ACS ES&T Air最新文献

{"title":"Informing Near-Airport Satellite NO2 Retrievals Using Pandora Sky-Scanning Observations","authors":"Asher P. Mouat,&nbsp;Elena Spinei and Jennifer Kaiser*,&nbsp;","doi":"10.1021/acsestair.4c0015810.1021/acsestair.4c00158","DOIUrl":"https://doi.org/10.1021/acsestair.4c00158https://doi.org/10.1021/acsestair.4c00158","url":null,"abstract":"<p >Airports are a large and growing source of NO_<i>x</i>. Tracking airport-related emissions is especially difficult, as a portion of emissions are elevated above the surface. While satellite-based NO₂ observations show hot-spots near airports, near-source retrievals often have large biases related to uncertainties in the NO₂ vertical distribution and resultant air mass factors (AMF). Here we use observations from UV–vis spectrometers (Pandora 1S, SciGlob) deployed near the Atlanta Hartsfield-Jackson International airport from April 2020–May 2021 to assess the impact of aviation on NO₂ vertical profiles. We show the first near-airport sky-scanning Pandora observations, which are used to distinguish the airport plume from the urban background. We find that increasing aviation leads to higher NO₂ over the airport, and the enhancement is distributed across the mixed layer near-equally. We compare observed profiles with those modeled by the Goddard Earth Observing System composition forecast (GEOS-CF) system. We find that modeled profiles attribute a larger portion of the column closer to the surface and underestimate the NO₂ mixing height. Observed profiles typically exhibited greater NO₂ concentrations up to 2.5 km above ground level. Air mass factors (AMF) calculated using observations (AMF_Fused) are similar over Hartsfield-Jackson to those calculated using GEOS-CF (AMF_GEOS-CF). The unexpected similarity in alternative AMFs is attributed to the altitude-dependent sensitivity of AMF_Fused to changes in NO₂ concentration. Using either AMF_Fused or AMF_GEOS-CF to evaluate TROPOMI NO₂ against independent direct-sun observations produces consistent normalized mean differences of −22% and −29%, respectively. Overall, these results demonstrate the benefits of a combined ground and satellite-based approach for probing a complex distribution of NO_<i>x</i> emissions in an urban area.</p><p >Observational constraints on airport-related emissions are limited. We use 3D observations from the Pandora instrument to map the near-airport NO₂ enhancement and discuss implications of discrepancies in measured and modeled NO₂ vertical profiles for interpretation of satellite-based observations.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 12","pages":"1617–1628 1617–1628"},"PeriodicalIF":0.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsestair.4c00158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849895","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":"Informing Near-Airport Satellite NO₂ Retrievals Using Pandora Sky-Scanning Observations.","authors":"Asher P Mouat, Elena Spinei, Jennifer Kaiser","doi":"10.1021/acsestair.4c00158","DOIUrl":"10.1021/acsestair.4c00158","url":null,"abstract":"<p><p>Airports are a large and growing source of NO _<i>x</i> . Tracking airport-related emissions is especially difficult, as a portion of emissions are elevated above the surface. While satellite-based NO₂ observations show hot-spots near airports, near-source retrievals often have large biases related to uncertainties in the NO₂ vertical distribution and resultant air mass factors (AMF). Here we use observations from UV-vis spectrometers (Pandora 1S, SciGlob) deployed near the Atlanta Hartsfield-Jackson International airport from April 2020-May 2021 to assess the impact of aviation on NO₂ vertical profiles. We show the first near-airport sky-scanning Pandora observations, which are used to distinguish the airport plume from the urban background. We find that increasing aviation leads to higher NO₂ over the airport, and the enhancement is distributed across the mixed layer near-equally. We compare observed profiles with those modeled by the Goddard Earth Observing System composition forecast (GEOS-CF) system. We find that modeled profiles attribute a larger portion of the column closer to the surface and underestimate the NO₂ mixing height. Observed profiles typically exhibited greater NO₂ concentrations up to 2.5 km above ground level. Air mass factors (AMF) calculated using observations (AMF_Fused) are similar over Hartsfield-Jackson to those calculated using GEOS-CF (AMF_GEOS-CF). The unexpected similarity in alternative AMFs is attributed to the altitude-dependent sensitivity of AMF_Fused to changes in NO₂ concentration. Using either AMF_Fused or AMF_GEOS-CF to evaluate TROPOMI NO₂ against independent direct-sun observations produces consistent normalized mean differences of -22% and -29%, respectively. Overall, these results demonstrate the benefits of a combined ground and satellite-based approach for probing a complex distribution of NO _<i>x</i> emissions in an urban area.