This work presents 30-year observations of dust chemical composition by the IMPROVE network in the United States. Analysis of 1,253 large dust storms detected at the IMPROVE sites shows that dust PM2.5 (particles less than 2.5 micrometers in fresh dust plumes) crustal materials (64%), organic matter (13%), sulfate (7%), nitrate (2%), Cl, Br, and heavy metals. Dust composition stays relatively stable during near source transport. There are distinct spatial variations in dust composition, including high carbon and sulfate in Oklahoma, high Cl in Washington, and high fractions of heavy metals in Arizona. Compared to the Earth’s crust, dust PM2.5 contains less crustal elements but more OC, EC, sulfate, nitrate, and halogen elements due to influence by human activities and biogeochemical processes. This rich pool of dust composition data provides useful information to study the roles played by dust in the Earth system and its effects on human society.

Biomass burning has shaped many of the ecosystems of the planet and for millennia humans have used it as a tool to manage the environment. When widespread fires occur, the health and daily lives of millions of people can be affected by the smoke, often at unhealthy to hazardous levels leading to a range of short-term and long-term health consequences such as respiratory issues, cardiovascular issues, and mortality. It is critical to adequately represent and include smoke and its consequences in atmospheric modeling systems to meet needs such as addressing the global climate carbon budget and informing and protecting the public during smoke episodes. Many scientific and technical challenges are associated with modeling the complex phenomenon of smoke. Variability in fire emissions estimates has an order of magnitude level of uncertainty, depending upon vegetation type, natural fuel heterogeneity, and fuel combustion processes. Quantifying fire emissions also vary from ground/vegetation-based methods to those based on remotely sensed fire radiative power data. These emission estimates are input into dispersion and air quality modeling systems, where their vertical allocation associated with plume rise, and temporal release parameterizations influence transport patterns, and, in turn affect chemical transformation and interaction with other sources. These processes lend another order of magnitude of variability to the downwind estimates of trace gases and aerosol concentrations. This chapter profiles many of the global and regional smoke prediction systems currently operational or quasi-operational in real time or near-real time. It is not an exhaustive list of systems, but rather is a profile of many of the systems in use to give examples of the creativity and complexity needed to simulate the phenomenon of smoke. This chapter, and the systems described, reflect the needs of different agencies and regions, where the various systems are tailored to the best available science to address challenges of a region. Smoke forecasting requirements range from warning and informing the public about potential smoke impacts to planning burn activities for hazard reduction or resource benefit. Different agencies also have different mandates, and the lines blur between the missions of quasi-operational organizations (e.g. research institutions) and agencies with operational mandates. The global smoke prediction systems are advanced, and many are self-organizing into a powerful ensemble, as discussed in section 2. Regional and national systems are being developed independently and are discussed in sections 3-5 for Europe (11 systems), North America (7 systems), and Australia (3 systems). Finally, the World Meteorological Organization (WMO) effort (section 6) is bringing together global and regional systems and building the Vegetation Fire and Smoke Pollution Advisory and Assessment Systems (VFSP-WAS) to support countries with smoke issues and who lack resources.

Most countries around the world including the United States took actions to control COVID-19 spread that lead to an abrupt shift in human activity. On-road NOx emissions from light and heavy-duty vehicles decreased by 9% to 19% between February and March at the onset of the lockdown period in the middle of March in most of the US; between March and April, the on-road NOx emissions dropped further by 8% to 31% when lockdown measures were the most stringent. These precipitous drops in NOx emissions correlated well with tropospheric NO2 column amount observed by the Sentinel 5 Precursor TROPOspheric Monitoring Instrument (S5P TROPOMI). Furthermore, the changes in TROPOMI tropospheric NO2 across the continental U.S. between 2020 and 2019 correlated well with changes in on-road NOx emissions (r = 0.68) but correlated weakly with changes in emissions from the power plants (r = 0.35). At the height of lock-down related unemployment in the second quarter of 2020, the NO2 values decreased at the rate of 0.8 µmoles/m2 per unit percentage increase in the unemployment rate. Despite the lifting of lockdown measures, parts of the US continued to have ~20% below normal on-road NOx emissions. To achieve this new normal urban air quality in the US, continuing remote work policies that do not impede economic growth may become one of the many options.

Vegetation is a major control on dust emission because it extracts momentum from the wind and shelters the soil surface, protecting dry and loose material from erosion by winds. Many traditional dust emission models (TEMs) assume that the Earth’s land surface is devoid of vegetation, adjust dust emission using a vegetation cover complement, and calibrate the magnitude of modelled emissions to atmospheric dust. We compare this approach with a novel albedo-based dust emission model (AEM) which calibrates Earth’s land surface normalised shadow (1-albedo) to shelter depending on wind speed, to represent aerodynamic roughness spatio-temporal variation. Existing datasets of satellite observed dust emission from point sources (DPS) and dust optical depth (DOD) show little spatial relation and DOD frequency exceeds DPS frequency by up to two orders of magnitude. Relative to DPS frequency, both dust emission models showed strong relations, but over-estimate dust emission frequency, suitable for calibration to observed dust emission. Our results show that TEMs over-estimate large dust emission over vast vegetated areas and produce considerable false change in dust emission, relative to the AEM. It is difficult to avoid the conclusion, raised by other literature, that calibrating dust cycle models to atmospheric dust has hidden for more than two decades, these TEM modelling weaknesses and its poor performance. The AEM overcomes these weaknesses and improves performance without masks or vegetation cover. Considerable potential exists for Earth System Models driven by prognostic albedo, to reveal new insights of aerosol effects on, and responses to, contemporary and environmental change projections.

