Articles | Volume 25, issue 8
https://doi.org/10.5194/acp-25-4571-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-4571-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Apr 2025
Research article |
| 25 Apr 2025
| 25 Apr 2025
Locating and quantifying CH4 sources within a wastewater treatment plant based on mobile measurements
Junyue Yang
Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Zhengning Xu
Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Zheng Xia
Ecological and Environmental Monitoring Center of Zhejiang Province, Hangzhou 310012, China
Zhejiang Key Laboratory of Modern Ecological and Environmental Monitoring, Hangzhou 310012, China
Xiangyu Pei
Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Yunye Yang
Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Botian Qiu
Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310030, China
Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou 310024, China
Shuang Zhao
Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310030, China
Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou 310024, China
Yuzhong Zhang
CORRESPONDING AUTHOR
Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310030, China
Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou 310024, China
Zhibin Wang
CORRESPONDING AUTHOR
Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311200, China
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Kewei Zhang, Zhengning Xu, Fei Zhang, and Zhibin Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-4421, https://doi.org/10.5194/egusphere-2025-4421, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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This study investigates how organics contribute to initial particle growth in the 3–10 nm size range. Through laboratory experiments, it was found that sulfuric acid dominates the growth of smaller particles, while organics play an increasingly important role as particle size increases. Elevated humidity also significantly enhances the contribution of organics. These findings further our understanding of new particle formation and subsequent growth.
Xiaobo Wang, Yuzhong Zhang, Tamás Bozóki, Ruosi Liang, Xinchun Xie, Shutao Zhao, Rui Wang, Yujia Zhao, and Shuai Sun
Atmos. Chem. Phys., 25, 8929–8942, https://doi.org/10.5194/acp-25-8929-2025, https://doi.org/10.5194/acp-25-8929-2025, 2025
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Schumann resonance observations are used to parameterise lightning NOx emissions to better capture global lightning trends and variability. Updated simulations reveal insignificant trends but greater variability in lightning NOx emissions, impacting tropospheric NOx, O3, and OH. Lightning generally counteracts non-lightning factors, reducing the inter-annual variability of tropospheric O3 and OH. Variations in global lightning play an important role in understanding the atmospheric methane budget.
Jing Wei, Jin-Mei Ding, Yao Song, Xiao-Yuan Wang, Xiang-Yu Pei, Sheng-Chen Xu, Fei Zhang, Zheng-Ning Xu, Xu-Dong Tian, Bing-Ye Xu, and Zhi-Bin Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2844, https://doi.org/10.5194/egusphere-2025-2844, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Black carbon (BC) is a light-absorbing particle that contributes to atmospheric warming, but its radiative impact remains highly uncertain. We conducted field measurements in Hangzhou, China, to examine how mass ratio (coating-to-BC) and morphology influence light absorption. Our results show that widely used optical models overestimate absorption especially under clean conditions. A new morphology-based method improves model accuracy and reduces this uncertainty.
Xinchun Xie, Yuzhong Zhang, Ruosi Liang, and Xuan Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2305, https://doi.org/10.5194/egusphere-2025-2305, 2025
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Brown carbon (BrC), mainly from biomass burning, absorbs short wavelength sunlight and affects climate and atmospheric chemistry. This study implemented an improved parameterization of BrC bleaching in a model with which BrC can survive much longer in cold, dry air, especially when lofted into the upper atmosphere by wildfires. The results reveal stronger warming effects and impacts on atmospheric oxidation, highlighting the need to consider BrC in climate and pollution control strategies.
Pengfei Han, Ning Zeng, Bo Yao, Wen Zhang, Weijun Quan, Pucai Wang, Ting Wang, Minqiang Zhou, Qixiang Cai, Yuzhong Zhang, Ruosi Liang, Wanqi Sun, and Shengxiang Liu
Atmos. Chem. Phys., 25, 4965–4988, https://doi.org/10.5194/acp-25-4965-2025, https://doi.org/10.5194/acp-25-4965-2025, 2025
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Methane (CH4) is a potent greenhouse gas. Northern China contributes a large proportion of CH4 emissions, yet large observation gaps exist. Here we compiled a comprehensive dataset, which is publicly available, that includes ground-based, satellite-based, inventory, and modeling results to show the CH4 concentrations, enhancements, and spatial–temporal variations. The data can benefit the research community and policy-makers for future observations, atmospheric inversions, and policy-making.
Yao Song, Jing Wei, Wenlong Zhao, Jinmei Ding, Xiangyu Pei, Fei Zhang, Zhengning Xu, Ruifang Shi, Ya Wei, Lu Zhang, Lingling Jin, and Zhibin Wang
Atmos. Chem. Phys., 25, 4755–4766, https://doi.org/10.5194/acp-25-4755-2025, https://doi.org/10.5194/acp-25-4755-2025, 2025
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This study investigates the size-resolved effective density (ρeff) of aerosol particles in Hangzhou using a tandem aerodynamic aerosol classifier and scanning mobility particle sizer system. The ρeff values ranged from 1.47 to 1.63 g cm-3, increasing with particle diameter. The relationship between ρeff and the particle diameter varies due to differences in the chemical composition of the particles. A new method to derive the size-resolved chemical composition of particles from ρeff is proposed.
Shutao Zhao, Yuzhong Zhang, Shuang Zhao, Xinlu Wang, and Daniel J. Varon
Atmos. Chem. Phys., 25, 4035–4052, https://doi.org/10.5194/acp-25-4035-2025, https://doi.org/10.5194/acp-25-4035-2025, 2025
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We target the challenge of detecting methane super-emitters in oil and gas fields, which is critical for mitigating climate change. Traditional satellite-based detectors struggle due to interference from complex surfaces. We developed a novel method using deep transfer learning that improves detection efficiency and accuracy by reducing artifacts and adapting methane knowledge to different regions. Application revealed significant methane emissions, demonstrating the potential of our method.
Elise Penn, Daniel J. Jacob, Zichong Chen, James D. East, Melissa P. Sulprizio, Lori Bruhwiler, Joannes D. Maasakkers, Hannah Nesser, Zhen Qu, Yuzhong Zhang, and John Worden
Atmos. Chem. Phys., 25, 2947–2965, https://doi.org/10.5194/acp-25-2947-2025, https://doi.org/10.5194/acp-25-2947-2025, 2025
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The hydroxyl radical (OH) destroys many air pollutants, including methane. Global-mean OH cannot be directly measured, and thus it is inferred with the methyl chloroform (MCF) proxy. MCF is decreasing, and a replacement is needed. We use satellite observations of methane in two spectral ranges as a proxy for OH. We find shortwave infrared observations can characterize yearly OH and its seasonality but not the latitudinal distribution. Thermal infrared observations add little information.
