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Pan S, Gan L, Jung J, Yu W, Roy A, Diao L, Jeon W, Souri AH, Gao HO, Choi Y. Quantifying the premature mortality and economic loss from wildfire-induced PM 2.5 in the contiguous U.S. Sci Total Environ 2023; 875:162614. [PMID: 36871727 DOI: 10.1016/j.scitotenv.2023.162614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
Emissions from wildfires worsen air quality and can adversely impact human health. This study utilized the fire inventory from NCAR (FINN) as wildfire emissions, and performed air quality modeling of April-October 2012, 2013, and 2014 using the U.S. Environmental Protection Agency CMAQ model under two cases: with and without wildfire emissions. This study then assessed the health impacts and economic values attributable to PM2.5 from fires. Results indicated that wildfires could lead annually to 4000 cases of premature mortality in the U.S., corresponding to $36 billion losses. Regions with high concentrations of fire-induced PM2.5 were in the west (e.g., Idaho, Montana, and northern California) and Southeast (e.g., Alabama, Georgia). Metropolitan areas located near fire sources, exhibited large health burdens, such as Los Angeles (119 premature deaths, corresponding to $1.07 billion), Atlanta (76, $0.69 billion), and Houston (65, $0.58 billion). Regions in the downwind of western fires, although experiencing relatively low values of fire-induced PM2.5, showed notable health burdens due to their large population, such as metropolitan areas of New York (86, $0.78 billion), Chicago (60, $0.54 billion), and Pittsburgh (32, $0.29 billion). Results suggest that impacts from wildfires are substantial, and to mitigate these impacts, better forest management and more resilient infrastructure would be needed.
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Affiliation(s)
- Shuai Pan
- Emergency Management College, Nanjing University of Information Science and Technology (NUIST), Nanjing, Jiangsu 210044, China; School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Lu Gan
- Emergency Management College, Nanjing University of Information Science and Technology (NUIST), Nanjing, Jiangsu 210044, China
| | - Jia Jung
- Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77204, USA; Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Wendi Yu
- Emergency Management College, Nanjing University of Information Science and Technology (NUIST), Nanjing, Jiangsu 210044, China
| | | | | | - Wonbae Jeon
- Department of Atmospheric Sciences, Pusan National University, Busan 46241, Republic of Korea
| | - Amir H Souri
- Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA
| | - H Oliver Gao
- School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Yunsoo Choi
- Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77204, USA.
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2
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Gao J, Qiu Z, Cheng W, Gao HO. Children's exposure to BC and PM pollution, and respiratory tract deposits during commuting trips to school. Ecotoxicol Environ Saf 2022; 232:113253. [PMID: 35121261 DOI: 10.1016/j.ecoenv.2022.113253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/23/2022] [Accepted: 01/25/2022] [Indexed: 06/14/2023]
Abstract
Although children have been identified as a vulnerable group highly susceptible to traffic-related air pollution, their exposure during school commutes to traffic-related pollutants and the relevant health impact is rarely studied. In this study, we measured black carbon (BC) and particulate matter (PM: PM1, PM2.5, and PM10) concentrations that children are exposed to during their multi-modal (walking, private cars, and e-bikes) commuting trips to schools in Xi'an, China. A multi-parameter inhalation rate assessment model was developed in combination with the Multi-Path Particle Dosimetry (MPPD) model to quantify the deposition dose in different parts of children's respiratory system (head, tracheobronchial (TB), pulmonary (PUL)). Results show that walking to school exposed children to the lowest PM1, PM2.5, and BC concentrations, whereas riding an e-bike led to significantly elevated exposure to PM1 and BC than the other two modes. This is due to children's closer proximity to vehicle tail pipe emissions when they bike to school on road or roadside. The PM and BC concentrations showed remarkable increases in comparison to background concentrations during children's school commutes. Urban background (UB) concentration, traffic volume (TV), time of day, and meteorological parameters could influence a child's personal exposure, and the impact of each factor vary across different transportation modes. Particle size of the pollutant affects its deposition site in the respiratory system. Deposition fractions (DFs) and deposition doses in the head region (DF > 50%) were the highest for PM and BC, for which fine particles (BC, PM1, and PM2.5) were then most easily deposited in the PUL region while coarse particles rarely reach PUL. Children inhaled higher doses of polluted air during active commuting (walking) than passive commuting (private cars, e-bikes), due to longer times of exposure coupled with more active breathing.
