@article{brantley_guinness_chi_2020, title={BASELINE DRIFT ESTIMATION FOR AIR QUALITY DATA USING QUANTILE TREND FILTERING}, volume={14}, ISSN={["1932-6157"]}, DOI={10.1214/19-AOAS1318}, abstractNote={We address the problem of estimating smoothly varying baseline trends in time series data. This problem arises in a wide range of fields, including chemistry, macroeconomics, and medicine; however, our study is motivated by the analysis of data from low cost air quality sensors. Our methods extend the quantile trend filtering framework to enable the estimation of multiple quantile trends simultaneously while ensuring that the quantiles do not cross. To handle the computational challenge posed by very long time series, we propose a parallelizable alternating direction method of moments (ADMM) algorithm. The ADMM algorthim enables the estimation of trends in a piecewise manner, both reducing the computation time and extending the limits of the method to larger data sizes. We also address smoothing parameter selection and propose a modified criterion based on the extended Bayesian Information Criterion. Through simulation studies and our motivating application to low cost air quality sensor data, we demonstrate that our model provides better quantile trend estimates than existing methods and improves signal classification of low-cost air quality sensor output.}, number={2}, journal={ANNALS OF APPLIED STATISTICS}, author={Brantley, Halley L. and Guinness, Joseph and Chi, Eric C.}, year={2020}, month={Jun}, pages={585–604} }
 @article{brantley_hagler_herndon_massoli_bergin_russell_2019, title={Characterization of Spatial Air Pollution Patterns Near a Large Railyard Area in Atlanta, Georgia}, volume={16}, ISSN={["1660-4601"]}, DOI={10.3390/ijerph16040535}, abstractNote={Railyards are important transportation hubs, and they are often situated near populated areas with high co-located density of manufacturing, freight movement and commercial enterprises. Emissions occurring within railyards can affect nearby air quality. To better understand the air pollution levels in proximity to a major railyard, an intensive mobile air monitoring study was conducted in May 2012 around a major railyard area in Atlanta, GA, constituted of two separate facilities situated side-by-side. A total of 19 multi-hour mobile monitoring sessions took place over different times of day, days of the week, and under a variety of wind conditions. High time resolution measurements included black carbon (BC), particle number concentration (PN), particle optical extinction (EXT), oxides of nitrogen (NO, NO2, NOy), carbon monoxide (CO), and speciated air toxics. Urban background was estimated to contribute substantially (>70%) to EXT and CO, whereas BC, oxides of nitrogen (NOx) and toluene had comparably low background contributions (<30%). Mobile monitoring data were aggregated into 50 meter spatial medians by wind categories, with categories including low speed wind conditions (<0.5 m s−1) and, for wind speeds above that threshold, by wind direction relative to the railyard. Spatial medians of different pollutants measured had a wide range of correlation—gas-phase air toxics (benzene, toluene, acetaldehyde) had moderate correlation with each other (r = 0.46–0.59) and between toluene and CO (r = 0.53), but lower correlation for other pairings. PN had highest correlation with oxides of nitrogen (r = 0.55–0.66), followed by BC (r = 0.4), and lower correlation with other pollutants. Multivariate regression analysis on the full set of 50 m medians found BC and NO as having the strongest relationship to railyard emissions, in comparison to their respective background levels. This was indicated by an increase associated with transiting through the yard and inverse relationship with distance from the railyard; NO and BC decreased by a factor of approximately 0.5 and 0.7 over 1 km distance of the railyard boundary, respectively. Low speed, variable wind conditions were related to higher concentrations of all measured parameters.}, number={4}, journal={INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH}, author={Brantley, Halley L. and Hagler, Gayle S. W. and Herndon, Scott C. and Massoli, Paola and Bergin, Michael H. and Russell, Armistead G.}, year={2019}, month={Feb} }
 @article{thoma_george_duvall_wu_whitaker_oliver_mukerjee_brantley_spann_bell_et al._2019, title={Rubbertown Next Generation Emissions Measurement Demonstration Project}, volume={16}, ISSN={["1660-4601"]}, DOI={10.3390/ijerph16112041}, abstractNote={Industrial facilities and other sources can emit air pollutants from fugitive leaks, process malfunctions and area sources that can be difficult to understand and to manage. Next generation emissions measurement (NGEM) approaches executed near facilities are enabling new ways to assess these sources and their impacts to nearby populations. This paper describes complementary uses of emerging NGEM systems in a Louisville, KY industrial district (Rubbertown), focusing on an important area air toxic, 1,3-butadiene. Over a one-year deployment starting in September 2017, two-week average passive samplers (PSs) at 11 sites showed both geospatial and temporal trends. At 0.24 ppbv annual average 1,3-butadiene concentration, a group of PSs located near facility fence lines was elevated compared to a PS group located in the community and upwind from facilities (0.07 ppbv average). Two elevated PS periods capturing emission events were examined using time-resolved NGEM approaches as case studies. In one event a 1.18 ppbv PS reading was found to be relatively localized and was caused by a multiday emission from a yet to be identified, non-facility source. In the other event, the airshed was more broadly impacted with PS concentrations ranging from 0.71 ppbv for the near-facility group to 0.46 ppbv for the community group. This case was likely influenced by a known emission event at an industrial facility. For both case studies, air pollutant and wind data from prototype NGEM systems were combined with source location models to inform the emission events. This research illustrates the power of applying NGEM approaches to improve both the understanding of emissions near sources and knowledge of impacts to near-source communities.}, number={11}, journal={INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH}, author={Thoma, Eben and George, Ingrid and Duvall, Rachelle and Wu, Tai and Whitaker, Donald and Oliver, Karen and Mukerjee, Shaibal and Brantley, Halley and Spann, Jane and Bell, Tiereny and et al.}, year={2019}, month={Jun} }