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Air quality has a significant impact on human health, in order to alleviate the air pollution and improve the ability to predict the air quality. In this paper, a prediction model of air quality index composed of variational mode decomposition and temporal convolutional network was proposed. First, in order to reduce the non-stationarity and randomness of the time series, the original air quality index sequence was decomposed by variational mode decomposition, and the decomposition number was determined by the central frequency method to decompose into multiple relatively stable sub-sequences with different frequency scales. Then, the decomposed sub-stable sequence was predicted by the time convolutional network. Finally, the prediction data were integrated and reconstructed to obtain the final prediction results. Comparing the results of other forecasting models by performance evaluation metrics, the combined forecasting model proposed in this paper reduced RMSE by 20.9%, 19.2%, 5.1%, 29.9%, 23.7% on the Beijing dataset. MAPE reduced by 26.6%, 22.3%, 19.5%, 28.9%, 15.0%, respectively. MAE decreased by 19.1%, 20.6%, 9.6%, 29.5%, 23.5%. R2 increased by 4.6%, 4.0%, 0.8%, 14.9%, 5.5% respectively. This proves the accuracy and reliability of the proposed model. [ABSTRACT FROM AUTHOR]

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This paper examines the effects of interventions to reduce air pollution during two international events on air quality in Beijing and its neighbor cities. Air quality data were gathered from China's Ministry of Environmental Protection, meteorological data from the China Meteorological Administration and economic data from the China Statistical Yearbook. The paper uses fixed-effect panel data models to empirically evaluate air quality improvement in Beijing and other affected cities before, during, and after the 2008 Olympic Games and the 2014 Asia-Pacific Economic Cooperation summit. Results show substantial improvement in air quality in Beijing and neighboring cities during the two events. However, some of the air quality improvement achieved reverted within a year after the games and within a week after the summit. Furthermore, the improvement achieved during the summit completely reverted and air quality deteriorated severely five days after the summit. It is also found that air quality in China, at least in the cities included in this study, gradually improved over the past 15 years or so. The findings suggest that sustainable interventions and incentive-based programs to reduce emissions from industry production and traffic are the key to maintaining the air pollution reduction achieved during the events. [ABSTRACT FROM AUTHOR]

This paper checks the asymmetrical impact of Beijing's and Shanghai's air quality (AQ) on cross-industries stock returns (SR) by using the quantile-on-quantile (QQ) regression method. The major empirical findings as shown as followings. There are heterogeneous responses from SR to AQ within the same city. Different links are discovered for Beijing and Shanghai within the same industry. Air pollution does not have political or economic properties for all industries. Our research provides useful contributions compared with past literature. First of all, we distinguish whether air pollution is political or economic. Apart from psychology and physiology, government intervention and economic expectation are also important components in interpreting the influence from AQ to SR. Second, this study adequately considers the heterogeneity of industries. Industries differently react to the identical extrinsic shock, depending on the nature of their industry. Besides, the QQ approach captures quantile-varying relationship between variables, and does not need to consider structural fracture and time lag effects. The practical significance is that investors need to focus on national industrial policies, and avoiding biased decisions in stock market from air pollution. [ABSTRACT FROM AUTHOR]

High-resolution urban surface information, e.g., the fraction of impervious/pervious surface, is pivotal in studies of local thermal/wind environments and air pollution. In this study, we introduced and validated a domain adaptive land cover classification model, to automatically classify Google Earth images into pixel-based land cover maps. By combining domain adaptation (DA) and semi-supervised learning (SSL) techniques, our model demonstrates its effectiveness even when trained with a limited dataset derived from Gaofen2 (GF2) satellite images. The model's overall accuracy on the translated GF2 dataset improved significantly from 19.5% to 75.2%, and on the Google Earth image dataset from 23.1% to 61.5%. The overall accuracy is 2.9% and 3.4% higher than when using only DA. Furthermore, with this model, we derived land cover maps and investigated the impact of land surface composition on the local meteorological parameters and air pollutant concentrations in the three most developed urban agglomerations in China, i.e., Beijing, Shanghai and the Great Bay Area (GBA). Our correlation analysis reveals that air temperature exhibits a strong positive correlation with neighboring artificial impervious surfaces, with Pearson correlation coefficients higher than 0.6 in all areas except during the spring in the GBA. However, the correlation between air pollutants and land surface composition is notably weaker and more variable. The primary contribution of this paper is to provide an efficient method for urban land cover extraction which will be of great value for assessing the urban surface composition, quantifying the impact of land use/land cover, and facilitating the development of informed policies. [ABSTRACT FROM AUTHOR]

