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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Arch Intern Med. Author manuscript; available in PMC 2013 April 10.
Published in final edited form as:
PMCID: PMC3622279

Exposure to Particulate Air Pollution and Cognitive Decline in Older Women



Chronic exposure to particulate air pollution may accelerate cognitive decline in older adults, although data on this association are limited. Our objective was to examine long-term exposure to particulate matter (PM) air pollution, both coarse ([PM 2.5–10 μm in diameter [PM2.5-10]) and fine (PM <2.5 μm in diameter [PM2.5]), in relation to cognitive decline.


The study population comprised the Nurses’ Health Study Cognitive Cohort, which included 19 409 US women aged 70 to 81 years. We used geographic information system–based spatiotemporal smoothing models to estimate recent (1 month) and long-term (7–14 years) exposures to PM2.5-10, and PM2.5 preceding base-line cognitive testing (1995–2001) of participants residing in the contiguous United States. We used generalized estimating equation regression to estimate differences in the rate of cognitive decline across levels of PM2.5-10 and PM2.5 exposures. The main outcome measure was cognition, via validated telephone assessments, administered 3 times at approximately 2-year intervals, including tests of general cognition, verbal memory, category fluency, working memory, and attention.


Higher levels of long-term exposure to both PM2.5-10 and PM2.5 were associated with significantly faster cognitive decline. Two-year decline on a global score was 0.020 (95% CI, −0.032 to −0.008) standard units worse per 10 μg/m3 increment in PM2.5-10 exposure and 0.018 (95% CI, −0.035 to −0.002) units worse per 10 μg/m3 increment in PM2.5 exposure. These differences in cognitive trajectory were similar to those between women in our cohort who were approximately 2 years apart in age, indicating that the effect of a 10-μg/m3 increment in long-term PM exposure is cognitively equivalent to aging by approximately 2 years.


Long-term exposure to PM2.5-10 and PM2.5 at levels typically experienced by many individuals in the United States is associated with significantly worse cognitive decline in older women.

Despite the tremendous public health importance of cognitive decline and dementia in older age18 and much effort to develop effective preventive and treatment regimes, few modifiable risk factors have been identified.9 One model has forecasted that a broadly applied intervention that delays the onset of Alzheimer disease (AD) by 2 years could reduce the number of prevalent cases in the United States by approximately 2 million over a 40-year interval.7

Exposures to environmental toxins are potential risk factors that can be modified. Among the most pervasive environmental toxins is particulate air pollution. Extensive toxicologic and epidemiologic research has documented the relation of exposure to ambient particles with mortality and adverse respiratory and cardiovascular outcomes.10,11 Regulatory actions have reduced ambient particulate matter (PM), resulting in decreased mortality.12,13 However, reductions have not occurred uniformly,14 and health effects remain associated with PM, even at current levels.15,16

Very little is known about the role of PM exposure in relation to cognitive decline. Evidence supporting such a relation could implicate exposure reduction as a potential means for reducing the public health burden of cognitive impairment. Yet existing studies in humans are rare,1722 few have used measures that account for the exposures’ complex spatial and temporal patterns, and none has evaluated the cognitive effects of exposure to fine particulate air pollution (<2.5 μm in diameter [PM2.5]). More notably, to our knowledge, no study has evaluated air pollution exposures in relation to longitudinal change in cognition. Therefore, using an established longitudinal study of older women living throughout the contiguous United States, we explored the hypothesis, specified a priori, that higher levels of exposure to PM would correspond to faster subsequent rates of decline in cognitive function.


The Nurses’ Health Study (NHS) began in 1976 when 121 700 female registered nurses, aged 30 to 55 years and living in 11 US states, returned a mailed questionnaire about their medical history and health-related behaviors.23 Since then, women have completed questionnaires every 2 years. To date, we have maintained follow-up of more than 90% of the original participants. This study was approved by the institutional review board of Brigham and Women’s Hospital, Boston, Massachusetts.


