Development of surface and upper air synoptic catalogues for the Central Mediterranean and an application to the analysis of nitrogen dioxide in the Rome winter season. 

Development of surface and upper air synoptic catalogues for the Central Mediterranean and an application to the analysis of nitrogen dioxide in the Rome winter season. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development of surface and upper air synoptic catalogues for the Central Mediterranean and an application to the analysis of nitrogen dioxide in the Rome winter season. Danielle Bird, Greg Spellman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3087575/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Synoptic climatological approaches provide an effective framework for the analysis of atmospheric circulation patterns and the relationship with environmental variables. The objective circulation classification method of Jenkinson and Collison is applied to the central Mediterranean for the period 1948–2021. In order to capture more synoptic information a modified version of the method is used to also develop a series using the 500 hPa surface. Results capture key modes of variability of the Mediterranean atmosphere. The technique is used to examine variability in daily nitrogen dioxide (NO 2 ) concentration in Rome. Analysis shows an evident surface synoptic control in the winter months and consideration of upper patterns renders this approach more effective. SYNOPTIC TYPING UPPER AIR PATTERNS CENTRAL MEDITERRANEAN AIR QUALITY Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Synoptic climatological techniques have been applied to a variety of locations with the aim of characterising atmospheric circulation and investigating circulation-related environmental variables, such as precipitation (Lorenzo et al. 2008 ; Cortesi et al. 2013 ), surface temperature (Post et al. 2002 ; Farukh and Yamada 2017 ) and air quality (Russo et al. 2014 ; Graham et al. 2020 ). Studies typically use patterns in the sea level pressure (SLP) field as, in most mid-latitude contexts, they are the principal driver of regional meteorology (Conway and Jones 1998 ). The moisture, thermal and dynamic properties of air will be modified by surface type (land, sea) which itself will vary according with season due to changes to the surface radiation balance. In addition, pattern classification techniques also incorporate isobaric curvature (vorticity), which indicates if the lower atmosphere is dominated by subsidence or ascent. Synoptic typing simplifies the continuum of atmospheric circulation into a number of discrete categories (Huth et al. 2015 ). In the past few decades this has been achieved by techniques which divide into those that use expert knowledge (so-called ‘subjective’ approaches) and ‘objective’ techniques based on statistical methods ( e.g. Principal Component Analysis (Von Storch and Zweirs 1999; Cuell and Bonsal 2009 )) or non-linear approaches such as Self Organising Maps (Nishiyama et al. 2007 ). A well-known subjective approach is the Lamb Weather Type (LWT) classification developed by Lamb for the British Isles (1950, 1972). This daily classification extends from 1861 to 1997 and has been used to study rainfall chemistry (Davies et al. 1991 ), flooding (Burt and Ferranti 2012 ) and drought (Richardson et al. 2017 ). Jenkinson and Collison ( 1977 ) devised an automated procedure (hereby called JC sfc ) which employed a set of equations and daily mean SLP taken from a 16-point grid (5 o x5 o ) centred on the British Isles. They were able to reproduce circulation types with negligible differences from the original LWT catalogue (Jones et al 1993 ). JC sfc has been regularly updated by the Climatic Research Unit as a replacement for the Lamb Catalogue since Lamb’s death in 1997. This technique built upon work previously undertaken by El Dessouky and Jenkinson ( 1977 ) who used similar empirical tools to determine circulation classes over Egypt using a smaller area 9-point (5 o x5 o ) grid. The method has been applied elsewhere using both 9-point ( e.g. Grimalt et al. 2013 ) and 16-point grids ( e.g. Vicente-Serrano and Lopez-Moreno 2006 ). The methodological decision concerning domain size and grid point spacing has been explored by Demuzere et al. ( 2009 ) who conclude that, whilst a fine grid reduces the number of classified days, it is important to choose a spatial resolution that accommodates the scale of the typical synoptic patterns within the classification domain. They demonstrate neither a grid spacing of 10° or 5° performs better when attempting to classify patterns with little sea level pressure variability. The choice is therefore context specific, so, for instance, typical synoptic structures in the central Mediterranean which are often unrelated to the polar front, result in a smaller sized features than those occurring in the Atlantic in northern Europe and these can be captured by a simple 9-point grid. One of the advantages of the JC sfc method is that it can be applied to any mid-latitude location (Jones et al. 1998) and consequently classifications can be found for southern Scandinavia (Chen 2000 ), Central Europe (Donat et al. 2010), south-west Russia (Spellman 2017 ), Ireland (Fealy and Mills 2018 ), Serbia (Putnikovic et al. 2016), Chile (Sarricolea et al. 2018 ) and southeastern China (Wu et al. 2020 ; Gu et al 2023 ). There have been a large number of applications to the Mediterranean area most notably the Iberian peninsula (Spellman 2000 ; Grimalt et al. 2013 ). In this current study, the method is applied to the Central Mediterranean area. This region shares some characteristics of atmospheric circulation over the Iberian peninsula notably the ‘ barometric swamps’ (Jorba et al. 2004 ) of the summer months where surface pressure gradients are often unremarkable. There are some significant and influential differences in geography, for instance, the Atlantic Ocean has a reduced control on regional climate. Travelling depressions (with consequent baroclinicity) are quite different in nature if they reach the central Mediterranean and there will be thermal and dynamic modification of near surface air flow by the extensive high plateau of Spain and the relatively warm and moist Mediterranean Sea surface. Finally, the Gulf of Genoa is a major region of cyclogenesis and therefore ‘new’ unstable disturbances arise downwind of the Iberian peninsula. Most studies in synoptic climatology have employed SLP as the principal circulation field however in some regions, such as the Mediterranean, it is also important to consider conditions at higher atmospheric levels. Here, at the surface, especially in the summer half of the year, there is a very slack pressure gradient, but often significant and influential circulation differences aloft (Martin-Vide 2001 ; Miro et al. 2020). Typically the 500hPa contour pattern is most useful in capturing atmospheric conditions in the mid-troposphere. This is regarded as the ‘level of non-divergence’ as it represents the point at which vertical motion is at a maximum. The practical value of a synoptic classification is judged by its ability to account for different weather conditions or the explain the variability in circulation-related environmental variables. In this study, we aim to detect a synoptic signal in urban air quality (represented by nitrogen dioxide, NO 2 ) recorded in the city of Rome. It has long been recognised that the concentration of atmospheric pollutants is a response to both local and regional meteorological factors ( e.g . Popescu and Ionel 2010 ; Tang et al. 2011 ; Gu et al. 2023 ), such as wind speed, mixing depth and instability and these can be captured by synoptic typing (Pope et al. 2014 ). Anticyclonic patterns, for instance, are generally associated with weak air movements which reduce the possibility of the horizontal transport and dilution of pollutants from an area. Furthermore stability and the development of nocturnal radiation inversions both suppress convection and vertical mixing with cleaner air aloft. Similarly, an increase in wind speed and turbulence, especially if associated with an unstable lapse rate will promote dispersal of most pollutants. However, in addition to atmospheric factors, measured nitrogen dioxide concentration displays several ‘modes of variability’ explained by changes in emission sources such as traffic circulation (hence time of day, day of the week) and energy production and consumption ( i.e. the time of year). Furthermore improved air quality management and technological change to vehicles and fuel has resulted in a long-term decline NO 2 concentration (ARPA 2023). This research has three main aims. First to investigate how the Jenkinson Collison synoptic typing method can account for the principal surface circulation types and variability in the Central Mediterranean region in. Secondly, we explore if a modified version of this approach (after Miro et al. 2020) can capture the nature of upper atmospheric flow, and finally, we explore the value of the combined surface and upper air catalogue to explain NO 2 variability in Rome. 2. Synoptic Classification 2.1 Data and Method Sea level pressure (SLP) and geopotential height (500hPa) data for this study was obtained from NCEP-NCAR Reanalysis (Kalnay 1996). This dataset yields 6-hourly data which can be converted to a daily mean to correspond with the single daily weather type in the catalogue. The classification domain in this study is defined as the intersections of the parallels 35 o N, 40 o N and 45 o N with the meridians 7.5 o E, 12.5 o E and 17.5 o E and is centred over the Tyrrhenian Sea (central point is 40 o N, 12.5 o E). Values of SLP (p1-p9) were taken for the nine-point grid (Fig. 1) and used to calculate the eight circulation parameters following the equations below. The scaling factors that appear in the formulae are dependent on geographical latitude. P = 0.0625[(P1 + P3 + P7 + P9) + 2(P2 + P4 + P6 + P8) + 4P5] W = 0.25[(P7 + 2P8 + P9) - (P1 + 2P2 + P3)] S = 0.653[0.25(P3 + 2P6 + P9) − 0.25(P1 + 2P4 + P7)] D = arctan( W / S ) F = ( W 2 + S 2 ) 1/2 ZW = 1.056[(P7 + 2P8 + P9) - (P4 + 2P5 + P6)] – 0.951[(P4 + 2P5 + P6) - (P1 + 2P2 + P3)] ZS = 1.305[0.25(P3 + 2P6 + P9) − 0.25(P2 + 2P5 + P8) – 0.25(P2 + 2P5 + P8) + 0.25(P1 + 2P4 + P7)] Z = ZW + ZS Where, P = Surface pressure (hPa). W = Zonal component of geostrophic (surface) wind calculated as the pressure gradient between 35 o and 45 o N. S = Meridional component of geostrophic (surface) wind calculated as the pressure gradient between 7.5 o E and 17.5 o N. D = Wind direction (azimuth degrees). F = Wind speed (m/s). ZW = Zonal vorticity component. ZS = Meridional vorticity component. Z = Total vorticity. A daily weather type is then assigned following a set of guidelines (see Jenkinson and Collison 1977 ). These determine if the weather type is pure directional ( i.e. N, NE, E, SE, S, SW, W, NW or N), cyclonic or anticyclonic (C, A) or a hybrid (CNE, CE, CSE, CS, CSW, CW, CNW, CN. ANE, AE, ASE, AS, ASW, AW, ANW, AN) and are as follows: The flow direction ( D ) uses eight compass points. If | Z | is less than F then an advective or pure directional type exists, determined by D (rule one). If | Z | is greater than 2 F , then the weather type will be cyclonic, if Z > 0, or anticyclonic if Z <0. If F is less than | Z | and 2 F , a hybrid type exists, vorticity is determined by rule three, and direction by rule one. Hybrid types are CNE, CE, CSE, CS, CSW, CW, CNW, CN, ANE, AE, ASE, AS, ASW, AW, ANW and AN. If F and | Z | are less than 6, an undetermined (U) weather type is reported. Atmospheric circulation at height in the mid-latitudes is much less complex than at surface. It reflects the position of axis of the polar front and whether this exhibits a zonal or meridional configuration. Key patterns that dominate the region are positive and negative. Geopotential height data for the 500hPa surface was used to construct the upper air classification ( JC 500 ). Given the equivalent latitude it is appropriate to follow the same methodology developed by Miro et al. (2020) for the Iberian Peninsula. First, the centre of the grid is displaced five degrees to the west to capture the fact that structures at this level will move from west to east and it is an approaching upper air pattern that has an influence on lower atmospheric conditions. At this height, isobaric curvature tends to increase more than flow strength and thus the threshold for discriminating between pure cyclonic/anticyclonic and hybrid classifications can be increased from 2 (as in the original surface method) to 6. The threshold between a trough pattern (cyclonic) and pure advection is lowered from 1 to 1/3, which enables weak (but influential) troughs to be captured. In contrast, the threshold separating a ridge from pure advection is raised from 1 to 4/3 allowing strong winds, so anticyclonic curvature is considered as advection in stable conditions. Consequently the rules for classifying the upper air types ( JC 500 ) are as follows: 1. If Z > 0 and | Z | < (1/3)*F or Z < 0 and | Z | 6 F , the type is cyclonic C if Z > 0 or anticyclonic A when Z < 0). 3. If (1/3)* F < | Z | 0 (cyclonic) a trough above is defined (N, NE, E, SE, S, SW, W, NW). 4. If (4/3)* F < | Z | < 6 F and Z < 0 (anticyclonic) a ridge above is defined (N, NE, E, SE, S, SW, W, NW). 5. If F < 6 and | Z | < 6 the type is unclassified (U). 2.2 Results of the surface synoptic classification (JC sfc ) A 1% sample of days is taken from the resulting 27,028-day catalogue and cross referenced with the respective 1200 UTC and 1800 UTC SLP charts it is clear that the technique captures the characteristics of surface circulation effectively. The marked seasonality of Central Mediterranean atmospheric circulation is reflected by differences in the intra-annual occurrence of the principal JC sfc types. Seasonal circulation change is a consequence of the poleward shift in the axis of the polar jet in the summer and a reduction in flow strength due to the decrease in the hemispheric temperature gradient. As the polar jet migrates south in winter months there is a strong effect on circulation patterns at surface and a higher variability in weather type occurrence. Days classified as either ‘U’, ‘C’ or ‘A’ types dominate the surface catalogue (Table 1 ). Unclassified ‘U’ types, representing little pressure gradient and SLP close to the annual mean, are most common and are a main persistent feature of summer circulation (53% of all days). It is often the case in summer months that long runs of ‘U’ types have an incidence of single ‘C’ type or ‘A’ type days, yet the vorticity index, z , (upon which the type is determined) indicates these patterns are often very close to the threshold of ‘6’ (-6)[1] and thus represent only very weak positive (or negative) vorticity. In the summer period more intense cyclonicity[2], for instance, occurs for periods of 2–3 days once every three years on average. Table 1 Frequency (percentage days of occurrence) of JC sfc during the full all year classified period (1948-2021) and the winter (DJF) season but also the sample period (2001-2021) all year and the winter season (DJF) used in the air pollution analysis. Only types with more than one percentage of occurrences are listed. Days C U W A CW E CE AW SW CNE 1948-2021 27028 33.27 33.65 4.43 13.05 1.71 2.49 2.25 1.71 1948-2021 Winter 6677 47.40 17.39 8.21 4.63 3.31 3.82 3.47 1.27 1.48 1.51 2001-2021 7668 37.14 35.08 4.11 8.69 1.81 2.26 3.12 1.17 2001-2021 Winter 1895 49.5 17.41 7.92 4.43 3.17 2.85 4.01 1.11 1.69 Classified ‘C’ types are also frequent in the JC sfc series at over 33% of days and exhibit considerable annual variability from 19% of days in 1948 to 44% in 2018. ‘C’ types include all surface cyclonic flow and therefore comprise patterns with gentle cyclonic curvature of the surface isobars which can be a response to summer heat, inducing surface ‘thermal low’ circulation, or shallow upper air troughs ( Fig. 2 a) and more active storm events ( Fig. 2 b). ‘C’ patterns in the area arise due to travelling depressions from the Atlantic ( Fig. 3 a) however it is more common that cyclonicity develops within the region itself ( Fig. 3 b ) . The Gulf of Genoa, west of Italy, is an important zone of cyclogenesis (low pressure formation) with these systems then travelling eastwards. The Alps and other coastal topography are the principal factor for cyclones forming here due to orographic lift. Furthermore, these barriers can also hinder eastward moving upper level troughs encouraging cyclones to strengthen (Trigo et al 2002). Cyclogenesis is more prevalent in late autumn and winter by the strong baroclinic conditions arising due to the enhanced maritime-continental temperature gradient at this time of year. ‘C’ types are most common in the winter (47.75%) and autumn months (35.68%). Anticyclonic types are infrequent compared to their occurrence in northern European catalogues. There is a distinct seasonality with many more incidences in spring (27% including all hybrid types) than other times of the year (winter is less than 8%). ‘W’ and ‘E’ are the most common directional weather types but are noticeably less so in the summer under the slack pressure gradients. More than half of the 27 weather types together occur on less than 3% of all days although there is more representation by the less frequent types in the winter months. The decrease in the number of annual occurrences of ‘A’ type days (and concomitant increase in ‘C ‘ days) is the principal temporal trend to emerge from the catalogue (Fig. 4 ). The annual number of ‘C’ types has increased in the time period at a rate of 5.9 days per decade ( R 2 = 41.6%, F = 49.1 = p < 0.001 ) and there has been an even clearer decline (8.8 days per decade) in ‘A’ types ( R 2 = 60.4%, F = 105.25 p < 0.001 ). The vorticity component ( z ) demonstrates a shift towards cyclonicity (an increase in mean annual z ) in this region in the second half of the twentieth and first two decades of the twenty-first century. It is notable that this increase happens in all seasons but not in the winter months. This is contrary to future modelled scenarios for circulation change ( e.g . Otero et al. 2018 ) that suggest greater anticyclonicity across the Mediterranean region however the classified domain is at a smaller scale within the region, is a centre of cyclogenesis and therefore likely to be highly responsive to more localised disturbances. On the other hand, Otrero et al. (2018) do report that that the set of models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) did underestimate the number of cyclonic days currently occurring in Southern Europe. 