- Email: [email protected]
A regional modelling study of the high ozone episode of June 2001 in southern Ontario
ARTICLE IN PRESS
Atmospheric Environment 41 (2007) 3777–3788 www.elsevier.com/locate/atmosenv
A regional modelling study of the high ozone episode of June 2001 in southern Ontario G. Brulfert, O. Galvez, F. Yang, J.J. Sloan Department of Chemistry, Waterloo Centre for Atmospheric Sciences, University of Waterloo, 200 University Ave W., Waterloo, Ont., Canada N2L 3G1 Received 13 July 2006; received in revised form 9 January 2007; accepted 10 January 2007
Abstract High ozone levels were observed in southern Ontario in the summer of 2001, particularly in June, when the observed maximum was 137 ppb at Long Point. Development of effective ozone abatement strategies to prevent such episodes requires acknowledge of the chemistry in the appropriate source regions. Comprehensive high-resolution Eulerian chemical transport models, when used with accurate emissions data and meteorology, can elucidate the atmospheric chemical and physical processes responsible for episodes like these. In this work, the MM5/SMOKE/CMAQ regional air quality modelling system was used to investigate the chemistry involved in ozone formation during the episode in question and also more generally in the target domain. Some of the important simulations were further developed using Taylor diagrams to explore the ozone background and understand the sensitivity of ozone to NOX and VOC concentrations. Results from an arbitrary reduction of road traffic are discussed, based on NOX and VOC species in the traffic emission inventory. The ozone production rate was extracted from the model and mapped for June 2001 to assist in the identification of the source regions contributing to the ozone episode. r 2007 Elsevier Ltd. All rights reserved. Keywords: Regional air quality; Air pollution; Ozone; NOX ; VOC; Mobile emission
1. Introduction Ground level ozone is a major concern for southern Ontario air quality because of its effects on respiratory health (Ontario Medical Association Toronto, 2001) and agricultural crops (Linzon et al., 1986). Tropospheric ozone is formed by a series of photochemical reactions among a variety of precursors emitted by multiple sources and tracing its evolution is difficult. It is generally accepted, Corresponding author. Tel.: +1 519 888 4401.
E-mail address: [email protected] (J.J. Sloan). 1352-2310/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2007.01.030
however, that the long range transport of ozone depends on the relative locations of the source and receptor regions and the meteorology connecting them (Brankov et al., 2003; Brook et al., 2002; Farrell and Keating, 2002) and that these aspects can be addressed by the use of Eulerian chemical transport modelling. Some early studies (Yap et al., 1988) suggest that the contribution from long range transport could be the most important factor in the high ozone episodes that occur regularly in summer in southern Ontario and for this reason, it is particularly important to understand the origins of the background ozone in this region.
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
3778
Southern Ontario contains a population of approximately 11 million, over 90% of whom are located in the region just north of lakes Erie and Ontario, in an area including the Windsor–Toronto corridor, the Greater Toronto Area (GTA) and Central and Eastern Ontario. The air quality in this region is assessed using an air quality index (AQI) that is published by the Ontario Ministry of the Environment (2005). The value of the AQI is determined largely by the concentrations of the six most common air pollutants ðSO2 ; O3 ; NO2 ; CO, total reduced sulphur compounds and fine particulate matter). In 2001, high O3 concentrations were responsible for 99% of the hours during which the AQI was listed as ‘poor’, and these periods of very high ozone occurred during the summer months. The present study examines one of these high ozone episodes, which occurred during the months of June and July 2001. We first describe the model and present a novel methodology that we have developed to validate the modelling results. We then compare the regional concentration of OX ðOX ¼ O3 þ NO2 Þ obtained from the model with that measured at the Province of Ontario AQI measurement stations. Then, the background ozone concentration will be determined using these validated results together with various emission scenarios. In order to understand the impact of the emission sources on ozone production, several simulations will be presented that have different reductions in specific NOX and VOC emissions, as well as a reduction of road traffic. Finally, an analysis of the ozone production rate during June 2001 will identify the areas of ozone production and consumption.
2. Estimation of the regional contribution of OX Clapp and Jenkin (2001) show that the OX concentration is made up of two identifiable contributions: an NO2 -independent (or regional) contribution and an NO2 -dependent (or local) contribution. It is possible to determine the regional contributions of these to OX concentrations using measurements over appropriate temporal and spatial scales. Data from 28 AQI stations have been used to investigate the relationships among the maximum daily values of OX and NOX in June 2001. During the summer of 2001, June was the most polluted month, with maximum concentrations of 137 ppb of O3 and 245 ppb of NOX observed over all measurement stations. The measured data are classified into different kinds of environments and analysis of the measured data permits us to estimate the regional and local contributions to the background OX concentration in these different locales. For the present case, we focus on commercial and industrial areas. Fig. 1 shows that OX concentrations increase with NOX during daylight over all measurement sites. Following Clapp and Jenkin (2001) we interpret the intercepts of the regression lines shown in the figure as being the regional contribution to OX , which is independent of NOX , and the gradient as the contribution that correlates with the local level of pollution. The linear regression demonstrates a high regional contribution to oxidants with 37.5 ppb. These background concentrations typically represent 50% of the total averaged concentration of OX in these areas.
120
[Ox] (ppb)
100 80 y = 0.3138x + 37.506 60 40 20 0 0
20
40
60
80 100 [NOx] (ppb)
120
140
160
180
Fig. 1. Observed daily maximum OX vs. NOX concentrations (ppb) over all measurement sites during June 2001. Lines were defined by regression analysis.
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
3779
3. Model description
4. Validation of modelling results
It has been accepted for some time that synoptic meteorological patterns play an important role in the long range transport of tropospheric ozone in southern Ontario (Heidorn and Yap, 1986). In this study, the meteorological fields were obtained using the MM5 mesoscale meteorology model, version 3.6 (Grell et al., 1994), which was used with two nested domains. The inner domain covers the northeastern US and southeastern Canada with 42 50 grid cells having a horizontal resolution of 36 km. The outer domain covers most of North America with a horizontal resolution of 108 km. The air quality simulations were carried out using the sparse matrix operator kernal emission (SMOKE)/CMAQ regional modelling system (Byun and Schere, 2006; Carolina Environmental Programs, 2003; Grell et al., 1994) on the domain having the 36 km resolution. Version 4.3 of the CMAQ model with the Carbon Bond IV Mechanism (CB-IV) (Gery et al., 1989) was used. Both MM5 and CMAQ were configured with 15 sigma layers, with the top layer at 100 mb. The MRF boundary layer scheme (Grell et al., 1994) was used in the MM5 parameterization. Time-invariant climatological profiles for ozone and its precursors were used as boundary conditions. Version 2.0 of the (SMOKE) modelling system was used to generate the gridded, hourly, speciated emission inputs for CMAQ. The 1996 EPA National Emissions Trends (NET96) US inventory and the 1995 Canadian emissions inventory were used for criteria emissions. Tests were made using the more recent 1999 EPA emissions data and the results differed by only a few percent from those obtained with the NET96 data, so the latter were used, in order to be temporally selfconsistent with the Canadian data. Canadian measured data for comparison with the simulations were obtained from 53 sites (37 located in Southern Ontario) of the National Air Pollution Surveillance (NAPS) network A map of AQI monitoring station is available at http://www. ene.gov.on.ca/envision/techdocs/4521e01.pdf, p. 36, Figure 5.1. Since these are surface measurements, only the model predictions for the surface layer can be compared with them, which makes the comparison somewhat sensitive to such model details as the vertical transport and deposition algorithms.
The 37 Ontario monitoring stations are grouped by common characteristics: residential (19), commercial (9), industrial (2), forested (2), agricultural (3) and indeterminate (2). For our comparisons, the agricultural and forested stations will be grouped under the heading ‘‘rural’’. Comparisons between modelled and measured results will be illustrated by Taylor diagrams (Taylor, 2001). Before discussing the comparisons, we will describe these diagrams briefly. They convey overall comparisons between measured and modelled fields in a compact way by exploiting certain geometric relationships among the statistical parameters used to characterize the fields. As a result of these relationships, it is possible to construct a modified two-dimensional polar plot that represents simultaneously the correlation coefficient, and root-mean-square (RMS) difference and the standard deviations of the measured and modelled fields. For cases such as the present one, in which several different measurement stations are involved, it is most convenient to normalize the statistical parameters in order to remove differences in absolute values at the different stations. The normalization is done by dividing the reduced RMS difference and the standard deviations for the measured and modelled quantities by the standard deviation of the measured quantity. The reduced RMS difference, defined by ( )1=2 N 1X ½ðsn sÞ ðmn mÞ2 D¼ (1) N n¼1 is just the RMS difference between the measured (mÞ and simulated (sÞ results, after removal of the bias in each quantity. To illustrate how the Taylor diagram is constructed, we show in Fig. 2 the statistics for the maximum 8 h ozone concentrations obtained in the model validation runs, compared with the same concentrations recorded at several observation stations during June 2001. We have constructed separate plots for (a) commercial, (b) residential and (c) rural stations. In each plot, the radial distance of each point is the normalized standard deviation of the modelled result for the specific station. The normalized standard deviations of the measurements are all represented by the point on the abscissa labelled ‘‘Observed’’. Due to the definition of the normalization, this is necessarily 1.0 for each station. The normalization does not change the
ARTICLE IN PRESS 3780
G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
Fig. 2. Normalized pattern statistics comparing the maximum ozone concentration over the 8 h simulation with the observed in June 2001 for (a) commercial, (b) residential and (c) rural stations. The radial distance from the origin is proportional to the standard deviation of the pattern. The correlation between the two fields is given by the azimuthal position of the points (Taylor, 2001). Each point represents a measurement station.