To study urban air quality, this paper proposes a novel categorical time series model, which is based on a linear combination of bounded Poisson distribution and discrete distribution to describe the dynamic and systemic features of air quality, respectively. Daily air quality level data of three major cities in China, including Beijing, Shanghai and Guangzhou, are analyzed. It is concluded that the air quality in Beijing is the worst among the three cities but is gradually improving, and its dynamics is also the most pronounced. Theoretically, the design of our model increases the flexibility of the probabilistic structure while ensuring a dynamic feedback mechanism without high computational stress. We estimate the parameters through an adaptive Bayesian Markov chain Monte Carlo sampling scheme and show the satisfactory finite sample performance of the model through simulation studies. [ABSTRACT FROM AUTHOR]

Microorganisms are an important part of atmospheric particulate matter and are closely related to human health. In this paper, the variations in the characteristics of the chemical components and bacterial communities in PM10 and PM2.5 grouped according to season, pollution degree, particle size, and winter heating stage were studied. The influence of environmental factors on community structure was also analyzed. The results showed that seasonal variations were significant. NO3− contributed the most to the formation of particulate matter in spring and winter, while SO42− contributed the most in summer and autumn. The community structures in summer and autumn were similar, while the community structure in spring was significantly different. The dominant phyla were similar among seasons, but their proportions were different. The dominant genera were no-rank_c_Cyanobacteria, Acidovorax, Escherichia-Shigella and Sphingomonas in spring; Massilia, Bacillus, Acinetobacter, Rhodococcus, and Brevibacillus in summer and autumn; and Rhodococcus in winter. The atmospheric microorganisms in Beijing mainly came from soil, water, and plants. The few pathogens detected were mainly affected by the microbial source on the sampling day, regardless of pollution level. RDA (redundancy analysis) showed that the bacterial community was positively correlated with the concentration of particulate matter and that the wind speed in spring was positively correlated with NO3− levels, NH4+ levels, temperature, and relative humidity in summer and autumn, but there was no clear consistency among winter samples. This study comprehensively analyzed the variations in the characteristics of the airborne bacterial community in Beijing over one year and provided a reference for understanding the source, mechanism, and assessment of the health effects of different air qualities. [ABSTRACT FROM AUTHOR]

Clean energy transition has been considered as an indispensable way to attain sustainable development for China, where the coal-to-gas initiative plays a vital role towards the goal. This paper takes Beijing, China's political and economic center as well as a national pioneer in the energy transition, as a case to systematically analyze the co-mitigation of air pollution (PM2.5) and carbon emissions (CO2) achieved by the policy-driven natural gas-coal consumption substitution. Firstly, a qualitative analysis of the relationship of Beijing's coal-to-gas policies and its air quality has been conducted. Then, VAR and ARDL models are employed to quantitatively analyze the impacts of coal-to-gas policies on PM2.5 and CO2, respectively. Results show that (i) an innovation of natural gas/coal consumption ratio will reduce PM2.5 concentrations, and the effect decreases over time; and (ii) an increase of 1% in natural gas/coal consumption ratio in Beijing will cause a decrease of 0.0784% in CO2 emissions in the long run. Therefore, the coal-to-gas policies do increase the usage of natural gas and improve Beijing's air quality. The assessment methods and conclusions can be regarded as a reference for not only China's policymakers, but also other countries, especially nowadays when air quality is becoming more valued and GHGs are being tightly controlled. [ABSTRACT FROM AUTHOR]

