IGSNRR OpenIR
Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions
Li, Lianfa1,2; Girguis, Mariam1; Lurmann, Frederick3; Wu, Jun4; Urman, Robert1; Rappaport, Edward1; Ritz, Beate5,6; Franklin, Meredith1; Breton, Carrie1; Gilliland, Frank1; Habre, Rima1
2019-07-01
Source PublicationENVIRONMENT INTERNATIONAL
ISSN0160-4120
Volume128Pages:310-323
Corresponding AuthorLi, Lianfa(lianfali@usc.edu)
AbstractBackground: Accurate estimation of nitrogen dioxide (NO2) and nitrogen oxide (NOx) concentrations at high spatiotemporal resolutions is crucial for improving evaluation of their health effects, particularly with respect to short-term exposures and acute health outcomes. For estimation over large regions like California, high spatial density field campaign measurements can be combined with more sparse routine monitoring network measurements to capture spatiotemporal variability of NO2 and NOx concentrations. However, monitors in spatially dense field sampling are often highly clustered and their uneven distribution creates a challenge for such combined use. Furthermore, heterogeneities due to seasonal patterns of meteorology and source mixtures between sub-regions (e.g. southern vs. northern California) need to be addressed. Objectives: In this study, we aim to develop highly accurate and adaptive machine learning models to predict high-resolution NO2 and NOx concentrations over large geographic regions using measurements from different sources that contain samples with heterogeneous spatiotemporal distributions and clustering patterns. Methods: We used a comprehensive Kruskal-K-means method to cluster the measurement samples from multiple heterogeneous sources. Spatiotemporal cluster-based bootstrap aggregating (bagging) of the base mixed-effects models was then applied, leveraging the clusters to obtain balanced and less correlated training samples for less bias and improvement in generalization. Further, we used the machine learning technique of grid search to find the optimal interaction of temporal basis functions and the scale of spatial effects, which, together with spatiotemporal covariates, adequately captured spatiotemporal variability in NO2 and NOx at the state and local levels. Results: We found an optimal combination of four temporal basis functions and 200 m scale spatial effects for the base mixed-effects models. With the cluster-based bagging of the base models, we obtained robust predictions with an ensemble cross validation R-2 of 0.88 for both NO2 and NOx [RMSE (RMSEIQR): 3.62 ppb (0.28) and 9.63 ppb (0.37) respectively]. In independent tests of random sampling, our models achieved similarly strong performance (R-2 of 0.87-0.90; RMSE of 3.97-9.69 ppb; RMSEIQR of 0.21-0.27), illustrating minimal over-fitting. Conclusions: Our approach has important implications for fusing data from highly clustered and heterogeneous measurement samples from multiple data sources to produce highly accurate concentration estimates of air pollutants such as NO2 and NOx at high resolution over a large region.
KeywordAir pollution Nitrogen oxides Spatiotemporal variability Generalization Machine learning Cluster methods
DOI10.1016/j.envint.2019.04.057
WOS KeywordLAND-USE REGRESSION ; LEVEL PM2.5 CONCENTRATIONS ; AIR-POLLUTION EXPOSURE ; NO2 ; VARIABILITY ; PM10
Indexed BySCI
Language英语
Funding ProjectLifecourse Approach to Developmental Repercussions of Environmental Agents on Metabolic and Respiratory Health NIH ECHO grants[5UG3OD023287] ; Lifecourse Approach to Developmental Repercussions of Environmental Agents on Metabolic and Respiratory Health NIH ECHO grants[4UH3OD023287] ; Southern California Environmental Health Sciences Center (National Institute of Environmental Health Sciences' grant)[P30ES007048] ; National Natural Science Foundation of China[41871351] ; National Natural Science Foundation of China[41471376] ; National Institute of Environmental Health Sciences[R21ES016379] ; National Institute of Environmental Health Sciences[R21ES022369] ; California Air Resources Board grant[04-323]
Funding OrganizationLifecourse Approach to Developmental Repercussions of Environmental Agents on Metabolic and Respiratory Health NIH ECHO grants ; Southern California Environmental Health Sciences Center (National Institute of Environmental Health Sciences' grant) ; National Natural Science Foundation of China ; National Institute of Environmental Health Sciences ; California Air Resources Board grant
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000467938500034
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/59548
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLi, Lianfa
Affiliation1.Univ Southern Calif, Dept Prevent Med, Los Angeles, CA 90007 USA
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
3.Sonoma Technol Inc, Petaluma, CA USA
4.Univ Calif Irvine, Susan & Henry Samueli Coll Hlth Sci, Program Publ Hlth, Irvine, CA USA
5.Univ Calif Los Angeles, Fileding Sch Publ Hlth, Dept Epidemiol, Los Angeles, CA USA
6.Univ Calif Los Angeles, Fileding Sch Publ Hlth, Dept Environm Hlth, Los Angeles, CA USA
Recommended Citation
GB/T 7714
Li, Lianfa,Girguis, Mariam,Lurmann, Frederick,et al. Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions[J]. ENVIRONMENT INTERNATIONAL,2019,128:310-323.
APA Li, Lianfa.,Girguis, Mariam.,Lurmann, Frederick.,Wu, Jun.,Urman, Robert.,...&Habre, Rima.(2019).Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions.ENVIRONMENT INTERNATIONAL,128,310-323.
MLA Li, Lianfa,et al."Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions".ENVIRONMENT INTERNATIONAL 128(2019):310-323.
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