From 1995 to 2001, we invited participants 70 years and older with no history of stroke to participate in a study of cognition. Of the 22 715 women who were eligible, we were unable to contact 1031 (4.5%). Of those remaining, 7.7% declined participation. Our analyses of exposure to coarse PM (2.5–10 μm in diameter [PM2.5-10]) and PM2.5 in relation to cognitive decline were based on data from up to 19 409 women with relevant data. Second (1997–2004) and third (2002–2008) cognitive assessments were administered a mean (SD) of 1.9 (0.4) years (n=17 089) and 4.3 (0.8) years (n=14204) after initial testing, reflecting at least 83% participation at each follow-up cycle.


We used geographic information system (GIS)-based spatiotemporal smoothing models to estimate exposures to PM10 (<10 μm in diameter) and PM2.5 for women residing in the contiguous United States. Coarse PM (PM2.5-10) was the difference between PM10 and PM2.5. The methods for estimating these exposures for a 13-state region have been described previously.2426 These methods have been extended to estimate PM10 and PM2.5 for the contiguous United States. Briefly, PM10 and PM2.5 monitor data were obtained from the US Environmental Protection Agency’s (USEPA) Air Quality System (AQS).2628 Monitor data on PM10 were available nationwide from 1988 through 2007. Monitor data on PM2.5 were not widely available before 1999. Thus, separate PM2.5 models were developed for the pre-1999 and post-1999 periods, as in previous work.26 The pre-1999 PM2.5 model described seasonal spatial and monthly temporal patterns in the PM2.5 to PM10 ratio; we multiplied this ratio by PM10 to obtain PM2.5 predictions during this earlier period. Generalized additive mixed models were constructed to explain variation in measured PM10 and PM2.5 (post-1999) levels as the sum of effects of GIS-derived covariates (eg, distance to nearest road by road class, urban land use), meteorological data, and smooth spatial terms. These models were used with GIS-derived and meteorological data specific to each geocoded residential location for each nurse, to provide highly spatially resolved estimates of monthly PM10 and PM2.5 concentrations.

We averaged month-specific exposures to PM2.5-10, PM2.5, and PM10 over several intervals preceding the initial cognitive interview: preceding month, year, 2 years, 5 years, and from 1988 through the preceding month. (See the eFigure and eAppendix 1 for further detail on PM exposure models and exposure estimation, and the timing of exposure and cognitive assessments;


Cognitive testing was administered using validated telephone interviews. In the initial interviewing, we administered only the Telephone Interview for Cognitive Status (TICS)29 and gradually added 5 more tests as high participation in the cognitive testing became apparent. Thus, the sample size differs somewhat across the cognitive tests, although participation rates remained identical for all tests. The TICS (n=19 409) is modeled on the Mini-Mental State Examination (MMSE), and scores on the 2 tests are strongly correlated (Pearson correlation, 0.94).29 A test of delayed recall of the 10-word list from the TICS (N=16 908) was one of the 5 tests added to our battery. We also added the East Boston Memory Test (EBMT)30,31 to assess immediate (n=18 662) and delayed (=18 635) paragraph recall. We administered a test of category fluency in which participants were asked to name as many animals as they could in 1 minute32 (n=18 652). Finally, participants were administered the Digit Span Backward test33 (n=16 916), measuring working memory and attention. We used the full testing battery in the second and third assessment waves.

Our 2 prespecified primary outcomes were composite measures of cognition.5,34 Specifically, to summarize the overall association of the air pollution exposure measures with cognitive performance, for women given all 6 tests (n=16 887), we constructed a global score by averaging z scores from all tests. In addition, to assess overall verbal memory, a strong predictor of developing AD,35 we combined the immediate and delayed recalls of the EBMT and the TICS 10-word list, for women given all 4 tests (n=16 906), by averaging z scores from these tests. We extensively tested the reliability and validity of our telephone procedure for assessing cognition in high functioning, educated women (eAppendix 2).