The effectiveness of national policies for air pollution control has been demonstrated, but the relative effectiveness of short-term emission reduction measures in comparison with national policies has not. Here we show that short-term abatement measures during important international events substantially reduced PM2.5 concentrations, but air quality rebounded to pre-event levels after the measures ceased. Long-term adherence to strict emission reduction policies led to successful decreases of 54% in PM2.5 concentrations in Beijing, and 23% in atmospheric nitrogen deposition in China from 2012 to 2020. Incentivized by "blue skies" type campaigns, economic development and reactive nitrogen pollution are quickly decoupled, showing that a combination of inspiring but aggressive short-term measures and effective but durable long-term policies delivers sustainable air quality improvement. However, increased ammonia concentrations, transboundary pollutant flows, and the complexity to achieving reduction targets under climate change scenarios, underscore the need for the synergistic control of multiple pollutants and inter-regional action. In response to the rebound of air pollution during large events in China, the authors propose that a combination of ambitious short-term measures and enduring long-term pollution control policies is a sustainable solution for improving air quality. [ABSTRACT FROM AUTHOR]

Air pollution is a major environmental urban issue, particularly in fast-growing cities in developing countries. Reducing air pollution is thus a challenge while evaluating the economic value of air quality is crucial for environmental policies made. However, few studies accurately estimate this value as they neglect the possible endogeneity issues, as well as the dynamic and heterogeneous effects of air pollution. Under the hedonic framework, we therefore assess the economic effect of fine particulate matter (PM2.5) on housing prices in Beijing, China. We construct a panel based on resale apartment transactions matched with average quarterly PM2.5 data between 2013 and 2019. To reduce the risk of an estimation bias, we apply an instrumental variable (IV) approach. Our results show that PM2.5 is negatively associated with housing prices. Households were willing to pay an extra 0.0852% per housing unit price for an average quarterly reduction in PM2.5 of 1 µg/m3. Furthermore, we argue that high-income dwellers tend to pay more for clean air. The negative effects of PM2.5 across regions are significant and different. Compared with that in the basic year 2013, the negative effect increases in the first 3 years and then decreases in the last 3 years. Our findings enhance our comprehension of the economic impact of air quality and make a valuable contribution to the nuanced understanding of willingness to pay for air quality, which is beneficial in assessing and optimizing environmental regulations. [ABSTRACT FROM AUTHOR]

The aim of the paper is to estimate and assess residents' willingness to pay to improve air quality in the urban area of Beijing using the Contingent Valuation Method (CVM). The mean willingness to pay (WTP) for a 50% reduction of harmful substances in the air was 143 CNY per household per year, and the total WTP of the residents in the study area was 336 million CNY per year. As a proportion of household annual income, the mean WTP amounted to 0.7%. Four socio-economic variables were found to have significant impacts on residents' willingness to pay. The analysis results illustrated that WTP increases with income and education level, and decreases with household population and age. The willingness was larger for residents in the urban districts than those in the suburban districts. It was found that most of the protest bids lay on interviewees' incomplete perception of the values of environmental amenity. The influence of household income on people's willingness to pay illustrated in this study reflects a causal force of Beijing's transition to market economy that households created to have disposable income are more likely to care about environmental quality. The results of this study strike an optimistic note on the possibility of measuring the total economic value of environmental quality improvement by using the CVM in China. Suggestions on implication of the CVM for both academics and policy makers are provided in the paper. [ABSTRACT FROM AUTHOR]

