Further Analysis of VOC Reactivity Metrics and Scales. Final Report. to the

Transcription

1 Further Analysis of VOC Reactivity Metrics and Scales Final Report to the Environmental Protection Agency Research Triangle Park, NC Contract #4D-5751-NAEX By Amir Hakami 1, Mohammad Arhami and A.G. (Ted) Russell Atlanta, GA July, Currently at the California Institute of Technology, Pasadena, CA.

2 Table of Contents Table of Contents...ii List of Tables. iii List of Figures... iii Introduction Reactivity Metrics from Prior Studies.4 Task 1: Domain-wide additional metrics for May and July Episodes...6 Taks 2: Subdomain Analyses..10 Task 3. Review of Reactivity Metrics: Appropriateness for Use in Air Quality Management Task 4. Assistance with Implementing DDM-3D in CMAQ for Reactivity Calculations Conclusion References Attachments: Appendix A: Task 1 Supporting Figures and Tables Appendix B: Task 2 Supporting Figures and Tables ii

3 List of Tables Table 1. Composition of the base reactivity mixture (Carter, 2002). 23 Table 2. Number of days that are available for calculations of each domain/episode reactivities.. 24 List of Figures Figure 1. Modeling domains for the Eastern US and central California..25 Figure 2. Comparison of different 3-D reactivity metrics for Eastern US: a) M2M, 1- hour; b) M2M, 8-hour; c) AVG, 1-hour; d) AVG, 8-hour; e) AVS, 1-hour; f) AVS, 8- hour Figure 3. Spatial plots of a) relative reactivity of benzaldehyde, and b) base mixture absolute reactivity.. 29 Figure 4. Relative reactivities for 1-hour a) M2M, and b) AVG metrics during July 1995 episode and with two different base mixture reactivity cutpoints for urban domain.30 Figure 5. The effect of the applied concentration threshold (standard) on the a) 1-hour, and b) 8-hour domainwide AVS metric for the July 1995 episode...31 Figure 6. Different subdomains used for the analysis...32 Figure 7. 1-hour M2M (top) and AVS (bottom) metrics for all domains: a) July 1995, b) July 2010, c) May 1995, and d) May Figure 8. Average and standard deviation of different metrics among all subdomains/episodes for 1-hour (top) and 8-hour (bottom) averaging periods Figure 9. Comparison between urban and non-urban subdomains for 1-hour M2M (top) and AVS (bottom) metrics: a) July 1995, b) May Figure 10. Comparison between domainwide and California Central Valley for 1-hour M2M (top) and AVS (bottom) metrics.. 40 Figure 11. Comparison between domainwide M2M (top) and AVS (bottom) metrics using two different emissions inventories for a) July and b) May episode Figure 12. Comparison between all domainwide 1-hour (top) and 8-hour (bottom) metrics for a) July 1995, b) July 2010, c) May 1995, and d) May 2010 episode...46 iii

4 Introduction Tropospheric ozone is formed from nonlinear reactions between VOCs and NOx, therefore its control is based on strategies for reducing one or both of the precursors. VOCs can differ significantly in their ozone formation potential (also referred to as reactivity, incremental reactivity, ozone production efficiency, etc.), therefore, VOCbased regulations that take into account these differences have the potential to reduce ozone levels in a more cost-effective manner. In the initial studies, organic reactivities were traditionally calculated using a box model simulation (i.e., steering the chemistry of the species). However, different environmental factors (e.g., meteorologies, emission variability, NOx availability, etc.) can affect the real-world ozone formation potential of an organic compound. To address this limitation, 3-D air quality models are now being used to simulate incremental reactivities while considering the most important physical processes that take place in the atmosphere. 3-D modeling of organic reactivities has been the subject of recent studies, in particular an effort coordinated by the Reactivity Research Working Group (RRWG) (e.g., Hakami et al. 2003b, Carter et al. 2003, Arunachalam et al., 2003). Those studies have led to a variety of results, including a comparison of reactivity metrics between regions of the country, using different inventories, using different models, using different chemical mechanisms, and the development of a variety of different scales. By and large, those studies suggest that reactivity scales can be developed using a variety of metrics, and that well constructed metrics can lead to scales that are applicable over a wide range of conditions, and do not vary significantly from one region to another, between episodes or 1

5 among different inventories. This work warranted further investigation, e.g., considering different metrics, looking at sub-domains of the previously analyzed areas, and considering the attributes of a good reactivity scale, and metrics that appear to best satisfy those attributes. Here, as a follow on study to the work previously conducted by Hakami et al. (2003b), with inspiration of the work by Carter et al., (2003), four new tasks were performed to provide information relating to comparing and assessing reactivity scales. As a first task, three new reactivity metrics were calculated using the same domains, episodes and inventories used previously in Hakami et al., (2003ab). This required application of both the MAQSIP and URM models to Central Valley of California and eastern US domains, respectively. The new metrics that were calculated were the Regional Average Ozone (AVG), Regional Average Ozone over Standard (AVS) and Regional MIR to MOIR (M2M) metrics. These metrics (as well as others used in previous studies) are discussed in more detail below. A second task included assessing the sub-domain variability of the reactivity scales. In this case, the eastern US domain being used in the URM modeling was divided into subdomains representing different parts of the country, as well as urban vs. non-urban areas. As discussed below, this involved utilizing new (NO x sensitivity) and prior calculations, as well as reanalysis of the results. A third task was to assess the attributes of the various reactivity metrics and the resulting scales. Metrics and scales are used rather interchangeably here; however the difference between the two is worth mentioning. The 2

