The Spatio-Temporal Distribution of Particulate Matter during Natural Dust Episodes at an Urban Scale

Abstract

Dust storms are a common phenomenon in arid and semi-arid areas, and their impacts on both physical and human environments are of great interest. Number of studies have associated atmospheric PM pollution in urban environments with origin in natural soil/dust, but less evaluated the dust spatial patterns over a city. We aimed to analyze the spatial-temporal behavior of PM concentrations over the city of Beer Sheva, in southern Israel, where dust storms are quite frequent. PM data were recorded during the peak of each dust episode simultaneously in 23 predetermined fixed points around the city. Data were analyzed for both dust days and non-dust days (background). The database was constructed using Geographic Information System and includes distributions of PM that were derived using inverse distance weighted (IDW) interpolation. The results show that the daily averages of atmospheric PM10 concentrations during the background period are within a narrow range of 31 to 48 μg m-3 with low variations. During dust days however, the temporal variations are significant and can range from an hourly PM10 concentration of 100 μg m-3 to more than 1280 μg m-3 during strong storms. IDW analysis demonstrates that during the peak time of the storm the spatial variations in PM between locations in the city can reach 400 μg m-3. An analysis of site and storm contribution to total PM concentration revealed that higher concentrations are found in parts of the city that are proximal to dust sources. The results improve the understanding of the dynamics of natural PM and the dependence on wind direction. This may have implications for environmental and health outcomes.

Validating and Comparing Highly Resolved Commercial “Off the Shelf” PM Monitoring Sensors with Satellite Based Hybrid Models, for Improved Environmental Exposure Assessment

“…A study on surveying hourly PM levels, performed by Krasnov [ 31 ], discovered that a slight increase in PM10, from 38 μg/m 3 to 45 μg/m 3 , is induced during the morning and afternoon rush hours, with a decrease after 5 p.m. This could attest to a single measurement method being a more accurate one, but on the other hand, possibly lacking in the explanation of different variables.…”

“…High resolution of PM measurements is important, since exposure levels have been observed to vary even in close proximity. Krasnov [ 31 ] has shown that fine grained measurements with the TSI DustTrak DRX 8534 gave a strong correlation between the spatial distribution values of PM2.5 and PM10 for the different measurement points. During dust days, however, the DustTrak’s RMSE values, when compared with the nearby station, for the averaged dust data were high, from 48 for PM10 to 25 for PM2.5, which shows that the calculated average PM spatial distributions during dust storm events were not accurate representations of the presented model, despite the relatively proximate measurements.…”

Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health Studies

“…More fine-grained measurements are important, since exposure levels have been observed to vary even in close proximity during dust storm events. Despite the relatively nearby measurements of dust-PM by portable industrial instruments (such as the portable TSI DustTrak Aerosol Monitor) [70], the accuracy of the assessment for dust atmospheric distribution remains questionable.…”

Comment on acp-2021-57

“…One of the most critical pollutants in the atmosphere is particulate matter (PM), especially in urban areas that are heavily impacted by anthropogenic emissions (David et al, 2019;Qiao et al, 2018;Schwela, 2000). Recent studies showed increasing interest in PM at a city-level allowing assessment of fine-scale pollution variability (Boppana et al, 2019;Dionisio et al, 2010;Etyemezian et al, 2005;Krasnov et al, 2016;Padhi and Padhy, 2008). The intricate topography and seasonality of particulate air pollution in the city of Grenoble (France) makes it an ideal location to explore both the small-and large-scale variabilities of PM pollution accounting for local variations in different urban environments (Calas et al, 2019;Favez et al, 2010;Srivastava et al, 2018;Tomaz et al, 2016Tomaz et al, , 2017.…”