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Analysis of ozone and VOCs measured in Shanghai: A case study
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Atmospheric Environment 41 (2007) 989–1001 www.elsevier.com/locate/atmosenv
Analysis of ozone and VOCs measured in Shanghai: A case study Fuhai Genga,b, Chunsheng Zhaoa,, Xu Tanga,b, Guoliang Lub, Xuexi Tiec a
Department of Atmospheric Science, School of Physics, Peking University, Beijing, China b Shanghai Meteorological Bureau, Shanghai, China c National Center for Atmospheric Research, Boulder, CO, USA
Received 22 June 2006; received in revised form 2 September 2006; accepted 14 September 2006
Abstract Shanghai Meteorological Administration has established a volatile organic compounds (VOCs) laboratory and an observational network for VOCs and ozone (O3) measurements in the city of Shanghai. In this study, the measured VOCs and O3 concentrations from 15 November (15-Nov) to 26 November (26-Nov) of 2005 in Shanghai show that there are strong day-to-day and diurnal variations. The measured O3 and VOCs concentrations have very different characterizations between the two periods. During 15-Nov to 21-Nov (defined as the first period), VOCs and O3 concentrations are lower than the values during 22-Nov to 28-Nov (defined as the second period). There is a strong diurnal variation of O3 during the second period with maximum concentrations of 40–80 ppbv at noontime, and minimum concentrations at nighttime. By contrast, during the first period, the diurnal variation of O3 is in an irregular pattern with maximum concentrations of only 20–30 ppbv. The VOC concentrations change rapidly from 30–50 ppbv during the first period to 80–100 ppbv during the second period. Two chemical models are applied to interpret the measurements. One model is a regional chemical/ dynamical model (WRF-Chem) and another is a detailed chemical mechanism model (NCAR MM). Model analysis shows that the meteorological conditions are very different between the two periods, and are mainly responsible for the different chemical characterizations of O3 and VOCs between the two periods. During the first period, meteorological conditions are characterized by cloudy sky and high-surface winds in Shanghai, resulting in a higher nighttime planetary boundary layer (PBL) and faster transport of air pollutants. By contrast, during the second period, the meteorological conditions are characterized by clear sky and weak surface winds, resulting in a lower nighttime PBL and slower transport of air pollutants. The chemical mechanism model calculation shows that different VOC species has very different contributions to the high-ozone concentrations during the second period. Alkane (40 ppbv) and aromatic (30 ppbv) are among the highest VOC concentrations observed in Shanghai. The analysis suggests that the aromatic is a main contributor for the O3 chemical production in Shanghai, with approximately 79% of the O3 being produced by aromatic. This analysis implies that future increase in VOC (especially aromatic) emissions could lead to significant increase in O3 concentrations in Shanghai. r 2006 Elsevier Ltd. All rights reserved. Keywords: VOCs; Ozone; Shanghai; WRF-Chem
Corresponding author. Tel.:+86 10 62754684.
E-mail address: [email protected] (C. Zhao). 1352-2310/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2006.09.023
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1. Introduction Shanghai is the largest city in China, with population of 16 millions. The recent rapid increase in the urbanization and human activities has important impacts on atmospheric air quality in this region. For example, the number automobiles increase from 100,000 (1980) to 1,500,000 (2005). As a result, the emissions of volatile organic compounds (VOCs) have significantly increased. Because VOCs are important precursors for the formation of ozone pollution (Crutzen, 1975; Chameides and Walker, 1976), to understand the relationship between VOCs and ozone concentrations is an important issue for the environmental concerns in the Shanghai region. There have been several studies on the air quality in the Shanghai region (from 1211 to 1221E and 30.51 to 31.51N). Jiang et al. (2004) studied the air pollution index (API) for particulate matter up to 10 mm in size (PM10), SO2, and NO2. Yang et al. (2005) measured black and organic carbons in Shanghai. They found that the concentrations of organic carbons were normally about twice the concentrations of black carbon. Xiu et al. (2005) measured mercury concentrations in Shanghai, and show that coal burning was estimated to contribute approximately 80% of total atmospheric mercury in Shanghai. Zhao et al. (2004) studied the ozone (O3) concentrations in the Shanghai region, their results suggest that small-scale dynamical processes have important influences on the distribution of O3 concentrations in this region. Lu et al. (2005) analyzed meteorological conditions in the Shanghai region. However, O3 concentrations and its precursors (NOx, VOCs, etc.) are not systemically measured and the relationship between O3 and its precursors (NOx, VOCs, etc.) has not been analyzed, such measurements have been urgently needed in the Shanghai region. In this study, we investigate the ozone chemical formation, variability, and diurnal variation during November 2005, when surface O3 and VOCs concentrations are simultaneously measured in Shanghai. A regional dynamical/chemical model (WRF-Chem) and a chemical mechanism model (NCAR MM) are used to study the causes of the ozone formation and ozone variability in Shanghai. In Section 2, we will describe the instruments and measurements. In Section 3, model configurations are described. In Section 4, the meteorological conditions during this period are analyzed. The
simulated O3 and VOCs concentrations will be compared to the measurement values to analyze causes of the ozone variability and the ozone production due to different VOC oxidants over the two periods. 2. Instrumentations and measurements 2.1. Instrumentations Ozone is measured using an EC 9810 ozone analyzer, together with an UV photometer which accurately and reliably measures ozone concentrations in ambient air. The instruments are installed on the top of a building which is 15 m high above the ground in the center of Shanghai. This instrument has automated to set to be zero and include an optional external valve manifold and external calibration sources. The instrument meets the technical specifications for US EPA (Environmental Protection Agency). Quality control checks were performed every 3 days including inspection of the shelter and instruments as well as zero, precision and span checks. Filter was replaced once in every 2 weeks and calibration was made every month. The ozone concentration was recorded every 5 s. VOCs concentrations were sampled for 24 h every day with a 6L silonite canister with silonitecoated valve (29-10622 model, Entech Instruments Inc., USA). The instrument has a large enough volume to provide detection of volatile chemicals down to low-pptv range. The internal silonite coating improves long-term VOCs storage. Absorption is eliminated by using nupro packless valves and by eliminating Teflon tape in the valve stem. These canisters are recognized to meet or exceed the technical specifications required for EPA methods TO14-A and TO15. Gases samples were pre-processed using the Model 7100 VOCs preconcentrator (Entech Instruments Inc., USA). Samples are analyzed for VOCs using a gas chromatography system (Agilent GC6890) coupled with mass-selective detection (Agilent MSD5975N) with length of 60 m, diameter of 0.32 mm, and film thickness of 1.0 mm. The column temperature is controlled by an initial temperature of 50 1C. The programmed temperature was used with helium as carrier gas and flow gas at 1.5 ml min1. The initial temperature program with 3 min hold time was 4 1C min1 to 170 1C switching to 14 1C min1 to 220 1C.
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2.2. Measured O3 and VOCs Fig. 1 shows the measured O3 and VOCs concentrations during 15-Nov to 26-Nov. The measured O3 has a strong day-to-day variability. For example, during 15-Nov to 21-Nov (the first period), the daily maximum ozone concentrations are low (20–30 ppbv) compared to the values during 22-Nov to 26-Nov (the second period) with a daily maximum of 40–80 ppbv. In addition, the ozone concentrations during the first period have an irregular diurnal variation. By contrast, during the
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second period, the diurnal variation of ozone is predominated by a strong diurnal variation with high-ozone concentrations at noontime and lowozone concentrations at nighttime. The measured VOCs concentrations are also very different during these two periods. During the first period, the concentrations of VOCs are lower than the concentrations during the second period. The measurements also indicate that the dominant VOCs species are primarily alkanes and aromatics. During the first period, the averaged alkane and aromatic concentrations are approximately 5 and 15 ppbv,
80 70
Ozone (ppbv)
60 50 40 30 20 10
15/11 16/11 17/11 18/11 19/11 20/11 21/11 22/11 23/11 24/11 25/11 26/11 Date of 2005 45 40 35
VOCs (ppbv)
30 25
alkyl aromaticity ether halohydrocarbon ketone alkene hydroxy aldehyde allyhalide halogenated aromatic hydrocarbons
20 15 10 5 0
16/11
18/11
20/11
22/11
24/11
26/11
Date of 2005 Fig. 1. Measured surface O3 and VOCs concentrations during 15 November to 26 November in Shanghai.
