Characterizing PM2.5 Emissions and Temporal Evolution of Organic Composition from Incense Burning in a California Residence.

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 (R2 > 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.

Air quality policy should quantify effects on disparities.

New tools can guide US policies to better target and reduce racial and socioeconomic disparities in air pollution exposure.

Emissions and meteorological impacts on PM2.5 species concentrations in Southern California using generalized additive modeling.

The chemical composition of PM2.5 has a significant impact on human health and air quality, and its accurate knowledge can be used to identify contributing emission sources. Assessing and quantifying the impacts of various factors (e.g., emissions, meteorology, and large-scale climate patterns) on the main PM2.5 chemical components can give guidance for implementing effective regulations to improve air quality in the future. In this study, we developed generalized additive models (GAMs) to assess how emissions, meteorological factors, and large-scale climate indices affected ammonium, sulfate, nitrate, elemental carbon, and organic carbon from 2002 to 2019 in the South Coast Air Basin (SoCAB). Concentration trends from three sites in the SoCAB are studied. The statistical results showed that GAMs can capture the variability of these species' daily concentrations (R2 = 0.6 to 0.7) and annual concentrations (R2 = 0.93 to 0.99). Precursor emissions most significantly affect PM2.5 species production, though meteorological factors like maximum temperature, relative humidity, wind speed, and boundary layer height, also influence PM2.5 composition. In the future, these meteorological factors will become more significant in affecting PM2.5 speciation, although emissions will continue to strongly affect formation. Results show that the composition of most PM2.5 species will decrease in the future except for OC, which will become the largest contributor to PM2.5.

Machine learning with spatial interpolation techniques for constructing 2-dimensional ozone concentrations in Southern California during the COVID-19 shutdown.

In this study, we combine machine learning and geospatial interpolations to create a two-dimensional high-resolution ozone concentration fields over the South Coast Air Basin for the entire year of 2020. Three spatial interpolation methods (bicubic, IDW, and ordinary kriging) were employed. The predicted ozone concentration fields were constructed using 15 building sites, and random forest regression was employed to test predictability of 2020 data based on input data from past years. Spatially interpolated ozone concentrations were evaluated at twelve sites that were independent of the actual spatial interpolations to find the most suitable method for SoCAB. Ordinary kriging interpolation had the best performance overall for 2020: concentrations were overestimated for Anaheim, Compton, LA North Main Street, LAX, Rubidoux, and San Gabriel sites and underestimated for Banning, Glendora, Lake Elsinore, and Mira Loma sites. The model performance improved from the West to the East, exhibiting better predictions for inland sites. The model is best at interpolating ozone concentrations inside the sampling region (bounded by the building sites), with R2 ranging from 0.56 to 0.85 for those sites, as prediction deficiencies occurred at the periphery of the sampling region, with the lowest R2 of 0.39 for Winchester. All the interpolation methods poorly predicted and underestimated ozone concentrations in Crestline during summer (up to 19 ppb). Poor performance for Crestline indicates that the site has a distribution air pollution levels independent from all other sites. Therefore, historical data from coastal and inland sites should not be used to predict ozone in Crestline using data-driven spatial interpolation approaches. The study demonstrates the utility of machine learning and geospatial techniques for evaluating air pollution levels during anomalous periods.

Emissions, meteorological and climate impacts on PM2.5 levels in Southern California using a generalized additive model: Historic trends and future estimates.

