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Chen F, Zhang W, Mfarrej MFB, Saleem MH, Khan KA, Ma J, Raposo A, Han H. Breathing in danger: Understanding the multifaceted impact of air pollution on health impacts. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 280:116532. [PMID: 38850696 DOI: 10.1016/j.ecoenv.2024.116532] [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: 12/08/2023] [Revised: 04/25/2024] [Accepted: 05/29/2024] [Indexed: 06/10/2024]
Abstract
Air pollution, a pervasive environmental threat that spans urban and rural landscapes alike, poses significant risks to human health, exacerbating respiratory conditions, triggering cardiovascular problems, and contributing to a myriad of other health complications across diverse populations worldwide. This article delves into the multifarious impacts of air pollution, utilizing cutting-edge research methodologies and big data analytics to offer a comprehensive overview. It highlights the emergence of new pollutants, their sources, and characteristics, thereby broadening our understanding of contemporary air quality challenges. The detrimental health effects of air pollution are examined thoroughly, emphasizing both short-term and long-term impacts. Particularly vulnerable populations are identified, underscoring the need for targeted health risk assessments and interventions. The article presents an in-depth analysis of the global disease burden attributable to air pollution, offering a comparative perspective that illuminates the varying impacts across different regions. Furthermore, it addresses the economic ramifications of air pollution, quantifying health and economic losses, and discusses the implications for public policy and health care systems. Innovative air pollution intervention measures are explored, including case studies demonstrating their effectiveness. The paper also brings to light recent discoveries and insights in the field, setting the stage for future research directions. It calls for international cooperation in tackling air pollution and underscores the crucial role of public awareness and education in mitigating its impacts. This comprehensive exploration serves not only as a scientific discourse but also as a clarion call for action against the invisible but insidious threat of air pollution, making it a vital read for researchers, policymakers, and the general public.
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Affiliation(s)
- Fu Chen
- School of Public Administration, Hohai University, Nanjing 211100, China.
| | - Wanyue Zhang
- School of Public Administration, Hohai University, Nanjing 211100, China
| | - Manar Fawzi Bani Mfarrej
- Department of Environmental Sciences and Sustainability, College of Natural and Health Sciences, Zayed University, Abu Dhabi 144534, United Arab Emirates
| | - Muhammad Hamzah Saleem
- Office of Academic Research, Office of VP for Research & Graduate Studies, Qatar University, Doha 2713, Qatar
| | - Khalid Ali Khan
- Applied College, Center of Bee Research and its Products, Unit of Bee Research and Honey Production, and Research Center for Advanced Materials Science (RCAMS), King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
| | - Jing Ma
- School of Public Administration, Hohai University, Nanjing 211100, China
| | - António Raposo
- CBIOS (Research Center for Biosciences and Health Technologies), Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, Lisboa 1749-024, Portugal
| | - Heesup Han
- College of Hospitality and Tourism Management, Sejong University, 98 Gunja-Dong, Gwanjin-Gu, Seoul 143-747, South Korea.
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Qu K, Yan Y, Wang X, Jin X, Vrekoussis M, Kanakidou M, Brasseur GP, Lin T, Xiao T, Cai X, Zeng L, Zhang Y. The effect of cross-regional transport on ozone and particulate matter pollution in China: A review of methodology and current knowledge. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 947:174196. [PMID: 38942314 DOI: 10.1016/j.scitotenv.2024.174196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 05/29/2024] [Accepted: 06/20/2024] [Indexed: 06/30/2024]
Abstract
China is currently one of the countries impacted by severe atmospheric ozone (O3) and particulate matter (PM) pollution. Due to their moderately long lifetimes, O3 and PM can be transported over long distances, cross the boundaries of source regions and contribute to air pollution in other regions. The reported contributions of cross-regional transport (CRT) to O3 and fine PM (PM2.5) concentrations often exceed those of local emissions in the major regions of China, highlighting the important role of CRT in regional air pollution. Therefore, further improvement of air quality in China requires more joint efforts among regions to ensure a proper reduction in emissions while accounting for the influence of CRT. This review summarizes the methodologies employed to assess the influence of CRT on O3 and PM pollution as well as current knowledge of CRT influence in China. Quantifying CRT contributions in proportion to O3 and PM levels and studying detailed CRT processes of O3, PM and precursors can be both based on targeted observations and/or model simulations. Reported publications indicate that CRT contributes by 40-80 % to O3 and by 10-70 % to PM2.5 in various regions of China. These contributions exhibit notable spatiotemporal variations, with differences in meteorological conditions and/or emissions often serving as main drivers of such variations. Based on trajectory-based methods, transport pathways contributing to O3 and PM pollution in major regions of China have been revealed. Recent studies also highlighted the important role of horizontal transport in the middle/high atmospheric boundary layer or low free troposphere, of vertical exchange and mixing as well as of interactions between CRT, local meteorology and chemistry in the detailed CRT processes. Drawing on the current knowledge on the influence of CRT, this paper provides recommendations for future studies that aim at supporting ongoing air pollution mitigation strategies in China.
