# Presentation: 530034

## Regional Air Quality Modeling Issues related to Animal Feeding Operations (AFOs) Thomas Pierce*1, Robin Dennis1, Alice Gilliland1, Donna Schwede1, and John Walker2 1Atmospheric Sciences Modeling Division, Air Resources Laboratory, NOAA, in partnership with the National Exposure Research Lab., ORD, USEPA, RTP, NC2 National Risk Management Research Laboratory, ORD, USEPA, RTP, NCEPA Regional Science Workshop on Animal Feeding OperationsDecember 6-9, 2004College Park, MD*Email: pierce.tom@epa.gov, phone: 919/541-1375

## Regional AQ Modeling Issues related to AFOs Focus on Ammonia

- _Outline of presentation_
- Why focus on ammonia?
- Emissions
- Deposition
- Regional air quality modeling

## Regional AQ Modeling Issues related to AFOs Why focus on ammonia?

- Animal feeding operations are dominant sources of NH3.
- NH3 forms a significant part of PM2.5 aerosols ... and it strongly influences control strategies of NOx and SOx.
- NH3 contributes to watershed nutrient loading and N deposition to streams, lakes, estuaries, and oceans.

## NH3 – Precursor to Ammonium Sulfate & Nitrate(National Emissions ~ 4.8 M TPY)

**... ****animal husbandry is the dominant source of NH3 emissions ...**

## PM2.5 in Ambient Air – A Complex Mixture

**PM2.5 in Ambient Air – A Complex Mixture**

## Atmospheric Emissions, Transport, Transformation and Deposition of Trace Gases

***Atmospheric Emissions, Transport, Transformation and Deposition of Trace Gases***

## Ammonia contributes to PM2.5 concentrations

**Ammonia contributes to PM2.5 concentrations**

## Ammonia contributes to nutrient loading

**Ammonia contributes to nutrient loading**

## Ammonia contributes to visibility reduction

**Ammonia contributes to visibility reduction**

## Ammonia emissions from an air quality modeling perspective ...

## USEPA, Nat’l Air Pollutant Emission Trends (1990-98), EPA-454/R-00-002 [2000]

| 1990 National Emissions Inventory (NEI): 
Annual U.S. Ammonia Emissions (103 short tons) |  |  |
| --- | --- | --- |
| Livestock | 3307 | 76% |
| Fertilizer | 420 | 10% |
| On-road vehicles | 192 | 4% |
| Chemical and Allied Product Manufacturing | 183 | 4% |
| Other (Industrial Processes, Off-road vehicles) | 229 | 5% |
|  |  |  |
| TOTAL | 4,331 |  |

- USEPA, *Nat’l Air Pollutant Emission Trends (1990-98)*, EPA-454/R-00-002 [2000]

## NH3 Emissions ~ All Area Sources

**NH3 Emissions ~ All Area Sources**

- Source: USEPA 1999 NEI v3
-   Area Sources   4,857,366 tons/yr
-   Point Sources        87,587 tons/yr
-   Onroad Sources   266,821 tons/yr
-   Nonroad Sources     3,130 tons/yr

## Farm Emissions ModelNitrogen Flows

- From Pinder- CMU

**... ****inventory methods are improving and are beginning to include climatic effects ...**

## National Practices Model

- Farm Emissions Model Inputs:
- Climate:
-  - Temperature
-  - Precipitation
-  - Wind speed
- Tuned Parameters:
-  - Resistance
-  - Infiltration

- Farm Emissions Model
*kg NH**3** cow**-1** year**-1*

- County Animal
- Population
*cows*

- County
- Inventory
*kg NH**3 **year**-1*

- Housing = Freestall,
- Storage = Lagoon,
- Winter Confined,
- etc.

- Housing = Tiestall,
- Storage = Lagoon,
- Drylot,
- etc.

**.**
**.**
**.**
**.**

- From Pinder- CMU

## Seasonal Variability in National Emissions

- From Pinder- CMU

## Measurements can be used to verify emissions over a range of climatic conditions

- Source: V. Aneja, NCSU

**Measurements can be used to verify emissions over a range of climatic conditions**

## Inverse modeling can be used to verify 

**seasonal NH3 emission estimates**
- State reported annual values go into EPA’s NH3 emission inventory
- Majority (~85%) of emissions from agricultural sources including animal husbandry and fertilizer [*Nat’l Air Pollutant Emission Trends (1990-98)*, EPA-454/R-00-002, 2000]
- NH3 emissions from animal waste are a function of temperature
- Fertilizer application patterns are seasonal
- Therefore, we expect seasonality in NH3 emission rate because of source types