correlation coefficient, which is plotted as the azimuthal position of the point. Finally, the reduced RMS difference, D, is the distance from the ‘‘Observed’’ point to each modelled point. The three Taylor diagrams in Fig. 2 compare the measurements with the simulations for residential, commercial and rural stations. Most of the points in each diagram are clustered together, suggesting that the designations of the sites were grouped correctly (in terms of emissions) in spite of their very different geographical locations. The Taylor diagram also
highlights sites with unusual results. For example, station 1 for the commercial sites (Fig. 2a) and stations 7 and 14 for the residential sites (Fig. 2b) do not fall close to the points representing the other sites and thus they are less representative of the designated group than the other stations. For this reason, these three stations will be omitted from the analysis of the results to be presented later. We justify this decision in statistical terms by noting that stations 1 and 7 have low correlation coefficients and large RMS differences, suggesting that
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
they are subject to sources not well described by the model. Station 14 has a low normalized standard deviation by comparison with the other residential stations, also suggesting that it is not representative of this category. Neglecting these points, the correlations for the three groups range from 0.6 to 0.85, and the standard deviations range from 0.5 to 0.75. A skillful model should be able to simulate accurately both the concentration of a species and its pattern of variability. There are no universally agreed criteria for satisfactory model performance, but a useful set of statistical measures has been provided by the US EPA (US EPA, 1991) for use in evaluating models where the monitoring data are sufficiently dense. The first if these is the mean normalized bias error (MNBE), defined by N 1X mn sn MNBE ¼ . (2) N n¼1 mn This test measures the model’s ability to replicate observed patterns. Since statistics from periods having relatively low predicted and measured ozone levels are not very meaningful, this test should be limited to cases where the observed concentration is greater than a reasonable minimum. In this case, we select 50 ppb, which is slightly above the naturally occurring ozone background value in this region (around 40 ppb). The next measure is the mean normalized gross error (MNGE) which is also applied for concentration above a prescribed threshold, as defined by N jmn sn j 1X MNGE ¼ . (3) N n¼1 mn This test, which is a measure of model precision, compares the differences between all pairs of predictions and observations that are greater than 50 ppb. The final measure is the unpaired peak prediction accuracy (UPA), which compares the difference between the highest observed value and
3781
the highest predicted value found over all hours, as defined by smax mmax . (4) UPA ¼ mmax The US EPA suggests that for model simulations of O3 that are used for regulatory applications, the MNBE should be 10–15%; the MNGE should be 30–35% and the UPA should be 15–20%. Table 1 shows results of MNBE, MNGE and UPA for each classification of site and for all sites together. Clearly the modelled results achieve the EPA goals in all cases. The O3 bias (MNBE) is positive at each site, ranging from 1.8% for residential sites to 7.7% for agricultural sites, suggesting that the model has a slight tendency for overestimation. The observed episode peak ð137 ppbÞ is well-captured by the model, however, and the peak prediction accuracy is slightly overestimated for commercial, industrial, agricultural and forest sites (4.5%, 8.2%, 0.9% and 2%, respectively), while it is underestimated for residential sites ð9:5%Þ. We note that the EPA objectives are met despite the relatively old emission inventories. We assume the agreement would be improved if newer emission data were used. 5. Background ozone evaluation If we ignore injections from the stratosphere, we can define ‘background’ tropospheric ozone as that which arises from in situ formation in the free troposphere, including contributions from both anthropogenic and natural VOCs (Yap et al., 1988). Taking this approach, we can separate the contributions from natural and anthropogenic sources using model scenarios. We carried out three scenarios during the period from 1 May to 30 September 2001 as follows:
Base case: All emissions (anthropogenic and biogenic) are present.
Table 1 Statistical measures of model performance for l-h O3 concentration based on (US EPA, 1991) methodology
Observed peak (ppb) Modelled peak (ppb) MNGE (%) MNBE (%) UPA (%)
EPA goal
Residential
Commercial
Industrial
Agricultural
Forest
Domain
o 30235 o 10215 o 15220
128 124 20.3 1.8 3.22
112 117 25 2.5 4.3
97 105 24.3 4.5 7.6
114 115 19.1 7.7 0.9
98 100 16 3.2 2
137 145 21.9 3.2 5.5
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
3782
100
percentage
80 60 40 20 0 Base case
No emissions Only beogenic
Observation
Fig. 3. Percentage of 8-h daily maximum ozone concentration, when ozone concentration is greater than 65 ppb, averaged over southern Ontario for selected scenarios from 1 May to 30 September 2001 (black column), and from 1 June to 31 July (grey column). The definition of the scenarios is in Section 5.
No emissions: No anthropogenic or biogenic emissions in the modelling domain. Only biogenic: No anthropogenic emissions in the modelling domain, but normal biogenic emissions are present.
without emissions and with only biogenic emissions is 31 ppb for both cases, while it is 53 ppb for the ‘base case’, which includes all emissions. This result corresponds closely to the estimate of Yap et al. (1988), who reported the local ‘background’ ozone level in southern Ontario for May to September to be approximately 20–30 ppb, based on observed data from 30 monitoring stations from 1979 to 1985. Although, the ‘background’ ozone concentration does not have large variations, it shows a small reduction when the ozone concentration is high during the smog episodes. A possible explanation for this result could be the scavenging of ozone, which happens in stagnant meteorological conditions. However, these episodes need further investigation. These results are also consistent with those in Fig. 1, which gives an averaged OX background concentration of 37.5 ppb. 6. Ozone abatement strategies 6.1. Emissions reductions
The ozone concentrations averaged over southern Ontario for these scenarios are illustrated in Fig. 3. The ozone concentrations for each scenario for the period from 1 May to 30 September, expressed as percentages of the 8-h daily maximum for the base case, are shown by the black bars. The corresponding concentrations for the high ozone period from 1 June 1 to 31 July are indicated by the grey bars. In both cases, the model base case reproduces the observations to within 3%. The scenarios without any emissions and with only biogenic emissions are almost identical in each case, indicating that the contribution from the biogenic emissions in the domain is not significant. This result agrees qualitatively with the modelling study from Lurmann et al. (1984), which estimated the contribution from biogenic emissions to be 2–9% of the predicted maximum ozone concentration in urban areas. Those authors attribute this small contribution to fast ozone-‘biogenic hydrocarbon’ reactions which scavenge almost as much ozone as is produced by the biogenic VOCs. The 8-h daily maximum ozone concentrations with only biogenic emissions are significantly lower (relative to the base case) during the high ozone period than during the whole study period, indicating that more ozone originates from anthropogenic emissions during the period of high ozone than at other times during the summer. The 8-h daily maximum ozone concentration averaged over the entire period for the scenarios
To develop an ozone abatement strategy for a specific area, it is necessary to know whether the ozone production is limited by NOX or by VOC and how this chemical limitation varies with season, meteorology and emissions. Using the numerical model, we examined the consequences of reductions in either NOX or VOC emissions for June 2001 by simulating three scenarios:
A base case with the meteorology and emissions of June 2001. A 50% reduction in NOX emission relative to the base case. A 50% reduction in VOC emission relative to the base case.
