Motor vehicles are the main source of many hazardous air pollutants in California. Previous studies have shown that low-income and minority populations are more likely to live near industrial sources of pollution and in areas that do not meet national air quality standards. We estimated neighborhood exposures to motor vehicle emissions from a road network with daily traffic counts using a geographic information system. To calculate traffic density, we summed the average daily vehicle miles of travel per square mile of land area for each census block group in the state. We used 1990 census data to characterize the population by age, race and socioeconomic status in block groups with high traffic density. Block groups with more than 500,000 vehicle miles of travel per square mile were defined to be high traffic density. Statewide, about 5% of all block groups met this criterion and more than 215,000 children under 15 years of age lived in these high traffic density areas. Block groups in the lowest quartile of median family income were three times more likely to have high traffic density than block groups in the highest income quartile. The percentage of children living in high traffic density block groups increased with decreasing median family income for all race and ethnicities except White. Overall, children of color were about three times more likely to live in high-traffic areas than were white children. Based on this analysis, low-income and children of color have higher potential exposure to vehicle emissions. Future exposure assessment studies should target the highest traffic density areas, and health studies should consider the differences by income and race or ethnicity during design.
Motor vehicle emissions are a major source of air pollution in California, accounting for most of the estimated emissions of several air toxics including benzene (66%), 1,3-butadiene (57%) and diesel particulate matter (49%) for 1996 (California Air Resources Board, 2000). The estimated emissions of several criteria air pollutants also indicate that a large component comes from motor vehicles including carbon monoxide (68%), reactive organic gases (47%) and oxides of nitrogen (60%) in California for 1995 (California Air Resources Board, 1999a). The concentration of traffic-related pollutants is generally higher near highways and major roads based on available air monitoring data. The mean concentrations of benzene, 1,3-butadiene and carbon monoxide were consistently higher near highways and major roads than at ambient monitoring sites in Los Angeles and Sacramento (California Air Resources Board, 1998). Measured outdoor nitrogen dioxide levels decreased in proportion to distance from the road and/or traffic volume in a Tokyo study (Nakai et al., 1995). Personal monitoring on sidewalks in New York City found that elemental carbon concentrations, a marker for diesel exhaust, were associated with bus and truck counts on adjacent streets (Kinney et al., 2000). Traffic volume was significantly associated with indoor concentrations of polycyclic aromatic hydrocarbons at urban, semiurban and suburban locations, although other combustion sources such as smoking, cooking and wood burning can also be important determinants of indoor PAH levels (Dubowsky et al., 1999). Mobile sources were also found to be the largest contributor to estimated cancer risk in an analysis conducted using recent air monitoring data from Southern California (South Coast Air Quality Management District, 1999).
Exposure to motor vehicle exhaust has been associated with adverse health outcomes in several epidemiological studies of children. Elevated risks of childhood cancer were associated with several surrogate measures of vehicle exhaust exposure, including traffic volume, car density, estimated concentration of nitrogen dioxide in outdoor air and proximity to sources of vehicle exhaust (Savitz and Feingold, 1989; Knox and Gilman, 1997; Nordlinder and Jarvholm, 1997; Feychting et al., 1998; Harrison et al., 1999; Pearson et al., 2000). Recent studies in California evaluating childhood cancer and traffic density have generally found no association (Reynolds et al., 2001; Langholz et al., 2002; Reynolds et al., 2002). Some studies have found an association between traffic volume and the number of hospital admissions for asthma (English et al., 1999), vehicle or truck traffic and self-reported symptoms of asthma (Weiland et al., 1994; Duhme et al., 1996), but others have found no association between asthma and traffic volume (Wilkinson et al., 1999). Truck and road traffic were related to chronic respiratory symptoms and reduced lung function in studies of adolescents (Wjst et al., 1993; Oosterlee et al., 1996; Van Vliet et al., 1997; Brunekreef et al., 1997; Ciccone et al., 1998). Most of these studies used traffic counts as a surrogate for exposure to vehicle emissions.
