1
|
Wu Y, Bi J, Gassett AJ, Young MT, Szpiro AA, Kaufman JD. Integrating traffic pollution dispersion into spatiotemporal NO 2 prediction. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 925:171652. [PMID: 38485010 PMCID: PMC11027090 DOI: 10.1016/j.scitotenv.2024.171652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 02/18/2024] [Accepted: 03/09/2024] [Indexed: 03/25/2024]
Abstract
Accurately predicting ambient NO2 concentrations has great public health importance, as traffic-related air pollution is of major concern in urban areas. In this study, we present a novel approach incorporating traffic contribution to NO2 prediction in a fine-scale spatiotemporal model. We used nationally available traffic estimate dataset in a scalable dispersion model, Research LINE source dispersion model (RLINE). RLINE estimates then served as an additional input for a validated spatiotemporal pollution modeling approach. Our analysis uses measurement data collected by the Multi-Ethnic Study of Atherosclerosis and Air Pollution in the greater Los Angeles area between 2006 and 2009. We predicted road-type-specific annual average daily traffic (AADT) on road segments via national-level spatial regression models with nearest-neighbor Gaussian processes (spNNGP); the spNNGP models were trained based on over half a million point-level traffic volume measurements nationwide. AADT estimates on all highways were combined with meteorological data in RLINE models. We evaluated two strategies to integrate RLINE estimates into spatiotemporal NO2 models: 1) incorporating RLINE estimates as a space-only covariate and, 2) as a spatiotemporal covariate. The results showed that integrating the RLINE estimates as a space-only covariate improved overall cross-validation R2 from 0.83 to 0.84, and root mean squared error (RMSE) from 3.58 to 3.48 ppb. Incorporating the estimates as a spatiotemporal covariate resulted in similar model improvement. The improvement of our spatiotemporal model was more profound in roadside monitors alongside highways, with R2 increasing from 0.56 to 0.66 and RMSE decreasing from 3.52 to 3.11 ppb. The observed improvement indicates that the RLINE estimates enhanced the model's predictive capabilities for roadside NO2 concentration gradients even after considering a comprehensive list of geographic covariates including the distance to roads. Our proposed modeling framework can be generalized to improve high-resolution prediction of NO2 exposure - especially near major roads in the U.S.
Collapse
Affiliation(s)
- Yunhan Wu
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jianzhao Bi
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
| | - Amanda J Gassett
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Michael T Young
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Adam A Szpiro
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Joel D Kaufman
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| |
Collapse
|
2
|
Yu Q, He BY, Ma J, Zhu Y. California's zero-emission vehicle adoption brings air quality benefits yet equity gaps persist. Nat Commun 2023; 14:7798. [PMID: 38086805 PMCID: PMC10716132 DOI: 10.1038/s41467-023-43309-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 11/06/2023] [Indexed: 12/18/2023] Open
Abstract
Zero-emission vehicle (ZEV) adoption is a key climate mitigation tool, but its environmental justice implications remain unclear. Here, we quantify ZEV adoption at the census tract level in California from 2015 to 2020 and project it to 2035 when all new passenger vehicles sold are expected to be ZEVs. We then apply an integrated traffic model together with a dispersion model to simulate air quality changes near roads in the Greater Los Angeles. We found that per capita ZEV ownership in non-disadvantaged communities (non-DACs) as defined by the state of California is 3.8 times of that in DACs. Racial and ethnic minorities owned fewer ZEVs regardless of DAC designation. While DAC residents receive 40% more pollutant reduction than non-DACs due to intercommunity ZEV trips in 2020, they remain disproportionately exposed to higher levels of traffic-related air pollution. With more ZEVs in 2035, the exposure disparity narrows. However, to further reduce disparities, the focus must include trucks, emphasizing the need for targeted ZEV policies that address persistent pollution burdens among DAC and racial and ethnic minority residents.
