Prevention Of Atmospheric Pollution Research Articles

Vertical distributions of VOCs in the Tibetan Plateau background region

Exploring the vertical variation in volatile organic compounds (VOCs) in background regions can provide information on the spatial distribution of pollutants, providing a scientific basis for atmospheric pollution prevention and control strategies. From 15 August to 5 September 2023, at the Southeast Tibet Mountain Comprehensive Environmental Observation Station (SETS), a tethered balloon was used to sample VOCs every 100 m from the ground to 1000 m. A total of 403 air bag samples were collected, and 39 vertical profiles of VOCs were obtained. Ninety-two VOC species were detected. The VOC concentration at the SETS did not change significantly vertically, and the average VOC concentration was 11.1 ± 2.4 ppbv. The main components were alkanes (51.4 %), alkenes (18.7 %), and halohydrocarbons (18.1 %). There was no obvious diurnal change in VOCs and no significant difference between the different layers. When the surface VOC concentration was less than 10 ppbv, the concentrations, components, and sources of VOCs were evenly distributed vertically, and the main sources of VOCs at different heights were vehicle exhaust and background. When the surface VOC concentration exceeded 10 ppbv, the VOC concentration gradually decreased with height. The proportion of alkanes in surface VOCs increased, and the source was mainly vehicle exhaust. This study confirmed that VOCs are vertically homogeneous in the background of the Tibetan Plateau, emphasizing the importance of vehicle emissions as a potential source of VOCs.摘要研究背景区域挥发性有机物 (VOCs) 的垂直变化规律, 可以提供污染物的空间分布信息, 为制定大气污染防治策略提供科学依据. 2023年夏季, 利用系留气艇对藏东南背景区域VOCs垂直分布规律进行研究, 共获取92种VOC的39条垂直廓线. 结果表明, VOC浓度垂直分布均一, 平均浓度为11.1 ± 2.4 ppbv; 主要成分为烷烃 (51.4 %), 烯烃 (18.7 %) 和卤代烃 (18.1 %). 地表VOC浓度小于10 ppbv时, 不同高度VOCs的主要来源为机动车尾气和背景贡献. 而地表VOC浓度超过10 ppbv时, 烷烃占比增加, 来源以机动车尾气为主. 该研究证实了青藏高原VOC在垂直方向上分布均一, 强调了机动车排放作为VOCs潜在来源的重要性.

The variability of NO2 concentrations over China based on satellite and influencing factors analysis during 2019–2021

The variation of tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs) indirectly reflects the difference in pollution emissions from industrial production and transportation. Accurately analyzing its pollution sources and driving factors plays an important role in energy conservation, emission reduction, and air pollution reduction. NO2 concentration products of Sentinel-5P (Sentinel-5 Precursor) TROPOMI (TROPOspheric Monitoring Instrument) from 2019 to 2021 and Aura OMI (Ozone Monitoring Instrument) from 2009 to 2021, combined with China’s main energy consumption, the growth value of the industry, Gross Domestic Product (GDP), and other data were used to analyze the influencing factors of NO2 variations. Firstly, NO2 tropospheric vertical column densities (NO2 TVCDs) of China increased by 14.72% and 3.26% in 2021 and 2020 compared with the 2019. The secondary and tertiary industry and the national energy consumption increased synchronously, which was highly related to the increase in NO2 TVCDs. Secondly, the impact of COVID-19 (coronavirus disease 2019) on China’s industrial production and residents was mainly concentrated in the first quarter of 2020, which leading to a decline in the annual average NO2 concentration in densely populated areas in 2020 compared to the same period in 2019. The industrial production scale and production capacity has gradually recovered since April 2020, and the NO2 concentration has gradually reached or exceeded the level of the same period of 2019. Finally, atmospheric pollution prevention and control measures played a positive role in the decline of NO2 of China.

