A diagnostic comparison of measured and model-predicted speciated VOC concentrations

Atmospheric Environment 43 (2009) 5759–5770

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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

A diagnostic comparison of measured and model-predicted speciated VOC concentrations Prakash Doraiswamy a, *,1, Christian Hogrefe a, b, Winston Hao b, Robert F. Henry b, Kevin Civerolo b, Jia-Yeong Ku b, Gopal Sistla b, James J. Schwab a, Kenneth L. Demerjian a a b

Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, NY, USA Division of Air Resources, New York State Department of Environmental Conservation (NYSDEC), Albany, NY, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 18 April 2009 Received in revised form 30 July 2009 Accepted 31 July 2009

This study compares speciated model-predicted concentrations (i.e., mixing ratios) of volatile organic compounds (VOCs) with measurements from the Photochemical Assessment Monitoring Stations (PAMS) network at sites within the northeastern US during June–August of 2006. Measurements of total nonmethane organic compounds (NMOC), ozone (O3), oxides of nitrogen (NOx) and reactive nitrogen species (NOy) are used for supporting analysis. The measured VOC species were grouped into the surrogate classes used by the Carbon Bond IV (CB4) chemical mechanism. It was found that the model typically over-predicted all the CB4 VOC species, except isoprene, which might be linked to overestimated emissions. Even with over-predictions in the CB4 VOC species, model performance for daily maximum O3 was typically within 15%. Analysis at an urban site in NY, where both NMOC and NOx data were available, suggested that the reasonable ozone performance may be possibly due to compensating overestimated NOx concentrations, thus modulating the NMOC/NOx ratio to be in similar ranges as that of observations. Ó 2009 Elsevier Ltd. All rights reserved.

Keywords: CMAQ VOC Model performance Non-methane organic compounds Ozone Carbon bond mechanism Photochemical assessment monitoring stations

1. Introduction Ozone (O3) and fine particles continue to be major air quality issues for many regions in the US. Volatile organic compounds (VOCs) are one of the important precursors to the formation of ground-level O3 (Seinfeld and Pandis, 1998). O3 is formed through a series of photochemical reactions involving VOCs and oxides of nitrogen (NOx). Regional-scale air quality models such as the Community Multiscale Air Quality (CMAQ) model are being used on a regular basis to simulate O3 for regulatory/policy-oriented studies, to demonstrate attainment of ambient air quality standards, evaluate the effect of emission controls, and more recently to forecast daily air quality. Limited studies (Kang et al., 2003; Stroud et al., 2008) have evaluated the performance of photochemical models in predicting the VOC species. Kang et al. (2003) evaluated the performance of the MAQSIP (Multi-scale Air Quality Simulation Platform) model with the Carbon Bond IV (CB4) mechanism (Gery et al., 1989) in

simulating non-methane organic compounds (NMOC) and O3 at three rural sites in the eastern US. Stroud et al. (2008) examined the predictions by Environment Canada’s AURAMS (A Unified Regional Air Quality Modeling System), using the ADOM-II chemical mechanism. In this study, model predictions of VOCs using the CB4 mechanism with a different modeling system (CMAQ) are compared against measurements from monitors at the Photochemical Assessment Monitoring Stations (PAMS) and the NMOC measurements at selected sites during summer (June–August) of 2006. The objectives of this study are to: (1) compare model-predicted VOC species concentrations (mixing ratios) with hourly measurements from the PAMS network, grouped by CB4 classes and the total NMOC concentrations; (2) explore observed and predicted diurnal profiles of the individual CB4 VOC species; and (3) examine the effect of VOC performance on O3 predictions. 2. Model and observational database 2.1. Model set-up and archived database

* Corresponding author at: Bureau of Air Quality Analysis and Research, New York State Department of Environmental Conservation, 625 Broadway, Albany, NY 12233-3259, USA. Tel.: þ1 518 402 8402; fax: þ1 518 402 9035. E-mail address: [email protected] (P. Doraiswamy). 1 On assignment to NYSDEC. 1352-2310/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2009.07.056

The analyses presented here utilize archived model-predicted concentration fields from daily air quality forecast simulations (Hogrefe et al., 2007) for June 13–August 31, 2006. The modeling

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system consisted of the WRF-NMM (Non-hydrostatic Mesoscale Model of the Weather Research and Forecasting System) meteorological model coupled with the PREMAQ emissions and meteorology processor (Otte et al., 2004, 2005), and the CMAQ (version 4.5.1) photochemical model (Byun and Ching, 1999), applied in a forecasting mode following the approach of National Oceanic & Atmospheric Administration (NOAA) and the US Environmental Protection Agency (EPA). The PREMAQ processor, developed by EPA specifically for use in air quality forecasting simulations, preprocesses the meteorological fields for use in emissions processing and the subsequent CMAQ simulations. Biogenic emissions were estimated using the Biogenic Emissions Inventory System (BEIS3.12) incorporated into PREMAQ. The modeling domain covered almost the entire Eastern US with a 12 km horizontal grid resolution. The surface layer is w35 m thick. A detailed description of the modeling system is available elsewhere (Hogrefe et al., 2006, 2007). Model simulations were initialized on June 1, 2005 using ‘‘clean’’ initial conditions. Subsequent simulations were initialized using modeled concentration fields from the previous day. Time-invariant clean boundary conditions were used for all days. Each simulation was performed for 48 h starting at 12:00 Greenwich Mean Time (GMT). 2.1.1. Chemical mechanism The Carbon Bond IV (CB4) chemical mechanism (Gery et al., 1989) with modifications to account for aerosol and cloud processes (Byun and Ching, 1999), was applied for all the simulations. A detailed description of the CB4 mechanism and its representation is available in literature (Byun and Ching, 1999; Gery et al., 1989). Briefly, the CB4 mechanism groups the organic compounds according to the type of carbon bond structure (single, double, etc.). The following are the lumped CB4 VOC species: (1) single-bonded one-carbon surrogate, PAR, representing alkanes; (2) the double-bonded two-carbon surrogate, OLE, representing alkenes; (3) the seven-carbon aromatic hydrocarbon species, TOL, representing monoalkylbenzene structures such as toluene; (4) the eight-carbon species, XYL, representing di- and tri-alkylbenzenes such as xylene; (5) the two-carbon carbonyl species, ALD2, representing acetaldehyde and higher aldehydes; (6) the explicitly treated species, the one-carbon compound, formaldehyde (FORM), the two-carbon compound, ethylene (ETH) and the five-carbon compound, isoprene (ISOP); and (7) the nonreactive species, NR. 2.2. Observational database Hourly concentrations of 55 VOC species and total NMOC measured at the PAMS, and O3 and NOx were obtained from the EPA Air Quality System (AQS) data archive. However, the NOx data reported to AQS suffer from insufficient significant digits and have three digits after the decimal in terms of parts per million (ppm). As a result, in terms of parts per billion (ppb), the data are available only in steps of integers, and thus appears to lose ‘‘continuity’’. This issue affected all the PAMS sites, except at the rural site at Cadillac Mountain in Maine. At the Cadillac Mountain site, a trace-level instrument was used and the data were already reported in ppb in AQS, and hence did not suffer from the above issue. At all other sites, due to low ambient concentrations, particularly at rural areas (typically <2–10 ppb of NOx), these data become unsuitable for comparison with model predictions. To enable comparisons of NOx at least for a few sites in NY, quality-assured raw data prior to input into AQS were obtained directly from the New York State Department of Environmental Conservation (NYSDEC). In addition, NMOC measured at non-PAMS sites in NY were also obtained from NYSDEC. Additional data for a rural site in NY, the Pinnacle State Park (PSP), were obtained from the Atmospheric Sciences Research Center at University at Albany. These data included measurements

of O3, NOx, nitric acid (HNO3) and total reactive nitrogen species (NOy, NOy ¼ NOx þ HNO3 þ nitrous acid [HONO] þ nitrate radical [NO 3 ] þ N2O5 þ RNO3 þ aerosol nitrate þ .). No VOC measurements were available during this time period due to problems in instrumentation. The analysis presented here is restricted to the PAMS monitoring period of June through August of 2006, and includes comparisons at monitors within the New England and Mid-Atlantic State region comprising of Maine, Vermont, New Hampshire, New York, Massachusetts, Connecticut, Rhode Island, Pennsylvania, New Jersey, Maryland, Delaware and the District of Columbia. Table 1 lists the sites with hourly measurements that were used in this analysis. It must be noted that all measured and predicted concentrations of the gaseous species are expressed as mixing ratios (such as ppb).

