Estimating the global abundance of ground level presence of particulate matter (PM2.5) (original) (raw)
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Estimating the Global Abundance of Ground Level Particulate Matter (PM2.5) Since 1997
With the increasing awareness of the health impacts of particulate matter there is a growing need to comprehend the spatial and temporal variations of the global abundance of ground level airborne particulate matter (PM2.5). Here we use a suite of remote sensing and meteorological data products together with ground based observations of PM2.5 from 8,329 measurement sites in 55 countries taken between 1997-2014 to train a machine learning algorithm to estimate the daily distributions of PM2.5 from 1997-present. In this first paper of a series we present the methodology and global average results from 1997-2014 and demonstrate that the new PM2.5 data product can reliably represent global observations of PM2.5 for epidemiological studies.
Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies
With the increasing awareness of health impacts of particulate matter, there is a growing need to comprehend the spatial and temporal variations of the global abundance of ground-level airborne particulate matter (PM 2.5 ). Here we use a suite of remote sensing and meteorological data products together with ground based observations of PM 2.5 from 8,329 measurement sites in 55 countries taken between 1997 and 2014 to train a machine learning algorithm to estimate the daily distributions of PM 2.5 from 1997 to the present. We demonstrate that the new PM 2.5 data product can reliably represent global observations of PM 2.5 for epidemiological studies. An analysis of Baltimore schizophrenia emergency room admissions is presented in terms of the levels of ambient pollution. PM 2.5 appears to have an impact on some aspects of mental health.
Atmospheric Measurement Techniques, 2015
Ground-based observations have insufficient spatial coverage to assess long-term human exposure to fine particulate matter (PM 2.5 ) at the global scale. Satellite remote sensing offers a promising approach to provide information on both short-and long-term exposure to PM 2.5 at local-toglobal scales, but there are limitations and outstanding questions about the accuracy and precision with which groundlevel aerosol mass concentrations can be inferred from satellite remote sensing alone. A key source of uncertainty is the global distribution of the relationship between annual average PM 2.5 and discontinuous satellite observations of columnar aerosol optical depth (AOD). We have initiated a global network of ground-level monitoring stations designed to evaluate and enhance satellite remote sensing estimates for application in health-effects research and risk assessment. This Surface PARTiculate mAtter Network (SPARTAN) includes a global federation of ground-level monitors of hourly PM 2.5 situated primarily in highly populated regions and collocated with existing ground-based sun photometers that measure AOD. The instruments, a three-wavelength nephelometer and impaction filter sampler for both PM 2.5 and PM 10 , are highly autonomous. Hourly PM 2.5 concentrations are inferred from the combination of weighed filters and nephelometer data. Data from existing networks were used to develop and evaluate network sampling characteristics. SPARTAN filters are analyzed for mass, black carbon, watersoluble ions, and metals. These measurements provide, in a variety of regions around the world, the key data required to evaluate and enhance satellite-based PM 2.5 estimates used for assessing the health effects of aerosols. Mean PM 2.5 concentrations across sites vary by more than 1 order of magnitude. Our initial measurements indicate that the ratio of AOD to ground-level PM 2.5 is driven temporally and spatially by the vertical profile in aerosol scattering. Spatially this ratio is also strongly influenced by the mass scattering efficiency.
PM2.5 Estimation using Machine Learning Models and Satellite Data: A Literature Review
International Journal of Advanced Computer Science and Applications
Most researchers are beginning to appreciate the use of remote sensing satellites to assess PM 2.5 levels and use machine learning algorithms to automate the collection, make sense of remote sensing data, and extract previously unseen data patterns. This study reviews delicate particulate matter (PM 2.5) predictions from satellite aerosol optical depth (AOD) and machine learning. Specifically, we review the characteristics and gap-filling methods of satellite-based AOD products, sources and components of PM 2.5 , observable AOD products, data mining, and the application of machine learning algorithms in publications of the past two years. The study also included functional considerations and recommendations in covariate selection, addressing the spatiotemporal heterogeneity of the PM 2.5-AOD relationship, and the use of cross-validation, to aid in determining the final model. A total of 79 articles were included out of 112 retrieved records consisting of articles published in 2022 totaling 43 articles, as of 2023 (until February) totaling 19 articles, and other years totaling 18 articles. Finally, the latest method works well for monthly PM 2.5 estimates, while daily PM 2.5 and hourly PM 2.5 can also be achieved. This is due to the increased availability and computing power of large datasets and increased awareness of the potential benefits of predictors working together to achieve higher estimation accuracy. Some key findings are also presented in the conclusion section of this article.
Remote Sensing of Environment, 2015
Although ground-level monitoring can provide accurate PM 2.5 measurements, it has limited spatial coverage and resolution. In contrast, satellite-based monitoring can provide aerosol optical depth (AOD) products with higher spatial resolution and continuous spatial coverage, but it cannot directly measure ground-level PM 2.5 concentration. Observation-based and simulation-based approaches have been actively developed to retrieve ground-level PM 2.5 concentrations from satellite AOD and sparse ground-level observations. However, the effect of aerosol characteristics (e.g., aerosol composition and size distribution) on the AOD-PM 2.5 relationship is seldom considered in observation-based methods. Although these characteristics are considered in simulation-based methods, the results still suffer from model uncertainties. In this study, we propose an observation-based algorithm that considers the effect of the main aerosol characteristics. Their related effects on hygroscopic growth, particle mass extinction efficiency, and size distribution are estimated and incorporated into the AOD-PM 2.5 relationship. The method is applied to quantify the PM 2.5 distribution in China. Good agreements between satellite-retrieved and ground-observed PM 2.5 annual and monthly averages are identified, with significant spatial correlations of 0.90 and 0.76, respectively, at 565 stations in China. The results suggest that this approach can measure large scale PM distributions with verified results that are at least as good as those from simulation-based estimations. The results also show the method's capacity to identify PM 2.5 spatial distribution with high-resolution at national, regional, and urban scales and to provide useful information for air pollution control strategies, health risk assessments, etc.
Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty
Environmental Science & Technology, 2021
Annual global satellite-based estimates of fine particulate matter (PM 2.5) 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 PM 2.5 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 PM 2.5 concentrations exceed 90 μg/m 3 , with local concentrations of approximately 200 μg/m 3 for parts of the Indo-Gangetic Plain. In East Asia, monthly mean PM 2.5 concentrations have decreased over the period 2010−2019 by 1.6−2.6 μg/m 3 /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 PM 2.5 exposure, with large measurement gaps in the Global South. Uncertainty estimates exhibit regional consistency with observed differences between ground-based and satellitederived PM 2.5. The evaluation of uncertainty for agglomerated values indicates that hybrid PM 2.5 estimates provide precise regionalscale representation, with residual uncertainty inversely proportional to the sample size.
Particulate Matter Sampling Techniques and Data Modelling Methods
Air Quality - Measurement and Modeling, 2016
Particulate matter with 10 μm or less in diameter (PM 10) is known to have adverse effects on human health and the environment. For countries committed to reducing PM 10 emissions, it is essential to have models that accurately estimate and predict PM 10 concentrations for reporting and monitoring purposes. In this chapter, a broad overview of recent empirical statistical and machine learning techniques for modelling PM 10 is presented. This includes the instrumentation used to measure particulate matter, data preprocessing, the selection of explanatory variables and modelling methods. Key features of some PM 10 prediction models developed in the last 10 years are described, and current work modelling and predicting PM 10 trends in New Zealand-a remote country of islands in the South Pacific Ocean-are examined. In conclusion, the issues and challenges faced when modelling PM 10 are discussed and suggestions for future avenues of investigation, which could improve the precision of PM 10 prediction and estimation models are presented.
Optimal estimation for global ground‐level fine particulate matter concentrations
Journal of Geophysical Research: Atmospheres, 2013
We develop an optimal estimation (OE) algorithm based on top‐of‐atmosphere reflectances observed by the MODIS satellite instrument to retrieve near‐surface fine particulate matter (PM2.5). The GEOS‐Chem chemical transport model is used to provide prior information for the Aerosol Optical Depth (AOD) retrieval and to relate total column AOD to PM2.5. We adjust the shape of the GEOS‐Chem relative vertical extinction profiles by comparison with lidar retrievals from the CALIOP satellite instrument. Surface reflectance relationships used in the OE algorithm are indexed by land type. Error quantities needed for this OE algorithm are inferred by comparison with AOD observations taken by a worldwide network of sun photometers (AERONET) and extended globally based upon aerosol speciation and cross correlation for simulated values, and upon land type for observational values. Significant agreement in PM2.5 is found over North America for 2005 (slope = 0.89; r = 0.82; 1‐σ error = 1 µg/m3 + 27...
National Prediction of Ambient Fine Particulates: 2000-2009
Open journal of air pollution, 2016
A large body of evidence links ambient fine particulates (PM 2.5) to chronic disease. Efforts continue to be made to improve large scale estimation of this pollutant for within-urban environments and sparsely monitored areas. Still questions remain about modeling choices. The purpose of this study was to evaluate the performance of spatial only models in predicting national monthly exposure estimates of fine particulate matter at different time aggregations during the time period 2000-2009 for the contiguous United States. Additional goals were to evaluate the difference in prediction between federal reference monitors and non-reference monitors, assess regional differences, and compare with traditional methods. Using spatial generalized additive models (GAM), national models for fine particulate matter were developed, incorporating geographical information systems (GIS)-derived covariates and meteorological variables. Results were compared to nearest monitor and inverse distance weighting at different time aggregations and a comparison was made between the Federal Reference Method and all monitors. Cross-validation was used for model evaluation. Using all monitors, the cross-validated R 2 was 0.76, 0.81, and 0.82 for monthly, 1 year, and 5-year aggregations, respectively. A small decrease in performance was observed when selecting Federal Reference monitors only (R 2 = 0.73, 0.78, and 0.80 respectively). For Inverse distance weighting (IDW), there was a significantly larger decrease in R 2 (0.68, 0.71, and 0.73, respectively). The spatial GAM showed the weakest performance for the northwest region. In conclusion, National exposure estimates of fine particulates at different time aggregations can be significantly improved over traditional methods by using spatial GAMs that are relatively easy to produce. Furthermore, these models are comparable in performance to other national prediction models.
Global chemical composition of ambient fine particulate matter for exposure assessment
Environmental science & technology, 2014
Epidemiologic and health impact studies are inhibited by the paucity of global, long-term measurements of the chemical composition of fine particulate matter. We inferred PM2.5 chemical composition at 0.1° × 0.1° spatial resolution for 2004-2008 by combining aerosol optical depth retrieved from the MODIS and MISR satellite instruments, with coincident profile and composition information from the GEOS-Chem global chemical transport model. Evaluation of the satellite-model PM2.5 composition data set with North American in situ measurements indicated significant spatial agreement for secondary inorganic aerosol, particulate organic mass, black carbon, mineral dust, and sea salt. We found that global population-weighted PM2.5 concentrations were dominated by particulate organic mass (11.9 ± 7.3 μg/m(3)), secondary inorganic aerosol (11.1 ± 5.0 μg/m(3)), and mineral dust (11.1 ± 7.9 μg/m(3)). Secondary inorganic PM2.5 concentrations exceeded 30 μg/m(3) over East China. Sensitivity simula...