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Ozone changes in response to the heavy-duty diesel truck control in the Pearl River Delta
Atmospheric Environment 88 (2014) 269e274
Contents lists available at ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Short communication
Ozone changes in response to the heavy-duty diesel truck control in the Pearl River Delta Xin Yu a, Zibing Yuan a, b, J.C.H. Fung a, b, Jian Xue a, Ying Li a, b, Junyu Zheng c, A.K.H. Lau a, b, d, * a
Division of Environment, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China Atmospheric Research Center, The Hong Kong University of Science and Technology, Fok Ying Tung Graduate School, Nansha, Guangzhou, China College of Environmental Science and Engineering, South China University of Technology, Guangzhou, China d Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 August 2013 Accepted 11 November 2013 Available online 8 December 2013
In recent years, restricting heavy-duty diesel trucks from driving within urban areas during the daytime is implemented in major PRD cities (e.g. Guangzhou and Shenzhen). Potential effects of this traffic control policy on spatial and temporal variations of O3 concentrations are examined by CMAQ model system. Temporal profiles of mobile source emissions are modified to reflect the emission characteristics after the control. Our results show that: (1) with the updated mobile emission profile, there is a notable improvement in O3 simulation performance for urban sites, with reductions in both the nighttime O3 overestimation (up to 25 ppb) and the daytime underestimation on O3 peak values (up to 20 ppb); (2) although the control policies are only applied in urban locations, their effects may extend to much larger downwind areas. The results from this study provide basic information that is useful in understanding the effects of mobile control policies on ambient O3 in highly developing regions of China where similar strategies have been widely implemented. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Mobile source emission Traffic control policy Ozone Pearl River Delta CMAQ
1. Introduction The elevation of ground-level O3 concentrations is one of the top atmospheric pollution concerns in the Pearl River Delta (PRD) region of China (Wang et al., 2009; Zhang et al., 2007, 2008; Zheng et al., 2010). Tropospheric O3 is a secondary species, produced through a series of complex atmospheric photochemical reactions between volatile organic compounds (VOCs) and nitrogen oxide (NOx ¼ NO þ NO2) that are emitted from various anthropogenic and natural sources. Mobile sources represent one of the major emission source categories, accounting for approximately 30e56% of total regional NOx and VOC emissions in the PRD region (Che et al., 2008; Liu et al., 2008; Zheng et al., 2009). Therefore, mobile sources are inevitably major culprits for ozone pollution in the PRD. To reduce vehicle noise and congestion and to improve air quality, a series of laws, regulations and policies have been
* Corresponding author. Division of Environment, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China. Tel.: þ86 852 2358 6944. E-mail address: [email protected] (A.K.H. Lau). 1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2013.11.022
formulated and promulgated across the PRD. One of the most widely implemented regulations is the restriction of heavyduty trucks from driving within urban areas during the daytime (7:00e21:00 local time) (Garland et al., 2008; Hofzumahaus et al., 2009). The implementation of this control policy leads to changes in temporal variations of mobile emissions. However, to date, such changes have not been quantified and their effects on O3 diurnal behavior have not been well characterized. In this study, we modify the temporal profiles of on-road mobile source emissions to reflect their changes after implementation of heavy-duty diesel trucks control policies. A three-dimensional nested modeling system, which incorporate the latest PRD emission inventory (Zheng et al., 2009), is then applied to assess the effect of the updated mobile source emissions profile in O3 simulation during the summer months in the PRD region. Modeling results are validated by using the recent available observations from PRD monitoring sites network (Department, 2011). The findings from this work provide basic information for studying the effects of mobile control policies on O3 simulations in highly developed regions of China (e.g. the BeijingeTianjineTangshan economic zone and the Yangtze Delta), where similar strategies have been widely implemented.
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2. Methodology 2.1. Model system and its localization in the PRD In this study, Version 4.6 of the Community Multiscale Air Quality (CMAQ) model is used. The meteorological fields for the CMAQ simulation were generated by the Penn State/NCAR fifthgeneration mesoscale model (MM5). Triple-nested domains for CMAQ and MM5 are selected and shown in Fig. 1. The outer domain (D1) with a horizontal grid spacing of 27 km covers almost the entire China; the inner domain (D2), with 9 km grid spacing, covers southeastern China; the innermost domain (D3) with a 3 km horizontal grid resolution covering the whole PRD region. The gridded and speciated hourly emission inputs for CMAQ were prepared using the SMOKE model (version 2.4). We use the INTEX-B emission inventory (Zhang et al., 2009) for the coarse domains (D1 and D2). The latest air pollutant emission inventory for the PRD is incorporated in D3 domain (Zheng et al., 2009). Biogenic emissions were modeled using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.03, with modifications made by ENVIRON (Guenther et al., 2006). 2.2. Modeling scenarios Two scenarios are specifically designed to investigate the effects of mobile emission changes on O3 simulations. The base scenario (B-scenario) uses two types of default diurnal profiles generated by USEPA for mobile sources. One is for Heavy Duty Diesel Vehicles (HDDV), which consists of Heavy Duty Diesel Trucks (HDDT) and Heavy Duty Diesel Buses (HDDB), and the other is for other types of vehicles (OTV). In the modification scenario (M-scenario), OTV diurnal profile is determined based on the recently observed
variations in traffic flows in the urban areas of the PRD region (Zheng et al., 2009), while new HDDV diurnal profiles are derived from the following equations to reflect the effects of heavy-duty truck control policies.