3. Data analysis Model predictions of the CB4 species were extracted from the archived simulations. Since the model fields were from a daily 48-h forecast simulation starting at 1200 GMT, the data for the first 17 h of each simulation were ignored and only the following 24-h period (midnight to midnight Eastern Standard Time [EST]) was used. In order to compare model predictions with measurements, the measured concentrations of the various VOCs were grouped into the CB4 classes, as per the Gery et al. (1989) mapping procedure presented by Yarwood et al. (2003). Table 2 summarizes the mapping factors for the 55 VOCs. To calculate observed CB4 groupings, the procedure involves multiplying the measured concentration for each compound (in ppb carbon [ppbC]) with the factor corresponding to the CB4 group, and then adding up the resultant values, as shown in Equation (1):

Cj ¼

55  X  Ci  Fi;j

(1)

i¼1

where, Cj ¼ observed lumped concentration of CB4 group ‘j’, in ppbC; j ¼ CB4 group (FORM, ALD2, ETH, ISOP, PAR, OLE, TOL and XYL); Ci ¼ observed concentration of individual VOC species,‘i’ (ethane, acetylene, etc.) in ppbC; Fi,j ¼ factor for mapping compound ‘i’ to CB4 class ‘j’. All measured species concentrations were maintained in ppbC. The model predictions were converted to ppbC using the respective number of carbon atoms for each surrogate. While mapping the multiple measured VOC species to the appropriate CB4 classes, if a species was absent (for example, not reported, not measured, etc.) for the entire period at a particular monitor, it was neglected from the calculation and the concentration of the mapped CB4 class at that monitor was estimated utilizing only the remaining species with available data. Thus, the mapped values derived from measurements may be underestimated depending on the compounds, if any, that were missing. The two CB4 groups, FORM and ALD2, were not considered for the individual species comparisons because most of the constituent VOCs required to calculate the mapped concentration were not available at an hourly time resolution at these PAMS sites. Modeled concentrations of the six CB4 VOC species (ETH, ISOP, PAR, OLE, TOL and XYL) were compared with the measurementderived CB4-mapped data. The performance of the model on a variety of parameters was explored. Average 1-h concentrations were calculated for the summer period. In order to explore the average diurnal profile, the data were also averaged by hour over the w2.5-month period. The analysis was then repeated after stratifying the data into weekdays and weekends, to examine if the model replicated weekday–weekend patterns, if any, that were noticed in the measurements. In this analysis, weekdays included

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Table 1 List of sites used in this analysis. State

County

Site ID

Latitude (degrees)

Longitude (degrees)

Site description

Location setting

Data availability PAMSa

NMOCb

Connecticut Connecticut Connecticut

Fairfield Hartford New Haven

090019003 090031003 090090027

41.1183 41.7847 41.3011

73.3367 72.6317 72.9028

Sherwood Island State Park McAuliffe Park 1, James Street

Rural Suburban Urban

x x x

A A A

110010043

38.9189

77.0125

S.E. End McMillian Reservoir

Urban

x

B

Maine Maine

Cumberland Hancock

230052003 230090102

43.5608 44.3517

70.2078 68.2272

Two Lights State Park Top of Cadillac Mountain

Rural Rural

x x

B B

Maryland

Baltimore

240053001

39.3108

76.4744

Woodward and Franklin Roads, Essex

Suburban

x

B

Massachusetts Massachusetts Massachusetts Massachusetts

Essex Essex Hampden Hampshire

250092006 250094004 250130008 250154002

42.4744 42.7894 42.1945 42.2983

70.9725 70.8092 72.5557 72.3347

390, Parkland Sunset Blvd Anderson Rd, AFB Quabbin Summit

Urban Suburban Suburban Rural

x x x x

A A A A

New Hampshire New Hampshire

Hillsborough Hillsborough

330111011 330115001

42.7204 42.8619

71.5231 71.8786

Gilson Road Pack Monadnock Summit

Suburban Rural

x x

B B

New Jersey New Jersey New Jersey

Camden Mercer Middlesex

340070003 340210005 340230011

39.9228 40.2828 40.4619

75.0972 74.7467 74.4298

Copewood & E. Davis Streets, Trailer Rider College, Lawrence Township (NJRC) R.U. Veg Research Farm, 3 Ryders Ln

Suburban Suburban Rural

x x x

B B B

New York

Bronx

360050083

40.8659

73.8808

Urban

n/a

C

New York

Bronx

360050133

40.8680

73.8782

Urban

x

B

New New New New

Richmond Richmond Steuben Suffolk

360850067 360850132 361010003 361030009

40.5973 40.5806 42.0907 40.8275

74.1262 74.1516 77.2103 73.0569

New York Botanical Gardens (NYBG), 200th Street and Southern Blvd Pfizer lab, also located in the NY botanical gardens, 200th Street and Southern Blvd Susan Wagner HS, Brielle Ave & Manor Rd 1001, Richmond Hill Rd Pinnacle State Park (PSP), Addison, NY 57, Division Street

Suburban Suburban Rural Suburban

n/a n/a n/a n/a

C C n/a D

Adams

420010001

39.9200

77.3100

NARSTO Site, Arendtsville (PANARSTO)

Rural

x

B

District of Columbia

York York York York

Pennsylvania a

Data availability at the PAMS network is indicated by an ‘x’. Measurements are made using a Perkin–Elmer automatic GC system; n/a: not available. b NMOC data in the AQS archive were listed as originating from different techniques, as indicated by alphabet: A represents NMOC obtained through summation of peaks (C2 to C12) from GC/flame ionization detector (FID) analysis; B represents NMOC reported by the Perkin–Elmer automatic GC systems at the PAMS sites; C represents NMOC measured using the Horiba ambient total hydrocarbon monitor; D represents NMOC measured using the Byron model 301 hydrocarbon analyzer using flame ionization detector; n/a: not available.

Tuesday through Thursday, while weekends included Saturday and Sunday. Monday and Friday were excluded from the weekday category to avoid any ramp-up or ramp-down of activity associated with the beginning and the end of a work week. Further, the week of July 4th was also excluded from the analysis (July 1–6, 2006) to minimize the effect of increased vehicular traffic and emissions from fireworks display associated with this holiday. Given that VOC and NOx are precursors to O3 formation, this work also examines the extent to which the VOC performance affected the model predictions of daily maximum O3 concentrations. 4. Results and discussion 4.1. Summer-average concentrations Across the 18 PAMS sites, summer-time average measured 1-h concentrations ranged from 0.2 to 3.3 ppbC for ETH, 0.8 to 8.7 ppbC for ISOP, 3 to 42 ppbC for PAR, 0.1 to 3.4 ppbC for OLE, 0.4 to 7.5 ppbC for TOL, and 0.8 to 7 ppbC for XYL (considering only those sites with valid data for at least 50% of the total possible hours). Except ISOP and PAR, the median concentrations of ETH, OLE, TOL and XYL across the urban sites were 1.8–2.4 times higher than the median concentrations across the rural sites. Median ISOP and PAR concentrations at the urban sites were similar to that at the rural sites. Fig. 1a shows the distribution of the average hourly modeled to observed ratio for each CB4 VOC species across the sites in the Northeast. Ratios greater than one indicate an over-prediction. The model typically over-predicted ETH, PAR and TOL by w1.6–4.3 times, and OLE by 4 to more than 10 times, as shown by the 25th to 75th percentile values. The higher ratios for OLE are a result of low