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Affiliation(s)
- Kun Qu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China; Laboratory for Modeling and Observation of the Earth System (LAMOS), Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
| | - Yu Yan
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China; Sichuan Academy of Environmental Policy and Planning, Chengdu 610041, China
| | - Xuesong Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China.
| | - Xipeng Jin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China; 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
| | - Mihalis Vrekoussis
- Laboratory for Modeling and Observation of the Earth System (LAMOS), Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany; Center of Marine Environmental Sciences (MARUM), University of Bremen, Bremen, Germany; Climate and Atmosphere Research Center (CARE-C), The Cyprus Institute, Nicosia, Cyprus
| | - Maria Kanakidou
- Laboratory for Modeling and Observation of the Earth System (LAMOS), Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany; Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, Heraklion, Greece; Center of Studies of Air quality and Climate Change, Institute for Chemical Engineering Sciences, Foundation for Research and Technology Hellas, Patras, Greece
| | - Guy P Brasseur
- Max Planck Institute for Meteorology, Hamburg, Germany; National Center for Atmospheric Research, Boulder, CO, USA
| | - Tingkun Lin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China
| | - Teng Xiao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China
| | - Xuhui Cai
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China
| | - Limin Zeng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100816, China; Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, Beijing 100871, China; CAS Center for Excellence in Regional Atmospheric Environment, Chinese Academy of Sciences, Xiamen 361021, China.
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Xing C, Liu C, Li Q, Wang S, Tan W, Zou T, Wang Z, Lu C. Observations of HONO and its precursors between urban and its surrounding agricultural fields: The vertical transports, sources and contribution to OH. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 915:169159. [PMID: 38232854 DOI: 10.1016/j.scitotenv.2023.169159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 11/21/2023] [Accepted: 12/05/2023] [Indexed: 01/19/2024]
Abstract
The insufficient study on vertical observations of main atmospheric reactive nitrogen oxides (NO2 and HONO) posed a great challenge to evaluate their intertransport between urban and agricultural areas, and to further learn the atmospheric nitrogen chemistry and the atmospheric oxidation capacity at high altitudes. A stereoscopic measurement campaign (satellite remote sensing, hyperspectral unmanned aerial vehicle (UAV) remote sensing and MAX-DOAS observation) was performed in a typical inland city Hefei and its surrounding agricultural fields from June to October 2022. Average aerosol vertical profiles exhibited a Gaussian shape above 100 m with maximum values of 0.67 km-1 and 0.55 km-1 at 300-400 m layer at Anhui University (AHU) and Changfeng (CF), respectively. The distinct layered structure was mainly attributed to regional transport. Average H2O and NO2 vertical profiles all showed a Gaussian shape and an exponential shape at AHU and CF, respectively. Moreover, the diurnal evolution of H2O profiles performed one peak and bi-peak patterns at AHU and CF, respectively, whereas the diurnal evolution of NO2 at two stations all exhibited bi-peak patterns attributed to vehicle emissions. Average HONO vertical profiles showed an exponential shape and a Gaussian shape at AHU and CF, respectively. Higher HONO (> 0.05 ppb) above 1.0 km at 14:00-16:00 was observed at CF. The transport flux analysis showed that the northern transport flux always larger than southern transport flux for aerosol and H2O. The maximum northern transport fluxes appeared at 300 m and surface for aerosol and H2O, respectively. It indicated that surrounding agricultural fields was an important source of atmospheric H2O of city. The southern transport flux was larger than northern transport flux for NO2, with a maximum net transport flux of 9.20 ppb m s-1 at 100 m. It demonstrated that NO2 transported from urban areas was an important source of NO2 in agricultural fields. For HONO, the southern transport flux was larger than northern transport flux under 100 m, whereas it was opposite above 100 m. It indicated that the HONO distributed at high altitudes at agricultural fields had potential to enhance the atmospheric