6 reactivity metric is how a specific organic compound s reactivity is calculated, while the scale is the set of those metrics that quantitatively represent the compound in the spectrum of comparison. Metrics have one set of attributes (e.g., being based on a single location/time/episode vs. being spatially integrative), while scales have others (e.g., being consistent from one region to another). A fourth task called for in the project was to assist EPA and/or its contractors in implementing DDM-3D in CMAQ. This report documents the results of the first 3 tasks. As such, the objectives of this report are to:! Provide the scientific and policy-making community with results that are comparable with similar studies,! Give further insight into the applicability of reactivity scales for VOC-based ozone control, its dependence on different environmental factors, and major areas of uncertainty, and! Lay the foundation for adopting a robust reactivity scale by comparing a multitude of metrics/scales of choice. The report focuses on reactivity simulations for the Eastern US, but also includes (for fewer species and part of the analyses) the results for central California. Simulation domains are shown in Figure 1. The modeling for the eastern US is done using URM, and applying a multi-scale grid structure that focuses on the areas with large population and/or intensive anthropogenic emissions. For the reactivity study, SAPRC-99 chemical 3

7 mechanism with 102 species and direct decoupled method in 3-D (DDM-3D) are implemented in URM. Modeling is carried out for episodes of May and July 1995, and for similar episodes in 2010 with projected emission inventories. By simulating multiple episodes/domains the behavior of different reactivity scales under various environmental conditions can be better investigated. Simulations for central California are carried out for SARMAP episode and domain (Damassa et al., 1996) in MAQSIP (Odman and Ingram, 1996), for the same species (except dodecane and acrolein) and using the same chemical mechanism and sensitivity analysis technique. The calculations detailed and analyzed are extensions of prior studies, and details can be found elsewhere (Hakami et al., 2003ab, Martien et al., 2002). Reactivity Metrics from Prior Studies Unlike box model calculations, 3-D simulation of organic reactivities will result in temporal and spatial distribution of reactivity values. This brings up a number of complexities. Previous studies have shown that absolute reactivities of different species exhibit great deal of temporal and spatial variability (potentially by few orders of magnitudes). To create a comparative spectrum of species (based on which they can be ranked for their ozone formation potentials), the reactivities are normalized to that of a base mixture. This normalized reactivity (usually referred to as relative reactivity or relative incremental reactivity), is shown to have far less variability in time and space (Russell et al., 1995; Bergin et al., 1995; Hakami et al 2003ab). However, the remaining variability in the relative reactivities can lead to the ability to construct and calculate a large number of scientifically valid metrics, though some have better justification and 4

8 properties than others. For regulatory applications it is desirable (and even necessary) to be able to rank organic compounds by single measure/scale. In addition to the reactivity metrics to be calculated for the analysis here, other metrics have been used as ensemble measures to represent regional reactivities of organic compounds. Emulating (as closely as possible) the box model reactivity scales (ref), 3-D Peak Ozone Incremental reactivity (POIR-3D) and Maximum Incremental Reactivity (MIR-3D) refer to the conditions of maximum ozone concentration or base mixture reactivity, respectively (ref). MIR-3D is the closest emulation of the box model MIR as found in the modeling domain/episode, and is found to be a fairly robust metric. POIR- 3D, however, is fundamentally different than the box model MOIR, as the former is calculated for the conditions where the sensitivity of maximum ozone concentration to the initial NOx is zero. In 3-D context, MOIR-3D metric disregards the sensitivity to NOx availability (primarily emissions). Both MOIR-3D and MIR-3D are domainwide, but point metrics, i.e., they are based on a single point value for each daily metric. Another reactivity metric that takes into account multiple points is the least square metric. This metric is the least square fit to the following line for each species and over all qualified cells: (Absolute reactivity) = (Relative reactivity) x (Base mixture reactivity) 5

9 Therefore, the least square metric is more heavily biased to find an accurate relative reactivity scale for the cells that are more sensitive to VOCs. An alternative least square fit can be used to find the relative reactivity metrics based on the inverse of the above equation. Such a metric (inverse least square) is more biased towards cells with small base mixture reactivity. These cells are inherently prone to erratic relative reactivity values, and therefore, the inverse least square metric is not considered a robust relative reactivity scale. Least square, POIR-3D, and MIR-3D metrics for the simulated episodes in eastern US and central California are discussed in more detail elsewhere (Hakami et al, 2003ab). Task 1: Domain-wide additional metrics for May and July Episodes Some of the metrics calculated previously were based on a single point, and taken from, or inspired by the box model definitions (MIR-3D and POIR-3D). Other metrics take into account the spatial distribution in 3-D results and are based on a number of points (least square relative reactivity [LS-RR], population/spatially-weighted average, etc.). Here, we calculate and closely examine three new multi-point metrics, and later, briefly discuss prior single-point or multi-point scales. The regional average metric (AVG) is calculated as the average of the relative reactivity over a defined area. The regional average above standard (AVS) is the average of the relative reactivities for each species for all grid cells and times where ozone concentration is above the air quality standards. Regional MIR-to- MOIR (M2M) scale is the average of the relative reactivities for each species for cells 6

