Temperature of coastal waters and of watercourses from ASTER images

This paper presents an algorithm to improve the spatial resolution, from 90 m to 30 m, of the thermal mapping of small bodies of water or near coasts obtained from the ASTER satellite sensor. The entire procedure is based only on ASTER images. The frst part of the work deals with the physical and mathematical basis on which the algorithm was constructed, the schema of the main steps and the methods of validation of the algorithm. In the second part two applications of the algorithm are shown, the frst on the area of the delta of the Po River (Italy), the second on the lagoon of Venice (Italy)

Mt. Etna aerosol optical thickness from MIVIS images

This work focuses on the evaluation of Aerosol Optical Thickness (AOT) in Mt. Etna volcano area starting from the analysis of MIVIS VIS images. MIVIS images and ancillary data (atmospheric profiles, photometric measurements, atmospheric infrared radiances, surface temperatures, ground reflectances, SO2 abundances) were collected during the Sicily '97 campaign. Data elaboration was performed with extensive use of 6S radiative transfer model, determining optical thickness with an inversion algorithm that uses atmospheric vertical profile, ground reflectance data and radiance measured by the first MIVIS spectrometer (channels 1-20; range 0.44-0.82 mu). Ground reflectance is the most problematic parameter for the algorithm. In order to have a low and 'uniform' surface reflectance, only pixels located at an altitude between 2000-3000 in a.s.l. were analysed. At this altitude, AOT is very low during non-eruptive periods: at Torre del Filosofo (2920 in a.s.l.) on June 16th 1997, during one MIVIS flight, AOT at 0.55 mu was 0.19. The uncertainty about ground reflectance produces significant errors on volcanic background AOT, and in some cases the error is up to 100%. The developed algorithm worked well on volcanic plume, allowing us to determine the plume related pixels' AOT. High plume AOT values minimize the problems deriving from reflectance uncertainty. Plume optical thickness shows values included in a range from 0.5 to 1.0. The plume AOT map of Mt. Etna volcano, derived from a MIVIS image of June 16th 1997, is presented

Mt. Etna aerosol optical thickness from MIVIS images

This work focuses on the evaluation of Aerosol Optical Thickness (AOT) in Mt. Etna volcano area starting from the analysis of MIVIS VIS images. MIVIS images and ancillary data (atmospheric profiles, photometric measurements, atmospheric infrared radiances, surface temperatures, ground reflectances, SO2 abundances) were collected during the «Sicily '97» campaign. Data elaboration was performed with extensive use of 6S radiative transfer model, determining optical thickness with an inversion algorithm that uses atmospheric vertical profile, ground reflectance data and radiance measured by the first MIVIS spectrometer (channels 1-20; range 0.44-0.82 n). Ground reflectance is the most problematic parameter for the algorithm. In order to have a low and 'uniform' surface reflectance, only pixels located at an altitude between 2000-3000 m a.s.l. were analysed. At this altitude,AOT is very low during non-eruptive periods: at Torre del Filosofo (2920 m a.s.l.) on June 16th 1997, during one MIVIS flight, AOT at 0.55 n was 0.19. The uncertainty about ground reflectance produces significant errors on volcanic background AOT, and in some cases the error is up to 100%. The developed algorithm worked well on volcanic plume, allowing us to determine the plume related pixels'AOT. High plume AOT values minimize the problems deriving from reflectance uncertainty. Plume optical thickness shows values included in a range from 0.5 to 1.0. The plume AOT map of Mt. Etna volcano, derived from a MIVIS image of June 16th 1997, is presented

Spectral emissivity and temperature maps of the Solfatara crater from DAIS hyperspectral images

Quantitative maps of surface temperature and spectral emissivity have been retrieved on the Solfatara crater at Pozzuoli (Naples) from remote sensing hyperspectral data. The present study relies on thermal infrared images collected on July 27, 1997 by the DAIS hyperspectral sensor owned by the German aerospace center (DLR). The Emissivity Spectrum Normalization method was used to make temperature and emissivity estimates. Raw data were previously transformed in radiance and corrected for the atmospheric contributions using the MODTRAN radiative transfer code and the sensor response functions. During the DAIS flight a radiosonde was launched to collect the atmospheric profiles of pressure, temperature and humidity used as input to the code. Retrieved temperature values are in good agreement with temperature measurements performed in situ during the campaign. The spectral emissivity map was used to classify the image in different geo-mineralogical units with the Spectral Angle Mapper method. Areas of geologic interest were previously selected using a mask obtained from an NDVI image calculated with two channels of the visible (red) and the near infrared respectively

Micro–scale simulation of atmospheric emissions from power–plant stacks in the Po Valley

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

Air pollution assessment over Po valley (Italy) using satellite data and ground station measurements