oxidation capacity of urban area. The net horizontal transport fluxes of HONO of our defined cropland were 5.25 μg m-2 s-1 and -3.65 μg m-2 s-1 during non-fertilization and fertilization periods, respectively. It indicated that the cropland could obviously export HONO to surrounding atmosphere during the fertilization period. Deducing the contribution of direct emission, heterogeneous process was a major source of HONO at urban and agricultural areas. The average surface conversion rate of NO2-to-HONO (CHONO) was 0.01467 h-1, and this value decreased with the increase of height at urban station. While average surface CHONO was 0.0322 h-1 at agricultural fields, which was ~1.2-2.8 times higher than that at urban area. The CHONO at agricultural fields significantly increased with the increase of height. The average CHONO at 1.0 km was ~2.0-3.6 times higher than that at surface. That suggested that the heterogeneous process was the main HONO source at high altitudes at CF, and this process obviously correlated with aerosol and H2O. The higher OH production from HONO (P(OH)HONO) occurred at 0-200 m and 100-400 m with averaged values of 0.31 ppb h-1 and 0.39 ppb h-1 at AHU and CF, respectively. The high P(OH)HONO above 1.0 km at CF from September to October was strongly correlated with high O3 (> 80 ppb). This study emphasized the importance of the stereoscopic of HONO on the analysis of its distribution, evolution, source and atmospheric oxidizing contribution.
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Affiliation(s)
- Chengzhi Xing
- Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Cheng Liu
- Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei 230026, China.
| | - Qihua Li
- Institute of Physical Science and Information Technology, Anhui University, Hefei 230601, China
| | - Shanshan Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Shanghai Institute of Eco-Chongming (SIEC), No.3663 Northern Zhongshan Road, Shanghai 200062, China
| | - Wei Tan
- Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Tiliang Zou
- School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei 230026, China
| | - Zhuang Wang
- Anhui Province Key Laboratory of Atmospheric Science and Satellite Remote Sensing, Anhui Institute of Meteorological Sciences, Hefei 230031, China; Shouxian National Climatology Observatory, Shouxian 232200, China; Huaihe River Basin Typical Farmland Ecological Meteorological Field Science Experiment Base of CMA, Shouxian 232200, China.
| | - Chuan Lu
- Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
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Bahadur FT, Shah SR, Nidamanuri RR. Applications of remote sensing vis-à-vis machine learning in air quality monitoring and modelling: a review. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1502. [PMID: 37987882 DOI: 10.1007/s10661-023-12001-2] [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: 05/28/2023] [Accepted: 10/22/2023] [Indexed: 11/22/2023]
Abstract
Environmental contamination especially air pollution is an exponentially growing menace requiring immediate attention, as it lingers on with the associated risks of health, economic and ecological crisis. The special focus of this study is on the advances in Air Quality (AQ) monitoring using modern sensors, integrated monitoring systems, remote sensing and the usage of Machine Learning (ML), Deep Learning (DL) algorithms, artificial neural networks, recent computational techniques, hybridizing techniques and different platforms available for AQ modelling. The modern world is data-driven, where critical decisions are taken based on the available and accessible data. Today's data analytics is a consequence of the information explosion we have reached. The current research also tends to re-evaluate its scope with data analytics. The emergence of artificial intelligence and machine learning in the research scenario has radically changed the methodologies and approaches of modern research. The aim of this review is to assess the impact of data analytics such as ML/DL frameworks, data integration techniques, advanced statistical modelling, cloud computing platforms and constantly improving optimization algorithms on AQ research. The usage of remote sensing in AQ monitoring along with providing enormous datasets is constantly filling the spatial gaps of ground stations, as the long-term air pollutant dynamics is best captured by the panoramic view of satellites. Remote sensing coupled with the techniques of ML/DL has the most impact in shaping the modern trends in AQ research. Current standing of research in this field, emerging trends and future scope are also discussed.
