Asociación entre las mediciones de PM2.5 por absorbancia y la distancia a vías de alto tráfico en la zona metropolitana de la Ciudad de México
Marlene Cortez-Lugo, M en CI; Consuelo Escamilla-Núñez, M en CI; Albino Barraza-Villarreal, M en C, D en CI; José Luis Texcalac-Sangrador, M en CI; Judith Chow, D en CII; John Watson, D en CII; Leticia Hernández-Cadena, M en C, D en CI; Isabelle Romieu, MD, MPH, DScI
ICentro de Investigación en Salud Poblacional, Instituto Nacional de Salud Pública. Cuernavaca, Morelos, México
IIDesert Research Institute. Reno, Nevada, EUA
OBJECTIVE: To study the relationship between light absorption measurements of PM2.5 at various distances from heavy traffic roads and diesel vehicle counts in Mexico City.
MATERIALS AND METHODS: PM2.5 samples were obtained from June 2003-June 2005 in three MCMA regions. Light absorption (babs) in a subset of PM2.5 samples was determined. We evaluated the effect of distance and diesel vehicle counts to heavy traffic roads on PM2.5 babs using generalized estimating equation models.
RESULTS: Median PM2.5 babs measurements significantly decrease as distance from heavy traffic roads increases (p<0.002); levels decreased by 7% (CI95% 0.9-14) for each 100 additional meters from heavy traffic roads. Our model predicts that PM2.5 babs measurements would increase by 20% (CI95% 3-38) as the hourly heavy diesel vehicle count increases by 150 per hour.
CONCLUSION: PM2.5 babs measurements are significantly associated with distance from motorways and traffic density and therefore can be used to assess human exposure to traffic-related emissions.
Key words: Carbon; vehicle emissions; roads; diesel exhaust; Mexico
OBJETIVO: Evaluar la relación entre las mediciones de absorción de luz de las PM2.5 a diferentes distancias de vías de tráfico y el aforo vehicular de diesel en la Ciudad de México.
MATERIAL Y MÉTODOS: Se realizaron mediciones de PM2.5 y su análisis de babs en tres zonas de la Ciudad de México. Se usaron modelos GEE para evaluar el efecto de la distancia y el aforo vehicular de tráfico pesado sobre PM2.5 babs.
RESULTADOS: Se observó una tendencia decreciente en la mediana de PM2.5 babs conforme se incrementó la distancia a las avenidas de alto tráfico (p<0.002); los niveles decrecen en 7% (CI95% 0.9-14) por cada 100 metros de incremento. Las mediciones de PM2.5 babs se incrementan en 20% (CI95% 3-38) cuando el aforo vehicular a diesel es mayor de 150 en una hora.
CONCLUSIONES: Las mediciones de PM2.5 babs están significativamente asociadas con la distancia de avenidas con alto tránsito vehicular y con vehículos de diesel.
Palabras clave: carbono; emisiones vehiculares; avenidas; diesel; México
Vehicular traffic is a major source of air pollution, in particular, nitrogen oxides (NOx) and particulate matter. various studies have suggested that vehicular exhaust traffic is associated with respiratory health effects.1-8 many of these studies obtained measurements near roadways in the vicinity of homes or schools, especially those with heavy vehicular traffic.
Some studies have concurrently incorporated air contaminant measurements with vehicular traffic counts.9,10 A study carried out by Brunekreef et al.11 in the netherlands reported that black smoke concentrations were correlated with the density of truck traffic and the percentage of time children were exposed downwind of the motorway. black or elemental carbon (BC-EC) originates mostly from incomplete combustion of fossil fuels and is the main factor in particle light absorption or light transmission (babs) , expressed in inverse megameters [Mm-1]) in ambient air. This measurement could therefore be a good indicator of exposure to vehicular traffic.
About 85% of air pollution in the Mexico City Metropolitan Area (MCMA) comes from mobile sources (emission estimates). One of the main pollutants is PM2.5, emission inventories in 2004 reported a PM2.5 emission of 6 622 ton / year, of which 56.6% comes from mobile sources: the 83.2% from diesel fuels and 16.8% from gasoline vehicles.12 The MCMA has carried out several studies to evaluate the composition of fine particles and their relationship to mobile sources.13-18 High concentrations of EC were observed in areas with heavy diesel vehicle traffic.
The toxicity of the particles is a subject of interest to both toxicological and epidemiological investigations.19 Elemental and organic carbon originating from vehicular exhaust have been recognized as likely being the most toxic components of the particles.20-22 This paper describes the relationship among PM2.5 babs(light absorption, babs), vehicle density, and the distance between the measurement location and roadways in different parts of the MCMA.