Fi;s ¼ Ti;t Ei;t þ Ti;b Ei;b ;
23 X
Ti;t ¼ 1;
i¼0
Ti;t ¼ 0; ð7 i 21Þ
Ti;t ¼ Ti;HDDVþOTV 1= þ
23 X
(1)
Ti;HDDVþOTV
! Ti;HDDVþOTV
Ti;b ¼ 1:
i¼0
(2)
i ¼ 22
6 X
23 X
(3)
ði 22; i 6Þ
i¼0
where Fi,s is the fraction of hourly NOx emissions from HDDV with respect to daily NOx emissions from HDDV at hour i (local time); the subscript s represents species emitted from HDDV (NOx, for example); Ti,t, Ti,b and Ti,HDDVþOTV are the ratios of hourly traffic flow to the overall daily traffic flow at hour i for HDDT, HDDB and all types of vehicles, respectively; Ti,b and Ti,HDDVþOTV are also determined based on the recently observed variations in traffic flows in the urban areas of the PRD region (Zheng et al., 2009). Et is the ratio of NOx emissions from HDDT to the total NOx emissions from HDDV; and Eb is the ratio of NOx emissions from HDDB to the total NOx emissions from HDDV. The values of Et and Eb are derived from recent studies by Che et al. (2008). In light of the heavy-duty truck control policies, Ti,t is set to be 0 from 7:00 in the morning to 21:00. After the 21:00 until the next 6:00, Ti,t shares the overall daily traffic flow.
Fig. 1. (a) and (b) The 3-nested CMAQ (solid line) and MM5 (dashed line) simulation domains; (c) Map of the PRD region and locations of eight monitoring sites in air quality monitoring networks in the PRD region. Monitoring sites, 1 e Luhu, 2 e Dangxiao, 3 e Wanqingsha, 4 e Zimaling, 5 e Tap Mun, 6 e Liyuan, 7 e Jinguowan, 8 e Xiapu. (d) Map of Hong Kong region.
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days, except that there are no notable emission peaks. Turning to the modified temporal profile of mobile emissions, the emissions for OTV change slightly, with the modified timeline revealing emission troughs around lunch time at w13:00 local time. In comparison, the diurnal temporal profile of HDDV emissions shows a substantially different diurnal pattern, with rather high levels at night and low levels during the daytime. This difference is due to the new traffic control policies that ban HDDT, one of the major contributors to on-road vehicle emissions, from entering urban areas during the daytime. The sensitivity tests show that modification on OTV resulted in a negligible shift in O3 concentration. We thus attribute all effects observed in this study to the modification of the diurnal profile of emissions from HDDV. Besides, aerosols, like black carbon, my decrease in the daytime due to the truck controls. This will increase UV actinic flux and lead to a slight increase in ozone production. However, the version 4.6 of CMAQ model used in this study is one-way system and is not able to calculate aerosol feedback on solar radiation. 3. Results and discussion 3.1. Spatial distribution of NOx mobile source emissions before and after the implementation of the heavy-duty truck control policy
Fig. 2. Diurnal temporal profiles of NOx mobile source emission used in B-scenario (BS_) and M-scenario (MS_) on (a) weekdays and (b) weekends.
Fig. 2 depicts the timelines for the mobile source emissions of NOx on (a) weekdays and (b) weekends in both the B-scenario and the M-scenario. In the B-scenario, mobile source emissions show a clear bimodal distribution in the daytime of weekdays, mobile source emissions show a clear bimodal distribution with peaking at w7:00 and w17:00 local time. The trend is similar over weekend
It can be seen that mobile sources are concentrated in the center of the major cities in the PRD region, e.g. Guangzhou and Shenzhen. The corresponding emission intensities are w0.1 and w0.8 mol s1 at 3:00 and 15:00 in the B-scenario (Fig. 3a and d), respectively. For the M-scenario, nighttime emissions of NOx increase by w150% (to 0.25 mol s1) in the center of Guangzhou and Shenzhen (Fig. 3c), whereas daytime emissions of NOx decrease by w20% (to 0.65 mol s1). The results indicate that HDDV are important contributors to NOx emissions in this region. Implementation of the HDDT control policy results in a notable change in the temporal profile of on-road mobile emissions that decreases in the daytime while increases at night.
Fig. 3. Spatial distribution of NOx mobile source emission (moles s1) used in CMAQ. (a), (b), (c) represent the emissions in the B-scenario, M-scenario and the differences between the M-scenario and B-scenario at 3:00 on 24 July 2006, respectively. (d), (e) and (f) show the same information at 15:00.
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Table 1 Summary of statistical meteorological parameters from MM5 simulations. Obs-sim statistics (Guangdong and Hong Kong) Stat-Para.
IOA
NMB (%)
RMSE (ppb)
MB (ppb)
IOAwdir
Mete.-Vari. Surface wind (m s1) Surface temperature ( C) Relative humid (%)
0.69 0.84 0.75
12 3 6
2.8 2.1 2.8
0.63 1.2 1.6
0.76 e e
Stat.-Para.: statistical parameter; Mete.-Vari.: meteorological variables; IOA: index of agreement; NMB: normalized mean bias; RMSE: root mean square error; MB: the mean bias. IOAwdir. Table 2 CMAQ performance statistics for hourly concentrations of ground-level O3 and NOx in the B-scenario and M-scenario. Monitoring Station(1) Hourly-O3 (ppb)
Hourly-NOx (ppb)
IOA NMB (%) RMSE MB Dangxiao (FS)
BS MS Jinguowan (HZ) BS MS Liyuan (SZ) BS MS Luhu (GZ) BS MS TapMun (HK) BS MS Wanqingsha (GZ) BS MS Xiapu (HZ) BS MS Zimaling (ZS) BS MS Average BS MS
0.85 0.91 0.76 0.78 0.59 0.77 0.76 0.91 0.72 0.73 0.86 0.87 0.69 0.78 0.78 0.82 0.75 0.82
18 15 14 14 54 22 24 7 22 21 7 5 25 19 6 8 e e
22.6 19.3 26.1 25.3 25.1 21.7 26.2 19.9 27.0 26.9 20.1 19.6 30.8 27.2 22.2 21.1 25.1 22.6
6.6 5.7 6.2 6.0 12.6 5.3 6.9 1.9 6.3 5.9 2.0 1.7 9.9 7.4 2.0 0.3 e e
IOA NMB (%) RMSE MB 0.51 0.53 0.54 0.55 e e 0.51 0.63 0.56 0.59 0.76 0.78 0.55 0.58 0.44 0.46 0.55 0.59
7 20 36 43 e e 53 31 11 6 17 2 24 48 58 36 e e
21.1 20.3 16.0 16.7 e e 40.1 26.7 17.9 18.1 13.3 13.2 19.3 21.8 18.4 16.1 20.9 19.0
2.4 6.5 6.8 8.2 e e 28.5 11.7 1.8 1.0 3.7 4.3 7.4 14.3 5.6 4.9 e e
a Urban sites comprise Liyuan, Luhu, Dangxiao; rural sites comprise Jinguowan, TapMun; rural/suburban sites comprise Wanqingsha, Xiapu, Zimaling. Cities: GZ (Guangzhou), SZ (Shenzhen), ZS (Zhongshan), FS (Foshan), HK (Hong Kong), and Huizhou (HZ).