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respectively. During the second period, the alkane and aromatic concentrations increased to 40 and 30 ppbv, respectively. In the following sections, these striking different characterizations of O3 and VOCs concentrations during these two periods will be analyzed by dynamical and chemical models. 3. Description of the models 3.1. WRF-Chem model The WRF-Chem model is mainly based on the frame of the weather research and forecasting (WRF) model. It is a newly developed mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. The effort to develop WRF has been a collaborative partnership, principally among the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (NOAA), the National Centers for Environmental Prediction (NCEP), the Forecast Systems Laboratory (FSL), the Air Force Weather Agency (AFWA), the Naval Research Laboratory, Oklahoma University, and the Federal Aviation Administration (FAA). The WRF model is a fully compressible and Euler non-hydrostatic model. It calculates winds (u, v, and w), perturbation potential temperature, perturbation geopotential, and perturbation surface pressure of dry air. It also can optionally output other variables, including turbulent kinetic energy, water vapor mixing ratio, rain/ snow mixing ratio, and cloud water/ice mixing ratio. The model physics include bulk schemes sophisticated mixed-phase physics for cloud-resolving modeling, multi-layer land surface models ranging from a simple thermal model to full vegetation and soil moisture models, including snow cover and sea ice, turbulent kinetic energy prediction or non-local K schemes for planetary boundary layer (PBL) calculation, and longwave and shortwave schemes with multiple spectral and a simple shortwave scheme. In addition to dynamical calculation, a chemical model is fully (on-line) coupled with the WRF model (WRF-Chem). A detailed description of WRF-Chem is given by Grell et al. (2005). The version of the model, as used in the present study, includes simultaneous calculation of dynamical parameters (winds, temperature, boundary layer, clouds, etc.), transport (advective, convective, and diffusive), dry deposition (Wesely, 1989), gasphase chemistry, radiation and photolysis rates
(Madronich and Flocke, 1999; Tie et al., 2003), and surface emissions (including on-line calculation of biogenic emission). The ozone chemistry is represented in the model by a modified regional acid deposition model, version 2 (RADM2) gasphase chemical mechanism (Chang et al., 1989) which includes 158 reactions among 36 species. Hourly emissions for 17 chemical species, including SO2, CO, NO, and 14 different hydrocarbons are implemented in the model. The emission inventory contains temporal, spatial, and detailed chemical composition of emissions from anthropogenic. The emission inventory is based on the study by Streets et al. (2003). In this study, a higher resolution of the emissions (1/6 degree in longitude and latitude) are used by the later works of Streets et al. (2003). Table 1 shows the emission inventory used in this study. Fig. 2 illustrates the spatial distribution of total VOCs emissions. It indicates that there is a very large variability in the emissions in the model domain with very high VOCs emissions located in the Shanghai region (from 1211 to 1221E and 30.51 to 31.51N). In this study, the model horizontal resolution is 12 12 km2, with a 1200 1200 km2 domain in the Yangtze Rive Delta (YRD) region (from 1141 to 1261E and 25.81 to 36.51N). The domain is selected by considering that there are a number of large cities located in the YRD region, and the interaction between these cities and Shanghai should be taken into account. The model has 31 vertical levels, nonuniformly spaced, from the surface to 20 mb. The lateral boundary and initial conditions of meteorological inputs (winds, temperature, etc.) are constrained from National Center for Environmental Prediction (NCEP) data for the same period to the measurements. The chemical initial conditions are constrained by a global chemical transport model (MOZART-2; Model for Ozone and Related Tracer, version 2). The detailed description of the global model is given by Horowitz et al. (2003). The model runs from 15-Nov to 26-Nov, 2005.
Table 1 Emission inventory of CO, NOx, and VOCs (ton year1) used in the model domain shown in Fig. 2 CO
NOx
VOC
3.20 107
5.09 106
5.99 106
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Fig. 2. The estimated emissions of total VOCs (mol s1), which is used in the WRF-Chem calculation.
3.2. Chemical mechanism model (NCAR MM) The NCAR master mechanism (NCAR MM) is a chemistry box model that includes a detailed and flexible chemical scheme (Madronich and Calvert, 1990). The model is a box (0D) model with detailed gas-phase chemistry consisting of 5000 reactions among 2000 species. This model computes the time-dependent chemical evolution of an air parcel initialized with known composition, assuming no additional emissions, no dilution, and no heterogeneous processes. This model is useful to study complex interactions with regional emissions. On the other hand, the box model does offer an opportunity to examine chemical transformations of an isolated urban plume at a level of detail that would be impractical to implement in current 3D models. The hydrocarbon chemistry in the master mechanism is treated explicitly and includes the photo-oxidation of partly oxygenated organic species. Alkanes, alkenes, and aromatics are considered as initial hydrocarbon reagents in the gas-phase mechanism. The chemistry of the methyl peroxy
radical is treated explicitly; a counter scheme is used for the other organic peroxy radicals (Madronich and Calvert, 1990). The rate coefficients for organic peroxy radical reactions were updated based on recent recommendations (Tyndall et al., 2001). Rate coefficients for hydrocarbons reactions with OH were updated based on the latest JPL compilations (DeMore, 2000). The OH-initiated ethene oxidation mechanism was modified to include multiple branching for the b-hydroxyl ethoxy radical reaction with NO (Orlando et al., 1998). OH-initiated rate coefficients for oxygenated hydrocarbons were updated from the (Atkinson, 1994) compilation. The kinetics of the HO2 self-reaction were recently measured (Christensen et al., 2002) to be lower than the current recommendation. The photolysis rate (J values) is calculated on-line with NCAR MM using radiative transfer code (TUV) (Madronich and Flocke, 1999). The TUV module was initialized for the various cases with measured median values for latitude, Julian day, altitude, O3 column and albedo. The TUV module was updated with cross section and quantum yields from recent evaluations
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for inorganic species (DeMore, 2000) and organic species (Atkinson et al., 2000). 4. Analysis of the result 4.1. The characterizations of meteorological conditions To study the causes of the different characterizations of O3 and VOCs between the two periods, meteorological conditions are analyzed. Fig. 3 shows the calculated cloud conditions (integrated cloud water contents from the surface to 10 km) on 16-Nov, 19-Nov, 23-Nov, and 26-Nov. It shows
that during the first period (16-Nov and 19-Nov), the Shanghai region is under cloudy conditions. By contrast, during the second period (23-Nov and 26-Nov), the Shanghai region is under clear sky conditions. As a result, during the first period, the diurnal variation of the PBL height is very irregular. The cloud layer provides ‘‘green house’’ effects during night, resulting in a higher altitude of the PBL during the first period. During daytime, the clouds scatter the sunlight, and the temporal variation of the cloud cover results in an irregular diurnal variation of the PBL height. Another important impact of the existence of clouds is their effect on the photochemistry (Tie et al., 2003). This
Fig. 3. The calculated cloud water contents integrated from the surface to 10 km (Kg(w) Kg1(a) 105) at noontime on 16-Nov, 19-Nov, 23-Nov, and 23-Nov, respectively.
