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Air quality sensors are becoming increasingly available to the general public, providing individuals and communities with information on fine-scale, local air quality in increments as short as 1 min. Current health studies do not support linking 1-min exposures to adverse health effects; therefore, the potential health implications of such ambient exposures are unclear. The U.S. Environmental Protection Agency (EPA) establishes the National Ambient Air Quality Standards (NAAQS) and Air Quality Index (AQI) on the best science available, which typically uses longer averaging periods (e.g., 8 hr; 24 hr). Another consideration for interpreting sensor data is the variable relationship between pollutant concentrations measured by sensors, which are short-term (1 min to 1 hr), and the longer term averages used in the NAAQS and AQI. In addition, sensors often do not meet federal performance or quality assurance requirements, which introduces uncertainty in the accuracy and interpretation of these readings. This article describes a statistical analysis of data from regulatory monitors and new real-time technology from Village Green benches to inform the interpretation and communication of short-term air sensor data. We investigate the characteristics of this novel data set and the temporal relationships of short-term concentrations to 8-hr average (ozone) and 24-hr average (PM2.5) concentrations to examine how sensor readings may relate to the NAAQS and AQI categories, and ultimately to inform breakpoints for sensor messages. We consider the empirical distributions of the maximum 8-hr averages (ozone) and 24-hr averages (PM2.5) given the corresponding short-term concentrations, and provide a probabilistic assessment. The result is a robust, empirical comparison that includes events of interest for air quality exceedances and public health communication. Concentration breakpoints are developed for short-term sensor readings such that, to the extent possible, the related air quality messages that are conveyed to the public are consistent with messages related to the NAAQS and AQI. Implications: Real-time sensors have the potential to provide important information about fine-scale current air quality and local air quality events. The statistical analysis of short-term regulatory and sensor data, coupled with policy considerations and known health effects experienced over longer averaging times, supports interpretation of such short-term data and efforts to communicate local air quality. [ABSTRACT FROM AUTHOR]

Salinity is an indicator of the interaction between ocean circulation and the global water cycle, which in turn affects the regulation of the earth's climate. To thoroughly understand sea surface salinity's connection to processes that define the hydrological cycle, such as surface forcing and ocean mixing, there is need for proper validation of remotely sensed salinity products with independent measurements, beyond central tendencies, across the entire distribution of salinity. Because of its fine spatial and temporal coverage, Aquarius presents an ideal measurement system for fully characterizing the distribution and properties of sea surface salinity. Using the first 33 months of Aquarius, version 3.0, level 2 sea surface salinity data, both central tendencies and distributional quantile characteristics across time and space are investigated, and a statistical validation of Aquarius measurements with Argo in situ observations is conducted. Several aspects are considered, including regional characteristics and temporal agreement, as well as seasonal differences by ocean basin and hemisphere. Regional studies examine the time and space scales of variability through time series comparisons and an analysis of quantile properties. Results indicate that there are significant differences between the tails of their respective distributions, especially the lower tail. The Aquarius data show longer, fatter lower tails, indicating higher probability to sample low-salinity events. There is also evidence of differences in measurement variation between Aquarius and Argo. These results are seen across seasons, ocean basins, hemispheres, and regions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Many analyses of the paleoclimate record include conclusions about extremes, with a focus on the unprecedented nature of recent climate events. While the use of extreme value theory is becoming common in the analysis of the instrumental climate record, applications of this framework to the spatio-temporal analysis of paleoclimate records remain limited. This article develops a Bayesian hierarchical model to investigate spatially varying trends and dependencies in the parameters characterizing the distribution of extremes of a proxy data set, and applies it to the site-wise decadal maxima and minima of a gridded network of temperature sensitive tree ring density time series over northern North America. The statistical analysis reveals significant spatial associations in the temporal trends of the location parameters of the generalized extreme value distributions: maxima are increasing as a function of time, with stronger increases in the north and east of North America; minima are significantly increasing in the west, possibly decreasing in the east, and exhibit no changes in the center of the region. Results indicate that the distribution varies as a function of both space and time, with tree ring density maxima becoming more extreme as a function of time and minima having diverging temporal trends, by spatial location. Results of this proxy-only analysis are a first step towards directly reconstructing extremal climate behavior, as opposed to mean climate behavior, by linking extremes in the proxy record to extremes in the instrumental record. [ABSTRACT FROM AUTHOR]

