Associations between Dust Exposure and Hospitalizations in El Paso, Texas, USA

Mortality risks from a spectrum of causes associated with sand and dust storms in China

Sand and Dust Storms (SDS) pose considerable health risks worldwide. Previous studies only indicated risk of SDS on overall mortality. This nationwide multicenter time-series study aimed to examine SDS-associated mortality risks extensively. We analyzed 1,495,724 deaths and 2024 SDS events from 1 February to 31 May (2013-2018) in 214 Chinese counties. The excess mortality risks associated with SDS were 7.49% (95% CI: 3.12-12.05%), 5.40% (1.25-9.73%), 4.05% (0.41-7.83%), 3.45% (0.34-6.66%), 3.37% (0.28-6.55%), 3.33% (0.07-6.70%), 8.90% (4.96-12.98%), 12.51% (6.31-19.08%), and 11.55% (5.55-17.89%) for ischemic stroke, intracerebral hemorrhagic stroke, hypertensive heart disease, myocardial infarction, acute myocardial infarction, acute ischemic heart disease, respiratory disease, chronic lower respiratory disease, and chronic obstructive pulmonary disease (COPD), respectively. SDS had significantly added effects on ischemic stroke, chronic lower respiratory disease, and COPD mortality. Our results suggest the need to implement public health policy against SDS.

Evaluating Data Product Exposure Metrics for Use in Epidemiologic Studies of Dust Storms

Dust storms are increasing in frequency and correlate with adverse health outcomes but remain understudied in the United States (U.S.), partially due to the limited spatio-temporal coverage, resolution, and accuracy of current data sets. In this work, dust-related metrics from four public areal data products were compared to a monitor-based "gold standard" dust data set. The data products included the National Weather Service (NWS) storm event database, the Modern-Era Retrospective analysis for Research and Applications-Version 2, the EPA's Air QUAlity TimE Series (EQUATES) Project using the Community Multiscale Air Quality Modeling System (CMAQ), and the Copernicus Atmosphere Monitoring Service global reanalysis product. California, Nevada, Utah, and Arizona, which account for most dust storms reported in the U.S., were examined. Dichotomous and continuous metrics based on reported dust storms, particulate matter concentrations (PM10 and PM2.5), and aerosol-type variables were extracted or derived from the data products. Associations between these metrics and a validated dust storm detection method utilizing Interagency Monitoring of Protected Visual Environments monitors were estimated via quasi-binomial regression. In general, metrics from CAMS yielded the strongest associations with the "gold standard," followed by the NWS storm database metric. Dust aerosol (0.9-20 μm) mixing ratio, vertically integrated mass of dust aerosol (9-20 μm), and dust aerosol optical depth at 550 nm from CAMS generated the highest standardized odds ratios among all metrics. Future work will apply machine-learning methods to the best-performing metrics to create a public dust storm database suitable for long-term epidemiologic studies.

Quantifying international and interstate contributions to primary ambient PM2.5 and PM10 in a complex metropolitan atmosphere

We quantify the contributions of long-range and regionally transported aerosols to ambient primary PM_2.5 and PM₁₀ in a representative United States industrialized/urban atmosphere via detailed elemental analysis and chemical mass balance (CMB) modeling after identifying their presence using a variety of publicly available satellite data/information, software products, and synoptic-scale aerosol models. A year-long study in Houston, Texas identified North African dust as the principal long-range global source of primary particulate matter (PM). CMB estimated transatlantic dust from the Sahara-Sahel region to be dominant in the summer months contributing an average of 3.5 μg m^-3 to PM_2.5 and 7.9 μg m^-3 to PM₁₀ during May-August, i.e., the active Saharan dust season. Biomass burning was the chief source of regionally transported PM impacting air quality on different occasions throughout the year depending on the fire location. Four major biomass combustion events affecting air quality in Texas were calculated to contribute an average of 1.3 μg m^-3 to PM_2.5 and 1.4 μg m^-3 to PM₁₀ in corresponding samples whose origins were tracked to Canada, southeastern states of USA, and Central America using fire maps, HYSPLIT back trajectories, and the Navy Aerosol Analysis and Prediction System global aerosol model. Elemental concentrations and signature ratios revealed significant mixing of potassium, rare earth metals, and vanadium from proximal and distal crustal (natural) sources with anthropogenically emitted PM. This demonstrates the need to isolate the non-mineral components of these metals to employ them as tracers for primary PM emitted by biomass burning, petroleum refineries, and oil combustion. Transboundary contributions to primary PM_2.5 were 1.5 μg m^-3 and 3.1 μg m^-3 to PM₁₀ adding 16% to annual average mass concentration of both size fractions demonstrating that local sources were primarily responsible for ambient air quality with non-trivial contributions from international and interstate sources. Rigorously identifying and quantifying aerosol sources assists in improving air quality management policies designed to protect public health and comply with ever-decreasing federal PM standards that allow state agencies to exclude contributions that are not reasonably controllable or preventable from regulatory decisions and actions.

