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The application of data from photochemical assessment monitoring stations to the observation-based model
Atmospheric Environment 34 (2000) 2325}2332
The application of data from photochemical assessment monitoring stations to the observation-based model Carlos A. Cardelino*, William L. Chameides School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA Received 9 June 1999; accepted 23 September 1999
Abstract The observation-based model } a diagnostic model designed to estimate ozone sensitivity to changes in the concentration of volatile organic compounds, nitrogen oxides, and CO } was used to analyze data gathered during the summer of 1995 at three photochemical assessment monitoring sites (PAMS). The sites were located in Washington DC, Bronx, New York, and Houston, Texas, with the "rst two being categorized as urban/cental city commercial and the latter as industrial/suburban. Our analysis indicated that: (i) natural hydrocarbons (primarily isoprene) represented a signi"cant fraction of the total hydrocarbon reactivity at all three sites and signi"cantly degraded the e$cacy of VOC emission reductions as an O mitigation strategy; and (ii) afternoon NO concentrations at all three sites typically fell to 3 levels at or below the limit of detection of the PAMS instrumentation and, as a result, it was not possible to determine whether O at the sites was more sensitive to reductions in anthropogenic hydrocarbons or nitrogen oxides. ( 2000 3 Elsevier Science Ltd. All rights reserved. Keywords: Observation-based model; Hydrocarbon reactivities; Ozone precursors; PAMS; Ozone control strategies
1. Introduction Emission-based models (EBMs) have played a major role in studying the relationship between the emission of ozone (O ) precursors and the production and accumu3 lation of ozone in urban areas (Russell and Dennis, 2000). There are, however, signi"cant uncertainties in many aspects of EBMs, e.g., the emission inventories used to de"ne the sources of the O precursors and the meteoro3 logical "elds and parameterizations used to simulate boundary layer dynamics. For this reason, more empirical or observation-based approaches have been developed to provide an alternate and complimentary method for assessing O precursor relationships in urban 3 areas (Hidy, 2000). One such alternate approach is the observation-base model (OBM) (Cardelino and Chameides, 1995 and hereafter referred to CC). The OBM uses measurements of ambient concentrations of O and pre3 cursors along with an alogorithm for simulating the
* Corresponding author.
photochemical production and destruction of O . From 3 these inputs, the OBM assesses the sensitivity of net O production in an urban area to changes in the emis3 sions of nitrogen oxides (NO "NO#NO ), volatile x 2 organic compounds (VOC), and carbon monoxide (CO), as well individual and various groupings of VOCs and CO. Unlike EBMs, the OBM does not use emission inventories and does not require the simulation of boundary layer dynamics. Since the OBM is driven by observations, it requires accurate and complete atmospheric chemical datasets. In the past such datasets were, for the most part, only gathered during intensive "eld campaigns launched in a particular area for a limited period of time (Hidy, 2000). Indeed, the initial application of the OBM was to Atlanta using data gathered during an EPA "eld sampling program conducted during the summer of 1990 (CC). In recent years however, monitoring networks have begun to routinely measure O precursor concentrations 3 as well as O in urban areas (Demerjian, 2000). One 3 such network is that of the photochemical assessment monitoring stations (PAMS) which have been
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established in the United States to corroborate emission inventories and support photochemical modeling in O non-attainment areas (EPA, 1996). Within a given 3 non-attainment area, there can be as many as "ve PAMS depending on the area's population. These stations are located within the urban core as well as at sites that generally lie upwind and downwind of the core. The data reported from each PAMS is supposed to include hourly averaged concentrations for O , nitric oxide (NO), 56 3 target hydrocarbons, and surface meteorological parameters. Thus, in principle, the PAMS data should be ideally suited for analysis by the OBM. In this work we use the OBM to analyze data gathered during the summer of 1995 from PAMS located in three metropolitan areas: Washington DC; Bronx, New York; and Houston Texas. Our objectives are two-fold: (i) to assess the utility of using PAMS data to assess O pre3 cursor relationships using diagnostic photochemical models such as the OBM; and (ii) to assess the applicability of conclusions reached by CC for Atlanta using the OBM to three other metropolitan areas. With regard to the latter objective, we speci"cally focus on two "ndings. The "rst has to do with the role of natural or biogenic hydrocarbons. CC found that the observed isoprene concentrations in Atlanta were su$ciently large to signi"cantly decrease the sensitivity of O production to VOC and enhance its sensitiv3 ity to NO . However, Atlanta is especially sylvan, and it x is not clear whether natural hydrocarbons have a signi"cant role in other large metropolitan areas. The second "nding relates to the role of afternoon NO concentrations. CC found that the magnitude of