2.3 Results of the upper air synoptic classification (JC 500 ) A JC 500 series is generated using the modified procedure of Miro et al. (2020). This is manually validated against a 1% sample of 500 hPa charts and it is seen to effectively capture the flow at this level. As expected it is typified by westerly zonal advection, which can be subdivided into anticyclonic, cyclonic or pure westerly flow (Table 2 ). The dominant westerly configuration is a trough pattern over the area. Although all possible JC types occur, hybrids and any type with easterly or southerly flow components are much less numerous (for instance, ‘SE’ types only occur on 20 days (0.07%) in the 27,028-day catalogue). There are significantly less ‘U’ types (6.23% compared to 33% at surface) at this height. Given the small number of hybrid weather types they can be grouped together as Ridge Westerly (RW) and Ridge Easterly (RE) or Trough Westerly (TW) and Trough Easterly (TE). Across the year the overall flow is relatively consistent although there is a slight increase in easterly flow in the winter. Table 2 Frequency (percentage of days of occurrence) of JC 500 during the full all year classified period (1948–2021) and the winter (DJF) season but also the sample period (2001–2021 all year and the winter season (DJF) used in the air pollution study. Only types with more than one percentage of occurrences are listed. . Days TW TE RW RE C A W NW SW U N NE 1948–2021 27028 39.12 3.28 9.78 1.18 3.45 1.00 27.47 4.97 1.68 6.23 1948–2021 Winter 6677 37.90 5.62 10.14 2.31 4.51 1.38 20.42 7.39 1.18 5.24 1.56 1.17 2001-21 7668 41.42 3.91 10.07 1.51 3.71 1.03 27.06 5.05 1.75 6.07 2001-21 Winter 1895 41.88 5.66 10.21 2.22 4.49 1.32 19.46 8.14 1.37 5.02 2.49 1.27 Surface circulation is strongly influenced by upper air flow particularly on westerly JC sfc days which almost entirely (98%) reflect the dominant direction aloft (either TW, RW and W). Anticyclonic and easterly surface types are less associated with zonal patterns aloft. For example, under surface A types, the relatively uncommon ridge easterly flow is present on 15% of days. The JC 500 catalogue gives synoptic information on the many instances where surface patterns are unidentifiable (‘U’ types). Table 3 summarises the variability in upper atmospheric circulation that accompanies surface U types. Although all upper types can occur, some are over-represented compared to the overall frequency of incidence. ‘U’ is more associated with negative rather than positive vorticity pattens aloft but not exclusively. JC 500 indicates different mechanisms can operate away from the surface which are implicated in the behaviour of a circulation-related environmental variable on these days. Surface C types, the second most common pattern in the JC sfc series, is strongly associated with dominant W and TW flow, but can also be accompanied by ridge patterns aloft (11% of occurrences) suggesting that many of these are shallow features. Unlike at surface there is no statistical trend in the annual or seasonal frequency of any of the principal weather types. Table 3 Upper atmospheric circulation accompanying surface U types. The ratio between JC 500 type is associated with surface ‘U’ to its frequency for all types with bold values representing above average occurrences. A C NW RE RW SW TE TW U W % occurrence under JC sfc U types 1.14 1.96 3.60 1.76 12.20 1.90 1.92 31.19 8.54 34.48 Ratio occurrence under weather type: occurrence under all types 1.18 0.57 0.72 1.13 1.30 1.13 0.51 0.81 1.37 1.26 3. Air Quality in Rome Rome is one of the most populous cities in the European Union with 2.9 million residents living in a, generally, densely urbanised area of 1285 km 2 Rome has been recorded as one of the worst cities in Europe for air quality levels (Phelan 2018 ). Levels of smog (a mixture of air pollutants) have caused concerns for health issues due to frequent episodes. For instance, in November 2011, high levels of pollution prompted authorities to limit motor vehicle use (UPI 2011 ). In 2015 (BBC 2015) and then in early 2020, Rome exceeded the safe, legal limits of air pollution across the city and this winter smog episode was largely driven by prevailing meteorological conditions (BBC 2020). Air quality data was obtained from the Agenzia Regionale Protecione (ARPA 2023) who operate a network of fixed air quality monitoring sites in the province of Lazio. Overall, there are 55 urban, suburban, industrial and rural stations with 18 stations in the Rome agglomeration. For the purposes of this study the ten urban sites (Table 4 ) within the Grande Raccordo Anulare (Fig. 5 ) were selected and these are identified as either ‘urban background’ or ‘urban traffic’ reflecting the proximity to traffic and exposure of the population (Fig. 6). Daily mean values of NO 2 were extracted for each site and there is a strong significant spatial correlation between locations indicating regional coherence in air quality behaviour. Poor air quality is exacerbated by the built environment which reduces wind speed and encourages air stagnation, inhibiting pollutant dispersion (Di Bernardino et al. 2018 ; Barbano et al. 2020). It is often the case that vehicular emissions, for example, remain trapped at the pedestrian level, especially when meteorological conditions are settled (Palmieri et al. 2008 ). Table 4 Air quality monitoring stations used in this study (Source: ARPA 2023). Station Coordinates Altitude (m ASL) Type Preneste 41.886018, 12.541614 28 Urban Background Corso Francia 41.947447, 12.469588 35 Urban Traffic Magna Grecia 41.883064, 12.508939 37 Urban Traffic Cinecitta 41.857720, 12.568665 48 Urban Background Ada 41.932874, 12.506971 50 Urban Background Fermi 41.864194, 12.469531 17 Urban Traffic Bufalotta 41.947649, 12.533682 36 Urban Background Cipro 41.906358, 12.447596 24 Urban Background Tiburtina 41.910257, 12.548870 26 Urban Traffic Arenula 41.894020, 12.475368 17 Urban Background In order to extract a signal related to synoptic conditions, rather than the influence of changing patterns of traffic, or the successful introduction of air quality management schemes, the full dataset was first examined for any patterns of regular variability that could then be addressed. At all sites NO 2 exhibits coherent modes of temporal variability according to: ( i ) time of day; ( ii ) day of the week ( iii ) time of year and ( iv ) point in the sample period. Figure 7 shows distinct morning and evening peaks in NO 2 related to elevated traffic circulation. In the time period there has been a clear reduction in mean hourly values across the day (a 40% reduction in the morning peak NO 2 concentration at Magna Grecia for instance). Many stations display a shift to an earlier peak in the 21-year period however the timing of the afternoon peak remains variable. Although selected daily maximum NO 2 might capture the acute influence on health daily mean concentration is a valid measure of the longer term exposure to pollution (Krzyzanowski 1997). Mean NO 2 values exhibit reduced values at a weekend of 25% on average on a Sunday at Preneste for instance (Fig. 8). This is in common with other studies such as the 10-year study by Karandinos et al. ( 2006 ) in Volos, Greece which showed up to a 20% decline at the weekends and also similar investigations in Detroit (Batterman et al. 2015 ) and Delhi (Gour et al. 2013 ). In this analysis both weekend days are removed as the lower residual values are independent of prevailing meteorology. There is a distinct NO 2 ‘season’ at Rome city centre sites. At Bufalotta to the north of the centre, for instance, the mean weekday daily NO 2 concentration in winter (95.17 µg/m 3 ) exceeds that of summer (63.63 µg/m 3 ) by almost 50%. In fact, in the top decile of daily NO 2 concentration at this site, 47% of days are in winter months and only 3% in summer. This seasonal pattern is replicated within the city area. Analysis concentrated on this season where health impacts will be more severe. Since 2001 there has been a considerable improvement in air quality in the city of Rome (Fig. 9 ), a consequence of improved air quality management, more stringent vehicle emission standards for petrol vehicles and changes in the nature of the vehicle fleet (an increasing proportion of hybrid cars). This would mean that, irrespective of meteorological conditions, years towards the end of the sample period would have better air quality then those at the start. In order to isolate the influence of synoptic conditions this background decline was removed by detrending the data. An equivalent procedure was undertaken by Grundstrom et al. ( 2015 ) in the analysis of pollution in Gothenburg, Sweden. In 2020, Italy, in common with other EU nations lockdown restrictions were imposed to address the COVID-19 pandemic. This resulted in a decrease in the emissions of primary pollutants, due to the temporary closure of certain economic activities and reduced vehicle use. Consequently, there was a general reduction in atmospheric concentration of most urban air pollutants (Campanelli et al. 2021 . Basasani et al. 2021). This analysis uses the standardised residual values around the pre-COVID background trend (2001–2019) rather than the actual measured concentrations and the variation of these values should more clearly respond to the synoptic control. Synoptic analysis is therefore undertaken on the mean daily concentration of the standardised residuals (about the 2001–2019 trend) of weekday NO 2 . during the winter (DJF) season. 4. Analysing air quality using synoptic typing The large number of low frequency circulation classes reduces the effectiveness for exploring circulation-pollution links however weather types with easterly components (AE, ANE, SE, CSE and E) are typified by positive mean NO 2 residuals and in contrast days with negative residuals are associated with westerly weather types (for instance, ANW, CNW, CSW, SW). Synoptic control strengthens in winter months and commonly occurring weather types exhibit contrasting air quality regimes (‘A’ types record a mean residual of + 42.58 µg/m 3 (total incidence 41 days) and ‘C’ mean residual + 5.44 µg/m 3 (384 days)). On the other hand there is a wide distribution of daily mean NO 2 values accompanying each type. In order to simplify the analysis, weather types can be amalgamated into groups where infrequently occurring types exert a similar control on air quality at the surface to more commonly occurring types. Expert knowledge, one-way ANOVA and post hoc testing of mean NO 2 concentration under each JC sfc type allows the series to be reduced to four principal groups based on their association with daily mean NO 2 concentration (Table 5 ) . Group 1 (14% of all days) is dominated by westerly circulation types including those A and C hybrids with a south-westerly component. These days are accompanied by mean nitrogen dioxide residuals which are close to the annual average but well below the seasonal mean. Group 2 types (27%) are typified by the indistinct SLP field of unclassified (U) and anticyclonic (A) patterns. These are associated with low wind speeds and stable atmospheres encouraging the build up of pollution. Group 2 also includes the south and easterly directional flow types and hybrids which occur when blocking high pressure systems anchor over central Europe. The distribution of residual nitrogen dioxide values in this group has the highest winter mean. Group 3 includes the uncommon (3%) weather types that have a north westerly component which also lead to relatively high NO 2 residual. Finally Group 4 is composed of the most frequent types (56%) and is associated with cyclonicity in the region. Examples of each group are depicted in Fig. 10 . Table 5 Grouping of JC sfc and mean daily NO 2 residuals (µg/m 3 ) at selected sites in Winter. Group JC sfc Occurrences (%) Preneste Cinecitta 1 W, SW, CW, CSW, AW, ASW 14 1.63 -0.77 2 A, E, S, U, AE, SE, ASE, CSE 27 31.21 35.60 3 NW, CN, ANW, CNW 3 19.46 20.73 4 C, NE, CNE, CE, CS 56 11.07 9.91 F = 106.5 F = 108.7 There is a strong association between large residuals and upper air patterns with easterly components and negative vorticity (E, NE, RE, A) and significantly lower residual values for westerly and cyclonic patterns (W, TW, C). JC 500 types can be grouped with respect to similar atmospheric conditions/air quality response. Seven principal patterns emerge (examples are shown in Fig. 11 ) which display significantly different NO 2 distributions. These are as follows: AE , comprising settled anticyclonic and unclassified patterns or ridge patterns with a weak easterly component; AW , which includes ridge patterns with westerly advection; NW , made up of pure N and NW directional type days; W which are pure directional from the west; TSE , troughs bringing air from the south and east; C , cyclonic patterns and TW which are troughs with associated westerly advection. Table 3 lists the frequencies of each type in the whole catalogue, in winter and in the sampled years used to analyse air quality. An important advantage of the JC 500 catalogue is that it can account for the variability of NO 2 concentration occurring under ‘U’ type surface patterns. Key atmospheric mechanisms that can lead to the dispersal (or accumulation) of pollution, which are not clear from surface pattern can be identified. Table 6 shows how certain types will lead to NO 2 concentrations which are different to the group mean. Generally stronger westerly flow and cyclonic patterns aloft will lead to a lower mean residual compared to the group mean. Unexpected results are due to the low sample size rendering some combinations unusual. Table 6 Mean residual NO 2 ( μg/m 3 ) associated with occurrences of JC 500 types for each surface JCsfc group in winter. * less than 5 occurrences. JC sfc Group 1 JC sfc Group 2 JC sfc Group 3 JC sfc Group 4 JC 500 Mean 1.63 31.21 19.46 11.07 AE 47.14* 46.29 51.48 32.70* AW -2.75 28.20 21.49 8.08* NW 11.4 59.28 21.10 23.76 W -1.04 12.68 17.89 17.05 TSE 27.10 6.35 C 57.90* 8.74 TW 4.90 29.97 7.5 3.27 The sequence of SLP and 500 hPa charts in Fig. 12 demonstrate very different upper patterns on example days which result in contrasting NO 2 in each case. For instance, the JC sfc U type on 12th December 2014 leads to high NO 2 residuals under the stable AW pattern aloft compared to that of 20th February 2014 which is accompanied by the unstable TW JC 500 type (Fig. 12c and d). Overall though the results confirm the value of the information provided by upper air patterns. 6.Concluding Remarks The Jenkinson-Collison method for objectively determining atmospheric circulation-based weather types was applied over the central Mediterranean. Although this technique has been employed by other authors in both the eastern and western regions of this circulation domain it has not been used in the central region. As expected, the overall weather type catalogue ( JC sfc ) obtained is not as varied as that of more northerly latitudes (for instance, that derived by Tang et al. ( 2009 ) for Sweden) and also yields less occurrences of certain weather types than further west over the Iberian peninsula ( e.g. Grimalt et al. 2013 ). The slack gradients in the summer half of the year result in a domination of ‘U’ types which is characteristic of the Mediterranean summer with its mixture of very light surface air movements. Of significant interest in the JC sfc catalogue is the fact that the more frequent weather types demonstrate clear directional trends in their occurrence over the time period. The overall increase in cyclonic types (and reduction in anticyclonic types) in the region is notable. The vorticity index, z , describes this increased cyclonicity (an increase in mean annual z values) in this region in the second half of the twentieth and first two decades of the twenty-first century. To some extent this finding contradicts that of other authors such as Kouroutzoglou, et al. ( 2014 ) who suggest that all forms of cyclonicity have become less frequent in the Mediterranean basin as a whole. Stryhal and Huth ( 2018 ) state that issues with circulation modelling exist such as the overestimation of the positive phase in the North Atlantic Oscillation (NAO) and the limited consideration of effects of Arctic amplification. This, in theory, would increase the temperature gradient at the polar front and potentially lead to deep meridional flow (Francis and Vavrus 2012 ; Peings et al. 2017 ). This in turn could promote cyclogenesis in the northern Mediterranean basin accounting for this observed z increase in this sub-region. The application of a modified Jenkinson Collison method (after Miro et al. 2020) effectively captures the principal modes of circulation variability at 500 hPa in this domain. As expected the JC 500 catalogue is dominated by westerly advection with the most frequent category being a trough over the central Mediterranean. Pure’ W‘ flow and ridges also feature frequently although there are also some easterly flow components at this level which are likely to accompany blocking high pressure events over Central Eurpope.. None of the frequent types show any directional trend in the time period. Overall the technique provides an effective way of capturing flow pattern at this height although arguably a larger gris to reflect the greater size of flow patterns at this level might yield a different categorization. In order to evaluate the utility of this method it was applied to the analysis of daily winter NO 2 in Rome. The 10 monitoring stations included in a sample (2001–2021) show an overall decline in weekday NO 2 and, as each of the sites within the networks were significantly correlated, it is suggested that air pollution variability around the background trend is influenced by regional scale circulation patterns. The pre-COVID trend was removed to attempt to extract the synoptic signal as it would be expected that negative residuals might be linked to those atmospheric conditions favourable to pollutant dispersal and vice versa on very short timescales. A clear synoptic control emerges although the infrequent occurrence and transitionary nature of certain JC sfc types means that some types can be merged into groups. The subsequent four surface groups demonstrate a significant control on residual NO 2 , in winter, however synoptic control is less clear in the summer half of the year due to the prevalence of persistent ‘U’ types. Daily winter NO 2 light anticyclonic and easterly conditions, typical of the circulation regime in the Central Mediterranean which discourages the vertical mixing of air, limits the transport of air pollutants away from source. This is compounded by the built environment which will reduce the movement of air at street level. Combining information from the JC sfc and JC 500 catalogues can improve an explanation of NO 2 variability especially when pressure patterns at the surface are slack. Considerable improvements have been made to the quality of the air in Rome and therefore significant benefits for public health are realised. Of great interest is the observation that if the observed trend in NO 2 in Rome were to continue the reduction in the frequency of regional meteorological conditions conducive to the build-up of NO 2 (decreased anticyclonicity and increased cyclonicity) would further improve air quality. However, on the other hand, the nitrogen dioxide problem in Rome is more associated with winter months and this is the only time of year in which a rise in surface cyclonic patterns have not been observed. Declarations Funding The authors declare that no funds, grants or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions Both authors contributed to the study conception and design. Material preparation, collection and analysis were performed by both authors (Danielle Bird and Greg Spellman). The first draft of the manuscript was written by both authors (Danielle Bird and Greg Spellman). Both authors read and approved the final manuscript. Data Availability The pressure and 500 hPa datasets used in the study can be available from the Climatic Research Unit, University of East Anglia at https://crudata.uea.ac.uk/cru/data/ncep/?