Search

Atmospheric visibility is an important indicator that reflects the transparency of the atmosphere and characterizes the air quality, so it is of great significance to study the long-term change in visibility. This paper is based on the global surface summary of day data (GSOD) site dataset and other relevant data, using the Mann–Kendall (MK) mutation point test, wavelet transform, and seasonal autoregressive integrated moving average (SARIMA) model forecasting. The time-frequency domain variation characteristics and related influencing factors of regional visibility in China were studied in detail, and the visibility was predicted; the results of the study showed the following: (1) the overall interannual variation of regional visibility in China has a decreasing trend, and the four-season variation has a decreasing trend, except for the rising trend in summer, with abrupt change points in both the overall interannual variation and the four-season variation. (2) There are main cycles of visibility in the Chinese region with time scales of 180 months and 18 months. Under the time scale of 180 months for the main cycle, the variation period of visibility is about 123 months, experiencing two high to low variations; under the time scale of 18 months for the main cycle, the variation period of visibility is 12 months, experiencing 21 high to low variations. (3) The development of the economy indirectly affects changes in visibility. Cities with high economies are densely populated, with concentrations of various particulate emissions and high concentrations of particulate matter, which can directly reduce visibility. (4) Two prediction models, SARIMA and long and the short-term memory (LSTM) neural network, were used to predict the visibility in China, both of which achieved good evaluation indexes, and the visibility in China may show an increasing trend in the future. [ABSTRACT FROM AUTHOR]

This paper investigated the physical and chemical properties of gasoline direct injection (GDI) engine particulate matter (PM). The physical properties mainly included the particulate aggregate morphology, primary particle size, and internal nanostructure. High-resolution transmission electron microscopy (HRTEM) and scanning electron microscopy (SEM) were used to obtain particle morphology information and to conduct image processing and analysis. The chemical characterization tests included X-ray photoelectron spectroscopy (XPS), energy dispersive scanning (EDS), Raman spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and thermal gravimetric analysis (TGA). XPS can be used to observe the content of carbon and oxygen components and the surface carbon chemistry status, EDS can be used to obtain the elemental composition of particles, and TGA is used to analyze the oxidative kinetics of particles. Samples were collected from the exhaust emissions of a passenger vehicle compliant with China's VI emission standards under China Light-Duty Vehicle Test Cycle (CLTC) test conditions. The study found that the particle morphology mainly comprised primary particles stacked on top of each other to form agglomerate structures, and the primary particles exhibited a core–shell structure. Analysis showed that carbon and oxygen were the predominant components of the particles, with other metallic elements also present. The XPS observations agreed with the FTIR results, indicating a small amount of oxygen was present on the particle surface and that the carbon components consisted mainly of sp2 hybridized graphite and sp3 hybridized organic carbon. The TGA results indicated high characteristic temperatures and low oxidation activity. [ABSTRACT FROM AUTHOR]

Cooking food in water or soup, such as hot pot, is a widely used cooking method in China. This type of cooking requires no oil and cooks at a lower temperature, but that does not mean it produces fewer pollutants or is less harmful. There are few research studies on the emission characteristics and mechanisms of particulate matter emissions when eating hot pot (the boiling process), which leads to the unreasonable design of ventilation systems for this kind of catering. In this paper, the effects of boiling different ingredients (including noodles, potatoes, fish, tofu, meatballs, and pork) on particle number concentration emissions were studied. The particle number concentration and particle size distribution of PM with diameters of 0.3 μm and less, 0.3–0.5 μm and 0.5–1.0 μm (PM0.3, PM0.3–0.5 and PM0.5–1.0, respectively) were measured in an experimental chamber. The food type and shape showed very little change in the PM emission characteristics of boiling. When the boiling state was reached, the number concentration, particle size distribution, and arithmetic mean diameter of particles all fluctuated within 60 s. The emission characteristics of particles produced by boiling water and heating oil were compared. Heating oil produced more small particles, and boiling water released more large particles. Transient and steady methods were used to calculate the emission rate of particles, and the steady-state calculation has a high estimation of the emission rate. [ABSTRACT FROM AUTHOR]

Particulate matter PM2.5 pollution affects the Chinese population, particularly in cities such as Shenyang in northeastern China, which occupies a number of traditional heavy industries. This paper proposes a semi-supervised learning model used for predicting PM2.5 concentrations. The model incorporates rich data from the real world, including 11 air quality monitoring stations in Shenyang and nearby cities. There are three types of data: air monitoring, meteorological data, and spatiotemporal information (such as the spatiotemporal effects of PM2.5 emissions and diffusion across different geographical regions). The model consists of two classifiers: genetic programming (GP) to forecast PM2.5 concentrations and support vector classification (SVC) to predict trends. The experimental results show that the proposed model performs better than baseline models in accuracy, including 3% to 18% over a classic multivariate linear regression (MLR), 1% to 11% over a multi-layer perceptron neural network (MLP-ANN), and 21% to 68% over a support vector regression (SVR). Furthermore, the proposed GP approach provides an intuitive contribution analysis of factors for PM2.5 concentrations. The data of backtracking points adjacent to other monitoring stations are critical in forecasting shorter time intervals (1 h). Wind speeds are more important in longer intervals (6 and 24 h). [ABSTRACT FROM AUTHOR]