Collapse
Affiliation(s)
- Qiao Yu
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Brian Yueshuai He
- Department of Civil and Environmental Engineering, Samueli School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jiaqi Ma
- Department of Civil and Environmental Engineering, Samueli School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Yifang Zhu
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.
| |
Collapse
|
3
|
Cheng S, Zhang B, Peng P, Lu F. Health and economic benefits of heavy-duty diesel truck emission control policies in Beijing. ENVIRONMENT INTERNATIONAL 2023; 179:108152. [PMID: 37598595 DOI: 10.1016/j.envint.2023.108152] [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: 06/19/2023] [Revised: 08/03/2023] [Accepted: 08/14/2023] [Indexed: 08/22/2023]
Abstract
PM2.5 emissions from heavy-duty diesel trucks (HDDTs) have a significant impact on air quality, human health, and climate change, and seriously threaten the UN Sustainable Development Goals. Globally, a series of emission control measures have been implemented to reduce pollution emissions from HDDTs. Current studies assessing the impact of these measures on air quality and human health have mainly used coarse-grained emission data as input to dispersion model, resulting in the inability to capture the spatiotemporal variability of pollutant concentrations and tending to increase the uncertainty of health impact assessment results. In this study, we quantified the impact of pollution control policies for HDDTs in Beijing on PM2.5 concentrations, human health, and economic losses by integrating policy scenario analysis, pollution dispersion simulation, public health impact and economic benefit assessment models, supported by high spatiotemporal resolution emission data from HDDTs. The results show that PM2.5 concentrations from HDDTs exhibit significant spatial aggregation characteristics, with the intensity of aggregation at night being about twice as high as that during the day. The emission hotspots are mainly concentrated in the sixth, fifth and fourth rings and major highways. Compared to the "business as usual" scenario in 2018, the current policy of updating the fuel standard to China VI and the emission standard to China 6 can reduce PM2.5 concentrations by 96.72%, thereby avoiding 612 premature deaths, which is equivalent to obtaining economic benefits of 1.65 billion CNY. This study further emphasizes the importance of high spatiotemporal resolution emission data during traffic dispersion modeling. The results can help improve the understanding of the effectiveness of emission reduction measures for HDDTs from a health benefit perspective.
Collapse
Affiliation(s)
- Shifen Cheng
- State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Beibei Zhang
- State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peng Peng
- State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Feng Lu
- State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; The Academy of Digital China, Fuzhou University, Fuzhou, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
| |
Collapse
|
4
|
Frey HC, Grieshop AP, Khlystov A, Bang JJ, Rouphail N, Guinness J, Rodriguez D, Fuentes M, Saha P, Brantley H, Snyder M, Tanvir S, Ko K, Noussi T, Delavarrafiee M, Singh S. Characterizing Determinants of Near-Road Ambient Air Quality for an Urban Intersection and a Freeway Site. Res Rep Health Eff Inst 2022; 2022:1-73. [PMID: 36314577 PMCID: PMC9620485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2023] Open
Abstract
INTRODUCTION Near-road ambient air pollution concentrations that are affected by vehicle emissions are typically characterized by substantial spatial variability with respect to distance from the roadway and temporal variability based on the time of day, day of week, and season. The goal of this work is to identify variables that explain either temporal or spatial variability based on case studies for a freeway site and an urban intersection site. The key hypothesis is that dispersion modeling of near-road pollutant concentrations could be improved by adding estimates or indices for site-specific explanatory variables, particularly related to traffic. Based on case studies for a freeway site and an urban intersection site, the specific aims of this project are to (1) develop and test regression models that explain variability in traffic-related air pollutant (TRAP) ambient concentration at two near-roadway locations; (2) develop and test refined proxies for land use, traffic, emissions and dispersion; and (3) prioritize inputs according to their ability to explain variability in ambient concentrations to help focus efforts for future data collection and model development. The key pollutants that are the key focus of this work include nitrogen