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Low-cost electrochemical air quality sensors can provide deep insights into the intra-urban variability of different air pollutants after proper calibration using field co-location with regulatory Air Quality Monitoring stations.

Light Absorbing Aerosols (LAA) are short-lived climate forcers with a significant impact on Earth radiative balance and include dust aerosols, Black Carbon (BC) and organic light-absorbing carbonaceous aerosol (collectively termed as Brown Carbon, BrC), which have been also proven to be highly toxic. Aerosol absorption at 5 wavelengths (UV, B, G, R, IR) by filter photometer was monitored continuously during two winter seasons in 2020 and 2021 in the city of Modena (South-central Po valley, Northern Italy) at the two regulatory air quality monitoring sites, along with other pollutants (PM10, PM2.5, O3, NO, NO2, C6H6) and vehicular traffic rate. Columnar levels of AOD and of other aerosol optical properties were concurrently monitored at multiple wavelengths by a local sun-photometer at urban background conditions and within the AERONET network. In-situ absorption levels were apportioned both to sources (fossil fuel, biomass burning) and to species (BC, BrC), while columnar absorption was apportioned to BC, BrC and mineral dust. The combined analysis of the atmospheric aerosol and gas levels and of the meteorological conditions (in-situ and by ERA5 reanalysis) identified the location of potential urban sources for BC and BrC, most likely traffic and biomass burning. In-situ data shown different diurnal/weekly patterns for BrC by biomass burning and BC by traffic, with minor differences among the background and the traffic urban conditions. AERONET version 3 Absorption Aerosol Optical Depth (AAOD) retrievals at 4 wavelengths allowed the estimate of the Absorptive Direct Radiative Effect by LAA over the same period under the reasonable assumption that the AOD signal is built up within the mixing layer. AERONET retrievals showed a modest correlation of columnar absorption with PBL-scaled in-situ observations, although the correlation improves significantly during a desert dust transport event, affecting both in-situ aerosol and columnar absorption, particularly in the Blue spectrum range. Low correlation occurred between the contribution of BrC to aerosol absorption for the in-situ and the columnar observations, with this contribution being generally larger at the former. Finally, evidences of a strongly layered atmosphere during the study period, featured by large spatial mixing and modest vertical mixing, were shown by ERA5-based atmospheric temperature profiles and by the large correlation of concurrent AERONET AOD retrievals in Modena and in Ispra (on the NW side of the Po valley, ca. 225 km distant from Modena).

The COVID lockdown presented a unique opportunity to study the anthropogenic emissions from different sectors under relatively cleaner conditions in India. The complex interplays of power production, industry, and transport could be dissected due to the significantly reduced influence of the latter two emission sources. Here, based on measurements of cloud condensation nuclei (CCN) activity and chemical composition of atmospheric aerosols during the lockdown, we report an episodic event showing rapid growth and high hygroscopicity of new aerosol particles formed in the SO2 plume from a large coal-fired power plant. These sulfate-rich particles had high CCN activity and number concentration, indicating high cloud-forming potential. Examining the sensitivity of CCN properties under relatively clean conditions over India provides important new clues to delineate contributions of different anthropogenic emission sectors and further to understand their perturbations of past and future climate forcing.

Covid lockdown presented an important opportunity to study relatively cleaner conditions in India. The complex factors of power production, industry, and transportation could be more carefully dissected because of the extreme reduction in the influence of the latter two emission sources. Measurements of cloud condensation nuclei (CCN) activity and other chemical properties of atmospheric aerosols showed that newly formed aerosol particles were produced in the SO2 plume from a large coal-fired power plant, contrary to normal conditions of heavy pollution. The sulfate-rich particles had high CCN activity and number concentration, indicating high cloud-forming potential. Examining the sensitivity of CCN properties under relatively clean conditions over India provides important new constraints on the perturbations of past and future climate forcing by anthropogenic emissions. Because most sensitive regime of aerosol climate forcing on cloud development is the midpoint of relatively clean conditions afforded by the Covid lockdown between background and polluted conditions.

Atmospheric nitrogen oxides ($$\hbox {NO}_{x} = \hbox {NO}+\hbox {NO}_{2}$$ NO x = NO + NO 2 ) are key pollutants and short-lived climate forcers contributing to acid rain, photochemical smog, aerosol formation and climate change. Exposure to nitrogen dioxide ($$\hbox {NO}_{2}$$ NO 2 ) emitted mainly from transportation, causes adverse health effects associated with respiratory illnesses and increased mortality even at low concentration. Application of titanium dioxide ($$\hbox {TiO}_{2}$$ TiO 2 )-based photocatalysis in urban environment is a new air cleaning solution, activated by sunlight and water vapour to produce OH radicals, able to remove $$\hbox {NO}_{x}$$ NO x and other pollutants from the planetary boundary layer. This study is a large-scale evaluation of $$\hbox {NO}_{x}$$ NO x removal efficiency at a near-road environment with applied photocatalytic NOxOFF™ technology on an urban road west of Copenhagen, thus supporting local municipality in meeting their clean-air Agenda 2030. The photocatalytic NOxOFF™ granulate containing $$\hbox {TiO}_{2}$$ TiO 2 nanoparticles was applied on an asphalt road in July 2020 and ambient $$\hbox {NO}_{x}$$ NO x was measured during a six-month monitoring campaign. It is the first $$\hbox {NO}_{x}$$ NO x monitoring campaign carried out at this road and specific efforts have been devoted to evaluate the reduction in ambient $$\hbox {NO}_{x}$$ NO x levels with NOxOFF™-treated asphalt. Several methods were used to evaluate the photocatalytic effect, taking into account analysis limitations such as the short reference period prior to application and the highly uncertain measurement period during which SARS-CoV-2 lockdown measures impacted air quality. There was no statistically significant difference in $$\hbox {NO}_{x}$$ NO x concentrations between the reference period and the photocatalytic active period and NO removal efficiency resulted in $$-\,0.17$$ - 0.17 (± 1.27). An upper limit removal of 17.5% $$\hbox {NO}_{x}$$ NO x was estimated using a kinetic tunnel model. While $$\hbox {NO}_{2}$$ NO 2 comparison with COPERT V street traffic model projection was roughly estimated to decrease by 39% (± 38%), although this estimate is subject to high uncertainty. The observed annual mean $$\hbox {NO}_{2}$$ NO 2 concentration complies with Frederiksberg clean-air Agenda 2030 and air quality standards. Graphical abstract A graphical abstract illustrating the air cleaning properties of $$\hbox{TiO}_{2}$$ TiO 2 -based photocatalytic-treated asphalt