Due to their effect on human health, the study of atmospheric pollutants is an important concern in the Po valley – northern Italy – one of the main industrialized and populated areas of the country. Our work focuses on the applicability of satellite Aerosol Optical Depth (AOD) retrievals in support of air quality monitoring and assessment in urban environments within the Po valley. This has been accomplished by using the implementation of the International MODIS/AIRS Processing Package (IMAPP) Air Quality Applications software, IDEA-I (Infusing satellite Data into Environmental Applications-International) over the Po valley study area. IDEA-I is a globally configurable software package that uses either Terra or Aqua MODerate resolution Imaging Spectro-radiometer (MODIS) AOD product retrievals to identify local domains of high values of aerosol. For our specific analyses, IDEA-I has been used over the large European domain, centred over the Po Valley. One year (2012) of MODIS AOD product retrievals from MODIS on board NASA’s Terra (MOD04) or Aqua (MYD04) satellite has been considered using IDEA-I in a retrospective study. These retrieved data have been also compared with the Particulate Matter (PM 10 ) measurements from the Italian Agency for Environmental Protection (ARPA) ground-based network stations. The acceptable results obtained by the correlation PM 10 – AOD suggest the satellite AOD as a good substitute for monitoring air quality over the Po valley domain. Yet the 10 km resolution of MODIS – AOD product is considered too large for air quality studies at urban scale. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been developed for MODIS which provides AOD data at 1 km of spatial resolution. We have evaluated ability of MODIS product MOD04 and MAIAC products to characterize the spatial distribution of aerosols in the urban area through comparison with surface PM 10 measurements. Using MAIAC data at 1 km, we have examined the relationship between PM 10 concentrations, AOD, and AOD normalized by Planetary Boundary Layer (PBL) depths obtained from NOAA National Center for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS), for the same period of analysis. Results show that the MAIAC retrieval provides a high resolution depiction of the AOD within the Po Valley and performs nearly as well in a statistical sense as the standard MODIS retrieval during the time period considered. Results also highlight that normalization by the analyzed PBL depth to obtain an estimate of the mean boundary layer extinction is needed to capture the seasonal cycle of the observed PM 10 over the Po Valley

Association between outdoor traffic air pollutants and spread of SARS-CoV-2 pandemic in Modena, Northern Italy

BACKGROUND AND AIM: Italy was the first European country severely affected by the SARS-CoV-2 pandemic, especially during the first wave in the North of the country. In particular, Modena is a city of Emilia-Romagna which is a region entirely included in the Po valley, one of European areas characterized by the heaviest air pollution levels due also to its orography. Previous studies yielded at a regional level suggested that higher air pollutant levels may increase both SARS-CoV-2 infection and mortality. METHODS: In this study, we further investigated the association between air pollutant exposure and spread of the SARS-CoV-2 using data collected from Modena municipality in the period February 2020-April 2021. We used traffic pollutant levels collected from an urban air quality monitoring station in the period January 2020-January 2021, including particulate matter (PM10), nitrogen oxides (NO2 and NOx), benzene, and black carbon (BC). We used a random-effects linear regression model within panel data analysis over the study period and we computed beta correlation coefficients (beta) with 95% confidence interval-CI between mean daily pollutant concentrations and SARS-CoV-2 daily positive cases diagnosed in Modena. RESULTS:We found a positive correlation between all traffic pollutants and SARS-CoV-2 cases, namely for PM10 beta was 1.23 (95%CI 1.00-1.46), lower for NOx (beta=0.66, 95%CI 0.56-0.75), and higher for NO2 (beta=1.95, 95%CI 1.59-2.31), benzene (beta=41.41, 95%CI 36.23-46.59), and BC (beta=5.95, 95%CI 1.19-10.72). CONCLUSIONS:Notwithstanding the limitations of use of aggregated data and potential the residual confounding, these findings seem to support the hypothesis that high levels of air pollution may favor the spread of SARS-CoV-2 infection, or alternatively that they reflect a higher mobility and number of social contacts that favor the spread of the infection. KEYWORDS: Air pollution, Traffic-related, Infectious diseases, Environmental epidemiology, Exposure assessmen

GPS Zenith Total Delays and precipitable water in comparison with special meteorological observations in Verona (Italy) during MAP-SOP

Continuous meteorological examination of the Pre-Alpine zones in Northern Italy (Po Valley) is important for determination of atmospheric water cycles connected kith floods and rainfalls. During a special meteorological observing period (MAP-SOP). radiosounding and other measurements were made in the site of Verona (Italy), This paper deals with Zenith Total Delay (ZTD) and Precipitable Water (PW) comparisons obtained by GPS, radiosounding and other meteorological measurements. PW and ZTD from ground-based GPS data in comparison with classical techniques (e.g.. WVR, radiosounding,) from recent literature present an accurate tool for use in meteorology applications (e.g., assimilation in Numerical Weather Prediction (NWP) models oil short-range precipitation forecasts). Comparison of such ZTD for MAP-SOP showed a standard deviation of 16.1 mm and PW comparison showed a standard deviation of 2.7 mm, confirming the accuracy of GPS measurements for meteorology applications. In addition, PW data and its time variation are also matched with time series of meteorological situations. Those results indicate that changes in PW values could be connected to changes in air masses, i.e. to passages of both cold and warm fronts. There is also a correlation between precipitation. forthcoming increase and the following decrease of PW. A good agreement between oscillation of PW and precipitation and strong cyclonic activities is found

SURFACE PARAMETERS EVALUATED FROM SATELLITE REMOTE SENSING IMAGES FOR POLLUTANT ATMOSPHERIC DISPERSION MODELLING

This contribute deals with the use of surface parameters extracted from satellite remote sensing images for the setup of the input dataset required by pollutants atmospheric dispersion models (PATM). These models need 2D distributions (grids) of many surface parameters to model turbulence parameters, as roughness length, albedo, leaf area index and Bowen ratio. Very often these parameters are set using predefined tables defined as a function of land cover (LC). Usually, this last information is extracted from public datasets, such as, for European countries, the CORINE Land Cover (CLC). Some of these parameters can be computed directly from remote sensing. Moreover, land cover classification evaluated from remote sensing can be used to update existing LC datasets. In this work ASTER images have been used to evaluate, using a supervised classification method, the LC map of the area. This LC is used to update the CLC. Moreover, albedo was directly calculated from the image. The importance of information extracted from remote sensing is evaluated using the SPRAY lagrangian PATM. SPRAY has been used to simulate the dispersion of an inert generic pollutant emitted from two virtual sources on a 30 km x 40 km domain in a study area located at Venice (Northern Italy), where a big industrial site is found (Porto Marghera). Real (measured) meteorological data have been used