Air Pollution Monitoring Design for Epidemiological Application in a Densely Populated City
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection and Processing
2.1.1. Air Pollution Monitoring Data
2.1.2. Location Data
2.1.3. Geographic Variables
2.2. Air Pollution Monitoring Design
2.2.1. Variable Selection
2.2.2. Cluster Analysis
2.2.3. Site Selection
2.2.4. Sensitivity Analysis
3. Results
3.1. Distributions of Locations and Air Pollution Concentrations
3.1.1. Three Types of Locations
3.1.2. Annual Average Concentrations of PM2.5
3.2. Air Pollution Monitoring Design
3.2.1. Variable Selection
3.2.2. Cluster Analysis
3.2.3. Site Selection
3.2.4. Sensitivity Analysis
4. Discussion
5. Conclusions
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Hoek, G.; Krishnan, R.M.; Beelen, R.; Peters, A.; Ostro, B.; Brunekreef, B.; Kaufman, J.D. Long-term air pollution exposure and cardio-respiratory mortality: A review. Environ. Health 2013, 12, 43. [Google Scholar] [CrossRef] [PubMed]
- Pope, C.A., III; Dockery, D.W. Health effects of fine particulate air pollution: Lines that connect. J. Air Waste Manag. Assoc. 2006, 56, 709–742. [Google Scholar] [CrossRef] [PubMed]
- Cohen, M.A.; Adar, S.D.; Allen, R.W.; Avol, E.; Curl, C.L.; Gould, T.; Hardie, D.; Ho, A.; Kinney, P.; Larson, T.V.; et al. Approach to estimating participant pollutant exposures in the multi-ethnic study of atherosclerosis and air pollution (mesa air). Environ. Sci. Technol. 2009, 43, 4687–4693. [Google Scholar] [CrossRef] [PubMed]
- Raaschou-Nielsen, O.; Andersen, Z.J.; Beelen, R.; Samoli, E.; Stafoggia, M.; Weinmayr, G.; Hoffmann, B.; Fischer, P.; Nieuwenhuijsen, M.J.; Brunekreef, B.; et al. Air pollution and lung cancer incidence in 17 european cohorts: Prospective analyses from the european study of cohorts for air pollution effects (escape). Lancet Oncol. 2013, 14, 813–822. [Google Scholar] [CrossRef]
- Kukkonen, J.; Härkönen, J.; Karppinen, A.; Pohjola, M.; Pietarila, H.; Koskentalo, T. A semi-empirical model for urban PM10 concentrations, and its evaluation against data from an urban measurement network. Atmos. Environ. 2001, 35, 4433–4442. [Google Scholar] [CrossRef]
- Smith, L.; Mukerjee, S.; Gonzales, M.; Stallings, C.; Neas, L.; Norris, G.; Özkaynak, H. Use of gis and ancillary variables to predict volatile organic compound and nitrogen dioxide levels at unmonitored locations. Atmos. Environ. 2006, 40, 3773–3787. [Google Scholar] [CrossRef]
- Kanaroglou, P.S.; Jerrett, M.; Morrison, J.; Beckerman, B.; Arain, M.A.; Gilbert, N.L.; Brook, J.R. Establishing an air pollution monitoring network for intra-urban population exposure assessment: A location-allocation approach. Atmos. Environ. 2005, 39, 2399–2409. [Google Scholar] [CrossRef]
- Kumar, N.; Nixon, V.; Sinha, K.; Jiang, X.; Ziegenhorn, S.; Peters, T. An optimal spatial configuration of sample sites for air pollution monitoring. J. Air Waste Manag. Assoc. 2009, 59, 1308–1316. [Google Scholar] [CrossRef] [PubMed]
- Ross, M.A. Integrated Science Assessment for Particulate Matter; US Environmental Protection Agency: Washington DC, USA, 2009; pp. 61–161.
- Korea National Institute of Environmental Research. Annual Report of Ambient Air Quality in Korea; Korea Ministry of Environment: Seoul, Koera, 2010; pp. 97–243, 461–466.
