Bayesian Bivariate Semiparametric Spatial Models for Ozone and PM2.5 Emissions
Tóm tắt
This study focused on the development of Bayesian bivariate semiparametric models for ozone and PM2.5 emissions. The semiparametric models rely on a Dirichlet mixture, which accounts for unobserved heterogeneity by employing random distribution for the intercept of each entity. The models with and without spatially structured random effects were also evaluated. Overall, four models were developed: three semiparametric with a flexible intercept and one parametric to serve as a reference. The significance of simultaneous estimation of PM2.5 and ozone was demonstrated by selection of common contributing factors as well as the statistically significant bivariate error term. In terms of relationship between dependent and independent variables, the areas with higher population and poverty, along with lesser educated residents, were observed to experience higher ozone and PM2.5 concentration. Also, the entities with higher vehicular traffic density and larger geographic areas were observed to be positively correlated with ozone and PM2.5 concentration. The underlying influential factor for both such variables may be the vehicular emissions, which is directly associated with traffic density and indirectly with land area as larger entities may tend to have higher traffic activity. The superiority of the semiparametric models, compared with parametric, signified the advantage associated with the flexible Dirichlet approach. The models with and without spatial correlation structures illustrated the mixed performance across the cases of parametric and semiparametric bivariate ones. The lack of consistent advantages associated with the inclusion of spatial effects may be due to the fact that the spatial correlation was not observed to be significant for the current dataset. Other spatial structure specifications may lead to different performance results.
Tài liệu tham khảo
El-Shawarby, I., Ahn, K., & Rakha, H. (2005). Comparative field evaluation of vehicle cruise speed and acceleration level impacts on hot stabilized emissions. Transportation Research Part D: Transport and Environment, 10(1), 13–30.
Raheem, A. A., Adekola, F. A., & Obioh, I. O. (2009). The seasonal variation of the concentrations of ozone, sulfur dioxide, and nitrogen oxides in two Nigerian cities. Environmental Modeling and Assessment, 14(4), 497–509.
Prada, F.P. & Monzon, A., (2017). Identifying traffic emissions hotspots for urban air quality interventions: the case of Madrid City, (No. 17-05015).
Rao, S., Chirkov, V., Dentener, F., Van Dingenen, R., Pachauri, S., Purohit, P., et al. (2012). Environmental modeling and methods for estimation of the global health impacts of air pollution. Environmental Modeling and Assessment, 17(6), 613–622.
Hackstadt, A. J., & Peng, R. D. (2014). A Bayesian multivariate receptor model for estimating source contributions to particulate matter pollution using national databases. Environmetrics, 25(7), 513–527.
Ibarra-Berastegi, G., Madariaga, I., Agirre, E., & Uria, J. (2003). Short-term forecasting of ozone and NO2 levels using traffic data in Bilbao (Spain). WIT Transactions on The Built Environment, 64.
Miranda-Moreno, L., Fu, S. U., & Lord, D. (2010). Incorporation of accident severity and vehicle occupancy into the hot spot identification process. Transportation Research Record: Journal of the Transportation Research Board, 2102, 53–60.
Pont, V., & Fontan, J. (2001). Comparison between weekend and weekday ozone concentration in large cities in France. Atmospheric Environment, 35(8), 1527–1535.
Dockery, D. W., Pope, C. A., Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., Ferris Jr., B. G., & Speizer, F. E. (1993). An association between air pollution and mortality in six US cities. New England Journal of Medicine, 329(24), 1753–1759.
Ito, K., Inoue, S., Hiraku, Y., & Kawanishi, S. (2005). Mechanism of site-specific DNA damage induced by ozone. Mutation Research, Genetic Toxicology and Environmental Mutagenesis, 585(1), 60–70.
McConnell, R., Berhane, K., Yao, L., Lurmann, F. W., Avol, E., & Peters, J. M. (2006). Predicting residential ozone deficits from nearby traffic. Science of the Total Environment, 363(1), 166–174.
Spiegelhalter, D., Thomas, A., Best, N., & Lunn, D., (2003). WinBUGS version 1.4 user manual. MRC Biostatistics Unit, Cambridge, http://www.mrc-cam.ac.uk/bugs
Prakash, A., Kumar, U., Kumar, K., & Jain, V. K. (2011). A wavelet-based neural network model to predict ambient air pollutants’ concentration. Environmental Modeling and Assessment, 16(5), 503–517.
