Key role of atmospheric water content in the formation of regional haze in southern China

Kong, Liuwei; Hu, Min; Tan, Qinwen; Feng, Miao; Yu, Qian; An, Junling; Zhang, Yuanhang; Liu, Xingang; Cheng, Ning; Deng, Yijun; Zhai, Ruixiao; Wang, Zheng

doi:10.1016/j.atmosenv.2019.116918

Cited by 12 publications

(3 citation statements)

References 68 publications

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“…The increase in NSA can increase the hygroscopicity properties of aerosols, and more AWC can increase the pH by diluting the hydrogen ion concentration (Kong et al, 2020;Ding et al, 2019). Previous studies have also shown that SO 2− 4 formation reduces aerosol pH (Sun et al, 2014).…”

Section: Sensitivity Analysismentioning

confidence: 98%

Elucidating the pollution characteristics of nitrate, sulfate and ammonium in PM<sub>2.5</sub> in Chengdu, southwest China, based on 3-year measurements

et al. 2020

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Abstract. Nitrate, sulfate and ammonium (NSA) are the main secondary inorganic aerosols of PM2.5 and play an important role in air pollution. In this study, a 3-year observational experiment was conducted from 1 January 2015 to 31 December 2017, in Chengdu, southwest China. NSA pollution characteristics, chemical conversion generation, emission reduction control sensitivity and pollutant regional transport characteristics were analyzed. NSA are the most important chemical compositions of particles with aerodynamic equivalent diameter ≤2.5 µm in ambient air (PM2.5), and the contribution of nitrate to the accumulation of PM2.5 concentration is greater than that of sulfate and ammonium. NSA also have obvious characteristics of annual, monthly, seasonal, diurnal and weekly variations. Through observation data and model simulation, it was also found that the existence of an aerosol aqueous environment plays an important role in the formation and existence of NSA. Sensitivity analysis between NSA found that controlling NO3- and SO42- plays an important role in reducing the contribution of NSA to PM2.5, which also implies that the current control of NOx and SO2 is important for improving air pollution. Combined with meteorological conditions and potential source contribution function (PSCF) analysis, local emissions and regional emissions of pollutants are found to have important impacts on Chengdu's atmospheric environment. This research result not only provides an assessment of the current atmospheric emission reduction effect but also provides an important reference for atmospheric pollution control.

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Section: Sensitivity Analysismentioning

confidence: 98%

Elucidating the pollution characteristics of nitrate, sulfate and ammonium in PM<sub>2.5</sub> in Chengdu, southwest China, based on 3-year measurements

et al. 2020

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“…Primary pollutant can change under the influence of physical and chemical factors or react with other substances in the air to produce secondary pollutant with different properties (Drozd et al, 2018). The formation and transformation mechanism of secondary pollutant involves complex physical and chemical reactions between different pollutants, and its harm to the environment and biology is also greater than that of primary pollutant (Kong et al, 2019;Menares et al, 2020). For example, SO 2 and NO 2 , as precursors, can undergo photochemical oxidation reaction and gas-solid conversion to form sulfate and nitrate, becoming secondary particles.…”

Section: Pollution Sourcementioning

confidence: 99%

Design a regional and multistep air quality forecast model based on deep learning and domain knowledge

Zhang

2022

Front. Earth Sci.

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Air pollution is an issue across the world. It not only directly affects the environment and human health, but also influences the regional and even global climate by changing the atmospheric radiation budget, resulting in extensive and serious adverse effects. It is of great significance to accurately predict the concentration of pollutant. In this study, the domain knowledge of Atmospheric Sciences, advanced deep learning methods and big data are skillfully combined to establish a novel integrated model TSTM, derived from its fundamental features of Time, Space, Type and Meteorology, to achieve regional and multistep air quality forecast. Firstly, Expectation Maximization and Min-Max algorithms are used for the interpolation and normalization of data. Secondly, feature selection and construction are accomplished based on domain knowledge and correlation coefficient, and then Sliding Time Window algorithm is employed to build the supervised learning task. Thirdly, the features of pollution source and meteorological condition are learned and predicted by CNN-BiLSTM-Attention model, the integrated model of convolutional neural network and Bidirectional long short-term memory network based on Sequence to Sequence framework with Attention mechanism, and then Convolutional Long Short-Term Memory Neural Network (ConvLSTM) integrates the two determinant features to obtain predicted pollutant concentration. The multiple-output strategy is also employed for the multistep prediction. Lastly, the forecast performance of TSTM for pollutant concentration, air quality and heavy pollution weather is tested systematically. Experiments are conducted in Beijing-Tianjin-Hebei Air Pollution Transmission Channel (“2+26” cities) of China for multistep prediction of hourly concentration of six conventional air pollutants. The results show that the performance of TSTM is better than other benchmark models especially for heavy pollution weather and it has good robustness and generalization ability.