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chemical effect of clouds will be discussed in the following section (Table 2). Fig. 4 shows the calculated wind speed and wind direction near the surface on—16-Nov, 19-Nov, 23Nov, and 26-Nov. It shows that during the first period, the wind direction is from the northeast direction, which transports air masses from the ocean (relatively clean air) to the Shanghai region. However, during the second period, the wind direction is switched from northeast to the northwest direction on 23-Nov, which transports air masses from inland around the Shanghai region. On 26-Nov, the wind circulates around the Shanghai region. As a result, during the second period, the wind direction is favorable to keep the air pollutants inside the Shanghai region. The wind speed is also very different between the two periods. During the first period, the wind speed is relatively strong (about 5 m s1 in the Shanghai region). By contrast, during the second period, the wind speed reduces significantly to about 1–2 m s1 in the Shanghai region. Both the wind direction and speed tend to produce lower air pollutants during the second period than the first period. The calculated surface temperature and wind speed are compared to the surface measurement in two stations in Shanghai. Stations 1 and 2 are located in longitudes (121.251E, 121.761E) and latitudes (31.381N, 31.051N), respectively. Fig. 5 shows the comparison between the calculation and measurement. Both the calculated Table 2 The configuration of NCAR MM with different model runs
H2O CO CH4 NOx C2H6 C3H8 C4H10 C2H2 C3H6 C2H4 BEN TOL XYL
Run-1 Base
Run-2 Run-3 Run-4 Run-5 No-alkane No-alkene No-ethene No-aromatic
0.02 ppmv 1 ppmv 2 ppmv 25 ppbv 13 ppbv 13 ppbv 13 ppbv 4 ppbv 3 ppbv 8 ppbv 6 ppbv 12 ppbv 12 ppbv
X X X X 0 0 0 X X X X X X
X X X X X X X 0 0 X X X X
X X X X X X X X X 0 X X X
X X X X X X X X X X 0 0 0
BEN represents benzene; TOL represents toluene; and XYL represents xylene.The X represents that same concentrations are used as the base run (RUN-1), and the 0 means that the concentrations are different from the base run and equal to zero. The date is 11-Nov at 311N with clear sky conditions.
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and measured results show that there are strong differences between the first and second periods. During the first period, the wind speed is higher than the second period, and the diurnal variation of temperature is more evident during the second period. Although the calculations have a similar feature as the measured results, discrepancies between the calculations and measurements are also evident. To quantitatively study the difference between calculated and measured values, we analyze the root mean square error (RMSE), the root mean square error systematic (RMSEs), root mean square error unsystematic (RMSEu), and the index of agreement (d) (Willmott, 1981; Tesche, 1988). Table 3 shows a detailed statistical analysis of the calculated surface temperature and wind speed. It indicates that the index of agreement (d) ranges from 0.65 to 0.67 for the calculated temperature, and 0.76–0.87 for the calculated wind speed. There is no clear indication that the model is systematically different with the measurement. The values of RMSEs range from 0.97 to 1.1 1C for temperature and 0.32–0.80 m s1 for wind speed, and the unsystematical errors (RMSEu) range from 1.6 to 2.1 1C for temperature and 0.51–0.85 m s1 for wind speed. The weather map (see Fig. 6) as the same period indicates that on 13-Nov, a strong cold highpressure system developed at the Mongolia plateau. The cold air mass moved to south and temperatures in the Shanghai region were dropped accompanied by strong winds. As a result, during 14–20 November , it was cold and cloudy in Shanghai influenced by this cold air mass (Fig. 6a, b) with strong northeast winds. From 21 November to 26 November, the cold high-pressure system decreases rapidly and disappears on 26-Nov (Fig. 6c, d). As a result, the Shanghai region was under clear sky conditions during 21–26 November, and surface winds were very weak. The measured weather system is consistent with the model calculated meteorological conditions (see Figs. 3 and 4). 4.2. The analysis of measured O3 and VOCs Fig. 7 shows the calculated PBL, O3, VOCs, and air mass outward flux. The calculated O3 and VOC concentrations are compared to the measured values. The calculated results show that the PBL height has an irregular diurnal variation during the first period, which is resulted from the variations of cloud cover (see Fig. 4). During the
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Fig. 4. Same as Fig. 3, except for wind direction and wind speed (m s1).
second period, the PBL height has a clear diurnal variation, with a maximum during noontime and a very low-PBL height during night. There is a clear indication that the nighttime PBL height is much higher during the first period than the second period. The measured daytime O3 concentrations are lower (with a maximum of 20–30 ppbv) than the second period (with a maximum of 40–80 ppbv). The calculated O3 by WRF-Chem shows a similar daily and day-to-day variation compared to the measured results. During the first period, the calculated O3 diurnal variation has an irregular pattern, which is due to the effect of clouds on the photochemistry. During the second period, Shanghai is under clear sky condition, and
the calculated O3 concentrations have a strong diurnal variation with a maximum of 40–60 ppbv during noontime and low-ozone concentrations during nighttime. The calculated noontime O3 concentrations also significantly increase from the first period to the second period, which is consistent with the measurements. However, some discrepancies are also shown between the measured and the calculated O3 concentrations. For example, the calculated O3 concentrations are generally 10 ppbv lower than the measured values. The statistical analysis of the calculated O3 (Table 3) shows that the index of agreement (d) is 0.58. The RMSEs is 9.6 ppbv and RMSEu is 11.1 ppbv, respectively.
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Fig. 5. Calculated surface temperature (oC) and wind speed (m s1) (solid lines) and compared to measured values in two stations of Shanghai. Table 3 Statistical analysis for calculated surface temperature, wind speed, and O3 concentrations RMSE (1C)
RMSEs (1C)
RMSEu (1C)
d
Temperature(1)a Temperature(2)a
2.1 2.3
1.1 0.97
1.6 2.1
0.67 0.65
Wind speed (1) Wind speed (2)
RMSE (m s1) 0.62 1.1
RMSEs (m s1) 0.32 0.80
RMSEu (m s1) 0.51 0.85
d 0.87 0.76
O3
RMSE (ppbv) 13.0
RMSEs (ppbv) 9.6
RMSEu (ppbv) 11.1
d 0.58
a
(1) and (2) represent the stations 1 and 2 defined in text.
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Fig. 6. The weather map for on 16-Nov, 19-Nov, 23-Nov, and 23-Nov, respectively.
The measured VOC concentrations, especially alkane and aromatic which are the two most dominant VOCs in Shanghai as indicated in Fig. 1, are lower during the first period than during the second period. The sum of alkane and aromatic concentrations are about 15–25 ppbv, and rapidly increased about 60–70 ppbv in the second period. There is a clear indication that the variations of VOC concentrations are anti-correlated to the out flow of the air mass indicated in the last panel of Fig. 7. The out flow of the air mass through the PBL is defined by
pollutants which are locally emitted. The out flow of the air mass (F) during the first period is about 5 times higher than the second period. The higher F during the first period is resulted from the combination of the higher PBL height and wind speeds (see Figs. 4 and 7). As a result, the higher F quickly dilutes the VOC concentrations as they are emitted from the surface in the Shanghai region (see Fig. 2). By contrast, during the second period, the lower F intends to keep air pollutants inside the city, resulting in higher VOC concentrations.
F ¼ W PH;
(1)
4.3. Interactions between O3 and VOCs
(2)
It is well known that VOCs are important precursors for the formation of O3, especially in large cities (Kleinman et al., 2001; Zhang et al., 2004; Tie et al., 2006). O3 photochemical production is related non-linearly to NOx and VOCs (Sillman, 1995). When NOx concentrations are small, O3 production is sensitive to NOx concentrations and enhanced by increased NOx emissions. Otherwise, O3 production is sensitive to VOCs but not to NOx (VOC limited). According to Sillman (1995), a value of 0.28 for the ratio CH2O/NOy
W¼
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi U 2 þ V 2,
where F represents the out flow of the air mass in the PBL (m2 s1); W represents the magnitude of horizontal wind speed in the PBL (m s1); U and V represent the wind speeds in latitudinal and longitudinal directions (m s1); and PH represents the PBL height (m). The values PH, U, and V are calculated from the model. From the above definition, the out flow of the air mass should be a good indicator to determine the magnitude of air
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Fig. 8. Calculated afternoon ratio of CH2O to NOy (NO+ NO2+HNO3+PAN+NO3+2N2O5) in the Shanghai region.