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. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Several climate modeling groups have recently generated ensembles of last-millennium climate simulations under different forcing scenarios. These experiments represent an ideal opportunity to establish the baseline feasibility of using proxy-based reconstructions of late-Holocene climate as out-of-calibration tests of the fidelity of the general circulation models used to project future climate. This paper develops a formal statistical model for assessing the agreement between members of an ensemble of climate simulations and the ensemble of possible climate histories produced from a hierarchical Bayesian climate reconstruction. As the internal variabilities of the simulated and reconstructed climate are decoupled from one another, the comparison is between the two latent, or unobserved, forced responses. Comparisons of the spatial average of a 600-yr high northern latitude temperature reconstruction to suites of last-millennium climate simulations from the GISS-E2 and CSIRO models, respectively, suggest that the proxy-based reconstructions are able to discriminate only between the crudest features of the simulations within each ensemble. Although one of the three volcanic forcing scenarios used in the GISS-E2 ensemble results in superior agreement with the reconstruction, no meaningful distinctions can be made between simulations performed with different estimates of solar forcing or land cover changes. In the case of the CSIRO model, sequentially adding orbital, greenhouse gas, solar, and volcanic forcings to the simulations generally improves overall consensus with the reconstruction, though the distinctions are not individually significant. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: Reconstructing a climate process in both space and time from incomplete instrumental and climate proxy time series is a problem with clear societal relevance that poses both scientific and statistical challenges. These challenges, along with the interdisciplinary nature of the reconstruction problem, point to the need for greater cooperation between the earth science and statistics communities – a sentiment echoed in recent parliamentary reports. As a step in this direction, it is prudent to formalize what is meant by the paleoclimate reconstruction problem using the language and tools of modern statistics. This article considers the challenge of inferring, with uncertainties, a climate process through space and time from overlapping instrumental and climate sensitive proxy time series that are assumed to be well dated – an assumption that is likely only reasonable for certain proxies over at most the last few millennia. Within a unifying, hierarchical space–time modeling framework for this problem, the modeling assumptions made by a number of published methods can be understood as special cases, and the distinction between modeling assumptions and analysis or inference choices becomes more transparent. The key aims of this article are to 1) establish a unifying modeling and notational framework for the paleoclimate reconstruction problem that is transparent to both the climate science and statistics communities; 2) describe how currently favored methods fit within this framework; 3) outline and distinguish between scientific and statistical challenges; 4) indicate how recent advances in the statistical modeling of large space–time data sets, as well as advances in statistical computation, can be brought to bear upon the problem; 5) offer, in broad strokes, some suggestions for model construction and how to perform the required statistical inference; and 6) identify issues that are important to both the climate science and applied statistics communities, and encourage greater collaboration between the two. [Copyright &y& Elsevier]

Daily maximum 8-h average (MDA8) ozone (O 3) concentrations are well-known to be influenced by local meteorological conditions, which vary across both daily and seasonal temporal scales. Previous studies have adjusted long-term trends in O 3 concentrations for meteorological effects using various statistical and mathematical methods in order to get a better estimate of the long-term changes in O 3 concentrations due to changes in precursor emissions such as nitrogen oxides (NO X) and volatile organic compounds (VOCs). In this work, the authors present improvements to the current method used by the United States Environmental Protection Agency (US EPA) to adjust O 3 trends for meteorological influences by making refinements to the input data sources and by allowing the underlying statistical model to vary locally using a variable selection procedure. The current method is also expanded by using a quantile regression model to adjust trends in the 90th and 98th percentiles of the distribution of MDA8 O 3 concentrations, allowing for a better understanding of the effects of local meteorology on peak O 3 levels in addition to seasonal average concentrations. The revised method is used to adjust trends in the May to September mean, 90th percentile, and 98th percentile MDA8 O 3 concentrations at over 700 monitoring sites in the U.S. for years 2000–2016. The utilization of variable selection and quantile regression allow for a more in-depth understanding of how weather conditions affect O 3 levels in the U.S. This represents a fundamental advancement in our ability to understand how interannual variability in weather conditions in the U.S. may impact attainment of the O 3 National Ambient Air Quality Standards (NAAQS). • Improvements made to the U.S. EPA's method for adjusting ozone trends for weather • Refinements include improvements to data sources and underlying statistical model • Variable selection allows location-specific formulation of meteorological effects • Develops ability to adjust trends in peak concentrations using quantile regression • Results have the potential to better inform air quality policy and decision-making [ABSTRACT FROM AUTHOR]

The United States Environmental Protection Agency (EPA) has implemented a Bayesian spatial data fusion model called the Downscaler (DS) model to generate daily air quality surfaces for PM2.5 across the contiguous U.S. Previous implementations of DS relied on monitoring data from EPA's Air Quality System (AQS) network, which is largely concentrated in urban areas. In this work, we introduce to the DS modeling framework an additional PM2.5 input dataset from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network located mainly in remote sites. In the western U.S. where IMPROVE sites are relatively dense (compared to the eastern U.S.), the inclusion of IMPROVE PM2.5 data to the DS model runs reduces predicted annual averages and 98th percentile concentrations by as much as 1.0 and 4 μg m−3, respectively. Some urban areas in the western U.S., such as Denver, Colorado, had moderate increases in the predicted annual average concentrations, which led to a sharpening of the gradient between urban and remote areas. Comparison of observed and DS-predicted concentrations for the grid cells containing IMPROVE and AQS sites revealed consistent improvement at the IMPROVE sites but some degradation at the AQS sites. Cross-validation results of common site-days withheld in both simulations show a slight reduction in the mean bias but a slight increase in the mean square error when the IMPROVE data is included. These results indicate that the output of the DS model (and presumably other Bayesian data fusion models) is sensitive to the addition of geographically distinct input data, and that the application of such models should consider the prediction domain (national or urban focused) when deciding to include new input data. [ABSTRACT FROM AUTHOR]