Detection of PM2.5 spatiotemporal patterns and driving factors in urban agglomerations in China

As important urban gathering places for new urbanization construction, urban agglomerations (UAs) have long suffered from high concentrations of PM2.5 pollution. By analyzing the changes of the temporal and spatial distribution patterns and the natural and human drivers of PM2.5 in UAs in China can provide an effective reference basis for atmospheric pollution prevention and control. In this study, based on PM2.5 concentration data from 2000 to 2020, PM2.5 pollution in 19 UAs in China was investigated. First, the annual average PM2.5 concentration was used to analyze the temporal variation, spatial distribution, and agglomeration characteristics of PM2.5 pollution in each UA. Then, factor detection was used to analyze the driving mechanisms of the effects of natural and human factors on PM2.5 pollution in UAs in China. Finally, the explanatory intensities of the interactions between different factors of PM2.5 pollution were analyzed based on interaction detection. The results show that from 2000 to 2020, the PM2.5 concentration exhibited an evolutionary trend of “M”-shape. An inflection point occurred in 2007, and 2020 had the lowest value for the study period. PM2.5 of high concentration took the Central Plains and the Beijing-Tianjin-Hebei UAs as the core of pollution. 2013 was an important turning point with a spatial pattern of expansion to the surrounding areas and then contraction to the center. UAs presented an elliptical spatial distribution, and outliers occurred in individual years. Humidity and temperature were the dominant natural factors for the variation of the PM2.5 concentration in 2013, 2018, and 2020, and their q-values were 0.276(humidity), 0.429(temperature), and 0.375(temperature), respectively. The total population and energy consumption were the dominant human factors for these three years, with q-values of 0.150(total population), 0.259(total population), and 0.196(energy consumption), respectively. The explanatory effect of the two-factor interaction was higher than that of a singular factor. Furthermore, the factor and interaction detection of natural factors found them to be more significant driving mechanisms than human factors. The results of this study not only allow for the understanding of the spatiotemporal variation of the PM2.5 concentration in UAs and the effects of different influencing factors, but also provide a scientific reference for the steady development of UAs and air pollution prevention and management in China.

Full-coverage spatiotemporal estimation of surface ozone over China based on a high-efficiency deep learning model

Ozone concentration Monitoring is essential to atmospheric pollution prevention and control. Against the background of severe ozone pollution over China in recent years, a spatiotemporal contiguous ozone concentration mapping method was developed. We imputed the significant data gaps of the Ozone Monitoring Instrument's tropospheric NO2 content by using an efficient machine learning model named LightGBM. Then, we developed a deep learning model based on three-dimensional Convolutional Neural Network architecture for daily maximum 8 h average ozone concentration estimation over China. With the support of the satellite-retrieved precursor, meteorological data and other ancillary data, our model achieved excellent performance with sample-based 10-fold cross-validation R2 = 0.88. Furthermore, we generated daily maximum 8 h average ozone datasets covering the whole of China from 2016 to 2020. This study presents a novel method for surface ozone modeling, which can provide fundamental data for the study of the ecological changes caused by ozone pollution, such as crop loss, or the harmful effects on humans, such as the increased incidence of respiratory diseases.

The optimization of XGBoost model and its application in PM2.5 concentrations estimation based on MODIS data in the Guanzhong region, China

ABSTRACT Atmospheric pollution affects air quality and can pose a serious risk to public health. Traditional PM2.5 monitoring is limited by the uneven distribution of ground stations, which makes it difficult to obtain spatially continuous and accurate PM2.5 concentrations information. Using aerosol optical depth (AOD) retrieved from satellite remote sensing to study the temporal and spatial variation characteristics of PM2.5 can accurately predict the PM2.5 concentrations in a wide range and provide a basis for atmospheric pollution prevention and control. Considering the spatio-temporal correlations among the data, this study uses AOD data, introduces geographic location data, temporal data, meteorological data, and elevation data to construct an Optimized XGBoost (O-XGBoost) model with spatial and temporal characteristics to estimate PM2.5 concentrations in the Guanzhong region from 2019 to 2021 and analyse its spatial and temporal distribution characteristics. The results show that compared with the random forest (RF) and XGBoost models, the O-XGBoost model has higher estimation accuracy, with , , and of 0.873, 11.460, and 8.061, respectively. The estimation of PM2.5 concentrations based on the O-XGBoost model makes up for the uneven distribution of ground monitoring stations and improves the estimation accuracy.

