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Home , Air quality index, Water quality

INVESTIGATION OF THE AS RELATED TO REGIME

Paul J. Croft

Department of & Meteorology Kean University Union, New Jersey Belkys V. Melendez

NOAA/National Weather Service Weather Forecast Office San Juan,

Corresponding Author: Paul J. Croft Kean University, Department of Geology & Meteorology 1000 Morris Avenue Union, New Jersey 07083 Email: [email protected] Croft and Melendez

Abstract

Daily Air Quality Index (AQI) values reported by the United States Agency were examined for the months of April through July of 2004 in the northern Mid-Atlantic region. Data were stratified by county for Delaware, Maryland, New Jersey, New York, and Pennsylvania and included maxima, minima, and mean AQI values for each day. Summary information for each of the 45 counties revealed both a similarity of behaviors, such as variations in maxima and minima, as well as a dichotomy and divergence of values based on their positions relative to obvious sources and sinks in the area, such as urban versus rural locations. Possible relationships to physical features, including mountain-valley and coastal plain regions were also noted. Data were also separated according to the prevailing weather regimes and flows (upper air and surface) to determine any apparent dependencies on specific weather features. Basic weather regimes included 500 hPa features (trough, zonal, ridge) and flow directions (north, northwest, et cetera); and surface features (high , low pressure, cold front, and warm front). The surface features were further separated into subtypes that specified the position of the weather feature relative to the study region. Mean AQI values plotted for the region according to these prevailing weather regimes, flows, and subtypes revealed several instances in which variations in the orientation, gradients, and characteristic patterns of AQI were related to the weather patterns. This was particularly evident when subtypes were grouped according to the location of the weather feature relative to the study area. These variations suggest a general basis exists to improve operational prediction and assessment of AQI patterns according to specific weather regimes. In other situations the patterns reflected topographic variations, urban or industrial centers or sources, and the possibility of observational network bias, or the role of mesoscale phenomena. 1. Introduction

quality index (AQI) value daily intended to portray the quality of the air and its attendant risks to, or impacts on, The occurrence of poor air quality, and even those the population. This index is an outgrowth of the original instances of moderate or fair air quality, due to a single, Standards Index previously used by the agency. or combinations of several , poses a significant The AQI was developed by the EPA in concert with health risk to the general population (Lippman 1989). NOAA, the National Park Service, and state and local More than 100 million people in the United States live agencies. The AQI is a summary measure based uponhttp:// the in counties with poor air quality (e.g., Neher and Koenig epa.gov/air/criteria.htmcriteria pollutants specifiedl in the National Ambient Air 1994) and experience its associated impacts (e.g., Quality Standards (NAAQS as per Table 1; or see Bascom 1996a,b; Fauroux et al. 2000). These may include ). Five of the criteria pollutants degradation of and quality and aesthetic changes (, particulate matter, , sulfur in the local environment. In addition, air quality impacts dioxide, and ) are standardized against and (e.g., Haagen-Smit et al. 1951; Heck et their maximum allowable value so as to assign a normalized al. 1982; or Godish 1997) – such as habitat weighting factor such that the observed of all and disruption of reproductive cycles – and has been pollutants combined may be expressed on an incremental documented with regard to its impacts on exposed scale of impact (Table 2). This provides a summative structures as well as ongoing industrial or commercial measure of air quality based upon the pollutants present processes. In New Jersey, concerns have included and their concentration relative to the NAAQS. related to pollution in the area (e.g., Lecher 1974, 1976) The AQI is determined operationally for each county and particulate matter loading downstream of sources in the United States so that air quality can be qualitatively (e.g., Beresford and Murphy 1978). described according to a quantification of the relationship For these reasons, air quality is monitored on a daily between pollutant values and measured health impacts. basis by the United States Environmental Protection Since it is not dependent upon any one particular pollutant, Agency (EPA) through www.epa.go state networksv for the public’s it may be used to examine the prevailing day-to-day air health and well-being. The EPA provides daily predictions quality across a region regardless of source locations. of air quality (e.g., see http://www.emc.ncep.) in a collaborative This suggests that the AQI may be examined with regard noaa.gov/mmb/aeffort betweenq the National Oceanic and Atmospheric to prevailing weather conditions across a multi-county Administration (NOAA) and EPA ( region to establish whether the observed behavioral ) for the purpose of planning and characteristics of the values are related to the weather response by the general public and various government pattern and any local features. 132 National Weather Digest agencies. As part of this effort, the EPA provides an air Investigation of the Air Quality Index as Related to Weather Regime

Primary Averaging The calculation of AQI values for each county is based Standard Time(s) upon an existing network of air quality monitoring devices and therefore is dependent upon the local distribution of Carbon Monoxide 9 ppm 8 h sites as well as the character of pollutants measured. For 35 ppm 1 h example, it is possible for one county to measure only ozone and particulate matter, another carbon monoxide, 1.5 ȝg/m3 quarterly , and nitrogen dioxide; and yet another to have no monitoring equipment. There are three scenarios Nitrogen Dioxide 0.053 ppm annual that may occur for observing sites: they may be located (1) within the county; (2) outside the county; or (3) Particulate Matter within and outside the county. These variations arise as PM10 150 ȝg/m3 24 h specific monitors for the criteria pollutants are placed by PM2.5 15 ȝg/m3 annual 35 ȝg/m3 24 h state agencies (in coordination with and according to EPA approval) to record the air pollutants that are most likely Ozone 0.08 ȝg/m3 8 h to be exceeded in that local area. Therefore, while the AQI 0.12 ȝg/m3 1 h are reported by county, there may be instances in which the calculated value is dependent upon nearby counties Sulfur Oxides 0.03 ȝg/m3 annual in which the monitoring equipment is located. The sites 0.14 ȝg/m3 24 h may also be clustered in only a portion of a county based on knowledge of the sources in that region. In spite of these limitations, the AQI can offer valuable information for a region in an urban zone in which there Table 1. Table 1 is a diversity of and use with a high National Ambient Air Quality Standards (EPA) for population density – such as that found in the northern each criteria pollutant. Only the primary standards are listed Mid-Atlantic region in the United States. While regional according to parts per million (ppm) or micrograms per cubic variations in the local pose multiple issues of meter (μg/m3) and their averaging times as measured by in concern (Dabberdt et al. 2000) with many impacts, their situ monitoring sites. impacts may vary considerably from place to place (e.g., Smoyer et al. 2000) as well. Throughout the year, the New Jersey region is prone to periods of poor air quality AQI Rating Values Color Code Significance of Level

Good 0 - 50 Green AirAir quality quality is is considered considered satisfactory, satisfactory, and air and pollution poses littleposes or littleno risk. or no risk.