- Yi, S.-J.; Kim, H.; Kim, S.-Y. Exploration and application of regulatory PM10 measurement data for developing long-term prediction models in South Korea. J. Korean Soc. Atmos. Environ. 2016, 32, 114–126. [Google Scholar] [CrossRef]
- Hong, S.; Son, D.K.; Lim, W.R.; Kim, S.H.; Kim, H.; Yum, H.Y.; Kwon, H. The prevalence of atopic dermatitis, asthma, and allergic rhinitis and the comorbidity of allergic diseases in children. Environ. Health Toxicol. 2012, 27, e2012006. [Google Scholar] [CrossRef] [PubMed]
- Eum, Y.; Song, I.; Kim, H.C.; Leem, J.H.; Kim, S.Y. Computation of geographic variables for air pollution prediction models in South Korea. Environ. Health Toxicol 2015, 30, e2015010. [Google Scholar] [CrossRef] [PubMed]
- Beelen, R.; Hoek, G.; Vienneau, D.; Eeftens, M.; Dimakopoulou, K.; Pedeli, X.; Tsai, M.-Y.; Künzli, N.; Schikowski, T.; Marcon, A.; et al. Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe-The ESCAPE project. Atmos. Environ. 2013, 72, 10–23. [Google Scholar] [CrossRef]
- Kashima, S.; Yorifuji, T.; Tsuda, T.; Doi, H. Application of land use regression to regulatory air quality data in Japan. Sci. Total Environ. 2009, 407, 3055–3062. [Google Scholar] [CrossRef] [PubMed]
- Ross, Z.; Jerrett, M.; Ito, K.; Tempalski, B.; Thurston, G.D. A land use regression for predicting fine particulate matter concentrations in the New York City region. Atmos. Environ. 2007, 41, 2255–2269. [Google Scholar] [CrossRef]
- Yu, H.L.; Wang, C.H.; Liu, M.C.; Kuo, Y.M. Estimation of fine particulate matter in taipei using landuse regression and bayesian maximum entropy methods. Int. J. Environ. Res. Public Health 2011, 8, 2153–2169. [Google Scholar] [CrossRef] [PubMed]
- Hoek, G.; Beelen, R.; de Hoogh, K.; Vienneau, D.; Gulliver, J.; Fischer, P.; Briggs, D. A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos. Environ. 2008, 42, 7561–7578. [Google Scholar] [CrossRef]
- Keller, J.P.; Olives, C.; Kim, S.Y.; Sheppard, L.; Sampson, P.D.; Szpiro, A.A.; Oron, A.P.; Lindstrom, J.; Vedal, S.; Kaufman, J.D. A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the multi-ethnic study of atherosclerosis and air pollution. Environ. Health Perspect. 2015, 123, 301–309. [Google Scholar] [CrossRef] [PubMed]
- Kim, S.Y.; Sheppard, L.; Bergen, S.; Szpiro, A.A.; Sampson, P.D.; Kaufman, J.D.; Vedal, S. Prediction of fine particulate matter chemical components with a spatio-temporal model for the Multi-Ethnic Study of Atherosclerosis cohort. J. Expo. Sci. Environ. Epidemiol. 2016, 26, 520–528. [Google Scholar] [CrossRef] [PubMed]
- Jin, X.; Han, J. K-means clustering. In Encyclopedia of Machine Learning; Sammut, C., Webb, G.I., Eds.; Springer: Boston, MA, USA, 2010; pp. 563–564. [Google Scholar]
- Hartigan, J.A.; Wong, M.A. Algorithm as 136: A k-means clustering algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.) 1979, 28, 100–108. [Google Scholar] [CrossRef]
- Dhillon, I.S.; Modha, D.S. Concept decompositions for large sparse text data using clustering. Mach. Learn. 2001, 42, 143–175. [Google Scholar] [CrossRef]
- Kijewska, A.; Bluszcz, A. Research of varying levels of greenhouse gas emissions in European countries using the k-means method. Atmos. Pollut. Res. 2016, 7, 935–944. [Google Scholar] [CrossRef]
- Austin, E.; Coull, B.A.; Zanobetti, A.; Koutrakis, P. A framework to spatially cluster air pollution monitoring sites in US based on the PM2.5 composition. Environ. Int. 2013, 59, 244–254. [Google Scholar] [CrossRef] [PubMed]
- Diggle, P.J.; Menezes, R.; Su, T.l. Geostatistical inference under preferential sampling. J. R. Stat. Soc. Ser. C (Appl. Stat.) 2010, 59, 191–232. [Google Scholar] [CrossRef]
- Lee, A.; Szpiro, A.; Kim, S.Y.; Sheppard, L. Impact of preferential sampling on exposure prediction and health effect inference in the context of air pollution epidemiology. Environmetrics 2015, 26, 255–267. [Google Scholar] [CrossRef]
- Szpiro, A.A.; Paciorek, C.J.; Sheppard, L. Does more accurate exposure prediction necessarily improve health effect estimates? Epidemiology 2011, 22, 680–685. [Google Scholar] [CrossRef] [PubMed]
- Szpiro, A.A.; Paciorek, C.J. Measurement error in two-stage analyses, with application to air pollution epidemiology. Environmetrics 2013, 24, 501–517. [Google Scholar] [CrossRef] [PubMed]
- Kim, S.Y.; Song, I. National Scale exposure prediction for long-term concentrations of particulate matter and nitrogen dioxide in South Korea. Environ. Pollut. 2017, 226, 21–29. [Google Scholar] [CrossRef] [PubMed]
- Rousseuw, P.J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Comput. Appl. Math. 1987, 20, 53–65. [Google Scholar] [CrossRef]
- Sugar, C.A.; James, G.M. Finding the number of clusters in a data set: An information-theoretic approach. J. Am. Stat. Assoc. 2003, 98, 750–763. [Google Scholar] [CrossRef]
- Schwartz, J. Air pollution and children’s health. Pediatrics 2004, 113, 1037–1043. [Google Scholar] [PubMed]
- Seoul Development Institute. Development of Urban Climate Map in Seoul (Korean); Seoul Metropolitan Government: Seoul, Korea, 2008; pp. 10–58.