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 12","pages":"1617-1628"},"PeriodicalIF":0.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11650605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857500","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":"Synergistic Disinfection by 222 nm Far-UVC and Negative Air Ions of Airborne Bacteria and the Induced Oxidative Stress Responses: A Bioaerosol Chamber Study","authors":"Haoxiang Wu,&nbsp;Yuhao Lu,&nbsp;Ruixuan Wang and Alvin Chi Keung Lai*,&nbsp;","doi":"10.1021/acsestair.4c0016210.1021/acsestair.4c00162","DOIUrl":"https://doi.org/10.1021/acsestair.4c00162https://doi.org/10.1021/acsestair.4c00162","url":null,"abstract":"<p >The understanding of the mechanisms of synergistic bioaerosol disinfection using 222 nm far-UVC and negative air ions is limited. In this study, we employed a fabricated aerosol chamber to investigate the synergistic disinfection effects of these two technologies and their impact on oxidative stress responses in airborne <i>Staphylococcus epidermidis</i>, <i>Salmonella enterica</i>, and <i>Escherichia coli</i>. We measured the log reduction in airborne survival of bacteria, lipid peroxidation, and the activities of antioxidant enzymes and total antioxidants. The results show that bacterial inactivation by the simultaneous operation of both technologies was significantly greater than the sum of the inactivation values by individual treatments, demonstrating a synergistic disinfection effect. The Pearson correlation coefficient indicates a strong correlation between airborne bacterial survival and lipid peroxidation (<i>r</i> ≥ 0.83). Furthermore, 222 nm far-UVC was found to be able to inactivate the antioxidant defense in airborne bacteria. Our results together suggest that the simultaneous operation of 222 nm far-UVC and negative air ions induces oxidative stress while deactivating antioxidant defense, contributing to the observed synergistic disinfection effect. Findings from the current study contribute to the mechanistic understanding of the synergistic effect by 222 nm far-UVC and negative air ions in bioaerosol disinfection, offering a new research opportunity for system design in high-efficiency air disinfection.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 12","pages":"1629–1636 1629–1636"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142850165","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":"North American Fine Particulate Matter Chemical Composition for 2000-2022 from Satellites, Models, and Monitors: The Changing Contribution of Wildfires.","authors":"Aaron van Donkelaar, Randall V Martin, Bonne Ford, Chi Li, Amanda J Pappin, Siyuan Shen, Dandan Zhang","doi":"10.1021/acsestair.4c00151","DOIUrl":"10.1021/acsestair.4c00151","url":null,"abstract":"<p><p>Air quality management benefits from an in-depth understanding of the emissions associated with, and composition of, local PM_2.5 concentrations. Here, we investigate the changing role of biomass burning emissions to North American PM_2.5 exposure by combining multiple satellite-, ground-, and simulation-based data sets biweekly at a 0.01° × 0.01° resolution from 2000 to 2022. We also developed a Buffered Leave Cluster Out (BLeCO) method to address autocorrelation and computational cost in cross-validation. Biomass burning emissions contribute an increasingly large fraction to PM_2.5 exposure in the United States and Canada, with national annual population-weighted mean contributions increasing from 0.4 μg/m³ (3-5%) in 2000-2004 to 0.8-0.9 μg/m³ (9-14%) by 2019-2022, led by western North American 2019-2022 annual contributions of 1.4-1.9 μg/m³ (15-27%) and maximum seasonal contributions of 3.3-5.5 μg/m³ (29-49%). Other components such as nonbiomass burning Organic Matter (OM) and nitrate can be regionally as (or more) important, albeit with distinct seasonal variability. The contribution of total OM to PM_2.5 exposure in the United States in 2016-2022 is 42.2%, comparable to all other anthropogenically sourced components combined. Comparison of BLeCO and random 10-fold cross-validation suggests that random 10-fold cross-validation may significantly underrepresent true uncertainty for total PM_2.5 concentrations due to the clustered nature of PM_2.5 ground-based monitoring.