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Affiliation(s)
- Jingwen Gao
- School of Automobile, Chang'an University, Chang'an Road, Xi'an 710064 Shaanxi, PR China
| | - Zhaowen Qiu
- School of Automobile, Chang'an University, Chang'an Road, Xi'an 710064 Shaanxi, PR China.
| | - Wen Cheng
- China National Heavy Duty Truck Group Co., Ltd. (SINOTRUK), Huaao Road, Jinan, 250101 Shandong, PR China
| | - H Oliver Gao
- School of Civil and Environmental Engineering, Cornell University 468 Hollister Hall, Ithaca, 14853 NY, USA
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3
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Wang D, Tayarani M, Yueshuai He B, Gao J, Chow JYJ, Oliver Gao H, Ozbay K. Mobility in post-pandemic economic reopening under social distancing guidelines: Congestion, emissions, and contact exposure in public transit. Transp Res Part A Policy Pract 2021; 153:151-170. [PMID: 34566278 PMCID: PMC8450489 DOI: 10.1016/j.tra.2021.09.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 06/04/2021] [Accepted: 09/06/2021] [Indexed: 05/25/2023]
Abstract
COVID-19 has raised new challenges for transportation in the post-pandemic era. The social distancing requirement, with the aim of reducing contact risk in public transit, could exacerbate traffic congestion and emissions. We propose a simulation tool to evaluate the trade-offs between traffic congestion, emissions, and policies impacting travel behavior to mitigate the spread of COVID-19 including social distancing and working from home. Open-source agent-based simulation models are used to evaluate the transportation system usage for the case study of New York City. A Post Processing Software for Air Quality (PPS-AQ) estimation is used to evaluate the air quality impacts. Finally, system-wide contact exposure on the subway is estimated from the traffic simulation output. The social distancing requirement in public transit is found to be effective in reducing contact exposure, but it has negative congestion and emission impacts on Manhattan and neighborhoods at transit and commercial hubs. While telework can reduce congestion and emissions citywide, in Manhattan the negative impacts are higher due to behavioral inertia and social distancing. The findings suggest that contact exposure to COVID-19 on subways is relatively low, especially if social distancing practices are followed. The proposed integrated traffic simulation models and air quality estimation model can help policymakers evaluate the impact of policies on traffic congestion and emissions as well as identifying hot spots, both temporally and spatially.
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Affiliation(s)
- Ding Wang
- C2SMART University Transportation Center, New York University Tandon School of Engineering, Brooklyn, NY, USA
| | - Mohammad Tayarani
- School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA
- Center for Transportation, Environment, and Community Health, Cornell University, Ithaca, NY, USA
| | - Brian Yueshuai He
- C2SMART University Transportation Center, New York University Tandon School of Engineering, Brooklyn, NY, USA
- Department of Civil and Environmental Engineering, UCLA, Los Angeles, CA, USA
| | - Jingqin Gao
- C2SMART University Transportation Center, New York University Tandon School of Engineering, Brooklyn, NY, USA
| | - Joseph Y J Chow
- C2SMART University Transportation Center, New York University Tandon School of Engineering, Brooklyn, NY, USA
| | - H Oliver Gao
- School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA
- Center for Transportation, Environment, and Community Health, Cornell University, Ithaca, NY, USA
| | - Kaan Ozbay
- C2SMART University Transportation Center, New York University Tandon School of Engineering, Brooklyn, NY, USA
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4
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Zheng J, Qiu Z, Gao HO, Li B. Commuter PM exposure and estimated life-expectancy loss across multiple transportation modes in Xi'an, China. Ecotoxicol Environ Saf 2021; 214:112117. [PMID: 33690005 DOI: 10.1016/j.ecoenv.2021.112117] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 02/13/2021] [Accepted: 02/28/2021] [Indexed: 06/12/2023]
Abstract
