Yougeng Lu

All Publications

• Acute mental health benefits of urban nature NATURE CITIES Li, Y., Mao, Y., Mandle, L., Rydstrom, A., Remme, R. P., Lan, X., Wu, T., Song, C., Lu, Y., Nadeau, K. C., Meyer-Lindenberg, A., Daily, G. C., Guerry, A. D. 2025; 2 (8)

  View details for DOI 10.1038/s44284-025-00286-y

  View details for Web of Science ID 001630851800003

• Reexamining exposure from truck emissions considering daily movement of individuals TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT Xiao, T., Qin, Z., Lu, Y., Chao, Y., Yang, C., Yuan, Q. 2024; 136

  View details for DOI 10.1016/j.trd.2024.104441

  View details for Web of Science ID 001331052400001

• Integrated strategies for road transportation-related multi-pollutant control: A cross-departmental policy mix TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT Yu, C., Qin, Z., Lu, Y., Lin, H., Yang, C., Yuan, Q., Wu, Q. 2024; 132

  View details for DOI 10.1016/j.trd.2024.104257

  View details for Web of Science ID 001245192800001

• Urban street network design and transport-related greenhouse gas emissions around the world TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT Boeing, G., Pilgram, C., Lu, Y. 2024; 127

  View details for DOI 10.1016/j.trd.2023.103961

  View details for Web of Science ID 001166191000001

• Assessing air pollution exposure misclassification using high-resolution PM2.5 concentration model and human mobility data AIR QUALITY ATMOSPHERE AND HEALTH Lu, Y. 2023

  View details for DOI 10.1007/s11869-023-01404-2

  View details for Web of Science ID 001044702600001

• Impacts of distinct travel behaviors on potential air pollution exposure measurement error ATMOSPHERIC ENVIRONMENT Lu, Y., Habre, R. 2023; 306

  View details for DOI 10.1016/j.atmosenv.2023.119820

  View details for Web of Science ID 001007331300001

• Where do people meet? Time-series clustering for social interaction levels in daily-life spaces during the COVID-19 pandemic CITIES Lu, Y., Giuliano, G. 2023; 137

  View details for DOI 10.1016/j.cities.2023.104298

  View details for Web of Science ID 000954713300001

• Drive less but exposed more? Exploring social injustice in vehicular air pollution exposure. Social science research Lu, Y. 2023; 111: 102867

  Abstract

  Despite growing understanding of racial and class injustice in vehicular air pollution exposure, less is known about the relationship between people's exposure to vehicular air pollution and their contribution to it. Taking Los Angeles as a case study, this study examines the injustice in vehicular PM2.5 exposure by developing an indicator that measures local populations' vehicular PM2.5 exposure adjusted by their vehicle trip distances. This study applies random forest regression models to assess how travel behavior, demographic, and socioeconomic characteristics affect this indicator. The results indicate that census tracts of the periphery whose residents drive longer distances are exposed to less vehicular PM2.5 pollution than tracts in the city center whose residents drive shorter distances. Ethnic minority and low-income tracts emit little vehicular PM2.5 and are particularly exposed to it, while White and high-income tracts generate more vehicular PM2.5 pollution but are less exposed.

  View details for DOI 10.1016/j.ssresearch.2023.102867

  View details for PubMedID 36898795

• Local inequities in the relative production of and exposure to vehicular air pollution in Los Angeles URBAN STUDIES Boeing, G., Lu, Y., Pilgram, C. 2023

  View details for DOI 10.1177/00420980221145403

  View details for Web of Science ID 000920368200001

• Beyond air pollution at home: Assessment of personal exposure to PM2.5 using activity-based travel demand model and low-cost air sensor network data ENVIRONMENTAL RESEARCH Lu, Y. 2021; 201: 111549

  Abstract

  Assessing personal exposure to air pollution is challenging due to the limited availability of human movement data and the complexity of modeling air pollution at high spatiotemporal resolution. Most health studies rely on residential estimates of outdoor air pollution instead which introduces exposure measurement error. Personal exposure for 100,784 individuals in Los Angeles County was estimated by integrating human movement data simulated from the Southern California Association of Governments (SCAG) activity-based travel demand model with hourly PM2.5 predictions from my 500 m gridded model incorporating low-cost sensor monitoring data. Individual exposures were assigned considering PM2.5 levels at homes, workplaces, and other activity locations. These dynamic exposures were compared to the residence-based exposures, which do not consider human movement, to examine the degree of exposure estimation bias. The results suggest that exposures were underestimated by 13% (range 5-22%) on average when human movement was not considered, and much of the error was eliminated by accounting for work location. Exposure estimation bias increased for people who exhibited higher mobility levels, especially for workers with long commute distances. Overall, the personal exposures of workers were underestimated by 22% (5-61%) relative to their residence-based exposures. For workers who commute >20 miles, their exposure levels can be at most underestimated by 61%. Omitting mobility resulted in underestimating exposures for people who reside in areas with cleaner air but work in more polluted areas. Similarly, exposures were overestimated for people living in areas with poorer air quality and working in cleaner areas. These could lead to differential estimation biases across racial, ethnic and socioeconomic lines that typically correlate with where people live and work and lead to important exposure and health disparities. This study demonstrates that ignoring human movement and spatiotemporal variability of air pollution could lead to differential exposure misclassification potentially biasing health risk assessments. These improved dynamic approaches can help planners and policymakers identify disadvantaged populations for which exposures are typically misrepresented and might lead to targeted policy and planning implications.

  View details for DOI 10.1016/j.envres.2021.111549

  View details for Web of Science ID 000703606300003

  View details for PubMedID 34153337

• Estimating hourly PM2.5 concentrations at the neighborhood scale using a low-cost air sensor network: A Los Angeles case study. Environmental research Lu, Y., Giuliano, G., Habre, R. 2021; 195: 110653

  Abstract

  Predicting PM2.5 concentrations at a fine spatial and temporal resolution (i.e., neighborhood, hourly) is challenging. Recent growth in low cost sensor networks is providing increased spatial coverage of air quality data that can be used to supplement data provided by monitors of regulatory agencies. We developed an hourly, 500*500m gridded PM2.5 model that integrates PurpleAir low-cost air sensor network data for Los Angeles County. We developed a quality control scheme for PurpleAir data. We included spatially and temporally varying predictors in a random forest model with random oversampling of high concentrations to predict PM2.5. The model achieved high prediction accuracy (10-fold cross-validation (CV) R2=0.93, root mean squared error (RMSE)=3.23mug/m3; spatial CV R2=0.88, spatial RMSE=4.33mug/m3; temporal CV R2=0.90, temporal RMSE=3.85mug/m3). Our model was able to predict spatial and diurnal patterns in PM2.5 on typical weekdays and weekends, as well as non-typical days, such as holidays and wildfire days. The model allows for far more precise estimates of PM2.5 than existing methods based on few sensors. Taking advantage of low-cost PM2.5 sensors, our hourly random forest model predictions can be combined with time-activity diaries in future studies, enabling geographically and temporally fine exposure estimation for specific population groups in studies of acute air pollution health effects and studies of environmental justice issues.

  View details for DOI 10.1016/j.envres.2020.110653

  View details for PubMedID 33476665

• Analyzing Traffic Impacts of Planned Major Events TRANSPORTATION RESEARCH RECORD Giuliano, G., Lu, Y. 2021; 2675 (8): 432-442

  View details for DOI 10.1177/0361198121998710

  View details for Web of Science ID 000683828100001