Materials and Methods
As part of the larger cohort of school children in Mexico City previously evaluated,23 we conducted local PM2.5 in public schools in three areas of the southeastern part of the MCMA (Iztapalapa, Iztacalco and Nezahualcoyotl) from June 2003 to June 2005.
Location and population
The overall study population consisted of school children living in three Mexico City municipalities: Iztapalapa, Iztacalco and Netzahualcoyotl. These regions are characterized by high levels of traffic-related emissions. The present report includes data from a subsample of thirty-seven of the 107 schools (34.6%) attended by the children and were selected based on their distance to the closest roadways with heavy vehicular traffic (range: 24 -800 m). The study area was divided into four zones for local PM2.5 sampling: Iztapalapa-west, Iztapalapa-east, Iztacalco and Nezahualcoyotl (figure 1). Local PM2.5 babs were obtained at 20 (54.1%) schools: five in Iztapalapawest, six in Iztapalapa-east, four in Iztacalco and five in Nezahualcoyotl.
Air pollutant and traffic assessment exposure
PM2.5 and babs measurements
Battery powered Minivol portable samplers with flow rates of 5 liters/min using 47 mm teflon-membrane filters were used to monitor local daily 24-hour outdoor PM2.5 concentrations. Measurements was conducted for two consecutive weeks in two zones (the first 11 schools) and then rotated to the other two zones (the additional nine schools). Minivols were located on school rooftops (3 m) and care was taken not to place monitors < 90 cm from the walls and windows or close to plants or trees. each school was measured on average 14 times (range: 1-26) for two weeks during the period june 2003 to june 2005, for 20 months of monitoring.
PM2.5 babswas analyzed in a subset of Teflon filters (n=207, 11.5%) using transmission densitometry at the Desert Research Institute (DRI), Nevada, Usa. The transmission densitometry method measures optical density with an incandescent broadband lamp (400-650 nm, peaking at 575 nm) transmitted through a glass diffuser. Transmittance is measured before and after Teflon filter exposure, to determine particle babs and the difference in the logarithms of the transmitted light is proportional to the absorption of the particle deposit.24 These results were used as a marker of diesel engine exhaust.25 The transmission of "white" light through the teflon-membrane filter was measured before and after aerosol sampling to determine particle babs.26 Absorption measurement on the Teflon-membrane filters is highly correlated with elemental carbon (EC), measured with thermal/optical reflectance (TOR) analysis27 PM2.5 babs have been found to be highly correlated (r>0.86) with elemental carbon (EC), measured with thermal/optical reflectance analysis,27-29 babs measurements in this study serve as ec surrogate.
NO2 concentrations and meteorological data (temperature, humidity and wind velocity) were also obtained from the mexico city government from four fixed-site central monitoring [Red Automática de Monitoreo Atmosférico (RAMA)] locations within the study area for the study period.
Traffic counts and distance from roadways
Design of the traffic count study was based on the following criteria:
1. Geographic locations: traffic points sufficiently close to the selected roadways with heavy vehicular traffic (average 127 712 veh/hr).
2. Vehicle types: vehicle fleet was divided into the following five classifications:
A= Private cars (gasoline);
B= Small buses for public transportation (gasoline or natural gas)
C= School buses, other buses, pick-up trucks(diesel)
D= Light duty (diesel): 3.5 tons and double-axis pick-ups, small trucks and delivery vans
E= High duty (diesel): two-and three-axle trucks, more than 3.5 tons pick-ups, autotanks, tractors, trailer-cabins with or without trailers, etc.
3. Traffic density: measured with pneumatic sensors every day for one week. The week selected was considered representative of traffic in that area. The average density of motor vehicles by type of vehicle, time of day, and day of the week was calculated for the study period.
A total of 51 roadway intersections distributed across the four study zones were measured. Nineteen out of the 51 intersections correspond to school locations selected for the babs analysis.
The distances between each selected school and the nearest to the main vehicular traffic roadways (mean: 224 m, range: 24 - 838 m) were measured by means of a geographic information system (GIS). Schools were represented by points (sampling locations) and roadways by lines in the GIS. Distances between points and lines were calculated using NEAR command, in ARCGIS.