Moderate 51 - 100 Yellow AirAir quality quality is isacceptable; acceptable; however, however, for some for some pollutants pollutants there may there be maya be a moderate moderatehealth concern health concern for a very for a smallvery small number number of people of people who who are are unusually sensitive unusuallyto air pollution. sensitive to air pollution.

Unhealthy (Sensitive) 101 - 150 Orange MembersMembers of ofsensitive sensitive groups groups may mayexperience experience health effects.health Theeffects. general The general public publicis not is likely not likely to be to affected. be affected.

Unhealthy 151 - 200 Red EveryoneEveryone may may begin begin to experience to experience health health effects; effects; members members of sensitive of sensitive groups groupsmay experiencemay experience more more serious serious health health effects. effects.

Very Unhealthy 201 - 300 Purple HealthHealth alert: alert: everyone everyone may may experience experience more seriousmore serious health effects. health effects.

Hazardous > 300 Maroon HealthHealth warnings warnings of emergencyof emergency conditions. conditions. The entire The populationentire population is more is more likely likelyto be to affected. be affected.

Table 2. Table 2 AQI Ratings by numeric values and color code. The significance of each level is summarized with regard to health impacts. Volume 33 Number 2 ~ December 2009 133 Croft and Melendez

(e.g., Zhang et al. 1998) and certain types of pollutants weather patterns – would ideally identify patterns that may (e.g., ozone; Sistla et al. 2001) of an episodic nature that reflect monitoring site placement, equipment behaviors require predictive methods to protect the local population unique to each location, and allow inferences to be made (e.g., Ryan et al. 2000). In some instances these may as to the role of sources, sinks, and local physiography lead to air pollution episodes or long term hazards that (i.e. the physical features of the region and related human have far reaching implications (e.g., Kuni et al. 2002; or interactions) in the observed patterns of air quality for the Donora, PA event – Davis 2002); some of which are the region, as a function of the local weather conditions. favored during certain times of day and/or seasons of the Operationally, these would have the potential to improve year (e.g., Godish 1997). forecasts of the spatial distributions of AQI in real-time Therefore, air quality forecasting is of significant 2.and Methods possibly relate features to mesoscale phenomena. value, as evidenced by various workshops (e.g.¸ Dabberdt et al. 2006) and related mitigation strategies (Boylan et al. 2005). The AQI approach is universal (Mohan and Kandya 2007) with similar methods in development (e.g., Kyrkilis The study area Figure selected 1a for investigation was et al. 2007; Cheng et al. 2007), and is relevant to the EPA air designed to include coastal, interior, varying , quality forecasting system (Ottea et al. 2005). This system and physiographic regions in and around the Northern provides regional predictionswww.airnow.go for both urbanv and rural Mid-Atlantic region (Fig. 1a). These included counties environments across the United States. For example, the EPA Airnow system online ( ) provides forecasts that are readily disseminated to broad user communities (e.g., web based information systems as per Triantafyllou et al. 2006). Forecast information includes a “National Outlook”, “Ozone Now”, and “Particle Now” for all regions of the United States. Therefore, it is of interest to examine the values, trends, and spatial distributions of AQI, particularly in relation to local weather regimes and physical features. While a number of studies have examined the relationships between select pollutants and specific atmospheric variables (whether observed or modeled; e.g., Clark and Karl 1982), few have considered the use of a summative measure such as the AQI. The AQI provides greater continuity in the examination of air quality variations from day to day as individual pollutant values may change substantially from hour to hour. In addition, the use of prediction methods is particularly sensitive to the numerical models used to generate the predicted values, and also to the model chemistry (e.g., Alapaty et al. 1995). Therefore, in order to ascertain how air quality varies in a more consistent manner, the use of AQI across a region, and its variations according to the prevailing weather regime, would be of greater value. Therefore, the intent of this study was to determine whether any easily recognized patterns of the AQI existed Fig. 1a. for individual counties, or across a region, and to determine whether these had any dependence upon the prevailing Study region (counties are listed in Table 3) in the Northern Mid-Atlantic of the United States. Color coding is weather regime or local features. Historically, similar used to indicate zones within the study region: coastal plain efforts have been applied over a lengthy period of time (blue), metropolitan and/or suburban with varying terrain (e.g., Holzworth 1962; Panofsky and Prasad 1967; Beaver (yellow), and higher elevation (green). Monitoring sites used and Palazoglu 2006) for many locations (e.g., McKendry for AQI are plotted in red. Counties excluded from study, or for 1994; Niccum et al. 1995; Pryor et al. 1995), using many which missing (or suspect) data were significant, are indicated different methods (Angevine et al. 2006; Schwarzhoff and by hatching (Burlington, Essex, and Warren Counties of New Reid134 2000).National This Weather approach Digest – the use of specific synoptic Jersey). Investigation of the Air Quality Index as Related to Weather Regime