We performed separate analyses for each of the PM measures, evaluating exposures in quintiles and as continuous variables, in relation to each cognitive score, including the verbal and global scores. All individual test scores were expressed as z scores, computed from the means and standard deviations in our study population. We compared trajectories in cognitive function over the 3 repeated measures across levels of the exposure measures, using generalized estimating equations regression models,36 which allowed us to account for the correlations among repeated cognitive scores. In these models, we included terms for time, in years, since baseline cognitive assessment (as a continuous variable), air pollution exposure, and cross-products between the time and air pollution exposure terms. We adjusted these analyses for potential confounding variables, including age at cognitive assessment, education (registered nurse degree, bachelor’s degree, or advanced graduate degree), husband’s education (high school diploma or less, college degree, advanced graduate degree, or other), energy expended on recreational physical activity37 (mean of responses to 4–7 questionnaires from 1986 through initial cognitive assessment, in quartiles), and alcohol consumption (mean of responses to 5–8 questionnaires from 1986 through initial cognitive assessment; none, up to 1 drink/wk, 2–6 drinks/wk, or ≥1 drink/d). We also included terms for the cross-products between each covariate and time. Additional adjustment for body mass index, diabetes, smoking (status and pack-years), aspirin use (3 frequency categories), and ibuprofen use (ever/never), did not change our findings. We conducted tests for linear trend across the PM quintiles using an ordinal variable that took on values corresponding to each quintile (1, 2, 3, 4, or 5).

All associations are reported as mean differences in cognitive score change over a 2-year interval, across exposure levels, as 2 years is the approximate interval between the testing cycles. In addition, to help interpret these mean differences, we compared findings on the relation of PM exposure to cognitive decline with age-related differences in cognitive decline, generated from the women in our data set. While our primary focus was on PM2.5-10 and PM2.5, we also evaluated PM10.

In secondary analyses, we further adjusted our analyses for 3 measures of socioeconomic position in the census tract of residence: percentage of adults who have less than high school education, median home value, and median income. We also evaluated potential mediation of air pollution’s association with cognitive decline by respiratory and cardiovascular conditions. In additional analyses, we further adjusted for self-reported emphysema and indicators of cardiovascular and cerebrovascular disease (high blood pressure, coronary heart disease, congestive heart failure, coronary artery bypass graft, transient ischemic attack, and carotid endarterectomy). Finally, we conducted sensitivity analyses restricted to women who did not move between 1988 and their first cognitive assessment (62% of the study population).


Estimated exposures to PM2.5-10 and PM2.5 varied widely among the women (Table 1). Estimated exposures over the month preceding the initial cognitive assessment were significantly correlated with longer-term exposures, but these correlations weakened with increasing measurement interval length (Table 1). For any given interval, estimated exposure to PM2.5-10 was significantly correlated with estimated exposure to PM2.5 but at magnitudes lower than the correlations between measures of the same PM type.

Table 1
Distributions of and Correlations Between the Measures of Exposure to Air Particulate Matter (PM) Pollution


There were few meaningful differences in characteristics of women across quintiles of long-term PM2.5-10 exposure (Table 2). From our multivariable-adjusted analyses, we observed rates of change in global cognitive function score that were significantly worse with higher levels of long-term exposure to PM2.5-10 (P value for trend, .01; Table 3) and were significantly worse in the highest vs the lowest quintile of exposure (P=.003). Higher estimated PM2.5-10 exposures in the 1, 2, and 5 years before the initial cognitive assessment were also associated with significantly worse subsequent decline on the global cognitive score. By contrast, exposure to PM2.5-10 in the previous month was weakly and not significantly associated with cognitive decline. This pattern also was apparent in the findings for decline in the TICS and verbal memory scores, while short- and long-term PM2.5-10 exposures were associated with comparable increases of rates of decline in digit span backward and verbal fluency scores.

Table 2
Characteristics of Women by Quintile of Long-term Exposurea to Coarse Air Particulate Matter (PM2.5-10 b)
Table 3
Adjusteda Difference (95% CI) in Cognitive Score Change per 2 Years by Level of Exposure to Coarse Particulate Matter (PM2.5-10 b)


The distributions of key characteristics across quintiles of estimated long-term PM2.5 exposure were similar to those for PM2.5-10 (Table 4), with few notable or consistent differences across quintiles. Similar to PM2.5-10, women in the highest quintile of long-term exposure to PM2.5 experienced significantly worse rates of change in the global score than did women in the lowest quintile (P=.03; Table 5). The trend of across quintiles were borderline significant (P value for trend, .11), but, when modeled as continuous variables, higher levels of both long-term PM2.5 exposure (since 1988) and PM2.5 exposure in the 5 years before the initial cognitive assessment were associated with significantly worse decline in global cognition. Decline in the individual cognitive domains generally was more strongly predicted by long-term than recent exposure to PM2.5.

