1
|
Torbatian S, Saleh M, Xu J, Minet L, Gamage SM, Yazgi D, Yamanouchi S, Roorda MJ, Hatzopoulou M. Societal Co-benefits of Zero-Emission Vehicles in the Freight Industry. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:7814-7825. [PMID: 38668733 DOI: 10.1021/acs.est.3c08867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
This study was set in the Greater Toronto and Hamilton Area (GTHA), where commercial vehicle movements were assigned across the road network. Implications for greenhouse gas (GHG) emissions, air quality, and health were examined through an environmental justice lens. Electrification of light-, medium-, and heavy-duty trucks was assessed to identify scenarios associated with the highest benefits for the most disadvantaged communities. Using spatially and temporally resolved commercial vehicle movements and a chemical transport model, changes in air pollutant concentrations under electric truck scenarios were estimated at 1-km2 resolution. Heavy-duty truck electrification reduces ambient black carbon and nitrogen dioxide on average by 10 and 14%, respectively, and GHG emissions by 10.5%. It achieves the highest reduction in premature mortality attributable to fine particulate matter chronic exposure (around 200 cases per year) compared with light- and medium-duty electrification (less than 150 cases each). The burden of all traffic in the GTHA was estimated to be around 600 cases per year. The benefits of electrification accrue primarily in neighborhoods with a high social disadvantage, measured by the Ontario Marginalization Indices, narrowing the disparity of exposure to traffic-related air pollution. Benefits related to heavy-duty truck electrification reflect the adverse impacts of diesel-fueled freight and highlight the co-benefits achieved by electrifying this sector.
Collapse
Affiliation(s)
- Sara Torbatian
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario,Canada M5S 1A4
| | - Marc Saleh
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario,Canada M5S 1A4
| | - Junshi Xu
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario,Canada M5S 1A4
| | - Laura Minet
- Department of Civil Engineering, University of Victoria, Victoria, British Columbia, Canada V8W 2Y2
| | | | - Daniel Yazgi
- Department of Research and Development, Swedish Meteorological and Hydrological Institute, Norrköping 60176, Sweden
| | - Shoma Yamanouchi
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario,Canada M5S 1A4
| | - Matthew J Roorda
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario,Canada M5S 1A4
| | - Marianne Hatzopoulou
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario,Canada M5S 1A4
| |
Collapse
|
2
|
Wen Y, Zhang S, Wang Y, Yang J, He L, Wu Y, Hao J. Dynamic Traffic Data in Machine-Learning Air Quality Mapping Improves Environmental Justice Assessment. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 38261755 DOI: 10.1021/acs.est.3c07545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
Air pollution poses a critical public health threat around many megacities but in an uneven manner. Conventional models are limited to depict the highly spatial- and time-varying patterns of ambient pollutant exposures at the community scale for megacities. Here, we developed a machine-learning approach that leverages the dynamic traffic profiles to continuously estimate community-level year-long air pollutant concentrations in Los Angeles, U.S. We found the introduction of real-world dynamic traffic data significantly improved the spatial fidelity of nitrogen dioxide (NO2), maximum daily 8-h average ozone (MDA8 O3), and fine particulate matter (PM2.5) simulations by 47%, 4%, and 15%, respectively. We successfully captured PM2.5 levels exceeding limits due to heavy traffic activities and providing an "out-of-limit map" tool to identify exposure disparities within highly polluted communities. In contrast, the model without real-world dynamic traffic data lacks the ability to capture the traffic-induced exposure disparities and significantly underestimate residents' exposure to PM2.5. The underestimations are more severe for disadvantaged communities such as black and low-income groups, showing the significance of incorporating real-time traffic data in exposure disparity assessment.
Collapse
Affiliation(s)
- Yifan Wen
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
| | - Shaojun Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, P. R. China
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, P. R. China
| | - Yuan Wang
- Department of Earth System Science, Stanford University, Stanford, California 94305, United States
| | - Jiani Yang
- Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California 91125, United States
| | - Liyin He
- Department of Global Ecology, Carnegie Institution for Science, Stanford, California 94305, United States
| | - Ye Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, P. R. China
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, P. R. China
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, P. R. China
| |
Collapse
|
3
|
McNeil W, Tong F, Harley RA, Auffhammer M, Scown CD. Corridor-Level Impacts of Battery-Electric Heavy-Duty Trucks and the Effects of Policy in the United States. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:33-42. [PMID: 38109378 PMCID: PMC10785805 DOI: 10.1021/acs.est.3c05139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/15/2023] [Accepted: 11/15/2023] [Indexed: 12/20/2023]
Abstract
Electrifying freight trucks will be key to alleviating air pollution burdens on disadvantaged communities and mitigating climate change. The United States plans to pursue this aim by adding vehicle charging infrastructure along specific freight corridors. This study explores the coevolution of the electricity grid and freight trucking landscape using an integrated assessment framework to identify when each interstate and drayage corridor becomes advantageous to electrify from a climate and human health standpoint. Nearly all corridors achieve greenhouse gas emission reductions if electrified now. Most can reduce health impacts from air pollution if electrified by 2040 although some corridors in the Midwest, South, and Mid-Atlantic regions remain unfavorable to electrify from a human health standpoint, absent policy support. Recent policy, namely, the Inflation Reduction Act, accelerates this timeline to 2030 for most corridors and results in net human health benefits on all corridors by 2050, suggesting that near-term investments in truck electrification, particularly drayage corridors, can meaningfully reduce climate and health burdens.