oxides (NOx), carbon monoxide (CO), black carbon (BC), fine particulate matter (PM2.5; PM ≤ 2.5 μm in aerodynamic diameter), ultrafine particles (UFPs; PM ≤ 0.1 μm in aerodynamic diameter), and ozone (O3). NOx, CO, and BC are tracers of vehicle emissions and dispersion. PM2.5 is influenced by vehicle table emissions and regional sources. UFPs are sensitive to primary vehicle emissions. Secondary particles can form near roadways and on regional scales, influencing both PM2.5 and UFP concentrations. O3 concentrations are influenced by interaction with NOx near the roadway. Nitrogen dioxide (NO2), CO, PM2.5, and O3 are regulated under the National Ambient Air Quality Standards (NAAQS) because of demonstrated health effects. BC and UFPs are of concern for their potential health effects. Therefore, these pollutants are the focus of this work. METHODS The methodological approach includes case studies for which variables are identified and assesses their ability to explain either temporal or spatial variability in pollutant ambient concentrations. The case studies include one freeway location and one urban intersection. The case studies address (1) temporal variability at a fixed monitor 10 meters from a freeway; (2) downwind concentrations perpendicular to the same location; (3) variability in 24-hour average pollutant concentrations at five sites near an urban intersection; and (4) spatiotemporal variability along a walking path near that same intersection. The study boundary encompasses key factors in the continuum from vehicle emissions to near-road exposure concentrations. These factors include land use, transportation infrastructure and traffic control, vehicle mix, vehicle (traffic) flow, on-road emissions, meteorology, transport and evolution (transformation) of primary emissions, and production of secondary pollutants, and their resulting impact on measured concentrations in the near-road environment. We conducted field measurements of land use, traffic, vehicle emissions, and near-road ambient concentrations in the vicinity of two newly installed fixed-site monitors. One is a monitoring station jointly operated by the U.S. Environmental Protection Agency (U.S. EPA) and the North Carolina Department of Environmental Quality (NC DEQ) on I-40 between Airport Boulevard and I-540 in Wake County, North Carolina. The other is a fixed-site monitor for measuring PM2.5 at the North Carolina Central University (NCCU) campus on E. Lawson Street in Durham, North Carolina. We refer to these two locations as the freeway site and the urban site, respectively. We developed statistical models for the freeway and urban sites. RESULTS We quantified land use metrics at each site, such as distances to the nearest bus stop. For the freeway site, we quantified lane-by-lane total vehicle count, heavy vehicle (HV) count, and several vehicle-activity indices that account for distance from each lane to the roadside monitor. For the urban site, we quantified vehicle counts for all 12 turning movements through the intersection. At each site, we measured microscale vehicle tailpipe emissions using a portable emission measurement system. At the freeway site, we measured the spatial gradient of NOx, BC, UFPs, and PM, quantified particle size distributions at selected distances from the roadway and assessed partitioning of particles as a function of evolving volatility. We also quantified fleet-average emission factors for several pollutants. At the urban site, we measured daily average concentrations of nitric oxide (NO), NOx, O3, and PM2.5 at five sites surrounding the intersection of interest; we also measured high resolution (1-second to 10-second averages) concentrations of O3, PM2.5, and UFPs along a pedestrian transect. At both sites, the Research LINE-source (R-LINE) dispersion model was applied to predict concentration gradients based on the physical dispersion of pollution. Statistical models were developed for each site for selected pollutants. With variables for local wind direction, heavy-vehicle index, temperature, and day type, the multiple coefficient of determination (R2) was 0.61 for hourly NOx concentrations at the freeway site. An interaction effect of the dispersion model and a real-time traffic index contributed only 24% of the response variance for NOx at the freeway site. Local wind direction, measured near the road, was typically more important than wind direction measured some distance away, and vehicle-activity metrics directly related to actual real-time traffic were important. At the urban site, variability in pollutant concentrations measured for a pedestrian walk-along route was explained primarily by real-time traffic metrics, meteorology, time of day, season, and real-world vehicle tailpipe emissions, depending on the