3.2. O3 and NOx simulation with the modified temporal profile of mobile emissions As shown in Table 1, the model generally performs better in the M-scenario than the B-scenario in predicting O3 and NOx, with improvements in the averaged IOA and RMSE, especially at urban sites (Luhu of Guangzhou, Dangxiao of Foshan, and Liyuan
of Shenzhen). In particular, at the Luhu site, the IOA for the O3 concentration increases from 0.76 to 0.91, and the IOA for the NOx concentration increases from 0.51 to 0.63. There is no obvious improvement in the O3 and NOx model simulations at rural sites such as Zimaling of Zhongshan and Tap Mun of Hong Kong. Table 2 Two sites, Luhu and Wanqingsha of Guangzhou, are selected to quantitatively evaluate the improvements on an hourly scale. Luhu is a typical urban and Wanqingsha is a suburban (Fig. 4). The Bscenario simulation frequently overestimates the nighttime O3 concentrations by up to 25 ppb. In comparison, a notable improvement in the nighttime O3 simulation at Luhu is observed when the modified temporal profile of mobile emissions is applied. At the Wanqingsha site, however, only slight improvements are observed. The overestimation of nighttime O3 by 3-D air quality models has been documented in previous studies (Castellanos et al., 2009; Jiang et al., 2010; Jin et al., 2010). However, there is little agreement on the possible reasons for this discrepancy. As other factors remain the same in the B- and M-scenarios, our results indicate that in the PRD region, underestimation of NOx emissions partly explains the overestimation of nighttime O3 concentrations in the urban locations. With respect to the daytime O3 simulations, the CMAQ predictions in the B-scenario only account for w50% and w80% of O3 daily peak values for Luhu and Wanqingsha, respectively. When the modified diurnal temporal profile of mobile emissions is applied, there is a clear elevation in the model-simulated O3 concentrations for Luhu. Compared with the predictions from the B-scenario, the daily maximum O3 concentration increases by w35% in the M-scenario. The chemistry of O3 formation may help to explain this result. Atmospheric conditions in urban areas of the PRD are frequently demonstrated under VOCsensitive (or NOx-saturated) regime (Chan and Yao, 2008; Wang et al., 2005; Zhang et al., 2008). In such circumstances, a decrease in NOx emissions will result in less OH being consumed by reacting with NO2, which allows more OH to react with VOCs and favors O3 formation (Sillman, 1999). In comparison with Luhu, the improvement in the O3 simulation for Wanqingsha is rather limited. This is expected because there is only a small difference in the NOx emissions for the two scenarios at this site (Fig. 3). It can be seen that in the B-scenario, the CMAQ model generally overpredicts ground-level NOx concentrations in the daytime but underpredicts them at night. When the modified temporal profile is applied, the model performance for Luhu
Fig. 4. Comparison of model predictions in B-scenario (blue curve), M-scenario (red curve) with observations (black dots) for (a) ground-level O3 at Luhu, (b) ground-level O3 at Wanqingsha, (c) ground-level NOx at Luhu, and (d) ground-level NOx at Wanqingsha. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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4. Conclusions
Fig. 5. (a) and (c) show the differences in the model predicted ground-level O3 concentration between M-scenario and B-scenario at 3:00 and 15:00 on 24 July 2006, respectively. (b) and (d) show the modeled wind field at ground level at 3:00 and 15:00, respectively.
improves considerably. This suggests that the discrepancy in the B-scenario may largely originate from the inaccurate emissions applied in the model. At Wanqingsha, the improvement in the NOx simulation is negligible. 3.3. Effects of implementing the HDDV control policy on O3 during the episode To characterize the effects of HDDV control policy on groundlevel O3 concentrations, the differences between the modelpredicted O3 concentrations in the B- and M-scenario at 3:00 and 15:00 on 24 July are illustrated in Fig. 5. The predictions for the B-scenario and M-scenario are used to represent O3 concentrations before and after the implementation of the HDDV control policy, respectively. 24 July is chosen because it is a typical summertime O3 episode resulted from the approaching of a tropical cyclone. As shown in Fig. 5a, two areas associated with negative O3 shifts are predicted at 3:00, including (1) a large area extending from western Guangzhou to Zhuhai and Macau, and (2) a small patch covering eastern Huizhou and Shenzhen. Implementation of the HDDV control policy lowers nighttime O3 concentrations by up to 20 ppb in the centers of Guangzhou and Shenzhen. In comparison with Fig. 3c, it is notable that the areas with lowered nighttime O3 concentrations are in good agreement with the zones with increased NOx mobile source emissions, which further implies the enhanced NOx titration at night. In addition, the negative O3 shift is extended from the road networks to the downwind suburban/rural area to the north/northeast of the major mobile sources (Fig. 5b). This suggests that although the control policy is mainly limited to urban locations, its effects may extend to much larger areas due to O3 transport. In contrast, positive O3 shifts are generally predicted during the daytime (Fig. 5c). Again, the most significant shifts are concentrated around the centers of Guangzhou and Shenzhen. Ground O3 concentrations increase by up to 20 ppb in Luhu and Liyuan following the implementation of the HDDV control policy. The shift extends from urban locations to the adjacent downwind areas. However, as the wind speed is relatively lower (Fig. 5d), the extended area is smaller.
