Dataset Information

Assessing air quality index awareness and use in Mexico City.

ABSTRACT: The Mexico City Metropolitan Area has an expansive urban population and a long history of air quality management challenges. Poor air quality has been associated with adverse pulmonary and cardiac health effects, particularly among susceptible populations with underlying disease. In addition to reducing pollution concentrations, risk communication efforts that inform behavior modification have the potential to reduce public health burdens associated with air pollution.This study investigates the utilization of Mexico's IMECA risk communication index to inform air pollution avoidance behavior among the general population living in the Mexico City Metropolitan Area. Individuals were selected via probability sampling and surveyed by phone about their air quality index knowledge, pollution concerns, and individual behaviors.The results indicated reasonably high awareness of the air quality index (53% of respondents), with greater awareness in urban areas, among older and more educated individuals, and for those who received air quality information from a healthcare provider. Additionally, behavior modification was less influenced by index reports as it was by personal perceptions of air quality, and there was no difference in behavior modification among susceptible and non-susceptible groups.Taken together, these results suggest there are opportunities to improve the public health impact of risk communication through an increased focus on susceptible populations and greater encouragement of public action in response to local air quality indices.

SUBMITTER: Borbet TC

PROVIDER: S-EPMC5913808 | biostudies-other | 2018 Apr

REPOSITORIES: biostudies-other

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Assessing air quality index awareness and use in Mexico City.

Borbet Timothy C TC Gladson Laura A LA Cromar Kevin R KR

BMC public health 20180423 1

<h4>Background</h4>The Mexico City Metropolitan Area has an expansive urban population and a long history of air quality management challenges. Poor air quality has been associated with adverse pulmonary and cardiac health effects, particularly among susceptible populations with underlying disease. In addition to reducing pollution concentrations, risk communication efforts that inform behavior modification have the potential to reduce public health burdens associated with air pollution.<h4>Meth ...[more]

PMID: 29688852

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Project description:UNLABELLED: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, PM2.5, 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. IMPLICATIONS: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 geostatistical interpolation models. 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.

Project description:BackgroundParticulate matter ≤10 μm in aerodynamic diameter (PM10) and diet quality are risk factors for systemic inflammation, which is associated with preterm birth (PTB). PM10 and a pro-inflammatory diet (assessed by the Dietary Inflammatory Index [DII®]) have been individually evaluated as causes of PTB and differences by offspring sex have been reported for the DII. However, additional studies are needed to evaluate joint effects of these associations to inform intervention efforts.ObjectivesTo evaluate the independent and joint effects of PM10 and energy-adjusted DII (E-DII) on PTB risks.MethodsPM10 estimates were generated from daily citywide averages for 1216 pregnant women from three subcohorts of the Early Life Exposures in Mexico to Environmental Toxicants study using data from the Mexico City Outdoor Air Monitoring Network. Among a subset of participants (N = 620), E-DII scores were calculated using a validated food frequency questionnaire. Cox Proportional Hazards models were run for select periods during pregnancy and entire pregnancy averages for E-DII and PM10. We assessed for potential non-linear associations using natural splines.ResultsIn adjusted models, PM10 exposure was associated with increased risks of PTB for a range of values (58-72 μg/m3) during the second trimester, while negative associations were seen during the second (≥74 μg/m3) and third trimesters (55-65 μg/m3). Analyses conducted using distributed lag models for periods closer to delivery (max lag = 90) did not show negative associations between PM10 exposure and preterm birth, and indeed positive significant associations were observed (estimates and figures). E-DII was not associated with PTB and there was no evidence of effect modification by infant sex. There was no evidence of interaction between PM10 and E-DII and the risk of preterm birth.DiscussionAssociations between PM10 and PTB in Mexico City varied over time and across levels of PM10. Our findings of negative associations in the second and third trimesters, which are contrary to the hypothesized relationship between PM10 and PTB, may be due to a number of factors, including live birth bias and the exposure period evaluated. Differences in results for the periods evaluated suggest that PM10 from shorter exposure windows may play a more proximal role in initiating preterm labor.

Project description:Meteorology and long-term trends in air pollutant concentrations may obscure the results from short-term policies implemented to improve air quality. This study presents changes in CO, NO2, O3, SO2, PM10, and PM2.5 based on their anomalies during the COVID-19 partial (Phase 2) and total (Phase 3) lockdowns in Mexico City (MCMA). To minimise the impact of the air pollutant long-term trends, pollutant anomalies were calculated using as baseline truncated Fourier series, fitted with data from 2016 to 2019, and then compared with those from the lockdown. Additionally, days with stagnant conditions and heavy rain were excluded to reduce the impact of extreme weather changes. Satellite observations for NO2 and CO were used to contrast the ground-based derived results. During the lockdown Phase 2, only NO2 exhibited significant decreases (p < 0.05) of between 10 and 23% due to reductions in motor vehicle emissions. By contrast, O3 increased (p < 0.05) between 16 and 40% at the same sites where NO2 decreased. During Phase 3, significant decreases (p < 0.05) were observed for NO2 (43%), PM10 (20%), and PM2.5 (32%) in response to the total lockdown. Although O3 concentrations were lower in Phase 3 than during Phase 2, those did not decrease (p < 0.05) from the baseline at any site despite the total lockdown. SO2 decreased only during Phase 3 in a near-road environment. Satellite observations confirmed that NO2 decreased and CO stabilised during the total lockdown. Air pollutant changes during the lockdown could be overestimated between 2 and 10-fold without accounting for the influences of meteorology and long-term trends in pollutant concentrations. Air quality improved significantly during the lockdown driven by reduced NO2 and PM2.5 emissions despite increases in O3, resulting in health benefits for the MCMA population. A health assessment conducted suggested that around 588 deaths related to air pollution exposure were averted during the lockdown. Our results show that to reduce O3 within the MCMA, policies must focus on reducing VOCs emissions from non-mobile sources. The measures implemented during the COVID-19 lockdowns provide valuable information to reduce air pollution through a range of abatement strategies for emissions other than from motor vehicles.

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Assessing air quality index awareness and use in Mexico City.

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Assessing air quality index awareness and use in Mexico City.

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