PurposeA food tourism destination can fully exploit its competitiveness if food-related attributes are consistently highlighted both in its promotion and in user-generated content. However, in the context of food tourism research, a possible image incongruence has not yet been studied. Tourism destination image incongruence occurs when different travel information sources reflect inconsistent representations of a destination's attributes. This study addresses this gap, focusing on Italian food and wine as drivers to attract visitors. This study examines whether food-related attributes are present in online travel-related conversations and are perceived differently by people with and without knowledge about the destination.Design/methodology/approachContent analysis based on a Bayesian machine-learning technique utilizing Leximancer software was applied to analyze questions and answers posted on TripAdvisor forums by potential and past visitors of four destinations in Italy (Naples, Florence, Parma and Ferrara). Questions and answers expressed by people with different knowledge in Italian and English were analyzed separately to gain deeper understanding.FindingsContrary to expectations, food-related themes were almost completely absent in the conversations analyzed, with only a few exceptions in Italian question sections. This situation depicts a sort of “cannibalism”, in the sense that the centrality of food-related attributes is engulfed by other, less sensorial, enjoyable and memorable aspects of the travel experience.Research limitations/implicationsAnalysis suggests that hype may exist in food tourism promotion related to destination image incongruence. However, while based on a large volume of conversations, the analysis covers only four Italian cities.Practical implicationsDestination management organizations (DMOs) should develop their strategy and communication considering internal and external elements: their marketing targets on one side and the local culture and attractions' perceptions on the other. Standard marketing processes (segmenting, targeting, positioning) and theories should be put in place. The application of standard marketing dynamics and studies should push the DMOs to understand that the internally perceived cultural values of the touristic destinations could not be known or joint univocally by the global external customers and that a local promotional activity should start with branding and not commercial activities.Originality/valueThis is the first study to suggest the existence of hype in food tourism promotion of Italian destinations and to provide evidence supporting this argument.

Atmospheric nitrogen oxides (A graphical abstract illustrating the air cleaning properties of

High accuracy in Equations of State (EoSs) is becoming a more and more critical aspect for design and operational purposes in different areas of chemical and process engineering. It is well-known that improper predictions of mixture properties provide large deviations in process simulations, process control actions, estimation of operative conditions, assessment of performance indexes and optimal unit/process design. These deviations are strongly emphasized when typical operating conditions appear rather severe. One of the most challenging application sectors is represented by cryogenic separations and especially in case their nominal operating conditions approach the critical point. Air Separation Units (ASUs) are the ideal application field to demonstrate these criticisms. In addition, dedicated EoSs sometimes lacks in implementation details and thermodynamic parameters, making the reliability target impossible to be achieved. The present paper is aimed at bridging the current gaps in the implementation of Bender EoS for the reliable prediction of air mixture properties as well as the reliable simulation of ASU plants. The implementation requires the description of robust and efficient algorithms. At last, a quantitative comparison between the proposed approach and the existing solutions is provided.

This paper considers a sample of successful wine bloggers, and clusters them into four distinct segments that could be targeted by wine marketers in different ways. Using text that they wrote in response to being named to a survey of 100 top wine blogs, we employ an advanced textual analysis tool (LIWC) to categorize the writings according to the following characteristics: analytical thinking, clout, authenticity, and emotional tone. This data is then used in a clustering procedure that distinguishes four distinct groups of bloggers: the Analysts, the Agnostics, the Authentic Pessimists, and the Confident Optimists.

