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Patel A, Mallik C, Chandra N, Patra PK, Steinbacher M. Revisiting regional and seasonal variations in decadal carbon monoxide variability: Global reversal of growth rate. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 909:168476. [PMID: 37984655 DOI: 10.1016/j.scitotenv.2023.168476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/09/2023] [Accepted: 11/08/2023] [Indexed: 11/22/2023]
Abstract
Carbon monoxide (CO) is one of the important trace gases in the atmosphere capturing the evolution of chemical properties of the troposphere. Here we analyze the growth rates of CO during the period of 1991-2020 using in situ measurements from the World Meteorological Organization's (WMO) Global Atmospheric Watch (GAW) program. The analysis of trends has been done on different spatial and temporal scales. Our analysis supports the decline in the overall CO mixing ratios over the globe but inter-decadal and regional trend analysis has shown heterogeneous changes in the given period of study. On average, there has been a decrease of -16.22 ± 1.92 ppb and -4.5 ± 0.64 ppb observed at the sites in the northern hemisphere (NH) and southern hemisphere (SH), respectively. This decline occurred at rates of -0.80 ± 0.12 ppb yr-1 in the NH and - 0.12 ± 0.03 ppb yr-1 in the SH. Bifurcating the annual trends for seasonal analysis reveals the impact of emissions, chemistry and atmospheric transport on CO variation over different regional clusters of stations. Seasonal trend analysis provides further evidence regarding heterogeneous patterns in the South-East Asia region. Our study highlights a slowdown in CO decline during the 2011-2020 decade when compared to the rate of decrease observed in 2001-2010. This is inferred from the variability and much slower decline of CO emissions across different regions, contributing to a weakening in CO trends.
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Affiliation(s)
- Ankit Patel
- Department of Atmospheric Science, Central University of Rajasthan, Ajmer 305801, India
| | - Chinmay Mallik
- Department of Atmospheric Science, Central University of Rajasthan, Ajmer 305801, India.
| | - Naveen Chandra
- Research Institute for Global Change, JAMSTEC, Yokohama 2360001, Japan
| | - Prabir K Patra
- Research Institute for Global Change, JAMSTEC, Yokohama 2360001, Japan; Research Institute for Humanity and Nature, Kyoto, Japan
| | - Martin Steinbacher
- Empa, Swiss Federal Laboratories for Materials Science and Technology, CH-8600 Duebendorf, Switzerland
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Chai W, Wang M, Li J, Tang G, Zhang G, Chen W. Pollution characteristics, sources, and photochemical roles of ambient carbonyl compounds in summer of Beijing, China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 336:122403. [PMID: 37595733 DOI: 10.1016/j.envpol.2023.122403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/23/2023] [Accepted: 08/16/2023] [Indexed: 08/20/2023]
Abstract
Ambient carbonyls are important precursors of radicals and ground-level ozone (O3). In this study, sources, precursors, and impacts on radicals and O3 of carbonyls were investigated based on online observations of volatile organic compounds (VOCs) at an urban site in Beijing during June 2021. Carbonyls accounted for 36% and 42% of mixing ratios and OH reactivity for total measured VOCs, respectively. Formaldehyde was the most abundant carbonyl, with the mean level of 4.13 ± 2.28 ppb. Source apportionment results based on the multi linear regression (MLR) method suggested that secondary production contributed 41%, 25%, 36%, and 30% of formaldehyde, acetaldehyde, propanal, and acetone, respectively. Key precursors of carbonyls were then identified based on the calculation of their production rates. It was found that alkenes contributed 59%-80% of aldehydes production. Impacts of carbonyls on HOx radicals (OH and HO2) and O3 production were explored using a box model based on observations (OBM). Photolysis of HONO, formaldehyde, and O3 were the dominant primary sources of HOx radicals during daytime of O3 pollution days, with average relative contributions of 52%, 28%, and 19% to the total primary production rate of HOx, respectively. Aldehydes accounted for 32% (20% from formaldehyde) of average HOx removal rates. The relative incremental reactivity (RIR) values of NOx determined by the OBM were negative, suggesting that the O3-VOCs-NOx sensitivity was in the VOCs-limited regime. Using the observed concentrations of carbonyls as constraints of OBM, the absolute values of RIR for NOx tended to increase but those for anthropogenic VOCs tended to decrease. Formaldehyde showed the largest RIR value for anthropogenic VOCs during O3 pollution days. These findings indicated the important impacts of carbonyls on O3 production and O3-VOCs-NOx sensitivity.