A dual attention-based fusion network for long- and short-term multivariate vehicle exhaust emission prediction

The increasing number of vehicles is one main cause of atmospheric environment pollution problems. Timely and accurate long- and short-term (LST) prediction of the on-road vehicle exhaust emission could contribute to atmospheric pollution prevention, public health protection, and government decision-making for environmental management. Vehicle exhaust emission has strong non-stationary and nonlinear characteristics due to the inherent randomness and imbalance nature of meteorological factors and traffic flow. Therefore accurate LST vehicle exhaust emission prediction encounters many challenges, such as the LST temporal dependencies and complicated nonlinear correlation on various emission gases, including carbon monoxide (CO), hydrocarbon (HC), and nitric oxide (NO), and external influence factors. To resolve these challenging issues, we propose a novel hybrid deep learning framework, namely Dual Attention-based Fusion Network (DAFNet), to effectively predict LST multivariate vehicle exhaust emission with the temporal convolutional network, convolutional neural network, long short term memory (LSTM)-skip based on recurrent neural network, dual attention mechanism, and autoregressive decomposition model. The proposed DAFNet consists of three major parts: 1) a nonlinear component to effectively capture the dynamic LST temporal dependency of multivariate gas by the temporal convolutional network, convolutional neural network, and LSTM-skip. Moreover, the above two networks employ an attention mechanism to model the internal relevance of the LST temporal patterns and multivariate gas, respectively. 2) a linear component to tackle the scale-insensitive problem of the neural network model by an autoregressive decomposition model. 3) the external components are taken to compensate the impact of external factors on vehicle exhaust emission by the multilayer perceptron model. Finally, the proposed DAFNet is evaluated on two real-world vehicle emission datasets in Zibo and Hefei, China. Experimental results demonstrate that the proposed DAFNet is a powerful tool to provide highly accurate prediction for LST multivariate vehicle exhaust emission in the field of vehicle environmental management.

Analysis of pollutants transport in heavy air pollution processes using a new complex-network-based model

A transport analysis model of air pollutants based on complex networks is proposed to study the spatiotemporal variation characteristics of air pollution during heavy air pollution processes and further analyze the contribution and interrelationship of air pollution of different cities. To construct the transmission network for each heavy air pollution process, a link is established between two cities according to the correlation coefficient of pollutants, the angle between the direction of cities and the wind direction, and its weight is assigned by integrating the factors of pollution, meteorology and geography. Then the PageRank algorithm is improved to identify the main contribution areas, and the label propagation algorithm is improved to find the associated air pollution areas. Finally, the period from January 1, 2014 to February 28, 2021 in Beijing–Tianjin–Hebei (BTH) region are used to demonstrate the performance of this model. The results show that a total of 58 heavy air pollution processes occur during the study period, the shortest lasting 3 days and the longest 13 days. These pollution processes occur mainly in winter, especially in January, and there are sufficient data to show that pollution levels decline year on year. Long-term correlation analysis for single-pollutant between different cities shows that geography is one of the most important factors affecting the regional transport of air pollutants. Moreover, according to the statistical analysis of each heavy pollution process, Tangshan, Tianjin and Langfang are the main contribution areas in the pollutant regional transport. In addition, apart from that in Zhangjiakou, Chengde and Qinhuangdao, the pollutants in the other cities in the BTH region have an effect on one another so that these cities are formed as an associated contaminated area. This study can provide better insights into local control and joint prevention of atmospheric pollution.

Carbonaceous aerosols in Lvliang, China: seasonal variation, spatial distribution and source apportionment

Environmental context Lvliang, one of the main cities on the Fenwei Plain, is a key atmospheric pollution prevention area in China. Identification of sources of aerosols is essential to improving environmental air quality in this region. The quantitative source apportionment of carbonaceous aerosols performed in this study provides a better understanding of their sources and implications for climate and air-quality management policies in the Fenwei plain. Rationale Organic carbon (OC) and elemental carbon (EC) are major components of fine particulate matter (PM2.5), and they are of concern due to their significant impacts on human health and climate. Methodology PM2.5 samples were collected daily during four consecutive seasons from 2018 to 2019. This paper highlights the seasonal variations, sources and transport characteristics of carbonaceous aerosol in Lvliang, China. Results The OC and EC concentrations exhibited strong seasonal variations, with the highest in winter, mainly due to high pollution caused by winter heating in northern cities, and secondary OC (SOC) contribution. The average OC/EC ratio (1.72) in Lvliang was lower than those in most regions in China, further indicating that this region was greatly affected by primary source emissions. The highest SOC/OC ratio in summer (25.3%) was due to the positive correlation between SOC and temperature. Through the positive matrix factorisation (PMF) model, four sources of carbonaceous aerosols were identified: vehicle emissions (31.26%), coal combustion (30.83%), biomass combustion (24.36%) and dust emissions (13.55%). Potential source contribution function (PSCF) results indicated that in addition to the impact of local emissions, coal emissions from Ningxia and Shaanxi, motor vehicle emissions and biomass from Inner Mongolia and Ningxia and dust from Shaanxi and Henan Provinces were the major contributors to pollution. Discussion These data provide key information for formulating emission reduction policies and improving air quality on the Fenwei Plain and highlights the urgent need for inter-regional prevention and control measures for the cities in Lvliang.