in Delaware, Maryland, New Jersey, New York, and suburban with varying terrain regions. The study region is Pennsylvania (Table 3) and were sorted according to quite varied in terms of its physiographic relief (Fig. 1b) and their location type (i.e. coastal plain – blue, metropolitan thus represents an amalgam of distinct and overlapping and/or suburban with varying terrain – yellow; and climatic zones as part of the megalopolis corridor that higher elevation - green). The ‘higher elevation’ locations runs from Washington, DC to Boston, Massachusetts. were identified as those regions where altitude was the Basic statistical and spatial analyses were completed in dominant feature as compared with ‘metropolitan and/or lieu of complex methodology (e.g., Lu 1995; Riccio 2005) Days Percent State County Missing Missing Pollutants Measured for the sake of brevity and clarity. The approach of this study was to 1 Maryland Charles 0 O3 establish simple relationships for 2 Maryland Prince George's 0 O3 easy operational implementation 3 Maryland Anne Arundel 0 O , NO , PM & PM (manual) 3 2 2.5 10 and to build the case for the 4 Maryland Kent 0 O3, NO2, PM2.5 & PM10 (manual) application of more sophisticated 5 Maryland Baltimore 0 O3, CO, SO2, NO2, PM2.5 & PM10 (manual)

6 Maryland Baltimore City 0 CO, NO2, PM2.5 &PM10 (manual) techniques.

7 Maryland Harford 0 O3, NO2, PM2.5 (manual) For this investigation, 8 Maryland Cecil 1 < 1 O3, PM2.5 (manual) mean daily AQI values were 9 Delaware New Castle 0 O3,SO2, PM2.5, PM10, NO2, NO, NOx, CO gathered for 117 of 122 days 10 Delaware Kent 0 O3, PM2.5 from April through July of 2004 11 Delaware Sussex 0 O ,PM 3 2.5 (five days of data were missing or 12 Pennsylvania Berks 0 PM10, PM2.5, SO2, NO2, O3, CO

13 Pennsylvania Chester 4 3 O3 unavailable). The year selected for

14 Pennsylvania Lehigh 0 PM10, PM2.5, SO2, NO2, O3, CO study was based upon a review of 15 Pennsylvania Montgomery 0 PM10, PM2.5, SO2, NO2, O3, CO the same seasons in surrounding 16 Pennsylvania Delaware 0 PM10, PM2.5, SO2, NO2, O3 years from 2000 to 2007. The 17 Pennsylvania Northampton 0 PM10, PM2.5, SO2, NO2, O3, CO year 2004 was observed (by 18 Pennsylvania Bucks 0 PM , PM , SO , NO , O , CO 10 2.5 2 2 3 inspection of daily weather maps) 19 Pennsylvania Luzerne 0 PM10, PM2.5, SO2, NO2, O3, CO

20 Pennsylvania Lackawanna 0 PM10, PM2.5, SO2, NO2, O3, CO to contain a greater frequency

21 New Jersey Gloucester 0 O3, SO2, PM2.5 of multiple weather regimes, as

22 New Jersey Cumberland 0 NOx, O3, SO2 compared with other years, such 23 New Jersey Camden 0 CO, O3, SO2, NOx, PM2.5 that the dataset would provide a 24 New Jersey Burlington 0 CO, SO2 sufficient number of samples of 25 New Jersey Atlantic 0 PM , PM , O , SO 2.5 10 3 2 each weather regime for study. 26 New Jersey 0 O , PM 3 2.5 The April through July period 27 New Jersey Monmouth 0 O3, CO

28 New Jersey Hunterdon 0 O3 was selected to provide a glimpse

29 New Jersey Mercer 0 NOx, O3, PM2.5, PM10 of potential health impacts that 30 New Jersey Middlesex 0 PM2.5, CO, SO2, NOx,O3 are typically exacerbated during 31 New Jersey Union 0 NOx, SO2, PM2.5,CO this time of year by the weather 32 New Jersey Essex 83 71 PM2.5, (CO & PM10 SHUTDOWN DURING 2004) regime (e.g., pollen season and 33 New Jersey Hudson 0 NO , O , SO , CO, PM , PM x 3 2 2.5 10 windy weather; intense sunshine 34 New Jersey Bergen 0 CO, PM2.5, PM10, SO2, NOx, O3

35 New Jersey Warren 86 74 PM2.5 with dry atmosphere and ozone

36 New Jersey Morris 0 NOx, O3, SO2, PM2.5, CO production). For the 117 days 37 New Jersey Passaic 1 0.01 PM2.5, O3 selected, the majority (85%) 38 New York New York 0 SO2, NOx, CO, PM2.5, PM10 indicated ozone as the primary 39 New York Kings/ Richmond 0 CO, PM2.5 pollutant (used in the AQI 40 New York Bronx 0 O , SO , NO , CO, PM , PM 3 2 x 2.5 10 calculation) for the majority of 41 New York Queens 0 O3, SO2, NOx, CO, PM2.5 sites (i.e. often 90% or more each 42 New York Orange 0 O3 (seasonal), PM2.5

43 New York Westchester 0 O3, PM2.5 day), and therefore no further breakdown by pollutant type was Table 3 Table 3. performed for this sample set. Listing of AQI counties by state and color-coding to indicate the type of region Missing data (Table 3) (i.e. coastal plain, blue; metropolitan and/or suburban and varying terrain, yellow; and was the result of (1) a lack of AQI higher elevation, green). Missing data amounts and percentages (based on 117 days values (five days); (2) missing available) and the pollutants and monitoring sites used for AQI are listed for each county. data for select counties; and/or The number assigned to each location in the left most column is in reference to FigureVolume 2. 33(3) Number suspect 2 ~ or December poor data 2009 quality. 135 Croft and Melendez

3. Results

In the first instance, the missing days represented a a. Basic AQI statistics and analysis very small percentage of days and were not considered (4%). When each county was examined for missing data, it was determined that Essex and Warren (New Jersey) reported more than 70 percent of AQI values missing. The AQI data were examined to determine distribution However, these counties were retained for analysis given and behavior characteristics for reference according to the the network of monitoring sites and data-rich region prevailing weather regime and local features in the study (Essex, near New York City) as well as the limited region region. This was first accomplished by calculation and (Warren, in western New Jersey) covered and theFigure AQI graphing 1b of the basic statistical parameters of AQI for each as determined from surrounding counties was deemed county (Table 4). Secondly, as a means to visually inspect appropriate. However, Burlington County (southern New the distribution of the AQI data for each county, descriptive Jersey) was excluded from study based on suspect data as statistics in the form of box pots (Fig.2) were employed. described in the next section. Figure 2 shows the relative values of AQI corresponding

Fig. 1b.

