This article presents a regression-tree-based meta-analysis of rodent pulmonary toxicity studies of uncoated, nonfunctionalized carbon nanotube (CNT) exposure. The resulting analysis provides quantitative estimates of the contribution of CNT attributes (impurities, physical dimensions, and aggregation) to pulmonary toxicity indicators in bronchoalveolar lavage fluid: neutrophil and macrophage count, and lactate dehydrogenase and total protein concentrations. The method employs classification and regression tree (CART) models, techniques that are relatively insensitive to data defects that impair other types of regression analysis: high dimensionality, nonlinearity, correlated variables, and significant quantities of missing values. Three types of analysis are presented: the RT, the random forest (RF), and a random-forest-based dose-response model. The RT shows the best single model supported by all the data and typically contains a small number of variables. The RF shows how much variance reduction is associated with every variable in the data set. The dose-response model is used to isolate the effects of CNT attributes from the CNT dose, showing the shift in the dose-response caused by the attribute across the measured range of CNT doses. It was found that the CNT attributes that contribute the most to pulmonary toxicity were metallic impurities (cobalt significantly increased observed toxicity, while other impurities had mixed effects), CNT length (negatively correlated with most toxicity indicators), CNT diameter (significantly positively associated with toxicity), and aggregate size (negatively correlated with cell damage indicators and positively correlated with immune response indicators). Increasing CNT N2 -BET-specific surface area decreased toxicity indicators.

The development of alternative testing strategies (ATS) for hazard assessment of new and emerging materials is high on the agenda of scientists, funders, and regulators. The relatively large number of nanomaterials on the market and under development means that an increasing emphasis will be placed on the use of reliable, predictive ATS when assessing their safety. We have provided recommendations as to how ATS development for assessment of nanomaterial hazard may be accelerated. Predefined search terms were used to identify the quantity and distribution of peer-reviewed publications for nanomaterial hazard assessment following inhalation, ingestion, or dermal absorption. A summary of knowledge gaps relating to nanomaterial hazard is provided to identify future research priorities and areas in which a rich data set might exist to allow ATS identification. Consultation with stakeholders (e.g., academia, industry, regulators) was critical to ensure that current expert opinion was reflected. The gap analysis revealed an abundance of studies that assessed the local and systemic impacts of inhaled particles, and so ATS are available for immediate use. Development of ATS for assessment of the dermal toxicity of chemicals is already relatively advanced, and these models should be applied to nanomaterials as relatively few studies have assessed the dermal toxicity of nanomaterials to date. Limited studies have investigated the local and systemic impacts of ingested nanomaterials. If the recommendations for research prioritization proposed are adopted, it is envisioned that a comprehensive battery of ATS can be developed to support the risk assessment process for nanomaterials. Some alternative models are available for immediate implementation, while others require more developmental work to become widely adopted. Case studies are included that can be used to inform the selection of alternative models and end points when assessing the pathogenicity of fibers and mode of action of nanomaterial toxicity.

We identify bacteria types on collected dust samples in Dakar Senegal, a region that experiences frequent Saharan dust events. We use classical techniques to identify bacteria types from dust samples. Seventy-seven bacteria types are identified from samples collected by spatula and the QuickTake® 30 air sampling pump. The dominant groups in the first batch of 51 bacteria (collected via deposition) are Micrococcus (33.33%), Bacillus (13.73%), Kytococcus (11.76%), Pseudomonas (9.80%), and Burkholderia (7.84%) and dominants in the second batch of 26 bacteria (collected with aerosol sampling vacuum pump): Pseudomonas (38.61%), Burkholderia (26.92%), Micrococcus (11.54%), and Brucella spp (7.69%). These bacteria are found in earlier studies from desert sources and can potentially cause respiratory diseases to exposed populations. Future work will use molecular methods is necessary to search for additional pathogens, including viruses on dust aerosols.Plain Language Summary Bacteria on the surfaces of Saharan dust samples collected from 2013-2016 were analyzed using traditional techniques at Dakar, Senegal. The samples were collected using a spatula and the QuickTake® 30 air sampling pump. The analysis finds some bacteria that are linked to respiratory disease, including Micrococcus, Burkholderia, and Pseudomonas. We believe that the spatula technique may include bacteria such as Bacillus from soils, which was not present in airborne samples. Additional analysis using genomic techniques will assist in better identifying bacteria and potential pathogens, which can impacts West African populations and are transported downstream over long distances to the Caribbean, Southeastern United States, South America, and Europe. During the dry season, PM 10 surface dust concentrations can reach hazardous levels with Marticornea et al. (2010) and Diokhane et al. (2016), showing frequent daily surface PM 10 concentrations exceeding 500 μg m −3 , which is 10 times the World Health Organization (2006) recommended daily levels of 50 μg m −3 . During the summer season, Toure et al. (2019) show that PM 10 monthly concentrations at Dakar, Senegal fall below the U.S. Environmental Protection Agency criteria of unhealthy levels (250 μg m −3 ), and monthly values are approximately 50 μg m −3 during August and September. The reduction in summer PM 10 and PM 2.5

The Marcellus Shale gas is known as a significant source of criteria pollutants and studies show that the current setback distance in Pennsylvania is not adequate to protect the residents from exceeding the established limits. Even an effective setback distance to meet the annual exposure limit may not be adequate to meet the daily limit. The probability of exceeding the annual limit increases with number of wells per site. We use a probabilistic dispersion model to introduce a technical basis to select appropriate setback distances.

Note: This file is a pre-print or un-reviewed and un-edited version of a work submitted for publication. The author provides this work publicly under a CC4.0 license: attribution, noncommercial, no derivatives. Any other uses are by expressed written permission only.