Temporal and Spatial Distribution Analysis of Atmospheric Pollutants in Chengdu-Chongqing Twin-City Economic Circle.

In order to study the temporal and spatial distribution characteristics of atmospheric pollutants in cities (districts and counties) in the Chengdu–Chongqing Twin-city Economic Circle (CCEC) and to provide a theoretical basis for atmospheric pollution prevention and control, this paper combined Ambient Air Quality Standards (AAQS) and WHO Global Air Quality Guidelines (GAQG) to evaluate atmospheric pollution and used spatial correlation to determine key pollution areas. The results showed that the distribution of atmospheric pollutants in CCEC presents a certain law, which was consistent with the air pollution transmission channels. Except for particulate matter with an aerodynamic diameter equal to or less than 2.5 μm (PM2.5) and ozone (O3), other pollutants reached Grade II of AAQS in 2020, among which particulate matter with an aerodynamic diameter equal to or less than 10 μm (PM10), PM2.5, sulfur dioxide (SO2), nitrogen dioxide (NO2) and carbon monoxide (CO) have improved. Compared with the air quality guidelines given in the GAQG, PM10, PM2.5, NO2 and O3 have certain effects on human health. The spatial aggregation of PM10 and PM2.5 decreased year by year, while the spatial aggregation of O3 increased with the change in time, and the distribution of NO2 pollution had no obvious aggregation. Comprehensive analysis showed that the pollution problems of particulate matter, NO2 and O3 in CCEC need to be further controlled.

Investigation and practical application of particle sink technology for haze removal: Pilot-scale testing

How to effectively purify fine particles from the atmosphere has always been the key and difficult point in atmospheric pollution prevention and control. This paper reported a new capture technology for haze particles based on the particle sink effect. The capture characteristics of haze particles and haze removal effect were analyzed in detail. The results showed that particulate matter concentrations (PMC) in the spatial area within a radius of 30 m could be reduced by 20%–75% in about one hour unaffected by environmental factors. Up to nearly 10% of haze particles in the outdoor working area were removed by the cooperation of different operation modes of the haze removal device (HRD). Additionally, the influences of wind direction (WD) and wind speed (WS) on the haze removal effect were revealed. The technology offers unique advantages in collecting haze particles and can be applied to alleviate urban haze.

Optimization of mechanisms for preventing air pollution

The present study has shown that the atmosphere, being a vital natural resource for humans, is constantly negatively influenced by the side effects of human social activities. As a resource that has no territorial boundaries, the atmosphere needs not only modern technologies to ensure its prosperity, but also intensive methods of protection. In this connection, the purpose of this article is to determine the necessary directions for optimizing the mechanisms for preventing atmospheric pollution. The isolation of mechanisms that control and guarantee the observance of the procedure for using this natural object, ensuring respect for the interaction of human society with the specified component of the environment, as well as identifying problems that create an obstacle to the full implementation of atmospheric pollution prevention mechanisms, contribute to the achievement of the set goal and predetermine the results of this study.

Analysis of the impact of the Beijing-Tianjin-Hebei coordinated development on environmental pollution and its mechanism.

Taking detailed rules for implementing of the plan on atmospheric pollution prevention and control in Beijing-Tianjin-Hebei region and surrounding areas promulgated and implemented in 2013 as the starting point, taking air pollution as the research object, using the propensity score matching method and difference-in-differences method to answer whether coordinated development of Beijing-Tianjin-Hebei is conducive to resolving the problem of air pollution. Using panel data from 251 cities in China 2003-2016, this paper discusses the environmental effects and mechanisms of coordinated urban cluster development. The results showed that the coordinated development of Beijing-Tianjin-Hebei had a significant and continuous effect on the convergence of PM2.5 to low levels. The results of further mechanism identification show that the policy of atmospheric joint defense rules in the process of Beijing-Tianjin-Hebei coordinated development mainly promotes the convergence of PM2.5 through population and economic scale effect, structural effect and technical effect, and promotes the convergence of PM2.5 in the low direction by reducing the economic growth scale of the city itself. It is suggested to further promote the formation and development of Beijing-Tianjin-Hebei integration, strengthen cooperation among local governments in environmental governance e across regions, and promote the green growth and coordinated development of urban clusters.