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 12","pages":"1589-1600"},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11651298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857504","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":"North American Fine Particulate Matter Chemical Composition for 2000–2022 from Satellites, Models, and Monitors: The Changing Contribution of Wildfires","authors":"Aaron van Donkelaar*,&nbsp;Randall V. Martin,&nbsp;Bonne Ford,&nbsp;Chi Li,&nbsp;Amanda J. Pappin,&nbsp;Siyuan Shen and Dandan Zhang,&nbsp;","doi":"10.1021/acsestair.4c0015110.1021/acsestair.4c00151","DOIUrl":"https://doi.org/10.1021/acsestair.4c00151https://doi.org/10.1021/acsestair.4c00151","url":null,"abstract":"<p >Air quality management benefits from an in-depth understanding of the emissions associated with, and composition of, local PM_2.5 concentrations. Here, we investigate the changing role of biomass burning emissions to North American PM_2.5 exposure by combining multiple satellite-, ground-, and simulation-based data sets biweekly at a 0.01° × 0.01° resolution from 2000 to 2022. We also developed a Buffered Leave Cluster Out (BLeCO) method to address autocorrelation and computational cost in cross-validation. Biomass burning emissions contribute an increasingly large fraction to PM_2.5 exposure in the United States and Canada, with national annual population-weighted mean contributions increasing from 0.4 μg/m³ (3–5%) in 2000–2004 to 0.8–0.9 μg/m³ (9–14%) by 2019–2022, led by western North American 2019–2022 annual contributions of 1.4–1.9 μg/m³ (15–27%) and maximum seasonal contributions of 3.3–5.5 μg/m³ (29–49%). Other components such as nonbiomass burning Organic Matter (OM) and nitrate can be regionally as (or more) important, albeit with distinct seasonal variability. The contribution of total OM to PM_2.5 exposure in the United States in 2016–2022 is 42.2%, comparable to all other anthropogenically sourced components combined. Comparison of BLeCO and random 10-fold cross-validation suggests that random 10-fold cross-validation may significantly underrepresent true uncertainty for total PM_2.5 concentrations due to the clustered nature of PM_2.5 ground-based monitoring.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 12","pages":"1589–1600 1589–1600"},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142843086","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":"Pristine and Aged Microplastics Can Nucleate Ice through Immersion Freezing","authors":"Heidi L. Busse,&nbsp;Devaka Dharmapriya Ariyasena,&nbsp;Jessica Orris and Miriam Arak Freedman*,&nbsp;","doi":"10.1021/acsestair.4c0014610.1021/acsestair.4c00146","DOIUrl":"https://doi.org/10.1021/acsestair.4c00146https://doi.org/10.1021/acsestair.4c00146","url":null,"abstract":"<p >Microplastics (MP) are ubiquitous in the environment; their atmospheric relevance is being increasingly recognized. Because of their atmospheric concentrations, there is the question of whether MP can act as ice nucleating particles in the atmosphere. This study investigates the immersion freezing activity of lab-prepared MP of four different compositions─low density polyethylene (LDPE), polypropylene (PP), poly(vinyl chloride) (PVC), and poly(ethylene terephthalate) (PET)─using droplet freezing assays. The MP are also exposed to ultraviolet light, ozone, sulfuric acid, and ammonium sulfate to mimic environmental aging of the plastics to elucidate the role that these processes play in the ice nucleating activity of MP. Results show that all studied MP act as immersion nuclei, and aging processes can modify this ice nucleating activity, leading, primarily, to decreases in ice nucleating activity for LDPE, PP, and PET. The ice nucleating activity of PVC generally increased following aging, which we attribute to a cleaning of chemical defects present on the surface of the stock material. Chemical changes were monitored with infrared spectroscopy (ATR-FTIR), and the growth of a peak at 1650–1800 cm^–1 was associated with a decrease in ice nucleating activity while loss of an existing peak in that region was associated with an increase in ice nucleating activity. The studied MP have ice nucleating activities sufficient to be a non-negligible source of ice nucleating particles in the atmosphere if present in sufficiently high concentrations.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 12","pages":"1579–1588 1579–1588"},"PeriodicalIF":0.