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Julin J, Murphy BN, Patoulias D, Fountoukis C, Olenius T, Pandis SN, Riipinen I. Impacts of Future European Emission Reductions on Aerosol Particle Number Concentrations Accounting for Effects of Ammonia, Amines, and Organic Species. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:692-700. [PMID: 29185762 PMCID: PMC6056894 DOI: 10.1021/acs.est.7b05122] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Although they are currently unregulated, atmospheric ultrafine particles (<100 nm) pose health risks because of, e.g., their capability to penetrate deep into the respiratory system. Ultrafine particles, often minor contributors to atmospheric particulate mass, typically dominate aerosol particle number concentrations. We simulated the response of particle number concentrations over Europe to recent estimates of future emission reductions of aerosol particles and their precursors. We used the chemical transport model PMCAMx-UF, with novel updates including state-of-the-art descriptions of ammonia and dimethylamine new particle formation (NPF) pathways and the condensation of organic compounds onto particles. These processes had notable impacts on atmospheric particle number concentrations. All three emission scenarios (current legislation, optimized emissions, and maximum technically feasible reductions) resulted in substantial (10-50%) decreases in median particle number concentrations over Europe. Consistent reductions were predicted in Central Europe, while Northern Europe exhibited smaller reductions or even increased concentrations. Motivated by the improved NPF descriptions for ammonia and methylamines, we placed special focus on the potential to improve air quality by reducing agricultural emissions, which are a major source of these species. Agricultural emission controls showed promise in reducing ultrafine particle number concentrations, although the change is nonlinear with particle size.
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Affiliation(s)
- Jan Julin
- Department of Environmental Science and Analytical Chemistry (ACES) and Bolin Centre for Climate Research, Stockholm University , SE-10691, Stockholm, Sweden
- Department of Applied Physics, University of Eastern Finland , FI-70211, Kuopio, Finland
| | - Benjamin N Murphy
- National Exposure Research Laboratory, US Environmental Protection Agency, Research Triangle Park , Durham, North Carolina 27709, United States
| | - David Patoulias
- Department of Chemical Engineering, University of Patras , GR-26504, Patras, Greece
| | - Christos Fountoukis
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Qatar Foundation , P.O. Box 5825, Doha, Qatar
| | - Tinja Olenius
- Department of Environmental Science and Analytical Chemistry (ACES) and Bolin Centre for Climate Research, Stockholm University , SE-10691, Stockholm, Sweden
| | - Spyros N Pandis
- Department of Chemical Engineering, University of Patras , GR-26504, Patras, Greece
- Department of Chemical Engineering, Carnegie Mellon University , Pittsburgh, Pennsylvania 15213, United States
- Institute of Chemical Engineering Sciences, Foundation for Research and Technology , GR-26504, Patras, Greece
| | - Ilona Riipinen
- Department of Environmental Science and Analytical Chemistry (ACES) and Bolin Centre for Climate Research, Stockholm University , SE-10691, Stockholm, Sweden
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Machine Learning to Predict the Global Distribution of Aerosol Mixing State Metrics. ATMOSPHERE 2018. [DOI: 10.3390/atmos9010015] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Dunne EM, Gordon H, Kurten A, Almeida J, Duplissy J, Williamson C, Ortega IK, Pringle KJ, Adamov A, Baltensperger U, Barmet P, Benduhn F, Bianchi F, Breitenlechner M, Clarke A, Curtius J, Dommen J, Donahue NM, Ehrhart S, Flagan RC, Franchin A, Guida R, Hakala J, Hansel A, Heinritzi M, Jokinen T, Kangasluoma J, Kirkby J, Kulmala M, Kupc A, Lawler MJ, Lehtipalo K, Makhmutov V, Mann G, Mathot S, Merikanto J, Miettinen P, Nenes A, Onnela A, Rap A, Reddington CLS, Riccobono F, Richards NAD, Rissanen MP, Rondo L, Sarnela N, Schobesberger S, Sengupta K, Simon M, Sipila M, Smith JN, Stozkhov Y, Tome A, Trostl J, Wagner PE, Wimmer D, Winkler PM, Worsnop DR, Carslaw KS. Global atmospheric particle formation from CERN CLOUD measurements. Science 2016; 354:1119-1124. [DOI: 10.1126/science.aaf2649] [Citation(s) in RCA: 213] [Impact Index Per Article: 26.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Accepted: 10/12/2016] [Indexed: 11/03/2022]
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Simulating Ultrafine Particle Formation in Europe Using a Regional CTM: Contribution of Primary Emissions Versus Secondary Formation to Aerosol Number Concentrations. ACTA ACUST UNITED AC 2012. [DOI: 10.1007/978-3-642-29172-2_133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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Zhang Y, Liu P, Liu XH, Jacobson MZ, McMurry PH, Yu F, Yu S, Schere KL. A comparative study of nucleation parameterizations: 2. Three-dimensional model application and evaluation. ACTA ACUST UNITED AC 2010. [DOI: 10.1029/2010jd014151] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Zhang Y, McMurry PH, Yu F, Jacobson MZ. A comparative study of nucleation parameterizations: 1. Examination and evaluation of the formulations. ACTA ACUST UNITED AC 2010. [DOI: 10.1029/2010jd014150] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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