afternoon NO concentrations in Atlanta was critical to the relative e$cacy of VOC and NO emission controls. If x afternoon NO was less than about 0.75 pbbv, they found that NO reductions would be most e!ective; if afternoon x NO was greater than about 0.75 ppbv, VOC reductions would be most e!ective. Because afternoon NO concentrations in Atlanta typically fell below 1}2 ppbv and the NO instrumentation used in the 1990 study were unable to accurately quantify NO at these concentrations, CC could not determine whether O production in Atlanta 3 during the study period was more sensitive to VOC or to NO . On the basis of this "nding, CC recommended that x monitoring networks use NO instrumentation with a sub-ppbv limit of detection. However, this recommendation is only valid if the conclusions of CC are applicable to other metropolitan areas, and this has heretofore not been assessed. We begin our presentation with a brief review of the main characteristics of the OBM and a discussion of the data used in our analysis. 2. The observation-based model As described in more detail by CC, the OBM utilizes hourly averaged concentrations of VOCs, NO, CO, and
O , as well as meteorological parameters, measured at one 3 or more sites within an urban area. These data are used to constrain a coupled set of photochemical box models which calculate the total amount of O photochemically produc3 ed during the daylight hours at each of the measurement sites, as well as the sensitivity of the O photochemical 3 production to changes in the concentrations of the precursor compounds at these sites. Because most datasets only contain observations for speciated hydrocarbons (HC), in practice OBM analyses are limited to consideration of only the HC portion of the total VOC burden. The approach used by the OBM is based on the concept of relative incremental reactivity (RIR) developed by Carter and Atkinson (1989). The ozone-forming potential at a given site is de"ned as the model-calculated net amount of ozone formed and NO consumed over a 12-hour period using the observed concentrations at the site. The model is then run with the concentrations of individual or groups of precursors changed by a speci"ed percentage. The RIR of precursor X at a site is then estimated as the % change in O produced per % change 3 in precursor concentration, and thus provides a relative measure of the e!ectiveness of reducing the emissions of one compound or group of compounds over that of another compound or group of compounds. Many di!erent types of RIR functions can be determined. For instance, in addition to calculating the RIR for a speci"c hydrocarbon species, the e!ectiveness of reducing a group of compounds } for instance, all hydro-
Table 1 Ozone and hydrocarbons concentrations for the 18 episode days analyzed Site location
DC DC DC DC DC DC NY NY NY NY NY NY TX TX TX TX TX TX
Date
6/18 7/14 7/15 7/31 8/20 8/21 6/19 6/20 7/14 7/22 8/1 8/27 6/21 6/22 7/9 7/11 7/13 8/3
O maximum 3 (ppbv)
125 124 155 116 115 115 116 120 131 115 113 110 131 125 131 122 136 148
Propy-equivalents (ppbC) average (7 a.m.}7 p.m.) AHC#CO
NHC
30.5 32.7 28.9 48.5 17.9 37.9 99.6 76.5 29.6 96.4 84.0 41.0 39.6 31.8 53.0 31.0 82.4 89.9
5.4 23.4 28.1 15.1 6.8 15.6 22.4 24.8 32.5 35.5 35.2 13.5 11.1 6.6 12.0 12.6 8.3 18.4
C.A. Cardelino, W.L. Chameides / Atmospheric Environment 34 (2000) 2325}2332
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Fig. 1. Concentrations of anthropogenic hydrocarbons (AHC), natural hydrocarbons (NHC), nitric oxide (NO), and ozone (O ) 3 observed at the Washington DC site as a function of time of day for 6 episode days and the average of the 6 d. Note that hydrocarbon concentrations are presented in terms of propylene equivalents and in the case of NO, a high and low average are presented.
carbons from mobile (MHC) or stationary sources (SHC) } can be estimated. Similarly, the e!ectiveness of reducing all anthropogenic hydrocarbons (AHC) can be estimated by allowing all hydrocarbons that arise from anthropogenic emissions to change at the same time and the sensitivity of O to natural hydrocarbons (NHC) by 3 allowing all hydrocarbons arising from biogenic sources to vary simultaneously. The relative e$cacy of reducing NO emissions over that of reducing hydrocarbon emissions can be inferred by simply comparing the RIR function for hydrocarbons with the RIR for NO. 3. PAMS data used in this analysis Data collected from the PAMS network are archived as part the aerometric information retrieval system
(AIRS). Of the many PAMS in operation during the summer of 1995, only a few of them reported the complete set of hourly measurements that are needed to run the OBM. From these, we selected three sites located within three major metropolitan areas: (i) a site in Washington, DC (39.0423N, 71.0123W; ii); a site in Bronx, NY (40.8653N, 73.8813W); (iii) a site in Houston, TX (29.4403N, 95.1523W). The "rst two of these are categorized as Urban/Cental City Commercial. The Houston site is categorized as mixed Industrial/Suburban. For each site, hourly averaged concentrations of O , CO, 3 NO, and many tens of hydrocarbon species (see http://www.epa.gov/airs) were measured over a period that spanned the 1995 ozone season. The data for each of the three sites were retrieved from the AIRS website (http://www.epa.gov/airs) and used to
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Fig. 2. As in Fig. 1 except for the Bronx, New York site.
