Aijun Xiu

Development of a Land Surface Model. Part I: Application in a Mesoscale Meteorological Model

Parameterization of land surface processes and consideration of surface inhomogeneities are very important to mesoscale meteorological modeling applications, especially those that provide information for air quality modeling. To provide crucial, reliable information on the diurnal evolution of the planetary boundary layer (PBL) and its dynamic characteristics, it is necessary in a mesoscale model to include a land surface parameterization that simulates the essential physics processes and is computationally efficient. A land surface model is developed and implemented in the Fifth-Generation Pennsylvania State University‐ National Center for Atmospheric Research Mesoscale Model (MM5) to enable MM5 to respond to changing soil moisture and vegetation conditions. This land surface model includes explicit soil moisture, which is based on the Interactions between Soil, Biosphere, and Atmosphere model and three pathways for evaporation, including soil evaporation, canopy evaporation, and vegetative evapotranspiration. The stomatal conductance, leaf-tocanopy scaling, and surface moisture parameterizations are newly developed based on a variety of sources in the current literature. Also, a processing procedure for gridding soil and vegetation parameters and simulating seasonal growth has been developed. MM5 with the land surface model is tested and evaluated against observations and the ‘‘standard’’ MM5, which uses a simple surface moisture availability scheme to estimate the soil wetness and then the latent heat flux, for two cases from the First International Satellite Land Surface Climatology Project Field Experiment. The evaluation analysis focuses primarily on surface fluxes of heat and moisture, near-surface temperature, soil temperature, PBL height, and vertical temperature profiles. A subsequent article will describe extensions of this model to simulate chemical dry deposition.

Development of a Land Surface Model. Part II: Data Assimilation

Abstract Part I described a land surface model, its implementation in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), and some model evaluation results. Part II describes the indirect soil moisture data assimilation scheme. As described in Part I, the land surface model includes explicit soil moisture, which is based on the Interactions between Soil, Biosphere, and Atmosphere (ISBA) model, and three pathways for evaporation: soil evaporation, evaporation from the wet canopy, and vegetative transpiration. The data assimilation scheme presented here also follows similar work on data assimilation for ISBA and uses model biases of the 2-m air temperature and humidity against observed analyses to nudge soil moisture. An important difference from the ISBA schemes is that the nudging strengths are computed from model parameters such as solar radiation, temperature, leaf area, vegetation coverage, and aerodynamic resistance rather than from statis...

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.

Comparison of Measured and Modeled Surface Fluxes of Heat, Moisture, and Chemical Dry Deposition

Realistic air quality modeling requires accurate simulation of both meteorological and chemical processes within the planetary boundary layer (PBL). Surface energy and moisture fluxes control the temperature and humidity profiles. Similarly, chemical fluxes (dry deposition) have an important influence on PBL concentrations of trace chemical species. Therefore, accurate and consistent methods for simulation of both meteorological and chemical surface exchange processes are critical for realistic modeling of boundary layer atmospheric chemistry.

Cardiovascular Outcomes and the Physical and Chemical Properties of Metal Ions Found in Particulate Matter Air Pollution: A QICAR Study

Background: This paper presents an application of quantitative ion character–activity relationships (QICAR) to estimate associations of human cardiovascular (CV) diseases (CVDs) with a set of metal ion properties commonly observed in ambient air pollutants. QICAR has previously been used to predict ecotoxicity of inorganic metal ions based on ion properties. Objectives: The objective of this work was to examine potential associations of biological end points with a set of physical and chemical properties describing inorganic metal ions present in exposures using QICAR. Methods: Chemical and physical properties of 17 metal ions were obtained from peer-reviewed publications. Associations of cardiac arrhythmia, myocardial ischemia, myocardial infarction, stroke, and thrombosis with exposures to metal ions (measured as inference scores) were obtained from the Comparative Toxicogenomics Database (CTD). Robust regressions were applied to estimate the associations of CVDs with ion properties. Results: CVD was statistically significantly associated (Bonferroni-adjusted significance level of 0.003) with many ion properties reflecting ion size, solubility, oxidation potential, and abilities to form covalent and ionic bonds. The properties are relevant for reactive oxygen species (ROS) generation, which has been identified as a possible mechanism leading to CVDs. Conclusion: QICAR has the potential to complement existing epidemiologic methods for estimating associations between CVDs and air pollutant exposures by providing clues about the underlying mechanisms that may explain these associations.

Sensitivity of the Weather Research and Forecast/Community Multiscale Air Quality modeling system to MODIS LAI, FPAR, and albedo

This study aims to improve land surface processes in a retrospective meteorology and air quality modeling system through the use of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation and albedo products for more realistic vegetation and surface representation. MODIS leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FPAR), and albedo are incorporated into the Pleim-Xiu land surface model (PX LSM) used in a combined meteorology and air quality modeling system. The current PX LSM intentionally exaggerates vegetation coverage and LAI in western dry lands so that its soil moisture nudging scheme is more effective in simulating surface temperature and mixing ratio. Reduced vegetation coverage from the PX LSM with MODIS input results in hotter and dryer daytime conditions with reduced ozone dry deposition velocities in much of western North America. Evaluations of the new system indicate greater error and bias in temperature, but reduced error and bias in moisture with the MODIS vegetation input. Hotter daytime temperatures and reduced dry deposition result in greater ozone concentrations in the western arid regions even with deeper boundary layer depths. MODIS albedo has much less impact on the meteorology simulations than MODIS LAI and FPAR. The MODIS vegetation and albedo input does not have much influence in the east where differences in vegetation and albedo parameters are less extreme. Evaluation results showing increased temperature errors with more accurate representation of vegetation suggests that improvements are needed in the model surface physics, particularly the soil processes in the PX LSM.

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.

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.