Xiangyu Pei, Yikan Meng, Yueling Chen, Huichao Liu, Yao Song, Zhengning Xu, Fei Zhang, Thomas C. Preston, and Zhibin Wang
Atmos. Chem. Phys., 24, 5235–5246, https://doi.org/10.5194/acp-24-5235-2024, https://doi.org/10.5194/acp-24-5235-2024, 2024
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An aerosol optical tweezer (AOT) Raman spectroscopy system is developed to capture a single aerosol droplet for phase transition monitoring and morphology studies. Rapid droplet capture is achieved and accurate droplet size and refractive index are retrieved. Results indicate that mixed inorganic/organic droplets are more inclined to form core–shell morphology when RH decreases. The phase transitions of secondary mixed organic aerosol/inorganic droplets vary with their precursors.
Zhengning Xu, Jian Gao, Zhuanghao Xu, Michel Attoui, Xiangyu Pei, Mario Amo-González, Kewei Zhang, and Zhibin Wang
Atmos. Meas. Tech., 16, 5995–6006, https://doi.org/10.5194/amt-16-5995-2023, https://doi.org/10.5194/amt-16-5995-2023, 2023
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Planar differential mobility analyzers (DMAs) have higher ion transmission efficiency and sizing resolution compared to cylindrical DMAs and are more suitable for use with mass spectrometers (MSs). Performance of the latest planar DMA (P5) was characterized. Sizing resolution and ion transmission efficiency were 5–16 times and ∼10 times higher than cylindrical DMAs. Sulfuric acid clusters were measured by DMA(P5)-MSs. This technique can be applied for natural products and biomolecule analysis.
Yueling Chen, Xiangyu Pei, Huichao Liu, Yikan Meng, Zhengning Xu, Fei Zhang, Chun Xiong, Thomas C. Preston, and Zhibin Wang
Atmos. Chem. Phys., 23, 10255–10265, https://doi.org/10.5194/acp-23-10255-2023, https://doi.org/10.5194/acp-23-10255-2023, 2023
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The impact of acidity on the phase transition behavior of levitated aerosol particles was examined. Our results revealed that lower acidity decreases the separation relative humidity of aerosol droplets mixed with ammonium sulfate and secondary organic aerosol proxy. Our research suggests that in real atmospheric conditions, with the high acidity found in many ambient aerosol particles, droplets encounter heightened impediments to phase separation and tend to display a homogeneous structure.
Chun Xiong, Binyu Kuang, Fei Zhang, Xiangyu Pei, Zhengning Xu, and Zhibin Wang
Atmos. Chem. Phys., 23, 8979–8991, https://doi.org/10.5194/acp-23-8979-2023, https://doi.org/10.5194/acp-23-8979-2023, 2023
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In hydration, an apparent water diffusion hindrance by an organic surfactant shell was confirmed, raising the inorganic deliquescence relative humidity (RH) to a nearly saturated condition. In dehydration, phase separations were observed for inorganic surfactant systems, showing a strong dependence on the organic molecular
oxygen-to-carbon ratio. Our results could improve fundamental knowledge about aerosol mixing states and decrease uncertainty in model estimations of global radiative effects.
Ruosi Liang, Yuzhong Zhang, Wei Chen, Peixuan Zhang, Jingran Liu, Cuihong Chen, Huiqin Mao, Guofeng Shen, Zhen Qu, Zichong Chen, Minqiang Zhou, Pucai Wang, Robert J. Parker, Hartmut Boesch, Alba Lorente, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 8039–8057, https://doi.org/10.5194/acp-23-8039-2023, https://doi.org/10.5194/acp-23-8039-2023, 2023
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We compare and evaluate East Asian methane emissions inferred from different satellite observations (GOSAT and TROPOMI). The results show discrepancies over northern India and eastern China. Independent ground-based observations are more consistent with TROPOMI-derived emissions in northern India and GOSAT-derived emissions in eastern China.
Juan Hong, Min Tang, Qiaoqiao Wang, Nan Ma, Shaowen Zhu, Shaobin Zhang, Xihao Pan, Linhong Xie, Guo Li, Uwe Kuhn, Chao Yan, Jiangchuan Tao, Ye Kuang, Yao He, Wanyun Xu, Runlong Cai, Yaqing Zhou, Zhibin Wang, Guangsheng Zhou, Bin Yuan, Yafang Cheng, and Hang Su
Atmos. Chem. Phys., 23, 5699–5713, https://doi.org/10.5194/acp-23-5699-2023, https://doi.org/10.5194/acp-23-5699-2023, 2023
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A comprehensive investigation of the characteristics of new particle formation (NPF) events was conducted at a rural site on the North China Plain (NCP), China, during the wintertime of 2018 by covering the particle number size distribution down to sub–3 nm. Potential mechanisms for NPF under the current environment were explored, followed by a further discussion on the factors governing the occurrence of NPF at this rural site compared with other regions (e.g., urban areas) in the NCP region.
Chun Xiong, Xueyan Chen, Xiaolei Ding, Binyu Kuang, Xiangyu Pei, Zhengning Xu, Shikuan Yang, Huan Hu, and Zhibin Wang
Atmos. Chem. Phys., 22, 16123–16135, https://doi.org/10.5194/acp-22-16123-2022, https://doi.org/10.5194/acp-22-16123-2022, 2022
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Water surface tension is applied widely in current aerosol–cloud models but could be inappropriate in the presence of atmospheric surfactants. With cloud condensation nuclei (CCN) activity and atomic force microscopy (AFM) measurement results of mixed inorganic salt and dicarboxylic acid particles, we concluded that surface tension reduction and phase state should be carefully considered in aerosol–cloud interactions. Our results could help to decease uncertainties in climate models.
Rui Zhang, Yuying Wang, Zhanqing Li, Zhibin Wang, Russell R. Dickerson, Xinrong Ren, Hao He, Fei Wang, Ying Gao, Xi Chen, Jialu Xu, Yafang Cheng, and Hang Su
Atmos. Chem. Phys., 22, 14879–14891, https://doi.org/10.5194/acp-22-14879-2022, https://doi.org/10.5194/acp-22-14879-2022, 2022
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Factors of cloud condensation nuclei number concentration (NCCN) profiles determined in the North China Plain include air mass sources, temperature structure, anthropogenic emissions, and terrain distribution. Cloud condensation nuclei (CCN) spectra suggest that the ability of aerosol activation into CCN is stronger in southeasterly than in northwesterly air masses and stronger in the free atmosphere than near the surface. A good method to parameterize NCCN from aerosol optical data is found.
Zhenqi Luo, Yuzhong Zhang, Wei Chen, Martin Van Damme, Pierre-François Coheur, and Lieven Clarisse
Atmos. Chem. Phys., 22, 10375–10388, https://doi.org/10.5194/acp-22-10375-2022, https://doi.org/10.5194/acp-22-10375-2022, 2022
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We quantify global ammonia (NH3) emissions over the period from 2008 to 2018 using an improved fast top-down method that incorporates Infrared Atmospheric
Sounding Interferometer (IASI) satellite observations and GEOS-Chem atmospheric chemical simulations. The top-down analysis finds a global total NH3 emission that is 30 % higher than the bottom-up estimate, largely reconciling a large discrepancy of more than a factor of 2 found in previous top-down studies using the same satellite data.