10 where the maximum ozone in that cell has a negative sensitivity to NOx, but positive sensitivity to VOCs. By strict definition of MIR, a further condition would be that the second derivative of the VOC sensitivity is negative, i.e., past where the reactivity of the VOC mixture is maximized with respect to adding more VOC, though this would not frequently occur in areas with significant ozone (since the ozone formation would be very NOx-inhibited), and would require calculating the second order ozone sensitivities. Thus, we used only the MOIR bound, as defined where the NOx sensitivity goes to zero. In this report, all of these metrics are calculated for each day in the episode, and for 1-hour or 8- hour averaging periods. For the AVS metrics, a lower threshold (65 and 60 ppb, for 1- hour and 8-hour averages) is used than the NAAQS standard values (120 and 80 ppb for 1- and 8-hour averages) because the use of those cut-off levels result in very few qualified cells for metric calculations in low-ozone episodes (July 2010, and May episodes). In the calculation of the metrics, cells/times with negative base mixture reactivity, or those with very small base mixture reactivities are excluded as they can cause misleading or erroneous results as discussed below. The minimum base mixture reactivity used for filtering out cells with small or negative base mixture reactivity was set to a very small value (0.05 ppb). The regional reactivity metrics were calculated by computing the sensitivity of ozone to equal amounts of emissions of individual VOCs in order to readily compare the reactivities. This is very similar to perturbing the emissions by the same amount for each pollutant, but does not alter the base calculation at all, and does not require running as many simulations. One set of sensitivity equations are integrated using the DDM-3D for 7

11 each VOC. The species and the base mixture composition are shown in Table 1, and discussed in more detail in Hakami et al., 2003b. For the eastern US domain, the reactivity metrics are calculated for four different episode/inventory combinations (meteorologies corresponding to May and July of 1995 and emissions corresponding to 1995 and 2010). For the Central Valley Calculation, only one episode is used (known as the SARMAP episode). This leads to a total of five sets of results that are shown and tabulated in Appendix A. Daily variability in the metrics (vertical bars in the graphs) is calculated for the number of days available for each episode/metric. In general, the metrics are consistent with each other for each episode, although marked differences for some species (e.g., benzaldehyde) are seen. For each episode, the order of the reactivities of some of the species among the three metrics remains very nearly the same. California results reflect a much more homogenous domain, and therefore, different metrics for the California episode are very similar to each other (Hakami et al., 2003). The domainwide results for the 1-hour and 8-hour averaging periods are essentially identical (1-hr vs. 8-hr) for all metrics/episodes, suggesting that the use of either averaging duration is appropriate. Comparing different episodes, the scale based on the MIR to MOIR (M2M) metric seems to be the most robust among the three, and is consistent among different episodes and for all species (Figure 2). More radical-limited episodes of May 1995 and May 2010 have higher relative reactivities for the more reactive species as discussed elsewhere (Hakami et al., 2003b). The effect of the two inventories on the reactivities is fairly small for the 8

12 M2M metric, but rather large for AVG and AVS metrics. The average metrics (both AVG and AVS) are affected by a large number of points, many of which have a small base mixture reactivity. As the criteria for grouping into these metrics is broad, the absolute reactivity of species for these cells can differ. These differences are magnified by the small base mixture reactivity, resulting in a rather large variability in the AVG and AVS scales between episodes and domains. For many of the points included in the average metrics, the small base mixture reactivity is the results of an offsetting effect between the negative benzaldehyde reactivity and positive reactivity of the more reactive species. Acting as a NOx and radical sink, benzaldehyde has significant negative reactivity in the NOx-limited environments away from urban core. The result is an unrealistically large magnitude of relative reactivity (negative) for benzaldehyde over many cells/days that have small base mixture reactivity reactivity (Figure 2). Such points, however, are excluded from M2M metric (because of their positive NOx sensitivity), resulting is more moderate and realistic values. The effect benzaldehyde has on the relative reactivities of all species can be seen in Figure 3, where the spatial plots of absolute reactivity of benzaldehyde and the base mixture reactivity are shown for some time. Benzaldehyde has the largest magnitude of its (negative) reactivity where the base mixture has the lowest values. This behavior suggests that, at least for the average metrics, including benzaldehyde in the base mixture can lead to misleading results. In general, having an all-positive base mixture can significantly improve the behavior of the relative reactivity metrics. 9

13 As mentioned earlier, to curb the effect small base mixture reactivity (or a negative one, for that matter) has on the relative reactivity metrics, all the metrics are calculated with a minimum base mixture reactivity cut-point. The magnitude of this cut-point has fairly small effect on the metrics results as shown in Figure 4, where the metrics for cut-point values of 0.05 and 0.2 ppb of the base mixture reactivity are compared. Similarly, a comparison for the concentration cut-point (120 ppb vs. 65 ppb for 1-hour, and 80 ppb vs. 60 ppb for 8-hour) in the calculation of the AVS metrics is shown in Figure 5 for the July 1995 episode (the only episode that has ozone concentrations high enough for such comparison). For the 1-hr time averaging based metrics, using a 120 ppb cut off does impact the most reactive species as it drastically changes the cells included in calculations. There was little difference between using an 80 ppb vs. 60 ppb cutoff when averaging over 8 hours. Taks 2: Subdomain Analyses One concern in devising and utilizing a reactivity scale is that the ozone forming potentials, an absolute value of the amount of ozone formed per unit of a specific organic compound, can vary significantly spatially and regionally. As discussed previously, using a relative reactivity reduces the spatial variation. Still, one issue that needs to be addressed further is if the reactivity scale being developed and considered for use in air quality management varies significantly between regions of the country. One could ask, will a scale that works in New York, with a very high population density and somewhat 10