Commuters are reportedly exposed to severe traffic-related air pollution (TRAP) during their commuting trips. This study was designed and implemented to (1) compare particulate matter (PM) exposure across four common transportation modes; (2) examine and analyze various determining factors; and (3) estimate public health effects caused by commuting exposure to PM. All analyses and calculations were based on the experimental data collected from 13 volunteers, including heart-rate data on 336 commuting trips in four travel modes in Xi'an China. The results indicate highest PM exposure associated with cycling (average PM10, PM2.5 and PM1.0 of 114.35, 72.37 and 56.51 μg/m3, respectively), followed by riding transit buses (116.29, 67.60 and 51.12 μg/m3 for the same pollutants, respectively), then taking a taxi (97.61, 58.87 and 45.11 μg/m3), and the lowest exposure onboard subways (55.86, 46.20 and 40.20 μg/m3). A multivariable linear regression model was used to examine major influences on PM concentration variations, with results corroborating significant PM variance across commuting modes, which is also affected by background pollution concentration and relative humidity. Further, years of life expectancy (YLE) loss were estimated using an inhalation dose model together with the life table method: cycling commuters experienced the greatest YLE loss (5.51-6.43 months per capita for the studied age group). During severe pollution periods, substituting other modes (like subway) for cycling could effectively avoid acute exposure. PM2.5 levels in taxi cabins powered by CNG or methanol were comparatively lower, indicating that implementing alternative energy strategies could effectively lower traffic emissions and population exposure.
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Affiliation(s)
- Jinlong Zheng
- School of Automobile, Chang'an University, Chang'an Road, Xi'an, 710064 Shaanxi, PR China
| | - Zhaowen Qiu
- School of Automobile, Chang'an University, Chang'an Road, Xi'an, 710064 Shaanxi, PR China.
| | - H Oliver Gao
- School of Civil and Environmental Engineering, Cornell University, 468 Hollister Hall, Ithaca, 14853 NY, USA
| | - Bing Li
- School of Automobile, Chang'an University, Chang'an Road, Xi'an, 710064 Shaanxi, PR China
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5
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von Grafenstein L, Gao HO. Infrastructure policy and public health: Evidence from OECD countries. Sci Total Environ 2021; 750:141157. [PMID: 32835960 DOI: 10.1016/j.scitotenv.2020.141157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 07/19/2020] [Accepted: 07/20/2020] [Indexed: 06/11/2023]
Abstract
When making infrastructure policies, decision makers insufficiently consider negative consequences for the environment or health. This lack of multi-sectorial awareness in policymaking triggers poor public health outcomes. To illustrate this issue, this interdisciplinary work presents evidence for the association of road infrastructure investment (as infrastructure policy) with the incidences of deaths due to transport accidents, chronic obstructive pulmonary disease, and pneumonia using nationally aggregated data from the Organisation for Economic Co-operation and Development for 27 countries over an 18-year period (1995-2012). We conduct an explorative analysis using descriptive statistics and fixed-effects panel-data regression models that include the interaction of the policy variable with the Environmental Policy Stringency Index, which proxies the awareness of negative consequences of policies. We show that countries which never achieved a score of 3 or higher for the Environmental Policy Stringency Index had higher levels of standardized death rates. This is supported by Pearson's correlation coefficients and by the results of t-tests for deaths due to transport accidents. Following the fixed-effects analysis, we find that an increase in road infrastructure investment of 1% of gross domestic product is associated, on average, with about three additional deaths per 100,000 population due to transport accidents and about 18 fewer deaths per 100,000 population due to chronic obstructive pulmonary disease using standardized death rates. A one unit increase in the Environmental Policy Stringency Index is related to about 7 fewer deaths per 100,000 population due to chronic obstructive pulmonary diseases. Marginal effects of the interaction of road infrastructure investment and the Environmental Policy Stringency Index are significant for standardized death rates due to transport accidents and chronic obstructive pulmonary disease. Multi-sectorial awareness in infrastructure policy mediates health effects for deaths due to transport accidents and chronic obstructive pulmonary disease.