Study area depiction according to physiographic features and elevation relief (feet 136 National Weather Digest above MSL). Investigation of the Air Quality Index as Related to Weather Regime

th th to the extremes (i.e., 10 & 90 percentiles), the first and that most locations shared AQI values from the same third quartiles, and the median. population (based on simple inspection of the median). The AQI values (Table 4) indicate that most of the study This commonality in the county sample distributions area can experience unhealthy air quality (AQI > 100) as might be expected given the contiguous nature of the study well as extremely good air quality (AQI < 50). Mean and region, and when transport and mixing are considered. median values were in close proximity for the majority This similarity is also seen according to location type of counties. While the average AQI varied from higher (i.e. coastal plain, et cetera). The plot also identified the to lower according to location, it did so inconsistently. presence of an outlier distribution for Burlington County Lowest mean values tended to be at higher elevations and which was removed from further analysis due to: (1) large select coastal sections. distributional inconsistencies with regional and nearby A closer examination of each county’s box plots (Fig. AQI sample distributions; and the fact that (2) monitoring 2, with counties numbered as in Table 3) provided a sites in the county measure only carbon monoxide and more comprehensive view for comparison and indicated sulfur dioxide (Table 3).

State County Max Min Mean Median While the above analyses were useful, it 1 Maryland Charles 132.0 13.0 43.4 41.0 was important to consider AQI values across 2 Maryland Prince George's 140.0 11.0 48.9 45.0 the study region to discern any patterns 3 Maryland Anne Arundel 156.0 12.0 51.1 46.0 that might suggest physical relationships 4 Maryland Kent 100.0 18.0 44.6 41.0 5 Maryland Baltimore 147.0 19.0 52.5 48.0 between AQI and the local weather regime 6 Maryland Baltimore City 113.0 20.0 55.3 55.0 and physiography. Therefore, a plot of the 7 Maryland Harford 132.0 17.0 51.7 45.0 mean AQI observed in each county during the 8 Maryland Cecil 106.0 16.0 45.0 40.0 9 Delaware New Castle 139.0 37.0 66.2 62.0 period of study was constructed to examine 10 Delaware Kent 115.0 21.0 52.6 52.0 the spatial characteristics or behaviors of the 11 Delaware Sussex 104.0 16.0 48.3 45.0 AQI distribution (Fig. 3). The analysis revealed 12 Pennsylvania Berks 104.0 16.0 42.9 38.0 13 Pennsylvania Chester 109.0 16.0 51.0 45.0 that although within the realm of GOOD to 14 Pennsylvania Lehigh 127.0 23.0 52.5 48.0 MODERATE air quality for the region, urban 15 Pennsylvania Montgomery 101.0 34.0 66.9 65.5 maxima (New York City vicinity, Philadelphia- 16 Pennsylvania Delaware 114.0 18.0 46.7 41.0 17 Pennsylvania Northampton 151.0 24.0 55.6 50.0 Metro, and Baltimore) and coastal and higher 18 Pennsylvania Bucks 109.0 11.0 43.0 38.0 terrain minima were present. This analysis 19 Pennsylvania Luzerne 111.0 21.0 45.5 42.0 spans the period from April through July 20 Pennsylvania Lackawanna 114.0 21.0 44.4 40.0 21 New Jersey Gloucester 129.0 16.0 47.3 42.0 during which a wide variety of atmospheric 22 New Jersey Cumberland 111.0 17.0 44.7 42.5 conditions may occur. 23 New Jersey Camden 147.0 22.0 55.7 50.0 The lower-case “lambda-like” pattern of 24 New Jersey Burlington 15.0 3.0 7.2 7.0 25 New Jersey Atlantic 92.0 14.0 40.2 38.0 peak AQI values in southeastern Pennsylvania 26 New Jersey Ocean 122.0 19.0 51.6 43.5 (as shown in Fig. 3) suggested possible land 27 New Jersey Monmouth 90.0 16.0 40.6 38.0 use (and cover) impacts and the interactive 28 New Jersey Hunterdon 135.0 14.0 45.4 41.0 29 New Jersey Mercer 105.0 13.0 45.2 41.0 role of changing elevations and coastal 30 New Jersey Middlesex 114.0 18.0 48.4 44.0 environments in association with local 31 New Jersey Union 141.0 8.0 52.3 49.0 sources and sinks. When specific features of 32 New Jersey Essex 102.0 10.0 48.1 51.0 33 New Jersey Hudson 99.0 13.0 39.1 34.0 the region were superimposed (e.g., see Fig. 34 New Jersey Bergen 151.0 27.0 57.9 56.0 1b), there is a suggestion that local features 35 New Jersey Warren 94.0 11.0 46.8 46.0 may modulate AQI values and behaviors, 36 New Jersey Morris 94.0 16.0 42.6 38.0 37 New Jersey Passaic 127.0 8.0 41.5 38.0 particularly as a function of directions 38 New York New York 136.0 22.0 55.0 54.0 (e.g., as evidenced by studies such as Guerra 39 New York Kings/ Richmond 128.0 21.0 52.0 50.0 et al. 2006). The pattern observed suggests 40 New York Bronx 136.0 25.0 53.1 52.0 41 New York Queens 139.0 25.0 54.1 49.0 that AQI values offer a realistic portrayal of 42 New York Orange 137.0 24.0 46.4 41.0 regional air quality. This is similar to results 43 New York Westchester 112.0 12.0 42.3 39.0 by Ryan et al. (2000) for the Baltimore and Table 4. Table 4 b.Washington, AQI and 500hPa DC region. regimes AQI descriptive statistics for each county with observed maximum, minimum, mean, and median for the entire study period. Counties are color-coded as in Table 3 according to the type of region. The number assigned to each location in the left most column is in reference to While the first analysis provided some Figure 2. insightVolume as to33 the Number observed 2 ~ December pattern variations 2009 137 in Croft and Melendez

