Long-term mortality burden trends attributed to black carbon and PM from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study.

Background: Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate trends in black carbon and PM concentrations and their attributable mortality burden across the USA.

Methods: In this study, we derived daily concentrations of PM and its highly toxic black carbon component at a 1-km resolution in the USA from 2000 to 2020 via deep learning that integrated big data from satellites, models, and surface observations.

Evaluating the Performance of Low-Cost PM Sensors in Mobile Settings.

Low-cost sensors (LCSs) for measuring air pollution are increasingly being deployed in mobile applications, but questions concerning the quality of the measurements remain unanswered. For example, what is the best way to correct LCS data in a mobile setting? Which factors most significantly contribute to differences between mobile LCS data and those of higher-quality instruments? Can data from LCSs be used to identify hotspots and generate generalizable pollutant concentration maps? To help address these questions, we deployed low-cost PM sensors (Alphasense OPC-N3) and a research-grade instrument (TSI DustTrak) in a mobile laboratory in Boston, MA, USA. We first collocated these instruments with stationary PM reference monitors (Teledyne T640) at nearby regulatory sites.

An analysis of degradation in low-cost particulate matter sensors.

Low-cost sensors (LCS) are increasingly being used to measure fine particulate matter (PM) concentrations in cities around the world. One of the most commonly deployed LCS is the PurpleAir with ~ 15,000 sensors deployed in the United States, alone. PurpleAir measurements are widely used by the public to evaluate PM levels in their neighborhoods.

Opportunistic experiments to constrain aerosol effective radiative forcing.

Aerosol-cloud interactions (ACIs) are considered to be the most uncertain driver of present-day radiative forcing due to human activities. The nonlinearity of cloud-state changes to aerosol perturbations make it challenging to attribute causality in observed relationships of aerosol radiative forcing. Using correlations to infer causality can be challenging when meteorological variability also drives both aerosol and cloud changes independently.

Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty.

Annual global satellite-based estimates of fine particulate matter (PM) 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 PM concentrations in winter, exceeding summertime concentrations by factors of 1.

Effects of COVID-19 lockdowns on fine particulate matter concentrations.

Lockdowns during the COVID-19 pandemic provide an unprecedented opportunity to examine the effects of human activity on air quality. The effects on fine particulate matter (PM) are of particular interest, as PM is the leading environmental risk factor for mortality globally. We map global PM concentrations for January to April 2020 with a focus on China, Europe, and North America using a combination of satellite data, simulation, and ground-based observations.

Large global variations in measured airborne metal concentrations driven by anthropogenic sources.

Globally consistent measurements of airborne metal concentrations in fine particulate matter (PM) are important for understanding potential health impacts, prioritizing air pollution mitigation strategies, and enabling global chemical transport model development. PM filter samples (N ~ 800 from 19 locations) collected from a globally distributed surface particulate matter sampling network (SPARTAN) between January 2013 and April 2019 were analyzed for particulate mass and trace metals content. Metal concentrations exhibited pronounced spatial variation, primarily driven by anthropogenic activities.

Estimates of African Dust Deposition Along the Trans-Atlantic Transit Using the Decade-long Record of Aerosol Measurements from CALIOP, MODIS, MISR, and IASI.

Deposition of mineral dust into ocean fertilizes ecosystems and influences biogeochemical cycles and climate. In-situ observations of dust deposition are scarce, and model simulations depend on the highly parameterized representations of dust processes with few constraints. By taking advantage of satellites' routine sampling on global and decadal scales, we estimate African dust deposition flux and loss frequency (LF, a ratio of deposition flux to mass loading) along the trans-Atlantic transit using the three-dimensional distributions of aerosol retrieved by spaceborne lidar (CALIOP) and radiometers (MODIS, MISR, and IASI).

Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998-2018).

Exposure to outdoor fine particulate matter (PM) is a leading risk factor for mortality. We develop global estimates of annual PM 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 PM concentrations using geophysical relationships between surface PM and AOD simulated by the GEOS-Chem chemical transport model with updated algorithms.

Constraining chemical transport PM modeling outputs using surface monitor measurements and satellite retrievals: application over the San Joaquin Valley.

Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM.

Global Sources of Fine Particulate Matter: Interpretation of PM Chemical Composition Observed by SPARTAN using a Global Chemical Transport Model.

Exposure to ambient fine particulate matter (PM) is a leading risk factor for the global burden of disease. However, uncertainty remains about PM sources. We use a global chemical transport model (GEOS-Chem) simulation for 2014, constrained by satellite-based estimates of PM to interpret globally dispersed PM mass and composition measurements from the ground-based surface particulate matter network (SPARTAN).

Karymsky volcano eruptive plume properties based on MISR multi-angle imagery, and volcanological implications.

Space-based, operational instruments are in unique positions to monitor volcanic activity globally, especially in remote locations or where suborbital observing conditions are hazardous. The Multi-angle Imaging SpectroRadiometer (MISR) provides hyper-stereo imagery, from which the altitude and microphysical properties of suspended atmospheric aerosols can be derived. These capabilities are applied to plumes emitted at Karymsky volcano from 2000 to 2017.