carry out `multiple-day analyses.a As described by CC, this involves selecting from the entire record for each site, a subset of `episode-daysa and then averaging the data from these days (from 7 a.m. to 7 p.m.) to produce a single composite dataset for each site that is then input into the OBM. By averaging the data from several episode days, we tend to minimize the impact of transient extremes in the data that may have been caused by local meteorological conditions, spurious sources, and/or instrumental problems as well as avoid analytical problems arising from missing data. For the Houston site, we initially selected 15 days in which the peak 1-h averaged O concentration at the site 3 exceeded 120 ppbv. However, only 6 days had a su$ciently complete record of data to allow analysis with the OBM. For the Bronx site, 6 days were identi"ed for analysis by selecting all days with a peak O in excess of 3 110 ppbv. For the Washington DC site, 6 days were
identi"ed for analysis by selecting days with peak O in 3 excess of 115 ppbv. A summary of all days selected for analysis from the three sites along with their peak O concentrations is 3 presented in Table 1. The hourly concentrations of hydrocarbons, NO, and O for each episode-day selected 3 along with their composite averages are illustrated in Figs. 1}3 for Washington DC, Bronx, and Houston, respectively. 3.1. Hydrocarbon and CO data The hourly hydrocarbon concentrations displayed in Figs. 1}3, as well as the 12-hour averaged total concentrations listed for each day in Table 1 are expressed using the propylene-equivalents scale of Chameides et al. (1992). Moreover, the hydrocarbons are aggregated into anthropogenic hydrocarbons (AHC) and natural
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Fig. 3. As in Fig. 1, except for the Houston, Texas site.
hydrocarbons (NHC). The reactivity of NHC came primarily from isoprene; isoprene was the only NHC measured in Washington, DC and the terpenes (alpha- and beta-pinene) measured at the Bronx, NY and Houston, TX had very small concentrations. All other hydrocarbons were assumed to be of anthropogenic origin. CO concentrations (not illustrated here) generally contributed about 10% or less to the total of the propyleneequivalents for AHC#CO listed in Table 1. It should also be noted with regard to CO, that of the 6 days data from the Washington, DC, site, four of them lacked CO measurements. Since our OBM calculations are not very sensitive to CO, the absence of this data is not likely to have seriously a!ected our results. An interesting aspect of the data from all three sites is the relatively high NHC reactivity. In fact, some episode-days in Washington, DC (with isoprene concentrations in the range 1.22}1.69 ppb) and most days in the
Bronx, NY (where the average isoprene concentrations were in the range 1.17}1.85 ppb) had isoprene concentrations that approached those found in Atlanta (Cardelino and Chameides, 1995). In addition, during the July 14 episode at Bronx, NY and the July 15 episode at Washington, DC, the average NHC reactivity was greater than that of the AHC reactivity (see Table 1). To further substantiate that the species listed in the datasets as isoprene and pinenes were properly identi"ed and were in fact of biogenic origin, we carried out a number of tests. One test compared the correlation of m}p-xylene and 1,2,4-trimethylbenzene with the correlation of isoprene and 1,2,4-trimethylbenzene. Strong correlations were found between the concentrations of m}p-xylene and 1,2,4-trimethylbenzene, consistent with inventories that indicate that mobile emissions are the overwhelming sources of both compounds (Sawyer et al., 2000 and the references cited therein). On the other hand, there was no
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Fig. 4. OBM-calculated relative incremental reactivities (RIRs) for nitric oxide (NO), total hydrocarbons (THC), anthropogenic hydrocarbons (AHC), natural hydrocarbons (NHC), and CO for the three PAMS sites using the high average and low average NO concentrations.
signi"cant correlation between isoprene and 1,2,4trimethylbenzene, suggesting that observed isoprene was indeed emitted from a source other than from mobile emissions. Another test examined the variation in the ratio of NHC to AHC as a function of time of day at each site. The minimum in the ratios were found to occur in the early morning, while the maximum in the mid- to lateafternoon. This result is consistent with AHCs having a major contribution from mobile sources and NHC having a source closely tied to photosynthesis (Guenther et al., 2000). 3.2. NO data In virtually all cases, NO concentrations start out in the morning quite high (i.e., *10 ppbv) but rapidly fall to concentrations that appear to be essentially equivalent
to the detection limit of the NO detectors used at the sites. Each site dealt with the problem of low afternoon NO concentrations di!erently. For example at the Washington DC site, all afternoon data were reported to be 3 pbbv before 15 July. On 31 July, afternoon NO data were reported as either 2 or 1 ppbv, and after 31 July, all NO data were recorded as 1 ppbv. At the New York site, all afternoon NO data were simply reported as 2 pbbv, with the exception of 1 datapoint (1900 hours on 19 June) when a concentration of 1 ppbv was reported. The data reported from the Houston site, however, are somewhat di!erent. On 22 June, NO concentrations at Houston exceeded 2 ppbv throughout most of the afternoon. For much of the rest of the afternoon data, NO is recorded as either 0 or 1 ppbv. The fact that the same NO concentration is often reported at the sites hour after hour during the afternoon suggests that the concentrations were at or below the
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instruments' detection limit. This is in turn suggests that much of the afternoon NO data in our datasets are not quantitative. To bracket the possible uncertainty this might give rise to in our OBM analyses, we use two sets of average NO concentrations as input to the OBM. The `high averagea NO was obtained by assuming that all afternoon NO concentrations recorded as 1 ppbv or less were in fact equal to 1 ppbv and then averaging the data. The `low averagea NO was obtained by assuming that all afternoon NO concentrations recorded as )2 ppbv (and 3 ppbv for the Washington DC site) were equal 0.4 ppbv and then averaging the data. In order to minimize the impact of extreme NO concentration observations, geometric instead of arithmetic averages were calculated. The resulting high and low averages are illustrated in Figs. 1}3. Note that in each case, the two averages are the same in the morning, but diverge signi"cantly in the afternoon.
