Dust events in southwestern Iran: Estimation of PM10 concentration based on horizontal visibility during dust events

Introduction: Recently, local dust events increased in Khuzestan province. Therefore, knowledge on its properties can have a crucial role in future prediction and planning. Materials and methods: This study investigated the effect of different boundary layer schemes for dust simulation by WRF_Chem model on March 14th 2012 in Khuzestan province. To validate the model, observation data such as horizontal visibility, 10-m wind speed and PM10 were provided. Results: The results indicated that the MYN scheme has the highest correlation between model outputs and observation for 10-m wind speed, PM10 and horizontal visibility. Due to the highest correlation of the 10 m wind speed, horizontal visibility, PM10 respectively with 0.83, -0.76 and 0.76 values and the highest consistency with the day-night variation of PM10, MYN scheme can be selected as the most suitable scheme. At the second level, UW scheme seems to be an appropriate option. In MYN and UW schemes, the maximum wind speed in 925 hPa level was estimated 24 m/s at 03 UTC, March 14th which caused an increase in the 10 m wind speed at 06 and 09UTC. Therefore, the dust emitted from the surface to the air. Although the results of MYJ scheme showed proper correlation and temporal variation with observed, but as it determined PM10 concentration with high difference, it can’t be considered as a suitable scheme for simulation dust concentration. Conclusion: Although the PM10 concentration obtained by WRF_Chem showed difference with the observation for all the selected boundary layer schemes, MYN scheme gives the most appropriate result.

Download Full-text

Monitoring the contribution of desert dust intrusion to PM10 concentration in Northern Cyprus

Clean Air Journal ◽

10.17159/2410-972x/2017/v27n2a8 ◽

2017 ◽

Vol 27 (2) ◽

Author(s):

HY Sulaiman ◽

S Çakir

Keyword(s):

Pm10 Concentration ◽

Hysplit Model ◽

Desert Dust ◽

Regional Background ◽

Northern Cyprus ◽

Local Soil ◽

Dust Events ◽

Standard Limit ◽

Urban Sites ◽

Daily Pm10

Air quality in the Mediterranean basin has been affected by PM10 pollution induced by transported desert dust and local emission. The study used PM10 data from Nicosia, Kyrenia, Guzelyurt and Famagusta urban representatives, Kalecik rural background and Alevkayasi regional background. HYSPLIT model and satellite data were used to identify dust days and dust input was quantified using the method suggested by the European Commission. Anthropogenic background contribution of each site was then estimated by subtracting the regional background concentrations. A total of 35 dust days occurred on Cyprus island within the 3-years period; mostly during winter and spring. Daily PM10 concentration on dust days can reach up to 400 μg/m3. After removing dust background, annual PM10 concentrations were 48-58 μg/m3 in Nicosia, 42-47 μg/m3 in Famagusta, 40-50 μg/m3 in Kyrenia, 33-41 μg/m3 in Guzelyurt, 21-28 μg/ m3 in Alevkayasi, and 32-38 μg/m3 in Kalecik. PM10 concentrations were higher during winters in the urban sites. Despite the high frequency of dust events, only a fraction of exceedances of the standard limit in the urban sites were attributable to dust. Anthropogenic background sources contributions were 12.3 μg/m3 in Guzelyurt, 18 μg/m3 in Kyrenia, 18.4 μg/m3 in Famagusta, 27.8 μg/m3 in Nicosia and 9.7 μg/m3 in Kalecik. Effects of other natural sources that the study did not assess, such as sea salt and local soil resuspension, could be the reason for exceedances.