Fig. 7. The calculated PBL (m), O3 (ppbv), VOCs (ppbv), and air mass outward flux through the PBL (m2 s1) (black lines). The O3 and VOC concentrations are compared to the measured values (red lines) from 15-Nov to 26-Nov in Shanghai.
(NOy ¼ NO+NO2+HNO3+PAN+NO3+2N2O5) usually marks a transition between VOC limited and NOx limited regimes. Fig. 8 shows that in the Shanghai region, calculated CH2O/NOy is less than 0.28, indicating that O3 production is likely VOC limited in this region. As a result, the O3 production rate will be likely increased when VOC emission is enhanced. However, the different types of VOCs lead to very different contributions on the O3 productions due to their different reaction rates with other oxidants (mainly hydroxyl radical OH) (Lin et al., 1988). In addition, different VOCs are resulted from different sources, for example, aromatic is mainly resulted from automobile exhaust (Na et al., 2005). To better understand the chemical formation of O3 in Shanghai, a detailed chemical model (NCAR MM) is used to assess the individual contribution of different VOCs on the O3 production. Table 2 gives the configurations of the model runs. There are five model runs to study the sensitive
of O3 production resulted from different VOCs. The base run (Run-1) includes alkane concentrations of 39 ppbv, alkene concentrations of 7 ppbv, ethene concentrations of 8 ppbv, and aromatic concentrations of 30 ppbv. These concentrations are selected according to the VOC measurement in the second period, when the ozone photochemistry is more active than the first period, and diurnal variation is clearly indicated. The Run-2, Run-3, Run-4, and Run-5 are the same conditions with the base run, except that the alkene, alkene, ethane, and aromatic concentrations set to be zero, respectively. Fig. 9 shows that calculated O3 diurnal variation using NCAR MM. The different contributions to O3 concentrations due to alkanes, alkenes, ethenes, and aromatics are calculated with different model runs. The result suggests that alkane have very small impact (3%) on the O3 due to their low chemical reactivity. Alkenes and ethenes have important impacts on the O3 concentrations, by 37% and 16%, respectively. Without alkenes and ethenes, the maximum O3 concentration is reduced from 80 ppbv (with all VOCs taking into account) to 40 and 60 ppbv, respectively. The most dominant VOCs on the O3 formation is resulted from aromatics, which contributes about 79% of O3 concentrations from total VOCs contributions.
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Fig. 9. The calculated O3 diurnal variation using NCAR MM. The different contributions due to alkane (red line), alkene (blue line), ethene (yellow line), and aromatic (light-blue line) are calculated with different model runs shown in Table 2.
low concentrations at night. By contrast, during the first period, the diurnal variation of O3 is very irregular. The measured daily averaged VOCs concentrations are higher during the first period than the second period. The model analysis shows that regional meteorological conditions play important roles in controlling the concentrations of O3 and VOCs between the two periods. During the first period, meteorological conditions are characterized by cloudy skies, high surface winds, and a high nighttime PBL height in Shanghai. By contrast, during the second period, meteorological conditions are characterized by clear skies, low surface winds, and low nighttime PBL height. These different meteorological conditions lead to the different O3 and VOCs characterizations between the two different periods. The sensitivity of different VOCs concentrations to the ozone concentrations are studied by using the NCAR MM model. The model calculation suggests that the O3 production in Shanghai results from the aromatic emission. This result implies that a future increase in aromatic emission in Shanghai due to economic development, especially with increase in the numbers of automobiles, could results in an enhancement in O3 concentration.
Without aromatic, the maximum of O3 concentration is reduced from 80 ppbv to about 15 ppbv. This analysis implies that with a rapid increase in aromatic emissions in Shanghai, which is likely due to the rapid increase in the numbers of automobiles in the Shanghai region, the O3 concentration could be significantly enhanced in the future.
Acknowledgments
5. Summary
References
In this paper, the measured VOCs and O3 concentrations from 15 November to 26 November in Shanghai are analyzed. A regional chemical/ dynamical model (WRF-Chem) and a detailed chemical mechanism model (NCAR MM) are used to interpret the measurements. The results show that O3 concentrations have strong day-to-day and diurnal variability, with very different characterizations between the two periods. During 15-Nov to 21-Nov (first period), O3 concentrations are lower than the values during 22-Nov to 26-Nov (second period). There are also a strong diurnal variation of O3 during the second period with maximum concentrations of 40–70 ppbv at noontime, and very
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This work is funded by the National Natural Science Foundation of China (NSFC) under Grant no. 40318001 and 40575060. The National Center for Atmospheric Research is sponsored by the National Science Foundation.
ARTICLE IN PRESS F. Geng et al. / Atmospheric Environment 41 (2007) 989–1001 Kinetics of HO2+HO2 ¼ H2O2+O2: implications for stratospheric H2O2. Geophysical Research Letters 29. Crutzen, P.J., 1975. Physical and chemical processes which control the production, destruction, and distribution of ozone and some other chemically active minor constituents. GARP Publications Series, 16, pp. 236–243. Demore, W.B., 2000. Chemical Kinetics and Photochemical Data for Use in Stratospheric Modeling. JPL Publications. Grell, G.A., Peckham, S.E., Schmitz, R., McKeen, S.A., Forest, G., Skamarock, W., Eder, B., 2005. Fully coupled ‘‘online’’ chemistry within the WRF model. Atmospheric Environment 39, 6957–6975. Horowitz, L.W., Walters, S., Mauzerall, D., Emmons, L., Rasch, P., Granier, C., Tie, X., Lamarque, J.F., Schultz, M., Brasseur, G., 2003. A global simulation of tropospheric ozone and related tracers: description and evaluation of MOZART, version 2. Journal of Geophysical Research 108, 4784. Jiang, D., Zhang, Y., Hu, X., Zeng, Y., Tan, J., Shao, D., 2004. Progress in developing an ANN model for air pollution index forecast. Atmospheric Environment 38 (40), 7055–7064. Kleinman, L.I., Daum, P.H., Lee, Y.N., Nunnermacker, L.J., Springston, S.R., Weinstein-Lloyd, J., Rudolf, J., 2001. Sensitivity of O3 production rate to O3 precursors. Geophysical Research Letters 28 (15), 2903–2906. Lin, X., Trainer, M., Liu, S.C., 1988. On the nonlinearity of the tropospheric ozone production. Journal of Geophysical Research 93 (D12), 15879–15888. Lu, Q., Gao, W., Gao, Z., Wu, W., Pan, X., Wang, D., Du, B., Slusser, J., 2005. Simulations of a rainfall process in Southeastern China R with WRF and MM5. Proceedings of SPIE—The International Society for Optical Engineering 5884, 1–9. Madronich, S., Calvert, J.G., 1990. Permutation reactions of organic peroxy-radicals in the troposphere. Journal of Geophysical Research 95, 5697–5715. Madronich, S., Flocke, S., 1999. The role of solar radiation in atmospheric chemistry. In: Boule, P. (Ed.), Handbook of Environmental Chemistry. Springer, Heidelberg, pp. 1–26. Na, K., Moon, K.C., Kim, Y.P., 2005. Source contribution to aromatic VOC concentration and ozone formation potential in the atmosphere of Seoul. Atmospheric Environment 39, 5517–5524. Orlando, J.J., Tyndall, G.S., Bilde, M., Ferronato, C., Wallington, T.J., Vereecken, L., Peeters, I., 1998. Laboratory and
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theoretical study of the oxy radicals in the OH- and CIinitiated oxidation of ethene. Journal of Physical Chemistry A 102, 8116–8123. Sillman, S., 1995. The use of NOy, H2O2, and HNO3 as indicators for ozone-NOXhydrocarbon sensitivity in urban locations. Journal of Geophysical Research 100, 14175–14188. Streets, D.G., et al., 2003. An Inventory of gaseous and primary aerosol emissions in Asia in the year 2000. Journal of Geophysical Research, 108, 8809, doi:10.1029/2002JD003093. Tesche, T.W., 1988. Accuracy of ozone air quality models. Journal of Environmental Engineering 114 (4), 739–752. Tie, X., Madronich, S., Walters, S., Rasch, P., Collins, W., 2003. Effect of clouds on photolysis and oxidants in the troposphere. Journal of Geophysical Research 108, 4642. Tie, X., Brasseur, G., Zhao, C., Granier, C., Massie, S., Qin, Y., Wang, P.C., Wang, G.L., Yang, P.C., 2006. Chemical characterization of air pollution in Eastern China and the Eastern United States. Atmospheric Environment 40, 2607–2625. Tyndall, G.S., Cox, R.A., Granier, C., Lesclaux, R., Moortgat, G.K., Pilling, M.J., Ravishankara, A.R., Wallington, T.J., 2001. Atmospheric chemistry of small organic peroxy radicals. Journal of Geophysical Research 106 (Dl1), 12157–12182. Wesely, M.L., 1989. Parameterization of surface resistance to gaseous dry deposition in regional-scale numerical models. Atmospheric Environment 23, 1293–1304. Willmott, C.J., 1981. On the validation of models. Physical Geography 2, 184–194. Xiu, G.L., Jin, Q., Zhang, D., Shi, S., Huang, X., Zhang, W., Bao, L., Gao, P., Chen, B., 2005. Characterization of sizefractionated particulate mercury in Shanghai ambient air. Atmospheric Environment 39 (3), 419–427. Yang, F., He, K., Ye, B., Chen, X., Cha, L., Cadle, S.H., Chan, T., Mulawa, P.A., 2005. One-year record of organic and elemental carbon in fine particles in downtown Beijing and Shanghai. Atmospheric Chemical Physics Discussions 5 (1), 217–241. Zhang, R.W., Lei, X., Tie, P., Hess, 2004. Industrial emissions cause extreme diurnal urban ozone variability. Proceedings of National Academic Science USA 101, 6346–6350. Zhao, C., Peng, L., Sun, A., et al., 2004. Numerical modeling of tropospheric ozone over Yangtze Delta region. Acta Scientiae Circumstantiae 24 (3), 525–533.