14 lower organic emissions, work for a place like Atlanta, which has large biogenic VOC emissions. In prior studies, we addressed the concern about how the scales vary between areas by comparing our results found for the Central California modeling with that for the eastern US (We have also compared those scales with the box model scales, as well as results from calculations conducted for the Los Angeles region.). We found that for the MIR-3D and LS-RR metric-based scales, and to a lesser degree the POIR-3D-based scale, the results were very similar. Here, we calculate the AVG, AVS and M2M metrics for the subdomains of the eastern US. The subdomains investigated include urban areas, non-urban areas, New England, Mid Atlantic, Southeastern, Midwestern, southern states, northern states (Figure 6). Other than geographically defined subdomains which are delineated explicitly on the map in Figure 6, the urban subdomain is defined based on the grid structure, i.e., the finest resolution (24 km) in the multiscale domain, and the non-urban domain is comprised of the coarse grids. It should be recognized that there are urban areas in the coarser domain areas, and more rural areas in the finer resolution areas. Also, as some of the times/cells are excluded from the metric calculations (below thresholds for base mixture reactivity or ozone concentration, etc.), subdomain metrics may include fewer days. This results in missing values for some subdomain/metric/episode combinations. Table 2 shows a summary of the number of days that are available for subdomain calculations in each episode. 11

15 A complete set of graphs and tables for the subdomain results are included in Appendix B, and a few examples of the results (for select species) are shown in Figure 7. Comparing the metrics, M2M is the most consistent of the new metric scales over the different subdomains, as it has the most specific characterization for including the cells in its calculation. However, for the heavily NOx-limited episodes of May, even M2M metrics are inconsistent for some subdomains (mostly for 8-hour averaging periods). Average metrics (AVG and AVS; both 1-hour and 8-hour) show significantly more inconsistency among different subdomains. This fact is also shown in Figure 8, where the average of each metric (for select species) over all subdomains/episodes, with the corresponding standard deviation is shown. MIR-to-MOIR metrics show significantly lower variability (particularly for 1-hour averages). In general, subdomains whose emissions are dominated by anthropogenic sources (urban, north-east, etc.) show more consistency among different metrics. On the other hand, subdomains with large biogenic emissions (south-east, south, etc.) are more likely to have differences among different metrics. Figure 9 shows a comparison between urban vs. non-urban subdomains for the 1-hour M2M and AVS metrics, verifying more spatial robustness for M2M than average metrics. 12

16 Task 3. Review of Reactivity Metrics: Appropriateness for Use in Air Quality Management One end point that might be desired of conducting the type of research discussed above, and in associated studies, is the identification of a reactivity scale that has a variety of desirable properties and general applicability, preferably at a national (or possibly international) scale, i.e., the scale would be robust. Such a scale would use one, or more metrics. One desirable property of such a scale might include minimal spatial and temporal variability. For example, the same scale would work just about as well in Houston as New York, and would work on a high ozone day about as well as a more typical day. (It could be argued that low ozone days are of less interest). A second attribute would be one with relatively low uncertainty. Such uncertainty could stem from uncertainties in the rate constants, product yields, knowledge of environmental conditions, other emissions, etc. A third property would be that the scale minimizes risk of being in error, leading to a region having significantly more ozone than would be expected by using the scale at face value. Another important property would be that the scale benefits the most people as much as possible. A fifth is that it would protect any one individual relatively equally as compared to any other person or group of persons. A sixth is that it is calculated in a manner that integrates as much information across the region as possible, and is not overly dependent upon conditions at a single point or a single species. To a degree, this work, and the prior research, addresses each one of these properties, many of which are closely related. A final desirable property is ease of calculation. 13

17 First, our prior research (Hakami et al., 2003ab) suggested that the Peak Ozone Incremental Reactivity-3d (POIR-3D) scale was less robust than either the MIR-3D or LS-RR scales. It showed more variability between domains and episodes. It also is based on the environmental conditions at only one point (as is the MIR-3D). Such a single point may be relatively rare, and thus a scale based upon those conditions would not have as much general applicability. The conditions at that point are more NOx-limited than for MIR-3D, and this tends to make the negative sensitivity of benzaldehyde, and the lower mixture reactivity, lead to less consistency. Also, the conditions are less typical of population centers, so the metric is less protective of the largest number of individuals as the MIR-3D scale would be. Further, such conditions are also less sensitive to VOC reductions, so areas with such conditions would be less likely to rely upon or benefit as much from VOC controls. For these reasons, the POIR-3D scale is viewed to be inferior to the MIR-3D and LS-RR-based scales for air quality management application. A primary focus of this work was to address the question of metric variability within the scale, both spatially and temporally. If the scale changes little quantitatively between domains or from day-to-day, it would be viewed as consistent. As noted above, comparing scales based on specific metrics was done previously by comparing results from studies based in the eastern United States with those being conducted in California, including the Central Valley and the Los Angeles area. In the prior studies (Hakami et al., 2003ab; Martien et al., 2003ab), it was found that of the reactivity metrics calculated, MIR-3D and LS-RR, were relatively consistent across domains between the Central Valley of California and the complete eastern US domain. Using the LS-RR with an 14