Download Full-text

Identification and assessment of intense African dust events and contribution to PM10 concentration in Tunisia

The European Physical Journal Plus ◽

10.1140/epjp/i2019-13066-4 ◽

2019 ◽

Vol 134 (11) ◽

Author(s):

Houda Chtioui ◽

Karim Bouchlaghem ◽

Mohamed Hichem Gazzah

Keyword(s):

Pm10 Concentration ◽

African Dust ◽

Dust Events

Download Full-text

A Study on Characteristics of Atmospheric Boundary Layer during Dust Events in Lanzhou, China and their Relation with PM10 Pollution

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.113-116.372 ◽

2010 ◽

Vol 113-116 ◽

pp. 372-377 ◽

Cited By ~ 1

Author(s):

Xin Yuan Feng ◽

Shi Gong Wang

Keyword(s):

Boundary Layer ◽

Wind Speed ◽

Atmospheric Boundary Layer ◽

Momentum Flux ◽

Turbulent Flux ◽

Layer Structure ◽

Pm10 Concentration ◽

Flux Transport ◽

Strong Inversion ◽

Dust Events

In order to understand the influence mechanism of dust events on PM10 pollution, the characteristics of atmospheric boundary-layer structure and turbulent flux transport during different kinds of dust events in Lanzhou and their relation with PM10 pollution were analyzed by using the data of PM10 concentration, gradient measurements of meteorological tower and turbulent flux measurements observed at SACOL. As dust events break out, the strong inversion in the boundary layer breaks and a mixing layer develops quickly. With the rise of wind speed, PM10 concentration increases sharply. If there is obvious dust transport from upstream regions, the peak of PM10 concentration can lag behind the peak of wind speed. The vertical transport of momentum flux is very strong. The turbulent momentum downward transport, especially the downward momentum flux of meridional velocity, is the main factor which causes the dust blowing and the heavy PM10 pollution during dust events.

Download Full-text

Long-Term Variability of Dust Events in Southwestern Iran and Its Relationship with the Drought

Atmosphere ◽

10.3390/atmos12101350 ◽

2021 ◽

Vol 12 (10) ◽

pp. 1350

Author(s):

Nasim Hossein Hamzeh ◽

Dimitris G. Kaskaoutis ◽

Alireza Rashki ◽

Kaveh Mohammadpour

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Distribution ◽

Dust Storms ◽

Modis Aod ◽

Spatio Temporal ◽

Dust Events ◽

Southwestern Iran ◽

Local Dust ◽

Southwest Iran

Dust storms represent a major environmental challenge in the Middle East. The southwest part of Iran is highly affected by dust events transported from neighboring desert regions, mostly from the Iraqi plains and Saudi Arabia, as well as from local dust storms. This study analyzes the spatio-temporal distribution of dust days at five meteorological stations located in southwestern Iran covering a period of 22 years (from 1997 to 2018). Dust codes (06, 07, 30 to 35) from meteorological observations are analyzed at each station, indicating that 84% of the dust events are not of local origin. The average number of dust days maximizes in June and July (188 and 193, respectively), while the dust activity weakens after August. The dust events exhibit large inter-annual variability, with statistically significant increasing trends in all of five stations. Spatial distributions of the aerosol optical depth (AOD), dust loading, and surface dust concentrations from a moderate resolution imaging spectroradiometer (MODIS) and Modern-Era Retrospective analysis for Research and Applications (MERRA-2) retrievals reveal high dust accumulation over southwest Iran and surrounding regions. Furthermore, the spatial distribution of the (MODIS)-AOD trend (%) over southwest Iran indicates a large spatial heterogeneity during 2000–2018 with trends ranging mostly between −9% and 9% (not statistically significant). 2009 was the most active dust year, followed by 2011 and 2008, due to prolonged drought conditions in the fertile crescent and the enhanced dust emissions in the Iraqi plains during this period. In these years, the AOD was much higher than the 19-year average (2000 to 2018), while July 2009 was the dustiest month with about 25–30 dust days in each station. The years with highest dust activity were associated with less precipitation, negative anomalies of the vegetation health index (VHI) and normalized difference vegetation index (NDVI) over the Iraqi plains and southwest Iran, and favorable meteorological dynamics triggering stronger winds.