We conducted a descriptive analysis for PM2.5 babs measurements by study zone, vehicular count and distance. We used Kruskal Wallis nonparametric test and a test for trend across ordered groups to evaluate the association between PM2.5 babsmeasurements and distance of the school to nearest major roadway. We also used generalized estimating equation models (GEE) to evaluate the association between distance to heavy traffic roads and the diesel vehicle counts on PM2.5 babs measurement (n=200). The predictor variables included in these models were distance between each selected school and the nearest heavy vehicular traffic roadway (m) and vehicle type C, D or E (1= >150 vehicles 0= < 150 vehicles). The pm2.5 babs (Mm-1) were log-transformed to achieve normality. The model was adjusted for the following variables: local PM2.5, average ambient NO2, minimum temperature, study zone, wind velocity, average relative humidity on the day that samples were taken and time trend. monitoring data were analyzed using the statistical software package STATA (version 9.2).
The largest proportion of vehicles (76%) consisted of private cars (transport type A); small buses for public transportation (transport type B) represented 15.3% of the vehicles. Transport types C, D and E--predominantly diesel vehicles--represented 8.7% of the hourly 24-h average (data not shown). On average, the distance from the school monitored to the closest roadway with heavy vehicular traffic was 175 m (SD = 147) (table I).
PM2.5 babs measurements were slightly higher (115.3 Mm-1) in the area of Iztapalapa east (table II and figure 2), which is the same area that presented a higher density of diesel vehicles (ratio gasoline/diesel=5.6). The highest PM2.5 babs were within the first 50 meters of the roadway with heavy vehicular traffic. The median of PM2.5 babs measurements was different between distance groups (p*= 0.06) and decreased significantly (test or trend p**= 0.02) with increased distance from the roadways from 50 to >250 (table III). Our multivariate model confirm the role of distance and the number vehicular using diesel as predictor of EC levels often adjusting for local PM2.5, average ambient NO2, minimum temperature, study zone, wind velocity, average relative humidity on the day the samples were taken and time trend (table IV).
Our results suggest that, on average, the highest PM2.5 babs were within the first 50 meters of roadways with heavy vehicular traffic and that exposure to PM2.5 babs decreases (p<0.002) at distances greater than 50 meters. The results from the multivariate regression analysis suggest that PM2.5 babs measurements would increase by 20% (CI95% 3-38) when the hourly traffic count for type C, D and E vehicles increases by 150 vehicles per hour In addition, for each additional 100 meters from heavy traffic roads with high a percentage (>10%) of diesel vehicles, PM2.5 babs measurements would decrease by 7% (CI95% 0.9-14.0).
These results suggest that distance from heavy traffic roads and traffic intensity can be used as a surrogate for exposure to traffic-related EC in epidemiological studies, especially where diesel vehicles are present.
In our study, the PM2.5 babs measurements ranged from 14.9 to 222.4 Mm-1. These measurements were conducted in a very densely populated area (Iztapalapa east) and are higher than that reported by Vega et al.,30 however, Chow et al.17 have reported spatial variation of among areas in the MCMA, with the highest concentrations being in the eastern area, corresponding to our study area.
Compared to concentrations reported in other cities, our babs measurements are four to five times higher,31-33 suggesting poor quality diesel vehicles are driven and a significant fleet of vehicles emit diesel, producing a large amount of fine and ultrafine particles in the MCMA. Even though different studies used different methods for measuring carbon, high correlations have been observed among the different methods.28,34,35
Our results are concordant with other studies that reported that levels of EC emitted by vehicles increase proportionally to the distance from avenues or mobile sources of emissions.9,15,36-38 Consequences for public health are likely to be large given the adverse health effects observed in people living near roads with high traffic density. 5,6,8,11,33,39,40
This study suggests that PM2.5 babs measurements from diesel vehicles are significantly associated with distance to the motorway and traffic density. These variables can therefore be used to assess exposure to traffic-related EC in subjects living near motorways.
This study was supported by the National Center for Environmental Health at the Centers for Disease Control. Filter light transmission analysis was carried out by Desert Research Institute (DRI), Nevada USA. We are indebted to the Mexico City General Department of Environmental Monitoring and for all the data given to us from the automatic network system (RAMA) reading service.
The ethics committee of the National Institute of Public Health approved the research protocol which are the data analyzed in this article.
1. Escamilla-Nuñez MC, Barraza-Villarreal A, Hernández-Cadena L, Moreno-Macías H, Ramirez-Aguilar M, Sienra-Monge JJ, et al. Traffic-related air pollution and respiratory symptoms among asthmatic children, resident in Mexico City: the EVA cohort study. Respir Res 2008;9:74.
2. Holguin F, Flores S, Zev Ross, Cortez M, Molina M, Molina L, et al. Traffic-related Exposures, Airway Function, Inflammation, and Respiratory Symptoms in Children. Am J Respir Crit Care Med 2007;176(12):1236-1242.
