published journal article
Car2work: Shared mobility concept to connect commuters with workplaces
Transportation Research Record
Publication Date
January 1, 2016
Area of Expertise: Unspecified
published journal article
Development of a dynamic cathode ejector model for solid oxide fuel cell-gas turbine hybrid systems
Journal of Fuel Cell Science and Technology
Publication Date
June 1, 2011
Author(s)
James D. Maclay, Jack Brouwer, Scott Samuelsen
Suggested Citation
James D. Maclay, Jacob Brouwer and G. Scott Samuelsen (2011) “Development of a dynamic cathode ejector model for solid oxide fuel cell-gas turbine hybrid systems”, Journal of Fuel Cell Science and Technology, 8(5). Available at: 10.1115/1.4003774. published journal article
Handbook of logistics and supply-chain management.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
Publication Date
January 1, 2003
Author(s)
Suggested Citation
AC Regan (2003) “Handbook of logistics and supply-chain management.”, TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, 37(2), pp. 189–190. Available at: 10.1016/S0965-8564(02)00040-X.
conference paper
Evaluating the potential to predict activity types from GPS and GIS data
Proceedings of the 86th annual meeting of the transportation research board
Publication Date
January 1, 2007
Author(s)
Abstract
Current travel forecasting models have had limited sensitivity to policy decisions. One of the primary challenges is limitations in the primary data source, the daily travel diary (e.g., accuracy and sample size). The daily travel diary has known problems with underreporting, time inaccuracies, respondent fatigue, and other human errors. The Global Positioning System (GPS) has been recently used to supplement the daily travel diary. As GPS becomes more accurate, reliable, and cost effective, could it entirely replace the daily travel diary? GPS devices can be used to record times and locations of each activity and the trips in between. To use GPS data to replace the daily travel diary one needs to predict the activity types. The goal of this research is to test the feasibility of a model that predicts activity types based solely on: (1) GPS data from devices placed on the individualâ??s vehicle or person, (2) Land use data, such as location type, expressed as GIS data, and (3) Individual and household demographic data. This report summarizes models developed with surrogate geo-coded data using discriminant analysis and classification/ regression trees. The models predicted in which of 26 different activity types the individual participated. Accuracy for the best model was: (1) 63% for out of home activities (2) 79% when including the â??at homeâ?? activity (3) 72% considering that GPS data may miss as much as 10% of trips Since travel diaries have known underreporting problems as high as 30%, GPS data with the model developed seems competitive.
Suggested Citation
Patrick Tracy McGowen and Michael G. McNally (2007) “Evaluating the potential to predict activity types from GPS and GIS data”, in Proceedings of the 86th annual meeting of the transportation research board, p. 22p. conference paper
System performance and controller design of the PI-ALINEA ramp metering scheme
Proceedings of the 95th annual meeting of the transportation research board
Publication Date
January 1, 2016
Author(s)
Abstract
Ramp metering (RM) has been deployed for decades and it is considered an efficient technique to control lane-drop bottlenecks by limiting ramp demand and avoiding the so-called capacity-drop phenomena, drop in the downstream flux that occurs when queues form up- stream of bottleneck. In this study, the authors use a simple link queue model to describe traffic dynamics inside a merge zone with an ordinary differential equation, which combines a capacity drop model and a proportional-integral feedback control algorithm (PI-ALINEA). This enables us to analytically study the system performance and controller design for the ramp metering problem. First they analyze the systemâ??s equilibrium states, their stability, and transition subject to varying demand levels. They consider impacts of both fixed and dynamical metering rates on the equilibrium states of the system and examine the reachability of the system. They further analyze the closed-loop systems and design parameters of PI-ALINEA such that the system can be stabilized at the optimal state at a high demand level. With numerical examples they verify the analytical results with respect to the systemâ??s stability and robustness.
