Showing papers on "Haze published in 2021"

Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China

Abstract: To control the spread of the 2019 novel coronavirus (COVID-19), China imposed nationwide restrictions on the movement of its population (lockdown) after the Chinese New Year of 2020, leading to large reductions in economic activities and associated emissions Despite such large decreases in primary pollution, there were nonetheless several periods of heavy haze pollution in eastern China, raising questions about the well-established relationship between human activities and air quality Here, using comprehensive measurements and modeling, we show that the haze during the COVID lockdown was driven by enhancements of secondary pollution In particular, large decreases in NOx emissions from transportation increased ozone and nighttime NO3 radical formation, and these increases in atmospheric oxidizing capacity in turn facilitated the formation of secondary particulate matter Our results, afforded by the tragic natural experiment of the COVID-19 pandemic, indicate that haze mitigation depends upon a coordinated and balanced strategy for controlling multiple pollutants

Ozone pollution in the North China Plain spreading into the late-winter haze season.

Abstract: Surface ozone is a severe air pollution problem in the North China Plain, which is home to 300 million people. Ozone concentrations are highest in summer, driven by fast photochemical production of hydrogen oxide radicals (HOx) that can overcome the radical titration caused by high emissions of nitrogen oxides (NOx) from fuel combustion. Ozone has been very low during winter haze (particulate) pollution episodes. However, the abrupt decrease of NOx emissions following the COVID-19 lockdown in January 2020 reveals a switch to fast ozone production during winter haze episodes with maximum daily 8-h average (MDA8) ozone concentrations of 60 to 70 parts per billion. We reproduce this switch with the GEOS-Chem model, where the fast production of ozone is driven by HOx radicals from photolysis of formaldehyde, overcoming radical titration from the decreased NOx emissions. Formaldehyde is produced by oxidation of reactive volatile organic compounds (VOCs), which have very high emissions in the North China Plain. This remarkable switch to an ozone-producing regime in January-February following the lockdown illustrates a more general tendency from 2013 to 2019 of increasing winter-spring ozone in the North China Plain and increasing association of high ozone with winter haze events, as pollution control efforts have targeted NOx emissions (30% decrease) while VOC emissions have remained constant. Decreasing VOC emissions would avoid further spreading of severe ozone pollution events into the winter-spring season.

Enhanced aerosol particle growth sustained by high continental chlorine emission in India

Abstract: Many cities in India experience severe deterioration of air quality in winter. Particulate matter is a key atmospheric pollutant that impacts millions of people. In particular, the high mass concentration of particulate matter reduces visibility, which has severely damaged the economy and endangered human lives. But the underlying chemical mechanisms and physical processes responsible for initiating haze and fog formation remain poorly understood. Here we present the measurement results of chemical composition of particulate matter in Delhi and Chennai. We find persistently high chloride in Delhi and episodically high chloride in Chennai. These measurements, combined with thermodynamic modelling, suggest that in the presence of excess ammonia in Delhi, high local emission of hydrochloric acid partitions into aerosol water. The highly water-absorbing and soluble chloride in the aqueous phase substantially enhances aerosol water uptake through co-condensation, which sustains particle growth, leading to haze and fog formation. We therefore suggest that the high local concentration of gas-phase hydrochloric acid, possibly emitted from plastic-contained waste burning and industry, causes some 50% of the reduced visibility. Our work implies that identifying and regulating gaseous hydrochloric acid emissions could be critical to improve visibility and human health in India. Half of the reduced visibility due to haze formation in cities in India is attributed to local emission of gas-phase hydrochloric acid from waste-burning and industry, according to measurements of particulate matter and thermodynamic modelling.

Sulfate formation is dominated by manganese-catalyzed oxidation of SO2 on aerosol surfaces during haze events.

Abstract: The formation mechanism of aerosol sulfate during wintertime haze events in China is still largely unknown. As companions, SO2 and transition metals are mainly emitted from coal combustion. Here, we argue that the transition metal-catalyzed oxidation of SO2 on aerosol surfaces could be the dominant sulfate formation pathway and investigate this hypothesis by integrating chamber experiments, numerical simulations and in-field observations. Our analysis shows that the contribution of the manganese-catalyzed oxidation of SO2 on aerosol surfaces is approximately one to two orders of magnitude larger than previously known routes, and contributes 69.2% ± 5.0% of the particulate sulfur production during haze events. This formation pathway could explain the missing source of sulfate and improve the understanding of atmospheric chemistry and climate change. Sulfate aerosols are an important component of wintertime haze events in China, but their production mechanisms are not well known. Here, the authors show that transition metal-catalyzed oxidation of SO2 on aerosol surfaces could be the dominant sulfate formation pathway in Northern China.

