Developing an Advanced PM2.5 Exposure Model in Lima, Peru
Abstract
:1. Introduction
2. Data and Methods
2.1. Study Area
2.2. Ground PM2.5 Data
2.3. Satellite Data
2.4. Chemical Transport Model (CTM) Data
2.5. Meteorological Variables
2.6. Land Use Variables
2.7. Random Forest Model
3. Results
3.1. Description of PM2.5 Ground-Based Measurements
3.2. Random Forest Model Performance and Cross-Validation
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
- Ma, Z.; Hu, X.; Huang, L.; Bi, J.; Liu, Y. Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing. Environ. Sci. Technol. 2014, 48, 7436–7444. [Google Scholar] [CrossRef] [PubMed]
- Prieto-Parra, L.; Yohannessen, K.; Brea, C.; Vidal, D.; Ubilla, C.A.; Ruiz-Rudolph, P. Air pollution, PM2.5 composition, source factors, and respiratory symptoms in asthmatic and nonasthmatic children in Santiago, Chile. Environ. Int. 2017, 101, 190–200. [Google Scholar] [CrossRef] [PubMed]
- Liu, Q.; Xu, C.; Ji, G.; Liu, H.; Shao, W.; Zhang, C.; Gu, A.; Zhao, P. Effect of exposure to ambient PM2.5 pollution on the risk of respiratory tract diseases: A meta-analysis of cohort studies. J. Biomed. Res. 2017, 31, 130–142. [Google Scholar]
- WHO (World Health Organization). WHO Global Urban. Ambient Air Pollution Database. 2016. Available online: http://www.who.int/phe/health_topics/outdoorair/databases/cities/en/ (accessed on 25 August 2017).
- WHO (World Health Organization). Climate and Health Country Profile—2015: Peru; WHO: Geneva, Switzerland, 2015. [Google Scholar]
- BBVA Research. Peru Automobile Market Outlook 2010. Available online: https://www.bbvaresearch.com/KETD/fbin/mult/automobile_market_outlook_peru_tcm348-259266.pdf (accessed on 5 July 2018).
- Mead, N.V. Pant by Numbers: The Cities with the Most Dangerous Air-Listed. 2017. Available online: https://www.theguardian.com/cities/datablog/2017/feb/13/most-polluted-cities-world-listed-region (accessed on 4 March 2019).
- González, C.M.; Ynoue, R.Y.; Vara-Vela, A.; Rojas, N.Y.; Aristizábal, B.H. High-resolution air quality modeling in a medium-sized city in the tropical Andes: Assessment of local and global emissions in understanding ozone and PM10 dynamics. Atmos. Pollut. Res. 2018, 9, 934–948. [Google Scholar] [CrossRef]
- Della Ceca, L.S.; García Ferreyra, M.F.; Lyapustin, A.; Chudnovsky, A.; Otero, L.; Carreras, H.; Barnaba, F. Satellite-based view of the aerosol spatial and temporal variability in the Córdoba region (Argentina) using over ten years of high-resolution data. ISPRS J. Photogramm. Remote Sens. 2018, 145, 250–267. [Google Scholar] [CrossRef]
- Gómez, C.D.; González, C.M.; Osses, M.; Aristizábal, B.H. Spatial and temporal disaggregation of the on-road vehicle emission inventory in a medium-sized Andean city. Comparison of GIS-based top-down methodologies. Atmos. Environ. 2018, 179, 142–155. [Google Scholar] [CrossRef]
