Variation in the Composition and In Vitro Proinflammatory Effect of Urban Particulate Matter from Different Sites

Spatial variation in particulate matter–related health and toxicological outcomes is partly due to its composition. We studied spatial variability in particle composition and induced cellular responses in Mexico City to complement an ongoing epidemiologic study. We measured elements, endotoxins, and polycyclic aromatic hydrocarbons in two particle size fractions collected in five sites. We compared the in vitro proinflammatory response of J774A.1 and THP‐1 cells after exposure to particles, measuring subsequent TNFα and IL‐6 secretion. Particle composition varied by site and size. Particle constituents were subjected to principal component analysis, identifying three components: C 1 (Si, Sr, Mg, Ca, Al, Fe, Mn, endotoxin), C 2 (polycyclic aromatic hydrocarbons), and C 3 (Zn, S, Sb, Ni, Cu, Pb). Induced TNFα levels were higher and more heterogeneous than IL‐6 levels. Cytokines produced by both cell lines only correlated with C 1 , suggesting that constituents associated with soil induced the inflammatory response and explain observed spatial differences. © 2013 Wiley Periodicals, Inc. J BiochemMol Toxicol 27:87‐97, 2013; View this article online at wileyonlinelibrary.com . DOI 10.1002/jbt.21471Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96321/1/jbt21471.pd

Air pollution, inflammation and preterm birth in Mexico City: Study design and methods

Preterm birth is one of the leading causes of perinatal mortality and is associated with long-term adverse health consequences for surviving infants. Preterm birth rates are rising worldwide, and no effective means for prevention currently exists. Air pollution exposure may be a significant cause of prematurity, but many published studies lack the individual, clinical data needed to elucidate possible biological mechanisms mediating these epidemiological associations. This paper presents the design of a prospective study now underway to evaluate those mechanisms in a cohort of pregnant women residing in Mexico City. We address how air quality may act together with other factors to induce systemic inflammation and influence the duration of pregnancy. Data collection includes: biomarkers relevant to inflammation in cervico-vaginal exudate and peripheral blood, along with full clinical information, pro-inflammatory cytokine gene polymorphisms and air pollution data to evaluate spatial and temporal variability in air pollution exposure. Samples are collected on a monthly basis and participants are followed for the duration of pregnancy. The data will be used to evaluate whether ambient air pollution is associated with preterm birth, controlling for other risk factors. We will evaluate which time windows during pregnancy are most influential in the air pollution and preterm birth association. In addition, the epidemiological study will be complemented with a parallel toxicology invitro study, in which monocytic cells will be exposed to air particle samples to evaluate the expression of biomarkers of inflammation

An assessment of air pollutant exposure methods in Mexico City, Mexico

<div><p>Geostatistical interpolation methods to estimate individual exposure to outdoor air pollutants can be used in pregnancy cohorts where personal exposure data are not collected. Our objectives were to a) develop four assessment methods (citywide average (CWA); nearest monitor (NM); inverse distance weighting (IDW); and ordinary Kriging (OK)), and b) compare daily metrics and cross-validations of interpolation models. We obtained 2008 hourly data from Mexico City’s outdoor air monitoring network for PM₁₀, PM_2.5, O₃, CO, NO₂, and SO₂ and constructed daily exposure metrics for 1,000 simulated individual locations across five populated geographic zones. Descriptive statistics from all methods were calculated for dry and wet seasons, and by zone. We also evaluated IDW and OK methods’ ability to predict measured concentrations at monitors using cross validation and a coefficient of variation (COV). All methods were performed using SAS 9.3, except ordinary Kriging which was modeled using R’s gstat package. Overall, mean concentrations and standard deviations were similar among the different methods for each pollutant. Correlations between methods were generally high (r = 0.77 to 0.99). However, ranges of estimated concentrations determined by NM, IDW, and OK were wider than the ranges for CWA. Root mean square errors for OK were consistently equal to or lower than for the IDW method. OK standard errors varied considerably between pollutants and the computed COVs ranged from 0.46 (least error) for SO2 and PM10 to 3.91 (most error) for PM2.5. OK predicted concentrations measured at the monitors better than IDW and NM. Given the similarity in results for the exposure methods, OK is preferred because this method alone provides predicted standard errors which can be incorporated in statistical models. The daily estimated exposures calculated using these different exposure methods provide flexibility to evaluate multiple windows of exposure during pregnancy, not just trimester or pregnancy-long exposures.</p><p><i>Implications</i>: Many studies evaluating associations between outdoor air pollution and adverse pregnancy outcomes rely on outdoor air pollution monitoring data linked to information gathered from large birth registries, and often lack residence location information needed to estimate individual exposure. This study simulated 1,000 residential locations to evaluate four air pollution exposure assessment methods, and describes possible exposure misclassification from using spatial averaging versus <b>geostatistical interpolation models.</b> An implication of this work is that policies to reduce air pollution and exposure among pregnant women based on epidemiologic literature should take into account possible error in estimates of effect when spatial averages alone are evaluated.</p></div