ACS ES&T Air最新文献

{"title":"Significant Changes of δ15N-NOx in Diesel Vehicle Emissions with Emission Standards and Cumulative Mileage: Insight into Isotope-Based NOx Source Analysis","authors":"Weijie Huang,&nbsp;Xingnan Ye*,&nbsp;Xiaofei Wang,&nbsp;Cheng Huang,&nbsp;Bingyue Fu,&nbsp;Yinghui Yao,&nbsp;Yuanqiao Zhou and Jianmin Chen,&nbsp;","doi":"10.1021/acsestair.4c0028910.1021/acsestair.4c00289","DOIUrl":"https://doi.org/10.1021/acsestair.4c00289https://doi.org/10.1021/acsestair.4c00289","url":null,"abstract":"<p >Nitrogen isotope (δ¹⁵N) analysis is a robust method for tracing atmospheric NO_<i>x</i> and nitrate. However, the isotopic signatures of NO_<i>x</i> emissions from heavy-duty diesel vehicles (HDDVs) remain poorly characterized. In this study, we conducted on-site tests on 18 on-road HDDVs with different emission standards in Shanghai under the condition of China World Transient Vehicle Cycle for trucks (C-WTVC). The average δ¹⁵N value under C-WTVC (−5.71 ± 2.60‰) was significantly higher than that in idle mode (−13.85 ± 1.97‰), which is attributed to the lower temperatures in the combustion chamber due to the lower engine output in idle mode, favoring the breakage of the lighter ¹⁴N¹⁴N molecules. The δ¹⁵N-NO_<i>x</i> values under C-WTVC were significantly lower than those in driving mode in the United States, suggesting that δ¹⁵N values may be localized in isotope-based NO_<i>x</i> source analysis. NO_<i>x</i> emissions were reduced considerably with the upgrade of emission standards while δ¹⁵N-NO_<i>x</i> levels showed an opposite trend. The NH₃-selective catalytic reduction (SCR) reduced automobile NO_<i>x</i> emissions by more than 50% and significantly enriched ¹⁵N relative to diesel-generated NO_<i>x</i>. Indicated by the correlations between NO_<i>x</i> emissions, δ¹⁵N-NO_<i>x</i>, and cumulative mileage, the SCR de-NO_<i>x</i> efficiency decreased linearly with the increase of cumulative mileage. Our results suggest that the routine maintenance and mandatory inspection of aged SCR systems should be strengthened. The distribution of provincial δ¹⁵N-NO_<i>x</i> values of diesel vehicle emissions was established based on a weighted average model. The contribution of vehicle NO_<i>x</i> emissions in typical megacities in China has been revisited by upgrading the δ¹⁵N-NO_<i>x</i> values of diesel vehicles, demonstrating that the application of regional δ¹⁵N-NO_<i>x</i> values of diesel vehicles can narrow the gap between isotope-based analysis and emission inventory. This study will enrich the NO_<i>x</i> isotopic fingerprint database in China and minimize the uncertainty in isotope-based NO_<i>x</i> source apportionment, helping to formulate air pollution prevention and control strategies.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 3","pages":"396–405 396–405"},"PeriodicalIF":0.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609018","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":"Cross-Reactions of Glyoxal and Glycolaldehyde in Aqueous Aerosol Mimics: Implications for Brown Carbon Product Formation","authors":"Brian P. Henesey,&nbsp;Stephanie M. Ingwer,&nbsp;Hope S. Tracey,&nbsp;Elizabeth G. Obarow,&nbsp;Rachael E. Holappa,&nbsp;Adelaide M. King,&nbsp;Heidi P. Hendrickson,&nbsp;Daniel R. Griffith and Melissa M. Galloway*,&nbsp;","doi":"10.1021/acsestair.4c0019210.1021/acsestair.4c00192","DOIUrl":"https://doi.org/10.1021/acsestair.4c00192https://doi.org/10.1021/acsestair.4c00192","url":null,"abstract":"<p >The formation of brown carbon (BrC) in aqueous atmospheric aerosols is well-documented and often attributed to aldehyde-ammonia reactions. However, many studies have focused on individual aldehyde precursors, overlooking the complex composition of organic aerosols, which comprise a diverse mix of organic and inorganic compounds. To address this, a complex BrC system was investigated by generating aqueous atmospheric aerosol mimics containing glyoxal (Gly), glycolaldehyde (GAld), and ammonium sulfate. Structural analysis using supercritical fluid chromatography–mass spectrometry (SFC-MS) showed that adjusting the Gly:GAld mole ratio leads to variations in the composition and abundance of BrC products formed. Notably, aromatic heterocycles (e.g., imidazoles and pyrazines) as well as acyclic carbonyl oligomers were identified to form at different concentrations depending on the Gly:GAld mole ratio. UV–visible spectroscopy analysis demonstrated that light absorption in these mixed Gly + GAld + AS systems cannot be modeled as a simple weighted average of the Gly:GAld mole ratio; observed changes in light absorbance can be explained by compositional changes in solution. These observations indicate that cross-reactions are occurring between the Gly and GAld in solution, potentially leading to changes in the physical properties of the aerosol. Given the thousands of reactive compounds found in atmospheric aerosol, these findings could have important implications for our understanding of organic reactions within the aerosol.