Kevin Talgo

Associations between ozone and morbidity using the Spatial Synoptic Classification system

BackgroundSynoptic circulation patterns (large-scale tropospheric motion systems) affect air pollution and, potentially, air-pollution-morbidity associations. We evaluated the effect of synoptic circulation patterns (air masses) on the association between ozone and hospital admissions for asthma and myocardial infarction (MI) among adults in North Carolina.MethodsDaily surface meteorology data (including precipitation, wind speed, and dew point) for five selected cities in North Carolina were obtained from the U.S. EPA Air Quality System (AQS), which were in turn based on data from the National Climatic Data Center of the National Oceanic and Atmospheric Administration. We used the Spatial Synoptic Classification system to classify each day of the 9-year period from 1996 through 2004 into one of seven different air mass types: dry polar, dry moderate, dry tropical, moist polar, moist moderate, moist tropical, or transitional. Daily 24-hour maximum 1-hour ambient concentrations of ozone were obtained from the AQS. Asthma and MI hospital admissions data for the 9-year period were obtained from the North Carolina Department of Health and Human Services. Generalized linear models were used to assess the association of the hospitalizations with ozone concentrations and specific air mass types, using pollutant lags of 0 to 5 days. We examined the effect across cities on days with the same air mass type. In all models we adjusted for dew point and day-of-the-week effects related to hospital admissions.ResultsOzone was associated with asthma under dry tropical (1- to 5-day lags), transitional (3- and 4-day lags), and extreme moist tropical (0-day lag) air masses. Ozone was associated with MI only under the extreme moist tropical (5-day lag) air masses.ConclusionsElevated ozone levels are associated with dry tropical, dry moderate, and moist tropical air masses, with the highest ozone levels being associated with the dry tropical air mass. Certain synoptic circulation patterns/air masses in conjunction with ambient ozone levels were associated with increased asthma and MI hospitalizations.

Creating locally-resolved mobile-source emissions inputs for air quality modeling in support of an exposure study in Detroit, Michigan, USA.

This work describes a methodology for modeling the impact of traffic-generated air pollutants in an urban area. This methodology presented here utilizes road network geometry, traffic volume, temporal allocation factors, fleet mixes, and emission factors to provide critical modeling inputs. These inputs, assembled from a variety of sources, are combined with meteorological inputs to generate link-based emissions for use in dispersion modeling to estimate pollutant concentration levels due to traffic. A case study implementing this methodology for a large health study is presented, including a sensitivity analysis of the modeling results reinforcing the importance of model inputs and identify those having greater relative impact, such as fleet mix. In addition, an example use of local measurements of fleet activity to supplement model inputs is described, and its impacts to the model outputs are discussed. We conclude that with detailed model inputs supported by local traffic measurements and meteorology, it is possible to capture the spatial and temporal patterns needed to accurately estimate exposure from traffic-related pollutants.

Projecting wildfire area burned in the south-eastern United States, 2011–60

Future changes in society and climate are expected to affect wildfire activity in the south-eastern United States. The objective of this research was to understand how changes in both climate and society may affect wildfire in the coming decades. We estimated a three-stage statistical model of wildfire area burned by ecoregion province for lightning and human causes (1992–2010) based on precipitation, temperature, potential evapotranspiration, forest land use, human population and personal income. Estimated parameters from the statistical models were used to project wildfire area burned from 2011 to 2060 under nine climate realisations, using a combination of three Intergovernmental Panel on Climate Change-based emissions scenarios (A1B, A2, B2) and three general circulation models. Monte Carlo simulation quantifies ranges in projected area burned by county by year, and in total for higher-level spatial aggregations. Projections indicated, overall in the Southeast, that median annual area burned by lightning-ignited wildfire increases by 34%, human-ignited wildfire decreases by 6%, and total wildfire increases by 4% by 2056–60 compared with 2016–20. Total wildfire changes vary widely by state (–47 to +30%) and ecoregion province (–73 to +79%). Our analyses could be used to generate projections of wildfire-generated air pollutant exposures, relevant to meeting the National Ambient Air Quality Standards.