Collapse
Affiliation(s)
- Wilson
H. McNeil
- Energy
Technologies Area, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
- Department
of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California 94720, United States
- Department
of Civil and Natural Resources Engineering, University of Canterbury, Christchurch 8041, New Zealand
| | - Fan Tong
- School
of Economics and Management, Beihang University, Beijing 100191, People’s Republic of China
- Lab
for Low-carbon Intelligent Governance, Beihang
University, Beijing 100191, People’s Republic
of China
- Peking
University Ordos Research Institute of Energy, Ordos City 017000, Inner Mongolia, People’s Republic of
China
| | - Robert A. Harley
- Department
of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California 94720, United States
| | - Maximilian Auffhammer
- Energy
Technologies Area, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
- Department
of Agricultural and Resource Economics, University of California, Berkeley, Berkeley, California 94720, United States
- National
Bureau of Economic Research, Cambridge, Massachusetts 02138, United States
| | - Corinne D. Scown
- Energy
Technologies Area, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
- Life-Cycle,
Economics and Agronomy Division, Joint BioEnergy
Institute, Emeryville, California 94608, United States
- Biosciences
Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Energy
and Biosciences Institute, University of
California, Berkeley, Berkeley, California 94720, United States
| |
Collapse
|
4
|
Gohlke JM, Harris MH, Roy A, Thompson TM, DePaola M, Alvarez RA, Anenberg SC, Apte JS, Demetillo MAG, Dressel IM, Kerr GH, Marshall JD, Nowlan AE, Patterson RF, Pusede SE, Southerland VA, Vogel SA. State-of-the-Science Data and Methods Need to Guide Place-Based Efforts to Reduce Air Pollution Inequity. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:125003. [PMID: 38109120 PMCID: PMC10727036 DOI: 10.1289/ehp13063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 11/19/2023] [Accepted: 11/27/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND Recently enacted environmental justice policies in the United States at the state and federal level emphasize addressing place-based inequities, including persistent disparities in air pollution exposure and associated health impacts. Advances in air quality measurement, models, and analytic methods have demonstrated the importance of finer-scale data and analysis in accurately quantifying the extent of inequity in intraurban pollution exposure, although the necessary degree of spatial resolution remains a complex and context-dependent question. OBJECTIVE The objectives of this commentary were to a) discuss ways to maximize and evaluate the effectiveness of efforts to reduce air pollution disparities, and b) argue that environmental regulators must employ improved methods to project, measure, and track the distributional impacts of new policies at finer geographic and temporal scales. DISCUSSION The historic federal investments from the Inflation Reduction Act, the Infrastructure Investment and Jobs Act, and the Biden Administration's commitment to Justice40 present an unprecedented opportunity to advance climate and energy policies that deliver real reductions in pollution-related health inequities. In our opinion, scientists, advocates, policymakers, and implementing agencies must work together to harness critical advances in air quality measurements, models, and analytic methods to ensure success. https://doi.org/10.1289/EHP13063.
Collapse
Affiliation(s)
- Julia M. Gohlke
- Environmental Defense Fund, Washington, District of Columbia, USA
- Department of Population Health Sciences, Virginia Tech, Blacksburg, Virginia, USA
| | - Maria H. Harris
- Environmental Defense Fund, Washington, District of Columbia, USA
| | - Ananya Roy
- Environmental Defense Fund, Washington, District of Columbia, USA
| | | | - Mindi DePaola
- Environmental Defense Fund, Washington, District of Columbia, USA
| | - Ramón A. Alvarez
- Environmental Defense Fund, Washington, District of Columbia, USA
| | - Susan C. Anenberg
- Department of Environmental and Occupational Health, George Washington University, Washington, District of Columbia, USA
| | - Joshua S. Apte
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California, USA
- School of Public Health, University of California, Berkeley, Berkeley, California, USA
| | | | - Isabella M. Dressel
- Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - Gaige H. Kerr
- Department of Environmental and Occupational Health, George Washington University, Washington, District of Columbia, USA
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA
| | - Aileen E. Nowlan
- Environmental Defense Fund, Washington, District of Columbia, USA
| | - Regan F. Patterson
- Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Sally E. Pusede
- Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - Veronica A. Southerland
- Environmental Defense Fund, Washington, District of Columbia, USA
- Department of Environmental and Occupational Health, George Washington University, Washington, District of Columbia, USA
| | - Sarah A. Vogel
- Environmental Defense Fund, Washington, District of Columbia, USA
| |
Collapse
|