18 intercept improved consistency some as compared to the no-intercept LS-RR. Here, a similar comparison was done for three additional metrics: AVG, AVS and M2M. It was found that M2M was also consistent between the California Central Valley calculation and the eastern US domain. AVG and AVS showed less consistency. (Figure 10). As discussed above, we also assessed the consistency between subdomains using the M2M, AVG, AVS, MIR and LS-RR metrics. Of the three new metrics, M2M showed the greatest inter-subdomain consistency, and both AVG and AVS showed considerable variability. Comparison of metrics between domains and subdomains is one component of assessing spatial variability. Such an analysis does not account for variability at a level below subdomain, e.g., at the grid level. Indeed, the definition of some of the metrics (MIR-3D and POIR-3D) are point metrics, so they do not have spatial variability within a defined domain. LS-RR and AVG are defined using all points, and calculation of those quantities grid-by-gird removes the averaging inherent in their definition. If one did calculate MIR-3D, POIR-3D, LS-RR and AVG on a grid-by-grid basis, they would give the very similar results. There would be grids for which AVS is undefined. We also assessed the temporal consistency by calculating each metric on a day-to-day basis, and using two different episodes (for the eastern US). Similar results were found. From the prior studies, the MIR-3D and LS-RR showed relatively little variability between days. In these new calculations, the M2M showed a similarly small daily variability, but the AVG and AVS showed more. A third component of variability assessed was how the scales would change with emissions. This was done using two 15

19 different emissions inventories. Again, M2M, like MIR-3D and LS-RR, showed consistency (Figures11). Comparing different episodes/inventories (Figure 12), M2M and LS-RR showed little variability. From the above considerations of spatial and temporal consistency, M2M joins MIR-3D and LS-RR as having the greatest consistency. AVG and AVS were significantly more variable. POIR-3D is likewise deemed less robust. This study tangentially considered uncertainty of the scales. In previous assessments of reactivity uncertainties, Harley et al., 2003, used a Lagrangian trajectory model and studies how uncertainties in rate constants and other model inputs impacted uncertainty. Russell et al., 1995, showed that using relative reactivities minimized the uncertainty in a reactivity scale. However, neither of those studies assessed the comparative uncertainty across metrics. Here, the use of two different emissions inventories, domains and episodes can be used to infer which metrics are prone to less uncertainty. This should not be viewed as equating variability and uncertainty, but those factors that lead to variability (emissions, temperatures, land uses, background conditions, etc.), are also uncertain factors in the modeling. Other factors contributing to uncertainty (e.g., chemical rate constants), would likely contribute similarly to all scales. Thus, the scales that show the least variability would likely have the lower uncertainties. The third attribute noted above is probably one of the most interesting, that being which scales likely minimize the risk of being most wrong, and thus reduce the likelihood of 16

20 leading to a location having significantly higher ozone than expected if air quality managers used a reactivity-based strategy. In part, this is addressed by using a scale with relatively low spatial and temporal variability and a lower uncertainty. However, there are other components to this as well. It should be one that implicitly or explicitly accounts for spatial variability in the reactivities, and averages those variabilities to reduce their impact on the scale. Such scales would include the LS-RR, AVG and AVS approaches. The scale should also be one that minimizes the maximum errors. Here, the formulation of the LS-RR metric is the best. As defined, it minimizes the sum of the squared errors. Given that the error is squared, large errors have a dominating effect on the sum, and are thus minimized. Further, it does this using the reactivities for each compound for each cell of interest. Thus, it accounts for spatial variability at the gridscale, and reduces the risk of a major deviation from the ozone impact expected using that reactivity scale for air quality management. The AVG and AVS scales do not try to minimize major deviations from the calculated values, and thus specific locations might have somewhat larger deviations than would be found using the LS-RR scale. MIR-3D is not formulated to be a spatially integrated, or to explicitly minimize the largest errors. M2M is not designed to explicitly minimize the largest errors. One set of metrics not calculated here, but worthy of consideration is based on the desire to protect as many individuals as possible (attribute four), would be those using some level of population weighting. It should be noted that all of the spatially integrative scales (e.g., M2M, AVS, AVG and LS-RR) could be reformulated to include populations weighting. Prior work suggests that population weighting leads to scales more similar to MIR-3D, as locations with high populations also tend to be higher in NO x and are more near MIR-like 17

21 conditions (Bergin et al., 1995; Hakami et al., 2003a). However, given that the LS-RR and MIR-3D scales are very similar, and that the LS-RR scale is designed to minimize large deviations and is spatially integrative, the LS-RR scale should probably satisfy this property well. One reason that the LS-RR scale and MIR-3D scales give similar results is that the LS-RR scale more heavily weights those regions with higher reactivities, i.e., more MIR-like conditions. The M2M scale is also an integrative scale that is more weighted to conditions where the population is high, so should reasonably satisfy this desired attribute. The fifth attribute identified is that the choice of scale would protect any one individual relatively equally as compared to any other person or group of persons. While this may seem orthogonal to protecting a large number of individuals preferentially (attribute four), it is not. This attribute suggests that the scale would be defined in such a way as to identify the one location (if inhabited) where the deviations are largest, and minimize those deviations. Such a scale was not developed or tested here. The LS-RR scale is spatially integrative, so it does not, identically, satisfy this property, but it is designed to minimize, on average, maximum errors, so it will, likely reduce the maximum error. M2M does not have, in its formulation, this property, but it is designed using conditions where VOC controls have their greatest impacts, and are integrative, so they should tend to minimize significant deviations, similar to the AVS and AVG scales. The sixth attribute is that the metric is calculated in a manner that integrates as much information across the region as possible, and is not overly dependent upon conditions at 18

