@article{guan_johnson_katzfuss_mannshardt_messier_reich_song_2020, title={Fine-Scale Spatiotemporal Air Pollution Analysis Using Mobile Monitors on Google Street View Vehicles}, volume={115}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2019.1665526}, abstractNote={Abstract People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. To make informed decisions on their day-to-day activities, they are interested in real-time information on a localized scale. Publicly available, fine-scale, high-quality air pollution measurements acquired using mobile monitors represent a paradigm shift in measurement technologies. A methodological framework utilizing these increasingly fine-scale measurements to provide real-time air pollution maps and short-term air quality forecasts on a fine-resolution spatial scale could prove to be instrumental in increasing public awareness and understanding. The Google Street View study provides a unique source of data with spatial and temporal complexities, with the potential to provide information about commuter exposure and hot spots within city streets with high traffic. We develop a computationally efficient spatiotemporal model for these data and use the model to make short-term forecasts and high-resolution maps of current air pollution levels. We also show via an experiment that mobile networks can provide more nuanced information than an equally sized fixed-location network. This modeling framework has important real-world implications in understanding citizens’ personal environments, as data production and real-time availability continue to be driven by the ongoing development and improvement of mobile measurement technologies. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.}, number={531}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Guan, Yawen and Johnson, Margaret C. and Katzfuss, Matthias and Mannshardt, Elizabeth and Messier, Kyle P. and Reich, Brian J. and Song, Joon J.}, year={2020}, month={Jul}, pages={1111–1124} }
 @article{mannshardt_sucic_fuentes_bingham_2016, title={Comparison of Distributional Statistics of Aquarius and Argo Sea Surface Salinity Measurements}, volume={33}, ISSN={["1520-0426"]}, DOI={10.1175/jtech-d-15-0068.1}, abstractNote={Abstract}, number={1}, journal={JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY}, author={Mannshardt, Elizabeth and Sucic, Katarina and Fuentes, Montserrat and Bingham, Frederick M.}, year={2016}, month={Jan}, pages={103–118} }
 @article{tingley_craigmile_haran_li_mannshardt_rajaratnam_2015, title={On Discriminating between GCM Forcing Configurations Using Bayesian Reconstructions of Late-Holocene Temperatures}, volume={28}, ISSN={["1520-0442"]}, DOI={10.1175/jcli-d-15-0208.1}, abstractNote={Abstract}, number={20}, journal={JOURNAL OF CLIMATE}, author={Tingley, Martin and Craigmile, Peter F. and Haran, Murali and Li, Bo and Mannshardt, Elizabeth and Rajaratnam, Bala}, year={2015}, month={Oct}, pages={8264–8281} }
 @article{mannshardt_sucic_jiao_dominici_frey_reich_fuentes_2013, title={Comparing exposure metrics for the effects of fine particulate matter on emergency hospital admissions}, volume={23}, ISSN={["1559-064X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84886725530&partnerID=MN8TOARS}, DOI={10.1038/jes.2013.39}, abstractNote={A crucial step in an epidemiological study of the effects of air pollution is to accurately quantify exposure of the population. In this paper, we investigate the sensitivity of the health effects estimates associated with short-term exposure to fine particulate matter with respect to three potential metrics for daily exposure: ambient monitor data, estimated values from a deterministic atmospheric chemistry model, and stochastic daily average human exposure simulation output. Each of these metrics has strengths and weaknesses when estimating the association between daily changes in ambient exposure to fine particulate matter and daily emergency hospital admissions. Monitor data is readily available, but is incomplete over space and time. The atmospheric chemistry model output is spatially and temporally complete but may be less accurate than monitor data. The stochastic human exposure estimates account for human activity patterns and variability in pollutant concentration across microenvironments, but requires extensive input information and computation time. To compare these metrics, we consider a case study of the association between fine particulate matter and emergency hospital admissions for respiratory cases for the Medicare population across three counties in New York. Of particular interest is to quantify the impact and/or benefit to using the stochastic human exposure output to measure ambient exposure to fine particulate matter. Results indicate that the stochastic human exposure simulation output indicates approximately the same increase in the relative risk associated with emergency admissions as using a chemistry model or monitoring data as exposure metrics. However, the stochastic human exposure simulation output and the atmospheric chemistry model both bring additional information, which helps to reduce the uncertainly in our estimated risk.}, number={6}, journal={JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY}, author={Mannshardt, Elizabeth and Sucic, Katarina and Jiao, Wan and Dominici, Francesca and Frey, H. Christopher and Reich, Brian and Fuentes, Montserrat}, year={2013}, pages={627–636} }
 @article{baxter_dionisio_burke_sarnat_sarnat_hodas_rich_turpin_jones_mannshardt_et al._2013, title={Exposure prediction approaches used in air pollution epidemiology studies: Key findings and future recommendations}, volume={23}, ISSN={["1559-064X"]}, DOI={10.1038/jes.2013.62}, abstractNote={Many epidemiologic studies of the health effects of exposure to ambient air pollution use measurements from central-site monitors as their exposure estimate. However, measurements from central-site monitors may lack the spatial and temporal resolution required to capture exposure variability in a study population, thus resulting in exposure error and biased estimates. Articles in this dedicated issue examine various approaches to predict or assign exposures to ambient pollutants. These methods include combining existing central-site pollution measurements with local- and/or regional-scale air quality models to create new or “hybrid” models for pollutant exposure estimates and using exposure models to account for factors such as infiltration of pollutants indoors and human activity patterns. Key findings from these articles are summarized to provide lessons learned and recommendations for additional research on improving exposure estimation approaches for future epidemiological studies. In summary, when compared with use of central-site monitoring data, the enhanced spatial resolution of air quality or exposure models can have an impact on resultant health effect estimates, especially for pollutants derived from local sources such as traffic (e.g., EC, CO, and NOx). In addition, the optimal exposure estimation approach also depends upon the epidemiological study design. We recommend that future research develops pollutant-specific infiltration data (including for PM species) and improves existing data on human time-activity patterns and exposure to local source (e.g., traffic), in order to enhance human exposure modeling estimates. We also recommend comparing how various approaches to exposure estimation characterize relationships between multiple pollutants in time and space and investigating the impact of improved exposure estimates in chronic health studies.}, number={6}, journal={JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY}, author={Baxter, Lisa K. and Dionisio, Kathie L. and Burke, Janet and Sarnat, Stefanie Ebelt and Sarnat, Jeremy A. and Hodas, Natasha and Rich, David Q. and Turpin, Barbara J. and Jones, Rena R. and Mannshardt, Elizabeth and et al.}, year={2013}, pages={654–659} }
 @article{mannshardt_craigmile_tingley_2013, title={Statistical modeling of extreme value behavior in North American tree-ring density series}, volume={117}, ISSN={["1573-1480"]}, DOI={10.1007/s10584-012-0575-5}, number={4}, journal={CLIMATIC CHANGE}, author={Mannshardt, Elizabeth and Craigmile, Peter F. and Tingley, Martin P.}, year={2013}, month={Apr}, pages={843–858} }