Perspectives in Designing and Operating a Regional Ammonia Monitoring Network Gary Lear USEPA Clean Air Markets Division

Overview Ammonia gas isn’t ordinary! Monitoring is expensive! How useful are traditional monitoring networks for measuring ammonia?

Overview Not examining: –Organizational or infrastructure needs –Spatial design of campaign-style monitoring –Methodology (Mark Sutton) –Covariance of ammonium particulate concentrations (John Walker)

Objectives of regional monitoring networks Provide quantifiable estimates of regional concentrations or depositions –Interpolations performed using number of techniques –Concentrations may be used to estimate deposition using inferential models or some other technique –Identify and characterize extent of “hot spots”

Objectives of regional monitoring networks Identify and characterize conditions in sensitive areas Estuaries and critical environments Low buffer capacity soils Chemically sensitive atmospheres –Particulate nurseries Understanding atmospheric conditions at existing monitoring stations

Optimize measurement methods Known accuracy and precision –Identify acceptable risk of: False negative: Area is identified as not being an area of concern when it is False positive: Area is identified as being an area of concern when it is not Reliable Affordable! –Capital equipment –Labor

Optimize monitoring locations Minimize uncertainty as function of cost –Predictive variance over grid –Must have understanding of granularity in concentrations Representative of regional conditions Measurement locations are spatially correlated at scale of interpolation technique

What density of measurements do we need? Extremely high spatial variance Concentrations can vary by factor of 100 on spatial scale of 1 km Time to call in the modelers?

REMSAD (Regulatory Modeling System for Aerosols and Deposition) Three dimensional grid-based Eulerian air quality model. Resample modeled output using monitoring locations from 4 different networks Interpolated re-sampled data using IDW Compared re-sampled interpolations to original output

REMSAD 30km Output Interpolated to 10km Grids (2000) Wet NH 4 + Dry (NH 3 +NH 4 +)

NADP/NTN ~250 mostly rural and isolated sites

CASTNET Rural dry deposition network with regional distribution of 84 sites –Rural82 –Suburban2 –Urban0

Speciation Trends Network Established to characterize the annual and seasonal spatial patterns of aerosols and track the progress of control programs –Rural7 –Suburban34 –Urban30

NAMS The NAMS (1,092 stations) are a subset of the NAMS network with emphasis being given to urban and multi- source areas. In effect, they are key sites under SLAMS, with emphasis on areas of maximum concentrations and high population density. –Rural158 –Suburban468 –Urban459 –Unknown7

Wet NH 4 + REMSAD 2000Actual NADP

Wet NH 4 + REMSAD 2000Re-Sampled as NADP

Wet NH 4 + REMSAD 2000Re-Sampled as NADP

Wet NH 4 + REMSAD 2000Re-Sampled as CASTNET

Wet NH 4 + REMSAD 2000Re-Sampled as CASTNET

Wet NH 4 + REMSAD 2000Re-Sampled as STN

Wet NH 4 + REMSAD 2000Re-Sampled as STN

Histograms of Differences Between REMSAD and Re-Sampled Wet NH 4 + Re-Sampled as STN NADP STN CASTNET NAMS

REMSAD 2000 Dry (NH 3 +NH 4 +) Re-Sampled as NADP

REMSAD 2000 Dry (NH 3 +NH 4 +) Re-Sampled as NADP

REMSAD 2000 Dry (NH 3 +NH 4 +) Re-Sampled as CASTNET

REMSAD 2000 Dry (NH 3 +NH 4 +) Re-Sampled as CASTNET

REMSAD 2000 Dry (NH 3 +NH 4 +) Re-Sampled as STN

REMSAD 2000 Dry (NH 3 +NH 4 +) Re-Sampled as STN

REMSAD 2000 Dry (NH 3 +NH 4 +) Re-Sampled as NAMS

REMSAD 2000 Dry (NH 3 +NH 4 +) Re-Sampled as NAMS

Histograms of Differences Between REMSAD and Re-Sampled Dry (NH 3 +NH 4 +) NADP STN CASTNET NAMS

Summary Traditional monitoring networks do have a role, but… –are likely to miss areas of highest concentrations –may have a substantial bias in interpolations Rural monitoring appear to have less bias than urban networks

Future Plans at EPA’s Office of Atmospheric Protection Establish pilot high temporal-resolution sites –Denuders –Passive ammonia sampling –Gas Particle Ion Chromatograph? Geostatistical inferential modeling Use CMAQ to define spatial structure of pollutant concentrations