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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]

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 CO 2 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 O 3 /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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Abstract: 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, NO x 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 NO x 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 NO x 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. [Copyright &y& Elsevier]

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: 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 < 90% in both regions, with a high correlation of 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 < 1.4. The obvious increase in SSA at 440 nm for 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 < 1.4, compared to Beijing, may be attributed to lower aerosol concentrations in all 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. [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: Air pollutant emission inventory is an important input parameter for chemical transport models (CTMs). Since great uncertainties exist in the emission inventory, further improvements and refinements are required. In this paper, genetic algorithm (GA), a global search and optimization method, was applied to optimize the emission inventory for the Models-3/Community Multiscale Air Quality (CMAQ) model. An emission optimizing system based on GA was developed and embedded to the CMAQ through the design of several core modules, which implemented the basic functions such as emission adjusting, GA population initializing, CMAQ results evaluating and GA operating. Hypothetical and real-data experiments were respectively performed to examine the validity of GA for emission calibrating. GA showed good performance in both experiments and was always able to find the global minimum. The emission optimizing system was then used to calibrate seasonal PM10 emission inventories of Beijing. Results revealed that PM10 emission in Beijing was underestimated in 2002, an average of 62.74% higher adjustment factor should be imposed on the original emission in target months of different seasons. With the calibrated emission inventories, CMAQ model errors were decreased by 6.46% on average in different seasons. It was concluded that GA was a promising search technique in calibrating emission inputs for CTMs. [ABSTRACT FROM AUTHOR]

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]

Abstract: 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 (NOx) and particulate matter with an aerodynamic diameter of 10 μm or less (PM10) 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 NOx 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

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 95gkg−1-fuel, 0.3gkg−1-fuel and 1.8×1015 particleskg−1-fuel, respectively. The emission factors for on-road heavy-duty vehicles are 50gkg−1-fuel, 1.3gkg−1-fuel and 1.1×1016 particleskg−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. [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]

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 2h. 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 189tonsd−1 during April and 174tonsd−1 during August. The estimated emission of SO2 varies greatly between the two periods, with an average emission of 293tonsd−1 during April and 52tonsd−1 during August. [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]

Abstract: 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 PM2.5 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 2.3–16ngm−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. [Copyright &y& Elsevier]

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. • A regional AHI aerosol retrieval algorithm over Beijing area is developed. • Our new AHI AOD product performs better than the JAXA AOD product. • The AHI assumed surface reflectance relationship performs well over Beijing area. • The single urban aerosol model is inappropriate over Beijing area. [ABSTRACT FROM AUTHOR]

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]

Precipitation chemistry and atmospheric nitrogen (N) deposition are of great concern worldwide due to their close relationships with air quality and impacts on ecosystems. However, evaluation of the chemical composition of precipitation and N deposition flux in rural areas of Beijing has received little attention to date. This paper presents the chemical constituents, possible sources and wet deposition fluxes of water-soluble ions (NH 4 +, NO 3 −, SO 4 2−, Cl−, Na+, K+, Ca2+, Mg2+) in precipitation samples collected during 2017–2018 at a rural site located at northwest of Beijing city; meanwhile, dry deposition of reactive N species (gaseous ammonia, nitrogen dioxide, nitric acid, and particulate (p) NH 4 + and NO 3 −) were also quantified. During this 2-year period, the volume-weighted mean (VWM) pH of precipitation was 6.73, and all samples had pH values above 6.0. The VWM electric conductivity of precipitation and the mean sum of all measured ions was 43.8 μS cm−1 and 591.9 μeq L−1, respectively, indicating a significant impact of atmospheric pollution. Ca2+ and NH 4 + were the dominant neutralizing species for precipitation acidity. Positive matrix factorization analysis further confirmed five sources for water-soluble ions, including sea salt aging, secondary formation, agriculture, crust, and biomass burning. The annual mean wet N deposition was 4.6, 3.4 and 8.0 kg N ha−1 yr−1 for NH 4 +, NO 3 − and total inorganic N, respectively. The total dry N deposition was dominated by gaseous ammonia (11.5 kg N ha−1 yr−1). The total N deposition (wet plus dry) was 27.7 kg N ha−1 yr−1, where dry deposition contributed to 75% and wet deposition 25% of the total. The simulations from the GEOS-Chem model indicate that agricultural (fertilizer use and livestock) and nonagricultural sources (industry, power plant, and transportation) are both important contributors to total N deposition. These results could be useful in evaluating/developing emission control policies to protect the eco-environment in Beijing. • Precipitation chemistry and nitrogen (N) deposition were investigated in Beijing. • The pH values of precipitation samples were all above 6.0 and averaged 6.7. • Ion source was sea salt, secondary source, agriculture, crust, and biomass burning. • Total N deposition was 27.7 kg N ha−1 yr−1, of which 75% was from dry deposition. • Agricultural and nonagricultural sources contributed equally to total N deposition. [ABSTRACT FROM AUTHOR]

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 PM 2.5 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 PM 2.5 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. • Model is applied to interval-valued PM 2.5 concentration forecasting. • BEMD and MLPI model the lower and upper bound of ITS simultaneously. • A new interval-valued reconstruction technique is proposed. • The proposed model takes into account influencing factors. • The enhanced model improves accuracy and stability. [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]