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“…Atmospheric aerosols possess complex compositions of organic and inorganic components that dictate their phase and mixing states. With variations of the ambient relative humidity (RH), changes in aerosol water content led to phase transitions such as effloresce, deliquescence, and liquid–liquid phase separation. − Effloresced particles, formed via crystallization of dissolved salts upon water evaporation, can directly or indirectly affect the radiative forcing, cloud formation, and atmospheric chemistry due to the changes in physicochemical properties (e.g., morphology, size, refractive index). − Therefore, aerosol efflorescence plays a crucial role in atmospheric processes and has profound impact on climate, air quality, , and human health. − …”

Section: Introductionmentioning

confidence: 99%

Role of WSOCs and pH on Ammonium Nitrate Aerosol Efflorescence: Insights into Secondary Aerosol Formation

Sun,

Hu,

Cao

et al. 2023

Environ. Sci. Technol.

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Efflorescence of ammonium nitrate (AN) aerosols significantly impacts atmospheric secondary aerosol formation, climate, and human health. We investigated the effect of representative water-soluble organic compounds (WSOCs) (sucralose (SUC), glycerol (GLY), and citric acid (CA) on AN:WSOC aerosol efflorescence using vacuum Fourier transform infrared spectroscopy. Combining efflorescence relative humidity (ERH) measurements, heterogeneous nucleation rates, and model predictions, we found that aerosol viscosity, correlating with molecular diffusion, effectively predicted ERH variations among the AN:WSOC aerosols. WSOCs with higher viscosity (SUC and CA) hindered efflorescence, while GLY with a lower viscosity showed a minor effect. At a low AN:CA molar ratio (10:1), CA promoted ERH, likely due to CA crystallization. Increasing the droplet pH inhibited AN:CA aerosol efflorescence. In contrast, for AN:SUC and AN:GLY aerosols, efflorescence is pH-insensitive. With the addition of trivial sulfate, AN:SUC droplets exhibited two-stage efflorescence, coinciding with ammonium sulfate and AN efflorescence. Given the atmospheric abundance, the morphology, phase, and mixing state of nitrate aerosols are significant for atmospheric chemistry and physics. Our results suggest that AN:WSOCs aerosols can exist in the amorphous phase in the atmosphere, with efflorescence behavior depending on the aerosol composition, viscosity, pH, and the cation and anion interactions in a complex manner.

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Key role of atmospheric water content in the formation of regional haze in southern China

Cited by 12 publications

References 68 publications

Elucidating the pollution characteristics of nitrate, sulfate and ammonium in PM<sub>2.5</sub> in Chengdu, southwest China, based on 3-year measurements

Elucidating the pollution characteristics of nitrate, sulfate and ammonium in PM<sub>2.5</sub> in Chengdu, southwest China, based on 3-year measurements

Design a regional and multistep air quality forecast model based on deep learning and domain knowledge

Role of WSOCs and pH on Ammonium Nitrate Aerosol Efflorescence: Insights into Secondary Aerosol Formation

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Key role of atmospheric water content in the formation of regional haze in southern China

Cited by 12 publications

References 68 publications

Elucidating the pollution characteristics of nitrate, sulfate and ammonium in PM&lt;sub&gt;2.5&lt;/sub&gt; in Chengdu, southwest China, based on 3-year measurements

Elucidating the pollution characteristics of nitrate, sulfate and ammonium in PM&lt;sub&gt;2.5&lt;/sub&gt; in Chengdu, southwest China, based on 3-year measurements

Design a regional and multistep air quality forecast model based on deep learning and domain knowledge

Role of WSOCs and pH on Ammonium Nitrate Aerosol Efflorescence: Insights into Secondary Aerosol Formation

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Product

Resources

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Elucidating the pollution characteristics of nitrate, sulfate and ammonium in PM<sub>2.5</sub> in Chengdu, southwest China, based on 3-year measurements

Elucidating the pollution characteristics of nitrate, sulfate and ammonium in PM<sub>2.5</sub> in Chengdu, southwest China, based on 3-year measurements