Weathering Properties Research Articles

Different molecular structure of pigments versus traditional UV Stabilizers on the UV irradiation life of HDPE

As the demands on high-density polyethylene for weather resistance, aesthetics and functionality continue to improve, copper phthalocyanine has recently received attention for enhancing the weathering properties of high-density polyethylene. However, little research has been done on other pigments whether to improve weathering resistance equally. In this work, inorganic and organic pigments added to matrix were explored and compared with the industrialized UV stabilizers. It was found that pigmented polyethylene showed very little color change during 2000 h irradiation except for polyethylene/ultramarine. After 2000 h irradiation, polyethylene/copper phthalocyanine was the best among pigmented polyethylene in tensile strength, copper polychlorinated phthalocyanine following. These two pigments were similar to commonly used light stabilizers in their ability to stop the aging of polyethylene. Chrome oxide and ultramarine were worse, basically aging after irradiation of 500 h. This was consistent with the order of UV absorption ability of pigments at 310 nm - 400 nm.

Preparation and weathering properties of Ce/TiO2 anti-UV coating on heat-treated wood

Heat-treated wood is widely used as building exterior wall panels and landscape materials due to its excellent dimensional stability and durability. However, heat-treated wood is susceptible to discoloration and cracking due to ultraviolet radiation, temperature and humidity changes in the outdoor environment. In this study, Ce/TiO2 alcohol solution was prepared by sol-gel method, and the inorganic anti-ultraviolet coating was attached to the surface of wood by brush coating. Then artificial accelerated weathering test was carried out for 672 h to evaluate the weathering behavior of heat-treated wood. UV–Vis spectroscopy and macroscopic morphology analysis of the glass substrate show that the coating has good transparency and effective UV shielding ability. Scanning electron microscopy, Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy analysis showed that the coating successfully adhered to the wood surface and played a good anti-ultraviolet effect. After artificial weathering, the color difference values of unmodified wood, Ce modified wood, TiO2 modified wood and Ce/TiO2 modified wood were 6.04, 4.97, 3.67 and 3.29, respectively. Based on the changes of color and wood chemical composition, the Ce/TiO2 anti-ultraviolet coating can significantly improve the weathering resistance of heat-treated wood, and ultimately limit the degradation degree of surface chemical composition of heat-treated wood.

Heatwave attribution based on reliable operational weather forecasts

The 2021 Pacific Northwest heatwave was so extreme as to challenge conventional statistical and climate-model-based approaches to extreme weather attribution. However, state-of-the-art operational weather prediction systems are demonstrably able to simulate the detailed physics of the heatwave. Here, we leverage these systems to show that human influence on the climate made this event at least 8 [2–50] times more likely. At the current rate of global warming, the likelihood of such an event is doubling every 20 [10–50] years. Given the multi-decade lower-bound return-time implied by the length of the historical record, this rate of change in likelihood is highly relevant for decision makers. Further, forecast-based attribution can synthesise the conditional event-specific storyline and unconditional event-class probabilistic approaches to attribution. If developed as a routine service in forecasting centres, it could provide reliable estimates of human influence on extreme weather risk, which is critical to supporting effective adaptation planning.

Enhancing weatherability and mechanical properties of tung oil wood finishes through natural rubber modification via the Diels-Alder reaction

Tung oil is commonly utilized for coating protection in wooden products, often attracting attention for its appearance, antimicrobial capabilities, and insect-resistant coatings. However, its poor mechanical properties and poor weather resistance stem from excessive self-crosslinking of surplus conjugated double bonds and molecular chains, resulting in poor film wrinkling. Therefore, this study introduces natural rubber via the Diels-Alder reaction to consume the residual double bonds in tung oil, resulting in tung oil/natural rubber composite coatings (NRTO) with excellent mechanical properties and weather resistance. The results indicate that NRTO exhibits excellent mechanical properties, including high elongation (32 %) and strong adhesion (4.55 MPa). Furthermore, NRTO demonstrates outstanding acid resistance and UV aging resistance. Given its many benefits, NRTO film emerges as a promising candidate for substantially protecting wood surfaces in demanding environments.

