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Beijing, China
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Cheng N.-L.,Airborne | Cheng N.-L.,Beijing Normal University | Cheng N.-L.,Chinese Research Academy of Environmental Sciences | Li Y.-T.,Airborne | And 7 more authors.
Huanjing Kexue/Environmental Science | Year: 2016

The spatial-temporal distribution characteristics of O3 and the correlations between O3 and meteorological elements in Beijing urban area were investigated based on the hourly O3 monitoring data from January to December in 2014 released by Beijing Municipal Environmental Monitoring Center. The annual concentration of O3 in Beijing was about 56.18 μg·m-3 in 2014. In the over polluted days during May and September, the O3 concentration could reach as high as 148.05 μg·m-3. The diurnal distribution of ozone presented a clear unimodal pattern with its peak appearing at 15:00 or 16:00 and trough at 06:00 or 07:00 and the concentrations of O3 during 09:00 and 23:00 was significantly higher than those in the Summer time. For the spatial distribution of O3, the concentration was lower in central urban area with the highest concentration appearing at plant garden site in the west of the urban area. Ground weather type of O3 over polluted days was divided into three categories including high-pressure, low-pressure, equalizing field, which accounted for 16%, 36%, 48%, respectively. The concentration of O3 was negatively correlated with the air pressure, humidity and visibility while it was positively correlated with the wind speed and temperature. In one heavy pollution episode of O3 caused by local photochemical pollution and regional transport from May 29th to June 1st in 2014 in Beijing, regional transport showed a very important influence on the concentration of O3 in Beijing. © 2016, Science Press. All right reserved.

News Article | December 14, 2015

« HeidelbergCement and Joule partnering to explore carbon-neutral fuel application in cement manufacturing | Main | New $7M XPRIZE competition for rapid and unmanned ocean exploration; $1M bonus from NOAA » Researchers from Tsinghua University and Peking University have investigated the effects of fuel properties on particulate emissions gasoline direct injection engines (GDI). The study results, reported in the journal Fuel, demonstrated that the fuel composition has a significant on particulate emissions from GDI engines. Although turbocharged GDI engines offer the attractive combination of both increased fuel efficiency and performance due to their higher volumetric efficiencies at high load, they also tend to produce more PM than PFI engines, with PM mass levels exceeding those of diesels equipped with diesel particulate filters, as well as conventional port-fuel injected vehicles. (Storey et al., 2014). A recent open access study published in Nature’s Scientific Reports (Zhang & Cao, 2015) found that only 25 out of 190 cities in China could meet the National Ambient Air Quality Standards of China; the population-weighted mean of PM in Chinese cities is 61 μg/m3—about 3 times as high as global population-weighted mean, highlighting a high health risk. According to data released by the Beijing Environmental Protection Bureau (EPB) in 2014, motor vehicles were responsible for 31% of local PM emissions in 2014. In the introduction to their paper, the Tsinghua/Peking team noted that in GDI operation, gasoline is injected into the cylinder during the intake stroke, leaving insufficient time for the gasoline to evaporate and to mix with air before ignition. This inhomogeneity unavoidably leads to diffusion flame, in which soot-like particulates are formed. It is known that vehicular particulate emissions can be affected by the fuel properties, such as aromatics, olefin, sulfur content, volatility and oxygenate. … The objective of this research work is to investigate different fuel compositions impacts on primary particulate emissions, VOCs and conventional gaseous emissions from gasoline powered vehicles (including both PFI and DIG). Since it is known that secondary particulate emission is a significant contributor to air quality, this research work also takes this into consideration. In the new paper, the research team blended six test fuels with different aromatics, olefin, sulfur, Methyl-cyclopentadienyl Manganese Tricarbonyl (MMT) and ethanol content to use with a GDI engine (certified to China Phase 4, equivalent to Euro 4) produced by a Chinese OEM to examine the influences on primary particulate emission including mass; number; size distribution; compounds including Polycyclic Aromatic Hydrocarbons (PAHs); and the toxicity of PAHs emissions. Fuel composition—especially aromatics content—had a significant impact on PM emissions. Higher aromatics in gasoline resulted in much higher PM (mass), PN (particle number) and PAHs emissions, with higher toxicity to human health. The researchers concluded that reducing aromatics content is an important means to reduce primary particulate emissions and improve air quality. Reducing olefin content resulted in reduced PM and PN emissions especially under high-load operation, but did not improve PAHs emissions levels by much. It did, however, contribute to the reduction of toxicity of PAHs. E10 showed limited improvement on PM emissions compared with the effect of reducing aromatics and olefin content. Additionally, E10 increased PN emissions under low-load conditions. Typical China Phase V gasoline did not definitely reduce vehicle emissions versus the typical Phase IV gasoline with higher sulfur ( The GDI tailpipe particulates consist mainly of EC (elemental carbon), OM (organic matter) and small amounts of inorganic ions. The mass percentage of EC in the total tailpipe PM increased as load increased. Three-way catalysts have a significant impact on PM, helping to reduce OM greatly (67–85%), resulting in an increase of mass percentage of EC in the the total PM of post-TWC versus pre-TWC configurations.