22 a single point or a single species. Obviously, POIR-3D and MIR-3D fail here as they are defined for a point. LS-RR, M2M, AVG and AVS have this desired property. In terms of ease of calculation, all of the 3d-model-based scales are of similar complexity to calculate, and most of the work is in conducting the base calculations. However, the M2M scale requires additional knowledge of the sensitivity to NO x. Obviously, using a box model would be easier, but the results less robust. Given the above discussion, the LS-RR and M2M scales would appear to have the greatest number of desired features. MIR-3D has the desired consistency, and also can be viewed as protecting more heavily populated areas. AVG, AVS and POIR-3D have significant deficiencies. Assistance with Implementing DDM-3D in CMAQ for Reactivity Calculations A fourth potential activity of this project was to help implement DDM-3D in the version of CMAQ being used by EPA contractors. A version of CMQ-DDM3D was provided to EPA and has been used by EPA contractors. Relatively minimal assistance was required, suggesting that the current version is reasonably stable. Conclusion A project, composed of four tasks, was conducted to quantify and analyze various organic compound reactivity metrics for possible use in air quality management. Similar to prior research upon which this project was built, three dimensional air quality model calculations were conducted for the Central Valley of California and the eastern United States using direct sensitivity analysis to quantify compound reactivities. Results of these 19

23 calculations were used to calculate a new set of organic compound reactivity metrics that could be compared to those previously developed. These new metrics included two that used spatial averaging across the model domains (AVS and AVG), and one that is based upon conditions that are conducive to VOC controls being most effective, particularly compared to NO x controls (M2M). These metrics were compared to each other and the previous metrics, and their day-to-day variability was found. Further, we calculated how those metrics might change in response to emissions changes. Sub-domain analysis was conducted for the eastern United States modeling to assess how those metrics varied in different parts of the eastern US, particularly between more heavily populated locations, and areas with high and low biogenic VOC inputs. A minor conclusion from this study is that including benzaldehyde in the base mixture leads to greater variability in some of the scales. Benzaldehyde can have very negative reactivity, and this can significantly alter how the reactivity of the base mixture changes in respect to most organic compounds that have positive reactivities. Those scales where this phenomenon has a smaller impact tended to work better. It was found that the scale based on using the M2M metric was similar to those found using MIR-3D and LS-RR. Like the MIR-3D and LS-RR scales, the M2M scale had relatively lower temporal variability, and the results were more consistent between the California and eastern US domains, as well as between the sub-domains within the eastern US. The AVS and AVG scales showed greater variability. Reasons for the variability had to do, in part, with averaging over cells with very small base-mixture 20

24 reactivities, often dominated by the reactivity of a single species, benzaldehyde, that had a negative reactivity in that location. A set of desirable attributes of a reactivity scale was identified. From this work, and prior studies, it would appear as though the LS-RR, M2M and MIR-3D scales best satisfy the desire for consistency. LS-RR and M2M are integrative. LS-RR and MIR-3D are likely more readily computed than M2M, but not significantly so. LS-RR is formulated to minimize the largest deviations, so should provide the lowest risk. LS-RR, M2M and MIR-3D also led to similar scales, so the choice between them is not likely to lead to any significant differences. AVG, AVS and POIR-3D were found to have significant deficiencies. It was noted that using a population-weighted metric might provide the greatest benefits and lower risks to the greatest number of individuals. While no population-weighted metrics were analyzed here, prior studies suggest that the MIR-3D scale would reflect conditions in the most heavily populated areas. Given that the LS-RR and M2M scales were similar to the MIR-3D scale, those scales should also provide that desired feature. From this, further analyses should probably concentrate on the LS-RR, M2M and MIR-3D scales. 21

