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Dr. Giovanni Gualtieri
National Research Council, Institute of Biometeorology (CNR-IBIMET), Via Caproni 8, 50145 Firenze, Italy

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0 Air Quality
0 Wind Energy
0 Wind Resource Assessment
0 boundary layer meteorology
0 ROAD TRANSPORTATION

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Journal article
Published: 19 August 2021 in Atmosphere
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Low-cost air quality stations can provide useful data that can offer a complete picture of urban air quality dynamics, especially when integrated with daily measurements from reference air quality stations. However, the success of such deployment depends on the measurement accuracy and the capability of resolving spatial and temporal gradients within a spatial domain. In this work, an ensemble of three low-cost stations named “AirQino” was deployed to monitor particulate matter (PM) concentrations over three different sites in an area affected by poor air quality conditions. Data of PM2.5 and PM10 concentrations were collected for about two years following a protocol based on field calibration and validation with a reference station. Results indicated that: (i) AirQino station measurements were accurate and stable during co-location periods over time (R2 = 0.5–0.83 and RMSE = 6.4–11.2 μg m−3; valid data: 87.7–95.7%), resolving current spatial and temporal gradients; (ii) spatial variability of anthropogenic emissions was mainly due to extensive use of wood for household heating; (iii) the high temporal resolution made it possible to detect time occurrence and strength of PM10 concentration peaks; (iv) the number of episodes above the 1-h threshold of 90 μg m−3 and their persistence were higher under urban and industrial sites compared to the rural area.

ACS Style

Lorenzo Brilli; Federico Carotenuto; Bianca Patrizia Andreini; Alice Cavaliere; Andrea Esposito; Beniamino Gioli; Francesca Martelli; Marco Stefanelli; Carolina Vagnoli; Stefania Venturi; Alessandro Zaldei; Giovanni Gualtieri. Low-Cost Air Quality Stations’ Capability to Integrate Reference Stations in Particulate Matter Dynamics Assessment. Atmosphere 2021, 12, 1065 .

AMA Style

Lorenzo Brilli, Federico Carotenuto, Bianca Patrizia Andreini, Alice Cavaliere, Andrea Esposito, Beniamino Gioli, Francesca Martelli, Marco Stefanelli, Carolina Vagnoli, Stefania Venturi, Alessandro Zaldei, Giovanni Gualtieri. Low-Cost Air Quality Stations’ Capability to Integrate Reference Stations in Particulate Matter Dynamics Assessment. Atmosphere. 2021; 12 (8):1065.

Chicago/Turabian Style

Lorenzo Brilli; Federico Carotenuto; Bianca Patrizia Andreini; Alice Cavaliere; Andrea Esposito; Beniamino Gioli; Francesca Martelli; Marco Stefanelli; Carolina Vagnoli; Stefania Venturi; Alessandro Zaldei; Giovanni Gualtieri. 2021. "Low-Cost Air Quality Stations’ Capability to Integrate Reference Stations in Particulate Matter Dynamics Assessment." Atmosphere 12, no. 8: 1065.

Journal article
Published: 10 July 2021 in Energies
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The reliability of ERA5 reanalyses for directly predicting wind resources and energy production has been assessed against observations from six tall towers installed over very heterogeneous sites around the world. Scores were acceptable at the FINO3 (Germany) offshore platform for both wind speed (bias within 1%, r = 0.95−0.96) and capacity factor (CF, at worst biased by 6.70%) and at the flat and sea-level site of Cabauw (Netherlands) for both wind speed (bias within 7%, r = 0.93−0.94) and CF (bias within 6.82%). Conversely, due to the ERA5 limited resolution (~31 km), large under-predictions were found at the Boulder (US) and Ghoroghchi (Iran) mountain sites, and large over-predictions were found at the Wallaby Creek (Australia) forested site. Therefore, using ERA5 in place of higher-resolution regional reanalysis products or numerical weather prediction models should be avoided when addressing sites with high variation of topography and, in particular, land use. ERA5 scores at the Humansdorp (South Africa) coastal location were generally acceptable, at least for wind speed (bias of 14%, r = 0.84) if not for CF (biased by 20.84%). However, due to the inherent sea–land discontinuity resulting in large differences in both surface roughness and solar irradiation (and thus stability conditions), a particular caution should be paid when applying ERA5 over coastal locations.

ACS Style

Giovanni Gualtieri. Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers. Energies 2021, 14, 4169 .

AMA Style

Giovanni Gualtieri. Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers. Energies. 2021; 14 (14):4169.

Chicago/Turabian Style

Giovanni Gualtieri. 2021. "Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers." Energies 14, no. 14: 4169.

