600 – E.g. mgpollutant /kgfuel or mgpollutant /mile OC Emission Factor (mg OC/ kg fuel) Hildemann, 1991 Organic Carbon we concentrated on characterizing the composition, • Emission profiles can be determined for use • Actual combustion also yields: CO, carbonaceous PM, NOx, etc. •MOUDI samples size distribution and mass and number emission rates OC(TQQ) in source-receptor modeling. • CO < 2% of emitted carbon, PM << 1% 500 OC (BareQ) Kirchstetter, 1999 collected on Al foil emitted by automobiles and trucks (Heavy Duty Diesel OC (MOUDI) Rogge, 1993 • Emissions from different vehicle • Therefore, fuel mass can be determined with [CO ] and [CO] HDDV EFs from Literature substrates Vehicles – HDDV) in an urban traffic tunnel in classes/types can be determined. 2 Automobile EFs from Literature Pittsburgh, Pennsylvania. Later work examined the • However, to calculate EFs from our data, we must: 400 TQQ: Y = 246*X + 35 (R2=0.32) •BareQ indicates – E.g. gasoline, heavy-duty diesel (HDDV), MOUDI: Y = 256*X + 2 (R2=0.82) effect of dilution on the measured PM mass. A light-duty (LDV), ‘smokers’ – Adjust concentrations for background Bare Q: Y = 299*X + 43 (R2=0.41) sample on quartz sampling of this work is presented here. – Account for the fraction of carbon in fuels 300 fiber filter (QFF) Sagebiel, 1997 •TQQ indicates QFF • Emission Factors calculated with: 200 corrected for positive artifact with mass Rogge, 1993 from a second QFF The Squirrel Hill Tunnel ⎡ P − P ⎤⎡ MW ⎤ 100 EF = tun amb p w sampling behind a The Squirrel Hill Tunnel is a 4-lane highway tunnel on ⎢ ⎥⎢ ⎥ c Kirchstetter, 1999 Teflon membrane []()()CO − CO + []()CO − ()CO MW Gray, 1986 Interstate 376 on the eastern edge of the City of ⎣ 2 tun 2 amb tun amb ⎦⎣ C ⎦ 0 filter Pittsburgh. It is 4,225 feet long, has a 2.5% up-grade in the 1600 westerly direction and carries both commercial and non- EC Emission Factor (mg EC/ kg fuel) Hildemann, 1991 (Dyno) – P is species concentration 1200 Kirchstetter, 1999 (Tunnel) Elemental Carbon commercial traffic. Our testing took place in the tunnel’s –wis carbon fraction in fuel: weighted average of gasoline (85%) and diesel fuel (87%) Rogge, 1993 (Dyno) •MOUDI data are the west-bound tube. The majority of the data collection took c –MW are molecular weights of species/carbon sum of stages with place in November of 2002, while later work on the effects p/c EC (Quartz) 400 EC (MOUDI) size cuts of 2.5 µm dilution of aerosol mass took place during the summer of 2 Quartz : Y = 445*X + 28.1 (R =0.63) and below . 2 2004. MOUDI : Y = 383*X + 3.2 (R =0.783) 300 HDDV EFs from Literature •Background Measurements taken in the tunnel were corrected for Automobile EFs from Literature correction based on background levels using data taken from remote sites: Study Average PM Tunnel Emission Factors scaled data from stations run by the Allegheny County Health Department Nitrate 200 Photo Source: Bridges and Tunnels of Allegheny County, PA; 2001 Bruce S. Cridlebaugh TEOM at a remote for gas concentrations and instruments on the CMU Low-speed High Truck 3% High-speed location The western portal of the Squirrel Hill Tunnel campus for PM measurements. (per kg fuel) Overall (Rush-hour) (Early Chloride (Mid-day) Ammonium 2% 100 Morning) 4% Sagebiel, 1997 (Remote Sensing) Other Inorganic Data collected during study: These measurements and samples were Sulfate Kirchstetter, 1999 (Tunnel) NOX (g/kg) 10.4 ± 1.9 11.0 ± 1.9 8.0 ± 1.4 17.9± 3.8 2% Hildemann, 1991 (Dyno) collected from the ventilation shaft above the 10% 0 Continuous Air Quality Measurements traffic tunnel (as shown in the schematic below). PM2.5 (mg/kg) 194 ± 28 174 ± 24 175 ± 23 253± 72 2500 Kirchstetter, 1999 (Tunnel) – Gases: CO2, CO, SO2, NOx, NH3 PM2.5 Emission Factor (mg PM2.5/ kg fuel) –PM: 30° C TEOM Mazzoleni, 2003 (Roadside) Total PM2.5 2.5 Exhaust OC (mg/kg) 58 ± 14 56 ± 10 51 ± 16 101 ± 20 2000 – Size/Number: SMPS and