Dataset Information

Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005-2014.

ABSTRACT: Accurate spatiotemporal air quality data are critical for use in assessment of regulatory effectiveness and for exposure assessment in health studies. A number of data fusion methods have been developed to combine observational data and chemical transport model (CTM) results. Our approach focuses on preserving the temporal variation provided by observational data while deriving the spatial variation from the community multiscale air quality (CMAQ) simulations, a type of CTM. Here we show the results of fusing regulatory monitoring observational data with 12 km resolution CTM simulation results for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, NO3-, NH4+, EC, OC, SO42-) over the contiguous United States on a daily basis for a period of ten years (2005-2014). An annual mean regression between the CTM simulations and observational data is used to estimate the average spatial fields, and spatial interpolation of observations normalized by predicted annual average is used to provide the daily variation. Results match the temporal variation well (R2 values ranging from 0.84-0.98 across pollutants) and the spatial variation less well (R2 values 0.42-0.94). Ten-fold cross validation shows normalized root mean square error values of 60% or less and spatiotemporal R2 values of 0.4 or more for all pollutants except SO2.

SUBMITTER: Senthilkumar N

PROVIDER: S-EPMC6765984 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005-2014.

Senthilkumar Niru N Gilfether Mark M Metcalf Francesca F Russell Armistead G AG Mulholland James A JA Chang Howard H HH

International journal of environmental research and public health 20190909 18

Accurate spatiotemporal air quality data are critical for use in assessment of regulatory effectiveness and for exposure assessment in health studies. A number of data fusion methods have been developed to combine observational data and chemical transport model (CTM) results. Our approach focuses on preserving the temporal variation provided by observational data while deriving the spatial variation from the community multiscale air quality (<i>CMAQ</i>) simulations, a type of CTM. Here we show ...[more]

PMID: 31505818

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Project description:BACKGROUND:In the last decade unconventional oil and gas (UOG) extraction has rapidly proliferated throughout the United States (US) and the world. This occurred largely because of the development of directional drilling and hydraulic fracturing which allows access to fossil fuels from geologic formations that were previously not cost effective to pursue. This process is known to use greater than 1,000 chemicals such as solvents, surfactants, detergents, and biocides. In addition, a complex mixture of chemicals, including heavy metals, naturally-occurring radioactive chemicals, and organic compounds are released from the formations and can enter air and water. Compounds associated with UOG activity have been linked to adverse reproductive and developmental outcomes in humans and laboratory animal models, which is possibly due to the presence of endocrine active chemicals. METHODS:Using systematic methods, electronic searches of PubMed and Web of Science were conducted to identify studies that measured chemicals in air near sites of UOG activity. Records were screened by title and abstract, relevant articles then underwent full text review, and data were extracted from the studies. A list of chemicals detected near UOG sites was generated. Then, the potential endocrine activity of the most frequently detected chemicals was explored via searches of literature from PubMed. RESULTS:Evaluation of 48 studies that sampled air near sites of UOG activity identified 106 chemicals detected in two or more studies. Ethane, benzene and n-pentane were the top three most frequently detected. Twenty-one chemicals have been shown to have endocrine activity including estrogenic and androgenic activity and the ability to alter steroidogenesis. Literature also suggested that some of the air pollutants may affect reproduction, development, and neurophysiological function, all endpoints which can be modulated by hormones. These chemicals included aromatics (i.e., benzene, toluene, ethylbenzene, and xylene), several polycyclic aromatic hydrocarbons, and mercury. CONCLUSION:These results provide a basis for prioritizing future primary studies regarding the endocrine disrupting properties of UOG air pollutants, including exposure research in wildlife and humans. Further, we recommend systematic reviews of the health impacts of exposure to specific chemicals, and comprehensive environmental sampling of a broader array of chemicals.

Dataset Information

Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005-2014.

Publications

Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005-2014.

Similar Datasets

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