- Martins, L.D.; Wikuats, C.F.H.; Capucim, M.N.; de Almeida, D.S.; da Costa, S.C.; Albuquerque, T.; Barreto Carvalho, V.S.; de Freitas, E.D.; de Fátima Andrade, M.; Martins, J.A. Extreme value analysis of air pollution data and their comparison between two large urban regions of South. America. Weather Clim. Extremes 2017, 18, 44–54. [Google Scholar] [CrossRef]
- Zalakeviciute, R.; Rybarczyk, Y.; López-Villada, J.; Diaz Suarez, M.V. Quantifying decade-long effects of fuel and traffic regulations on urban ambient PM2.5 pollution in a mid-size South. American city. Atmos. Pollut. Res. 2018, 9, 66–75. [Google Scholar] [CrossRef]
- Lin, C.A.; Martins, M.A.; Farhat, S.C.; Conceição, G.M.; Anastácio, V.M.; Hatanaka, M.; Andrade, W.C.; Hamaue, W.R.; Böhm, G.M.; Saldiva, P.H. Air pollution and respiratory illness of children in São Paulo, Brazil. Paediatr. Perinat. Epidemiol. 1999, 13, 475–488. [Google Scholar] [CrossRef]
- Ribeiro, A.G.; Downward, G.S.; de Freitas, C.U.; Neto, F.C.; Cardoso, M.R.A.; de Oliveira, M.D.R.D.; Hystad, P.; Vermeulen, R.; Nardocci, A.C. Incidence and mortality for respiratory cancer and traffic-related air pollution in São Paulo, Brazil. Environ. Res. 2019, 170, 243–251. [Google Scholar] [CrossRef]
- Amarillo, A.C.; Carreras, H.A. The effect of airborne particles and weather conditions on pediatric respiratory infections in Cordoba, Argentine. Environ. Pollut. 2012, 170, 217–221. [Google Scholar] [CrossRef] [PubMed]
- De Miranda, R.M.; de Fatima Andrade, M.; Fornaro, A.; Astolfo, R.; de Andre, P.A.; Saldiva, P. Urban air pollution: A representative survey of PM2.5 mass concentrations in six Brazilian cities. Air Qual. Atmos. Health 2012, 5, 63–77. [Google Scholar] [CrossRef] [PubMed]
- Scholl, L.; Guerrero, A.; Quintanilla, O.; L’Hoste, M.C. Comparative Case Studies of Three IDB-Supported Urban Transport Projects; Inter.-American Development Bank: Washington, DC, USA, 2015. [Google Scholar]
- Silva, J.; Rojas, J.; Norabuena, M.; Molina, C.; Toro, R.A.; Leiva-Guzmán, M.A. Particulate matter levels in a South. American megacity: The metropolitan area of Lima-Callao, Peru. Environ. Monit. Assess. 2017, 189, 635. [Google Scholar] [CrossRef] [PubMed]
- Baumann, L.M.; Robinson, C.L.; Combe, J.M.; Gomez, A.; Romero, K.; Gilman, R.H.; Cabrera, L.; Hansel, N.N.; Wise, R.A.; Breysse, P.N.; et al. Effects of distance from a heavily transited avenue on asthma and atopy in a periurban shantytown in Lima, Peru. J. Allergy Clin. Immunol. 2011, 127, 875–882. [Google Scholar] [CrossRef] [PubMed]
- Carbajal-Arroyo, L.; Barraza-Villarreal, A.; Durand-Pardo, R.; Moreno-Macías, H.; Espinoza-Laín, R.; Chiarella-Ortigosa, P.; Romieu, I. Impact of Traffic Flow on the Asthma Prevalence Among School Children in Lima, Peru. J. Asthma 2007, 44, 197–202. [Google Scholar] [CrossRef]
- CEHTP (California Environmental Health Tracking Program). Air Quality: Measures and Limitations. Available online: http://www.cehtp.org/faq/air/air_quality_measures_and_limitations (accessed on 11 April 2018).
- ESRL (Earth System Research Laboratory: Global Monitoring Division). SURFRAD Aerosol Optical Depth. Available online: https://www.esrl.noaa.gov/gmd/grad/surfrad/aod/ (accessed on 25 August 2019).