pollutant. The regression models explained most of the variance in measured concentrations for BC, PM, UFPs, NO, and NOx at the freeway site and for UFPs and O3 at the urban site pedestrian transect. CONCLUSIONS Among the set of candidate explanatory variables, typically only a few were needed to explain most of the variability in observed ambient concentrations. At the freeway site, the concentration gradients perpendicular to the road were influenced by dilution, season, time of day, and whether the pollutant underwent chemical or physical transformations. The explanatory variables that were useful in explaining temporal variability in measured ambient concentrations, as well as spatial variability at the urban site, were typically localized real-time traffic-volume indices and local wind direction. However, the specific set of useful explanatory variables was site, context (e.g., next to road, quadrants around an intersection, pedestrian transects), and pollutant specific. Among the most novel of the indicators, variability in real-time measured tailpipe exhaust emissions was found to help explain variability in pedestrian transect UFP concentrations. UFP particle counts were very sensitive to real-time traffic indicators at both the freeway and urban sites. Localized site-specific data on traffic and meteorology contributed to explaining variability in ambient concentrations. HV traffic influenced near-road air quality at the freeway site more so than at the urban site. The statistical models typically explained most of the observed variability but were relatively simple. The results here are site-specific and not generalizable, but they are illustrative that near-road air quality can be highly sensitive to localized real-time indicators of traffic and meteorology.
Collapse
Affiliation(s)
| | | | | | - J J Bang
- North Carolina Central University
| | | | | | | | | | - P Saha
- North Carolina State University
| | | | - M Snyder
- University of North Carolina, Chapel Hill
| | | | - K Ko
- North Carolina State University
| | - T Noussi
- North Carolina Central University
| | | | - S Singh
- North Carolina State University
| |
Collapse
|
5
|
Demetriou E, Hadjistassou C. Lowering mortality risks in urban areas by containing atmospheric pollution. ENVIRONMENTAL RESEARCH 2022; 211:113096. [PMID: 35276194 DOI: 10.1016/j.envres.2022.113096] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 02/23/2022] [Accepted: 03/05/2022] [Indexed: 06/14/2023]
Abstract
OBJECTIVES Although studies collectively examining the traffic and residential heat pollutant emissions are abundant, research investigations dedicated to Cyprus are scarce. This investigation has simulated the levels of air pollutants, namely, CO, NOx, PM2.5, and PM10 and reconciled them with actual air quality measurements in Nicosia, Cyprus, during a 9-month period at an hourly resolution. To this end, several scenarios and cases were formulated to tackle emissions and minimise human mortality risks in the city. METHODS The GRAL dispersion model was used to project pollution levels. Nine different traffic scenarios were devised to estimate variations in concentration of PM2.5 and NOx under various policies, such as banning diesel passenger vehicles (PV), light duty vehicles (LDV), non-Euro 6 standards vehicles, stringent speed limits and a ubiquitous roll-out of electric passenger vehicles. Moreover, 4 distinct cases were analysed to year 2030 considering a fluctuation in traffic of ±20% whereas all vehicles conform to Euro 6 standards. Three additional policies examined the prohibition of diesel PV and LDV, 80% electric PV and outlawing fireplaces. Drawing on the findings of these scenarios and cases, the total cardiovascular and respiratory mortality rates at the capital of Cyprus, Nicosia, were deduced. RESULTS The most promising scenario in terms of curbing emissions was to ban non-Euro 6 vehicles and diesel PV and LDV which could contain average NOx concentration, in Nicosia, from 52.9 μg/m3 to 15.0 μg/m3. If this policy were to be implemented, it could have saved 70% of the premature deaths tied to NOx emissions. For particulate matter, banning fireplaces and abandoning non-Euro 6 vehicles could lower average concentrations from 18.3 μg/m3 to 13.1 μg/m3, saving at least 30% of the people poised to lose their lives from particulate matter risks. CONCLUSION Traffic and residential heat policies are not easy to implement. However, our study has demonstrated that the most effective policies for curbing NOx emissions would be to ensure that all vehicles abide with the Euro 6 standards and, concurrently, ban diesel passenger and light duty vehicles. Lastly, phasing out domestic fireplaces appears to be the most promising solution for containing particulate matter, in 2030.
