Entity

Time filter

Source Type


Li J.,Tsinghua University | Wu Y.,Tsinghua University | Wu Y.,State Environmental Protection Key Laboratory of Source and Control of Air Pollution Complex | Song S.,Massachusetts Institute of Technology | And 4 more authors.
Huanjing Kexue Xuebao/Acta Scientiae Circumstantiae | Year: 2015

A sampling campaign was performed to characterize the elemental composition of submicron particulate matter (i.e., PM1) near the North 4th Ring Road, a typical urban freeway in Beijing. This field study was carried out during December 2011 and August 2012. Results showed that average PM1 concentrations on normal days were (52.5±29.9) μg·m-3 in the summer and (59.6±32.5) μg·m-3 in the winter, but increased to (154.2±36.3) μg·m-3 on hazy days in the summer. Twenty one elements including Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Cd, Ba and Pb were analyzed by XRF. Elements with the highest concentrations were S, Cl, K, Na, Si, Zn, Fe and Ca, altogether accounting for more than 90% of the total measured element mass concentrations. Concentration of the elements varied seasonally: S was higher in summer, while As, Cl, K, Pb, Mn, V, Cd and P were higher in winter. Using enrichment factor analysis we found that those pollution elements were enriched in PM1 more than in PM2.5. Three factors were identified using factor analysis: factor 1 was mainly associated with vehicle emissions, coal combustion and biomass burning; factor 2 was attributed by dust sources; and factor 3 was related to vehicle and possibly industrial emissions. The sum of factors 1 and 3 contributed as high as 46.8% in summer and 68.3% in winter, respectively, which indicated that these major anthropogenic sources (e.g., vehicles, coal burning, and biomass burning) played the leading role to PM1 elemental concentrations near the road. During hazy days, concentrations of S, As and Pb were much higher than for non-hazy days while crustal elements showed no significant difference between these two periods. ©, 2014, Science Press. All right reserved. Source


Yang L.,Tsinghua University | Yang L.,Transport planning and research institute | Wu Y.,Tsinghua University | Wu Y.,State Environmental Protection Key Laboratory of Source and Control of Air Pollution Complex | And 5 more authors.
Frontiers of Environmental Science and Engineering | Year: 2015

Mass concentrations of PM10, PM2.5 and PM1 were measured near major roads in Beijing during six periods: summer and winter of 2001, winter of 2007, and periods before, during and after the 2008 Beijing Olympic Games. Since the control efforts for motor vehicles helped offset the increase of emissions from the rapid growth of vehicles, the averaged PM2.5 concentrations at roadsides during the sampling period between 2001 and 2008 fluctuated over a relatively small range. With the implementation of temporary traffic control measures during the Olympics, a clear “V” shaped curve showing the concentrations of particulate matter and other gaseous air pollutants at roadsides over time was identified. The average concentrations of PM10, PM2.5, CO and NO decreased by 31.2%, 46.3%, 32.3% and 35.4%, respectively, from June to August; this was followed by a rebound of all air pollutants in December 2008. Daily PM10 concentrations near major roads exceeded the National Ambient Air Quality Standard (Grade II) for 61.2% of the days in the non-Olympic periods, while only for 12.5% during the Olympics. The mean ratio of PM2.5/ PM10 near major roads remained relatively stable at 0.55 (±0.108) on non-Olympic days. The ratio decreased to 0.48 (±0.099) during the Olympics due to a greater decline in fine particles than in coarse-mode PM. The ratios PM1/ PM2.5 fluctuated over a wide range and were statistically different from each other during the sampling periods. The average ratios of PM1/PM2.5 on non-Olympic days were 0.71. © 2014, Higher Education Press and Springer-Verlag Berlin Heidelberg. Source


Yan H.,Tsinghua University | Wu Y.,Tsinghua University | Wu Y.,State Environmental Protection Key Laboratory of Source and Control of Air Pollution Complex | Zhang S.,Tsinghua University | And 6 more authors.
Huanjing Kexue Xuebao/Acta Scientiae Circumstantiae | Year: 2014

