Grantee Research Project Results
Final Report: Observation-Based Approaches to VOC Emissions Inventory Reconciliation And Control Strategies for Photochemical Smog
EPA Grant Number: R826238Title: Observation-Based Approaches to VOC Emissions Inventory Reconciliation And Control Strategies for Photochemical Smog
Investigators: Henry, Ronald C. , Chang, Yu-Shuo
Institution: University of Southern California
EPA Project Officer: Hahn, Intaek
Project Period: February 1, 1998 through January 31, 2001 (Extended to August 31, 2003)
Project Amount: $441,574
RFA: Ambient Air Quality (1997) RFA Text | Recipients Lists
Research Category: Air , Air Quality and Air Toxics
Objective:
The objective of this research project was to reconcile volatile organic compound (VOC) emission inventories and observations of concentrations of VOC species, with a view to improving the deficiencies in the inventories. High levels of VOC in ambient air are a direct threat to human health because some VOCs are toxic. All VOCs are an indirect threat to health because these compounds can combine with nitrogen oxides in the air to produce dangerous levels of ozone and airborne particulate matter in urban and suburban atmospheres. As with all primary air pollutants, the emission inventory is a major tool for devising rational, cost-effective control strategies to reduce the threat to human heath by reducing emissions of VOCs. VOC emission inventories usually are self-reported and fraught with errors that lead to overestimates and underestimates of VOC emission rates. Often, the inventories only report total VOCs or general categories such as “olefins” rather than specific compounds, which are needed for accurate modeling of health effects.
Multivariate receptor modeling is one of the tools used in this project; specifically, the Unmix model invented and developed by the principal investigator. Given sufficient VOC or particulate composition data, Unmix can estimate the number, composition, and contribution of the sources affecting the monitor. Hourly VOC data are available at some sites, so wind direction and speed information can be used to discriminate further between sources. Another major objective of this study was to improve the methods of using wind data to relate output of multivariate receptor models to emission inventories.
This study also sought to produce empirical estimates of ozone production rates using hourly wind and ozone data. Average ozone production rates for each hour of the day were produced for summer and winter months. Ideally, these ozone production rates could be related statistically to meteorological parameters such as sunlight intensity, temperature, humidity, and levels of nitrogen oxides and VOCs.
Summary/Accomplishments (Outputs/Outcomes):
As is often the case in research, the most significant finding of this project was not foreseen at the start. The use of wind direction in conjunction with hourly concentrations of VOCs to point toward significant local sources always was a part of the emission inventory reconciliation originally proposed. The use of “pollution roses” and other forms of wind direction histograms was unsatisfactory because the direction of maximum concentration could not be determined to better than ±10 degrees. The solution to this problem was to use nonparametric regression (this is believed to be the first use of this technique in receptor modeling/source apportionment). By using nonparametric regression, the direction of a nearby source can be estimated to within a few tenths of a degree, an improvement of about a factor of 5 to 10 in accuracy. The nonparametric regression also provides error estimates that are very useful in distinguishing peaks in concentration that are real versus those caused by random events. Nonparametric regression has proven to be a very valuable tool for source identification for pollutants other than VOCs. In cooperation with researchers in Hong Kong, nonparametric regression made it possible to use SO2 as a tracer for airport emissions. In this research project, the main use of nonparametric regression was to attempt to correlate VOC sources determined by the Unmix multivariate receptor model with sources in the emission inventory. To this end, attention was focused on two photochemical assessment monitoring station (PAMS) sites in Houston, TX, near the ship channel, a major complex of refineries and petrochemical industries.
From hourly PAMS VOC data, Unmix estimated seven sources; the contribution of each to average total nonmethane hydrocarbons is given in Figure 1. After vehicle emissions from roadways, the largest source was dominated by isobutane. Isobutane is a feedstock for methyltertiarybutyl ether (MTBE) production. This source was not in the inventory at all. The wind direction dependence of this source was determined by nonparametric regression and is given in Figure 2.
Figure 1. Contribution of Sources to Average VOC Concentration at Clinton Dr. in Houston, TX, for 1997
Figure 2. Comparison of Wind Direction Dependence of the Isobutane Source With Distance-Weighted Emissions From Specific VOC Sources
The peak in the isobutane source occurs in a region with emissions dominated by the Valero refinery, although the inventory does not list it as a producer of MTBE or an emitter of isobutane. Indeed, of the seven sources determined by receptor modeling, only one small source could be unequivocally related to a source in the inventory.
Unmix multivariate receptor modeling was a major part of this project. In the spirit of scientific cooperation, improvements to Unmix by other investigators were explored. This included the algorithm for estimating the number of sources and advanced error analysis by Markov Chain Monte Carlo methods. Also, we assisted the U.S. Environmental Protection Agency (EPA) and holders of EPA grants with application of Unmix to source apportionment of particulate matter.
Estimates of photochemical production rates of ozone were determined by the Source Identification by Empirical Orthogonal Functions method. These showed the expected diurnal and seasonal variation. Ozone production in the South Coast Air Basin in the summer (August) of 1985, 1986, and 1995 peaks at 1,500 hours were about 9, 7, and 3 ppb/h, respectively. In the winter (December) of 1985, 1986, and 1995, the rates were close to 0 ppb/h in all cases.
Conclusions:
- This research project pioneered the use of nonparametric regression for air quality data analysis and receptor modeling. This technique shows great promise for environmental data analysis in general. It makes no assumptions about the distribution of the data and works well for arbitrary nonlinear relationships. The main limitation at the present time is that there is seldom enough data to apply it to more than two or three explanatory variables.