In this study, the impact of restrictions on heavy-duty diesel trucks on O3 is examined through a three-dimensional nested MM5/SMOKE/CMAQ model system with modified temporal profile of mobile source emissions. The results show that without modification on the temporal profile of HDDT emissions, the model tends to overestimate nighttime O3 concentrations and underestimate daytime concentrations. Such discrepancies can be obscured when the modified emission temporal profile is applied. Daytime O3 peak simulations increase by w35% in the center of Guangzhou, roughly capturing the observational levels. The O3 simulation in the rural/suburban sites also improves to a less extent. Although the control policies are limited to urban locations, their effects extend to much larger downwind areas. As traffic control policies are becoming increasingly common in Asian metropolis, our results highlight the importance of their effects on the temporal and spatial distributions of emission inventories to generate reasonable modeling simulation results. This study also provides important information on the effects of the HDDT control policy in a regional scale. Acknowledgments This work was supported by grant SRHIPO01, Joint Funding of the National Science Foundation of China e Guangdong Province (U1033001), and the HKUST Fok Ying Tung Graduate School (NRC06/07.SC01). The authors sincerely thank the Hong Kong Environmental Protection Department for providing the emission and air quality data, and the Hong Kong Observatory for providing the meteorological data. References Castellanos, P., Stehr, J.W., Dickerson, R.R., Ehrman, S.H., 2009. The sensitivity of modeled ozone to the temporal distribution of point, area, and mobile source emissions in the eastern United States. Atmos. Environ. 43, 4603e4611. Chan, C.K., Yao, X., 2008. Air pollution in mega cities in China. Atmos. Environ. 42, 1e42. Che, W.W., Zheng, J.Y., Zhong, L.J., 2008. Mobile source emission characteristics and contributions in the Pearl River Delta region. Res. Environ. Sci. 22, 456e461. Department, G.P.E.P, 2011. Pearl River Delta Regional Air Quality Monitoring Network: A Report of Monitoring Results in 2010 (Hong Kong). Garland, R.M., Yang, H., Schmid, O., Rose, D., Nowak, A., Achtert, P., Wiedensohler, A., Takegawa, N., Kita, K., Miyazaki, Y., Kondo, Y., Hu, M., Sha, M., Zeng, L.M., Zhang, Y.H., Andreae, M.O., Poschl, U., 2008. Aerosol optical properties in a rural environment near the mega-city Guangzhou, China: implications for regional air pollution, radiative forcing and remote sensing. Atmos. Chem. Phys. 8, 5161e5186. Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P.I., Geron, C., 2006. Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmos. Chem. Phys. 6, 3181e3210. Hofzumahaus, A., Rohrer, F., Lu, K.D., Bohn, B., Brauers, T., Chang, C.C., Fuchs, H., Holland, F., Kita, K., Kondo, Y., Li, X., Lou, S.R., Shao, M., Zeng, L.M., Wahner, A., Zhang, Y.H., 2009. Amplified trace gas removal in the troposphere. Science 324, 1702e1704. Jiang, F., Guo, H., Wang, T.J., Cheng, H.R., Wang, X.M., Simpson, I.J., Ding, A.J., Saunders, S.M., Lam, S.H.M., Blake, D.R., 2010. An ozone episode in the Pearl River Delta: field observation and model simulation. J. Geophys. Re. Atmos. 115, D22305. http://dx.doi.org/10.1029/2009JD013583. Jin, L., Brown, N.J., Harley, R.A., Bao, J.W., Michelson, S.A., Wilczak, J.M., 2010. Seasonal versus episodic performance evaluation for an Eulerian photochemical air quality model. J. Geophys. Res. Atmos. 115, D09302. http://dx.doi.org/10.1029/ 2009JD012680. Liu, Y., Shao, M., Lu, S.H., Chang, C.C., Wang, J.L., Fu, L.L., 2008. Source apportionment of ambient volatile organic compounds in the Pearl River Delta, China: part II. Atmos. Environ. 42, 6261e6274. Sillman, S., 1999. The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments. Atmos. Environ. 33, 1821e1845. Wang, T., Wei, X.L., Ding, A.J., Poon, C.N., Lam, K.S., Li, Y.S., Chan, L.Y., Anson, M., 2009. Increasing surface ozone concentrations in the background atmosphere of Southern China, 1994e2007. Atmos. Chem. Phys. 9, 6216e6226. Wang, X.M., Carmichael, G., Chen, D.L., Tang, Y.H., Wang, T.J., 2005. Impacts of different emission sources on air quality during March 2001 in the Pearl River Delta (PRD) region. Atmos. Environ. 39, 5227e5241.
