Analysis of improvement effect of PM2.5 and gaseous pollutants in Beijing based on self-organizing map network

Liu, Shuang; Gautam, Atul; Yang, Xiankun; Tao, Jinhui; Wang, Xinyu; Zhao, Wenji

doi:10.1016/j.scs.2021.102827

Cited by 13 publications

(8 citation statements)

References 34 publications

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“…A few studies have analyzed the spatial and temporal heterogeneity of relationships among these air pollutants in China. Xie et al (2015), Li et al (2017), Zhang et al (2018), andLiu et al (2021) conducted the regional or national analysis of relationships among PM 2.5 , PM 10 , CO, SO 2 , NO 2 , and O 3 in China. These studies concluded that relationships among these air pollutants were heterogeneous in different cities and time periods.…”

Section: Discussionmentioning

confidence: 99%

“…Variations of these air pollutants, except O 3 , were closest to each other in January, which was typically the coldest month of the year. This is because these gaseous precursors were mainly generated from anthropogenic activities, e.g., coal and biomass burning for heating in this period, which directly contaminated the environment and also facilitated the formation of PM 2.5 (Liu et al, 2021).…”

Section: Spatio-temporal Heterogeneity Of Relationships Among Air Pol...mentioning

confidence: 99%

“…Zhang et al (2018) characterized spatial and temporal heterogeneity of relationships between air pollutants influenced by meteorological and geographical conditions by gray correlation analysis. Liu et al (2021) explored spatiotemporal changes in relationships among pollutants by Pearson correlation analysis and identified seasonal variations because of weather conditions. In addition to being spatially and temporally heterogeneous, relationships among these air pollutants are also non-linear due to complex chemical reactions, which refer to the concurrent formation of different air pollutants (Wang et al, 2014;Chu et al, 2015;Abdullah et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Tri-clustering–based exploration of spatio-temporal heterogeneity of six criteria air pollutants and their relationships in China

2022

Front. Earth Sci.

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Severe air pollution in China has become a challenging issue because of its adverse health effects. The distribution of air pollutants and their relationships exhibits spatio-temporal heterogeneity due to influences by meteorological and socioeconomic factors. Investigation of spatio-temporal variations of criteria air pollutants and their relationships, thus, helps understand the current status and further assist pollution prevention and control. Even though many studies have been conducted, relationships among pollutants are non-linear due to complicated chemical reactions and were difficult to model by linear analyses in previous studies. Here, we presented a tri-clustering–based method, the Bregman cuboid average tri-clustering algorithm with I-divergence (BCAT_I), to explore spatio-temporal heterogeneity of air pollutants and their relationships in China. Concentrations of PM2.5, PM10, CO, SO2, NO2, and O3 in 31 provincial cities in 2021 were used as the case study dataset. Results showed that air pollutants except O3 exhibited spatial and seasonal variations, i.e., low in summer in southern cities and high in winter in northern cities. Variations of PMs were more similar to those of CO than other pollutants in southern cities in 2021. Results also found that relationships among these air pollutants were heterogeneous in different regions and time periods in China. Moreover, with the increasing level of NO2 from summer to winter in northern cities, concentrations of O3 first decreased and then increased. This is because the response of O3 to NO2 was negative at the low pollution level due to the titration reaction, which, however, changed to positive when concentrations of NO2 became high.

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Section: Discussionmentioning

confidence: 99%

Section: Spatio-temporal Heterogeneity Of Relationships Among Air Pol...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tri-clustering–based exploration of spatio-temporal heterogeneity of six criteria air pollutants and their relationships in China

2022

Front. Earth Sci.

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“…Hourly averages of three sets of data (R std , L bj , and S bj ) were input into the SOM. Determining the size of the output map is crucial for the SOM (Chang et al, 2020;Liu et al, 2021). To reduce the subjectivity, the K-means cluster method was used for the decision-making regarding size.…”