observed concentrations, often less than 1 ppbC. XYL over-predictions were typically less than a factor of 2. For ISOP, slightly more than half of the sites showed an under-prediction. In addition to comparisons of the individual CB4-mapped VOC species, hourly total NMOC concentrations were obtained from AQS for the same time period. As indicated in Table 1, the NMOC data were derived or measured through different measurement or analytical techniques. Hence the measured NMOC data do not represent a consistent parameter across the different sites. The modeled NMOC was calculated by adding all the CB4 VOC species (FORM þ ALD2 þ ETH þ ISOP þ PAR þ OLE þ TOL þ XYL), weighted by the number of carbon atoms in each species. Fig. 1b shows a box plot of the average ratio of modeled to observed NMOC concentrations across these sites. Similar to that seen for the individual CB4 species, the model still overpredicts the total NMOC concentrations as indicated by ratios greater than 1.0. However, the over-prediction is less severe (for example, 75th percentile < 2.5) than that found for most of the individual CB4 species, suggesting that the PAMS measurements may not capture all the compounds represented by the CB4 species, which may account for part of the model over-prediction. As seen from Table 1, NMOC measurements by two methods (NMOC reported by PAMS and that measured by the Horiba total hydrocarbon monitor) were available at two nearby (<0.25 miles) sites located at Bronx, NY. The PAMS instrument was located at site 360050133 (Pfizer lab) while the Horiba instrument was located at site 360050083 (New York Botanical Gardens, NYBG). Both of these sites are located within the botanical gardens separated by less than a quarter mile. Fig. 2 shows a comparison of the NMOC concentrations obtained by the two methods. Clearly, the Horiba instrument measures higher values than the PAMS. The ratio of NMOC by Horiba

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Table 2 Mapping factors to assign measured VOC to Carbon Bond IV (CB4) classes (based on Yarwood et al. (2003)). PAMS species in ppbC

Ethene Acetylene Ethane Propene n-Propane Isobutane 1-Butene n-Butane t-2-Butene c-2-Butene Isopentane 1-Pentene n-Pentane Isoprene t-2-Pentene c-2-Pentene 2,2-Dimethylbutane Cyclopentane 2,3-Dimethylbutane 2-Methylpentane 3-Methylpentane 2-Methyl-1-pentene n-Hexane Methylcyclopentane 2,4-Dimethylpentane Benzene Cyclohexane 2-Methylhexane 2,3-Dimethylpentane 3-Methylhexane 2,2,4-Trimethylpentane n-Heptane Methylcyclohexane 2,3,4-Trimethylpentane Toluene 2-Methylheptane 3-Methylheptane n-Octane Ethylbenzene mp-Xylene Styrene o-Xylene n-Nonane Isopropylbenzene n-Propylbenzene m-Ethyltoluene p-Ethyltoluene 1,3,5-Trimethylbenzene o-Ethyltoluene 1,2,4-Trimethylbenzene n-Decane 1,2,3-Trimethylbenzene m-Diethylbenzene p-Diethylbenzene n-Undecane

# Carbon

2 2 2 3 3 4 4 4 4 4 5 5 5 5 5 5 6 5 6 6 6 6 6 6 7 6 6 7 7 7 8 7 7 8 7 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 10 9 10 10 11

CMAQ species in ppbC NR

OLE

PAR

TOL

XYL

FORM

ALD2

ETH

ISOP

0 0 0.70 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.17 0 0 0 0 0 0 0.83 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0.67 0 0 0.50 0 0 0 0 0.40 0 0 0 0 0 0 0 0 0 0.33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.13 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1.00 0.30 0.33 1.00 1.00 0.50 1.00 0 0 1.00 0.60 1.00 0 0.20 0.20 1.00 1.00 0.83 1.00 1.00 0.67 1.00 1.00 1.00 0.17 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0 1.00 1.00 1.00 0.13 0 0 0 1.00 0.22 0.22 0.11 0.11 0.11 0.11 0.11 1.00 0.11 0.20 0.20 1.00

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.00 0 0 0 0.88 0 0.88 0 0 0.78 0.78 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.00 0 1.00 0 0 0 0.89 0.89 0.89 0.89 0.89 0 0.89 0.80 0.80 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1.00 1.00 0 0 0 0 0.80 0.80 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1.00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1.00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

to NMOC by PAMS was 1.67 on average (median ¼ 1.64). Hence, it appears that the Horiba measurement is a better indicator of the total NMOC and likely captures a larger array of compounds. Therefore, the high modeled to observed ratios found at PAMS sites may be partly due to incomplete measurement by the other two techniques (peak summation and PAMS reported NMOC). Fig. 3a shows the relative composition of VOC concentrations as a fraction of the sum of the species (without considering FORM and ALD2), averaged across the PAMS sites. On average, the model appeared to capture the relative distribution of the CB4 VOC species. The PAR constituted w64% of the VOC, followed by ISOP (w11%), XYL (w11%), TOL (w9%), ETH (w3%) and OLE (w2%) based on measurements. Discrepancies in the modeled composition include

the larger contribution of PAR (73%) and OLE (w5%) and the smaller contribution of ISOP (w4%) and XYL (6%) than observations. The potential contribution of a species to O3 formation is dependent upon its reactivity as well as its concentration. Thus, reactivityweighted concentrations of each CB4 VOC species were determined by multiplying the measured and predicted concentrations of that species with the rate coefficient of the OH reaction at 298 K, similar to the approach followed by Stroud et al. (2008). Table 3 lists the OH-reaction rate coefficients used by the CB4 mechanism in the CMAQ model that was applied in the above calculation to estimate the reactivity of each species. Subsequently, relative distributions of these reactivity-weighted concentrations were calculated for both measurements and model predictions.

P. Doraiswamy et al. / Atmospheric Environment 43 (2009) 5759–5770

10

15

10

5

b

Average Relative Composition of VOC Based on Concentrations (excluding FORM and ALD2)

a 100

8

% of VOC

a Mean Predicted/Observed Ratio

Mean Predicted/Observed Ratio

20

6

4

Observed Predicted

80 60 40 20 0 ETH

2

ISOP

PAR OLE CB4 Group

TOL

XYL

0

0 ETH

ISOP

PAR

OLE

TOL

XYL

Average Relative Composition of VOC Based on Reactivity (excluding FORM and ALD2)

b

NMOC

CB4 VOC Species

% of VOC

100

Fig. 1. Distribution of the mean modeled to observed ratio for a) each CB4 VOC species and b) NMOC across the sites in the Northeast (excluding PSP). The ordinate is dimensionless. The dotted line indicates a ratio of 1.0. The box indicates the 25th, 50th and 75th percentiles. The upper (or lower) whiskers represent the largest (or the lowest) data that is less than or equal to the 75th percentile plus 1.5 times the interquartile range [IQR] (or greater than or equal to the 25th percentile minus 1.5 times IQR). Data that fall outside the whiskers are outliers.