Atmospheric Environment 41 (2007) 989–1001 www.elsevier.com/locate/atmosenv
Analysis of ozone and VOCs measured in Shanghai: A case study Fuhai Genga,b, Chunsheng Zhaoa,, Xu Tanga,b, Guoliang Lub, Xuexi Tiec a
Department of Atmospheric Science, School of Physics, Peking University, Beijing, China b Shanghai Meteorological Bureau, Shanghai, China c National Center for Atmospheric Research, Boulder, CO, USA
Received 22 June 2006; received in revised form 2 September 2006; accepted 14 September 2006
Abstract Shanghai Meteorological Administration has established a volatile organic compounds (VOCs) laboratory and an observational network for VOCs and ozone (O3) measurements in the city of Shanghai. In this study, the measured VOCs and O3 concentrations from 15 November (15-Nov) to 26 November (26-Nov) of 2005 in Shanghai show that there are strong day-to-day and diurnal variations. The measured O3 and VOCs concentrations have very different characterizations between the two periods. During 15-Nov to 21-Nov (defined as the first period), VOCs and O3 concentrations are lower than the values during 22-Nov to 28-Nov (defined as the second period). There is a strong diurnal variation of O3 during the second period with maximum concentrations of 40–80 ppbv at noontime, and minimum concentrations at nighttime. By contrast, during the first period, the diurnal variation of O3 is in an irregular pattern with maximum concentrations of only 20–30 ppbv. The VOC concentrations change rapidly from 30–50 ppbv during the first period to 80–100 ppbv during the second period. Two chemical models are applied to interpret the measurements. One model is a regional chemical/ dynamical model (WRF-Chem) and another is a detailed chemical mechanism model (NCAR MM). Model analysis shows that the meteorological conditions are very different between the two periods, and are mainly responsible for the different chemical characterizations of O3 and VOCs between the two periods. During the first period, meteorological conditions are characterized by cloudy sky and high-surface winds in Shanghai, resulting in a higher nighttime planetary boundary layer (PBL) and faster transport of air pollutants. By contrast, during the second period, the meteorological conditions are characterized by clear sky and weak surface winds, resulting in a lower nighttime PBL and slower transport of air pollutants. The chemical mechanism model calculation shows that different VOC species has very different contributions to the high-ozone concentrations during the second period. Alkane (40 ppbv) and aromatic (30 ppbv) are among the highest VOC concentrations observed in Shanghai. The analysis suggests that the aromatic is a main contributor for the O3 chemical production in Shanghai, with approximately 79% of the O3 being produced by aromatic. This analysis implies that future increase in VOC (especially aromatic) emissions could lead to significant increase in O3 concentrations in Shanghai. r 2006 Elsevier Ltd. All rights reserved. Keywords: VOCs; Ozone; Shanghai; WRF-Chem
Corresponding author. Tel.:+86 10 62754684.
E-mail address: [email protected] (C. Zhao). 1352-2310/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2006.09.023
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1. Introduction Shanghai is the largest city in China, with population of 16 millions. The recent rapid increase in the urbanization and human activities has important impacts on atmospheric air quality in this region. For example, the number automobiles increase from 100,000 (1980) to 1,500,000 (2005). As a result, the emissions of volatile organic compounds (VOCs) have significantly increased. Because VOCs are important precursors for the formation of ozone pollution (Crutzen, 1975; Chameides and Walker, 1976), to understand the relationship between VOCs and ozone concentrations is an important issue for the environmental concerns in the Shanghai region. There have been several studies on the air quality in the Shanghai region (from 1211 to 1221E and 30.51 to 31.51N). Jiang et al. (2004) studied the air pollution index (API) for particulate matter up to 10 mm in size (PM10), SO2, and NO2. Yang et al. (2005) measured black and organic carbons in Shanghai. They found that the concentrations of organic carbons were normally about twice the concentrations of black carbon. Xiu et al. (2005) measured mercury concentrations in Shanghai, and show that coal burning was estimated to contribute approximately 80% of total atmospheric mercury in Shanghai. Zhao et al. (2004) studied the ozone (O3) concentrations in the Shanghai region, their results suggest that small-scale dynamical processes have important influences on the distribution of O3 concentrations in this region. Lu et al. (2005) analyzed meteorological conditions in the Shanghai region. However, O3 concentrations and its precursors (NOx, VOCs, etc.) are not systemically measured and the relationship between O3 and its precursors (NOx, VOCs, etc.) has not been analyzed, such measurements have been urgently needed in the Shanghai region. In this study, we investigate the ozone chemical formation, variability, and diurnal variation during November 2005, when surface O3 and VOCs concentrations are simultaneously measured in Shanghai. A regional dynamical/chemical model (WRF-Chem) and a chemical mechanism model (NCAR MM) are used to study the causes of the ozone formation and ozone variability in Shanghai. In Section 2, we will describe the instruments and measurements. In Section 3, model configurations are described. In Section 4, the meteorological conditions during this period are analyzed. The
simulated O3 and VOCs concentrations will be compared to the measurement values to analyze causes of the ozone variability and the ozone production due to different VOC oxidants over the two periods. 2. Instrumentations and measurements 2.1. Instrumentations Ozone is measured using an EC 9810 ozone analyzer, together with an UV photometer which accurately and reliably measures ozone concentrations in ambient air. The instruments are installed on the top of a building which is 15 m high above the ground in the center of Shanghai. This instrument has automated to set to be zero and include an optional external valve manifold and external calibration sources. The instrument meets the technical specifications for US EPA (Environmental Protection Agency). Quality control checks were performed every 3 days including inspection of the shelter and instruments as well as zero, precision and span checks. Filter was replaced once in every 2 weeks and calibration was made every month. The ozone concentration was recorded every 5 s. VOCs concentrations were sampled for 24 h every day with a 6L silonite canister with silonitecoated valve (29-10622 model, Entech Instruments Inc., USA). The instrument has a large enough volume to provide detection of volatile chemicals down to low-pptv range. The internal silonite coating improves long-term VOCs storage. Absorption is eliminated by using nupro packless valves and by eliminating Teflon tape in the valve stem. These canisters are recognized to meet or exceed the technical specifications required for EPA methods TO14-A and TO15. Gases samples were pre-processed using the Model 7100 VOCs preconcentrator (Entech Instruments Inc., USA). Samples are analyzed for VOCs using a gas chromatography system (Agilent GC6890) coupled with mass-selective detection (Agilent MSD5975N) with length of 60 m, diameter of 0.32 mm, and film thickness of 1.0 mm. The column temperature is controlled by an initial temperature of 50 1C. The programmed temperature was used with helium as carrier gas and flow gas at 1.5 ml min1. The initial temperature program with 3 min hold time was 4 1C min1 to 170 1C switching to 14 1C min1 to 220 1C.