Suleiman, A., Tight, M. R., & Quinn, A. D. (2016). Hybrid neural networks and boosted regression tree models for predicting roadside particulate matter. Environmental Modeling and Assessment, 21(6), 731–750.
Valavanidis, A., Loridas, S., Vlahogianni, T., & Fiotakis, K. (2009). Influence of ozone on traffic-related particulate matter on the generation of hydroxyl radicals through a heterogeneous synergistic effect. Journal of Hazardous Materials, 162(2), 886–892.
Dellinger, B., Pryor, W. A., Cueto, R., Squadrito, G. L., Hegde, V., & Deutsch, W. A. (2001). Role of free radicals in the toxicity of airborne fine particulate matter. Chemical Research in Toxicology, 14(10), 1371–1377.
Pawlovich, M. D., Li, W., Carriquiry, A., & Welch, T. (2006). Iowa’s experience with “road diet” measures: impacts on crash frequencies and crash rates assessed following a Bayesian approach. Transportation Research Record: Journal of the Transportation Research Board, 1953, 163–171.
Bobb, J.F., Dominici, F. & Peng, R.D., (2011). Reduced Bayesian hierarchical models: Estimating health effects of simultaneous exposure to multiple pollutants. Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 230. http://biostats.bepress.com/jhubiostat/paper230
Park, E., & Lord, D. (2007). Multivariate Poisson-lognormal models for jointly modeling crash frequency by severity. Transportation Research Record: Journal of the Transportation Research Board, 2019, 1–6.
Forkenbrock, D. J., & Schweitzer, L. A. (1999). Environmental justice in transportation planning. Journal of the American Planning Association, 65(1), 96–112.
Gill, G. S., Cheng, W., Xie, M., Vo, T., Jia, X., & Zhou, J. (2017). Evaluating influence of neighboring structures on spatial crash frequency modeling and site-ranking performance. Transportation Research Record: Journal of the Transportation Research Board, 2659, 117–126.
Pirani, M., Papageorgiou, G., Best, N., Atkinson, R.W. & Fuller, G.W., (2013). Bayesian modelling for estimating adverse health effects of exposure to multiple air pollutants in a time series framework. ISI Proceedings.
Qiu, Z. & Li, X., (2016). Quantitative particulate matter hot-spot analysis for a transportation project in China. Transportation Research Board 95th Annual Meeting (No. 16-2588).
Brauer, A. C. M. (2000). Ambient atmospheric particles in the airways of human lungs. Ultrastructural Pathology, 24(6), 353–361.
Park, S., & Rakha, H. (2006). Energy and environmental impacts of roadway grades. Transportation Research Record: Journal of the Transportation Research Board, 1987, 148–160.
Fecht, D., Hansell, A. L., Morley, D., Dajnak, D., Vienneau, D., Beevers, S., Toledano, M. B., Kelly, F. J., Anderson, H. R., & Gulliver, J. (2016). Spatial and temporal associations of road traffic noise and air pollution in London: Implications for epidemiological studies. Environment International, 88, 235–242.
Pryor, W. A. (1994). Mechanisms of radical formation from reactions of ozone with target molecules in the lung. Free Radical Biology and Medicine, 17(5), 451–465.
Pope III, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., & Thurston, G. D. (2002). Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. Jama, 287(9), 1132–1141.
Lingwall, J. W., Christensen, W. F., & Reese, C. S. (2008). Dirichlet based Bayesian multivariate receptor modeling. Environmetrics, 19(6), 618–629.
McBride, S. J., Williams, R. W., & Creason, J. (2007). Bayesian hierarchical modeling of personal exposure to particulate matter. Atmospheric Environment, 41(29), 6143–6155.
WILMAPCO, (2013). Transportation equity report: an environmental justice study & title VI plan for the Wilmapco Region. <http://www.wilmapco.org/EJ/2013_EJ_T6_Report.pdf>.
Mardia, K. V. (1988). Multi-dimensional multivariate Gaussian Markov random fields with application to image processing. Journal of Multivariate Analysis, 24(2), 265–284.
Liu, L. J., Delfino, R., & Koutrakis, P. (1997). Ozone exposure assessment in a southern California community. Environmental Health Perspectives, 105(1), 58–65.
Bakka, H., Rue, H., Fuglstad, G. A., Riebler, A., Bolin, D., Illian, J., Krainski, E., Simpson, D., & Lindgren, F. (2018). Spatial modeling with R-INLA: a review. Wiley Interdisciplinary Reviews: Computational Statistics, 10(6), e1443.
