ACS ES&T Air最新文献

{"title":"Neighborhood-Level Nitrogen Dioxide Inequalities Contribute to Surface Ozone Variability in Houston, Texas","authors":"Isabella M. Dressel,&nbsp;Sixuan Zhang,&nbsp;Mary Angelique G. Demetillo,&nbsp;Shan Yu,&nbsp;Kimberly Fields,&nbsp;Laura M. Judd,&nbsp;Caroline R. Nowlan,&nbsp;Kang Sun,&nbsp;Alexander Kotsakis,&nbsp;Alexander J. Turner and Sally E. Pusede*,&nbsp;","doi":"10.1021/acsestair.4c0000910.1021/acsestair.4c00009","DOIUrl":"https://doi.org/10.1021/acsestair.4c00009https://doi.org/10.1021/acsestair.4c00009","url":null,"abstract":"<p >In Houston, Texas, nitrogen dioxide (NO₂) air pollution disproportionately affects Black, Latinx, and Asian communities, and high ozone (O₃) days are frequent. There is limited knowledge of how NO₂ inequalities vary in urban air quality contexts, in part from the lack of time-varying neighborhood-level NO₂ measurements. First, we demonstrate that daily TROPOspheric Monitoring Instrument (TROPOMI) NO₂ tropospheric vertical column densities (TVCDs) resolve a major portion of census tract-scale NO₂ inequalities in Houston, comparing NO₂ inequalities based on TROPOMI TVCDs and spatiotemporally coincident airborne remote sensing (250 m × 560 m) from the NASA TRacking Aerosol Convection ExpeRiment–Air Quality (TRACER-AQ). We further evaluate the application of daily TROPOMI TVCDs to census tract-scale NO₂ inequalities (May 2018–November 2022). This includes explaining differences between mean daily NO₂ inequalities and those based on TVCDs oversampled to 0.01° × 0.01° and showing daily NO₂ column-surface relationships weaken as a function of observation separation distance. Second, census tract-scale NO₂ inequalities, city-wide high O₃, and mesoscale airflows are found to covary using principal component and cluster analysis. A generalized additive model of O₃ mixing ratios versus NO₂ inequalities reproduces established nonlinear relationships between O₃ production and NO₂ concentrations, providing observational evidence that neighborhood-level NO₂ inequalities and O₃ are coupled. Consequently, emissions controls specifically in Black, Latinx, and Asian communities will have co-benefits, reducing both NO₂ disparities and high O₃ days city wide.</p><p >Most neighborhood-level NO₂ inequalities can be observed with daily TROPOspheric Monitoring Instrument (TROPOMI) observations; the unequal NO₂ distribution affects O₃ chemistry in Houston, Texas.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 9","pages":"973–988 973–988"},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsestair.4c00009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Model Spatial Resolution on Global Geophysical Satellite-Derived Fine Particulate Matter.","authors":"Dandan Zhang, Randall V Martin, Aaron van Donkelaar, Chi Li, Haihui Zhu, Alexei Lyapustin","doi":"10.1021/acsestair.4c00084","DOIUrl":"https://doi.org/10.1021/acsestair.4c00084","url":null,"abstract":"<p><p>Global geophysical satellite-derived ambient fine particulate matter (PM_2.5) inference relies upon a geophysical relationship (η) from a chemical transport model to relate satellite retrievals of aerosol optical depth (AOD) to surface PM_2.5. The resolution dependence of simulated η warrants further investigation. In this study, we calculate geophysical PM_2.5 with simulated η from the GEOS-Chem model in its high-performance configuration (GCHP) at cubed-sphere resolutions of C360 (∼25 km) and C48 (∼200 km) and satellite AOD at 0.01° (∼1 km). Annual geophysical PM_2.5 concentrations inferred from satellite AOD and GCHP simulations at ∼25 km and ∼200 km resolutions exhibit remarkable similarity (<i>R</i> ² = 0.96, slope = 1.03). This similarity in part reflects opposite resolution responses across components with population-weighted normalized mean difference (PW-NMD) increasing by 5% to 11% for primary species while decreasing by -30% to -5% for secondary species at finer resolution. Despite global similarity, our results also identify larger resolution sensitivities of η over isolated pollution sources and mountainous regions, where spatial contrast of aerosol concentration and composition is better represented at fine resolution. Our results highlight the resolution dependence of representing near-surface concentrations and the vertical distribution of chemically different species with implications for inferring ground-level PM_2.5 from columnar AOD.