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Affiliation(s)
- Liza von Grafenstein
- Cornell Institute for Public Affairs (CIPA), Cornell University, Ithaca, NY 14853, USA; Department of Agricultural Economics and Rural Development, Georg-August-Universität Göttingen, Heinrich-Düker-Weg 12, 37073 Göttingen, Germany
| | - H Oliver Gao
- Cornell Institute for Public Affairs (CIPA), Cornell University, Ithaca, NY 14853, USA; School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA.
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6
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He HD, Gao HO. Particulate matter exposure at a densely populated urban traffic intersection and crosswalk. Environ Pollut 2021; 268:115931. [PMID: 33187848 DOI: 10.1016/j.envpol.2020.115931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 10/25/2020] [Accepted: 10/26/2020] [Indexed: 05/24/2023]
Abstract
Exposure to elevated particulate matter (PM) pollution is of great concern to both the general public and air quality management agencies. At urban traffic intersections, for example, pedestrians are often at a higher risk of exposure to near-source PM pollution from traffic while waiting on the roadside or while walking in the crosswalk. This study offers an in-depth investigation of pedestrian exposure to PM pollution at an urban traffic intersection. Fixed-site measurements near an urban intersection were conducted to examine the variations in particles of various sizes through traffic signal cycles. This process aids in the identification of major PM dispersion patterns on the roadside. In addition, mobile measurements of pedestrian exposure to PM were conducted across six time intervals that correspond to different segments of a pedestrian's journey when passing through the intersection. Measurement results are used to estimate and compare the cumulative deposited doses of PM by size categories and journey segments for pedestrians at an intersection. Furthermore, comparisons of pedestrian exposure to PM on a sunny day and a cloudy day were analyzed. The results indicate the importance of reducing PM pollution at intersections and provide policymakers with a foundation for possible measures to reduce pedestrian PM exposure at urban traffic intersections.
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Affiliation(s)
- Hong-di He
- Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications Research, State-Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - H Oliver Gao
- School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, 14853, USA; Center for Transportation, Environment, and Community Health, Cornell University, Ithaca, NY, 14853, USA
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7
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Pan S, Roy A, Choi Y, Sun S, Gao HO. The air quality and health impacts of projected long-haul truck and rail freight transportation in the United States in 2050. Environ Int 2019; 130:104922. [PMID: 31226557 DOI: 10.1016/j.envint.2019.104922] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 06/07/2019] [Accepted: 06/10/2019] [Indexed: 06/09/2023]
Abstract
Diesel emissions from freight transportation activities are a key threat to public health. This study examined the air quality and public health impacts of projected freight-related emissions in 2050 over the continental United States. Three emission scenarios were considered: (1) a projected business-as-usual socioeconomic growth with freight fleet turnover and stringent emission control (CTR); (2) the application of a carbon pricing climate policy (PO); and (3) further technology improvements to eliminate high-emitting conditions in the truck fleet (NS). The PO and NS cases are superimposed on the CTR case. Using a WRF-SMOKE-CMAQ-BenMAP modeling framework, we quantified the impacts of diesel fine particulate matter (PM2.5) emissions change on air quality, health, and economic benefits. In the CTR case, we simulate a widespread reduction of PM2.5 concentrations, between 0.5 and 1.5 μg m-3, comparing to a base year of 2011. This translates into health benefits of 3600 (95% CI: 2400-4800) prevented premature deaths, corresponding to $38 (95% CI: $3.5-$100) billion. Compared to CTR case, the PO case can obtain ~9% more health benefits nationally, however, climate policy also affects the health outcomes regionally due to transition of demand from truck to rail; regions with fewer trucks could gain in health benefits, while regions with added rail freight may potentially experience a loss in health benefits due to air quality degradation. The NS case provides substantial additional benefits (~20%). These results support that a combination of continuous adoption of stringent emission standards and strong improvements in vehicle technology are necessary, as well as rewarding, to meet the sustainable freight and community health goals. States and metropolitan areas with high population density and usually high freight demand and emissions can take more immediate actions, such as accelerating vehicle technology improvements and removing high-emitting trucks, to improve air quality and health benefits.
