DOI: 10.1016/j.atmosenv.2018.01.045
Scopus记录号: 2-s2.0-85041846771
论文题名: Cross-comparison and evaluation of air pollution field estimation methods
作者: Yu H ; , Russell A ; , Mulholland J ; , Odman T ; , Hu Y ; , Chang H ; H ; , Kumar N
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2018
卷: 179 起始页码: 49
结束页码: 60
语种: 英语
英文关键词: Air pollution
; Data fusion
; Exposure estimation
; Health impacts
; Hybrid model
Scopus关键词: Air quality
; Atmospheric aerosols
; Autocorrelation
; Data fusion
; Estimation
; Health
; Interpolation
; Inverse problems
; Land use
; Pollution
; Time series analysis
; Air pollutant concentrations
; Chemical transport models
; Exposure estimation
; Health impact
; Hybrid model
; Interspecies correlations
; Inverse distance weighting
; Spatial correlation coefficients
; Air pollution
; air quality
; atmospheric pollution
; comparative study
; concentration (composition)
; data assimilation
; estimation method
; field method
; health impact
; numerical model
; pollution exposure
; pollution monitoring
; air monitoring
; air pollutant
; air pollution
; air quality
; Article
; concentration (parameters)
; correlation coefficient
; environmental impact
; evaluation study
; Georgia (republic)
; intermethod comparison
; kriging
; measurement accuracy
; measurement error
; observational study
; particulate matter
; pollution monitoring
; priority journal
; spatiotemporal analysis
; statistical analysis
; statistical bias
; urban rural difference
; Atlanta
; Georgia
; United States
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: Accurate estimates of human exposure is critical for air pollution health studies and a variety of methods are currently being used to assign pollutant concentrations to populations. Results from these methods may differ substantially, which can affect the outcomes of health impact assessments. Here, we applied 14 methods for developing spatiotemporal air pollutant concentration fields of eight pollutants to the Atlanta, Georgia region. These methods include eight methods relying mostly on air quality observations (CM: central monitor; SA: spatial average; IDW: inverse distance weighting; KRIG: kriging; TESS-D: discontinuous tessellation; TESS-NN: natural neighbor tessellation with interpolation; LUR: land use regression; AOD: downscaled satellite-derived aerosol optical depth), one using the RLINE dispersion model, and five methods using a chemical transport model (CMAQ), with and without using observational data to constrain results. The derived fields were evaluated and compared. Overall, all methods generally perform better at urban than rural area, and for secondary than primary pollutants. We found the CM and SA methods may be appropriate only for small domains, and for secondary pollutants, though the SA method lead to large negative spatial correlations when using data withholding for PM2.5 (spatial correlation coefficient R = −0.81). The TESS-D method was found to have major limitations. Results of the IDW, KRIG and TESS-NN methods are similar. They are found to be better suited for secondary pollutants because of their satisfactory temporal performance (e.g. average temporal R2 > 0.85 for PM2.5 but less than 0.35 for primary pollutant NO2). In addition, they are suitable for areas with relatively dense monitoring networks due to their inability to capture spatial concentration variabilities, as indicated by the negative spatial R (lower than −0.2 for PM2.5 when assessed using data withholding). The performance of LUR and AOD methods were similar to kriging. Using RLINE and CMAQ fields without fusing observational data led to substantial errors and biases, though the CMAQ model captured spatial gradients reasonably well (spatial R = 0.45 for PM2.5). Two unique tests conducted here included quantifying autocorrelation of method biases (which can be important in time series analyses) and how well the methods capture the observed interspecies correlations (which would be of particular importance in multipollutant health assessments). Autocorrelation of method biases lasted longest and interspecies correlations of primary pollutants was higher than observations when air quality models were used without data fusing. Use of hybrid methods that combine air quality model outputs with observational data overcome some of these limitations and is better suited for health studies. Results from this study contribute to better understanding the strengths and weaknesses of different methods for estimating human exposures. © 2018 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82951
Appears in Collections: 气候变化事实与影响
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作者单位: School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, United States; Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, United States; Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, United States; Electric Power Research Institute, Palo Alto, CA, United States
Recommended Citation:
Yu H,, Russell A,, Mulholland J,et al. Cross-comparison and evaluation of air pollution field estimation methods[J]. Atmospheric Environment,2018-01-01,179