Research article
Published: 11 February 2021 in Environmental Science and Pollution Research
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A multi-year dataset of measurements of CO2 concentrations, eddy covariance fluxes, and meteorological parameters over the city centre of Florence (Italy) has been analysed to assess the role of anthropogenic emissions and meteorology in controlling urban CO2 concentrations. The latter exhibited a negative correlation with air temperature, wind speed, solar radiation, and sensible heat flux and a positive one with relative humidity and emissions. A linear and an artificial neural network (ANN) model have been developed and validated for short-term modelling of 3-h CO2 concentrations. The ANN model performed better, with mean bias of 0.58 ppm, root mean square error within 30 ppm, and r2=0.49. Data clustering through the self-organized maps allowed to disentangle the role played by emissions and meteorological parameters in influencing CO2 concentrations. Sensitivity analysis of CO2 concentrations revealed a primary role played by the meteorological parameters, particularly wind speed. These results highlighted that (i) emission reduction actions at local urban scale should be better tied to actual and expected meteorological conditions and (ii) those actions alone have limited effects (e.g. a 20% emission reduction would result in a 3% CO2 concentrations reduction). For all these reasons, large-scale policies would be needed.

ACS Style

Giovanni Gualtieri; Sara Di Lonardo; Federico Carotenuto; Piero Toscano; Carolina Vagnoli; Alessandro Zaldei; Beniamino Gioli. The role of emissions and meteorology in driving CO2 concentrations in urban areas. Environmental Science and Pollution Research 2021, 1 -11.

AMA Style

Giovanni Gualtieri, Sara Di Lonardo, Federico Carotenuto, Piero Toscano, Carolina Vagnoli, Alessandro Zaldei, Beniamino Gioli. The role of emissions and meteorology in driving CO2 concentrations in urban areas. Environmental Science and Pollution Research. 2021; ():1-11.

Chicago/Turabian Style

Giovanni Gualtieri; Sara Di Lonardo; Federico Carotenuto; Piero Toscano; Carolina Vagnoli; Alessandro Zaldei; Beniamino Gioli. 2021. "The role of emissions and meteorology in driving CO2 concentrations in urban areas." Environmental Science and Pollution Research , no. : 1-11.

Research article
Published: 28 September 2020 in Meteorological Applications
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The performances of limited area weather models are affected by the choice of core solvers, domain resolutions, and initial and boundary conditions. To understand the extent of such differences on simulated wind fields, weather research and forecast (WRF) simulations initialized by different forcings were extensively compared with an aircraft‐derived high‐resolution data set. The two used forcings were the European Centre for Medium‐Range Weather Forecasts (ECMWF) ERA‐Interim reanalysis and the National Centers for Environmental Predictions (NCEP) Climate Forecast System Reanalysis (CFSR). The model domain covered a large portion of central western Italy (including part of the Tyrrhenian coast) encompassing the aircraft track and allowed the characterization of their performance across the simulation domain rather than a small set of point‐based observations. The WRF results show good agreement with the aircraft data across the whole flight track with both forcings (root mean square errors (RMSEs) < 2.3 m·s−1 and an average r2 = 0.7). Orography and coasts show an effect on simulated wind fields. The presence of a strong orography (which is smoothed by the model internal terrain elevation model) is associated with increased errors. Distance from the coast is also associated with a variation in RMSE (even if in a non‐straightforward manner) because of potential breeze effects. No forcing data set clearly outperforms the other, while the ECMWF has higher correlation co‐efficients when considering wind direction.

ACS Style

Federico Carotenuto; Giovanni Gualtieri; Piero Toscano; Franco Miglietta; Beniamino Gioli. WRF wind field assessment under multiple forcings using spatialized aircraft data. Meteorological Applications 2020, 27, 1 .

AMA Style

Federico Carotenuto, Giovanni Gualtieri, Piero Toscano, Franco Miglietta, Beniamino Gioli. WRF wind field assessment under multiple forcings using spatialized aircraft data. Meteorological Applications. 2020; 27 (5):1.

Chicago/Turabian Style

Federico Carotenuto; Giovanni Gualtieri; Piero Toscano; Franco Miglietta; Beniamino Gioli. 2020. "WRF wind field assessment under multiple forcings using spatialized aircraft data." Meteorological Applications 27, no. 5: 1.

