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Yong H, Allen G, Mcquilkin J, Ricketts H, Shaw JT. Lessons learned from a UAV survey and methane emissions calculation at a UK landfill. WASTE MANAGEMENT (NEW YORK, N.Y.) 2024; 180:47-54. [PMID: 38507836 DOI: 10.1016/j.wasman.2024.03.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 03/01/2024] [Accepted: 03/16/2024] [Indexed: 03/22/2024]
Abstract
Accurate quantification of methane emissions from landfills is crucial for improving greenhouse gas inventories and mitigating climate change impacts. Existing methodologies, such as theoretical gas production models and labour-intensive measurement approaches, present limitations including large uncertainties and high operational costs. This study adds to a growing body of research and applications which aim to bridge this gap. To this end, we present a case study using Unmanned Aerial Vehicles (UAVs) equipped with methane and wind instrumentation for a survey of a landfill site in Bury, Manchester, UK, in summer 2022, in order to evaluate and reflect the challenges of the UAV-based mass balance method for quantification of methane emissions from a large heterogeneous source such as landfill. This study offers guidance on defining an appropriate methane background concentration, geospatial interpolation of sampled date, survey sampling strategy, and more importantly, addresses the challenges surrounding UAV wind measurements and spatial characterisation of emission plumes. For the period of the case study, we quantified methane flux for the landfill site to be 150.7 kg h-1 with a 1 standard deviation uncertainty range of 83.0 kg h-1 to 209.5 kg h-1.
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Affiliation(s)
- Han Yong
- Department of Earth and Environmental Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom.
| | - Grant Allen
- Department of Earth and Environmental Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom
| | - Jamie Mcquilkin
- Department of Earth and Environmental Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom
| | - Hugo Ricketts
- Department of Earth and Environmental Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom; National Centre for Atmospheric Science, University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom
| | - Jacob T Shaw
- Department of Earth and Environmental Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom
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2
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Sadeghi B, Ghahremanloo M, Mousavinezhad S, Lops Y, Pouyaei A, Choi Y. Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 310:119863. [PMID: 35963387 DOI: 10.1016/j.envpol.2022.119863] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 07/07/2022] [Accepted: 07/23/2022] [Indexed: 06/15/2023]
Abstract
From hourly ozone observations obtained from three regions⸻Houston, Dallas, and West Texas⸻we investigated the contributions of meteorology to changes in surface daily maximum 8-h average (MDA8) ozone from 2000 to 2019. We applied a deep convolutional neural network and Shapely additive explanation (SHAP) to examine the complex underlying nonlinearity between variations of surface ozone and meteorological factors. Results of the models showed that between 2000 and 2019, specific humidity (38% and 27%) and temperature (28% and 37%) contributed the most to ozone formation over the Houston and Dallas metropolitan areas, respectively. On the other hand, the results show that solar radiation (50%) strongly impacted ozone variation over West Texas during this time. Using a combination of the Kolmogorov-Zurbenko (KZ) filter and multiple linear regression, we also evaluated the influence of meteorology on ozone and quantified the contributions of meteorological parameters to trends in surface ozone formation. Our findings showed that in Houston and Dallas, meteorology influenced ozone variations to a large extent. The impacts of meteorology on West Texas, however, showed meteorological factors had fewer influences on ozone variabilities from 2000 to 2019. This study showed that SHAP analysis and the KZ approach can investigate the contributions of the meteorological factors on ozone concentrations and help policymakers enact effective ozone mitigation policies.
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Affiliation(s)
- Bavand Sadeghi
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA
| | - Masoud Ghahremanloo
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA
| | | | - Yannic Lops
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA
| | - Arman Pouyaei
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA
| | - Yunsoo Choi
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA.
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Chossière GP, Xu H, Dixit Y, Isaacs S, Eastham SD, Allroggen F, Speth RL, Barrett SRH. Air pollution impacts of COVID-19-related containment measures. SCIENCE ADVANCES 2021; 7:eabe1178. [PMID: 34020946 PMCID: PMC8139585 DOI: 10.1126/sciadv.abe1178] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 04/02/2021] [Indexed: 05/05/2023]
Abstract
Responses to the COVID-19 outbreak resulted in one of the largest short-term decreases in anthropogenic emissions in modern history. To date, there has been no comprehensive assessment of the impact of lockdowns on air quality and human health. Using global satellite observations and ground measurements from 36 countries in Europe, North America, and East Asia, we find that lockdowns led to reductions in NO2 concentrations globally, resulting in ~32,000 avoided premature mortalities, including ~21,000 in China. However, we do not find corresponding reductions in PM2.5 and ozone globally. Using satellite measurements, we show that the disconnect between NO2 and ozone changes stems from local chemical regimes. The COVID-related lockdowns demonstrate the need for targeted air quality policies to reduce the global burden of air pollution, especially related to secondary pollutants.