Previous environmental equity studies in the US have shown that low-income neighborhoods and communities of color are more likely to be located near sources of pollution. The geographic scale of analysis can be an important factor and should reflect populations with homogeneous exposures as much as possible (Sheppard et al., 1999). County-level analyses conducted in the US found that people of color were more likely to live in counties with higher toxic industrial emissions, but also that household income was higher in counties with higher industrial releases (Perlin et al., 1995). A census tract level analysis in three parts of the US observed that African Americans and those living below the poverty level were more likely to live closer to the nearest industrial facility and to live near multiple facilities (Perlin et al., 1999, 2001). A similar evaluation conducted in Oregon found that industrial facilities were disproportionately located in minority and low-income neighborhoods, but there was no relation between hazard ranking and overall socioeconomic status (SES) of the community (Neumann et al., 1998). An assessment of ozone exposure in Southern California concluded that low-income areas generally experience higher ozone concentrations than high-income areas (Korc, 1996). Finally, a recent investigation of environmental justice in Southern California found that race and income were important predictors of estimated cancer risk based on modeled outdoor air toxics exposures from both mobile and point sources (Morello-Frosch et al., 2002).
Unfortunately, existing air monitoring stations are too geographically sparse to characterize the differences in ambient pollutant concentrations for small areas like a census block group for all of California. For a specific residence or point, models have been developed to estimate pollutant concentrations as a function of traffic volume and distance from the road (Kono and Ito, 1990; Versluis, 1994). In this study, we calculated the traffic density for all census block groups in California using traffic volume data and a geographic information system (GIS). We used cen-sus data to determine the SES and ethnicity of residents living in the highest traffic density block groups.
We used traffic count data for 1992 from the Highway Performance and Monitoring System maintained by the California Department of Transportation (California Department of Transportation, 1993). These data provide the annual average daily traffic (AADT) for all highways and most major roads in the state. The AADT represents the average number of vehicles per day traveling in both directions on a road segment. The road segments are spatially referenced in a GIS database. AADT is based upon new traffic data collected every 3 years; during noncount years, the AADT is estimated using traffic trends for that location. The AADT for local roads is not typically measured and therefore not included in the database.
Block group boundaries from the 1990 census were identified from TIGER line files (United States Bureau of the Census, 1995). The land area of block groups in California had a huge range from 0.0001 to 3610 square miles (mi2), with a median value of 0.2 mi2. A 200-m buffer was drawn around each census block group to allow for dispersion of pollutants and spatial discrepancies between the road and census geography. For each block group, we calculated the vehicle miles of travel (VMT) for each attributed road segment within the buffered area by multiplying the AADT by the road segment length. We calculated traffic density by summing the VMT for all road segments and dividing by the buffered land area for each census block group. The units of traffic density are vehicle miles of travel per day per square mile (VMT/mi2),
where TD is the traffic density (vehicles × miles/day/miles2), AADT the annual average daily traffic (vehicles/day), L the length of road segment (miles) and AB the buffered land area of census block group (miles2).
Vehicle tailpipe emissions are directly proportional to VMT based on established emission rates (Watson et al., 1988). To evaluate our exposure measure, we compared block group traffic density values to available air monitoring data from 1990 for three compounds with a high proportion of emissions from mobile sources: carbon monoxide (n=93 sites), benzene (n=20) and 1,3-butadiene (n=20) (California Air Resources Board, 1999b). We used a GIS to address geocode the air monitoring locations, create a buffer with a radius of 500 m around each monitor and determine the proportion of area occupied by all census block groups within the circle. We calculated the average traffic density around each monitor by summing the traffic density for all relevant block groups weighted by the proportion of area each block group contained within the buffer. We determined the correlation between the average traffic density and the annual median concentration for the three compounds using the Spearman rank correlation coefficient (Snedecor and Cochran, 1989).
We used data from the 1990 census on population by age group and ethnicity for block groups (United States Bureau of the Census, 1992). The median population in California block groups was 1154 and ranged from 1 to 35,682. We used block group summarized data as measures of SES including median family income, percentage of the population below the poverty level, percentage of adults in managerial and professional occupations and percentage of adults with a college degree. We calculated the percentage of block groups with high traffic density (>95th percentile) by quartiles of median family income. The percentage of block groups with high traffic density in each quartile was also determined for the other measures of SES. We calculated the percentage of children less than 15 years of age in high traffic density block groups by race or ethnicity and by median family income quartile combined with race or ethnicity. We also compared the racial/ethnic composition of all block groups statewide to that in high traffic density block groups. We used χ2 tests for proportions to test for differences in traffic density and SES and/or race/ethnicity.