Fig. 4 shows the changes in ozone concentration associated with either reduced VOC or reduced NOX for the locations corresponding to the measurement stations introduced earlier. The top four sections of Fig. 4 refer the results of a 50% reduction in VOC. For this scenario, there is never a statistically significant increase in ozone at any station and there is no significant change in ozone for ozone concentrations below the (approximately) 40 ppb background value. For ozone concentrations above the background, the reduction in VOCs causes a reduction in ozone for all sites and the reduction increases with ozone concentration up
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788 [O3 ] (ppb)
[O3 ] (ppb) 40
60
80
100
0
120
20
40
100
120
0 -5 -10 -15 -20 Commercial stations with 50% decrease of VOC
[O3 ] (ppb)
[O3 ] (ppb)
20
40
60
80
100
0
120 ozone change (ppb)
5 0 -5 -10 -15 -20
20
40
20
40
60
80
100
120
Industrial stations with 50% decrease of VOC
[O3 ] (ppb) 0
60
4 2 0 -2 -4 -6 -8 -10 -12 -14
Agricultural stations with 50% decrease of VOC
[O3 ] (ppb) 80
100
120
40 30 20 10 0 -10 -20 -30
0 ozone change (ppb)
ozone change (ppb)
80
Residential stations with 50% decrease of VOC
-25
20
40
60
80
100
120
30 20 10 0 -10 -20 -30
Residential stations with 50% decrease of NOx
Commercial stations with 50% decrease of NOx
[O3 ] (ppb) 0
20
40
60
[O3 ] (ppb) 80
100
0
120
15 10 5 0 -5 -10 -15 -20
ozone change (ppb)
ozone change (ppb)
60
5
-25
0 ozone change (ppb)
20
5 0 -5 -10 -15 -20 -25 -30 -35 -40
ozone change (ppb)
ozone change (ppb)
0
3783
Agricultural stations with 50% decrease of NOx
20
40
60
80
100
120
30 25 20 15 10 5 0 -5 -10 -15 Industrial stations with 50% decrease of NOx
Fig. 4. O3 reduction (ppb) against O3 concentration (ppb) with a decrease of 50% of NOX or VOC for residential, commercial, industrial and agricultural sites (June 2001).
to the maximum values encountered during the month. Residential, commercial and industrial ozone concentrations are more sensitive to VOC, however, than it is to NOX . The bottom four sections of Fig. 4 show that a 50% reduction in NOX may cause the ozone to
increase or decrease, depending on the initial O3 concentration. In general, for low ozone levels, a decrease in the NOX causes an increase in ozone, while the opposite is the case for high ozone levels. The changes appear to be roughly linear with initial ozone concentration and assuming that they are, the
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
3784
intercept with the abscissa of the linear regression line gives the ozone concentration at which the sensitivity to NOX changes from positive to negative. The values of this intercept are very similar for all categories of sites: they are 41, 38, 40 and 37 ppb for residential, commercial, industrial and agricultural sites, respectively. This coincides with the mean natural background ozone level calculated, as shown in Fig. 3, from the maximum daily concentrations observed under conditions of no local emissions. In all cases, the maximum and minimum changes in the ozone concentration resulting from the 50% NOX reduction are roughly 20 ppb. Depending on the initial ozone concentration, the slope of the response is about 0:3 ppb per ppb of initial ozone. The NOX =O3 titration chemistry plays a major role in the ozone response to a reduction in NOX . Fig. 5 shows the O3 change against hours of the day, changes in concentration are positive and negative at each hour of daylight and night: any diurnal signal can be found due to the O3 titration by NO, which is not a photolysis reaction. Fig. 6 shows the change in ozone for a 50% NOX reduction, plotted as a function of NOX concentration. There is an approximately negative linear relationship between the ozone change and the NOX concentration. The regression line intercepts NOX concentration axis at 97 ppb, indicating that a 50% reduction in NOX increases the ozone for NOX concentrations below 97 ppb and decreases it for concentrations above this value. This suggests that the NO þ O3 reaction dominates the NOX 2O3 system below the intercept, while the production of O3 by the photolysis of NO2 dominates above this value. Fig. 6 is plotted for residential sites, but
similar behaviour is observed for the other categories of sites as well.
a
b
6.2. NOX reduction from 20% to 80% The previous section illustrated the result of a 50% reduction in NOX emission. To complete the analysis, NOX emission reductions of 20%, 70% and 80% were also simulated. Fig. 7 shows the resulting changes in ozone resulting from the above NOX emission reductions, plotted against total ozone concentration. In all cases, the ozone change is linear with ozone concentration, so only the regression lines are plotted in the figure. The response of the ozone change varies with the amount of NOX reduction, but for all site categories, the intercept with the abscissa (ozone concentration) is approximately the same (38, 39, 37 and 37 ppb for residential, industrial, commercial and agricultural, respectively). For ozone concentrations above this value, a decrease in NOX leads to a decrease in ozone that is linear with total ozone concentration. In this region, therefore, ozone
ozone change (ppb)
[NOx] (ppb)
ozone change (ppb)
40 20
0 -20 -40 -60 0
3
6
9
12
15
hour of the day
18
21
100
50
0
150
200
250
y = -0.11 x + 10.7
Fig. 6. Ozone reduction (ppb) against NOX concentration (ppb) with a decrease of 50% of NOX for residential sites (June 2001).
60
-80
40 30 20 10 0 -10 -20 -30
24
80 60 40 20 0 -20 -40 -60 -80 -100
0
3
6
9
12
15
18
21
hour of the day
Fig. 5. O3 change against hours of the day over all sites with (a) a decrease of 50% of NOX and (b) a decrease of 50% of VOC.
24
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788 [ozone] (ppb)
[ozone] (ppb) 0
20
40
60
80
100
ozone change (ppb)
ozone change (ppb)
0
120
60 40 20 0 -20 -40 -60
20
40
60
80
40
60
120
Commercial [ozone] (ppb)
[ozone] (ppb) 20
100
40 30 20 10 0 -10 -20 -30 -40 -50
Residential
0
80
20 10 0 -10 -20 -30 -40 -50
100
120
0 ozone change (ppb)
ozone change (ppb)
3785
20
40
60
40 30 20 10 0 -10 -20 -30 -40 -50
Agricultural
80
100
120
Industrial
Fig. 7. Ozone reduction (ppb) against O3 concentration (ppb) with a decrease of 20% (blue line), 50% (dark line), 70% (red line) and 80% (green line) of NOX for residential, commercial, industrial and agricultural sites (June 2001).
Table 2 Linear regression parameters for the O3 response to NOX reductions of 20%, 50%, 70% and 80%
Residential Industrial Commercial Agricultural
the O3 decreases more rapidly than can be described by a linear relationship.
S20
I 20
S50
I 50
S70
I 70
S80
I 80
7. Mobile source reductions
0.10 0.09 0.11 0.09
5.05 3.75 4.12 3.76
0.27 0.28 0.30 0.29
11.00 11.12 12.16 10.54
0.48 0.47 0.51 0.49
17.82 17.6 18.92 17.16
0.66 0.61 0.66 0.64
24.35 21.78 21.98 22.15
Due to the chemical nonlinearities discussed above, the effects on O3 of separate changes in NOX and VOC emissions are difficult to predict unless the exact circumstances are known. Since motor vehicle traffic is a source of both NOX and VOC emissions, then decreases in traffic cause changes in both of these in (approximately) fixed ratios and it is useful to consider the consequences of this kind of change, rather than considering the NOX and VOC emissions independently. In Fig. 8, we show the simulated results from a decrease of 50% in traffic emission for each category of monitoring site for the month of June 2001. For all sites, the reduction of traffic has a similar effect on ozone: it causes an increase for O3 concentrations below the regional background level of about 40 ppb and a decrease if the O3 concentration above this value. In this respect, the response is qualitatively similar to that for both NOX and VOC reductions separately (see Fig. 4). The effects differ, however, for different classes of locations. The changes in O3 are large and approximately linear with O3 concentration for residential, commercial and agricultural sites, while the trend is not as
production increases with NOX emission, so the production of ozone by NO2 photolysis predominates. For ozone concentrations below the intersection point, a decrease in NOX emission leads to an increase in ozone concentration that is also linear with total ozone concentration. In this region, therefore, the titration of ozone by NO is predominant. The larger the reduction in NOX emissions, the larger is the slope of the regression line. The parameters of the regression lines for reductions of 20%, 50%, 70% and 80% are shown in Table 2, where the S N and I N indicate the slope and intercept for an NOX reduction of N%. This shows that the change in O3 for a change in NOX is nonlinear. The average slopes for reductions of 20%, 50%, 70% and 80% are 0:10, 0:28, 0:49 and 0:64, respectively. Thus for a given reduction in NOX ,
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
3786
[O 3] (ppv)
[O 3] (ppv) 0
20
40
60
80
100
0
120
40
ozone change (ppb)
10 5 0 -5
-10
100
120
5 0 -5
Commercial stations
[O 3] (ppv) 20
40
60
[O 3] (ppv) 80
100
0
120
ozone change (ppb)
ozone change (ppb)
80
10
-10
Residential stations
0 0 -2 -4 -6 -8 -10 -12 -14
60
15
15
ozone change (ppb)
20
Agricultural stations
16 14 12 10 8 6 4 2 0 -2 -4
20
40
60
80
100
120
Industrial stations
Fig. 8. Ozone reduction (ppb) against O3 concentration with a decrease of 50% of traffic for residential, commercial, industrial and agricultural sites (June 2001).
