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The PM2.5 problem is proving to be a major public crisis and is of great public-concern requiring an urgent response. Information about, and prediction of PM2.5 from the perspective of atmospheric dynamic theory is still limited due to the complexity of the formation and development of PM2.5. In this paper, we attempted to realize the relevance analysis and short-term prediction of PM2.5 concentrations in Beijing, China, using multi-source data mining. A correlation analysis model of PM2.5 to physical data (meteorological data, including regional average rainfall, daily mean temperature, average relative humidity, average wind speed, maximum wind speed, and other pollutant concentration data, including CO, NO2, SO2, PM10) and social media data (microblog data) was proposed, based on the Multivariate Statistical Analysis method. The study found that during these factors, the value of average wind speed, the concentrations of CO, NO2, PM10, and the daily number of microblog entries with key words ‘Beijing; Air pollution’ show high mathematical correlation with PM2.5 concentrations. The correlation analysis was further studied based on a big data's machine learning model- Back Propagation Neural Network (hereinafter referred to as BPNN) model. It was found that the BPNN method performs better in correlation mining. Finally, an Autoregressive Integrated Moving Average (hereinafter referred to as ARIMA) Time Series model was applied in this paper to explore the prediction of PM2.5 in the short-term time series. The predicted results were in good agreement with the observed data. This study is useful for helping realize real-time monitoring, analysis and pre-warning of PM2.5 and it also helps to broaden the application of big data and the multi-source data mining methods.

This paper is the second in a series of papers aimed at understanding volatile organic compound (VOC) emissions from motor vehicles in Beijing using on-board emission measurements, focusing specifically on rural vehicles (RVs). In this work, 13 RVs, including 6 different 3-wheel (3-W) RVs and 7 different 4-wheel (4-W) RVs, were examined using a portable emissions measurement system (PEMS) as the vehicles were driven on predesigned fixed test routes in rural areas of Beijing. Overall, 50 VOC species were quantified in this study, including 18 alkanes, 5 alkenes, 11 aromatics, 13 carbonyls and 3 other compounds. The average emission factor (EF) of the total VOCs for the 4-W RVs based on the distance traveled was 326.2 ± 129.3 mg/km, which is 2.5 times greater than that of the 3-W RVs. However, the VOC emissions for the 3-W RVs had higher EFs based on their CO2 emissions due to the different fuel economies of the two types of RVs. Formaldehyde, toluene, acetaldehyde, m-xylene, p-xylene, isopentane, benzene, ethylbenzene, n-pentane, 2-methoxy-2-methylpropane and butenal were the dominant VOC species from the RVs, accounting for an average of 68.6% of the total VOC emissions. Overall, the RVs had high proportions of aromatics and carbonyls. The ozone formation potentials (OFPs) were 670.6 ± 227.2 and 1454.1 ± 643.0 mg O3/km for the 3-W and 4-W RVs, respectively, and approximately 60%–70% of the OFP resulted from carbonyls. We estimated that the 3-W and 4-W RVs accounted for approximately 50% and 10%, respectively, of the total OFP caused by diesel vehicles (including diesel trucks and RVs) in Beijing in 2012. Thus, more attention should be given to VOC emissions and their impact on ozone formation.

This paper is the first in our series of papers aimed at understanding the volatile organic compound (VOC) emissions of vehicles in Beijing by conducting on-board emission measurements. This paper focuses on diesel vehicles. In this work, 18 China III diesel vehicles, including seven light-duty diesel trucks (LDDTs), four medium-duty diesel trucks (MDDTs) and seven heavy-duty diesel trucks (HDDTs), were examined when the vehicles were driven on predesigned fixed test routes in Beijing in China using a portable emissions measurement system (PEMS). Tedlar bag sampling and 2,4-dinitrophenyhydrazine (DNPH) cartridge sampling were used to collect VOC species, and gas chromatography-mass spectrometry (GC/MS) and high-performance liquid chromatography (HPLC) were used to analyze these samples. We obtained the VOC emission factors and relative compositions for diesel trucks of different sizes under different driving patterns. In total, 64 VOC species were quantified in this study, including 25 alkanes, four alkenes, 13 aromatics, 13 carbonyls and nine other compounds. The emission factors of the total VOCs based on mileage traveled for HDDTs were higher than those of LDDTs and MDDTs. Carbonyls, aromatics and alkanes were the dominant VOC species. Carbonyls accounted for 42.7%–69.2% of the total VOCs in the three types of tested diesel trucks. The total VOC emission factors of the tested vehicles that were driven on non-highway routes were 1.5–2.0 times higher than those of the vehicles driven on the highway. As for the OFP calculation results, with increased vehicle size, the ozone formation potential presented an increasing trend. Among the VOC components, carbonyls were the primary contributor to OFP. In addition, the OFPs under non-highway driving cycles were 1.3–1.7 times those under highway driving cycles. The results of this study will be helpful in improving our understanding of VOCs emitted from on-road diesel trucks in China.