Li, N., Hao, M., Phalen, R. F., Hinds, W. C., & Nel, A. E. (2003). Particulate air pollutants and asthma: a paradigm for the role of oxidative stress in PM-induced adverse health effects. Clinical Immunology, 109(3), 250–265.
Piersanti, A., Monforti, F., & Zanini, G. (2005). Simulation of PM 10 concentration patterns for a 2010 traffic scenario in Bologna, Italy. Environmental Modeling and Assessment, 10(4), 291–301.
Heydari, S., Fu, L., Miranda-Moreno, L. F., & Jopseph, L. (2017). Using a flexible multivariate latent class approach to model correlated outcomes: a joint analysis of pedestrian and cyclist injuries. Analytic Methods in Accident Research, 13, 16–27.
Thurston, G. D., & Ito, K. (2001). Epidemiological studies of acute ozone exposures and mortality. Journal of Exposure Science & Environmental Epidemiology, 11(4), 286–294.
FHWA, (2014). Environmental justice-training, U.S. DOT, <http://www.fhwa.dot.gov/environment/environmental_justice/training/>.
Wang, M., Keller, J. P., Adar, S. D., Kim, S. Y., Larson, T. V., Olives, C., Sampson, P. D., Sheppard, L., Szpiro, A. A., Vedal, S., & Kaufman, J. D. (2015). Development of long-term spatiotemporal models for ambient ozone in six metropolitan regions of the United States: the MESA Air study. Atmospheric Environment, 123, 79–87.
Cheng, W., Gill, G. S., Ensch, J. L., Kwong, J., & Jia, X. (2018a). Multimodal crash frequency modeling: multivariate space-time models with alternate spatiotemporal interactions. Accident Analysis and Prevention, 113, 159–170.
McCormack, M. C., Breysse, P. N., Matsui, E. C., Hansel, N. N., Peng, R. D., Curtin-Brosnan, J., D'Ann, L. W., Wills-Karp, M., Diette, G. B., & Center for Childhood Asthma in the Urban Environment. (2011). Indoor particulate matter increases asthma morbidity in children with non-atopic and atopic asthma. Annals of Allergy, Asthma & Immunology, 106(4), 308–315.
Parra, M. A., Elustondo, D., Bermejo, R., & Santamaria, J. M. (2009). Ambient air levels of volatile organic compounds (VOC) and nitrogen dioxide (NO2) in a medium size city in Northern Spain. Science of the Total Environment, 407(3), 999–1009.
Ohlssen, D. I., Sharples, L. D., & Spiegelhalter, D. J. (2007). Flexible random-effects models using Bayesian semi-parametric models: applications to institutional comparisons. Statistics in Medicine, 26(9), 2088–2112.
Molina-Garcia, A., Reyes-Hernandez, A., & Rodriguez-Gomez, A. (2017). A year air quality measures, 2016, and risk communication in Morelia City, Michoacan, Mexico. Journal of Transport & Health, 5, S110.
Werner, M., Kryza, M., & Dore, A. J. (2014). Differences in the spatial distribution and chemical composition of PM 10 between the UK and Poland. Environmental Modeling and Assessment, 19(3), 179–192.
Congdon, P. (2007). Bayesian statistical modelling (Vol. 704). John Wiley & Sons.
Cheng, W., Gill, G., Sakrani, T., Ralls, D., & Jia, X. (2018). Modeling the endogeneity of lane-mean speeds and lane-speed deviations using a Bayesian structural equations approach with spatial correlation. Journal of Transportation Research Part A: Policy and Practice, 116, 220–231.
El-Basyouny, K., Barua, S., & Islam, M. T. (2014). Investigation of time and weather effects on crash types using full Bayesian multivariate Poisson lognormal models. Accident; Analysis and Prevention, 73, 91–99.
Liu, Y., Guo, H., Mao, G., & Yang, P. (2008). A bayesian hierarchical model for urban air quality prediction under uncertainty. Atmospheric Environment, 42(36), 8464–8469.
Boriboonsomsin, K., & Barth, M. (2009). Impacts of road grade on fuel consumption and carbon dioxide emissions evidenced by use of advanced navigation systems. Transportation Research Record: Journal of the Transportation Research Board, 2139, 21–30.