clearly identifiable for the industrial sites. The maximum reduction for the first three sites is similar (about 5 ppb), while the 50% decrease in traffic has virtually no effect on O3 for the industrial sites. The amount of increase for low O3 concentrations, which we associate with a reduction in the consumption of background O3 by NO, is up to about 10 ppb for the residential, commercial and industrial stations, but smaller for the agricultural sites. We conclude that the latter are not as strongly influenced by traffic emissions and the other three, which is consistent with their classification. The intercepts of the regression lines with the O3 concentration axis occur at approximately the background level, as seen in the case of the NOX reduction as well. There are small but significant differences in the values of the intercepts, however, due to the difference in the NOX emission sources. The intercepts for the residential and commercial sites in this case are 40 and 46 ppb, respectively. These can be compared with the changes for the pure NOX reductions at these sites, which are 41 and 38 ppb, respectively. The similarity of the response at the residential sites compared with the difference of 20% at the commercial sites emphasizes the point that the local conditions must be considered when assessing the effects of changes in emissions. The traffic reduction changes several
emissions that affect the O3 chemistry, including NOX , VOCs and CO, whereas changes in only one of these can have a different effect on the O3 . It is important to take account of this complexity when considering abatement strategies rather than considering the effects of only one or two important emissions. 8. Ozone production and consumption rate Ozone production and consumption have systematic temporal and spatial patterns due to chemistry, superimposed on stochastic changes caused, among other things, by meteorological variability. Abatement strategies, therefore, must be based on the predictability of the chemistry, while accepting that there will be a range of effectiveness due to random factors that are not included in the chemical system. A numerical CTM simulation is capable of addressing both aspects of the problem, as long as the simulation time is long enough to get adequate statistics on the stochastic part of the system. The calculations reported here only describe one summer season, so they cannot be taken as a general description of the ozone variability in the target domain. The results averaged for one month, however, are adequate to show the spatial distribution of ozone production
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
Fig. 9. Average production rate of ozone ðppb h1 Þ over June 2001 from the modelling system. Negative values stand for consumption area and positive values for production area.
and consumption regions in the domain and their relative NOX =VOC sensitivities that month. Although not extensive, this information is useful to indicate the abatement strategies that would be most effective in the region. Where such an analysis is carried out for a specific case, of course, the receptor regions must also be considered, since they will be affected differently by the different source regions. The first step in this analysis is to identify the spatial and temporal distributions of the production rates of ozone and the other species that are chemically related to it. This information is produced directly by the CTM at the resolution of the grid. (It is not, however, available from any other source.) Fig. 9 shows a map of the average net O3 production rate during June 2001 at the 36 km resolution of the modelling grid used in this study. Negative values (indicating ozone consumption) are evident at the locations of urban areas such as Toronto, Hamilton, Detroit, Cleveland and the Baltimore–Washington conurbation, presumably due to NOX emissions from traffic. There are O3 sources in western Ohio, across Pennsylvania and in southern New York State as well as on the north shore of Lake Erie. Most of the latter can be identified with large industrial sources. 9. Conclusion For the domain including southern Ontario, a linear regression of the observed concentration of
3787
OX against that of NOX demonstrates a high regional contribution to oxidants, amounting to OX concentrations of 69 ppb for residential areas and 44 ppb for commercial areas. To understand the chemistry responsible for this, the MM5 /SMOKE/ CMAQ regional modelling system was applied for the summer months of 2001, with a focus on June, using nested domains covering north eastern North America with a resolution of 108 km and southern Ontario at 36 km. This relatively low resolution is adequate to explore the ozone precursor dependences on a regional scale. The model was first used to determine the regional ozone background. The 8h daily maximum ozone concentration averaged over the entire period using the full emission data set is 53 ppb, while it is 31 ppb for scenarios without any emissions at all and only about 2% higher with only biogenic emissions. From this and the temporal and spatial distributions of the ozone concentration, we conclude that the contribution from the biogenic emissions to ozone concentration in southern Ontario is not significant for this time period. Scenario studies with this model configuration show that a reduction in VOC emissions never leads to an increase in ozone concentration for any initial concentration of ozone. If the ozone concentration is below the regional background level, reduction in VOCs causes no change. If the ozone is above the regional background, a reduction in VOCs causes a reduction in ozone that increases with increasing ozone concentration. A reduction in NOX emission, on the other hand, can lead to either an ozone decrease or an increase, depending on the initial ozone concentration. A reduction of 50% in NOX emission decreases or increases the ozone concentration by up to 20 ppb, if the initial ozone concentration is above or below the background level (respectively). The largest changes correspond to the highest and lowest ozone concentrations. For areas that are highly polluted (NOX concentration greater than 100 ppb), ozone concentration change significantly (up to 20 ppb) with a reduction of NOX emissions. For low concentrations of NOX , a reduction in NOX emissions increases the ozone concentration (up to 10 sppb), presumably due to a reduction in ozone removal by reaction with NO. Simulations with reductions of 10%, 20%, 50%, 70% and 80% in NOX emissions lead to a linear increase or decrease of ozone with initial ozone concentration. The larger the reduction in NOX emission, the more sensitive the system is to initial
ARTICLE IN PRESS 3788
G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
ozone concentration. These results indicate where NOX and/or VOC reductions are likely to be helpful in reducing ozone concentrations and—equally importantly—cases where such abatement measures will have little or no effect. In particular, if the ozone is already at or below the background levels, then it is possible to increase it by reducing NOX emissions. Another counterintuitive conclusion from this work (that has also been seen in other similar studies) is that the major urban areas are net ozone sinks due to significant NOX emissions from the high densities of motor vehicles, which consume ozone in the oxidation of NOX to HNO3 , which is partially removed by wet deposition, thereby removing some of the NO2 , which could otherwise be a source of ozone. Computation of the average net ozone production rate during June 2001 shows negative values coinciding with all large urban areas in the domain, which are likely due to high NOX emissions from traffic. The most important areas of ozone production are in western Pennsylvania, where a maximum average production rate of about 13 ppb h1 was observed at one location during the month of June. There are also significant ozone production rates at other locations in south-western and north-central Pennsylvania, with lower rates at locations in Ohio, south-western New York State and the north shore of Lake Erie. The isolated nature of these locations indicate that the ozone production is due to large point sources located in the grid squares indicated in Fig. 9. Measures to reduce ozone production, therefore, should be focussed on the point sources in these locations and if higher resolution is required to separate individual sources, this is easily achieved with current models, which can resolve the contributions of sources separated by as little as 3–4 km with good accuracy. Acknowledgements Financial support for this work was provided by the Natural Sciences and Engineering Research Council of Canada, Ontario Power Generation, the Ontario Research and Development Challenge Fund and the Secretarı´ a de Estado de Educacio´n y Universidades de Espan˜a and Fondo Social Europeo. We wish to acknowledge Mr. Jonatan Aronsson for technical assistance.
References Brankov, E., Henry, R., Civerolo, K., Hao, W., Rao, S.T., Misra, P.K., Bloxam, R., Reid, N., 2003. Assessing the effects of transboundary ozone pollution between Ontario, Canada and New York, USA. Environmental Pollution 123, 403–411. Brook, J.R., Lillyman, C.D., Shepherd, M.F., Mamedov, A., 2002. Regional transport and urban contributions to fine particle concentrations in Southeastern Canada. Journal of the Air & Waste Management Association 52, 855–866. Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and other components of the models-3 community multiscale air quality (CMAQ) modeling system. Applied Mechanics Reviews 59, 51–77. Carolina Environmental Programs, 2003. Sparse Matrix Operator Kernel Emission (SMOKE) Modeling System. University of Carolina, Carolina Environmental Programs, Research Triangle Park, NC. Clapp, L.J., Jenkin, M.E., 2001. Analysis of the relationship between ambient levels of O3 , NO2 and NO as a function of NOX in the UK. Atmospheric Environment 35, 6391–6405. Farrell, A.E., Keating, T.J., 2002. Transboundary environmental assessment: lessons from OTAG. Environmental Science and Technology 36, 2537–2544. Gery, M.W., Whitten, G.Z., Killus, J.P., Dodge, M.C., 1989. A photochemical kinetics mechanism for urban and regional scale computer modelling. Journal of Geophysical Research 94, 925–956. Grell, G.A., Dudhia, J., Stauffer, D.R., 1994. A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR/TN-398+STR. Heidorn, K.C., Yap, D., 1986. A synoptic climatology for surface ozone concentrations in southern ontario. Atmospheric Environment 20, 695–703. Linzon, S.N., Pearson, R.G., Donnan, J.A., Durham, F.N., 1984. Ozone effects on crops in Ontario and related monetary values. MOE Report ARB-13-84-Phyyo. Lurmann, F.W., Nitta, B., Ganesan, K., Lloyd, A.C., 1984. Modeling potential ozone impacts from natural sources-III. Ozone modeling in Tampa/St. Petersburg, Florida. Atmospheric Environment 18, 1133–1143. Ontario Medical Association Toronto, 2001. Ontario’s air: years of stagnation hhttp://www.oma.org/phealth/report/airq001. pdfi. Ontario Ministry of the Environment, 2005. Ontario’s Air Quality Index 2004 Report, Toronto Ontario hhttp://www. ene.gov.on.ca/programs/3958e02.pdfi. Taylor, K.E., 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research 10, 7183–7192. US EPA, 1991. Guideline for regulatory application of the urban airshead model. US EPA Report No. EPA-450/4-91-013. Office of Air and Radiation, Office of Air Quality Planning and Standards, Technical Support Division, Research Triangle Park, North Carolina, US. Yap, D., Ning, D.T., Dong, W., 1988. An assessment of source contributions to the ozone concentration in southern Ontario 1979–1985. Atmospheric Environment 22, 1161–1168.