Understanding the impact of temperature fluctuations on air quality and public health has gained popularity among environmental and epidemiological researchers. Potentially, increase and decrease in temperature between neighboring days have increased the environmental and health risk worldwide. Based on ordinary least-squares method, this paper aims to examine the impact of temperature fluctuations on air quality index (AQI) and respiratory health outcomes (RHOs) during 2008-2012 in Beijing. Our results show that a drop of more than 3 °C results in the increased impact on AQI and RHO in the heating period. At the same time, a raise of more than 3 °C results in the similar increased impact on AQI in the whole study period and heating period. Furthermore, for a raise of more than 3 °C, a larger impact on RHO is observed in the heating period compared with the whole study period. Additionally, an increase in temperature also results in the increased influence of health risk on females during the heating period. Our results suggest that the air quality and public health in Beijing are significantly influenced by decrease and increase in temperature in the heating period. [ABSTRACT FROM AUTHOR]

An innovative approach of mean emission by vehicle type was used in this paper to assess the impact of new vehicle emission standards in Beijing, China during the period of 2000-2005. It was found that CO and NO emissions decreased by 48% and 23%, respectively, from Type O (before 2000) to Type I (year 2000) vehicles. The reductions from Type O to Type II (year 2002) vehicles were 85% and 73% for CO and NO, respectively. When all three types of vehicles (Types O, I and II) are combined, the annual per vehicle CO emissions decreased from 586 kg per vehicle per year in 2000 to 324 kg per vehicle per year in 2005, while that of NO decreased from 66.9 to 43.4 kg per vehicle per year, which was mainly resulted from the impact of stringent new vehicle emission standards implemented in years 2000 and 2002. However, the vehicle population increased by 70% during the same time period, which offset the impact of cleaner vehicles. Thus, the total vehicle emission decreased little for CO (885,000 tons in 2000, 837,000 tons in 2005) and even increased slightly for NO (101,000 and 112,000 tons in 2000 and 2005, respectively). The ambient concentrations of CO decreased significantly throughout 2000-2005, the same trend was not observed for NO. Correlation analysis (grey correlation and Pearson correlation) between the annual vehicle emissions and annual concentrations of CO, the annual NO emission and annual NO concentration indicated that the implementation of new vehicle emission standards was associated with the abatement of ambient CO and NO concentrations in Beijing. [ABSTRACT FROM AUTHOR]

This paper reports the use of LIDAR technique, vertical wind profiler (VWP) and the coupled MM5-CMAQ air quality modeling system to investigate a high PM10 concentration episode occurred in Beijing, China during January 8–9, 2004. Through the regression analysis between the observed PM10 concentrations and the observed extinction coefficients, the converting formulas from the LIDAR records to the PM10 mass concentrations in Beijing have been found. Further, a 2-level-nested grid domain with spatial resolutions of 36 and 12 km have been designed and employed for this study, and the coupled MM5-CMAQ modeling system has then been evaluated using both the ground-level PM10 observations and the vertical profiles of PM10 deduced from the measured LIDAR extinction coefficients. Based on the verified modeling system, two emission scenarios were designed to quantitatively assess the trans-boundary PM10 contributions from the surrounding provinces of Beijing. The results illustrated that the particulate matter buildup over the Beijing region was due to a number of factors, including pollutants brought in from its surrounding provinces by the southwest winds and the emission from local sources within Beijing. It indicates 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 its surrounding provinces. [ABSTRACT FROM AUTHOR]

With the industrialization of society, air pollution has become a critical environmental issue, leading to excessive morbidity and mortality from cardiovascular and respiratory diseases in humans. Accurate air pollution prediction has strongly promoted air quality control, which is important for human health. However, previous studies have failed to model spatiotemporal dependencies simultaneously with non-Euclidean distributions considering meteorological factors. In this study, a novel multigraph convolutional neural network for air pollution prediction is proposed. First, a spatial graph, an air pollution pattern graph and a meteorological pattern graph are constructed to model different relationships among non-Euclidean areas. Second, the graph convolutional network is applied to learn and incorporate the information of neighbour nodes of the corresponding graph, and then the graphs after convolution are fused. Finally, the fused matrix of GCNs is input into the gate recurrent units to capture temporal dependencies. Experimental results on the real dataset collected at air quality monitoring stations in Beijing validate the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