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 9","pages":"1112-1123"},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11407304/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142305500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Model Spatial Resolution on Global Geophysical Satellite-Derived Fine Particulate Matter","authors":"Dandan Zhang*,&nbsp;Randall V. Martin,&nbsp;Aaron van Donkelaar,&nbsp;Chi Li,&nbsp;Haihui Zhu and Alexei Lyapustin,&nbsp;","doi":"10.1021/acsestair.4c0008410.1021/acsestair.4c00084","DOIUrl":"https://doi.org/10.1021/acsestair.4c00084https://doi.org/10.1021/acsestair.4c00084","url":null,"abstract":"<p >Global geophysical satellite-derived ambient fine particulate matter (PM_2.5) inference relies upon a geophysical relationship (η) from a chemical transport model to relate satellite retrievals of aerosol optical depth (AOD) to surface PM_2.5. The resolution dependence of simulated η warrants further investigation. In this study, we calculate geophysical PM_2.5 with simulated η from the GEOS-Chem model in its high-performance configuration (GCHP) at cubed-sphere resolutions of C360 (∼25 km) and C48 (∼200 km) and satellite AOD at 0.01° (∼1 km). Annual geophysical PM_2.5 concentrations inferred from satellite AOD and GCHP simulations at ∼25 km and ∼200 km resolutions exhibit remarkable similarity (<i>R</i>² = 0.96, slope = 1.03). This similarity in part reflects opposite resolution responses across components with population-weighted normalized mean difference (PW-NMD) increasing by 5% to 11% for primary species while decreasing by −30% to −5% for secondary species at finer resolution. Despite global similarity, our results also identify larger resolution sensitivities of η over isolated pollution sources and mountainous regions, where spatial contrast of aerosol concentration and composition is better represented at fine resolution. Our results highlight the resolution dependence of representing near-surface concentrations and the vertical distribution of chemically different species with implications for inferring ground-level PM_2.5 from columnar AOD.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 9","pages":"1112–1123 1112–1123"},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chemical Insights into the Molecular Composition of Organic Aerosols in the Urban Region of Houston, Texas","authors":"Tania Gautam,&nbsp;Gregory W. Vandergrift,&nbsp;Nurun Nahar Lata,&nbsp;Zezhen Cheng,&nbsp;Ashfiqur Rahman,&nbsp;Annalisa Minke,&nbsp;Zhenli Lai,&nbsp;Darielle N. Dexheimer,&nbsp;Damao Zhang,&nbsp;Matthew A. Marcus,&nbsp;Maria A. Zawadowicz,&nbsp;Chongai Kuang,&nbsp;Ran Zhao,&nbsp;Allison L. Steiner and Swarup China*,&nbsp;","doi":"10.1021/acsestair.4c0014110.1021/acsestair.4c00141","DOIUrl":"https://doi.org/10.1021/acsestair.4c00141https://doi.org/10.1021/acsestair.4c00141","url":null,"abstract":"<p >Molecular functional groups, such as organosulfates (CHOS) and organonitrates (CHNO) are important tracers for field observations of secondary organic aerosols (SOA). While CHOS and CHNO are prevalent in the atmosphere, there is a lack of knowledge regarding daily and day- and night-time variations in these species in the urban atmosphere. Meteorological factors such as wind speed/direction, relative humidity (RH), and temperature can influence the formation of CHOS/CHNO. To investigate these trends, we utilized multimodal chemical imaging and advanced high resolution mass spectrometry techniques to acquire particle speciation and molecular formulas (MFs) associated with day and night sampling periods. Back trajectory analyses revealed the oceanic influence of southern wind airmasses in later June sampling periods with organic fractions &lt;10%. Conversely, northern winds in early June sampling periods contributed to the episodic emergence of extremely low volatile organics (ELVOCs) and organic factions up to 41%. The observed unique MFs to June 3 (223 MFs) and to June 4 (144 MFs) were largely found to be of biogenic rather than anthropogenic origin. Our findings reveal episodic prevalence and temporal distribution of SOA constituents across the urban region of Houston, Texas.