Wang Z.,Chinese Research Academy of Environmental Sciences | Li Y.,Beijing Municipal Environmental Monitoring Center | Chen T.,Beijing Environmental Protection Bureau | Zhang D.,Tsinghua University | And 5 more authors.
Bulletin of the American Meteorological Society | Year: 2016

An examination of the major air pollution control measures leading to the improvement in air quality in Beijing from 2008 to 2014. © 2016 American Meteorological Society.

Wang Z.,Beijing Municipal Environmental Monitoring Center | Wang Z.,Chinese Research Academy of Environmental Sciences | Li Y.,Beijing Municipal Environmental Monitoring Center | Chen T.,Beijing Environmental Protection Bureau | And 8 more authors.
Journal of Geophysical Research: Atmospheres | Year: 2015

Five sites were selected to investigate the impact of regional-scale air pollutant control strategies during the Asia-Pacific Economic Cooperation (APEC) conference (1-12 November 2014) in and around Beijing. Concentrations of most of the air pollutants in the APEC period were significantly lower than those in the adjacent time period, especially when the enhanced reduction measures were implemented. Compared with the same time period in the previous 5 years (PM2.5 was compared with the last year), average concentrations of SO2, NO2, PM10, and PM2.5 in the five sites during the APEC period decreased by 62%, 41%, 36%, and 47% respectively, whereas average concentration of O3 increased by 102%. A possible cause of the increase of O3 concentrations is the stricter reduction measure on NOx compared to that applied to volatile organic compounds. Compared with the non-APEC period in autumn 2014, concentrations of most of the chemical compositions of PM2.5 decreased significantly in the APEC period, especially SO4 2-, NO3 -, and NH4 + (sulfate, nitrate, and ammonium). The aerosol optical depth and the columnar NO2 in the area of 39.5°-40.5°N, 116°-117°E showed a changing pattern similar to the typical gas pattern. The net effectiveness of the emission reduction measures was calculated through a comparison of concentrations of air pollutants under similar meteorological conditions. Through the reduction measures imposed during the APEC period, concentrations of CO, SO2, NO, NO2, PM10, and PM2.5 decreased by 54%, 74%, 64%, 48%, 67%, and 65%, respectively, whereas concentrations of O3 increased by 189%. © 2015. American Geophysical Union. All Rights Reserved.

Wang Z.,Beijing Municipal Environmental Monitoring Center | Zhang D.,Beijing Municipal Environmental Monitoring Center | Chen T.,Beijing Environmental Protection Bureau | Li Y.,Beijing Municipal Environmental Monitoring Center | And 3 more authors.
Huanjing Kexue Xuebao/Acta Scientiae Circumstantiae | Year: 2015

NO2 data from 35 automatic air quality monitoring stations in Beijing in 2013 were analyzed to investigate spatiotemporal characteristics of NO2 and also correlation between NO2, PM2.5 and also atmospheric oxidation. The results showed that the average concentration of NO2 is the highest in winter followed by autumn, spring and summer with the average concentration of 66.6, 58.3, 54.7 μg·m-3 and 45.8 μg·m-3, respectively. The average concentration of NO2 is the highest at the traffic station, followed by the urban station, the suburban station and the regional station with the average concentration of 78.6, 57.9, 48.5 μg·m-3 and 40.3 μg·m-3, respectively. Monthly average concentration of NO2 changes in a wave-shape curve with peak values during January, March, May and also October. Generally, diurnal variation of NO2 at the regional station showed unimodal distribution, while other stations showed bimodal distribution. Concentrations of NO2 were higher during weekends most of the time, which indicated anti weekend effect. Annual average concentration of NO2 in different regions show different concentrations at different stations. The highest concentration can be found at the central six districts, while lower concentration at the southwest, southeast, northwest and northeast. Concentrations of NO2 were significantly positively correlated with concentrations of PM2.5 and OX, which indicated that NO2 could be the factor behind increase in PM2.5 concentrations by increasing precursor concentrations and enhancing atmospheric oxidation. ©, 2015, Science Press. All right reserved.