Atmospheric Environment 41 (2007) 3777–3788 www.elsevier.com/locate/atmosenv
A regional modelling study of the high ozone episode of June 2001 in southern Ontario G. Brulfert, O. Galvez, F. Yang, J.J. Sloan Department of Chemistry, Waterloo Centre for Atmospheric Sciences, University of Waterloo, 200 University Ave W., Waterloo, Ont., Canada N2L 3G1 Received 13 July 2006; received in revised form 9 January 2007; accepted 10 January 2007
Abstract High ozone levels were observed in southern Ontario in the summer of 2001, particularly in June, when the observed maximum was 137 ppb at Long Point. Development of effective ozone abatement strategies to prevent such episodes requires acknowledge of the chemistry in the appropriate source regions. Comprehensive high-resolution Eulerian chemical transport models, when used with accurate emissions data and meteorology, can elucidate the atmospheric chemical and physical processes responsible for episodes like these. In this work, the MM5/SMOKE/CMAQ regional air quality modelling system was used to investigate the chemistry involved in ozone formation during the episode in question and also more generally in the target domain. Some of the important simulations were further developed using Taylor diagrams to explore the ozone background and understand the sensitivity of ozone to NOX and VOC concentrations. Results from an arbitrary reduction of road traffic are discussed, based on NOX and VOC species in the traffic emission inventory. The ozone production rate was extracted from the model and mapped for June 2001 to assist in the identification of the source regions contributing to the ozone episode. r 2007 Elsevier Ltd. All rights reserved. Keywords: Regional air quality; Air pollution; Ozone; NOX ; VOC; Mobile emission
1. Introduction Ground level ozone is a major concern for southern Ontario air quality because of its effects on respiratory health (Ontario Medical Association Toronto, 2001) and agricultural crops (Linzon et al., 1986). Tropospheric ozone is formed by a series of photochemical reactions among a variety of precursors emitted by multiple sources and tracing its evolution is difficult. It is generally accepted, Corresponding author. Tel.: +1 519 888 4401.
E-mail address: [email protected] (J.J. Sloan). 1352-2310/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2007.01.030
however, that the long range transport of ozone depends on the relative locations of the source and receptor regions and the meteorology connecting them (Brankov et al., 2003; Brook et al., 2002; Farrell and Keating, 2002) and that these aspects can be addressed by the use of Eulerian chemical transport modelling. Some early studies (Yap et al., 1988) suggest that the contribution from long range transport could be the most important factor in the high ozone episodes that occur regularly in summer in southern Ontario and for this reason, it is particularly important to understand the origins of the background ozone in this region.
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
3778
Southern Ontario contains a population of approximately 11 million, over 90% of whom are located in the region just north of lakes Erie and Ontario, in an area including the Windsor–Toronto corridor, the Greater Toronto Area (GTA) and Central and Eastern Ontario. The air quality in this region is assessed using an air quality index (AQI) that is published by the Ontario Ministry of the Environment (2005). The value of the AQI is determined largely by the concentrations of the six most common air pollutants ðSO2 ; O3 ; NO2 ; CO, total reduced sulphur compounds and fine particulate matter). In 2001, high O3 concentrations were responsible for 99% of the hours during which the AQI was listed as ‘poor’, and these periods of very high ozone occurred during the summer months. The present study examines one of these high ozone episodes, which occurred during the months of June and July 2001. We first describe the model and present a novel methodology that we have developed to validate the modelling results. We then compare the regional concentration of OX ðOX ¼ O3 þ NO2 Þ obtained from the model with that measured at the Province of Ontario AQI measurement stations. Then, the background ozone concentration will be determined using these validated results together with various emission scenarios. In order to understand the impact of the emission sources on ozone production, several simulations will be presented that have different reductions in specific NOX and VOC emissions, as well as a reduction of road traffic. Finally, an analysis of the ozone production rate during June 2001 will identify the areas of ozone production and consumption.
2. Estimation of the regional contribution of OX Clapp and Jenkin (2001) show that the OX concentration is made up of two identifiable contributions: an NO2 -independent (or regional) contribution and an NO2 -dependent (or local) contribution. It is possible to determine the regional contributions of these to OX concentrations using measurements over appropriate temporal and spatial scales. Data from 28 AQI stations have been used to investigate the relationships among the maximum daily values of OX and NOX in June 2001. During the summer of 2001, June was the most polluted month, with maximum concentrations of 137 ppb of O3 and 245 ppb of NOX observed over all measurement stations. The measured data are classified into different kinds of environments and analysis of the measured data permits us to estimate the regional and local contributions to the background OX concentration in these different locales. For the present case, we focus on commercial and industrial areas. Fig. 1 shows that OX concentrations increase with NOX during daylight over all measurement sites. Following Clapp and Jenkin (2001) we interpret the intercepts of the regression lines shown in the figure as being the regional contribution to OX , which is independent of NOX , and the gradient as the contribution that correlates with the local level of pollution. The linear regression demonstrates a high regional contribution to oxidants with 37.5 ppb. These background concentrations typically represent 50% of the total averaged concentration of OX in these areas.
120
[Ox] (ppb)
100 80 y = 0.3138x + 37.506 60 40 20 0 0
20
40
60
80 100 [NOx] (ppb)
120
140
160
180
Fig. 1. Observed daily maximum OX vs. NOX concentrations (ppb) over all measurement sites during June 2001. Lines were defined by regression analysis.
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
3779
3. Model description
4. Validation of modelling results
It has been accepted for some time that synoptic meteorological patterns play an important role in the long range transport of tropospheric ozone in southern Ontario (Heidorn and Yap, 1986). In this study, the meteorological fields were obtained using the MM5 mesoscale meteorology model, version 3.6 (Grell et al., 1994), which was used with two nested domains. The inner domain covers the northeastern US and southeastern Canada with 42 50 grid cells having a horizontal resolution of 36 km. The outer domain covers most of North America with a horizontal resolution of 108 km. The air quality simulations were carried out using the sparse matrix operator kernal emission (SMOKE)/CMAQ regional modelling system (Byun and Schere, 2006; Carolina Environmental Programs, 2003; Grell et al., 1994) on the domain having the 36 km resolution. Version 4.3 of the CMAQ model with the Carbon Bond IV Mechanism (CB-IV) (Gery et al., 1989) was used. Both MM5 and CMAQ were configured with 15 sigma layers, with the top layer at 100 mb. The MRF boundary layer scheme (Grell et al., 1994) was used in the MM5 parameterization. Time-invariant climatological profiles for ozone and its precursors were used as boundary conditions. Version 2.0 of the (SMOKE) modelling system was used to generate the gridded, hourly, speciated emission inputs for CMAQ. The 1996 EPA National Emissions Trends (NET96) US inventory and the 1995 Canadian emissions inventory were used for criteria emissions. Tests were made using the more recent 1999 EPA emissions data and the results differed by only a few percent from those obtained with the NET96 data, so the latter were used, in order to be temporally selfconsistent with the Canadian data. Canadian measured data for comparison with the simulations were obtained from 53 sites (37 located in Southern Ontario) of the National Air Pollution Surveillance (NAPS) network A map of AQI monitoring station is available at http://www. ene.gov.on.ca/envision/techdocs/4521e01.pdf, p. 36, Figure 5.1. Since these are surface measurements, only the model predictions for the surface layer can be compared with them, which makes the comparison somewhat sensitive to such model details as the vertical transport and deposition algorithms.
The 37 Ontario monitoring stations are grouped by common characteristics: residential (19), commercial (9), industrial (2), forested (2), agricultural (3) and indeterminate (2). For our comparisons, the agricultural and forested stations will be grouped under the heading ‘‘rural’’. Comparisons between modelled and measured results will be illustrated by Taylor diagrams (Taylor, 2001). Before discussing the comparisons, we will describe these diagrams briefly. They convey overall comparisons between measured and modelled fields in a compact way by exploiting certain geometric relationships among the statistical parameters used to characterize the fields. As a result of these relationships, it is possible to construct a modified two-dimensional polar plot that represents simultaneously the correlation coefficient, and root-mean-square (RMS) difference and the standard deviations of the measured and modelled fields. For cases such as the present one, in which several different measurement stations are involved, it is most convenient to normalize the statistical parameters in order to remove differences in absolute values at the different stations. The normalization is done by dividing the reduced RMS difference and the standard deviations for the measured and modelled quantities by the standard deviation of the measured quantity. The reduced RMS difference, defined by ( )1=2 N 1X ½ðsn sÞ ðmn mÞ2 D¼ (1) N n¼1 is just the RMS difference between the measured (mÞ and simulated (sÞ results, after removal of the bias in each quantity. To illustrate how the Taylor diagram is constructed, we show in Fig. 2 the statistics for the maximum 8 h ozone concentrations obtained in the model validation runs, compared with the same concentrations recorded at several observation stations during June 2001. We have constructed separate plots for (a) commercial, (b) residential and (c) rural stations. In each plot, the radial distance of each point is the normalized standard deviation of the modelled result for the specific station. The normalized standard deviations of the measurements are all represented by the point on the abscissa labelled ‘‘Observed’’. Due to the definition of the normalization, this is necessarily 1.0 for each station. The normalization does not change the
ARTICLE IN PRESS 3780
G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
Fig. 2. Normalized pattern statistics comparing the maximum ozone concentration over the 8 h simulation with the observed in June 2001 for (a) commercial, (b) residential and (c) rural stations. The radial distance from the origin is proportional to the standard deviation of the pattern. The correlation between the two fields is given by the azimuthal position of the points (Taylor, 2001). Each point represents a measurement station.