4. Results of the OBM calculations The RIR values for NO, THC, AHC, NHC, and CO calculated for each site using high and low average NO concentrations are illustrated Fig. 4. The results are quite consistent with those obtained by CC using data from Atlanta. In the case of natural hydrocarbons, we "nd that at all three sites, and for both high and low average NO, the RIR for NHC is similar in magnitude to the RIR for AHC. As a result, the sensitivity of O production to 3 reductions in AHC, the controllable portion of the HC loading, is about 50% smaller than the O sensitivity to 3 THC (i.e., RIR(AHC)&0.5 RIR(THC)). These results provide further evidence that biogenic hydrocarbons are ubiquitous, even within some of the United States' largest metropolitan areas, and they can signi"cantly a!ect the relative e$cacy of VOC- and NO -based O mitigation x 3 strategies. As was the case for CC's analysis in Atlanta, our results are highly sensitive to the afternoon NO concentration. Note in Fig. 4 that for each site, the RIR(AHC) is signi"cantly larger than RIR(NO) when the high average NO was used. However, the complete opposite result, with RIR(NO) larger the RIR(AHC), is obtained when the low average NO was used. Recall that the low average NO pro"les were derived assuming a minimum afternoon NO concentration of 0.4 ppbv. In Fig. 5 we illustrate how our results for the New York site vary as we allow this minimum NO concentration to vary from 0.4 to 2.0 ppbv. We "nd, for this case, that O is most sensitive 3 to NO when the minimum NO concentration is )0.9 ppbv, and is most sensitive to AHC when NO )0.9 ppbv. The clear implication of these results, like those of CC, are that, if data from monitoring networks are to be used to diagnose O precursor relationships, 3 these networks will need to use instrumentation capable
Fig. 5. Variation in OBM-calculated relative incremental reactivities (RIRs) for nitric oxide (NO), anthropogenic hydrocarbons (AHC), natural hydrocarbons (NHC), and CO at the Bronx, New York site as a function of the assumed minimum afternoon NO concentration.
of reliably quantifying NO concentrations at the subppbv level.
5. Conclusion Diagnostic models such as the OBM can provide useful insights into the relationships between O and its 3 precursor compounds. However, their application requires the existence of appropriate chemical databases. In the case of the OBM, the data requirement has heretofore limited its application to only one urban area. The recent implementation of more comprehensive chemical measurement networks such as that of PAMS is creating a rich database that could in principle greatly enhance the applicability of models such as the OBM. Our application of the OBM to data from PAMS con"rms that the basic design of these networks makes them well suited to analysis with the OBM. However, our study also highlights how de"ciencies in these data (for example from missing data or from instrumentation without adequate sensitivity) can severely limit the reliability and utility of the analysis.
Acknowledgements This work was supported in part by the U. S. Environmental Protection Agency through Cooperative
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Agreements CR816963 and CR824849 as part of the Southern Oxidant Study. References Cardelino, C.A., Chameides, W.L., 1995. An observation-based model for analyzing ozone precursor relationship in the ambient atmosphere. Journal of Air Waste Management Association 45, 161}180. Carter, W.L., Atkinson, R., 1989. Computer modeling of incremental hydrocrbon reactivity. Environmental Science and Technology 21, 864}880. Chameides, W.L., Fehsenfeld, F., Rodgers, M.O., Cardelino, C.A., Martinez, J., Parrish, D., Lonneman, W., Lawson, D.R., Rasmussen, R.A., Zimmerman, P., Greengaeg, J., Middleton, P., Wang, T., 1992. Ozone precursor relationships in the ambient atmosphere. Journal of Geophysical Research 97, 6037}6055.
Demerjian, K.L., 2000. A review of national monitoring networks in north America. Atmospheric Environment 34, 1861}1884. Environmental Protection Agency, 1996. Photochemical assessment monitoring stations. 1996 Data Analysis Results Report, EPA-454/R-96-006, O$ce of Air Quality, October 1996. Guenther, A., Geron, C., Pierce, T., Lamb, B., Harley, P., Fall, R., 2000. Natural emissions of non-methane volatile organic compounds, carbon monoxide, and oxides of nitrogen from North America. Atmospheric Environment 34, 2205}2230. Hidy, G.M., 2000. Ozone process insights from "eld experiments. Atmospheric Environment 34, 2001}2022. Russell, A., Dennis, R., 2000. NARSTO critical review of photochemical models and modeling. Atmospheric Environment 34, 2283}2324. Sawyer, R.F., Harley, R.A., Cadle, S.H., Norbeck, J.M., Slott, R., Bravo, H.A., 2000. Mobile sources critical review: 1998 NARSTO assessment. Atmospheric Environment 34, 2161}2181.