Table 4
Characteristics of Women, by Quintile of Long-term Exposurea to Fine Particulate Matter (PM2.5 b)
Table 5
Adjusteda Difference (95% CI) in Cognitive Score Change per 2 Years by Level of Exposure to Fine Particulate Matter (PM2.5 b)

We observed similar differences in rates of global cognitive change per 10 μg/m3 increment in long-term exposure to PM2.5-10 and PM2.5 (−0.020 [95% CI, −0.032 to −0.008] and −0.018 [−0.035 to −0.002] standard units/2 years, respectively). These differences were similar to the difference in rates of change we observed between women in our data who were 1 to 2 years apart in age. Expressed per SD increment of each PM measure, these differences were −0.008 (95% CI, −0.013 to −0.003) and −0.006 (95% CI, −0.011 to −0.001) standard units/2 years.

Results from analyses of thoracic PM (PM10) indicated generally faster rates of cognitive decline with higher level of long-term exposure (P value for trend, .<.001; eTable).


The associations of PM2.5-10 and PM2.5 with cognitive decline remained nearly identical when we adjusted our analyses for area socioeconomic position measures and for potential respiratory and cardiovascular intermediates. Analyses restricted to women who did not move yielded modestly stronger associations than those in our primary analyses. For example, 10-μg/m3 increments in long-term exposure to PM2.5-10 and PM2.5 corresponded to 2-year rates of decline in the global cognitive score that were worse by 0.025 (95% CI, −0.040 to −0.009) and 0.021 (95% CI, −0.043 to −0.000) standard units, respectively.


In this large, prospective study of older women, higher levels of long-term exposure to both PM2.5-10 and PM2.5 were associated with significantly faster cognitive decline. Placing these results in context, the differences in cognitive trajectory per 10-μg/m3 increment in long-term exposure to PM2.5-10 and PM2.5 were similar to the differences in trajectories between women in our study who were 1 to 2 years apart in age; that is, 10-μg/m3 higher exposure to PM was cognitively equivalent to aging by up to 2 years.

Several lines of indirect evidence indicate that PM may cause cognitive decline. Results from animal studies indicate that PM may access the brain either via circulation, or intranasally by direct translocation through the olfactory bulb.3840 Once in the central nervous system, fine particles appear to exert adverse effects. Several animal studies have shown increased brain inflammation in response to air particulate exposures.4144 In one experiment, mice were exposed either to filtered air, ambient ultrafine particles, or a mixture of fine and ultrafine particles sampled from Los Angeles, California, air. Two weeks after exposure, the brains of mice in both exposed groups contained higher levels of inflammatory markers, as compared with the mice in the control group.41 In dogs, signs of blood-brain barrier dysfunction, neural degeneration, cerebrovascular pathologic signs, and apoptosis in glial cells were present more often in those who had lived in Mexico City, Mexico, an area of high air pollution, than in dogs from less polluted cities.44 In a postmortem study of 19 humans aged 34 to 83 years, who had died of nonneurologic causes, brain levels of cyclooxygenase-2, an inflammatory mediator, in the frontal cortex and hippocampus were higher among those who had lived in highly polluted cities than among those who had lived in less polluted cities. Importantly, brain levels of amyloid-β42, a pathologic hallmark of AD, were also higher among residents of the polluted cities.45

The relation of PM exposure to cognitive decline may also be mediated through cardiovascular mechanisms. Extensive experimental and epidemiologic data indicate an association between exposure to air pollution and cardiovascular diseases and risk factors.10,11,46,47 This link is important because vascular factors have also been found to predict cognitive decline and dementia.48,49 In our data, however, adjustment for vascular factors did not change our findings, indicating that this is not likely a key pathway by which PM influences cognition.