This paper is the second in a series of three papers aimed at understanding the emissions of vehicles in China by conducting on-board emission measurements. This paper focuses on light-duty gasoline vehicles. In this study, we measured 57 light-duty gasoline vehicles (LDGVs) in three Chinese mega-cites (Beijing, Guangzhou, and Shenzhen), covering Euro 0 through Euro IV technologies, and generated CO, HC, and NOx emission factors and deterioration rates for each vehicle technology. The results show that the vehicle emission standards have played a significant role in reducing vehicle emission levels in China. The vehicle emission factors are reduced by 47–81%, 53–64%, 46–71%, and 78–82% for each phase from Euro I to Euro IV. Euro 0 vehicles have a considerably high emission level, which is hundreds of times larger than that of Euro IV vehicles. Three old taxis and four other Euro I and Euro II LDGVs are also identified as super emitters with equivalent emission levels to Euro 0 vehicles. Of the measured fleet, 23% super emitters were estimated to contribute 50–80% to total emissions. Besides vehicle emission standards, measures for restricting super emitters are equally important to reduce vehicle emissions. This study is intended to improve the understanding of the vehicle emission levels in China, but some key issues such as emission deterioration rates are yet to be addressed with the presence of a sufficient amount of vehicle emission measurements.

This paper focused on VOCs (referring to C2 C12 hydrocarbons) in Beijing and conducted an integrated comparison study on VOCs concentration, composition, chemical degradation and regional transport impact between haze and non-haze days in July and December of 2015, through VOCs monitoring and WRF-Chem modeling simulation. Our monitoring results show that in July VOCs was 20.1% higher in haze days (24.5 ± 6.6 ppbv) than in non-haze days (20.4 ± 7.8 ppbv), but VOCs:CO in haze days was 13.4% lower than the non-haze level. The relative change in VOCs:CO from non-haze days to haze days responded negatively to VOCs reaction ability with OH radical (KOH). Combining VOCs:CO decline during 8:00–14:00 with KOH, OH level was estimated to increase from 0.75 × 106 molec/cm3 in non-haze days to 1.17 × 106 molec/cm3 in haze days. WRF-Chem simulations calculate the regional transport contribution to Beijing VOCs, averagely −0.5 ppbv in haze days and 6.3 ppbv in non-haze days in July. These analyses implied a significant VOCs chemical degradation and a weak VOCs regional transport in summer haze days. In December, VOCs reached 91.0 ± 37.3 ppbv in haze days and 21.4 ± 14.1 ppbv in non-haze days, and VOCs:CO still dropped by 21.2% lower in haze days. However, the VOCs:CO decrease was mostly contributed by alkanes, acetylene and C4 C5 alkenes. The relative change in VOCs:CO from non-haze days to haze days seemingly responded positively to KOH values and VOCs chemical degradation was never explicitly found in both haze and non-haze days. Model simulations obtain a higher regional transport contribution to VOCs in haze days (4.4–31.0 ppbv) than in non-haze days (0.5–6.5 ppbv). The winds transport also explained the difference in VOCs composition and VOCs:CO ratio between haze and non-haze days of winter, for prevalent south winds in haze days transporting air masses of South Beijing and South Hebei that were more polluted and characterized by higher proportions of alkenes, aromatics and CO.

This paper examines the impact of variability in impact of air pollution on life satisfaction (LS). Previous studies have shown robust negative impact of air pollution on subjective well-being (SWB). However, empirical studies that consider variability in air pollution effects through comparative city study are limited. This study provides comparative evaluation of two major Chinese cities: Beijing and Shanghai. We apply a geo-statistical spatial interpolation technique on pollution data from monitoring sites to estimate the Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), coarse particles with a diameter between 2.5 and 10 μm (PM10) and fine particles with a diameter of 2.5 μm or less (PM2.5) pollution exposure of respondents of a survey conducted in 2016. The results show that all pollutants have robust negative impacts on LS for Beijing residents, whereas only SO2 and NO2 have significant negative impacts on LS for Shanghai residents; Per unit impact of SO2 is greater in Shanghai, and that of NO2 is greater in Beijing. Beijing and Shanghai residents have almost same monetary valuation for SO2 reduction but Beijing residents place approximately 1.5 times valuation on NO2 reduction compared to Shanghai residents. Moreover, the LS of Beijing residents is sensitive to temporal changes in the pollution level, whereas Shanghai residents are unaffected by such changes.