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 10","pages":"1304–1316 1304–1316"},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determination of Legacy and Emerging Per- and Polyfluoroalkyl Substances (PFAS) in Indoor and Outdoor Air","authors":"Wen-Long Li,&nbsp; and ,&nbsp;Kurunthachalam Kannan*,&nbsp;","doi":"10.1021/acsestair.4c0009510.1021/acsestair.4c00095","DOIUrl":"https://doi.org/10.1021/acsestair.4c00095https://doi.org/10.1021/acsestair.4c00095","url":null,"abstract":"<p >Despite increasing awareness of health risks associated with exposure to per- and polyfluoroalkyl substances (PFAS), studies on analysis of these chemicals in air are limited. In this study, an extensive sampling of indoor and outdoor air (at a residential neighborhood) was performed to determine the occurrence, temporal variation, and gas/particle partitioning of PFAS. Among 58 PFAS analyzed in air (sum of gas and particulate phases), fluorotelomer alcohols (FTOHs) were found at the highest concentrations (1900 ± 2000 pg/m³). The concentrations of FTOHs and perfluorooctane sulfonamides (FOSA/E) were 4.9−5.9 times higher in indoor air than those in residential outdoor air (<i>p</i> &lt; 0.05). Emerging PFAS such as hexafluoropropylene oxide dimer acid (HFPO-DA), chlorinated polyfluoroether sulfonate (Cl-PFESA), and ADONA were detected at average concentrations ranging from 0.10 to 4.4 pg/m³. We found significant temporal variations in PFAS concentrations, with concentrations higher in warmer than colder months. The majority of ionic PFAS (&gt;50%) such as PFOS were detected in the particulate phase, whereas FTOHs partition predominantly to the vapor phase. This study establishes baseline indoor air concentrations of emerging PFAS and contributes to the understanding of gas−particle partitioning of PFAS.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 9","pages":"1147–1155 1147–1155"},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secondary Organic Aerosol Formation from Aqueous Ethylamine Oxidation Mediated by Particulate Nitrate Photolysis","authors":"Xiaomeng Tian,&nbsp;Valeria YeeWan Chan and Chak K. Chan*,&nbsp;","doi":"10.1021/acsestair.3c0009510.1021/acsestair.3c00095","DOIUrl":"https://doi.org/10.1021/acsestair.3c00095https://doi.org/10.1021/acsestair.3c00095","url":null,"abstract":"<p >Atmospheric ethylamine (EA) is emitted by various primary sources and can be found abundantly in the gas and particle phases. Nitrate (NO₃^–) is one of the most abundant inorganic compounds and has been found to coexist with amines in ambient particles. The photolysis of NO₃^– can produce oxidants such as the OH radical, NO₂, O(³P), and N(III), which can lead to the decay of particulate EA. Moreover, the degradation of EA forms carbonyl species, which are precursors to brown carbon (BrC) formation. In this study, we investigated the aging of EA-containing particles mediated by NO₃^– photolysis under different relative humidity (RH) and initial pH conditions under 300 nm UV irradiation. The more acidic (pH 0.0, 0.2, and 0.6) particles (EA:H⁺ molar ratio = 4:4.25, 4:4.5, 4:5 at 70% RH) exhibited an increase in pH, while the less acidic (pH 5.0, 4.8, 4.7, and 5.1) particles (EA:H⁺ = 4:4 at 40%, 55%, 70%, and 85% RH) showed a decrease in pH as a result of photooxidation. We attributed these contrary pH changes to the combination of the HONO evaporation, which increases the pH, and the EA reactions, which decrease the pH. The decay rates of NO₃^– and EA appear not to be sensitive to RH and pH within experimental uncertainties. We proposed EA reaction pathways in the presence of oxidants produced from NO₃^– photolysis based on product speciation. We also observed the formation of water-soluble organics (BrC and an organic phase) as a potential secondary organic aerosol (SOA). This study sheds light on the particulate sink of EA and its potential in BrC and SOA formation mediated by NO₃^– photolysis in the atmosphere, providing new insights into the aging of amines in atmospheric aerosols.</p><p >This study shows particulate ethylamine decay during nitrate photolysis could form water-soluble secondary organics (BrC and an organic phase), providing insight into the atmospheric amine sink and aging.