1 DUST modeling AirWare AQMS: urban/industrial and regional air quality modeling and management: DUST modeling DDr. Kurt Fedra Environmental Software and Services GmbH

2 Basic AQMS functions: Data management (GIS, emissions, monitoring, scenarios, permits, ….) Emission inventories: traffic, industry, households, land cover, soils (wind entrainment), biogenic sources (VOC) Emission modelling (PEMS, natural dust: PM10,2.5, traffic - road, rail, shipping, air ) Transport and dispersion modeling, (photo)chemistry, wet and dry deposition Impact assessment Data management (GIS, emissions, monitoring, scenarios, permits, ….) Emission inventories: traffic, industry, households, land cover, soils (wind entrainment), biogenic sources (VOC) Emission modelling (PEMS, natural dust: PM10,2.5, traffic - road, rail, shipping, air ) Transport and dispersion modeling, (photo)chemistry, wet and dry deposition Impact assessment

3 Basic functions: Air quality modelling: Main pollutants: SO2 (acid rain), NOx, CO, O3, PM10/2.5, benzene, lead, air toxics, POP, … Forecasting, public information Monitoring compliance Impact assessment, scenario analysis Emission control (optimization, cost efficient solutions: BATNEEC) Air quality modelling: Main pollutants: SO2 (acid rain), NOx, CO, O3, PM10/2.5, benzene, lead, air toxics, POP, … Forecasting, public information Monitoring compliance Impact assessment, scenario analysis Emission control (optimization, cost efficient solutions: BATNEEC)

4 Auxiliary tools: Energy efficiency optimization, GHG emission control (Kyoto) Technological risk assessment and management, accidental release Noise modelling, mapping Urban development (land use dynamics) Water resources/supply, urban flooding ISO environmental management Technical training (eLearning) Energy efficiency optimization, GHG emission control (Kyoto) Technological risk assessment and management, accidental release Noise modelling, mapping Urban development (land use dynamics) Water resources/supply, urban flooding ISO environmental management Technical training (eLearning)

5 Policy orientation: Web Based implementation: distributed remote access, possibility for cooperative use: Continuous Analysis and Decision Support Continuous record and data analysis/display Shared information basis Regulatory requirements: monitor and demonstrate compliance, reporting Cooperation between institutions, “stakeholders” Public information (web server) Direct integration of economic criteria (economic efficiency, net benefit, CBA) Web Based implementation: distributed remote access, possibility for cooperative use: Continuous Analysis and Decision Support Continuous record and data analysis/display Shared information basis Regulatory requirements: monitor and demonstrate compliance, reporting Cooperation between institutions, “stakeholders” Public information (web server) Direct integration of economic criteria (economic efficiency, net benefit, CBA)

6 AirWare AQMS For more information, please visit the AirWare related web pages and on-line demos (login with default: guest) – – several “live” case studies on-line: Henan(China), Gulf, Turkey, Cyprus, Malta, Croatia, Tehran/Iran, …

7 Implementation:Implementation: Fully web based implementation (cooperative data management) Flexible client-server architecture, from stand-alone systems to distributed networks and shared clusters; Clients: PC with standard web browser, mobile clients Ease of use: integration of simulation models, data bases (emission modeling), and monitoring (data assimilation) Fully web based implementation (cooperative data management) Flexible client-server architecture, from stand-alone systems to distributed networks and shared clusters; Clients: PC with standard web browser, mobile clients Ease of use: integration of simulation models, data bases (emission modeling), and monitoring (data assimilation)

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User interface examples

10 Geographic domain: From large-scale (continental) to local street canyon nested models, nested grid From large-scale (continental) to local street canyon nested models, nested grid

11 Modelling natural DUST PM10/2.5: Modelling of “natural” sources of “DUST” in nested domains with a new dynamic wind erosion and “entrainment” model, using wind speed (Weibull), vegetation/land cover, soils, soil moisture, relief Simulate long-range transport in this domain with a standard transport model (CAMx) to estimate local versus external inputs to the observed PM10 levels. Modelling of “natural” sources of “DUST” in nested domains with a new dynamic wind erosion and “entrainment” model, using wind speed (Weibull), vegetation/land cover, soils, soil moisture, relief Simulate long-range transport in this domain with a standard transport model (CAMx) to estimate local versus external inputs to the observed PM10 levels.