Assessing the Added Value of Dynamical Downscaling Using the Standardized Precipitation Index

In this study, the Standardized Precipitation Index (SPI) is used to ascertain the added value of dynamical downscaling over the contiguous United States. WRF is used as a regional climate model (RCM) to dynamically downscale reanalysis fields to compare values of SPI over drought timescales that have implications for agriculture and water resources planning. The regional climate generated by WRF has the largest improvement over reanalysis for SPI correlation with observations as the drought timescale increases. This suggests that dynamically downscaled fields may be more reliable than larger-scale fields for water resource applications (e.g., water storage within reservoirs). WRF improves the timing and intensity of moderate to extreme wet and dry periods, even in regions with homogenous terrain. This study also examines changes in SPI from the extreme drought of 1988 and three “drought busting” tropical storms. Each of those events illustrates the importance of using downscaling to resolve the spatial extent of droughts. The analysis of the “drought busting” tropical storms demonstrates that while the impact of these storms on ending prolonged droughts is improved by the RCM relative to the reanalysis, it remains underestimated. These results illustrate the importance and some limitations of using RCMs to project drought.

Guidance for Quantifying the Contribution of Airport Emissions to Local Air Quality

This report is a guide for airport operators on effective procedures for using air quality models in combination with on-site measurement equipment to prepare a comprehensive assessment of air pollutant concentrations in the vicinity of airports. It is designed to help practitioners generate information desired by local communities as they seek to develop more detailed local air quality assessments as well as respond to regulatory needs, including those of the National Environmental Policy Act (NEPA). The guide provides in-depth information on the capabilities and limitations of modeling and measurement tools, adding to an increasing knowledge base concerning preparation of air quality assessments near airports. Starting with the Federal Aviation Administrations (FAAs) regulatory EDMS/AEDT, it describes how best to use available models, in combination with potential on-site monitoring programs, to conduct air quality assessments. Detailed information on the monitoring campaigns and modeling assessments is included in a set of appendices that accompany the guide. The appendices (available in CRP-CD-115) describe the models tested and the various equipment used to collect data, the rationale behind the selection of Washington Dulles International Airport as a case study application, and the components and steps involved in the measurement campaigns, and include an assessment of the various model outputs.

Projecting wildfire emissions over the south-eastern United States to mid-century

Wildfires can impair human health because of the toxicity of emitted pollutants, and threaten communities, structures and the integrity of ecosystems sensitive to disturbance. Climate and socioeconomic factors (e.g. population and income growth) are known regional drivers of wildfires. Reflecting changes in these factors in wildfire emissions estimates is thus a critical need in air quality and health risk assessments in the south-eastern United States. We developed such a methodology leveraging published statistical models of annual area burned (AAB) over the US Southeast for 2011–2060, based on county-level socioeconomic and climate projections, to estimate daily wildfire emissions in selected historical and future years. Projected AABs were 7 to 150% lower on average than the historical mean AABs for 1992–2010; projected wildfire fine-particulate emissions were 13 to 62% lower than those based on historical AABs, with a temporal variability driven by the climate system. The greatest differences were in areas of large wildfire impacts from socioeconomic factors, suggesting that historically based (static) wildfire inventories cannot properly represent future air quality responses to changes in these factors. The results also underscore the need to correct biases in the dynamical downscaling of wildfire climate drivers to project the health risks of wildfire emissions more reliably.

Assessment of port-related air quality impacts: geographic analysis of population

Increased global trade has led to greater transportation by rail, road and ships to move cargo. Based upon multiple near-road and near-source monitoring studies, the busy roadways and large emission sources at ports may impact local air quality within several hundred metres of the ports. Health effects have been associated with near-road exposures and proximity to large emission sources, so characterising emission sources is important for understanding potential health effects. To address this need, we have developed a new community-scale tool called C-PORT to model emissions related to all port-area activities and predict concentrations of multiple criteria and hazardous air pollutants at fine spatial scales in the near-source environment. We present a geographical information system analysis of areas surrounding five US ports (Ports of New York and New Jersey, Virginia, Savannah, Miami, and Houston) to determine potential sources of concern related to freight transport and demographics of the near-source population that could be affected.