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2.2. Measured O3 and VOCs Fig. 1 shows the measured O3 and VOCs concentrations during 15-Nov to 26-Nov. The measured O3 has a strong day-to-day variability. For example, during 15-Nov to 21-Nov (the first period), the daily maximum ozone concentrations are low (20–30 ppbv) compared to the values during 22-Nov to 26-Nov (the second period) with a daily maximum of 40–80 ppbv. In addition, the ozone concentrations during the first period have an irregular diurnal variation. By contrast, during the
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second period, the diurnal variation of ozone is predominated by a strong diurnal variation with high-ozone concentrations at noontime and lowozone concentrations at nighttime. The measured VOCs concentrations are also very different during these two periods. During the first period, the concentrations of VOCs are lower than the concentrations during the second period. The measurements also indicate that the dominant VOCs species are primarily alkanes and aromatics. During the first period, the averaged alkane and aromatic concentrations are approximately 5 and 15 ppbv,
80 70
Ozone (ppbv)
60 50 40 30 20 10
15/11 16/11 17/11 18/11 19/11 20/11 21/11 22/11 23/11 24/11 25/11 26/11 Date of 2005 45 40 35
VOCs (ppbv)
30 25
alkyl aromaticity ether halohydrocarbon ketone alkene hydroxy aldehyde allyhalide halogenated aromatic hydrocarbons
20 15 10 5 0
16/11
18/11
20/11
22/11
24/11
26/11
Date of 2005 Fig. 1. Measured surface O3 and VOCs concentrations during 15 November to 26 November in Shanghai.
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respectively. During the second period, the alkane and aromatic concentrations increased to 40 and 30 ppbv, respectively. In the following sections, these striking different characterizations of O3 and VOCs concentrations during these two periods will be analyzed by dynamical and chemical models. 3. Description of the models 3.1. WRF-Chem model The WRF-Chem model is mainly based on the frame of the weather research and forecasting (WRF) model. It is a newly developed mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. The effort to develop WRF has been a collaborative partnership, principally among the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (NOAA), the National Centers for Environmental Prediction (NCEP), the Forecast Systems Laboratory (FSL), the Air Force Weather Agency (AFWA), the Naval Research Laboratory, Oklahoma University, and the Federal Aviation Administration (FAA). The WRF model is a fully compressible and Euler non-hydrostatic model. It calculates winds (u, v, and w), perturbation potential temperature, perturbation geopotential, and perturbation surface pressure of dry air. It also can optionally output other variables, including turbulent kinetic energy, water vapor mixing ratio, rain/ snow mixing ratio, and cloud water/ice mixing ratio. The model physics include bulk schemes sophisticated mixed-phase physics for cloud-resolving modeling, multi-layer land surface models ranging from a simple thermal model to full vegetation and soil moisture models, including snow cover and sea ice, turbulent kinetic energy prediction or non-local K schemes for planetary boundary layer (PBL) calculation, and longwave and shortwave schemes with multiple spectral and a simple shortwave scheme. In addition to dynamical calculation, a chemical model is fully (on-line) coupled with the WRF model (WRF-Chem). A detailed description of WRF-Chem is given by Grell et al. (2005). The version of the model, as used in the present study, includes simultaneous calculation of dynamical parameters (winds, temperature, boundary layer, clouds, etc.), transport (advective, convective, and diffusive), dry deposition (Wesely, 1989), gasphase chemistry, radiation and photolysis rates
(Madronich and Flocke, 1999; Tie et al., 2003), and surface emissions (including on-line calculation of biogenic emission). The ozone chemistry is represented in the model by a modified regional acid deposition model, version 2 (RADM2) gasphase chemical mechanism (Chang et al., 1989) which includes 158 reactions among 36 species. Hourly emissions for 17 chemical species, including SO2, CO, NO, and 14 different hydrocarbons are implemented in the model. The emission inventory contains temporal, spatial, and detailed chemical composition of emissions from anthropogenic. The emission inventory is based on the study by Streets et al. (2003). In this study, a higher resolution of the emissions (1/6 degree in longitude and latitude) are used by the later works of Streets et al. (2003). Table 1 shows the emission inventory used in this study. Fig. 2 illustrates the spatial distribution of total VOCs emissions. It indicates that there is a very large variability in the emissions in the model domain with very high VOCs emissions located in the Shanghai region (from 1211 to 1221E and 30.51 to 31.51N). In this study, the model horizontal resolution is 12 12 km2, with a 1200 1200 km2 domain in the Yangtze Rive Delta (YRD) region (from 1141 to 1261E and 25.81 to 36.51N). The domain is selected by considering that there are a number of large cities located in the YRD region, and the interaction between these cities and Shanghai should be taken into account. The model has 31 vertical levels, nonuniformly spaced, from the surface to 20 mb. The lateral boundary and initial conditions of meteorological inputs (winds, temperature, etc.) are constrained from National Center for Environmental Prediction (NCEP) data for the same period to the measurements. The chemical initial conditions are constrained by a global chemical transport model (MOZART-2; Model for Ozone and Related Tracer, version 2). The detailed description of the global model is given by Horowitz et al. (2003). The model runs from 15-Nov to 26-Nov, 2005.
Table 1 Emission inventory of CO, NOx, and VOCs (ton year1) used in the model domain shown in Fig. 2 CO
NOx
VOC
3.20 107
5.09 106
5.99 106
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Fig. 2. The estimated emissions of total VOCs (mol s1), which is used in the WRF-Chem calculation.
