No abstract

This study investigates the effect of grid resolution on the particulate matter (PM 10 , PM 2.5 ) mass concentrations and its chemical composition simulated with the AMS-MINNI modelling system. The air pollution was simulated over Italy with grid resolutions of 20 and 4 km, for a whole year. The gridded emissions were produced performing speciation and spacetime disaggregation of aggregated inventory data, using both land use information and anthropogenic activity-based profiles. Often, the fine grid simulations, based on high resolution gridded emissions, improved the agreement between model and measurements. In particular, the use of a fine grid improved predictions of primary species such as elemental carbon (EC), PM 10 and PM 2.5 mainly at urban stations. An improvement of predicted PM components and mass concentration at high altitudes sites was also observed, especially during winter. However, a general overestimation of nitrate (NO 3 -) and of secondary inorganic species, more evident at night than during the day, was increased by employing a finer grid. Organic carbon (OC) was more affected by the grid resolution than the other species. At urban and kerbside stations, the use of a finer grid resulted in an overestimation of primary organic carbon aerosol (POC) but had a negligible effect on secondary organic carbon aerosol (SOC). The overestimation of carbonaceous aerosol (defined as the sum of EC, POC and SOC), at an urban station, opposite to general underestimation of this component by air quality (AQ) models, indicates that the anthropogenic emissions can contribute as much as organic model formulation at the success of simulation in reproducing experimental data.The modelling results obtained under stable meteorological conditions characterised by weak winds, which are often encountered in the Po Valley, did not improve substantially by the increase of the modelling system resolution.

The aim of the study is to find the relationship between the land surface temperature and air temperature and to determine the hot spots in the urban area of Bucharest, the capital of Romania. The analysis was based on images from both moderate-resolution imaging spectroradiometer (MODIS), located on both Terra and Aqua platforms, as well as on data recorded by the four automatic weather stations existing in the endowment of The National Air Quality Monitoring Network, from the summer of 2017. Correlation coefficients between land surface temperature and air temperature were higher at night (0.8-0.87) and slightly lower during the day (0.71-0.77). After the validation of satellite data with in-situ temperature measurements, the hot spots in the metropolitan area of Bucharest were identified using Getis-Ord spatial statistics analysis. It has been achieved that the “very hot” areas are grouped in the center of the city and along the main traffic streets and dense residential areas. During the day the "very hot spots” represent 33.2% of the city's surface, and during the night 31.6%. The area where the mentioned spots persist, falls into the "very hot spot" category both day and night, it represents 27.1% of the city’s surface and it is mainly represented by the city center.

No abstract

The impact of anthropogenic actions on the environment and climate has recently increased the need to map the afforested areas. In this context, the three-dimensional (3D) measurement of vegetation structures plays an important role in having an efficient forest inventory and management. Nowadays, the airborne LiDAR (Light Detection And Ranging) system offers high horizontal resolution as well as vertical dimension information, making it possible to estimate both three-dimensional characteristics of individual trees and to identify the distribution of forest resources in the region. This study aims to present a processing approach for the determination of each tree’s position (X and Y location, as well as tree height) and its dimensions (crown diameter, area and volume) using geometrically accurate 3D point clouds (data sets were collected in a forested area in Argeș County, Romania). To a better understanding of the forest features and to explore the potential of remote sensing for such analysis, it was further exploited Digital Terrain Model (DTM), Digital Surface Model (DSM), and Canopy Height Model (CHM) derivation.