Suggested Citation
Felipe Augusto de Souza and Wenlong Jin (2016) “System performance and controller design of the PI-ALINEA ramp metering scheme”, in Proceedings of the 95th annual meeting of the transportation research board, p. 24p. published journal article
Exposure measurement error in air pollution studies: the impact of shared, multiplicative measurement error on epidemiological health risk estimates
Air Quality, Atmosphere & Health
Publication Date
June 1, 2020
Author(s)
Mariam S. Girguis, Lianfa Li, Fred Lurmann, Jun Wu, Carrie Breton, Frank Gilliland, Daniel Stram, Rima Habre
Abstract
Spatiotemporal air pollution models are increasingly being used to estimate health effects in epidemiological studies. Although such exposure prediction models typically result in improved spatial and temporal resolution of air pollution predictions, they remain subject to shared measurement error, a type of measurement error common in spatiotemporal exposure models which occurs when measurement error is not independent of exposures. A fundamental challenge of exposure measurement error in air pollution assessment is the strong correlation and sometimes identical (shared) error of exposure estimates across geographic space and time. When exposure estimates with shared measurement error are used to estimate health risk in epidemiological analyses, complex errors are potentially introduced, resulting in biased epidemiological conclusions. We demonstrate the influence of using a three-stage spatiotemporal exposure prediction model and introduce formal methods of shared, multiplicative measurement error (SMME) correction of epidemiological health risk estimates. Using our three-stage ensemble learning-based nitrogen oxides (NOx) exposure prediction model, we quantified SMME. We conducted an epidemiological analysis of wheeze risk in relation to NOx exposure among school-aged children. To demonstrate the incremental influence of exposure modeling stage, we iteratively estimated the health risk using assigned exposure predictions from each stage of the NOx model. We then determined the impact of SMME on the variance of health risk estimates under various scenarios. Depending on the stage of the spatiotemporal exposure model used, we found that wheeze odds ratio ranged from 1.16 to 1.28 for an interquartile range increase in NOx. With each additional stage of exposure modeling, the health effect estimate moved further away from the null (OR = 1). When corrected for observed SMME, the health effects confidence intervals slightly lengthened, but our epidemiological conclusions were not altered. When the variance estimate was corrected for the potential “worst case scenario” of SMME, the standard error further increased, having a meaningful influence on epidemiological conclusions. Our framework can be expanded and used to understand the implications of using exposure predictions subject to shared measurement error in future health investigations.
Suggested Citation
Mariam S. Girguis, Lianfa Li, Fred Lurmann, Jun Wu, Carrie Breton, Frank Gilliland, Daniel Stram and Rima Habre (2020) “Exposure measurement error in air pollution studies: the impact of shared, multiplicative measurement error on epidemiological health risk estimates”, Air Quality, Atmosphere & Health, 13(6), pp. 631–643. Available at: 10.1007/s11869-020-00826-6. published journal article
The role of extreme heat exposure on premature rupture of membranes in Southern California: A study from a large pregnancy cohort
Environment International
Publication Date
March 1, 2023
Author(s)
Anqi Jiao, Yi Sun, David A. Sacks, Chantal Avila, Vicki Chiu, John Molitor, Jiu-Chiuan Chen, Kelly T Sanders, John T Abatzoglou, Jeff Slezak, Tarik Benmarhnia, Darios Getahun, Jun Wu
Abstract
Background Significant mortality and morbidity in pregnant women and their offspring are linked to premature rupture of membranes (PROM). Epidemiological evidence for heat-related PROM risk is extremely limited. We investigated associations between acute heatwave exposure and spontaneous PROM. Methods We conducted this retrospective cohort study among mothers in Kaiser Permanente Southern California who experienced membrane ruptures during the warm season (May-September) from 2008 to 2018. Twelve definitions of heatwaves with different cut-off percentiles (75th, 90th, 95th, and 98th) and durations (≥ 2, 3, and 4 consecutive days) were developed using the daily maximum heat index, which incorporates both daily maximum temperature and minimum relative humidity in the last gestational week. Cox proportional hazards models were fitted separately for spontaneous PROM, term PROM (TPROM), and preterm PROM (PPROM) with zip codes as the random effect and gestational week as the temporal unit. Effect modification by air pollution (i.e., PM2.5 and NO2), climate adaptation measures (i.e., green space and air conditioning [AC] penetration), sociodemographic factors, and smoking behavior was examined. Results In total, we included 190,767 subjects with 16,490 (8.6%) spontaneous PROMs. We identified a 9–14% increase in PROM risks associated with less intense heatwaves. Similar patterns as PROM were found for TPROM and PPROM. The heat-related PROM risks were greater among mothers exposed to a higher level of PM2.5 during pregnancy, under 25 years old, with lower education and household income level, and who smoked. Even though climate adaptation factors were not statistically significant effect modifiers, mothers living with lower green space or lower AC penetration were at consistently higher heat-related PROM risks compared to their counterparts. Conclusion Using a rich and high-quality clinical database, we detected harmful heat exposure for spontaneous PROM in preterm and term deliveries. Some subgroups with specific characteristics were more susceptible to heat-related PROM risk.
