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Meteorological Developments of Dust Events in Senegal

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Meteorological Developments of Dust Events in Senegal Meteorological Developments of Dust Events in Senegal Kayla McCauley, Takhari Thompson, and Gregory Jenkins The Pennsylvania State University, Department of Meteorology and Atmospheric Science Introduction Data 2017 Data 2019 Results 2017 Every year dust storms in the Sahara Desert bring copious Ø For the 2017 storm, the AOD data, averaged over all of amounts of dust into Senegal, exposing a population of over 16 Senegal, closely matches the CGQA surface recorded levels from Dakar million to unhealthy levels PM10 (Senegal, 2019). Senegal is located on the western coast of Africa, with 14 administrative Ø Although there are no PM10 values for 2019, the surface districts. The capital of Senegal, Dakar, is home to over 3 observations in Dakar from March 13 align with the highest million people in the city and the surrounding metropolitan area AOD values from the storm (Senegal, 2019). Due to Senegal’s location southwest of the Ø The levels of PM10 from the WRF model data for Senegal's Sahara, the country is subjected to dust at a high frequency. 14 administrative districts coincides with the AOD and CGQA Surface Observations December CGQA Surface Observations March 13, 27, 2017 2019 observation data for both 2017 and 2019 Average PM Distribution by Region (μg/m3) 3 Statistics from NASA have found that over 180 million tons of 3 3 10 Average PM10 Distribution by Region (μg/m ) 3 3 Average PM10 Distribution by Region (μg/m ) Average PM10 Distribution by Region (μg/m ) Average PM10 Distribution by Region (μg/m ) Average PM10 Distribution by Region (μg/m ) March 11th March 12th December 26th December 27th March 13th December 25th ZSAT031119.MEAN ZSAT031219.MEAN ZSAT122617.MEAN ZSAT122717.MEAN ZSAT031319.MEAN ZSAT122517.MEAN 0-54.5 (Healthy) 0-54.5 (Healthy) 0-54.5 (Healthy) 0-54.5 (Healthy) Ø The regional WRF model for both storms displays the large- 0-54.5 (Healthy) 0-54.5 (Healthy) 54.6-154.5 (Moderate) 54.6-154.5 (Moderate) 54.6-154.5 (Moderate) 54.6-154.5 (Moderate) 54.6-154.5 (Moderate) 54.6-154.5 (Moderate) 154.6-254.5 (Unhealthy for Sensitive Groups) 154.6-254.5 (Unhealthy for Sensitive Groups) dust are picked up in the Sahara every year (Carlocwiz). These 154.6-254.5 (Unhealthy for Sensitive Groups) 154.6-254.5 (Unhealthy for Sensitive Groups) 154.6-254.5 (Unhealthy for Sensitive Groups) 254.6-354.5 (Unhealthy) 254.6-354.5 (Unhealthy) 154.6-254.5 (Unhealthy for Sensitive Groups) 254.6-354.5 (Unhealthy) 254.6-354.5 (Unhealthy) 254.6-354.5 (Unhealthy) 354.6-425 (Very Unhealthy) 354.6-425 (Very Unhealthy) 254.6-354.5 (Unhealthy) 354.6-425 (Very Unhealthy) 354.6-425 (Very Unhealthy) 354.6-425 (Very Unhealthy) 425+ (Hazardous) 425+ (Hazardous) 354.6-425 (Very Unhealthy) 425+ (Hazardous) 425+ (Hazardous) 425+ (Hazardous) Light Gray Canvas Base Light Gray Canvas Base scale distribution and impact of dust the events had across 425+ (Hazardous) Light Gray Canvas Base Light Gray Canvas Base Light Gray Canvas Base Saint Louis Saint Louis Light Gray Canvas Base Saint Louis storms pick up dust from active dust regions, as well as local Saint Louis Saint Louis Saint Louis µ µ Northwest Africa dust in Senegal, and bring hazardous levels of dust into the µ µ Louga Louga µ µ Louga Louga Louga Louga Matam Matam Matam Matam Matam Matam Ø 2017 Meteorology: The event formed due to a Southerly Thies DakarThies Thies DakarThies Dakar Diourbel Thies country for multiple days. Exposure from dust at this frequency DakarThies Diourbel Diourbel Dakar Diourbel Diourbel Dakar Diourbel Kaffrine Kaffrine Kaffrine Kaffrine Kaffrine Kaffrine Azores high, picking up dust and funneling between the high Kaolack Kaolack Fatick Tambacounda Kaolack Kaolack Kaolack Kaolack has been associated with health impacts to the respiratory and Fatick Tambacounda Fatick Tambacounda Fatick Tambacounda Fatick Tambacounda Fatick Tambacounda and low pressure systems cardiovascular systems (World Health, 2006). There are limited Kolda Kolda Sedhiou Kolda Sedhiou Kedougou Ziguinchor Kedougou Sedhiou Kolda Kolda Kolda Ziguinchor Ziguinchor Kedougou Sedhiou Kedougou Sedhiou Kedougou Sedhiou Ziguinchor Ziguinchor Ziguinchor Kedougou Ø 2019 Meteorology: The event formed due to a strong low 