Low-carbon cities have become a new trend in regional development around the world. Whether they can improve the environment in China, especially the air quality, remains to be tested. In this paper we take low-carbon city construction as a quasi-natural experiment and empirically test the net effects, influencing factors, and dynamic effects of low-carbon city construction on air quality by constructing a multistage propensity score matching and Difference-in-Differences model. After a series of robustness tests, the following conclusions are drawn: first, low-carbon city construction reduces the regional Air Quality Index, inhalable particulate matter, fine particulate matter, and NO2 concentrations. Among them, the construction effect in 2017 was the most significant. Therefore, it is necessary to continue to promote low-carbon city policies and accurately identify different types of air pollutants to improve the overall effectiveness of low-carbon city policies. Second, temperature, humidity, wind level, and other meteorological factors, as well as gross domestic product for the proportion of secondary industry, will affect air quality. Therefore, it is necessary to comprehensively consider meteorological, economic, social, and other influencing factors in an early stage of the construction of the next batch of low-carbon cities, so as to avoid falling into the trap of "building first and managing later". Third, the impact of secondary industry on air quality is significantly greater than that of tertiary industry. Therefore, the upgrading of industrial structure promoted by low-carbon city policy is effective in improving air quality. Fourth, the construction of low-carbon cities in western China has the most significant impact on air quality improvement. Therefore, the joint prevention and control mechanism of air pollution control in urban agglomeration should be established. [ABSTRACT FROM AUTHOR]

Haze is the phenomenon of visibility degradation caused by extinction effects related to the physicochemical properties of atmospheric particulate matter (APM). Atmosphere heterogeneous reactions can alter the physicochemical properties of APM. Therefore, it is important to understand the atmospheric heterogeneous reactions of APM in order to reveal the cause of haze. Herein, the current situation, developmental trend, source, and composition of APM pollution in China are reviewed. Additionally, we introduce the reaction characteristics and key chemical processes of common inorganic, organic, and mixed pollutant gases on the surface of mineral particles. The effects of mineral particulate matter on aggregation, regulation, and catalysis in the formation of atmospheric aerosols and the synergistic reaction mechanism of SO2, NO2, O3, and VOCs on the surfaces of different mineral particles are summarized. The problems existing in the current research on heterogeneous reactions on the surfaces of mineral particles are also evaluated. This paper aims to gain a deep understanding of the mechanism of mineral particulate matter promoting the formation of secondary aerosols and attempts to provide theoretical support for effective haze control. [ABSTRACT FROM AUTHOR]

Air pollution contributes to a large fraction of the total mortality estimated under the global burden of disease project (GBD) of the World Health Organization (WHO). This paper discusses an integrated study to obtain the spatiotemporal characteristics of particulate matter (PM2.5 and PM10) and trace gases (O3, SO2, NO2, and CO) pollutants in Hangzhou City (China) for the years 2014–2016. Our detailed analysis shows a relationship between air pollutants and land-use/land-cover change. Air quality parameters (PM2.5 and PM10) and trace gases (SO2, NO2, and CO) show strong monthly variations in the months of January (higher values) and July (lower values). During monsoon and summer seasons, air quality and trace gases show low values, whereas ozone (O3) is higher in the summer and lower in the winter. The spatial distribution of air pollutants is retrieved using the kriging method at the monitoring sites in Hangzhou City. We have considered normalized difference vegetation index (NDVI) and land surface temperature (LST) from the Landsat 8 data. The correlation between air pollutants and land use at the street-town unit suggests that areas with low NDVI, high road density, large built-up density, and LST are consistent with high concentrations of particulate matter and SO2, NO2, and CO. Among the trace gases, NO2 is found to be the most sensitive element affected by land use patterns, and O3 shows weak correlation with land use. SO2 shows a strong positive correlation with road density and LST, whereas CO shows positive correlation with the built-up density, LST, and population density. [ABSTRACT FROM AUTHOR]