Collapse
Affiliation(s)
- E Demetriou
- University of Nicosia, Marine and Carbon Lab, Department of Engineering, 46 Makedonitissas Ave., Engomi, 1700, Nicosia, Cyprus
| | - C Hadjistassou
- University of Nicosia, Marine and Carbon Lab, Department of Engineering, 46 Makedonitissas Ave., Engomi, 1700, Nicosia, Cyprus.
| |
Collapse
|
6
|
Filigrana P, Milando C, Batterman S, Levy JI, Mukherjee B, Pedde M, Szpiro AA, Adar SD. Exposure to Primary Air Pollutants Generated by Highway Traffic and Daily Mortality Risk in Near-Road Communities: A Case-Crossover Study. Am J Epidemiol 2022; 191:63-74. [PMID: 34347034 DOI: 10.1093/aje/kwab215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 07/20/2021] [Accepted: 07/30/2021] [Indexed: 11/13/2022] Open
Abstract
Most epidemiologic studies fail to capture the impact of spatiotemporal fluctuations in traffic on exposure to traffic-related air pollutants in the near-road population. Using a case-crossover design and the Research LINE source (R-LINE) dispersion model with spatiotemporally resolved highway traffic data, we quantified associations between primary pollutants generated by highway traffic-particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5), oxides of nitrogen (NOx), and black carbon (BC)-and daily nonaccidental, respiratory, cardiovascular, and cerebrovascular mortality among persons who had resided within 1 km (0.6 mile) of major highways in the Puget Sound area of Washington State between 2009 and 2013. We estimated these associations using conditional logistic regression, adjusting for time-varying covariates. Although highly resolved modeled concentrations of PM2.5, NOx, and BC from highway traffic in the hours before death were used, we found no evidence of an association between mortality and the preceding 24-hour average PM2.5 exposure (odds ratio = 0.99, 95% confidence interval: 0.96, 1.02) or exposure during shorter averaging periods. This work did not support the hypothesis that mortality risk was meaningfully higher with greater exposures to PM2.5, NOx, and BC from highways in near-road populations, though we did incorporate a novel approach to estimate exposure to traffic-generated air pollution based on detailed traffic congestion data.
Collapse
|
7
|
Wang A, Xu J, Tu R, Zhang M, Adams M, Hatzopoulou M. Near-road air quality modelling that incorporates input variability and model uncertainty. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 284:117145. [PMID: 33910134 DOI: 10.1016/j.envpol.2021.117145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 03/10/2021] [Accepted: 04/12/2021] [Indexed: 06/12/2023]
Abstract
Dispersion modelling is an effective tool to estimate traffic-related fine particulate matter (PM2.5) concentrations in near-road environments. However, many sources of uncertainty and variability are associated with the process of near-road dispersion modelling, which renders a single-number estimate of concentration a poor indicator of near-road air quality. In this study, we propose an integrated traffic-emission-dispersion modelling chain that incorporates several major sources of uncertainty. Our approach generates PM2.5 probability distributions capturing the uncertainty in emissions and meteorological conditions. Traffic PM2.5 emissions from 7 a.m. to 6 p.m. were estimated at 3400 ± 117 g. Modelled PM2.5 levels were validated against measurements along a major arterial road in Toronto, Canada. We observe large overlapping areas between modelled and measured PM2.5 distributions at all locations along the road, indicating a high likelihood that the model can reproduce measured concentrations. A policy scenario expressing the impact of reductions in truck emissions revealed that a 30% reduction in near-road PM2.5 concentrations can be achieved by upgrading close to 55% of the current trucks circulating along the corridor. A speed limit reduction of 10 km/h could lead to statistically significant increases in PM2.5 concentrations at twelve out of the eighteen locations.
