Investigating the enhancement of air pollutant predictions and understanding air quality disparities across racial, ethnic, and economic lines at US public schools

2022 Spring.Includes bibliographical references.Ambient air pollution has significant health and economic impacts worldwide. Even in the most developed countries, monitoring networks often lack the spatiotemporal density to resolve air pollution gradients. Though air pollution affects the entire population, it can disproportionately affect the disadvantaged and vulnerable communities in society. Pollutants such as fine particulate matter (PM2.5), nitrogen oxides (NO and NO2), and ozone, which have a variety of anthropogenic and natural sources, have garnered substantial research attention over the last few decades. Over half the world and over 80% of Americans live in urban areas, and yet many cities only have one or several air quality monitors, which limits our ability to capture differences in exposure within cities and estimate the resulting health impacts. Improving sub-city air pollution estimates could improve epidemiological and health-impact studies in cities with heterogeneous distributions of PM2.5, providing a better understanding of communities at-risk to urban air pollution. Biomass burning is a source of PM2.5 air pollution that can impact both urban and rural areas, but quantifying the health impacts of PM2.5 from biomass burning can be even more difficult than from urban sources. Monitoring networks generally lack the spatial density needed to capture the heterogeneity of biomass burning smoke, especially near the source fires. Due to limitations of both urban and rural monitoring networks several techniques have been developed to supplement and enhance air pollution estimates. For example, satellite aerosol optical depth (AOD) can be used to fill spatial gaps in PM monitoring networks, but AOD can be disconnected from time-resolved surface-level PM in a multitude of ways, including the limited overpass times of most satellites, daytime-only measurements, cloud cover, surface reflectivity, and lack of vertical-profile information. Observations of smoke plume height (PH) may provide constraints on the vertical distribution of smoke and its impact on surface concentrations. Low-cost sensor networks have been rapidly expanding to provide higher density air pollution monitoring. Finally, both geophysical modeling, statistical techniques such as machine learning and data mining, and combinations of all of the aforementioned datasets have been increasingly used to enhance surface observations. In this dissertation, we explore several of these different data sources and techniques for estimating air pollution and determining community exposure concentrations. In the first chapter of this dissertation, we assess PH characteristics from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and evaluate its correlation with co-located PM2.5 and AOD measurements. PH is generally highest over the western US. The ratio PM2.5:AOD generally decreases with increasing PH:PBLH (planetary boundary layer height), showing that PH has the potential to refine surface PM2.5 estimates for collections of smoke events. Next, to estimate spatiotemporal variability in PM2.5, we use machine learning (Random Forests; RFs) and concurrent PM2.5 and AOD measurements from the Citizen Enabled Aerosol Measurements for Satellites (CEAMS) low-cost sensor network as well as PM2.5 measurements from the Environmental Protection Agency's (EPA) reference monitors during wintertime in Denver, CO, USA. The RFs predicted PM2.5 in a 5-fold cross validation (CV) with relatively high skill (95% confidence interval R2=0.74-0.84 for CEAMS; R2=0.68-0.75 for EPA) though the models were aided by the spatiotemporal autocorrelation of the PM22.5 measurements. We find that the most important predictors of PM2.5 are factors associated with pooling of pollution in wintertime, such as low planetary boundary layer heights (PBLH), stagnant wind conditions, and, to a lesser degree, elevation. In general, spatial predictors are less important than spatiotemporal predictors because temporal variability exceeds spatial variability in our dataset. Finally, although concurrent AOD is an important predictor in our RF model for hourly PM2.5, it does not improve model performance during our campaign period in Denver. Regardless, we find that low-cost PM2.5 measurements incorporated into an RF model were useful in interpreting meteorological and geographic drivers of PM2.5 over wintertime Denver. We also explore how the RF model performance and interpretation changes based on different model configurations and data processing. Finally, we use high resolution PM2.5 and nitrogen dioxide (NO2) estimates to investigate socioeconomic disparities in air quality at public schools in the contiguous US. We find that Black and African American, Hispanic, and Asian or Pacific Islander students are more likely to attend schools in locations where the ambient concentrations of NO2 and PM2.5 are above the World Health Organization's (WHO) guidelines for annual-average air quality. Specifically, we find that ~95% of students that identified as Asian or Pacific Islander, 94% of students that identified as Hispanic, and 89% of students that identified as Black or African American, attended schools in locations where the 2019 ambient concentrations were above the WHO guideline for NO2 (10 μg m-3 or ~5.2 ppbv). Conversely, only 83% of students that identified as white and 82% of those that identified as Native American attended schools in 2019 where the ambient NO2 concentrations were above the WHO guideline. Similar disparities are found in annually averaged ambient PM2.5 across racial and ethnic groups, where students that identified as white (95%) and Native American (83%) had a smallest percentage of students above the WHO guideline (5 μg m-3), compared to students that identified with minoritized groups (98-99%). Furthermore, the disparity between white students and other minoritized groups, other than Native Americans, is larger at higher PM2.5 concentrations. Students that attend schools where a higher percentage of students are eligible for free or reduced meals, which we use as a proxy for poverty, are also more likely to attend schools where the ambient air pollutant concentrations exceed WHO guidelines. These disparities also tend to increase in magnitude at higher concentrations of NO2 and PM2.5. We investigate the intersectionality of disparities across racial/ethnic and poverty lines by quantifying the mean difference between the lowest and highest poverty schools, and the most and least white schools in each state, finding that most states have disparities above 1 ppbv of NO2 and 0.5 μg m-3 of PM2.5 across both. We also identify distinct regional patterns of disparities, highlighting differences between California, New York, and Florida. Finally, we also highlight that disparities do not only exist across an urban and non-urban divide, but also within urban areas