Journal article
Published: 18 September 2020 in Environmental Pollution
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Covid19-induced lockdown measures caused modifications in atmospheric pollutant and greenhouse gas emissions. Urban road traffic was the most impacted, with 48‒60% average reduction in Italy. This offered an unprecedented opportunity to assess how a prolonged (∼2 months) and remarkable abatement of traffic emissions impacted on urban air quality. Six out of the eight most populated cities in Italy with different climatic conditions were analysed: Milan, Bologna, Florence, Rome, Naples, and Palermo. The selected scenario (24/02/2020‒30/04/2020) was compared to a meteorologically comparable scenario in 2019 (25/02/2019–02/05/2019). NO2, O3, PM2.5 and PM10 observations from 58 air quality and meteorological stations were used, while traffic mobility was derived from municipality-scale big data. NO2 levels remarkably dropped over all urban areas (from ‒24.9% in Milan to ‒59.1% in Naples), to an extent roughly proportional but lower than traffic reduction. Conversely, O3 concentrations remained unchanged or even increased (up to 13.7% in Palermo and 14.7% in Rome), likely because of the reduced O3 titration triggered by lower NO emissions from vehicles, and lower NOx emissions over typical VOCs-limited environments such as urban areas, not compensated by comparable VOCs emissions reductions. PM10 exhibited reductions up to 31.5% (Palermo) and increases up to 7.3% (Naples), while PM2.5 showed reductions of ∼13–17% counterbalanced by increases up to ∼9%. Higher household heating usage (+16–19% in March), also driven by colder weather conditions than 2019 (‒0.2 to ‒0.8 °C) may partly explain primary PM emissions increase, while an increase in agriculture activities may account for the NH3 emissions increase leading to secondary aerosol formation. This study confirmed the complex nature of atmospheric pollution even when a major emission source is clearly isolated and controlled, and the need for consistent decarbonisation efforts across all emission sectors to really improve air quality and public health. Main finding A 2-month urban traffic ban extended to the whole Italy only significantly reduced NO2 levels, while O3, PM2.5 and PM10 concentrations were affected to a minor extent.

ACS Style

Giovanni Gualtieri; Lorenzo Brilli; Federico Carotenuto; Carolina Vagnoli; Alessandro Zaldei; Beniamino Gioli. Quantifying road traffic impact on air quality in urban areas: A Covid19-induced lockdown analysis in Italy. Environmental Pollution 2020, 267, 115682 -115682.

AMA Style

Giovanni Gualtieri, Lorenzo Brilli, Federico Carotenuto, Carolina Vagnoli, Alessandro Zaldei, Beniamino Gioli. Quantifying road traffic impact on air quality in urban areas: A Covid19-induced lockdown analysis in Italy. Environmental Pollution. 2020; 267 ():115682-115682.

Chicago/Turabian Style

Giovanni Gualtieri; Lorenzo Brilli; Federico Carotenuto; Carolina Vagnoli; Alessandro Zaldei; Beniamino Gioli. 2020. "Quantifying road traffic impact on air quality in urban areas: A Covid19-induced lockdown analysis in Italy." Environmental Pollution 267, no. : 115682-115682.

Journal article
Published: 30 March 2020 in Sensors
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The Arctic is an important natural laboratory that is extremely sensitive to climatic changes and its monitoring is, therefore, of great importance. Due to the environmental extremes it is often hard to deploy sensors and observations are limited to a few sparse observation points limiting the spatial and temporal coverage of the Arctic measurement. Given these constraints the possibility of deploying a rugged network of low-cost sensors remains an interesting and convenient option. The present work validates for the first time a low-cost sensor array (AIRQino) for monitoring basic meteorological parameters and atmospheric composition in the Arctic (air temperature, relative humidity, particulate matter, and CO2). AIRQino was deployed for one year in the Svalbard archipelago and its outputs compared with reference sensors. Results show good agreement with the reference meteorological parameters (air temperature (T) and relative humidity (RH)) with correlation coefficients above 0.8 and small absolute errors (≈1 °C for temperature and ≈6% for RH). Particulate matter (PM) low-cost sensors show a good linearity (r2 ≈ 0.8) and small absolute errors for both PM2.5 and PM10 (≈1 µg m−3 for PM2.5 and ≈3 µg m−3 for PM10), while overall accuracy is impacted both by the unknown composition of the local aerosol, and by high humidity conditions likely generating hygroscopic effects. CO2 exhibits a satisfying agreement with r2 around 0.70 and an absolute error of ≈23 mg m−3. Overall these results, coupled with an excellent data coverage and scarce need of maintenance make the AIRQino or similar devices integrations an interesting tool for future extended sensor networks also in the Arctic environment.

ACS Style

Federico Carotenuto; Lorenzo Brilli; Beniamino Gioli; Giovanni Gualtieri; Carolina Vagnoli; Mauro Mazzola; Angelo Pietro Viola; Vito Vitale; Mirko Severi; Rita Traversi; Alessandro Zaldei. Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment. Sensors 2020, 20, 1919 .

AMA Style

Federico Carotenuto, Lorenzo Brilli, Beniamino Gioli, Giovanni Gualtieri, Carolina Vagnoli, Mauro Mazzola, Angelo Pietro Viola, Vito Vitale, Mirko Severi, Rita Traversi, Alessandro Zaldei. Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment. Sensors. 2020; 20 (7):1919.