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Affiliation(s)
- Shuai Pan
- School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA; Center for Transportation, Environment, and Community Health, Cornell University, Ithaca, NY 14853, USA
| | - Anirban Roy
- Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77204, USA
| | - Yunsoo Choi
- Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77204, USA
| | - ShiQuan Sun
- School of Hydraulic Engineering, Changsha University of Science & Technology, China
| | - H Oliver Gao
- School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA; Center for Transportation, Environment, and Community Health, Cornell University, Ithaca, NY 14853, USA.
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Qiu Z, Liu W, Gao HO, Li J. Variations in exposure to in-vehicle particle mass and number concentrations in different road environments. J Air Waste Manag Assoc 2019; 69:988-1002. [PMID: 31184551 DOI: 10.1080/10962247.2019.1629357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Accepted: 05/15/2019] [Indexed: 06/09/2023]
Abstract
Road environments significantly affect in cabin concentration of particulate matter (PM). This study conducted measurements of in-vehicle and on-road concentrations of PM10, PM2.5, PM1, and particle number (PN) in size of 0.02-1 µm, under six ventilation settings in different urban road environments (tunnels, surface roads and elevated roads). Linear regression was then used to analyze the contributions of multiple predictor variables (including on-road concentrations, temperature, relative humidity, time of day, and ventilation settings) to measured variations. On-road measurements of PM2.5, PM1, and PN concentrations from the open surface roads were 5.5%, 3.7%, and 16% lower, respectively, than those measured in tunnels, but 7.6%, 7.1% and 24% higher, respectively, than those on elevated roads. The highest on-road PM10 concentration was observed on surface roads. The time series pattern of in-vehicle particle concentrations closely tracked the on-road concentrations outside of the car and exhibited a smoother profile. Irrespective of road environment, the average I/O ratio of particles was found to be the lowest when air conditioning was on with internal recirculation, the highest purification efficiency via ventilation was obtained by switching on external air recirculation and air conditioning. Statistical models showed that on-road concentration, temperature, and ventilation setting are common factors of significance that explained 58%-80%, 64%-97%, and 87%-98% of the variations in in-vehicle PM concentrations on surface roads, on elevated roads, and in tunnels, respectively. Implications: Inside vehicles, both driver and passengers will be exposed to elevated particle concentrations. However, for in-vehicle particles, there has been no comprehensive comparative study of the three-dimensional traffic environment including tunnels surface roads and elevated roads. This study focuses on the analysis of the trends and main influencing factors of particle concentrations in different road environments. The results can provide suggestions for the driver's behavior, and provide data support for the environmental protection department to develop pollutant concentration limits within the vehicle.
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Affiliation(s)
- Zhaowen Qiu
- a School of Automobile, Chang'an University , Xi'an , Shaanxi , People's Republic of China
| | - Wenyue Liu
- a School of Automobile, Chang'an University , Xi'an , Shaanxi , People's Republic of China
| | - H Oliver Gao
- b School of Civil and Environmental Engineering, Cornell University , Ithaca , NY , USA
| | - Jianghao Li
- c School of Environmental Science and Engineering, Chang'an University , Xi'an , Shaanxi , People's Republic of China
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Heo J, Adams PJ, Gao HO. Public health costs accounting of inorganic PM 2.5 pollution in metropolitan areas of the United States using a risk-based source-receptor model. Environ Int 2017; 106:119-126. [PMID: 28633084 DOI: 10.1016/j.envint.2017.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Revised: 06/02/2017] [Accepted: 06/07/2017] [Indexed: 05/25/2023]
Abstract
In order to design effective strategies to reduce the public health burden of ambient fine particulate matter (PM2.5) imposed in an area, it is necessary to identify the emissions sources affecting that location and quantify their contributions. However, it is challenging because PM2.5 travels long distances and most constituents are the result of complex chemical processes. We developed a reduced-form source-receptor model for estimating locations and magnitudes of downwind health costs from a source or, conversely, the upwind sources that contribute to health costs at a receptor location. Built upon outputs from a state-of-the-art air quality model, our model produces comprehensive risk-based source apportionment results with trivial computational costs. Using the model, we analyzed all the sources contributing to the inorganic PM2.5 health burden in 14 metropolitan statistical areas (MSAs) in the United States. Our analysis for 12 source categories shows that 80-90% of the burden borne by these areas originates from emissions sources outside of the area and that emissions sources up to 800 km away need to be included to account for 80% of the burden. Conversely, 60-80% of the impacts of an MSA's emissions occurs outside of that MSA. The results demonstrate the importance of regionally coordinated measures to improve air quality in metropolitan areas.