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Zhang, J., Wang, T., Chameides, W.L., Cardelino, C., Kwok, J., Blake, D.R., Ding, A., So, K.L., 2007. Ozone production and hydrocarbon reactivity in Hong Kong, Southern China. Atmos. Chem. Phys. 7, 557e573. Zhang, Q., Streets, D.G., Carmichael, G.R., He, K.B., Huo, H., Kannari, A., Klimont, Z., Park, I.S., Reddy, S., Fu, J.S., Chen, D., Duan, L., Lei, Y., Wang, L.T., Yao, Z.L., 2009. Asian emissions in 2006 for the NASA INTEX-B mission. Atmos. Chem. Phys. 9, 5131e5153. Zhang, Y.H., Su, H., Zhong, L.J., Cheng, Y.F., Zeng, L.M., Wang, X.S., Xiang, Y.R., Wang, J.L., Gao, D.F., Shao, M., Fan, S.J., Liu, S.C., 2008. Regional ozone pollution
and observation-based approach for analyzing ozone-precursor relationship during the PRIDE-PRD2004 campaign. Atmos. Environ. 42, 6203e6218. Zheng, J.Y., Zhang, L.J., Che, W.W., Zheng, Z.Y., Yin, S.S., 2009. A highly resolved temporal and spatial air pollutant emission inventory for the Pearl River Delta region, China and its uncertainty assessment. Atmos. Environ. 43, 5112e5122. Zheng, J.Y., Zhong, L.J., Wang, T., Louie, P.K.K., Li, Z.C., 2010. Ground-level ozone in the Pearl River Delta region: analysis of data from a recently established regional air quality monitoring network. Atmos. Environ. 44, 814e823.
Contents lists available at ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Short communication
Ozone changes in response to the heavy-duty diesel truck control in the Pearl River Delta Xin Yu a, Zibing Yuan a, b, J.C.H. Fung a, b, Jian Xue a, Ying Li a, b, Junyu Zheng c, A.K.H. Lau a, b, d, * a
Division of Environment, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China Atmospheric Research Center, The Hong Kong University of Science and Technology, Fok Ying Tung Graduate School, Nansha, Guangzhou, China College of Environmental Science and Engineering, South China University of Technology, Guangzhou, China d Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 August 2013 Accepted 11 November 2013 Available online 8 December 2013
In recent years, restricting heavy-duty diesel trucks from driving within urban areas during the daytime is implemented in major PRD cities (e.g. Guangzhou and Shenzhen). Potential effects of this traffic control policy on spatial and temporal variations of O3 concentrations are examined by CMAQ model system. Temporal profiles of mobile source emissions are modified to reflect the emission characteristics after the control. Our results show that: (1) with the updated mobile emission profile, there is a notable improvement in O3 simulation performance for urban sites, with reductions in both the nighttime O3 overestimation (up to 25 ppb) and the daytime underestimation on O3 peak values (up to 20 ppb); (2) although the control policies are only applied in urban locations, their effects may extend to much larger downwind areas. The results from this study provide basic information that is useful in understanding the effects of mobile control policies on ambient O3 in highly developing regions of China where similar strategies have been widely implemented. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Mobile source emission Traffic control policy Ozone Pearl River Delta CMAQ
1. Introduction The elevation of ground-level O3 concentrations is one of the top atmospheric pollution concerns in the Pearl River Delta (PRD) region of China (Wang et al., 2009; Zhang et al., 2007, 2008; Zheng et al., 2010). Tropospheric O3 is a secondary species, produced through a series of complex atmospheric photochemical reactions between volatile organic compounds (VOCs) and nitrogen oxide (NOx ¼ NO þ NO2) that are emitted from various anthropogenic and natural sources. Mobile sources represent one of the major emission source categories, accounting for approximately 30e56% of total regional NOx and VOC emissions in the PRD region (Che et al., 2008; Liu et al., 2008; Zheng et al., 2009). Therefore, mobile sources are inevitably major culprits for ozone pollution in the PRD. To reduce vehicle noise and congestion and to improve air quality, a series of laws, regulations and policies have been
* Corresponding author. Division of Environment, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China. Tel.: þ86 852 2358 6944. E-mail address: [email protected] (A.K.H. Lau). 1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2013.11.022
formulated and promulgated across the PRD. One of the most widely implemented regulations is the restriction of heavyduty trucks from driving within urban areas during the daytime (7:00e21:00 local time) (Garland et al., 2008; Hofzumahaus et al., 2009). The implementation of this control policy leads to changes in temporal variations of mobile emissions. However, to date, such changes have not been quantified and their effects on O3 diurnal behavior have not been well characterized. In this study, we modify the temporal profiles of on-road mobile source emissions to reflect their changes after implementation of heavy-duty diesel trucks control policies. A three-dimensional nested modeling system, which incorporate the latest PRD emission inventory (Zheng et al., 2009), is then applied to assess the effect of the updated mobile source emissions profile in O3 simulation during the summer months in the PRD region. Modeling results are validated by using the recent available observations from PRD monitoring sites network (Department, 2011). The findings from this work provide basic information for studying the effects of mobile control policies on O3 simulations in highly developed regions of China (e.g. the BeijingeTianjineTangshan economic zone and the Yangtze Delta), where similar strategies have been widely implemented.
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2. Methodology 2.1. Model system and its localization in the PRD In this study, Version 4.6 of the Community Multiscale Air Quality (CMAQ) model is used. The meteorological fields for the CMAQ simulation were generated by the Penn State/NCAR fifthgeneration mesoscale model (MM5). Triple-nested domains for CMAQ and MM5 are selected and shown in Fig. 1. The outer domain (D1) with a horizontal grid spacing of 27 km covers almost the entire China; the inner domain (D2), with 9 km grid spacing, covers southeastern China; the innermost domain (D3) with a 3 km horizontal grid resolution covering the whole PRD region. The gridded and speciated hourly emission inputs for CMAQ were prepared using the SMOKE model (version 2.4). We use the INTEX-B emission inventory (Zhang et al., 2009) for the coarse domains (D1 and D2). The latest air pollutant emission inventory for the PRD is incorporated in D3 domain (Zheng et al., 2009). Biogenic emissions were modeled using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.03, with modifications made by ENVIRON (Guenther et al., 2006). 2.2. Modeling scenarios Two scenarios are specifically designed to investigate the effects of mobile emission changes on O3 simulations. The base scenario (B-scenario) uses two types of default diurnal profiles generated by USEPA for mobile sources. One is for Heavy Duty Diesel Vehicles (HDDV), which consists of Heavy Duty Diesel Trucks (HDDT) and Heavy Duty Diesel Buses (HDDB), and the other is for other types of vehicles (OTV). In the modification scenario (M-scenario), OTV diurnal profile is determined based on the recently observed
variations in traffic flows in the urban areas of the PRD region (Zheng et al., 2009), while new HDDV diurnal profiles are derived from the following equations to reflect the effects of heavy-duty truck control policies.