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Affiliation(s)
- Wenxuan Chai
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Ming Wang
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
| | - Jingyan Li
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Guigang Tang
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Guohan Zhang
- The Ecological and Environmental Monitoring Station of DEEY in Kunming, Kunming, 650032, China
| | - Wentai Chen
- Nanjing Intelligent Environmental Science and Technology Co., Ltd., Nanjing, 211800, China
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Hael MA. Unveiling air pollution patterns in Yemen: a spatial-temporal functional data analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:50067-50095. [PMID: 36790700 PMCID: PMC9930045 DOI: 10.1007/s11356-023-25790-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 02/03/2023] [Indexed: 04/16/2023]
Abstract
The application of spatiotemporal functional analysis techniques in environmental pollution research remains limited. As a result, this paper suggests spatiotemporal functional data clustering and visualization tools for identifying temporal dynamic patterns and spatial dependence of multiple air pollutants. The study uses concentrations of four major pollutants, named particulate matter (PM2.5), ground-level ozone (O3), carbon monoxide (CO), and sulfur oxides (SO2), measured over 37 cities in Yemen from 1980 to 2022. The proposed tools include Fourier transformation, B-spline functions, and generalized-cross validation for data smoothing, as well as static and dynamic visualization methods. Innovatively, a functional mixture model was used to capture/identify the underlying/hidden dynamic patterns of spatiotemporal air pollutants concentration. According to the results, CO levels increased 25% from 1990 to 1996, peaking in the cities of Taiz, Sana'a, and Ibb before decreasing. Also, PM2.5 pollution reached a peak in 2018, increasing 30% with severe concentrations in Hodeidah, Marib, and Mocha. Moreover, O3 pollution fluctuated with peaks in 2014-2015, 2% increase and pollution rate of 265 Dobson. Besides, SO2 pollution rose from 1997 to 2010, reaching a peak before stabilizing. Thus, these findings provide insights into the structure of the spatiotemporal air pollutants cycle and can assist policymakers in identifying sources and suggesting measures to reduce them. As a result, the study's findings are promising and may guide future research on predicting multivariate air pollution statistics over the analyzed area.
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Affiliation(s)
- Mohanned Abduljabbar Hael
- School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, China.
- Department of Data Science and Information Technology, Taiz University, 9674, Taiz, Yemen.
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Sung WC, Jung HS, Bae JW, Kim JY, Lee DH. Hydrodynamic effects on the direct conversion of syngas to methyl acetate in a two-stage fixed-bed/fluidized-bed combined reactor. J CO2 UTIL 2023. [DOI: 10.1016/j.jcou.2023.102411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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Liu Q, Sheng J, Wu Y, Ma Z, Sun J, Tian P, Zhao D, Li X, Hu K, Li S, Shen X, Zhang Y, He H, Huang M, Ding D, Liu D. Source characterization of volatile organic compounds in urban Beijing and its links to secondary organic aerosol formation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 860:160469. [PMID: 36464057 DOI: 10.1016/j.scitotenv.2022.160469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 11/01/2022] [Accepted: 11/20/2022] [Indexed: 06/17/2023]
Abstract
Volatile organic compounds (VOCs) are precursors for ozone and secondary organic aerosol (SOA) formation, thereby playing a vital role in atmospheric chemistry and urban air quality. To characterize the relationship between VOCs and SOA, organics both in gas and particulate phases were concurrently measured in urban Beijing. The VOCs and organic aerosol (OA) were apportioned into factors with different oxidation levels by applying the factorization analysis on their detailed mass spectra. Six factors of VOCs were identified, including four primary VOCs (PVOC) factors and two secondary VOCs (SVOC) factors. The PVOC factors dominated the total VOCs when the air mass originated in the cleaner northern areas, while SVOC factors dominated for polluted southern air masses. The normalized concentrations of PVOC and primary OA factors showed consistent diurnal variations regardless of air mass directions, owing to the relatively stable local emissions during the experimental period. This contrasted with the secondary factors due to more complex transformation processes. The traffic-related VOCs and solid fuel combustion VOCs negatively correlated with SOA, implying that they may have contributed to the SOA formation through photooxidation. The VOCs in lower oxidation levels were found to have poor correlations with the less oxidized SOA, whereas they correlated strongly to the more oxidized SOA. This implied that the less oxidized SOA may be in a transition state, where its production and loss rates were balanced. These served as products of VOCs oxidation and reactants of more oxidized SOA formation, playing important roles on the VOC to SOA transformation. The identified VOC emission sources and their photochemical production of SOA should be considered in air quality policy planning.