- Combining multivariate receptor modeling with nonparametric regression for wind direction dependence makes a powerful tool for reconciliation of VOC emission inventories and short-time average VOC concentrations.
- VOC emission inventories in the Houston ship channel could not be reconciled with observed VOC concentrations.
- Unmix multivariate receptor modeling of VOCs uncovered unexpected sources not consistent with the inventory.
- Rates of photochemical production of ozone can be estimated from routine monitoring data for ozone.
- Ozone production rates in the South Coast Air Basin in August decreased from 9 ppb/h in 1985 to 3 ppb/h in 1995.
The specific results of this research project will be useful to state agencies that are responsible for the compilation of VOC and air toxics inventories. EPA’s Office of Air Quality Planning and Standards and Office of Research and Development can use these results to improve guidelines for compiling VOC inventories and to improve the design of air quality research studies. More generally, the potential for the widespread use of nonparametric regression in environmental data analysis cannot be overstated. Hopefully, the results of this project will be a catalyst to spur the use of nonparametric regression throughout the air quality and environmental communities.
Journal Articles on this Report : 13 Displayed | Download in RIS Format
Other project views: | All 15 publications | 15 publications in selected types | All 14 journal articles |
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Chen L-WA, Doddridge BG, Dickerson RR, Chow JC, Henry RC. Origins of fine aerosol mass in the Baltimore–Washington corridor:implications from observation, factor analysis, and ensemble air parcel back trajectories. Atmospheric Environment 2002;36(28):4541-4554. |
R826238 (2001) R826238 (Final) R826373 (2002) |
Exit Exit Exit |
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Henry RC, Park ES, Spiegelman CH. Comparing a new algorithm with the classic methods for estimating the number of factors. Chemometrics and Intelligent Laboratory Systems 1999;48(1):91-97. |
R826238 (2001) R826238 (Final) R825173 (1999) |
Exit |
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Henry RC, Chang Y-S, Spiegelman CH. Locating nearby sources of air pollution by nonparametric regression of atmospheric concentrations on wind direction. Atmospheric Environment 2002;36(13):2237-2244. |
R826238 (2001) R826238 (Final) |
Exit Exit |
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Henry RC. Multivariate receptor models—current practice and future trends. Chemometrics and Intelligent Laboratory Systems 2002;60(1-2):43-48. |
R826238 (2001) R826238 (Final) |
Exit |
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Henry RC. Multivariate receptor modeling by N-dimensional edge detection. Chemometrics and Intelligent Laboratory Systems 2003;65(2):179-189. |
R826238 (2001) R826238 (Final) |
Exit |
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Kim BM, Henry RC. Diagnostics for determining influential species in the chemical mass balance receptor model. Journal of the Air & Waste Management Association 1999;49(12):1449-1455. |
R826238 (2001) R826238 (Final) |
Exit |
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Kim BM, Henry RC. Application of SAFER model to the Los Angeles PM10 data. Atmospheric Environment 2000;34(11):1747-1759. |
R826238 (2001) R826238 (Final) |
Exit Exit |
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Kim BM, Henry RC. Extension of self-modeling curve resolution to mixtures of more than three components. Part 3. Atmospheric aerosol data simulation studies. Chemometrics and Intelligent Laboratory Systems 2000;52(2):145-154. |
R826238 (2001) R826238 (Final) |
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Lewis CW, Norris GA, Conner TL, Henry RC. Source apportionment of Phoenix PM2.5 aerosol with the Unmix receptor model. Journal of the Air & Waste Management Association 2003;53(3):325-338. |
R826238 (2001) R826238 (Final) |
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Park ES, Spiegelman CH, Henry RC. Bilinear estimation of pollution source profiles and amounts by using multivariate receptor models. Environmetrics 2002;13(7):775-798. |
R826238 (Final) R825173 (1999) R825173 (2000) |
Exit Exit |
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Park ES, Henry RC, Spiegelman CH. Estimating the number of factors to include in a high-dimensional multivariate bilinear model. Communications in Statistics-Simulation and Computation 2000;29(3):723-746. |
R826238 (Final) R825173 (1999) R825173 (2000) |
Exit |
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Park ES, Guttorp P, Henry RC. Multivariate receptor modeling for temporally correlated data by using MCMC. Journal of the American Statistical Association 2001;96(456):1171-1183. |
R826238 (2001) R826238 (Final) R825173 (1999) R825173 (2000) |
Exit Exit |
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Yu KN, Cheung YP, Cheung T, Henry RC. Identifying the impact of large urban airports on local air quality by nonparametric regression. Atmospheric Environment 2004;38(27):4501-4507. |
R826238 (Final) |
Exit Exit |
Supplemental Keywords:
air quality, data analysis, volatile organic compounds, VOCs, ozone, emission inventory, statistics, receptor modeling, Unmix, nonparametric regression, Houston, TX, empirical orthogonal function, meteorological parameters, human health, nitrogen oxides, pollution roses, isobutane,, RFA, Scientific Discipline, Air, particulate matter, Environmental Chemistry, mobile sources, Ecological Risk Assessment, Ecology and Ecosystems, Atmospheric Sciences, tropospheric ozone, photochemical assessment monitoring, phototchemical modeling, multivariate receptor modeling, fine particles, ambient measurement methods, ozone, air quality models, ambient air, VOCs, photochemical smog, air sampling, photochemistry, emissions inventory, atmospheric transport, Volatile Organic Compounds (VOCs), measurement methods , atmospheric chemistry, chemical speciation sampling, particle transportProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.