correlation coefficient, which is plotted as the azimuthal position of the point. Finally, the reduced RMS difference, D, is the distance from the ‘‘Observed’’ point to each modelled point. The three Taylor diagrams in Fig. 2 compare the measurements with the simulations for residential, commercial and rural stations. Most of the points in each diagram are clustered together, suggesting that the designations of the sites were grouped correctly (in terms of emissions) in spite of their very different geographical locations. The Taylor diagram also
highlights sites with unusual results. For example, station 1 for the commercial sites (Fig. 2a) and stations 7 and 14 for the residential sites (Fig. 2b) do not fall close to the points representing the other sites and thus they are less representative of the designated group than the other stations. For this reason, these three stations will be omitted from the analysis of the results to be presented later. We justify this decision in statistical terms by noting that stations 1 and 7 have low correlation coefficients and large RMS differences, suggesting that
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
they are subject to sources not well described by the model. Station 14 has a low normalized standard deviation by comparison with the other residential stations, also suggesting that it is not representative of this category. Neglecting these points, the correlations for the three groups range from 0.6 to 0.85, and the standard deviations range from 0.5 to 0.75. A skillful model should be able to simulate accurately both the concentration of a species and its pattern of variability. There are no universally agreed criteria for satisfactory model performance, but a useful set of statistical measures has been provided by the US EPA (US EPA, 1991) for use in evaluating models where the monitoring data are sufficiently dense. The first if these is the mean normalized bias error (MNBE), defined by N 1X mn sn MNBE ¼ . (2) N n¼1 mn This test measures the model’s ability to replicate observed patterns. Since statistics from periods having relatively low predicted and measured ozone levels are not very meaningful, this test should be limited to cases where the observed concentration is greater than a reasonable minimum. In this case, we select 50 ppb, which is slightly above the naturally occurring ozone background value in this region (around 40 ppb). The next measure is the mean normalized gross error (MNGE) which is also applied for concentration above a prescribed threshold, as defined by N jmn sn j 1X MNGE ¼ . (3) N n¼1 mn This test, which is a measure of model precision, compares the differences between all pairs of predictions and observations that are greater than 50 ppb. The final measure is the unpaired peak prediction accuracy (UPA), which compares the difference between the highest observed value and
3781
the highest predicted value found over all hours, as defined by smax mmax . (4) UPA ¼ mmax The US EPA suggests that for model simulations of O3 that are used for regulatory applications, the MNBE should be 10–15%; the MNGE should be 30–35% and the UPA should be 15–20%. Table 1 shows results of MNBE, MNGE and UPA for each classification of site and for all sites together. Clearly the modelled results achieve the EPA goals in all cases. The O3 bias (MNBE) is positive at each site, ranging from 1.8% for residential sites to 7.7% for agricultural sites, suggesting that the model has a slight tendency for overestimation. The observed episode peak ð137 ppbÞ is well-captured by the model, however, and the peak prediction accuracy is slightly overestimated for commercial, industrial, agricultural and forest sites (4.5%, 8.2%, 0.9% and 2%, respectively), while it is underestimated for residential sites ð9:5%Þ. We note that the EPA objectives are met despite the relatively old emission inventories. We assume the agreement would be improved if newer emission data were used. 5. Background ozone evaluation If we ignore injections from the stratosphere, we can define ‘background’ tropospheric ozone as that which arises from in situ formation in the free troposphere, including contributions from both anthropogenic and natural VOCs (Yap et al., 1988). Taking this approach, we can separate the contributions from natural and anthropogenic sources using model scenarios. We carried out three scenarios during the period from 1 May to 30 September 2001 as follows:
Base case: All emissions (anthropogenic and biogenic) are present.
Table 1 Statistical measures of model performance for l-h O3 concentration based on (US EPA, 1991) methodology
Observed peak (ppb) Modelled peak (ppb) MNGE (%) MNBE (%) UPA (%)
EPA goal
Residential
Commercial
Industrial
Agricultural
Forest
Domain
o 30235 o 10215 o 15220
128 124 20.3 1.8 3.22
112 117 25 2.5 4.3
97 105 24.3 4.5 7.6
114 115 19.1 7.7 0.9
98 100 16 3.2 2
137 145 21.9 3.2 5.5
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
3782
100
percentage
80 60 40 20 0 Base case
No emissions Only beogenic
Observation
Fig. 3. Percentage of 8-h daily maximum ozone concentration, when ozone concentration is greater than 65 ppb, averaged over southern Ontario for selected scenarios from 1 May to 30 September 2001 (black column), and from 1 June to 31 July (grey column). The definition of the scenarios is in Section 5.
No emissions: No anthropogenic or biogenic emissions in the modelling domain. Only biogenic: No anthropogenic emissions in the modelling domain, but normal biogenic emissions are present.
without emissions and with only biogenic emissions is 31 ppb for both cases, while it is 53 ppb for the ‘base case’, which includes all emissions. This result corresponds closely to the estimate of Yap et al. (1988), who reported the local ‘background’ ozone level in southern Ontario for May to September to be approximately 20–30 ppb, based on observed data from 30 monitoring stations from 1979 to 1985. Although, the ‘background’ ozone concentration does not have large variations, it shows a small reduction when the ozone concentration is high during the smog episodes. A possible explanation for this result could be the scavenging of ozone, which happens in stagnant meteorological conditions. However, these episodes need further investigation. These results are also consistent with those in Fig. 1, which gives an averaged OX background concentration of 37.5 ppb. 6. Ozone abatement strategies 6.1. Emissions reductions
The ozone concentrations averaged over southern Ontario for these scenarios are illustrated in Fig. 3. The ozone concentrations for each scenario for the period from 1 May to 30 September, expressed as percentages of the 8-h daily maximum for the base case, are shown by the black bars. The corresponding concentrations for the high ozone period from 1 June 1 to 31 July are indicated by the grey bars. In both cases, the model base case reproduces the observations to within 3%. The scenarios without any emissions and with only biogenic emissions are almost identical in each case, indicating that the contribution from the biogenic emissions in the domain is not significant. This result agrees qualitatively with the modelling study from Lurmann et al. (1984), which estimated the contribution from biogenic emissions to be 2–9% of the predicted maximum ozone concentration in urban areas. Those authors attribute this small contribution to fast ozone-‘biogenic hydrocarbon’ reactions which scavenge almost as much ozone as is produced by the biogenic VOCs. The 8-h daily maximum ozone concentrations with only biogenic emissions are significantly lower (relative to the base case) during the high ozone period than during the whole study period, indicating that more ozone originates from anthropogenic emissions during the period of high ozone than at other times during the summer. The 8-h daily maximum ozone concentration averaged over the entire period for the scenarios
To develop an ozone abatement strategy for a specific area, it is necessary to know whether the ozone production is limited by NOX or by VOC and how this chemical limitation varies with season, meteorology and emissions. Using the numerical model, we examined the consequences of reductions in either NOX or VOC emissions for June 2001 by simulating three scenarios:
A base case with the meteorology and emissions of June 2001. A 50% reduction in NOX emission relative to the base case. A 50% reduction in VOC emission relative to the base case.
Fig. 4 shows the changes in ozone concentration associated with either reduced VOC or reduced NOX for the locations corresponding to the measurement stations introduced earlier. The top four sections of Fig. 4 refer the results of a 50% reduction in VOC. For this scenario, there is never a statistically significant increase in ozone at any station and there is no significant change in ozone for ozone concentrations below the (approximately) 40 ppb background value. For ozone concentrations above the background, the reduction in VOCs causes a reduction in ozone for all sites and the reduction increases with ozone concentration up
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788 [O3 ] (ppb)
[O3 ] (ppb) 40
60
80
100
0
120
20
40
100
120
0 -5 -10 -15 -20 Commercial stations with 50% decrease of VOC
[O3 ] (ppb)
[O3 ] (ppb)
20
40
60
80
100
0
120 ozone change (ppb)
5 0 -5 -10 -15 -20
20
40
20
40
60
80
100
120
Industrial stations with 50% decrease of VOC
[O3 ] (ppb) 0
60
4 2 0 -2 -4 -6 -8 -10 -12 -14
Agricultural stations with 50% decrease of VOC
[O3 ] (ppb) 80
100
120
40 30 20 10 0 -10 -20 -30
0 ozone change (ppb)
ozone change (ppb)
80
Residential stations with 50% decrease of VOC
-25
20
40
60
80
100
120
30 20 10 0 -10 -20 -30
Residential stations with 50% decrease of NOx
Commercial stations with 50% decrease of NOx
[O3 ] (ppb) 0
20
40
60
[O3 ] (ppb) 80
100
0
120
15 10 5 0 -5 -10 -15 -20
ozone change (ppb)
ozone change (ppb)
60
5
-25
0 ozone change (ppb)
20
5 0 -5 -10 -15 -20 -25 -30 -35 -40
ozone change (ppb)
ozone change (ppb)
0
3783
Agricultural stations with 50% decrease of NOx
20
40
60
80
100
120
30 25 20 15 10 5 0 -5 -10 -15 Industrial stations with 50% decrease of NOx
Fig. 4. O3 reduction (ppb) against O3 concentration (ppb) with a decrease of 50% of NOX or VOC for residential, commercial, industrial and agricultural sites (June 2001).