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Affiliation(s)
- Jinhyok Heo
- School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, United States
| | - Peter J Adams
- Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, United States; Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - H Oliver Gao
- School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, United States.
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10
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Abstract
Accurate real time monitoring of atmospheric conditions at ground level is vital for hazard warning, meteorological forecasting, and various environmental applications required for public health and safety. However, conventional monitoring facilities are costly and often insufficient, for example, since they are not representative of the larger space and are not deployed densely enough in the field. There have been numerous scientific works showing the ability of commercial microwave links that comprise the data transmission infrastructure in cellular communication networks to monitor hydrometeors as a potential complementary solution. However, despite the large volume of research carried out in this emerging field during the past decade, no study has shown the ability of the system to provide critical information regarding air quality. Here we reveal the potential for identifying atmospheric conditions prone to air pollution by detecting temperature inversions that trap pollutants at ground level. The technique is based on utilizing standard signal measurements from an existing cellular network during routine operation.
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Affiliation(s)
- Noam David
- The School of Civil and Environmental Engineering, Cornell University , Ithaca, New York 14853, United States
| | - H Oliver Gao
- The School of Civil and Environmental Engineering, Cornell University , Ithaca, New York 14853, United States
- School of Hydraulics, Changsha University of Science and Technology , Changsha, Hunan Province 410004, China
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11
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Heo J, Adams PJ, Gao HO. Public Health Costs of Primary PM2.5 and Inorganic PM2.5 Precursor Emissions in the United States. Environ Sci Technol 2016; 50:6061-70. [PMID: 27153150 DOI: 10.1021/acs.est.5b06125] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Current methods of estimating the public health effects of emissions are computationally too expensive or do not fully address complex atmospheric processes, frequently limiting their applications to policy research. Using a reduced-form model derived from tagged chemical transport model (CTM) simulations, we present PM2.5 mortality costs per tonne of inorganic air pollutants with the 36 km × 36 km spatial resolution of source location in the United States, providing the most comprehensive set of such estimates comparable to CTM-based estimates. Our estimates vary by 2 orders of magnitude. Emission-weighted seasonal averages were estimated at $88,000-130,000/t PM2.5 (inert primary), $14,000-24,000/t SO2, $3,800-14,000/t NOx, and $23,000-66,000/t NH3. The aggregate social costs for year 2005 emissions were estimated at $1.0 trillion dollars. Compared to other studies, our estimates have similar magnitudes and spatial distributions for primary PM2.5 but substantially different spatial patterns for precursor species where secondary chemistry is important. For example, differences of more than a factor of 10 were found in many areas of Texas, New Mexico, and New England states for NOx and of California, Texas, and Maine for NH3. Our method allows for updates as emissions inventories and CTMs improve, enhancing the potential to link policy research to up-to-date atmospheric science.
