Operational Real Time Air Quality Modeling

Air quality modeling and AQMs predict air quality based on emissions, meteorological conditions, topography, and other factors. To do this, AQMs imitate the physical and chemical processes that take place in the atmosphere. The term 'air quality modeling' is a fairly generic term, and often includes studies of ozone levels, concentrations of particulate matter (PM), acid rain deposition, and the like. Most often, however, AQMs seem to be concerned with ozone concentrations and the very real problem of regulatory compliance. It is important to understand where AQMs stand in the 'larger scheme' of integrated systems. Figure 2 shows a flowchart of an air pollution system, which includes the science and the public policy/legislative components. Notice that the modeling part is only one

Figure 2 Main aspects of an air pollution control system.

component of the overall air quality assessment system. 'The specific purpose of air quality modeling assessment system is to determine the best control strategy by which air quality can be improved in a given geographical area'. If there is anything missing from this chart, it is the direct mention of the economic implications of control measures. One should recognize that there are significant political issues related to boxes Control strategy options, Legislation and Control measures. Evidence of this is the recent Supreme Court case ('US Supreme Court backs EPA in air quality cases', http://archives.cnn.com/2001/LAW/02/27/scotus. cleanair/ (last visited 12 November 2007)) in US regarding new air quality standards for atmospheric pollutants.

A complete understanding of the air pollution system diagram (as in Figure 2) is essential for success in any part of air quality work. Figure 3 shows a general scheme for AQMs. In this graphic, meteorological, and emissions data, combined with the users control strategies, all combine to provide input to the AQM, resulting in some type of data set as a result.

In many AQMs, the meteorological data are approximated, collected in the field, or both, while the emissions inventories are often predicted using an emissions model. Emission models typically use the principle of mass balance, and assume that emissions from a particular source for a specific pollutant in a specified time frame are equal to the product of the activity of the source in the unit activity; in fact, product should be of two quantize.

Most practical or operational AQMs require the analyst to be able to deal with the majority of the areas listed below:

• meteorology and atmospheric physics,

• atmospheric chemistry,

• emissions inventories,

• computer science (and computational science) including numerical analysis, and

• regulatory issues and processes

Real-time AQMs require reliable and robust chemical information related to the initial and boundary conditions. Ideally, real-time mesoscale AQMs should be nested to real-time global chemical models which will produce proper initial and boundary condition for our mesoscale and urban and local-scale applications. Computer capabilities have substantially increased during the last decade. Cluster platforms or parallel systems are used more often due to the fact that single processor capabilities are reaching their limits with existing computing architecture. Important efforts are made on developing software parallel applications which can parallel complex AQMs to optimize the performance on these platforms. Because of these advances, real-time air quality forecasting systems have started to be developed using complex 4D grid systems which include reliable, robust, and efficient chemical carbon mechanism. Real-time air quality modeling imposes specific characteristics on modeling tools. A proper combination of data assimilation techniques, computer capabilities, parallel options, and visualization techniques is required to perform a consistent, robust, efficient, and reliable real-time air quality modeling system. Internet technology is also an essential technical element not only to disseminate the air quality forecasting information but also to make the simulation data accessible in real-time in an efficient way.

The design of real-time air quality applications depends on the specific type of application. Typically for a European real-time air quality modeling system, a mother domain of about 6000 x 6000 km should be prepared. The spatial resolution should be as high as possible but resolutions on about 80-25 km are routinely used. If there is no access to real-time global chemical model results, prescribed profiles can be used based on averaged values obtained from historical global chemical model runs.

The real-time emission module is another essential element of the overall assessment system. The emissions should be calculated in a consistent way as the nesting domains and mother domain should maintain a full mass balance. This is an important point as many of the methods relying on GIS processing do not ensure this

consistency. On the other hand, traffic, industrial, and biogenic emissions clearly depend on the weather variables such as temperature. This fact obliges us to link the weather forecasting system with the 'emission forecasting system' since forecasting emissions are required to the prescribed simulation period.

The temporal design of the real-time air quality forecasting system is normally in two periods, the first period uses data assimilation technique, typically 4DVAR with assimilated meteorological and chemical monitoring data if available. This process is important to ensure the maximum quality of these data sets. New nondirect monitored data coming from satellite probes are starting to be available in real-time although much more work is necessary on this aspect. The advantage of satellite information is that it can cover large spatial areas which is relevant to the mother AQM domain. Although satellites provide good spatial resolution they suffer from the limitation of cloud covering and lack of vertical resolution.