Atmospheric aerosols are known to play a key role in Earth’s radiative budget, although the quantification of their climate forcing is still highly uncertain. In order to improve the scientific understanding of their climatic effect, in-situ ground-based aerosol properties observations are needed by the research community. Such data would also allow the global assessment of the effect of environmental policies over both the short and the long term.To develop a robust and consistent view over time of the worldwide variability of aerosol properties, data resulting from a fully-characterized value chain, including uncertainty estimation, is needed.The present work is part of a wider project, having among its goals the investigation of the variability of climate-relevant aerosol properties observed at all sites connected to the Global Atmospheric Watch network, whose data are publicly available from the World Data Centre for Aerosols and follow the aforementioned specifications.This work focuses on aerosol optical proprieties, i.e. the aerosol light scattering coefficient (σsp), the aerosol light absorption coefficient (σap), single scattering albedo (ωo) and both scattering and absorption Ångström exponents (åsp and åap).The analysis includes 108 yearly datasets collected either during 2016 or 2017 at different sites: 53 for absorption and 55 for scattering coefficient datasets, respectively. For 29 of these sites it was also possible to compute single scattering albedo.The spatial variability in extensive and intensive optical properties was analysed in terms of each site’s geographical location (either polar, continental, coastal or mountain) and its footprint (from pristine to urban, representing increasing levels of anthropogenic influence).The results highlight the impact of anthropogenic emissions and biomass burning on absolute levels and annual variability. The effect of sea spray or long range transport of dust is also evident for several sites, along with the influence of regional emissions. The largest seasonality in aerosol loading was observed at mountain sites under mixed footprint conditions, while the lowest seasonality occurred at urban sites. Urban sites also exhibited the highest σsp and σap values. The lowest levels in σsp and σap were observed at some polar sites, along with few coastal and mountain sites, despite their typically mixed footprint.AcknowledgementsThe authors acknowledge WMO-GAW World Data Centre on Aerosol for providing data available at http://ebas.nilu.no

Most of worldwide population lives in urban areas, demanding for air quality information with a high spatio-temporal resolution. The most promising approaches for estimating urban air quality within the complex urban topography are small sensor networks and simulation models.The TRAFAIR project focuses on understanding the role of traffic emissions on urban air quality by the combination of dispersion modelling, space- and time-resolved gas monitoring by lower cost sensors and realistic traffic flow rates by dynamic traffic model based on real time traffic data. Test cities of TRAFAIR are Modena, Florence, Pisa, Livorno, Zaragoza and Santiago de Compostela.Depending on the size of the urban area, from 6 to 13 sensors units are deployed across each city since August 2019, providing estimates of NO, NO2, CO and O3, along with RH and temperature. Metal oxide sensors are deployed in Tuscany (Florence, Pisa, Livorno) and electrochemical cells are used elsewhere. The units are calibrated on a regular basis by co-location at the air quality regulatory stations and subsequently deployed across the town to monitor several representative locations (e.g. Low Emission Zones, hospital surroundings). For each sensor the raw readings (e.g. mV for electrochemical cells) are collected and a regression model (e.g. Random Forest) is applied to derive a calibration function, exploiting the data from the regulatory stations during co-location periods; for instance in Modena, the first short-term calibration provided a model with a Mean Absolute Error between 5 – 6 ppb and 2 – 4 ppb for NO and NO2 respectively.The sensors are used for both real-time urban air quality mapping and to test and validate the 24hr forecast service of NOx by the microscale lagrangian dispersion model GRAL. The simulation domains, covering the urban area of each TRAFAIR city, have a horizontal resolution of 4 m and allow to account for the presence of buildings. The dispersion model mainly focuses on NOx by traffic emissions, although domestic heating will be also included in the analysis. Vehicular emissions are based either upon historical traffic data (e.g. induction loops), or upon previously available traffic flow simulation, or upon traffic pattern reconstruction using a traffic flow model followed by a cluster analysis to group streets with similar pattern.The final goal of the project is the development of a tool to support local policymakers and to inform citizenship about the quality of air and the impact of urban emission sources, particularly traffic. A secondary goal of the project is the development of a valuable QA/QC protocol for small sensor units and the optimization of the modelling chain for the forecast of traffic and domestic heating impact on local air quality at the urban scale.

In order to support environmental policies, epidemiological studies and urban mobility planning, a multi-scale modelling system was developed to provide hourly NOx (NO + NO2) concentration fields at a building-resolving scale in the urban area of Modena, a city in the middle of the Po Valley (Italy). The modelling system relied on two different models: the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), which is able to compute concentration fields over regional domain by considering specific emission scenarios, and Parallel Micro SWIFT SPRAY (PMSS), a Lagrangian particle model accounting for dispersion phenomena within the urban area. PMSS was used to simulate at building-scale resolution the NOx dispersion produced by urban traffic flows in the city of Modena. Conversely, WRF-Chem was selected to estimate the NOx background concentrations over three nested domains with resolution of 15, 3 and 1 km in order to take into account emissions both at regional and local scale by excluding traffic emissions sources over the city of Modena. The estimation of traffic emissions in the urban area of Modena was based on a bottom-up approach relying on the Emission Factors suggested by the European Monitoring and Evaluation Programme (EMEP/EEA) and traffic fluxes estimated by the PTV VISUM model. By contrast, other anthropogenic emissions were taken from the TNO-MACC III inventory at the scales resolved by the WRF-Chem model. Simulation was performed between 28 October and 8 November 2016, the same period whereby a direct vehicle flow measurement campaign was carried out continuously, with 4 Doppler radar counters in a four-lane road in Modena, to reproduce the hourly modulation rates of the emissions. The performances of the model chain were finally assessed by comparing modelled NOx concentrations with observations at two air quality monitoring stations located inside the urban domain. Simulated and observed NOx hourly concentrations exhibit a large agreement, in particular for urban traffic site where detailed traffic emissions estimation (real traffic modulation combined with a bottom-up approach) proved to be very successful in reproducing the observed NOx pattern. At the urban background station, notwithstanding a general underestimation of the observed concentrations (more pronounced than at the urban traffic site), the analysis of hourly daily modelled concentrations shows that PMSS combined with WRF-Chem provided a daily pattern in line with observations. These features highlight the strength of this modelling chain in representing urban air quality, in particular at traffic sites, whose concentration levels make them the most critical area of the city; characteristics that chemical transport models alone cannot express, due to the coarser resolution to which they operate and to their inability to reproduce street canyons and urban structures.