The application of data from photochemical assessment monitoring stations to the observation-based model Carlos A. Cardelino*, William L. Chameides School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA Received 9 June 1999; accepted 23 September 1999
Abstract The observation-based model } a diagnostic model designed to estimate ozone sensitivity to changes in the concentration of volatile organic compounds, nitrogen oxides, and CO } was used to analyze data gathered during the summer of 1995 at three photochemical assessment monitoring sites (PAMS). The sites were located in Washington DC, Bronx, New York, and Houston, Texas, with the "rst two being categorized as urban/cental city commercial and the latter as industrial/suburban. Our analysis indicated that: (i) natural hydrocarbons (primarily isoprene) represented a signi"cant fraction of the total hydrocarbon reactivity at all three sites and signi"cantly degraded the e$cacy of VOC emission reductions as an O mitigation strategy; and (ii) afternoon NO concentrations at all three sites typically fell to 3 levels at or below the limit of detection of the PAMS instrumentation and, as a result, it was not possible to determine whether O at the sites was more sensitive to reductions in anthropogenic hydrocarbons or nitrogen oxides. ( 2000 3 Elsevier Science Ltd. All rights reserved. Keywords: Observation-based model; Hydrocarbon reactivities; Ozone precursors; PAMS; Ozone control strategies
1. Introduction Emission-based models (EBMs) have played a major role in studying the relationship between the emission of ozone (O ) precursors and the production and accumu3 lation of ozone in urban areas (Russell and Dennis, 2000). There are, however, signi"cant uncertainties in many aspects of EBMs, e.g., the emission inventories used to de"ne the sources of the O precursors and the meteoro3 logical "elds and parameterizations used to simulate boundary layer dynamics. For this reason, more empirical or observation-based approaches have been developed to provide an alternate and complimentary method for assessing O precursor relationships in urban 3 areas (Hidy, 2000). One such alternate approach is the observation-base model (OBM) (Cardelino and Chameides, 1995 and hereafter referred to CC). The OBM uses measurements of ambient concentrations of O and pre3 cursors along with an alogorithm for simulating the
* Corresponding author.
photochemical production and destruction of O . From 3 these inputs, the OBM assesses the sensitivity of net O production in an urban area to changes in the emis3 sions of nitrogen oxides (NO "NO#NO ), volatile x 2 organic compounds (VOC), and carbon monoxide (CO), as well individual and various groupings of VOCs and CO. Unlike EBMs, the OBM does not use emission inventories and does not require the simulation of boundary layer dynamics. Since the OBM is driven by observations, it requires accurate and complete atmospheric chemical datasets. In the past such datasets were, for the most part, only gathered during intensive "eld campaigns launched in a particular area for a limited period of time (Hidy, 2000). Indeed, the initial application of the OBM was to Atlanta using data gathered during an EPA "eld sampling program conducted during the summer of 1990 (CC). In recent years however, monitoring networks have begun to routinely measure O precursor concentrations 3 as well as O in urban areas (Demerjian, 2000). One 3 such network is that of the photochemical assessment monitoring stations (PAMS) which have been
1352-2310/00/$ - see front matter ( 2000 Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 9 9 ) 0 0 4 6 9 - 0
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C.A. Cardelino, W.L. Chameides / Atmospheric Environment 34 (2000) 2325}2332
established in the United States to corroborate emission inventories and support photochemical modeling in O non-attainment areas (EPA, 1996). Within a given 3 non-attainment area, there can be as many as "ve PAMS depending on the area's population. These stations are located within the urban core as well as at sites that generally lie upwind and downwind of the core. The data reported from each PAMS is supposed to include hourly averaged concentrations for O , nitric oxide (NO), 56 3 target hydrocarbons, and surface meteorological parameters. Thus, in principle, the PAMS data should be ideally suited for analysis by the OBM. In this work we use the OBM to analyze data gathered during the summer of 1995 from PAMS located in three metropolitan areas: Washington DC; Bronx, New York; and Houston Texas. Our objectives are two-fold: (i) to assess the utility of using PAMS data to assess O pre3 cursor relationships using diagnostic photochemical models such as the OBM; and (ii) to assess the applicability of conclusions reached by CC for Atlanta using the OBM to three other metropolitan areas. With regard to the latter objective, we speci"cally focus on two "ndings. The "rst has to do with the role of natural or biogenic hydrocarbons. CC found that the observed isoprene concentrations in Atlanta were su$ciently large to signi"cantly decrease the sensitivity of O production to VOC and enhance its sensitiv3 ity to NO . However, Atlanta is especially sylvan, and it x is not clear whether natural hydrocarbons have a signi"cant role in other large metropolitan areas. The second "nding relates to the role of afternoon NO concentrations. CC found that the magnitude of