Yao Song, Xiangyu Pei, Huichao Liu, Jiajia Zhou, and Zhibin Wang
Atmos. Meas. Tech., 15, 3513–3526, https://doi.org/10.5194/amt-15-3513-2022, https://doi.org/10.5194/amt-15-3513-2022, 2022
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Accurate particle classification is very important in aerosol studies. Differential mobility analyzers (DMAs), centrifugal particle mass analyzers (CPMAs), aerodynamic aerosol classifiers (AACs) and their tandem systems are commonly used. We demonstrated that DMA–CPMA is more susceptible to the multiple charging effect than DMA–AAC. It is not suggested to reduce the resolutions of the instruments, especially when selecting small-size soot particles.
Cuiqi Zhang, Zhijun Wu, Jingchuan Chen, Jie Chen, Lizi Tang, Wenfei Zhu, Xiangyu Pei, Shiyi Chen, Ping Tian, Song Guo, Limin Zeng, Min Hu, and Zamin A. Kanji
Atmos. Chem. Phys., 22, 7539–7556, https://doi.org/10.5194/acp-22-7539-2022, https://doi.org/10.5194/acp-22-7539-2022, 2022
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The immersion ice nucleation effectiveness of aerosols from multiple sources in the urban environment remains elusive. In this study, we demonstrate that the immersion ice-nucleating particle (INP) concentration increased dramatically during a dust event in an urban atmosphere. Pollutant aerosols, including inorganic salts formed through secondary transformation (SIA) and black carbon (BC), might not act as effective INPs under mixed-phase cloud conditions.
John R. Worden, Daniel H. Cusworth, Zhen Qu, Yi Yin, Yuzhong Zhang, A. Anthony Bloom, Shuang Ma, Brendan K. Byrne, Tia Scarpelli, Joannes D. Maasakkers, David Crisp, Riley Duren, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 6811–6841, https://doi.org/10.5194/acp-22-6811-2022, https://doi.org/10.5194/acp-22-6811-2022, 2022
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This paper is intended to accomplish two goals: 1) describe a new algorithm by which remotely sensed measurements of methane or other tracers can be used to not just quantify methane fluxes, but also attribute these fluxes to specific sources and regions and characterize their uncertainties, and 2) use this new algorithm to provide methane emissions by sector and country in support of the global stock take.
Tia R. Scarpelli, Daniel J. Jacob, Shayna Grossman, Xiao Lu, Zhen Qu, Melissa P. Sulprizio, Yuzhong Zhang, Frances Reuland, Deborah Gordon, and John R. Worden
Atmos. Chem. Phys., 22, 3235–3249, https://doi.org/10.5194/acp-22-3235-2022, https://doi.org/10.5194/acp-22-3235-2022, 2022
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We present a spatially explicit version of the national inventories of oil, gas, and coal methane emissions as submitted by individual countries to the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. We then use atmospheric modeling to compare our inventory emissions to atmospheric methane observations with the goal of identifying potential under- and overestimates of oil–gas methane emissions in the national inventories.
Yaqing Zhou, Nan Ma, Qiaoqiao Wang, Zhibin Wang, Chunrong Chen, Jiangchuan Tao, Juan Hong, Long Peng, Yao He, Linhong Xie, Shaowen Zhu, Yuxuan Zhang, Guo Li, Wanyun Xu, Peng Cheng, Uwe Kuhn, Guangsheng Zhou, Pingqing Fu, Qiang Zhang, Hang Su, and Yafang Cheng
Atmos. Chem. Phys., 22, 2029–2047, https://doi.org/10.5194/acp-22-2029-2022, https://doi.org/10.5194/acp-22-2029-2022, 2022
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This study characterizes size-resolved particle effective densities and their evolution associated with emissions and aging processes in a rural area of the North China Plain. Particle effective density exhibits a high-frequency bimodal distribution, and two density modes exhibit opposite trends with increasing particle size. SIA and BC mass fractions are key factors of particle effective density, and a value of 0.6 g cm−3 is appropriate to represent BC effective density in bulk particles.
Xiao Lu, Daniel J. Jacob, Haolin Wang, Joannes D. Maasakkers, Yuzhong Zhang, Tia R. Scarpelli, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Hannah Nesser, A. Anthony Bloom, Shuang Ma, John R. Worden, Shaojia Fan, Robert J. Parker, Hartmut Boesch, Ritesh Gautam, Deborah Gordon, Michael D. Moran, Frances Reuland, Claudia A. Octaviano Villasana, and Arlyn Andrews
Atmos. Chem. Phys., 22, 395–418, https://doi.org/10.5194/acp-22-395-2022, https://doi.org/10.5194/acp-22-395-2022, 2022
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We evaluate methane emissions and trends for 2010–2017 in the gridded national emission inventories for the United States, Canada, and Mexico by inversion of in situ and satellite methane observations. We find that anthropogenic methane emissions for all three countries are underestimated in the national inventories, largely driven by oil emissions. Anthropogenic methane emissions in the US peak in 2014, in contrast to the report of a steadily decreasing trend over 2010–2017 from the US EPA.
Yuliang Liu, Wei Nie, Yuanyuan Li, Dafeng Ge, Chong Liu, Zhengning Xu, Liangduo Chen, Tianyi Wang, Lei Wang, Peng Sun, Ximeng Qi, Jiaping Wang, Zheng Xu, Jian Yuan, Chao Yan, Yanjun Zhang, Dandan Huang, Zhe Wang, Neil M. Donahue, Douglas Worsnop, Xuguang Chi, Mikael Ehn, and Aijun Ding
Atmos. Chem. Phys., 21, 14789–14814, https://doi.org/10.5194/acp-21-14789-2021, https://doi.org/10.5194/acp-21-14789-2021, 2021
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Oxygenated organic molecules (OOMs) are crucial intermediates linking volatile organic compounds to secondary organic aerosols. Using nitrate time-of-flight chemical ionization mass spectrometry in eastern China, we performed positive matrix factorization (PMF) on binned OOM mass spectra. We reconstructed over 1000 molecules from 14 derived PMF factors and identified about 72 % of the observed OOMs as organic nitrates, highlighting the decisive role of NOx in OOM formation in populated areas.
Zhen Qu, Daniel J. Jacob, Lu Shen, Xiao Lu, Yuzhong Zhang, Tia R. Scarpelli, Hannah Nesser, Melissa P. Sulprizio, Joannes D. Maasakkers, A. Anthony Bloom, John R. Worden, Robert J. Parker, and Alba L. Delgado
Atmos. Chem. Phys., 21, 14159–14175, https://doi.org/10.5194/acp-21-14159-2021, https://doi.org/10.5194/acp-21-14159-2021, 2021
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The recent launch of TROPOMI offers an unprecedented opportunity to quantify the methane budget from a top-down perspective. We use TROPOMI and the more mature GOSAT methane observations to estimate methane emissions and get consistent global budgets. However, TROPOMI shows biases over regions where surface albedo is small and provides less information for the coarse-resolution inversion due to the larger error correlations and spatial variations in the number of observations.