- Liu, Y.; Paciorek, C.J.; Koutrakis, P. Estimating Regional Spatial and Temporal Variability of PM2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information. Environ. Health Perspect. 2009, 117, 886–892. [Google Scholar] [CrossRef]
- Remer, L.A.; Kaufman, Y.J.; Tanré, D.; Mattoo, S.; Chu, D.A.; Martins, J.V.; Li, R.R.; Ichoku, C.; Levy, R.C.; Kleidman, R.G.; et al. The MODIS Aerosol Algorithm, Products, and Validation. J. Atmos. Sci. 2005, 62, 947–973. [Google Scholar] [CrossRef] [Green Version]
- Meng, X.; Garay, M.J.; Diner, D.J.; Kalashnikova, O.V.; Xu, J.; Liu, Y. Estimating PM2.5 speciation concentrations using prototype 4.4 km-resolution MISR aerosol properties over Southern California. Atmos. Environ. 2018, 181, 70–81. [Google Scholar] [CrossRef]
- Russell, M.C.; Belle, J.H.; Liu, Y. The impact of three recent coal-fired power plant closings on Pittsburgh air quality: A natural experiment. J. Air Waste Manag. Assoc. 2017, 67, 3–16. [Google Scholar] [CrossRef]
- Zheng, Y.; Zhang, Q.; Liu, Y.; Geng, G.; He, K. Estimating ground-level PM2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements. Atmos. Environ. 2016, 124, 232–242. [Google Scholar] [CrossRef]
- Hu, X.; Waller, L.A.; Lyapustin, A.; Wang, Y.; Al-Hamdan, M.Z.; Crosson, W.L.; Estes, M.G.; Estes, S.M.; Quattrochi, D.A.; Puttaswamy, S.J.; et al. Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model. Remote Sens. Environ. 2014, 140, 220–232. [Google Scholar] [CrossRef]
- Kloog, I.; Sorek-Hamer, M.; Lyapustin, A.; Coull, B.; Wang, Y.; Just, A.C.; Schwartz, J.; Broday, D.M. Estimating daily PM2.5 and PM10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data. Atmos. Environ. 2015, 122, 409–416. [Google Scholar] [CrossRef] [PubMed]
- Xiao, Q.; Wang, Y.; Chang, H.H.; Meng, X.; Geng, G.; Lyapustin, A.; Liu, Y. Full-coverage high-resolution daily PM2.5 estimation using MAIAC AOD in the Yangtze River Delta of China. Remote Sens. Environ. 2017, 199, 437–446. [Google Scholar] [CrossRef]
- Liang, F.; Xiao, Q.; Wang, Y.; Lyapustin, A.; Li, G.; Gu, D.; Pan, X.; Liu, Y. MAIAC-based long-term spatiotemporal trends of PM2.5 in Beijing, China. Sci. Total Environ. 2018, 616–617, 1589–1598. [Google Scholar] [CrossRef] [PubMed]
- Bose, S.; Romero, K.; Psoter, K.J.; Curriero, F.C.; Chen, C.; Johnson, C.M.; Kaji, D.; Breysse, P.N.; Williams, D.A.L.; Ramanathan, M.; et al. Association of traffic air pollution and rhinitis quality of life in Peruvian children with asthma. PLoS ONE 2018, 13, e0193910. [Google Scholar] [CrossRef]
- Scientific, T.F. 5014i Beta Continuous Ambient Particulate Monitor. Available online: https://www.thermofisher.com/order/catalog/product/5014I (accessed on 4 March 2019).
- Underhill, J.L.; Bose, S.; Williams, L.D.A.; Romero, M.K.; Malpartida, G.; Breysse, N.P.; Klasen, M.E.; Combe, M.J.; Checkley, W.; Hansel, N.N. Association of Roadway Proximity with Indoor Air Pollution in a Peri-Urban. Community in Lima, Peru. Int. J. Environ. Res. Publ. Health 2015, 12, 13466–13481. [Google Scholar] [CrossRef]
- Lyapustin, A.; Wang, Y.; Korkin, S.; Huang, D. MODIS Collection 6 MAIAC algorithm. Atmos. Meas. Tech. 2018, 11, 5741–5765. [Google Scholar] [CrossRef]
- Gile, D.M. AERONET: AEROSOL ROBOTIC NETWORK: Site: Arica. Available online: https://aeronet.gsfc.nasa.gov/cgi-bin/type_one_station_opera_v2_new?site=Arica&nachal=2&level=1&place_code=10 (accessed on 6 March 2019).