to the maximum values encountered during the month. Residential, commercial and industrial ozone concentrations are more sensitive to VOC, however, than it is to NOX . The bottom four sections of Fig. 4 show that a 50% reduction in NOX may cause the ozone to
increase or decrease, depending on the initial O3 concentration. In general, for low ozone levels, a decrease in the NOX causes an increase in ozone, while the opposite is the case for high ozone levels. The changes appear to be roughly linear with initial ozone concentration and assuming that they are, the
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
3784
intercept with the abscissa of the linear regression line gives the ozone concentration at which the sensitivity to NOX changes from positive to negative. The values of this intercept are very similar for all categories of sites: they are 41, 38, 40 and 37 ppb for residential, commercial, industrial and agricultural sites, respectively. This coincides with the mean natural background ozone level calculated, as shown in Fig. 3, from the maximum daily concentrations observed under conditions of no local emissions. In all cases, the maximum and minimum changes in the ozone concentration resulting from the 50% NOX reduction are roughly 20 ppb. Depending on the initial ozone concentration, the slope of the response is about 0:3 ppb per ppb of initial ozone. The NOX =O3 titration chemistry plays a major role in the ozone response to a reduction in NOX . Fig. 5 shows the O3 change against hours of the day, changes in concentration are positive and negative at each hour of daylight and night: any diurnal signal can be found due to the O3 titration by NO, which is not a photolysis reaction. Fig. 6 shows the change in ozone for a 50% NOX reduction, plotted as a function of NOX concentration. There is an approximately negative linear relationship between the ozone change and the NOX concentration. The regression line intercepts NOX concentration axis at 97 ppb, indicating that a 50% reduction in NOX increases the ozone for NOX concentrations below 97 ppb and decreases it for concentrations above this value. This suggests that the NO þ O3 reaction dominates the NOX 2O3 system below the intercept, while the production of O3 by the photolysis of NO2 dominates above this value. Fig. 6 is plotted for residential sites, but
similar behaviour is observed for the other categories of sites as well.
a
b
6.2. NOX reduction from 20% to 80% The previous section illustrated the result of a 50% reduction in NOX emission. To complete the analysis, NOX emission reductions of 20%, 70% and 80% were also simulated. Fig. 7 shows the resulting changes in ozone resulting from the above NOX emission reductions, plotted against total ozone concentration. In all cases, the ozone change is linear with ozone concentration, so only the regression lines are plotted in the figure. The response of the ozone change varies with the amount of NOX reduction, but for all site categories, the intercept with the abscissa (ozone concentration) is approximately the same (38, 39, 37 and 37 ppb for residential, industrial, commercial and agricultural, respectively). For ozone concentrations above this value, a decrease in NOX leads to a decrease in ozone that is linear with total ozone concentration. In this region, therefore, ozone
ozone change (ppb)
[NOx] (ppb)
ozone change (ppb)
40 20
0 -20 -40 -60 0
3
6
9
12
15
hour of the day
18
21
100
50
0
150
200
250
y = -0.11 x + 10.7
Fig. 6. Ozone reduction (ppb) against NOX concentration (ppb) with a decrease of 50% of NOX for residential sites (June 2001).
60
-80
40 30 20 10 0 -10 -20 -30
24
80 60 40 20 0 -20 -40 -60 -80 -100
0
3
6
9
12
15
18
21
hour of the day
Fig. 5. O3 change against hours of the day over all sites with (a) a decrease of 50% of NOX and (b) a decrease of 50% of VOC.
24
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788 [ozone] (ppb)
[ozone] (ppb) 0
20
40
60
80
100
ozone change (ppb)
ozone change (ppb)
0
120
60 40 20 0 -20 -40 -60
20
40
60
80
40
60
120
Commercial [ozone] (ppb)
[ozone] (ppb) 20
100
40 30 20 10 0 -10 -20 -30 -40 -50
Residential
0
80
20 10 0 -10 -20 -30 -40 -50
100
120
0 ozone change (ppb)
ozone change (ppb)
3785
20
40
60
40 30 20 10 0 -10 -20 -30 -40 -50
Agricultural
80
100
120
Industrial
Fig. 7. Ozone reduction (ppb) against O3 concentration (ppb) with a decrease of 20% (blue line), 50% (dark line), 70% (red line) and 80% (green line) of NOX for residential, commercial, industrial and agricultural sites (June 2001).
Table 2 Linear regression parameters for the O3 response to NOX reductions of 20%, 50%, 70% and 80%
Residential Industrial Commercial Agricultural
the O3 decreases more rapidly than can be described by a linear relationship.
S20
I 20
S50
I 50
S70
I 70
S80
I 80
7. Mobile source reductions
0.10 0.09 0.11 0.09
5.05 3.75 4.12 3.76
0.27 0.28 0.30 0.29
11.00 11.12 12.16 10.54
0.48 0.47 0.51 0.49
17.82 17.6 18.92 17.16
0.66 0.61 0.66 0.64
24.35 21.78 21.98 22.15
Due to the chemical nonlinearities discussed above, the effects on O3 of separate changes in NOX and VOC emissions are difficult to predict unless the exact circumstances are known. Since motor vehicle traffic is a source of both NOX and VOC emissions, then decreases in traffic cause changes in both of these in (approximately) fixed ratios and it is useful to consider the consequences of this kind of change, rather than considering the NOX and VOC emissions independently. In Fig. 8, we show the simulated results from a decrease of 50% in traffic emission for each category of monitoring site for the month of June 2001. For all sites, the reduction of traffic has a similar effect on ozone: it causes an increase for O3 concentrations below the regional background level of about 40 ppb and a decrease if the O3 concentration above this value. In this respect, the response is qualitatively similar to that for both NOX and VOC reductions separately (see Fig. 4). The effects differ, however, for different classes of locations. The changes in O3 are large and approximately linear with O3 concentration for residential, commercial and agricultural sites, while the trend is not as
production increases with NOX emission, so the production of ozone by NO2 photolysis predominates. For ozone concentrations below the intersection point, a decrease in NOX emission leads to an increase in ozone concentration that is also linear with total ozone concentration. In this region, therefore, the titration of ozone by NO is predominant. The larger the reduction in NOX emissions, the larger is the slope of the regression line. The parameters of the regression lines for reductions of 20%, 50%, 70% and 80% are shown in Table 2, where the S N and I N indicate the slope and intercept for an NOX reduction of N%. This shows that the change in O3 for a change in NOX is nonlinear. The average slopes for reductions of 20%, 50%, 70% and 80% are 0:10, 0:28, 0:49 and 0:64, respectively. Thus for a given reduction in NOX ,
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
3786
[O 3] (ppv)
[O 3] (ppv) 0
20
40
60
80
100
0
120
40
ozone change (ppb)
10 5 0 -5
-10
100
120
5 0 -5
Commercial stations
[O 3] (ppv) 20
40
60
[O 3] (ppv) 80
100
0
120
ozone change (ppb)
ozone change (ppb)
80
10
-10
Residential stations
0 0 -2 -4 -6 -8 -10 -12 -14
60
15
15
ozone change (ppb)
20
Agricultural stations
16 14 12 10 8 6 4 2 0 -2 -4
20
40
60
80
100
120
Industrial stations
Fig. 8. Ozone reduction (ppb) against O3 concentration with a decrease of 50% of traffic for residential, commercial, industrial and agricultural sites (June 2001).