Several limitations of our study warrant consideration. First, our estimates of PM exposure were indirect, based on spatiotemporal modeling of measurements from air pollution monitors located near each woman’s residence. Measurement via personal air monitoring devices is not practical for long-term exposures in large-scale epidemiologic studies. Yet, exposure measurement errors in our study were likely to be nondifferential with respect to degree of cognitive decline, resulting in attenuated estimates of association. In addition, our PM exposure estimation50,51 features GIS-based spatiotemporal statistical models with little bias and high precision—particularly relative to other modeling approaches24,26—accounting for small-scale variations in exposure at each participant’s residential address using GIS-based covariates. This enabled us to assign estimated PM exposure levels to each address for each participant throughout the study period. Therefore, exposure estimates should be more accurate over time than estimates from initial addresses only. If chronic PM exposure particularly affects cognitive aging or if the biologic response occurs over longer than 1 month, this improved accuracy over time may explain, in part, why many associations corresponding to recent PM exposure were weaker than associations corresponding to long-term PM exposures, although it is also plausible that recent PM exposure is less biologically relevant.

It is possible that our results from this observational study are due to confounding. However, we did not find meaningful differences in numerous potential confounding variables across levels of PM exposure, and adjustment for a variety of known confounding variables—some of them measured repeatedly over several study cycles—did not eliminate the observed associations.

Although ours is the first study, to our knowledge, to investigate PM2.5-10 and PM2.5 in relation to cognitive aging, previous epidemiologic studies of adults generally suggest an adverse association between exposure to other forms of ambient particulates and cognition.1922 In a study of 399 older women residing in urban and rural areas of Germany, residential proximity to busy road, a source of exposure to ultrafine particles, was associated with performance on several tests of cognition and olfaction, but higher PM10 exposure in the previous 5 years was not.20 Another study of 671 older men measured exposure to black carbon, a marker of traffic-related particles, and observed that higher level of exposure over the previous 1 to 11 years was associated with worse cognitive function.22 In the largest study to date, among 15 973 older adults in China, residents of areas with poorer air quality over the previous 7 to 10 years, measured by an index of ambient particulate and gas concentrations, were more likely to have poor cognitive function.21 Finally, in a study of 1764 adults aged 20 to 59 years living throughout the contiguous United States, higher exposure to ozone over the previous year was associated with worse performance on several cognitive and motor tests, but exposure to PM10 was not. Neither of the 2 studies that examined PM10 observed an association with cognitive function, yet the range of PM10 exposure in the German study may have been too narrow (eg, 39.3–53.6 μg/m3 from 1980–1993) to observe a measureable effect,20 and the 1-year measurement interval for PM10 exposure used in the study of younger adults may have been too brief.19 These previous findings also suggest that traffic-related exposures may be important contributors to cognitive aging. Our findings complement and extend these previous findings not only by directly examining cognitive decline in a large population, but also by using detailed modeling of short- and long-term PM2.5-10 and PM2.5 exposures.

In conclusion, we found that higher levels of exposure to ambient PM are associated with worse cognitive decline. Importantly, these associations were present at levels of PM exposure typical in many areas of the United States. Therefore, if our findings are confirmed in other research, air pollution reduction is a potential means for reducing the future population burden of age-related cognitive decline, and eventually, dementia.

Supplementary Material

Supplementary text and tables


Funding/Support: This study was funded by the National Institute of Environmental Health Sciences (NIEHS) grant R21ES016829. Development of the air pollution model was funded by NIEHS grant R01ES017017, and the Nurses’ Health Study is supported by National Cancer Institute (NCI) grant P01CA87969. Dr Schwartz’s contributions were additionally supported by Environmental Protection Agency (EPA) grant RD832416.

Role of the Sponsor: The NIEHS, NCI, and EPA had no involvement in the design and conduct of the study; in the collection, management, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.


Financial Disclosure: None reported.

Previous Presentation: This work was presented at the annual meeting of the International Society for Environmental Epidemiology; September 16, 2011; Barcelona, Spain.