Figure 1 shows the categorical distribution of traffic density for the 21,222 census block groups included in this analysis. Block groups with no population or land area were not included in our analysis. The distribution appears lognormal, with a mean statewide value of 132,472 VMT/mi2 and a median value of 82,734. We defined high traffic density block groups as those at the 95th percentile of the distribution and above (≥500,000 VMT/mi2).
The results from our comparison of block group traffic density values and median annual air concentrations for traffic-related pollutants are shown in Table 1. The Spearman correlation coefficients (r) between the median annual concentration for the three compounds analyzed and average traffic density around the monitoring site were between 0.6 and 0.7 and statistically significant.
The relation between block group median family income and traffic density is presented in Figure 2. The percentage of block groups with high traffic density decreases from 7 to 2% from the lowest to the highest income quartiles and was significantly different (P<0.0001). Figure 3 shows the percentage of block groups with high traffic density by quartile for each measure of SES. All four SES measures provided the same general results and were also statistically significant, but median family income had a slightly stronger relation with traffic density than the other measures.
The percentage of children living in block groups with high traffic density varied by race or ethnicity (Figure 4). Hispanic children were most likely (5.2%) and White children least likely (1.5%) to live in high-traffic areas. Hispanic, African-American and Asian/Other children were significantly more likely than White children to live in high traffic density block groups (P<0.001). Figure 5 shows the percentage of children living in block groups with high traffic density by ethnicity and quartiles of median family income. There was a strong and significant relationship between quartile of median family income and the percentage living in high traffic density block groups for Hispanic, African-American and Asian/other children. For these children, those in the lowest income quartile were on average five times more likely than children in the highest income quartile to live in block groups with high traffic density. The percentage of White children living in block groups with high traffic density was about the same for all income quartiles. A very low percentage of all children in the highest income quartile lived in block groups with high traffic density regardless of race or ethnicity.
Table 2 shows the total number of children under 15 years of age by race or ethnicity for all 1990 California census block groups, high-traffic block groups and high-traffic/low-income block groups. The total number of children in the state was 6,647,645. The number of children living in high traffic density block groups was 215,979 or just over 3% of the total population. Of those children living in high traffic density block groups, over half (116,676) also lived in block groups in the lowest quartile of median family income. White children were 46% of the total child population, but only 21 and 7% of the child population in high-traffic and high-traffic/low-income block groups, respectively. Hispanic children showed the opposite trend with 35% of total population, but 56% of those in high traffic density block groups and 71% of children in high-traffic/low-income block groups. As a result, there were 10 times more Hispanic children than White children living in high-traffic/low-income block groups statewide.
We found that low-income children of color were more likely than White children and higher income children to live in block groups with high traffic density. Since block group traffic density is related to vehicle emissions and was moderately correlated with the ambient concentrations of several vehicle-related pollutants, children living in these areas have higher potential for exposure. Other studies in the US have found that low-income neighborhoods and communities of color are more likely to be located near sources of pollution (Perlin et al., 1995; Korc, 1996; Neumann et al., 1998; Perlin et al., 1999, 2001; Morello-Frosch et al., 2002). Our results, from a large and heterogeneous state, provide further evidence that low-income and people of color are more likely to live near sources of toxic emissions, in this case freeways and major roads.
Future exposure assessment studies should target the highest traffic density areas in the state. Given the large percentage of low-income Hispanic children living in high traffic density neighborhoods, study and educational materials should be prepared in Spanish as well as English to encourage participation among adults in these communities. The fact that income and race/ethnicity are related to traffic density is also an important consideration for the design of future epidemiological studies and care should be taken to control for them in the analysis. If the controls had higher income than the cases or were under-represented by children of color, a potentially false association between the health outcome of interest and traffic could result. We recently evaluated the relation between childhood cancer rates and traffic density for census block groups in California and generally did not find an association even after adjusting for race or ethnicity and block group SES (Reynolds et al., 2002).
Traffic density as an estimate of exposure to vehicle-related pollutants needs to be validated using measured air concentrations. Air monitoring studies conducted near mobile sources suggest that pollutant concentrations are dependent on the distance to the road and traffic volume (Rodes and Holland, 1981; Nakai et al., 1995; South Coast Air Quality Management District, 1999; Rijnders et al., 2001). Evaluation of our traffic density measure with available air monitoring data showed significant, but moderate, correlations of 0.6–0.7 with three traffic-related pollutants. Other published methods for estimating traffic exposure at a residence have achieved correlation coefficients ranging from 0.7 to more than 0.9 with measured pollutant concentrations (Raaschou-Nielsen et al., 2000; Briggs et al., 2000; Bellander et al., 2001).