80

Observed Predicted

60 40 20 0 ETH

While PAR was the largest species in terms of VOC concentrations, Fig. 3b shows that ISOP contributes the largest (66%) to the overall reactivity of observed VOC distributions (excluding FORM and ALD2), followed by XYL (19%). PAR, OLE and TOL each contributed w4%, while ETH was w2% of the VOC reactivity. Modeled reactivity distributions showed a lower ISOP reactivity (43%), and higher reactivity of OLE (18%) and TOL (8%) than that seen in measurements. 4.2. Weekday–weekend patterns Tables 4–6 summarize the average 1-h concentration on weekdays, the ratio of the average weekday to weekend concentration, and the number of pairs with valid data for each of the CB4 VOC species. As discussed above, in terms of absolute concentrations, the model typically over-predicted all species except ISOP. The average relative composition of observed and predicted concentrations of the CB4 VOC species were similar between weekdays and weekends (not shown), and similar to the all-week average relative composition shown in Fig. 3. It must be noted that in order to elucidate the weekday–weekend effect arising from differences in the quantity and the temporal profile of anthropogenic emissions between weekdays and weekends, it is essential to have a larger dataset (e.g., >10 years) that

Bronx, NY NMOC by PAMS at Site 360050133 (ppbC)

5763

600

2

R = 0.44 Slope=0.76 Intercept = -10.1 ppbC

500 400 300

ISOP

PAR OLE CB4 Group

TOL

XYL

Fig. 3. Relative composition of observed and predicted ambient VOC in terms of a) concentrations and b) reactivity with hydroxyl radical, averaged across the PAMS sites with at least 50% of all possible data. The error bars represent one standard deviation from the mean.

would essentially minimize spurious variations arising from differences in meteorology. Hence, for the short-term comparisons presented here, the objective is to merely examine whether the model reproduced the weekday–weekend pattern noticed in measurements, which can be influenced by both meteorology and emissions. Ratios greater than one indicate higher average concentrations on weekdays than weekends. Median observed ratios at the rural and the suburban/urban sites were, respectively, 0.88 and 1.03 for ETH, 0.87 and 0.96 for ISOP, 0.98 and 1.13 for PAR, 0.91 and 1.05 for OLE, 1.01 and 1.14 for TOL and 0.98 and 1.13 for XYL. ISOP is emitted by biogenic sources, with negligible contribution from anthropogenic sources. ISOP emissions are affected mainly by temperature and solar radiation but not anthropogenic activity. The fact that observed ISOP ratios deviated from unity (ranging from 0.69 to 1.32) suggests that these differences arise from differences in meteorology between weekdays and weekends and cannot be attributed to anthropogenic activity. Thus, the weekday–weekend patterns presented here would represent a synergistic effect of differences in both anthropogenic activity and meteorology. With the exception of a few sites, model-predicted weekday to weekend ratios showed similar directional tendencies (i.e., ratio >1 or <1) as observed ratios and were in the same order of magnitude for each of the CB4 VOC species. Overall, median weekday to weekend ratios across the suburban/urban sites were higher than

200 Table 3 Reaction rate coefficient of CB4 VOC species with hydroxyl radical (OH) used by CB-IV mechanism in the CMAQ model.

100 0 0

100 200 300 400 500 NMOC by Horiba at Site 360050083 (ppbC)

600

Fig. 2. Comparison of NMOC concentrations derived from PAMS measurement with NMOC measured by Horiba instrument at two different, but nearby, sites in the NY Botanical Gardens at Bronx, NY, separated by less than a quarter mile, during June 13–August 31, 2006. The PAMS instrument was located at site 360050133 (Pfizer lab), while the Horiba instrument was located at site 360050083 (NYBG). The dotted line represents the 1:1 line.

CB4 VOC species

OH-reaction rate coefficient at 298 K (1012 ppm1 min1)

ETH ISOP PAR OLE TOL XYL

7.94 99.7 0.81 28.2 6.19 25.1

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Table 4 Weekday versus weekend concentrations of ETH and ISOP from June 13–August 31, 2006. Shown are the number of pairs of valid 1-h data, average weekday concentration and the ratio of the average weekday to weekend concentration. Site ID

Rural 090019003 230052003 230090102 250154002 330115001 340230011 420010001

No. of Pairs (Weekday, weekend)

702, 773, 725, 652, 651, 764, 765,

420 480 480 464 384 468 445

ETH Predicted

Predicted

Weekday/ weekend

Weekday Conc.

Weekday/ weekend

Weekday Conc.

Weekday/ weekend

Weekday Conc.

Weekday/ weekend

ppbC

Ratio

ppbC

Ratio

ppbC

Ratio

ppbC

Ratio

1.32 0.26 0.15 0.46 0.18 1.56 0.57

0.86 0.77 0.96 0.90 0.79 1.17 0.88

1.92 1.27 0.55 1.25 1.05 3.48 1.26

1.21 1.03 0.98 1.05 0.98 1.20 0.96

4.00 0.76 1.30 7.95 3.94 2.59 2.63

0.84 0.69 0.87 1.00 0.96 0.94 0.83

0.85 0.55 0.22 1.55 2.24 3.46 3.95

1.19 1.08 1.13 0.95 0.75 1.05 0.99

0.88 630, 302 632–695, 363–376 600, 365 564, 356 626, 334 712, 379 706, 467 757, 480 550, 391 782, 480 631, 417

Observed

Weekday Conc.

Mediana Suburban/urban 090031003 090090027 110010043 240053001 250092006 250094004 250130008 330111011 340070003 340210005 360050133

ISOP

Observed

1.26 2.23 0.84 1.79 1.18 0.80 1.20 0.57 1.90 0.92 3.25

Mediana

0.96 1.01 1.62 1.23 1.00 0.78 1.11 0.92 1.14 1.11 1.03

1.03 1.85 1.57 2.70 1.93 1.77 0.96 1.64 1.86 3.69 2.31 4.10

1.03

1.09 1.14 1.08 1.39 1.11 1.12 1.05 1.00 1.19 1.22 1.18

0.87 2.43 1.32 2.19 2.50 3.62 2.41 3.84 2.69 1.19 1.07 3.62

1.12

0.95 0.83 1.32 1.21 1.05 1.10 0.96 0.90 1.19 0.86 0.92

1.05 0.72 1.04 2.90 1.96 1.31 0.48 1.26 1.89 3.32 2.16 0.89

0.96

1.19 1.37 0.86 1.19 1.13 1.28 1.09 0.92 1.06 1.07 0.91 1.09

a

Median includes only those sites with at least 50% or more valid data out of the maximum possible 792 weekday hours or 480 weekend hours. Excluded sites, if any, are shown in italics.

Table 5 Same as Table 4, but for PAR and OLE. Site ID

Rural 090019003 230052003 230090102 250154002 330115001 340230011 420010001

No. of Pairs (Weekday, weekend)

702, 420 773, 480 706–725, 456–480 158–623, 96–464 651, 384 764, 468 764, 444

PAR

Mediana a

Predicted

Predicted

Weekday/ weekend

Weekday Conc.

Weekday/ weekend

Weekday Conc.

Weekday/ weekend

Weekday Conc.

Weekday/ weekend

ppbC

Ratio

ppbC

Ratio

ppbC

Ratio

ppbC

Ratio

1.12 0.97 0.94 0.61 0.96 1.23 0.93

0.59 0.18 0.10 0.26 0.25 0.84 0.34

0.84 0.85 0.89 0.91 1.02 1.19 0.92

2.09 2.21 1.31 2.47 2.64 3.20 2.15

1.25 1.05 1.09 1.02 0.98 1.22 0.95

18.71 9.17 3.15 5.72 21.36 21.13 8.96

0.88 0.90 0.97 0.46 1.04 1.16 0.99

44.74 34.26 14.76 22.60 25.08 56.26 29.04

0.98

154–630, 129–302 168–435, 70–225 598, 365 563, 356 613–626, 330–334 329–712, 190–379 706, 467 757, 480 550, 391 762–782, 480 319–631, 186–417

Observed

Weekday Conc.

Mediana Suburban/urban 090031003 090090027 110010043 240053001 250092006 250094004 250130008 330111011 340070003 340210005 360050133

OLE

Observed

18.00 145.74 20.89 40.02 17.17 11.84 15.56 10.98 43.94 17.59 41.51

0.84 1.12 1.53 1.21 0.92 1.05 1.13 0.83 1.17 1.05 0.97 1.13

0.97

43.08 43.05 70.48 45.66 42.99 24.73 36.41 35.89 65.59 48.46 120.85

0.89 0.84 1.01 1.33 1.07 1.31 1.03 0.97 1.21 1.19 0.98 1.07

0.91

0.54 3.32 0.30 0.85 0.70 0.44 0.49 0.19 1.42 0.61 0.24

0.94 1.11 2.86 1.20 1.00 0.86 1.11 0.78 1.07 1.11 1.03 1.05

1.05

2.24 2.18 3.32 2.58 2.38 1.36 2.40 3.00 3.17 2.65 3.77

1.14 1.07 1.05 1.38 1.11 1.11 1.02 1.00 1.20 1.17 1.17 1.13

Median includes only those sites with at least 50% or more valid data out of the maximum possible 792 weekday hours or 480 weekend hours. Excluded sites, if any, are shown in italics.