3. Lanki T, Ahokas A, Alm S, Janssen NA, Hoek G, De Hartog JJ, et al. Determinants of personal and indoor PM2.5 and absorbance among elderly subjects with coronary heart disease. J Exp Sci Environ Epidemiology 2007;17(2):124-133.
4. Nyberg F, Gustavsson P, Jarup L, Bellander T, Berglind N, Jakobsson R, et al. Urban air pollution and lung cancer in Stockholm. Epidemiology 2000;11(5): 487-495.
5. Ciccone G, Forastiere F, Agabiti N, Biggeri A, Bisanti L, Chellini E, et al. Road traffic and adverse respiratory effects in children. SIDRA Collaborative Group. Occup Environ Med 1998;55:771-778.
6. Weiland SK, Mundt KA, Rueckmann A, Keil U. Self-reported wheezing and allergic rhinitis in children and traffic density on street of residence. Am Epidemiology 1994;4:243-247.
7. Wjst M, Reitmeir P, Doid S, Wulff A, Nicola T, von Loeffelholz Colberg E, et al. Road traffic and adverse effects on respiratory health in children. Br Med J 1993;307:596-600.
8. Nitta HST, Nakai S, Maeda K, Aoki S, Ono M. Respiratory health associated with exposure to automobile exhaust. I. Results of cross-sectional studies in 1979, 1982 and 1983. Arch Environ Health 1993;48:53-58.
9. Janssen NAH, Van Vliet PH, Aarts F, Harssema H, Brunekreef B. Assessment of exposure to traffic related air pollution of children attending schools near motorways. Atmos Environ 2001;35:3875-3884.
10. Van Vliet P, Knape M, Harlog de J, Janssen N, Harssema H, Brunekreef B. Motor vehicle exhaust and chronic respiratory symptoms in children living near motorways. Environ Res 1997;74:122-132.
11. Brunekreef B, Jansen NAH, Hartog de J, Harssema H, Knape M, Vlict van P. Air pollution from truck traffic and lung function in children living near motorways. Epidemilogy 1997;8:298-303.
12. Inventario de emisiones. Zona Metropolitana del Valle de México. México: Secretaría del Medio Ambiente del Gobierno del Distrito Federal, 2007:46-47.
13. Vega E, Ruiz H, Martinez-Villa G, Sosa G, González-Avalos E, Reyes E, et al. Fine and Coarse Particulate Matter Chemical Characterization in a Heavily Industrialized City Central Mexico during winter 2003. J Air & Waste Manage Assoc 2007;57:6206-6233.
14. Rosas-Pérez I, Serrano J, Alfaro-Moreno E, Baumgardner D, García-Cuellar C, Miranda J, et al. Relations between PM10 composition and cell toxicity: A multivariate and graphical approach. Chemosphere 2007;67:1218-1228.
15. Marr LC, Grogan LA, Wohrnschimmel H, Molina L, Molina MJ. Vehicle traffic as a source of particulate polycyclic aromatic hydrocarbon exposure in the Mexico City Metropolitan Area. Environ Sci Technol 2004;38(9):2584-2592.
16. Chow JC, Watson JG, Edgerton SA, Vega E. Chemical composition of PM2.5 and PM10 in Mexico City during winter 1997. Sci Total Environ 2002;287(3):177-201.
17. Chow JC, Watson, JG, Edgerton SA, Vega E, Ortiz E. Spatial Differences in Outdoor PM10 Mass and Aerosol Composition in Mexico City. J Air & Waste Manage Assoc 2002;52:423-434.
18. Watson JG, Chow JC. Estimating middle-, neighborhood-, and urbanscale contributions to elemental carbon in Mexico City with a rapid response aethalometer. J Air & Waste Manag Assoc 2001;51(11):1522-1528.
19. Harrison RM, Yin J. Particulate matter in the atmosphere: which particle properties are important for its effects on health? Sci total Env 2000;249:85-101.
20. Laden F, Neas LM, Dockery DW, Schwartz J. Association of fine particle in six U.S. cities. Environ Health Perspect 2000;108(10):941-947.
21. Katsouyanni K, Touloumi G, Samoli E, Gryparis A, Le Tertre A, Monopolis Y, et al. Confounding and effect modification in the short-term effects of ambient particles on total mortality: results from 29 European cities within the APHEA2 project. Epidemiology 2001;12(5):521-531.
