Exposure to outdoor fine particulate matter (PM2.5) is a leading risk factor for mortality. We develop global estimates of annual PM2.5 concentrations and trends for 1998–2018 using advances in ...satellite observations, chemical transport modeling, and ground-based monitoring. Aerosol optical depths (AODs) from advanced satellite products including finer resolution, increased global coverage, and improved long-term stability are combined and related to surface PM2.5 concentrations using geophysical relationships between surface PM2.5 and AOD simulated by the GEOS-Chem chemical transport model with updated algorithms. The resultant annual mean geophysical PM2.5 estimates are highly consistent with globally distributed ground monitors (R 2 = 0.81; slope = 0.90). Geographically weighted regression is applied to the geophysical PM2.5 estimates to predict and account for the residual bias with PM2.5 monitors, yielding even higher cross validated agreement (R 2 = 0.90–0.92; slope = 0.90–0.97) with ground monitors and improved agreement compared to all earlier global estimates. The consistent long-term satellite AOD and simulation enable trend assessment over a 21 year period, identifying significant trends for eastern North America (−0.28 ± 0.03 μg/m3/yr), Europe (−0.15 ± 0.03 μg/m3/yr), India (1.13 ± 0.15 μg/m3/yr), and globally (0.04 ± 0.02 μg/m3/yr). The positive trend (2.44 ± 0.44 μg/m3/yr) for India over 2005–2013 and the negative trend (−3.37 ± 0.38 μg/m3/yr) for China over 2011–2018 are remarkable, with implications for the health of billions of people.
Our understanding of the global dust cycle is limited by a dearth of information about dust sources, especially small-scale features which could account for a large fraction of global emissions. Here ...we present a global-scale high-resolution (0.1 deg) mapping of sources based on Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue estimates of dust optical depth in conjunction with other data sets including land use. We ascribe dust sources to natural and anthropogenic (primarily agricultural) origins, calculate their respective contributions to emissions, and extensively compare these products against literature. Natural dust sources globally account for 75% of emissions; anthropogenic sources account for 25%. North Africa accounts for 55% of global dust emissions with only 8% being anthropogenic, mostly from the Sahel. Elsewhere, anthropogenic dust emissions can be much higher (75% in Australia). Hydrologic dust sources (e.g., ephemeral water bodies) account for 31% worldwide; 15% of them are natural while 85% are anthropogenic. Globally, 20% of emissions are from vegetated surfaces, primarily desert shrublands and agricultural lands. Since anthropogenic dust sources are associated with land use and ephemeral water bodies, both in turn linked to the hydrological cycle, their emissions are affected by climate variability. Such changes in dust emissions can impact climate, air quality, and human health. Improved dust emission estimates will require a better mapping of threshold wind velocities, vegetation dynamics, and surface conditions (soil moisture and land use) especially in the sensitive regions identified here, as well as improved ability to address small-scale convective processes producing dust via cold pool (haboob) events frequent in monsoon regimes.