overall mean AQI values, it did not identify synoptic scale be expected to provide regional subsidence that could contributions from an upper air (e.g., 500mb) or surface enhance or maintain higher AQI values and thus the synoptic perspective. In an effort to better understand the “lambda-like” pattern. In contrast, the trough situations distribution of mean AQI values, as well as the relative – which accounted for nearly half of all cases – yielded influence of synoptic scale (upper air and surface) or a relatively flat pattern of mean AQI with lower values local features, the data were parsed according to the 500 throughout and local maxima found primarily in urban mb flow pattern (i.e., trough, zonal flow, ridge) and flow and industrial locations. In these situations it appeared direction (i.e. north, northwest, et cetera). Each of these that local effects or monitor location could be the more allowed for an investigation of their contributions to the important factor. observed pattern, mean values, and the variability of AQI Plots of the mean AQI values were also prepared for across the study region. All cases were then collected for 500 mb flows (Fig. 5 a–c), regardless of feature, for cases the 500 mb feature according to their number and relative in which sufficient sample size existed: northwest (26; frequency: trough (53; 47%), zonal flow (13; 11%), and 21%), west (52; 43%), and southwest (36; 30%). When ridge (51; 42%). compared with the overall mean AQI analysis (Fig. 3), it Plots of the mean AQI values (Fig. 4a–c) indicated that was clear that an upper air flow from the northwest and the zonal flow and ridge patterns (Fig. 4b-c) at 500mb west not only dominated the flows (78 cases), but was also appear to be the key contributors to the overall “lambda- integral to producing the “lambda-like” pattern and had like” mean pattern observed for the entire data set (Fig. high mean values. These would more likely be associated 3). The ridge pattern generally had higher mean values with the ridge or zonal flow patterns in the study region of AQI within the “lambda” region. Peak mean valuesFigure (as2 per Fig. 4b-c) which accounted for 64 of the cases were observed in the Philadelphia region and northern examined. While the westerly flow cases had higher mean Delaware and were evident in both the zonal flow and values (and twice as many occurrences), the northwest ridge cases. The zonal flow and ridge patterns wouldOverall Values Overall Values 180

160

140

120

100

80 AQI Value AQI

60

40

Extreme and Percentile AQI Values AQI and Percentile Extreme 20 Extreme and Percentile AQI Values

0

CountyCounty County,Numbers Numbers State from Table 3 Fig. 2.

Box plots for each county for the period of study. AQI values plotted (from bottom to top of graph) are lowest extremes, first and third quartiles, median, and highest extremes. Median values (yellow) are connected to aid interpretation of the variation138 National in values Weather between Digest counties. Investigation of the Air Quality IndexFigure as Related 3 to Weather Regime

flow cases indicated a greater range of peak mean values. The southwest cases showed only a weak reflection of the overall pattern of AQI values. All of the 500 mb flow patterns and wind flow regimes were also examined according to a variety of parameter (or diagnostic) fields (e.g., contours, omega, and others – not shown) to determine any specific characteristics of each (not shown) with regard to the observed AQI patterns. For example, 500 mb trough cases indicated a weak omega axis located along an “alley” of peak mean AQI values (in Delaware and southeastern Pennsylvania) and in the vicinity of the minimum mean wind – suggestive of pooling. In the case of zonal O flow maximum mean AQI values were found within the eastern portion of a weak omega gradient (increasing from east to west), but with a gradient in motion showing reduced mixing inland (to the north and west) relative to the coastal region (south and east). The ridge cases indicated peak values just east of the mean ridge axis in the vicinity of the surface thermal ridge but with little distinction. Values were relatively invariant in regions where mixing (wind flow) was enhanced or reduced. A similar analysis was performed for the 500 mb wind flow cases but yielded Fig. 3. no distinctive information with regard to Plot of mean AQI values for study region using full period of record the observed AQI patterns. For example, with values centered according to monitoring site locations. Chloropleth northwest flow cases showed higher mean colors are according to AQI Ranking (see legend). The lower-case “lambda- values in areas of higher heights, warmer like” pattern (λ) is shown in overlay. surface , and very light but only weakly. While these analyses were useful in confirming “expectations” of the behavior of air 1992; Elder et al. 1994; Ortega et al. 2006) and may be quality value patterns according to basic meteorological accomplished through combined methods (e.g., Carroll principles, they did not provide a comprehensive and Baskett 1979) and application to specific locations operational understanding of the variations experienced (e.g., Rohli et al. 2004). This would be relevant to the use of across the region on a day to day basis. Therefore, the AQI in an air quality forecasting system and its verification investigation considered the use of simple surface synoptic and evaluation (e.g., Kang et al. 2007), when considering weather regimes that result from the 500 mb patterns and spatial variations in pollutant . It is also c.flows. AQI and surface synoptic regimes essential for application to an operational forecasting environment. I Therefore, an examination of the data according to specific weather regimes was considered in hopes of n an attempt to provide forecasters with useful delineating regional variations and characteristic patterns guidance with respect to the spatial variations in air in air quality associated with atmospheric flow, and in quality as related to specific weather conditions, surface relation to local features. Data were parsed according synoptic regimes were considered. Such an application to atmospheric conditions. Four basic continued weather page patterns 142 is not unusual (e.g., Yarnal 1993; El-Kadi and Smithson Volume 33 Number 2 ~ December 2009 139 Croft and Melendez Figure 4a Figure 4b Fig. 4(a). Fig. 4(b).

Figure 4c Fig. 4(c).