When a Dust Storm Is Not a Dust Storm: Reliability of Dust Records From the Storm Events Database and Implications for Geohealth Applications

Windblown dust impacts human health, air quality, and climate. The National Weather Service Storm Events Database (SED) is a widely used dataset of significant or unusual weather, including dust storms (DS), and resulting deaths, injuries, and material losses in the USA. The SED is frequently used by medical, social, and atmospheric scientists. However, it is uncertain whether this dataset reliably represents spatial and temporal variations and trends of DS. Analyzing the SED from 2000 to 2020 identified 1,167 DS reports; removing reports of the same event from multiple locations left 647 DS in 21 USA states. The number of DS ranged from 12 in 2008 to 53 in 2018, with no strong interannual trends detected (R ² was 0.3). By examining the DS events reported in the SED based on meteorological observations including wind speed, visibility, and weather codes, we determined that the SED was not only missing many DS (visibility <1 km), but also included many blowing dust (BLDU) events. 49.9% of 491 reported DS events in SED had visibility >1 km and were incorrectly reported as DS. Underrepresentation of DS and inclusion of BLDU may be partially due to the diverse sources contributing to the SED and a lack of verification of the reports and their consistency. Although the SED is an extremely useful and valuable database of impactful weather, including DS, the issues found in this study warrant caution in use of this dataset for many geohealth applications.

Dust Storms, Valley Fever, and Public Awareness

We discuss several issues raised by Comrie (2021, https://doi.org/10.1029/2021GH000504), which uses a crowdsourced data set to study dust storms and coccidioidomycosis (Valley fever). There is inconsistency in the term "dust storm" used by science communities. The dust data from National Oceanic and Atmospheric Administration Storm Events Database are from diverse sources, unsuitable for assessing dust-coccidioidomycosis relationships. Population exposure to dust or Coccidioides needs to consider the frequency, magnitude, and duration of dust events. Given abundant evidence that dust storms are a viable driver to transport pathogens, it is in best public interest to advocate dust storms may put people at risk for contracting Valley fever.

Exploring Meteorological Conditions and Human Health Impacts during Two Dust Storm Events in Northern Cape Province, South Africa: Findings and Lessons Learnt

Dust storms are meteorological hazards associated with several adverse health impacts including eye irritations, respiratory and cardiovascular disorders, and vehicular road accidents due to poor visibility. This study investigated relations between admissions from a large, public hospital that serves people living in Northern Cape and Free State provinces, South Africa during 2011 to 2017, and meteorological variables (temperature and air quality) during two dust storms, one in October 2014 (spring) and the second in January 2016 (summer), identified from the media as no repository of such events exists for South Africa. Distributed nonlinear lag analysis and wavelet transform analysis were applied to explore the relationships between hospital admissions for respiratory and cardiovascular diseases, eye irritation, and motor vehicle accidents; maximum temperature, and two air quality ‘proxy measures,’ aerosol optical depth and Ångström exponent, were used as ground-based air quality data were unavailable. Eye irritation was the most common dust-related hospital admission after both dust storm events. No statistically significant changes in admissions of interest occurred at the time of the two dust storm events, using either of the statistical methods. Several lessons were learnt. For this type of study, ground-based air quality and local wind data are required; alternative statistical methods of analysis should be considered; and a central dust storm repository would help analyze more than two events. Future studies in South Africa are needed to develop a baseline for comparison of future dust storm events and their impacts on human health.