Tropospheric NO2 and SO2 concentrations are of great importance with regard to air quality, atmospheric chemistry, and climate change. Due to lack of surface monitoring stations, this study analyzes long term trend of NO2 and SO2 levels (2005–2014), retrieved from Ozone Monitoring Instrument (OMI) board on the NASA's Aura satellite, in an important region of China – Henan Province. Henan Province, located in North China Plain, has encountered serious air pollution problems including extremely high PM2.5 concentrations and as one of the most polluted region in China. The satellite spatial images clearly show that high levels of both NO2 and SO2 are concentrated in north and northeastern regions with much lower levels observed in other parts of Henan. Both pollutants exhibit the highest levels in winter with the least in summer/spring. The temporal trend analysis based on moving average of deseasonalized and decyclic data indicates that for NO2, there is a continuous increasing pattern from 2005 to 2011 at 6.4% per year, thereafter, it shows a decreasing trend (10.6% per year). As for SO2, the increasing trend is about 16% per year from 2005 to 2007 with decreasing rate 7% per year from 2007 to 2014. The economic development with incredible annual 11% GDP growth in Henan is responsible for increasing levels of NO2 and SO2. The observed decreasing SO2 level starting in 2007 is due to reduced SO2 emission, utilization of flue gas desulfurization (FGD) devices and to some extent, in preparation of Beijing 2008 Olympic Games. On the other hand, increasing vehicle numbers (155% from 2006 to 2012) and coal consumption (37% during the same span), along with the lack of denitration process for removing flue/exhaust gas NOx are responsible for increasing NO2 trend until 2011. The ratio of SO2/NO2 started decreasing in 2007 and dropped significantly from 2011 to 2013 indicating good performance of FGD and ever increasing NOx contribution from mobile sources. Unlike those observed in developed countries (US, EU and Japan) where a decreasing trend for both SO2 and NO2 has been observed since 1990s, the observed upward and downward trend found in Henan is similar to those found in North China Plain and other parts of China. The spatial and temporal trend analyses of SO2 and NO2 in four other regions in Henan further indicate a similar trend to those observed in Henan Province, albeit with different increasing and decreasing rate. The results provide regulatory agency to develop action plans to combat air pollution problem in general and SO2 and NO2 problems in particular in Henan. The implications of our findings and recommendations for decision makers are discussed in the paper.

Himawari-8, a new generation geostationary satellite, with the Advanced Himawari Imager (AHI) providing Full Disk observation with high temporal resolution (10 min) exhibits prominent advantage in monitoring aerosols over East Asia region. The AHI has 16 channels from 0.46 to 13.3 to capture visible and infrared spectral data. In this paper, we developed a regional algorithm to retrieve the Aerosol Optical Depth (AOD) over Beijing area based on the AHI visible and short-wave infrared (SWIR) data from June to August 2016. The AHI surface reflectance at 0.46, 0.64, 0.86 and 2.3 μm channel was obtained using the lowest gas-corrected reflectance based on the three-month top of the atmosphere (TOA) data. The algorithm employed superposing technique and linear regression to iteratively train the AHI surface reflectance, and built optimal surface reflectance relationships between visible and SWIR bands with three Normalized Difference Vegetation Index (NDVI) classifications over Beijing area. Based on these surface reflectance relationships, the AHI AOD data were retrieved through the radiation transmission function. The AOD products from the AErosol RObotic NETwork (AERONET) ground measurement, the MODerate resolution Imaging Spectroradiometer (MODIS) and the Japan Aerospace Exploration Agency (JAXA) official AHI were used to evaluate the performance of our AOD retrievals. At diurnal scale, more than 66% of AHI AOD retrievals fell within the EE in the afternoon, which indicated that these retrievals could capture the regional aerosol variability over Beijing area. In contrast, the AHI JAXA AOD product significantly overestimated aerosol loadings. The low AOD biases (13:00–16:00) under low aerosol conditions from our retrieval algorithm demonstrated that the AHI surface reflectance assumptions are available. However, AHI retrievals in XiangHe overestimated AOD under high aerosol conditions, which indicated that the urban aerosol model is inappropriate over this site. In addition, our AHI AOD algorithm was only applied over Beijing area in the summer of 2016. The retrieval time-independence in this algorithm has not been estimated in fall and winter when NDVI is lower. Future study will investigate this surface algorithm at larger temporal and spatial scales.

This study is the third in a series of three papers aimed at characterizing the VOC emissions of vehicles in Beijing. In this study, 30 light-duty vehicles fueled with gasoline were evaluated using a portable emission measurement system (PEMS) as they were driven on a predesigned, fixed test route. All of the tested vehicles were rented from private vehicle owners and spanned regulatory compliance guidelines ranging from Pre-China I to China IV. Alkanes, alkenes, aromatics and some additional species in the exhaust were collected in Tedlar bags and analyzed using gas chromatography/mass spectrometry (GC–MS). Carbonyls were collected on 2,4-dinitrophenyhydrazine (DNPH) cartridges and analyzed using high-performance liquid chromatography (HPLC). Overall, 74 VOC species were detected from the tested vehicles, including 22 alkanes, 6 alkenes, 1 alkyne, 16 aromatics, 3 cyclanes, 10 halohydrocarbons, 12 carbonyls and 4 other compounds. Alkanes, aromatics and carbonyls were the dominant VOCs with weight percentages of approximately 36.4%, 33.1% and 17.4%, respectively. The average VOC emission factors and standard deviations of the Pre-China I, China I, China II, China III and China IV vehicles were 469.3 ± 200.1, 80.7 ± 46.1, 56.8 ± 37.4, 25.6 ± 11.7 and 14.9 ± 8.2 mg/km, respectively, which indicated that the VOC emissions significantly decreased under stricter vehicular emission standards. Driving cycles also influenced the VOC emissions from the tested vehicles. The average VOC emission factors based on the travel distances of the tested vehicles under urban driving cycles were greater than those under highway driving cycles. In addition, we calculated the ozone formation potential (OFP) using the maximum incremental reactivity (MIR) method. The results of this study will be helpful for understanding the true emission levels of light-duty gasoline vehicles and will provide information for controlling VOC emissions from vehicles in Beijing, China.