- Wang, A.; Brauer, M. Review of next generation air monitors for air pollution; Environment Canada: Vancouber, BC, Canada, 2014; pp. 5–13.
Variable | β a | p Value | LOOCV R2 |
---|---|---|---|
Length of major road b (100 m buffer) | 3.58 | <0.001 | 0.69 |
Proportion of water surface land use (500 m) | 0.67 | <0.001 | |
Number of construction companies (1000 m) | 3.01 | 0.001 | |
Distance to the nearest bus stop | −2.46 | 0.013 | |
Number of employees in construction industries (100 m) | −1.91 | 0.025 |
Cluster 1 | Cluster 2 | Cluster 3 | Cluster 4 | Cluster 5 | Cluster 6 | Cluster 7 | Cluster 8 | Cluster 9 | Total | |
---|---|---|---|---|---|---|---|---|---|---|
Current a | 9 (24.3) | 0 (0.0) | 16 (43.2) | 0 (0.0) | 1 (2.7) | 3 (8.1) | 0 (0.0) | 3 (8.1) | 5 (13.5) | 37 (100) |
Subject b | 2587 (8.3) | 505 (1.6) | 14,888 (47.9) | 34 (0.1) | 187 (0.6) | 303 (1.0) | 2246 (7.2) | 6780 (21.8) | 3567 (11.5) | 31,097 (100) |
Candidate c | 60 (14.6) | 19 (4.6) | 131 (31.8) | 0 (0.0) | 4 (1.0) | 7 (1.7) | 25 (6.1) | 136 (33.0) | 30 (7.3) | 412 (100) |
Current/Subject d | 34.8 | 0 | 10.8 | 0 | 53.5 | 99.0 | 0 | 4.4 | 14.0 | |
New sites | 1 | 6 | 4 | 8 | 1 |
Cluster 1 | Cluster 2 | Cluster 3 | Cluster 4 | Cluster 5 | Cluster 6 | Cluster 7 | Cluster 8 | Total | |
---|---|---|---|---|---|---|---|---|---|
Current a | 2 (5.4) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 21 (56.8) | 1 (2.7) | 1 (2.7) | 12 (32.4) | 37 (100) |
Subject b | 228 (0.3) | 2 (3.5) | 2131 (0.6) | 1577 (9.4) | 20,935 (63.3) | 3336 (6.8) | 187 (4.5) | 2701 (8.1) | 31,097 (100) |
Candidate c | 5 (1.2) | 0 (0.0) | 33 (8.0) | 20 (4.9) | 247 (60.0) | 32 (7.8) | 4 (1.0) | 71 (17.2) | 412 (100) |
Current/Subject d | 87.72 | 0 | 0 | 0 | 10.03 | 3.00 | 53.48 | 44.43 | |
New sites | 4 | 3 | 9 | 4 |
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Min, K.-D.; Kwon, H.-J.; Kim, K.; Kim, S.-Y. Air Pollution Monitoring Design for Epidemiological Application in a Densely Populated City. Int. J. Environ. Res. Public Health 2017, 14, 686. https://doi.org/10.3390/ijerph14070686
Min K-D, Kwon H-J, Kim K, Kim S-Y. Air Pollution Monitoring Design for Epidemiological Application in a Densely Populated City. International Journal of Environmental Research and Public Health. 2017; 14(7):686. https://doi.org/10.3390/ijerph14070686
Chicago/Turabian StyleMin, Kyung-Duk, Ho-Jang Kwon, KyooSang Kim, and Sun-Young Kim. 2017. "Air Pollution Monitoring Design for Epidemiological Application in a Densely Populated City" International Journal of Environmental Research and Public Health 14, no. 7: 686. https://doi.org/10.3390/ijerph14070686