Aerosol particles are essential constituents of the Earth’s atmosphere, impacting the earth radiation balance directly by scattering and absorbing solar radiation, and indirectly by acting as cloud condensation nuclei. In contrast to most greenhouse gases, aerosol particles have short atmospheric residence time resulting in a highly heterogeneous distribution in space and time. There is a clear need to document this variability at regional scale through observations involving, in particular, the in-situ near-surface segment of the atmospheric observations system. This paper will provide the widest effort so far to document variability of climate-relevant in-situ aerosol properties (namely wavelength dependent particle light scattering and absorption coefficients, particle number concentration and particle number size distribution) from all sites connected to the Global Atmosphere Watch network. High quality data from more than 90 stations worldwide have been collected and controlled for quality and are reported for a reference year in 2017, providing a very extended and robust view of the variability of these variables worldwide. The range of variability observed worldwide for light scattering and absorption coefficients, single scattering albedo and particle number concentration are presented together with preliminary information on their long-term trends and comparison with model simulation for the different stations. The scope of the present paper is also to provide the necessary suite of information including data provision procedures, quality control and analysis, data policy and usage of the ground-based aerosol measurements network. It delivers to users of the World Data Centre on Aerosol, the required confidence in data products in the form of a fully-characterized value chain, including uncertainty estimation and requirements for contributing to the global climate monitoring system.

Aerosol particles are essential constituents of the Earth’s atmosphere, impacting the earth radiation balance directly by scattering and absorbing solar radiation, and indirectly by acting as cloud condensation nuclei. In contrast to most greenhouse gases, aerosol particles have short atmospheric residence time resulting in a highly heterogeneous distribution in space and time. There is a clear need to document this variability at regional scale through observations involving, in particular, the in-situ near-surface segment of the atmospheric observations system. This paper will provide the widest effort so far to document variability of climate-relevant in-situ aerosol properties (namely wavelength dependent particle light scattering and absorption coefficients, particle number concentration and particle number size distribution) from all sites connected to the Global Atmosphere Watch network. High quality data from more than 90 stations worldwide have been collected and controlled for quality and are reported for a reference year in 2017, providing a very extended and robust view of the variability of these variables worldwide. The range of variability observed worldwide for light scattering and absorption coefficients, single scattering albedo and particle number concentration are presented together with preliminary information on their long-term trends and comparison with model simulation for the different stations. The scope of the present paper is also to provide the necessary suite of information including data provision procedures, quality control and analysis, data policy and usage of the ground-based aerosol measurements network. It delivers to users of the World Data Centre on Aerosol, the required confidence in data products in the form of a fully-characterized value chain, including uncertainty estimation and requirements for contributing to the global climate monitoring system.

A multi-scale modelling system was developed to provide hourly NOx concentration fields at a building-resolving scale in the urban area of Modena, a city in the middle of the Po Valley (Italy), one of the most polluted areas in Europe. The WRF-Chem model was applied over three nested domains and employed with the aim of reproducing local background concentrations, taking into account meteorological and chemical transformation at the regional scale with nested resolutions of 15 km, 3 km and 1 km. Conversely, the PMSS modelling system was applied to simulate 3D air pollutant dispersion, due to traffic emissions, with a very high-resolution (4 m) on a 6 km × 6 km domain covering the city of Modena. The methodology employed to account for anthropogenic emissions relies on two different strategies. Traffic emissions were based on a bottom-up approach using emission factors suggested by the European Environmental Agency with traffic fluxes estimated by the PTV VISUM model in the urban area of Modena, combined with direct traffic flow measurements performed between October 28 and November 8, 2016 which was used for the hourly vehicle modulation. Other anthropogenic emissions were taken from the TNO-MACC III inventory at the scales resolved by the WRF-Chem model. Simulations were performed for the same period whereby the traffic measurement campaign was carried out. 2 m temperature and 10 m wind speed were captured quite well by the WRF-Chem model with statistical metrics in line with similar case studies related to the Northern Italy. The NOx concentrations reproduced in the Po Valley area by WRF-Chem were on average simulated reasonably well with a general negative bias in almost all the examined rural background monitoring stations. Additionally, the deployment of an emission inventory at the original resolution (7 km) highlighted that increasing resolution from 3 km to 1 km does not generally improve the model performance. Nevertheless, simulated and observed NOx hourly concentrations in the urban area of Modena exhibit a large agreement in particular for urban traffic site where detailed traffic emission estimations proved to be very successful in reproducing the observed NOx trend. At urban background stations, despite a general underestimation of the observed concentrations, the combination of WRF-Chem with PMSS provided daily pattern in line with observations. The analysis of the modelled NOx daily cycle pointed out also that at both traffic and background urban stations the morning NOx peak concentration was on average underestimated. This could be explained with an overestimation of mixing phenomena between 07:30 a.m. and 10:00 a.m. by WRF-Chem which leads to a greater dispersion of NOx along the vertical and thus a morning underestimation. The statistical analysis showed finally that PMSS combined with WRF-Chem at both the resolutions (3 km and 1 km) and at both traffic and background sites fulfilled standard acceptance criteria for urban dispersion model evaluation, confirming that the proposed multi-modelling system can be employed as a tool to support environmental policies, epidemiological studies and urban mobility planning.