The Himalayas have a profound effect on the South Asian climate and the regional hydrological cycle, as it forms a barrier for the strong monsoon winds and serves as an elevated heat source, thus ...controlling the onset and distribution of precipitation during the Indian summer monsoon. Recent studies have suggested that radiative heating by absorbing aerosols, such as dust and black carbon over the Indo‐Gangetic Plains (IGP) and slopes of the Himalayas, may significantly accelerate the seasonal warming of the Hindu Kush–Himalayas–Tibetan Plateau (HKHT) and influence the subsequent evolution of the summer monsoon. This paper presents a detailed characterization of aerosols over the IGP and their radiative effects during the premonsoon season (April‐May‐June) when dust transport constitutes the bulk of the regional aerosol loading, using ground radiometric and spaceborne observations. During the dust‐laden period, there is a strong response of surface shortwave flux to aerosol absorption indicated by the diurnally averaged forcing efficiency of −70 Wm−2 per unit optical depth. The simulated aerosol single‐scattering albedo, constrained by surface flux and aerosol measurements, is estimated to be 0.89 ± 0.01 (at ∼550 nm) with diurnal mean surface and top‐of‐atmosphere forcing values ranging from −11 to −79.8 Wm−2 and +1.4 to +12 Wm−2, respectively, for the premonsoon period. The model‐simulated solar heating rate profile peaks in the lower troposphere with enhanced heating penetrating into the middle troposphere (5–6 km), caused by vertically extended aerosols over the IGP with peak altitude of ∼5 km as indicated by spaceborne Cloud‐Aerosol Lidar with Orthogonal Polarization observations. On a long‐term climate scale, our analysis, on the basis of microwave satellite measurements of tropospheric temperatures from 1979 to 2007, indicates accelerated annual mean warming rates found over the Himalayan–Hindu Kush region (0.21°C/decade ± 0.08°C/decade) and underscores the potential role of enhanced aerosol solar absorption in the maximum warming localized over the western Himalayas (0.26°C/decade ± 0.09°C/decade) that significantly exceed the entire HKHT and global warming rates. We believe the accelerated warming rates reported here are critical to both the South Asian summer monsoon and hydro‐glaciological resource variability in the Himalayan–Hindu Kush snowpack and therefore to the densely populated downstream regions.
Mineral dust interacts with radiation and impacts both the regional and global climate. The relative contribution of natural and anthropogenic dust sources, however, remains largely uncertain. ...Although human activities disturb soils and therefore enhance wind erosion, their contribution to global dust emission has never been directly evaluated because of a lack of data. The retrieval of aerosol properties over land, including deserts, using the Moderate Resolution Imaging Spectroradiometer Deep Blue algorithm makes the first direct characterization of the origin of individual sources possible. In order to separate freshly emitted dust from other aerosol types and aged dust particles, the spectral dependence of the single scattering albedo and the Angstrom wavelength exponent are used. Four years of data from the eastern part of West Africa, which includes one of the most active natural dust sources and the highest population density on the continent, are processed. Sources are identified on the basis of the persistence of significant aerosol optical depth from freshly emitted dust, and the origin is characterized as natural or anthropogenic on the basis of a land use data set. Our results indicate that although anthropogenic dust is observed less frequently and with lower optical depth than dust from natural sources in this particular region, it occupies a large area covering most of northern Nigeria and southern Chad, around Lake Chad. In addition, smaller anthropogenic sources are found as far south as 5° of latitude north, well outside the domain of most dust source inventories.
We estimated global fine particulate matter (PM2.5) concentrations using information from satellite-, simulation- and monitor-based sources by applying a Geographically Weighted Regression (GWR) to ...global geophysically based satellite-derived PM2.5 estimates. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations for 1998–2014. The GWR predictors included simulated aerosol composition and land use information. The resultant PM2.5 estimates were highly consistent (R 2 = 0.81) with out-of-sample cross-validated PM2.5 concentrations from monitors. The global population-weighted annual average PM2.5 concentrations were 3-fold higher than the 10 μg/m3 WHO guideline, driven by exposures in Asian and African regions. Estimates in regions with high contributions from mineral dust were associated with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM2.5 data sources can yield valuable improvements to PM2.5 characterization on a global scale.
Annual global satellite-based estimates of fine particulate matter (PM2.5) are widely relied upon for air-quality assessment. Here, we develop and apply a methodology for monthly estimates and ...uncertainties during the period 1998–2019, which combines satellite retrievals of aerosol optical depth, chemical transport modeling, and ground-based measurements to allow for the characterization of seasonal and episodic exposure, as well as aid air-quality management. Many densely populated regions have their highest PM2.5 concentrations in winter, exceeding summertime concentrations by factors of 1.5–3.0 over Eastern Europe, Western Europe, South Asia, and East Asia. In South Asia, in January, regional population-weighted monthly mean PM2.5 concentrations exceed 90 μg/m3, with local concentrations of approximately 200 μg/m3 for parts of the Indo-Gangetic Plain. In East Asia, monthly mean PM2.5 concentrations have decreased over the period 2010–2019 by 1.6–2.6 μg/m3/year, with decreases beginning 2–3 years earlier in summer than in winter. We find evidence that global-monitored locations tend to be in cleaner regions than global mean PM2.5 exposure, with large measurement gaps in the Global South. Uncertainty estimates exhibit regional consistency with observed differences between ground-based and satellite-derived PM2.5. The evaluation of uncertainty for agglomerated values indicates that hybrid PM2.5 estimates provide precise regional-scale representation, with residual uncertainty inversely proportional to the sample size.