Concentrations of Major and Trace Elements within the Snowpack of Tyumen, Russia

A study on the composition of snow allowed for a quantitative determination of pollutants deposited from the atmosphere. Concentrations of dissolved (<0.45 μm) and particulate fractions of 62 chemical elements were determined by ICP–MS and ICP–AES in 41 samples of snow from Tyumen (Russia). The background sites were characterized by a predominance of the dissolved phase of elements, except for Al, Sn, Cr, Co and Zr. The increased concentrations of dissolved Cd, Cu, Zn, Pb, Ni, As and Mo can be explained by a long-range atmospheric transport from the sources located in the Urals. The urban sites showed multiple increases in particulate depositions and a predominance of the particulate phase, with a high degree of enrichment in many heavy metals. Sources of trace elements were determined according to the enrichment factor (EF). Highly enriched elements (Pb, Sb, Cd, Ag, Mo, As, Zn and Cu) with an EF > 100 were emitted from anthropogenic sources. According to the potential ecological risk index (RI), the worst ecological conditions were identified in Tyumen’s historical center, industrial zone and along roads with the heaviest traffic. The data obtained in the present study allowed us to identify the most polluted parts of the city, which are located in the center and along the roads with the most intensive traffic. This research could offer a reference for the atmospheric pollution prevention and control in Tyumen.

Efficiency assessment of coal mine use and land restoration: Considering climate change and income differences

Coal burning is still the main source of power generation in many Asian countries and is also the biggest cause of climate change. This research thus employs meta dynamic two-stage SBM (slack-based measure) under the exogenous model to examine 24 provinces in China as the research object. The first stage evaluates the efficiency of coal mining, taking employment in the coal industry and fixed assets in the mining industry as inputs, the development of non-oil and gas mineral resources a desirable output, and land damage as undesirable output and link. The second stage assesses the efficiency of land recovery, using land recovery funding as the input and recovery area as the output, and considers the climate change effect. The contribution herein is to discuss the degree of land destruction and the efficiency of land recovery through the different economic environments of different provinces and to divide the two groups by GDP. We also introduce high and low temperature days as exogenous variables to highlight the impact of climate change factors and to expose efficiency issues that may be underestimated. The results are as follows. (1) The efficiency value of the coal mining stage is higher than that of the land recovery stage, showing that China still pays more attention to economic benefits and ignores environmental responsibility issues. (2) More than 90% of China's provinces have coal mining expertise, but 70% of the provinces have low land recovery performance. (3) Eight provinces did not follow the Atmospheric Pollution Prevention Action Plan (APCP), the efficiency of coal mining deteriorated after the implementation of the policy. (4) Before the implementation of the APCP, land recovery in high GDP provinces was less efficient than in low GDP provinces. (5) Activating the APCP policy does not help technology upgrade. (6) The APCP policy caused the provinces to misevaluate their investments in fixed assets and misdirected their decisions on land recovery funding. However, the efficiency of land recovery after the implement of APCP was better. (7) Neglecting climatic factors causes serious errors in coal mine efficiency.

Characteristics of heavy air pollution in Beijing-Tianjin-Hebei and the surrounding areas during autumn and winter and policy recommendations

Beijing-Tianjin-Hebei and its surrounding areas is the most polluted region in China. Autumn and winter are the most heavily polluted seasons. It is essential to reduce the PM2.5 pollution in autumn and winter in this region for winning the blue sky defense war. The characteristics of PM2.5 pollution during autumn and winter in this region were analysed and crucial issues in atmospheric pollution prevention were identified from the perspectives of time, space and pollution sources, using a combination of techniques including air quality observation, emission inventory, PM2.5 source apportionment and air quality model simulation. The results showed that average PM2.5 concentrations in autumn and winter were twice that in spring and summer, and frequent heavy pollution elevated PM2.5 concentration significantly. Six cities including Handan, Xingtai, Shijiazhuang, Xinxiang, Anyang and Baoding, located along the south-western transport channel in the region, have great effects on regional air quality. The severe PM2.5 pollution in this region could be attributed to heavy industrial structure, large NOx emissions from mobile sources, re-burn of loose coal, insufficient dust control and impacts from cities out of the region. Suggestions for air pollution control in autumn and winter in this region were proposed. More efforts should be devoted to the most vulnerable links in the key cities.