To investigate the medium- and long-term effects of a driving-restriction policy under the guidance of carbon emission peak goals, Beijing was taken as an example to construct a private-car energy-conservation and emission-reduction model with dual-driving factors from the perspective of the economy, environment, technology, and energy. Through causal analysis, the negative effects of a driving-restriction policy were revealed. The simulation results revealed the following. (1) A driving-restriction policy is effective in the short term, but in the long run, it cannot effectively reduce CO2 emissions and the quantity of fuel used in private cars (e.g., the increase in the amount of CO2 pollution and degree of parking space tension, as well as the rebound of air pollution). (2) Compared with the driving-restriction policy, the combined policy reduces the amount of CO2 pollution by approximately 52.45%, the PM2.5 concentration by 21.98%, and the quantity of fuel in private cars by 33.42% by 2035, while improving the air quality by approximately 144.79%. Therefore, this article provides a theoretical basis for optimizing driving-restriction policies and achieving the carbon emission peak target. [ABSTRACT FROM AUTHOR]

In this study, the characteristics of turbulence transport and intermittency and the evolutionary mechanisms were studied in different pollution stages of heavy haze weather from December 2016 to January 2017 in the Beijing area using the method developed by Ren et al. (2019) as the automatic identification of atmospheric spectral gaps and the reconstruction of atmospheric turbulence sequences. The results reveal that turbulence intermittency is the strongest in the cumulative stage (CS) of heavy haze weather, followed by in the transport stage (TS), and it is the weakest in the dissipation stage (DS). During the development and accumulation of haze pollution, buoyancy contributes negatively to turbulent kinetic energy (TKE), and horizontal wind speed is low. The classical turbulent motion is often affected by submesoscale motion. As a result, the calculation results of turbulence parameters are affected by submesoscale motion, which causes intensified turbulence intermittency. During the dissipation of pollution, the downward momentum transfer induced by low-level jets provides kinetic energy for turbulent motion in the near surface layer. The turbulent mixing effect is enhanced, and intermittency is weakened. Due to the intermittency of atmospheric turbulence, turbulence parameters calculated from the original fluctuation of meteorological elements may be overestimated. The overestimation of turbulence parameters in the CS is the strongest, followed by the TS, and the DS is the weakest. The overestimation of turbulent fluxes results in an overestimation of atmospheric dissipation capability that may cause an underestimation of pollutant concentrations in the numerical simulations of air quality. [ABSTRACT FROM AUTHOR]

Accumulation of airborne particulate matter (PM) has profoundly affected the atmospheric environment of Beijing, China. Although studies on health risks have increased, characterization of specific factors that contribute to increased health risks remains an area of needed exploration. Chemical composition studies on PM can readily be found in the literature but researches on biological composition are still limited. In this study, the concentration and size distribution of viable airborne bacteria and fungi were determined in the atmosphere from May to July 2013 in Beijing, China. Samples were collected during non-haze days and haze days based on the value of air quality index (AQI) PM2.5. Multiple linear regression results indicated that concentrations of viable bioaerosol exhibited a negative correlation with PM2.5 (AQI) ranging from 14 to 452. There was a little difference in size distribution of bioaerosol between non-haze and haze days that all airborne bacteria showed skewed trends toward larger sizes and airborne fungi followed a Gaussian distribution. Spearman's correlation analysis showed that a fraction of bioaerosol with fine and coarse particles had negative and positive relations with PM2.5 (AQI), respectively. Moreover, the temporal variation of d (aerodynamic diameter) of bioaerosol with PM2.5 (AQI) fluctuated from 9:00 to 21:00, which suggested that their deposition pattern would vary during a day. The primary research in this study implied that aerodynamic size variation should be considered in assessing the bioaerosol exposure during haze weather. [ABSTRACT FROM AUTHOR]

