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Yassine MM, Dabek-Zlotorzynska E, Celo V, Sofowote UM, Mooibroek D, Hopke PK. Effect of industrialization on the differences in sources and composition of ambient PM 2.5 in two Southern Ontario locations. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 341:123007. [PMID: 38006992 DOI: 10.1016/j.envpol.2023.123007] [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: 09/08/2023] [Revised: 10/27/2023] [Accepted: 11/18/2023] [Indexed: 11/27/2023]
Abstract
PM2.5 was sampled over a seven-year period (2013-2019) at two locations ∼50 km apart in Southern Ontario (concurrently for five years: 2015-2019). One is a heavily industrialized site (Hamilton), while the other was a rural site (Simcoe). To assess the impact of industrialization on the composition and sources of PM affecting air quality in these two locations, positive matrix factorization coupled with dispersion normalization (DN-PMF) was used to identify six and eight factors at Simcoe and Hamilton, respectively. The Simcoe factors in order of diminishing PM mass contribution were: particulate sulphate (pSO4), secondary organic aerosol (SOA), crustal matter, particulate nitrate (pNO3), biomass burning, and vehicular emissions. At Hamilton, the effects of industrialization were observed by the ∼36% higher average ambient PM2.5 concentration for the study period as well as the presence of factors unique to metallurgy, i.e., coking and steelmaking, compared to Simcoe. The coking and steelmaking factors contributed ∼15% to the PM mass at Hamilton. Seasonal variants of appropriate nonparametric trend tests with the associated slopes (Sen's) were used to assess statistically significant changes in the factor contributions to PM2.5 over time. Specifically at Hamilton, a significant decline in PM contributions was noted for coking (-0.03 μg/m³/yr or -4.1%/yr) while steelmaking showed no statistically significant decline over the study period. Other factors at Hamilton that showed statistically significant declines over the study period were: pSO4 (-0.27 μg/m³/yr or -12.6%/yr), biomass burning (-0.05 μg/m³/yr or -9.02%/yr), crustal matter (-0.03 μg/m³/yr or -5.28%/yr). These factors mainly accounted for the significant decline in PM2.5 over the study period (-0.35 μg/m³/yr or -4.24%/yr). This work shows the importance of long-term monitoring in assessing the unique contributions and temporal changes of industrialization on air quality in Ontario and similarly affected locations.
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Affiliation(s)
- Mahmoud M Yassine
- Analysis and Air Quality Section, Air Quality Research Division, Science and Technology Branch, Environment and Climate Change Canada, 335 River Road, Ottawa, ON, K1A 0H3, Canada
| | - Ewa Dabek-Zlotorzynska
- Analysis and Air Quality Section, Air Quality Research Division, Science and Technology Branch, Environment and Climate Change Canada, 335 River Road, Ottawa, ON, K1A 0H3, Canada
| | - Valbona Celo
- Analysis and Air Quality Section, Air Quality Research Division, Science and Technology Branch, Environment and Climate Change Canada, 335 River Road, Ottawa, ON, K1A 0H3, Canada
| | - Uwayemi M Sofowote
- Environmental Monitoring and Reporting Branch, Ontario Ministry of the Environment, Conservation and Parks, Toronto, Canada.
| | - Dennis Mooibroek
- Centre for Environmental Monitoring, National Institute for Public Health and the Environment (RIVM), A. van Leeuwenhoeklaan 9, P.O. Box 1, 3720, BA, Bilthoven, the Netherlands
| | - Philip K Hopke
- Institute for a Sustainable Environment, Clarkson University, Potsdam, NY, USA; Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
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Dajnak D, Assareh N, Kitwiroon N, Beddows AV, Stewart GB, Hicks W, Beevers SD. Can the UK meet the World Health Organization PM 2.5 interim target of 10 μg m -3 by 2030? ENVIRONMENT INTERNATIONAL 2023; 181:108222. [PMID: 37948865 DOI: 10.1016/j.envint.2023.108222] [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: 06/20/2023] [Revised: 09/13/2023] [Accepted: 09/19/2023] [Indexed: 11/12/2023]
Abstract
The recent United Kingdom (UK) Environment Act consultation had the intention of setting two targets for PM2.5 (particles with an aerodynamic diameter less than 2.5 μm), one related to meeting an annual average concentration and the second to reducing population exposure. As part of the consultation, predictions of PM2.5 concentrations in 2030 were made by combining European Union (EU) and UK government's emissions forecasts, with the Climate Change Committee's (CCC) Net Zero vehicle forecasts, and in London with the addition of local policies based on the London Environment Strategy (LES). Predictions in 2018 showed 6.4% of the UK's area and 82.6% of London's area had PM2.5 concentrations above the World Health Organization (WHO) interim target of 10 μg m-3, but by 2030, over 99% of the UK's area was predicted to be below it. However, kerbside concentrations in London and other major cities were still at risk of exceeding 10 μg m-3. With local action on PM2.5 in London, population weighted concentrations showed full compliance with the WHO interim target of 10 μg m-3 in 2030. However, predicting future PM2.5 concentrations and interpreting the results will always be difficult and uncertain for many reasons, such as imperfect models and the difficulty in estimating future emissions. To help understand the sensitivity of the model's PM2.5 predictions in 2030, current uncertainty was quantified using PM2.5 measurements and showed large areas in the UK that were still at risk of exceeding the WHO interim target despite the model predictions being below 10 μg m-3. Our results do however point to the benefits that policy at EU, UK and city level can have on achieving the WHO interim target of 10 μg m-3. These results were submitted to the UK Environment Act consultation. Nevertheless, the issues addressed here could be applicable to other European cities.
