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.

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 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.

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.

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.

On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies.

Appropriately characterizing spatiotemporal individual mobility is important in many research areas, including epidemiological studies focusing on air pollution. However, in many retrospective air pollution health studies, exposure to air pollution is typically estimated at the subjects' residential addresses. Individual mobility is often neglected due to lack of data, and exposure misclassification errors are expected. In this study, we demonstrate the potential of using location history data collected from smartphones by the Google Maps application for characterizing historical individual mobility and exposure. Here, one subject carried a smartphone installed with Google Maps, and a reference GPS data logger which was configured to record location every 10 s, for a period of one week. The retrieved Google Maps Location History (GMLH) data were then compared with the GPS data to evaluate their effectiveness and accuracy of the GMLH data to capture individual mobility. We also conducted an online survey (n = 284) to assess the availability of GMLH data among smartphone users in the US. We found the GMLH data reasonably captured the spatial movement of the subject during the one-week time period at up to 200 m resolution. We were able to accurately estimate the time the subject spent in different microenvironments, as well as the time the subject spent driving during the week. The estimated time-weighted daily exposures to ambient particulate matter using GMLH and the GPS data logger were also similar (error less than 1.2%). Survey results showed that GMLH data may be available for 61% of the survey sample. Considering the popularity of smartphones and the Google Maps application, detailed historical location data are expected to be available for large portion of the population, and results from this study highlight the potential of these location history data to improve exposure estimation for retrospective epidemiological studies.

Aerosols in an arid environment: The role of aerosol water content, particulate acidity, precursors, and relative humidity on secondary inorganic aerosols.

Meteorological conditions, gas-phase precursors, and aerosol acidity (pH) can influence the formation of secondary inorganic aerosols (SIA) in fine particulate matter (PM2.5). Most works related to the influence of pH and gas-phase precursors on SIA have been laboratory research, but field observation research is very scarce, especially in arid environments. The relationship among SIA, pH, gas-phase precursors, and meteorological conditions are investigated in Hohhot, a major city in China with an arid environment. Secondary inorganic species, e.g., SO42-, NO3-, were typically found at low levels, reflecting the low level of secondary aerosol. It is interesting to note that the level of SO2 in Hohhot was higher than in other cities while SO42- was relatively lower than in other cities. Multiple receptor models were used to explore the contributions to the SIA and quantify the source impacts on the SIA. Annual average aerosol pH in Hohhot was 5.6 (range 1.1-8.4) which was estimated by a thermodynamic equilibrium model. Additionally, a statistical method was used to evaluate the influence of SIA sources on ambient aerosol concentrations. Aerosol water content and particulate acidity were found to be positively associated with secondary SO42-, while NO2 and RH had a significant impact on secondary NO3- in an arid atmosphere. The findings explain the relationship between gaseous precursors, relative humidity, aerosol pH and temperature in the arid city of Hohhot.

Source contributions to water-soluble organic carbon and water-insoluble organic carbon in PM2.5 during Spring Festival, heating and non-heating seasons.

To investigate the influences of anthropogenic activities on carbon aerosols, especially on water-soluble organic carbon (WSOC), PM2.5 samples were collected at an urban site in a northern city of China during Spring Festival (SF), heating season (HS), and non-heating season (NHS). Carbonaceous species and ions (Ca2+, SO42-, NO3-, etc.) were analyzed. Mass concentrations of WSOC and WSIC exhibited higher levels in SF and HS, and high WSOC/OC ratios (67.4%) on average were found. Stronger correlations between WSOC and K+, Cl- occurred in SF, which might due to contributions of firework emissions. Six major sources of PM2.5 were quantified by PMF model, which contributed in aerosol mass differently in different periods: biomass & firework burning exhibited higher contribution (11.2%) in SF; crustal dust accounted for 19.4% during NHS; secondary particles contributed most (41.0%) in HS; during SF and HS, coal combustion devoted more to aerosol mass. Contributions to WSOC were in the order of vehicular exhaust (41.0% of WSOC) > coal combustion (29.3%) > secondary formation (17.0%) > biomass & firework burning (12.7%). The 82.0% of WIOC were from coal combustion and the rest were devoted by vehicular exhaust. Obvious peaks of firework burning contributions to WSOC were observed on SF's Eve and Lantern Festival. Coal combustion contributed to organic carbons highly in SF and HS. Results implied that anthropogenic activities contributions, like firework burning and coal combustion, significantly influenced the levels of PM2.5 and WSOC.

Source apportionment for fine particulate matter in a Chinese city using an improved gas-constrained method and comparison with multiple receptor models.

PM2.5 is one of the most studied atmospheric pollutants due to its adverse impacts on human health and welfare and the environment. An improved model (the chemical mass balance gas constraint-Iteration: CMBGC-Iteration) is proposed and applied to identify source categories and estimate source contributions of PM2.5. The CMBGC-Iteration model uses the ratio of gases to PM as constraints and considers the uncertainties of source profiles and receptor datasets, which is crucial information for source apportionment. To apply this model, samples of PM2.5 were collected at Tianjin, a megacity in northern China. The ambient PM2.5 dataset, source information, and gas-to-particle ratios (such as SO2/PM2.5, CO/PM2.5, and NOx/PM2.5 ratios) were introduced into the CMBGC-Iteration to identify the potential sources and their contributions. Six source categories were identified by this model and the order based on their contributions to PM2.5 was as follows: secondary sources (30%), crustal dust (25%), vehicle exhaust (16%), coal combustion (13%), SOC (7.6%), and cement dust (0.40%). In addition, the same dataset was also calculated by other receptor models (CMB, CMB-Iteration, CMB-GC, PMF, WALSPMF, and NCAPCA), and the results obtained were compared. Ensemble-average source impacts were calculated based on the seven source apportionment results: contributions of secondary sources (28%), crustal dust (20%), coal combustion (18%), vehicle exhaust (17%), SOC (11%), and cement dust (1.3%). The similar results of CMBGC-Iteration and ensemble method indicated that CMBGC-Iteration can produce relatively appropriate results.