Fig. 4

. Same as Fig. 3 except for 500mb synoptic regimes:

(a - above) Trough (b) Zonal Flow (c) Ridge

140 National Weather Digest Investigation of the Air Quality Index as Related to Weather Regime Figure 5a Figure 5b Fig. 5(a). Fig. 5(b).

Fig. 5(c). Figure 5c

Fig. 5.

Same as Fig. 3 except for selected 500mb flow regimes:

(a - above) Northwest (b) West (c) Southwest

Volume 33 Number 2 ~ December 2009 141 Croft and Melendez

www.hpc.ncep.noaa.gov/ weredwm/dwm.shtm used for eachl day based on the Daily Weather Map identified (Fig. 3) based on overall mean AQI values. In Series (available online at high pressure cases the pattern extended westward, and ) and included: (a) High Pressure; (b) shared similarities with the 500 mb ridge and northwest Low Pressure; (c) Cold Front; and (d) Warm Front (Table flow patterns (Figs. 4c and 5a). This would be consistent 5). These simple weather regimes were selected because with a pattern of subsidence across the region. For the they represent basic atmospheric processes that can be low pressure cases, the portion of the “lambda-like” used as a basis to develop air quality forecasting programs pattern extending into southern New Jersey (Fig. 6b) was or for comparison to techniques using numeric model much less emphatic, and the observed mean AQI pattern output (e.g., Touma et al. 2007). exhibited some similarity to those for the 500 mb trough Studies have shown that concentrations vary and northwest flow cases (Figs. 4a and 5a). These would on the same scale as these simplistic types of weather be consistent with a pattern in which lift and/or mixing systems (e.g., Targino et al. 2005). While more sophisticated could reduce AQI values in the region. synoptic techniques might prove useful (e.g., Kalkstein The “lambda-like” feature was more pronounced et al. 1998; Bower et al. 2007) and provide very specific (although somewhat distorted) and had higher mean applications (e.g., Sheridan 2002 and 2003; Rainham et values for the cold and warm front cases (based on a al. 2005), this study’s intent was to demonstrate whether comparable sample size) than high and low pressure relationships between AQI and weather regime exist, cases. The cold front pattern was consistent with the by investigating a small sampling of AQI values. If so, 500 mb zonal regime and west and southwest flows, suggesting a greater significance to local sources and forecasting applications and methods could be developed Fig. 6 (facing page). for operational use with specific scenarios modified according to local mesoscale phenomena. Same as Fig. 3 except for surface weather The mean AQI values for each weather regime (High regimes: Pressure, Low Pressure, and each frontal type) were plotted in order to assess any spatial patterns and/ (a) High Pressure or variability based on the prevailing atmospheric (b) Low Pressure conditions (Fig. 6a-d). Common to each weather regime (c) Cold Front (d) Warm Front to some extent was the “lambda-like” pattern previously Observed Frequency Percent Dates by month Weather (sample Frequency Regime size) April May June July High Pressure 45 38.5 6, 12, 16, 1, 3, 4, 6, 8, 4, 14, 15, 17, 1, 3, 10, 11, 17, 20, 25, 9, 11, 12, 13, 20, 21, 23, 20, 21, 24, 28, 29, 30 16, 18, 20, 27, 29, 30 25, 29, 31 22, 23, 29, 30 Low Pressure 42 35.9 1, 2, 3, 4, 2, 25 1, 2, 3, 5, 4, 5, 6, 7, 8, 5, 8, 9, 13, 6, 7, 11, 22, 9, 12, 13, 14, 14, 15, 23, 24, 26 15, 16, 17, 18, 27 19, 22, 23, 26, 27 Cold Front 19 16.2 10, 18, 22, 5, 7, 15, 19, 16, 18, 19, 2, 28 24, 26 21, 27, 28 25, 28 Warm Front 11 9.4 7, 11, 19, 21 10, 14, 17, 24, 30 26, 31 Total 117 100 30 31 25 31

Table 5.

Weather regime frequency and percent frequency including dates of occurrence (all events from 2004). 142 National Weather Digest Investigation of the Air Quality Index as Related to Weather Regime Figure 6a Figure 6b Fig. 6(a). Fig. 6(b).

