Using Satellites to Track Indicators of Global Air Pollution and Climate Change Impacts: Lessons Learned From a NASA-Supported Science-Stakeholder Collaborative.

The 2018 NASA Health and Air Quality Applied Science Team (HAQAST) "Indicators" Tiger Team collaboration between NASA-supported scientists and civil society stakeholders aimed to develop satellite-derived global air pollution and climate indicators. This Commentary shares our experience and lessons learned. Together, the team developed methods to track wildfires, dust storms, pollen counts, urban green space, nitrogen dioxide concentrations and asthma burdens, tropospheric ozone concentrations, and urban particulate matter mortality. Participatory knowledge production can lead to more actionable information but requires time, flexibility, and continuous engagement. Ground measurements are still needed for ground truthing, and sustained collaboration over time remains a challenge.

Effects of exposure to ambient ultrafine particles on respiratory health and systemic inflammation in children.

It is known that ultrafine particles (UFP, particles smaller than 0.1 µm) can penetrate deep into the lungs and potentially have adverse health effects. However, epidemiological data on the health effects of UFP is limited. Therefore, our objective was to test the hypothesis that exposure to UFPs is associated with respiratory health status and systemic inflammation among children aged 8 to 11 years. We conducted a cross-sectional study among 655 children (43.3% male) attending 25 primary (elementary) schools in the Brisbane Metropolitan Area, Australia. Ultrafine particle number concentration (PNC) was measured at each school and modelled at homes using Land Use Regression to derive exposure estimates. Health outcomes were respiratory symptoms and diagnoses, measured by parent-completed questionnaire, spirometric lung function, exhaled nitric oxide (FeNO), and serum C reactive protein (CRP). Exposure-response models, adjusted for potential personal and environmental confounders measured at the individual, home and school level, were fitted using Bayesian methods. PNC was not independently associated with respiratory symptoms, asthma diagnosis or spirometric lung function. However, PNC was positively associated with an increase in CRP (1.188-fold change per 1000 UFP cm-3 day/day (95% credible interval 1.077 to 1.299)) and an increase in FeNO among atopic participants (1.054 fold change per 1000 UFP cm-3 day/day (95% CrI 1.005 to 1.106)). UFPs do not affect respiratory health outcomes in children but do have systemic effects, detected here in the form of a positive association with a biomarker for systemic inflammation. This is consistent with the known propensity of UFPs to penetrate deep into the lung and circulatory system.

Improving the linkages between air pollution epidemiology and quantitative risk assessment.

BACKGROUND: Air pollution epidemiology plays an integral role in both identifying the hazards of air pollution as well as supplying the risk coefficients that are used in quantitative risk assessments. Evidence from both epidemiology and risk assessments has historically supported critical environmental policy decisions. The extent to which risk assessors can properly specify a quantitative risk assessment and characterize key sources of uncertainty depends in part on the availability, and clarity, of data and assumptions in the epidemiological studies. OBJECTIVES: We discuss the interests shared by air pollution epidemiology and risk assessment communities in ensuring that the findings of epidemiological studies are appropriately characterized and applied correctly in risk assessments. We highlight the key input parameters for risk assessments and consider how modest changes in the characterization of these data might enable more accurate risk assessments that better represent the findings of epidemiological studies. DISCUSSION: We argue that more complete information regarding the methodological choices and input data used in epidemiological studies would support more accurate risk assessments-to the benefit of both disciplines. In particular, we suggest including additional details regarding air quality, demographic, and health data, as well as certain types of data-rich graphics. CONCLUSIONS: Relatively modest changes to the data reported in epidemiological studies will improve the quality of risk assessments and help prevent the misinterpretation and mischaracterization of the results of epidemiological studies. Such changes may also benefit epidemiologists undertaking meta-analyses. We suggest workshops as a way to improve the dialogue between the two communities.

The influence of genetic polymorphisms on population variability in six xenobiotic-metabolizing enzymes.

This review provides variability statistics for polymorphic enzymes that are involved in the metabolism of xenobiotics. Six enzymes were evaluated: cytochrome P-450 (CYP) 2D6, CYP2E1, aldehyde dehydrogenase-2 (ALDH2), paraoxonase (PON1), glutathione transferases (GSTM1, GSTT1, and GSTP1), and N-acetyltransferases (NAT1 and NAT2). The polymorphisms were characterized with respect to (1) number and type of variants, (2) effects of polymorphisms on enzyme function, and (3) frequency of genotypes within specified human populations. This information was incorporated into Monte Carlo simulations to predict the population distribution and describe interindividual variability in enzyme activity. The results were assessed in terms of (1) role of these enzymes in toxicant activation and clearance, (2) molecular epidemiology evidence of health risk, and (3) comparing enzyme variability to that commonly assumed for pharmacokinetics. Overall, the Monte Carlo simulations indicated a large degree of interindividual variability in enzyme function, in some cases characterized by multimodal distributions. This study illustrates that polymorphic metabolizing systems are potentially important sources of pharmacokinetic variability, but there are a number of other factors including blood flow to liver and compensating pathways for clearance that affect how a specific polymorphism will alter internal dose and toxicity. This is best evaluated with the aid of physiologically based pharmacokinetic (PBPK) modeling. The population distribution of enzyme activity presented in this series of articles serves as inputs to such PBPK modeling analyses.

Genetic polymorphism in N-Acetyltransferase (NAT): Population distribution of NAT1 and NAT2 activity.

N-Acetyltransferases (NAT) are key enzymes in the conjugation of certain drugs and other xenobiotics with an arylamine structure. Polymorphisms in NAT2 have long been recognized to modulate toxicity produced by the anti-tubercular drug isoniazid, with molecular epidemiologic studies suggesting a link between acetylator phenotype and increased risk for bladder cancer. Recent evidence indicates that the other major NAT isozyme, NAT1, is also polymorphic. The current analysis characterizes the main polymorphisms in both NAT2 and NAT1 in terms of their effect on enzyme activity and frequency in the population. Multiple NAT2 alleles (NAT2*5, *6, *7, and *14) have substantially decreased acetylation activity and are common in Caucasians and populations of African descent. In these groups, most individuals carry at least one copy of a slow acetylator allele, and less than 10% are homozygous for the wild type (fast acetylator) trait. Incorporation of these data into a Monte Carlo modeling framework led to a population distribution of NAT2 activity that was bimodal and associated with considerable variability in each population assessed. The ratio of the median to the first percentile of NAT2 activity ranged from 7 in Caucasians to 18 in the Chinese population. This variability indicates the need for more quantitative approaches (e.g., physiologically based pharmacokinetic [PBPK] modeling) to assess the full distribution of internal dose and adverse responses to aromatic amines and other NAT2 substrates. Polymorphisms in NAT1 are generally associated with relatively minor effects on acetylation function, with Monte Carlo analysis indicating less interindividual variability than seen in NAT2 analysis.