Hannah Nesser, Daniel J. Jacob, Joannes D. Maasakkers, Tia R. Scarpelli, Melissa P. Sulprizio, Yuzhong Zhang, and Chris H. Rycroft
Atmos. Meas. Tech., 14, 5521–5534, https://doi.org/10.5194/amt-14-5521-2021, https://doi.org/10.5194/amt-14-5521-2021, 2021
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Analytical inversions of satellite observations of atmospheric composition can improve emissions estimates and quantify errors but are computationally expensive at high resolutions. We propose two methods to decrease this cost. The methods reproduce a high-resolution inversion at a quarter of the cost. The reduced-dimension method creates a multiscale grid. The reduced-rank method solves the inversion where information content is highest.
Xiao Lu, Daniel J. Jacob, Yuzhong Zhang, Joannes D. Maasakkers, Melissa P. Sulprizio, Lu Shen, Zhen Qu, Tia R. Scarpelli, Hannah Nesser, Robert M. Yantosca, Jianxiong Sheng, Arlyn Andrews, Robert J. Parker, Hartmut Boesch, A. Anthony Bloom, and Shuang Ma
Atmos. Chem. Phys., 21, 4637–4657, https://doi.org/10.5194/acp-21-4637-2021, https://doi.org/10.5194/acp-21-4637-2021, 2021
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We use an analytical solution to the Bayesian inverse problem to quantitatively compare and combine the information from satellite and in situ observations, and to estimate global methane budget and their trends over the 2010–2017 period. We find that satellite and in situ observations are to a large extent complementary in the inversion for estimating global methane budget, and reveal consistent corrections of regional anthropogenic and wetland methane emissions relative to the prior inventory.
Joannes D. Maasakkers, Daniel J. Jacob, Melissa P. Sulprizio, Tia R. Scarpelli, Hannah Nesser, Jianxiong Sheng, Yuzhong Zhang, Xiao Lu, A. Anthony Bloom, Kevin W. Bowman, John R. Worden, and Robert J. Parker
Atmos. Chem. Phys., 21, 4339–4356, https://doi.org/10.5194/acp-21-4339-2021, https://doi.org/10.5194/acp-21-4339-2021, 2021
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We use 2010–2015 GOSAT satellite observations of atmospheric methane over North America in a high-resolution inversion to estimate methane emissions. We find general consistency with the gridded EPA inventory but higher oil and gas production emissions, with oil production emissions twice as large as in the latest EPA Greenhouse Gas Inventory. We find lower wetland emissions than predicted by WetCHARTs and a small increasing trend in the eastern US, apparently related to unconventional oil/gas.
Yuzhong Zhang, Daniel J. Jacob, Xiao Lu, Joannes D. Maasakkers, Tia R. Scarpelli, Jian-Xiong Sheng, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Jinfeng Chang, A. Anthony Bloom, Shuang Ma, John Worden, Robert J. Parker, and Hartmut Boesch
Atmos. Chem. Phys., 21, 3643–3666, https://doi.org/10.5194/acp-21-3643-2021, https://doi.org/10.5194/acp-21-3643-2021, 2021
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We use 2010–2018 satellite observations of atmospheric methane to interpret the factors controlling atmospheric methane and its accelerating increase during the period. The 2010–2018 increase in global methane emissions is driven by tropical and boreal wetlands and tropical livestock (South Asia, Africa, Brazil), with an insignificant positive trend in emissions from the fossil fuel sector. The peak methane growth rates in 2014–2015 are also contributed by low OH and high fire emissions.
Shaojie Song, Tao Ma, Yuzhong Zhang, Lu Shen, Pengfei Liu, Ke Li, Shixian Zhai, Haotian Zheng, Meng Gao, Jonathan M. Moch, Fengkui Duan, Kebin He, and Michael B. McElroy
Atmos. Chem. Phys., 21, 457–481, https://doi.org/10.5194/acp-21-457-2021, https://doi.org/10.5194/acp-21-457-2021, 2021
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We simulate the atmospheric chemical processes of an important sulfur-containing organic aerosol species, which is produced by the reaction between sulfur dioxide and formaldehyde. We can predict its distribution on a global scale. We find it is particularly rich in East Asia. This aerosol species is more abundant in the colder season partly because of weaker sunlight.
Ting Lei, Nan Ma, Juan Hong, Thomas Tuch, Xin Wang, Zhibin Wang, Mira Pöhlker, Maofa Ge, Weigang Wang, Eugene Mikhailov, Thorsten Hoffmann, Ulrich Pöschl, Hang Su, Alfred Wiedensohler, and Yafang Cheng
Atmos. Meas. Tech., 13, 5551–5567, https://doi.org/10.5194/amt-13-5551-2020, https://doi.org/10.5194/amt-13-5551-2020, 2020
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We present the design of a nano-hygroscopicity tandem differential mobility analyzer (nano-HTDMA) apparatus that enables high accuracy and precision in hygroscopic growth measurements of aerosol nanoparticles with diameters less than 10 nm. We further introduce comprehensive methods for system calibration and validation of the performance of the system. We then study the size dependence of the deliquescence and the efflorescence of aerosol nanoparticles for sizes down to 6 nm.
Cited articles
Abeywickrama, H. G. K., Bajón-Fernández, Y., Srinamasivayam, B., Turner, D., and Rivas Casado, M.: Development of a UAV based framework for CH4 monitoring in sludge treatment centres, Remote Sens.-Basel, 15, 3704, https://doi.org/10.3390/rs15153704, 2023.
Albertson, J. D., Harvey, T., Foderaro, G., Zhu, P., Zhou, X., Ferrari, S., Amin, M. S., Modrak, M., Brantley, H., and Thoma, E. D.: A Mobile Sensing Approach for regional surveillance of fugitive methane emissions in oil and gas production, Environ. Sci. Technol., 50, 2487–2497, https://doi.org/10.1021/acs.est.5b05059, 2016.
Allen, G., Hollingsworth, P., Kabbabe, K., Pitt, J. R., Mead, M. I., Illingworth, S., Roberts, G., Bourn, M., Shallcross, D. E., and Percival, C. J.: The development and trial of an unmanned aerial system for the measurement of methane flux from landfill and greenhouse gas emission hotspots, Waste Manage., 87, 883–892, https://doi.org/10.1016/j.wasman.2017.12.024, 2019.
Al-Shalan, A., Lowry, D., Fisher, R. E., Nisbet, E. G., Zazzeri, G., Al-Sarawi, M., and France, J. L.: Methane emissions in Kuwait: Plume identification, isotopic characterisation and inventory verification, Atmos. Environ., 268, 118763, https://doi.org/10.1016/j.atmosenv.2021.118763, 2022.