Figure 6c Fig. 6(c). Fig. 6(d). Figure 6d

Volume 33 Number 2 ~ December 2009 143 Croft and Melendez

pooling. The warm front pattern, although more diffuse, when the high pressure center is to the EAST of the study shared some similarity to the 500 mb zonal regime but region; the lowest and least extensive mean values occur was a poor match to any of the 500 mb flows. To varying when the high pressure center is located to the WEST. degrees each surface weather pattern retained the urban In each case, some remnant of the “lambda-like” pattern signatures (New York City, Baltimore, and Philadelphia) identified previously may be discerned – although the found earlier, and indicated minima at higher elevations pattern is oriented more east to west across the region. and in the vicinity of coastal sections. Given the nature of This pattern is more obvious, albeit elongated, for the Low the data set, and the use of mean daily values, relation of Pressure subtypes (Fig. 8 a-c), and is comparable to all these to specific mesoscale phenomena was not possible. cases combined (Fig. 6b). In general, AQI values for the low However, while some common elements between pressure cases are reduced compared with high pressure various partitions of the data do exist in the analyses cases. The most extensive and highest mean values occur above, it must be emphasized that these are occurring for low pressure WEST and least for EAST – the opposite during a period (April through July) in which surface of high pressure – which is not unexpected given that and upper air parameters typically exhibit a very large the air flow for low pressure EAST would be comparable range. Therefore, examination of the surface patterns also with that for high pressure WEST. The AQI pattern for low considered various diagnostic fields. For high pressure pressure EAST is more similar to high pressure WEST days, there was some similarity between mean peak given the comparable wind flow of the two subtypes (i.e. AQI values and the thermal and moisture fields (i.e. both with a downslope component). In combination, these higher in peak regions) as well as weak mean wind flow results suggest that the “lambda-like” pattern identified at the surface. In the cases of cold and warm fronts, there is more likely the result of specific sources and pooling of was a tendency for the peak mean AQI values to follow pollutants, and could also represent a bias in the structure the axis of mean rising motion (omega), yet exhibiting of the observational network. some inconsistency with regard to values for urban and higher elevation locations. These mixed results suggested consideration of the location of the surface feature as a determinant in the observed AQI patterns. d. AQI and weather regimes subtypes Weather Regime Sample Size Percent (frequency) Frequency High Pressure WEST 17 37.8 The investigation considered mean AQI values as a OVER 10 22.2 function of the location of the surface weather regime EAST 18 40.0 feature. The intent was to determine whether the specific surface weather regime details were important to AQI Low Pressure spatial distributions, and/or related to local physical WEST 19 45.2 features. Therefore the basic weather regime types OVER 14 33.3 (except fronts) were broken-down into subtypes (Table EAST 9 21.4 6) according to their location relative to the study region: (a) WEST (northwest, west, or southwest); (b) OVER Cold Front 19 100.0 (north, over, or south); and (c) EAST (northeast, east, or southeast). These groupings were made to ensure Warm Front 11 100.0 adequate sample size, to account for the typical west to east flow of weather sequences in the study region, and Total 117 100 to consider the prevailing mean air flow that would be common or similar in direction – and thus important to Table 6. . In each subtype, weather conditions and air Weather regime subtypes (for high and low pressure: flow patterns would be expected to differ based on the WEST,Table OVER, 6 and EAST; as described in the text) with sample location of the center of the high or low pressure system size and percent frequency. and its interactions with local features. In the case of High Pressure subtypes (Fig. 7a-c), the pattern and magnitude of AQI in each is comparable to that for all cases of high pressure combined (Fig. 6a) but with variation in the location and shape of the maximum AQI.144 TheNational most Weather extensive Digest and highest mean AQI occurs Investigation of the Air Quality Index as Related to Weather Regime Figure 7b Fig. 7(a). Figure 7a Fig. 7(b).

Figure 7c Fig. 7(c).

Fig. 7.

Same as Fig. 3 except for High Pressure:

(a) WEST (b) OVER (c) EAST

Volume 33 Number 2 ~ December 2009 145 Croft and MelendezFigure 8a Figure 8b Fig. 8(a). Fig. 8(b).

Figure 8c Fig. 8(c).

Fig. 8.

Same as Fig. 3 except for Low Pressure subtypes

(a) WEST (b) OVER (c) EAST

146 National Weather Digest Investigation of the Air Quality Index as Related to Weather Regime e. AQI operational composites

to the local wind flow. In the case of low pressure located to the EAST of the study region, the characteristic behavior In order to identify the potential factors creating these and pattern of AQI mimics that of the high pressure sample observed AQI patterns and variations, composite weather population (particularly WEST), but with mean values maps were generated for further analysis. The composite slightly lower across the region. When low pressure is maps (Fig. 9a-c) for each of the resulting mean weather situated OVER or WEST of the region, a distinctly different pattern subtypes above were generated (based on the sample population characteristic is revealed as an isolated reanalysis data set as described by Kalnay et al. 1996) to “lambda-like” pattern appears. This indicates a region of indicate the mean location of the pressure system (or front) peak AQI that appears to be related to both local sources relative to the study region. Each map identifies: (1) the and the resulting transport of higher values – or the mean center of high(low) pressure, or the mean location observational network itself. At the same time, it provides of cold(warm) front; (2) the general surface wind flow for an air flow from the ocean to the east and south that (arrow) which has been added (when definitive) based on results in minima for coastal areas. In each of these cases, simple meteorological principles; and (3) the location and the local physiographic features may block or channel shape of the observed maximum AQI analyzed (derived pollutants depending upon the actual wind flow (rather from Figs. 6c-d, 7a-c, and 8a-c). The composite maps (Fig. than mean). 9a-c) provide verification of the subtype classifications 4. Conclusions made during data analysis and provide some measure of the within subtype variability, given they represent the mean state of the atmosphere. They also provide a basis for the development of specific operational air quality An examination of mean AQI values during late spring forecasting methods attuned to the study region. and early summer of 2004 was completed to determine Examination of the composite maps for the high the ability of the EPA-derived measure to characterize air pressure configurations are shown in figure 9a. The quality across a region. The intent was to ascertain any WEST and OVER sub-types reveal a simple tendency for obvious patterns based on the location of air monitoring the isolation or possibly pooling of higher AQI values in sites, local physiographic features, the prevailing weather southeastern Pennsylvania (at low elevations), given the regime, and any potential interactions between these wind flow and topographic barriers. However, it is also factors. Analyses revealed that in the mean, air quality quite possible that local emissions, warm temperatures, is GOOD to MODERATE (for the 2004 sample), and that and local (or sea breeze) circulations may be responsible. most counties shared similar statistical distributions from Further investigation would be required in order to isolate the same population. There was some evidence of AQI and identify the precise cause. In addition, a secondary variation from coastal zones to metropolitan/suburban maximum occurs in the vicinity of the New York City area and higher elevation/terrain locations. and other industrial or urban zones. The down slope flow When examined spatially, a “lambda-like” pattern of of air, and the tendency for sinking and divergence, may peak AQI appeared across the region with other maxima be responsible for the minima observed from the higher located in the New York City and Baltimore urban zones. elevations of Pennsylvania south and eastward to the This basic pattern was relatively invariant (i.e. apparent coastal regions of New Jersey. However, when the high in most cases), regardless of the varying surface weather pressure system is located to the EAST of the study region regimes that were examined. This would suggest the higher AQI become pervasive and appear independent influences of a key source region, and/or pooling, or of the observational network site locations. This may transport along the I-95 corridor) – or potentially an be due to transport and forced upslope flow that allows observational bias resulting from the location of network penetration of valley regions in the higher terrain locations sites. However, when composites of synoptic subtypes as the flow becomes parallel with the physiography. In were generated for these weather regimes (according to these cases AQI minima occur along New Jersey coastal their location relative to the study area), it was apparent sections and in southern Delaware. that some relationship existed between mean AQI values When considering low pressure systems (Fig. 9b), it and the physiographic features, local and nearby source appears that lower mean AQI values reflect air “cleansing”, regions, and specific weather patterns. This implies possibly related to rainout-removal by cloud and that synoptic and mesoscale assessments of regional air hydrometeor production and/or washout-removal by the quality and its change with time (and synoptic features) fall of hydrometeors, as well as a spatial “confinement” of are possible in a systematic manner. continued The obvious page AQI150 higher AQI values to specific source regions and according Volume 33 Number 2 ~ December 2009 147 Croft and Melendez