P. Doraiswamy et al. / Atmospheric Environment 43 (2009) 5759–5770

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Table 6 Same as Table 4, but for TOL and XYL. Site ID

Rural 090019003 230052003 230090102 250154002 330115001 340230011 420010001

No. of Pairs (Weekday, weekend)

702, 773, 725, 158, 651, 764, 765,

420 480 480 96 384 468 444

TOL Predicted

Mediana

Predicted

Weekday/ weekend

Weekday Conc.

Weekday/ weekend

Weekday Conc.

Weekday/ weekend

Weekday Conc.

Weekday/ weekend

ppbC

Ratio

ppbC

Ratio

ppbC

Ratio

ppbC

Ratio

3.02 0.97 0.41 1.41 1.72 3.69 1.02

0.90 0.77 0.93 0.56 1.11 1.46 1.08

6.72 5.49 0.75 2.66 2.26 8.41 2.29

1.01 630, 302 307–356, 123–164 599, 365 564, 356 628, 334 329–540, 190–313 706, 467 757, 480 550, 391 782, 480 330–587, 234–372

Observed

Weekday Conc.

Mediana Suburban/urban 090031003 090090027 110010043 240053001 250092006 250094004 250130008 330111011 340070003 340210005 360050133

XYL

Observed

5.71 20.84 2.96 6.28 3.19 1.80 2.29 1.71 5.26 2.40 7.55

0.99 0.94 1.90 1.18 1.01 1.05 1.25 0.82 1.37 1.12 1.14

1.12 0.97 0.88 0.58 0.93 1.27 1.00

2.92 0.72 0.91 1.18 5.54 2.53 0.77

0.99 7.61 6.10 10.27 5.40 9.63 2.98 6.47 5.04 10.73 6.69 24.46

1.14

1.02 0.89 1.02 1.36 1.01 0.99 1.03 0.93 1.20 1.21 1.08 1.03

0.86 0.60 1.00 0.66 1.05 1.26 0.95

4.59 5.45 0.44 1.29 1.13 4.62 1.12

0.98 4.86 16.57 3.27 6.89 3.46 2.14 3.12 5.46 3.07 1.55 11.02

0.95 1.21 2.20 1.18 1.03 0.98 1.13 0.73 1.37 1.16 0.91 1.13

1.21 0.99 1.00 0.67 0.97 1.41 1.03 1.02

4.36 3.62 6.01 3.13 5.16 1.73 3.27 2.95 5.84 3.61 13.86

1.12 0.97 1.08 1.48 1.08 1.25 1.11 1.01 1.27 1.32 1.06 1.12

a

Median includes only those sites with at least 50% or more valid data out of the maximum possible 792 weekday hours or 480 weekend hours. Excluded sites, if any, are shown in italics.

that across the rural sites in both the observations (by 10–17%) and the model predictions (4–10%). In terms of NMOC (Table 7), there appeared to be a better agreement between modeled and observed ratios of average weekday to weekend concentrations. Ratios were typically near or greater than unity. On a median basis, observed and predicted weekday to weekend ratios were near unity (0.98 and 0.96 respectively) at the rural sites. At the suburban/urban sites, the observations showed a median ratio of 1.02, although the predicted weekday to weekend ratio was 1.10. This suggests that during the short period of study, the weekday and weekend concentrations were similar in general, and the model-predicted ratio of 1.10 may be within the error of the model predictions. 4.3. Diurnal profiles In order to explore the observed and predicted diurnal profiles, measured and predicted hourly concentrations were averaged by hour over all days from June 13 to August 31, 2006. Fig. 4 presents the average observed and predicted diurnal profiles of each of the aforementioned six CB4 VOC species at the following three sites: an urban site in NY (360050133, Pfizer lab) located at Bronx, NY, a suburban site in NJ (340210005) located at Rider College (NJRC) and a rural site in PA (420010001) located at Arendtsville, PA (PANARSTO). These sites were selected such that there was one each representing a highly urban site, a suburban site, and an upwind rural site within the extent of the transport corridor affecting the urban site in NY city. Note that the predicted concentrations are plotted on the right ordinate, while the measured concentrations are plotted on the left ordinate, as they often differ by a factor of two or more. The intent here is to examine the shape of the profile to gain an insight into differences in the physical and chemical processes between observations and predictions. Overall, the shape of the

observed and predicted profiles was similar at the three sites, except for isoprene at the Pfizer lab and NJRC sites, and OLE at PANARSTO site. The typical diurnal profile for ETH, PAR, OLE, TOL and XYL consisted of a morning and an evening peak with a trough in the afternoon. The evening peak was more pronounced in the modeled profile. For isoprene, the predicted profile at Pfizer lab showed an afternoon trough, while the measured profile showed a mid-day maximum (bell-shaped curve). One possible explanation is that the measurements are influenced by the localized isoprene emissions from the vegetation in the botanical gardens. On the other hand, the 12-km model grid cell covers a much larger urban area resulting in a lower average isoprene concentration across the grid cell which was insufficient to offset the dilution due to expansion of the boundary layer. At the NJRC and PANARSTO sites, the isoprene emissions were possibly overestimated resulting in an over-prediction of isoprene concentrations. The afternoon trough noted in the profiles is likely due to loss by photochemical reactions combined with the expansion of the boundary layer. As mentioned above, the model typically overpredicted most of these species. The over-prediction could be due to a combination of reasons: a) the VOC emissions may be overestimated in the model and/or b) the observations may not include all the compounds within the respective CB4 grouping. At the suburban/urban sites, a pronounced evening peak was typically noticed in the model predictions, while being less pronounced, or absent, in observations. This is probably due to trapping of emissions at the surface layer combined with rapidly falling predicted boundary layer heights. 4.4. Discussion Fig. 5a shows a scatter plot of modeled versus observed NMOC at NYBG between 5 am and 8 am EST (6–9 am local time). This time

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P. Doraiswamy et al. / Atmospheric Environment 43 (2009) 5759–5770

Table 7 Same as Table 4, but for NMOC. Site ID

Rural 090019003 230052003 230090102 250154002 330115001 340230011 420010001

Meas. Methoda

A B B A B B B

No. of Pairs (Weekday, weekend)

702, 773, 706, 158, 651, 764, 765,

420 480 456 96 384 468 445

Observed

Predicted

Weekday Conc.

Weekend Conc.

Weekday/weekend

Weekday Conc.

Weekend Conc.

