scholarly journals Dust storm index anomaly for sand-dust events monitoring in western Iran and its association with the NDVI and LST anomalies

2021 ◽  
Author(s):  
Zohre Ebrahimi-Khusfi ◽  
Fatemeh Roustaei
Keyword(s):  
Dust Storm ◽  
Western Iran ◽  
Dust Events ◽  
Storm Index ◽  
Sand Dust

Dust Storm Index Anomaly for Sand-Dust Events Monitoring in Western Iran and its Association with the NDVI and LST Anomalies

2021 ◽  
Author(s):  
Zohre Ebrahimi-Khusfi ◽  
Fatemeh Roustaei
Keyword(s):  
Dust Storm ◽  
Land Surface ◽  
Arid Regions ◽  
Decision Makers ◽  
Negative Consequences ◽  
Dust Pollution ◽  
Western Iran ◽  
Dust Events ◽  
Storm Index ◽  
Sand Dust

Abstract Sand-dust events (SDE) are an increasing concern in many arid and semi-arid regions of the world, which have severely damaged air quality and human health in recent years. This study was conducted to monitor the SDE in western Iran using the dust storm index anomaly (DSIA) during 2000–2018. The spatiot-emporal change detection and statistical analysis were used to understand the impacts of normalized difference vegetation cover anomaly (NDVIA) and land surface temperature anomaly (LSTA) on the SDE activities. The area has suffered from the highest dust pollution in 2004, 2009, and 2012 (DSIA > + 40) while it experienced the lowest dust pollution in 2002 and 2017 (DSIA<-40). Approximately 48% of western Iran experienced decreasing changes and 52 % of the total area experienced increasing changes in dust pollution during 2010–2018 compared to the previous years. Incremental changes in NDVIA and LSTA were observed in 73.2% and 7.5% of the study area while their decreasing changes were observed in 26.8% and 92.5% of the total area, respectively. Spatially, regions affected by the increase in dust pollution are mainly distributed in the eastern and southern regions of the study area. Significant effects of changes in anomalies of both terrestrial parameters on DSIA were observed throughout the study period (R LSTA−DSIA = + 0.52; R NDVIA−DSIA= -0.41); P < 0.05). It was also found that spatial correlation between LSTA and DSIA as well as NDVIA and DSIA in many parts of the study area were significant at the 95% confidence level (). These findings can be useful for decision-makers to assess the risks of dust pollution and reduce its negative consequences in western Iran.

Mapping Sand-dust Storm Risk of the World

2015 ◽  
pp. 115-150 ◽  
Author(s):  
Huimin Yang ◽  
Xingming Zhang ◽  
Fangyuan Zhao ◽  
Jing’ai Wang ◽  
Peijun Shi ◽  
...  
Keyword(s):  
Dust Storm ◽  
The World ◽  
Sand Dust

Dust storm episodes at the aerodromes in the Asian part of Russia

2021 ◽  
Vol 2 ◽  
pp. 20-42
Author(s):  
A.R. Ivanova ◽  
◽  
E.N. Skriptunova ◽  
N.I. Komasko ◽  
A.A. Zavialova ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Dust Storm ◽  
Trajectory Analysis ◽  
Dust Storms ◽  
Strong Wind ◽  
Dust Transport ◽  
Air Masses ◽  
Classical Definition ◽  
Dust Events ◽  
Definition Of

Dust storm episodes at the aerodromes in the Asian part of Russia / Ivanova A.R., Skriptunova E.N., Komasko N.I., Zavialova A.A.// Hydrometeorological Research and Forecasting, 2021, no. 2 (380), pp. 20-42. According to 2001-2020 METAR data, episodes of dust storms at 26 international aerodromes in the Asian Russia causing poor visibility are studied. The conditions for issuing reports on dust storms, their correspondence to the definition of a dust storm are discussed. It was found that out of 337 reports describing dust transport by strong wind, only 7 episodes registered at the aerodromes of Irkutsk, Abakan, Omsk, and Blagoveshchensk corresponded to the classical definition. The others detected at 15 of 26 aerodromes may be defined as “dust events” – the episodes of dust transfer causing the nonessential visibility reduction. The seasonal variation in such episodes and its connection with changes in visibility are studied. The characteristics of dusty air masses and the direction of their advection are given. Keywords: dust storm, dust events, aerodromes of Asian Russia, seasonal variation, trajectory analysis

