A hybrid approach is proposed to estimate exposure to fine particulate matter (PM_2.5) at a given location and time. This approach builds on satellite-based aerosol optical depth (AOD), air pollution data from sparsely distributed Environmental Protection Agency (EPA) sites and local time–space Kriging, an optimal interpolation technique. Given the daily global coverage of AOD data, we can develop daily estimate of air quality at any given location and time. This can assure unprecedented spatial coverage, needed for air quality surveillance and management and epidemiological studies. In this paper, we developed an empirical relationship between the 2 km AOD and PM_2.5 data from EPA sites. Extrapolating this relationship to the study domain resulted in 2.3 million predictions of PM_2.5 between 2000 and 2009 in Cleveland Metropolitan Statistical Area (MSA). We have developed local time–space Kriging to compute exposure at a given location and time using the predicted PM_2.5. Daily estimates of PM_2.5 were developed for Cleveland MSA between 2000 and 2009 at 2.5 km spatial resolution; 1.7 million ( ∼ 79.8%) of 2.13 million predictions required for multiyear and geographic domain were robust. In the epidemiological application of the hybrid approach, admissions for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) was examined with respect to time–space lagged PM_2.5 exposure. Our analysis suggests that the risk of AECOPD increases 2.3% with a unit increase in PM_2.5 exposure within 9 days and 0.05° ( ∼ 5 km) distance lags. In the aggregated analysis, the exposed groups (who experienced exposure to PM_2.5 >15.4 μg/m³) were 54% more likely to be admitted for AECOPD than the reference group. The hybrid approach offers greater spatiotemporal coverage and reliable characterization of ambient concentration than conventional in situ monitoring-based approaches. Thus, this approach can potentially reduce exposure misclassification errors in the conventional air pollution epidemiology studies.

The Eastern Pacific leatherback turtle population ( Dermochelys coriacea ) has declined precipitously in recent years. One of the major causes is bycatch from coastal and pelagic fisheries. Fisheries observations are often underutilized, despite strong potential for this data to affect policy. In this study, we created a spatiotemporal species distribution model that synthesizes fisheries observations with remotely sensed environmental data. The model will be developed into a dynamic management tool for the Eastern Pacific leatherback population. We obtained leatherback observation data from multiple fisheries that have operated in the Southeast Pacific (2001–2018). A dynamic Poisson point process model was applied to predict leatherback intensity (observation per unit area) as a function of dynamic environmental covariates. This model serves as a tool for application by managers and stakeholders toward the reduction of leatherback turtle bycatch and provides a modeling framework for analyzing fisheries observations from other vulnerable populations and species.

The unpredictable timing and magnitude of precipitation events and the spatiotemporal variability of constituent concentrations are major complications to effective monitoring of watershed nutrient and sediment loads. Furthermore, detecting small changes in constituent loads in response to implementation of Stormwater control measures (SCMs) against natural variability is a challenge. Nevertheless, regulatory frameworks that direct reductions of pollutants to streams frequently depend on the ability to quantify changes in loads after management interventions. The before-after-control impact (BACI) sampling design is often used to assess the effects of an environmental change made at a known point in time. However, this approach may be complicated to apply to nutrient and sediment loads in streams as the relative impact of SCMs on nutrient concentration conditional on the long term variability of discharges has not been evaluated. Multi-scale monitoring studies that provide estimates of the natural temporal and spatial variability of discharge and concentrations could provide useful information in designing a BACI study. Here we use data from the Baltimore Long Term Ecological Research (LTER) sites and urban restoration sites to develop multiple statistical measures of the effectiveness of a given monitoring scheme in revealing the hypothesized restoration effects in terms of hydrology and nutrient loads. Stratified sampling over baseflow and stormflow and the use of multiple control streams were useful tools to detect long term cumulative reductions in concentrations due to SCMs. Moderate reductions in concentration (20%), however, were not detectable with the design options considered. We emphasize that appropriate pre-planning of monitoring schemes and sampling frequency is essential to determine if the effects on constituent loads resulting from a given watershed restoration activity are measurable.