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The PM 2.5 problem is proving to be a major public crisis and is of great public-concern requiring an urgent response. Information about, and prediction of PM 2.5 from the perspective of atmospheric dynamic theory is still limited due to the complexity of the formation and development of PM 2.5 . In this paper, we attempted to realize the relevance analysis and short-term prediction of PM 2.5 concentrations in Beijing, China, using multi-source data mining. A correlation analysis model of PM 2.5 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, NO 2 , SO 2 , PM 10 ) 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, NO 2 , PM 10 , and the daily number of microblog entries with key words ‘Beijing; Air pollution’ show high mathematical correlation with PM 2.5 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 PM 2.5 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 PM 2.5 and it also helps to broaden the application of big data and the multi-source data mining methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper aims to study the vehicular emissions trends in the Beijing–Tianjin–Hebei (BTH) region, located in northern China. The multiyear emission inventories of NO X , CO, VOC and PM10 from road vehicles in the period 1999–2010 were developed by the COPERT IV model. Results show that vehicular emissions of CO and VOC have decreased by annual average change rates (AACR) of −3.1% to −5.2% and −4.4% to −6.9% in the study area, respectively. However, due to the rapid development of freight traffic, emissions of NO X and PM10 have kept increasing in Tianjin and Hebei. Based on the vehicular emission inventories, trends of emission levels for vehicles with different standards, as well as the overall effects of implementing vehicular emission mitigation strategies were assessed. It is suggested that passenger cars (PC) with Euro 0 and Euro I standards, which were at higher emission level in the PC fleet, should be gradually eliminated. Although the increasing rates (IR) of emissions from PC were lower than those of the PC population, the sharp growth of PC population in recent years contributed to a remarkable increase of emissions, weakening the overall mitigation effect. Total vehicle population capacity and other mitigation measures should be studied in China in order to develop new and more effective vehicular emission control strategies. [Copyright &y& Elsevier]

Abstract: 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 (PM0.5) are derived from the measurements, which are consistent with the results from laboratory dynamometer tests. The PM0.5 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 PM0.5 number emissions, 60% PM0.5 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 PM0.5 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

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 (8m, 47m, 120m, and 280m) of the 325-m high meteorological observation tower of the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences. A maximal SO2 concentration of 172.3ppb was measured during this pollution episode, and SO2 concentration increased with altitude and reached its maximal value at ∼50m. The study also analyzed the meteorological situation before, during, and after the pollution episode. [Copyright &y& Elsevier]

In this paper, an integrated MM5–CMAQ modeling approach was employed to investigate the PM10 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 12km was designed for the study region. Seven monitoring stations across Beijing municipality were selected to provide hourly PM10 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 PM10 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. [Copyright &y& Elsevier]

Air pollution, such as PM 2.5 (particulate matter with an aerodynamic equivalent diameter of less than 2.5 μm), PM 10 (particulate matter with an aerodynamic equivalent diameter of less than 10 μm), NO x , and SO x , 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 PM 2.5 , PM 10 , O 3 , NO 2 , SO 2 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. • Social media were selected as a proxy for public perceptions for air quality. • LSTM model was proposed for air quality prediction. • Weibo data was suitable for air quality prediction during short-term social events. [ABSTRACT FROM AUTHOR]

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 PM 2.5 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 PM 2.5 concentration and the number of hospital ER visits for cardiovascular diseases. For each interquartile range (IQR) increase in PM 2.5 , 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 PM 2.5 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 PM 2.5 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. • Particulate matter pollution has worsened recently in Beijing, China. • Short-term impacts of PM 2.5 on different cardiovascular diseases were evaluated by a generalized additive model. • Obvious lag effects of PM 2.5 were observed for different subgroups. • Relative risks of cardiovascular diseases showed the shortest lag effect for the 60–75 yr subgroup. [ABSTRACT FROM AUTHOR]