_ga=2.224632154.1939107129.1687253023-1349553202.1687253023 The air quality data is available from ARPALAZIO at https://www.arpalazio.net/main/aria/sci/basedati/chimici/chimici.php References ARPA Lazio (2023) https://www.arpalazio.it/ [Accessed 21 March 2023]. Barbano, F., Brattich, E. and Di Sabatino, S., (2021), Characteristic Scales for Turbulent Exchange Processes in a Real Urban Canopy. Boundary Layer Meteorology , 178 , 119–142. https://doi.org/10.1007/s10546-020-00554-5 Bassani C., Vichi, F., Esposito, G., Montagnoli, M., Giusto, M. and Ianniello, A., (2021), Nitrogen dioxide reductions from satellite and surface observations during COVID-19 mitigation in Rome (Italy). Environmental Science Pollution Research , 28 , 22981–23004. https://doi.org/10.1007/s11356-020-12141-9 Batterman, S., Cook, R. and Justin, T., (2015), Temporal variation of traffic on highways and the development of accurate temporal allocation factors for air pollution analyses. Atmospheric Environment , 107 , 351-363. https://doi.org/10.1016/j.atmosenv.2015.02.047 BBC News, (2015), Italian Cities Ban Cars Due To Smog . [online] Available at: [Accessed 21 March 2023]. BBC News. (2020), European Cities Grapple With Dangerous Smog Levels . [online] Available at: [Accessed 21 March 2023]. Burt, T. and Ferranti E., (2012), Changing patterns of heavy rainfall in upland areas: A case study from northern England. International Journal of Climatology, 32 , 518 - 532. https://doi.org/10.1002/joc.2287 Campanelli, M., Iannarelli, A.M., Mevi, G., Casadio, S., Diémoz, H., Finardi, S., Dinoi, A., Castelli, E., di Sarra, A., Di Bernardino, A., Casasanta, G., Bassani, C., Siani, A.M., Cacciani, M,, Barnaba, F., Di Liberto, L. and Argentini, S., (2021), A wide-ranging investigation of the COVID-19 lockdown effects on the atmospheric composition in various Italian urban sites (AER–LOCUS). Urban Climate , 39 , 100954. https://doi.org//10.1016/j.uclim.2021.100954 Chen, D., (2000), A monthly circulation climatology for Sweden and its application to a winter temperature case study. International Journal of Climatology , 20 , 1067-1076. https://doi.org/10.1002/1097-0088(200008)20:103.0.CO;2-Q Conway, D. and Jones, P.D., (1998), The use of weather types and air flow indices for GCM downscaling , Journal of Hydrology , 212 , 348-361. https://doi.org//10.1016/S0022-1694(98)00216-9 Cortesi, N., Trigo, R., Gonzalez-Hidalgo, J. and Ramos, A., (2013), Modelling monthly precipitation with circulation weather types for a dense network of stations over Iberia. Hydrology and Earth System Sciences , 17 , 665-678. https://doi.org/:10.5194/hess-17-665-2013 Crudata.uea.ac.uk. (n.d.). Lamb Weather Types . [online] Available at: https://crudata.uea.ac.uk/cru/data/lwt/ [Accessed 14 March 2023]. Cuell, C. and Bonsal, B.R., (2009), An assessment of climatological synoptic typing by principal component analysis and k-means clustering. Theoretical. Applied Climatology, 98, 361–373. https://doi.org/10.1007/s00704-009-0119-8 Davies, T.D., Farmer, G., and Barthelmie, R.J., (1991), Use of simple daily atmospheric circulation types for the interpretation of precipitation composition at a site (Eskdalemuir) in Scotland, 1978–1984, Atmospheric Environment. Part A. General Topics , 24 , 63-72. https://doi.org/:10.1016/0960-1686(90)90441-O Demuzere, M., Werner, M., Van Lipzig, N.P.M and Roeckner, E., (2009), An analysis of present and future ECHAMS pressure fields using a classification of circulation patterns. International Journal of Climatology , 29 , 1796-1810. https://doi.org//10.1002/joc.1821 Di Bernardino, A., Monti, P., Leuzzi, G., Querzoli, G., (2018), Pollutant fluxes in two-dimensional street canyons. Urban Climate , 24 , 80-93. https://doi.org/10.1016/j.uclim.2018.02.002 Donat, M., Leckebusch, G., Pinto, J. and Ulbrich, U., (2009), Examination of wind storms over Central Europe with respect to circulation weather types and NAO phases. International Journal of Climatology , 30 , 1289-1300. https://doi.org/10.1002/joc.1982 El-Dessouky T.M., and Jenkinson A.F., (1975), An objective daily catalogue of surface pressure, flow and vorticity indices for Egypt and its use in monthly rainfall forecasting. Synoptic Climatology Branch Memorandum no. 46 , Bracknell, Meteorological Office, London. Farukh, M.A. and Yamada, T.J., (2017), Synoptic climatology of winter daily temperature extremes in Sapporo, northern Japan. International Journal of Climatology , 38 , 2230-2238. https://doi.org/10.1002/joc.5329 Francis, J. and Vavrus, S., (2012), Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophysical Research Letters , 39, L06801. https://doi.org/10.1029/2012GL051000 Gour, A., Singh, S., Tyagi, S. and Mandal, A., (2013), Weekday/Weekend differences in air quality parameters in Delhi, India. International Journal of Research in Engineering and Technology , 1 , 69 – 76. Graham, A.M., Pringle, K.J., Arnold, S.R., Pope, R.J., Vieno, M., Butt, E.W., Conibear, L., Stirling,. and McQuaid, J.B., (2020), Impact of weather types on UK ambient particulate matter concentrations, Atmospheric Environment , X5 , 100061. https://doi.org/10.1016/j.aeaoa.2019.100061 Fealy, R. and Mills, G., (2018), Deriving Lamb weather types suited to regional climate studies: A case study on the synoptic origins of precipitation over Ireland. International Journal of Climatology . 38, 3439-3448. https://doi.org/10.1002/joc.5495 Grimalt, M., Tomàs, M., Alomar, G., Martin-Vide, J. and Moreno-García, M., (2013), Determination of the Jenkinson and Collison’s weather types for the western Mediterranean basin over the 1948-2009 period. Temporal analysis. Atmósfera , 26 , 75-94. https://doi.org/10.1016/S0187-6236(13)71063-4 Grundstrom, M., Tang, L., Hallquist, M., Nguyen, H., Chen, D. and Pleijel, H., (2015), Influence of atmospheric circulation patterns on urban air quality during the winter. Atmospheric Pollution Research , 6 , 278-285. https://doi.org/10.5094/APR.2015.032 Gu, S., Wu, S., Yang, L.; Hu, Y., Tian, B., Yu, Y., Ma, N,, Ji, P. and Zhang, B., (2023), Synoptic Weather Patterns and Atmospheric Circulation Types of PM 2.5 Pollution Periods in the Beijing-Tianjin-Hebei Region. Atmosphere , 14 , 942. https://doi.org/10.3390/atmos14060942 Huth, R., Beck, C. and Kučerová, M., (2015), Synoptic-climatological evaluation of the classifications of atmospheric circulation patterns over Europe. International Journal of Climatology , 36 , 2710-2726. https://doi.org/10.1002/joc.4546 Jenkinson, A.F. and Collison, F.P., (1977), An initial climatology of gales over the North Sea. Synoptic Climatology Branch Memorandum No. 62 , Meteorological Office, Bracknell. Jones P.D., Hulme, M. and Briffa, K.R., (1993), A comparison of Lamb circulation types with an objective classification scheme. International Journal of Climatology , 13 , 655–663. V] https://doi.org/10.1002/joc.3370130606 Jorba, O., Gassó, S. and Baldasano, J.M., (2004), Regional Circulations Within the Iberian Peninsula East Coast. In: Borrego, C., Incecik, S. (eds) Air Pollution Modeling and Its Application XVI . Springer, Boston, MA. Kalnay E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D. Gandin, L.S., Iredell, M., Saha, S., White, G., Woollen,J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Ropelewski, C., Wang, J. and Leetmaa, A., (1996), The NMC/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society , 77 , 437-470. Karandinos, A., Saitanis, C. and Arapis, G., (2006), Study of the weekday-weekend variation of air pollutants in a typical Mediterranean coastal town. International Journal of Environment and Pollution , 27 , 300-312. https://doi.org/10.1504/IJEP.2006.010574 Kouroutzoglou, J., Flocas, H.A., Hatzaki, M., Keay, K. and Simmonds. I., (2014), A high-resolution climatological study on the comparison between surface explosive and ordinary cyclones in the Mediterranean. Regional Environmental Change , 14 , 1833–1846. https://doi.org/10.1007/s10113-013-0461-3 Krzyzanowdki, M., (1997), Methods for assessing the extent of exposure and effects of air pollution. Occupational and Environmental Medicine, 54 , 145-151. https://doi.org/10.1136/oem.54.3.145 Lamb H.H., (1950), Types and spells of weather around the year in the British Isles: annual trends, seasonal structure of the year, singularities. Quarterly Journal of the Royal Meteorological Society , 76 , 393–429. Lamb, H.H., (1972), British Isles Weather types and a register of daily sequence of circulation patterns, 1861-1971. Geophysical Memoir 116 , HMSO, London, 85pp. Lorenzo, N., Taboada, J. and Gimeno, L., (2008), Links between circulation weather types and teleconnection patterns and their influence on precipitation patterns in Galicia (NW Spain). International Journal of Climatology , 28 , 1493 - 1505. https://doi.org/10.1002/joc.1646 Martin-Vide, J. (2001), Limitations of an objective weather-typing system for the Iberian Peninsula. Weather, 56 ,248-250. Miró, J.R., Pepin, N., Peña, J.C. and Martin-Vide, J., (2020), Daily atmospheric circulation patterns for Catalonia (northeast Iberian Peninsula) using a modified version of Jenkinson and Collison method, Atmospheric Research , 231 , 104674, https://doi.org/10.1016/j.atmosres.2019.104674 Nishiyama, K., Endo, S., Jinno, K., Uvo, C.B., Olsson, J. and Berndtsson, R., (2007), Identification of typical synoptic patterns causing heavy rainfall in the rainy season in Japan by a Self-Organizing Map, Atmospheric Research , 83 , 185-200, https://doi.org/10.1016/j.atmosres.2005.10.015 Otero, N., Sillmann, J. and Butler, T., (2018), Assessment of an extended version of the Jenkinson–Collison classification on CMIP5 models over Europe. Climate Dynamics . 50 , 1559-1579. https://doi.org/:10.1007/s00382-017-3705-y Palmieri, S., Durante, G., Siani, A. M. and Casale, G. R., (2008), Atmospheric stagnation episodes and hospital admissions. Public Health , 122 , 1128-1130. https://doi.org//10.1016/j.puhe.2008.02.006 Peings, Y., Cattiaux, J., Vavrus, S. and Magnusdottir, G., (2017), Late Twenty-First-Century changes in the midlatitude atmospheric circulation in the CESM large ensemble. Journal of Climate , 30 , 5943-5960. https://doi.org/10.1175/JCLI-D-16-0340.1 Phelan, J., (2018), Rome among worst cities in Europe for road safety, traffic and pollution: Greenpeace. [online] Thelocal.it. Available at: https://www.thelocal.it/20180522/italy-rome-road-safety-traffic-air-quality-greenpeace [Accessed 9 Oct. 2021]. Pope, R.J., Savage, N.H., Chipperfield, M.P. Arnold, S.R and Osborn, T.J., (2014), The influence of synoptic weather regimes on UK air quality: analysis of satellite column NO 2 . Atmospheric Science Letters , 15 , 211–217. https://doi.org/10.1002/asl2.492 Popescu, F., and Ionel, I., (2010), Anthropogenic Air Pollution Sources. In Kumar, A. (ed) Air Quality. Post, P., Truija, V. and Tuulik, J., (2002), Circulation weather types and their influence on temperature and precipitation in Estonia. Boreal Environment Research , 7 , 281-289. Putniković, S., Tošić, I. and Đurđević, V., (2016), Circulation weather types and their influence on precipitation in Serbia. Meteorology and Atmospheric Physics , 128 , 649-662. https://doi.org/10.1007/s00703-016-0432-6 Richardson, D., Fowler, H., Kilsby, C. and Neal, R., (2017), A new precipitation and drought climatology. International Journal of Climatology , 38 , 630-648. https://doi.org/10.1002/joc.5199 Russo, A., Trigo, R., Martins, H. and Mendes, M., (2014), NO 2 , PM 10 and O 3 urban concentrations and its association with circulation weather types in Portugal. Atmospheric Environment, 89 , 768-785. https://doi.org/10.1016/j.atmosenv.2014.02.010 Sarricolea, P., Meseguer-Ruiz, O., Martín-Vide, J. and Outeiro, L., (2018), Trends in the frequency of synoptic types in central-southern Chile in the period 1961–2012 using the Jenkinson and Collison synoptic classification. Theoretical and Applied Climatology , 134 , 193-204. https://doi.org/10.1007/s00704-017-2268-5 Spellman, G., (2000), The use of an index-based regression model for precipitation analysis on the Iberian peninsula. Theoretical and Applied Climatology , 66 , 229-239. https://doi.org/10.1007/s007040070027 Spellman, G., (2017), An assessment of the Jenkinson and Collison synoptic classification to a continental mid-latitude location. Theoretical and Applied Climatology , 128 , 731-744. https://doi.org/10.1007/s00704-015-1711-8 Stryhal, J. and Huth, R., (2018), Trends in winter circulation over the British Isles and central Europe in twenty-first century projections by 25 CMIP5 GCMs. Climate Dynamics , 52 , 1063-1075. https://doi.org/10.1007/s00382-018-4178-3 Tang, L., Chen, D., Karlsson, P.-E., Gu, Y. & Ou, T., (2009), Synoptic circulation and its influence on spring and summer surface ozone concentrations in southern Sweden. Boreal Environment Research , 14 , 889–902. Tang, L., Rayner, D. and Haeger-Eugensson, M., (2011), Have meteorological conditions reduced NO 2 concentrations from local emission sources in Gothenburg. Water, Air, & Soil Pollution , 221 , 275-286. http:// doi.org/10.1007/s11270-011-0789-6 Trigo R.M. and DaCamara C.C., (2000), Circulation weather types and their influence on the precipitation regime in Portugal. International Journal of Climatology, 20 , 1559-1581. https://doi.org/10.1002/1097-0088(20001115)20:13%3C1559::AID-JOC555%3E3.0.CO;2-5 UPI, (2011), Rome, Milan restrict driving to cut smog. https://www.upi.com/Top_News/World-News/2011/11/19/Rome-Milan-restrict-driving-to-cut-smog/36571321746264/ [Accessed 9 Oct. 2021]. Vicente-Serrano, S. and Lopez-Moreno, J.I., (2006), Influence of atmospheric circulation at different spatial scales on winter drought variability through a semiarid climatic gradient in North-east Spain. International Journal of Climatology , 26 , 1427 –1453. https://doi.org/10.1002/joc.1387 Von Storch, H. and Zorita, E., (2019), The history of ideas of downscaling—From synoptic dynamics and spatial interpolation. Frontiers in Environmental Science , 7 , 1-8. https://doi.org/10.3389/fenvs.2019.00021 Von Storch, H. and Zwiers, F.W., (1999), Statistical Analysis in Climate Research . Cambridge University Press, 484 p. https://doi.org/10.1017/CBO9780511612336 Wu, H. Y., Li, Z., Li, W., and Zheng, J., (2020), Characteristics analysis of extremely severe precipitation based on regional automatic weather stations in Guangdong. Meteorological Monthly , 46 , 801–812. https://doi.org/10.3389/feart.2021.678230 Footnotes According to rule 5. Above a |z| value of 20 (annual mean + 1 SD). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Jan, 2024 Reviews received at journal 09 Aug, 2023 Reviewers agreed at journal 26 Jul, 2023 Reviewers invited by journal 25 Jul, 2023 Submission checks completed at journal 26 Jun, 2023 Editor assigned by journal 26 Jun, 2023 First submitted to journal 20 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3087575","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":213246338,"identity":"da01e6ac-9c2d-42b5-bfd1-58b524a2057a","order_by":0,"name":"Danielle Bird","email":"","orcid":"","institution":"University of Northampton","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Danielle","middleName":"","lastName":"Bird","suffix":""},{"id":213246341,"identity":"71b7ebce-530d-49f8-a222-cea9b254d1da","order_by":1,"name":"Greg Spellman","email":"data:image/png;base64,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","orcid":"","institution":"University of Northampton","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Greg","middleName":"","lastName":"Spellman","suffix":""}],"badges":[],"createdAt":"2023-06-20 13:29:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3087575/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3087575/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":39322587,"identity":"00f03f50-e8b3-4659-9032-a89b676f3deb","added_by":"auto","created_at":"2023-06-29 18:35:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e9-point grid for classification of sea level JC\u003c/em\u003e\u003csub\u003e\u003cem\u003esfc\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e in the Central Mediterranean \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e 45\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE 7.5\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e 45\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE 12.5\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e 45\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE 17.5\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e 40\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE 7.5\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e 40\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE 12.5\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e 40\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE 17.5\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e 35\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE 7.5\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e 35\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE 12.5\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e 35\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE 17.5\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eE\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3087575/v1/728c04013dbb9cb40d67da6a.png"},{"id":39322307,"identity":"325eec9c-d593-48cf-bfa6-b0cffad294d3","added_by":"auto","created_at":"2023-06-29 18:27:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":268827,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003e2a\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Central Mediterranean C type 31.07.07 \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e2b \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eC type 25.01.09\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3087575/v1/12b3973f0a10878da53e27e5.png"},{"id":39322585,"identity":"5753887d-51d9-48b3-9afe-77ee3f89565f","added_by":"auto","created_at":"2023-06-29 18:35:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":275032,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003e3(a\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e) Travelling Atlantic depressions 04.02.15 \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e3(b)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Depression originating in Gulf of Genoa 05.03.15\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3087575/v1/691021ba5ef63d5a260f444a.png"},{"id":39322309,"identity":"2c6c3603-9615-4ab5-b520-43dad9ce30a7","added_by":"auto","created_at":"2023-06-29 18:27:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Annual number of A days and \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e C days 1948-2021\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3087575/v1/1c58f1bb6c5da6782b747ed5.png"},{"id":39322588,"identity":"8c410dbc-d089-4cb7-b435-88e0bde66665","added_by":"auto","created_at":"2023-06-29 18:35:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":751899,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLocation of air quality monitoring stations in Rome within the Grande Raccordo Anulare\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3087575/v1/d8f7f2d8acfc20f5fce0819d.png"},{"id":39321673,"identity":"c883f45b-0ff6-4221-b471-eda03fdbb110","added_by":"auto","created_at":"2023-06-29 18:19:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1883388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAir quality monitoring sites at \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Magna Grecia and \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Preneste\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3087575/v1/7780642a7bd6099cd689e18b.png"},{"id":39321664,"identity":"93dbc556-ac30-4837-9e1f-68f8f8367e8f","added_by":"auto","created_at":"2023-06-29 18:19:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":81051,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMean hourly concentration of nitrogen dioxide μg/m\u003c/em\u003e\u003csup\u003e\u003cem\u003e3 \u003c/em\u003e\u003c/sup\u003e\u003cem\u003eat\u003c/em\u003e\u003csup\u003e\u003cem\u003e \u003c/em\u003e\u003c/sup\u003e\u003cem\u003eMagna Grecia\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3087575/v1/5fe797ed0af506f1cd2a63a8.png"},{"id":39322304,"identity":"ac344898-bc00-43cf-a2a9-d5c102162f10","added_by":"auto","created_at":"2023-06-29 18:27:33","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":23672,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMean NO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e daily concentration μg/m\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e at Preneste by day of the week Monday = 1 \u0026nbsp;\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3087575/v1/794f51b5ce45fac305f13276.png"},{"id":39323315,"identity":"8b6d27d2-6b84-4492-94dd-b858463cdf78","added_by":"auto","created_at":"2023-06-29 18:43:33","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":38478,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eReduction in annual nitrogen dioxide concentration μg/m\u003c/em\u003e\u003csup\u003e\u003cem\u003e3 \u003c/em\u003e\u003c/sup\u003e\u003cem\u003eat Magna Grecia, F = 72.55 p\u0026lt;0.05 R sq. = 79.25%\u003c/em\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3087575/v1/b5d5ec2143581b5369674e69.png"},{"id":39321670,"identity":"9a74e302-a02e-4669-95fe-d00aa56e2a24","added_by":"auto","created_at":"2023-06-29 18:19:33","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":372825,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExamples of \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eJC\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u003cstrong\u003esfc\u003c/strong\u003e\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e groupings\u003c/em\u003e\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3087575/v1/2233f6e6d78942e15464d74c.png"},{"id":39321667,"identity":"bfac0d03-686b-4a3d-8417-cfbb74245f55","added_by":"auto","created_at":"2023-06-29 18:19:33","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":323844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePrincipal pattern groups in the \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eJC\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u003cstrong\u003e500\u003c/strong\u003e\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e catalogue Overall frequency of occurrence in parenthesis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-3087575/v1/5724f94106945578bb40d010.png"},{"id":39321674,"identity":"e6eb13cf-09dd-4dff-9fb5-39ebf5329aa1","added_by":"auto","created_at":"2023-06-29 18:19:34","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":533129,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExample \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eJC\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u003cstrong\u003esfc\u003c/strong\u003e\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e and \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eJC\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u003cstrong\u003e500\u003c/strong\u003e\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e patterns for contrasting surface and upper atmospheric patterns\u003c/em\u003e\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-3087575/v1/c542fb2448fbf01d65e1f469.png"},{"id":39324284,"identity":"1595ea38-f7e2-46a3-9892-3ce78fe96e2b","added_by":"auto","created_at":"2023-06-29 18:51:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5036154,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3087575/v1/d5b26b9f-35a9-4fc5-bbc8-01f3f3601857.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of surface and upper air synoptic catalogues for the Central Mediterranean and an application to the analysis of nitrogen dioxide in the Rome winter season. ","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSynoptic climatological techniques have been applied to a variety of locations with the aim of characterising atmospheric circulation and investigating circulation-related environmental variables, such as precipitation (Lorenzo et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Cortesi et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), surface temperature (Post et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Farukh and Yamada \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and air quality (Russo et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Graham et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Studies typically use patterns in the sea level pressure (SLP) field as, in most mid-latitude contexts, they are the principal driver of regional meteorology (Conway and Jones \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The moisture, thermal and dynamic properties of air will be modified by surface type (land, sea) which itself will vary according with season due to changes to the surface radiation balance. In addition, pattern classification techniques also incorporate isobaric curvature (vorticity), which indicates if the lower atmosphere is dominated by subsidence or ascent.\u003c/p\u003e \u003cp\u003eSynoptic typing simplifies the continuum of atmospheric circulation into a number of discrete categories (Huth et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In the past few decades this has been achieved by techniques which divide into those that use expert knowledge (so-called \u0026lsquo;subjective\u0026rsquo; approaches) and \u0026lsquo;objective\u0026rsquo; techniques based on statistical methods (\u003cem\u003ee.g.\u003c/em\u003e Principal Component Analysis (Von Storch and Zweirs 1999; Cuell and Bonsal \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)) or non-linear approaches such as Self Organising Maps (Nishiyama et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). A well-known subjective approach is the Lamb Weather Type (LWT) classification developed by Lamb for the British Isles (1950, 1972). This daily classification extends from 1861 to 1997 and has been used to study rainfall chemistry (Davies et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), flooding (Burt and Ferranti \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and drought (Richardson et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Jenkinson and Collison (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) devised an automated procedure (hereby called \u003cb\u003eJC\u003c/b\u003e\u003csub\u003e\u003cb\u003esfc\u003c/b\u003e\u003c/sub\u003e) which employed a set of equations and daily mean SLP taken from a 16-point grid (5\u003csup\u003eo\u003c/sup\u003ex5\u003csup\u003eo\u003c/sup\u003e) centred on the British Isles. They were able to reproduce circulation types with negligible differences from the original LWT catalogue (Jones et al \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). \u003cb\u003eJC\u003c/b\u003e\u003csub\u003e\u003cb\u003esfc\u003c/b\u003e\u003c/sub\u003e has been regularly updated by the Climatic Research Unit as a replacement for the Lamb Catalogue since Lamb\u0026rsquo;s death in 1997. This technique built upon work previously undertaken by El Dessouky and Jenkinson (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) who used similar empirical tools to determine circulation classes over Egypt using a smaller area 9-point (5\u003csup\u003eo\u003c/sup\u003ex5\u003csup\u003eo\u003c/sup\u003e) grid. The method has been applied elsewhere using both 9-point (\u003cem\u003ee.g.\u003c/em\u003e Grimalt et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and 16-point grids (\u003cem\u003ee.g.\u003c/em\u003e Vicente-Serrano and Lopez-Moreno \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The methodological decision concerning domain size and grid point spacing has been explored by Demuzere et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) who conclude that, whilst a fine grid reduces the number of classified days, it is important to choose a spatial resolution that accommodates the scale of the typical synoptic patterns within the classification domain. They demonstrate neither a grid spacing of 10\u0026deg; or 5\u0026deg; performs better when attempting to classify patterns with little sea level pressure variability. The choice is therefore context specific, so, for instance, typical synoptic structures in the central Mediterranean which are often unrelated to the polar front, result in a smaller sized features than those occurring in the Atlantic in northern Europe and these can be captured by a simple 9-point grid.\u003c/p\u003e \u003cp\u003eOne of the advantages of the \u003cb\u003eJC\u003c/b\u003e\u003csub\u003e\u003cb\u003esfc\u003c/b\u003e\u003c/sub\u003e method is that it can be applied to any mid-latitude location (Jones et al. 1998) and consequently classifications can be found for southern Scandinavia (Chen \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), Central Europe (Donat et al. 2010), south-west Russia (Spellman \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Ireland (Fealy and Mills \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Serbia (Putnikovic et al. 2016), Chile (Sarricolea et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and southeastern China (Wu et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gu et al \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). There have been a large number of applications to the Mediterranean area most notably the Iberian peninsula (Spellman \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Grimalt et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this current study, the method is applied to the Central Mediterranean area. This region shares some characteristics of atmospheric circulation over the Iberian peninsula notably the \u0026lsquo;\u003cem\u003ebarometric swamps\u0026rsquo;\u003c/em\u003e (Jorba et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) of the summer months where surface pressure gradients are often unremarkable. There are some significant and influential differences in geography, for instance, the Atlantic Ocean has a reduced control on regional climate. Travelling depressions (with consequent baroclinicity) are quite different in nature if they reach the central Mediterranean and there will be thermal and dynamic modification of near surface air flow by the extensive high plateau of Spain and the relatively warm and moist Mediterranean Sea surface. Finally, the Gulf of Genoa is a major region of cyclogenesis and therefore \u0026lsquo;new\u0026rsquo; unstable disturbances arise downwind of the Iberian peninsula.\u003c/p\u003e \u003cp\u003eMost studies in synoptic climatology have employed SLP as the principal circulation field however in some regions, such as the Mediterranean, it is also important to consider conditions at higher atmospheric levels. Here, at the surface, especially in the summer half of the year, there is a very slack pressure gradient, but often significant and influential circulation differences aloft (Martin-Vide \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Miro et al. 2020). Typically the 500hPa contour pattern is most useful in capturing atmospheric conditions in the mid-troposphere. This is regarded as the \u0026lsquo;level of non-divergence\u0026rsquo; as it represents the point at which vertical motion is at a maximum.\u003c/p\u003e \u003cp\u003eThe practical value of a synoptic classification is judged by its ability to account for different weather conditions or the explain the variability in circulation-related environmental variables. In this study, we aim to detect a synoptic signal in urban air quality (represented by nitrogen dioxide, NO\u003csub\u003e2\u003c/sub\u003e) recorded in the city of Rome. It has long been recognised that the concentration of atmospheric pollutants is a response to both local and regional meteorological factors (\u003cem\u003ee.g\u003c/em\u003e. Popescu and Ionel \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), such as wind speed, mixing depth and instability and these can be captured by synoptic typing (Pope et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Anticyclonic patterns, for instance, are generally associated with weak air movements which reduce the possibility of the horizontal transport and dilution of pollutants from an area. Furthermore stability and the development of nocturnal radiation inversions both suppress convection and vertical mixing with cleaner air aloft. Similarly, an increase in wind speed and turbulence, especially if associated with an unstable lapse rate will promote dispersal of most pollutants.\u003c/p\u003e \u003cp\u003eHowever, in addition to atmospheric factors, measured nitrogen dioxide concentration displays several \u0026lsquo;modes of variability\u0026rsquo; explained by changes in emission sources such as traffic circulation (hence time of day, day of the week) and energy production and consumption (\u003cem\u003ei.e.\u003c/em\u003e the time of year). Furthermore improved air quality management and technological change to vehicles and fuel has resulted in a long-term decline NO\u003csub\u003e2\u003c/sub\u003e concentration (ARPA 2023).\u003c/p\u003e \u003cp\u003eThis research has three main aims. First to investigate how the Jenkinson Collison synoptic typing method can account for the principal surface circulation types and variability in the Central Mediterranean region in. Secondly, we explore if a modified version of this approach (after Miro et al. 2020) can capture the nature of upper atmospheric flow, and finally, we explore the value of the combined surface and upper air catalogue to explain NO\u003csub\u003e2\u003c/sub\u003e variability in Rome.\u003c/p\u003e"},{"header":"2. Synoptic Classification","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Data and Method\u003c/h2\u003e\n \u003cp\u003eSea level pressure (SLP) and geopotential height (500hPa) data for this study was obtained from NCEP-NCAR Reanalysis (Kalnay 1996). This dataset yields 6-hourly data which can be converted to a daily mean to correspond with the single daily weather type in the catalogue. The classification domain in this study is defined as the intersections of the parallels 35\u003csup\u003eo\u003c/sup\u003eN, 40\u003csup\u003eo\u003c/sup\u003eN and 45\u003csup\u003eo\u003c/sup\u003e N with the meridians 7.5\u003csup\u003eo\u003c/sup\u003eE, 12.5\u003csup\u003eo\u003c/sup\u003eE and 17.5\u003csup\u003eo\u003c/sup\u003eE and is centred over the Tyrrhenian Sea (central point is 40\u003csup\u003eo\u003c/sup\u003eN, 12.5\u003csup\u003eo\u003c/sup\u003eE). Values of SLP (p1-p9) were taken for the nine-point grid (Fig. 1) and used to calculate the eight circulation parameters following the equations below. The scaling factors that appear in the formulae are dependent on geographical latitude.