A coupled MM5-CAMx air quality modeling system was used to simulate SO concentrations in Beijing, China during the heating season. Particulate matter source apportionment technology was employed to investigate the apportionment of SO sources in the study area. Comprehensive analysis of the industry and region revealed that the most important SO contributors were publicly and privately supplied heating emission sources and other industry emission sources from the urban areas of Beijing, with 66.1 % of the emission source contribution ratio. Four SO emission reduction scenarios based on our SO source apportionment research were established to assess the potential for improving the SO air quality in Beijing during the heating season. By weighing the desired SO improvement, the availability of technology, and economic considerations, a suitable SO reduction plan was able to be recommended for Beijing city. [ABSTRACT FROM AUTHOR]

The alternative transportation strategy implemented during the 2008 Summer Olympic Games in Beijing provided an opportunity to study the impact of the control measures and weather conditions on air quality and asthma morbidity. An ecological study compared the 41 days of the Olympic Games (8 August-17 September 2008) to a baseline period (1-30 June). Also, in order to emphasize the impact of weather conditions on air quality, a pollution linking meteorological index (Plam) was introduced to represent the air pollution meteorological condition. Our study showed that the average number of outpatient visits for asthma was 12.5 per day at baseline and 7.3 per day during the Olympics-a 41.6% overall decrease. Compared with the baseline, the Games were associated with a significant reduction in asthma visits (RR 0.58, 95%CI: 0.52-0.65). At 16.5 visits per day, asthma visits were also significantly higher, during the pre-Olympic period (RR 1.32, 95% CI: 1.15-1.52). The study also showed that the RR of asthma events on a given day, as well as the average daily peak ozone concentration during the preceding 48-72 h, increased at cumulative ozone concentrations of 70 to 100 ppb and 100 ppb or more compared with ozone concentrations of less than 70 ppb ( P < 0.05). We concluded that along with 'good' weather conditions, efforts to reduce traffic congestion in Beijing during the Olympic Games were associated with a prolonged reduction in air pollution and significantly lower rates of adult asthma events. These data provide support for efforts to reduce air pollution and improve health via reductions in motor vehicle traffic. [ABSTRACT FROM AUTHOR]

One-minute PM2.5 concentration was obtained with LD-5C pocket microcomputer laser dust instrument from Dec. 15th, 2005 to Jan. 16th, 2006 and Mar. 17th to Apr. 28th, 2006 in Beijing. The concentration of SO2, NO2, O3, CO, and PM10 from Jan. 1st, 2001 to Dec. 31st, 2004 were obtained from the conversion of air pollution index. Results showed that all the pollutants showed cyclic characteristics. The longer yearly cycles was shown from SO2, NO2, O3, CO, and PM10, as the sampling time was 4-year long and daily collected. The shorter hourly and daily cycle was shown from 1-min PM2.5, as the sampling time was about 1-month long and one collected at 1 min. The spectral density analysis confirmed this from the periodogram graphs. The longer yearly cycle (365, 180 days), the seasonal cycle (120, 60–90 days), and monthly cycle (21, 23, 27 days) of SO2, NO2, CO, O3, and PM10 were obviously shown. In addition, the shorter weekly cycle of 5–7 days is obviously shown, too. The shorter hourly cycle (8–12, 4–6, 3, 1–2 h, 20 min) of 1-min PM2.5 was also indicated from spectral density analysis. Two major factors contribute the 1-min PM2.5 cycles, i.e., the meteorological factors and source effects. Both the relative humidity and dew point showed consistent variation with PM2.5, but the wind speed showed inverse variations with PM2.5. Furthermore, the spectral density analysis of the meteorological factors (4–5, 2–2.5, 1–1.5 days, 12, 6–8, 3 h) may partially explain the cycles of PM2.5. As for the sources effects, it can be shown from the strong dust storm of April 16–18th, 2006. PM2.5 constantly increased tens and even hundreds of times high concentration within a few minutes due to the intensity of the dust sources. [ABSTRACT FROM AUTHOR]