Distinguishing remobilized ash from erupted volcanic plumes using space-borne multi-angle imaging.

Volcanic systems are comprised of a complex combination of ongoing eruptive activity and secondary hazards, such as remobilized ash plumes. Similarities in the visual characteristics of remobilized and erupted plumes, as imaged by satellite-based remote sensing, complicate the accurate classification of these events. The stereo imaging capabilities of the Multi-angle Imaging SpectroRadiometer (MISR) were used to determine the altitude and distribution of suspended particles.

SAM-CAAM: A Concept for Acquiring Systematic Aircraft Measurements to Characterize Aerosol Air Masses.

A modest program of systematic aircraft measurements can resolve key satellite-aerosol-data-record limitations. Satellite observations provide frequent, global aerosol-amount maps, but offer only loose aerosol property constraints needed for climate and air quality applications. We define and illustrate the feasibility of flying an aircraft payload to measure key aerosol optical, microphysical, and chemical properties .

Aerosol Absorption: Progress Towards Global and Regional Constraints.

Purpose Of Review: Some aerosols absorb solar radiation, altering cloud properties, atmospheric stability and circulation dynamics, and the water cycle. Here we review recent progress towards global and regional constraints on aerosol absorption from observations and modeling, considering physical properties and combined approaches crucial for understanding the total (natural and anthropogenic) influences of aerosols on the climate.

Recent Findings: We emphasize developments in black carbon absorption alteration due to coating and ageing, brown carbon characterization, dust composition, absorbing aerosol above cloud, source modeling and size distributions, and validation of high-resolution modeling against a range of observations.

Aerosol indirect effects on the nighttime Arctic Ocean surface from thin, predominantly liquid clouds.

Aerosol indirect effects have potentially large impacts on the Arctic Ocean surface energy budget, but model estimates of regional-scale aerosol indirect effects are highly uncertain and poorly validated by observations. Here we demonstrate a new way to quantitatively estimate aerosol indirect effects on a regional scale from remote sensing observations. In this study, we focus on nighttime, optically thin, predominantly liquid clouds.

Reducing Multi-sensor Monthly Mean Aerosol Optical Depth Uncertainty Part II: Optimal Locations for Potential Ground Observation Deployments.

Surface remote sensing of aerosol properties provides "ground truth" for satellite and model validation, and is an important component of aerosol observation system. Due to the different characteristics of background aerosol variability, information obtained at different locations usually have different spatial representativeness, implying that the location should be carefully chosen so that its measurement could be extended to a greater area. In this study, we present an objective observation array design technique that automatically determines the optimal locations with the highest spatial representativeness based on the Ensemble Kalman Filter (EnKF) theory.

Reducing multisensor satellite monthly mean aerosol optical depth uncertainty: 1. Objective assessment of current AERONET locations.

Various space-based sensors have been designed and corresponding algorithms developed to retrieve aerosol optical depth (AOD), the very basic aerosol optical property, yet considerable disagreement still exists across these different satellite data sets. Surface-based observations aim to provide ground truth for validating satellite data; hence, their deployment locations should preferably contain as much spatial information as possible, i.e.

Improving our fundamental understanding of the role of aerosol-cloud interactions in the climate system.

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth's clouds is the most uncertain component of the overall global radiative forcing from preindustrial time. General circulation models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol-cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured.

Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors.

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.

Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application.

Background: Epidemiologic and health impact studies of fine particulate matter with diameter < 2.5 microm (PM2.5) are limited by the lack of monitoring data, especially in developing countries.

Modeling optical properties of mineral aerosol particles by using nonsymmetric hexahedra.

We explore the use of nonsymmetric geometries to simulate the single-scattering properties of airborne dust particles with complicated morphologies. Specifically, the shapes of irregular dust particles are assumed to be nonsymmetric hexahedra defined by using the Monte Carlo method. A combination of the discrete dipole approximation method and an improved geometric optics method is employed to compute the single-scattering properties of dust particles for size parameters ranging from 0.

Single-scattering properties of triaxial ellipsoidal particles for a size parameter range from the Rayleigh to geometric-optics regimes.

The single-scattering properties of randomly oriented triaxial ellipsoids with size parameters from the Rayleigh to geometric-optics regimes are investigated. A combination of the discrete dipole approximation (DDA) technique and an improved geometric optics method (IGOM) is applied to the computation of ellipsoidal particle scattering properties for a complete range of size parameters. Edge effect contributions to the extinction and absorption efficiencies are included in the present IGOM simulation.

Estimating fine particulate matter component concentrations and size distributions using satellite-retrieved fractional aerosol optical depth: part 2--a case study.

We use the fractional aerosol optical depth (AOD) values derived from Multiangle Imaging Spectroradiometer (MISR) aerosol component measurements, along with aerosol transport model constraints, to estimate ground-level concentrations of fine particulate matter (PM2.5) mass and its major constituents in the continental United States. Regression models using fractional AODs predict PM2.