We did not have traffic counts for local roads in this study. Depending on the pollutants of interest, the traffic counts on local roads could be important. In San Diego, California the traffic volume for local roads was estimated at 700 vehicles/day (San Diego Association of Governments, Transportation Program, 2001). Based on this estimate, the contribution of local road traffic to the total block group traffic density would be relatively small. In a previous study, we calculated road density by summing the miles of all roads in a block group per square mile of land area. The correlations between road density and the measured concentrations of vehicle-related pollutants were weaker than the correlations with traffic density and were nonsignificant for benzene and butadiene (Reynolds et al., 2002). We calculated traffic density at a neighborhood (census block group) rather than individual (residence) level, and as a result there is likely to be some exposure and SES misclassification. We performed this analysis at the block group level because this is the smallest geographic unit with data available on SES and population by ethnicity. Individual time activity information is also an important consideration because children can be exposed to similar combustion-related pollutants at school or day care, during transit and from indoor sources such as cigarette smoke.
Further studies of environmental equity using measured or estimated concentrations of traffic-related air pollutants should be conducted. We plan to repeat this analysis using 2000 census and traffic count data. The annual vehicle miles traveled on California highways increased from 139 to 164 billion between 1990 and 2000 (California Department of Transportation, Traffic Operations Division, 2001). Whether the growth in traffic density alters the relation we found with socioeconomic factors and race/ethnicity of community residents is not currently known. Race/ethnicity reporting in the 2000 census was also changed allowing respondents to choose multiple race/ethnicity categories, which will make direct comparisons to some of our 1990 findings difficult. Ideally, future studies would collect individual-level data on other sources of exposure to combustion products, family income and race or ethnicity. Air dispersion modeling techniques could also be combined with a GIS to provide the best estimate of exposure at a residence. Block group traffic density is fairly simple to calculate and might be a useful method for identifying the highest exposed areas for future air monitoring studies. If air concentrations exceed health-based standards in high traffic density neighborhoods, policy changes will be needed to reduce vehicle emissions and address the issue of disproportionate exposure among communities of color and the poor.
annual average daily traffic
California Department of Transportation
geographic information system
vehicle miles traveled
Bellander T., Berglind N., Gustavsson P., Jonson T., Nyberg F., Pershagen G., and Jarup L. Using geographic information systems to assess individual historical exposure to air pollution from traffic and house heating in Stockholm. Environ Health Perspect 2001: 109 (6): 633–639.
Briggs D.J., de Hoogh C., Gulliver J., Wills J., Elliott P., Kingham S., and Smallbone K. A regression-based method for mapping traffic-related air pollution: application and testing in four contrasting urban environments. Sci Total Environ 2000: 253: 151–167.
Brunekreef B., Janssen N.A.H., de Hartog J., Harssema H., Knape M., and van Vliet P. Air pollution from truck traffic and lung function in children living near motorways. Epidemiology 1997: 8: 298–303.
California Air Resources Board. Measuring concentrations of selected air pollutants inside California vehicles. (Report No.: 95-339) California Air Resources Board, Research Division, Sacramento, CA, 1998.
California Air Resources Board. The 1999 California almanac of emissions and air quality. California Air Resources Board, Planning and Technical Support Division, Sacramento, CA, 1999a.
California Air Resources Board, Air quality data branch. California ambient air quality data 1980–1998 [data file]. California Air Resources Board, Sacramento, CA, 1999b.
California Air Resources Board. California toxics inventory for 1996. [Web Page] 2000; http://www.arb.ca.gov/toxics/cti/cti.htm. [Accessed 24 January 2001].
California Department of Transportation. Highway performance and monitoring system [data file] California Department of Transportation, Sacramento, CA, 1993.
California Department of Transportation, Traffic operations division. Historical monthly vehicle miles of travel report. [Web Page] 2001 [Accessed 27 December 2002].
Ciccone G., Forastiere F., and Agabiti N. Road traffic and adverse respiratory effects in children. SIDRIA Collaborative Group. Occup Environ Med 1998: 55 (11): 771–778.
Dubowsky S., Wallace L., and Buckley T. The contribution of traffic to indoor concentrations of polycyclic aromatic hydrocarbons. J Expo Anal Environ Epidemiol 1999: 9 (4): 312–321.