Chicago/Turabian Style

Federico Carotenuto; Lorenzo Brilli; Beniamino Gioli; Giovanni Gualtieri; Carolina Vagnoli; Mauro Mazzola; Angelo Pietro Viola; Vito Vitale; Mirko Severi; Rita Traversi; Alessandro Zaldei. 2020. "Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment." Sensors 20, no. 7: 1919.

Journal article
Published: 18 February 2020 in Energy Conversion and Management
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Among the main grid-based wind farm layout optimization studies addressed in the literature, 14 layouts have been recomputed by selecting the levelized cost of energy as a primary objective function. Relying on 120 wind turbine combinations, a previously developed optimization method targeting best turbine selection has then been applied. All literature layouts were optimized, as capacity factors were (slightly) increased (78.89–80.90 to 83.02–83.07%), while levelized costs of energy were (significantly) reduced (130.37–370.42 to 54.01–142.64 $/MWh). This study concluded that neither the discrete nor the continuous optimization model can be recommended in all scenarios. In general, a capacity factor increase does not necessarily imply a decrease in levelized cost of energy. The latter may be minimized by decreasing the overall wind farm capacity, the number of turbines, or selecting turbines with lower rotor diameters or rated powers. By contrast, capacity factor may be maximized by installing turbines with higher hub heights or lower rated speeds. Contradicting various findings, using turbines with different rotor diameters, rated powers or hub heights is not recommended to minimize the levelized cost of energy. Although addressed within several optimization studies, maximization of energy production is a misleading target, as involving the highest costs of energy.

ACS Style

Giovanni Gualtieri. Comparative analysis and improvement of grid-based wind farm layout optimization. Energy Conversion and Management 2020, 208, 112593 .

AMA Style

Giovanni Gualtieri. Comparative analysis and improvement of grid-based wind farm layout optimization. Energy Conversion and Management. 2020; 208 ():112593.

Chicago/Turabian Style

Giovanni Gualtieri. 2020. "Comparative analysis and improvement of grid-based wind farm layout optimization." Energy Conversion and Management 208, no. : 112593.

Journal article
Published: 26 September 2019 in Journal of Economic Behavior & Organization
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A society that believes wealth to be determined by random “luck”, rather than by merit, demands more redistribution. We present evidence of this behavior by exploiting a natural experiment provided by the L’Aquila earthquake in 2009, which hit a large area of Central Italy through a series of destructive shakes over eight days. Matching detailed information on the ground acceleration registered during each shock with survey data about individual opinions on redistribution we show that the average intensity of the shakes is associated with subsequent stronger beliefs that, for a society to be fair, income inequalities should be leveled by redistribution. The shocks, however, are not all alike. We find that only the last three shakes - occurred on the fourth and the eighth day of the earthquake - have a statistically significant impact. Overall, we find that the timing and repetition of the shocks play a role in informing redistributive preferences.

ACS Style

Giovanni Gualtieri; Marcella Nicolini; Fabio Sabatini. Repeated shocks and preferences for redistribution. Journal of Economic Behavior & Organization 2019, 167, 1 .

AMA Style

Giovanni Gualtieri, Marcella Nicolini, Fabio Sabatini. Repeated shocks and preferences for redistribution. Journal of Economic Behavior & Organization. 2019; 167 ():1.

Chicago/Turabian Style

Giovanni Gualtieri; Marcella Nicolini; Fabio Sabatini. 2019. "Repeated shocks and preferences for redistribution." Journal of Economic Behavior & Organization 167, no. : 1.

Journal article
Published: 25 April 2019 in Energy Conversion and Management
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A novel method was developed to detect the optimal onshore wind farm layout driven by the characteristics of all commercially-available wind turbines. A huge number of turbine combinations (577) was processed, resulting in 22,721 generated layouts. Various assumptions and constraints were considered, mostly derived from the literature, including site features, wind conditions, and layout design. For the latter, an irregularly staggered turbine array configuration was assumed. Wake effects were simulated through the Jensen’s model, while a typical turbine thrust coefficient curve as a function of wind speed was originally developed. A detailed cost model was used, with levelized cost of energy selected as primary and capacity factor as secondary objective function. The self-organizing maps were used to address a thorough analysis, proving to be a powerful means to straightforwardly achieve a comprehensive pattern of wind farm layout optimization. In general, the two optimization functions basically match, while for higher wind potential sites, increasing capacity factor did not necessarily result in decreasing levelized cost of energy. The latter may be minimised by reducing the total number of turbines or the overall wind farm capacity, as well as maximising rotor diameters or minimising rated wind speeds; increasing rated power or hub height is only beneficial for mid-potential sites. The mere maximisation of wind farm energy production is a misleading target, as corresponding to mid-to-high values of levelized cost of energy. In contrast to previous studies, the use of turbines with different rated power, rotor diameter or hub height should be avoided.

ACS Style

Giovanni Gualtieri. A novel method for wind farm layout optimization based on wind turbine selection. Energy Conversion and Management 2019, 193, 106 -123.