Weekday/weekend

ppbC

ppbC

Ratio

ppbC

ppbC

Ratio

48.7 15.1 12.0 19.6 55.1 45.5 19.31

53.77 17.96 12.10 30.66 54.18 39.26 19.98

Medianb Suburban/urban 090031003 090090027 110010043 240053001 250092006 250094004 250130008 330111011 340070003 340210005 360050083 360050133 360850067 360850132 361030009

0.91 0.84 1.00 0.64 1.02 1.16 0.97

66.6 53.1 20.8 34.4 38.6 86.7 45.45

58.82 54.36 22.00 53.99 40.89 70.28 48.13

0.98 A A B B A A A B B B C B C C D

630, 695, 600, 564, 628, 712, 706, 757, 550, 782, 811, 631, 743, 316, 664,

302 376 365 356 334 379 467 480 391 480 477 417 450 153 432

64.7 186.0 55.8 86.7 36.1 24.9 31.0 26.8 75.1 34.6 150.6 101.5 119.89 86.1 100

62.64 165.01 32.11 73.93 37.79 27.75 27.69 32.84 64.58 32.36 148.79 102.57 129.31 70.96 99.79

Medianb

1.03 1.13 1.74 1.17 0.96 0.90 1.12 0.82 1.16 1.07 1.01 0.99 0.93 1.21 1.00 1.02

1.13 0.98 0.94 0.64 0.94 1.23 0.94 0.96

64.2 57.3 104.5 68.6 68.3 37.4 56.2 55.4 99.7 72.6 167.2 172.2 114.7 139.5 49.7

62.08 52.31 103.36 51.17 64.27 34.03 54.47 57.40 82.72 61.12 151.25 157.99 103.56 120.34 45.19

1.03 1.10 1.01 1.34 1.06 1.10 1.03 0.97 1.21 1.19 1.11 1.09 1.11 1.16 1.10 1.10

a

Meas. Method: Measurement method. See footnote to Table 1 for details on measurement/analytical technique used to determine NMOC concentrations. b Median includes only those sites with at least 50% or more valid data out of the maximum possible 792 weekday hours or 480 weekend hours. Excluded sites, if any, are shown in italics.

period corresponds to a period of increasing emissions under an evolving boundary layer combined with low photochemistry. Hence, the concentrations during this period may be related to direct emissions of the respective species, and a comparison of predicted and observed concentrations may serve as an indicator of the extent of over-/under-estimated emissions. It appears that the total NMOC concentrations are predicted reasonably well by the model with an average modeled to observed ratio of 1.06, as seen from the nearly equal scatter on either side of the 1:1 line. This indicates that the total NMOC emissions in the inventory may be reasonable at this site. As seen from Fig. 4, the model-predicted individual species concentrations at the nearby Pfizer lab site during the 5–8 am time period were still higher than observed concentrations for ETH (by w1.2 times), PAR (by w3 times), OLE (by w15 times), TOL (by w3.2 times) and XYL (by w1.1 times). These over-predictions were also noticed at the suburban (NJRC) and rural (PANARSTO) sites but with different magnitudes. It must be remembered that ETH and ISOP are explicitly mapped species, and thus any over-prediction in these species cannot be attributed to an incomplete measurement by the PAMS network discussed previously. This suggests that the significant over-predictions noted for the individual species could be partly due to overestimated emissions. But the agreement in total NMOC also indicates that these over-predictions in individual species were either compensated by under-predictions in the unidentified species or that the observed concentrations of the mapped species may be underestimated due to incomplete measurement by the PAMS methodology. Although forecasted emissions segregated by source category were not available for this specific period, emission inventories utilized by NYSDEC in other modeling efforts were examined to determine the relative contributions of source categories that

emit each species. Examination of the county-wide emissions at the urban location, Bronx County, NY (where Pfizer lab is located) showed that area sources contributed more than 50% of PAR, TOL and XYL emissions and the remaining by on-road and non-road mobile sources, while ETH was dominated by on-road (43%) and non-road (27%) mobile sources. As expected, nearly 100% of ISOP is emitted by biogenic sources. The relatively large over-prediction in PAR and TOL compared to ETH suggests issues with area source emission inventories. Using the measured NOx concentrations obtained from NYSDEC, slight over-predictions (average ratio ¼ 1.7) were found at this site during the same time period, noticeably at lower observed concentrations (Fig. 5b), indicating possible over-estimation of ground-level NOx emissions. The emissions inventory indicated that about 46% of the NOx emissions in this county were contributed by area source categories, 41% from non-road and on-road mobile sources, and the remaining from point sources. It appears that the over-prediction in NOx may also be related to area source emissions. At the rural sites in NY (PSP, 361010003) and ME (Cadillac Mountain, 230090102), no consistent over-predictions of NOx were found. As shown in Fig. 6, the model under-predicted at higher measured concentrations and over-predicted at lower concentrations at both sites. Note that no NMOC measurements were available at PSP. At Cadillac Mountain, NMOC reported by only the PAMS methodology was available. Hourly model predictions of NMOC at this site were 0.2–6.4 times the observed values over the summer. These modeled to observed ratios were distributed such that about 28% were less than 1.0, w34% between 1 and 2, and w38% greater than 2. Based on the data at NYBG/Pfizer lab, NMOC by PAMS during the 5–8 am EST period may typically be a factor of 1.2–2.5 lower than that measured by Horiba instrument. This inherent underestimation in the PAMS reported NMOC values may be similar to or

P. Doraiswamy et al. / Atmospheric Environment 43 (2009) 5759–5770

Observed (ppbC)

10

Observed (ppbC)

14 12 10 8 6 4 2 0

5

10

15

6 4 2 0 5

10

15

10 5 0 10

15

10

15

3

0.4

2

0.2

1

0.0

0 5

10

15

6 4 2 0 10

15

2.5

3

1.5 1.0

2

0.5

1

0.0

0 0

5

10

15

10

15

30 20 10 0 5

10

15

4 3

0.3

2

0.2

1

0.1

0

0.0 10

15

20

2.0

3.0 2.5 2.0 1.5 1.0 0.5 0.0

1.5 1.0 0.5 0.0 5

10

15

20

1.5

1.5

1.0

1.0

0.5

0.5

0.0

0.0

Observed (ppbC)

Predicted (ppbC)

20

0

Hour

5

10

15

Predicted (ppbC)

20

0.4

5

Predicted (ppbC)

20

Predicted (ppbC)

5

12 10 8 6 4 2 0

0

4

2.0

20

0

20

5

Hour

2

0

8

5

4

20

3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0

20

12 10 8 6 4 2 0

0

0.6

15

6

20

4

10

Predicted (ppbC)

5

5

8

0

0.8

0

15

5

0

20

20

0

5

0

35 30 25 20 15 10 5 0 0

10

0.0

0.0

20

15

20

8

15

20

0

5 4 3 2 1 0

10

0.5

0.2

Predicted (ppbC)

0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 0

1

20

Predicted (ppbC)

15

0.5

5

1.0

0.4

Predicted (ppbC)

0

0

Observed (ppbC)

10

Observed (ppbC)

50

10

2

Observed (ppbC)

20

5

3

1.0

Observed (ppbC)

30

60 50 40 30 20 10 0

1.5

Observed (ppbC)

100

Predicted (ppbC)

40

Observed (ppbC)

150

50

0

4

0.0

1.5

0.6

0

5

0

2.0 0.8

20

2.0

20

Observed (ppbC)

15

Predicted (ppbC)

10

15

Predicted (ppbC)

0.0 5

10

Predicted (ppbC)

0.5

5

Predicted (ppbC)

1.0

Predicted (ppbC)

0

Observed (ppbC)

20

Observed (ppbC)

ISOP

Observed (ppbC) Observed (ppbC)

PAR OLE

15

1.5

0

Observed (ppbC)

10

7 6 5 4 3 2 1 0 0

TOL

5

Predicted (ppbC)

0

Predicted (ppbC)

1

3.0 2.5 2.0 1.5 1.0 0.5 0.0

1.2 1.0 0.8 0.6 0.4 0.2 0.0

Predicted (ppbC)

Observed Predicted

Predicted (ppbC)

2

Observed (ppbC)

3

0

XYL

5 4 3 2 1 0

4

PANARSTO, 420010001 (Rural)

NJRC, 340210005 (Suburban)

Observed (ppbC)

ETH

Observed (ppbC)

Pfizer lab, 360050133 (Urban)

5767

20

Hour

Fig. 4. Average diurnal profile of CB4 VOC species at the urban (Pfizer lab), suburban (NJRC) and rural (PANARSTO) sites during June 13–August 31, 2006. Note that the observed concentration (filled diamond) is plotted on the left ordinate, while the predicted concentration (unfilled triangle) is plotted on the right ordinate (often on different scales).