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 9","pages":"951–959 951–959"},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsestair.3c00095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Model to Predict Saturation Vapor Pressures of Atmospheric Aerosol Constituents","authors":"Noora Hyttinen*,&nbsp;Linjie Li,&nbsp;Mattias Hallquist and Cheng Wu,&nbsp;","doi":"10.1021/acsestair.4c0011310.1021/acsestair.4c00113","DOIUrl":"https://doi.org/10.1021/acsestair.4c00113https://doi.org/10.1021/acsestair.4c00113","url":null,"abstract":"<p >We present a novel machine learning (ML) model for predicting saturation vapor pressures (<i>p</i>_sat), a physical property of use to describe transport, distribution, mass transfer, and fate of environmental toxins and contaminants. The ML model uses σ-profiles from the conductor-like screening model (COSMO) as molecular descriptors. The main advantages in using σ-profiles instead of other types of molecular representations are the relatively small size of the descriptor and the fact that the addition of new elements does not affect the size of the descriptor. The ML model was trained separately for liquid and solid compounds using experimental vapor pressures at various temperatures. The 95% confidence intervals of the error in the liquid- and solid-phase log₁₀(<i>p</i>_sat/Pa) are 1.02 and 1.4, respectively. Especially our solid-phase model outperforms all group-contribution models in predicting experimental sublimation pressures of solid compounds. To demonstrate its applicability, the model was used to predict <i>p</i>_sat of atmospherically relevant species, and the values were compared with those obtained from a new experimental method. Here, our model provided a tool for a better description of this critical property and gave a higher confidence in the measurements.</p><p >Accurate saturation vapor pressure estimates of environmental contaminants are lacking in the low volatility range. Our quantum chemistry-based machine learning model provides a novel tool for predicting vapor pressure.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 9","pages":"1156–1163 1156–1163"},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsestair.4c00113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach","authors":"Augustine Wigle*,&nbsp;Audrey Béliveau,&nbsp;Daniel Blackmore,&nbsp;Paule Lapeyre,&nbsp;Kirk Osadetz,&nbsp;Christiane Lemieux and Kyle J. Daun,&nbsp;","doi":"10.1021/acsestair.4c0003010.1021/acsestair.4c00030","DOIUrl":"https://doi.org/10.1021/acsestair.4c00030https://doi.org/10.1021/acsestair.4c00030","url":null,"abstract":"<p >An accurate understanding of uncertainty is needed to properly interpret methane emission estimates from upstream oil and gas sources in a variety of contexts, from component-level measurements to yearly jurisdiction-wide inventories. To characterize measurement uncertainty, we examine controlled release (CR) data from five different technology providers including quantitative gas imaging (QOGI), tunable diode laser-absorption spectroscopy (TDLAS); and airborne near-infrared hyperspectral (NIR HS) imaging. We introduce a novel empirical method to develop probability distributions of measurements given a true emission rate using the CR data. The approach includes flexible likelihoods which capture complex relationships in the data. An algorithm which provides the distribution of the true emission rate given a measurement is also developed, which synthesizes the measurement with the CR data and external information about the possible true emission rate. The results show that flexible models that accommodate complex nonlinear behavior are needed to adequately model measurement error. We also show that measurement error can vary under different conditions. We demonstrate that measurement uncertainty can be reduced by performing repeated measurements. A limitation of the study is that the collected CR data is collected under controlled conditions that may differ from those in industrial settings. As new CR data become available, the models presented in this paper can be refit to consider more diverse scenarios. The methodology can be extended to explicitly model different conditions to improve performance.</p><p >The uncertainty in measurements from methane emissions quantification technologies has important implications for emissions monitoring and reduction efforts. We show how a novel flexible model can be used to quantify measurement uncertainty.