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;0.0625[(P1\u0026thinsp;+\u0026thinsp;P3\u0026thinsp;+\u0026thinsp;P7\u0026thinsp;+\u0026thinsp;P9)\u0026thinsp;+\u0026thinsp;2(P2\u0026thinsp;+\u0026thinsp;P4\u0026thinsp;+\u0026thinsp;P6\u0026thinsp;+\u0026thinsp;P8)\u0026thinsp;+\u0026thinsp;4P5]\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eW\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;0.25[(P7\u0026thinsp;+\u0026thinsp;2P8\u0026thinsp;+\u0026thinsp;P9) - (P1\u0026thinsp;+\u0026thinsp;2P2\u0026thinsp;+\u0026thinsp;P3)]\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;0.653[0.25(P3\u0026thinsp;+\u0026thinsp;2P6\u0026thinsp;+\u0026thinsp;P9) \u0026minus;\u0026thinsp;0.25(P1\u0026thinsp;+\u0026thinsp;2P4\u0026thinsp;+\u0026thinsp;P7)]\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;arctan(\u003cstrong\u003eW\u003c/strong\u003e/\u003cstrong\u003eS\u003c/strong\u003e)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e = (\u003cstrong\u003eW\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;\u003cstrong\u003eS\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e)\u003csup\u003e1/2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eZW\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;1.056[(P7\u0026thinsp;+\u0026thinsp;2P8\u0026thinsp;+\u0026thinsp;P9) - (P4\u0026thinsp;+\u0026thinsp;2P5\u0026thinsp;+\u0026thinsp;P6)] \u0026ndash; 0.951[(P4\u0026thinsp;+\u0026thinsp;2P5\u0026thinsp;+\u0026thinsp;P6) - (P1\u0026thinsp;+\u0026thinsp;2P2\u0026thinsp;+\u0026thinsp;P3)]\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eZS\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;1.305[0.25(P3\u0026thinsp;+\u0026thinsp;2P6\u0026thinsp;+\u0026thinsp;P9) \u0026minus;\u0026thinsp;0.25(P2\u0026thinsp;+\u0026thinsp;2P5\u0026thinsp;+\u0026thinsp;P8) \u0026ndash; 0.25(P2\u0026thinsp;+\u0026thinsp;2P5\u0026thinsp;+\u0026thinsp;P8)\u0026thinsp;+\u0026thinsp;0.25(P1\u0026thinsp;+\u0026thinsp;2P4\u0026thinsp;+\u0026thinsp;P7)]\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;\u003cstrong\u003eZW\u003c/strong\u003e\u0026thinsp;+\u0026thinsp;\u003cstrong\u003eZS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWhere,\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;Surface pressure (hPa).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eW\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;Zonal component of geostrophic (surface) wind calculated as the pressure gradient between 35\u003csup\u003eo\u003c/sup\u003e and 45\u003csup\u003eo\u003c/sup\u003eN.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;Meridional component of geostrophic (surface) wind calculated as the pressure gradient between 7.5\u003csup\u003eo\u003c/sup\u003eE and 17.5\u003csup\u003eo\u003c/sup\u003eN.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;Wind direction (azimuth degrees).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;Wind speed (m/s).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eZW\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;Zonal vorticity component.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eZS\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;Meridional vorticity component.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;Total vorticity.\u003c/p\u003e\n \u003cp\u003eA daily weather type is then assigned following a set of guidelines (see Jenkinson and Collison \u003cspan class=\"CitationRef\"\u003e1977\u003c/span\u003e). These determine if the weather type is pure directional (\u003cem\u003ei.e.\u003c/em\u003e N, NE, E, SE, S, SW, W, NW or N), cyclonic or anticyclonic (C, A) or a hybrid (CNE, CE, CSE, CS, CSW, CW, CNW, CN. ANE, AE, ASE, AS, ASW, AW, ANW, AN) and are as follows:\u003c/p\u003e\n \u003col\u003e\n \u003cli\u003eThe flow direction (\u003cstrong\u003eD\u003c/strong\u003e) uses eight compass points.\u003c/li\u003e\n \u003cli\u003eIf |\u003cstrong\u003eZ\u003c/strong\u003e| is less than \u003cstrong\u003eF\u003c/strong\u003e then an advective or pure directional type exists, determined by \u003cstrong\u003eD\u003c/strong\u003e (rule one).\u003c/li\u003e\n \u003cli\u003eIf |\u003cstrong\u003eZ\u003c/strong\u003e| is greater than 2\u003cstrong\u003eF\u003c/strong\u003e, then the weather type will be cyclonic, if \u003cstrong\u003eZ\u003c/strong\u003e \u0026gt; 0, or anticyclonic if \u003cstrong\u003eZ\u003c/strong\u003e\u0026lt;0.\u003c/li\u003e\n \u003cli\u003eIf F is less than |\u003cstrong\u003eZ\u003c/strong\u003e| and 2\u003cstrong\u003eF\u003c/strong\u003e, a hybrid type exists, vorticity is determined by rule three, and direction by rule one. \u0026nbsp;Hybrid types are CNE, CE, CSE, CS, CSW, CW, CNW, CN, ANE, AE, ASE, AS, ASW, AW, ANW and AN.\u003c/li\u003e\n \u003cli\u003eIf \u003cstrong\u003eF\u003c/strong\u003e and |\u003cstrong\u003eZ\u003c/strong\u003e| are less than 6, an undetermined (U) weather type is reported.\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003cp\u003eAtmospheric circulation at height in the mid-latitudes is much less complex than at surface. It reflects the position of axis of the polar front and whether this exhibits a zonal or meridional configuration. Key patterns that dominate the region are positive and negative. Geopotential height data for the 500hPa surface was used to construct the upper air classification (\u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e500\u003c/strong\u003e\u003c/sub\u003e). Given the equivalent latitude it is appropriate to follow the same methodology developed by Miro et al. (2020) for the Iberian Peninsula. First, the centre of the grid is displaced five degrees to the west to capture the fact that structures at this level will move from west to east and it is an approaching upper air pattern that has an influence on lower atmospheric conditions. At this height, isobaric curvature tends to increase more than flow strength and thus the threshold for discriminating between pure cyclonic/anticyclonic and hybrid classifications can be increased from 2 (as in the original surface method) to 6. The threshold between a trough pattern (cyclonic) and pure advection is lowered from 1 to 1/3, which enables weak (but influential) troughs to be captured. In contrast, the threshold separating a ridge from pure advection is raised from 1 to 4/3 allowing strong winds, so anticyclonic curvature is considered as advection in stable conditions. Consequently the rules for classifying the upper air types (\u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e500\u003c/strong\u003e\u003c/sub\u003e) are as follows:\u003c/p\u003e\n \u003cp\u003e1. If \u003cstrong\u003eZ\u003c/strong\u003e \u0026gt; 0 and |\u003cstrong\u003eZ\u003c/strong\u003e| \u0026lt; (1/3)*F or \u003cstrong\u003eZ\u003c/strong\u003e \u0026lt; 0 and |\u003cstrong\u003eZ\u003c/strong\u003e| \u0026lt; (4/3)*\u003cstrong\u003eF\u003c/strong\u003e\u0026nbsp; types \u0026nbsp;are considered as pure advection (N, NE, E, SE, S, SW, W, NW).\u003c/p\u003e\n \u003cp\u003e2. When |\u003cstrong\u003eZ\u003c/strong\u003e| \u0026gt; 6\u003cstrong\u003eF\u003c/strong\u003e, the type is cyclonic C if \u003cstrong\u003eZ\u003c/strong\u003e \u0026gt; 0 or anticyclonic A when \u003cstrong\u003eZ\u003c/strong\u003e \u0026lt; 0).\u003c/p\u003e\n \u003cp\u003e3. If (1/3)*\u003cstrong\u003eF\u003c/strong\u003e \u0026lt; |\u003cstrong\u003eZ\u003c/strong\u003e| \u0026lt; 6\u003cstrong\u003eF\u003c/strong\u003e and \u003cstrong\u003eZ\u003c/strong\u003e \u0026gt; 0 (cyclonic) a trough above is defined (N, NE, E, SE, S, SW, W, NW).\u003c/p\u003e\n \u003cp\u003e4. If (4/3)*\u003cstrong\u003eF\u003c/strong\u003e \u0026lt; |\u003cstrong\u003eZ\u003c/strong\u003e| \u0026lt; 6\u003cstrong\u003eF\u003c/strong\u003e and \u003cstrong\u003eZ\u003c/strong\u003e \u0026lt; 0 (anticyclonic) a ridge above is defined (N, NE, E, SE, S, SW, W, NW).\u003c/p\u003e\n \u003cp\u003e5. If \u003cstrong\u003eF\u003c/strong\u003e \u0026lt; 6 and |\u003cstrong\u003eZ\u003c/strong\u003e| \u0026lt; 6 the type is unclassified (U).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Results of the surface synoptic classification (JC\u003csub\u003esfc\u003c/sub\u003e)\u003c/h2\u003e\n \u003cp\u003eA 1% sample of days is taken from the resulting 27,028-day catalogue and cross referenced with the respective 1200 UTC and 1800 UTC SLP charts it is clear that the technique captures the characteristics of surface circulation effectively. The marked seasonality of Central Mediterranean atmospheric circulation is reflected by differences in the intra-annual occurrence of the principal \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003esfc\u003c/strong\u003e\u003c/sub\u003e types. Seasonal circulation change is a consequence of the poleward shift in the axis of the polar jet in the summer and a reduction in flow strength due to the decrease in the hemispheric temperature gradient. As the polar jet migrates south in winter months there is a strong effect on circulation patterns at surface and a higher variability in weather type occurrence.\u003c/p\u003e\n \u003cp\u003eDays classified as either \u0026lsquo;U\u0026rsquo;, \u0026lsquo;C\u0026rsquo; or \u0026lsquo;A\u0026rsquo; types dominate the surface catalogue (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Unclassified \u0026lsquo;U\u0026rsquo; types, representing little pressure gradient and SLP close to the annual mean, are most common and are a main persistent feature of summer circulation (53% of all days). It is often the case in summer months that long runs of \u0026lsquo;U\u0026rsquo; types have an incidence of single \u0026lsquo;C\u0026rsquo; type or \u0026lsquo;A\u0026rsquo; type days, yet the vorticity index, \u003cem\u003ez\u003c/em\u003e, (upon which the type is determined) indicates these patterns are often very close to the threshold of \u0026lsquo;6\u0026rsquo; (-6)[1] and thus represent only very weak positive (or negative) vorticity. In the summer period more intense cyclonicity[2], for instance, occurs for periods of 2\u0026ndash;3 days once every three years on average.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eTable 1 Frequency (percentage days of occurrence) of\u0026nbsp;\u003c/em\u003e\u003cstrong\u003eJC\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003esfc\u003c/sub\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;during the full all year classified period (1948-2021) and the winter (DJF) season but also the sample period (2001-2021) all year and the winter season (DJF) used in the air pollution analysis. \u0026nbsp;Only types with more than one percentage of occurrences are listed.\u003c/em\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.956810631229235%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.973421926910299%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDays\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.471760797342192%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637873754152825%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.973421926910299%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.471760797342192%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.973421926910299%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.641196013289036%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.807308970099668%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.475083056478406%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.475083056478406%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCNE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.956810631229235%\" valign=\"top\"\u003e\n \u003cp\u003e1948-2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.973421926910299%\" valign=\"top\"\u003e\n \u003cp\u003e27028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.471760797342192%\" valign=\"top\"\u003e\n \u003cp\u003e33.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637873754152825%\" valign=\"top\"\u003e\n \u003cp\u003e33.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.973421926910299%\" valign=\"top\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.471760797342192%\" valign=\"top\"\u003e\n \u003cp\u003e13.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.973421926910299%\" valign=\"top\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.641196013289036%\" valign=\"top\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.807308970099668%\" valign=\"top\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.475083056478406%\" valign=\"top\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.475083056478406%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.956810631229235%\" valign=\"top\"\u003e\n \u003cp\u003e1948-2021\u003c/p\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.973421926910299%\" valign=\"top\"\u003e\n \u003cp\u003e6677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.471760797342192%\" valign=\"top\"\u003e\n \u003cp\u003e47.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637873754152825%\" valign=\"top\"\u003e\n \u003cp\u003e17.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.973421926910299%\" valign=\"top\"\u003e\n \u003cp\u003e8.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.471760797342192%\" valign=\"top\"\u003e\n \u003cp\u003e4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.973421926910299%\" valign=\"top\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.641196013289036%\" valign=\"top\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.807308970099668%\" valign=\"top\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.475083056478406%\" valign=\"top\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.475083056478406%\" valign=\"top\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.956810631229235%\" valign=\"top\"\u003e\n \u003cp\u003e2001-2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.973421926910299%\" valign=\"top\"\u003e\n \u003cp\u003e7668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.471760797342192%\" valign=\"top\"\u003e\n \u003cp\u003e37.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637873754152825%\" valign=\"top\"\u003e\n \u003cp\u003e35.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.973421926910299%\" valign=\"top\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.471760797342192%\" valign=\"top\"\u003e\n \u003cp\u003e8.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.973421926910299%\" valign=\"top\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.641196013289036%\" valign=\"top\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.807308970099668%\" valign=\"top\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.475083056478406%\" valign=\"top\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.475083056478406%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.956810631229235%\" valign=\"top\"\u003e\n \u003cp\u003e2001-2021\u003c/p\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.973421926910299%\" valign=\"top\"\u003e\n \u003cp\u003e1895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.471760797342192%\" valign=\"top\"\u003e\n \u003cp\u003e49.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637873754152825%\" valign=\"top\"\u003e\n \u003cp\u003e17.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.973421926910299%\" valign=\"top\"\u003e\n \u003cp\u003e7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.471760797342192%\" valign=\"top\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.973421926910299%\" valign=\"top\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.641196013289036%\" valign=\"top\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.807308970099668%\" valign=\"top\"\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.475083056478406%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.475083056478406%\" valign=\"top\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eClassified \u0026lsquo;C\u0026rsquo; types are also frequent in the \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003esfc\u003c/strong\u003e\u003c/sub\u003e series at over 33% of days and exhibit considerable annual variability from 19% of days in 1948 to 44% in 2018. \u0026lsquo;C\u0026rsquo; types include all surface cyclonic flow and therefore comprise patterns with gentle cyclonic curvature of the surface isobars which can be a response to summer heat, inducing surface \u0026lsquo;thermal low\u0026rsquo; circulation, or shallow upper air troughs \u003cem\u003e(\u003c/em\u003eFig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea) and more active storm events \u003cem\u003e(\u003c/em\u003eFig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). \u0026lsquo;C\u0026rsquo; patterns in the area arise due to travelling depressions from the Atlantic \u003cem\u003e(\u003c/em\u003eFig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea) however it is more common that cyclonicity develops within the region itself \u003cem\u003e(\u003c/em\u003eFig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb\u003cem\u003e)\u003c/em\u003e. The Gulf of Genoa, west of Italy, is an important zone of cyclogenesis (low pressure formation) with these systems then travelling eastwards. The Alps and other coastal topography are the principal factor for cyclones forming here due to orographic lift. Furthermore, these barriers can also hinder eastward moving upper level troughs encouraging cyclones to strengthen (Trigo \u003cem\u003eet al\u003c/em\u003e 2002). Cyclogenesis is more prevalent in late autumn and winter by the strong baroclinic conditions arising due to the enhanced maritime-continental temperature gradient at this time of year. \u0026lsquo;C\u0026rsquo; types are most common in the winter (47.75%) and autumn months (35.68%).\u003c/p\u003e\n \u003cp\u003eAnticyclonic types are infrequent compared to their occurrence in northern European catalogues. There is a distinct seasonality with many more incidences in spring (27% including all hybrid types) than other times of the year (winter is less than 8%). \u0026lsquo;W\u0026rsquo; and \u0026lsquo;E\u0026rsquo; are the most common directional weather types but are noticeably less so in the summer under the slack pressure gradients. More than half of the 27 weather types together occur on less than 3% of all days although there is more representation by the less frequent types in the winter months.\u003c/p\u003e\n \u003cp\u003eThe decrease in the number of annual occurrences of \u0026lsquo;A\u0026rsquo; type days (and concomitant increase in \u0026lsquo;C \u0026lsquo; days) is the principal temporal trend to emerge from the catalogue (Fig.\u0026nbsp;4\u003cem\u003e).\u003c/em\u003e The annual number of \u0026lsquo;C\u0026rsquo; types has increased in the time period at a rate of 5.9 days per decade (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;41.6%, F\u0026thinsp;=\u0026thinsp;49.1\u0026thinsp;=\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) and there has been an even clearer decline (8.8 days per decade) in \u0026lsquo;A\u0026rsquo; types (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;60.4%, F\u0026thinsp;=\u0026thinsp;105.25 p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). The vorticity component (\u003cem\u003ez\u003c/em\u003e) demonstrates a shift towards cyclonicity (an increase in mean annual \u003cem\u003ez\u003c/em\u003e) in this region in the second half of the twentieth and first two decades of the twenty-first century. It is notable that this increase happens in all seasons but not in the winter months. This is contrary to future modelled scenarios for circulation change (\u003cem\u003ee.g\u003c/em\u003e. Otero et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) that suggest greater anticyclonicity across the Mediterranean region however the classified domain is at a smaller scale within the region, is a centre of cyclogenesis and therefore likely to be highly responsive to more localised disturbances. On the other hand, Otrero et al. (2018) do report that that the set of models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) did underestimate the number of cyclonic days currently occurring in Southern Europe.