NMOC 500 400 300

`

200

NOx

b 200

600

Predicted (ppb)

Predicted (ppbC)

a

100 0

150 100 50 0

0

100

200

300

400

500

Observed NMOC by Horiba (ppbC)

600

0

50

100

150

200

Observed (ppb)

Fig. 5. Predicted versus observed concentrations of (a) NMOC and (b) NOx at NYBG between 5 and 8 am EST (6–9 am local time) during June 13–August 31, 2006.

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P. Doraiswamy et al. / Atmospheric Environment 43 (2009) 5759–5770

a

6 4 2 0 0

b

NOx at PSP, NY Predicted (ppb)

Predicted (ppb)

8

1

2

3 4 5 Observed (ppb)

6

7

8

6 5 4 3 2 1 0 0

NOx at Cadillac Mountain, ME

1

2

3

4

5

6

Observed (ppb)

Fig. 6. Observed and predicted concentrations of NOx at a) PSP, NY and b) Cadillac Mountain, ME between 5 and 8 am EST (6–9 am local time) during June 13–August 31, 2006.

greater than a factor of 2.5 at the Cadillac Mountain site, as it is a downwind site representing an ‘‘aged’’ atmosphere (that contain a higher fraction of photochemically derived organic compounds). Thus, it can be inferred that the NMOC over-predictions at Cadillac Mountain may also be partly due to incomplete capture of the NMOC by the PAMS measurements. This suggests that the NMOC predictions did not show a consistent over-prediction. In comparison with the data presented for the urban site in Fig. 5, it can be inferred that a) the model appeared to reproduce the urban/rural contrasts, as seen from the range of predicted concentrations at the urban and rural sites; and b) the emissions at the rural sites may have been underestimated. Given the over-predictions found in the individual CB4 VOC species, it is of interest to examine the performance on the modeled O3 concentrations. Daily maximum 1-h O3 concentrations are

analyzed here to determine the peak effect. Fig. 7 shows the normalized mean bias (NMB) of daily maximum 1-h O3 predictions during summer of 2006 across all sites in the modeling domain. No minimum threshold concentration was used while calculating this statistic. In general, the NMB was within 15%. The southern and western portions of the domain tended to display relatively larger negative bias than the northeastern portion of the domain. Yu et al. (2007) found a NMB of 16% in daily maximum 1-h O3 predictions by CMAQ across the whole domain during July 1–August 15, 2004. NMB of daily maximum 8-h predictions at individual sites ranged from 25% to 50%. Cai et al. (2008) showed NMB of 5% to 15% in the maximum 8-h predictions by CAMx across the northeastern US for observed O3 concentrations greater than 60 ppb during July 2001. Thus the performance of O3 in this study is similar to those reported in other studies.

Fig. 7. Normalized mean bias (NMB) of daily maximum 1-h ozone predictions during June 13–August 31, 2006.

12 10 8 6 4 2 0

a

NOy Predicted (ppb)

Predicted (ppb)

P. Doraiswamy et al. / Atmospheric Environment 43 (2009) 5759–5770

0

2

4

6

8

10

12

12 10 8 6 4 2 0

NOz

b

0

2

4

Observed (ppb)

HNO3

c

5 4 3 2 1 0 0

1

2

3

4

6

10

8

12

Observed (ppb)

Ozone (ppb)

Predicted (ppb)

6

5769

5

6

Observed (ppb)

80 70 60 50 40 30 20 10 0

d

OPE

Observed y = 9.41x + 28.27

Predicted y = 4.36x + 34.58 R2 = 0.65

2

R = 0.54

0

1

2

3

4 5 NOz (ppb)

6

7

8

Observed Predicted

Fig. 8. Comparison of observed and predicted a) NOy, b) NOz, c) HNO3 and (d) ozone production efficiency (OPE) during June 13–August 31 of 2006 at PSP. Panels (a) through (c) use data over all hours, while panel (d) uses data from 9 am to 4 pm EST only.

At the NYBG site, O3 predictions were 17.7% (NMB) lower than observed concentrations. Hourly NMOC concentrations were highly correlated with NOx concentrations over all hours in both observations (R2 ¼ 0.81) and model predictions (R2 ¼ 0.93). The slopes of the best-fit regression lines were similar between observations (2.4) and model predictions (2.6), although intercepts were different (83 ppbC in observations versus 36 in model predictions). Similar slopes with high correlation indicate that the model captures on average the NMOC to NOx relationship over the whole simulation period. However, when hourly NMOC/NOx ratios were compared between observations and model predictions, it was found that the model severely under-predicted the NMOC/NOx ratio at the higher range (observed ratios ranged from 2 to w20, while modeled ratios ranged from 2 to 7.5). This indicates that although the best-fit NMOC to NOx relationships over the whole summer were similar, hourly concentration ranges and ratios were significantly different between observations and model predictions. This could also be the possible cause for the difference in the intercept. The average NMOC/NOx ratio was lower in model predictions (3.7) than observations (6.4). The negative bias in O3 predictions could be related to these differences in NMOC to NOx relationship and the lower ratio in model simulations. Thus, although the individual CB4 VOC species were significantly over-predicted, NMOC to NOx ratios were on the same order of magnitude as the measurements, which could explain the reason for reasonable O3 predictions within 17% bias. At other sites, collocated NOx and Horiba-based NMOC measurements are not available to perform such analysis. However, using NOx and NOy measurements at PSP, a few key diagnostic parameters were examined to gain additional insight into the overall performance of the model for O3 at this rural site. These diagnostic variables include modeled and observed NOy, NOz (NOz ¼ NOy–NOx), HNO3, NOz/NOy ratio and Ozone Production Efficiency (OPE). The ratio of NOz/NOy is typically used as an indicator of the age of the air mass (fresh versus photochemically transformed) (Arnold et al., 2003; Cai et al., 2008). The OPE is a measure of the amount of O3 produced per molecule of NOx consumed, and is estimated by examining the relationship between O3 and NOz (Arnold et al., 2003; Cai et al., 2008; Trainer et al., 1993). Arnold et al. (2003) used only those hours for which the O3/NOx ratio was greater than 46, while Cai et al. (2008) used only data from 9 am to 5 pm. In this study, data from 9 am to 4 pm EST (10 am to 5 pm local time) was used. Daily

maximum O3 predictions at PSP showed a NMB of 1.0%. Fig. 8 shows comparisons of NOy, NOz, HNO3 and OPE as measured and predicted by the model during summer 2006. As seen, there is significant scatter indicating hour-to-hour variation, but the model predictions were in similar ranges of concentrations seen in observations. In general, there appeared to be a slight over-prediction of NOy, NOz and HNO3 at lower observed concentrations. A regression of O3 against NOz resulted in a slope (estimate of OPE) of 4.4 for model predictions, w53% lower than the OPE of 9.4 for observations. This is similar to OPE values determined by Cai et al. (2008) for their 2001 simulations using the CAMx modeling system and by Godowitch et al. (2008) and Yu et al. (2006) for their 2002 simulations using the CMAQ system, all using the CB4 mechanism. The summeraverage NOz/NOy ratio indicated a slightly more transformed (‘‘aged’’) atmosphere in the model (0.64) compared to observations (0.58). The over-prediction in NOz and the under-prediction in OPE suggests more production of oxidized nitrogen species in the model relative to observations. As suggested by Cai et al. (2008) this could indicate an underestimation of deposition losses of NOz or an underestimation of OH concentrations. The similarity of modeled OPE between two different modeling systems (CMAQ and CAMx), but using the same chemical mechanism, also indicates a deficiency in the inherent chemical mechanism. Even with lower modeled OPE, the bias in O3 predictions was near zero, suggesting possible differences in NMOC and the constituent species predictions. As mentioned previously, NMOC and VOC measurements were absent at PSP to be able to compare model predictions with measurements. 5. Summary and conclusions This study compared hourly predictions of VOCs with measurements from the PAMS network, grouped by CB4 classes, during the summer (June through August) of 2006. Except isoprene (ISOP), the model typically over-predicted the CB4 classes of alkanes (paraffins, PAR), alkenes (olefins, OLE), ethylene (ETH), toluene (TOL) and xylene (XYL) by 1.1 to more than 10 times. A comparison of the total non-methane organic compound (NMOC) concentrations showed that model over-predictions were less severe than that found for the individual CB4 species. Further, a comparison of NMOC reported by PAMS against NMOC measured using the Horiba instrument at the New York Botanical Gardens