The concentration of gaseous elemental mercury (GEM) in the atmosphere of urban Beijing was measured from October 2003 to September 2004 to investigate the origins and spatial-temporal variations of atmospheric Hg. The mean value of Hg concentration is 17.1 ng⋅m-3 (n=653). The atmospheric Hg data showed spatial-temporal variations throughout the duration of our observation. The maximum GEM concentration (53.7 ng⋅m-3) was observed at Gucheng in the western area of urban Beijing. The GEM concentrations increased from the north to the south of the studied area, and were higher in winter than in summer. The highest and lowest monthly averages of GEM concentrations were measured to be 23.3 and 4.1 ng⋅m-3 in January and July, respectively. In addition, GEM concentrations are higher in the daytime than at night in Autumn and from 14 March to 15 April, but daily GEM variation showed an inverse pattern from 22 April to 22 May. In winter, two peak values of GEM concentrations occurred at 13:30 and 21:30. Daily variation of GEM concentrations in summer was the lowest in the four seasons. Mercury from coal combustion was estimated to be the main source of anthropogenic emissions in Beijing from October 2003 to September 2004. Additionally, Hg emission from natural gas burning was estimated to be another dominant source of atmospheric Hg in Beijing. [ABSTRACT FROM AUTHOR]

By utilizing the air quality monitoring data and the NCEP reanalysis data, the relationship between the PM1O air pollution episode and synoptic situation is analyzed in the Beijing area. It is interesting to find that PM1O air pollution episode in and around the Beijing area is correlated with the Western Pacific tropical cyclone track to some extent, namely when a tropical cyclone lands southward to the Changjiang River, PM10 air pollution episode is not easy to take place generally in the Beijing area; but when a tropical cyclone moves northward and finally lands at the Korea Peninsula or the Japanese mainland, and under this condition the Beijing area is generally controlled by weak high or weak low for several days, PM10 air pollution episode often takes place in one day or several days. Above findings indicate that predicting the Western Pacific tropical cyclone track can foretell whether or not PM10 air pollution episode takes place in the Beijing area, which can offer a technique for urban air quality prediction and air pollution source management in the Beijing area. [ABSTRACT FROM AUTHOR]

In China, “green” integrated waste management methods are being implemented in response to environmental concerns. We measured the air quality at several municipal solid waste (MSW) sites to provide information for the incorporation of logistics facilities within the current integrated waste management system. We monitored ambient air quality at eight MSW collecting stations, five transfer stations, one composting plant, and five disposal sites in Beijing during April 2006. Composite air samples were collected and analyzed for levels of odor, ammonia (NH3), hydrogen sulfide (H2S), total suspended particles (TSPs), carbon monoxide (CO), sulfur dioxide (SO2), and nitrogen dioxide (NO2). The results of our atmospheric monitoring demonstrated that although CO and SO2 were within acceptable emission levels according to ambient standards, levels of H2S, TSP, and NO2 in the ambient air at most MSW logistics facilities far exceeded ambient limits established for China. The primary pollutants in the ambient air at Beijing MSW logistics facilities were H2S, TSPs, NO2, and odor. To improve current environmental conditions at MSW logistics facilities, the Chinese government encourages the separation of biogenic waste from MSW at the source. [ABSTRACT FROM AUTHOR]

A coupled ARPS–CMAQ modeling system was applied to investigate the atmospheric assimilative capacity (AAC) of PM10 in the Beijing metropolitan region of China. The AAC was defined as the maximum allowable pollutant emission that can be discharged to the atmosphere without violating the desired air quality objective in the planning region. The coupled modeling system was firstly evaluated through comparing the simulation results of PM10 concentration with the corresponding observations during four representative months in 2002, which showed an acceptable modeling performance. By using a trial-and-error approach, the validated modeling system was run through gradually reducing the emission strength within the modeling domain from a GIS-based pollutant emission database until a desired air quality objective was achieved. The air quality objective was characterized by the air quality guideline-satisfaction ratio (AQGSR) which was defined as the ratio of days when the daily PM10 concentrations are below the air quality standard during a certain time period. The PM10 emission within the modeling domain after such trial-and-error reduction under the condition of satisfying a certain AQGSR was considered its corresponding assimilative capacity. Accordingly, the atmospheric assimilative capacities of PM10 in the study region were identified corresponding to the AQGSR of 60, 65, 70, 75 and 80%, respectively. The relation between such capacity and guideline-satisfaction ratio was then established through regression analysis. The results could provide sound basis for decision makers in terms of effective air quality management by understanding the allowable pollutant discharge under different desired air quality objectives. [ABSTRACT FROM AUTHOR]