AMA Style

Giovanni Gualtieri. A novel method for wind farm layout optimization based on wind turbine selection. Energy Conversion and Management. 2019; 193 ():106-123.

Chicago/Turabian Style

Giovanni Gualtieri. 2019. "A novel method for wind farm layout optimization based on wind turbine selection." Energy Conversion and Management 193, no. : 106-123.

Review
Published: 19 December 2018 in Renewable and Sustainable Energy Reviews
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A review spanning across a 40-year period (1978–2018) and including a total of 332 applications has been addressed on theoretical and empirical wind resource extrapolation models applied in wind energy, which can be grouped into three main families: (i) the logarithmic models; (ii) the Deaves and Harris (DH) model; (iii) the power law (PL). Applied over 96 very heterogeneous locations worldwide, models have been tested against observations at upper extrapolation height and assessed by location characteristics, extrapolation range skills, and application economical advantages. The logarithmic models can nowadays be considered unsuitable for extrapolating wind resource to hub height of current multi-MW WTs, mainly because exhibiting a limited extrapolation range capability (about 10–50 m median bin). Finer scores in extrapolating wind resource (mean absolute bias of 3.3%) and in predicting energy output (10.1%) were achieved by the DH model, also showing remarkable extrapolation range skills (10–80 m median bin). However, although among the most economical and forward-looking solutions, its need for accurate z0 assessment and u* observations resulted so far in great limitations to its large-scale application for wind energy purposes (less than 1%). Eventually, the PL confirmed the most reliable – and largely most commonly used (73.5%) – approach for wind energy applications. Out of the plethora of PL models developed in the literature, the PL(α)-αlower and the PL(α)-αI were the finest in predicting both extrapolated wind resource (mean absolute error of 4% and 4.4%, respectively) and energy output (8.9% and 5.5%), also exhibiting extrapolation range skills meeting modern WTs requirements. By contrast, the PL using α = 1/7 returned among the worst scores, yet resulting – since the simplest – the solution most frequently applied (19.6%). This study also demonstrated that extrapolation tools requiring the most expensive instrumentation equipment do not necessarily return the finest scores.

ACS Style

Giovanni Gualtieri. A comprehensive review on wind resource extrapolation models applied in wind energy. Renewable and Sustainable Energy Reviews 2018, 102, 215 -233.

AMA Style

Giovanni Gualtieri. A comprehensive review on wind resource extrapolation models applied in wind energy. Renewable and Sustainable Energy Reviews. 2018; 102 ():215-233.

Chicago/Turabian Style

Giovanni Gualtieri. 2018. "A comprehensive review on wind resource extrapolation models applied in wind energy." Renewable and Sustainable Energy Reviews 102, no. : 215-233.

Journal article
Published: 01 November 2018 in Atmospheric Pollution Research
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A linear and an artificial neural network (ANN) statistical model have been developed and validated for short-term forecasting of PM10 hourly concentrations in the city of Brescia (Italy). PM10 observed concentrations were biased by less than 1% by each model, though the ANN outperformed the linear model, as exhibiting NRMSE of 0.48 vs. 0.53, and r2 of 0.57 vs. 0.48. The self-organizing maps (SOMs) showed that both models predictions exhibit the same clustering as the observations, with the ANN at worst capable of under-estimating clustered PM10 peak concentrations by 5.8 μg/m3. In Brescia, PM10 most critical conditions were detected in wintertime in the early morning or late afternoon under unfavourable meteorological conditions, i.e. reduced advection enhancing PM10 stagnation, and lack of precipitations capable of reducing PM10 resuspension. Under these conditions, PM10 accumulation is driven by local anthropogenic emissions ascribing to two main sources: heating plants, responsible of emissions of primary PM10 (mostly PM2.5, likely resulting from wood and biomass burning); and road traffic (basically diesel vehicles), mainly responsible of emissions of secondary PM10 precursors (mostly NOx), and secondly of primary PM10 emissions. The SOM analysis clearly indicated that PM10 most critical conditions are driven by the secondary rather primary PM10 component.

ACS Style

Giovanni Gualtieri; Federico Carotenuto; Sandro Finardi; Mario Tartaglia; Piero Toscano; Beniamino Gioli. Forecasting PM10 hourly concentrations in northern Italy: Insights on models performance and PM10 drivers through self-organizing maps. Atmospheric Pollution Research 2018, 9, 1204 -1213.

AMA Style

Giovanni Gualtieri, Federico Carotenuto, Sandro Finardi, Mario Tartaglia, Piero Toscano, Beniamino Gioli. Forecasting PM10 hourly concentrations in northern Italy: Insights on models performance and PM10 drivers through self-organizing maps. Atmospheric Pollution Research. 2018; 9 (6):1204-1213.