Duhme H., Weiland S.K., Keil U., Kraemer B., Schmid M., Stender M., and Chambless L. The association between self-reported symptoms of asthma and allergic rhinitis and self-reported traffic density on street of residence in adolescents. Epidemiology 1996: 7: 578–582.
English P., Neutra R., Scalf R., Sullivan M., Waller L., and Zhu L. Examining associations between childhood asthma and traffic flow using a geographic information system. Environ Health Perspect 1999: 107 (9): 761–767.
Feychting M., Svensson D., and Ahlbom A. Exposure to motor vehicle exhaust and childhood cancer. Scand J Work Environ Health 1998: 24 (1): 8–11.
Harrison R.M., Leung P., Somervaille L., Smith R., and Gilman E. Analysis of incidence of childhood cancer in the West Midlands of the United Kingdom in relation to proximity to main roads and petrol stations. Occup Environ Med 1999: 56: 774–780.
Kinney P.L., Aggarwal M., Northridge M.E., Janssen N.A.H., and Shepard P. Airborne concentrations of PM2.5 and diesel exhaust particles on harlem sidewalks: a community-based pilot study. Environ Health Perspect 2000: 108 (3): 213–218.
Knox E.G., and Gilman E.A. Hazard proximities of childhood cancers in Great Britain from 1953–1980. J Epidemiol Community Health 1997: 51: 151–159.
Kono K., and Ito S. A micro-scale dispersion model for motor vehicle exhaust gas in urban areas – OMG volume–source model. Atmos Environ 1990: 24B (2): 243–251.
Korc M.E. A socioeconomic assessment of human exposure to ozone in the south coast air basin of California. J Air Waste Manage Assoc 1996: 46: 547–557.
Langholz B., Ebi K., Thomas D., Peters J., and London S. Traffic density and the risk of childhood leukemia in a Los Angeles case-control study. Ann Epidemiol 2002: 12 (7): 482–487.
Morello-Frosch R., Pastor M., Porras C., and Sadd J. Environmental justice and regional inequality in southern California: implications for future research. Environ Health Perspect 2002: 110 (Suppl 2): 149–154.
Nakai S., Nitta H., and Meada K. Respiratory health associated with exposure to automobile exhaust II. personal NO2 exposure levels according to distance from the roadside. J Expo Anal Environ Epidemiol 1995: 5 (2): 125–136.
Neumann C.M., Forman D.L., and Rothlein J.E. Hazard screening of chemical releases and environmental equity analysis of populations proximate to toxic release inventory facilities in Oregon. Environ Health Perspect 1998: 106 (4): 217–226.
Nordlinder R., and Jarvholm B. Environmental exposure to gasoline and leukemia in children and young adults – an ecology study. Int Arch Occup Environ Health 1997: 70: 57–60.
Oosterlee A., Drijver M., Lebret E., and Brunekreef B. Chronic respiratory symptoms in children and adults living along streets with high traffic density. Occup Environ Med 1996: 53 (4): 241–247.
Pearson R., Wachtel H., and Ebi K. Distance-weighted traffic density in proximity to a home is a risk factor for leukemia and other childhood cancers. J Air Waste Manage Assoc 2000: 50 (2): 175–180.
Perlin S.A., Setzer R.W., Creason J., and Sexton K. Distribution of industrial air emissions by income and race in the United States: an approach using the toxic release inventory. Environ Sci Technol 1995: 29: 69–80.
Perlin S.A., Sexton K., and Wong D.W. An examination of race and poverty for populations living near industrial sources of pollution. J Exp Anal Environ Epidemiol 1999: 9: 29–48.
Perlin S.A., Wong D., and Sexton K. Residential proximity to industrial sources of air pollution: interrelationships among race, poverty and age. J Air Waste Manage Assoc 2001: 51: 406–421.
Raaschou-Nielsen O., Hertel O., Vignati E., Berkowicz R., Jensen S.S., Larsen V.B., Lohse C., and Olsen J.H. An air pollution model for use in epidemiological studies: evaluation with measured levels of nitrogen dioxide and benzene. J Expo Anal Environ Epidemiol 2000: 10: 4–14f.
Reynolds P., Elkin E., Scalf R., Von Behren J., and Neutra R.R. Case–control pilot study of traffic exposures and early childhood leukemia using a geographic information system. Bioelectromagnetics 2001: 5 (Suppl): S58–S68.