## Inverse Methodology 

- If *E * 0, adjust
- emissions globally

**Inverse Methodology **
**(aka “adaptive iterative Kalman Filter”, **
**optimal estimation via cost function min.)**

## Previous work on NH3 inverse modeling

- Gilliland et al. (2003) applied an inverse modeling method to estimate monthly varying NH3 emissions
- Eastern U.S., 1990 (1 winter, 3 spring, 3 summer, and 1 fall month)
- Annually constant emissions for first series of simulations
- Monthly adjustment factors produced based on observed vs. modeled NH4+ wet concentration and inverse method
- A second posterior series of simulations then generated

*Adjustments led to  ~25-35% decrease in annual total NH**3*
*NH**3** emission factor for beef cattle suggested too high (EPA NRMRL report)*
*More recent inventories have addressed this bias*

**Percent Adjustment to the 1990 Annually-averaged NH3 emission Field**

##  Using Inverse Modeling to Improve 1990 NH4 and NO3 Aerosol Predictions

-  **Using Inverse Modeling to Improve 1990 NH****4**** and NO****3**** Aerosol Predictions**
**Annually Constant NH****3**** Emissions (top) and Inverse Estimated (bottom) Adjustments**

- (Gilliland et al., 2003)

**...****adjusted NH3 emissions => improved PM2.5 model performance**

## Inverse modeling is now being applied to the 

**2001 annual CMAQ simulations**
- Annual 2001 CMAQ simulations developed in collaboration with USEPA Office of Air Quality Planning and Standards (OAQPS) and Atmospheric Modeling Division
- Continental U.S. domain
- 36 km × 36 km horizontal grids, 14 vertical layers
- CB4 chemical mechanism
- MM5 version 3.6.1
- USEPA 2001 National Emission Inventory
- BEIS 3.12
- Mobile 6
- NH3 emission were distributed monthly to provide seasonality estimate
- Dairy cattle distribution based on Pinder et al., 2004
- Fertilizer distribution based on Goebes et al., 2003
- 1990 inverse modeling adjustment factors used all other sources (Gilliland et al., 2003)
- Factors normalized so that annual total NH3 emissions maintained
- Inverse modeling method is being applied to test the monthly NH3 emission estimates

## Deposition of ammonia from an air quality modeling perspective...

## Modeling Dry Deposition

**Modeling Dry Deposition**

- Function of wind speed and atmospheric turbulence

- Function of wind speed, solar radiation, plant characteristics, precipitation/moisture, and soil/air temperature

## Measuring Dry Deposition Near a Large Swine Facility

- 5000 head swine facility
- Long-term monitoring
- Local deposition accounts
- for 13% of emissions.
- Courtesy: J. Walker

- Annual 2003

- Deposition (kg NH3-N ha-1 yr-1)

## Measuring Air/Surface Exchange of Ammonia Over Crops

- Fertilized soybean
- Periods of emission
- and deposition
- Net uptake
- (11 ng NH3-N m-2 s-1)

- Courtesy: J. Walker

## The European scientific community is actively measuring and modeling ammonia deposition

**The European scientific community is actively measuring and modeling ammonia deposition**

- Source: FRAME modeling results from the
- Centre for Ecology, Edinburgh, U.K.

## Using a regional air quality model to assess the influence of ammonia ...

## Regional Eulerian (grid-model)Approaches to Air Quality Modeling of AmmoniaIncludes relevant chemistry & physics, with grid sizes ranging from local 2-4 km up to regional at 32-36km grids; 20-24 vertical layers up to top of free troposphere

- Atmospheric Diffusion Equation

**Computational**
**Planes**

**10-30**

**50-200**

**50-200**

## Air Quality Model: Integrating Science

**Air Quality**

**Sources**

**Transported**
**Pollutants**

**Sources **
**(E,S, BCs)**

**Geographical**
**Features**

**Numerical**
**Solution**
**Techniques**

**Computed **
**Concentrations**

**Meteorology**

**Chemical**
**Processes**
**(R)**

**Chemistry**

## Using regional air quality models to balance the NH3 budget

- Where does the ammonia go?