Chicago/Turabian Style

Giovanni Gualtieri; Federico Carotenuto; Sandro Finardi; Mario Tartaglia; Piero Toscano; Beniamino Gioli. 2018. "Forecasting PM10 hourly concentrations in northern Italy: Insights on models performance and PM10 drivers through self-organizing maps." Atmospheric Pollution Research 9, no. 6: 1204-1213.

Journal article
Published: 28 August 2018 in Sensors
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A low-cost air quality station has been developed for real-time monitoring of main atmospheric pollutants. Sensors for CO, CO2, NO2, O3, VOC, PM2.5 and PM10 were integrated on an Arduino Shield compatible board. As concerns PM2.5 and PM10 sensors, the station underwent a laboratory calibration and later a field validation. Laboratory calibration has been carried out at the headquarters of CNR-IBIMET in Florence (Italy) against a TSI DustTrak reference instrument. A MATLAB procedure, implementing advanced mathematical techniques to detect possible complex non-linear relationships between sensor signals and reference data, has been developed and implemented to accomplish the laboratory calibration. Field validation has been performed across a full “heating season” (1 November 2016 to 15 April 2017) by co-locating the station at a road site in Florence where an official fixed air quality station was in operation. Both calibration and validation processes returned fine scores, in most cases better than those achieved for similar systems in the literature. During field validation, in particular, for PM2.5 and PM10 mean biases of 0.036 and 0.598 µg/m3, RMSE of 4.056 and 6.084 µg/m3, and R2 of 0.909 and 0.957 were achieved, respectively. Robustness of the developed station, seamless deployed through a five and a half month outdoor campaign without registering sensor failures or drifts, is a further key point.

ACS Style

Alice Cavaliere; Federico Carotenuto; Filippo Di Gennaro; Beniamino Gioli; Giovanni Gualtieri; Francesca Martelli; Alessandro Matese; Piero Toscano; Carolina Vagnoli; Alessandro Zaldei. Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors. Sensors 2018, 18, 2843 .

AMA Style

Alice Cavaliere, Federico Carotenuto, Filippo Di Gennaro, Beniamino Gioli, Giovanni Gualtieri, Francesca Martelli, Alessandro Matese, Piero Toscano, Carolina Vagnoli, Alessandro Zaldei. Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors. Sensors. 2018; 18 (9):2843.

Chicago/Turabian Style

Alice Cavaliere; Federico Carotenuto; Filippo Di Gennaro; Beniamino Gioli; Giovanni Gualtieri; Francesca Martelli; Alessandro Matese; Piero Toscano; Carolina Vagnoli; Alessandro Zaldei. 2018. "Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors." Sensors 18, no. 9: 2843.

Journal article
Published: 01 May 2018 in Renewable Energy
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Following testing at the Cabauw (Netherlands) flat and inland site, and at the FINO3 offshore platform in the North Sea (Germany), the α–I wind resource extrapolating method was tested at the Boulder (CO, USA) mountain site (1855 m), another substantially different location in terms of surface characteristics, stability conditions, and wind energy pattern. Data from local 82-m M2 met mast between 10 and 80 m were used, with extrapolations to 50-m and 80-m turbine hub heights performed based on 10-m and 20-m turbulence intensity observations. Trained over a 2-year period (1997–1998), the method was validated on the year 1999. Slightly better results than those at both Cabauw and FINO3 were achieved in 50-m and 80-m wind speed extrapolations, with bias within 5%, NRMSE = 0.17–0.23, and r = 0.96–0.98. In predicting the annual energy yield, a bias within 1% was achieved at 50 m, which at worst increased to 6.44% at 80 m. The method was less stability-sensitive than at Cabauw and particularly FINO3. It proved to be reliable even over a mountain site affected by fairly complex terrain, which is noteworthy if considering the power law the method is based upon was actually developed for flat and homogeneous terrain.

ACS Style

Giovanni Gualtieri. Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height: method's test at a mountain site. Renewable Energy 2018, 120, 457 -467.

AMA Style

Giovanni Gualtieri. Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height: method's test at a mountain site. Renewable Energy. 2018; 120 ():457-467.

Chicago/Turabian Style

Giovanni Gualtieri. 2018. "Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height: method's test at a mountain site." Renewable Energy 120, no. : 457-467.