22. Mauderly JL, Chow JC. Health effects of organic aerosols. Inhal Toxicol 2008;20(3):257-288.
23. Barraza-Villarreal A, Sunyer J, Hernandez-Cadena L, Escamilla-Nuñez MC, Sienra-Monge JJ, Ramírez-Aguilar M, et al. Air Pollution, Airway Inflammation, and Lung Function in a Cohort Study of Mexico City Schoolchildren. Environ Health Perspec 2008;116(6):832-838.
24. Barraza-Villarreal A, Escamilla-Nuñez MC, Hernández-Cadena L, Texcalac-Sangrador JL, Sierra-Monge JJ, del Río-Navarro BR, et al. Elemental carbon exposure and lung function in schoolchildren from Mexico City. Eur Respir J 2011;38:548-552.
25. Wolff GT. Characteristics and consequences of soot in the atmosphere. Environment International 1985;11:259-269.
26. Chow JC, Lowenthal DH, Watson JG, Kohl SD, Hinsvark BA, Hackett E, et al. Light absorption by black sand dust. Appl Opt 2000;39(27):4232-4236.
27. Chow JC, Watson JG, Pritchett LC, Pierson WR, Frazier CA, Purcell RG. The DRI Thermal/Optical reflectance carbon analysis system: Description, evaluation and applications in U.S. air quality studies. Atmospheric Env 1993;27A(8):1185-1201.
28. Park K, Chow JC, Watson JG, Trimble DL, Doraiswamy P. Comparison of continuous and filter-based carbon measurements at the Fresno Supersite. J Air & Waste Manage Assoc 2006;56:474-491.
29. Chow JC, Watson JG, Chen LWA, Chang MCO, Robinson NF, Trimble DL, et al. The IMPROVE_A temperature protocol for thermal/optical carbon analysis: Maintaining consistency with a long-term data base. J Air & Waste Manage Assoc 2007;57(9):1014-1023.
30. Vega E, Reyes E, Ruiz H, García J, Sánchez G, Martinez-Villa G, et al. Analysis of PM2.5 and PM10 in the Atmosphere of Mexico City during 2000-2002. J Air & Waste Manage Assoc (2004);54:786-798.
31. Cyrys J, Joachim H, Gerard H, Kees M, Marie L, Ulrike G, et al. Comparison between different traffic-related particle indicators: Elemental carbon (EC), PM2.5 mass, and absorbance. J Exp Analysis Env Epidemology 2003;13:134-143.
32. Brauer M, Hoek G, van Vliet P, Meliefste K, Fischer P, Gehring U, et al. Estimating Long-Term Average Particulate Air pollution Concentrations: Aplication of Traffic Indicators and Geographic Information Systems. Epidemiology 2003;14(2):228-239.
33. Brauer M, Gehring U, Brunekreef B, de Jongste J, Gerritsen J, Rovers M, et al. Traffic-Related Air Pollution and Otitis Media. Env Health Perspect 2006;114:1414-1418.
34. Watson JG, Chow JC. Comparison and evaluation of in situ and filter carbon measurements at the Fresno Supersite. J Geophys Res 2002;107(D21).8341,doi:10.1029/20001JD000573
35. Watson JG, Chow JC, Chen LWA. Summary of organic and elemental carbon/black carbon analysis methods and Intercomparisons. Aerosol Air Qual Res 2005;1:69-102.
36. Yifang Zhu, William CH. Concentration and size distribution of ultra fine particles near a major highway. J Air & Waste Manage Assoc 2002;52:1032-1042.
37. Kinney PL, Aggarwal M, Northridge ME, Janssen NA, Shepard P. Airborne concentrations of PM2.5 and diesel exhaust particles on Harlem sidewalks: a community-based pilot study. Environ Health Perspect 200;108(3):213-218.
38. Ross K, Karg E, Brand P. Short term evaluation of size distribution and concentrations of atmospheric aerosol particles. Aerosol Sci 1991;22:S629-S632.
39. Wyler C, Braun-Fahrlander C, Kunzli N, Schindler C, Ackermann- Liebrich U, Perruchoud AP, et al. Exposure to motor vehicle traffic and allergic sensitization. Epidemiology 2000;11:450-456.
40. De Hartog JJ, Van Vliet PH, Brunekreef B, Knape MC, Janssen NA, Harssema H. Relationship between air pollution due to traffic, decreased lung function and airway symptoms in children. Ned Tijdsch Geneeskd 1997;141(38):1814-1818.
Albino Barraza Villarreal
Instituto Nacional de Salud Publica
Av. Universidad 655, Col. Santa María Ahuacatitlán. 62100
Cuernavaca, Morelos, México
Received on: february 28, 2012
Accepted on: january 7, 2013
Declaration of conflict of interests. The authors declare that they have no conflict of interests.