Is reducing new particle formation a plausible solution to mitigate particulate air pollution in Beijing and other Chinese megacities

Abstract: Atmospheric gas-to-particle conversion is a crucial or even dominant contributor to haze formation in Chinese megacities in terms of aerosol number, surface area and mass. Based on our comprehensive observations in Beijing during 15 January 2018-31 March 2019, we are able to show that 80-90% of the aerosol mass (PM2.5) was formed via atmospheric reactions during the haze days and over 65% of the number concentration of haze particles resulted from new particle formation (NPF). Furthermore, the haze formation was faster when the subsequent growth of newly formed particles was enhanced. Our findings suggest that in practice almost all present-day haze episodes originate from NPF, mainly since the direct emission of primary particles in Beijing has considerably decreased during recent years. We also show that reducing the subsequent growth rate of freshly formed particles by a factor of 3-5 would delay the buildup of haze episodes by 1-3 days. Actually, this delay would decrease the length of each haze episode, so that the number of annual haze days could be approximately halved. Such improvement in air quality can be achieved with targeted reduction of gas-phase precursors for NPF, mainly dimethyl amine and ammonia, and further reductions of SO2 emissions. Furthermore, reduction of anthropogenic organic and inorganic precursor emissions would slow down the growth rate of newly-formed particles and consequently reduce the haze formation.

A Haze Prediction Method Based on One-Dimensional Convolutional Neural Network

Abstract: In recent years, more and more people are paying close attention to the environmental problems in metropolitan areas and their harm to the human body. Among them, haze is the pollutant that people are most concerned about. The demand for a method to predict the haze level for the public and academics keeps rising. In order to predict the haze concentration on a time scale in hours, this study built a haze concentration prediction method based on one-dimensional convolutional neural networks. The gated recurrent unit method was used for comparison, which highlights the training speed of a one-dimensional convolutional neural network. In summary, the haze concentration data of the past 24 h are used as input and the haze concentration level on the next moment as output such that the haze concentration level on the time scale in hours can be predicted. Based on the results, the prediction accuracy of the proposed method is over 95% and can be used to support other studies on haze prediction.

Explosive Secondary Aerosol Formation during Severe Haze in the North China Plain.

Abstract: Severe haze events with exceedingly high-levels of fine aerosols occur frequently over the past decades in the North China Plain (NCP), exerting profound impacts on human health, weather, and climate. The development of effective mitigation policies requires a comprehensive understanding of the haze formation mechanisms, including identification and quantification of the sources, formation, and transformation of the aerosol species. Haze evolution in this region exhibits distinct physical and chemical characteristics from clean to polluted periods, as evident from increasing stagnation and relative humidity, but decreasing solar radiation as well as explosive secondary aerosol formation. The latter is attributed to highly elevated concentrations of aerosol precursor gases and is reflected by rapid increases in the particle number and mass concentrations, both corresponding to nonequilibrium chemical processes. Considerable new knowledge has been acquired to understand the processes regulating haze formation, particularly in light of the progress in elucidating the aerosol formation mechanisms. This review synthesizes recent advances in understanding secondary aerosol formation, by highlighting several critical chemical/physical processes, that is, new particle formation and aerosol growth driven by photochemistry and aqueous chemistry as well as the interaction between aerosols and atmospheric stability. Current challenges and future research priorities are also discussed.

Aqueous production of secondary organic aerosol from fossil-fuel emissions in winter Beijing haze.