3.2. Chemical mechanism model (NCAR MM) The NCAR master mechanism (NCAR MM) is a chemistry box model that includes a detailed and flexible chemical scheme (Madronich and Calvert, 1990). The model is a box (0D) model with detailed gas-phase chemistry consisting of 5000 reactions among 2000 species. This model computes the time-dependent chemical evolution of an air parcel initialized with known composition, assuming no additional emissions, no dilution, and no heterogeneous processes. This model is useful to study complex interactions with regional emissions. On the other hand, the box model does offer an opportunity to examine chemical transformations of an isolated urban plume at a level of detail that would be impractical to implement in current 3D models. The hydrocarbon chemistry in the master mechanism is treated explicitly and includes the photo-oxidation of partly oxygenated organic species. Alkanes, alkenes, and aromatics are considered as initial hydrocarbon reagents in the gas-phase mechanism. The chemistry of the methyl peroxy
radical is treated explicitly; a counter scheme is used for the other organic peroxy radicals (Madronich and Calvert, 1990). The rate coefficients for organic peroxy radical reactions were updated based on recent recommendations (Tyndall et al., 2001). Rate coefficients for hydrocarbons reactions with OH were updated based on the latest JPL compilations (DeMore, 2000). The OH-initiated ethene oxidation mechanism was modified to include multiple branching for the b-hydroxyl ethoxy radical reaction with NO (Orlando et al., 1998). OH-initiated rate coefficients for oxygenated hydrocarbons were updated from the (Atkinson, 1994) compilation. The kinetics of the HO2 self-reaction were recently measured (Christensen et al., 2002) to be lower than the current recommendation. The photolysis rate (J values) is calculated on-line with NCAR MM using radiative transfer code (TUV) (Madronich and Flocke, 1999). The TUV module was initialized for the various cases with measured median values for latitude, Julian day, altitude, O3 column and albedo. The TUV module was updated with cross section and quantum yields from recent evaluations
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for inorganic species (DeMore, 2000) and organic species (Atkinson et al., 2000). 4. Analysis of the result 4.1. The characterizations of meteorological conditions To study the causes of the different characterizations of O3 and VOCs between the two periods, meteorological conditions are analyzed. Fig. 3 shows the calculated cloud conditions (integrated cloud water contents from the surface to 10 km) on 16-Nov, 19-Nov, 23-Nov, and 26-Nov. It shows
that during the first period (16-Nov and 19-Nov), the Shanghai region is under cloudy conditions. By contrast, during the second period (23-Nov and 26-Nov), the Shanghai region is under clear sky conditions. As a result, during the first period, the diurnal variation of the PBL height is very irregular. The cloud layer provides ‘‘green house’’ effects during night, resulting in a higher altitude of the PBL during the first period. During daytime, the clouds scatter the sunlight, and the temporal variation of the cloud cover results in an irregular diurnal variation of the PBL height. Another important impact of the existence of clouds is their effect on the photochemistry (Tie et al., 2003). This
Fig. 3. The calculated cloud water contents integrated from the surface to 10 km (Kg(w) Kg1(a) 105) at noontime on 16-Nov, 19-Nov, 23-Nov, and 23-Nov, respectively.
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chemical effect of clouds will be discussed in the following section (Table 2). Fig. 4 shows the calculated wind speed and wind direction near the surface on—16-Nov, 19-Nov, 23Nov, and 26-Nov. It shows that during the first period, the wind direction is from the northeast direction, which transports air masses from the ocean (relatively clean air) to the Shanghai region. However, during the second period, the wind direction is switched from northeast to the northwest direction on 23-Nov, which transports air masses from inland around the Shanghai region. On 26-Nov, the wind circulates around the Shanghai region. As a result, during the second period, the wind direction is favorable to keep the air pollutants inside the Shanghai region. The wind speed is also very different between the two periods. During the first period, the wind speed is relatively strong (about 5 m s1 in the Shanghai region). By contrast, during the second period, the wind speed reduces significantly to about 1–2 m s1 in the Shanghai region. Both the wind direction and speed tend to produce lower air pollutants during the second period than the first period. The calculated surface temperature and wind speed are compared to the surface measurement in two stations in Shanghai. Stations 1 and 2 are located in longitudes (121.251E, 121.761E) and latitudes (31.381N, 31.051N), respectively. Fig. 5 shows the comparison between the calculation and measurement. Both the calculated Table 2 The configuration of NCAR MM with different model runs
H2O CO CH4 NOx C2H6 C3H8 C4H10 C2H2 C3H6 C2H4 BEN TOL XYL
Run-1 Base
Run-2 Run-3 Run-4 Run-5 No-alkane No-alkene No-ethene No-aromatic
0.02 ppmv 1 ppmv 2 ppmv 25 ppbv 13 ppbv 13 ppbv 13 ppbv 4 ppbv 3 ppbv 8 ppbv 6 ppbv 12 ppbv 12 ppbv
X X X X 0 0 0 X X X X X X
X X X X X X X 0 0 X X X X
X X X X X X X X X 0 X X X
X X X X X X X X X X 0 0 0
BEN represents benzene; TOL represents toluene; and XYL represents xylene.The X represents that same concentrations are used as the base run (RUN-1), and the 0 means that the concentrations are different from the base run and equal to zero. The date is 11-Nov at 311N with clear sky conditions.
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and measured results show that there are strong differences between the first and second periods. During the first period, the wind speed is higher than the second period, and the diurnal variation of temperature is more evident during the second period. Although the calculations have a similar feature as the measured results, discrepancies between the calculations and measurements are also evident. To quantitatively study the difference between calculated and measured values, we analyze the root mean square error (RMSE), the root mean square error systematic (RMSEs), root mean square error unsystematic (RMSEu), and the index of agreement (d) (Willmott, 1981; Tesche, 1988). Table 3 shows a detailed statistical analysis of the calculated surface temperature and wind speed. It indicates that the index of agreement (d) ranges from 0.65 to 0.67 for the calculated temperature, and 0.76–0.87 for the calculated wind speed. There is no clear indication that the model is systematically different with the measurement. The values of RMSEs range from 0.97 to 1.1 1C for temperature and 0.32–0.80 m s1 for wind speed, and the unsystematical errors (RMSEu) range from 1.6 to 2.1 1C for temperature and 0.51–0.85 m s1 for wind speed. The weather map (see Fig. 6) as the same period indicates that on 13-Nov, a strong cold highpressure system developed at the Mongolia plateau. The cold air mass moved to south and temperatures in the Shanghai region were dropped accompanied by strong winds. As a result, during 14–20 November , it was cold and cloudy in Shanghai influenced by this cold air mass (Fig. 6a, b) with strong northeast winds. From 21 November to 26 November, the cold high-pressure system decreases rapidly and disappears on 26-Nov (Fig. 6c, d). As a result, the Shanghai region was under clear sky conditions during 21–26 November, and surface winds were very weak. The measured weather system is consistent with the model calculated meteorological conditions (see Figs. 3 and 4). 4.2. The analysis of measured O3 and VOCs Fig. 7 shows the calculated PBL, O3, VOCs, and air mass outward flux. The calculated O3 and VOC concentrations are compared to the measured values. The calculated results show that the PBL height has an irregular diurnal variation during the first period, which is resulted from the variations of cloud cover (see Fig. 4). During the
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Fig. 4. Same as Fig. 3, except for wind direction and wind speed (m s1).
second period, the PBL height has a clear diurnal variation, with a maximum during noontime and a very low-PBL height during night. There is a clear indication that the nighttime PBL height is much higher during the first period than the second period. The measured daytime O3 concentrations are lower (with a maximum of 20–30 ppbv) than the second period (with a maximum of 40–80 ppbv). The calculated O3 by WRF-Chem shows a similar daily and day-to-day variation compared to the measured results. During the first period, the calculated O3 diurnal variation has an irregular pattern, which is due to the effect of clouds on the photochemistry. During the second period, Shanghai is under clear sky condition, and
the calculated O3 concentrations have a strong diurnal variation with a maximum of 40–60 ppbv during noontime and low-ozone concentrations during nighttime. The calculated noontime O3 concentrations also significantly increase from the first period to the second period, which is consistent with the measurements. However, some discrepancies are also shown between the measured and the calculated O3 concentrations. For example, the calculated O3 concentrations are generally 10 ppbv lower than the measured values. The statistical analysis of the calculated O3 (Table 3) shows that the index of agreement (d) is 0.58. The RMSEs is 9.6 ppbv and RMSEu is 11.1 ppbv, respectively.