There are different types of real-time air quality modeling tools. There are tools which can be applied to forecast the air concentrations at urban and regional domains which are applicable over cities and/or regions. These types of systems have been applied in the past over large cities and/ or regions. Different examples have been applied in the past for Madrid City and Bilbao (Spain) under several EU projects such as EMMA, APNEE, and APNEE-TU (see Figure 4). These models were using limited area domains such as REMEST mesoscale meteorological model (based on the MEMO model) and SMVGEAR with CBM-IV. An example of real-time air quality forecast nowadays is shown in Figures 5 and 6. Plans to implement a National Air Quality Forecasting System which includes chemical data assimilation and a 72 h time horizon with 5 km spatial resolution for US has been recently approved by NOAA (6 December 2005) for 2008.

Since atmospheric chemistry plays a major role in complex air pollution problems, the representation of chemical interactions among atmospheric constituents is an essential element of an air quality model. All important chemical transformations relevant to the problem being studied must be included to make accurate predictions of ambient pollutant concentrations. There are different chemical schemes which can be implemented into AQMs.

The CMAQ system, for example, currently includes four chemical mechanisms that have been developed primarily to address issues associated with urban and regional scale ozone formation and acid deposition - the CBIV, CB05, SAPRC99, and RADM2. Interactions in the gas-phase are represented in AQMs by means of chemical mechanisms. Variants of these mechanisms have been developed for the CMAQ system to provide the necessary linkages to the aerosol and aqueous chemistry processes. An added flexibility of CMAQ is its modular structure and hence chemical mechanisms can be modified or even replaced with another scheme to address specific issues. Brief characteristics of the main schemes are summarized in Table 1.

Figure 4 Air quality forecasting system for Madrid city as developed for APNEE EU (1ST) project (2000-02) by using OPANA V2.

EUROPEAN OPERATIONAL AIR QUALITY FORECASTS: MM5-CMAQ by Universidad Politécnica de Madrid (UPM) 02-01 -2006 TO 05-01 -2006 UPDATED: January 02 200611 :«:02.



Figure 5 European operational air quality forecasts by using OPANA V3 (MM5-CMAQ) for 5 January 2006 at 16:00 for ozone concentrations.
Figure 6 Verification statistics for a nationwide air quality forecasting system by using the MM5-CMAQ air quality modeling system to be in operation in US on Summer of 2008.

Table 1 Brief comparison of the gas-phase chemistry mechanisms in CMAQ

Chemical CB05

mechanisms CB4 New version RADM2 SAPRC99

Species Reactions

Primary organic species Features

3e 93

Simple to use (only nine primary organic species)

Relatively smaller temperature and pressure dependence Main focus on urban area

51 57

156 158

13 15

(1) Account for conditions of (1) temperature, pressure, and chemical environment for annual simulations

(2) Improved nighttime chemistry (2)

(3) For urban and regional scale

(4) Explicit secondary organic aerosol precursors

Comprehensive for chemical reactions

More output for different species

Over 500

Fully comprehensive scheme

About 550

(1) Capability to incorporate semi-explicit chemistry of selected organics

(2) Each of these organics can be modeled individually

The new version of the Carbon Bond mechanism, CB05, is a condensed mechanism of atmospheric oxidant chemistry that provides a basis for computer modeling studies of ozone, PM, visibility, acid deposition, and air toxics issues. It incorporates the latest kinetic and photolysis data in the core mechanism. This chemical mechanism is extended to better support PM modeling needs such as the formation of secondary organic aerosols (SOA). Inorganic reaction set is updated to account for conditions of temperature, pressure, and chemical environment encountered in annual simulations at scales ranging from urban to continental.

Aerosol Treatment

The chemistry and physics of fine and coarse particles are also simulated within the CMAQ. Models-3/CMAQis capable of investigating the complex multiphase interactions contributing to the evolution of aerosol distributions on urban to regional scales. The aerosol module implemented in the standard CMAQ is derived from the Regional Particulate Model (RPM) and is designed to be an efficient and economical depiction of aerosol dynamics in the atmosphere. The approach taken represents the particle size distribution as the superposition of three lognormal subdistributions, called modes. The module considers both PM2.5 and PM10. The secondary species considered are sulfate, nitrate, ammonium, water, and organic from precursors of anthropogenic and bio-genic origin. Each mode is subject to wet and dry deposition. From CMAQ v4.5 the aerosol module (AERO4) contains calculations of sea-salt emissions and thermodynamic equilibrium between the accumulation-mode and the gas-phase treated within the ISORROPIA equilibrium module. The new aerosol module can calculate the volume fraction of each mode that is composed of particles smaller than 2.5 um aerodynamic diameter. This provides a more rigorous calculation of PM2 5 which is more comparable to measurements than the summation of Aitken and accumulation modes.

The Model for Aerosol Dynamics, Reaction, Ionisation and Dissolution (MADRID) module was released as an optional description of aerosol processes within CMAQ in 2002. The updated MADRID 2004 of CMAQbased on the October 2004 core was released in 2005. MADRID uses a discrete size bins approach, that is, sectional method, to capture particle size distributions and provides a significant improvement in the treatment of aerosol dynamics and chemistry including a variety of new SOA production and aqueous chemistry routines.

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