PurposeThis paper explores if and how Artificial Intelligence can contribute to marketing strategy formulation.Design/methodology/approachQualitative research based on exploratory in-depth interviews with industry experts currently working with artificial intelligence tools.FindingsKey themes include: (1) Importance of AI in strategic marketing decision management; (2) Presence of AI in strategic decision management; (3) Role of AI in strategic decision management; (4) Importance of business culture for the use of AI; (5) Impact of AI on the business’ organizational model. A key consideration is a “creative-possibility perspective,” highlighting the future potential to use AI not only for rational but also for creative thinking purposes.Research limitations/implicationsThis work is focused only on strategy creation as a deliberate process. For this, AI can be used as an effective response to the external contingencies of high volumes of data and uncertain environmental conditions, as well as being an effective response to the external contingencies of limited managerial cognition. A key future consideration is a “creative-possibility perspective.”Practical implicationsA practical extension of the Gartner Analytics Ascendancy Model (Maoz, 2013).Originality/valueThis paper aims to contribute knowledge relating to the role of AI in marketing strategy formulation and explores the potential avenues for future use of AI in the strategic marketing process. This is explored through the lens of contingency theory, and additionally, findings are expressed using the Gartner analytics ascendancy model.

In this work, a comprehensive characterisation and source apportionment of size-segregated aerosol collected using a multistage cascade impactor was performed. The samples were collected during wintertime in Milan (Italy), which is located in the Po Valley, one of the main pollution hot-spot areas in Europe. For every sampling, size-segregated mass concentration, elemental and ionic composition, and levoglucosan concentration were determined. Size-segregated data were inverted using the program MICRON to identify and quantify modal contributions of all the measured components. The detailed chemical characterisation allowed the application of a three-way (3-D) receptor model (implemented using Multilinear Engine) for size-segregated source apportionment and chemical profiles identification. It is noteworthy that - as far as we know - this is the first time that three-way source apportionment is attempted using data of aerosol collected by traditional cascade impactors. Seven factors were identified: wood burning, industry, resuspended dust, regional aerosol, construction works, traffic 1, and traffic 2. Further insights into size-segregated factor profiles suggested that the traffic 1 factor can be associated to diesel vehicles and traffic 2 to gasoline vehicles. The regional aerosol factor resulted to be the main contributor (nearly 50%) to the droplet mode (accumulation sub-mode with modal diameter in the range 0.5–1 μm), whereas the overall contribution from the two factors related to traffic was the most important one in the other size modes (34–41%). The results showed that applying a 3-D receptor model to size-segregated samples allows identifying factors of local and regional origin while receptor modelling on integrated PM fractions usually singles out factors characterised by primary (e.g. industry, traffic, soil dust) and secondary (e.g. ammonium sulphate and nitrate) origin. Furthermore, the results suggested that the information on size-segregated chemical composition in different size classes was exploited by the model to relate primary emissions to rapidly-formed secondary compounds.

Operational forecasting systems based on chemical transport models (CTMs) nowadays generally produce concentration maps with a resolution in the order of 2–5 km, very rarely exceeding the sub-kilometre scale. The main reason for this restriction is the prohibitive computing cost that a simulation covering an entire country would have if set-up with a resolution in the order of meters. In this paper a hybrid forecast system, relying on the WRF-Chem model coupled with the PMSS Lagrangian modelling suite, has been developed and applied for each day of February 2019, to predict hourly NO2 and NOx concentrations with a spatial resolution of 4 m, for the urban area of Modena (a city located in the central Po Valley). Simulated meteorological fields (temperature, wind speed and direction) were assessed at three urban stations, compliant with WMO standards, and modelled concentrations were compared with measurements at two urban air quality stations located at background and traffic sites. Results show that meteorological variables are well captured by the hybrid system and statistical performances are in line with the benchmark values suggested by the European Environmental Agency and with similar case studies focusing on the same area. Modelled NO2 and NOx concentrations, notwithstanding a slight underestimation mainly evident at urban traffic stations for NOx, present a large agreement with related observations. The NO2 Model Quality Objective, as defined by Fairmode guidelines, was met for both the urban stations and the other statistical indexes considered in the evaluation fulfilled the acceptance criteria for dispersion modelling in urban environment, for both NO2 and NOx concentrations. In the second section of the study, the population exposure to forecasted NO2 concentrations has been evaluated adopting a generic model of dynamic population activity. The population was distributed at hourly time steps in specific urban micro-environments at the same resolution of the concentration maps (4 m) and the short-term exposure has been computed as the product between the population density in each model cell and related surface NO2 concentrations. An infiltration factor was also applied to estimate indoor concentrations. The hybrid system was shown to be particularly suited for assessing short-term peak exposure in areas influenced by traffic emissions. On the other hand, due to the limited time spent by the population within traffic related environments, the long-term population exposure calculated by the hybrid system tends to be similar to the WRF-Chem stand-alone estimate.