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 9","pages":"1000–1014 1000–1014"},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsestair.4c00030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atmospheric Aerosol Sulfur Distribution and Speciation in Mexico City: Sulfate, Organosulfates, and Isoprene-Derived Secondary Organic Aerosol from Low NO Pathways","authors":"Madeline E. Cooke,&nbsp;Cara M. Waters,&nbsp;Joel Y. Asare,&nbsp;Jessica A. Mirrielees,&nbsp;Andrew L. Holen,&nbsp;Molly P. Frauenheim,&nbsp;Zhenfa Zhang,&nbsp;Avram Gold,&nbsp;Kerri A. Pratt,&nbsp;Jason D. Surratt,&nbsp;Luis A. Ladino and Andrew P. Ault*,&nbsp;","doi":"10.1021/acsestair.4c0004810.1021/acsestair.4c00048","DOIUrl":"https://doi.org/10.1021/acsestair.4c00048https://doi.org/10.1021/acsestair.4c00048","url":null,"abstract":"<p >Poor air quality is a persistent challenge in Mexico City, and addressing this issue requires an understanding of the chemical composition of PM_2.5 (particulate matter less than 2.5 μm in diameter). Sulfate and secondary organic aerosol (SOA) are two of the largest contributors to PM_2.5 in Mexico City, but uncertainties exist regarding their sources, distribution across individual particles, and ability to form organosulfates. Herein, we show using electron dispersive x-ray spectroscopy that only 41 ± 1% and 25 ± 1% of particles (aerodynamic diameter, 0.32–0.56 μm) by number at two sites in Mexico City, respectively, contain sulfur. Vibrational spectroscopy (Optical-Photothermal Infrared + Raman Microspectroscopy) shows that these sulfur-containing particles consist of inorganic sulfate (SO₄^2–) and organosulfates (ROSO₃^–). In addition, we unexpectedly measured abundant isoprene-derived SOA from low nitric oxide reaction pathways, specifically organosulfates (methyltetrol sulfates = avg. 50 ng/m³, max. 150 ng/m³) and polyols (methyltetrols = avg. 70 ng/m³, max. 190 ng/m³) using liquid chromatography with high-resolution mass spectrometry. Differences in SO₂ and NO_<i>x</i> concentrations between sites likely contribute to these spatial differences in sulfate, organosulfate, and SOA formation. These findings improve understanding of sulfur distribution and sources of SOA in Mexico City, which can inform efforts to improve air quality.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 9","pages":"1037–1052 1037–1052"},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Indoor Surface pH in Controlling the Fate of Acids and Bases in an Unoccupied Residence","authors":"Jenna C. Ditto*,&nbsp;Marc Webb,&nbsp;Han N. Huynh,&nbsp;Jie Yu,&nbsp;Glenn C. Morrison,&nbsp;Barbara J. Turpin,&nbsp;Michael R. Alves,&nbsp;Kathryn Mayer,&nbsp;Michael F. Link,&nbsp;Allen H. Goldstein,&nbsp;Dustin Poppendieck,&nbsp;Marina E. Vance,&nbsp;Delphine K. Farmer,&nbsp;Arthur W. H. Chan and Jonathan P. D. Abbatt,&nbsp;","doi":"10.1021/acsestair.4c0004410.1021/acsestair.4c00044","DOIUrl":"https://doi.org/10.1021/acsestair.4c00044https://doi.org/10.1021/acsestair.4c00044","url":null,"abstract":"<p >The chemical composition of indoor air is strongly driven by the composition and properties of indoor surfaces. At the Chemical Assessments of Surfaces and Air (CASA) campaign, we performed controlled additions of ammonia (reaching up to 297 ppb to 662 ppb) to investigate the impacts of changing surface basicity on the fate of gaseous and particulate acids and bases in an unoccupied house. In response to ammonia injections, nitrogen-containing compounds (C_2–7H_3–11N₁O_0–3) were emitted from surfaces to the gas phase with signals increasing 10¹% to 10⁴% compared to their signals prior to ammonia addition. At the same time, oxygen-containing compounds (C_1–7H_2–6O_2–3) were removed from the gas phase by indoor surface partitioning. Indoor surface pH and aerosol pH likely increased during these controlled ammonia injections relative to their baseline conditions. We estimate indoor surface pH to be nearly 5 and indoor aerosol pH to range from 2 to 4 during this experiment. At each ammonia injection, we observed ammonium and nitrate concentrations in the aerosol phase to increase due to gas-particle partitioning of ammonia and nitric acid. This gas-particle-surface exchange showed strong dependence on relative humidity; evaporation of gaseous bases was more pronounced at lower relative humidity when surface-associated water volume was reduced, while gas-to-particle partitioning of inorganic species was greater in the presence of more aerosol liquid water at higher relative humidity. From cooking experiments, which represent realistic sources of acids and bases to the indoor environment but which emit 10 times less ammonia than was introduced to the house via pure ammonia injection experiments, we predict that surfaces may still be important sources of these basic gases to indoor air.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 9","pages":"1015–1027 1015–1027"},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}