DOI: 10.1016/j.atmosenv.2020.117309
论文题名: Ensemble averaging based assessment of spatiotemporal variations in ambient PM2.5 concentrations over Delhi, India, during 2010–2016
作者: Mandal S. ; Madhipatla K.K. ; Guttikunda S. ; Kloog I. ; Prabhakaran D. ; Schwartz J.D. ; GeoHealth Hub India Team
刊名: Atmospheric Environment
ISSN: 1352-2310
出版年: 2020
卷: 224 语种: 英语
英文关键词: Additives
; Decision trees
; Forecasting
; Geographical regions
; Land use
; Mean square error
; Particles (particulate matter)
; Pollution
; Population statistics
; Random forests
; Support vector regression
; Annual average concentration
; Dose response relationships
; Generalized additive model
; Hybrid model
; Particulate Matter
; Root mean squared errors
; Satellite observations
; Spatio-temporal variation
; Learning systems
; ambient air
; assessment method
; atmospheric pollution
; land use
; machine learning
; mortality
; observational method
; particulate matter
; pollution exposure
; risk factor
; spatiotemporal analysis
; article
; averaging
; calibration
; dose response
; elastic tissue
; exercise
; human
; India
; land use
; particulate matter
; population density
; prediction
; random forest
; support vector machine
; Delhi
; India
学科: Hybrid models
; Machine learning
; Particulate matter
; Pollution exposure
; Satellite observations
中文摘要: Elevated levels of ambient air pollution has been implicated as a major risk factor for morbidities and premature mortality in India, with particularly high concentrations of particulate matter in the Indo-Gangetic plain. High resolution spatiotemporal estimates of such exposures are critical to assess health effects at an individual level. This article retrospectively assesses daily average PM2.5 exposure at 1 km × 1 km grids in Delhi, India from 2010 to 2016, using multiple data sources and ensemble averaging approaches. We used a multi-stage modeling exercise involving satellite data, land use variables, reanalysis based meteorological variables and population density. A calibration regression was used to model PM2.5: PM10 to counter the sparsity of ground monitoring data. The relationship between PM2.5 and its spatiotemporal predictors was modeled using six learners; generalized additive models, elastic net, support vector regressions, random forests, neural networks and extreme gradient boosting. Subsequently, these predictions were combined under a generalized additive model framework using a tensor product based spatial smoothing. Overall cross-validated prediction accuracy of the model was 80% over the study period with high spatial model accuracy and predicted annual average concentrations ranging from 87 to 138 μg/m3. Annual average root mean squared errors for the ensemble averaged predictions were in the range 39.7–62.7 μg/m3 with prediction bias ranging between 4.6 and 11.2 μg/m3. In addition, tree based learners such as random forests and extreme gradient boosting outperformed other algorithms. Our findings indicate important seasonal and geographical differences in particulate matter concentrations within Delhi over a significant period of time, with meteorological and land use features that discriminate most and least polluted regions. This exposure assessment can be used to estimate dose response relationships more accurately over a wide range of particulate matter concentrations. © 2020 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/160489
Appears in Collections: 气候变化与战略
There are no files associated with this item.
作者单位: Center for Chronic Disease Control, New Delhi, India; Public Health Foundation of India, New Delhi, India; Urban Emissions, India; Division of Atmospheric Sciences, Desert Research Institute, Reno, United States; Ben Gurion University of the Negev, Israel; London School of Hygiene and Tropical Medicine, London, United Kingdom; Harvard TH Chan School of Public Health, Boston, United States
Recommended Citation:
Mandal S.,Madhipatla K.K.,Guttikunda S.,et al. Ensemble averaging based assessment of spatiotemporal variations in ambient PM2.5 concentrations over Delhi, India, during 2010–2016[J]. Atmospheric Environment,2020-01-01,224