The impact of transportation control measures on emission reductions during the 2008 Olympic Games in Beijing, China

Zhou, Yu; Wu, Ye; Yang, Liu; Fu, Lixin; He, Kebin; Wang, Yafei; Hao, Jiming; Chen, Jinchuan; Li, Chunyan

doi:10.1016/j.atmosenv.2009.10.040

Cited by 208 publications

(123 citation statements)

References 17 publications

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“…Even in Beijing, with a large number of private gasoline cars, HDDVs emitted 25.0 Gg NO x , accounting for 30 % of total NO x emissions from vehicles based on our previous study . To mitigate emissions from HDDVs, MEP and local environmental protection bureaus (EPBs) have launched a series of control measures since 2000 (ICCT, 2010;Wu et al, 2011 Zhou et al, 2010). However, such engine tests have often been criticized as an inaccurate representation of real-world on-road operation (Krishnamurthy et al, 2007;US DOJ and US EPA, 1998 (Krishnamurthy et al, 2007;US EPA, 2005a).…”

Section: Y Wu Et Al: the Challenge To No X Emission Control For Heamentioning

confidence: 99%

“…For example, with MOBILE-China we estimate a significant decrease in NO x emission factors (54 %) from Euro II to Euro III buses, and a significant decrease of 50 % from Euro III to Euro IV buses. Such estimates using a continuous decrease in NO x emission factors as emission standards tightened for HDDV have been used widely in previous policy evaluations Zhou et al, 2010). Thus, NO x emissions for China's diesel bus fleet may be significantly underestimated based on these new NO x results.…”

Section: Emission Rates and Emission Factorsmentioning

confidence: 99%

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The challenge to NOx emission control for heavy-duty diesel vehicles in China

Zhang

et al. 2012

Atmos. Chem. Phys.

151

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Abstract. China's new "Twelfth Five-Year Plan" set a target for total NO x emission reduction of 10 % for the period of 2011-2015. Heavy-duty diesel vehicles (HDDVs) have been considered a major contributor to NO x emissions in China. Beijing initiated a comprehensive vehicle test program in 2008. This program included a sub-task for measuring onroad emission profiles of hundreds of HDDVs using portable emission measurement systems (PEMS). The major finding is that neither the on-road distance-specific (g km −1 ) nor brake-specific (g kWh −1 ) NO x emission factors for diesel buses and heavy-duty diesel trucks improved in most cases as emission standards became more stringent. For example, the average NO x emission factors for Euro II, Euro III and Euro IV buses are 11.3 ± 3.3 g km −1 , 12.5 ± 1.3 g km −1 , and 11.8 ± 2.0 g km −1 , respectively. No statistically significant difference in NO x emission factors was observed between Euro II and III buses. Even for Euro IV buses equipped with SCR systems, the NO x emission factors are similar to Euro III buses. The data regarding real-time engine performance of Euro IV buses suggest the engine certification cycles did not reflect their real-world operating conditions. These new on-road test results indicate that previous estimates of total NO x emissions for HDDV fleet may be significantly underestimated. The new estimate in total NO x emissions for the Beijing HDDV fleet in 2009 is 37.0 Gg, an increase of 45 % compared to the previous study. Further, we estimate that the total NO x emissions for the na-

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Section: Y Wu Et Al: the Challenge To No X Emission Control For Heamentioning

confidence: 99%

Section: Emission Rates and Emission Factorsmentioning

confidence: 99%

The challenge to NOx emission control for heavy-duty diesel vehicles in China

Zhang

et al. 2012

Atmos. Chem. Phys.

151

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“…As a result, the traffic volume and vehicular exhaust decreased significantly (Wang et al, 2010a;Zhou et al, 2010). For example, during the Olympic Games the traffic volume and average PM 2.5 concentration near a major road -about 1.2 km away from the TH site -reduced by 21 % and 46.3 % compared to those in June, respectively (Kong et al, 2010).…”

Section: Seasonal Patterns Of Carbonaceous Speciesmentioning

confidence: 99%

Carbonaceous species in PM2.5 at a pair of rural/urban sites in Beijing, 2005–2008