Investigation of AC and DC Electrical Conductivity in Ethyl Cellulose (EC) and Poly Methyl Methacrylate (PMMA) Polyblends

The study of AC and DC electrical conductivity is crucial for understanding the behavior of charge carriers within materials and their mobility. Ethyl cellulose (EC) stands out among cellulose ethers due to its favorable electrical, mechanical, and weathering properties. Poly Methyl Methacrylate (PMMA) is a thermoplastic known for its rigidity, transparency, and outdoor durability, making it a valuable material. Despite being insulating materials, both EC and PMMA exhibit limited free charge carriers and low mobility. In this research, AC and DC electrical properties of Ethyl Cellulose (EC), Poly Methyl Methacrylate (PMMA), and their blends doped with tetrahydrofuran (THF) film were investigated using isothermal evaporation techniques. The investigation focused on the effects of temperature, electric field, and frequency on electrical conduction mechanisms. Measurements were conducted across frequencies ranging from 1 KHz to 1 MHz at temperatures between 323 K and 373 K. Results indicate that AC electrical conductivity of Ethyl Cellulose (EC), Poly Methyl Methacrylate (PMMA), and their blend (EC/PMMA) increases with higher frequencies of the applied electric field. Meanwhile, DC electrical conductivity of Ethyl Cellulose (EC), Poly Methyl Methacrylate (PMMA), and their blend (EC/PMMA) rises with increasing temperature. X-ray diffraction (XRD) analysis further supports these conductivity changes in the blend.

Representing natural climate variability in an event attribution context: Indo-Pakistani heatwave of 2022

Attribution of extreme climate events to global climate change as a result of anthropogenic greenhouse gas emissions has become increasingly important. Extreme climate events arise at the intersection of natural climate variability and a forced response of the Earth system to anthropogenic greenhouse gas emissions, which may alter the frequency and severity of such events. Accounting for the effects of both natural climate variability and the forced response to anthropogenic climate change is thus central for the attribution. Here, we investigate the reproducibility of probabilistic extreme event attribution results under more explicit representations of natural climate variability. We employ well-established methodologies deployed in statistical Earth System Model emulators to represent natural climate variability as informed from its spatio-temporal covariance structures. Two approaches towards representing natural climate variability are investigated: (1) where natural climate variability is treated as a single component; and (2) where natural climate variability is disentangled into its annual and seasonal components. We showcase our approaches by attributing the 2022 Indo-Pakistani heatwave to human-induced climate change. We find that explicit representation of annual and seasonal natural climate variability increases the overall uncertainty in attribution results considerably compared to established approaches such as the World Weather Attribution Initiative. The increase in likelihood of such an event occurring as a result of global warming differs slightly between the approaches, mainly due to different assessments of the pre-industrial return periods. Our approach that explicitly resolves annual and seasonal natural climate variability indicates a median increase in likelihood by a factor of 41 (95% range: 6-603). We find a robust signal of increased likelihood and intensification of the event with increasing global warming levels across all approaches. Compared to its present likelihood, under 1.5 °C (2 °C) of global near-surface air temperature increase relative to pre-industrial temperatures, the likelihood of the event would be between 2.2 to 2.5 times (8 to 9 times) higher. We note that regardless of the different statistical approaches to represent natural variability, the outcomes on the conducted event attribution are similar, with minor differences mainly in the uncertainty ranges. Possible reasons for differences are evaluated, including limitations of the proposed approach for this type of application, as well as the specific aspects in which it can provide complementary information to established approaches.

Development and experimental characterization study of cesium doped zinc oxide polymer conductive films for sensing applications

Development and experimental characterization study of cesium doped zinc oxide polymer conductive films for sensing applications

Empowering Agriculture: Leveraging Intelligent Systems for Sustainable Farming

Climate change, market volatility, water scarcity, and pest control issues are just a few of the challenges that farmers face. Limited access to resources and education, as well as the slow adoption of new technologies, aggravate these problems. Creating intelligent systems is necessary to handle these pressing issues. This method makes use of key components such as crop type, weather, and soil properties to forecast agricultural yields. This system offers significant yield prediction capabilities for 53 crops by utilizing complex algorithms including XG-Boost, random forest, and decision tree. It takes into account crucial factors to accurately assess crop production potential, such as temperature, rainfall, nutrient levels (N, P, and K), soil pH, and temperature data. Cutting-edge machine learning techniques look at past data and trends to provide farmers with crucial information they need to make wise decisions. In order to solve the problems farmers, encounter in the modern agricultural sector, this intelligent system aims to enhance resource efficiency, farming systems' resilience and production, and the use of resources.