A special and unusual purple (magenta) haze episode was observed in Nanjing, China, at 17:00 on 22 December 2015. Many local and national news outlets reported this event. Based on an analysis of the pollution features and meteorological factors, including boundary layer characteristics, we concluded that this haze event was similar in most respects to other local haze episodes. We discuss the reasons and the possibilities about this rare color haze at the end of the paper. One way to attain a combination of blue and red light is to have the green wavelengths selectively absorbed, and this seems unlikely for typical atmospheric constituents. Another way involves pollution gases or particles together with small liquid-water drops, which need further confirmation. A third possibility is that the combination of transmitted red light from the sun and scattered blue light from above could produce a purple/magenta color in the sky. In general, further studies are required to assess the physical, chemical, and optical features of this purple haze in order to explain and predict this phenomenon in the future. [ABSTRACT FROM AUTHOR]

This paper reveals a study on air pollution process over North China, applying remote sensing technology, using satellite observation and in situ measurements during the twenty-first Asia-Pacific Economic Cooperation (APEC) meeting, which was held in Beijing between 6 and 12 November when the clear weather was called "APEC-Blue". In the meantime, pollutants concentrations including PM2.5 and PM10 in Beijing were lower than 100 μg/m3 owing to the effective government measures and policies, as well as meteorological conditions. High aerosol loading (AOD > 1) was observed over south of Beijing and vertical observations showed that the pollutants were prominent near the land surface. Different from the meeting period, high pollutants concentrations with explosive growth (the values of PM2.5 and PM10 peaking at 291 μg/m3 and 360 μg/m3 respectively) appeared over Beijing after the meeting period, accompanied by strong temperature inversion and high Relative Humidity (RH) values. The pollution particles transferred from Beijing area to south part of North China. Otherwise, fine-mode particles with strong absorption characteristic (UVAI > 1.5, AOD > 1) covered the Beijing sky in the meantime, indicating the existence of black carbon aerosols. [ABSTRACT FROM AUTHOR]

In this paper, based on a sample selection rule and a Back Propagation (BP) neural network, a new model of forecasting daily SO2, NO2, and PM10 concentration in seven sites of Guangzhou was developed using data from January 2006 to April 2012. A meteorological similarity principle was applied in the development of the sample selection rule. The key meteorological factors influencing SO2, NO2, and PM10 daily concentrations as well as weight matrices and threshold matrices were determined. A basic model was then developed based on the improved BP neural network. Improving the basic model, identification of the factor variation consistency was added in the rule, and seven sets of sensitivity experiments in one of the seven sites were conducted to obtain the selected model. A comparison of the basic model from May 2011 to April 2012 in one site showed that the selected model for PM10 displayed better forecasting performance, with Mean Absolute Percentage Error (MAPE) values decreasing by 4% and R² values increasing from 0.53 to 0.68. Evaluations conducted at the six other sites revealed a similar performance. On the whole, the analysis showed that the models presented here could provide local authorities with reliable and precise predictions and alarms about air quality if used at an operational scale. [ABSTRACT FROM AUTHOR]

In September 2018, China's air quality monitoring protocol was amended from the standard conditions to actual conditions for particulate matter and to reference conditions for gaseous pollutants. Due to the amendment, the reported concentrations of the gaseous pollutants decreased by a constant rate of 8.4%, and the averages of PM2.5 (particulate matter that has an aerodynamic diameter of 2.5 microns or smaller) reported during September 2017 and August 2018 decreased by 7.9 ± 6.1% at 99% of the monitoring stations. Comparing the periods before and after the amendment, the 12-month PM2.5 concentrations at 17.2% of the stations actually increased despite appearing to decrease if the amendments were not considered. We reviewed 370 papers published in 2020 that utilized this air quality dataset, and 21% of these papers used the data before and after the amendment without explicitly stating whether or how conversions were conducted. It is urgent to widely broadcast the protocol amendment to ensure proper use of this extensively cited dataset. [ABSTRACT FROM AUTHOR]

In this paper, the Weather Research Forecast (WRF) Comprehensive Air Quality Model with Extensions (CAMx) modeling system with the particulate source apportionment technology (PSAT) module was used to study and analyze the spatial and temporal distribution of atmospheric pollutant concentrations and the source apportionment of fine particles (PM2.5) under the base year and an emission reduction scenario in the Shandong province, China. Our results show that industry is the largest contributor of PM2.5. In addition, the contribution of key energy-related industries was as high as 29.5%, with the thermal power industry being the largest individual contributor. In January, the largest contribution came from residents, reaching 41.3%. Moreover, loose coal burning in rural areas contributed up to 19.4% in winter. Our results also show that the emission reduction scenario had palpable effects on the reduction of air pollution. The more the emissions of SO2, NOX, PM2.5, and PM10 were reduced, the more the average concentration was decreased. The implementation of energy conservation and emission reduction policies by industry and resident is conducive to improving the quality of the atmospheric environment. In particular, a comprehensive control of loose coal burning in winter could significantly improve heavy pollution by particulate matter in winter. [ABSTRACT FROM AUTHOR]