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A Kalman Filter based sensitivity study of the influence of boundary layer parameters (e.g., vertical turbulent exchange coefficient and the mixing layer height) and emission data was conducted on the predicted-observed concentration errors of the REM-CALGRID (RCG) model. To accomplish this, operational ground-based O3 and NO2 were assimilated into the RCG model during a simulation covering summer and winter conditions. Analysis of the noise values indicated that a systematic error in the vertical exchange process was periodically impacting deposition fluxes and ground level concentrations. While the Kalman filtering analysis didn’t tell us, in detail, the correct way to proceed, it did identify the problematic module and the statistical-based findings were the starting point for revisiting the vertical flux formulation within the model.

Airborne particulate matter (PM) is important because it causes health problems and environmental degradation and accordingly many countries implement programs to control PM pollution (e.g., EPA, 1996). In recent years the emphasis on controlling PM pollution has shifted toward problems associated with fine PM (PM2.5 with particle diameter less than 2.5 μm) because it is more strongly associated with serious health effects than coarse PM. Knowing what sources contribute to fine PM2.5 is essential for developing effective control strategies. Many components of PM2.5 are secondary pollutants and so photochemical models are important tools for PM air quality planning. The Comprehensive Air quality Model with extensions CAMx; (ENVIRON, 2003) is one of the photochemical grid models being used to understand PM pollution and visibility impairment in the US and Europe. The Particulate Matter Source Apportionment Technology (PSAT) has been developed for CAMx to provide geographic and source category specific PM source apportionment. PM source apportionment information from PSAT is useful for:

Uncertainties in key elements of emissions and meteorology inputs to air quality models (AQMs) can range from 50 to 100% with some areas of emissions uncertainty even higher (Russell and Dennis, 2000). Uncertainties in the chemical mechanisms are thought to be smaller (Russell and Dennis, 2000) but can range to 30% or more as new techniques are applied to re-measure reaction rate constants and yields. Single perturbation sensitivity analyses have traditionally been used with AQMs to characterize effects of these uncertainties on peak predicted ozone concentration ([O3]).

During the summer of 2004, the International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) coordinated the largest field study related to air quality so far. Over 500 scientists from 5 different countries, 11 planes, 1 ship, 2 satellites and numerous ground sites were involved in characterizing the emissions of particulate matter (PM), ozone and their precursors, their chemical transformations, and their removal during transport to and over the North Atlantic and into Europe. The study was organized around three main objectives: characterizing the regional air quality in eastern North America; assessing the export of smog related air pollution from eastern North America and its evolution as it travels towards Europe; and assessing the possible effects of the PM associated with the smog on our climate.

In this paper, two major sources of bias in the Community Multi-scale Air Quality Model (CMAQ), one physical and one chemical process, are examined. The examination is conducted with hourly gas and particle data for the inorganic system of sulfate, total ammonia, also called NHX, (gaseous ammonia, NH3 plus aerosol ammonium, NH4) and total nitrate (gaseous nitric acid, HNO3 plus aerosol nitrate, NO3-) and with hourly gas and particle data for inert or conservative species. The physical source of bias stems from the meteorological inputs related to mixing, in particular the behavior of the simulated mixed layer in the evening. The chemical source of bias stems from the nighttime heterogeneous production of HNO3 from N2O5. The analyses are carried out for a summer and a winter period to examine the seasonal dependence of the biases.

Like in other Western European countries, the summer of 2003 was characterized by exceptional warm weather conditions and two strong ozone episodes from 14-20 July and in the first half of August. The August episode counted 12 consecutive days of ozone exceedances, which never happened before. The total number of days of ozone exceedances in 2003 was 65, which is the highest number ever registered. At the European level, long-term objectives for the reduction of ozone concentrations have been defined in the Framework Directive 96/62/EC. According to the ozone daughter directive (2002/3/EC), the target values should be attained by the member states by the year 2010. In order to reach these objectives, most of the member states will have to reduce drastically the emission of pollutants responsible for ozone formation, i.e. nitrogen oxides (NOx) and non-methane volatile organic compounds (VOC). The emission reductions are prescribed for each of the EU member states, by means of national emission ceilings under the Gothenburg Protocol (1999) and the EU directive on National Emission Ceilings (2001/81/EC).

