The Research on Air Pollution Laws in Guanzhong Urban Agglomeration Based on High Frequency AQI Data

Hu, Qiu-Ling; Yang, Zhe

doi:10.12783/dteees/seee2016/6578

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The air quality is best in summer, then deteriorates from autumn gradually, and gets worst in winter and spring, in which the major pollutants are PM2.5 and PM10. Ozone mainly occurs in summer, which is consistent with the previous research that ozone density is inversely proportional to the concentration of suspended particulate matters [6]. The differences become more evident when the internal cities of each regions were compared.…”

Section: Regional and Descriptive Statistical Analysissupporting

confidence: 91%

The Allocation of Responsibilities in Air Pollution: Evidence from Ethnic Minority Urban Agglomerations

Hu¹,

Liu²,

Yang³

2018

dteees

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Abstract. This paper constructed AQI hourly & monthly index to make a comparison based on the high-frequency AQI data and major pollutants statistical data of four ethnic minority urban agglomerations, then established a VAR model to analyze the Variance Decomposition. The results indicated that the air pollution of four urban agglomerations had exhibited noticeable seasonal effect and clustering feature. Once there was a deterioration of air quality in one city, the other neighboring cities would spread it rapidly but decayed slowly. According to the results of the Variance Decomposition, whose orders was determined by the average velocity to the peak of the impulse response function, a scheme was proposed in this paper to solve the issues in the allocation of responsibilities for regional joint prevention in each city.

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Section: Regional and Descriptive Statistical Analysissupporting

confidence: 91%

The Allocation of Responsibilities in Air Pollution: Evidence from Ethnic Minority Urban Agglomerations

Hu¹,

Liu²,

Yang³

2018

dteees

Self Cite

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“…While in the spring,sandstorms are prone to occur in northern China,the wind will carry a lot of dust into the air and then causes serious air pollution [15] . In addition, Guanzhong urban agglomeration's contaminants may spread to Lan-Xi-Yin urban agglomeration by atmospheric circulation due to the near geographic distance between two urban agglomerations according to the study of Qiu-ling HU and Zhe YANG [16] .…”

Section: Analysis Of Causesmentioning

confidence: 99%

Mining on the Air Pollutants Association Rules of Lan-Xi-Yin Urban Agglomeration

Hu¹,

Guo²

2018

dteees

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Based on the high-frequency AQI data, this paper analyzed the air pollution laws of Lan-Xi-Yin urban agglomeration from the perspective of air quality grade distribution, the primary pollutant and pollutants' daily variation.Then it used the APRIORI algorithm to excavate the rules of pollutants. The results show that the overall air quality of the Lan-Xi-Yin urban agglomeration is better than that of national average,and PM10 and O 3 are the primary pollutants. The air quality in summer and autumn is obviously better than that in winter and spring. In addition to O 3 , the fluctuations of PM10, PM2.5, SO 2 , NO 2 and CO are both bimodal within a day, 9:00 -12:00 and 21:00 -23:00 are the concentration's high period; APRIORI algorithm finds 18 high confidence PM2.5 rules in total. According to the results of excavation, we should pay attention to the concentration of CO. The air pollution control of the urban agglomeration can start from the source of CO due to the homology characteristics of pollutants.

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Correlation networks of air particulate matter ($$\hbox {PM}_{2.5}$$): a comparative study

Vlachogiannis

Jin

et al. 2021

Appl Netw Sci

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Over the last decades, severe haze pollution constitutes a major source of far-reaching environmental and human health problems. The formation, accumulation and diffusion of pollution particles occurs under complex temporal scales and expands throughout a wide spatial coverage. Seeking to understand the transport patterns of haze pollutants in China, we review a proposed framework of time-evolving directed and weighted air quality correlation networks. In this work, we evaluate monitoring stations’ time-series data from China and California, to test the sensitivity of the framework to region size, climate and pollution magnitude across multiple years (2014–2020). We learn that the use of hourly $$\hbox {PM}_{2.5}$$ PM 2.5 concentration data is needed to detect periodicities in the positive and negative correlations of the concentrations. In addition, we show that the standardization of the correlation function method is required to obtain networks with more meaningful links when evaluating the dispersion of a severe haze event at the North China Plain or a wildfire event in California during December 2017. Post COVID-19 outbreak in China, we observe a significant drop in the magnitude of the assigned weights, indicating the improved air quality and the slowed transport of $$\hbox {PM}_{2.5}$$ PM 2.5 due to the lockdown. To identify regions where pollution transport is persistent, we extend the framework, partitioning the dynamic networks and reducing the networks’ complexity through node subsampling. The end result separates the temporal series of $$\hbox {PM}_{2.5}$$ PM 2.5 in set of regions that are similarly affected through the year.

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The Research on Air Pollution Laws in Guanzhong Urban Agglomeration Based on High Frequency AQI Data

Cited by 3 publications

References 7 publications

The Allocation of Responsibilities in Air Pollution: Evidence from Ethnic Minority Urban Agglomerations

The Allocation of Responsibilities in Air Pollution: Evidence from Ethnic Minority Urban Agglomerations

Mining on the Air Pollutants Association Rules of Lan-Xi-Yin Urban Agglomeration

Correlation networks of air particulate matter ($$\hbox {PM}_{2.5}$$): a comparative study

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