Fi;s ¼ Ti;t Ei;t þ Ti;b Ei;b ;
23 X
Ti;t ¼ 1;
i¼0
Ti;t ¼ 0; ð7 i 21Þ
Ti;t ¼ Ti;HDDVþOTV 1= þ
23 X
(1)
Ti;HDDVþOTV
! Ti;HDDVþOTV
Ti;b ¼ 1:
i¼0
(2)
i ¼ 22
6 X
23 X
(3)
ði 22; i 6Þ
i¼0
where Fi,s is the fraction of hourly NOx emissions from HDDV with respect to daily NOx emissions from HDDV at hour i (local time); the subscript s represents species emitted from HDDV (NOx, for example); Ti,t, Ti,b and Ti,HDDVþOTV are the ratios of hourly traffic flow to the overall daily traffic flow at hour i for HDDT, HDDB and all types of vehicles, respectively; Ti,b and Ti,HDDVþOTV are also determined based on the recently observed variations in traffic flows in the urban areas of the PRD region (Zheng et al., 2009). Et is the ratio of NOx emissions from HDDT to the total NOx emissions from HDDV; and Eb is the ratio of NOx emissions from HDDB to the total NOx emissions from HDDV. The values of Et and Eb are derived from recent studies by Che et al. (2008). In light of the heavy-duty truck control policies, Ti,t is set to be 0 from 7:00 in the morning to 21:00. After the 21:00 until the next 6:00, Ti,t shares the overall daily traffic flow.
Fig. 1. (a) and (b) The 3-nested CMAQ (solid line) and MM5 (dashed line) simulation domains; (c) Map of the PRD region and locations of eight monitoring sites in air quality monitoring networks in the PRD region. Monitoring sites, 1 e Luhu, 2 e Dangxiao, 3 e Wanqingsha, 4 e Zimaling, 5 e Tap Mun, 6 e Liyuan, 7 e Jinguowan, 8 e Xiapu. (d) Map of Hong Kong region.
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days, except that there are no notable emission peaks. Turning to the modified temporal profile of mobile emissions, the emissions for OTV change slightly, with the modified timeline revealing emission troughs around lunch time at w13:00 local time. In comparison, the diurnal temporal profile of HDDV emissions shows a substantially different diurnal pattern, with rather high levels at night and low levels during the daytime. This difference is due to the new traffic control policies that ban HDDT, one of the major contributors to on-road vehicle emissions, from entering urban areas during the daytime. The sensitivity tests show that modification on OTV resulted in a negligible shift in O3 concentration. We thus attribute all effects observed in this study to the modification of the diurnal profile of emissions from HDDV. Besides, aerosols, like black carbon, my decrease in the daytime due to the truck controls. This will increase UV actinic flux and lead to a slight increase in ozone production. However, the version 4.6 of CMAQ model used in this study is one-way system and is not able to calculate aerosol feedback on solar radiation. 3. Results and discussion 3.1. Spatial distribution of NOx mobile source emissions before and after the implementation of the heavy-duty truck control policy
Fig. 2. Diurnal temporal profiles of NOx mobile source emission used in B-scenario (BS_) and M-scenario (MS_) on (a) weekdays and (b) weekends.
Fig. 2 depicts the timelines for the mobile source emissions of NOx on (a) weekdays and (b) weekends in both the B-scenario and the M-scenario. In the B-scenario, mobile source emissions show a clear bimodal distribution in the daytime of weekdays, mobile source emissions show a clear bimodal distribution with peaking at w7:00 and w17:00 local time. The trend is similar over weekend
It can be seen that mobile sources are concentrated in the center of the major cities in the PRD region, e.g. Guangzhou and Shenzhen. The corresponding emission intensities are w0.1 and w0.8 mol s1 at 3:00 and 15:00 in the B-scenario (Fig. 3a and d), respectively. For the M-scenario, nighttime emissions of NOx increase by w150% (to 0.25 mol s1) in the center of Guangzhou and Shenzhen (Fig. 3c), whereas daytime emissions of NOx decrease by w20% (to 0.65 mol s1). The results indicate that HDDV are important contributors to NOx emissions in this region. Implementation of the HDDT control policy results in a notable change in the temporal profile of on-road mobile emissions that decreases in the daytime while increases at night.
Fig. 3. Spatial distribution of NOx mobile source emission (moles s1) used in CMAQ. (a), (b), (c) represent the emissions in the B-scenario, M-scenario and the differences between the M-scenario and B-scenario at 3:00 on 24 July 2006, respectively. (d), (e) and (f) show the same information at 15:00.
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Table 1 Summary of statistical meteorological parameters from MM5 simulations. Obs-sim statistics (Guangdong and Hong Kong) Stat-Para.