Section: Self-organizing Mapmentioning

confidence: 99%

The impact of atmospheric motions on source-specific black carbon and the induced direct radiative effects over a river-valley region

et al. 2022

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Abstract. Black carbon (BC) is one of the most important short-lived climate forcers, and atmospheric motions play an important role in determining its mass concentrations of pollutants. Here an intensive observation was launched in a typical river-valley city to investigate relationships between atmospheric motions and BC aerosols. Equivalent BC (eBC) source apportionment was based on an aethalometer model with the site-dependent absorption Ångström exponents (AAEs) and the mass absorption cross sections (MACs) retrieved using a positive matrix factorization (PMF) model based on observed chemical components (i.e., EC, POC, K+, Mg, Al, Si, S, Cl, Ca, V, Mn, Fe, Ni, Cu, As, Se, Br, Sr, Pb, Ga, and Zn) and primary absorption coefficients at selected wavelengths from λ=370 to 880 nm. The derived AAEs from 370 to 880 nm were 1.07 for diesel vehicular emissions, 2.13 for biomass burning, 1.74 for coal combustion, and 1.78 for mineral dust. The mean values for eBCfossil and eBCbiomass were 2.46 and 1.17 µg m−3, respectively. Wind run distances and the vector displacements of the wind in 24 h were used to construct a self-organizing map, from which four atmospheric motion categories were identified (local-scale dominant, local-scale strong and regional-scale weak, local-scale weak and regional-scale strong, and regional-scale dominant). BC pollution was found to be more likely when the influence of local-scale motions outweighed that of regional-scale motions. Cluster analysis for the back-trajectories of air mass calculated by the Hybrid Single-Particle Lagrangian Integrated Trajectory model at the study site indicated that the directions of air flow can have different impacts for different scales of motion. The direct radiative effects (DREs) of source-specific eBC were lower when the influence of regional-scale motions outweighed that of the local ones. However, due to chemical aging of the particles during transport – the DRE efficiencies under regional-scale motions were ∼ 1.5 times higher than those under more local influences. The finding that the DRE efficiency of BC increased during the regional transport suggested significant consequences in regions downwind of pollution sources and emphasizes the importance of regionally transported BC for potential climatic effects.

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“…Meanwhile, some previous research procedures and method settings were not comparatively clear. In 2021, Shuang Liu et al (2021) studied the spatial and temporal characteristics of PM2.5, SO2, CO, NO2, and O3 based on data from 2014 to 2019 in Beijing. Also, they explored the influence of land surface temperature (LST) on contamination.…”

Section: Introductionmentioning

confidence: 99%

Regression model and method settings for air pollution status analysis based on air quality data in Beijing (2017–2021)

Wang

2023

Int J Data Sci Anal

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Regression analysis is an essential tool for modeling and analyzing data, which can be utilized in various areas for predictive analysis and discovering relationships between variables. However, guidelines such as the model's features, dataset selection, and method settings for using regression models to explore air pollution status are not detailed. This paper applied regression analysis based on air quality data in Beijing from 2017 to 2021, to study the characteristics of regression models, provide research guidance, and update the air pollution research data based on the dataset. This paper drew the latest conclusions: (1) PM2.5 and NO2 are positively correlated on the test set from these five years, yielding a correlation coefficient of 0.7036 by using linear regression. The respective coefficient of determination on small-scale test sets for 2017, 2019, and 2021 is much lower than those derived from a five-year dataset. Single year dataset is not befitting for linear regression analysis. (2) The polynomial regression's coefficient of determination on the training set is higher than that of the linear regression model, which is more proper for regression analysis on a one-year dataset. (3) PM2.5 and NO2 concentrations are strongly positively correlated with whether the air is polluted or not, and the correlation coefficient on the test set from these five years is 0.9697. The accuracy of logistic regression in classifying air pollution status based on these two pollutants' concentrations reaches 0.9430. Besides, this paper proposed some appropriate parameter settings for the logistic regression method provided by Python third-party-library sklearn. Specifically, L2-type regularization is better optimized for the 2017-2021 dataset. L1-type regularization works better when applying a one-year dataset. A boost in the inverse of the regularization strength to 1.8 will optimize the regularization.

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Analysis of improvement effect of PM2.5 and gaseous pollutants in Beijing based on self-organizing map network

Cited by 13 publications

References 34 publications

Tri-clustering–based exploration of spatio-temporal heterogeneity of six criteria air pollutants and their relationships in China

Tri-clustering–based exploration of spatio-temporal heterogeneity of six criteria air pollutants and their relationships in China

The impact of atmospheric motions on source-specific black carbon and the induced direct radiative effects over a river-valley region

Regression model and method settings for air pollution status analysis based on air quality data in Beijing (2017–2021)

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