Air pollution, such as PM2.5 (particulate matter with an aerodynamic equivalent diameter of less than 2.5 μm), PM10 (particulate matter with an aerodynamic equivalent diameter of less than 10 μm), NOx, and SOx, is a global concern because it may cause many chronic and fatal diseases, especially in developing countries. To better address air pollution problems, an important step is the timely and accurate prediction of air quality. Traditional methods are mainly based on meteorological data, regression model data, remote sensing data and different retrieval methods. Numerous studies on deep learning methods have suggested that these approaches may be able to perform accurate predictions for complex systems. In this paper, a long short-term memory (LSTM) approach for predicting air quality is proposed; moreover, meteorological data are used and Chinese social media is investigated as a proxy for public perceptions and responses for air quality prediction. We gathered daily air quality data, meteorological data and Weibo check-in data for Beijing, China from January 1, 2015 to December 31, 2016. The average sentiment of the related Weibo posts was selected as the public response proxy. The performance of our proposed model is evaluated based on real data. The root-mean-square error (RMSE) and the mean absolute error (MAE) indicated that our method presented better prediction results than traditional methods in terms of the PM2.5, PM10, O3, NO2, SO2 and CO concentrations. We focused on the prediction performance during the 2015 China Victory Day Parade period, during which social and political factors played an important role in air quality predictions. The results indicated that the proposed method, which incorporates public response data, was especially suitable for predicting the air quality in extreme short-term social events and provides a timely social measurement and feedback for environmental problems.

In order to protect public health by providing an early warning of harmful air pollutants, various forecasting models are proposed to forecast the average values of daily pollutant concentrations. In fact, even on the same day, the concentration of pollutants will fluctuate greatly during different time periods, point-based models can not reflect the variability well. Thus, an enhanced interval PM2.5 concentration forecasting model is developed in this paper, which is based on interval decomposition ensemble and considering influencing factors. For the purpose of obtaining main influencing factors, interval grey incidence analysis (IGIA) is used to select input variables for model. The interval-valued time series (ITS) of PM2.5 concentration and its influencing factors are decomposed into a finite number of complex-valued intrinsic mode functions (IMFs) and one complex-valued residual by bivariate empirical mode decomposition (BEMD) algorithm. Considering the different amounts of various IMFs, the complex-valued IMFs and residual are clustered into fewer classes by reconstruction technique. Then, interval multilayer perceptron (MLPI) is employed to fit the lower and upper bound simultaneously of all classes to obtain the corresponding forecasting results, which are combined to generate the aggregated interval-valued output by a simple addition approach. The model is tested by the dataset collected from three environmental monitoring stations in Beijing, China. Experimental results show that the enhanced model outperforms other considered models by means of forecasting accuracy and stability.

This paper employs meteorological observation data from surface and high-balloon stations, China Meteorological Administration (CMA) model T639 output data, NCEP reanalysis data, PM2.5 observations and modeled HYSPLIT4 trajectory results to study the meteorological causes, including large-scale circulation and planetary boundary layer features, which led to the extended haze episode on January 6–16, 2013 in central-eastern China. It discusses the possible impact of pollutants transported from southern Hebei Province on Beijing. The study's results show that: (1) the re-adjustment of atmospheric circulation from a longitudinal to a latitudinal model provides a valuable interpretation of the large-scale circulation background to the haze episode experienced in the metropolitan regions of Beijing, Tianjin, Hebei and their surrounding regions; (2) the regional atmospheric stratification of the planetary boundary layer is stable and the mixing height is low, suppressing air turbulence in the planetary boundary layer and providing favorable meteorological conditions for the formation of haze; and (3) the southwesterly jet stream with wind speeds of 6–11 m/s at a height of 850–950 hPa and the below-700 m air mass trajectory tracking established using the HYSPLIT4 model interdependently suggest a transport of pollutants from southern Hebei Province to Beijing at 850–950 hPa.