IOA
NMB (%)
RMSE (ppb)
MB (ppb)
IOAwdir
Mete.-Vari. Surface wind (m s1) Surface temperature ( C) Relative humid (%)
0.69 0.84 0.75
12 3 6
2.8 2.1 2.8
0.63 1.2 1.6
0.76 e e
Stat.-Para.: statistical parameter; Mete.-Vari.: meteorological variables; IOA: index of agreement; NMB: normalized mean bias; RMSE: root mean square error; MB: the mean bias. IOAwdir. Table 2 CMAQ performance statistics for hourly concentrations of ground-level O3 and NOx in the B-scenario and M-scenario. Monitoring Station(1) Hourly-O3 (ppb)
Hourly-NOx (ppb)
IOA NMB (%) RMSE MB Dangxiao (FS)
BS MS Jinguowan (HZ) BS MS Liyuan (SZ) BS MS Luhu (GZ) BS MS TapMun (HK) BS MS Wanqingsha (GZ) BS MS Xiapu (HZ) BS MS Zimaling (ZS) BS MS Average BS MS
0.85 0.91 0.76 0.78 0.59 0.77 0.76 0.91 0.72 0.73 0.86 0.87 0.69 0.78 0.78 0.82 0.75 0.82
18 15 14 14 54 22 24 7 22 21 7 5 25 19 6 8 e e
22.6 19.3 26.1 25.3 25.1 21.7 26.2 19.9 27.0 26.9 20.1 19.6 30.8 27.2 22.2 21.1 25.1 22.6
6.6 5.7 6.2 6.0 12.6 5.3 6.9 1.9 6.3 5.9 2.0 1.7 9.9 7.4 2.0 0.3 e e
IOA NMB (%) RMSE MB 0.51 0.53 0.54 0.55 e e 0.51 0.63 0.56 0.59 0.76 0.78 0.55 0.58 0.44 0.46 0.55 0.59
7 20 36 43 e e 53 31 11 6 17 2 24 48 58 36 e e
21.1 20.3 16.0 16.7 e e 40.1 26.7 17.9 18.1 13.3 13.2 19.3 21.8 18.4 16.1 20.9 19.0
2.4 6.5 6.8 8.2 e e 28.5 11.7 1.8 1.0 3.7 4.3 7.4 14.3 5.6 4.9 e e
a Urban sites comprise Liyuan, Luhu, Dangxiao; rural sites comprise Jinguowan, TapMun; rural/suburban sites comprise Wanqingsha, Xiapu, Zimaling. Cities: GZ (Guangzhou), SZ (Shenzhen), ZS (Zhongshan), FS (Foshan), HK (Hong Kong), and Huizhou (HZ).
3.2. O3 and NOx simulation with the modified temporal profile of mobile emissions As shown in Table 1, the model generally performs better in the M-scenario than the B-scenario in predicting O3 and NOx, with improvements in the averaged IOA and RMSE, especially at urban sites (Luhu of Guangzhou, Dangxiao of Foshan, and Liyuan
of Shenzhen). In particular, at the Luhu site, the IOA for the O3 concentration increases from 0.76 to 0.91, and the IOA for the NOx concentration increases from 0.51 to 0.63. There is no obvious improvement in the O3 and NOx model simulations at rural sites such as Zimaling of Zhongshan and Tap Mun of Hong Kong. Table 2 Two sites, Luhu and Wanqingsha of Guangzhou, are selected to quantitatively evaluate the improvements on an hourly scale. Luhu is a typical urban and Wanqingsha is a suburban (Fig. 4). The Bscenario simulation frequently overestimates the nighttime O3 concentrations by up to 25 ppb. In comparison, a notable improvement in the nighttime O3 simulation at Luhu is observed when the modified temporal profile of mobile emissions is applied. At the Wanqingsha site, however, only slight improvements are observed. The overestimation of nighttime O3 by 3-D air quality models has been documented in previous studies (Castellanos et al., 2009; Jiang et al., 2010; Jin et al., 2010). However, there is little agreement on the possible reasons for this discrepancy. As other factors remain the same in the B- and M-scenarios, our results indicate that in the PRD region, underestimation of NOx emissions partly explains the overestimation of nighttime O3 concentrations in the urban locations. With respect to the daytime O3 simulations, the CMAQ predictions in the B-scenario only account for w50% and w80% of O3 daily peak values for Luhu and Wanqingsha, respectively. When the modified diurnal temporal profile of mobile emissions is applied, there is a clear elevation in the model-simulated O3 concentrations for Luhu. Compared with the predictions from the B-scenario, the daily maximum O3 concentration increases by w35% in the M-scenario. The chemistry of O3 formation may help to explain this result. Atmospheric conditions in urban areas of the PRD are frequently demonstrated under VOCsensitive (or NOx-saturated) regime (Chan and Yao, 2008; Wang et al., 2005; Zhang et al., 2008). In such circumstances, a decrease in NOx emissions will result in less OH being consumed by reacting with NO2, which allows more OH to react with VOCs and favors O3 formation (Sillman, 1999). In comparison with Luhu, the improvement in the O3 simulation for Wanqingsha is rather limited. This is expected because there is only a small difference in the NOx emissions for the two scenarios at this site (Fig. 3). It can be seen that in the B-scenario, the CMAQ model generally overpredicts ground-level NOx concentrations in the daytime but underpredicts them at night. When the modified temporal profile is applied, the model performance for Luhu
Fig. 4. Comparison of model predictions in B-scenario (blue curve), M-scenario (red curve) with observations (black dots) for (a) ground-level O3 at Luhu, (b) ground-level O3 at Wanqingsha, (c) ground-level NOx at Luhu, and (d) ground-level NOx at Wanqingsha. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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4. Conclusions
Fig. 5. (a) and (c) show the differences in the model predicted ground-level O3 concentration between M-scenario and B-scenario at 3:00 and 15:00 on 24 July 2006, respectively. (b) and (d) show the modeled wind field at ground level at 3:00 and 15:00, respectively.