Assessing the impact of Chinese FY-3/MERSI AOD data assimilation on air quality forecasts: Sand dust events in northeast China

2019 ◽  
Vol 205 ◽  
pp. 78-89 ◽  
Author(s):  
Yansong Bao ◽  
Liuhua Zhu ◽  
Qin Guan ◽  
Yuanhong Guan ◽  
Qifeng Lu ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Northeast China ◽  
Dust Events ◽  
The Impact ◽  
Sand Dust

scholarly journals The Impacts of Taklimakan Dust Events on Chinese Urban Air Quality in 2015

Atmosphere ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 281 ◽  
Author(s):  
Xiaoyu Li ◽  
Xiaodong Liu ◽  
Zhi-Yong Yin
Keyword(s):  
Air Quality ◽  
Dust Storm ◽  
Northwest China ◽  
Urban Air Quality ◽  
Urban Air ◽  
Taklimakan Desert ◽  
The Taklimakan Desert ◽  
Pm10 And Pm2.5 ◽  
Dust Events ◽  
Mass Concentrations

Aerosols are an important factor affecting air quality. As the largest source of dust aerosol of East Asia, the Taklimakan Desert in Northwest China witnesses frequent dust storm events, which bring about significant impacts on the downstream air quality. However, the scope and timing of the impacts of Taklimakan dust events on Chinese urban air quality have not yet been fully investigated. In this paper, based on multi-source dust data including ground observations, satellite monitoring, and reanalysis products, as well as air quality index (AQI) and the mass concentrations of PM10 and PM2.5 at 367 urban stations in China for 2015, we examined the temporal and spatial characteristics of the impacts of the Taklimakan dust events on downstream urban air quality in China. The results show that the Taklimakan dust events severely affected the air quality of most cities in Northwest China including eastern Xinjiang, Hexi Corridor and Guanzhong Basin, and even northern Southwest China, leading to significant increases in mass concentrations of PM10 and PM2.5 in these cities correlating with the occurrence of dust events. The mass concentrations of PM10 on dust days increased by 11–173% compared with the non-dust days, while the mass concentration of PM2.5 increased by 21–172%. The increments of the mass concentrations of PM10 and PM2.5 on dust days decreased as the distances increased between the cities and the Taklimakan Desert. The influence of the Taklimakan dust events on the air quality in the downstream cities usually persisted for up to four days. The mass concentrations of PM10 and PM2.5 increased successively and the impact duration shortened gradually with increasing distances to the source area as a strong dust storm progressed toward the southeast from the Taklimakan Desert. The peaks of the PM10 concentrations in the downstream cities of eastern Xinjiang, the Hexi Corridor and the Guanzhong Basin occurred on the second, third and fourth days, respectively, after the initiation of the Taklimakan dust storm.

scholarly journals Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Yonghua Xie ◽  
Yurong Liu ◽  
Qingqiu Fu
Keyword(s):  
Dust Storm ◽  
Adaptive Sampling ◽  
Imbalanced Data ◽  
Real Data ◽  
Classification Performance ◽  
Selection Strategy ◽  
Data Sets ◽  
Minority Class ◽  
Redundant Data ◽  
Sand Dust

In view of the SVM classification for the imbalanced sand-dust storm data sets, this paper proposes a hybrid self-adaptive sampling method named SRU-AIBSMOTE algorithm. This method can adaptively adjust neighboring selection strategy based on the internal distribution of sample sets. It produces virtual minority class instances through randomized interpolation in the spherical space which consists of minority class instances and their neighbors. The random undersampling is also applied to undersample the majority class instances for removal of redundant data in the sample sets. The comparative experimental results on the real data sets from Yanchi and Tongxin districts in Ningxia of China show that the SRU-AIBSMOTE method can obtain better classification performance than some traditional classification methods.

The Dust Storm Index (DSI): A method for monitoring broadscale wind erosion using meteorological records

2014 ◽  
Vol 12 ◽  
pp. 29-40 ◽  
Author(s):  
T. O’Loingsigh ◽  
G.H. McTainsh ◽  
E.K. Tews ◽  
C.L. Strong ◽  
J.F. Leys ◽  
...  
Keyword(s):  
Dust Storm ◽  
Wind Erosion ◽  
Storm Index
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