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(NYBG, Bronx, NY) site showed that NMOC by Horiba was w1.7 times that reported by PAMS, indicating that the PAMS measurement may not capture the entire range of VOCs. Although the model over-predicted most species, the relative distribution of these species was reasonably similar, except for the lower ISOP contributions in the predictions. In terms of reactivity with the hydroxyl radical, this was equivalent to about 43% contribution of ISOP reactivity in the modeled distribution, much lower than the 66% contribution in the measurements. The model also appeared to replicate the observed pattern of near unit ratios of weekday to weekend concentrations for the CB4 VOC species at the rural sites, and higher ratios at the suburban/urban sites, but reflects the net effect of both meteorology and emissions. Similarly, good agreement was found for NMOC at rural sites. At the suburban/urban sites, however, median observed ratios of the average weekday to weekend concentrations of NMOC suggested that the weekday and weekend concentrations were similar during the period of study, although the model predicted a higher ratio of 1.10. An examination of the average diurnal profile at an urban (Pfizer lab, Bronx, NY), a suburban (NJRC) and a rural site (PANARSTO) showed that the model appeared to track the shape of the diurnal profile at these sites, although the model typically over-predicted the concentrations. Noticeable discrepancies included the sharper evening peak in modeled profiles at the suburban/urban sites and the afternoon trough in predicted ISOP profiles at Pfizer lab and OLE at the rural PANARSTO site. Analyses of the NMOC and nitrogen oxides (NOx) concentrations at the NYBG site between 5 am and 8 am, a period of increasing emissions and low photochemistry, showed that the model predictions were in reasonable agreement with the observations for NMOC (average ratio ¼ 1.06), while showing slight over-predictions for NOx (average ratio 1.70). Although the NMOC predictions were within 10% of observed concentrations, the individual CB4 VOC species (ETH, OLE, PAR, TOL and XYL) were still over-predicted. Since overpredictions were found for ETH, a direct mapped species, it is possible that the over-prediction may be partly related to an overestimation of VOC emissions. Over-predictions in NOx suggest issues with overestimated NOx emissions. At the rural sites, NOx was underpredicted at higher observed concentrations. Results suggested that the model appeared to reproduce the urban/rural contrasts, and that the urban over-predictions may be resulting from overestimated emissions. Even with the significant over-prediction in the individual VOC species, model predictions of the daily maximum 1-h O3 concentrations were within 15%. Further examination of the NMOC to NOx ratio at the urban NYBG site showed that the predicted NMOC/NOx ratio to be on the same order of magnitude as the observations, albeit lower on average. Using diagnostic parameters such as reactive nitrogen oxides (NOy), NOz (NOy–NOx), NOz/NOy (a measure of the ‘‘age’’) and ozone production efficiency (OPE), it was found that the modeled OPE (4.4) was about 53% lower than observed, and indicated a slightly more transformed (‘‘aged’’) atmosphere at the rural Pinnacle State Park in NY. Near zero bias in O3 predictions at this site, even with lower OPE, may point to differences in NMOC concentrations. Overall, the model appeared to capture the urban/rural contrasts, the weekday–weekend patterns and the average diurnal profiles. Over-predictions in the individual CB4 VOC species concentrations did not appear to affect O3 performance. One possible reason could be due to modeled NMOC to NOx ratios being in similar ranges as observed. Over-predictions in VOC species and

NOx concentrations may point to possible overestimated emissions. This implies that any effort to address emission inventories must evaluate and rectify both VOC and NOx emissions. Acknowledgements This work was funded in part by the U.S. EPA under Cooperative Agreement CR83228001 and the New York State Energy Research and Development Authority (NYSERDA) under agreement #10599. In addition, Prakash Doraiswamy was partly supported by the Air Pollution Educational and Research Grant (APERG) funded by the Mid-Atlantic States Section of the Air and Waste Management Association. The results presented here have not been reviewed by the funding agencies. The views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of NYSDEC or those of the sponsoring agencies. The authors thank Russ Twadell of NYSDEC for providing us the measurement data for sites in NY. References Arnold, J.R., Dennis, R.L., Tonnesen, G.S., 2003. Diagnostic evaluation of numerical air quality models with specialized ambient observations: testing the Community Multiscale Air Quality modeling system (CMAQ) at selected SOS 95 ground sites. Atmospheric Environment 37, 1185–1198. Byun, D.W., Ching, J.K.S., 1999. Science Algorithms of the EPA MODELS-3 Community Multiscale Air Quality (CMAQ) Modeling System. Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC. Cai, C., et al., 2008. Performance evaluation of an air quality forecast modeling system for a summer and winter season – photochemical oxidants and their precursors. Atmospheric Environment 42, 8585–8599. Gery, M.W., et al., 1989. A photochemical kinetics mechanism for urban and regional scale computer modeling. Journal of Geophysical Research 94, 12925–12956. Godowitch, J.M., Hogrefe, C., Rao, S.T., 2008. Diagnostic analyses of a regional air quality model: changes in modeled processes affecting ozone and chemicaltransport indicators from NOx point source emission reductions. Journal of Geophysical Research 113 (D19303). doi:10.1029/2007JD009537. Hogrefe, C., et al., 2006. Exploring Approaches to Integrate Observations and CMAQ Simulations for Improved Air Quality Forecasts. Models-3 Users’ Workshop, Chapel Hill, NC. Hogrefe, C., et al., 2007. Daily simulation of ozone and fine particulates over New York state: findings and challenges. Journal of Applied Meteorology 46, 961–979. doi:10.1175/JAM2520.1. Kang, D., et al., 2003. Nonmethane hydrocarbons and ozone in three rural southeast United States national parks: a model sensitivity analysis and comparison to measurements. Journal of Geophysical Research 108 (D19). doi:10.1029/ 2002JD003054. Otte, T.L., Pouliot, G., Pleim, J.E., 2004. PREMAQ: A New Pre-Processor to CMAQ for Air Quality Forecasting. Models-3 Conference, Chapel Hill, NC. Otte, T.L., et al., 2005. Linking the Eta model with the Community Multiscale Air Quality (CMAQ) modeling system to build a national air quality forecasting system. Weather and Forecasting 20, 367–384. Seinfeld, J.H., Pandis, S.N., 1998. Atmospheric Chemistry and Physics: from Air Pollution to Climate Change. John Wiley & Sons, New York, NY. Stroud, C.A., et al., 2008. OH-reactivity of volatile organic compounds at urban and rural sites across Canada: evaluation of air quality model predictions using speciated VOC measurements. Atmospheric Environment 42, 7746–7756. Trainer, M., et al., 1993. Correlations of ozone with NOy in photochemically aged air. Journal of Geophysical Research 98, 2917–2925. Yarwood, G., et al., 2003. Impact of Updates to On-Road Mobile Source Emission Factor Models (EMFAC) for the Los Angeles Region: Ozone Model Sensitivity and Ambient/Inventory Reconciliation. CRC Project No. A-38 Final Report. Environ International Corporation, Novato, CA, 128 pp. Yu, S., et al., 2006. Performance and diagnostic evaluation of ozone predictions by the Eta-community multiscale air quality forecast system during the 2002 New England air quality study. Journal of the Air & Waste Management Association 56, 1459–1471. Yu, S., et al., 2007. A detailed evaluation of the Eta-CMAQ forecast model performance for O3, its related precursors, and meteorological parameters during the 2004 ICARTT study. Journal of Geophysical Research 112 (D12S14). doi:10.1029/ 2006JD007715.