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 3","pages":"309–318 309–318"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609017","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":"Formation of Ozone and PM2.5 in Smoke from Prescribed Burning in the Southeastern United States","authors":"Rime El Asmar,&nbsp;Zongrun Li,&nbsp;Haofei Yu,&nbsp;Susan O’Neill,&nbsp;David J. Tanner,&nbsp;L. Gregory Huey,&nbsp;M. Talat Odman and Rodney J. Weber*,&nbsp;","doi":"10.1021/acsestair.4c0023110.1021/acsestair.4c00231","DOIUrl":"https://doi.org/10.1021/acsestair.4c00231https://doi.org/10.1021/acsestair.4c00231","url":null,"abstract":"<p >Ground-based measurements of smoke from prescribed fires in the southeastern US during the burning seasons of 2021 through 2024 are used to assess ozone (O₃) and PM_2.5 mass formation and their changes with age in 69 smoke events. O₃ production occurred in nearly all plumes (31 out of 32) measured between 12:00 and 18:00. The O₃ to carbon monoxide ratio (ΔO₃/ΔCO) increased from 5.32 to 143 ppb ppm^–1 as plumes aged from 1 to 480 min, showing a rapid initial increase, doubling in approximately 60 min, followed by a gradual slow down. Residual O₃ from daytime fires was detected during the evening and night, disrupting the typical nighttime O₃ pattern. The ΔPM_2.5 mass/ΔCO ratio ranged from 40.6 to 466 μg m^–3 ppm^–1. Little age-related change was observed in smoke measured at night with ΔPM_2.5 mass/ΔCO levels similar to those observed at the time of emissions (132 μg m^–3 ppm^–1). However, in plumes of observed O₃ enhancement during photochemically active periods (12:00–18:00), ΔPM_2.5 mass/ΔCO followed a similar increasing trend as ΔO₃/ΔCO, and the two were correlated (<i>r</i>² = 0.5), although the O₃ trend was more pronounced. For these data, a stronger correlation between ΔPM_2.5 mass/ΔCO and age was found in plumes of higher PM_2.5 concentration (PM_2.5 mass &gt; 35 μg m^–3). The impact of the prescribed burning season in the southeast was evident in state-operated air quality monitors near burning areas, where PM_2.5 mass concentrations were 25–30% higher than nonburning seasons. In contrast, changes in daily maximum 8 h O₃ concentrations were less pronounced. Our data indicates that the formation of both O₃ and PM_2.5 frequently occurred in smoke from prescribed fires during photochemically active periods in the studied regions. These findings are significant, as exposure to O₃ and PM_2.5 can negatively impact human health.</p><p >We investigate the impact of prescribed burning on the production of secondary PM_2.5 mass and ozone (O₃) in a region where extensive prescribed burning is widely used for land management.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 3","pages":"343–357 343–357"},"PeriodicalIF":0.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsestair.4c00231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608962","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":"Forest Mercury Deposition Observation in Chongqing: Evaluating Effectiveness of Mercury Pollution Control over the Past Decade in Southwestern China","authors":"Ziyi Zhan,&nbsp;Dongwei Lv,&nbsp;Huang Zhou,&nbsp;Qingru Wu,&nbsp;Yuying Cui and Lei Duan*,&nbsp;","doi":"10.1021/acsestair.4c0026910.1021/acsestair.4c00269","DOIUrl":"https://doi.org/10.1021/acsestair.4c00269https://doi.org/10.1021/acsestair.4c00269","url":null,"abstract":"<p >As the largest Hg reservoir in terrestrial ecosystems, forests provide valuable insights into atmospheric Hg levels through observations of mercury deposition. In this study, we conducted a one-year observation of Hg deposition, by both throughfall and litterfall, in a forest located in Chongqing, an industrial hub in southwestern China, in 2021. We compared the results with observations made at the same sampling site in 2010–2011. The results showed that there were significant decreases in both Hg concentrations (from 115 μg·kg^–1 to 80.2 μg·kg^–1 for litterfall and from 68.4 ng·L^–1 to 4.01 ± 2.09 ng·L^–1 for throughfall) and Hg deposition fluxes (from 89.8 μg·m^–2·yr^–1 to 19.9 μg·m^–2·yr^–1 for total deposition, decreasing by 77.8%) in the forest from 2010 to 2021. The decrease in deposition might result from the synergistic effect of reduction in both anthropogenic (inventory-listed or nonquantitative) and re-emission sources. Over the decade since the initiation of the <i>Minamata Convention on Mercury</i>, atmospheric Hg levels have decreased significantly, mainly due to the co-benefits of conventional air pollutant control, indicating the effectiveness of China’s Hg pollution control policies.