Annual fine particulate matter (PM2.5) mass concentrations in the South Coast Air Basin (SoCAB) of California decreased from around 30 µg/m3 to 11 µg/m3 between 2000 and 2013 but rose from 11 µg/m3 to 13 µg/m3 between 2014 and 2018, raising important questions about the effectiveness of ongoing emission control policies. A two-step generalized additive model (GAM)-least squares approach was developed to explore the effects of emissions, large-scale climate events and meteorological factors on daily PM2.5 mass concentrations from 2000 to 2019 to quantitatively link impacts of emissions and meteorological on PM2.5 and to assess factors leading to the increase. The GAM had an R2 = 0.99 and root mean square error (RMSE) = 0.7 µg/m3 for the annual average PM2.5 concentrations. The two-step method had an R2 = 0.93 and RMSE = 4.07 µg/m3 for the 98th percentile 24-hr average PM2.5 concentrations. Variations in both emissions and relative humidity were of high importance compared with other included factors. Interactions of NH3 emissions with NOx and SO2 emissions, which lead to ammonium nitrate and sulfate aerosol formation, were the most important factors. Meteorological effects on PM2.5 explained the majority of the daily PM2.5 fluctuations. Emission changes (increases in SO2 and PM2.5) led to increases in predicted PM2.5 between 2014 and 2018. Predicted future PM2.5, using projected emissions and meteorological data from model simulations of representative concentration pathway (RCP) scenarios, are around 12 µg/m3 (annual) and 30 µg/m3 (98th percentile daily), which are both close to the current National Ambient Air Quality Standards (NAAQS) for PM2.5. Meteorological impacts on the predicted PM2.5 in future years lead to variations of ±2 µg/m3 for the annual average and ±5 µg/m3 for the 98th percentile daily level. Future climate changes lead to a probable year-to-year variation that will let PM2.5 levels in some years exceed the standard.

Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal.

Low-cost air quality (LCAQ) sensors are increasingly being used for community air quality monitoring. However, data collected by low-cost sensors contain significant noise, and proper calibration of these sensors remains a widely discussed, but not yet fully addressed, area of concern. In this study, several LCAQ sensors measuring nitrogen dioxide (NO2) and ozone (O3) were deployed in six cities in the United States (Atlanta, GA; New York City, NY; Sacramento, CA; Riverside, CA; Portland, OR; Phoenix, AZ) to evaluate the impacts of different climatic and geographical conditions on their performance and calibration. Three calibration methods were applied, including regression via linear and polynomial models and random forest methods. When signals from carbon monoxide (CO) sensors were included in the calibration models for NO2 and O3 sensors, model performance generally increased, with pronounced improvements in selected cities such as Riverside and New York City. Such improvements may be due to (1) temporal co-variation between concentrations of CO and NO2 and/or between CO and O3; (2) different performance levels of low-cost CO, NO2, and O3 sensors; and (3) different impacts of environmental conditions on sensor performance. The results showed an innovative approach for improving the calibration of NO2 and O3 sensors by including CO sensor signals into the calibration models. Community users of LCAQ sensors may be able to apply these findings further to enhance the data quality of their deployed NO2 and O3 monitors.

Location-specific strategies for eliminating US national racial-ethnic [Formula: see text] exposure inequality.

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 ([Formula: see text]), 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.

Application of an Improved Gas-constrained Source Apportionment Method Using Data Fused Fields: a Case Study in North Carolina, USA.

A number of studies have found differing associations of disease outcomes with PM2.5 components (or species) and sources (e.g., biomass burning, diesel vehicles and gasoline vehicles). Here, a unique method of fusing daily chemical transport model (Community Multiscale Air Quality Modeling) results with observations has been utilized to generate spatiotemporal fields of the concentrations of major gaseous pollutants (CO, NO2, NOx, O3, and SO2), total PM2.5 mass, and speciated PM2.5 (including crustal elements) over North Carolina for 2002-2010. The fused results are then used in chemical mass balance source apportionment model, CMBGC-Iteration, which uses both gas constraint and particulate matter concentrations to quantify source impacts. The method, as applied to North Carolina, quantifies the impacts of ten source categories and provides estimates of source contributions to PM2.5 concentrations. The ten source categories include both primary sources (diesel vehicles, gasoline vehicles, dust, biomass burning, coal-fired power plants and sea salt) and secondary components (ammonium sulfate, ammonium bisulfate, ammonium nitrate and secondary organic carbon). The results show a steady decrease in anthropogenic source impacts, especially from diesel vehicles and coal-fired power plants. Secondary pollutant components accounted for approximately 70% of PM2.5 mass. This study demonstrates an ability to provide spatiotemporal fields of both PM components and source impacts using a chemical transport model fused with observation data, linked to a receptor-based source apportionment method, to develop spatiotemporal fields of multiple pollutants.