Predictive analysis of the air quality indicators in the Yangtze River Delta in China: An application of a novel seasonal grey model

Foreknowledge of the air quality indicators (i.e. AQI, PM2.5, PM10, SO2, CO, NO2, and O3) provides decision-makers a possibility for building an early-warning system and tailoring related policies and plans accordingly so as to reduce the negative influences of these pollutants. However, accurate forecasts are hardly obtained because strong seasonal variations in meteorological circumstances can largely give rise to seasonal fluctuations in the time series of these indicators, which are difficult to be described and extracted by traditional forecasting tools. To address such issues, a seasonal nonlinear grey Bernoulli model is developed to provide skillful forecasts, which can effectively grasp the nonlinear and seasonal features. Subsequently, this paper elaborates on the model and method used for parameter estimations. For validation and verification purposes, operational seasonal forecasts of the air quality indicators in the four representative cities (Shanghai, Hangzhou, Nanjing, and Hefei) in the Yangtze River Delta are performed, in comparison with five prevalent forecasting tools including SFGM(1,1), SGM(1,1), LSSVM, SARIMA, and BPNN. Results show that the proposed model outperforms other competitors in improving the prediction accuracy of the seasonal air quality changes. Thus, the verified model is recommended to produce future estimations of the air quality indicators in the Yangtze River Delta from 2020 to 2021, revealing that Shanghai, Hangzhou, and Hefei will have better air quality than before, while Nanjing will be subjected to a poorer one. Eventually, some suggestions related to the prevention of atmospheric pollution are provided to further improve air quality.

Aerosol chemical component: Simulations with WRF-Chem and comparison with observations in Nanjing

Secondary inorganic aerosols, including sulfate, nitrate, and ammonium (SNA), are the predominant components of fine particles (PM2.5). Reasonable representations of SNA formation in numerical models can largely improve the predictions of PM2.5 concentrations and effectively help implement emission control strategies. Despite the Atmospheric Pollution Prevention and Control Action Plan has been implemented since 2013, PM2.5 concentration during 2017 in Nanjing, one of the megacities in China, still exceeded the World Health Organization-recommended safe level (35 μg m−3). In this study, WRF-Chem model was applied to simulate aerosol chemical components in PM2.5 during April 2016 and January 2017 in Nanjing, and the simulations are evaluated with in-situ observations. Our results show that the model can reasonably reproduce the temporal variability of PM2.5 in two seasons, but significantly underestimate the sulfate concentrations by 71% (84%) in April (January), and overestimate the nitrate concentrations by 67% (45%) in April (January). The simulated ammonium is overall consistent with the observations. Meanwhile, the model tends to overestimate SO2 concentrations by 20% (74%) in April (January). Several sensitivity studies are conducted to explore the mechanisms for underestimation of sulfate and overestimation of nitrate, and found that the conversion rate of SO2 to sulfate is significantly underestimated in the model. Tripling the gas-phase oxidation rate of SO2 by OH only enhances sulfate by 67% (72%) in April (January), indicating gas-phase oxidation is not the main causes for the underestimations in the model. However, inclusion of SO2 heterogeneous oxidation in aerosol water can largely increase the simulated sulfate by 84% (196%) in April (January), and also better reproduce the diurnal variations of sulfate compared to the reference run. It should be noted that the simulated sulfate is still 47% (53%) lower than the observations in April (January), though inclusion of heterogeneous reaction can substantially improve the simulation performance of SNA.

Vertical structures of physical and chemical properties of urban boundary layer and formation mechanisms of atmospheric pollution