Article
Published: 22 February 2018 in Environmental Monitoring and Assessment
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CO2 remains the greenhouse gas that contributes most to anthropogenic global warming, and the evaluation of its emissions is of major interest to both research and regulatory purposes. Emission inventories generally provide quite reliable estimates of CO2 emissions. However, because of intrinsic uncertainties associated with these estimates, it is of great importance to validate emission inventories against independent estimates. This paper describes an integrated approach combining aircraft measurements and a puff dispersion modelling framework by considering a CO2 industrial point source, located in Biganos, France. CO2 density measurements were obtained by applying the mass balance method, while CO2 emission estimates were derived by implementing the CALMET/CALPUFF model chain. For the latter, three meteorological initializations were used: (i) WRF-modelled outputs initialized by ECMWF reanalyses; (ii) WRF-modelled outputs initialized by CFSR reanalyses and (iii) local in situ observations. Governmental inventorial data were used as reference for all applications. The strengths and weaknesses of the different approaches and how they affect emission estimation uncertainty were investigated. The mass balance based on aircraft measurements was quite succesful in capturing the point source emission strength (at worst with a 16% bias), while the accuracy of the dispersion modelling, markedly when using ECMWF initialization through the WRF model, was only slightly lower (estimation with an 18% bias). The analysis will help in highlighting some methodological best practices that can be used as guidelines for future experiments.

ACS Style

Federico Carotenuto; Giovanni Gualtieri; Franco Miglietta; Angelo Riccio; Piero Toscano; Georg Wohlfahrt; Beniamino Gioli. Industrial point source CO2 emission strength estimation with aircraft measurements and dispersion modelling. Environmental Monitoring and Assessment 2018, 190, 1 -15.

AMA Style

Federico Carotenuto, Giovanni Gualtieri, Franco Miglietta, Angelo Riccio, Piero Toscano, Georg Wohlfahrt, Beniamino Gioli. Industrial point source CO2 emission strength estimation with aircraft measurements and dispersion modelling. Environmental Monitoring and Assessment. 2018; 190 (3):1-15.

Chicago/Turabian Style

Federico Carotenuto; Giovanni Gualtieri; Franco Miglietta; Angelo Riccio; Piero Toscano; Georg Wohlfahrt; Beniamino Gioli. 2018. "Industrial point source CO2 emission strength estimation with aircraft measurements and dispersion modelling." Environmental Monitoring and Assessment 190, no. 3: 1-15.

Journal article
Published: 01 October 2017 in Renewable Energy
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ACS Style

Giovanni Gualtieri. Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height: method's test at an offshore site. Renewable Energy 2017, 111, 175 -186.

AMA Style

Giovanni Gualtieri. Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height: method's test at an offshore site. Renewable Energy. 2017; 111 ():175-186.

Chicago/Turabian Style

Giovanni Gualtieri. 2017. "Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height: method's test at an offshore site." Renewable Energy 111, no. : 175-186.

Journal article
Published: 17 August 2017 in Journal of Wind Engineering and Industrial Aerodynamics
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Only relying on surface information, the Deaves and Harris (DH) model was tested in predicting annual mean wind speed, Weibull distribution and energy yield at turbine hub heights of 40, 80 and 140 m. Based on 3-year (2011–2013) 10-min observations from the mast of Cabauw, the DH model was compared to the power law. The DH model was forced being applied for all stability conditions, although actually developed for the strongest neutral wind conditions (when about 75% overall wind energy may be extracted). The DH model was the finest at higher levels and its accuracy generally increased with height: at 80 m, biases of 2% (mean wind speed) and 6.06% (energy yield) were achieved, while at 140 m biases of 1% and 6.16% were obtained, respectively. Since affected by a high sensitivity to site's roughness length, an accurate assessment of this parameter proved to be main model's shortcoming. In any case, currently-achieved scores encourage further applications of the DH model, which should be deemed as a challenging wind energy research topic. Since valid over the entire boundary layer, it may be regarded as an ideal and certainly forward-looking tool for addressing modern multi-MW turbines whose hub heights steadily increase.

ACS Style

Giovanni Gualtieri. Wind resource extrapolating tools for modern multi-MW wind turbines: Comparison of the Deaves and Harris model vs. the power law. Journal of Wind Engineering and Industrial Aerodynamics 2017, 170, 107 -117.

AMA Style

Giovanni Gualtieri. Wind resource extrapolating tools for modern multi-MW wind turbines: Comparison of the Deaves and Harris model vs. the power law. Journal of Wind Engineering and Industrial Aerodynamics. 2017; 170 ():107-117.

Chicago/Turabian Style

Giovanni Gualtieri. 2017. "Wind resource extrapolating tools for modern multi-MW wind turbines: Comparison of the Deaves and Harris model vs. the power law." Journal of Wind Engineering and Industrial Aerodynamics 170, no. : 107-117.

Journal article
Published: 01 July 2017 in Energy Conversion and Management
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Giovanni Gualtieri. Improving investigation of wind turbine optimal site matching through the self-organizing maps. Energy Conversion and Management 2017, 143, 295 -311.

AMA Style

Giovanni Gualtieri. Improving investigation of wind turbine optimal site matching through the self-organizing maps. Energy Conversion and Management. 2017; 143 ():295-311.

Chicago/Turabian Style

Giovanni Gualtieri. 2017. "Improving investigation of wind turbine optimal site matching through the self-organizing maps." Energy Conversion and Management 143, no. : 295-311.