Separating emissions and meteorological impacts on peak ozone concentrations in Southern California using generalized additive modeling.

Ozone levels have been declining in the Los Angeles, CA, USA area for the last four decades, but there was a recent uptick in the 4th highest daily maximum 8-h (MDA8) ozone concentrations from 2014 to 2018 despite continued reductions in the estimated precursor emissions. In this study, we assess the emissions and meteorological impacts on the 4th highest MDA8 ozone concentrations to better understand the factors affecting the observed MDA8 ozone using a two-step generalized additive model (GAM)/least squares approach applied to the South Coast Air Basin (SoCAB) for the 1990 to 2019 period. The GAM model includes emissions, meteorological factors, large-scale climate variables, date, and the interactions between meteorology and emissions. A least squares method was applied to the GAM output to better capture the 4th highest MDA8 ozone. The resulting two-step model had an R2 of 0.98 and a slope of 1 between the observed and predicted 4th highest MDA8 ozone. Emissions and the interactions between the maximum temperature and emissions explain most of the variation in the peak MDA8 ozone concentrations. Declining emissions have lowered the 4th highest MDA8 ozone concentration. Meteorology explains the higher than expected 4th-high, ozone levels observed in 2014-2018, indicating that meteorology was a stronger forcer than the continued reductions in emissions during that time period. The model was applied to estimate future ozone levels. Meteorology developed from climate modeling of the representative concentration pathway (RCP) scenarios, and two sets of emissions were used in the application. The modeling results indicated climate trends will push ozone levels slightly higher if no further emissions reductions are realized and that of two emissions trajectories modeled, the more stringent is required to reliably meet the federal ozone standard given annual meteorological variability.

Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks between atmospheric chemistry and climate change.

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.

Confronting Uncertainties of Simulated Air Pollution Concentrations during Persistent Cold Air Pool Events in the Salt Lake Valley, Utah.

Air pollutant accumulations during wintertime persistent cold air pool (PCAP) events in mountain valleys are of great concern for public health worldwide. Uncertainties associated with the simulated meteorology under stable conditions over complex terrain hinder realistic simulations of air quality using chemical transport models. We use the Community Multiscale Air Quality (CMAQ) model to simulate the gaseous and particulate species for 1 month in January 2011 during the Persistent Cold Air Pool Study (PCAPS) in the Salt Lake Valley (SLV), Utah (USA). Results indicate that the temporal variability associated with the elevated NOx and PM2.5 concentrations during PCAP events was captured by the model (r = 0.20 for NOx and r = 0.49 for PM2.5). However, concentrations were not at the correct magnitude (NMB = -35/12% for PM2.5 during PCAPs/non-PCAPs), where PM2.5 was underestimated during PCAP events and overestimated during non-PCAP periods. The underestimated PCAP strength is represented by valley heat deficit, which contributed to the underestimated PM2.5 concentrations compared with observations due to the model simulating more vertical mixing and less stable stratification than what was observed. Based on the observations, the dominant PM2.5 species were ammonium and nitrate. We provide a discussion that aims to investigate the emissions and chemistry model uncertainties using the nitrogen ratio method and the thermodynamic ammonium nitrate regime method.

Quantifying the impact of daily mobility on errors in air pollution exposure estimation using mobile phone location data.