High Pressure WEST – Figure 9a High Pressure OVER – Figure 9a continued Fig. 9(a). High Pressure WEST Fig. 9(a) continued. High Pressure OVER

H H

High Pressure EAST – Figure 9a continued Low Pressure WEST – Figure 9b Fig. 9(a) continued. High Pressure EAST Fig. 9(b). Low Pressure WEST

L

H

Fig. 9.

Composite surface weather maps of basic weather regime subtypes (a) High Pressure northwest, west, and southwest (or WEST); north, over, and south (or OVER); and northeast, east, and southeast (or EAST); and (b) Low Pressure WEST; OVER; and EAST; and (c) Cold Front (blue) and Warm Front (red). High or low pressure center (or frontal boundary) is indicated on each map based on the pressure pattern represented by the analyzed isobars. Arrow provides general mean surface flow of air across study region (only when well-defined) given the weather regime analyzed. Outlined area indicates region of highest mean AQI and148 is Nationallabeled for Weather each weather Digest regime subtype. Investigation of the Air Quality Index as Related to Weather Regime

High Pressure OVER – Figure 9a continued Low Pressure OVER – Figure 9b continued Low Pressure EAST – Figure 9b continued Fig. 9(b) continued. Low Pressure OVER Fig. 9(b) continued. Low Pressure EAST

H L L

Warm Front – Figure 9c continued Cold Front – Figure 9c

Fig. 9(c). Cold Front Fig. 9(c) continued. Warm Front

Volume 33 Number 2 ~ December 2009 149 Croft and Melendez

References pattern variations suggest a strong potential for real-time operational prediction of AQI across the region according to specific weather patterns and local features. Thus, case Alapaty, K., D. T. Olerud, K. L. Schere, and A. F. Hanna, 1995:J. studies could also be performed and this information Appl.Sensitivity Meteor., of regional oxidant model predictions to could be used with guidance products to more specifically prognostic and diagnostic meteorological fields. convey any “call to action” that might be needed in 34. 1787-1801. the case of poor air quality episodes or as to how such episodes might evolve over time across a forecast region Angevine, W. M., M. Tjernström,J. Appl. and Meteor. M. Žagar. and 2006: Clim. (e.g., extended forecast and hazardous outlooks). Modeling of the coastal boundary layer and pollutant It is recommended that local studies be conducted transport in New England: to determine base AQI patterns and distributions for a 45, 137–154. region, including their sensitivity to weather regime and local features. This would serve as a conceptual model Bascom, R., (for the Committee of the Environmental for the location such that day-to-day impacts could be and OccupationalAmer. HealthJ. Respir. Assembly Crit. Care ofMed. the American effectively tracked, and predicted with the use of real- Thoracic Society), 1996a: Health effects of outdoor air time measurements and operational model guidance and pollution, 1., 153, 3-50. Authorsforecast products. ______, (for the Committee of the Environmental and Paul J. Croft Occupational Amer. Health J. Respir. Assembly Crit. Care of Med. the American Thoracic Society), 1996b: Health effects of outdoor air , a Ph.D. graduate of Rutgers University (1991), pollution, 2., 153, 477- is a Meteorology Professor in the Department of Geology 498. and Meteorology at Kean University and is responsible for teaching, research, and service activities. As a member of the National Weather Association since 1983, he has Acknowledgments served in several capacities (including President in 2004) and his interests are in operations-based research and applications in meteorology, particularly mesoscale forecasting. He has worked with students and professional We thank the Department of Geology and Meteorology meteorologists, particularly in the operational community, faculty and staff at Kean University for their assistance Belkysfor more V. thanMelendez 20 years. and supporting infrastructure. We specifically appreciate the assistance from the Department graduated with a B.S. degree in Earth in terms of access to GIS software and Science (2005, Meteorology Option) from Kean University , particularly from Dr. John F. Dobosiewicz and a B.A. graduate from Montclair State University in and Will Heyniger, as well as from Aaron Burton and Geography (2003) with a concentration in Environmental Braden Ward. This support is possible through the Studies. She is currently with the National Weather College of Natural, Applied, and Health Sciences and Service in San Juan, Puerto Rico where she serves as the Studies Program at Kean University. a Meteorologist Intern. Her work includes operations We acknowledge the imageshttp://www.cdc.noaa.gov provided by the NOAA-/ (upper air observing, radar operations), interpretation CIRES Climate Diagnostics Center, Boulder Colorado of the effects of tropical weather, observation and through their Web site at recording weather/climatic conditions, and translation of based on the NCEP Re-Analysis data (NCEP Reanalysis weather products into Spanish for the Spanish-speaking data provided by the NOAA-CIRES Climate Diagnostics communities of Puerto Rico. Her specialties and interests Center, Boulder, Colorado, USA). Information from are in climatology, climate change, operational forecasting the EPA Regional Office in New Jersey as well as from and meteorological research. She has previously worked state environmental agencies (online in many cases) and volunteered in many sectors of the field including in Delaware, Maryland, New Jersey, New York, and private, broadcast and other research programs. Pennsylvania – including state air quality reports – were helpful in performing this study.

150 National Weather Digest Investigation of the Air Quality Index as Related to Weather Regime

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