scholarly journals Air quality changes in Ukraine during the April 2020 wildfire event

2020 ◽  
Vol 24 (4) ◽  
pp. 271-284
Author(s):  
Mykhailo Savenets ◽  
Volodymyr Osadchyi ◽  
Andrii Oreshchenko ◽  
Larysa Pysarenko
Keyword(s):  
Air Quality ◽  
Dust Storm ◽  
Storm Event ◽  
Quality Changes ◽  
Hysplit Model ◽  
Web Based ◽  
Air Masses ◽  
Pollutants Emission ◽  
Dust Storm Event ◽  
Event Based

The paper analyzes air quality changes in Ukraine during a wildfire event in April 2020 and a dust storm episode during the 16th of April 2020. The wildfire event contained two episodes of active fires and huge pollutants' emission: 4-14 April and 16-21 April, respectively. Using the Sentinel-5P data of CO and NO 2 column number density and ground-based measurements, there was estimated air quality deterioration. Advection of polluted air masses and analysis of affected territories were made in combination with a Web-based HYSPLIT model. Satellite data described air quality changes better than in-situ measurements. Data intercomparison showed better coincidence in regions that were not affected by wildfire emissions. The paper described the dust storm event based on absorbing aerosol index (AAI) data that occurred between two wildfire episodes.

scholarly journals Dust Transport from Inland Australia and Its Impact on Air Quality and Health on the Eastern Coast of Australia during the February 2019 Dust Storm

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 141
Author(s):  
Emilie Aragnou ◽  
Sean Watt ◽  
Hiep Nguyen Duc ◽  
Cassandra Cheeseman ◽  
Matthew Riley ◽  
...  
Keyword(s):  
Air Quality ◽  
Dust Storm ◽  
New South ◽  
New South Wales ◽  
The State ◽  
Dust Storms ◽  
Eastern Coast ◽  
Storm Event ◽  
South Wales ◽  
Dust Storm Event

Dust storms originating from Central Australia and western New South Wales frequently cause high particle concentrations at many sites across New South Wales, both inland and along the coast. This study focussed on a dust storm event in February 2019 which affected air quality across the state as detected at many ambient monitoring stations in the Department of Planning, Industry and Environment (DPIE) air quality monitoring network. The WRF-Chem (Weather Research and Forecast Model—Chemistry) model is used to study the formation, dispersion and transport of dust across the state of New South Wales (NSW, Australia). Wildfires also happened in northern NSW at the same time of the dust storm in February 2019, and their emissions are taken into account in the WRF-Chem model by using Fire Inventory from NCAR (FINN) as emission input. The model performance is evaluated and is shown to predict fairly accurate the PM2.5 and PM10 concentration as compared to observation. The predicted PM2.5 concentration over New South Wales during 5 days from 11 to 15 February 2019 is then used to estimate the impact of the February 2019 dust storm event on three health endpoints, namely mortality, respiratory and cardiac disease hospitalisation rates. The results show that even though as the daily average of PM2.5 over some parts of the state, especially in western and north western NSW near the centre of the dust storm and wild fires, are very high (over 900 µg/m3), the population exposure is low due to the sparse population. Generally, the health impact is similar in order of magnitude to that caused by biomass burning events from wildfires or from hazardous reduction burnings (HRBs) near populous centres such as in Sydney in May 2016. One notable difference is the higher respiratory disease hospitalisation for this dust event (161) compared to the fire event (24).

scholarly journals Dust Transport from Inland Australia and its Impact on Air Quality and Health on the Eastern Coast of Australia during the February 2019 Dust Storm.

2020 ◽  
Author(s):  
Emilie Aragnou ◽  
Sean Watt ◽  
Hiep Nguyen Duc ◽  
Cassandra Cheeseman ◽  
Matt Riley ◽  
...  
Keyword(s):  
Air Quality ◽  
Dust Storm ◽  
Respiratory Diseases ◽  
New South ◽  
New South Wales ◽  
The State ◽  
Storm Event ◽  
Cardiac Diseases ◽  
South Wales ◽  
Dust Storm Event

Dust storms originating from Central Australia and western New South Wales frequently cause high particles concentration at many sites across New South Wales, both inland and along the coast. This study focussed on a dust storm event in February 2019 which affect air quality across the state as detected at many ambient monitoring stations in the Department of Planning, Industry and Environment (DPIE) air quality monitoring network. The WRF-Chem (Weather Research and Forecast Model – Chemistry) model is used to study the formation, dispersion and transport of dust across the state of New South Wales (NSW, Australia). Wildfires also happened in northern NSW at the same time of the dust storm in February 2019, and their emissions are taken into account in WRF-Chem model by using Fire Inventory from NCAR (FINN) as emission input. The model performance is evaluated and is shown to predict fairly accurate the PM2.5 and PM10 concentration as compared to observation. The predicted PM2.5 concentration over New South Wales during 5 days from 11 to 15 February 2019 is then used to estimate the impact of the February 2019 dust storm event on three health endpoints namely mortality, respiratory and cardiac diseases hospitalisation rates. The results show that even though as the daily average of PM2.5 over some parts of the state, especially in western and north western NSW near the centre of the dust storm and wild fires, are very high (over 900 µg/m3), the population exposure is low due to the sparse population. The top five Statistical Area Level 4 regions with the most impact in term of mortality, respiratory diseases hospitalisation and cardiac disease hospitalisation are Far West and Orana, Newcastle and Lake Macquarie, New England and North West, Sydney – Inner South West and either Central Coast (mortality) or Sydney – Parramatta (respiratory diseases hospitalisation) or Sydney – Inner West (cardiac diseases hospitalisation). Generally, the health impact is similar in order of magnitude to that caused by biomass burnings events from wildfires or from hazardous reduction burnings (HRBs) near populous centres such as in Sydney in May 2016. One notable difference is the higher respiratory diseases hospitalisation for this dust event (161) compared to fire event (24).