Airborne particulate matter (PM2.5 and PM10) samples were collected at the Beijing Normal University sampling site in the urban area of Beijing, China in dry and wet seasons during 2001-2004. Concentrations of 23 elements and 14 ions in particulate samples were determined by ICP-AES and IC, respectively. Source apportionment results derived from both Positive Matrix Factorizatlon (PMF) and Chemical Mass Balance (CMB) models indicate that the major contributors of PM2.5 and PM10 in Beijing are: soil dust, fossil fuel combustion, vehicle exhausts, secondary particulate, biomass burning and some industrial sources. We have identified both regional common sources, such as vehicular emissions, particulate of secondary origin and biomass burning, as well as country-specific problems, such as sand storms and soil dust that should be addressed for effective air quality control. [ABSTRACT FROM AUTHOR]

On 20 March, 2002, a super dust storm attacked Beijing, which was stronger than any dust storm ever recorded. The concentration of total suspended particulates (TSP) reached 10.9 mg · m-3, 54 times as high as the national air quality standard. The concentrations of major crustal elements, such as Ca, Al, Fe, Na, Mg and Ti, were 30-58 times higher than those in non-dust storm days. The concentrations of pollution elements, such as Zn, Cu, Pb, As, Cd and S, were also about several or even nearly ten times higher than those in normal days. The enrichment factors of Pb, As, Cd and S in PM2.5 were as high as 12.7, 29.6, 43.5, 28.4, indicating that these pollutants came from the mixing of mineral aerosol with pollution aerosol emitted by pollution sources on the way of dust storm's long-range transport. The overlap of invaded air mass from dust with pollution air mass from Beijing local area was another reason for the enhancement of pollutants. During dust storm, fine particles (PM2.5) accounted for 30% of TSP and pollutants in PM2.5 accounted for even as high as 45%-69% of TSP. The increase of pollutants after dust storm proved further that mineral aerosol, especially the fine particles from dust storm favored the transformation and accumulation of pollutants. It must be noted that Fe (II) was detected again in this dust storm, which provided new evidence for the mechanism of coupling and feedback between iron and sulfur in the atmosphere and the ocean. The increase of both pollutants and nutrient, Fe(II), during dust storm illuminated that dust storm is an important factor affecting the global environment change. [ABSTRACT FROM AUTHOR]

Although the physical effects of air pollution on humans are well documented, there may be even greater impacts on the emotional state and health. Surveys have traditionally been used to explore the impact of air pollution on people's subjective well-being (SWB). However, the survey techniques usually take long periods to properly match the air pollution characteristics from monitoring stations to each respondent's SWB at both disaggregated spatial and temporal levels. Here, we used air pollution data to simulate fixed-scene images and psychophysical process to examine the impact from only air pollution on SWB. Findings suggest that under the atmospheric conditions in Beijing, negative emotions occur when PM2.5 (particulate matter with a diameter less than 2.5 µm) increases to approximately 150 AQI (air quality index). The British observers have a stronger negative response under severe air pollution compared with Chinese observers. People from different social groups appear to have different sensitivities to SWB when air quality index exceeds approximately 200 AQI. Air pollution can affect people's emotional status and well-being. Here, the authors simulate fixed-scene images to show that under the atmospheric conditions in Beijing, negative emotions occur when air quality index of PM2.5 increases to approximately 150. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented which discusses the clear blue skies in the Beijing, China area during the Asia-Pacific Economic Cooperation (APEC) summit in November 2014 which resulted from the enforcement of emissions-reduction measures.