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 2","pages":"286–294 286–294"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402390","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":"Wildland Fire Smoke Adds to Disproportionate PM2.5 Exposure in the United States","authors":"R. Byron Rice,&nbsp;Jason D. Sacks,&nbsp;Kirk R. Baker,&nbsp;Stephen D. LeDuc and J. Jason West,&nbsp;","doi":"10.1021/acsestair.4c0017310.1021/acsestair.4c00173","DOIUrl":"https://doi.org/10.1021/acsestair.4c00173https://doi.org/10.1021/acsestair.4c00173","url":null,"abstract":"<p >Wildland fire (i.e., prescribed fire and wildfire) smoke exposure is an emerging public health threat, in part due to climate change. Previous research has demonstrated disparities in ambient fine particulate matter (PM_2.5) exposure, with Black people, among others, exposed to higher concentrations; yet, it remains unclear how wildland fire smoke may contribute to additional disproportionate exposure. Here, we investigate the additional PM_2.5 burden contributed by wildland fire smoke in the contiguous United States by race and ethnicity, urbanicity, median household income, and language spoken at home, using modeled total, non-fire, and fire PM_2.5 concentrations from 2007 to 2018. Wildland fires contributed 7% to 14% of total population weighted PM_2.5 concentrations annually, while non-fire PM_2.5 concentrations declined by 24% over the study period. Wildland fires contributed to greater PM_2.5 exposure for Black and American Indian or Alaska Native people and those who live in nonurban areas. Disproportionate mean non-fire PM_2.5 concentrations for Black people (9.1 μg/m³, compared to 8.7 μg/m³ overall) were estimated to be further exacerbated by additional disproportionate concentrations from fires (1.0 μg/m³, compared to 0.9 μg/m³ overall). These results can inform equitable strategies by public health agencies and air quality managers to reduce smoke exposure in the United States.</p><p >This study investigates the contribution of wildland fire smoke to total fine particulate matter exposure for different population groups to inform equitable exposure reduction strategies in the United States.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 2","pages":"215–225 215–225"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402386","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":"Biochemical Conversion of Herbaceous Biomass to Renewable Diesel: Net Greenhouse Gas and Air Pollutant Trade-offs","authors":"David Quiroz,&nbsp;Vikram Ravi*,&nbsp;Yimin Zhang,&nbsp;Arpit Bhatt and Garvin Heath,&nbsp;","doi":"10.1021/acsestair.4c0015210.1021/acsestair.4c00152","DOIUrl":"https://doi.org/10.1021/acsestair.4c00152https://doi.org/10.1021/acsestair.4c00152","url":null,"abstract":"<p >This study examines greenhouse gas (GHG) and criteria air pollutant (CAP) emissions trade-offs for renewable diesel across 12 scenarios, involving different biochemical conversion designs, biorefinery scales, and feedstocks. A conventional design uses lignin for on-site heat and power, which exports excess power to the grid. An alternative design exports lignin pellets, offsetting other pellet production methods but requiring grid electricity to meet biorefinery power demands. Net emissions were quantified in Iowa and Georgia, selected considering feedstock availability, coproduct displacement, and regional power grids, assuming grid-exported power avoids coal or low-carbon electricity. Results for the conventional design remained consistent across the electricity displacement scenarios. When comparing lignin utilization strategies, pelletizing lignin reduces sulfur dioxide, carbon monoxide, nitrogen oxides, and volatile organic compounds (net emissions −0.66 mg MJ^–1, 25 mg MJ^–1, 25 mg MJ^–1, 7.8 mg MJ^–1, respectively). However, lignin pelletization increases net particulate matter (fine and coarse) and ammonia (net emissions of 4.7 mg MJ^–1, 13 mg MJ^–1, and 0.26 mg MJ^–1, respectively), alongside indirect GHG emissions due to grid electricity dependence. Additionally, processing 2000 tonnes corn stover daily minimizes emissions for both designs. Only lignin pelletization with renewable electricity and additional particulate matter and ammonia controls reduces all CAP and GHG emissions simultaneously.