Alshboul, Z., Encinas-Fernández, J., Hofmann, H., and Lorke, A.: Export of dissolved methane and carbon dioxide with effluents from municipal wastewater treatment plants, Environ. Sci. Technol., 50, 5555–5563, https://doi.org/10.1021/acs.est.5b04923, 2016.
Bai, R. L., Jin, L., Sun, S. R., Cheng, Y., and Wei, Y.: Quantification of greenhouse gas emission from wastewater treatment plants, Greenhouse Gas Sci. Technol., 12, 587–601, https://doi.org/10.1002/ghg.2171, 2022.
Balashov, N. V., Davis, K. J., Miles, N. L., Lauvaux, T., Richardson, S. J., Barkley, Z. R., and Bonin, T. A.: Background heterogeneity and other uncertainties in estimating urban methane flux: results from the Indianapolis Flux Experiment (INFLUX), Atmos. Chem. Phys., 20, 4545–4559, https://doi.org/10.5194/acp-20-4545-2020, 2020.
Bao, Z., Sun, S., and Sun, D.: Assessment of greenhouse gas emission from A/O and SBR wastewater treatment plants in Beijing, China, Int. Biodeter. Biodegr., 108, 108–114, https://doi.org/10.1016/j.ibiod.2015.11.028, 2016.
Bergamaschi, P., Karstens, U., Manning, A. J., Saunois, M., Tsuruta, A., Berchet, A., Vermeulen, A. T., Arnold, T., Janssens-Maenhout, G., Hammer, S., Levin, I., Schmidt, M., Ramonet, M., Lopez, M., Lavric, J., Aalto, T., Chen, H., Feist, D. G., Gerbig, C., Haszpra, L., Hermansen, O., Manca, G., Moncrieff, J., Meinhardt, F., Necki, J., Galkowski, M., O'Doherty, S., Paramonova, N., Scheeren, H. A., Steinbacher, M., and Dlugokencky, E.: Inverse modelling of European CH4 emissions during 2006–2012 using different inverse models and reassessed atmospheric observations, Atmos. Chem. Phys., 18, 901–920, https://doi.org/10.5194/acp-18-901-2018, 2018.
Briggs, G. A.: Diffusion estimation for small emissions. Preliminary report, Tech. Rep. TID-28289, National Oceanic and Atmospheric Administration, Atmospheric Turbulence and Diffusion Lab., Oak Ridge, TN, USA, https://doi.org/10.2172/5118833, 1973.
Cai, B., Gao, Q., Li, Z., Wu, J., Cao, D., and Liu, L.: Study on the methane emission factors of wastewater treatment plants in China, China Population Resources and Environment, 25, 118–124, https://doi.org/10.3969/j.issn.1002-2104.2015.04.015, 2015.
Caulton, D. R., Li, Q., Bou-Zeid, E., Fitts, J. P., Golston, L. M., Pan, D., Lu, J., Lane, H. M., Buchholz, B., Guo, X., McSpiritt, J., Wendt, L., and Zondlo, M. A.: Quantifying uncertainties from mobile-laboratory-derived emissions of well pads using inverse Gaussian methods, Atmos. Chem. Phys., 18, 15145–15168, https://doi.org/10.5194/acp-18-15145-2018, 2018.
Chen, J., Dietrich, F., Maazallahi, H., Forstmaier, A., Winkler, D., Hofmann, M. E. G., Denier van der Gon, H., and Röckmann, T.: Methane emissions from the Munich Oktoberfest, Atmos. Chem. Phys., 20, 3683–3696, https://doi.org/10.5194/acp-20-3683-2020, 2020.
Cui, Y. Y., Brioude, J., Angevine, W. M., Peischl, J., McKeen, S. A., Kim, S.-W., Neuman, J. A., Henze, D. K., Bousserez, N., Fischer, M. L., Jeong, S., Michelsen, H. A., Bambha, R. P., Liu, Z., Santoni, G. W., Daube, B. C., Kort, E. A., Frost, G. J., Ryerson, T., Wofsy, S. C., and Trainer, M.: Top-down estimate of methane emissions in California using a mesoscale inverse modeling technique: The San Joaquin Valley, J. Geophys. Res., 122, 3686–3699, https://doi.org/10.1002/2016JD026398, 2017.
Cusworth, D. H., Duren, R. M., Ayasse, A. K., Jiorle, R., Howell, K., Aubrey, A., Green, R. O., Eastwood, M. L., Chapman, J. W., Thorpe, A. K., Heckler, J., Asner, G. P., Smith, M. L., Thoma, E., Krause, M. J., Heins, D., and Thorneloe, S.: Quantifying methane emissions from United States landfills, Science, 383, 1499–1504, https://doi.org/10.1126/science.adi7735, 2024.
Defratyka, S. M., Paris, J. D., Yver-Kwok, C., Fernandez, J. M., Korben, P., and Bousquet, P.: Mapping urban methane sources in Paris, France, Environ. Sci. Technol., 55, 8583–8591, https://doi.org/10.1021/acs.est.1c00859, 2021.
Delre, A., Mønster, J., and Scheutz, C.: Greenhouse gas emission quantification from wastewater treatment plants, using a tracer gas dispersion method, Sci. Total. Environ., 605–606, 258–268, https://doi.org/10.1016/j.scitotenv.2017.06.177, 2017.
Delre, A., Mønster, J., Samuelsson, J., Fredenslund, A. M., and Scheutz, C.: Emission quantification using the tracer gas dispersion method: the influence of instrument, tracer gas species and source simulation, Sci. Total. Environ., 634, 59–66, https://doi.org/10.1016/j.scitotenv.2018.03.289, 2018.
Dietrich, F., Chen, J., Voggenreiter, B., Aigner, P., Nachtigall, N., and Reger, B.: MUCCnet: Munich Urban Carbon Column network, Atmos. Meas. Tech., 14, 1111–1126, https://doi.org/10.5194/amt-14-1111-2021, 2021.
Fredenslund, A. M., Gudmundsson, E., Falk, J. M., and Scheutz, C.: The Danish national effort to minimise methane emissions from biogas plants, Waste Manage., 157, 321–329, https://doi.org/10.1016/j.wasman.2022.12.035, 2023.
Guisasola, A., de Haas, D., Keller, J., and Yuan, Z.: Methane formation in sewer systems, Water Res., 42, 1421–1430, https://doi.org/10.1016/j.watres.2007.10.014, 2008.
Guo, S., Huang, H., Dong, X., and Zeng, S.: Calculation of greenhouse gas emissions of municipal wastewater treatment and its temporal and spatial trend in China, Water and Wastewater Engineering, 45, 56–62, https://doi.org/10.13789/j.cnki.wwe1964.2019.04.009, 2019.