- Giles, D.M. AERONET: AEROSOL ROBOTIC NETWORK. Available online: https://aeronet.gsfc.nasa.gov/ (accessed on 11 April 2018).
- Martins, V.S.; Novo, E.M.L.M.; Lyapustin, A.; Aragão, L.E.O.C.; Freitas, S.R.; Barbosa, C.C.F. Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon. (2000–2015): Insights for remote sensing and climate analysis. ISPRS J. Photogramm. Remote Sens. 2018, 145, 309–327. [Google Scholar] [CrossRef]
- Bi, J.; Wildani, A.; Wang, Y.; Lyapustin, A.; Liu, Y. Incorporating Snow and Cloud Fractions in Random Forest to Estimate High Resolution PM2.5 Exposures in New York State; Emory University: Atlanta, GA, USA, 2018. [Google Scholar]
- Ederer, G. EARTHDATA: LAADS DAAC. Available online: https://ladsweb.modaps.eosdis.nasa.gov (accessed on 6 March 2019).
- Sánchez-Ccoyllo, O.; Ordoñez-Aquino, C.; Muñoz, A.; Llacza, A.; Andrade, M.; Liu, Y. Modeling study of the particulate matter in lima with the WRF-Chem model: Case study of April 2016. Int. J. Appl. Eng. Res. 2018, 13, 10129–10141. [Google Scholar]
- Grell, G.A.; Peckham, S.E.; Schmitz, R.; McKeen, S.A.; Frost, G.; Skamarock, W.C.; Eder, B. Fully coupled “online” chemistry within the WRF model. Atmos. Environ. 2005, 39, 6957–6975. [Google Scholar] [CrossRef]
- ECMWF (European Centre for Medium-Range Weather Forecasts). ERA Interim, Daily. Available online: http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/ (accessed on 6 March 2019).
- ECMWF (European Centre for Medium-Range Weather Forecasts). Available online: https://www.ecmwf.int/en/about (accessed on 12 April 2018).
- McNoldy, B. Calculate Temperature, Dewpoint, or Relative Humidity. Available online: http://andrew.rsmas.miami.edu/bmcnoldy/Humidity.html (accessed on 6 March 2019).
- Berrick, S. EARTHDATA: EARTHDATA Search. Available online: https://search.earthdata.nasa.gov/search (accessed on 6 March 2019).
- UT-Battelle for the Department of Energy. Oak Ridge National Laboratory: LandScan Datasets. Available online: https://landscan.ornl.gov/index.php/landscan-datasets (accessed on 6 March 2019).
- Chen, J.; Chen, J.; Liao, A.; Cao, X.; Chen, L.; Chen, X.; He, C.; Han, G.; Peng, S.; Lu, M.; et al. Global land cover mapping at 30m resolution: A POK-based operational approach. ISPRS J. Photogramm. Remote Sens. 2015, 103, 7–27. [Google Scholar] [CrossRef] [Green Version]
- Didan, K. MYD13A1 MODIS/Aqua Vegetation Indices 16-day L3 Global 500m SIN Grid V006; NASA EOSDIS LP DAAC: Washington, DC, USA, 2015.
- Geofabrik GmbH. GEOFABRIK Downloads: Peru. Available online: http://download.geofabrik.de/south-america/peru.html (accessed on 5 May 2017).
- Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef] [Green Version]
- Liaw, A.; Wiener, M. Classification and Regression by RandomForest. R news 2002, 2, 18–22. [Google Scholar]
- Van Donkelaar, A.; Martin, R.V.; Brauer, M.; Kahn, R.; Levy, R.; Verduzco, C.; Villeneuve, P.J. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: Development and application. Environ. Health Perspect. 2010, 118, 847–855. [Google Scholar] [CrossRef]
- Nicolis, O.; Camaño, C.; Maŕın, J.C.; Sahu, S.K. Spatio-temporal modelling for assessing air pollution in Santiago de Chile. AIP Conf. Proc. 2017, 1798, 020113. [Google Scholar]
- Riojas-Rodriguez, H.; da Silva, A.S.; Texcalac-Sangrador, J.L.; Moreno-Banda, G.L. Air pollution management and control in Latin America and the Caribbean: Implications for climate change. Rev. Panam. Salud. Publ. 2016, 40, 150–159. [Google Scholar]
- Pearce, J.L.; Rathbun, S.L.; Aguilar-Villalobos, M.; Naeher, L.P. Characterizing the spatiotemporal variability of PM2.5 in Cusco, Peru using kriging with external drift. Atmos. Environ. 2009, 43, 2060–2069. [Google Scholar] [CrossRef]
- Kim, S.; Shen, S.; Sioutas, C.; Zhu, Y.; Hinds, W.C. Size Distribution and Diurnal and Seasonal Trends of Ultrafine Particles in Source and Receptor Sites of the Los Angeles Basin. J. Air Waste Manag. Assoc. 2002, 52, 297–307. [Google Scholar] [CrossRef] [Green Version]
- Yuval; Broday, D.M. Enhancement of PM2.5 exposure estimation using PM10 observations. Environ. Sci. Process. Impacts 2014, 16, 1094–1102. [Google Scholar] [CrossRef]
Network | Station | Elevation (m.) | # of Measurements |
---|---|---|---|
JHU | Station 02 | 94.6 | 339 |
JHU | Station 07 | 123.6 | 417 |
JHU | Station 08 | 74.2 | 288 |
JHU | Station 09 | 186.0 | 443 |
JHU | Station 10 | 192.1 | 287 |
JHU | Station 11 | 109.2 | 307 |
SENAMHI | ATE | 372.7 | 528 |
SENAMHI | CDM | 124.5 | 544 |
SENAMHI | CRB | 219.5 | 737 |
SENAMHI | HCH | 301.2 | 696 |
SENAMHI | PPD | 186.0 | 778 |
SENAMHI | SBJ | 131.3 | 581 |
SENAMHI | SJL | 237.5 | 757 |
SENAMHI | SMP | 58.5 | 775 |
SENAMHI | STA | 254.3 | 598 |
SENAMHI | VMT | 328.3 | 395 |
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Vu, B.N.; Sánchez, O.; Bi, J.; Xiao, Q.; Hansel, N.N.; Checkley, W.; Gonzales, G.F.; Steenland, K.; Liu, Y. Developing an Advanced PM2.5 Exposure Model in Lima, Peru. Remote Sens. 2019, 11, 641. https://doi.org/10.3390/rs11060641
Vu BN, Sánchez O, Bi J, Xiao Q, Hansel NN, Checkley W, Gonzales GF, Steenland K, Liu Y. Developing an Advanced PM2.5 Exposure Model in Lima, Peru. Remote Sensing. 2019; 11(6):641. https://doi.org/10.3390/rs11060641
Chicago/Turabian StyleVu, Bryan N., Odón Sánchez, Jianzhao Bi, Qingyang Xiao, Nadia N. Hansel, William Checkley, Gustavo F. Gonzales, Kyle Steenland, and Yang Liu. 2019. "Developing an Advanced PM2.5 Exposure Model in Lima, Peru" Remote Sensing 11, no. 6: 641. https://doi.org/10.3390/rs11060641
APA StyleVu, B. N., Sánchez, O., Bi, J., Xiao, Q., Hansel, N. N., Checkley, W., Gonzales, G. F., Steenland, K., & Liu, Y. (2019). Developing an Advanced PM2.5 Exposure Model in Lima, Peru. Remote Sensing, 11(6), 641. https://doi.org/10.3390/rs11060641