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Affiliation(s)
| | | | - H Oliver Gao
- School of Civil and Environmental Engineering, Cornell University , Ithaca, New York 14853, United States
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12
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Qiu Z, Li X, Hao Y, Deng S, Gao HO. Emission inventory estimation of an intercity bus terminal. Environ Monit Assess 2016; 188:367. [PMID: 27220506 DOI: 10.1007/s10661-016-5370-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Accepted: 05/16/2016] [Indexed: 06/05/2023]
Abstract
Intercity bus terminals are hotspots of air pollution due to concentrated activities of diesel buses. In order to evaluate the bus terminals' impact on air quality, it is necessary to estimate the associated mobile emission inventories. Since the vehicles' operating condition at the bus terminal varies significantly, conventional calculation of the emissions based on average emission factors suffers the loss of accuracy. In this study, we examined a typical intercity bus terminal-the Southern City Bus Station of Xi'an, China-using a multi-scale emission model-(US EPA's MOVES model)-to quantity the vehicle emission inventory. A representative operating cycle for buses within the station is constructed. The emission inventory was then estimated using detailed inputs including vehicle ages, operating speeds, operating schedules, and operating mode distribution, as well as meteorological data (temperature and humidity). Five functional areas (bus yard, platforms, disembarking area, bus travel routes within the station, and bus entrance/exit routes) at the terminal were identified, and the bus operation cycle was established using the micro-trip cycle construction method. Results of our case study showed that switching to compressed natural gas (CNG) from diesel fuel could reduce PM2.5 and CO emissions by 85.64 and 6.21 %, respectively, in the microenvironment of the bus terminal. When CNG is used, tail pipe exhaust PM2.5 emission is significantly reduced, even less than brake wear PM2.5. The estimated bus operating cycles can also offer researchers and policy makers important information for emission evaluation in the planning and design of any typical intercity bus terminals of a similar scale.
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Affiliation(s)
- Zhaowen Qiu
- School of Automobile, Chang'an University, Chang'an Road, Xi'an, Shaanxi, 710064, China.
| | - Xiaoxia Li
- School of Automobile, Chang'an University, Chang'an Road, Xi'an, Shaanxi, 710064, China
| | - Yanzhao Hao
- School of Automobile, Chang'an University, Chang'an Road, Xi'an, Shaanxi, 710064, China
| | - Shunxi Deng
- School of Environmental Science and Engineering, Chang'an University, Yanta Road, Xi'an, Shaanxi, 710064, China
| | - H Oliver Gao
- School of Civil and Environmental Engineering, Cornell University, 468 Hollister Hall, Ithaca, NY, 14853, USA
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13
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Nicholson CF, He X, Gómez MI, Gao HO, Hill E. Environmental and Economic Impacts of Localizing Food Systems: The Case of Dairy Supply Chains in the Northeastern United States. Environ Sci Technol 2015; 49:12005-14. [PMID: 26401757 DOI: 10.1021/acs.est.5b02892] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
We developed and evaluated an empirical model of the U.S. dairy supply chain with a high degree of spatial and product disaggregation to assess the impacts of increasing localization of the northeast region's fluid milk supply on food miles, supply chain costs, greenhouse gas and criteria pollutant emissions, economic activity, and employment. Evaluation included comparison to regional production values and sensitivity analysis of demand and unit cost assumptions. Our analysis compares a baseline to two localization scenarios based on state boundaries and multiple-state subregions. Localization scenarios increased total distances fluid milk traveled by 7-15%, overall supply chain costs by 1-2%, and emissions of greenhouse gases (CO2 equivalent) criteria pollutants such as oxides of nitrogen and particulate matter smaller than 2.5 μm associated with fluid milk transportation by 7-15% per month. The impacts of localization on employment and economic activity are positive, but changes are small on a percentage basis. Our analyses indicate that the definition used for localization has an impact on outcomes and that efforts to localize food systems may benefit from a more systems-oriented approach.
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Affiliation(s)
- Charles F Nicholson
- Department of Supply Chain and Information Systems, The Pennsylvania State University , University Park, Pennsylvania 16802, United States
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Abstract
This article considers the application of functional data analysis methods to modelling particulate matter emission profiles from dynamometer experiments. In particular the functional convolution model is introduced as an extension of the distributed lag model to functional (smooth and continuous) observations. We present a penalized ordinary least squares estimator for the model and a novel bootstrap procedure to provide pointwise confidence regions for the estimated convolution functions. The model is illustrated on the Coordinating Research Council E55/59 study of diesel truck emissions.