## Modeling regional dry deposition of NH3 

- Sampson County

**Modeling regional dry deposition of NH3 **
**in the vicinity of AFOs**

## Sampson County

**Gas to Particle**
**Conversion**

**Gas to Particle**
**Conversion**

**Free Troposphere**

**Mixed**
**Layer**

**Surface**

- 38m

- 2 km

**NH3**
**Emissions**

**NH3**
**Emissions**

**Dry Deposition**

**Dry Deposition**
**      ****(NH****3****+NH****4****)**

**Vertical**

**Vertical (NH****3****+NH****4****)**

**Horizontal**

**Horizontal**
**(NH****3****+NH****4****)**

***Layer 1***

***PBL***

**Sampson County**

**5.6%**

**8%**

**86%**

**2%**

**65%**

**5.9%**

**28%**

**28%**

- MAQSIP model calculations in collaboration with
- Rohit Mathur, Carolina Environmental Program

## NH3 Budget Appraisal

- Somewhat surprisingly, modeled NH3 moves rapidly away from the surface.
- This is consistent w/ an European analysis of a poultry operation, which showed that only 3-5% of the emissions were dry-deposited locally.
- These results are consistent w/ budget calculations for eastern North Carolina where Robarge (NCSU) estimated that 10-15% of the emissions dry-deposited (equal to the wet deposition).

## Regional models can estimate NH3 deposition from AFO-dominated source regions

**Regional models can estimate NH3 deposition from AFO-dominated source regions**

## Slide 35

## Ammonia Budget Appraisal

- There is (a serious) disagreement about ammonia budgets and where it goes.
- We need more empirical data and studies to sort this out more definitively.
- Ammonia is both regional and local.  Airsheds are still substantial, although not as large as for oxidized nitrogen.
- Conventional wisdom is distorting interpretations and assessments of responsibility.  This needs to be corrected.

## Further Elements of the NH3 Budget

- Mixing
- Vertical mixing away from the surface
- Day – do fairly well
- Night – we don’t know how to model the nighttime pbl
- Mixing height
- We are getting better, though still uncertainty of +/- 25%
- Aloft transfer to the free troposphere
- Ignorance reigns
- Transport
- For averaging time of a month or longer, transport is probably o.k.

## Further Elements of the Budget (cont.)

- Deposition
- Wet – not too bad, but there are some inconsistencies
- Dry – we need a lot of North American work on dry deposition
- Out-of-date and erroneous parameterizations.  We have been borrowing from the Europeans to improve the parameterizations
- Issue of bi-directionality and compensation point

## Additional Issues Regarding Loads

- Subgrid Hot-Spots
- Hot-spots are a local loading issue that is not going away.
- We are typically blind to hot-spots from a monitoring sense, because we try to avoid them. We basically depend on the emissions inventory to identify them.  We need some top-down remote-sensing help.
- The grid models dilute their effect, if we even have correct emissions to put into the air quality model.
- We have not studied them sufficiently to characterize the bias introduced by ignoring or misrepresenting them.

## Regional Air Quality Modeling Issues related to Animal Feeding Operations (AFOs)

**Regional Air Quality Modeling Issues related to Animal Feeding Operations (AFOs)**

## Research Recommendations – AFO  Emissions 

- Need more work on emissions inventories for ammonia
- better, more complete activity data; better spatial data
- improved emissions factors, leading to development of emissions models for ammonia
- better representation of diurnal patterns of emissions

## Research Recommendations – Dry Deposition 

- Need work on NH3 / NH4+ dry deposition algorithms
- compensation point and other effects.
- Need measurement methods to give diurnal deposition and eddy correlation-based estimates for ground truth
- New NH3 measurement techniques are becoming available with 1 second and better time resolution.  We need to take advantage of these (make the investment).
- Note the burst of ammonia in the a.m. seen by John Walker
- Need work on evasion/flux of NH3 from surface waters
- Need collocated wet and dry deposition measurements

## Research Recommendations - Methods 

- Need to have a full suite of inorganic measurements at all locations
- SO4=, HNO3, NO3-, NH3, NH4+
- Need to transition all sites to measuring the full suite of inorganic species at hourly time resolution.
- Need to replace integrated sampling with hourly measurements as quickly as possible.
- The new, semi-continuous techniques still need further shake-down, but we need to get ready.
- Need to include coarse particle composition at near-coastal and coastal sites as well.

## Research Recommendations – Studies 

- Need some North American budget experiments (general and around hot-spots)
- emissions
- around hot spots / concentration gradients
- flux gradients
- mixed-layer aloft measurements

## Research Recommendations – Loading 

- Develop detailed, location-specific deposition budgets for a check on the regional model
- The regional model is still best to develop large-scale regional estimate of loading and estimating long-term average wet-to-dry ratios for use with empirical wet deposition data.
- Apply new, sophisticated space-time techniques to better estimate deposition patterns (e.g., Bayesian techniques).
- These techniques combine model and measurement and address model bias.
- But, we need NH3 and NH4+ measurements.