Temporal distribution and source apportionment of PM2.5 chemical composition in Xinjiang, NW-China

Turap, Yusan; Talifu, Dilinuer; Wang, Xinming; Maihemuti, Mailikezhati; Abulizi, Abulikemu; Ding, Xiang; Aierken, Tuergong; Rekefu, Suwubinuer

doi:10.1016/j.atmosres.2018.12.010

Cited by 62 publications

(42 citation statements)

References 57 publications

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“…The gradual concentration trend of PM 2.5 concentrations in the north slope of the Tianshan Mountain from 2005 indicated some environment effects of Zhundong Coalfield mining activities. With collecting and analyzing daily PM 2.5 samples in Dushanzi, Tura, et al [30] found that coal combustion and soil dust contributions accounts for nearly half of PM 2.5 sources. A great deal of research shows that the coal mining operations, coal transport, coal processing, and coal burning would all generate significant atmospheric pollution [62,63].…”

Section: Discussionmentioning

confidence: 99%

“…Although cities and urban agglomeration are playing key roles in regional economic development, various environmental problems begin to emerge under the influence of their intense human activities and fragile ecological environment. The air pollution caused by mixtures of industrial emission, vehicle emission, and fine dust particles has become an urgent problem in this region [30]. [41].…”

Section: Study Areamentioning

confidence: 99%

“…This regional policy difference had an unintended effect of encouraging polluting enterprises to move their investments to western provinces, such as Xinjiang. In the northern part of the Tianshan Mountain and the western margin of the Tarim Basin, about 10 million people have suffered from serious air pollution in the past decade [30]. Meanwhile, the North Tianshan Mountain Economic Zone (NTMEZ), the largest and the most comprehensive economic belt of China, is located in this region [31].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Chen et al [32] investigated the sources of heavy metals (HMs) and per fluorinated compounds (PFCs) in PM 2.5 in Urumqi and Shihezi, two of the major industrial cities in northern Xinjiang. Turap et al [30] measured the major components of ambient PM 2.5 in four seasons in Dushanzi, finding that the mixing of anthropogenic aerosol sources and dust were the main sources of PM 2.5 .…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Variations of Satellite-Based PM2.5 Concentrations and Its Determinants in Xinjiang, Northwest of China

Wang

Samat

Abuduwaili

et al. 2020

IJERPH

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With the aggravation of air pollution in recent years, a great deal of research on haze episodes is mainly concentrated on the east-central China. However, fine particulate matter (PM2.5) pollution in northwest China has rarely been discussed. To fill this gap, based on the standard deviational ellipse analysis and spatial autocorrelation statistics method, we explored the spatio-temporal variation and aggregation characteristics of PM2.5 concentrations in Xinjiang from 2001 to 2016. The result showed that annual average PM2.5 concentration was high both in the north slope of Tianshan Mountain and the western Tarim Basin. Furthermore, PM2.5 concentrations on the northern slope of the Tianshan Mountain increased significantly, while showing an obviously decrease in the western Tarim Basin during the period of 2001–2016. Based on the result of the geographical detector method (GDM), population density was the most dominant factor of the spatial distribution of PM2.5 concentrations (q = 0.550), followed by road network density (q = 0.423) and GDP density (q = 0.413). During the study period (2001–2016), the driving force of population density on the distribution of PM2.5 concentrations showed a gradual downward trend. However, other determinants, like DEM (Digital elevation model), NSL (Nighttime stable light), LCT (Land cover type), and NDVI (Normalized Difference Vegetation Index), show significant increased trends. Therefore, further effort is required to reveal the role of landform and vegetation in the spatio-temporal variations of PM2.5 concentrations. Moreover, the local government should take effective measures to control urban sprawl while accelerating economic development.

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Section: Discussionmentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Variations of Satellite-Based PM2.5 Concentrations and Its Determinants in Xinjiang, Northwest of China

Wang

Samat

Abuduwaili

et al. 2020

IJERPH

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“…The temperature was the highest in the non-heating period and the lowest in the heating period. By contrast, relative humidity was the highest during the heating period and lowest during the sandstorm and non-heating periods [18]. The large day-night temperature difference and long sunshine duration provide favorable meteorological conditions for the transformation of VOCs [19].…”

Section: Introductionmentioning

confidence: 97%

Volatile Organic Compounds in a Petrochemical Region in Arid of NW China: Chemical Reactivity and Source Apportionment

et al. 2019

Self Cite

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We measured volatile organic compounds (VOCs) during the heating, non-heating, and sandstorm periods in the air of the Dushanzi district in NW China and investigated their concentrations, chemical reactivity, and sources. The observed concentrations of total VOCs (TVOCs) were 22. 35 ± 17.60, 33.20 ± 34.15, and 17.05 ± 13.61 ppbv in non-heating, heating, and sandstorm periods, respectively. C 2 -C 5 alkanes, C 2 -C 3 alkenes, benzene, and toluene were the most abundant species, contributing more than 60% of the TVOCs. Among these VOCs, alkenes such as propene had the highest chemical reactivity, accounting for more than 60% of total hydroxyl radical loss rate (L OH ) and ozone formation potential (OFP). Chemical reactivity was the highest in the heating period. The average reaction rate constant (K OH -avg) and average maximum incremental reactivity coefficient (MIR-avg) of the total observed VOCs were (8.72 ± 1.42) × 10 −12 cm 3 /mol·s and 2.42 ± 0.16 mol/mol, respectively. The results of the source apportionment via the Positive Matrix Factorization (PMF) model showed that coal combustion (43.08%) and industrial processes (38.86%) were the major sources of VOCs in the air of the Dushanzi district. The contribution of coal combustion to VOCs was the highest in the heating period, while that of industrial solvents and oil volatilization was the lowest.completion of each sampling. After the sampling, the samples were stored at room temperature and sent to the lab for analysis after a month.

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Chemical and biological components of atmospheric particulate matter and their impacts on human health and crops: a review

Kumar

Dwivedi

2022

Aerobiologia

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Temporal distribution and source apportionment of PM2.5 chemical composition in Xinjiang, NW-China

Cited by 62 publications

References 57 publications

Spatio-Temporal Variations of Satellite-Based PM2.5 Concentrations and Its Determinants in Xinjiang, Northwest of China

Spatio-Temporal Variations of Satellite-Based PM2.5 Concentrations and Its Determinants in Xinjiang, Northwest of China

Volatile Organic Compounds in a Petrochemical Region in Arid of NW China: Chemical Reactivity and Source Apportionment

Chemical and biological components of atmospheric particulate matter and their impacts on human health and crops: a review

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