Air quality in China has been continuously improved since the implementation of “Atmospheric Pollution Prevention and Control Action Plan” in 2013. However, the mass concentrations of fine particles particularly in Beijing, Tianjin and Hebei are still much higher than the National Ambient Air Quality Standard (35 μg m−3 as an annual average), and severely polluted events also occur occasionally in autumn and winter seasons. In recent years, Chinese research scientists have made significant progresses in vertical measurements of air pollution, formation mechanisms and source apportionment of atmospheric aerosol, and also the interactions between aerosol and atmospheric boundary layer, which greatly support the scientific prevention and control of atmospheric pollution. Despite this, many questions on atmospheric pollution have not been well addressed yet. This paper first summarizes the recent research progresses in vertical measurements of air pollution in China, in particular, the measurements from the research platform of the Beijing 325 m meteorological tower. In addition to the routine long-term measurements of boundary layer (turbulence, CO2 and H2O flux) and meteorological parameters (temperature, relative humidity, wind speed and wind direction) at 7 and 15 heights, respectively, this platform can have simultaneous real-time measurements of aerosol particle composition (organics, sulfate, nitrate, ammonium, chloride, and black carbon), gaseous species (CO, SO2, and O3), particle number size distributions, and optical properties (extinction and absorption coefficients) at ground level and 260 m, and also the continuously vertical measurements from ground to 260 m using the tower container equipped with specific instruments. Then, the vertical differences of aerosol composition in different seasons in Beijing, and their interactions with boundary layer are discussed. Also, the response of aerosol chemistry to regional source emission control are elucidated based on vertical measurements during the Asia-Pacific Economic Conference (APEC) summit in 2014 and the 70th anniversary parade in 2015. This paper also reviews the latest research progress on formation mechanisms of secondary inorganic aerosol (nitrate and sulfate) and secondary organic aerosol, and the life cycles of severe haze events in China. In particular, the heterogeneous formation of sulfate during severe haze episodes strongly depends on pH values of aerosol liquid water content. While techniques for direct measurements of aerosol particle pH are essential yet challenging, laboratory experiments are more needed now to prove the hypothesis of sulfate formation mechanisms. Compared with inorganic species, the formation and evolutionary mechanisms of secondary organic aerosol (SOA) are far more complex in polluted environments, and their roles in haze formation need to be better characterized. Finally, this paper lists some future recommendations for air pollution measurements (e.g., from long-term to vertical measurements in terms of significant emission changes in the near future), formation mechanism of secondary aerosol, and molecular composition and physical and chemical properties (phase, volatility, oxidation state, etc.) of organic aerosol.

Analysis on Effects of Energy Efficiency Standards of Industry Boiler in China

China industry boilers have characteristics that huge total amount, wide distribution, high energy consumption and heavy pollution. Since the Chinese government revised the energy conservation law in 2007, boiler and other high energy consuming special equipment energy conservation supervision was more and more concerned, and in 2014 the Chinese government has enacted the new revision “atmospheric pollution prevention and control law” in which boiler emissions management requirement was increased. But because of boiler energy conservation and environmental protection work started late in China, relevant working basis is weak and the indicators for boiler efficiency, emissions are not perfect, to a certain extent, it restricted the progress of boiler energy conservation and environmental protection work in China. In this paper, we analysis on the effect of energy efficiency standards of industry boiler in China, and give a suggestion to promote China industry boilers to more energy saving.

Chemical characteristics of precipitation and the indicative significance for sand dust events in the northern and southern slopes of Wushaoling Mountain, northwestern China

Precipitation chemistry analysis is essential to evaluate the atmospheric environmental quality and identify the sources of atmospheric pollutants. In this study, we collected a total of 480 precipitation samples at 6 sampling sites in the northern and southern slopes of Wushaoling Mountain from May 2013 to July 2014 to analyze the chemical characteristics of precipitation and to identify the main sources of ions in precipitation. Furthermore, we also explored the indicative significance for sand dust events in the northern and southern slopes of Wushaoling Mountain based on the precipitation chemistry analysis. During the sampling period (from May 2013 to July 2014), the pH values, EC (electrical conductivity) values and concentrations of cations (Ca2+, Mg2+, Na+, K+ and NH4+) and anions (SO42–, NO3–, Cl–, NO2– and F–) in precipitation were different in the northern and southern slopes at daily and seasonal time scales, with most of the values being higher in the northern slope than in the southern slope. The chemical type of precipitation in the southern and northern slopes was the same, i.e., SO42–-Ca2+-NO3–-Na+. The concentrations of ions in precipitation were mainly controlled by terrigenous material and anthropogenic activities (with an exception of Cl–). The concentration of Cl– in precipitation was mainly controlled by the sea salt fraction. The concentrations of Na+ and Cl– showed an increasing trend after the occurrence of sand dust events both in the northern and southern slopes. In addition, after the occurrence of sand dust events, the concentrations of K+, Mg2+, SO42–, NO3– and Ca2+ showed an increasing trend in the southern slope and a decreasing trend in the northern slope. It is our hope that the results may be helpful to further understand the atmospheric pollution caused by sand dust events in the Wushaoling Mountain and can also provide a scientific basis for the effective prevention of atmospheric pollution.