afternoon NO concentrations in Atlanta was critical to the relative e$cacy of VOC and NO emission controls. If x afternoon NO was less than about 0.75 pbbv, they found that NO reductions would be most e!ective; if afternoon x NO was greater than about 0.75 ppbv, VOC reductions would be most e!ective. Because afternoon NO concentrations in Atlanta typically fell below 1}2 ppbv and the NO instrumentation used in the 1990 study were unable to accurately quantify NO at these concentrations, CC could not determine whether O production in Atlanta 3 during the study period was more sensitive to VOC or to NO . On the basis of this "nding, CC recommended that x monitoring networks use NO instrumentation with a sub-ppbv limit of detection. However, this recommendation is only valid if the conclusions of CC are applicable to other metropolitan areas, and this has heretofore not been assessed. We begin our presentation with a brief review of the main characteristics of the OBM and a discussion of the data used in our analysis. 2. The observation-based model As described in more detail by CC, the OBM utilizes hourly averaged concentrations of VOCs, NO, CO, and
O , as well as meteorological parameters, measured at one 3 or more sites within an urban area. These data are used to constrain a coupled set of photochemical box models which calculate the total amount of O photochemically produc3 ed during the daylight hours at each of the measurement sites, as well as the sensitivity of the O photochemical 3 production to changes in the concentrations of the precursor compounds at these sites. Because most datasets only contain observations for speciated hydrocarbons (HC), in practice OBM analyses are limited to consideration of only the HC portion of the total VOC burden. The approach used by the OBM is based on the concept of relative incremental reactivity (RIR) developed by Carter and Atkinson (1989). The ozone-forming potential at a given site is de"ned as the model-calculated net amount of ozone formed and NO consumed over a 12-hour period using the observed concentrations at the site. The model is then run with the concentrations of individual or groups of precursors changed by a speci"ed percentage. The RIR of precursor X at a site is then estimated as the % change in O produced per % change 3 in precursor concentration, and thus provides a relative measure of the e!ectiveness of reducing the emissions of one compound or group of compounds over that of another compound or group of compounds. Many di!erent types of RIR functions can be determined. For instance, in addition to calculating the RIR for a speci"c hydrocarbon species, the e!ectiveness of reducing a group of compounds } for instance, all hydro-
Table 1 Ozone and hydrocarbons concentrations for the 18 episode days analyzed Site location
DC DC DC DC DC DC NY NY NY NY NY NY TX TX TX TX TX TX
Date
6/18 7/14 7/15 7/31 8/20 8/21 6/19 6/20 7/14 7/22 8/1 8/27 6/21 6/22 7/9 7/11 7/13 8/3
O maximum 3 (ppbv)
125 124 155 116 115 115 116 120 131 115 113 110 131 125 131 122 136 148
Propy-equivalents (ppbC) average (7 a.m.}7 p.m.) AHC#CO
NHC
30.5 32.7 28.9 48.5 17.9 37.9 99.6 76.5 29.6 96.4 84.0 41.0 39.6 31.8 53.0 31.0 82.4 89.9
5.4 23.4 28.1 15.1 6.8 15.6 22.4 24.8 32.5 35.5 35.2 13.5 11.1 6.6 12.0 12.6 8.3 18.4
C.A. Cardelino, W.L. Chameides / Atmospheric Environment 34 (2000) 2325}2332
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Fig. 1. Concentrations of anthropogenic hydrocarbons (AHC), natural hydrocarbons (NHC), nitric oxide (NO), and ozone (O ) 3 observed at the Washington DC site as a function of time of day for 6 episode days and the average of the 6 d. Note that hydrocarbon concentrations are presented in terms of propylene equivalents and in the case of NO, a high and low average are presented.
carbons from mobile (MHC) or stationary sources (SHC) } can be estimated. Similarly, the e!ectiveness of reducing all anthropogenic hydrocarbons (AHC) can be estimated by allowing all hydrocarbons that arise from anthropogenic emissions to change at the same time and the sensitivity of O to natural hydrocarbons (NHC) by 3 allowing all hydrocarbons arising from biogenic sources to vary simultaneously. The relative e$cacy of reducing NO emissions over that of reducing hydrocarbon emissions can be inferred by simply comparing the RIR function for hydrocarbons with the RIR for NO. 3. PAMS data used in this analysis Data collected from the PAMS network are archived as part the aerometric information retrieval system
(AIRS). Of the many PAMS in operation during the summer of 1995, only a few of them reported the complete set of hourly measurements that are needed to run the OBM. From these, we selected three sites located within three major metropolitan areas: (i) a site in Washington, DC (39.0423N, 71.0123W; ii); a site in Bronx, NY (40.8653N, 73.8813W); (iii) a site in Houston, TX (29.4403N, 95.1523W). The "rst two of these are categorized as Urban/Cental City Commercial. The Houston site is categorized as mixed Industrial/Suburban. For each site, hourly averaged concentrations of O , CO, 3 NO, and many tens of hydrocarbon species (see http://www.epa.gov/airs) were measured over a period that spanned the 1995 ozone season. The data for each of the three sites were retrieved from the AIRS website (http://www.epa.gov/airs) and used to
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Fig. 2. As in Fig. 1 except for the Bronx, New York site.