The Variation in Atmospheric Turbidity over a Tropical Site in Nigeria and Its Relation to Climate Drivers

Atmospheric turbidity exhibits substantial spatial–temporal variability due to factors such as aerosol emissions, seasonal changes, meteorology, and air mass transport. Investigating atmospheric turbidity is crucial for climatology, meteorology, and atmospheric pollution. This study investigates the variation in atmospheric turbidity over a tropical location in Nigeria, utilizing the Ångström exponent (α), the turbidity coefficient (β), the Linke turbidity factor (TL), the Ångström turbidity coefficient (βEST), the Unsworth–Monteith turbidity coefficient (KAUM), and the Schüepp turbidity coefficient (SCH). These parameters were estimated from a six-month uninterrupted aerosol optical depth dataset (January–June 2016) and a one-year dataset (January–December 2016) of solar radiation and meteorological data. An inverse correlation (R = −0.77) was obtained between α and β, which indicates different turbidity regimes based on particle size. TL and βEST exhibit pronounced seasonality, with higher turbidity during the dry season (TL = 9.62 and βEST = 0.60) compared to the rainy season (TL = 0.48 and βEST = 0.20) from May to October. Backward trajectories and wind patterns reveal that high-turbidity months align with north-easterly air flows from the Sahara Desert, transporting dust aerosols, while low-turbidity months coincide with humid maritime air masses originating from the Gulf of Guinea. Meteorological drivers like relative humidity and water vapor pressure are linked to turbidity levels, with an inverse exponential relationship observed between normalized turbidity coefficients and normalized water vapor pressure. This analysis provides insights into how air mass origin, wind patterns, and local climate factors impact atmospheric haze, particle characteristics, and solar attenuation variability in a tropical location across seasons. The findings can contribute to environmental studies and assist in modelling interactions between climate, weather, and atmospheric optical properties in the region.

Effects of TiO2 nanoparticles and silane coupling agents on the adhesion strength and weathering properties of silicone rubber coatings

Environmental contamination on high voltage outdoor insulators can cause leakage currents and electrical flashover and finally reduce the reliability of electric distribution systems. To prevent energy losses and enormous damages, the in-service life of insulators has to be extended. For this purpose, room-temperature-vulcanizing (RTV) silicone rubber coatings are used due to their hydrophobicity. The objective of the current paper is to enhance the weathering resistance and proper adhesion of these coatings to the insulator. In the present study, adhesion improvement was achieved by adding titanium dioxide (TiO2) nanoparticles and silane coupling agents to the silicone rubber. The mechanical properties of nanocomposite coatings were characterized using the pull-off test method. Also, the water immersion method was employed to investigate the weathering resistance. The results revealed that adding TiO2 nanoparticles and silane coupling improved nanoparticle dispersion, adhesion strength, hydrophobicity, and weathering properties of RTV coatings.

Incorporating Weather Attribution to Future Water Budget Projections

Weather attribution is a scientific study that estimates the relative likelihood of an observed weather event occurring under different climate regimes. Water budget models are widely used tools that can estimate future water resource management and conservation conditions using daily weather forcing. A stochastic weather generator (WG) is a statistical model of daily weather sequences designed to simulate or represent a climate description. A WG provides a means to generate stochastic, future weather forcing to drive a water budget model to produce future water resource projections. Observed drought magnitude and human-induced climate change likelihood from a weather attribution study provide targets for WG calibration. The attribution-constrained WG approximately reproduces the five-fold increase in probability attributed to observed drought magnitude under climate change. A future (2031–2060) climate description produced by the calibrated WG is significantly hotter, with lower expected soil moisture than the future description obtained from global climate model (GCM) simulation results. The attribution-constrained WG describes future conditions where historical extreme and severe droughts are significantly more likely to occur.