The Deep Blue (DB) algorithm has been used to retrieve aerosol optical depth (AOD) and Ångström exponent (AE) over land from multiple satellite instruments, including the Moderate Resolution Imaging ...Spectroradiometers (MODIS) aboard the Terra and Aqua platforms and the Visible Infrared Imaging Radiometer Suite (VIIRS). This study first validates the latest MODIS (Collection 6.1) and VIIRS (Version 1) DB data products against Aerosol Robotic Network observations. On global average, the typical level of uncertainty in AOD is slightly better than ±(0.05 + 20%) relative to Aerosol Robotic Network. AE is quantitatively more uncertain but qualitatively shows skill at distinguishing between fine‐mode and coarse‐mode dominated aerosol columns. Results are also compared with the previous MODIS Collection 6. The stability of the three DB data sets ranges from 0.005–0.01 AOD per decade. Second, spatial and temporal patterns in AOD and AE are compared between the three data sets. It is found that they all show similar patterns of spatial coverage, which is predominantly linked to cloud cover, snow, and polar night. Regional time series of AOD also show highly consistent seasonal and interannual variations and are strongly correlated, although have offsets in some regions due to a combination of algorithmic and sensor‐related differences.
Plain Language Summary
Aerosols are small particles in the atmosphere like desert dust, volcanic ash, smoke, industrial haze, and sea spray. Understanding them is important for applications such as hazard avoidance, air quality and human health, and climate studies. Satellite instruments provide an important tool to study aerosol loading over the world. However, individual satellites do not last forever, and newer satellites often have improved capabilities compared to older ones. This paper evaluates the latest version of the Deep Blue algorithm for monitoring aerosols as applied to the Moderate Resolution Imaging Spectroradiometers (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite instruments. The two MODIS sensors provide data from 2000 and 2002 onward, while the first VIIRS was launched in late 2011, and VIIRS will carry on the MODIS data records into the future. The evaluation is performed by comparing to ground‐truth data which are part of (National Aeronautics and Space Administration) NASA's global Aerosol Robotic Network. The stability in time and consistency between the MODIS and VIIRS data sets are also examined.
Key Points
VIIRS and MODIS Deep Blue show very similar validation results against AERONET
Decadal stability in retrieved AOD is about 0.01 per decade or better
The data sets show consistent seasonal and interannual variations in regional AOD
Each year, prior to the onset of the Indian Summer Monsoon, the Gangetic Plains (GP), bounded by the high‐altitude Himalayan mountains, are strongly influenced by the transport of dust outbreaks ...originating in the northwestern desert in India (known as the Thar Desert). Dust particles constitute the bulk of the regional aerosol loading which peaks annually during the pre‐monsoon season. This paper integrates observations from space‐borne sensors, namely MODIS and CALIPSO, together with ground sunphotometer measurements, to infer dust loading in the pre‐monsoon aerosol build‐up over source and sink regions in northern India. Detailed aerosol characterization from the synergetic observational assessment suggests that the two pre‐monsoon seasons of 2007 and 2008 were strikingly contrasting in terms of the dust loading over both the Thar Desert and the GP. Further analysis of aerosol loading and optical properties, from the entire record of MODIS and sunphotometer observations, reveals that the 2007 pre‐monsoon season was an unusually weak dust‐laden period. Our findings suggest the plausible role of the immediately preceding excess winter monsoon rainfall in the suppressed dust activity during the 2007 pre‐monsoon season.
PDF
