Workshop on Air Quality Data Analysis and Interpretation Evaluation of Emission Inventory

Emission Inventories  Emission inventories are routinely used for planning purposes and as input to comprehensive photochemical air quality models.  Significant biases in either VOC or NO x emission estimates can lead to poor baseline photochemical model performance and erroneous estimates of the effects of control strategies. Essential top-down emission inventory evaluation procedure: comparison of emission estimates with ambient air quality data.  Caution: Ambient/emission inventory comparisons are useful for examining the relative composition of emission inventories; they are not useful for verifying absolute amounts unless they are combined with bottom-up evaluations.

Approach Perform the following three tasks:  Compare early morning (e.g., LT) ambient- and emissions-derived NMOC/NO x and CO/NO x ratios.  Compare early morning ambient- and emissions-derived relative compositions of individual chemical species and species groups.  Compare early morning ambient- and emissions-derived relative reactivities of individual chemical species and species groups. Early morning sampling periods are more appropriate to use in these evaluations because they have the best potential to minimize the effects of upwind transport and photochemistry. Emissions are generally high, mixing depths are low, winds are usually light, and photochemical reactions are minimized.  Conduct a second evaluation following the incorporation of the recommendations made in the first evaluation, in order to verify improvement.

NMHC/NO x Emissions  PCD – 1997 (Bangkok Inventory) Total NMHC (MW=14 g/mol) on a per C basis (268,882 ton/yr)x(1000 kg/ton)/(0.014 kg/mol) = 19.2 x 10 9 mol/yr Total NO x (MW=46 g/mol as NO 2 ) (329,161 ton/yr)x(1000 kg/ton)/(0.046 kg/mol) = 7.16 x 10 9 mol/yr NMHC/NO x =2.7 (ppbC/ppb)

NMHC/NO x Emissions - Mobile  PCD – 1997 (Bangkok Inventory) Mobile NMHC (MW=14 g/mol C) (232,973 ton/yr)x(1000 kg/ton)/(0.014 kg/mol) = 16.6 x 10 9 mol/yr Mobile NO x (MW=46 g/mol) (264,648 ton/yr)x(1000 kg/ton)/(0.046 kg/mol) = 5.75 x 10 9 mol/yr NMHC/NO x =2.9 (ppbC/ppb)

Bangkok Emission Inventory Comparison NO x /CO  Ambient = 30 – 70 ppb/ppm  Inventory (Total) = 430  Inventory (Mobile) = 460 NMHC/NO x  Ambient (slope) = 9.3 ppbC/ppb  Ambient mean, median = 22.9, 17.2  Inventory (Total) = 2.7  Inventory (Mobile) = 2.9

Let’s look at the NMHC/CO ratio in emissions! Total Emissions  NMHC/CO = (19.2 x 10 9 mol/yr)/ (16.5 x 10 9 mol/yr) = 1.2 ppbC/ppb Mobile Emissions  NMHC/CO = (16.6 x 10 9 mol/yr)/ (12.5 x 10 9 mol/yr) = 1.3 ppbC/ppb Ambient  NMHC/CO = 0.5 (slope of scatter plot)  NMHC/CO = 1.3, 0.9 (Mean, median of ratio at National Housing 10T)

Bangkok Emissions Inventory Conclusions  NO x /CO – lower for ambient than inventory  NMHC/NO x – higher for ambient than inventory  NMHC/CO – reasonably close in ambient to inventory  These results make one question the NO x portion of the inventory specifically. It seems to be high in the inventory relative to both CO and NMHC.

Differences between Emission Inventories and Ambient are Common

Problems with Vehicle Emissions

Uncertainties in Evaluation of Emission Inventories EMISSION INVENTORY UNCERTAINTY ISSUES  Spatial and temporal allocation of activities  Adjustment of emission rates for temperature and day-specific activities  Assignment of accurate and representative source speciation profiles AMBIENT MEASUREMENTS UNCERTAINTY ISSUES  The representativeness of the monitoring sites  The influence of lower quantifiable limits and precision  The identification, misidentification, or lack of identification of all important species  Potential sampling or handling losses of total mass or individual species COMPARISONS-RELATED UNCERTAINTY ISSUES  The matching of emissions and ambient NMOC species  The temporal matching of the emissions and ambient data  The spatial matching of the emissions and ambient data  Meteorological factors such as wind speed and direction and mixing height  The level of ambient background NMOC and NO x concentrations  Chemical reactions

VOCs as tracers SpeciesMajor SourceComments AcetyleneMobile sources, combustion processes Tracer for vehicle exhaust EtheneMobile sources, petrochemical industry Tracer for vehicle exhaust EthaneNatural gas useNon-reactive PropaneLPG and natural gas use, oil and gas production Relatively non-reactive, often underestimated in E.I. i-butaneConsumer products, gasoline evaporative emissions, refining Replacement for CFCs in consumer products

VOCs as tracers (continued) SpeciesMajor SourceComments ButaneGasoline evaporative emission Tracer of gasoline use IsopreneBiogenics Tracer of biogenic emission, highly reactive BenzeneMotor vehicle exhaust, combustion processes, refining Tracer for combustion, motor vehicle exhaust TolueneSolvent use, refining, mobile sources One of most abundant species in urban air internal olefins Gasoline evaporative emissions, plastics production Reactive XylenesSolvent use, refining, mobile sources Reactive

SPECIATE  This is a very useful tool to provide estimates of the composition of emissions from a variety of sources.  Speciates the TOC emissions from a few hundred different sources into individual organic compounds.  Also, speciates the PM emissions from a few hundred different sources into individual “elemental” contributions.  Source profiles can be exported to the Chemical Mass Balance (CMB) model.

Source Contributions  Species contributions to sources are generally based on emission source measurements or standard source- contributions like SPECIATE.  Source characterization can be quite expensive and representative of operations during test conditions.  We will briefly discuss an option based on ambient measurements.

Comparison of Source Contributions

GRACE/SAFER Graphical Ratio Analysis for Composition Estimates (GRACE)  Correlations between acetylene (assumed to be emitted solely from vehicle exhaust) and other VOC are used to establish the minimum and maximum exhaust-related ratios of acetylene to other species. GRACE plots of each roadway-corrected species versus all others are also examined. Source Apportionment by Factors with Explicit Restrictions (SAFER)  SAFER is a multivariate receptor model that predicts the number of sources and their composition from the ambient data. SAFER requires that these predictions be consistent with observed intercorrelations of the concentrations and with physical constraints and explicit constraints derived from GRACE.  SAFER requires large data sets, thus, the PAMS auto-GC data are well suited for this analysis. Environ. Sci. Technol., 28, , 1994.

Plots of VOCs vs Acetylene

Edge Relationship Environ. Sci. Technol., 28, (1994).

Ratios to Acetylene

Ambient Data for Emissions Profiles GRACE/SAFER RESULTS 1990 ATLANTA OZONE STUDY  Using ambient data, obtained three source profiles: roadway emissions (acetylene), whole gasoline (roadway-corrected 2,3- dimethylpentane), gasoline headspace vapor (n-butane).  GRACE/SAFER-derived profiles compared well to source measurements.  Source profiles used in subsequent CMB modeling.  PAMS data well suited for these analyses.

VOC Source Contributions Roadway Whole Gasoline headspace White – model derived Black – source derived

Chemical Mass Balance Approach  The CMB model can be quite useful in identifying various source contributions to ambient air quality measurements.  CMB has been used extensively to understand source contributions to particulate measurement, based on the elemental composition of samples.  The same approach is quite useful for understanding various source contributions to ambient VOC measurements, based on speciated VOC composition of the samples.