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Results of the upper air synoptic classification (JC\u003csub\u003e500\u003c/sub\u003e)\u003c/h2\u003e\n \u003cp\u003eA \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e500\u003c/strong\u003e\u003c/sub\u003e series is generated using the modified procedure of Miro et al. (2020). This is manually validated against a 1% sample of 500 hPa charts and it is seen to effectively capture the flow at this level. As expected it is typified by westerly zonal advection, which can be subdivided into anticyclonic, cyclonic or pure westerly flow (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The dominant westerly configuration is a trough pattern over the area. Although all possible JC types occur, hybrids and any type with easterly or southerly flow components are much less numerous (for instance, \u0026lsquo;SE\u0026rsquo; types only occur on 20 days (0.07%) in the 27,028-day catalogue). There are significantly less \u0026lsquo;U\u0026rsquo; types (6.23% compared to 33% at surface) at this height. Given the small number of hybrid weather types they can be grouped together as Ridge Westerly (RW) and Ridge Easterly (RE) or Trough Westerly (TW) and Trough Easterly (TE). Across the year the overall flow is relatively consistent although there is a slight increase in easterly flow in the winter.\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFrequency (percentage of days of occurrence) of \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e500\u003c/strong\u003e\u003c/sub\u003e during the full all year classified period (1948\u0026ndash;2021) and the winter (DJF) season but also the sample period (2001\u0026ndash;2021 all year and the winter season (DJF) used in the air pollution study. Only types with more than one percentage of occurrences are listed. .\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNE\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1948\u0026ndash;2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1948\u0026ndash;2021\u003c/p\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2001-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2001-21\u003c/p\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eSurface circulation is strongly influenced by upper air flow particularly on westerly \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003esfc\u003c/strong\u003e\u003c/sub\u003e days which almost entirely (98%) reflect the dominant direction aloft (either TW, RW and W). Anticyclonic and easterly surface types are less associated with zonal patterns aloft. For example, under surface A types, the relatively uncommon ridge easterly flow is present on 15% of days.\u003c/p\u003e\n \u003cp\u003eThe \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e500\u003c/strong\u003e\u003c/sub\u003e catalogue gives synoptic information on the many instances where surface patterns are unidentifiable (\u0026lsquo;U\u0026rsquo; types). Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e summarises the variability in upper atmospheric circulation that accompanies surface U types. Although all upper types can occur, some are over-represented compared to the overall frequency of incidence. \u0026lsquo;U\u0026rsquo; is more associated with negative rather than positive vorticity pattens aloft but not exclusively. \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e500\u003c/strong\u003e\u003c/sub\u003e indicates different mechanisms can operate away from the surface which are implicated in the behaviour of a circulation-related environmental variable on these days. Surface C types, the second most common pattern in the \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003esfc\u003c/strong\u003e\u003c/sub\u003e series, is strongly associated with dominant W and TW flow, but can also be accompanied by ridge patterns aloft (11% of occurrences) suggesting that many of these are shallow features. Unlike at surface there is no statistical trend in the annual or seasonal frequency of any of the principal weather types.\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUpper atmospheric circulation accompanying surface U types. The ratio between \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e500\u003c/strong\u003e\u003c/sub\u003e type is associated with surface \u0026lsquo;U\u0026rsquo; to its frequency for all types with bold values representing above average occurrences.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% occurrence under \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003esfc\u003c/strong\u003e\u003c/sub\u003e U types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRatio occurrence under weather type: occurrence under all types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"3. Air Quality in Rome","content":"\u003cp\u003eRome is one of the most populous cities in the European Union with 2.9\u0026nbsp;million residents living in a, generally, densely urbanised area of 1285 km\u003csup\u003e2\u003c/sup\u003e Rome has been recorded as one of the worst cities in Europe for air quality levels (Phelan \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Levels of smog (a mixture of air pollutants) have caused concerns for health issues due to frequent episodes. For instance, in November 2011, high levels of pollution prompted authorities to limit motor vehicle use (UPI \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). In 2015 (BBC 2015) and then in early 2020, Rome exceeded the safe, legal limits of air pollution across the city and this winter smog episode was largely driven by prevailing meteorological conditions (BBC 2020).\u003c/p\u003e\n\u003cp\u003eAir quality data was obtained from the \u003cem\u003eAgenzia Regionale Protecione\u003c/em\u003e (ARPA 2023) who operate a network of fixed air quality monitoring sites in the province of Lazio. Overall, there are 55 urban, suburban, industrial and rural stations with 18 stations in the Rome agglomeration. For the purposes of this study the ten urban sites (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) within the \u003cem\u003eGrande Raccordo Anulare\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) were selected and these are identified as either \u0026lsquo;urban background\u0026rsquo; or \u0026lsquo;urban traffic\u0026rsquo; reflecting the proximity to traffic and exposure of the population (Fig.\u0026nbsp;6). Daily mean values of NO\u003csub\u003e2\u003c/sub\u003e were extracted for each site and there is a strong significant spatial correlation between locations indicating regional coherence in air quality behaviour. Poor air quality is exacerbated by the built environment which reduces wind speed and encourages air stagnation, inhibiting pollutant dispersion (Di Bernardino et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Barbano et al. 2020). It is often the case that vehicular emissions, for example, remain trapped at the pedestrian level, especially when meteorological conditions are settled (Palmieri et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAir quality monitoring stations used in this study\u003c/p\u003e\n \u003cdiv class=\"Credit\"\u003e\n \u003cp\u003e(Source: ARPA 2023).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoordinates\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAltitude\u003c/p\u003e\n \u003cp\u003e(m ASL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreneste\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.886018, 12.541614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Background\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorso Francia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.947447, 12.469588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Traffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagna Grecia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.883064, 12.508939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Traffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCinecitta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.857720, 12.568665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Background\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.932874, 12.506971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Background\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFermi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.864194, 12.469531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Traffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBufalotta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.947649, 12.533682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Background\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCipro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.906358, 12.447596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Background\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTiburtina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.910257, 12.548870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Traffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArenula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.894020, 12.475368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Background\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eIn order to extract a signal related to synoptic conditions, rather than the influence of changing patterns of traffic, or the successful introduction of air quality management schemes, the full dataset was first examined for any patterns of regular variability that could then be addressed. At all sites NO\u003csub\u003e2\u003c/sub\u003e exhibits coherent modes of temporal variability according to: (\u003cem\u003ei\u003c/em\u003e) time of day; (\u003cem\u003eii\u003c/em\u003e) day of the week (\u003cem\u003eiii\u003c/em\u003e) time of year and (\u003cem\u003eiv\u003c/em\u003e) point in the sample period. Figure\u0026nbsp;7 shows distinct morning and evening peaks in NO\u003csub\u003e2\u003c/sub\u003e related to elevated traffic circulation. In the time period there has been a clear reduction in mean hourly values across the day (a 40% reduction in the morning peak NO\u003csub\u003e2\u003c/sub\u003e concentration at Magna Grecia for instance). Many stations display a shift to an earlier peak in the 21-year period however the timing of the afternoon peak remains variable. Although selected daily maximum NO\u003csub\u003e2\u003c/sub\u003e might capture the acute influence on health daily mean concentration is a valid measure of the longer term exposure to pollution (Krzyzanowski 1997). Mean NO\u003csub\u003e2\u003c/sub\u003e values exhibit reduced values at a weekend of 25% on average on a Sunday at Preneste for instance (Fig. 8). This is in common with other studies such as the 10-year study by Karandinos et al. (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) in Volos, Greece which showed up to a 20% decline at the weekends and also similar investigations in Detroit (Batterman et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Delhi (Gour et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this analysis both weekend days are removed as the lower residual values are independent of prevailing meteorology.\u003c/p\u003e\n\u003cp\u003eThere is a distinct NO\u003csub\u003e2\u003c/sub\u003e \u0026lsquo;season\u0026rsquo; at Rome city centre sites. At Bufalotta to the north of the centre, for instance, the mean weekday daily NO\u003csub\u003e2\u003c/sub\u003e concentration in winter (95.17 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) exceeds that of summer (63.63 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) by almost 50%. In fact, in the top decile of daily NO\u003csub\u003e2\u003c/sub\u003e concentration at this site, 47% of days are in winter months and only 3% in summer. This seasonal pattern is replicated within the city area. Analysis concentrated on this season where health impacts will be more severe.\u003c/p\u003e\n\u003cp\u003eSince 2001 there has been a considerable improvement in air quality in the city of Rome (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e), a consequence of improved air quality management, more stringent vehicle emission standards for petrol vehicles and changes in the nature of the vehicle fleet (an increasing proportion of hybrid cars). This would mean that, irrespective of meteorological conditions, years towards the end of the sample period would have better air quality then those at the start. In order to isolate the influence of synoptic conditions this background decline was removed by detrending the data. An equivalent procedure was undertaken by Grundstrom et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) in the analysis of pollution in Gothenburg, Sweden. In 2020, Italy, in common with other EU nations lockdown restrictions were imposed to address the COVID-19 pandemic. This resulted in a decrease in the emissions of primary pollutants, due to the temporary closure of certain economic activities and reduced vehicle use. Consequently, there was a general reduction in atmospheric concentration of most urban air pollutants (Campanelli et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e. Basasani et al. 2021). This analysis uses the standardised residual values around the pre-COVID background trend (2001\u0026ndash;2019) rather than the actual measured concentrations and the variation of these values should more clearly respond to the synoptic control. Synoptic analysis is therefore undertaken on the mean daily concentration of the standardised residuals (about the 2001\u0026ndash;2019 trend) of weekday NO\u003csub\u003e2\u003c/sub\u003e. during the winter (DJF) season.\u003c/p\u003e"},{"header":"4. Analysing air quality using synoptic typing","content":"\u003cp\u003eThe large number of low frequency circulation classes reduces the effectiveness for exploring circulation-pollution links however weather types with easterly components (AE, ANE, SE, CSE and E) are typified by positive mean NO\u003csub\u003e2\u003c/sub\u003e residuals and in contrast days with negative residuals are associated with westerly weather types (for instance, ANW, CNW, CSW, SW). Synoptic control strengthens in winter months and commonly occurring weather types exhibit contrasting air quality regimes (\u0026lsquo;A\u0026rsquo; types record a mean residual of +\u0026thinsp;42.58 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (total incidence 41 days) and \u0026lsquo;C\u0026rsquo; mean residual\u0026thinsp;+\u0026thinsp;5.44 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (384 days)). On the other hand there is a wide distribution of daily mean NO\u003csub\u003e2\u003c/sub\u003e values accompanying each type. In order to simplify the analysis, weather types can be amalgamated into groups where infrequently occurring types exert a similar control on air quality at the surface to more commonly occurring types. Expert knowledge, one-way ANOVA and \u003cem\u003epost hoc\u003c/em\u003e testing of mean NO\u003csub\u003e2\u003c/sub\u003e concentration under each \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003esfc\u003c/strong\u003e\u003c/sub\u003e type allows the series to be reduced to four principal groups based on their association with daily mean NO\u003csub\u003e2\u003c/sub\u003e concentration (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGroup 1\u003c/strong\u003e (14% of all days) is dominated by westerly circulation types including those A and C hybrids with a south-westerly component. These days are accompanied by mean nitrogen dioxide residuals which are close to the annual average but well below the seasonal mean. \u003cstrong\u003eGroup 2\u003c/strong\u003e types (27%) are typified by the indistinct SLP field of unclassified (U) and anticyclonic (A) patterns. These are associated with low wind speeds and stable atmospheres encouraging the build up of pollution. Group 2 also includes the south and easterly directional flow types and hybrids which occur when blocking high pressure systems anchor over central Europe. The distribution of residual nitrogen dioxide values in this group has the highest winter mean. \u003cstrong\u003eGroup 3\u003c/strong\u003e includes the uncommon (3%) weather types that have a north westerly component which also lead to relatively high NO\u003csub\u003e2\u003c/sub\u003e residual. Finally \u003cstrong\u003eGroup 4\u003c/strong\u003e is composed of the most frequent types (56%) and is associated with cyclonicity in the region. Examples of each group are depicted in \u003cem\u003eFig.\u0026nbsp;10\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGrouping of \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003esfc\u003c/strong\u003e\u003c/sub\u003e and mean daily NO\u003csub\u003e2\u003c/sub\u003e residuals (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) at selected sites in Winter.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eJC\u003csub\u003esfc\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOccurrences (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePreneste\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCinecitta\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW, SW, CW, CSW, AW, ASW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA, E, S, U, AE, SE, ASE, CSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNW, CN, ANW, CNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC, NE, CNE, CE, CS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;106.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;108.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThere is a strong association between large residuals and upper air patterns with easterly components and negative vorticity (E, NE, RE, A) and significantly lower residual values for westerly and cyclonic patterns (W, TW, C). \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e500\u003c/strong\u003e\u003c/sub\u003e types can be grouped with respect to similar atmospheric conditions/air quality response. Seven principal patterns emerge (examples are shown in \u003cem\u003eFig.