scholarly journals Separated status of the natural dust plume and polluted air masses in an Asian dust storm event at coastal areas of China

2005 ◽  
Vol 110 (D6) ◽  
pp. n/a-n/a ◽  
Author(s):  
Daizhou Zhang ◽  
Yasunobu Iwasaka ◽  
Guangyu Shi ◽  
Jiaye Zang ◽  
Min Hu ◽  
...  
Keyword(s):  
Dust Storm ◽  
Coastal Areas ◽  
Asian Dust ◽  
Storm Event ◽  
Air Masses ◽  
Asian Dust Storm ◽  
Dust Storm Event

scholarly journals Observations and transport modeling of dust storm event over Northeast Asia using HYSPLIT: case study

2019 ◽  
Vol 99 ◽  
pp. 02002
Author(s):  
Gantuya Ganbat ◽  
Dulam Jugder
Keyword(s):  
Dust Storm ◽  
Northeast Asia ◽  
Thick Layer ◽  
Northern China ◽  
Storm Event ◽  
Hysplit Model ◽  
Lidar Measurements ◽  
Dust Storm Event ◽  
High Concentrations ◽  
Dust Events

This study analyzes a regional dust storm event that occurred in spring 2016 using data from observation sites, Lidar measurements, and satellite imageries. PM10 concentrations at surface observation stations are considered as a primary indicator of the dust events. The dust events occurred on 3-12 March with PM10 reaching a maximum beyond 1682, 1498, 706, and 165 μg m−3 at observation sites in Mongolia, China, Korea and Japan, respectively. The dust event in Northeast Asia is captured by time series of PM10 concentrations at observation sites. On 3-4 March, the dust storm event originated from Mongolia move toward China, Korea and Japan. Vertical distributions of dust observed by Lidar measurements from stations in AD-Net capture a thick layer of nearly 2.2 km of high concentrations above surface in the area of origin. The maximum PM10 concentration drops with downwind transport. Dust source identification and dust-loaded air parcel trajectories are calculated using the HYSPLIT model. According to the HYSPLIT model, the dust storm started on 3-4 March from Mongolia and reached northern Japan in about 4 days passing over northern China and Korea.

scholarly journals Measurements and modelling of particulate matter building ingress during a severe dust storm event

2020 ◽  
Vol 167 ◽  
pp. 106441 ◽  
Author(s):  
Christos D. Argyropoulos ◽  
Hala Hassan ◽  
Prashant Kumar ◽  
Konstantinos E. Kakosimos
Keyword(s):  
Particulate Matter ◽  
Dust Storm ◽  
Storm Event ◽  
Dust Storm Event ◽  
Severe Dust Storm

A Case Study of a Typical Dust Storm Event over the Loess Plateau of Northwest China

2011 ◽  
Vol 4 (6) ◽  
pp. 344-348 ◽  
Author(s):  
Ling Xiao-Lu ◽  
Guo Wei-Dong ◽  
Zhao Qian-Fei ◽  
Zhang Bei-Dou
Keyword(s):  
Loess Plateau ◽  
Dust Storm ◽  
Northwest China ◽  
Storm Event ◽  
The Loess Plateau ◽  
Dust Storm Event

scholarly journals Position correction in dust storm forecasting using LOTOS-EUROS v2.1: grid-distorted data assimilation v1.0

2021 ◽  
Vol 14 (9) ◽  
pp. 5607-5622
Author(s):  
Jianbing Jin ◽  
Arjo Segers ◽  
Hai Xiang Lin ◽  
Bas Henzing ◽  
Xiaohui Wang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Dust Storm ◽  
Storm Event ◽  
Initial Condition ◽  
Position Errors ◽  
Dust Clouds ◽  
Position Correction ◽  
Dust Storm Event ◽  
Dust Load ◽  
Distorted Data

Abstract. When calibrating simulations of dust clouds, both the intensity and the position are important. Intensity errors arise mainly from uncertain emission and sedimentation strengths, while position errors are attributed either to imperfect emission timing or to uncertainties in the transport. Though many studies have been conducted on the calibration or correction of dust simulations, most of these focus on intensity solely and leave the position errors mainly unchanged. In this paper, a grid-distorted data assimilation, which consists of an image-morphing method and an ensemble-based variational assimilation, is designed for realigning a simulated dust plume to correct the position error. This newly developed grid-distorted data assimilation has been applied to a dust storm event in May 2017 over East Asia. Results have been compared for three configurations: a traditional assimilation configuration that focuses solely on intensity correction, a grid-distorted data assimilation that focuses on position correction only and the hybrid assimilation that combines these two. For the evaluated case, the position misfit in the simulations is shown to be dominant in the results. The traditional emission inversion only slightly improves the dust simulation, while the grid-distorted data assimilation effectively improves the dust simulation and forecasting. The hybrid assimilation that corrects both position and intensity of the dust load provides the best initial condition for forecasting of dust concentrations.

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