Collapse
Affiliation(s)
- An Wang
- Department of Civil and Mineral Engineering, University of Toronto, Canada
| | - Junshi Xu
- Department of Civil and Mineral Engineering, University of Toronto, Canada
| | - Ran Tu
- School of Transportation, Southeast University, China
| | - Mingqian Zhang
- Department of Civil and Mineral Engineering, University of Toronto, Canada
| | - Matthew Adams
- Department of Geography, University of Toronto Mississauga, Canada
| | | |
Collapse
|
8
|
Zhang X, Just AC, Hsu HHL, Kloog I, Woody M, Mi Z, Rush J, Georgopoulos P, Wright RO, Stroustrup A. A hybrid approach to predict daily NO 2 concentrations at city block scale. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 761:143279. [PMID: 33162146 DOI: 10.1016/j.scitotenv.2020.143279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 10/12/2020] [Accepted: 10/19/2020] [Indexed: 06/11/2023]
Abstract
Estimating the ambient concentration of nitrogen dioxide (NO2) is challenging because NO2 generated by local fossil fuel combustion varies greatly in concentration across space and time. This study demonstrates an integrated hybrid approach combining dispersion modeling and land use regression (LUR) to predict daily NO2 concentrations at a high spatial resolution (e.g., 50 m) in the New York tri-state area. The daily concentration of traffic-related NO2 was estimated at the Environmental Protection Agency's NO2 monitoring sites in the study area for the years 2015-2017, using the Research LINE source (R-LINE) model with inputs of traffic data provided by the Highway Performance and Management System and meteorological data provided by the NOAA Integrated Surface Database. We used the R-LINE-predicted daily concentrations of NO2 to build mixed-effects regression models, including additional variables representing land use features, geographic characteristics, weather, and other predictors. The mixed model was selected by the Elastic Net method. Each model's performance was evaluated using the out-of-sample coefficient of determination (R2) and the square root of mean squared error (RMSE) from ten-fold cross-validation (CV). The mixed model showed a good prediction performance (CV R2: 0.75-0.79, RMSE: 3.9-4.0 ppb). R-LINE outputs improved the overall, spatial, and temporal CV R2 by 10.0%, 18.9% and 7.7% respectively. Given the output of R-LINE is point-based and has a flexible spatial resolution, this hybrid approach allows prediction of daily NO2 at an extremely high spatial resolution such as city blocks.
Collapse
Affiliation(s)
- Xueying Zhang
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Allan C Just
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hsiao-Hsien Leon Hsu
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Itai Kloog
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Matthew Woody
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Zhongyuan Mi
- Computational Chemodynamics Laboratory, Environmental and Occupational Health Science Institute, Rutgers University, New Brunswick, NJ, USA
| | - Johnathan Rush
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panos Georgopoulos
- Computational Chemodynamics Laboratory, Environmental and Occupational Health Science Institute, Rutgers University, New Brunswick, NJ, USA
| | - Robert O Wright
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Annemarie Stroustrup
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Neonatology, Department of Pediatrics, Cohen Children's Medical Center at Northwell Health, New Hyde Park, NY, USA
| |
Collapse
|
9
|
Amoatey P, Omidvarborna H, Baawain MS, Al-Mamun A. Evaluation of vehicular pollution levels using line source model for hot spots in Muscat, Oman. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2020; 27:31184-31201. [PMID: 32488708 DOI: 10.1007/s11356-020-09215-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 05/07/2020] [Indexed: 06/11/2023]
Abstract
A detailed investigation was carried out to assess the concentration of near-road traffic-related air pollution (TRAP) using a dispersion model in Muscat. Two ambient air quality monitoring (AQM) stations were utilized separately at six locations near major roadways (each location for 2 months) to monitor carbon monoxide (CO) and nitrogen oxides (NOx). The study aimed to measure the concentration of near-road TRAP in a city hot spots and develop a validated dispersion model via performance measures. The US Environmental Protection Agency (US EPA) Line Source Model was implemented in which the pollutant emission factors were obtained through Comprehensive Modal Emission Model (CMEM) and COmputer Programme to calculate Emissions from Road Transport (COPERT) model. Traffic data of all vehicle categories under normal driving conditions including average vehicle speed