et al. 2011

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Abstract. One-week integrated PM 2.5 samples were collected over four years (2005)(2006)(2007)(2008) at a pair of sites, one rural and one urban site, in the area of Beijing, China. Weekly organic and elemental carbon (OC and EC) concentrations from these samples were measured to investigate their atmospheric concentrations, temporal variation patterns and the factors influencing these aspects. A slightly systematic decrease in annual mean concentration of OC and an opposite trend for EC at both sites was observed. Since OC accounted for about 70 % of total carbon (TC, i.e. OC + EC) the total carbonaceous mass experienced a slight decline on a yearly basis over the study period. Its fraction of PM 2.5 mass at the urban site in 2008 was significantly lower than observed 10 years earlier (1999), indicating that the relative importance of carbonaceous species in PM 2.5 has decreased. In urban Beijing clear seasonal variations (by factors of 1.35 ∼ 3.0) were shown in both OC and EC with higher weekly concentrations and fluctuations in winter and much lower values in summer and spring. The minimum seasonal urban excesses of OC (3.0 µg m −3 ) and EC (1.3 µg m −3 ) occurred in the summer of 2008, which were only one-ninth and one-seventh of their corresponding maximum values, respectively. This reduction in the urban-difference, coupled with more positive stable carbon isotope values of EC at the urban site in that summer relative to the preceding summers, supports that the special clean air campaign for the 2008 Summer Olympic Games very likely had noticeable impact on the ambient concentrations of carbonaceous aerosols in the Beijing area, particularly on the local urban scale. Less consistent seasonal patterns in OC and EC, their scattered correlation, andCorrespondence to: K. He (hekb@tsinghua.edu.cn) higher mass ratios of OC to EC (OC/EC) at the rural site indicate more complex and variable major sources regarding aerosol formation in the rural area. These emission sources include biomass burning during the harvest seasons, widely used high-polluting family stoves and small boilers for cooking and heating with high OC/EC ratios, and a greater relative quantity of secondary organic aerosols.

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“…The daily forecasts for the lead times of 1, 3, and 5 days were used in this study. The relative longer forecasting lead time (compared to 1-2 days lead time in previous studies [16,22]) would enable us to evaluate whether the post-correction method is useful for dynamic management practices [33], which usually requires about a 3-5 day lead time to take actions.…”

Section: Pm25 Model Forecastsmentioning

confidence: 99%

Improving PM2.5 Air Quality Model Forecasts in China Using a Bias-Correction Framework

Lyu

Zhang

2017

Atmosphere

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Abstract:Chinese cities are experiencing severe air pollution in particular, with extremely high PM 2.5 levels observed in cold seasons. Accurate forecasting of occurrence of such air pollution events in advance can help the community to take action to abate emissions and would ultimately benefit the citizens. To improve the PM 2.5 air quality model forecasts in China, we proposed a bias-correction framework that utilized the historic relationship between the model biases and forecasted and observational variables to post-process the current forecasts. The framework consists of four components: (1) a feature selector that chooses the variables that are informative to model forecast bias based on historic data; (2) a classifier trained to efficiently determine the forecast analogs (clusters) based on clustering analysis, such as the distance-based method and the classification tree, etc.; (3) an error estimator, such as the Kalman filter, to predict model forecast errors at monitoring sites based on forecast analogs; and (4) a spatial interpolator to estimate the bias correction over the entire modeling domain. One or more methods were tested for each step. We applied five combinations of these methods to PM 2.5 forecasts in 2014-2016 over China from the operational AiMa air quality forecasting system using the Community Multiscale Air Quality (CMAQ) model. All five methods were able to improve forecast performance in terms of normalized mean error (NME) and root mean square error (RMSE), though to a relatively limited degree due to the rapid changing of emission rates in China. Among the five methods, the CART-LM-KF-AN (a Classification And Regression Trees-Linear Model-Kalman Filter-Analog combination) method appears to have the best overall performance for varied lead times. While the details of our study are specific to the forecast system, the bias-correction framework is likely applicable to the other air quality model forecast as well.

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The impact of transportation control measures on emission reductions during the 2008 Olympic Games in Beijing, China

Cited by 208 publications

References 17 publications

The challenge to NO<sub>x</sub> emission control for heavy-duty diesel vehicles in China

The challenge to NO<sub>x</sub> emission control for heavy-duty diesel vehicles in China

Carbonaceous species in PM<sub>2.5</sub> at a pair of rural/urban sites in Beijing, 2005–2008

Improving PM2.5 Air Quality Model Forecasts in China Using a Bias-Correction Framework

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The impact of transportation control measures on emission reductions during the 2008 Olympic Games in Beijing, China

Cited by 208 publications

References 17 publications

The challenge to NO&lt;sub&gt;x&lt;/sub&gt; emission control for heavy-duty diesel vehicles in China

The challenge to NO&lt;sub&gt;x&lt;/sub&gt; emission control for heavy-duty diesel vehicles in China

Carbonaceous species in PM&lt;sub&gt;2.5&lt;/sub&gt; at a pair of rural/urban sites in Beijing, 2005–2008

Improving PM2.5 Air Quality Model Forecasts in China Using a Bias-Correction Framework

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The challenge to NO<sub>x</sub> emission control for heavy-duty diesel vehicles in China

The challenge to NO<sub>x</sub> emission control for heavy-duty diesel vehicles in China

Carbonaceous species in PM<sub>2.5</sub> at a pair of rural/urban sites in Beijing, 2005–2008