Online-Only Material: The eAppendixes, eFigure, and eTable are available at

Author Contributions: Drs Weuve and Puett had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Laden and Grodstein contributed equally to this article. Study concept and design: Weuve, Puett, Schwartz, Laden, and Grodstein. Acquisition of data: Schwartz, Yanosky, Laden, and Grodstein. Analysis and interpretation of data: Weuve, Puett, Schwartz, Yanosky, Laden, and Grodstein. Drafting of the manuscript: Weuve, Puett, and Yanosky. Critical revision of the manuscript for important intellectual content: Weuve, Puett, Schwartz, Yanosky, Laden, and Grodstein. Statistical analysis: Weuve, Schwartz, and Yanosky. Obtained funding: Weuve, Laden, and Grodstein. Administrative, technical, and material support: Puett, Yanosky, and Grodstein. Study supervision: Weuve, Laden, and Grodstein.


1. Tomaszewski Farias S, Cahn-Weiner DA, Harvey DJ, et al. Longitudinal changes in memory and executive functioning are associated with longitudinal change in instrumental activities of daily living in older adults. Clin Neuropsychol. 2009;23(3):446–461. [PMC free article] [PubMed]
2. Chodosh J, Seeman TE, Keeler E, et al. Cognitive decline in high-functioning older persons is associated with an increased risk of hospitalization. J Am Geriatr Soc. 2004;52(9):1456–1462. [PubMed]
3. Linn RT, Wolf PA, Bachman DL, et al. The “preclinical phase” of probable Alzheimer’s disease: a 13-year prospective study of the Framingham cohort. Arch Neurol. 1995;52(5):485–490. [PubMed]
4. Small BJ, Fratiglioni L, Viitanen M, Winblad B, Bäckman L. The course of cognitive impairment in preclinical Alzheimer disease: three- and 6-year follow-up of a population-based sample. Arch Neurol. 2000;57(6):839–844. [PubMed]
5. Bennett DA, Wilson RS, Schneider JA, et al. Natural history of mild cognitive impairment in older persons. Neurology. 2002;59(2):198–205. [PubMed]
6. Hebert LE, Scherr PA, Bienias JL, Bennett DA, Evans DA. Alzheimer disease in the US population: prevalence estimates using the 2000 census. Arch Neurol. 2003;60(8):1119–1122. [PubMed]
7. Brookmeyer R, Gray S, Kawas C. Projections of Alzheimer’s disease in the United States and the public health impact of delaying disease onset. Am J Public Health. 1998;88(9):1337–1342. [PubMed]
8. Wimo A, Winblad B, Jönsson L. The worldwide societal costs of dementia: estimates for 2009. Alzheimers Dement. 2010;6(2):98–103. [PubMed]
9. Daviglus ML, Bell CC, Berrettini W, et al. National Institutes of Health State-of-the-Science Conference Statement: preventing Alzheimer’s disease and cognitive decline. NIH Consens State Sci Statements. 2010 Apr 26–28;27(4):1–30. [PubMed]
10. Samet J, Krewski D. Health effects associated with exposure to ambient air pollution. J Toxicol Environ Health A. 2007;70(3–4):227–242. [PubMed]
11. Pope CA, III, Dockery DW. Health effects of fine particulate air pollution: lines that connect. J Air Waste Manag Assoc. 2006;56(6):709–742. [PubMed]
12. Laden F, Schwartz J, Speizer FE, Dockery DW. Reduction in fine particulate air pollution and mortality: extended follow-up of the Harvard Six Cities study. Am J Respir Crit Care Med. 2006;173(6):667–672. [PMC free article] [PubMed]
13. Clancy L, Goodman P, Sinclair H, Dockery DW. Effect of air-pollution control on death rates in Dublin, Ireland: an intervention study. Lancet. 2002;360(9341):1210–1214. [PubMed]
14. US Environmental Protection Agency. The Particle Pollution Report: Current Understanding of Air Quality and Emissions Through 2003: Office of Air Quality Planning and Standards. Research Triangle Park, NC: Emissions, Monitoring, and Analysis Division; 2004.