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851235","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":"Urban Air Quality Management at Low Cost Using Micro Air Sensors: A Case Study from Accra, Ghana","authors":"Collins Gameli Hodoli*,&nbsp;Iq Mead,&nbsp;Frederic Coulon,&nbsp;Cesunica E. Ivey,&nbsp;Victoria Owusu Tawiah,&nbsp;Garima Raheja,&nbsp;James Nimo,&nbsp;Allison Hughes,&nbsp;Achim Haug,&nbsp;Anika Krause,&nbsp;Selina Amoah,&nbsp;Maxwell Sunu,&nbsp;John K. Nyante,&nbsp;Esi Nerquaye Tetteh,&nbsp;Véronique Riffault and Carl Malings,&nbsp;","doi":"10.1021/acsestair.4c0017210.1021/acsestair.4c00172","DOIUrl":"https://doi.org/10.1021/acsestair.4c00172https://doi.org/10.1021/acsestair.4c00172","url":null,"abstract":"<p >Urban air quality management is dependent on the availability of local air pollution data. In many major urban centers of Africa, there is limited to nonexistent information on air quality. This is gradually changing in part due to the increasing use of micro air sensors, which have the potential to enable the generation of ground-based air quality data at fine scales for understanding local emission trends. Regional literature on the application of high-resolution data for emission source identification in this region is limited. In this study a micro air sensor was colocated at the Physics Department, University of Ghana, with a reference grade instrument to evaluate its performance for estimating PM_2.5 pollution accurately at fine scales and the value of these data in identification of local sources and their behavior over time. For this study, 15 weeks of data at hourly resolution with approximately 2500 data pairs were generated and analyzed (June 1, 2023, to September 15, 2023). For this time period a coefficient of determination (<i>r</i>²) of 0.83 was generated with a mean absolute error (MAE) of 5.44 μg m^–3 between the pre local calibration micro air sensor (i.e., out of the box) and the reference-grade instrument. Following currently accepted best practice methods (see, e.g., PAS4023) a domain specific (i.e., local) calibration factor was generated using a multilinear regression model, and when this factor is applied to the micro air sensor data, a reduction, i.e. improvement, in MAE to 1.43 μg m^–3 was found. Daily variation was calculated, a receptor model was applied, and time series plots as a function of wind direction were generated, including PM_2.5/PM₁₀ ratio scatter and count plots, to explore the utility of this observational approach for local source identification. The 3 data sets were compared (out of the box, domain calibrated, and reference-grade) and it was found that although there were variations in the data reported, source areas highlighted based on these data were similar, with input from local sources such as traffic emissions and biomass burning. As the temporal resolution of observational data associated with these micro air sensors is higher than for reference grade instruments (primarily due to costs and logistics limitations), they have the potential to provide insight into the complex, often hyperlocalized sources associated with urban areas, such as those found in major African cities.</p><p >Research on micro air sensor data for emission source identification is patchy. This study reports PM_2.5 emission sources in an urban setting using relative and calibrated micro air sensor data compared to reference grade data with implications for low-cost air pollution management and control.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 2","pages":"201–214 201–214"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsestair.4c00172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402106","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":"Urban Air Quality Management at Low Cost Using Micro Air Sensors: A Case Study from Accra, Ghana.","authors":"Collins Gameli Hodoli, Iq Mead, Frederic Coulon, Cesunica E Ivey, Victoria Owusu Tawiah, Garima Raheja, James Nimo, Allison Hughes, Achim Haug, Anika Krause, Selina Amoah, Maxwell Sunu, John K Nyante, Esi Nerquaye Tetteh, Véronique Riffault, Carl Malings","doi":"10.1021/acsestair.4c00172","DOIUrl":"10.1021/acsestair.4c00172","url":null,"abstract":"<p><p>Urban air quality management is dependent on the availability of local air pollution data. In many major urban centers of Africa, there is limited to nonexistent information on air quality. This is gradually changing in part due to the increasing use of micro air sensors, which have the potential to enable the generation of ground-based air quality data at fine scales for understanding local emission trends. Regional literature on the application of high-resolution data for emission source identification in this region is limited. In this study a micro air sensor was