Hase, F., Frey, M., Blumenstock, T., Groß, J., Kiel, M., Kohlhepp, R., Mengistu Tsidu, G., Schäfer, K., Sha, M. K., and Orphal, J.: Application of portable FTIR spectrometers for detecting greenhouse gas emissions of the major city Berlin, Atmos. Meas. Tech., 8, 3059–3068, https://doi.org/10.5194/amt-8-3059-2015, 2015.
Han, G., Pei, Z., Shi, T., Mao, H., Li, S., Mao, F., Ma, X., Zhang, X., and Gong, W.: Unveiling unprecedented methane hotspots in China's leading coal production hub: a satellite mapping revelation, Geophys. Res. Lett., 51, e2024GL109065, https://doi.org/10.1029/2024GL109065, 2024.
Heerah, S., Frausto-Vicencio, I., Jeong, S., Marklein, A. R., Ding, Y., Meyer, A. G., Parker, H. A., Fischer, M. L., Franklin, J. E., Hopkins, F. M., and Dubey, M.: Dairy methane emissions in California's San Joaquin Valley inferred with ground-based remote sensing observations in the summer and winter, J. Geophys. Res-Atmos., 126, e2021JD034785, https://doi.org/10.1029/2021JD034785, 2021.
Harada, T. and Alba, E.: Parallel genetic algorithms: a useful survey, ACM Comput. Surv., 53, 1–39, https://doi.org/10.1145/3400031, 2020.
He, Y., Li, Y., Li, X., Liu, Y., Wang, Y., Guo, H., Hou, J., Zhu, T., and Liu, Y.: Net-zero greenhouse gas emission from wastewater treatment: mechanisms, opportunities and perspectives, Renew. Sust. Energ. Rev., 184, 113547, https://doi.org/10.1016/j.rser.2023.113547, 2023.
IPCC: 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Institute for Global Environmental Strategies, Japan, Open File Rep., 1980 pp., 2006.
IPCC: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 35–115, https://doi.org/10.59327/IPCC/AR6-9789291691647, 2023.
Jackson, R. B., Down, A., Phillips, N. G., Ackley, R. C., Cook, C. W., Plata, D. L., and Zhao, K.: Natural gas pipeline leaks across Washington, DC, Environ. Sci. Technol., 48, 2051–2058, https://doi.org/10.1021/es404474x, 2014.
Jacob, D. J., Varon, D. J., Cusworth, D. H., Dennison, P. E., Frankenberg, C., Gautam, R., Guanter, L., Kelley, J., McKeever, J., Ott, L. E., Poulter, B., Qu, Z., Thorpe, A. K., Worden, J. R., and Duren, R. M.: Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane, Atmos. Chem. Phys., 22, 9617–9646, https://doi.org/10.5194/acp-22-9617-2022, 2022.
Jin, L., Gao, M., Liu, W., Lu, Y., Zhang, Y., Wang, Y., Zhang, T., Xu, L., Liu, Z., and Chen, J.: Application of SOF-FTIR method to measuring ammonia emission flux of chemical plant, Spectrosc. Spect. Anal., 30, 1478–1481, https://doi.org/10.3964/j.issa.1000-0593(2010)06-1478-04, 2010.
Karion, A., Lauvaux, T., Lopez Coto, I., Sweeney, C., Mueller, K., Gourdji, S., Angevine, W., Barkley, Z., Deng, A., Andrews, A., Stein, A., and Whetstone, J.: Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study, Atmos. Chem. Phys., 19, 2561–2576, https://doi.org/10.5194/acp-19-2561-2019, 2019.
Katoch, S., Chauhan, S. S., and Kumar, V.: A review on genetic algorithm: past, present, and future, Multimed. Tools Appl., 80, 8091–8126, https://doi.org/10.1007/s11042-020-10139-6, 2021.
Kissas, K., Ibrom, A., Kjeldsen, P., and Scheutz, C.: Methane emission dynamics from a Danish landfill: The effect of changes in barometric pressure, Waste Manage., 138, 234–242, https://doi.org/10.1016/j.wasman.2021.11.043, 2022.
Kumar, P., Broquet, G., Yver-Kwok, C., Laurent, O., Gichuki, S., Caldow, C., Cropley, F., Lauvaux, T., Ramonet, M., Berthe, G., Martin, F., Duclaux, O., Juery, C., Bouchet, C., and Ciais, P.: Mobile atmospheric measurements and local-scale inverse estimation of the location and rates of brief CH4 and CO2 releases from point sources, Atmos. Meas. Tech., 14, 5987–6003, https://doi.org/10.5194/amt-14-5987-2021, 2021.
Kupper, T., Bühler, M., Gruber, W., and Häni, C.: Methane and ammonia emissions from wastewater treatment plants: A brief literature review, Swiss Federal Office for the Environment, Switzerland, Open File Rep., 18 pp., 2018.
Li, H., You, L., Du, H., Yu, B., Lu, L., Zheng, B., Zhang, Q., He, K., and Ren, N.: Methane and nitrous oxide emissions from municipal wastewater treatment plants in China: a plant-level and technology-specific study, Environ. Sci. Technol., 20, 100345, https://doi.org/10.1016/j.ese.2023.100345, 2024.
Liang, R., Zhang, Y., Chen, W., Zhang, P., Liu, J., Chen, C., Mao, H., Shen, G., Qu, Z., Chen, Z., Zhou, M., Wang, P., Parker, R. J., Boesch, H., Lorente, A., Maasakkers, J. D., and Aben, I.: East Asian methane emissions inferred from high-resolution inversions of GOSAT and TROPOMI observations: a comparative and evaluative analysis, Atmos. Chem. Phys., 23, 8039–8057, https://doi.org/10.5194/acp-23-8039-2023, 2023.
Lin, X., Zhang, W., Crippa, M., Peng, S., Han, P., Zeng, N., Yu, L., and Wang, G.: A comparative study of anthropogenic CH4 emissions over China based on the ensembles of bottom-up inventories, Earth Syst. Sci. Data, 13, 1073–1088, https://doi.org/10.5194/essd-13-1073-2021, 2021.
Lopez, M., Sherwood, O. A., Dlugokencky, E. J., Kessler, R., Giroux, L., and Worthy, D. E. J.: Isotopic signatures of anthropogenic CH4 sources in Alberta, Canada, Atmos. Environ., 164, 280–288, https://doi.org/10.1016/j.atmosenv.2017.06.021, 2017.
Luhar, A. K. and Patil, R. S.: A General Finite Line Source Model for vehicular pollution prediction, Atmos. Environ., 23, 555–562, https://doi.org/10.1016/0004-6981(89)90004-8, 1989.
Luther, A., Kleinschek, R., Scheidweiler, L., Defratyka, S., Stanisavljevic, M., Forstmaier, A., Dandocsi, A., Wolff, S., Dubravica, D., Wildmann, N., Kostinek, J., Jöckel, P., Nickl, A.-L., Klausner, T., Hase, F., Frey, M., Chen, J., Dietrich, F., Nȩcki, J., Swolkień, J., Fix, A., Roiger, A., and Butz, A.: Quantifying CH4 emissions from hard coal mines using mobile sun-viewing Fourier transform spectrometry, Atmos. Meas. Tech., 12, 5217–5230, https://doi.org/10.5194/amt-12-5217-2019, 2019.