\u0026nbsp;11\u003c/em\u003e) which display significantly different NO\u003csub\u003e2\u003c/sub\u003e distributions. These are as follows: \u003cstrong\u003eAE\u003c/strong\u003e, comprising settled anticyclonic and unclassified patterns or ridge patterns with a weak easterly component; \u003cstrong\u003eAW\u003c/strong\u003e, which includes ridge patterns with westerly advection; \u003cstrong\u003eNW\u003c/strong\u003e, made up of pure N and NW directional type days; \u003cstrong\u003eW\u003c/strong\u003e which are pure directional from the west; \u003cstrong\u003eTSE\u003c/strong\u003e, troughs bringing air from the south and east; \u003cstrong\u003eC\u003c/strong\u003e, cyclonic patterns and \u003cstrong\u003eTW\u003c/strong\u003e which are troughs with associated westerly advection. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e lists the frequencies of each type in the whole catalogue, in winter and in the sampled years used to analyse air quality.\u003c/p\u003e\n\u003cp\u003eAn important advantage of the \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e500\u003c/strong\u003e\u003c/sub\u003e catalogue is that it can account for the variability of NO\u003csub\u003e2\u003c/sub\u003e concentration occurring under \u0026lsquo;U\u0026rsquo; type surface patterns. Key atmospheric mechanisms that can lead to the dispersal (or accumulation) of pollution, which are not clear from surface pattern can be identified. Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows how certain types will lead to NO\u003csub\u003e2\u003c/sub\u003e concentrations which are different to the group mean. Generally stronger westerly flow and cyclonic patterns aloft will lead to a lower mean residual compared to the group mean. Unexpected results are due to the low sample size rendering some combinations unusual.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 6 Mean residual NO\u003csub\u003e2\u003c/sub\u003e (\u003c/em\u003e\u003cem\u003e\u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e) associated with occurrences of \u003cstrong\u003eJC\u003csub\u003e500\u003c/sub\u003e\u003c/strong\u003e types for each surface \u003cstrong\u003eJCsfc\u003c/strong\u003e group in winter. * less than 5 occurrences.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" style=\"margin-right: calc(47%); width: 53%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 41.2717%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" rowspan=\"2\" valign=\"top\" style=\"width: 11.5054%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eJC\u003csub\u003esfc\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGroup 1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" rowspan=\"2\" valign=\"top\" style=\"width: 15.2987%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eJC\u003csub\u003esfc\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGroup 2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" rowspan=\"2\" valign=\"top\" style=\"width: 14.5068%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eJC\u003csub\u003esfc\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGroup 3\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\" rowspan=\"2\" valign=\"top\" style=\"width: 17.8643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eJC\u003csub\u003esfc\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGroup 4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\" style=\"width: 41.2717%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eJC\u003csub\u003e500\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 41.2717%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 11.5054%;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 15.2987%;\"\u003e\n \u003cp\u003e31.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 14.5068%;\"\u003e\n \u003cp\u003e19.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 17.8643%;\"\u003e\n \u003cp\u003e11.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 41.2717%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 11.5054%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e47.14*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 15.2987%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e46.29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 14.5068%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e51.48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 17.8643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e32.70*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 41.2717%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 11.5054%;\"\u003e\n \u003cp\u003e-2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 15.2987%;\"\u003e\n \u003cp\u003e28.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 14.5068%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e21.49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 17.8643%;\"\u003e\n \u003cp\u003e8.08*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 41.2717%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 11.5054%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 15.2987%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e59.28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 14.5068%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e21.10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 17.8643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e23.76\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 41.2717%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 11.5054%;\"\u003e\n \u003cp\u003e-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 15.2987%;\"\u003e\n \u003cp\u003e12.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 14.5068%;\"\u003e\n \u003cp\u003e17.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 17.8643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 41.2717%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 11.5054%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 15.2987%;\"\u003e\n \u003cp\u003e27.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 14.5068%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 17.8643%;\"\u003e\n \u003cp\u003e6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 41.2717%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 11.5054%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 15.2987%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e57.90*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 14.5068%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 17.8643%;\"\u003e\n \u003cp\u003e8.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 41.2717%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 11.5054%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 15.2987%;\"\u003e\n \u003cp\u003e29.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.634920634920636%\" valign=\"top\" style=\"width: 14.5068%;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\" valign=\"top\" style=\"width: 17.8643%;\"\u003e\n \u003cp\u003e3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe sequence of SLP and 500 hPa charts in \u003cem\u003eFig.\u0026nbsp;12\u003c/em\u003e demonstrate very different upper patterns on example days which result in contrasting NO\u003csub\u003e2\u003c/sub\u003e in each case. For instance, the \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003esfc\u003c/strong\u003e\u003c/sub\u003e U type on 12th December 2014 leads to high NO\u003csub\u003e2\u003c/sub\u003e residuals under the stable AW pattern aloft compared to that of 20th February 2014 which is accompanied by the unstable TW \u003cstrong\u003eJC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e500\u003c/strong\u003e\u003c/sub\u003e type (Fig. 12c \u003cem\u003eand d).\u003c/em\u003e Overall though the results confirm the value of the information provided by upper air patterns.\u003c/p\u003e"},{"header":"6.Concluding Remarks","content":"\u003cp\u003eThe Jenkinson-Collison method for objectively determining atmospheric circulation-based weather types was applied over the central Mediterranean. Although this technique has been employed by other authors in both the eastern and western regions of this circulation domain it has not been used in the central region. As expected, the overall weather type catalogue (\u003cb\u003eJC\u003c/b\u003e\u003csub\u003e\u003cb\u003esfc\u003c/b\u003e\u003c/sub\u003e) obtained is not as varied as that of more northerly latitudes (for instance, that derived by Tang et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) for Sweden) and also yields less occurrences of certain weather types than further west over the Iberian peninsula (\u003cem\u003ee.g.\u003c/em\u003e Grimalt et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The slack gradients in the summer half of the year result in a domination of \u0026lsquo;U\u0026rsquo; types which is characteristic of the Mediterranean summer with its mixture of very light surface air movements.\u003c/p\u003e \u003cp\u003eOf significant interest in the \u003cb\u003eJC\u003c/b\u003e\u003csub\u003e\u003cb\u003esfc\u003c/b\u003e\u003c/sub\u003e catalogue is the fact that the more frequent weather types demonstrate clear directional trends in their occurrence over the time period. The overall increase in cyclonic types (and reduction in anticyclonic types) in the region is notable. The vorticity index, \u003cem\u003ez\u003c/em\u003e, describes this increased cyclonicity (an increase in mean annual \u003cem\u003ez\u003c/em\u003e values) in this region in the second half of the twentieth and first two decades of the twenty-first century. To some extent this finding contradicts that of other authors such as Kouroutzoglou, et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) who suggest that all forms of cyclonicity have become less frequent in the Mediterranean basin as a whole. Stryhal and Huth (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) state that issues with circulation modelling exist such as the overestimation of the positive phase in the North Atlantic Oscillation (NAO) and the limited consideration of effects of Arctic amplification. This, in theory, would increase the temperature gradient at the polar front and potentially lead to deep meridional flow (Francis and Vavrus \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Peings et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This in turn could promote cyclogenesis in the northern Mediterranean basin accounting for this observed z increase in this sub-region.\u003c/p\u003e \u003cp\u003eThe application of a modified Jenkinson Collison method (after Miro et al. 2020) effectively captures the principal modes of circulation variability at 500 hPa in this domain. As expected the \u003cb\u003eJC\u003c/b\u003e\u003csub\u003e\u003cb\u003e500\u003c/b\u003e\u003c/sub\u003e catalogue is dominated by westerly advection with the most frequent category being a trough over the central Mediterranean. Pure\u0026rsquo; W\u0026lsquo; flow and ridges also feature frequently although there are also some easterly flow components at this level which are likely to accompany blocking high pressure events over Central Eurpope.. None of the frequent types show any directional trend in the time period. Overall the technique provides an effective way of capturing flow pattern at this height although arguably a larger gris to reflect the greater size of flow patterns at this level might yield a different categorization.\u003c/p\u003e \u003cp\u003eIn order to evaluate the utility of this method it was applied to the analysis of daily winter NO\u003csub\u003e2\u003c/sub\u003e in Rome. The 10 monitoring stations included in a sample (2001\u0026ndash;2021) show an overall decline in weekday NO\u003csub\u003e2\u003c/sub\u003e and, as each of the sites within the networks were significantly correlated, it is suggested that air pollution variability around the background trend is influenced by regional scale circulation patterns. The pre-COVID trend was removed to attempt to extract the synoptic signal as it would be expected that negative residuals might be linked to those atmospheric conditions favourable to pollutant dispersal and vice versa on very short timescales. A clear synoptic control emerges although the infrequent occurrence and transitionary nature of certain \u003cb\u003eJC\u003c/b\u003e\u003csub\u003e\u003cb\u003esfc\u003c/b\u003e\u003c/sub\u003e types means that some types can be merged into groups. The subsequent four surface groups demonstrate a significant control on residual NO\u003csub\u003e2\u003c/sub\u003e, in winter, however synoptic control is less clear in the summer half of the year due to the prevalence of persistent \u0026lsquo;U\u0026rsquo; types. Daily winter NO\u003csub\u003e2\u003c/sub\u003e light anticyclonic and easterly conditions, typical of the circulation regime in the Central Mediterranean which discourages the vertical mixing of air, limits the transport of air pollutants away from source. This is compounded by the built environment which will reduce the movement of air at street level. Combining information from the \u003cb\u003eJC\u003c/b\u003e\u003csub\u003e\u003cb\u003esfc\u003c/b\u003e\u003c/sub\u003e and \u003cb\u003eJC\u003c/b\u003e\u003csub\u003e\u003cb\u003e500\u003c/b\u003e\u003c/sub\u003e catalogues can improve an explanation of NO\u003csub\u003e2\u003c/sub\u003e variability especially when pressure patterns at the surface are slack.\u003c/p\u003e \u003cp\u003eConsiderable improvements have been made to the quality of the air in Rome and therefore significant benefits for public health are realised. Of great interest is the observation that if the observed trend in NO\u003csub\u003e2\u003c/sub\u003e in Rome were to continue the reduction in the frequency of regional meteorological conditions conducive to the build-up of NO\u003csub\u003e2\u003c/sub\u003e (decreased anticyclonicity and increased cyclonicity) would further improve air quality. However, on the other hand, the nitrogen dioxide problem in Rome is more associated with winter months and this is the only time of year in which a rise in surface cyclonic patterns have not been observed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth authors contributed to the study conception and design. \u0026nbsp;Material preparation, collection and analysis were performed by both authors (Danielle Bird and Greg Spellman). \u0026nbsp;The first draft of the manuscript was written by both authors (Danielle Bird and Greg Spellman). \u0026nbsp;Both authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pressure and 500 hPa datasets used in the study can be available from the Climatic Research Unit, University of East Anglia at https://crudata.uea.ac.uk/cru/data/ncep/?_ga=2.224632154.1939107129.1687253023-1349553202.1687253023\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe air quality data is available from ARPALAZIO at https://www.arpalazio.net/main/aria/sci/basedati/chimici/chimici.php\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eARPA Lazio (2023) https://www.arpalazio.it/ [Accessed 21 March 2023].\u003c/li\u003e\n\u003cli\u003eBarbano, F., Brattich, E. and Di Sabatino, S., (2021), Characteristic Scales for Turbulent Exchange Processes in a Real Urban Canopy. \u003cem\u003eBoundary Layer Meteorology\u003c/em\u003e, \u003cstrong\u003e178\u003c/strong\u003e, 119\u0026ndash;142. https://doi.org/10.1007/s10546-020-00554-5\u003c/li\u003e\n\u003cli\u003eBassani C., Vichi, F., Esposito, G., Montagnoli, M., Giusto, M. and Ianniello, A., (2021), Nitrogen dioxide reductions from satellite and surface observations during COVID-19 mitigation in Rome (Italy). \u003cem\u003eEnvironmental Science Pollution Research\u003c/em\u003e, \u003cstrong\u003e28\u003c/strong\u003e, 22981\u0026ndash;23004. https://doi.org/10.1007/s11356-020-12141-9\u003c/li\u003e\n\u003cli\u003eBatterman, S., Cook, R. and Justin, T., (2015), Temporal variation of traffic on highways and the development of accurate temporal allocation factors for air pollution analyses. \u003cem\u003eAtmospheric Environment\u003c/em\u003e, \u003cstrong\u003e107\u003c/strong\u003e, 351-363. https://doi.org/10.1016/j.atmosenv.2015.02.047\u003c/li\u003e\n\u003cli\u003eBBC News, (2015), \u003cem\u003eItalian Cities Ban Cars Due To Smog\u003c/em\u003e. [online] Available at: \u0026lt;https://www.bbc.co.uk/news/world-europe-35188685\u0026gt; [Accessed 21 March 2023].\u003c/li\u003e\n\u003cli\u003eBBC News. (2020), \u003cem\u003eEuropean Cities Grapple With Dangerous Smog Levels\u003c/em\u003e. [online] Available at: \u0026lt;https://www.bbc.co.uk/news/world-europe-51132764\u0026gt; [Accessed 21 March 2023].\u003c/li\u003e\n\u003cli\u003eBurt, T. and Ferranti E., (2012), Changing patterns of heavy rainfall in upland areas: A case study from northern England. \u003cem\u003eInternational Journal of Climatology,\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 518 - 532. https://doi.org/10.1002/joc.2287\u003c/li\u003e\n\u003cli\u003eCampanelli, M., Iannarelli, A.M., Mevi, G., Casadio, S., Di\u0026eacute;moz, H., Finardi, S., Dinoi, A., Castelli, E., di Sarra, A., Di Bernardino, A., Casasanta, G., Bassani, C., Siani, A.M., Cacciani, M,, Barnaba, F., Di Liberto, L. and Argentini, S., (2021), A wide-ranging investigation of the COVID-19 lockdown effects on the atmospheric composition in various Italian urban sites (AER\u0026ndash;LOCUS). \u003cem\u003eUrban Climate\u003c/em\u003e, \u003cstrong\u003e39\u003c/strong\u003e, 100954. https://doi.org//10.1016/j.uclim.2021.100954\u003c/li\u003e\n\u003cli\u003eChen, D., (2000), A monthly circulation climatology for Sweden and its application to a winter temperature case study. \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e, \u003cstrong\u003e20\u003c/strong\u003e, 1067-1076. https://doi.org/10.1002/1097-0088(200008)20:103.0.CO;2-Q\u003c/li\u003e\n\u003cli\u003eConway, D. and Jones, P.D., (1998), The use of weather types and air flow indices for GCM downscaling\u003cem\u003e, Journal of Hydrology\u003c/em\u003e, \u003cstrong\u003e212\u003c/strong\u003e, 348-361. https://doi.org//10.1016/S0022-1694(98)00216-9\u003c/li\u003e\n\u003cli\u003eCortesi, N., Trigo, R., Gonzalez-Hidalgo, J. and Ramos, A., (2013), Modelling monthly precipitation with circulation weather types for a dense network of stations over Iberia. \u003cem\u003eHydrology and Earth System Sciences\u003c/em\u003e, \u003cstrong\u003e17\u003c/strong\u003e, 665-678. https://doi.org/:10.5194/hess-17-665-2013\u003c/li\u003e\n\u003cli\u003eCrudata.uea.ac.uk. (n.d.). \u003cem\u003eLamb Weather Types\u003c/em\u003e. [online] Available at: https://crudata.uea.ac.uk/cru/data/lwt/ [Accessed 14 March 2023].