15. Adar SD, Gold DR, Coull BA, Schwartz J, Stone PH, Suh H. Focused exposures to airborne traffic particles and heart rate variability in the elderly. Epidemiology. 2007;18(1):95–103. [PubMed]
16. Dominici F, Peng RD, Bell ML, et al. Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA. 2006;295(10):1127–1134. [PMC free article] [PubMed]
17. Srám RJ, Benes I, Binková B, et al. Teplice program—the impact of air pollution on human health. Environ Health Perspect. 1996;104(suppl 4):699–714. [PMC free article] [PubMed]
18. Suglia SF, Gryparis A, Wright RO, Schwartz J, Wright RJ. Association of black carbon with cognition among children in a prospective birth cohort study. Am J Epidemiol. 2008;167(3):280–286. [PubMed]
19. Chen JC, Schwartz J. Neurobehavioral effects of ambient air pollution on cognitive performance in US adults. Neurotoxicology. 2009;30(2):231–239. [PubMed]
20. Ranft U, Schikowski T, Sugiri D, Krutmann J, Krämer U. Long-term exposure to traffic-related particulate matter impairs cognitive function in the elderly. Environ Res. 2009;109(8):1004–1011. [PubMed]
21. Zeng Y, Gu D, Purser J, Hoenig H, Christakis N. Associations of environmental factors with elderly health and mortality in China. Am J Public Health. 2010;100(2):298–305. [PMC free article] [PubMed]
22. Power MC, Weisskopf MG, Alexeeff SE, Coull BA, Spiro A, III, Schwartz J. Traffic-related air pollution and cognitive function in a cohort of older men. Environ Health Perspect. 2011;119(5):682–687. [PMC free article] [PubMed]
23. Colditz GA, Manson JE, Hankinson SE. The Nurses’ Health Study: 20-year contribution to the understanding of health among women. J Womens Health. 1997;6(1):49–62. [PubMed]
24. Yanosky JD, Paciorek CJ, Schwartz J, Laden F, Puett RC, Suh HH. Spatiotemporal modeling of chronic PM10 exposure for the Nurses’ Health Study. Atmos Environ. 2008;42(18):4047–4062. [PMC free article] [PubMed]
25. Paciorek CJ, Yanosky JD, Puett RC, Laden F, Suh H. Practical large-scale spatiotemporal modeling of particulate matter concentrations. Ann of Appl Stat. 2009;3(1):370–397.
26. Yanosky JD, Paciorek CJ, Suh HH. Predicting chronic fine and coarse particulate exposures using spatiotemporal models for the Northeastern and Midwestern United States. Environ Health Perspect. 2009;117(4):522–529. [PMC free article] [PubMed]
27. Spengler JD, Koutrakis P, Dockery DW, Raizenne M, Speizer FE. Health effects of acid aerosols on North American children: air pollution exposures. Environ Health Perspect. 1996;104(5):492–499. [PMC free article] [PubMed]
28. Suh HH, Nishioka Y, Allen GA, Koutrakis P, Burton RM. The metropolitan acid aerosol characterization study: results from the summer 1994 Washington, DC field study. Environ Health Perspect. 1997;105(8):826–834. [PMC free article] [PubMed]
29. Brandt J, Spencer M, Folstein M. The telephone interview for cognitive status. Neuropsychiatry Neuropsychol Behav Neurol. 1988;1:111–117.
30. Scherr PA, Albert MS, Funkenstein HH, et al. Correlates of cognitive function in an elderly community population. Am J Epidemiol. 1988;128(5):1084–1101. [PubMed]
31. Albert M, Smith LA, Scherr PA, Taylor JO, Evans DA, Funkenstein HH. Use of brief cognitive tests to identify individuals in the community with clinically diagnosed Alzheimer’s disease. Int J Neurosci. 1991;57(3–4):167–178. [PubMed]
32. Goodglass H, Kaplan E. The Assessment of Aphasia. Philadelphia, PA: Lea & Febiger; 1983.
33. Lezak MD. Neuropsychological Assessment. 3. New York, NY: Oxford; 1995. pp. 335–384.