Abstract: Secondary organic aerosol (SOA) produced by atmospheric oxidation of primary emitted precursors is a major contributor to fine particulate matter (PM2.5) air pollution worldwide. Observations during winter haze pollution episodes in urban China show that most of this SOA originates from fossil-fuel combustion but the chemical mechanisms involved are unclear. Here we report field observations in a Beijing winter haze event that reveal fast aqueous-phase conversion of fossil-fuel primary organic aerosol (POA) to SOA at high relative humidity. Analyses of aerosol mass spectra and elemental ratios indicate that ring-breaking oxidation of POA aromatic species, leading to functionalization as carbonyls and carboxylic acids, may serve as the dominant mechanism for this SOA formation. A POA origin for SOA could explain why SOA has been decreasing over the 2013-2018 period in response to POA emission controls even as emissions of volatile organic compounds (VOCs) have remained flat.

Separating emission and meteorological contributions to long-term PM 2.5 trends over eastern China during 2000–2018

Abstract: . The contribution of meteorology and emissions to long-term PM 2.5 trends is critical for air quality management but has not yet been fully analyzed. Here, we used the combination of a machine learning model, statistical method, and chemical transport model to quantify the meteorological impacts on PM 2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PM 2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PM 2.5 estimates over highly polluted days, thus allowing us to better characterize the meteorological effects on haze events. Then we used two methods to examine the meteorological contribution to PM 2.5 : a generalized additive model (GAM) driven by the satellite-based full-coverage daily PM 2.5 retrievals and the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling system. We found good agreements between GAM estimations and the CMAQ model estimations of the meteorological contribution to PM 2.5 on a monthly scale (correlation coefficient between 0.53–0.72). Both methods revealed the dominant role of emission changes in the long-term trend of PM 2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual variabilities in meteorology-associated PM 2.5 were dominated by the fall and winter meteorological conditions, when regional stagnant and stable conditions were more likely to happen and when haze events frequently occurred. From 2000 to 2018, the meteorological contribution became more unfavorable to PM 2.5 pollution across the North China Plain and central China but were more beneficial to pollution control across the southern part, e.g., the Yangtze River Delta. The meteorology-adjusted PM 2.5 over eastern China (denoted East China in figures) peaked in 2006 and 2011, mainly driven by the emission peaks in primary PM 2.5 and gas precursors in these years. Although emissions dominated the long-term PM 2.5 trends, the meteorology-driven anomalies also contributed −3.9 % to 2.8 % of the annual mean PM 2.5 concentrations in eastern China estimated from the GAM. The meteorological contributions were even higher regionally, e.g., − 6.3 % to 4.9 % of the annual mean PM 2.5 concentrations in the Beijing-Tianjin-Hebei region, − 5.1 % to 4.3 % in the Fenwei Plain, − 4.8 % to 4.3 % in the Yangtze River Delta, and − 25.6 % to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PM 2.5 and the possible worsening trend of meteorological conditions in the northern part of China where air pollution is severe and population is clustered, stricter clean air actions are needed to avoid haze events in the future.

A Haze Prediction Model in Chengdu Based on LSTM

Abstract: Air pollution with fluidity can influence a large area for a long time and can be harmful to the ecological environment and human health. Haze, one form of air pollution, has been a critical problem since the industrial revolution. Though the actual cause of haze could be various and complicated, in this paper, we have found out that many gases’ distributions and wind power or temperature are related to PM2.5/10’s concentration. Thus, based on the correlation between PM2.5/PM10 and other gaseous pollutants and the timing continuity of PM2.5/PM10, we propose a multilayer long short-term memory haze prediction model. This model utilizes the concentration of O3, CO, NO2, SO2, and PM2.5/PM10 in the last 24 h as inputs to predict PM2.5/PM10 concentrations in the future. Besides pre-processing the data, the primary approach to boost the prediction performance is adding layers above a single-layer long short-term memory model. Moreover, it is proved that by doing so, we could let the network make predictions more accurately and efficiently. Furthermore, by comparison, in general, we have obtained a more accurate prediction.