12 Geographical scope: Cyprus Nested model domains: – 4,800 km – 270 km – km nested “city” domains The basic entrainment model works on a 1 km resolution, independent of domain. Nested model domains: – 4,800 km – 270 km – km nested “city” domains The basic entrainment model works on a 1 km resolution, independent of domain.

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16 Dust model inputs: Vegetation index (derived from CVF data, land cover/land use) = “bare soil” Soil type (FAO soil map of the world) “Relief Energie” measures the variability of elevation within the 1 km 2 cells based on 30 m DEM Soil moisture (hourly results from a meteorological model, MM5 or WRF) Wind field (hourly averages from the meteo forecast or re-analysis model, Weibull function) Vegetation index (derived from CVF data, land cover/land use) = “bare soil” Soil type (FAO soil map of the world) “Relief Energie” measures the variability of elevation within the 1 km 2 cells based on 30 m DEM Soil moisture (hourly results from a meteorological model, MM5 or WRF) Wind field (hourly averages from the meteo forecast or re-analysis model, Weibull function)

17 Erodibility: vegetation Land cover from different RS/satellite or local map sources: NDVI, VCF, USGS, FAO, ….. composite data re-sampled at a 1 km resolution

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20 Dynamic wind field (3 km prognostic, diagnostic interpolation to 1km) two-parameter Weibull function two-parameter Weibull function

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23 Task 4.3 (PM 06 - PM 12)

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25 Emission model The DUST emission model is a non-linear ( a * windspeed ** b) dynamic (hourly) threshold model (minimal wind speed for the different soil classes) that estimates emission (g/km 2 /s) as a function of: the wind speed (Weibull function in 0.5 m/s steps up to N times the average wind speed MM5 (as cutoff) The DUST emission model is a non-linear ( a * windspeed ** b) dynamic (hourly) threshold model (minimal wind speed for the different soil classes) that estimates emission (g/km 2 /s) as a function of: the wind speed (Weibull function in 0.5 m/s steps up to N times the average wind speed MM5 (as cutoff)

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27 Emission model: results export the emission model output (set of 24 hourly matrices per day) is exported in NetCDF machine-independent standard format automatic with the daily forecasts interactively (from the main scenario or sensitivity analysis level of the dust model scenario) on demand, by the user for analysis and post-processing (e.g., MATLAB, 3D transport models) the emission model output (set of 24 hourly matrices per day) is exported in NetCDF machine-independent standard format automatic with the daily forecasts interactively (from the main scenario or sensitivity analysis level of the dust model scenario) on demand, by the user for analysis and post-processing (e.g., MATLAB, 3D transport models)

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29 Post-processing: long range transport modeling Using MM5/CAMx model system (from EUREKA E!3266 WEBAIR) to simulate the transport of the dynamic dust emissions: PM10 emissions from inventories and models (pyrogenic sources) DUST entrainment for natural sources → transport, dispersion, deposition Using MM5/CAMx model system (from EUREKA E!3266 WEBAIR) to simulate the transport of the dynamic dust emissions: PM10 emissions from inventories and models (pyrogenic sources) DUST entrainment for natural sources → transport, dispersion, deposition

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34 Long range transport modeling Daily 120 hours (revolving) forecast MM5/CAMx nested grid simulation Daily 120 hours (revolving) forecast MM5/CAMx nested grid simulation

35 Long range transport modeling CYPRUS: “Sahara Dust” one day event: May 5, 2011 observed: μg/m 3 simulated: 129/243 μg/m 3 CYPRUS: “Sahara Dust” one day event: May 5, 2011 observed: μg/m 3 simulated: 129/243 μg/m 3

36 Monitoring vs modelling: Monitoring: Daily gravimetric samples, 3 stations, chemical and mineralogical analysis Modelling: Hourly concentration values at the monitoring sites Monitoring: Daily gravimetric samples, 3 stations, chemical and mineralogical analysis Modelling: Hourly concentration values at the monitoring sites

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40 Post-processing: emission control optimization assign PM10 control technologies to individual or classes of sources; select a baseline (episode) generate large sets of alternative solutions identify non-dominated (pareto optimal subset) select a “preferred solution” = control strategy test/evaluate the effects of emission reductions with the transport model assign PM10 control technologies to individual or classes of sources; select a baseline (episode) generate large sets of alternative solutions identify non-dominated (pareto optimal subset) select a “preferred solution” = control strategy test/evaluate the effects of emission reductions with the transport model

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