25 References Bergin, M.S.; Russell, A.G.; Milford, J.B. Environ. Sci. Technol. 1995, 29, CARB Development of Reactivity Scales via 3-D Grid Modeling of California Episodes. Final Report, contract No , Sacramento, CA, Carter, W.P.L. J. Air Waste Mgmt. Assoc. 1994, 44, 881. Carter, W.P.L. Documentation of the SAPRC-99 Chemical Mechanism for VOC Reactivity Assessment; Draft report to the California Air Resources Board, Contracts and , Riverside, CA, Carter, W. P. L.; Tonnesen, G.; Yarwood, G. Investigation of VOC Reactivity Effects Using Existing Regional Air Quality Models Final report to American Chemistry Council, Contract SC-20.0-UCR-VOC-RRWG, April, Dunker, A.M. J. Chem. Phys. 1984, 81, Hakami, A.; Harley, R.A.; Milford, J.B.; Odman, M.T.; and Russell, A.G. Atmos. Environ. 2004, 38, Hakami, A.; Bergin, M. S.; Russell, A. G. (2003c). Assessment of the ozone and aerosol formation potentials (reactivities) of organic compounds over the eastern United States. Final report to American Chemical Council and California Air resources Board, Atlanta, GA. Martien, P.; Harley, R. A.; Milford, J. B.; Russell, A. G.. Environ. Sci. Technol., 2003, 37, Odman, M. T., Boylan, J. W., Wilkinson, J. G., Russell, A. G., Doty, K., Norris, W., McNider, R., Mueller S. F., and Imhoff R. E. SAMI Air Quality Modeling Final report, 205 pp., Southern Appalachian Mountains Initiative, Asheville, North Carolina, Russell, A.G.; Milford, J.; Bergin, M.S.; McBride, S.; McNair, L.; Yang, Y.; Stockwell, W.R.; Croes, B. Science 1995, 269, 491. Yang, Y.-J.; Wilkinson, J.G.; Russell, A.G. Environ. Sci. Technol. 1997, 31,

26 Table 1. Composition of the base reactivity mixture (Carter, 2002). species or group ethane ALK2 1 n-butane ALK4 2 ethene Lumped olefines 2-methyl 2-butene butadiene isoprene "-pinene benzene toluene m-xylene CRES 3 formaldehyde acetaldehyde propionaldehyde benzaldehyde acetone methyl ethyl ketone inert acetylene methanol methyl t-butyl ether n-butyl acetate ethanol acrolein moles/mole-c 8.68e e e e e e e e e e e e e e e e e e e e e e e e e e e-3 1- ALK2 is 0.5 (ethane + n-butane) 2- ALK4 is 0.25 (methyl cyclopentane + iso-pentane+ n-pentane + 2,2,4 tri-methyl pentane) 3- CRES is 0.5 (toluene + benzaldehyde) 23

27 Table 2. Number of days that are available for calculations of each domain/episode reactivities. July 1995 M2M-1hr AVG-1hr AVS-1hr M2M-8hr AVG-8hr AVS-8hr Domainwide Mid Atlantic Midwestern New England Southeastern Urban Non-urban Urban (base cut-point) Non-urban (base cut-point) Southern States Northern States July 2010 M2M-1hr AVG-1hr AVS-1hr M2M-8hr AVG-8hr AVS-8hr Domainwide Mid Atlantic Midwestern New England Southeastern Urban Non-Urban Southern States Northern States May 1995 M2M-1hr AVG-1hr AVS-1hr M2M-8hr AVG-8hr AVS-8hr Domainwide Mid Atlantic Midwestern New England Southeastern Urban Non-Urban Southern States Northern States May 2010 M2M-1hr AVG-1hr AVS-1hr M2M-8hr AVG-8hr AVS-8hr Domainwide Mid Atlantic Midwestern New England Southeastern Urban Non-Urban Urban (base cut-point) Non-urban (base cut-point) Southern States Northern States

28 Sacramento San Francisco San Jose Fresno Bakersfield Figure 1. Modeling domains for the Eastern US and central California 25

29 MBT BUTD PRPE ISOP XYLM ETHE HCHO ACRO 124B CCHO APIN XYLP TOLU MCPT ETOH IPNT N_C5 MEK 224P N_C4 C2H2 BACT C6H6 MTBE MEOH IPOH DODC ACET C2H6 CO CH4 RCHO ALK3 ALK4 ALK5 ARO1 ARO2 OLE1 OLE2 TRP1 BALD Relative Reactivity Jul-95 Jul-10 May-95 May-10 BALD x (-1) a b MBT BUTD PRPE ISOP XYLM ETHE HCHO ACRO 124B CCHO APIN XYLP TOLU MCPT ETOH IPNT N_C5 MEK 224P N_C4 C2H2 BACT C6H6 MTBE MEOH IPOH DODC ACET C2H6 CO CH4 RCHO ALK3 ALK4 ALK5 ARO1 ARO2 OLE1 OLE2 TRP1 BALD Relative Reactivity Jul-95 Jul-10 May-95 May-10 BALD x (-1) 26

30 MBT BUTD PRPE ISOP XYLM ETHE HCHO ACRO 124B CCHO APIN XYLP TOLU MCPT ETOH IPNT N_C5 MEK 224P N_C4 C2H2 BACT C6H6 MTBE MEOH IPOH DODC ACET C2H6 CO CH4 RCHO ALK3 ALK4 ALK5 ARO1 ARO2 OLE1 OLE2 TRP1 BALD Relative Reactivity Jul-95 Jul-10 May-95 May-10 BALD x (-1) 10.7 c MBT BUTD PRPE ISOP XYLM ETHE HCHO ACRO 124B CCHO APIN XYLP TOLU MCPT ETOH IPNT N_C5 MEK 224P N_C4 C2H2 BACT C6H6 MTBE MEOH IPOH DODC ACET C2H6 CO CH4 RCHO ALK3 ALK4 ALK5 ARO1 ARO2 OLE1 OLE2 TRP1 BALD Relative Reactivity Jul-95 Jul-10 May-95 May-10 BALD x (-1) 11.3 d 27