A comprehensive range of gas and particle phase pollutants were sampled at 1-hour time resolution in urban background Milan during summer 2012. Measurements include several soluble inorganic aerosols (Cl−,NO2−,NO3−,SO42−,Ca2+,K+,Mg2+,Na+,NH4+) and gases (HCl, HNO2,HNO3, NH3, NO, NO2,O3, SO2), organic, elemental and black carbon and meteorological parameters. Analysis methods used include mean diurnal pattern on weekdays and Sundays, pollution roses, bivariate polar plots and statistical models using backtrajectories. Results show how nitrous acid (HONO) was mainly formed heterogeneously at nighttime, with a dependence of its formation rate on NO2 consistent with observations during the last HONO campaign in Milan in summer 1998, although since 1998 a drop in HONO levels occurred following to the decrease of its precursors. Nitrate showed two main formation mechanisms: one occurring through N2O5 at nighttime and leading to nitrate formation onto existing particles; another occurring both daytime and nighttime following the homogeneous reaction of ammonia gas with nitric acid gas. Air masses reaching Milan influenced nitrate formation depending on their content in ammonia and the timing of arrival. Notwithstanding the low level of SO2 in Milan, its peaks were associated to point source emissions in the Po valley or shipping and power plant emissions SW of Milan, beyond the Apennines. A distinctive pattern for HCl was observed, featured by an afternoon peak and a morning minimum, and best correlated to atmospheric temperature, although it was not possible to identify any specific source. The ratio of primary-dominated organic carbon and elemental carbon on hourly PM2.5 resulted 1.7. Black carbon was highly correlated to elemental carbon and the average mass absorption coefficient resulted MAC = 13.8 ± 0.2 m2 g −1. It is noteworthy how air quality for a large metropolitan area, in a confined valley and under enduring atmospheric stability, is nonetheless influenced by sources within and outside the valley.

The competitive advantage of a tourist product or service, in the global market, is based on the seller’s ability to offer a successful experiential offering system. Recent literature (Voss 2004; Quan and Wang 2004) shows that the demand for experience is the major trend in the global tourist industry and destinations more and more compete by emphasizing their experiential content (Buhalis 2000). According to Pizam (2010), the creation of experiences is the essence and the raison d'etre of the tourism sector. The expectation of a gratifying and memorable experience motivates tourists to buy products and services (Tsaur et al. 2006).

In order to assess the impact of traffic on local air quality a microscale simulation of pollutant concentration fields was produced for two busy intersections, in Reggio Emilia and in Modena, Italy. The simulation was performed by the model suite Micro-Swift-Spray, a Lagrangian particle dispersion model accounting for buildings. Direct measurements of traffic flow were continuously collected in Reggio Emilia over the period January 13–24, 2014 by a two channel radar traffic counter and in Modena from October 28 to November 8, 2016 by four single channel radar traffic counters and used for the hourly modulation of vehicular emissions. Combining radar counts with vehicular fleet composition for each municipality, specific emission factors were obtained. For both cities, simulated concentration fields were compared to local air quality measurements at the nearest urban traffic and urban background sites. The simulated NOx showed large correlation with the observations, notwithstanding some underestimation. The results proved the reliability of the procedure and provided a fair estimate of the NO2 mass fraction of total NOx (primary NO2) due to vehicular emissions in the investigated traffic sites.

This work sets out the test of the GRAL model (Graz Lagrangian Model, vs.18.1) in the urban area of Modena (Po valley, Northern Italy). The simulation domain sizes 2 000 x 3 000 m2 and it features ‘microscale’ cells of 4 x 4 m2. The simulation focuses on an intersection featured by large traffic flows next to a school and a regulatory air quality monitoring station classified as an urban traffic site. The model is a lagrangian particle dispersion model and it takes into account the presence of buildings as obstacles and generating microscale wind fields accordingly, making this class of model suitable for investigating spatial pattern of atmospheric pollution in urban areas where local accumulation might occur. The simulation investigates traffic emissions of nitrogen oxides (NOx) over the period October 29 to November 10, 2016, when direct measurements of traffic flow were collected by four one-channel doppler radar traffic counters. These counters provided continuous estimate of vehicle length, speed and number. These latter data were combined with available traffic flows at rush hour by PTV VISUM mobility software and the fleet composition of the municipality to estimate the total NOx emissions by vehicular traffic over the roads included in the simulation domain. NOx simulated concentrations showed a moderate correlation with the NOx observations at the nearby monitoring site. To have a better insight on the potential and the limitations of the GRAL model, its results will be compared with the output of the lagrangian particle dispersion model PMSS over the same area.

This study looks for a correlation between visual hedonistic and technical variables and site success, simultaneously establishing whether there are any recorded evolutionary aspects of technical variables. A mixed method approach was adopted and the analysis was divided into the following phases: content analysis on technical and hedonistic characteristics; traffic analysis, analysis of the position of websites in relation to data traffic using a size reduction technique; cross-analysis of results obtained in the two previous phases to observe the cluster structure from a point of view of data traffic. The results state that there is no clear correlation between technical and hedonistic qualities of an e-commerce site in the apparel industry and the popularity and attractiveness of their site, and therefore, the authors cannot confirm the four proposed hypotheses.