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Affiliation(s)
- Guillaume P Chossière
- Laboratory for Aviation and the Environment, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Haofeng Xu
- Laboratory for Aviation and the Environment, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yash Dixit
- Laboratory for Aviation and the Environment, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stewart Isaacs
- Laboratory for Aviation and the Environment, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sebastian D Eastham
- Laboratory for Aviation and the Environment, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Florian Allroggen
- Laboratory for Aviation and the Environment, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Raymond L Speth
- Laboratory for Aviation and the Environment, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Steven R H Barrett
- Laboratory for Aviation and the Environment, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Mechanical Engineering, Seoul National University, Seoul, South Korea
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4
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O’Lenick CR, Baniassadi A, Michael R, Monaghan A, Boehnert J, Yu X, Hayden MH, Wiedinmyer C, Zhang K, Crank PJ, Heusinger J, Hoel P, Sailor DJ, Wilhelmi OV. A Case-Crossover Analysis of Indoor Heat Exposure on Mortality and Hospitalizations among the Elderly in Houston, Texas. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:127007. [PMID: 33300819 PMCID: PMC7727721 DOI: 10.1289/ehp6340] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 11/09/2020] [Accepted: 11/13/2020] [Indexed: 05/31/2023]
Abstract
BACKGROUND Despite the substantial role indoor exposure has played in heat wave-related mortality, few epidemiological studies have examined the health effects of exposure to indoor heat. As a result, knowledge gaps regarding indoor heat-health thresholds, vulnerability, and adaptive capacity persist. OBJECTIVE We evaluated the role of indoor heat exposure on mortality and morbidity among the elderly (≥65 years of age) in Houston, Texas. METHODS Mortality and emergency hospital admission data were obtained through the Texas Department of State Health Services. Summer indoor heat exposure was modeled at the U.S. Census block group (CBG) level using building energy models, outdoor weather data, and building characteristic data. Indoor heat-health associations were examined using time-stratified case-crossover models, controlling for temporal trends and meteorology, and matching on CBG of residence, year, month, and weekday of the adverse health event. Separate models were fitted for three indoor exposure metrics, for individual lag days 0-6, and for 3-d moving averages (lag 0-2). Effect measure modification was explored via stratification on individual- and area-level vulnerability factors. RESULTS We estimated positive associations between short-term changes in indoor heat exposure and cause-specific mortality and morbidity [e.g., circulatory deaths, odds ratio per 5°C increase=1.16 (95% CI: 1.03, 1.30)]. Associations were generally positive for earlier lag periods and weaker across later lag periods. Stratified analyses suggest stronger associations between indoor heat and emergency hospital admissions among African Americans compared with Whites. DISCUSSION Findings suggest excess mortality among certain elderly populations in Houston who are likely exposed to high indoor heat. We developed a novel methodology to estimate indoor heat exposure that can be adapted to other U.S. LOCATIONS In locations with high air conditioning prevalence, simplified modeling approaches may adequately account for indoor heat exposure in vulnerable neighborhoods. Accounting for indoor heat exposure may improve the estimation of the total impact of heat on health. https://doi.org/10.1289/EHP6340.
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Affiliation(s)
- Cassandra R. O’Lenick
- Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
| | - Amir Baniassadi
- School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, Arizona, USA
- Harvard University, Cambridge, Massachusetts, USA
| | - Ryan Michael
- Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
| | | | - Jennifer Boehnert
- Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
| | - Xiao Yu
- Department of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Mary H. Hayden
- University of Colorado-Colorado Springs, Colorado Springs, Colorado, USA
| | | | - Kai Zhang
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Southwest Center for Occupational and Environmental Health, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Peter J. Crank
- School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, Arizona, USA
| | - Jannik Heusinger
- School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, Arizona, USA
| | - Paige Hoel
- Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
- University of California, Los Angeles, Los Angeles, USA
| | - David J. Sailor
- School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, Arizona, USA
| | - Olga V. Wilhelmi
- Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
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Analysis of Spatio-temporal Characteristics and Driving Forces of Air Quality in the Northern Coastal Comprehensive Economic Zone, China. SUSTAINABILITY 2020. [DOI: 10.3390/su12020536] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Comprehensive analysis of air quality is essential to underpin knowledge-based air quality conservation policies and funding decisions by governments and managers. In this paper, air quality change characteristics for the Northern Coastal Comprehensive Economic Zone from 2008 to 2018 were analyzed using air quality indices. The spatio-temporal pattern of air quality was identified using centroid migration, spatial autocorrelation analysis and spatial analysis in a geographic information system (GIS). A spatial econometric model was established to confirm the natural and anthropogenic factors affecting air quality. Results showed that air pollution decreased significantly. PM2.5, PM10, and O3 were the primary pollutants. The air quality exhibited an inverted U-shaped trend from January to December, with the highest quality being observed in summer and the lowest during winter. Spatially, the air quality showed an increasing trend from inland to the coast and from north to south, with significant spatial autocorrelation and clustering. Population, energy consumption, temperature, and atmospheric pressure had significant negative impacts on air quality, while wind speed had a positive impact. This study offers an efficient and effective method to evaluate air quality change. The research provides important scientific information necessary for developing future air pollution prevention and control.
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