An enhanced research paradigm is presented to address the spatial and temporal gaps in fine particulate matter (PM) measurements and generate realistic and representative concentration fields for use in epidemiological studies of human exposure to ambient air particulate concentrations. The general approach for research designed to analyze health impacts of exposure to PM is to use concentration data from the nearest ground-based air quality monitor(s), which typically have missing data on the temporal and spatial scales due to filter sampling schedules and monitor placement, respectively. To circumvent these data gaps, this research project uses a Hierarchical Bayesian Model (HBM) to generate estimates of PM in areas with and without air quality monitors by combining PM concentrations measured by monitors, PM concentration estimates derived from satellite aerosol optical depth (AOD) data, and Community-Multiscale Air Quality (CMAQ) model predictions of PM concentrations. This methodology represents a substantial step forward in the approach for developing representative PM concentration datasets to correlate with inpatient hospitalizations and emergency room visits data for asthma and inpatient hospitalizations for myocardial infarction (MI) and heart failure (HF) using case-crossover analysis. There were two key objective of this current study. First was to show that the inputs to the HBM could be expanded to include AOD data in addition to data from PM monitors and predictions from CMAQ. The second objective was to determine if inclusion of AOD surfaces in HBM model algorithms results in PM air pollutant concentration surfaces which more accurately predict hospital admittance and emergency room visits for MI, asthma, and HF. This study focuses on the New York City, NY metropolitan and surrounding areas during the 2004-2006 time period, in order to compare the health outcome impacts with those from previous studies and focus on any benefits derived from the changes in the HBM model surfaces. Consistent with previous studies, the results show high PM exposure is associated with increased risk of asthma, myocardial infarction and heart failure. The estimates derived from concentration surfaces that incorporate AOD had a similar model fit and estimate of risk as compared to those derived from combining monitor and CMAQ data alone. Thus, this study demonstrates that estimates of PM concentrations from satellite data can be used to supplement PM monitor data in the estimates of risk associated with three common health outcomes. Results from this study were inconclusive regarding the potential benefits derived from adding AOD data to the HBM, as the addition of the satellite data did not significantly increase model performance. However, this study was limited to one metropolitan area over a short two-year time period. The use of next-generation, high temporal and spatial resolution satellite AOD data from geostationary and polar-orbiting satellites is expected to improve predictions in epidemiological studies in are...

The fine particulate matter baseline (PMB), which includes PM 2.5 monitor readings fused with Community Multiscale Air Quality (CMAQ) model predictions, using the Hierarchical Bayesian Model (HBM), is less accurate in rural areas without monitors. To address this issue, an upgraded HBM was used to form four experimental aerosol optical depth (AOD)-PM 2.5 concentration surfaces. A case-crossover design and conditional logistic regression evaluated the contribution of the AOD-PM 2.5 surfaces and PMB to four respiratory-cardiovascular hospital events in all 99 12 km 2 CMAQ grids, and in grids with and without ambient air monitors. For all four health outcomes, only two AOD-PM 2.5 surfaces, one not kriged (PMC) and the other kriged (PMCK), had significantly higher Odds Ratios (ORs) on lag days 0, 1, and 01 than PMB in all grids, and in grids without monitors. In grids with monitors, emergency department (ED) asthma PMCK on lag days 0, 1 and 01 and inpatient (IP) heart failure (HF) PMCK ORs on lag days 01 were significantly higher than PMB ORs. Warm season ORs were significantly higher than cold season ORs. Independent confirmation of these results should include AOD-PM 2.5 concentration surfaces with greater temporal-spatial resolution, now easily available from geostationary satellites, such as GOES-16 and GOES-17.In urban areas, PMB gives more "weight" to PM 2.5 monitor readings than CMAQ PM 2.5 model predictions. In rural areas, CMAQ PM 2.5 model predictions exert more influence than PM 2.5 monitor readings on PMB, since there are fewer monitors or no monitors. Ambient air monitors are usually found in urban areas. In the last 15 years, PMB has turned out to be a more representative PM 2.5 concentration surface, compared to the interpolation of PM 2.5 monitor data, as a method to resolve spatial gaps between ambient air monitors [16,18,22]. CDC subsequently incorporated PMB into its Environmental Public Health Tracking (EPHT) network of state and New York City partners [16,18,22,26]. To date, PMB has been used by federal and state epidemiologists completing EPHT projects in different parts of the US [16,18,22,26].Within this decade, the availability and use of satellite AOD data have become more routine [6,16,[27][28][29][30][31]. Newer generation satellite instruments measure AOD with increased temporal accuracy and finer spatial resolution [27,[32][33][34][35][36][37]. AOD is a unitless measure of the scattering and absorption of visible light by aerosols (particles) in the atmosphere [38][39][40]. AOD data are, by definition, actual physical measurements, an improvement over CMAQ PM 2.5 model predictions. Once AOD unitless measurements have been calibrated with actual PM 2.5 readings from on-the-ground ambient air monitors, it is then possible to utilize the derived AOD-PM 2.5 concentration readings to estimate actual ambient PM 2.5 concentration in areas where there are no on-the-ground air monitors. The relationship between AOD measurements and on-the-ground measurements of PM 2.5 concentration readings has b...

The Oregon Department of Environmental Quality (ODEQ) uses Total Maximum Daily Load (TMDL) calculations, and the associated regulatory process, to manage harmful cyanobacterial blooms (CyanoHABs) attributable to non-point source (NPS) pollution. TMDLs are based on response (lagging) indicators (e.g., measurable quantities of NPS (nutrients: nitrogen {N} and phosphorus {P}), and/or sediment), and highlight the negative outcomes (symptoms) of impaired water quality. These response indicators belatedly address water quality issues, if the cause is impaired riparian functions. Riparian functions assist in decreasing the impacts of droughts and floods (through sequestration of nutrients and excess sediment), allow water to remain on the land surface, improve aquatic habitats, improve water quality, and provide a focus for monitoring and adaptive management. To manage water quality, the focus must be on the drivers (leading indicators) of the causative mechanisms, such as loss of ecological functions. Success in NPS pollution control, and maintaining healthy aquatic habitats, often depends on land management/land use approaches, which facilitate the natural recovery of stream and wetland riparian functions. Focusing on the drivers of ecosystem functions (e.g., vegetation, hydrology, soil, and landform), instead of individual mandated response indicators, using the proper functioning condition (PFC) approach, as a best management practice (BMP), in conjunction with other tools and management strategies, can lead to pro-active policies and approaches, which support positive change in an ecosystem or watershed, and in water quality improvement.