Download Full-text

Evaluation of African Dust Events and Effect on PM10 Concentration in Tunisia

Recent Advances in Environmental Science from the Euro-Mediterranean and Surrounding Regions (2nd Edition) - Environmental Science and Engineering ◽

10.1007/978-3-030-51210-1_58 ◽

2021 ◽

pp. 359-365

Author(s):

Karim Bouchlaghem ◽

Houda Chtioui ◽

Mohamed Hichem Gazzah

Keyword(s):

Pm10 Concentration ◽

African Dust ◽

Dust Events

Download Full-text

Investigation of Aeolian Dust Deposition Rates in Different Climate Zones of Southwestern Iran

Atmosphere ◽

10.3390/atmos12020229 ◽

2021 ◽

Vol 12 (2) ◽

pp. 229

Author(s):

Mansour Ahmadi Foroushani ◽

Christian Opp ◽

Michael Groll

Keyword(s):

Deposition Rate ◽

Dust Deposition ◽

Deposition Rates ◽

Climate Zones ◽

Long Run ◽

Seasonal Deposition ◽

Dust Events ◽

Southwestern Iran ◽

Major Factors ◽

Modern Era

Dust and atmospheric particles have been described in southwestern Iran primarily in terms of load, concentration and transport. The passive deposition, however, has been discussed inadequately. Therefore, the relationships between different climate zones in southwestern Iran and dust deposition rates were quantified between 2014 and 2017 using both space- (second modern-era retrospective analysis for research and applications, version 2 reanalysis model) and ground-based (eolian ground deposition rate) tools. In addition, the surface meteorological records, including the wind patterns favoring the occurrence of dust events, were examined. A hot desert climate (BWh), hot semi-arid climate (BSh), and temperate hot and dry summer climate (Csa) were identified as the three dominant climate regions in the study area, exhibiting the highest average dust deposition rates. In this study, correlations between the most relevant climate patterns and deposition rate weather parameters were found to describe a region’s deposition rate when a dust event occurred. Based on these results, the BSh and Csa regions were found to be associated with the seasonal cycle of dust events in March, April, and May, revealing that in the long run meteorological conditions were responsible for the varying dust deposition rates. Relatively, precipitation and temperature were the two major factors influencing dust deposition rates, not wind speed. Moreover, the peak seasonal deposition rates in the spring and summer were 8.40 t km−2 month−1, 6.06 t km−2 month−1, and 3.30 t km−2 month−1 for the BWh, BSh, and Csa climate regions, respectively. However, each of these climate types was directly related to the specific quantity of the dust deposition rates. Overall, the highest dust deposition rates were detected over the years studied were 100.80 t km−2 year−1, 79.27 t km−2 year−1, and 39.60 t km−2 year−1 for BWh, BSh, and Csa, respectively.

Download Full-text

Estimating atmospheric visibility using synergy of MODIS data and ground-based observations

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-368-46-2015 ◽

2015 ◽

Vol 368 ◽

pp. 46-50

Author(s):

H. Komeilian ◽

S. Mohyeddin Bateni ◽

T. Xu ◽

J. Nielson

Keyword(s):

Meteorological Data ◽

Back Propagation ◽

Mean Square ◽

Atmospheric Visibility ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Dust Events ◽

Southwestern Iran

Abstract. Dust events are intricate climatic processes, which can have adverse effects on human health, safety, and the environment. In this study, two data mining approaches, namely, back-propagation artificial neural network (BP ANN) and supporting vector regression (SVR), were used to estimate atmospheric visibility through the synergistic use of Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B (L1B) data and ground-based observations at fourteen stations in the province of Khuzestan (southwestern Iran), during 2009–2010. Reflectance and brightness temperature in different bands (from MODIS) along with in situ meteorological data were input to the models to estimate atmospheric visibility. The results show that both models can accurately estimate atmospheric visibility. The visibility estimates from the BP ANN network had a root-mean-square error (RMSE) and Pearson’s correlation coefficient (R) of 0.67 and 0.69, respectively. The corresponding RMSE and R from the SVR model were 0.59 and 0.71, implying that the SVR approach outperforms the BP ANN.

Download Full-text