Classification of road sections according to the degree of danger to the formation of a wear track

Introduction. The intensification of the motorization of the Russian population has led to a significant increase in the number of passenger cars, which in many regions are supplied with studded tires for the winter period. This circumstance led to the large-scale appearance of a certain type of ruts caused by the wear of asphalt concrete pavement from the action of studded tires and abrasive materials used to combat winter slipperiness. The track formed on the surface of road surfaces has a complex character and includes three types of tracks: structural, shear track and wear track. The contribution of each type of track to the total depth of the complex track is different and depends on the characteristics of transport loads, the properties of the coating material, and weather and climatic factors.Materials and methods. The wear track is formed in winter from the impact of studded tires, and its depth is affected by many factors: the mode and intensity of movement of cars with studded tires, the type of spikes, their number on the tire, the size of the protrusion of the working insert of the spike, the properties of asphalt concrete pavement, weather and climatic factors, as well as winter road maintenance measures. On curves of a small radius, braking or acceleration sections, as well as sections with a normal mode of movement of cars, the horizontal loads from the studded tire are different. This is due to the occurrence of various types of friction, as well as the dependence of the coefficient of adhesion of the coated tire on the speed of movement, which is variable in the braking and acceleration sections. As a result, in certain areas, the contribution of wear to the depth of the complex track is greater than in areas with a different driving mode. In the article, a study was conducted to determine the susceptibility of road sections with different traffic modes to the formation of wear ruts. A method has been developed for calculating various components of the complex trackage caused by compaction and wear of the asphalt concrete coating, as well as deformations of the underlying layers of the pavement and the roadbed.Result. Based on the data of experiments carried out on the roads of Omsk, a classification of sections according to the degree of danger to the formation of a wear track is proposed, dividing sections of urban roads into heavily worn, worn and weakly worn. Based on the analysis of data from road diagnostics performed over the past five years, the approximate service life of the coatings until the formation of a 30 mm deep track has been determined.Discussion and conclusion. The authors’ ideas about the influence of friction and the speed of movement on the process of track formation caused by wear are presented. The correspondence of the experimental data obtained by the authors of the article with the data of colleagues who performed similar studies in Russia and abroad is shown. Clarifications introduced by the authors into the generally accepted ideas about asphalt wear are explained.

Composition Analysis and Subclass Division of Ancient Glass Artifacts Based on R-Q Type Clustering

The Silk Road served as a conduit for cultural exchange between ancient East and West, with glass being a valuable material evidence of early trade interactions. Archaeologists have classified ancient glassware primarily into two categories: high-potassium glass and lead-barium glass. This study focuses on the sub-classification of high-potassium glass. Firstly, the high-potassium glass is divided into two major categories, weathered and unweathered, based on their weathering properties. Subsequently, the R-Q clustering algorithm is employed to further subdivide the unweathered high-potassium glasswhile the same approach is applied to the weathered high-potassium glass. The results are validated through rational analysis and can be applied to other types of ancient glassware.

Microbial Catalysis for CO2 Sequestration: A Geobiological Approach.

One of the greatest threats facing the planet is the continued increase in excess greenhouse gasses, with CO2 being the primary driver due to its rapid increase in only a century. Excess CO2 is exacerbating known climate tipping points that will have cascading local and global effects including loss of biodiversity, global warming, and climate migration. However, global reduction of CO2 emissions is not enough. Carbon dioxide removal (CDR) will also be needed to avoid the catastrophic effects of global warming. Although the drawdown and storage of CO2 occur naturally via the coupling of the silicate and carbonate cycles, they operate over geological timescales (thousands of years). Here, we suggest that microbes can be used to accelerate this process, perhaps by orders of magnitude, while simultaneously producing potentially valuable by-products. This could provide both a sustainable pathway for global drawdown of CO2 and an environmentally benign biosynthesis of materials. We discuss several different approaches, all of which involve enhancing the rate of silicate weathering. We use the silicate mineral olivine as a case study because of its favorable weathering properties, global abundance, and growing interest in CDR applications. Extensive research is needed to determine both the upper limit of the rate of silicate dissolution and its potential to economically scale to draw down significant amounts (Mt/Gt) of CO2 Other industrial processes have successfully cultivated microbial consortia to provide valuable services at scale (e.g., wastewater treatment, anaerobic digestion, fermentation), and we argue that similar economies of scale could be achieved from this research.

Decay resistance and weathering properties of thermally-modified amazon woods – Parkia pendula (Willd.) and Simarouba amara (Aubl.)