The analysis and forecasting of PM concentrations play a significant role in regulatory planning on the reduction and control of PM emission and precautionary strategies. However, accurate PM forecasting, which is needed to establish an early warning system, is still a huge challenge and a critical issue. Determining how to address the accurate forecasting problem becomes an even more significant and urgent task. Based on gray correlation analysis (GCA), Ensemble Empirical Mode Decomposition (EEMD), Cuckoo search (CS) and Back-propagation artificial neutral networks (BPANN), this paper proposes the CS-EEMD-BPANN model for forecasting PM concentrations. Prior to establishing this model, gray correlation has been uniquely used to search for possible predictors of PM among other air pollutants (CO, NO2, O3 and SO2) and meteorological environments (wind speed, wind direction, temperature, humidity and pressure). The proposed method was investigated in four major cities of China (Beijing, Shanghai, Guangzhou and Lanzhou) with different characteristics of climatic, terrain and emission sources. The results of the gray correlation analysis indicate that CO, NO2 and SO2 are more related to PM and that the incorporation of these predictors can significantly improve the model performance predictability, suggesting the effectiveness of our developed method.

As a pioneer in controlling vehicle emissions within China, Beijing released the Clean Air Action Plan 2013–2017 document in August 2013 to improve its urban air quality. It has put forward this plan containing the most stringent emission control policies and strategies to be adopted for on-road vehicles of Beijing. This paper estimates the historic and future trends and uncertainties in vehicle emissions of Beijing from 1998 to 2020 by applying a new emission factor model for the Beijing vehicle fleet (EMBEV). Our updated results show that total emissions of CO, THC, NOx and PM2.5 from the Beijing vehicle fleet are 507 (395–819) kt, 59.1 (41.2–90.5) kt, 74.7 (54.9–103.9) kt and 2.69 (1.91–4.17) kt, respectively, at a 95% confidence level. This represents significant reductions of 58%, 59%, 31% and 62%, respectively, relative to the total vehicle emissions in 1998. The past trends clearly posed a challenge to NOx emission mitigation for the Beijing vehicle fleet, especially in light of those increasing NOx emissions from heavy-duty diesel vehicles (HDDVs) which have partly offset the reduction benefit from light-duty gasoline vehicles (LDGVs). Because of recently announced vehicle emission controls to be adopted in Beijing, including tighter emissions standards, limitations on vehicle growth by more stringent license control, promotion of alternative fuel technologies (e.g., natural gas) and the scrappage of older vehicles, estimated vehicle emissions in Beijing will continue to be mitigated by 74% of CO, 68% of THC, 56% of NOx and 72% of PM2.5 in 2020 compared to 2010 levels. Considering that many of the megacities in China are facing tremendous pressures to mitigate emissions from on-road vehicles, our assessment will provide a timely case study of significance for policy-makers in China.

This paper presents a multi-scale study on formation process of urban pollution and haze clouds as well as their interactions over northern China in spring using integrated satellite and surface observations. Several extreme haze events occurred in Beijing area in March 2013, but primary atmospheric pollutants in the urban pollution exhibited inconsistent variations with the widespread haze clouds observed by satellites. Two typical types of haze event were found in Beijing area. Type-1 haze pollution appeared in stagnant weather conditions, during which PM2.5 was

Recently, air pollution has been substantially decreased in China, including particulate matter (PM) pollution in Beijing. However, PM pollution still has a great impact on human health, as stated in several epidemiological studies. In this paper, we used a generalized additive model (GAM) to evaluate the associations between PM2.5 and cardiovascular diseases from 2009 to 2012 in the Beijing metropolitan area. In GAM, we controlled the confounding factors, such as day of the week and long-term trends. We used the number of daily hospital emergency room (ER) visits to represent the incidence of the studied diseases. We divided the entire study group by gender (i.e., male and female) and age (i.e., 15–60 yr, 60–75 yr, and ≥75 yr). The results showed strong links were observed between increase in PM2.5 concentration and the number of hospital ER visits for cardiovascular diseases. For each interquartile range (IQR) increase in PM2.5, the relative risks (RRs) of the incidence of ischemic heart disease, arrhythmia, high blood pressure, and cerebrovascular disease were 1.023 (95% CI: 1.007–1.040), 1.019 (95% CI: 0.992–1.047), 1.082 (95% CI: 0.985–1.188), 0.976 (95%CI: 0.957–0.996), and 1.038 (95% CI: 1.005–1.072), respectively. Overall, the short-term impact of PM2.5 on the incidence of cardiovascular diseases was the strongest for the 60–75 yr subgroup. For the two different gender subgroups, the short-term impact of PM2.5 on ischemic heart disease and high blood pressure was relatively stronger for the females than for the males, while the opposite was true for arrhythmia and cerebrovascular disease.

Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and almucantar retrievals (single scattering albedo (SSA) and aerosol size distribution) from 2005–2009 in Beijing and Kanpur are used to analyze differences and similarities in aerosol optical properties over these two regions. The examination of monthly mean AOD (440 nm) shows that maximum and minimum values occurred in summer and winter, respectively, for Beijing, while the range in AOD in Kanpur was lower. Precipitation in both Beijing and Kanpur peaked in summer; however, the columnar water vapor (CWV) exhibited a high correlation with AOD in Beijing ( R 2 = 0.79) but had a weak relationship with AOD in Kanpur ( R 2 = 0.13). The Angstrom exponent ( α , 440–870 nm) generally increased linearly as the fine mode fraction (FMF) of AOD (500 nm) increased for FMF R 2 > 0.96. However a clear decrease in α for FMF > 90% found in Beijing is not shown distinctly in Kanpur, and is mainly due to the higher aerosol loading in this FMF bin at Beijing (AOD at 440 nm > 2.2) which results in a stronger coagulation of fine mode particles. Bimodal seasonally-averaged size distributions reveals similar aerosol mixtures composed of fine pollution particles and coarse dust particles in both regions. The analysis of spectral SSA as a function of α is emphasized in this paper. The average SSA at 440 nm in both regions shows a similar low dynamic range of ∼0.03 for α α > 1.4 in both regions can be attributed to a higher FMF leading to fine mode coagulation. However, the distinctly smaller increase in Kanpur suggests that fine mode aerosols at Beijing are less absorbing than those at Kanpur. The visibly lower SSA at 675 nm at Kanpur compared to that at Beijing for α > 0.4 is due to a larger find-coarse mode separation radius of ∼0.76 μm in Beijing versus a value lower than ∼0.58 μm in Kanpur. Another reason lies in the weaker absorption by fine mode aerosols in Beijing. The distinctly lower near-infrared SSA in Kanpur when α α bins, resulting in less aggregation of absorbing black carbon particles on coarse particles in Kanpur. The classification of aerosol properties shows that the AOD accumulation mode in all seasons, even including spring, in Beijing and in post-monsoon and winter seasons in Kanpur can be attributed to fine particle coagulation or hygroscopic growth; during pre-monsoon and monsoon seasons in Kanpur, it is due to coarse mode particle accumulation or cloud contamination.

This paper reports findings of a recent field study that characterized the on-road emissions of individual diesel vehicles in and around Beijing, China during November and December of 2009. We successfully sampled 230 individual trucks on 4 major expressways around the city as well as 57 individual buses in the city using refined mobile chasing techniques and fast response instruments. Emission factors (EF) for carbon monoxide (CO), black carbon (BC) and particulate matter with diameters less than 0.5 μm (PM 0.5 ) are derived from the measurements, which are consistent with the results from laboratory dynamometer tests. The PM 0.5 number emission factor distributions demonstrate consistent bimodal modes with peaks around 10 nm and 80 nm, while the mass emission factor distributions demonstrate a unimodal maximum around 110 nm for a majority of the trucks. The BC emissions are shown to be highly correlated with the mass emission of particles with 100–250 nm diameters, which are in good agreement with the results from previous studies. A number of important policy implications are discussed based on the results from this study. First, we identified “heavy emitters” in the on-road fleet we encountered, finding that 5% of diesel trucks in this sample are responsible for 50% of total BC emissions, and 20% of the trucks are responsible for 50% CO and PM 0.5 number emissions, 60% PM 0.5 mass emissions and over 70% of BC emissions. This suggests that emissions control programs should include identifying and removing heavy emitters from the road or improving their emissions. Second, the BC and PM 0.5 number emission factors of trucks registered in regions outside Beijing are significantly higher than those of Beijing-registered trucks, suggesting that improving engine and fuel standards in Beijing alone is not sufficient in reducing the traffic-related air pollution in Beijing. Third, the significantly lower emissions from Euro IV and CNG buses compared to the Euro II and Euro III buses support that introducing clean-burning buses is an effective way to reduce the overall vehicle emissions for the pollutants studied. Fourth, our refined chasing method demonstrates a cost-effective approach to characterize the emissions from a large number of on-road diesel vehicles. Further studies with large sample sizes are critical to constructing more accurate mobile emission inventories to represent the on-road vehicle population.

Valuation of health effects of air pollution is becoming a critical component of the performance of cost–benefit analysis of pollution control measures, which provides a basis for setting priorities for action. Beijing has focused on control of transport emission as vehicular emissions have recently become an important source of air pollution, particularly during Olympic games and Post-games. In this paper, we conducted an estimation of health effects and economic cost caused by road transport-related air pollution using an integrated assessment approach which utilizes air quality model, engineering, epidemiology, and economics. The results show that the total economic cost of health impacts due to air pollution contributed from transport in Beijing during 2004–2008 was 272, 297, 310, 323, 298 million US$ (mean value), respectively. The economic costs of road transport accounted for 0.52, 0.57, 0.60, 0.62, and 0.58% of annual Beijing GDP from 2004 to 2008. Average cost per vehicle and per ton of PM10 emission from road transport can also be estimated as 106 US $/number and 3584 US $ t−1, respectively. These findings illustrate that the impact of road transport contributed particulate air pollution on human health could be substantial in Beijing, whether in physical and economic terms. Therefore, some control measures to reduce transport emissions could lead to considerable economic benefit.