34. Bretsky P, Guralnik JM, Launer L, Albert M, Seeman TE. MacArthur Studies of Successful Aging. The role of APOE-epsilon4 in longitudinal cognitive decline: MacArthur Studies of Successful Aging. Neurology. 2003;60(7):1077–1081. [PubMed]
35. Small BJ, Mobly JL, Laukka EJ, Jones S, Bäckman L. Cognitive deficits in preclinical Alzheimer’s disease. Acta Neurol Scand Suppl. 2003;179:29–33. [PubMed]
36. Fitzmaurice GM, Laird NM, Ware JH. Marginal Models: Generalized Estimating Equations (GEE): Applied Longitudinal Analysis. Hoboken, NJ: John Wiley & Sons, Inc; 2004. pp. 291–323.
37. Weuve J, Kang JH, Manson JE, Breteler MM, Ware JH, Grodstein F. Physical activity, including walking, and cognitive function in older women. JAMA. 2004;292(12):1454–1461. [PubMed]
38. Oberdörster G, Utell MJ. Ultrafine particles in the urban air: to the respiratory tract—and beyond? Environ Health Perspect. 2002;110(8):A440–A441. [PMC free article] [PubMed]
39. Peters A, Veronesi B, Calderón-Garcidueñas L, et al. Translocation and potential neurological effects of fine and ultrafine particles a critical update. Part Fibre Toxicol. 2006 Sep 8;3:13. [PMC free article] [PubMed]
40. Oberdörster G, Sharp Z, Atudorei V, et al. Translocation of inhaled ultrafine particles to the brain. Inhal Toxicol. 2004;16(6–7):437–445. [PubMed]
41. Campbell A, Oldham M, Becaria A, et al. Particulate matter in polluted air may increase biomarkers of inflammation in mouse brain. Neurotoxicology. 2005;26(1):133–140. [PubMed]
42. Levesque S, Taetzsch T, Lull ME, et al. Diesel exhaust activates and primes microglia: air pollution, neuroinflammation, & regulation of dopaminergic neurotoxicity. Environ Health Perspect. 2011;119(8):1149–1155. [PMC free article] [PubMed]
43. Morgan TE, Davis DA, Iwata N, et al. Glutamatergic neurons in rodent models respond to nanoscale particulate urban air pollutants in vivo and in vitro. Environ Health Perspect. 2011;119(7):1003–1009. [PMC free article] [PubMed]
44. Calderón-Garcidueñas L, Azzarelli B, Acuna H, et al. Air pollution and brain damage. Toxicol Pathol. 2002;30(3):373–389. [PubMed]
45. Calderón-Garcidueñas L, Reed W, Maronpot RR, et al. Brain inflammation and Alzheimer’s-like pathology in individuals exposed to severe air pollution. Toxicol Pathol. 2004;32(6):650–658. [PubMed]
46. Brook RD, Franklin B, Cascio W, et al. Expert Panel on Population and Prevention Science of the American Heart Association. Air pollution and cardiovascular disease: a statement for healthcare professionals from the Expert Panel on Population and Prevention Science of the American Heart Association. Circulation. 2004;109(21):2655–2671. [PubMed]
47. Miller KA, Siscovick DS, Sheppard L, et al. Long-term exposure to air pollution and incidence of cardiovascular events in women. N Engl J Med. 2007;356 (5):447–458. [PubMed]
48. Stampfer MJ. Cardiovascular disease and Alzheimer’s disease: common links. J Intern Med. 2006;260(3):211–223. [PubMed]
49. Breteler MM. Vascular involvement in cognitive decline and dementia: epidemiologic evidence from the Rotterdam Study and the Rotterdam Scan Study. Ann N Y Acad Sci. 2000;903:457–465. [PubMed]
50. Puett RC, Hart JE, Yanosky JD, et al. Chronic fine and coarse particulate exposure, mortality, and coronary heart disease in the Nurses’ Health Study. Environ Health Perspect. 2009;117(11):1697–1701. [PMC free article] [PubMed]
51. Puett RC, Schwartz J, Hart JE, et al. Chronic particulate exposure, mortality, and coronary heart disease in the nurses’ health study. Am J Epidemiol. 2008;168 (10):1161–1168. [PMC free article] [PubMed]