Operational implementation and performance evaluation of a PM10 and PM2.5 model for Santiago de Chile

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Neal Melton
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Meteorológica de Chile, Santiago, Chile.

1 Operational implementation and performance evaluation of a PM10 and PM2.5 model for Santiago de Chile Rodrigo Delgado 1*, Pablo Hernandez 2, Marcelo Mena- Carrasco 3 1 Dirección Meteorológica de Chile, Santiago, Chile. 2 Center for Sustainability Research, Universidad Andres Bello, Santiago, Chile. 3 Ministry of the Environment, Chile. *Corresponding author, rdelgado@meteochile.cl WWOSC August 16-21, Montreal, Canada

Atmospheric pollution and control measures in

24-hour moving avg (μg/m3) 120 100 Pre-Emergency

2 Atmospheric pollution and control measures in Santiago 2 Chilean PM10 standard Pre-Emergency Alert 150 Chilean PM2.5 standard Good+regular PM10 24-hour moving avg (μg/m3) Pre-Emergency 80 Alert 60 Good+regular PM hour moving avg (μg/m3)

3 Previous works 3 Cassmassi Model: multivariate regression tool with PM10 forecast horizon to 24 hours for the Santiago basin (Cassmassi, 1999). WRF-Chem Model: CO tracer is used as a proxy to obtain concentrations within the Santiago basin for PM10 and PM2.5 by a linear combination of factors (Saide et al., 2011).

1 3 domain telescoping nesting, with higher horizontal resolution domain centered over the Santiago basin 2, 6

4 Modeling tool and configuration 4 Domains configuration Maipo Valley Angostura de Paine Chacabuco Ridge Chemical Weather Model: WRF- Chem domain telescoping nesting, with higher horizontal resolution domain centered over the Santiago basin 2, 6 and 18 km horizontal resolution, 39 vertical levels Initial and boundary conditions for the first 24 hours: NCEP FNL Global Operational Analysis (1º) Boundary conditions for hour 25 to 120: GFS (0.5 )

5 Emission inventory 5 The emissions inventory is based on the official inventory of 2000 : Spatial resolution: 2 km Temporal resolution: 1 hour of a representative day of the year. Modulation of weekdays and weekends. This inventory considers 200,000 tons/year of CO

6 Simulation periods 6 Three periods are being considered: May 19 August 31, 2011 April 1 st August 31, 2012 April 1 st August 31, 2013 Model was run once per day.

7 Time-based ensemble forecast 7 Simulación 26/01 26/1/12 Simulación 27/01 27/1/12 SpinU Simulación 28/01 28/1/12 SpinU p D1 D2 D3 Simulación 29/01 29/1/12 p D1 D2 D3 Simulación 30/01 30/1/12 D4 D4 1/ SpinU 1/26 1/27 1/28 1/29 1/30 1/31 2/1 2/2 p D1 D2 D3 D4 Día de interés 30/1/12 Período sim 26/01 25/1/12 29/1/12 Período sim 27/01 26/1/12 30/1/12 Período sim 28/01 27/1/12 31/1/12 Período sim 29/01 28/1/12 1/2/12 Período sim 30/01 29/1/12 2/2/12

Methodology 8 Cassmassi comparison Statistic index

validation Temperature Correlation daily (R) cycles

Proportion Correct (PC) 1 Critical Sucess Index (CSI) 1

8 Methodology 8 Cassmassi comparison Statistic index Ideal value Statistics Meteorology for AQ evaluation validation Temperature Correlation daily (R) cycles RootWind mean speed square and error direction (RMSE) Proportion Correct (PC) 1 Critical Sucess Index (CSI) 1 Bias (BIAS) 1 False Alarm Rate (FAR) 0 Equitable Threat Score (ETS) 1

9 9 Results

10 Air quality model validation 10 PM10, 24h moving averages: observed vs. modeled values R R RMSE 0.7 Best 2011 Best 2012 Best R: R: moving moving PM10 PM10 averages, averages, observed observed vs. vs. modeled modeled Pudahuel D3: D4: D2: Cerro Navia D2: D4: D1: Pudahuel Pudahuel Cerro Cerro Navia Navia D1 D2 D3 D4 D4

11 Comparison with Cassmassi 11 Alert Cerro PC CSI BIAS FAR ETS Navia Cassmassi WRF Alert PC CSI BIAS FAR ETS Pudahuel Cassmassi WRF Pre-Emergency PC CSI BIAS FAR ETS Cerro Navia Cassmassi WRF N.A. 1 0 During 2013 Pre-Emergency PC CSI BIAS FAR ETS Pudahuel Cassmassi WRF N.A. 1 N.A.

modeled values 0.8 0.8 R: R: moving PM2.

modeled 2013 2012 2011 0.7 0.75 0.7 R 0.

12 Air quality model validation 12 PM2.5, 24h moving averages: observed vs. modeled values R: R: moving PM2.5 averages, observed vs. modeled R R RMSE Best 2011 Best 2012 Best 2013 Pudahuel D3: D4: D2: Pudahuel Cerro Navia D2: D4: D2: Cerro Navia D1 D1 D2 D2 D3 D4 D4

13 Meteorological validation 13 Temperature: AMB

14 Meteorological validation 10m wind: AMB Observed Modeled wind NORTH NORTH 20% 25% 16% 20% 12% 15% 8% 10% WEST WEST 5% 4% EAST EAST SOUTH SOUTH WIND WIND SPEED SPEED (m/s) (m/s) >= 11,1 >= 11,1 8,8-11,1 8,8-11,1 5,7-8,8 5,7-8,8 3,6-5,7 3,6-5,7 2,1-3,6 2,1-3,6 0,5-2,1 0,5-2,1 Calms: Calms: 21,70% 7,76%

15 15 Conclusions and final remarks

16 Conclusions 16 Model has an acceptable performance in the prediction of particulate matter when used for critical episode management Allows reasonably anticipate management of critical events of air pollution for Santiago, both for its air quality capabilities and for its highresolution meteorology over the Santiago basin. Meteorology: Temperature Winds

17 Conclusions 17 Possible performance change reasons: Preventive approach first applied in GEC period 2012, severely enforced in Change in measurement equipment during Evolution of emissions and change in the vehicle mix composition. Model was transfered to the Chilean Weather Service, institution that uses its output as one of the different tools in the air quality forecast process. It is recommended to move towards a multimodel air quality forecast, an ensemble forecast.

Analyze the relevance of the amount of time spent performing the spin-up of the model in the forecast quality and the increase in the simulated

18 Next steps 18 To update the conversion factors and the emission inventory used in this work. Include the impact of the operational decisions of the authorities regarding control measures. Analyze the relevance of the amount of time spent performing the spin-up of the model in the forecast quality and the increase in the simulated level density within the first few hundred meters of the atmosphere. Apply data assimilation techniques for information coming from different sources, including vertical profiler (LIDAR), air quality stations and meteorological stations for air quality.

19 19 Thank you!/merci!/ Gracias! Questions?

Recent Developments in Air Quality Forecasting in Chile.

Recent Developments in Air Quality Forecasting in Chile. Marcelo Mena-Carrasco 1, Pablo Saide 2, Gregory Carmichael 2 Scott Spak 3,Luisa Molina 4, Center for Sustainability Research, Universidad Andrés