Traffic congestion and air pollution were two major challenges for the planners of the 2008 Olympic Games in Beijing. The Beijing municipal government implemented a package of temporary transportation control measures during the event. In this paper, we report the results of a recent research project that investigated the effects of these measures on urban motor vehicle emissions in Beijing. Bottom–up methodology has been used to develop grid-based emission inventories with micro-scale vehicle activities and speed-dependent emission factors. The urban traffic emissions of volatile organic compounds (VOC), carbon monoxide (CO), nitrogen oxides (NO x ) and particulate matter with an aerodynamic diameter of 10 μm or less (PM 10 ) during the 2008 Olympics were reduced by 55.5%, 56.8%, 45.7% and 51.6%, respectively, as compared to the grid-based emission inventory before the Olympics. Emission intensity was derived from curbside air quality monitoring at the North 4th Ring Road site, located about 7 km from the National Stadium. Comparison between the emission intensity before and during the 2008 Olympics shows a reduction of 44.5% and 49.0% in daily CO and NO x emission from motor vehicles. The results suggest that reasonable traffic system improvement strategies along with vehicle technology improvements can contribute to controlling total motor vehicle emissions in Beijing after the Olympic Games.

The aggressive traffic interventions and emission control measures implemented to improve air quality during the 2008 Beijing Olympic Games created a valuable case study to evaluate the effectiveness of these measures on mitigating air pollution and protecting public health. In this paper, we report the results from our field campaign in summer 2008 on the on-road emission factors of carbon monoxide, black carbon (BC) and ultrafine particles (UFP) as well as the ambient BC concentrations. The fleet average emission factors for light-duty gasoline vehicles (LDGV) showed considerable reduction in the Olympic year (2008) compared to the pre-Olympic year (2007). Our measurement of Black Carbon (BC), a primary pollutant, at different elevations at the ambient site suggests consistent decrease in BC concentrations as the height increased near the ground level, which indicates that the nearby ground level sources, probably dominated by traffic, contributed to a large portion of BC concentrations in the lower atmospheric layer in Beijing during summertime. These observations indicate that people living in near ground levels experience higher exposures than those living in higher floors in Beijing. The BC diurnal patterns on days when traffic control were in place during the Olympic Games were compared to those on non-traffic-control days in both 2007 and in 2008. These patterns strongly suggest that diesel trucks are a major source of summertime BC in Beijing. The median BC concentration on Olympic days was 3.7 μg m−3, which was dramatically lower than the value on non-traffic-control days, indicating the effectiveness of traffic control regulations in BC reduction in Beijing.

In this paper, we report the results and analysis of a recent field campaign in August 2007 investigating the impacts of emissions from transportation on air quality and community concentrations in Beijing, China. We conducted measurements in three different environments, on-road, roadside and ambient. The carbon monoxide, black carbon and ultrafine particle number emission factors for on-road light-duty vehicles are derived to be 95 g kg−1-fuel, 0.3 g kg−1-fuel and 1.8 × 1015 particles kg−1-fuel, respectively. The emission factors for on-road heavy-duty vehicles are 50 g kg−1-fuel, 1.3 g kg−1-fuel and 1.1 × 1016 particles kg−1-fuel, respectively. The carbon monoxide emission factors from this study agree with those derived from remote sensing and on-board vehicle emission testing systems in China. The on-road black carbon and particle number emission factors for Chinese vehicles are reported for the first time in the literature. Strong traffic impacts can be observed from the concentrations measured in these different environments. Most clear is a reflection of diesel truck traffic activity in black carbon concentrations. The comparison of the particle size distributions measured at the three environments suggests that the traffic is a major source of ultrafine particles. A four-day traffic control experiment conducted by the Beijing Government as a pilot to test the effectiveness of proposed controls was found to be effective in reducing extreme concentrations that occurred at both on-road and ambient environments.

This paper reports altitude-resolved concentrations of sulfur dioxide (SO2) and particulate matter up to 10 microns in diameter (PM10) in the planetary boundary layer of major urban centers during extreme pollution episodes. The concentration of SO2 was observed continuously from November 24, 2004, to December 4, 2004, in Beijing during the heating period. Fluorescence SO2 analyzers were used to measure the atmospheric SO2 concentrations. Four SO2 analyzers were placed at 4 different levels (8 m, 47 m, 120 m, and 280 m) of the 325-m high meteorological observation tower of the Institute of Atmospheric Physics (IAP). Chinese Academy of Sciences. A maximal SO2 concentration of 172.3 ppb was measured during this pollution episode, and SO2 concentration increased with altitude and reached its maximal value at similar to 50 m. The study also analyzed the meteorological situation before, during, and after the pollution episode. (C) 2008 Elsevier Ltd. All rights reserved.

This paper presents a novel method for quantification of the total emission of gases from area sources using the mobile mini-DOAS (Differential Optical Absorption Spectroscopy) instrument, a ground based optical remote sensing technique. The presented method has been applied to measure the emission of SO2 and NO2 from the city of Beijing, P.R. China, on a time scale of approximately 2 h. The measurements have been performed during two field campaigns, in April and in August 2005. The estimated emission of NO2 is roughly the same during the two field campaigns with an average emission of 189 tons d 1 during April and 174 tons d 1 during August. The estimated emission of SO2 varies greatly between the two periods, with an average emission of 293 tons d 1 during April and 52 tons d 1 during August.