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Affiliation(s)
- Faizan Tahir Bahadur
- Department of Civil Engineering, National Institute of Technology, Srinagar, Jammu and Kashmir, 190006, India.
| | - Shagoofta Rasool Shah
- Department of Civil Engineering, National Institute of Technology, Srinagar, Jammu and Kashmir, 190006, India
| | - Rama Rao Nidamanuri
- Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Trivandrum, Kerala, 695547, India
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Herman DI, Mead G, Giorgetta FR, Baumann E, Malarich NA, Washburn BR, Newbury NR, Coddington I, Cossel KC. Open-path measurement of stable water isotopologues using mid-infrared dual-comb spectroscopy. ATMOSPHERIC MEASUREMENT TECHNIQUES 2023; 16:10.5194/amt-16-4053-2023. [PMID: 37961051 PMCID: PMC10642444 DOI: 10.5194/amt-16-4053-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
We present an open-path mid-infrared dual-comb spectroscopy (DCS) system capable of precise measurement of the stable water isotopologues H216O and HD16O. This system ran in a remote configuration at a rural test site for 3.75 months with 60% uptime and achieved a precision of < 2‰ on the normalized ratio of H216O and HD16O (δ D ) in 1000s. Here, we compare the δ D values from the DCS system to those from the National Ecological Observatory Network (NEON) isotopologue point sensor network. Over the multi-month campaign, the mean difference between the DCS δ D values and the NEON δ D values from a similar ecosystem is < 2‰ with a standard deviation of 18‰, which demonstrates the inherent accuracy of DCS measurements over a variety of atmospheric conditions. We observe time-varying diurnal profiles and seasonal trends that are mostly correlated between the sites on daily timescales. This observation motivates the development of denser ecological monitoring networks aimed at understanding regional- and synoptic-scale water transport. Precise and accurate open-path measurements using DCS provide new capabilities for such networks.
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Affiliation(s)
- Daniel I. Herman
- Spectrum Technology and Research Division, National Institute of Standards and Technology, Boulder, Colorado 80305, United States of America
- Department of Physics, University of Colorado Boulder, Boulder, Colorado 80309, United States of America
| | - Griffin Mead
- Spectrum Technology and Research Division, National Institute of Standards and Technology, Boulder, Colorado 80305, United States of America
| | - Fabrizio R. Giorgetta
- Spectrum Technology and Research Division, National Institute of Standards and Technology, Boulder, Colorado 80305, United States of America
- Department of Physics, University of Colorado Boulder, Boulder, Colorado 80309, United States of America
| | - Esther Baumann
- Spectrum Technology and Research Division, National Institute of Standards and Technology, Boulder, Colorado 80305, United States of America
- Department of Physics, University of Colorado Boulder, Boulder, Colorado 80309, United States of America
| | - Nathan A. Malarich
- Spectrum Technology and Research Division, National Institute of Standards and Technology, Boulder, Colorado 80305, United States of America
| | - Brian R. Washburn
- Spectrum Technology and Research Division, National Institute of Standards and Technology, Boulder, Colorado 80305, United States of America
- Department of Physics, University of Colorado Boulder, Boulder, Colorado 80309, United States of America
| | - Nathan R. Newbury
- Spectrum Technology and Research Division, National Institute of Standards and Technology, Boulder, Colorado 80305, United States of America
| | - Ian Coddington
- Spectrum Technology and Research Division, National Institute of Standards and Technology, Boulder, Colorado 80305, United States of America
| | - Kevin C. Cossel
- Spectrum Technology and Research Division, National Institute of Standards and Technology, Boulder, Colorado 80305, United States of America
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