clearly identifiable for the industrial sites. The maximum reduction for the first three sites is similar (about 5 ppb), while the 50% decrease in traffic has virtually no effect on O3 for the industrial sites. The amount of increase for low O3 concentrations, which we associate with a reduction in the consumption of background O3 by NO, is up to about 10 ppb for the residential, commercial and industrial stations, but smaller for the agricultural sites. We conclude that the latter are not as strongly influenced by traffic emissions and the other three, which is consistent with their classification. The intercepts of the regression lines with the O3 concentration axis occur at approximately the background level, as seen in the case of the NOX reduction as well. There are small but significant differences in the values of the intercepts, however, due to the difference in the NOX emission sources. The intercepts for the residential and commercial sites in this case are 40 and 46 ppb, respectively. These can be compared with the changes for the pure NOX reductions at these sites, which are 41 and 38 ppb, respectively. The similarity of the response at the residential sites compared with the difference of 20% at the commercial sites emphasizes the point that the local conditions must be considered when assessing the effects of changes in emissions. The traffic reduction changes several
emissions that affect the O3 chemistry, including NOX , VOCs and CO, whereas changes in only one of these can have a different effect on the O3 . It is important to take account of this complexity when considering abatement strategies rather than considering the effects of only one or two important emissions. 8. Ozone production and consumption rate Ozone production and consumption have systematic temporal and spatial patterns due to chemistry, superimposed on stochastic changes caused, among other things, by meteorological variability. Abatement strategies, therefore, must be based on the predictability of the chemistry, while accepting that there will be a range of effectiveness due to random factors that are not included in the chemical system. A numerical CTM simulation is capable of addressing both aspects of the problem, as long as the simulation time is long enough to get adequate statistics on the stochastic part of the system. The calculations reported here only describe one summer season, so they cannot be taken as a general description of the ozone variability in the target domain. The results averaged for one month, however, are adequate to show the spatial distribution of ozone production
ARTICLE IN PRESS G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
Fig. 9. Average production rate of ozone ðppb h1 Þ over June 2001 from the modelling system. Negative values stand for consumption area and positive values for production area.
and consumption regions in the domain and their relative NOX =VOC sensitivities that month. Although not extensive, this information is useful to indicate the abatement strategies that would be most effective in the region. Where such an analysis is carried out for a specific case, of course, the receptor regions must also be considered, since they will be affected differently by the different source regions. The first step in this analysis is to identify the spatial and temporal distributions of the production rates of ozone and the other species that are chemically related to it. This information is produced directly by the CTM at the resolution of the grid. (It is not, however, available from any other source.) Fig. 9 shows a map of the average net O3 production rate during June 2001 at the 36 km resolution of the modelling grid used in this study. Negative values (indicating ozone consumption) are evident at the locations of urban areas such as Toronto, Hamilton, Detroit, Cleveland and the Baltimore–Washington conurbation, presumably due to NOX emissions from traffic. There are O3 sources in western Ohio, across Pennsylvania and in southern New York State as well as on the north shore of Lake Erie. Most of the latter can be identified with large industrial sources. 9. Conclusion For the domain including southern Ontario, a linear regression of the observed concentration of
3787
OX against that of NOX demonstrates a high regional contribution to oxidants, amounting to OX concentrations of 69 ppb for residential areas and 44 ppb for commercial areas. To understand the chemistry responsible for this, the MM5 /SMOKE/ CMAQ regional modelling system was applied for the summer months of 2001, with a focus on June, using nested domains covering north eastern North America with a resolution of 108 km and southern Ontario at 36 km. This relatively low resolution is adequate to explore the ozone precursor dependences on a regional scale. The model was first used to determine the regional ozone background. The 8h daily maximum ozone concentration averaged over the entire period using the full emission data set is 53 ppb, while it is 31 ppb for scenarios without any emissions at all and only about 2% higher with only biogenic emissions. From this and the temporal and spatial distributions of the ozone concentration, we conclude that the contribution from the biogenic emissions to ozone concentration in southern Ontario is not significant for this time period. Scenario studies with this model configuration show that a reduction in VOC emissions never leads to an increase in ozone concentration for any initial concentration of ozone. If the ozone concentration is below the regional background level, reduction in VOCs causes no change. If the ozone is above the regional background, a reduction in VOCs causes a reduction in ozone that increases with increasing ozone concentration. A reduction in NOX emission, on the other hand, can lead to either an ozone decrease or an increase, depending on the initial ozone concentration. A reduction of 50% in NOX emission decreases or increases the ozone concentration by up to 20 ppb, if the initial ozone concentration is above or below the background level (respectively). The largest changes correspond to the highest and lowest ozone concentrations. For areas that are highly polluted (NOX concentration greater than 100 ppb), ozone concentration change significantly (up to 20 ppb) with a reduction of NOX emissions. For low concentrations of NOX , a reduction in NOX emissions increases the ozone concentration (up to 10 sppb), presumably due to a reduction in ozone removal by reaction with NO. Simulations with reductions of 10%, 20%, 50%, 70% and 80% in NOX emissions lead to a linear increase or decrease of ozone with initial ozone concentration. The larger the reduction in NOX emission, the more sensitive the system is to initial
ARTICLE IN PRESS 3788
G. Brulfert et al. / Atmospheric Environment 41 (2007) 3777–3788
ozone concentration. These results indicate where NOX and/or VOC reductions are likely to be helpful in reducing ozone concentrations and—equally importantly—cases where such abatement measures will have little or no effect. In particular, if the ozone is already at or below the background levels, then it is possible to increase it by reducing NOX emissions. Another counterintuitive conclusion from this work (that has also been seen in other similar studies) is that the major urban areas are net ozone sinks due to significant NOX emissions from the high densities of motor vehicles, which consume ozone in the oxidation of NOX to HNO3 , which is partially removed by wet deposition, thereby removing some of the NO2 , which could otherwise be a source of ozone. Computation of the average net ozone production rate during June 2001 shows negative values coinciding with all large urban areas in the domain, which are likely due to high NOX emissions from traffic. The most important areas of ozone production are in western Pennsylvania, where a maximum average production rate of about 13 ppb h1 was observed at one location during the month of June. There are also significant ozone production rates at other locations in south-western and north-central Pennsylvania, with lower rates at locations in Ohio, south-western New York State and the north shore of Lake Erie. The isolated nature of these locations indicate that the ozone production is due to large point sources located in the grid squares indicated in Fig. 9. Measures to reduce ozone production, therefore, should be focussed on the point sources in these locations and if higher resolution is required to separate individual sources, this is easily achieved with current models, which can resolve the contributions of sources separated by as little as 3–4 km with good accuracy. Acknowledgements Financial support for this work was provided by the Natural Sciences and Engineering Research Council of Canada, Ontario Power Generation, the Ontario Research and Development Challenge Fund and the Secretarı´ a de Estado de Educacio´n y Universidades de Espan˜a and Fondo Social Europeo. We wish to acknowledge Mr. Jonatan Aronsson for technical assistance.
References Brankov, E., Henry, R., Civerolo, K., Hao, W., Rao, S.T., Misra, P.K., Bloxam, R., Reid, N., 2003. Assessing the effects of transboundary ozone pollution between Ontario, Canada and New York, USA. Environmental Pollution 123, 403–411. Brook, J.R., Lillyman, C.D., Shepherd, M.F., Mamedov, A., 2002. Regional transport and urban contributions to fine particle concentrations in Southeastern Canada. Journal of the Air & Waste Management Association 52, 855–866. Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and other components of the models-3 community multiscale air quality (CMAQ) modeling system. Applied Mechanics Reviews 59, 51–77. Carolina Environmental Programs, 2003. Sparse Matrix Operator Kernel Emission (SMOKE) Modeling System. University of Carolina, Carolina Environmental Programs, Research Triangle Park, NC. Clapp, L.J., Jenkin, M.E., 2001. Analysis of the relationship between ambient levels of O3 , NO2 and NO as a function of NOX in the UK. Atmospheric Environment 35, 6391–6405. Farrell, A.E., Keating, T.J., 2002. Transboundary environmental assessment: lessons from OTAG. Environmental Science and Technology 36, 2537–2544. Gery, M.W., Whitten, G.Z., Killus, J.P., Dodge, M.C., 1989. A photochemical kinetics mechanism for urban and regional scale computer modelling. Journal of Geophysical Research 94, 925–956. Grell, G.A., Dudhia, J., Stauffer, D.R., 1994. A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR/TN-398+STR. Heidorn, K.C., Yap, D., 1986. A synoptic climatology for surface ozone concentrations in southern ontario. Atmospheric Environment 20, 695–703. Linzon, S.N., Pearson, R.G., Donnan, J.A., Durham, F.N., 1984. Ozone effects on crops in Ontario and related monetary values. MOE Report ARB-13-84-Phyyo. Lurmann, F.W., Nitta, B., Ganesan, K., Lloyd, A.C., 1984. Modeling potential ozone impacts from natural sources-III. Ozone modeling in Tampa/St. Petersburg, Florida. Atmospheric Environment 18, 1133–1143. Ontario Medical Association Toronto, 2001. Ontario’s air: years of stagnation hhttp://www.oma.org/phealth/report/airq001. pdfi. Ontario Ministry of the Environment, 2005. Ontario’s Air Quality Index 2004 Report, Toronto Ontario hhttp://www. ene.gov.on.ca/programs/3958e02.pdfi. Taylor, K.E., 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research 10, 7183–7192. US EPA, 1991. Guideline for regulatory application of the urban airshead model. US EPA Report No. EPA-450/4-91-013. Office of Air and Radiation, Office of Air Quality Planning and Standards, Technical Support Division, Research Triangle Park, North Carolina, US. Yap, D., Ning, D.T., Dong, W., 1988. An assessment of source contributions to the ozone concentration in southern Ontario 1979–1985. Atmospheric Environment 22, 1161–1168.