Purpose The purpose of this paper is to present a theory of consumer and product orientation in the realm of political branding to illustrate how politicians can choose to position and present themselves to voters. It is evident that some politicians play an active role in shaping the beliefs and actions of their constituents, while others are more influenced by voter sentiment. The effectiveness of the political strategy is highly influenced by the market realities of the voting body in question. Design/methodology/approach A dichotomy is presented to shed light on how consumer and product orientation might influence the way in which politicians choose to address the public. Specifically, four modified strategic orientation archetypes are presented and analyzed with particular focus on political brands and strategy. Findings Product and consumer orientations have been shown to also be applicable to the strategic positioning of political brands. While it can be argued that no strategy is superior over another, careful consideration of the political environment in question and subsequent execution of an appropriate stance can be used to better manage the relationship between the electorate and politicians. Research limitations/implications This study provides academics in this area with a comprehensive examination of strategic orientation literature in political contexts, and lays out a strong groundwork for future studies. In this burgeoning area of research, there are several opportunities for marketing and political strategy academics to dive deeper into the intricacies that drive politicians to adopt specific strategic orientations, and how these strategies evolve over time and in differing political environments. Practical implications This analysis suggests that there are opportunities for political strategists to explore the relationship between the identified strategic orientations and political brands, and for political marketing scholars to investigate the modes of focus presented. Originality/value This analysis provides better understanding of how politicians can influence voters and voters can influence political brands, and how the strategic orientation archetypes can be used to influence decisions about political strategy.

Low cost sensors for measuring atmospheric pollutants are experiencing an increase in popularity worldwide among practitioners, academia and environmental agencies, and a large amount of data by these devices are being delivered to the public. Notwithstanding their behaviour, performance and reliability are not yet fully investigated and understood. In the present study we investigate the medium term performance of a set of NO and NO2 electrochemical sensors in Switzerland using three different regression algorithms within a field calibration approach. In order to mimic a realistic application of these devices, the sensors were initially co-located at a rural regulatory monitoring site for a 4-month calibration period, and subsequently deployed for 4 months at two distant regulatory urban sites in traffic and urban background conditions, where the performance of the calibration algorithms was explored. The applied algorithms were Multivariate Linear Regression, Support Vector Regression and Random Forest; these were tested, along with the sensors, in terms of generalisability, selectivity, drift, uncertainty, bias, noise and suitability for spatial mapping intra-urban pollution gradients with hourly resolution. Results from the deployment at the urban sites show a better performance of the non-linear algorithms (Support Vector Regression and Random Forest) achieving RMSE

The development of a natural gas-fired tri-generation power plant (520 MW Combined Cycle Gas Turbines + 58 MW Tri-generation) in the Republic of San Marino, a small independent country in Northern Italy, is under assessment. This work investigates the impact of atmospheric emissions of NOx by the plant, under the Italian and European regulatory framework. The impact assessment was performed by the means of the Aria Industry package, including the 3D Lagrangian stochastic particle dispersion model SPRAY, the diagnostic meteorological model SWIFT, and the turbulence model SURFPRO (Aria Technologies, France, and Arianet, Italy). The Republic of San Marino is almost completely mountainous, 10 km west of the Adriatic Sea and affected by land-sea breeze circulation. SPRAY is suitable for simulations under non-homogenous and non-stationary conditions, over a complex topography. The emission scenario included both a worst-case meteorological condition and three 10-day periods representative of typical atmospheric conditions for 2014. The simulated NOx concentrations were compared with the regulatory air quality limits. Notwithstanding the high emission rate, the simulation showed a spatially confined environmental impact, with only a single NOx peak at ground where the plume hits the hillside of the Mount Titano (749 m a.s.l.), 5 km west of the future power plant.

Climate predictions are affected by high uncertainties partially due to an insufficient knowledge of aerosol–cloud interactions. One of the poorly understood processes is formation of mixed-phase clouds (MPCs) via heterogeneous ice nucleation. Field measurements of the atmospheric ice phase in MPCs are challenging due to the presence of much more numerous liquid droplets. The Ice Selective Inlet (ISI), presented in this paper, is a novel inlet designed to selectively sample pristine ice crystals in mixed-phase clouds and extract the ice residual particles contained within the crystals for physical and chemical characterization. Using a modular setup composed of a cyclone impactor, droplet evaporation unit and pumped counterflow virtual impactor (PCVI), the ISI segregates particles based on their inertia and phase, exclusively extracting small ice particles between 5 and 20 μm in diameter. The setup also includes optical particle spectrometers for analysis of the number size distribution and shape of the sampled hydrometeors. The novelty of the ISI is a droplet evaporation unit, which separates liquid droplets and ice crystals in the airborne state, thus avoiding physical impaction of the hydrometeors and limiting potential artefacts. The design and validation of the droplet evaporation unit is based on modelling studies of droplet evaporation rates and computational fluid dynamics simulations of gas and particle flows through the unit. Prior to deployment in the field, an inter-comparison of the optical particle size spectrometers and a characterization of the transmission efficiency of the PCVI was conducted in the laboratory. The ISI was subsequently deployed during the Cloud and Aerosol Characterization Experiment (CLACE) 2013 and 2014 – two extensive international field campaigns encompassing comprehensive measurements of cloud microphysics, as well as bulk aerosol, ice residual and ice nuclei properties. The campaigns provided an important opportunity for a proof of concept of the inlet design. In this work we present the setup of the ISI, including the modelling and laboratory characterization of its components, as well as field measurements demonstrating the ISI performance and validating the working principle of the inlet. Finally, measurements of biological aerosol during a Saharan dust event (SDE) are presented, showing a first indication of enrichment of bio-material in sub-2 μm ice residuals.

In this article, we propose a methodology in order to measure political positioning and constituent perception. Political leaders should be able to effectively define the distinctive characteristic ...