Maazallahi, H., Fernandez, J. M., Menoud, M., Zavala-Araiza, D., Weller, Z. D., Schwietzke, S., von Fischer, J. C., Denier van der Gon, H., and Röckmann, T.: Methane mapping, emission quantification, and attribution in two European cities: Utrecht (NL) and Hamburg (DE), Atmos. Chem. Phys., 20, 14717–14740, https://doi.org/10.5194/acp-20-14717-2020, 2020.
Maazallahi, H., Delre, A., Scheutz, C., Fredenslund, A. M., Schwietzke, S., Denier van der Gon, H., and Röckmann, T.: Intercomparison of detection and quantification methods for methane emissions from the natural gas distribution network in Hamburg, Germany, Atmos. Meas. Tech., 16, 5051–5073, https://doi.org/10.5194/amt-16-5051-2023, 2023.
Makarova, M. V., Alberti, C., Ionov, D. V., Hase, F., Foka, S. C., Blumenstock, T., Warneke, T., Virolainen, Y. A., Kostsov, V. S., Frey, M., Poberovskii, A. V., Timofeyev, Y. M., Paramonova, N. N., Volkova, K. A., Zaitsev, N. A., Biryukov, E. Y., Osipov, S. I., Makarov, B. K., Polyakov, A. V., Ivakhov, V. M., Imhasin, H. Kh., and Mikhailov, E. F.: Emission Monitoring Mobile Experiment (EMME): an overview and first results of the St. Petersburg megacity campaign 2019, Atmos. Meas. Tech., 14, 1047–1073, https://doi.org/10.5194/amt-14-1047-2021, 2021.
Masuda, S., Suzuki, S., Sano, I., Li, Y.-Y., and Nishimura, O.: The seasonal variation of emission of greenhouse gases from a full-scale sewage treatment plant, Chemosphere, 140, 167–173, https://doi.org/10.1016/j.chemosphere.2014.09.042, 2015.
Masuda, S., Sano, I., Hojo, T., Li, Y.-Y., and Nishimura, O.: The comparison of greenhouse gas emissions in sewage treatment plants with different treatment processes, Chemosphere, 193, 581–590, https://doi.org/10.1016/j.chemosphere.2017.11.018, 2017.
McKain, K., Down, A., Raciti, S. M., Budney, J., Hutyra, L. R., Floerchinger, C., Herndon, S. C., Nehrkorn, T., Zahniser, M. S., Jackson, R. B., Phillips, N., and Wofsy, S. C.: Methane emissions from natural gas infrastructure and use in the urban region of Boston, Massachusetts, P. Natl. Acad. Sci. USA, 112, 1941–1946, https://doi.org/10.1073/pnas.1416261112, 2015.
Mønster, J., Samuelsson, J., Kjeldsen, P., Rella, C. W., and Scheutz, C.: Quantifying methane emission from fugitive sources by combining tracer release and downwind measurements – A sensitivity analysis based on multiple field surveys, Waste Manage., 34, 1416–1428, https://doi.org/10.1016/j.wasman.2014.03.025, 2014.
Moore, D. P., Li, N. P., Wendt, L. P., Castañeda, S. R., Falinski, M. M., Zhu, J.-J., Song, C., Ren, Z. J., and Zondlo, M. A.: Underestimation of sector-wide methane emissions from united states wastewater treatment, Environ. Sci. Technol., 57, 4082–4090, https://doi.org/10.1021/acs.est.2c05373, 2023.
Nassar, R., Hill, T. G., McLinden, C. A., Wunch, D., Jones, D. B. A., and Crisp, D.: Quantifying CO2 emissions from individual power plants from Space, Geophys. Res. Lett., 44, 10045–10053, https://doi.org/10.1002/2017GL074702, 2017.
Picarro: Datasheet G2201-i δ13C in CH4 and CO2 Gas Analyzer, https://www.picarro.com/environmental/support/library/documents/g2201i_analyzer_datasheet (last access: 5 August 2024), 2010.
Rees-White, T. C., Mønster, J., Beaven, R. P., and Scheutz, C.: Measuring methane emissions from a UK landfill using the tracer dispersion method and the influence of operational and environmental factors, Waste Manage., 87, 870–882, https://doi.org/10.1016/j.wasman.2018.03.023, 2019.
Reinelt, T., Delre, A., Westerkamp, T., Holmgren, M. A., Liebetrau, J., and Scheutz, C.: Comparative use of different emission measurement approaches to determine methane emissions from a biogas plant, Waste Manage., 68, 173–185, https://doi.org/10.1016/j.wasman.2017.05.053, 2017.
Rella, C. W., Hoffnagle, J., He, Y., and Tajima, S.: Local- and regional-scale measurements of CH4,
δ13CH4, and C2H6 in the Uintah Basin using a mobile stable isotope analyzer, Atmos. Meas. Tech., 8, 4539–4559, https://doi.org/10.5194/amt-8-4539-2015, 2015.
Richardson, S. J., Miles, N. L., Davis, K. J., Lauvaux, T., Martins, D. K., Turnbull, J. C., McKain, K., Sweeney, C., and Cambaliza, M. O. L.: Tower measurement network of in-situ CO2, CH4, and CO in support of the Indianapolis FLUX (INFLUX) Experiment, Elem. Sci. Anth., 5, 59, https://doi.org/10.1525/elementa.140, 2017.
Riddick, S. N., Connors, S., Robinson, A. D., Manning, A. J., Jones, P. S. D., Lowry, D., Nisbet, E., Skelton, R. L., Allen, G., Pitt, J., and Harris, N. R. P.: Estimating the size of a methane emission point source at different scales: from local to landscape, Atmos. Chem. Phys., 17, 7839–7851, https://doi.org/10.5194/acp-17-7839-2017, 2017.
Scheutz, C. and Kjeldsen, P.: Guidelines for landfill gas emission monitoring using the tracer gas dispersion method, Waste Manage., 85, 351–360, https://doi.org/10.1016/j.wasman.2018.12.048, 2019.
Shi, T., Han, G., Ma, X., Mao, H., Chen, C., Han, Z., Pei, Z., Zhang, H., Li, S., and Gong, W.: Quantifying factory-scale CO2/CH4 emission based on mobile measurements and EMISSION-PARTITION model: cases in China, Environ. Res. Lett., 18, 034028, https://doi.org/10.1088/1748-9326/acbce7, 2023.
Song, C., Zhu, J.-J., Willis, J. L., Moore, D. P., Zondlo, M. A., and Ren, Z. J.: Methane emissions from municipal wastewater collection and treatment systems, Environ. Sci. Technol., 57, 2248–2261, https://doi.org/10.1021/acs.est.2c04388, 2023.