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Affiliation(s)
- David Dajnak
- Environmental Research Group, School of Public Health, Imperial College London, Sir Michael Uren Biomedical Engineering Hub, White City Campus, 80 Wood Lane, W12 0BZ London, United Kingdom.
| | - Nosha Assareh
- Environmental Research Group, School of Public Health, Imperial College London, Sir Michael Uren Biomedical Engineering Hub, White City Campus, 80 Wood Lane, W12 0BZ London, United Kingdom
| | - Nutthida Kitwiroon
- Environmental Research Group, School of Public Health, Imperial College London, Sir Michael Uren Biomedical Engineering Hub, White City Campus, 80 Wood Lane, W12 0BZ London, United Kingdom
| | - Andrew V Beddows
- Environmental Research Group, School of Public Health, Imperial College London, Sir Michael Uren Biomedical Engineering Hub, White City Campus, 80 Wood Lane, W12 0BZ London, United Kingdom
| | - Gregor B Stewart
- Environmental Research Group, School of Public Health, Imperial College London, Sir Michael Uren Biomedical Engineering Hub, White City Campus, 80 Wood Lane, W12 0BZ London, United Kingdom
| | - William Hicks
- Environmental Research Group, School of Public Health, Imperial College London, Sir Michael Uren Biomedical Engineering Hub, White City Campus, 80 Wood Lane, W12 0BZ London, United Kingdom
| | - Sean D Beevers
- Environmental Research Group, School of Public Health, Imperial College London, Sir Michael Uren Biomedical Engineering Hub, White City Campus, 80 Wood Lane, W12 0BZ London, United Kingdom
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Zhang W, Huang W, Tan J, Guo Q, Wu B. Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects. CHEMOSPHERE 2022; 308:136447. [PMID: 36116627 DOI: 10.1016/j.chemosphere.2022.136447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/08/2022] [Accepted: 09/11/2022] [Indexed: 06/15/2023]
Abstract
Energy crisis and environmental pollution have become the bottleneck of human sustainable development. Therefore, there is an urgent need to develop new catalysts for energy production and environmental remediation. Due to the high cost caused by blind screening and limited valuable computing resources, the traditional experimental methods and theoretical calculations are difficult to meet with the requirements. In the past decades, computer science has made great progress, especially in the field of machine learning (ML). As a new research paradigm, ML greatly accelerates the theoretical calculation methods represented by first principal calculation and molecular dynamics, and establish the physical picture of heterogeneous catalytic processes for energy and environment. This review firstly summarized the general research paradigms of ML in the discovery of catalysts. Then, the latest progresses of ML in light-, electricity- and enzyme-mediated heterogeneous catalysis were reviewed from the perspective of catalytic performance, operating conditions and reaction mechanism. The general guidelines of ML for heterogeneous catalysis were proposed. Finally, the existing problems and future development trend of ML in heterogeneous catalysis mediated by light, electricity and enzyme were summarized. We highly expect that this review will facilitate the interaction between ML and heterogeneous catalysis, and illuminate the development prospect of heterogeneous catalysis.
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Affiliation(s)
- Wentao Zhang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Wenguang Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China.
| | - Jie Tan
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China
| | - Qingwei Guo
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China
| | - Bingdang Wu
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, People's Republic of China; Key Laboratory of Suzhou Sponge City Technology, Suzhou, 215002, People's Republic of China.
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Hopke PK, Querol X. Is Improved Vehicular NOx Control Leading to Increased Urban NH 3 Emissions? ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:11926-11927. [PMID: 35939076 DOI: 10.1021/acs.est.2c04996] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Affiliation(s)
- Philip K Hopke
- Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, New York 14642, United States
- Institute for a Sustainable Environment, Clarkson University, Potsdam, New York 13699-5708, United States
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Road Traffic and Its Influence on Urban Ammonia Concentrations (France). ATMOSPHERE 2022. [DOI: 10.3390/atmos13071032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Ammonia (NH3) is an unregulated atmospheric gaseous pollutant in ambient air, involved in the formation of fine particles. Ammonia is therefore a major precursor of particulate matter (PM), the health effects of which have been widely demonstrated. NH3 emissions are clearly dominated by the agricultural sector (livestock and fertilizers), but other sources may also be important and less studied, such as road traffic with the increased use of catalytic converters in vehicles. This study is based on a long-term real-time measurements campaign (December 2019–September 2021) on two urban sites: a background site and a roadside site in the same agglomeration in France. The study of historical measurements at the background site clearly demonstrated the dominance of agriculture on the ammonia concentrations. This influence was also observed at both sites during the measurement campaign. The annual and monthly averages obtained in the study were similar to previous ones, with concentrations between 1–10 µg/m3 at both sites, indicating lower levels than previous studies for the roadside site. The ammonia levels measured during the campaign at the traffic site were significantly higher than those measured at the background site, highlighting the road traffic influence on ammonia in urban area. The biomass burning influence also seemed to be observed during this long measurement campaign at the agglomeration scale. The influences of road traffic and biomass burning on ammonia concentration remain small compared to agriculture.
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