Memory CD8⁺ T cells exhibit tissue imprinting and non-stable exposure-dependent reactivation characteristics following blood-stage Plasmodium berghei ANKA infections

Experimental cerebral malaria (ECM) is a severe complication of Plasmodium berghei ANKA (PbA) infection in mice, characterized by CD8(+) T‐cell accumulation within the brain. Whilst the dynamics of CD8(+) T‐cell activation and migration during extant primary PbA infection have been extensively researched, the fate of the parasite‐specific CD8(+) T cells upon resolution of ECM is not understood. In this study, we show that memory OT‐I cells persist systemically within the spleen, lung and brain following recovery from ECM after primary PbA‐OVA infection. Whereas memory OT‐I cells within the spleen and lung exhibited canonical central memory (Tcm) and effector memory (Tem) phenotypes, respectively, memory OT‐I cells within the brain post‐PbA‐OVA infection displayed an enriched CD69(+)CD103(−) profile and expressed low levels of T‐bet. OT‐I cells within the brain were excluded from short‐term intravascular antibody labelling but were targeted effectively by longer‐term systemically administered antibodies. Thus, the memory OT‐I cells were extravascular within the brain post‐ECM but were potentially not resident memory cells. Importantly, whilst memory OT‐I cells exhibited strong reactivation during secondary PbA‐OVA infection, preventing activation of new primary effector T cells, they had dampened reactivation during a fourth PbA‐OVA infection. Overall, our results demonstrate that memory CD8(+) T cells are systemically distributed but exhibit a unique phenotype within the brain post‐ECM, and that their reactivation characteristics are shaped by infection history. Our results raise important questions regarding the role of distinct memory CD8(+) T‐cell populations within the brain and other tissues during repeat Plasmodium infections

An optimized microarray platform for assaying genomic variation in Plasmodium falciparum field populations

We present an optimized probe design for copy number variation (CNV) and SNP genotyping in the Plasmodium falciparum genome. We demonstrate that variable length and isothermal probes are superior to static length probes. We show that sample preparation and hybridization conditions mitigate the effects of host DNA contamination in field samples. The microarray and workflow presented can be used to identify CNVs and SNPs with 95% accuracy in a single hybridization, in field samples containing up to 92% human DNA contamination

Memory CD8 + T cells exhibit tissue imprinting and non‐stable exposure‐dependent reactivation characteristics following blood‐stage Plasmodium berghei ANKA infections

From Wiley via Jisc Publications RouterHistory: received 2020-11-02, rev-recd 2021-08-09, accepted 2021-08-13, pub-electronic 2021-08-27Article version: VoRPublication status: PublishedFunder: Medical Research Council; Id: http://dx.doi.org/10.13039/501100000265; Grant(s): G0900487, MR/R010099/1Abstract: Experimental cerebral malaria (ECM) is a severe complication of Plasmodium berghei ANKA (PbA) infection in mice, characterized by CD8+ T‐cell accumulation within the brain. Whilst the dynamics of CD8+ T‐cell activation and migration during extant primary PbA infection have been extensively researched, the fate of the parasite‐specific CD8+ T cells upon resolution of ECM is not understood. In this study, we show that memory OT‐I cells persist systemically within the spleen, lung and brain following recovery from ECM after primary PbA‐OVA infection. Whereas memory OT‐I cells within the spleen and lung exhibited canonical central memory (Tcm) and effector memory (Tem) phenotypes, respectively, memory OT‐I cells within the brain post‐PbA‐OVA infection displayed an enriched CD69+CD103− profile and expressed low levels of T‐bet. OT‐I cells within the brain were excluded from short‐term intravascular antibody labelling but were targeted effectively by longer‐term systemically administered antibodies. Thus, the memory OT‐I cells were extravascular within the brain post‐ECM but were potentially not resident memory cells. Importantly, whilst memory OT‐I cells exhibited strong reactivation during secondary PbA‐OVA infection, preventing activation of new primary effector T cells, they had dampened reactivation during a fourth PbA‐OVA infection. Overall, our results demonstrate that memory CD8+ T cells are systemically distributed but exhibit a unique phenotype within the brain post‐ECM, and that their reactivation characteristics are shaped by infection history. Our results raise important questions regarding the role of distinct memory CD8+ T‐cell populations within the brain and other tissues during repeat Plasmodium infections

Population Structure Shapes Copy Number Variation in Malaria Parasites.

If copy number variants (CNVs) are predominantly deleterious, we would expect them to be more efficiently purged from populations with a large effective population size (Ne) than from populations with a small Ne. Malaria parasites (Plasmodium falciparum) provide an excellent organism to examine this prediction, because this protozoan shows a broad spectrum of population structures within a single species, with large, stable, outbred populations in Africa, small unstable inbred populations in South America and with intermediate population characteristics in South East Asia. We characterized 122 single-clone parasites, without prior laboratory culture, from malaria-infected patients in seven countries in Africa, South East Asia and South America using a high-density single-nucleotide polymorphism/CNV microarray. We scored 134 high-confidence CNVs across the parasite exome, including 33 deletions and 102 amplifications, which ranged in size from <500 bp to 59 kb, as well as 10,107 flanking, biallelic single-nucleotide polymorphisms. Overall, CNVs were rare, small, and skewed toward low frequency variants, consistent with the deleterious model. Relative to African and South East Asian populations, CNVs were significantly more common in South America, showed significantly less skew in allele frequencies, and were significantly larger. On this background of low frequency CNV, we also identified several high-frequency CNVs under putative positive selection using an FST outlier analysis. These included known adaptive CNVs containing rh2b and pfmdr1, and several other CNVs (e.g., DNA helicase and three conserved proteins) that require further investigation. Our data are consistent with a significant impact of genetic structure on CNV burden in an important human pathogen