nano-SMPS Fresh •TEOM was run with To Ambient Squirrel Hill Tunnel Hildemann, 1991 (Dyno) – Traffic Count: PennDOT Sensors Air Supply EC (mg/kg) 83 ± 18 95 ± 18 66 ± 17 140±27 1500 Miguel, 1998 (Tunnel) the SES (Sample Schematic EC – Traffic Video: Highway Patrol video 42% Allen, 2001 (Tunnel) Equilibration System) NH (mg/kg) 260 ± 65 209 ± 85 265 ± 57 294 ± 30 PM (MOUDI) 3 OC 1000 2.5 at 30° C, and does Ventilation Tunnels Integrated Air Quality Samples 37% PM (TEOM) 2.5 not show good – Artifact-corrected samples for OC/EC ~ 17% ~ 19% ~ 11% ~ 36% 2 Sampling MOUDI : Y = 1079*X + 33.3 (R =0.753) agreement with – Filters for Organic Speciation (results 2 Location HDDV HDDV HDDV HDDV TEOM : Y = 267*X + 143.3 (R =0.220) MOUDI data (see pending) HDDV EFs from Literature Morris, 1998 (On road opacity) •Carbonaceous aerosol Automobile EFs from Literature below). – MOUDI for size-resolved mass and 500 OC/EC •Overall tunnel emission factors were calculated using a fuel- predominates, representing ~80% •Data is background- – Inorganic gas/PM samples weighted average of sampling period emission factors. of PM mass. corrected using data Traffic Tunnel •Average PM mass concentration – Filters for metals analysis (results Hildemann, 1991 (Dyno) collected from a pending) (West bound) •NOx, PM2.5, Elemental Carbon and Organic Carbon emission 3 in the tunnel is ~ 50 µg/m . Kirchstetter, 1999 (Tunnel) second TEOM at a factors are strongly influenced by the proportion of HDDV in Mazzoleni, 2003 (Roadside) – Canisters for VOC speciation 0 the fleet. remote site in 0% 50% 100% Pittsburgh. HDDV Fuel Fraction Diurnal Patterns in Traffic and Concentration Data Determining the Fraction of Fuel Used by Trucks These plots show time series of average daily traffic conditions and background-corrected pollutant • Fleet composition has large impact on EFs concentration - all exhibit consistent diurnal patterns. Integrated sample collection periods are highlighted Loss of Semi-volatile Mass Limits Usefulness of on the plots. • Can we separate emissions from different vehicle types? • Identify HD vehicles from video tape TEOM for Automotive PM Measurements Rush Mid-day 0.4 Early morning Mid-day 90 hour ⎛ 1 ⎞ Comparison of data taken by the ) Rush • All HD vehicles assumed diesel-powered f ⎜ ⎟ 3 Early morning truck⎜ ⎟ 80 MOUDI (which samples at ambient hour ⎝ mpg⎠ 0.3 • Fraction of fuel consumed by HDDV: %fuel = truck conditions) and the TEOM (which 60 HDDV ⎛ 1 ⎞ ⎛ 1 ⎞ f ⎜ ⎟ + f ⎜ ⎟ samples at 30° C) indicate significant ] (ug/m 0.2 truck⎜ ⎟ car⎜ ⎟ loss of PM mass in the TEOM. This mpg mpg 60 2.5 ⎝ ⎠truck ⎝ ⎠car ) Assumed: 6 mpg for trucks, 21 mpg for cars 3 plot shows that the TEOM under- 30 0.1 predicted PM mass concentrations g/m 2.5 [PM µ in the tunnel by ~30%. The plot above shows that the variability is even more HDDV Fuel Fraction Fuel HDDV 0.0 40 0 pronounced during the early morning 2500 ( - TEOM hours, when a larger portion of the 80 2.5 fleet is HDDVs. 2000 Influence of Traffic Composition on NOx Emissions PM 20 PM2.5 - TEOM (µg/m3) This effect in the TEOM is presumably 60 2500 Background Corrected NOx (ppbv) 2 Comparing NOx emission factor and Y = 0.69*X (R = 0.71) 1500 2000 due to the loss to the vapor phase of (ppbv) fleet composition indicates vehicle 1:1 Line x 40 1500 the portion of engine exhaust PM 1000 1000 classes can be clearly separated. 0 made up of semi-volatile organics. NO 500 0 20406080Later work has similarly confirmed the 20 0 Comparison of the diurnal average time 500 3 PM - MOUDI (µg/m ) effects of dilution on the measured PM series of NOx emission factor and fleet 2.5 40 Average NO Emission Factor (g NO /kg fuel) mass in the tunnel. 0 (mph) Speed Average 0 x x composition (as the portion of fuel used by 30 HDDV) clearly suggests that vehicle types 20 should be separable by combining these 800 4000 10 data sets.
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