colocated at the Physics Department, University of Ghana, with a reference grade instrument to evaluate its performance for estimating PM_2.5 pollution accurately at fine scales and the value of these data in identification of local sources and their behavior over time. For this study, 15 weeks of data at hourly resolution with approximately 2500 data pairs were generated and analyzed (June 1, 2023, to September 15, 2023). For this time period a coefficient of determination (<i>r</i> ²) of 0.83 was generated with a mean absolute error (MAE) of 5.44 μg m^-3 between the pre local calibration micro air sensor (i.e., out of the box) and the reference-grade instrument. Following currently accepted best practice methods (see, e.g., PAS4023) a domain specific (i.e., local) calibration factor was generated using a multilinear regression model, and when this factor is applied to the micro air sensor data, a reduction, i.e. improvement, in MAE to 1.43 μg m^-3 was found. Daily variation was calculated, a receptor model was applied, and time series plots as a function of wind direction were generated, including PM_2.5/PM₁₀ ratio scatter and count plots, to explore the utility of this observational approach for local source identification. The 3 data sets were compared (out of the box, domain calibrated, and reference-grade) and it was found that although there were variations in the data reported, source areas highlighted based on these data were similar, with input from local sources such as traffic emissions and biomass burning. As the temporal resolution of observational data associated with these micro air sensors is higher than for reference grade instruments (primarily due to costs and logistics limitations), they have the potential to provide insight into the complex, often hyperlocalized sources associated with urban areas, such as those found in major African cities.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 2","pages":"201-214"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143461403","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":"Findings from Biomass Burning Field Campaigns Set Directions for Future Research on Atmospheric Impacts","authors":"Kelley C. Barsanti*,&nbsp;Steven S. Brown*,&nbsp;Emily V. Fischer,&nbsp;Johannes W. Kaiser,&nbsp;Chelsea E. Stockwell,&nbsp;Chelsea Thompson,&nbsp;Carsten Warneke and Robert J. Yokelson*,&nbsp;","doi":"10.1021/acsestair.4c0027610.1021/acsestair.4c00276","DOIUrl":"https://doi.org/10.1021/acsestair.4c00276https://doi.org/10.1021/acsestair.4c00276","url":null,"abstract":"","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 12","pages":"1507–1510 1507–1510"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844163","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":"Low Methane Emissions from the Natural Gas Distribution System Indicated by Mobile Measurements in a Chinese Megacity Hangzhou","authors":"Shuang Zhao,&nbsp;Yuzhong Zhang*,&nbsp;Ruosi Liang,&nbsp;Wei Chen,&nbsp;Xinchun Xie,&nbsp;Rui Wang,&nbsp;Zheng Xia,&nbsp;Jiandong Shen,&nbsp;Yilong Wang and Huilin Chen,&nbsp;","doi":"10.1021/acsestair.4c0006810.1021/acsestair.4c00068","DOIUrl":"https://doi.org/10.1021/acsestair.4c00068https://doi.org/10.1021/acsestair.4c00068","url":null,"abstract":"<p >The consumption of natural gas (NG) in China has quadrupled over the past decade. However, there is an absence of measurement-based assessment of methane emissions from NG consumption, including those from the urban distribution network. Here, we conducted a mobile survey with concurrent measurements of ambient methane concentrations, carbon dioxide concentrations, and ¹³CH₄ isotopic signatures in the center and suburban areas of Hangzhou, a megacity in East China, from November 2021 to June 2022. We detected 176 leak indications in the 1408 km road covered with 32% attributed to NG leaks based on the source isotopic signature and the methane-to-carbon dioxide ratio derived from the Keeling plot and measured concentrations. We quantified the flux using Weller’s empirical equation, which yielded an emission factor of 115 L·d^–1·km^–1 in Hangzhou. The value ranges from 16 to 314 L·d^–1·km^–1 by accounting for various uncertainty sources. This emission factor falls on the lower end compared to previous studies conducted in North American and European cities. Our findings confirm the effectiveness of mobile surveys in detecting methane emission sources in urban China.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"1 12","pages":"1511–1518 1511–1518"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844024","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}