Traffic flow data, including hourly profiles for total traffic volume, fleet composition by vehicle category and average speed, were investigated on a typical freeway (the North Fourth Ring Road) in Beijing during 2009. By applying the Emission Factor Model for Beijing Vehicle Fleet (EMBEV) in combination with previous studies on vehicle emissions of black carbon (BC), we estimated BC emission factors and emission intensity from on-road vehicles. In combination with simultaneously measured meteorological data in Beijing, dispersion of road traffic BC emissions was simulated with the AERMOD model in a roadside environment and further validated with concurrently observed BC concentration data. Our results showed that the hourly average BC emission factor was very strongly correlated with the proportion of the traffic volume of heavy-duty diesel vehicles (e.g., diesel-powered passenger buses and freight trucks). Due to the traffic restrictions on truck use in the urban area of Beijing during the day time (6 a.m. to 11 p.m.), the average BC emission factor was (9.3±1.2) mg·km-1·veh-1 during the day but increased to (29.5±11.1) mg·km-1·veh-1 during night time. On the other hand, BC hourly emission intensity ranged from 17.9 g·km-1·h-1 to 115.3 g·km-1·h-1 for this road segment. Two peaks of BC emission intensity were observed synchronized with traffic volume peaks, (106.1±13.0) g·km-1·h-1 during the morning rush period (7:00-9:00) and (102.6±6.2) g·km-1·h-1 during the evening rush period (17:00-19:00). The AERMOD was able to provide satisfactory simulation results of BC concentration at the road side due to traffic emissions as validated by observed concentration data. Road traffic emissions were estimated to contribute (2.8±3.5 μg·m-3) BC on average at the road side with the AERMOD model. In particular, due to the great differences of local meteorological conditions, substantial seasonal and diurnal variations were observed from the simulated BC concentrations. During the day, light-duty passenger cars were the largest contributor (1.07±1.57 μg·m-3) among all vehicle categories, followed by the public bus fleet (0.58±0.85 μg·m-3). During night time, trucks became the dominant contributor (2.44±2.31 μg·m-3) to BC concentration at the road site. Source


Wu Y.,Tsinghua University | Wu Y.,State Environmental Protection Key Laboratory of Source and Control of Air Pollution Complex | Yang L.,Transportation Institute | Zheng X.,Tsinghua University | And 5 more authors.
Science of the Total Environment | Year: 2014

The profiles of particulate polycyclic aromatic hydrocarbons (PAHs) near a major road and relative major sources were determined based on five 1-week intensive field campaigns in 2008 and 2009, and the impacts of temporary control measures on roadside PAHs during the Beijing Olympics are discussed. The annual average concentration of PAHs in the non-Olympic period was 42.3±52.4ng/m3 and clear seasonal variation was present. Diesel vehicles, gasoline vehicles and coal combustion were identified as the three possible major sources of roadside PAHs using positive matrix factorization analysis. During the Olympics, the average total PAH concentration decreased to 4.8±2.7ng/m3, which was attributed primarily to the reduction of local emissions. Temporary traffic control measures significantly changed the diurnal pattern of particulate PAHs at the roadside site. Diesel vehicle contribution, in particular, decreased to a negligible fraction because heavy-duty diesel vehicles were strictly banned. © 2013 Elsevier B.V. Source


Wu X.,Tsinghua University | Wu Y.,Tsinghua University | Wu Y.,State Environmental Protection Key Laboratory of Source and Control of Air Pollution Complex | Zhang S.,University of Michigan | And 6 more authors.
Environmental Pollution | Year: 2016

China has been embracing rapid motorization since the 1990s, and vehicles have become one of the major sources of air pollution problems. Since the late 1990s, thanks to the international experience, China has adopted comprehensive control measures to mitigate vehicle emissions. This study employs a local emission model (EMBEV) to assess China's first fifteen-year (1998-2013) efforts in controlling vehicles emissions. Our results show that China's total annual vehicle emissions in 2013 were 4.16 million tons (Mt) of HC, 27.4 Mt of CO, 7.72 Mt of NOX, and 0.37 Mt of PM2.5, respectively. Although vehicle emissions are substantially reduced relative to the without control scenarios, we still observe significantly higher emission density in East China than in developed countries with longer histories of vehicle emission control. This study further informs China's policy-makers of the prominent challenges to control vehicle emissions in the future. First, unlike other major air pollutants, total NOX emissions have rapidly increased due to a surge of diesel trucks and the postponed China IV standard nationwide. Simultaneous implementation of fuel quality improvements and vehicle-engine emission standards will be of great importance to alleviate NOX emissions for diesel fleets. Second, the enforcement of increasingly stringent standards should include strict oversight of type-approval conformity, in-use complacence and durability, which would help reduce gross emitters of PM2.5 that are considerable among in-use diesel fleets at the present. Third, this study reveals higher HC emissions than previous results and indicates evaporative emissions may have been underestimated. Considering that China's overall vehicle ownership is far from saturation, persistent efforts are required through economic tools, traffic management and emissions regulations to lower vehicle-use intensity and limit both exhaust and evaporative emissions. Furthermore, in light of the complex technology for emerging new energy vehicles, their real-world emissions need to be adequately evaluated before massive promotion. © 2016 Elsevier Ltd. Source

Discover hidden collaborations