Journal article
Published: 01 January 2017 in Transportation Research Procedia
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A. Zaldei; F. Camilli; T. De Filippis; F. Di Gennaro; S. Di Lonardo; F. Dini; B. Gioli; G. Gualtieri; A. Matese; W. Nunziati; L. Rocchi; P. Toscano; C. Vagnoli. An integrated low-cost road traffic and air pollution monitoring platform for next citizen observatories. Transportation Research Procedia 2017, 24, 531 -538.

AMA Style

A. Zaldei, F. Camilli, T. De Filippis, F. Di Gennaro, S. Di Lonardo, F. Dini, B. Gioli, G. Gualtieri, A. Matese, W. Nunziati, L. Rocchi, P. Toscano, C. Vagnoli. An integrated low-cost road traffic and air pollution monitoring platform for next citizen observatories. Transportation Research Procedia. 2017; 24 ():531-538.

Chicago/Turabian Style

A. Zaldei; F. Camilli; T. De Filippis; F. Di Gennaro; S. Di Lonardo; F. Dini; B. Gioli; G. Gualtieri; A. Matese; W. Nunziati; L. Rocchi; P. Toscano; C. Vagnoli. 2017. "An integrated low-cost road traffic and air pollution monitoring platform for next citizen observatories." Transportation Research Procedia 24, no. : 531-538.

Journal article
Published: 01 January 2017 in Transportation Research Procedia
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An integrated monitoring platform (IMP) was developed for real-time monitoring of traffic flows and related air pollution in urban areas. The IMP includes: (i) an air quality monitoring unit, integrating the “Arduino” open-source technology with low-cost and high-resolution sensors, to measure air pollutant concentrations; (ii) a traffic monitoring device, equipped with a camera sensor and a video analysis software, to detect vehicles’ counts, speed and category; (iii) a spatial data infrastructure, composed of a central GeoDatabase, a GIS engine, and a web interface, for data storage and management. The IMP was tested in Florence (Italy) by installing sensor devices at a road site where a 1-year measuring campaign was carried out. A reference meteorological station in the city centre was used to provide observations of wind speed and direction, air temperature, and relative humidity. In this work, a statistical analysis was performed to investigate the influence of local road traffic and meteorological conditions on CO, NO2 and CO2 concentrations. Two different methods were applied: a linear regression model and an artificial neural network. To investigate the role played by emissions from road traffic, the influence of all drivers by period of the year (cold vs. warm months) and day of the week (weekdays vs. weekends) was analysed. As a result, the contribution of local road traffic on pollutant concentrations proved to be lower than meteorological parameters.

ACS Style

G. Gualtieri; F. Camilli; A. Cavaliere; T. De Filippis; Salvatore Filippo Di Gennaro; Sara Di Lonardo; F. Dini; B. Gioli; A. Matese; W. Nunziati; L. Rocchi; Piero Toscano; C. Vagnoli; A. Zaldei. An integrated low-cost road traffic and air pollution monitoring platform to assess vehicles’ air quality impact in urban areas. Transportation Research Procedia 2017, 27, 609 -616.

AMA Style

G. Gualtieri, F. Camilli, A. Cavaliere, T. De Filippis, Salvatore Filippo Di Gennaro, Sara Di Lonardo, F. Dini, B. Gioli, A. Matese, W. Nunziati, L. Rocchi, Piero Toscano, C. Vagnoli, A. Zaldei. An integrated low-cost road traffic and air pollution monitoring platform to assess vehicles’ air quality impact in urban areas. Transportation Research Procedia. 2017; 27 ():609-616.

Chicago/Turabian Style

G. Gualtieri; F. Camilli; A. Cavaliere; T. De Filippis; Salvatore Filippo Di Gennaro; Sara Di Lonardo; F. Dini; B. Gioli; A. Matese; W. Nunziati; L. Rocchi; Piero Toscano; C. Vagnoli; A. Zaldei. 2017. "An integrated low-cost road traffic and air pollution monitoring platform to assess vehicles’ air quality impact in urban areas." Transportation Research Procedia 27, no. : 609-616.

Journal article
Published: 01 March 2016 in Renewable Energy
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Giovanni Gualtieri. Atmospheric stability varying wind shear coefficients to improve wind resource extrapolation: A temporal analysis. Renewable Energy 2016, 87, 376 -390.

AMA Style

Giovanni Gualtieri. Atmospheric stability varying wind shear coefficients to improve wind resource extrapolation: A temporal analysis. Renewable Energy. 2016; 87 ():376-390.

Chicago/Turabian Style

Giovanni Gualtieri. 2016. "Atmospheric stability varying wind shear coefficients to improve wind resource extrapolation: A temporal analysis." Renewable Energy 87, no. : 376-390.