Cheng, W., Gill, G.S., Choi, S., Zhou, J., Jia, X. & Xie, M., (2017). Comparative evaluation of temporal correlation treatment in crash frequency modelling Transportmetrica A: Transport Science, (just-accepted), 1-40. https://doi.org/10.1080/23249935.2017.1418458.
Gelfand, A.E., (1996). Model determination using sampling-based methods. Markov chain Monte Carlo in Practice, 145-161.
Hänninen, O., Zauli-Sajani, S., De Maria, R., Lauriola, P., & Jantunen, M. (2009). Integrated ambient and microenvironment model for estimation of PM10 exposures of children in annual and episode settings. Environmental Modeling and Assessment, 14(4), 419–429.
İçağa, Y., & Sabah, E. (2009). Statistical analysis of air pollutants and meteorological parameters in Afyon, Turkey. Environmental Modeling and Assessment, 14(2), 259–266.
Ntzoufras, I. (2011). Bayesian modeling using WinBUGS (Vol. 698). Hoboken: Wiley.
Cheng, W., Gill, G. S., Zhang, Y., & Cao, Z. (2018b). Bayesian spatiotemporal crash frequency models with mixture components for space-time interactions. Accident Analysis & Prevention, 112, 84–93.
Wang, H., Dong, Y. & Zhang, K., (2017). A spatial-temporal model to improve PM2. 5 inference. In Computer and Information Science (ICIS), 2017 IEEE/ACIS 16th international conference on (173–177). IEEE.
Earnest, A., Morgan, G., Mengersen, K., Ryan, L., Summerhayes, R., & Beard, J. (2007). Evaluating the effect of neighbourhood weight matrices on smoothing properties of conditional autoregressive (CAR) models. International Journal of Health Geographics, 6(1), 54.
Geedipally, S. R., Lord, D., & Dhavala, S. S. (2014). A caution about using deviance information criterion while modeling traffic crashes. Safety Science, 62, 495–498.
Cheng, W., Gill, G. S., Vo, T., Zhou, J., & Sakrani, T. (2018). Use of bivariate dirichlet process mixture spatial model to estimate active transportation-related crash counts. Transportation Research Record, 2672(38), 105–115.
Alai, D. H., Merz, M., & Wüthrich, M. V. (2009). Mean square error of prediction in the Bornhuetter–Ferguson claims reserving method. Annals of Actuarial Science, 4(1), 7–31.
England, P., & Verrall, R. (1999). Analytic and bootstrap estimates of prediction errors in claims reserving. Insurance: Mathematics & Economics, 25(3), 281–293.
Amemiya, T., & Wu, R. Y. (1972). The effect of aggregation on prediction in the autoregressive model. Journal of the American Statistical Association, 67(339), 628–632.
Bunke, O., & Droge, B. (1984). Estimators of the mean squared error of prediction in linear regression. Technometrics, 26(2), 145–155.
Rock, S., Ahern, A., & Caulfield, B. (2014). Equity and fairness in transport planning: the state of play. TRB Annual Meeting Compendium of Papers. Washington, D.C.: National Academy of Science.
Victorin, K. (1992). Review of the genotoxicity of ozone. Mutation Research/Reviews in Genetic Toxicology, 277(3), 221–238.
Fernandes, P., Salamati, K., Rouphail, N. M., & Coelho, M. C. (2015). Identification of emission hotspots in roundabouts corridors. Transportation Research Part D: Transport and Environment, 37, 48–64.
Faiz, A. (1993). Automotive emissions in developing countries-relative implications for global warming, acidification and urban air quality. Transportation Research Part A: Policy and Practice, 27(3), 167–186.
Frank, L. D., Stone Jr., B., & Bachman, W. (2000). Linking land use with household vehicle emissions in the central Puget Sound: methodological framework and findings. Transportation Research Part D: Transport and Environment, 5(3), 173–196.
Morrison, D.F., (1998). Multivariate analysis, overview. Encyclopedia of Biostatistics.
Cheng, W., Lin, W. H., Jia, X., Wu, X., & Zhou, J. (2018). Ranking cities for safety investigation by potential for safety improvement. Journal of Transportation Safety & Security, 10(4), 345–366.
Cheng, W., Gill, G. S., Zhang, Y., Vo, T., Wen, F., & Li, Y. (2020). Exploring the modeling and site-ranking performance of Bayesian spatiotemporal crash frequency models with mixture components. Accident Analysis & Prevention, 135, 105357.