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Affiliation(s)
| | - Giles Hooker
- Department of Biological Statistics and Computational Biology, Cornell University
| | - H Oliver Gao
- Department of Civil and Environmental Engineering, Cornell University
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15
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Gómez MI, Barrett CB, Buck LE, De Groote H, Ferris S, Gao HO, McCullough E, Miller DD, Outhred H, Pell AN, Reardon T, Retnanestri M, Ruben R, Struebi P, Swinnen J, Touesnard MA, Weinberger K, Keatinge JDH, Milstein MB, Yang RY. Agriculture. Research principles for developing country food value chains. Science 2011; 332:1154-5. [PMID: 21636760 DOI: 10.1126/science.1202543] [Citation(s) in RCA: 89] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
- M I Gómez
- Cornell University, Ithaca, NY 14853, USA.
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16
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Kamarianakis Y, Gao HO. Accounting for exhaust gas transport dynamics in instantaneous emission models via smooth transition regression. Environ Sci Technol 2010; 44:1320-1326. [PMID: 20070072 DOI: 10.1021/es901913k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Collecting and analyzing high frequency emission measurements has become very usual during the past decade as significantly more information with respect to formation conditions can be collected than from regulated bag measurements. A challenging issue for researchers is the accurate time-alignment between tailpipe measurements and engine operating variables. An alignment procedure should take into account both the reaction time of the analyzers and the dynamics of gas transport in the exhaust and measurement systems. This paper discusses a statistical modeling framework that compensates for variable exhaust transport delay while relating tailpipe measurements with engine operating covariates. Specifically it is shown that some variants of the smooth transition regression model allow for transport delays that vary smoothly as functions of the exhaust flow rate. These functions are characterized by a pair of coefficients that can be estimated via a least-squares procedure. The proposed models can be adapted to encompass inherent nonlinearities that were implicit in previous instantaneous emissions modeling efforts. This article describes the methodology and presents an illustrative application which uses data collected from a diesel bus under real-world driving conditions.
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Affiliation(s)
- Yiannis Kamarianakis
- School of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14853.
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Affiliation(s)
- Ameya Bapat
- Undergraduate Research Assistant, School of Operation Research and Information Engineering, Cornell Univ., Ithaca, NY 14853
- School of Civil and Environmental Engineering, Cornell Univ., Ithaca, NY 14853 (corresponding author)
| | - H. Oliver Gao
- Undergraduate Research Assistant, School of Operation Research and Information Engineering, Cornell Univ., Ithaca, NY 14853
- School of Civil and Environmental Engineering, Cornell Univ., Ithaca, NY 14853 (corresponding author)
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18
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Sonntag DB, Gao HO, Holmén BA. Variability of particle number emissions from diesel and hybrid diesel-electric buses in real driving conditions. Environ Sci Technol 2008; 42:5637-5643. [PMID: 18754487 DOI: 10.1021/es702359x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
A linear mixed model was developed to quantify the variability of particle number emissions from transit buses tested in real-world driving conditions. Two conventional diesel buses and two hybrid diesel-electric buses were tested throughout 2004 under different aftertreatments, fuels, drivers, and bus routes. The mixed model controlled the confounding influence of factors inherent to on-board testing. Statistical tests showed that particle number emissions varied significantly according to the after treatment, bus route, driver, bus type, and daily temperature, with only minor variability attributable to differences between fuel types. The daily setup and operation of the sampling equipment (electrical low pressure impactor) and mini-dilution system contributed to 30-84% of the total random variability of particle measurements among tests with diesel oxidation catalysts. By controlling for the sampling day variability, the model better defined the differences in particle emissions among bus routes. In contrast, the low particle number emissions measured with diesel particle filters (decreased by over 99%) did not vary according to operating conditions or bus type but did vary substantially with ambient temperature.
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Affiliation(s)
- Darrell B Sonntag
- School of Civil and Environmental Engineering, Cornell University, 220 Hollister Hall, Ithaca, New York 14853, USA
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