An advanced modeling system with a “one-atmosphere” perspective, Models-3/Community Multi-scale Air Quality (CMAQ) modeling system, driven by MM5/NCEP reanalysis data as the meteorology, and GEOS-Chem outputs as boundary values was applied to simulate the O3, and other gaseous pollutants (SO2 and NO2) evolution among other atmospheric chemicals for July 2001. Comparisons had been made with other models in the MICS-II exercise for the same period. Statistics of both monthly and daily means show that the model skill is very good in reproducing O3 and SO2 with small to moderate RMSE. The model species capture the day-to-day and spatial variability of the observations. The same O3 model concentrations that overpredict most of the EANET observations in the MICS-II study may have underpredicted ones from monitoring networks in Beijing area that is not included in this paper. Vertical O3 profiles at 4 ozonesonde sites are well predicted in July 2001. In fact, our model is among the best of those MICS-II models within the 2-km surface layer. The meteorology near surface and lower troposphere is well reproduced. Compared to SO2 and O3, the NO2 gas concentrations are simulated less well, but the correlation coefficient is still significant. The choice of reanalysis meteorological fields and different boundary conditions generated by different global models may result in diverse spatial patterns exhibited by MICS-II models and ours. Our spatial distributions of O3 shows a high concentration patch covering Beijing, a moderate to high pattern across Korea and Japan Sea, and a low but extensive pattern enclosing southern China, Taiwan, and East Sea. Extension of the pattern to southern China coincides with the existence of pollution problems in Guangdong and Taiwan, but overprediction of O3 over the region deserves further improvement by various factors. One of them can be the grid resolution to resolve the complex orography in or close to the ocean. Another factor can be the refinement of local land use data that changes the micro-meteorology in favor of more air pollution events.

In this paper, an integrated MM5–CMAQ modeling approach was employed to investigate the PM 10 air pollution issue in Beijing, China, with a focus on assessing pollution contributions from surrounding provinces. A 2-level-nested grid domain with spatial resolutions of 36 and 12 km was designed for the study region. Seven monitoring stations across Beijing municipality were selected to provide hourly PM 10 measurement data. The months of January, April, July and October in 2002 were taken as target periods for model performance evaluation. Five emission scenarios were designed and run in order to quantitatively assess the trans-boundary PM 10 contributions. The results show that, while Beijing needs to take positive steps to reduce its own pollution emissions, much effort should also be placed on demanding more pollution reduction and better environmental performance from surrounding provinces.

Beijing, the capital of China, is a rapidly developing mega-city with a huge population in the world. Beijing has been experiencing severe air pollution problems since 1980s due to more and more energy consumption. In the case of severe organic aerosol pollution in Beijing, a one-year-long observation on major organic compounds in PM 2.5 was performed at an urban site to gain information of their ambient concentration levels and sources in Beijing. The compound classes identified by GC/MS included alkanes, PAHs, fatty acids, dicarboxylic acids, and hopanes, whose seasonal average concentrations during the year were 39–477, 9–283, 260–483, 16–42, and 2.3–16 ng m −3 , respectively. Since the molecular distributions of particulate organic matter are indicative of different emission sources, their seasonal characteristics and variations were discussed in detail to explore the major contributing sources in Beijing. Conclusively, organic aerosol pollution in Beijing was found to be mostly associated with vehicle exhaust, coal combustion for residential heating, and cooking emissions, and the latter two sources are unique Chinese pollution sources. The dataset in this paper provides useful information for establishing effective control measures to reduce organic aerosol pollution in Beijing.

A method was developed in this paper to estimate the spatial and temporal distribution of vehicle emissions and concentrations in Beijing based on the limited database. The vehicle sources were treated as both line sources and area sources to make full use of the available data. For line sources, substantial counting of vehicles on site had been conducted to determine the traffic flow and vehicle type mixture; for area sources, the total non-line-source vehicle-traveled mileage was distributed to different grids based on population density and road area in the grids. A mathematical dispersion model, Industrial Sources Complex Short Term Version 3 (ISCST3), was used to simulate the pollutant concentration distribution in urban areas based on the meteorological data and the emissions distribution mentioned above. The verification data show that method is accurate in the Beijing urban area, and the results show that the vehicle emissions in Beijing contributed significantly to the simulated pollutant impact, and the polluted areas correlate with population.

This paper explores the adaptation of a regional Lagrangian approach for making long-term simulations of SO2 and sulfate ambient concentrations at the resolution needed for health effects risk assessment in Asian megacities and their surroundings. A Lagrangian trajectory model (UR-BAT) is described which simulates transport and diffusion of sulfur within and near urban areas, originating from area and major point sources. The long-range contribution is accounted for by the ATMOS model, simulating all Asian sources. The model has been applied to Beijing and Bombay, by using preliminary emission figures, and the results have been compared with available monitoring data. The computed concentrations in different cities are in the correct range, indicating the potential use of the model in an integrated assessment framework such as RAINS-Asia.