Wang Z.-S.,Beijing Municipal Environmental Monitoring Center | Li Y.-T.,Beijing Municipal Environmental Monitoring Center | Chen T.,Beijing Environmental Protection Bureau | Zhang D.-W.,Beijing Municipal Environmental Monitoring Center | And 4 more authors.
Zhongguo Huanjing Kexue/China Environmental Science | Year: 2014

Concentration of ozone and its precursors from 12 automatic air monitoring stations in urban area of Beijing from December 2012 to November 2013 were analyzed to investigate their diurnal changes and correlation. The results showed that ozone concentration maintained relatively high from May to August while it was at a low level in other months. Ozone concentration exhibited a single peak distribution, with the peak appeared around 15:00 and 16:00, and in weekend the concentration was higher than weekdays. Ozone precursors, such as CO, NO, NO2 and NOx, showed bimodal distribution in most cases. Ozone and its precursors showed a negative correlation, and the correlation was weak in summer. According to the fitting equation of OX and NOx concentrations, atmospheric oxidants OX was mainly affected by regional O3 transmission during daytime in winter, and by local NOx pollution during nighttime in winter. The calculated photolysis rate of NO2 in an ideal situation showed that photolysis rate in spring, summer, autumn and winter was 0.180, 0.209, 0.169, 0.149 min-1, respectively. The approximate photochemical equilibrium of O3, NO and NO2 was also observed in urban area with high O3 concentration during the day. ©, 2014, Chinese Society for Environmental Sciences. All right reserved.

Cheng N.,Beijing Municipal Environmental Monitoring Center | Li Y.,Beijing Municipal Environmental Monitoring Center | Zhang D.,Beijing Municipal Environmental Monitoring Center | Chen T.,Beijing Environmental Protection Bureau | And 3 more authors.
Research of Environmental Sciences | Year: 2015

Atmospheric environmental background data, weather conditions and formation mechanisms of four typical air pollution episodes in Beijing in October 2014 were investigated by combining observed data and result from the numerical model CAMx. The results showed that the occurrences of heavy air pollution resulted mainly from stable regional or local atmospheric conditions. Observed heavy pollution episodes were characterized by a stagnant atmospheric structure with average wind speed of 1.52 m/s, high humidity of 80.75% and, large inversion strongth of 2.26℃/100 m which were disadvantageous to the dispersion of air pollutants. The air pollution in October 8th-11st episode was most serious with daily average PM2.5 concentration of 264 μg/m3 and highest contribution from regional chemistry transport (63.75%), the second most serious pollution episode was October 24th-25th during which there was a strongest inversion (5.94℃/100 m). During the October 18th-20th heavy pollution episode, high concentrations PM2.5 (above 200 μg/m3) were found mainly in the northwest part of Beijing. The heavy air pollution episode of October 30th-31st was actually the lightest among the four episodes, with daily average PM2.5 concentration of 154 μg/m3. Model simulation analysis showed that regional transport contribution to Beijing's air pollution ranged between 42.36%-69.12%, and also that the formation of secondary inorganic and organic aerosols was significant. ©, 2015, Editorial department of Molecular Catalysis. All right reserved.

Li L.-J.,Beijing Municipal Environmental Monitoring Center | Li L.-J.,Airborne | Wang Z.-S.,Beijing Municipal Environmental Monitoring Center | Wang Z.-S.,Airborne | And 7 more authors.
Zhongguo Huanjing Kexue/China Environmental Science | Year: 2016

The temporal and spatial variations of air pollutant concentrations, and types of pollution were investigated during heavy air pollution episodes occurred in Beijing from 2013 to 2014. The results showed that there were 105 heavy pollution days in Beijing during 2013~2014, accounting for 14.4% of the total. And in these heavy air pollution episodes, Beijing suffered the PM2.5, PM10 and O3 as the primary pollutant for 103 days, 1day and 1day, respectively. The heavy pollution days in the winter half year accounted for 76.2%, and pollution episodes could be characterized by calm wind, high relative humidity and low visibility. For the heavy air pollution days, the concentration ratio of PM2.5 to PM10 reached to 91.3% which was significantly higher than the annual average level, indicating that PM2.5 was dominant. Air pollutant concentrations in the southern region of Beijing were higher than those in the northern parts. Areas with higher air pollutant concentrations were mainly located in the plains, and lower values are located in the mountain regions. Moreover, the frequency of heavy air pollution for traffic monitoring sites was higher than other urban sites in Beijing. The heavy air pollution episodes could be grouped into four typical types, namely the sustained-accumulated, the O3 pollution, the sand-dust caused and the combined type. The sustained-accumulated episodes were always accompanied by enhancements of regional air pollution level for the whole city, and by obvious increase of NO3 -, SO4 2- and NH4 + concentrations in PM2.5. It is also found that O3 pollution became more serious in recent years. © 2016, Chinese Society for Environmental Sciences. All right reserved.

