Nontuberculous mycobacteria (NTM) are environmental organisms that can cause opportunistic pulmonary disease with species diversity showing significant regional variation. In the United States, Hawai’i shows the highest rate of NTM pulmonary disease. The need for improved understanding of NTM reservoirs led us to identify NTM from patient respiratory specimens and compare NTM diversity between outdoor and indoor locations in Hawai’i. A total of 545 water biofilm samples were collected from 357 unique locations across Kaua’i (n = 51), O’ahu (n = 202), Maui (n = 159), and Hawai’i Island (n = 133) and divided into outdoor (n = 179) or indoor (n = 366) categories. rpoB sequence analysis was used to determine NTM species and predictive modeling applied to develop NTM risk maps based on geographic characteristics between environments. M. chimaera was frequently identified from respiratory and environmental samples followed by M. chelonae and M. abscessus; yet significantly less NTM were consistently recovered from outdoor compared to indoor biofilms, as exemplified by showerhead biofilm samples. While the frequency of M. chimaera recovery was comparable between outdoor and indoor showerhead biofilms, phylogenetic analyses demonstrate similar rpoB gene sequences between all showerhead and respiratory M. chimaera isolates, supporting outdoor and indoor environments as possible sources for pulmonary M. chimaera infections.

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Purpose of Review We reviewed the exposure assessments of ambient air pollution used in studies of fertility, fecundability, and pregnancy loss. Recent Findings Comprehensive literature searches were performed in the PUBMED, Web of Science, and Scopus databases. Of 168 total studies, 45 met the eligibility criteria and were included in the review. We find that 69% of fertility and pregnancy loss studies have used one-dimensional proximity models or surface monitor data, while only 35% have used the improved models, such as land-use regression models (4%), dispersion/chemical transport models (11%), or fusion models (20%). No published studies have used personal air monitors. Summary While air pollution exposure models have vastly improved over the past decade from a simple, one-dimensional distance or air monitor data to models that incorporate physiochemical properties leading to better predictive accuracy, precision, and increased spatiotemporal variability and resolution, the fertility literature has yet to fully incorporate these new methods. We provide descriptions of each of these air pollution exposure models and assess the strengths and limitations of each model, while summarizing the findings of the literature on ambient air pollution and fertility that apply each method.

Background Autoimmune (AI) diseases appear to be a product of genetic predisposition and environmental triggers. Disruption of the skin barrier causes exacerbation of psoriasis/eczema. Oxidative stress is a mechanistic pathway for pathogenesis of the disease and is also a primary mechanism for the detrimental effects of air pollution. Methods We evaluated the association between autoimmune skin diseases (psoriasis or eczema) and air pollutant mixtures in 9060 subjects from the Personalized Environment and Genes Study (PEGS) cohort. Pollutant exposure data on six criteria air pollutants are publicly available from the Center for Air, Climate, and Energy Solutions and the Atmospheric Composition Analysis Group. For increased spatial resolution, we included spatially cumulative exposure to volatile organic compounds from sites in the United States Environmental Protection Agency Toxic Release Inventory and the density of major roads within a 5 km radius of a participant’s address from the United States Geological Survey. We applied logistic regression with quantile g-computation, adjusting for age, sex, diagnosis with an autoimmune disease in family or self, and smoking history to evaluate the relationship between self-reported diagnosis of an AI skin condition and air pollution mixtures. Results Only one air pollution variable, sulfate, was significant individually (OR = 1.06, p = 3.99E−2); however, the conditional odds ratio for the combined mixture components of PM2.5 (black carbon, sulfate, sea salt, and soil), CO, SO2, benzene, toluene, and ethylbenzene is 1.10 (p-value = 5.4E−3). Significance While the etiology of autoimmune skin disorders is not clear, this study provides evidence that air pollutants are associated with an increased prevalence of these disorders. The results provide further evidence of potential health impacts of air pollution exposures on life-altering diseases. Significance and impact statement The impact of air pollution on non-pulmonary and cardiovascular diseases is understudied and under-reported. We find that air pollution significantly increased the odds of psoriasis or eczema in our cohort and the magnitude is comparable to the risk associated with smoking exposure. Autoimmune diseases like psoriasis and eczema are likely impacted by air pollution, particularly complex mixtures and our study underscores the importance of quantifying air pollution-associated risks in autoimmune disease.