</p><p >Optimizing biorefinery design for scale, feedstock, and lignin management can achieve simultaneous reductions in greenhouse gas and air pollutant emissions from renewable diesel production.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 2","pages":"175–186 175–186"},"PeriodicalIF":0.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsestair.4c00152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402208","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":"Kinetic Modeling of Secondary Organic Aerosol in a Weather-Chemistry Model: Parameterizations, Processes, and Predictions for GOAmazon","authors":"Yicong He*,&nbsp;Kelsey R. Bilsback,&nbsp;Manish Shrivastava,&nbsp;Rahul A. Zaveri,&nbsp;John E. Shilling,&nbsp;John H. Seinfeld,&nbsp;Bin Zhao,&nbsp;Shuxiao Wang,&nbsp;Christopher D. Cappa,&nbsp;Jeffrey R. Pierce and Shantanu H. Jathar,&nbsp;","doi":"10.1021/acsestair.4c0024010.1021/acsestair.4c00240","DOIUrl":"https://doi.org/10.1021/acsestair.4c00240https://doi.org/10.1021/acsestair.4c00240","url":null,"abstract":"<p >Secondary organic aerosol (SOA) forms and evolves in the atmosphere through many pathways and processes, over diverse spatial and time scales. Hence, there is a need to represent these widely varying kinetic processes in large-scale atmospheric models to allow for accurate predictions of the abundance, properties, and impacts of SOA. In this work, we integrated a kinetic, process-level model (simpleSOM-MOSAIC) into a weather-chemistry model (WRF-Chem) to simulate the oxidation chemistry and microphysics of atmospheric SOA. simpleSOM-MOSAIC simulates multigenerational gas-phase chemistry, autoxidation reactions, aqueous chemistry, heterogeneous oxidation, oligomerization, and phase-state-influenced gas/particle partitioning of SOA. As a case study, the integrated WRF-Chem-simpleSOM-MOSAIC (WC-SSM) model was used to simulate the photochemical evolution downwind of a large city (Manaus, Brazil) in the Amazon and, in turn, study the anthropogenic and biogenic interactions in an otherwise pristine environment. Consistent with previous work, we found that OA was enhanced by up to a factor of 4 in the urban plume due to elevated hydroxyl radical (OH) concentrations, relative to the background, and that this OA was dominated by SOA from biogenic precursors (80%). In addition to accurately simulating the OA enhancement in the urban plume, the model reproduced the magnitude of the OA oxygen-to-carbon (O:C) ratio and broadly tracked the evolution of the aerosol number size distribution. Our work highlights the importance of including an integrated, kinetic representation of SOA processes in an atmospheric model.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 2","pages":"249–263 249–263"},"PeriodicalIF":0.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402205","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":"TROPOMI Satellite Data Reshape NO₂ Air Pollution Land-Use Regression Modeling Capabilities in the United States.","authors":"M Omar Nawaz, Daniel L Goldberg, Gaige H Kerr, Susan C Anenberg","doi":"10.1021/acsestair.4c00153","DOIUrl":"10.1021/acsestair.4c00153","url":null,"abstract":"<p><p>Nitrogen dioxide (NO₂) pollution is associated with adverse health effects, but its spatial variability between ground monitors is poorly characterized. NO₂ column observations from the Tropospheric Monitoring Instrument (TROPOMI) have unprecedented spatial resolution and high accuracy over the globe. Land-use regression (LUR) models predict surface-level NO₂ with relevance for epidemiological and environmental justice studies. We use TROPOMI NO₂ columns in a land use regression (LUR) model to improve surface NO₂ concentration estimates over the United States. The TROPOMI LUR predictions have improved correlation with ground monitors (Adj. <i>R</i> ² = 0.72) and bias (Mean Bias, MB = 14.2%) compared with an existing LUR using less granular NO₂ data from a legacy satellite instrument (Adj. <i>R</i> ² = 0.54 and MB = 49%; for North America). Removing TROPOMI NO₂ from the LUR decreased <i>R</i> ² by 29.1%, 8.1 times the impact of removing road system information. These findings reveal that novel Earth observing satellites can enhance surface NO₂ surveillance by capturing pollution variation between monitors without relying heavily on other data sources.