The Clean Air Act and its Amendments require that the U.S. Environmental Protection Agency (EPA) establish National Ambient Air Quality Standards for O3 and particulate matter and to assess current and future air quality regulations, designed to protect human health and welfare. Air quality models, such as EPA’s Models-3 Community Multi-scale Air Quality (CMAQ) model, provide one of the most reliable tools for performing such assessments. CMAQ simulates air concentrations and deposition of numerous pollutants on a myriad of spatial and temporal scales to support both regulatory assessment as well as scientific studies conducted by research institutions. In order characterize its performance and to build confidence in the air quality regulatory community, CMAQ, like any model, needs to be evaluated using observational data. Accordingly, this evaluation compares concentrations of various species (SO4, NO3, PM2.5, NH4, EC, OC, and O3 (not available at press time)), simulated by CMAQ with data collected by the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network, the Clean Air Status and Trends Network (CASTNet) and the Speciated Trends Network (STN).

In the United States, photochemical air quality models are the principal tools used by governmental agencies to develop emission reduction strategies aimed at achieving National Ambient Air Quality Standards (NAAQS). Before they can be applied with confidence in a regulatory setting, models’ ability to simulate key features embedded in the air quality observations at an acceptable level must be assessed. With this concern in mind, the U.S. Environmental Protection Agency (EPA) has recently completed several runs of the Community Multiscale Air Quality model (CMAQ) and the Regional Modeling System for Aerosols and Deposition model (REMSAD) to simulate air quality over the contiguous United States during the year 2001 with a horizontal cell size of 36 km×36 km. The meteorological model MM5 and the emission processor SMOKE were used to generate the input fields necessary for CMAQ and REMSAD. See Hogrefe et al (2004a) and Eder and Yu (2004) for more information about model settings.

Clouds play an active role in the processing and cycling of chemicals in the atmosphere. Gases and aerosols can enter cloud droplets through absorption/condensation (of soluble gases) and activation and impact scavenging (of aerosol particles). Once inside the cloud droplets these tracers can dissolve, dissociate, and undergo chemical reactions. It is believed that aqueous phase chemistry in cloud is the largest contributor to sulphate aerosol production. Some of the aqueousphase tracers will be removed from the atmosphere when precipitation forms and reaches the ground. However, the majority of clouds are non-precipitating, and upon cloud dissipation and evaporation, the tracers, physically and chemically altered, will be released back to the atmosphere. Updrafts and downdrafts in convective clouds are also efficient ways of redistributing atmospheric tracers in the vertical. It is therefore important to represent these cloud-related physical and chemical processes when modelling the transport and transformation of atmospheric chemical tracers, particularly aerosols.

Motivated by growing concerns about the detrimental effects of fine particulate matter (PM2.5) on human health, the U.S. Environmental Protection Agency (EPA) recently promulgated a National Ambient Air Quality Standard (NAAQS) for PM2.5. The PM2.5 standard includes a 24-hour limit (65 μg/m3 for the 98th percentile) and annual (15 μg/m3) limit. Except for a few cases, the annual standard will be the primary concern for attainment issues. Over the next several years, grid-based photochemical models such as the Community Multiscale Air Quality (CMAQ) model (Byun and Ching, 1999) will be used by regulatory agencies to design emission control strategies aimed at meeting and maintaining the NAAQS for O3 and PM2.5. The evaluation of these models for a simulation of current conditions is a necessary prerequisite for using them to simulate future conditions. The evaluation presented in this study focuses on determining the temporal patterns in all components of the modeling system (meteorology, emissions and air quality) and comparing them against available observations. Furthermore, we briefly investigated the weekday/weekend differences in the observed and predicted pollutant concentrations and outlined steps for future research. Since anthropogenic emissions are known to have a distinct weekly cycle, such analyses would help us in evaluating the modeling system’s ability to accurately reproduce the observed response to emission changes.