One major source of uncertainty in accurately estimating human exposure to air pollution is that human subjects move spatiotemporally, and such mobility is usually not considered in exposure estimation. How such mobility impacts exposure estimates at the population and individual level, particularly for subjects with different levels of mobility, remains under-investigated. In addition, a wide range of methods have been used in the past to develop air pollutant concentration fields for related health studies. How the choices of methods impact results of exposure estimation, especially when detailed mobility information is considered, is still largely unknown. In this study, by using a publicly available large cell phone location dataset containing over 35 million location records collected from 310,989 subjects, we investigated the impact of individual subjects' mobility on their estimated exposures for five chosen ambient pollutants (CO, NO2, SO2, O3 and PM2.5). We also estimated exposures separately for 10 groups of subjects with different levels of mobility to explore how increased mobility impacted their exposure estimates. Further, we applied and compared two methods to develop concentration fields for exposure estimation, including one based on Community Multiscale Air Quality (CMAQ) model outputs, and the other based on the interpolated observed pollutant concentrations using the inverse distance weighting (IDW) method. Our results suggest that detailed mobility information does not have a significant influence on mean population exposure estimate in our sample population, although impacts can be substantial at the individual level. Additionally, exposure classification error due to the use of home-location data increased for subjects that exhibited higher levels of mobility. Omitting mobility could result in underestimation of exposures to traffic-related pollutants particularly during afternoon rush-hour, and overestimate exposures to ozone especially during mid-afternoon. Between CMAQ and IDW, we found that the IDW method generates smooth concentration fields that were not suitable for exposure estimation with detailed mobility data. Therefore, the method for developing air pollution concentration fields when detailed mobility data were to be applied should be chosen carefully. Our findings have important implications for future air pollution health studies.

Characterizing population exposure to coal emissions sources in the United States using the HyADS model.

In anticipation of the expanding appreciation for air quality models in health outcomes studies, we develop and evaluate a reduced-complexity model for pollution transport that intentionally sacrifices some of the sophistication of full-scale chemical transport models in order to support applicability to a wider range of health studies. Specifically, we introduce the HYSPLIT average dispersion model, HyADS, which combines the HYSPLIT trajectory dispersion model with modern advances in parallel computing to estimate ZIP code level exposure to emissions from individual coal-powered electricity generating units in the United States. Importantly, the method is not designed to reproduce ambient concentrations of any particular air pollutant; rather, the primary goal is to characterize each ZIP code's exposure to these coal power plants specifically. We show adequate performance towards this goal against observed annual average air pollutant concentrations (nationwide Pearson correlations of 0.88 and 0.73 with SO 4 2 - and PM2.5, respectively) and coal-combustion impacts simulated with a full-scale chemical transport model and adjusted to observations using a hybrid direct sensitivities approach (correlation of 0.90). We proceed to provide multiple examples of HyADS's single-source applicability, including to show that 22% of the population-weighted coal exposure comes from 30 coal-powered electricity generating units.

Source apportionment of primary and secondary PM2.5: Associations with pediatric respiratory disease emergency department visits in the U.S. State of Georgia.

We developed a hybrid chemical transport model and receptor model (CTM-RM) to conduct source apportionment of both primary and secondary PM2.5 (particulate matter ≤2.5 µm in diameter) at 36 km resolution throughout the U.S. State of Georgia for the years 2005 and 2007. This novel source apportionment model enabled us to estimate and compare associations of short-term changes in 12 PM2.5 source concentrations (agriculture, biogenic, coal, dust, fuel oil, metals, natural gas, non-road mobile diesel, non-road mobile gasoline, on-road mobile diesel, on-road mobile gasoline, and all other sources) with emergency department (ED) visits for pediatric respiratory diseases. ED visits for asthma (N = 49,651), pneumonia (N = 25,558), and acute upper respiratory infections (acute URI, N = 235,343) among patients aged ≤18 years were obtained from patient claims records. Using a case-crossover study, we estimated odds ratios per interquartile range (IQR) increase for 3-day moving average PM2.5 source concentrations using conditional logistic regression, matching on day-of-week, month, and year, and adjusting for average temperature, humidity, and holidays. We fit both single-source and multi-source models. We observed positive associations between several PM2.5 sources and ED visits for asthma, pneumonia, and acute URI. For example, for asthma, per IQR increase in the source contribution in the single-source model, odds ratios were 1.022 (95% CI: 1.013, 1.031) for dust; 1.050 (95% CI: 1.036, 1.063) for metals, and 1.091 (95% CI: 1.064, 1.119) for natural gas. These sources comprised 5.7%, 2.2%, and 6.3% of total PM2.5 mass, respectively. PM2.5 from metals and natural gas were positively associated with all three respiratory outcomes. In addition, non-road mobile diesel was positively associated with pneumonia and acute URI.