Development of a Health-Based Index to Identify the Association between Air Pollution and Health Effects in Mexico City

Abstract

Health risks from air pollution continue to be a major concern for residents in Mexico City. These health burdens could be partially alleviated through individual avoidance behavior if accurate information regarding the daily health risks of multiple pollutants became available. A split sample approach was used in this study to create and validate a multi-pollutant, health-based air quality index. Poisson generalized linear models were used to assess the impacts of ambient air pollution (i.e., fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ground-level ozone (O3)) on a total of 610,982 daily emergency department (ED) visits for respiratory disease obtained from 40 facilities in the metropolitan area of Mexico City from 2010 to 2015. Increased risk of respiratory ED visits was observed for interquartile increases in the 4-day average concentrations of PM2.5 (Risk Ratio (RR) 1.03, 95% CI 1.01–1.04), O3 (RR 1.03, 95% CI 1.01–1.05), and to a lesser extent NO2 (RR 1.01, 95% CI 0.99–1.02). An additive, multi-pollutant index was created using coefficients for these three pollutants. Positive associations of index values with daily respiratory ED visits was observed among children (ages 2–17) and adults (ages 18+). The use of previously unavailable daily health records enabled an assessment of short-term ambient air pollution concentrations on respiratory morbidity in Mexico City and the creation of a health-based air quality index, which is now currently in use in Mexico City.

Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources

“…Several important components of this complementary effort are capacity building activities through the NASA ARSET program (Section 3.1) and illustrative case studies (e.g., Section 3.3.1) for each city to demonstrate how the pilot cities may bring NASA resources into their standard operating procedures. An additional component of the Expanded CityAQ effort is to use pollutant concentrations (PM2.5, NO2, and O3) from the GEOS-CF forecasts (Section 2.2) as input to a health-based AQ index (HAQI; Cromar et al, 2021), another L4 data product (Figure 15). A HAQI is a simple way to communicate the combined health risks of forecasted levels of air pollution, such as by a color-coded numeric scale.…”

Establishment of a City-Based Index to Communicate Air Pollution-Related Health Risks to the Public in Bangkok, Thailand

“…However, the AQI only takes into account the single highest index, despite the fact that the health impact of air pollution depends on the multitude of air pollutants to which people are exposed. This tool is therefore of limited use for capturing the actual risk to the public due to exposure to air pollution [9].…”

“…This requires extensive information about air pollutant concentrations, meteorological conditions, and health factors (morbidity and mortality) in the locations of interest. Worldwide, the AQHI has so far been officially implemented in Canada, Hong Kong, and Mexico City [9,11,12]. AQHIs have been developed in many countries around the world, including Canada [10], Sweden [13], China [14][15][16][17], and Mexico [9].…”

“…Worldwide, the AQHI has so far been officially implemented in Canada, Hong Kong, and Mexico City [9,11,12]. AQHIs have been developed in many countries around the world, including Canada [10], Sweden [13], China [14][15][16][17], and Mexico [9]. However, to the best of our knowledge, no case studies of using an AQHI to represent air pollution in southeast Asia have yet been conducted.…”

Unorganized Machines to Estimate the Number of Hospital Admissions Due to Respiratory Diseases Caused by PM10 Concentration

“…Generalized Linear Models (GLM) [10][11][12][13][14] and Generalized Additive Models (GAM) [15,16] are statistical regression models usually used to assess air pollution consequences on human health. However, a minimum of data is required to assure that regression models will be able to capture the relationship between the inputs (predictors) and the output (response variable) [17].…”