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The chemical composition of incense-generated organic aerosol in residential indoor air has received limited attention in Western literature. In this study, we conducted incense burning experiments in a single-family California residence during vacancy. We report the chemical composition of organic fine particulate matter (PM2.5), associated emission factors (EFs), and gas-particle phase partitioning for indoor semivolatile organic compounds (SVOCs). Speciated organic PM2.5 measurements were made using two-dimensional gas chromatography coupled with high-resolution time-of-flight mass spectrometry (GC×GC-HR-ToF-MS) and semivolatile thermal desorption aerosol gas chromatography (SV-TAG). Organic PM2.5 EFs ranged from 7 to 31 mg g-1 for burned incense and were largely comprised of polar and oxygenated species, with high abundance of biomass-burning tracers such as levoglucosan. Differences in PM2.5 EFs and chemical profiles were observed in relation to the type of incense burned. Nine indoor SVOCs considered to originate from sources other than incense combustion were enhanced during incense events. Time-resolved concentrations of these SVOCs correlated well with PM2.5 mass (R 2 > 0.75), suggesting that low-volatility SVOCs such as bis(2-ethylhexyl)phthalate and butyl benzyl phthalate partitioned to incense-generated PM2.5. Both direct emissions and enhanced partitioning of low-volatility indoor SVOCs to incense-generated PM2.5 can influence inhalation exposures during and after indoor incense use.

Gohlke et al. (1) summarized approaches for air pollution measurements and modeling to guide the implementation and evaluation of federal and state policies to reduce disproportionate harms in impacted communities. [...]

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. Furthermore, the abrupt reduction in emissions during the lockdown periods led to clearly observable changes in atmospheric composition, which provide direct insight into feedbacks between the Earth system and human activity. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes, due in large part to their different lifetimes. Here, we discuss several key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed, in part due to decreased ocean uptake of CO2 and a likely increase in CH4 lifetime from reduced NO x emissions. Second, the response of O3 to decreased NO x emissions showed significant spatial and temporal variability, due to differing chemical regimes around the world. Finally, the overall response of atmospheric composition to emissions changes is heavily modulated by factors including carbon-cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality, such as wildfires and the ozone climate penalty.

An interdisciplinary undergraduate atmospheric science modeling course was cotaught at a midsize public university by three professors from atmospheric science, statistics, and design using a blended learning approach. The in-class portion of the course taught upper-level students numerical weather prediction modeling and statistical evaluation methods. Online modules were used to teach data visualization techniques and three foundational design principles upon which their efficacy depends: legibility, visual hierarchy, and appropriate use of color. Geoscience students need data visualization skills to prepare for careers in government, industry, and research that increasingly require work with big data and communication with diverse collaborators and audiences. This article focuses on the instructional approach for the visualization component of the course--specifically, the three teaching innovations used: online modules, an interdisciplinary teaching team, and design-focused data visualization instruction. Although course enrollment was low at four students, several valuable lessons were learned that can improve the teaching of visualization in geoscience courses, including the utility of structuring visualization instruction around two separate but complementary visualization skills: visualization for analysis and visualization for sharing knowledge. Evaluation of students' visualization work at the conclusion of the course demonstrated improvement in the foundational design principles as well as improved ability to select appropriate visualization strategies for different situations. The methods used to assess these improvements are presented alongside illustrative examples of student work. Pre- and postcourse surveys indicate the students felt more confident in creating data visualizations upon completion of the course, and qualitative assessments of student work confirm increased application of foundational design principles in visualizations created by the students. The authors argue that teaching visualization as an online supplement to other geoscience instruction is a potentially replicable model for improving students' learning about visualization. This is especially true when such instruction relies on open-source programs and materials and leverages interdisciplinary expertise in course design.

Air pollution levels in the United States have decreased dramatically over the past decades, yet national racial-ethnic exposure disparities persist. For ambient fine particulate matter (PM2.5), we investigate three emission-reduction approaches and compare their optimal ability to address two goals: 1) reduce the overall population average exposure ("overall average") and 2) reduce the difference in the average exposure for the most exposed racial-ethnic group versus for the overall population ("national inequalities"). We show that national inequalities in exposure can be eliminated with minor emission reductions (optimal: ~1% of total emissions) if they target specific locations. In contrast, achieving that outcome using existing regulatory strategies would require eliminating essentially all emissions (if targeting specific economic sectors) or is not possible (if requiring urban regions to meet concentration standards). Lastly, we do not find a trade-off between the two goals (i.e., reducing overall average and reducing national inequalities); rather, the approach that does the best for reducing national inequalities (i.e., location-specific strategies) also does as well as or better than the other two approaches (i.e., sector-specific and meeting concentration standards) for reducing overall averages. Overall, our findings suggest that incorporating location-specific emissions reductions into the US air quality regulatory framework 1) is crucial for eliminating long-standing national average exposure disparities by race-ethnicity and 2) can benefit overall average exposures as much as or more than the sector-specific and concentration-standards approaches. [ABSTRACT FROM AUTHOR]

The growing abundance of data is conducive to using numerical methods to relate air quality, meteorology, and emissions to address which factors impact pollutant concentrations. Often, it is the extreme values that are of interest for health and regulatory purposes (e.g., the National Ambient Air Quality Standard for ozone uses the annual, maximum, daily 4th highest, 8-hour average (MDA8) ozone), though such values are the most challenging to predict using empirical models. We developed four different computational models, including the Generalized Additive Model (GAM), the Multivariate Adaptive Regression Splines, the Random Forest, and the Support Vector Regression, to develop observation-based relationships between the 4th highest MDA8 ozone in the South Coast Air Basin and precursor emissions, meteorological factors, and large-scale climate patterns. All models had similar predictive performance, though the GAM showed a relatively higher R2 value (0.96) with a lower root mean square error and mean bias. [ABSTRACT FROM AUTHOR]