Statistical downscaling of coarse-resolution fine particulate matter predictions over the contiguous United States: model development, evaluation, and implication in health impact assessment.

Fine particulate matter (PM) predictions at a high spatial resolution (i.e., neighborhood scale) are critically needed to better understand the health impacts of air pollution, especially at neighborhood scales.

XIS-PM: A daily spatiotemporal machine-learning model for PM in the contiguous United States.

Air-pollution monitoring is sparse across most of the United States, so geostatistical models are important for reconstructing concentrations of fine particulate air pollution (PM) for use in health studies. We present XGBoost-IDW Synthesis (XIS), a daily high-resolution PM machine-learning model covering the contiguous US from 2003 through 2023. XIS uses aerosol optical depth from satellites and a parsimonious set of additional predictors to make predictions at arbitrary points, capturing near-roadway gradients and allowing the estimation of address-level exposures.

First close insight into global daily gapless 1 km PM pollution, variability, and health impact.

Here we retrieve global daily 1 km gapless PM concentrations via machine learning and big data, revealing its spatiotemporal variability at an exceptionally detailed level everywhere every day from 2017 to 2022, valuable for air quality monitoring, climate change, and public health studies. We find that 96%, 82%, and 53% of Earth's populated areas are exposed to unhealthy air for at least one day, one week, and one month in 2022, respectively. Strong disparities in exposure risks and duration are exhibited between developed and developing countries, urban and rural areas, and different parts of cities.

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.

Separating Daily 1 km PM Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data.

Fine particulate matter (PM) chemical composition has strong and diverse impacts on the planetary environment, climate, and health. These effects are still not well understood due to limited surface observations and uncertainties in chemical model simulations. We developed a four-dimensional spatiotemporal deep forest (4D-STDF) model to estimate daily PM chemical composition at a spatial resolution of 1 km in China since 2000 by integrating measurements of PM species from a high-density observation network, satellite PM retrievals, atmospheric reanalyses, and model simulations.

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.

Advancing methodologies for applying machine learning and evaluating spatiotemporal models of fine particulate matter (PM) using satellite data over large regions.

Reconstructing the distribution of fine particulate matter (PM) in space and time, even far from ground monitoring sites, is an important exposure science contribution to epidemiologic analyses of PM health impacts. Flexible statistical methods for prediction have demonstrated the integration of satellite observations with other predictors, yet these algorithms are susceptible to overfitting the spatiotemporal structure of the training datasets. We present a new approach for predicting PM using machine-learning methods and evaluating prediction models for the goal of making predictions where they were not previously available.

Spatial validation reveals poor predictive performance of large-scale ecological mapping models.

Mapping aboveground forest biomass is central for assessing the global carbon balance. However, current large-scale maps show strong disparities, despite good validation statistics of their underlying models. Here, we attribute this contradiction to a flaw in the validation methods, which ignore spatial autocorrelation (SAC) in data, leading to overoptimistic assessment of model predictive power.

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.

Estimating daily PM concentrations in New York City at the neighborhood-scale: Implications for integrating non-regulatory measurements.

Previous PM related epidemiological studies mainly relied on data from sparse regulatory monitors to assess exposure. The introduction of non-regulatory PM monitors presents both opportunities and challenges to researchers and air quality managers. In this study, we evaluated the advantages and limitations of integrating non-regulatory PM measurements into a satellite-based daily PM model at 100 m resolution in New York City in 2015.

Assessing NO Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging.

NO is a combustion byproduct that has been associated with multiple adverse health outcomes. To assess NO levels with high accuracy, we propose the use of an ensemble model to integrate multiple machine learning algorithms, including neural network, random forest, and gradient boosting, with a variety of predictor variables, including chemical transport models. This NO model covers the entire contiguous U.

Connecting Crop Productivity, Residue Fires, and Air Quality over Northern India.

Northwestern India is known as the "breadbasket" of the country producing two-thirds of food grains, with wheat and rice as the principal crops grown under the crop rotation system. Agricultural data from India indicates a 25% increase in the post-monsoon rice crop production in Punjab during 2002-2016. NASA's A-train satellite sensors detect a consistent increase in the vegetation index (net 21%) and post-harvest agricultural fire activity (net ~60%) leading to nearly 43% increase in aerosol loading over the populous Indo-Gangetic Plain in northern India.

Impacts of snow and cloud covers on satellite-derived PM levels.

Satellite aerosol optical depth (AOD) has been widely employed to evaluate ground fine particle (PM) levels, whereas snow/cloud covers often lead to a large proportion of non-random missing AOD values. As a result, the fully covered and unbiased PM estimates will be hard to generate. Among the current approaches to deal with the data gap issue, few have considered the cloud-AOD relationship and none of them have considered the snow-AOD relationship.

Satellite-based view of the aerosol spatial and temporal variability in the Córdoba region (Argentina) using over ten years of high-resolution data.

Space-based observations offer a unique opportunity to investigate the atmosphere and its changes over decadal time scales, particularly in regions lacking in situ and/or ground based observations. In this study, we investigate temporal and spatial variability of atmospheric particulate matter (aerosol) over the urban area of Córdoba (central Argentina) using over ten years (2003-2015) of high-resolution (1 km) satellite-based retrievals of aerosol optical depth (AOD). This fine resolution is achieved exploiting the capabilities of a recently developed inversion algorithm (Multiangle implementation of atmospheric correction, MAIAC) applied to the MODIS sensor datasets of the NASA-Terra and -Aqua platforms.

Estimation of daily PM and PM concentrations in Italy, 2013-2015, using a spatiotemporal land-use random-forest model.

Particulate matter (PM) air pollution is one of the major causes of death worldwide, with demonstrated adverse effects from both short-term and long-term exposure. Most of the epidemiological studies have been conducted in cities because of the lack of reliable spatiotemporal estimates of particles exposure in nonurban settings. The objective of this study is to estimate daily PM (PM < 10 μm), fine (PM < 2.

Assessing snow extent data sets over North America to inform and improve trace gas retrievals from solar backscatter.

Accurate representation of surface reflectivity is essential to tropospheric trace gas retrievals from solar backscatter observations. Surface snow cover presents a significant challenge due to its variability and thus snow-covered scenes are often omitted from retrieval data sets; however, the high reflectance of snow is potentially advantageous for trace gas retrievals. We first examine the implications of surface snow on retrievals from the upcoming TEMPO geostationary instrument for North America.

Earth Observations from DSCOVR/EPIC Instrument.

The NOAA Deep Space Climate Observatory (DSCOVR) spacecraft was launched on February 11, 2015, and in June 2015 achieved its orbit at the first Lagrange point or L1, 1.5 million km from Earth towards the Sun. There are two NASA Earth observing instruments onboard: the Earth Polychromatic Imaging Camera (EPIC) and the National Institute of Standards and Technology Advanced Radiometer (NISTAR).

Bayesian geostatistical modelling of PM and PM surface level concentrations in Europe using high-resolution satellite-derived products.

Air quality monitoring across Europe is mainly based on in situ ground stations, which are too sparse to accurately assess the exposure effects of air pollution for the entire continent. The demand for precise predictive models that estimate gridded geophysical parameters of ambient air at high spatial resolution has rapidly grown. Here, we investigate the potential of satellite-derived products to improve particulate matter (PM) estimates.