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 2","pages":"187-200"},"PeriodicalIF":0.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143461334","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":"TROPOMI Satellite Data Reshape NO2 Air Pollution Land-Use Regression Modeling Capabilities in the United States","authors":"M. Omar Nawaz*,&nbsp;Daniel L. Goldberg,&nbsp;Gaige H. Kerr and Susan C. Anenberg,&nbsp;","doi":"10.1021/acsestair.4c0015310.1021/acsestair.4c00153","DOIUrl":"https://doi.org/10.1021/acsestair.4c00153https://doi.org/10.1021/acsestair.4c00153","url":null,"abstract":"<p >Nitrogen dioxide (NO₂) pollution is associated with adverse health effects, but its spatial variability between ground monitors is poorly characterized. NO₂ column observations from the Tropospheric Monitoring Instrument (TROPOMI) have unprecedented spatial resolution and high accuracy over the globe. Land-use regression (LUR) models predict surface-level NO₂ with relevance for epidemiological and environmental justice studies. We use TROPOMI NO₂ columns in a land use regression (LUR) model to improve surface NO₂ concentration estimates over the United States. The TROPOMI LUR predictions have improved correlation with ground monitors (Adj. <i>R</i>² = 0.72) and bias (Mean Bias, MB = 14.2%) compared with an existing LUR using less granular NO₂ data from a legacy satellite instrument (Adj. <i>R</i>² = 0.54 and MB = 49%; for North America). Removing TROPOMI NO₂ from the LUR decreased <i>R</i>² by 29.1%, 8.1 times the impact of removing road system information. These findings reveal that novel Earth observing satellites can enhance surface NO₂ surveillance by capturing pollution variation between monitors without relying heavily on other data sources.</p><p >Improved satellite air pollution observations from TROPOMI further support that surface NO₂ can be estimated without heavy reliance on other data.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 2","pages":"187–200 187–200"},"PeriodicalIF":0.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsestair.4c00153","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402325","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":"Cost-Effective Mapping of Hyperlocal Air Pollution Using Large-Scale Mobile Monitoring and Land-Use Machine Learning","authors":"Tie Zheng,&nbsp;Yifan Wen*,&nbsp;Sheng Xiang,&nbsp;Pan Yang,&nbsp;Xuan Zheng,&nbsp;Yan You,&nbsp;Shaojun Zhang and Ye Wu*,&nbsp;","doi":"10.1021/acsestair.4c0013610.1021/acsestair.4c00136","DOIUrl":"https://doi.org/10.1021/acsestair.4c00136https://doi.org/10.1021/acsestair.4c00136","url":null,"abstract":"<p >The advent of large-scale mobile monitoring using fast-response instruments has enabled hyperlocal mapping (≤100 m) of traffic-related air pollution (TRAP), with important implications for air quality management. However, most related studies have been confined within small areas due to the high cost and labor intensity. This study pioneers a cost-effective TRAP mapping method by incorporating large-scale mobile monitoring and land-use machine learning (LUML). Here, over 4.6 million 1 Hz high-frequency measurements (∼1300 h) were collected on a part of major roadways in the Chinese megacity of Shenzhen. Unmeasured locations were estimated by LUML models to reduce measurement costs and labor intensity. Various ML algorithms and varying spatial aggregation segment lengths were incorporated to optimize the model performance. Hyperlocal maps of NO, NO₂, and PM_2.5 were predicted across the entire road network covering over 1700 km². Based on our results, LU-RF (random forest) for mapping NO and NO₂ and LU-GBM (Gradient Boosting Machine) for mapping PM_2.5, demonstrated superior performance. Deep learning models, in contrast, did not yield comparable results. Finer partitioning of road segments (≤100 m) improved NO prediction performance, but worsened that for NO₂ and PM_2.5. By deployment of optimal ML algorithms and segment lengths, the TRAP mapping accuracy increased by 20–80% compared to conventional land-use regression models. This study provides a promising and cost-effective approach to hyperlocal air pollution mapping and management in cities worldwide.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 2","pages":"151–161 151–161"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402056","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}