News Article | December 15, 2015

Artificial intelligence is the big, oft-misconstrued catchphrase of the day, making headlines recently with the launch of the new OpenAI organization, backed by Elon Musk, Peter Thiel, and other tech luminaries. AI is neither a synonym for killer robots nor a technology of the future, but one that is already finding new signals in the vast noise of collected data, ranging from weather reports to social media chatter to temperature sensor readings. Today IBM has opened up new access to its AI system, called Watson, with a set of application programming interfaces (APIs) that allow other companies and organizations to feed their data into IBM's big brain for analysis. Real AI isn't about building a know-it-all computer, but rather one that's a good learner, able to sort overwhelming amounts of data, and diligently catalog recurring patterns. For example, while working with sensor readings and other flight data from airliners, AI might spot the conditions that caused a plane to burn up too much fuel, a project that IBM is already undertaking with plane manufacturer Airbus. Data-spewing machines such as planes are components of (another catchphrase) the Internet of things (IoT). It's essentially the online linking of a turbine, water pump, plane, or any other device beyond the usual suspects: computers and smartphones. In this area, IBM is up against Predix, GE's own new cloud brain for analyzing data from machinery. IBM is going beyond industrial devices with Watson, though, opening it up to other "things" such as videos, people's voices, or text from Twitter. Like an isolated sound reading from a turbine, the meaning of a phrase such as "pedal is soft" (an example IBM gave me) isn't immediately clear to a computer. It's "unstructured data" that requires sorting out to understand. But after reading enough tweets and other text, AI can figure out that that particular phrase means the brakes aren't working well. The opening of Watson's interface exposes to the world what IBM has already been doing within a few pilot programs. The company has been combining unstructured data with straight-up traditional measurements in a project with the Beijing Environmental Protection Bureau (EPB), to track and forecast air pollution conditions for the city. "Using not just very structured stuff but videos that people are taking, call-center transcripts . . . and blogs, all this unstructured data . . . we've been able to identify very accurately exactly where the pollutants are coming from [and] how they are moving," says Harriet Green, IBM's general manager for Watson IoT and Education. IBM claims that its efforts have led to a 20% reduction of one pollutant, ultra-fine particulate matter, although Beijing has a long way to go, given recent reports of its most dangerous air pollution ever. Green will be running outreach from IBM's new 1000-person Watson IoT Global Headquarters in Munich. The new HQ will develop projects directly with clients, such as one with telecom company Vodaphone and local governments in the Andalucía region of southern Spain. Vodaphone plans to invest, over two years, ‎€243 million ($267 million) on new infrastructure that includes IoT sensors and data analytics. What exactly IBM and Vodaphone can achieve remains a bit murky. Green describes the undertaking with some vague phrasing like, "Using data about [people's] moods, their state of being around the services they provide, how they're texting and blogging—all with permissions naturally—to really develop systems, networks, responses . . . that are in concert with what ordinary people really want." Asked for a specific example, she brings up the topic of potholes. People post text, photos, and videos about "enormous numbers of potholes" in southern Spain, she says. Analyzing all those posts could help cities find out where the holes are, and which ones most frustrate drivers. It's not glamorous, but it's something that people really do care about.

Cheng N.-L.,Airborne | Chen T.,Beijing Environmental Protection Bureau | Zhang D.-W.,Airborne | Li Y.-T.,Airborne | And 5 more authors.
Huanjing Kexue/Environmental Science | Year: 2015

To analyze the impacts of emissions from fireworks on the air quality, monitoring data of PM2.5, PM10, SO2, NO2 chemical compositions of PM2.5 of automatic air quality stations in Beijing during Spring Festival(February 18th-24th) in 2015 were investigated. Moreover, we also estimated the fireworks on the New Year's Eve produced based on the ratio of PM2.5 to CO. Analysis results showed that the concentrations of PM2.5, PM10, SO2, NO2 during 2015 Spring Festival was 116.85, 184.71, 22.14, and 36.27 μg·m-3, respectively, which raised 52.61%, 92.41%, -40.15%, -0.46% respectively compared to the same period in 2014; the concentration peaks of PM2.5, PM10, SO2, NO2at 1:00 am on 19th was 412.69, 541.63, 152.73, 51.09 μg·m-3, respectively, which was increased 19.02%, 14.37%, 76.57%, 11.35% compared to that of 2014; the concentration peaks at dense population area were significantly higher than that in other districts; fireworks had great influence on the chemical compositions of PM2.5especially on the concentrations of chloride ion, potassium ion, magnesian ion, which were 18.85, 66.72, and 70.10 times than that in 2013-2014; fireworks resulted in severe air pollution in a short time and the estimated fireworks on the New Year's Eve was approximately 2.13×105 kg of PM2.5. Reduction of pollutants during Spring Festival had a positive significant impact on air quality in Beijing. ©, 2015, Science Press. All right reserved.

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