Stadler, C., Fusé, V. S., Linares, S., and Juliarena, P.: Estimation of methane emission from an urban wastewater treatment plant applying inverse Gaussian model, Environ. Monit. Assess., 194, 27, https://doi.org/10.1007/s10661-021-09660-4, 2022.
Sun, W., Deng, L., Wu, G., Wu, L., Han, P., Miao, Y., and Yao, B.: Atmospheric monitoring of methane in Beijing using a mobile observatory, Atmosphere-Basel, 10, 554, https://doi.org/10.3390/atmos10090554, 2019.
Sun, Y., Yang, T., Gui, H., Li, X., Wang, W., Duan, J., Mao, S., Yin, H., Zhou, B., Lang, J., Zhou, H., Liu, C., and Xie, P.: Atmospheric environment monitoring technology and equipment in China: a review and outlook, J. Environ. Sci., 123, 41–53, https://doi.org/10.1016/j.jes.2022.01.014, 2023.
Venkatram, A. and Horst, T. W.: Approximating dispersion from a finite line source, Atmos. Environ., 40, 2401–2408, https://doi.org/10.1016/j.atmosenv.2005.12.014, 2006.
Vítěz, T., Novák, D., Lochman, J., and Vítězová, M.: Methanogens diversity during anaerobic sewage sludge stabilization and the effect of temperature, Processes, 8, 822, https://doi.org/10.3390/pr8070822, 2020.
Vogel, F., Ars, S., Wunch, D., Lavoie, J., Gillespie, L., Maazallahi, H., Röckmann, T., Nęcki, J., Bartyzel, J., Jagoda, P., Lowry, D., France, J., Fernandez, J., Bakkaloglu, S., Fisher, R., Lanoiselle, M., Chen, H., Oudshoorn, M., Yver-Kwok, C., Defratyka, S., Morgui, J. A., Estruch, C., Curcoll, R., Grossi, C., Chen, J., Dietrich, F., Forstmaier, A., Denier van der Gon, H. A. C., Dellaert, S. N. C., Salo, J., Corbu, M., Iancu, S. S., Tudor, A. S., Scarlat, A. I., and Calcan, A.: Ground-based mobile measurements to track urban methane emissions from natural gas in 12 cities across eight countries, Environ. Sci. Technol., 58, 2271–2281, https://doi.org/10.1021/acs.est.3c03160, 2024.
von Fischer, J. C., Cooley, D., Chamberlain, S., Gaylord, A., Griebenow, C. J., Hamburg, S. P., Salo, J., Schumacher, R., Theobald, D., and Ham, J.: Rapid, vehicle-based identification of location and magnitude of urban natural gas pipeline leaks, Environ. Sci. Technol., 51, 4091–4099, https://doi.org/10.1021/acs.est.6b06095, 2017.
Wang, X., Wang, T., Chen, S., and Tang, Y.: Study on methane emission from wastewater treatment plants-A case study of Wuhu city, Adv. Geosci., 11, 677–689, https://doi.org/10.12677/AG.2021.115063, 2021.
WMO: WMO greenhouse gas Bulletin. The state of greenhouse gases in the atmosphere based on global observations through 2022, https://library.wmo.int/idurl/4/68532 (last access: 5 August 2024), 2023.
Weller, Z. D., Roscioli, J. R., Daube, W. C., Lamb, B. K., Ferrara, T. W., Brewer, P. E., and von Fischer, J. C.: Vehicle-based methane surveys for finding natural gas leaks and estimating their size: validation and uncertainty, Environ. Sci. Technol., 52, 11922–11930, https://doi.org/10.1021/acs.est.8b03135, 2018.
Weller, Z. D., Yang, D. K., and von Fischer, J. C.: An open source algorithm to detect natural gas leaks from mobile methane survey data, PLoS ONE, 14, e0212287, https://doi.org/10.1371/journal.pone.0212287, 2019.
Yacovitch, T. I., Herndon, S. C., Petron, G., Kofler, J., Lyon, D., Zahniser, M. S., and Kolb, C. E.: Mobile laboratory observations of methane emissions in the Barnett Shale Region, Environ. Sci. Technol., 49, 7889–7895, https://doi.org/10.1021/es506352j, 2015.
Yin, Y., Qi, X., Gao, L., Lu, X., Yang, X., Xiao, K., Liu, Y., Qiu, Y., Huang, X., and Liang, P.: Quantifying methane influx from sewer into wastewater treatment processes, Environ. Sci. Technol., 58, 9582–9590, https://doi.org/10.1021/acs.est.4c00820, 2024.
Yoshida, H., Mønster, J., and Scheutz, C.: Plant-integrated measurement of greenhouse gas emissions from a municipal wastewater treatment plant, Water Res., 61, 108–118, https://doi.org/10.1016/j.watres.2014.05.014, 2014.
Zhang, Y., Jacob, D. J., Lu, X., Maasakkers, J. D., Scarpelli, T. R., Sheng, J.-X., Shen, L., Qu, Z., Sulprizio, M. P., Chang, J., Bloom, A. A., Ma, S., Worden, J., Parker, R. J., and Boesch, H.: Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations, Atmos. Chem. Phys., 21, 3643–3666, https://doi.org/10.5194/acp-21-3643-2021, 2021.
Zhao, S., Zhang, Y., Liang, R., Chen, W., Xie, X., Wang, R., Xia, Z., Shen, J., Wang, Y., and Chen, H.: Low methane emissions from the natural gas distribution system indicated by mobile measurements in a Chinese megacity Hangzhou, Environ. Sci. Technol. Air, 1, 1511–1518, https://doi.org/10.1021/acsestair.4c00068, 2024.
Zhao, Y., Xue, M., Li, X., Liu, G., Liu, S., and Sun, X.: Application of vehicle-mounted methane detection method in the oil and gas industry, Environmental Protection of Oil and Gas Fields, 31, 4, https://doi.org/10.3969/j.issn.1005-3158.2021.04.001, 2021.
Zimnoch, M., Necki, J., Chmura, L., Jasek, A., Jelen, D., Galkowski, M., Kuc, T., Gorczyca, Z., Bartyzel, J., and Rozanski, K.: Quantification of carbon dioxide and methane emissions in urban areas: source apportionment based on atmospheric observations, Mitig. Adapt. Strateg. Gl., 24, 1051–1071, https://doi.org/10.1007/s11027-018-9821-0, 2018.
Short summary
Mobile CH4 measurements were conducted at a wastewater treatment plant in Hangzhou in the summer and winter of 2023. A multi-source Gaussian plume model, combined with a genetic algorithm inversion framework, was used to locate major CH4 sources at the plant and quantify emissions. Results indicate that the summer CH4 emissions (603.33 ± 152.66 t a-1) were 2.8 times as high as inventory values, and winter values (418.95 ± 187.59 t a-1) were twice as high. The main sources were the screen and primary clarifier.
Mobile CH4 measurements were conducted at a wastewater treatment plant in Hangzhou in the summer...
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