Spatiotemporal Analysis of Haze in Beijing Based on the Multi-Convolution Model

Abstract: As a kind of air pollution, haze has complex temporal and spatial characteristics. From the perspective of time, haze has different causes and levels of pollution in different seasons. From the perspective of space, the concentration of haze in adjacent areas will affect each other, showing some correlation. In this paper, we construct a multi-convolution haze-level prediction model for predicting haze levels in different areas of Beijing, which uses the remote sensing satellite image of the Beijing divided into nine regions as input and the haze pollution level as output. We categorize the predictions into four seasons in chronological order and use frequency histograms to analyze haze levels in different regions in different seasons. The results show that the haze pollution in the southern regions is significantly different from that in the northern regions. In addition, the haze tends to be clustered in adjacent areas. We use Global Moran’s I to analyze the predictions and find that haze is related to the geographical location in summer and autumn. We also use Local Moran’s I, Moran scatter plot, and Local Indicators of Spatial Association (LISA) to study the spatial characteristics of haze in adjacent areas. The results show, for the spatial distribution of haze in Beijing, that the southern regions present a high-high agglomeration, while the northern regions exhibit a ‘low-low agglomeration. The temporal evolution of haze on the seasonal scale, according to the chronological order of winter, spring, and summer to autumn, shows that the haze gradually becomes agglomerated. The main finding is that the haze pollution in southern Beijing is significantly different from that of northern regions, and haze tends to be clustered in adjacent areas.

Secondary aerosol formation in winter haze over the Beijing-Tianjin-Hebei Region, China

Abstract: Severe haze pollution occurs frequently in the winter over the Beijing-Tianjin-Hebei (BTH) region (China), exerting profound impacts on air quality, visibility, and human health. The Chinese Government has taken strict mitigation actions since 2013 and has achieved a significant reduction in the annual mean PM2.5 concentration over this region. However, the level of secondary aerosols during heavy haze episodes showed little decrease during this period. During heavy haze episodes, the concentrations of secondary aerosol components, including sulfate, nitrate and secondary organics, in aerosol particles increase sharply, acting as the main contributors to aerosol pollution. To achieve effective control of particle pollution in the BTH region, the precise and complete secondary aerosol formation mechanisms have been investigated, and advances have been made about the mechanisms of gas phase reaction, nucleation and heterogeneous reactions in forming secondary aerosols. This paper reviews the research progress in aerosol chemistry during haze pollution episodes in the BTH region, lays out the challenges in haze formation studies, and provides implications and directions for future research.

Haze Pollution Levels, Spatial Spillover Influence, and Impacts of the Digital Economy: Empirical Evidence from China

Abstract: With the development of digital technologies such as the Internet and digital industries such as e-commerce, the digital economy has become a new form of economic and social development, which has brought forth a new perspective for environmental governance, energy conservation, and emission reduction. Based on data from 30 Chinese provinces from 2011 to 2018, this study applies the space and threshold models to empirically examine the digital economy’s influence on haze pollution and its spatial spillover. Furthermore, it investigates the spatial diffusion effect of regional digital economic development and haze pollution by constructing a spatial weight matrix. Subsequently, an instrumental variable robustness test is performed. Results indicate the following: (1) Haze pollution has spatial spillover effects and high emission aggregation characteristics, with haze pollution in neighbouring provinces significantly aggravating pollution levels in the focal province. (2) China’s digital economy has positively impacted haze pollution, with digital economic development having a significant effect (i.e., most prominent in eastern China) on reducing haze pollution. (3) Changing the energy structure and supporting innovation can restrain haze pollution, and the digital economy can reduce the path mechanism of haze pollution through the mediating effect of an advanced industrial structure. It shows a non-linear characteristic that the influence of haze reduction continues to weaken. Thus, policymakers should include the digital economy as a mechanism for ecologically sustainable development in haze pollution control.

High-speed rail accessibility and haze pollution in China: A spatial econometrics perspective

Abstract: With deteriorating air quality in Chinese cities, high-speed rail (HSR) has attracted serious attention as an efficient transportation system to contain haze pollution across the country. This study introduces the concept of HSR accessibility and applies the generalized spatial two-stage least square method to examine the effect of HSR on haze pollution in China. A time-varying difference-in-difference strategy is then employed to recognize the causality from HSR to the haze pollution and eliminate endogeneity. Based on a panel dataset comprising 285 cities over 2010–2018, we find a negative effect of HSR accessibility on haze pollution. An increase of one standard deviation in HSR accessibility reduces PM2.5 concentration by 0.22%. However, the HSR pollution reduction effect varies significantly across cities. It is found that HSR can reduce haze pollution by improving the efficiency of resource allocation as well as promoting industrial structure change and technological innovation. This study proposes a new solution for pollution, i.e., improving urbanization quality through intercity transport efficiency enhancement.

