We present the analysis of the strongest North African desert dust (DD) intrusion that occurred over the Iberian Peninsula (IP) during the last decade, as registered by modern remote sensing techniques like Sun photometry. This event took place from 22 July to 3 August 2004. The most relevant features of this exceptional event, originated over the Saharan desert, were its great intensity and duration. We focus on the columnar aerosol properties measured by the AERONET‐Cimel photometers at El Arenosillo (southwest) and Palencia (north‐center) stations. Aerosol optical depth (AOD) reached a maximum of 2.7 at El Arenosillo during 22 July and 1.3 at Palencia on 23 July, with the Ånsgtröm exponent values near zero during the AOD peaks. In addition, PM10 concentration levels are also reported at various sites of the IP in order to establish the impact of this intrusion, reaching daily values as high as 200 μg/m³ and peaks near 600 μg/m³ in an hourly basis. The interest of this special event is increased because of the mixing with smoke particles from concurrent forest fires in the IP. Features of the columnar volume particle size distribution and derived microphysical parameters, the single scattering albedo, and a reliable estimation of the radiative forcing under these extreme conditions are also reported. Complementary information, as air mass back trajectories, synoptic charts, images, and AOD maps of satellite sensors (SeaWIFS, MODIS) together with NAAPS prognostic model, is used in the analysis in order to draw a detailed scenario of this dust‐smoke event over the IP.

This study focuses on the analysis of the sub-micron aerosol characteristics at El Arenosillo Station, a rural and coastal environment in South-western Spain between 1 August 2004 and 31 July 2006 (594 days). The mean total concentration (N_T) was 8660 cm⁻³ and the mean concentrations in the nucleation (N_NUC), Aitken (N_AIT) and accumulation (N_ACC) particle size ranges were 2830 cm⁻³, 4110 cm⁻³ and 1720 cm⁻³, respectively. Median size distribution was characterised by a single-modal fit, with a geometric diameter, median number concentration and geometric standard deviation of 60 nm, 5390 cm⁻³ and 2.31, respectively. Characterisation of primary emissions, secondary particle formation, changes to meteorology and long-term transport has been necessary to understand the seasonal and annual variability of the total and modal particle concentration. Number concentrations exhibited a diurnal pattern with maximum concentrations around noon. This was governed by the concentrations of the nucleation and Aitken modes during the warm seasons and only by the nucleation mode during the cold seasons. Similar monthly mean total concentrations were observed throughout the year due to a clear inverse variation between the monthly mean N_NUC and N_ACC. It was related to the impact of desert dust and continental air masses on the monthly mean particle levels. These air masses were associated with high values of N_ACC which suppressed the new particle formation (decreasing N_NUC). Each day was classified according to a land breeze flow or a synoptic pattern influence. The median size distribution for desert dust and continental aerosol was dominated by the Aitken and accumulation modes, and marine air masses were dominated by the nucleation and Aitken modes. Particles moved offshore due to the land breeze and had an impact on the particle burden at noon, especially when the wind was blowing from the NW sector in the morning during summer time. This increased N_NUC and N_AIT by factors of 3.1 and 2.4, respectively. Nucleation events with the typical "banana" shape were characterised by a mean particle nucleation rate of 0.74 cm⁻³ s⁻¹, a mean growth rate of 1.96 nm h⁻¹ and a mean total duration of 9.25 h (starting at 10:55 GMT and ending at 20:10 GMT). They were observed for 48 days. Other nucleation events were identified as those produced by the emissions from the industrial areas located at a distance of 35 km. They were observed for 42 days. Both nucleation events were strongly linked to the marine air mass origin.

Samples of atmospheric aerosol particles were collected in Valladolid, Spain, during the winter of 2003-2004. The measurements were made with a Dekati PM₁₀ cascade impactor with four size stages: greater than 10 µm, between 2.5 to 10 µm, 1 to 2.5 µm and less than 1 µm. The size and shape of the particles were analyzed with a scanning electron microscope (SEM) and elemental analysis was done with an energy dispersive x-ray analysis (EDX). We present an evaluation by size, shape and composition of the major particulate species in the Valladolid urban atmosphere. The total aerosol concentration is very variable, ranging from 39.86 µg·m^-3 to 184.88 µg·m^-3 with the coarse particles as the dominant mass fraction. Emphasis was given to fine particles (<1 µm), for which the visible (400 nm to 650 nm) light absorption coefficients were measured using the integrating plate technique. We have made some enhancements in the illumination system of this measurement system. The absorption coefficient, σ_a, is highly variable and ranges from 7.33×10^-6 m^-1 to 1.01×10^-4 m^-1 at a wavelength of 550 nm. There is an inverse power law relationship between σ_a and wavelength, with an average exponent of -0.8.

In this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.