Suggested Citation
Anqi Jiao, Yi Sun, David A. Sacks, Chantal Avila, Vicki Chiu, John Molitor, Jiu-Chiuan Chen, Kelly T Sanders, John T Abatzoglou, Jeff Slezak, Tarik Benmarhnia, Darios Getahun and Jun Wu (2023) “The role of extreme heat exposure on premature rupture of membranes in Southern California: A study from a large pregnancy cohort”, Environment International, 173, p. 107824. Available at: 10.1016/j.envint.2023.107824. published journal article
Solving the bicriteria traffic equilibrium problem with variable demand and nonlinear path costs
Applied Mathematics and Computation
Publication Date
December 1, 2010
Author(s)
Suggested Citation
Anthony Chen, Jun-Seok Oh, Dongjoo Park and Will Recker (2010) “Solving the bicriteria traffic equilibrium problem with variable demand and nonlinear path costs”, Applied Mathematics and Computation, 217(7), pp. 3020–3031. Available at: 10.1016/j.amc.2010.08.035. published journal article
Use of Radioisotope Ratios of Lead for the Identification of Historical Sources of Soil Lead Contamination in Santa Ana, California
Toxics
Publication Date
June 1, 2022
Author(s)
Shahir Masri, Alana M. W. LeBrón, Michael D. Logue, Patricia Flores, Abel Ruiz, Abigail Reyes, Juan Manuel Rubio, Jun Wu
Abstract
Lead (Pb) is an environmental neurotoxicant that has been associated with a wide range of adverse health conditions, and which originates from both anthropogenic and natural sources. In California, the city of Santa Ana represents an urban environment where elevated soil lead levels have been recently reported across many disadvantaged communities. In this study, we pursued a community-engaged research approach through which trained “citizen scientists” from the surrounding Santa Ana community volunteered to collect soil samples for heavy metal testing, a subset of which (n = 129) were subjected to Pb isotopic analysis in order to help determine whether contamination could be traced to specific and/or anthropogenic sources. Results showed the average 206Pb/204Pb ratio in shallow soil samples to be lower on average than deep samples, consistent with shallow samples being more likely to have experienced historical anthropogenic contamination. An analysis of soil Pb enrichment factors (EFs) demonstrated a strong positive correlation with lead concentrations, reinforcing the likelihood of elevated lead levels being due to anthropogenic activity, while EF values plotted against 206Pb/204Pb pointed to traffic-related emissions as a likely source. 206Pb/204Pb ratios for samples collected near historical urban areas were lower than the averages for samples collected elsewhere, and plots of 206Pb/204Pb against 206Pb/207 showed historical areas to exhibit very similar patterns to those of shallow samples, again suggesting lead contamination to be anthropogenic in origin, and likely from vehicle emissions. This study lends added weight to the need for health officials and elected representatives to respond to community concerns and the need for soil remediation to equitably protect the public.
Suggested Citation
Shahir Masri, Alana M. W. LeBrón, Michael D. Logue, Patricia Flores, Abel Ruiz, Abigail Reyes, Juan Manuel Rubio and Jun Wu (2022) “Use of Radioisotope Ratios of Lead for the Identification of Historical Sources of Soil Lead Contamination in Santa Ana, California”, Toxics, 10(6), p. 304. Available at: 10.3390/toxics10060304. published journal article
Modeling spatially varying compliance effects of PM2.5 exposure reductions on gestational diabetes mellitus in southern California: Results from electronic health record data of a large pregnancy cohort
Environmental Research
Publication Date
August 15, 2023
Author(s)
John Molitor, Yi Sun, Virgilio Gómez Rubio, Tarik Benmarhnia, Jiu-Chiuan Chen, Chantal Avila, David A. Sacks, Vicki Chiu, Jeff Slezak, Darios Getahun, Jun Wu
Abstract
Gestational diabetes mellitus (GDM) is a major pregnancy complication affecting approximately 14.0% of pregnancies around the world. Air pollution exposure, particularly exposure to PM2.5, has become a major environmental issue affecting health, especially for vulnerable pregnant women. Associations between PM2.5 exposure and adverse birth outcomes are generally assumed to be the same throughout a large geographical area. However, the effects of air pollution on health can very spatially in subpopulations. Such spatially varying effects are likely due to a wide range of contextual neighborhood and individual factors that are spatially correlated, including SES, demographics, exposure to housing characteristics and due to different composition of particulate matter from different emission sources. This combination of elevated environmental hazards in conjunction with socioeconomic-based disparities forms what has been described as a “double jeopardy” for marginalized sub-populations. In this manuscript our analysis combines both an examination of spatially varying effects of a) unit-changes in exposure and examines effects of b) changes from current exposure levels down to a fixed compliance level, where compliance levels correspond to the Air Quality Standards (AQS) set by the U.S. Environmental Protection Agency (EPA) and World Health Organization (WHO) air quality guideline values. Results suggest that exposure reduction policies should target certain “hotspot” areas where size and effects of potential reductions will reap the greatest rewards in terms of health benefits, such as areas of southeast Los Angeles County which experiences high levels of PM2.5 exposures and consist of individuals who may be particularly vulnerable to the effects of air pollution on the risk of GDM.