Reynolds P., Von Behren J., Gunier R.B., Goldberg D.E., Hertz A., and Smith D. Traffic patterns and childhood cancer incidence rates in California, United States. Cancer Causes Control 2002: 13 (7): 665–673.
Rijnders E., Janssen N.A., van Vliet P.H., and Brunekreef B. Personal and outdoor nitrogen dioxide concentrations in relation to degree of urbanization and traffic density. Environ Health Perspect 2001: 109 (Suppl 3): 411–417.
Rodes C.E., and Holland D.M. Variations of NO, NO2 and O3 concentrations downwind of a Los Angeles freeway. Atmos Environ 1981: 15: 243–250.
San Diego Association of Governments, Transportation Program. Historical vehicle miles of travel for the San Diego region. [Web Page] May 2001 [Accessed 3 December 2002].
Savitz D.A., and Feingold L. Association of childhood cancer with resi-dential traffic density. Scand J Work Environ Health 1989: 15: 360–363.
Sheppard E., Leitner H., McMaster R.B., and Tian H. GIS-based measures of environmental equity: exploring their sensitivity and significance. J Expo Anal Environ Epidemiol 1999: 9 (1): 18–28.
Snedecor G. and Cochran W. Statistical Methods. Iowa State University Press, Ames, IA, 1989.
South Coast Air Quality Management District. Multiple air toxics exposure study in the South Coast air basin. South Coast Air Quality Management District, Diamond Bar, CA, 1999.
United States Bureau of the Census. Census of population and housing, 1990: modified age/race, sex and hispanic origin (MARS) state and county file. [data file]. United States Bureau of the Census, Washington, DC. 1992.
United States Bureau of the Census. TIGER Line Files [data file]. United States Bureau of the Census, Washington, DC, 1995.
Van Vliet P., Knape M., de Hartog J., Janssen N., Harssema H., and Brunekreef B. Motor vehicle exhaust and chronic respiratory symptoms in children living near freeways. Environ Res 1997: 74 (2): 122–132.
Versluis A. Methodology for predicting vehicle emissions on motorways and their impact on air quality in the Netherlands. Sci Total Environ 1994: 146/147: 359–364.
Watson A.Y., Bates R.R. and Kennedy D. Air Pollution, the Automobile, and Public Health. National Academy Press, Washington, DC, 1988.
Weiland S.K., Mundt K.A., Ruckman A., and Keil U. Self-reported wheezing and allergic rhinitis in children and traffic denstiy on street of residence. Ann Epidemiol 1994: 4 (3): 243–247.
Wilkinson P., Elliott P., Grundy C., Shaddick G., Thakrar B., Walls P., and Falconer S. Case–control study of hospital admission with asthma in children aged 5–14 years: relation with road traffic in north west London. Thorax 1999: 54 (12): 1070–1074.
Wjst M., Reitmeir P., Dold S., Wulff A., Nicolai T., von Loeffelholz-Colberg E.F., and von Mutius E. Road traffic and adverse effects of respiratory health in children. BMJ 1993: 307: 596–600.
This work was funded by Grant Number R01 CA71745 from the National Cancer Institute. We thank Paul English for helpful comments and Theresa Saunders for preparation of the manuscript. The ideas and opinions expressed are those of the authors and no endorsement by the California Department of Health Services should be inferred.
About this article
Cite this article
Gunier, R., Hertz, A., von Behren, J. et al. Traffic density in California: Socioeconomic and ethnic differences among potentially exposed children. J Expo Sci Environ Epidemiol 13, 240–246 (2003). https://doi.org/10.1038/sj.jea.7500276
- air pollution
- environmental equity
- geographic information system
- socioeconomic status
Estimating exposure to fine particulate matter emissions from vehicle traffic: Exposure misclassification and daily activity patterns in a large, sprawling region
Environmental Research (2020)
Journal of Allergy and Clinical Immunology (2020)
Racial and Ethnic Disparities in Human Milk Intake at Neonatal Intensive Care Unit Discharge among Very Low Birth Weight Infants in California
The Journal of Pediatrics (2020)
Association of change in the neighborhood obesogenic environment with colorectal cancer risk: The Multiethnic Cohort Study
SSM - Population Health (2020)
Spatiotemporal variations in traffic activity and their influence on air pollution levels in communities near highways
Atmospheric Environment (2020)