limits and local meteorological conditions were included in the modeling study. The analysis of monitoring data showed that hourly (00:00 to 23:00) concentrations of CO were within the US EPA limits, while NOx concentration was exceeded in most locations. Also, the measured pollutant levels were consistent with hourly peak and off-peak traffic volumes. The overall primary statistical performance measures showed that COPERT model was better than CMEM due to the high sensitivity of CMEM to the local meteorological factors. The best fractional bias (0.47 and 0.39), normalized mean square error (0.44 and 0.50), correlation coefficient (0.64 and 0.70), geometric mean bias (1.07 and 1.57), and geometric variance (2.00 and 2.32) were obtained for CO and NOx, respectively. However, the bootstrap 95% CI estimates over normalized mean square error, fractional bias, and correlation coefficient for COPERT and CMEM were found to be statistically significant from 0 in the case of combined model comparison across all the traffic locations for both CO and NOx. In overall, certain roads showed weak performance mainly due to the terrain features and the lack of reliable background concentrations, which need to be considered in the future study.
Collapse
Affiliation(s)
- Patrick Amoatey
- Department of Civil and Architectural Engineering, College of Engineering, Sultan Qaboos University, P.O. Box 33, Al-Khoudh, 123, Muscat, Sultanate of Oman
| | - Hamid Omidvarborna
- Department of Civil and Architectural Engineering, College of Engineering, Sultan Qaboos University, P.O. Box 33, Al-Khoudh, 123, Muscat, Sultanate of Oman
- Global Centre for Clean Air Research, Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, UK
| | - Mahad Said Baawain
- Department of Civil and Architectural Engineering, College of Engineering, Sultan Qaboos University, P.O. Box 33, Al-Khoudh, 123, Muscat, Sultanate of Oman.
| | - Abdullah Al-Mamun
- Department of Civil and Architectural Engineering, College of Engineering, Sultan Qaboos University, P.O. Box 33, Al-Khoudh, 123, Muscat, Sultanate of Oman
| |
Collapse
|
10
|
Lal RM, Das K, Fan Y, Barkjohn KK, Botchwey N, Ramaswami A, Russell AG. Connecting Air Quality with Emotional Well-Being and Neighborhood Infrastructure in a US City. ENVIRONMENTAL HEALTH INSIGHTS 2020; 14:1178630220915488. [PMID: 32425542 PMCID: PMC7218333 DOI: 10.1177/1178630220915488] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 03/06/2020] [Indexed: 05/21/2023]
Abstract
Cities in the United States have announced initiatives to become more sustainable, healthy, resilient, livable, and environmentally friendly. However, indicators for measuring all outcomes related to these targets and the synergies between them have not been well defined or studied. One such relationship is the linkage between air quality with emotional well-being (EWB) and neighborhood infrastructure. Here, regulatory monitoring, low-cost sensors (LCSs), and air quality modeling were combined to assess exposures to PM2.5 and traffic-related NOx in 6 Minneapolis, MN, neighborhoods of varying infrastructure parameters (median household income, urban vs suburban, and access to light rail). Residents of the study neighborhoods concurrently took real-time EWB assessments using a smart phone application, Daynamica, to gauge happiness, tiredness, stress, sadness, and pain. Both LCS PM2.5 observations and mobile-source-simulated NOx were calibrated using regulatory observations in Minneapolis. No statistically significant (α = 0.05) PM2.5 differences were found between urban poor and urban middle-income neighborhoods, but average mobile-source NOx was statistically significantly (α = 0.05) higher in the 4 urban neighborhoods than in the 2 suburban neighborhoods. Close proximity to light rail had no observable impact on average observed PM2.5 or simulated mobile-source NOx. Home-based exposure assessments found that PM2.5 was negatively correlated with positive emotions such as happiness and to net affect (the sum of positive and negative emotion scores) and positively correlated (ie, a higher PM2.5 concentration led to higher scores) for negative emotions such as tiredness, stress, sadness, and pain. Simulated mobile-source NOx, assessed from both home-based exposures and in situ exposures, had a near-zero relationship with all EWB indicators. This was attributed to low NOx levels throughout the study neighborhoods and at locations were the EWB-assessed activities took place, both owing to low on-road mobile-source NOx impacts. Although none of the air quality and EWB responses were determined to be statistically significant (α = 0.05), due in part to the relatively small sample size, the results are suggestive of linkages between air quality and a variety of EWB outcomes.