improves considerably. This suggests that the discrepancy in the B-scenario may largely originate from the inaccurate emissions applied in the model. At Wanqingsha, the improvement in the NOx simulation is negligible. 3.3. Effects of implementing the HDDV control policy on O3 during the episode To characterize the effects of HDDV control policy on groundlevel O3 concentrations, the differences between the modelpredicted O3 concentrations in the B- and M-scenario at 3:00 and 15:00 on 24 July are illustrated in Fig. 5. The predictions for the B-scenario and M-scenario are used to represent O3 concentrations before and after the implementation of the HDDV control policy, respectively. 24 July is chosen because it is a typical summertime O3 episode resulted from the approaching of a tropical cyclone. As shown in Fig. 5a, two areas associated with negative O3 shifts are predicted at 3:00, including (1) a large area extending from western Guangzhou to Zhuhai and Macau, and (2) a small patch covering eastern Huizhou and Shenzhen. Implementation of the HDDV control policy lowers nighttime O3 concentrations by up to 20 ppb in the centers of Guangzhou and Shenzhen. In comparison with Fig. 3c, it is notable that the areas with lowered nighttime O3 concentrations are in good agreement with the zones with increased NOx mobile source emissions, which further implies the enhanced NOx titration at night. In addition, the negative O3 shift is extended from the road networks to the downwind suburban/rural area to the north/northeast of the major mobile sources (Fig. 5b). This suggests that although the control policy is mainly limited to urban locations, its effects may extend to much larger areas due to O3 transport. In contrast, positive O3 shifts are generally predicted during the daytime (Fig. 5c). Again, the most significant shifts are concentrated around the centers of Guangzhou and Shenzhen. Ground O3 concentrations increase by up to 20 ppb in Luhu and Liyuan following the implementation of the HDDV control policy. The shift extends from urban locations to the adjacent downwind areas. However, as the wind speed is relatively lower (Fig. 5d), the extended area is smaller.
In this study, the impact of restrictions on heavy-duty diesel trucks on O3 is examined through a three-dimensional nested MM5/SMOKE/CMAQ model system with modified temporal profile of mobile source emissions. The results show that without modification on the temporal profile of HDDT emissions, the model tends to overestimate nighttime O3 concentrations and underestimate daytime concentrations. Such discrepancies can be obscured when the modified emission temporal profile is applied. Daytime O3 peak simulations increase by w35% in the center of Guangzhou, roughly capturing the observational levels. The O3 simulation in the rural/suburban sites also improves to a less extent. Although the control policies are limited to urban locations, their effects extend to much larger downwind areas. As traffic control policies are becoming increasingly common in Asian metropolis, our results highlight the importance of their effects on the temporal and spatial distributions of emission inventories to generate reasonable modeling simulation results. This study also provides important information on the effects of the HDDT control policy in a regional scale. Acknowledgments This work was supported by grant SRHIPO01, Joint Funding of the National Science Foundation of China e Guangdong Province (U1033001), and the HKUST Fok Ying Tung Graduate School (NRC06/07.SC01). The authors sincerely thank the Hong Kong Environmental Protection Department for providing the emission and air quality data, and the Hong Kong Observatory for providing the meteorological data. References Castellanos, P., Stehr, J.W., Dickerson, R.R., Ehrman, S.H., 2009. The sensitivity of modeled ozone to the temporal distribution of point, area, and mobile source emissions in the eastern United States. Atmos. Environ. 43, 4603e4611. Chan, C.K., Yao, X., 2008. Air pollution in mega cities in China. Atmos. Environ. 42, 1e42. Che, W.W., Zheng, J.Y., Zhong, L.J., 2008. Mobile source emission characteristics and contributions in the Pearl River Delta region. Res. Environ. Sci. 22, 456e461. Department, G.P.E.P, 2011. Pearl River Delta Regional Air Quality Monitoring Network: A Report of Monitoring Results in 2010 (Hong Kong). Garland, R.M., Yang, H., Schmid, O., Rose, D., Nowak, A., Achtert, P., Wiedensohler, A., Takegawa, N., Kita, K., Miyazaki, Y., Kondo, Y., Hu, M., Sha, M., Zeng, L.M., Zhang, Y.H., Andreae, M.O., Poschl, U., 2008. Aerosol optical properties in a rural environment near the mega-city Guangzhou, China: implications for regional air pollution, radiative forcing and remote sensing. Atmos. Chem. Phys. 8, 5161e5186. Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P.I., Geron, C., 2006. Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmos. Chem. Phys. 6, 3181e3210. Hofzumahaus, A., Rohrer, F., Lu, K.D., Bohn, B., Brauers, T., Chang, C.C., Fuchs, H., Holland, F., Kita, K., Kondo, Y., Li, X., Lou, S.R., Shao, M., Zeng, L.M., Wahner, A., Zhang, Y.H., 2009. Amplified trace gas removal in the troposphere. Science 324, 1702e1704. Jiang, F., Guo, H., Wang, T.J., Cheng, H.R., Wang, X.M., Simpson, I.J., Ding, A.J., Saunders, S.M., Lam, S.H.M., Blake, D.R., 2010. An ozone episode in the Pearl River Delta: field observation and model simulation. J. Geophys. Re. Atmos. 115, D22305. http://dx.doi.org/10.1029/2009JD013583. Jin, L., Brown, N.J., Harley, R.A., Bao, J.W., Michelson, S.A., Wilczak, J.M., 2010. Seasonal versus episodic performance evaluation for an Eulerian photochemical air quality model. J. Geophys. Res. Atmos. 115, D09302. http://dx.doi.org/10.1029/ 2009JD012680. Liu, Y., Shao, M., Lu, S.H., Chang, C.C., Wang, J.L., Fu, L.L., 2008. Source apportionment of ambient volatile organic compounds in the Pearl River Delta, China: part II. Atmos. Environ. 42, 6261e6274. Sillman, S., 1999. The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments. Atmos. Environ. 33, 1821e1845. Wang, T., Wei, X.L., Ding, A.J., Poon, C.N., Lam, K.S., Li, Y.S., Chan, L.Y., Anson, M., 2009. Increasing surface ozone concentrations in the background atmosphere of Southern China, 1994e2007. Atmos. Chem. Phys. 9, 6216e6226. Wang, X.M., Carmichael, G., Chen, D.L., Tang, Y.H., Wang, T.J., 2005. Impacts of different emission sources on air quality during March 2001 in the Pearl River Delta (PRD) region. Atmos. Environ. 39, 5227e5241.
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