0 40 80 160 240 320 0 40 80 160 240 320 Miles 0 40 80 160 240 320 0 40 80 160 240 320 warnings established for dust storms in Senegal, with only a Miles 0 40 80 160 240 320 0 40 80 160 240 320 Miles Esri, HERE, Garmin, (c) OpenStreetMap contributors, andM thilees Esri, HERE, Garmin, (c) OpenStreetMap contributors, andM thilees Esri, HERE, Garmin, (c) OpenStreetMap contributors, and the GIS Esri, HERE, Garmin, (c) OpenStreetMap contributors, andM thilees Esri, HERE, Garmin, (c) OpenStreetMap contributors, and the GIS Esri, HERE, Garmin, (c) OpenStreetMap contributors, and the GIS GIS user community GIS user community user community GIS user community user community user community pressure system developing over Algeria, causing dust to be small population of the country having access to the Daily PM10 WRF Model values averaged for Senegal’s 14 administrative districts: December 25-27, 2017 Daily PM10 WRF Model values averaged for Senegal’s 14 administrative districts: March 11-13 , 2019 government alerts. Synoptic scale features can drive the picked up and carried through the trough storms, with both high and low pressure systems having caused severe dust events. In December 2017, a ten day dust Conclusion event in Senegal was driven by a high pressure system, whereas a ten day storm of similar severity in March 2019 was Ø The December 2017 event brought 7 or more days of driven by a low pressure system. unhealthy PM10 levels to over 12million people, 86% of the county’s population The objectives of this project are: Ø The March 2019 event brought unhealthy PM10 levels for 7 Ø Identify two ten day dust events: December 19-29, 2017 and or more days to 10million, 76% of the population March 5-15, 2019 3 Ø Unhealthy PM10 is between 255-354.5 µg/m Ø Evaluate models to assess how the entire country was Ø According to the World Health Organization (WHO), the safe impacted by both storms 3 guideline for 24-hour PM10 is 50µg/m (World Health, 2006) Ø Determine the meteorological driving forces behind each Ø Long term exposure to unhealthy levels of PM has shown to storm increase lung cancer and cardiopulmonary mortality (Pascal, 2013) Methodology Ø The WHO recommends immediate action for countries not meeting the safe daily PM averages Ø NASA’s Giovanni database was used to produce Aerosol Ø What this means for Senegal: PM monitoring, warnings, and Optical Depth (AOD) from MODIS-Terra. Area averaged of informative systems should be established throughout the 550nm AOD data was produced for the ten day periods of country both storms Ø Surface observations taken by the Centre de Gestin de la Qualtié de l’Air (CGQA) in Dakar, Senegal, were used to References compare the modeled data with what was actually recorded Carlowicz, M. (n.d.). Bits of the Sahara on the Move. Retrieved December 11, during the length of the storms. CGQA assesses the quality 2019, from https://earthobservatory.nasa.gov/images/91925/bits-of- the-sahara-on-the-move#targetText=Scientists estimate that winds of the air and measures PM10 values, although there is data missing and,Ocean and on the Americas. Pascal, M., Corso, M., Chanel, O., Declercq, C., Badaloni, C., Cesaroni, G., … Ø NCEP reanalysis using NOAA’s ESRL daily mean Medina, S. (2013). Assessing the public health impacts of urban air composites was used to create sea level pressure maps to pollution in 25 European cities: Results of the Aphekom project. assess the synoptic scale features driving each storm Science of The Total Environment, 449, 390–400. doi: Ø The WRF-Chem model is a regional model which analyzes 10.1016/j.scitotenv.2013.01.077 Senegal Population. (2019, August 27). Retrieved December 11, 2019, from the distribution and path of dust, and measures the amount http://worldpopulationreview.com/countries/senegal-population/. of PM10 in the atmosphere. The data calculated by WRF World Health Organization. Occupational and Environmental Health Team. was inputted into a GIS framework to develop imagery for (2006). WHO Air quality guidelines for particulate matter, ozone, the severity of PM for each day of the storms across the nitrogen dioxide and sulfur dioxide : global update 2005 : summary 10 of risk assessment. (n.d.). Retrieved December 11, 2019, from 14 districts and was categorized by the US Air Quality Index https://apps.who.int/iris/handle/10665/69477..
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