John L. Pearce

Using self-organizing maps to develop ambient air quality classifications: a time series example

BackgroundDevelopment of exposure metrics that capture features of the multipollutant environment are needed to investigate health effects of pollutant mixtures. This is a complex problem that requires development of new methodologies.ObjectivePresent a self-organizing map (SOM) framework for creating ambient air quality classifications that group days with similar multipollutant profiles.MethodsEight years of day-level data from Atlanta, GA, for ten ambient air pollutants collected at a central monitor location were classified using SOM into a set of day types based on their day-level multipollutant profiles. We present strategies for using SOM to develop a multipollutant metric of air quality and compare results with more traditional techniques.ResultsOur analysis found that 16 types of days reasonably describe the day-level multipollutant combinations that appear most frequently in our data. Multipollutant day types ranged from conditions when all pollutants measured low to days exhibiting relatively high concentrations for either primary or secondary pollutants or both. The temporal nature of class assignments indicated substantial heterogeneity in day type frequency distributions (~1%-14%), relatively short-term durations (<2 day persistence), and long-term and seasonal trends. Meteorological summaries revealed strong day type weather dependencies and pollutant concentration summaries provided interesting scenarios for further investigation. Comparison with traditional methods found SOM produced similar classifications with added insight regarding between-class relationships.ConclusionWe find SOM to be an attractive framework for developing ambient air quality classification because the approach eases interpretation of results by allowing users to visualize classifications on an organized map. The presented approach provides an appealing tool for developing multipollutant metrics of air quality that can be used to support multipollutant health studies.

Exploring associations between multipollutant day types and asthma morbidity: epidemiologic applications of self-organizing map ambient air quality classifications

BackgroundRecent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks.ObjectivePresent a methodology allowing health investigators to explore associations between categories of ambient air quality days (i.e., multipollutant day types) and adverse health.MethodsFirst, we applied a self-organizing map (SOM) to daily air quality data for 10 pollutants collected between January 1999 and December 2008 at a central monitoring location in Atlanta, Georgia to define a collection of multipollutant day types. Next, we conducted an epidemiologic analysis using our categories as a multipollutant metric of ambient air quality and daily counts of emergency department (ED) visits for asthma or wheeze among children aged 5 to 17 as the health endpoint. We estimated rate ratios (RR) for the association of multipollutant day types and pediatric asthma ED visits using a Poisson generalized linear model controlling for long-term, seasonal, and weekday trends and weather.ResultsUsing a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed). On days with only NO3 elevated (which tended to be relatively cool) and on days when only SO2 was elevated (which likely reflected plume touchdowns from coal combustion point sources), estimated associations were modestly positive but confidence intervals included the null.ConclusionsWe found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others. Relative to other health endpoints, asthma exacerbation may be driven more by total ambient pollutant exposure than by composition.

Community-based participatory research for the study of air pollution: a review of motivations, approaches, and outcomes

Neighborhood level air pollution represents a long-standing issue for many communities that, until recently, has been difficult to address due to the cost of equipment and lack of related expertise. Changes in available technology and subsequent increases in community-based participatory research (CBPR) have drastically improved the ability to address this issue. However, much still needs to be learned as these types of studies are expected to increase in the future. To assist, we review the literature in an effort to improve understanding of the motivations, approaches, and outcomes of air monitoring studies that incorporate CBPR and citizen science (CS) principles. We found that the primary motivations for conducting community-based air monitoring were concerns for air pollution health risks, residing near potential pollution sources, urban sprawl, living in “unmonitored” areas, and a general quest for improved air quality knowledge. Studies were mainly conducted using community led partnerships. Fixed site monitoring was primarily used, while mobile, personal, school-based, and occupational sampling approaches were less frequent. Low-cost sensors can enable thorough neighborhood level characterization; however, keeping the community involved at every step, understanding the limitations and benefits of this type of monitoring, recognizing potential areas of debate, and addressing study challenges are vital for achieving harmony between expected and observed study outcomes. Future directions include assessing currently unregulated pollutants, establishing long-term neighborhood monitoring sites, performing saturation studies, evaluating interventions, and creating CS databases.

Characterizing the spatial distribution of multiple pollutants and populations at risk in Atlanta, Georgia

BACKGROUND Exposure metrics that identify spatial contrasts in multipollutant air quality are needed to better understand multipollutant geographies and health effects from air pollution. Our aim is to improve understanding of: (1) long-term spatial distributions of multiple pollutants; and (2) demographic characteristics of populations residing within areas of differing air quality. METHODS We obtained average concentrations for ten air pollutants (p=10) across a 12 km grid (n=253) covering Atlanta, Georgia for 2002-2008. We apply a self-organizing map (SOM) to our data to derive multipollutant patterns observed across our grid and classify locations under their most similar pattern (i.e, multipollutant spatial type (MST)). Finally, we geographically map classifications to delineate regions of similar multipollutant characteristics and characterize associated demographics. RESULTS We found six MSTs well describe our data, with profiles highlighting a range of combinations, from locations experiencing generally clean air to locations experiencing conditions that were relatively dirty. Mapping MSTs highlighted that downtown areas were dominated by primary pollution and that suburban areas experienced relatively higher levels of secondary pollution. Demographics show the largest proportion of the overall population resided in downtown locations experiencing higher levels of primary pollution. Moreover, higher proportions of nonwhites and children in poverty reside in these areas when compared to suburban populations that resided in areas exhibiting relatively lower pollution. CONCLUSION Our approach reveals the nature and spatial distribution of differential pollutant combinations across urban environments and provides helpful insights for identifying spatial exposure and demographic contrasts for future health studies.

State Regulations Promoting Infant Physical Activity in Early Care and Education

BACKGROUND State policies have the potential to improve early care and education (ECE) settings, but little is known about the extent to which states are updating their licensing and administrative regulations, especially in response to national calls to action. In 2013, we assessed state regulations promoting infant physical activity in ECE and compared them with national recommendations. To assess change over time, we conducted this review again in 2018. METHODS We reviewed regulations for all US states for child care centers (centers) and family child care homes (homes) and compared them with three national recommendations: (1) provide daily tummy time; (2) use cribs, car seats, and high chairs for their primary purpose; and (3) limit the use of restrictive equipment (e.g., strollers). We performed exact McNemars tests to compare the number of states meeting recommendations from 2013 to 2018 to evaluate whether states had made changes over this period. RESULTS From 2013 to 2018, we observed significant improvement in one recommendation for homes-to use cribs, car seats, and high chairs for their primary purpose (odds ratio 11.0; 95% CI 1.6-47.3; p = 0.006). We did not observe any other significant difference between 2013 and 2018 regulations. CONCLUSIONS Despite increased awareness of the importance of early-life physical activity, we observed only modest improvement in the number of states meeting infant physical activity recommendations over the past 5 years. In practice, ECE programs may be promoting infant physical activity, but may not be required to do so through state regulations.