The atmospheric dispersion of the NOX plume that will be emitted from a new power–plant, at present under installation, was simulated at micro–scale with Micro–Swift–Spray (MSS) Model. The plant will be constructed in a residential urban area in the town of Modena (Po Valley, Northern Italy), where low wind speeds and thermal inversions are quite frequent. Simulation results point out a different behavior of urban canopy in influencing the 3D dispersion patterns among urban obstacles, according to atmospheric mixing conditions: in case of moderate wind events, urban canyon phenomena may occur with a consequent increasing of NOX concentration gradients among buildings, while with low winds the near–field influence of the buildings emphasizes pollutant accumulation. The MSS simulated NOX concentrations result always much lower than the regulatory limits for air quality. The comparison of simulation results with measured concentration data for NOX shows the importance of micro–scale dispersion modeling to perform an accurate and reliable assessment of meteorological condition effects on pollutant distribution, and the ability of MSS in providing reliable simulations of atmospheric dispersion.

The limits to atmospheric pollutant concentration set by the European Commission provide a challenging target for the municipalities in the Po Valley, because of the characteristic climatic conditions and high population density of this region. In order to assess climatology and trends in the concentration of atmospheric particles in the Po Valley, a data set of PM10 data from 41 sites across the Po Valley have been analysed, including both traffic and background sites (either urban, suburban or rural). Of these 41 sites, 18 with 10 yr or longer record have been analysed for long-term trend in deseasonalized monthly means, in annual quantiles and in monthly frequency distribution. A widespread significant decreasing trend has been observed at most sites, up to a few percent per year, by a generalized least squares and Theil–Sen method. All 41 sites have been tested for significant weekly periodicity by Kruskal–Wallis test for mean anomalies and by Wilcoxon test for weekend effect magnitude. A significant weekly periodicity has been observed for most PM10 series, particularly in summer and ascribed mainly to anthropic particulate emissions. A cluster analysis has been applied in order to highlight stations sharing similar pollution conditions over the reference period. Five clusters have been found, two encompassing the metropolitan areas of Turin and Milan and their respective nearby sites and the other three clusters gathering northeast, northwest and central Po Valley sites respectively. Finally, the observed trends in atmospheric PM10 have been compared to trends in provincial emissions of particulates and PM precursors, and analysed along with data on vehicular fleet age, composition and fuel sales. A significant basin-wide drop in emissions occurred for gaseous pollutants, contrarily to emissions of PM10 and PM2.5, whose drop was low and restricted to a few provinces. It is not clear whether the decrease for only gaseous emissions is sufficient to explain the observed drop in atmospheric PM10, or if the low drop in particulate emissions is indeed due to the uncertainty in the emission inventory data for this species.

The atmospheric impact of stack emissions from a power plant (tri-generator and boilers) that will be installed in an urban area in the central Po valley (Northern Italy), characterized by calm wind events, is studied and compared with the impact of the existing plant (conventional boilers). Both the plants are supplied by methane gas. The atmospheric dispersion of NOx emitted is simulated, both in the current and future scenario, by the software package ARIA INDUSTRY. The NOx emission rates are set equal to the regulatory emission limits for existing and future boilers, while the tri-generation system emission rates are set equal to the emission limits certified by the system manufacturer. The simulation periods focus over the 2010 winter season. The simulation estimates the impact of NOx emissions on air quality (vertical concentration profiles and concentration maps at the ground) in the urban area close to the plant. The future power plant impact on air quality results lower than the impact of the existing plant, even if the yearly total mass of pollutants emitted in atmosphere from the new power plant is higher than from the existing plant. The emissions of conventional boilers result the main responsible of the air pollution at the ground in the future scenario.

The Po Valley is one of the largest European regions with a remarkably high concentration level of atmospheric pollutants, both for particulate and gaseous compounds. In the last decade stringent regulations on air quality standards and on anthropogenic emissions have been set by the European Commission, including also for PM2.5 and its main components since 2008. These regulations have led to an overall improvement in air quality across Europe, including the Po Valley and specifically PM10, as shown in a previous study by Bigi and Ghermandi (2014). In order to assess the trend and variability in PM2.5 in the Po Valley and its role in the decrease in PM10, we analysed daily gravimetric equivalent concentration of PM2.5 and of PM10–2.5 at 44 and 15 sites respectively across the Po Valley. The duration of the times series investigated in this work ranges from 7 to 10 years. For both PM sizes, the trend in deseasonalized monthly means, annual quantiles and in monthly frequency distribution was estimated: this showed a significant decreasing trend at several sites for both size fractions and mostly occurring in winter. All series were tested for a significant weekly periodicity (a proxy to estimate the impact of primary anthropogenic emissions), yielding positive results for summer PM2.5 and for summer and winter PM10–2.5. Hierarchical cluster analysis showed moderate variability in PM2.5 across the valley, with two to three main clusters, dividing the area in western, eastern and southern/Apennines foothill sectors. The trend in atmospheric concentration was compared with the time series of local emissions, vehicular fleet details and fuel sales, suggesting that the decrease in PM2.5 and in PM10 originates from a drop both in primary and in precursors of secondary inorganic aerosol emissions, largely ascribed to vehicular traffic. Potentially, the increase in biomass burning emissions in winter and the modest decrease in NH3 weaken an otherwise even larger drop in atmospheric concentrations.