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Affiliation(s)
- Quan Liu
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Jiujiang Sheng
- Beijing Weather Modification Center, Beijing 100089, China
| | - Yangzhou Wu
- Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou, Zhejiang, 310027, China
| | - Zhiqiang Ma
- Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
| | - Junying Sun
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Ping Tian
- Beijing Weather Modification Center, Beijing 100089, China
| | - Delong Zhao
- Beijing Weather Modification Center, Beijing 100089, China
| | - Xia Li
- Beijing Weather Modification Center, Beijing 100089, China
| | - Kang Hu
- Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou, Zhejiang, 310027, China
| | - Siyuan Li
- Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou, Zhejiang, 310027, China
| | - Xiaojing Shen
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Yangmei Zhang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Hui He
- Beijing Weather Modification Center, Beijing 100089, China
| | - Mengyu Huang
- Beijing Weather Modification Center, Beijing 100089, China; Field Experiment Base of Cloud and Precipitation Research in North China, China Meteorological Administration, Beijing 101200, China
| | - Deping Ding
- Beijing Weather Modification Center, Beijing 100089, China; Beijing Key Laboratory of Cloud, Precipitation and Atmospheric Water Resources, Beijing 100089, China
| | - Dantong Liu
- Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou, Zhejiang, 310027, China.
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Che K, Liu Y, Cai Z, Yang D, Wang H, Ji D, Yang Y, Wang P. Characterization of Regional Combustion Efficiency using ΔXCO: ΔXCO 2 Observed by a Portable Fourier-Transform Spectrometer at an Urban Site in Beijing. ADVANCES IN ATMOSPHERIC SCIENCES 2022; 39:1299-1315. [PMID: 35578720 PMCID: PMC9093556 DOI: 10.1007/s00376-022-1247-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 12/17/2021] [Accepted: 01/05/2022] [Indexed: 06/15/2023]
Abstract
Measurements of column-averaged dry-air mole fractions of carbon dioxide and carbon monoxide, CO2 (XCO2) and CO (XCO), were performed throughout 2019 at an urban site in Beijing using a compact Fourier Transform Spectrometer (FTS) EM27/SUN. This data set is used to assess the characteristics of combustion-related CO2 emissions of urban Beijing by analyzing the correlated daily anomalies of XCO and XCO2 (e.g., ΔXCO and ΔXCO2). The EM27/SUN measurements were calibrated to a 125HR-FTS at the Xianghe station by an extra EM27/SUN instrument transferred between two sites. The ratio of ΔXCO over ΔXCO2 (ΔXCO:ΔXCO2) is used to estimate the combustion efficiency in the Beijing region. A high correlation coefficient (0.86) between ΔXCO and ΔXCO2 is observed. The CO:CO2 emission ratio estimated from inventories is higher than the observed ΔXCO:ΔXCO2 (10.46 ± 0.11 ppb ppm-1) by 42.54%-101.15%, indicating an underestimation in combustion efficiency in the inventories. Daily ΔXCO:ΔXCO2 are influenced by transportation governed by weather conditions, except for days in summer when the correlation is low due to the terrestrial biotic activity. By convolving the column footprint [ppm (µmol m-2 s-1)-1] generated by the Weather Research and Forecasting-X-Stochastic Time-Inverted Lagrangian Transport models (WRF-X-STILT) with two fossil-fuel emission inventories (the Multi-resolution Emission Inventory for China (MEIC) and the Peking University (PKU) inventory), the observed enhancements of CO2 and CO were used to evaluate the regional emissions. The CO2 emissions appear to be underestimated by 11% and 49% for the MEIC and PKU inventories, respectively, while CO emissions were overestimated by MEIC (30%) and PKU (35%) in the Beijing area.
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Affiliation(s)
- Ke Che
- Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, 100029 China
- Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, 100029 China
- University of Chinese Academy of Science, Beijing, 100049 China
| | - Yi Liu
- Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, 100029 China
- University of Chinese Academy of Science, Beijing, 100049 China
| | - Zhaonan Cai
- Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, 100029 China
| | - Dongxu Yang
- Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, 100029 China
| | - Haibo Wang
- Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, 100029 China
- University of Chinese Academy of Science, Beijing, 100049 China
| | - Denghui Ji
- Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, 100029 China
- University of Chinese Academy of Science, Beijing, 100049 China
| | - Yang Yang
- Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, 100029 China
- University of Chinese Academy of Science, Beijing, 100049 China
| | - Pucai Wang
- Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, 100029 China
- University of Chinese Academy of Science, Beijing, 100049 China
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