31 Relative Reactivity e Jul-95 Jul-10 May-95 May-10 BALD x (-1) MBT BUTD PRPE ISOP XYLM ETHE HCHO ACRO 124B CCHO APIN XYLP TOLU MCPT ETOH IPNT N_C5 MEK 224P N_C4 C2H2 BACT C6H6 MTBE MEOH IPOH DODC ACET C2H6 CO CH4 RCHO ALK3 ALK4 ALK5 ARO1 ARO2 OLE1 OLE2 TRP1 BALD Relative Reactivity f Jul-95 Jul-10 May-95 May-10 BALD x (-1) MBT BUTD PRPE ISOP XYLM ETHE HCHO ACRO 124B CCHO APIN XYLP TOLU MCPT ETOH IPNT N_C5 MEK 224P N_C4 C2H2 BACT C6H6 MTBE MEOH IPOH DODC ACET C2H6 CO CH4 RCHO ALK3 ALK4 ALK5 ARO1 ARO2 OLE1 OLE2 TRP1 BALD Figure 2. Comparison of different 3-D reactivity metrics for Eastern US: a) M2M, 1- hour; b) M2M, 8-hour; c) AVG, 1-hour; d) AVG, 8-hour; e) AVS, 1-hour; f) AVS, 8- hour. 28

32 Figure 3. Spatial plots of a) relative reactivity of benzaldehyde, and b) base mixture absolute reactivity. 29

33 Figure 4. Relative reactivities for 1-hour a) M2M, and b) AVG metrics during July 1995 episode and with two different base mixture reactivity cutpoints for urban domain. 30

34 Relative Reactivity, 120 ppb threshold BUTD OLE1 PRPE HCHO RCHO ETHE BALD x (-1) Relative Reactivity, 65 ppb threshold Relative Reactivity, 80 ppb threshold ETHE BALD x (-1) Relative Reactivity, 60 ppb threshold Figure 5. The effect of the applied concentration threshold (standard) on the a) 1-hour, and b) 8-hour domainwide AVS metric for the July 1995 episode. 31

35 Figure 6. Different subdomains used for the analysis. 32

36 a 33

37 34 b

38 c 35

39 d Figure 7. 1-hour M2M (top) and AVS (bottom) metrics for all domains: a) July 1995, b) July 2010, c) May 1995, and d) May

40 MIR to MOIR-3D Average Average Above Standard Relative Reactivity BALD x (-1) -1 2MBT BUTD PRPE ISOP XYLM ETHE HCHO RCHO 124B CCHO APIN XYLP TOLU MCPT ETOH IPNT N_C5 MEK 224P N_C4 C2H2 BACT C6H6 MTBE MEOH IPOH OLE ACET C2H6 CO CH4 BALD MIR to MOIR-3D Average Average Above Standard Relative Reactivity BALD x (-1) -1 2MBT BUTD PRPE ISOP XYLM ETHE HCHO RCHO 124B CCHO APIN XYLP TOLU MCPT ETOH IPNT N_C5 MEK 224P N_C4 C2H2 BACT C6H6 MTBE MEOH IPOH OLE ACET C2H6 CO CH4 BALD Figure 8. Average and standard deviation of different metrics among all subdomains/episodes for 1-hour (top) and 8-hour (bottom) averaging periods. 37

41 a Non-urban Relative Reactivity BALD x (-1) Urban Relative Reactivity 10 9 BALD x (-1) Non-urban Relative Reactivity ETHE Urban Relative Reactivity 38

42 b Non-urban Relative Reactivity HCHO 1 BALD x (-1) Urban Relative Reactivity HCHO ETHE Non-urban Relative Reactivity BALD x (-1) 2MBT 0 RCHO DODC Urban Relative Reactivity Figure 9. Comparison between urban and non-urban subdomains for 1-hour M2M (top) and AVS (bottom) metrics: a) July 1995, b) May

43 California Relative Reactivity MBT HCHO BUTD PRPE ISOP CCHO XYLM 1 APIN BALD x (-1) Domainwide Relative Reactivity for July 1995 episode California Relative Reactivity APIN 2MBT HCHO ISOP CCHO RCHO XYLM 124B ETOH N_C5 MEOH BUTD ETHE BALD x (-1) Domainwide Relative Reactivity for July 1995 episode Figure 10. Comparison between domainwide and California Central Valley for 1-hour M2M (top) and AVS (bottom) metrics. 40

44 Domainwide Relative Reactivity for July 2010 episode HCHO BALD x (-1) Domainwide Relative Reactivity for July 1995 episode a Domainwide Relative Reactivity for July 2010 episode 11 BALD x (-1) 10 9 ETHE HCHO PRPE 5 BUTD 4 OLE1 2MBT Domainwide Relative Reactivity for July 1995 episode 41

45 Domainwide Relative Reactivity for May 2010 episode b HCHO BALD x (-1) Domainwide Relative Reactivity for May 1995 episode Domainwide Relative Reactivity for May 2010 episode MEOH BALD x (-1) CCHO ACRO RCHO BUTD PRPE HCHO ETHE Domainwide Relative Reactivity for May 1995 episode Figure 11. Comparison between domainwide M2M (top) and AVS (bottom) metrics using two different emissions inventories for a) July and b) May episode. 42

46 43 a

47 44 b

48 45 c

49 d Figure 12. Comparison between all domainwide 1-hour (top) and 8-hour (bottom) metrics for a) July 1995, b) July 2010, c) May 1995, and d) May 2010 episode. 46