Hierarchical Feature Fusion with Mixed Convolution Attention for Single Image Dehazing

Abstract: Single image dehazing, which aims at restoring a haze-free image from its correspondingly unconstrained hazy scene, is highly challenging and has gained immense popularity in recent years. However, the images generated using existing haze-removal methods often contain haze, artifacts, and color distortions, which severely degrade the visual quality of the final images. To this end, we propose a network combining multi-scale hierarchical feature fusion and mixed convolution attention to progressively and adaptively enhance the dehazing performance. The haze levels and image structure information are accurately estimated by fusing multi-scale hierarchical features, thus the model restores images with less remaining haze. The proposed attention mechanism is capable of reducing feature redundancy, learning compact internal representations, highlighting task-relevant features and further helping the model to estimate images with sharper textural details and more vivid colors. Therefore, with the application of multi-scale features extracted from both diverse layers and filters, the dehazing performance is significantly improved. Furthermore, a deep semantic loss function is proposed to highlight more semantic information in deep features. The experimental results show that the proposed method outperforms state-of-the-art haze removal algorithms.

Do economic development and population agglomeration inevitably aggravate haze pollution in China? New evidence from spatial econometric analysis

Abstract: With sustained economic development, China's ecological environment is becoming increasingly fragile and the problem of haze pollution is becoming increasingly prominent, which has affected the normal life of human beings and the stable development of society. In this paper, 287 cities' panel data from 1998 to 2016 are used, PM2.5 is used to represent haze pollution, and the spatial Durbin model is used to explore the role of the economy and population agglomeration on smog pollution. The empirical results show that (1) haze pollution has obvious spatial spillover. From the perspective of China as a whole, the relationship between the economy and smog pollution is an inverted U shape. (2) China is divided into three economic regions, i.e., the east, the middle, and the west. In the east and middle regions, it is found that economic development also shows an inverted U-shaped relationship with haze pollution. (3) Regardless of the country or the three major economic regions, population agglomeration is the primary factor that aggravates haze pollution; the progress of technology and the optimization of the industrial structure can improve haze pollution. (4) Through further analysis of the indirect effects of haze in China, it is found that there is a significant spatial spillover effect. According to the results of this research, policy suggestions are put forward.

Does haze cloud decision making? A natural laboratory experiment

Abstract: The adverse impact of haze on health and its association with a range of economic outcomes have received increasing attention in the literature. A natural laboratory experiment involving more than 600 subjects enables a first attempt at investigating the causal effect of haze, proxied by particulate matter of up to 2.5 microns in diameter (PM2.5) on decision making. This study was conducted in Beijing in October 2012 over five days with highly varying levels of PM2.5, which only became commonly known in China in 2013. We observed several effects associated with an increase in haze. In terms of individual decision making, we found increases in risk aversion and ambiguity aversion over gains. In terms of other-regarding behavior, subjects became less prosocial, giving less in a dictator game, contributing less in a public goods game, and reciprocating less in a sequential prisoners’ dilemma. Our results underpin several reported findings in the literature linking short-term variations in air quality to real-world economic variables, including stock market performance, worker productivity, movie attendance and revenue, criminal activities, and subjective wellbeing.

3D simulations of photochemical hazes in the atmosphere of hot Jupiter HD 189733b

Abstract: Photochemical hazes have been suggested as candidate for the high-altitude aerosols observed in the transmission spectra of many hot Jupiters. We present 3D simulations of the hot Jupiter HD 189733b to study how photochemical hazes are transported by atmospheric circulation. The model includes spherical, constant-size hazes particles that gravitationally settle and are transported by the winds as passive tracers, with particle radii ranging from 1 nm to 300 $\mu$m. We identify two general types of haze distribution based on particle size: In the small-particle regime ( 30 nm), hazes settle out quickly on the nightside, resulting in more hazes at the evening terminator. For small particles, terminator differences in haze mass mixing ratio and temperature considered individually can result in significant differences in the transit spectra of the terminators. When combining both effects for HD189733b, however, they largely cancel out each other, resulting in very small terminator differences in the spectra. Transit spectra based on the GCM-derived haze distribution fail to reproduce the steep spectral slope at short wavelengths in the current transit observations of HD 189733b. Differing optical properties of hazes, hotter temperatures at low pressures because of heating by hazes, enhanced sub-grid-scale mixing, or star spots might explain the mismatch between the model and observations.