Collapse
Affiliation(s)
- Raj M. Lal
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Kirti Das
- Hubert H. Humphrey School of Public Affairs, University of Minnesota, Minneapolis, MN, USA
| | - Yingling Fan
- Hubert H. Humphrey School of Public Affairs, University of Minnesota, Minneapolis, MN, USA
| | - Karoline K. Barkjohn
- Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA
| | - Nisha Botchwey
- School of City and Regional Planning, Georgia Institute of Technology, Atlanta, GA, USA
| | - Anu Ramaswami
- Civil and Environmental Engineering, Princeton Institute for International and Regional Studies, Princeton Environmental Institute, Princeton University, Princeton, NJ, USA
| | - Armistead G. Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| |
Collapse
|
11
|
Park YM. Assessing personal exposure to traffic-related air pollution using individual travel-activity diary data and an on-road source air dispersion model. Health Place 2020; 63:102351. [DOI: 10.1016/j.healthplace.2020.102351] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 04/26/2020] [Accepted: 05/01/2020] [Indexed: 12/21/2022]
|
12
|
Milando CW, Batterman SA. Sensitivity analysis of the near-road dispersion model RLINE - an evaluation at Detroit, Michigan. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2018; 181:135-144. [PMID: 29632433 PMCID: PMC5889051 DOI: 10.1016/j.atmosenv.2018.03.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The development of accurate and appropriate exposure metrics for health effect studies of traffic-related air pollutants (TRAPs) remains challenging and important given that traffic has become the dominant urban exposure source and that exposure estimates can affect estimates of associated health risk. Exposure estimates obtained using dispersion models can overcome many of the limitations of monitoring data, and such estimates have been used in several recent health studies. This study examines the sensitivity of exposure estimates produced by dispersion models to meteorological, emission and traffic allocation inputs, focusing on applications to health studies examining near-road exposures to TRAP. Daily average concentrations of CO and NOx predicted using the Research Line source model (RLINE) and a spatially and temporally resolved mobile source emissions inventory are compared to ambient measurements at near-road monitoring sites in Detroit, MI, and are used to assess the potential for exposure measurement error in cohort and population-based studies. Sensitivity of exposure estimates is assessed by comparing nominal and alternative model inputs using statistical performance evaluation metrics and three sets of receptors. The analysis shows considerable sensitivity to meteorological inputs; generally the best performance was obtained using data specific to each monitoring site. An updated emission factor database provided some improvement, particularly at near-road sites, while the use of site-specific diurnal traffic allocations did not improve performance compared to simpler default profiles. Overall, this study highlights the need for appropriate inputs, especially meteorological inputs, to dispersion models aimed at estimating near-road concentrations of TRAPs. It also highlights the potential for systematic biases that might affect analyses that use concentration predictions as exposure measures in health studies, e.g., to estimate health impacts.
Collapse
|