carry out `multiple-day analyses.a As described by CC, this involves selecting from the entire record for each site, a subset of `episode-daysa and then averaging the data from these days (from 7 a.m. to 7 p.m.) to produce a single composite dataset for each site that is then input into the OBM. By averaging the data from several episode days, we tend to minimize the impact of transient extremes in the data that may have been caused by local meteorological conditions, spurious sources, and/or instrumental problems as well as avoid analytical problems arising from missing data. For the Houston site, we initially selected 15 days in which the peak 1-h averaged O concentration at the site 3 exceeded 120 ppbv. However, only 6 days had a su$ciently complete record of data to allow analysis with the OBM. For the Bronx site, 6 days were identi"ed for analysis by selecting all days with a peak O in excess of 3 110 ppbv. For the Washington DC site, 6 days were
identi"ed for analysis by selecting days with peak O in 3 excess of 115 ppbv. A summary of all days selected for analysis from the three sites along with their peak O concentrations is 3 presented in Table 1. The hourly concentrations of hydrocarbons, NO, and O for each episode-day selected 3 along with their composite averages are illustrated in Figs. 1}3 for Washington DC, Bronx, and Houston, respectively. 3.1. Hydrocarbon and CO data The hourly hydrocarbon concentrations displayed in Figs. 1}3, as well as the 12-hour averaged total concentrations listed for each day in Table 1 are expressed using the propylene-equivalents scale of Chameides et al. (1992). Moreover, the hydrocarbons are aggregated into anthropogenic hydrocarbons (AHC) and natural
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Fig. 3. As in Fig. 1, except for the Houston, Texas site.
hydrocarbons (NHC). The reactivity of NHC came primarily from isoprene; isoprene was the only NHC measured in Washington, DC and the terpenes (alpha- and beta-pinene) measured at the Bronx, NY and Houston, TX had very small concentrations. All other hydrocarbons were assumed to be of anthropogenic origin. CO concentrations (not illustrated here) generally contributed about 10% or less to the total of the propyleneequivalents for AHC#CO listed in Table 1. It should also be noted with regard to CO, that of the 6 days data from the Washington, DC, site, four of them lacked CO measurements. Since our OBM calculations are not very sensitive to CO, the absence of this data is not likely to have seriously a!ected our results. An interesting aspect of the data from all three sites is the relatively high NHC reactivity. In fact, some episode-days in Washington, DC (with isoprene concentrations in the range 1.22}1.69 ppb) and most days in the
Bronx, NY (where the average isoprene concentrations were in the range 1.17}1.85 ppb) had isoprene concentrations that approached those found in Atlanta (Cardelino and Chameides, 1995). In addition, during the July 14 episode at Bronx, NY and the July 15 episode at Washington, DC, the average NHC reactivity was greater than that of the AHC reactivity (see Table 1). To further substantiate that the species listed in the datasets as isoprene and pinenes were properly identi"ed and were in fact of biogenic origin, we carried out a number of tests. One test compared the correlation of m}p-xylene and 1,2,4-trimethylbenzene with the correlation of isoprene and 1,2,4-trimethylbenzene. Strong correlations were found between the concentrations of m}p-xylene and 1,2,4-trimethylbenzene, consistent with inventories that indicate that mobile emissions are the overwhelming sources of both compounds (Sawyer et al., 2000 and the references cited therein). On the other hand, there was no
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Fig. 4. OBM-calculated relative incremental reactivities (RIRs) for nitric oxide (NO), total hydrocarbons (THC), anthropogenic hydrocarbons (AHC), natural hydrocarbons (NHC), and CO for the three PAMS sites using the high average and low average NO concentrations.
signi"cant correlation between isoprene and 1,2,4trimethylbenzene, suggesting that observed isoprene was indeed emitted from a source other than from mobile emissions. Another test examined the variation in the ratio of NHC to AHC as a function of time of day at each site. The minimum in the ratios were found to occur in the early morning, while the maximum in the mid- to lateafternoon. This result is consistent with AHCs having a major contribution from mobile sources and NHC having a source closely tied to photosynthesis (Guenther et al., 2000). 3.2. NO data In virtually all cases, NO concentrations start out in the morning quite high (i.e., *10 ppbv) but rapidly fall to concentrations that appear to be essentially equivalent
to the detection limit of the NO detectors used at the sites. Each site dealt with the problem of low afternoon NO concentrations di!erently. For example at the Washington DC site, all afternoon data were reported to be 3 pbbv before 15 July. On 31 July, afternoon NO data were reported as either 2 or 1 ppbv, and after 31 July, all NO data were recorded as 1 ppbv. At the New York site, all afternoon NO data were simply reported as 2 pbbv, with the exception of 1 datapoint (1900 hours on 19 June) when a concentration of 1 ppbv was reported. The data reported from the Houston site, however, are somewhat di!erent. On 22 June, NO concentrations at Houston exceeded 2 ppbv throughout most of the afternoon. For much of the rest of the afternoon data, NO is recorded as either 0 or 1 ppbv. The fact that the same NO concentration is often reported at the sites hour after hour during the afternoon suggests that the concentrations were at or below the
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instruments' detection limit. This is in turn suggests that much of the afternoon NO data in our datasets are not quantitative. To bracket the possible uncertainty this might give rise to in our OBM analyses, we use two sets of average NO concentrations as input to the OBM. The `high averagea NO was obtained by assuming that all afternoon NO concentrations recorded as 1 ppbv or less were in fact equal to 1 ppbv and then averaging the data. The `low averagea NO was obtained by assuming that all afternoon NO concentrations recorded as )2 ppbv (and 3 ppbv for the Washington DC site) were equal 0.4 ppbv and then averaging the data. In order to minimize the impact of extreme NO concentration observations, geometric instead of arithmetic averages were calculated. The resulting high and low averages are illustrated in Figs. 1}3. Note that in each case, the two averages are the same in the morning, but diverge signi"cantly in the afternoon.
