Spaces of air pollution
While the detrimental effects of air pollution exposure have been documented through multiple medical studies, this information has not been leveraged against the industries and systemic patterns that cause the most vulnerable to develop serious respiratory illnesses. Children are one of the most vulnerable populations to air pollution, and yet their safety and health has not been prioritized through meaningful changes to transportation, heavy industry, or poor housing. Air pollution is categorized as “a risk factor for both acute and chronic respiratory disease” for children by the World Health Organization (WHO 2020). Since it is listed as a risk factor which worsens existing health conditions, it is not recognized as a primary cause of illness or death. A landmark inquest in the United Kingdom is challenging this assumption, with a legal case that, if successful, would allow air pollution to be listed as a cause on the death certificate of a nine-year-old girl who lived in one of London’s hotspots of diesel particulate matter (“Inquest”, 2019).
The field of health geography has focused primarily upon distribution and access to health services, while medical geography is more akin to the epidemiological study of the spread of disease (Kearns and Moon 2002). As the field of health geography has found its place in broader context of research, Kearns and Moon (2002) also noted some omissions in this field, particularly the engagement with literature on the body and with theories of risk in public health. This omission has created a space where studies on air pollution, listed as a risk factor for other illnesses, is studied from many different perspectives, including health, social sciences, environmental justice. Each of these modes of research could address the specific harmful effects of air pollution on children, but the interdisciplinary approach that would naturally emerge from this overlapping area of interests has not coalesced. As the following studies will demonstrate, air pollution has a detrimental effect on children’s health, disproportionately affects children of racial minorities, and is linked to higher school absenteeism and lower educational attainment.
In 2009, a study on air pollution, family, and neighborhood environment was conducted in Los Angeles. Focusing on three types of air pollution – ozone (O3), particulate matter (PM2.5 and PM10), and carbon monoxide (CO) – and the rates of asthma among children, the study concludes that higher rates of CO, which generally indicate heavier traffic pollution, were linked with higher rates of asthma in children. As part of their conclusions, they also noted the issues of spatial distribution with the air monitors, since traffic pollution in particular is extremely localized and might not have registered on a nearby air monitor. Additionally, their findings indicate that the health realities of non-English speaking families may be under-represented, posing a challenge to telling the whole story on asthma exposure and experience among children of non-English speaking families (Wilhelm et al 2009)
One study that utilizes geographic information systems (GIS) to analyze the spatial distribution of diesel particulate matter (DPM) in relation to lung cancer and asthma rates in Massachusetts is particularly informative. In this study, researchers found that asthma rates by town corresponded with areas with high rates of diesel particulate matter. In addition, they also considered which neighborhoods were listed as environmental justice regions (classified as household income at 65% or less of that state average and 25% of the population that are immigrants, minorities, or have low English proficiency). All of the environmental justice neighborhoods in the Boston area were found within town DPM hotspots (McEntee 2008).
Another study based in Toronto, Ontario analyzed the relationship between the birthplaces of children with atopic asthma and traffic-related air pollution. This study found the connection between location at birth to be particularly significant, as more than two thirds of the cases had moved residences between birth and the time of the study, reducing the chance of the results being related to factors in later childhood. In the results, the clusters of birth location of atopic asthma cases were either fully or partially explained by traffic-related air pollution. Other factors which may not have been picked up on in this study include differences in socioeconomic level, as well as other spatial conditions such as the air quality in the home (Shankardass 2015).
As many of the above studies have highlighted, the GIS analysis of asthma and air pollution in the Bronx emphasizes the need for better data and methodology around the distribution of air pollution health risks. While Mantaay (2007) points to many medical studies that show the negative health effects of air pollution, the gaps in reporting from Toxic Release Inventory (TRI) facilities means that linking actual cases of respiratory illness to specific sources is still a long way from being possible. Additionally, the cumulative effects of multiple sources of hazardous air pollutants are often aggregated at a scale (county or tract) that doesn’t allow for the granular level of data necessary for a health risk analysis (Mantaay 2007, USEPA 2003). The cumulative moments of exposure from large or small facilities result in real bodily harm, but are difficult to prove (Scott 2016, Lewis 2005).
In addition to establishing the geography of respiratory health, research has also linked elevated levels of air pollution to illness-related absenteeism among schoolchildren (Mohai 2011). A study conducted in Seoul, South Korea linked illness-related absenteeism with increased levels of PM2.5, ozone (O3), and sulfur dioxide (Park et 2002). Another study based in Los Angeles looking at six months of absentee rates and levels of various air pollutants found that levels of O3 were particularly correlated with increased illness-related absenteeism (Gilliland et al 2001). Further emphasizing this relationship is a study based in Utah in which the air pollutant PM10 was linked to higher hospitalization rates for respiratory illness among children, with the elevated air pollution and hospitalizations lining up with steel mill activity (Pope 1991).
While some of these studies have recommendations for research and policy to build upon their findings, the field of health geography is still missing an actionable directive for mitigating the health risks of air pollution or a methodology that will make environmental justice a reality for those suffering from or at risk of chronic respiratory illness. Because of the lack of empirical data on hazardous air pollutants at a higher resolution, linking respiratory illness cases to the sources of contaminants is extremely difficult. Additionally, the temporal aspect of air pollution complicates the relationship between the body and environment, suggesting a constitutive relationship with room for uncertainties, rather than one that requires the body and environment to be separate and static (Garnett 2017).
The body bears the evidence of the impacts of air pollution on respiratory health. However, in both distributive and procedural justice, that bodily knowledge is difficult to spatially correlate with the sources of something as mutable as air pollution. One research agenda could be to implement more fine-tuned air quality measurement with more air quality sensors, and to gather more detailed medical histories for those who suffer from respiratory illness (Wilhelm 2009). However, while this would undoubtedly produce a clearer picture of our current situation, air quality devices and medical records cannot measure history, social frameworks, or relationships. While this quantitative data is a valuable resource, relying strictly upon these statistics reinforces the legitimization of experts over experience, especially the bodily experiences of Black, Latinx, indigenous, and other marginalized and intersecting identities.