From the heated debates over the airborne transmission of the novel coronavirus to the abrupt Earth system changes caused by the sudden lockdowns, the dire circumstances resulting from the coronavirus disease 2019 (COVID-19) pandemic has brought the field of GeoHealth to the forefront of visibility in science and policy. The pandemic has inadvertently provided an opportunity to study how human response has impacted the Earth system, how the Earth system may impact the pandemic, and the capacity of GeoHealth to inform real-time policy. The lessons learned throughout our responses to the COVID-19 pandemic are shaping the future of GeoHealth.

The western United States experienced a record-breaking wildfire season in 2020. This study quantifies the contribution of wildfire emissions to the exceedances of health-based National Ambient Air Quality Standard (NAAQS) for fine particles (PM2.5) by comparing two CMAQ simulations, with and without wildfire emissions. During August to October 2020, western wildfires contributed 23% of surface PM2.5 in the contiguous US (CONUS), with a larger contribution in Pacific Coast (43%) and Mountain Region (42%). Consequently, wildfires were the primary contributor to the 3,720 observed exceedances. The wildfire influence peaked on September 14th, 2020, when 273 exceedances were recorded and wildfire emissions contributed 41%, 81%, and 72% to surface PM2.5 concentrations in the CONUS, Pacific Coast, and Mountain regions, respectively. Our finding highlights the predominating influence of wildfires on air quality, and potentially human health, that is expected to grow with increasing fire activities, while anthropogenic emissions decrease.

In connection to drought, the southwestern United States is experiencing an increase in blowing dust, an increase in incidence of valley fever, an increase in visibility-related traffic accidents, reduction in solar panel efficiency, and earlier mountain snowmelt. The response of the land surface to the wind is greatly dependent on the proportion of land cover, soil and sediment characteristics, weather conditions, and spatial distribution of vegetation, all factors affecting aeolian processes. Combined use of optical and radar satellite imagery products can provide invaluable benefits in characterizing surface properties of desert playas (ephemeral lakes)- the preferred landform for wind erosion. As a home for high temporal coverage of Landsat optical images and a cloud computing platform, Google Earth Engine (GEE) provides a multi-petabyte catalog of satellite imagery, powerful algorithms, and application programming interface capabilities. Processing and analyzing moderate to high spatial resolution images from Landsat and Sentinel-2 in GEE are crucial resources for extracting contributions of different endmembers to pixel mixtures. Radar images, with the capability to penetrate through clouds, darkness, and rain, have a power to detect the extent and levels of changes in land cover and soil moisture. In this study, we identified the fractional abundance of soil, vegetation, and water endmembers of each pixel of images using the spectral unmixing algorithm in GEE in Lordsburg Playa (New Mexico, USA), which is prone to aeolian erosion. Here, we used Landsat-8 and Landsat-5 at 30 meters spatial resolution and Sentinel-2 Multispectral instrument at 10-20 meters resolution. By employing Interferometric Synthetic Aperture Radar (InSAR) techniques, we explored the changes in the water level of the playa and the possible hot spots of erosion-inducing sediment loading to the playa. In this data recipe, we analyzed a pair of Sentinel-1 images that bracket a monsoon day with high rainfall event and a pair of images representing dry day and monsoon day. The results from this approach clearly show locations prone to the change in phase and displacement due to water and thus sediment loading susceptible to wind.

Dust is a meteorological phenomenon that has a strong impact on the environment, air quality, and human health. In the USA one of the most widely used databases of information on dust events is the Storm Events Database (SED). This project aims to examine the reliability and usefulness of the SED as a source for documenting the climatology of dust storms (DS) across the USA. While SED provides information potentially useful for understanding the frequency, distribution, and importance of DS across the USA, our analysis of DS from 2000 to 2020 shows that many DS were missing while some recorded events of less severe blowing dust (BLDU) in the SED were incorrectly reported as DS. Although the dust records from SED have been widely utilized to study dust related physical and societal issues, the limitations found in this study need to be taken into consideration in future studies.

Inspired by Comrie (2021), we discuss a few issues related to dust storms and Coccidioidomycosis (Valley fever). There is inconsistency in the term “dust storm” as used by science communities, and the dust data from NOAA 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, duration, and spatial coverage of dust events. Given abundant evidence that dust storms are a viable driver to transport Coccidioides, it is in best public interest to advocate that dust storms may put people at risk for contracting Valley fever.