4. Results of the OBM calculations The RIR values for NO, THC, AHC, NHC, and CO calculated for each site using high and low average NO concentrations are illustrated Fig. 4. The results are quite consistent with those obtained by CC using data from Atlanta. In the case of natural hydrocarbons, we "nd that at all three sites, and for both high and low average NO, the RIR for NHC is similar in magnitude to the RIR for AHC. As a result, the sensitivity of O production to 3 reductions in AHC, the controllable portion of the HC loading, is about 50% smaller than the O sensitivity to 3 THC (i.e., RIR(AHC)&0.5 RIR(THC)). These results provide further evidence that biogenic hydrocarbons are ubiquitous, even within some of the United States' largest metropolitan areas, and they can signi"cantly a!ect the relative e$cacy of VOC- and NO -based O mitigation x 3 strategies. As was the case for CC's analysis in Atlanta, our results are highly sensitive to the afternoon NO concentration. Note in Fig. 4 that for each site, the RIR(AHC) is signi"cantly larger than RIR(NO) when the high average NO was used. However, the complete opposite result, with RIR(NO) larger the RIR(AHC), is obtained when the low average NO was used. Recall that the low average NO pro"les were derived assuming a minimum afternoon NO concentration of 0.4 ppbv. In Fig. 5 we illustrate how our results for the New York site vary as we allow this minimum NO concentration to vary from 0.4 to 2.0 ppbv. We "nd, for this case, that O is most sensitive 3 to NO when the minimum NO concentration is )0.9 ppbv, and is most sensitive to AHC when NO )0.9 ppbv. The clear implication of these results, like those of CC, are that, if data from monitoring networks are to be used to diagnose O precursor relationships, 3 these networks will need to use instrumentation capable
Fig. 5. Variation in OBM-calculated relative incremental reactivities (RIRs) for nitric oxide (NO), anthropogenic hydrocarbons (AHC), natural hydrocarbons (NHC), and CO at the Bronx, New York site as a function of the assumed minimum afternoon NO concentration.
of reliably quantifying NO concentrations at the subppbv level.
5. Conclusion Diagnostic models such as the OBM can provide useful insights into the relationships between O and its 3 precursor compounds. However, their application requires the existence of appropriate chemical databases. In the case of the OBM, the data requirement has heretofore limited its application to only one urban area. The recent implementation of more comprehensive chemical measurement networks such as that of PAMS is creating a rich database that could in principle greatly enhance the applicability of models such as the OBM. Our application of the OBM to data from PAMS con"rms that the basic design of these networks makes them well suited to analysis with the OBM. However, our study also highlights how de"ciencies in these data (for example from missing data or from instrumentation without adequate sensitivity) can severely limit the reliability and utility of the analysis.
Acknowledgements This work was supported in part by the U. S. Environmental Protection Agency through Cooperative
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Agreements CR816963 and CR824849 as part of the Southern Oxidant Study. References Cardelino, C.A., Chameides, W.L., 1995. An observation-based model for analyzing ozone precursor relationship in the ambient atmosphere. Journal of Air Waste Management Association 45, 161}180. Carter, W.L., Atkinson, R., 1989. Computer modeling of incremental hydrocrbon reactivity. Environmental Science and Technology 21, 864}880. Chameides, W.L., Fehsenfeld, F., Rodgers, M.O., Cardelino, C.A., Martinez, J., Parrish, D., Lonneman, W., Lawson, D.R., Rasmussen, R.A., Zimmerman, P., Greengaeg, J., Middleton, P., Wang, T., 1992. Ozone precursor relationships in the ambient atmosphere. Journal of Geophysical Research 97, 6037}6055.
Demerjian, K.L., 2000. A review of national monitoring networks in north America. Atmospheric Environment 34, 1861}1884. Environmental Protection Agency, 1996. Photochemical assessment monitoring stations. 1996 Data Analysis Results Report, EPA-454/R-96-006, O$ce of Air Quality, October 1996. Guenther, A., Geron, C., Pierce, T., Lamb, B., Harley, P., Fall, R., 2000. Natural emissions of non-methane volatile organic compounds, carbon monoxide, and oxides of nitrogen from North America. Atmospheric Environment 34, 2205}2230. Hidy, G.M., 2000. Ozone process insights from "eld experiments. Atmospheric Environment 34, 2001}2022. Russell, A., Dennis, R., 2000. NARSTO critical review of photochemical models and modeling. Atmospheric Environment 34, 2283}2324. Sawyer, R.F., Harley, R.A., Cadle, S.H., Norbeck, J.M., Slott, R., Bravo, H.A., 2000. Mobile sources critical review: 1998 NARSTO assessment. Atmospheric Environment 34, 2161}2181.