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Fig. 5. Calculated surface temperature (oC) and wind speed (m s1) (solid lines) and compared to measured values in two stations of Shanghai. Table 3 Statistical analysis for calculated surface temperature, wind speed, and O3 concentrations RMSE (1C)
RMSEs (1C)
RMSEu (1C)
d
Temperature(1)a Temperature(2)a
2.1 2.3
1.1 0.97
1.6 2.1
0.67 0.65
Wind speed (1) Wind speed (2)
RMSE (m s1) 0.62 1.1
RMSEs (m s1) 0.32 0.80
RMSEu (m s1) 0.51 0.85
d 0.87 0.76
O3
RMSE (ppbv) 13.0
RMSEs (ppbv) 9.6
RMSEu (ppbv) 11.1
d 0.58
a
(1) and (2) represent the stations 1 and 2 defined in text.
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Fig. 6. The weather map for on 16-Nov, 19-Nov, 23-Nov, and 23-Nov, respectively.
The measured VOC concentrations, especially alkane and aromatic which are the two most dominant VOCs in Shanghai as indicated in Fig. 1, are lower during the first period than during the second period. The sum of alkane and aromatic concentrations are about 15–25 ppbv, and rapidly increased about 60–70 ppbv in the second period. There is a clear indication that the variations of VOC concentrations are anti-correlated to the out flow of the air mass indicated in the last panel of Fig. 7. The out flow of the air mass through the PBL is defined by
pollutants which are locally emitted. The out flow of the air mass (F) during the first period is about 5 times higher than the second period. The higher F during the first period is resulted from the combination of the higher PBL height and wind speeds (see Figs. 4 and 7). As a result, the higher F quickly dilutes the VOC concentrations as they are emitted from the surface in the Shanghai region (see Fig. 2). By contrast, during the second period, the lower F intends to keep air pollutants inside the city, resulting in higher VOC concentrations.
F ¼ W PH;
(1)
4.3. Interactions between O3 and VOCs
(2)
It is well known that VOCs are important precursors for the formation of O3, especially in large cities (Kleinman et al., 2001; Zhang et al., 2004; Tie et al., 2006). O3 photochemical production is related non-linearly to NOx and VOCs (Sillman, 1995). When NOx concentrations are small, O3 production is sensitive to NOx concentrations and enhanced by increased NOx emissions. Otherwise, O3 production is sensitive to VOCs but not to NOx (VOC limited). According to Sillman (1995), a value of 0.28 for the ratio CH2O/NOy
W¼
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi U 2 þ V 2,
where F represents the out flow of the air mass in the PBL (m2 s1); W represents the magnitude of horizontal wind speed in the PBL (m s1); U and V represent the wind speeds in latitudinal and longitudinal directions (m s1); and PH represents the PBL height (m). The values PH, U, and V are calculated from the model. From the above definition, the out flow of the air mass should be a good indicator to determine the magnitude of air
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Fig. 8. Calculated afternoon ratio of CH2O to NOy (NO+ NO2+HNO3+PAN+NO3+2N2O5) in the Shanghai region.
Fig. 7. The calculated PBL (m), O3 (ppbv), VOCs (ppbv), and air mass outward flux through the PBL (m2 s1) (black lines). The O3 and VOC concentrations are compared to the measured values (red lines) from 15-Nov to 26-Nov in Shanghai.
(NOy ¼ NO+NO2+HNO3+PAN+NO3+2N2O5) usually marks a transition between VOC limited and NOx limited regimes. Fig. 8 shows that in the Shanghai region, calculated CH2O/NOy is less than 0.28, indicating that O3 production is likely VOC limited in this region. As a result, the O3 production rate will be likely increased when VOC emission is enhanced. However, the different types of VOCs lead to very different contributions on the O3 productions due to their different reaction rates with other oxidants (mainly hydroxyl radical OH) (Lin et al., 1988). In addition, different VOCs are resulted from different sources, for example, aromatic is mainly resulted from automobile exhaust (Na et al., 2005). To better understand the chemical formation of O3 in Shanghai, a detailed chemical model (NCAR MM) is used to assess the individual contribution of different VOCs on the O3 production. Table 2 gives the configurations of the model runs. There are five model runs to study the sensitive
of O3 production resulted from different VOCs. The base run (Run-1) includes alkane concentrations of 39 ppbv, alkene concentrations of 7 ppbv, ethene concentrations of 8 ppbv, and aromatic concentrations of 30 ppbv. These concentrations are selected according to the VOC measurement in the second period, when the ozone photochemistry is more active than the first period, and diurnal variation is clearly indicated. The Run-2, Run-3, Run-4, and Run-5 are the same conditions with the base run, except that the alkene, alkene, ethane, and aromatic concentrations set to be zero, respectively. Fig. 9 shows that calculated O3 diurnal variation using NCAR MM. The different contributions to O3 concentrations due to alkanes, alkenes, ethenes, and aromatics are calculated with different model runs. The result suggests that alkane have very small impact (3%) on the O3 due to their low chemical reactivity. Alkenes and ethenes have important impacts on the O3 concentrations, by 37% and 16%, respectively. Without alkenes and ethenes, the maximum O3 concentration is reduced from 80 ppbv (with all VOCs taking into account) to 40 and 60 ppbv, respectively. The most dominant VOCs on the O3 formation is resulted from aromatics, which contributes about 79% of O3 concentrations from total VOCs contributions.
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Fig. 9. The calculated O3 diurnal variation using NCAR MM. The different contributions due to alkane (red line), alkene (blue line), ethene (yellow line), and aromatic (light-blue line) are calculated with different model runs shown in Table 2.
low concentrations at night. By contrast, during the first period, the diurnal variation of O3 is very irregular. The measured daily averaged VOCs concentrations are higher during the first period than the second period. The model analysis shows that regional meteorological conditions play important roles in controlling the concentrations of O3 and VOCs between the two periods. During the first period, meteorological conditions are characterized by cloudy skies, high surface winds, and a high nighttime PBL height in Shanghai. By contrast, during the second period, meteorological conditions are characterized by clear skies, low surface winds, and low nighttime PBL height. These different meteorological conditions lead to the different O3 and VOCs characterizations between the two different periods. The sensitivity of different VOCs concentrations to the ozone concentrations are studied by using the NCAR MM model. The model calculation suggests that the O3 production in Shanghai results from the aromatic emission. This result implies that a future increase in aromatic emission in Shanghai due to economic development, especially with increase in the numbers of automobiles, could results in an enhancement in O3 concentration.
Without aromatic, the maximum of O3 concentration is reduced from 80 ppbv to about 15 ppbv. This analysis implies that with a rapid increase in aromatic emissions in Shanghai, which is likely due to the rapid increase in the numbers of automobiles in the Shanghai region, the O3 concentration could be significantly enhanced in the future.
Acknowledgments
5. Summary
References
In this paper, the measured VOCs and O3 concentrations from 15 November to 26 November in Shanghai are analyzed. A regional chemical/ dynamical model (WRF-Chem) and a detailed chemical mechanism model (NCAR MM) are used to interpret the measurements. The results show that O3 concentrations have strong day-to-day and diurnal variability, with very different characterizations between the two periods. During 15-Nov to 21-Nov (first period), O3 concentrations are lower than the values during 22-Nov to 26-Nov (second period). There are also a strong diurnal variation of O3 during the second period with maximum concentrations of 40–70 ppbv at noontime, and very
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This work is funded by the National Natural Science Foundation of China (NSFC) under Grant no. 40318001 and 40575060. The National Center for Atmospheric Research is sponsored by the National Science Foundation.
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