\u003c/li\u003e\n\u003cli\u003eCuell, C. and Bonsal, B.R., (2009), An assessment of climatological synoptic typing by principal component analysis and k-means clustering. \u003cem\u003eTheoretical. Applied Climatology, \u003c/em\u003e\u003cstrong\u003e98,\u003c/strong\u003e 361\u0026ndash;373. https://doi.org/10.1007/s00704-009-0119-8\u003c/li\u003e\n\u003cli\u003eDavies, T.D., Farmer, G., and Barthelmie, R.J., (1991), Use of simple daily atmospheric circulation types for the interpretation of precipitation composition at a site (Eskdalemuir) in Scotland, 1978\u0026ndash;1984, \u003cem\u003eAtmospheric Environment. Part A. General Topics\u003c/em\u003e, \u003cstrong\u003e24\u003c/strong\u003e, 63-72. https://doi.org/:10.1016/0960-1686(90)90441-O\u003c/li\u003e\n\u003cli\u003eDemuzere, M., Werner, M., Van Lipzig, N.P.M and Roeckner, E., (2009), An analysis of present and future ECHAMS pressure fields using a classification of circulation patterns. \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e, \u003cstrong\u003e29\u003c/strong\u003e, 1796-1810. https://doi.org//10.1002/joc.1821\u003c/li\u003e\n\u003cli\u003eDi Bernardino, A., Monti, P., Leuzzi, G., Querzoli, G., (2018), Pollutant fluxes in two-dimensional street canyons. \u003cem\u003eUrban Climate\u003c/em\u003e, \u003cstrong\u003e24\u003c/strong\u003e, 80-93. https://doi.org/10.1016/j.uclim.2018.02.002\u003c/li\u003e\n\u003cli\u003eDonat, M., Leckebusch, G., Pinto, J. and Ulbrich, U., (2009), Examination of wind storms over Central Europe with respect to circulation weather types and NAO phases. \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e, \u003cstrong\u003e30\u003c/strong\u003e, 1289-1300. https://doi.org/10.1002/joc.1982\u003c/li\u003e\n\u003cli\u003eEl-Dessouky T.M., and Jenkinson A.F., (1975), An objective daily catalogue of surface pressure, flow and vorticity indices for Egypt and its use in monthly rainfall forecasting. \u003cem\u003eSynoptic Climatology Branch Memorandum no. 46\u003c/em\u003e, Bracknell, Meteorological Office, London.\u003c/li\u003e\n\u003cli\u003eFarukh, M.A. and Yamada, T.J., (2017), Synoptic climatology of winter daily temperature extremes in Sapporo, northern Japan. \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e, \u003cstrong\u003e38\u003c/strong\u003e, 2230-2238. https://doi.org/10.1002/joc.5329\u003c/li\u003e\n\u003cli\u003eFrancis, J. and Vavrus, S., (2012), Evidence linking Arctic amplification to extreme weather in mid-latitudes. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cstrong\u003e39,\u003c/strong\u003e L06801. https://doi.org/10.1029/2012GL051000\u003c/li\u003e\n\u003cli\u003eGour, A., Singh, S., Tyagi, S. and Mandal, A., (2013), Weekday/Weekend differences in air quality parameters in Delhi, India. \u003cem\u003eInternational Journal of Research in Engineering and Technology\u003c/em\u003e, \u003cstrong\u003e1\u003c/strong\u003e, 69 \u0026ndash; 76.\u003c/li\u003e\n\u003cli\u003eGraham, A.M., Pringle, K.J., Arnold, S.R., Pope, R.J., Vieno, M., Butt, E.W., Conibear, L., Stirling,. and McQuaid, J.B., (2020), Impact of weather types on UK ambient particulate matter concentrations, \u003cem\u003eAtmospheric Environment\u003c/em\u003e, \u003cstrong\u003eX5\u003c/strong\u003e, 100061. https://doi.org/10.1016/j.aeaoa.2019.100061\u003c/li\u003e\n\u003cli\u003eFealy, R. and Mills, G., (2018), Deriving Lamb weather types suited to regional climate studies: A case study on the synoptic origins of precipitation over Ireland. \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e. \u003cstrong\u003e38,\u003c/strong\u003e 3439-3448. https://doi.org/10.1002/joc.5495\u003c/li\u003e\n\u003cli\u003eGrimalt, M., Tom\u0026agrave;s, M., Alomar, G., Martin-Vide, J. and Moreno-Garc\u0026iacute;a, M., (2013), Determination of the Jenkinson and Collison\u0026rsquo;s weather types for the western Mediterranean basin over the 1948-2009 period. Temporal analysis. \u003cem\u003eAtm\u0026oacute;sfera\u003c/em\u003e, \u003cstrong\u003e26\u003c/strong\u003e, 75-94. https://doi.org/10.1016/S0187-6236(13)71063-4\u003c/li\u003e\n\u003cli\u003eGrundstrom, M., Tang, L., Hallquist, M., Nguyen, H., Chen, D. and Pleijel, H., (2015), Influence of atmospheric circulation patterns on urban air quality during the winter. \u003cem\u003eAtmospheric Pollution Research\u003c/em\u003e, \u003cstrong\u003e6\u003c/strong\u003e, 278-285. https://doi.org/10.5094/APR.2015.032\u003c/li\u003e\n\u003cli\u003eGu, S., Wu, S., Yang, L.; Hu, Y., Tian, B., Yu, Y., Ma, N,, Ji, P. and Zhang, B., (2023), Synoptic Weather Patterns and Atmospheric Circulation Types of PM\u003csub\u003e2.5 \u003c/sub\u003ePollution Periods in the Beijing-Tianjin-Hebei Region. \u003cem\u003eAtmosphere\u003c/em\u003e, \u003cstrong\u003e14\u003c/strong\u003e, 942. https://doi.org/10.3390/atmos14060942\u003c/li\u003e\n\u003cli\u003eHuth, R., Beck, C. and Kučerov\u0026aacute;, M., (2015), Synoptic-climatological evaluation of the classifications of atmospheric circulation patterns over Europe. \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e, \u003cstrong\u003e36\u003c/strong\u003e, 2710-2726. https://doi.org/10.1002/joc.4546\u003c/li\u003e\n\u003cli\u003eJenkinson, A.F. and Collison, F.P., (1977), An initial climatology of gales over the North Sea. \u003cem\u003eSynoptic Climatology Branch Memorandum No. 62\u003c/em\u003e, Meteorological Office, Bracknell.\u003c/li\u003e\n\u003cli\u003eJones P.D., Hulme, M. and Briffa, K.R., (1993), A comparison of Lamb circulation types with an objective classification scheme. \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e, \u003cstrong\u003e13\u003c/strong\u003e, 655\u0026ndash;663. V] https://doi.org/10.1002/joc.3370130606\u003c/li\u003e\n\u003cli\u003eJorba, O., Gass\u0026oacute;, S. and Baldasano, J.M., (2004), Regional Circulations Within the Iberian Peninsula East Coast. In: Borrego, C., Incecik, S. (eds) \u003cem\u003eAir Pollution Modeling and Its Application XVI\u003c/em\u003e. Springer, Boston, MA.\u003c/li\u003e\n\u003cli\u003eKalnay E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D. Gandin, L.S., Iredell, M., Saha, S., White, G., Woollen,J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Ropelewski, C., Wang, J. and Leetmaa, A., (1996), The NMC/NCAR 40-year reanalysis project. \u003cem\u003eBulletin of the American Meteorological Society\u003c/em\u003e, \u003cstrong\u003e77\u003c/strong\u003e, 437-470.\u003c/li\u003e\n\u003cli\u003eKarandinos, A., Saitanis, C. and Arapis, G., (2006), Study of the weekday-weekend variation of air pollutants in a typical Mediterranean coastal town. \u003cem\u003eInternational Journal of Environment and Pollution\u003c/em\u003e, \u003cstrong\u003e27\u003c/strong\u003e, 300-312. https://doi.org/10.1504/IJEP.2006.010574\u003c/li\u003e\n\u003cli\u003eKouroutzoglou, J., Flocas, H.A., Hatzaki, M., Keay, K. and Simmonds. I., (2014), A high-resolution climatological study on the comparison between surface explosive and ordinary cyclones in the Mediterranean. \u003cem\u003eRegional Environmental Change\u003c/em\u003e, \u003cstrong\u003e14\u003c/strong\u003e, 1833\u0026ndash;1846. https://doi.org/10.1007/s10113-013-0461-3\u003c/li\u003e\n\u003cli\u003eKrzyzanowdki, M., (1997), Methods for assessing the extent of exposure and effects of air pollution. \u003cem\u003eOccupational and Environmental Medicine, \u003c/em\u003e\u003cstrong\u003e54\u003c/strong\u003e, 145-151. https://doi.org/10.1136/oem.54.3.145\u003c/li\u003e\n\u003cli\u003eLamb H.H., (1950), Types and spells of weather around the year in the British Isles: annual trends, seasonal structure of the year, singularities. \u003cem\u003eQuarterly Journal of the Royal Meteorological Society\u003c/em\u003e, \u003cstrong\u003e76\u003c/strong\u003e, 393\u0026ndash;429.\u003c/li\u003e\n\u003cli\u003eLamb, H.H., (1972), British Isles Weather types and a register of daily sequence of circulation patterns, 1861-1971. \u003cem\u003eGeophysical Memoir 116\u003c/em\u003e, HMSO, London, 85pp.\u003c/li\u003e\n\u003cli\u003eLorenzo, N., Taboada, J. and Gimeno, L., (2008), Links between circulation weather types and teleconnection patterns and their influence on precipitation patterns in Galicia (NW Spain). \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e, \u003cstrong\u003e28\u003c/strong\u003e, 1493 - 1505. https://doi.org/10.1002/joc.1646\u003c/li\u003e\n\u003cli\u003eMartin-Vide, J. (2001), Limitations of an objective weather-typing system for the Iberian Peninsula. \u003cem\u003eWeather,\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e,248-250.\u003c/li\u003e\n\u003cli\u003eMir\u0026oacute;, J.R., Pepin, N., Pe\u0026ntilde;a, J.C. and Martin-Vide, J., (2020), Daily atmospheric circulation patterns for Catalonia (northeast Iberian Peninsula) using a modified version of Jenkinson and Collison method, \u003cem\u003eAtmospheric Research\u003c/em\u003e, \u003cstrong\u003e231\u003c/strong\u003e, 104674, https://doi.org/10.1016/j.atmosres.2019.104674\u003c/li\u003e\n\u003cli\u003eNishiyama, K., Endo, S., Jinno, K., Uvo, C.B., Olsson, J. and Berndtsson, R., (2007), Identification of typical synoptic patterns causing heavy rainfall in the rainy season in Japan by a Self-Organizing Map, \u003cem\u003eAtmospheric Research\u003c/em\u003e, \u003cstrong\u003e83\u003c/strong\u003e, 185-200, https://doi.org/10.1016/j.atmosres.2005.10.015\u003c/li\u003e\n\u003cli\u003eOtero, N., Sillmann, J. and Butler, T., (2018), Assessment of an extended version of the Jenkinson\u0026ndash;Collison classification on CMIP5 models over Europe. \u003cem\u003eClimate Dynamics\u003c/em\u003e. \u003cstrong\u003e50\u003c/strong\u003e, 1559-1579. https://doi.org/:10.1007/s00382-017-3705-y\u003c/li\u003e\n\u003cli\u003ePalmieri, S., Durante, G., Siani, A. M. and Casale, G. R., (2008), Atmospheric stagnation episodes and hospital admissions. \u003cem\u003ePublic Health\u003c/em\u003e, \u003cstrong\u003e122\u003c/strong\u003e, 1128-1130. https://doi.org//10.1016/j.puhe.2008.02.006\u003c/li\u003e\n\u003cli\u003ePeings, Y., Cattiaux, J., Vavrus, S. and Magnusdottir, G., (2017), Late Twenty-First-Century changes in the midlatitude atmospheric circulation in the CESM large ensemble. \u003cem\u003eJournal of Climate\u003c/em\u003e, \u003cstrong\u003e30\u003c/strong\u003e, 5943-5960. https://doi.org/10.1175/JCLI-D-16-0340.1\u003c/li\u003e\n\u003cli\u003ePhelan, J., (2018), Rome among worst cities in Europe for road safety, traffic and pollution: Greenpeace. [online] Thelocal.it. Available at: https://www.thelocal.it/20180522/italy-rome-road-safety-traffic-air-quality-greenpeace [Accessed 9 Oct. 2021].\u003c/li\u003e\n\u003cli\u003ePope, R.J., Savage, N.H., Chipperfield, M.P. Arnold, S.R and Osborn, T.J., (2014), The influence of synoptic weather regimes on UK air quality: analysis of satellite column NO\u003csub\u003e2\u003c/sub\u003e. \u003cem\u003eAtmospheric Science Letters\u003c/em\u003e, \u003cstrong\u003e15\u003c/strong\u003e, 211\u0026ndash;217. https://doi.org/10.1002/asl2.492\u003c/li\u003e\n\u003cli\u003ePopescu, F., and Ionel, I., (2010), Anthropogenic Air Pollution Sources. In Kumar, A. (ed) \u003cem\u003eAir Quality.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003ePost, P., Truija, V. and Tuulik, J., (2002), Circulation weather types and their influence on temperature and precipitation in Estonia. \u003cem\u003eBoreal Environment Research\u003c/em\u003e, \u003cstrong\u003e7\u003c/strong\u003e, 281-289. \u003c/li\u003e\n\u003cli\u003ePutniković, S., To\u0026scaron;ić, I. and Đurđević, V., (2016), Circulation weather types and their influence on precipitation in Serbia. \u003cem\u003eMeteorology and Atmospheric Physics\u003c/em\u003e, \u003cstrong\u003e128\u003c/strong\u003e, 649-662. https://doi.org/10.1007/s00703-016-0432-6\u003c/li\u003e\n\u003cli\u003eRichardson, D., Fowler, H., Kilsby, C. and Neal, R., (2017), A new precipitation and drought climatology. \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e, \u003cstrong\u003e38\u003c/strong\u003e, 630-648. https://doi.org/10.1002/joc.5199\u003c/li\u003e\n\u003cli\u003eRusso, A., Trigo, R., Martins, H. and Mendes, M., (2014), NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e urban concentrations and its association with circulation weather types in Portugal. \u003cem\u003eAtmospheric Environment,\u003c/em\u003e \u003cstrong\u003e89\u003c/strong\u003e, 768-785. https://doi.org/10.1016/j.atmosenv.2014.02.010\u003c/li\u003e\n\u003cli\u003eSarricolea, P., Meseguer-Ruiz, O., Mart\u0026iacute;n-Vide, J. and Outeiro, L., (2018), Trends in the frequency of synoptic types in central-southern Chile in the period 1961\u0026ndash;2012 using the Jenkinson and Collison synoptic classification. \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e, \u003cstrong\u003e134\u003c/strong\u003e, 193-204. https://doi.org/10.1007/s00704-017-2268-5\u003c/li\u003e\n\u003cli\u003eSpellman, G., (2000), The use of an index-based regression model for precipitation analysis on the Iberian peninsula. \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e, \u003cstrong\u003e66\u003c/strong\u003e, 229-239. https://doi.org/10.1007/s007040070027\u003c/li\u003e\n\u003cli\u003eSpellman, G., (2017), An assessment of the Jenkinson and Collison synoptic classification to a continental mid-latitude location. \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e, \u003cstrong\u003e128\u003c/strong\u003e, 731-744. https://doi.org/10.1007/s00704-015-1711-8\u003c/li\u003e\n\u003cli\u003eStryhal, J. and Huth, R., (2018), Trends in winter circulation over the British Isles and central Europe in twenty-first century projections by 25 CMIP5 GCMs. \u003cem\u003eClimate Dynamics\u003c/em\u003e, \u003cstrong\u003e52\u003c/strong\u003e, 1063-1075. https://doi.org/10.1007/s00382-018-4178-3\u003c/li\u003e\n\u003cli\u003eTang, L., Chen, D., Karlsson, P.-E., Gu, Y. \u0026amp; Ou, T., (2009), Synoptic circulation and its influence on spring and summer surface ozone concentrations in southern Sweden. \u003cem\u003eBoreal Environment Research\u003c/em\u003e, \u003cstrong\u003e14\u003c/strong\u003e, 889\u0026ndash;902.\u003c/li\u003e\n\u003cli\u003eTang, L., Rayner, D. and Haeger-Eugensson, M., (2011), Have meteorological conditions reduced NO\u003csub\u003e2\u003c/sub\u003e concentrations from local emission sources in Gothenburg. \u003cem\u003eWater, Air, \u0026amp; Soil Pollution\u003c/em\u003e, \u003cstrong\u003e221\u003c/strong\u003e, 275-286. http:// doi.org/10.1007/s11270-011-0789-6\u003c/li\u003e\n\u003cli\u003eTrigo R.M. and DaCamara C.C., (2000), Circulation weather types and their influence on the precipitation regime in Portugal. \u003cem\u003eInternational Journal of Climatology,\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1559-1581. https://doi.org/10.1002/1097-0088(20001115)20:13%3C1559::AID-JOC555%3E3.0.CO;2-5\u003c/li\u003e\n\u003cli\u003eUPI, (2011), Rome, Milan restrict driving to cut smog. https://www.upi.com/Top_News/World-News/2011/11/19/Rome-Milan-restrict-driving-to-cut-smog/36571321746264/ [Accessed 9 Oct. 2021].\u003c/li\u003e\n\u003cli\u003eVicente-Serrano, S. and Lopez-Moreno, J.I., (2006), Influence of atmospheric circulation at different spatial scales on winter drought variability through a semiarid climatic gradient in North-east Spain. \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e, \u003cstrong\u003e26\u003c/strong\u003e, 1427 \u0026ndash;1453. https://doi.org/10.1002/joc.1387\u003c/li\u003e\n\u003cli\u003eVon Storch, H. and Zorita, E., (2019), The history of ideas of downscaling\u0026mdash;From synoptic dynamics and spatial interpolation. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e, \u003cstrong\u003e7\u003c/strong\u003e, 1-8. https://doi.org/10.3389/fenvs.2019.00021\u003c/li\u003e\n\u003cli\u003eVon Storch, H. and Zwiers, F.W., (1999), \u003cem\u003eStatistical Analysis in Climate Research\u003c/em\u003e. Cambridge University Press, 484 p. https://doi.org/10.1017/CBO9780511612336\u003c/li\u003e\n\u003cli\u003eWu, H. Y., Li, Z., Li, W., and Zheng, J., (2020), Characteristics analysis of extremely severe precipitation based on regional automatic weather stations in Guangdong. \u003cem\u003eMeteorological Monthly\u003c/em\u003e, \u003cstrong\u003e46\u003c/strong\u003e, 801\u0026ndash;812. https://doi.org/10.3389/feart.2021.678230\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e According to rule 5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Above a |z| value of 20 (annual mean\u0026thinsp;+\u0026thinsp;1 SD).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"SYNOPTIC TYPING, UPPER AIR PATTERNS, CENTRAL MEDITERRANEAN, AIR QUALITY","lastPublishedDoi":"10.21203/rs.3.rs-3087575/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3087575/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSynoptic climatological approaches provide an effective framework for the analysis of atmospheric circulation patterns and the relationship with environmental variables. The objective circulation classification method of Jenkinson and Collison is applied to the central Mediterranean for the period 1948\u0026ndash;2021. In order to capture more synoptic information a modified version of the method is used to also develop a series using the 500 hPa surface. Results capture key modes of variability of the Mediterranean atmosphere. The technique is used to examine variability in daily nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) concentration in Rome. Analysis shows an evident surface synoptic control in the winter months and consideration of upper patterns renders this approach more effective.\u003c/p\u003e","manuscriptTitle":"Development of surface and upper air synoptic catalogues for the Central Mediterranean and an application to the analysis of nitrogen dioxide in the Rome winter season. ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-29 18:19:28","doi":"10.21203/rs.3.rs-3087575/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-01-25T20:45:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-08-09T12:49:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"b35c10a5-0587-4d6e-a4d4-452f4237c0af","date":"2023-07-26T07:22:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-07-25T20:41:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-06-26T10:53:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-06-26T10:53:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2023-06-20T13:28:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1829b7f9-f8b8-4d42-ba04-08c51b0e7ef4","owner":[],"postedDate":"June 29th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-02-15T13:31:49+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-29 18:19:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3087575","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3087575","identity":"rs-3087575","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}