ABSTRACT Thermal modification is a technique that can be used to increase the resistance of wood to the biodeterioration processes. In this context, the objective of this study was to evaluate the durability of Parkia pendula (angelim-saia) and Simarouba amara (marupá) woods thermally modified in rotting field tests and by the action of natural weathering. The tests were carried out in the Brazilian Amazon region, in the Mato Grosso State. The thermal modification of the wood was set with four treatments: control (T1); electric-oven treatment, 180°C for 3 h (T2); combination of autoclave and electric-oven, 125°C for 3 h, and 180°C for 3 h respectively (T3), and immersion in vegetable oil, 180°C for 3 h (T4). The samples were partially buried for the field rotting assays, and the mass loss and deterioration index were seasonally evaluated after the experiment was installed. These analyses were performed on the same samples which has been exposed to rotting essays. The action of natural weathering was assessed through the CIELAB* colour system. T4 was the most efficient for increasing the resistance to biological deterioration, under the tested conditions. T2 and T3 treatments conferred higher colorimetric stability for both types of woods after natural weathering, under the tested conditions.

Weathering and soil production under trees growing on sandstones – The role of tree roots in soil formation

Rock weathering drives both landform formation and soil production/evolution. The less studied biological component of weathering and soil production caused by tree root systems is the main focus of the present study. Weathering by trees, which likely has been important in soil formation since the first trees emerged in the middle and late Devonian, is accomplished through both physical and biological means, like acids excreted by plants and exudates from associated bacterial communities. However, these processes are relatively poorly known. We assessed the impact of tree roots and associated microbiota on the potential level of biological weathering. Three research plots were selected in two sandstone regions in Poland. Two plots were in the Stołowe Mountains (Złotno, Batorów), a tableland built of Cretaceous sandstones. The third plot (Żegiestów) was in the Sącz Beskidy Mountains, the Carpathians. Soil samples were taken from tree root zones of Norway spruces from predefined sampling positions. Soils from non-tree control positions were also sampled. Soil samples were a subject of laboratory analyses which included the content of Fe and Al (amorphous and labile forms), carbon (C), nitrogen (N), and soil pH. The microbial functional diversity of soil microorganisms was determined using the Biolog (EcoPlate) system. Rock fragments were collected for mineralogical and a subject of optical microscopy and cathodoluminescence analyses in order to examine their mineralogical composition.Significant differences (pHolm-corrected < 0.05) between sample locations were found mostly for the Żegiestów plot: Soils at control positions differed from the crack and bulk soil sample positions in terms of C, N, C/N, and pH.Tree roots were able to develop a great variety of sizes and forms by following the existing net of bedrock discontinuities and hillslope microrelief. They developed along the most accessible surfaces, and caused rockcliff retreat and scree slope formation. These two features can be considered as initial stages of soil production. Trees add to the complexity of the soil system and allow formation of rhizospheric soils, and horizons rich in organic matter which are zones of a high microbial activity. However, as our study shows, rock cracks with roots cannot be considered as zones of microbial weathering. In addition, C content and microbial activity decreases with depth but can stay on a high level along living and dead roots. When entering rock fractures, they change the intensity of biomechanical weathering and soil properties. The highest biological activity of microorganisms was found in the control samples.Overall, tree roots do change the pattern of soil formation and explain the existing pattern of soil chemical properties, microbial activity, and potentially biological weathering intensity, and the intensity of those processes in correlation with root presence varies in space.

Why do people take e-scooter trips? Insights on temporal and spatial usage patterns of detailed trip data

Electric scooters (e-scooters) are becoming one of the most popular micromobility options in the United States. Although there is some evidence of increased mobility, reduced carbon emissions, replaced car trips, and associated public health benefits, there is little known about the patterns of e-scooter use. This study proposes a framework for high-resolution analysis of micromobility data based on temporal, spatial, and weather attributes. As a case study, we scrutinized more than one million e-scooter trips of Nashville, Tennessee, from September 1, 2018, to August 31, 2019. Weather data and land use data from the Nashville Travel Demand Model and scraping of Google Maps Point of Interest (POI) data complemented the trip data. The combination of Principal Component Analysis (PCA) and a K-means unsupervised machine learning algorithm identified five distinct e-scooter usage patterns, namely morning work/school, daytime short errand, social, nighttime entertainment district, and utilitarian trips. Among other findings, the most common use of e-scooters in Nashville was daytime short errand trips, contributing to 29% of all e-scooter trips. We found that 7% of all e-scooter trips resembled morning commuting to work or school. Only 16% of trips were classified as Nighttime Entertainment District trips. The average daily number of trips on a typical weekend was 81% higher than a typical weekday. We also found variation in e-scooter usage patterns over a year with high summer ridership patterns. The findings of this study can help city administrations, planners, and micromobility operators to understand when and where people are using e-scooters. Such knowledge can guide them in making data-driven decisions regarding safety, sustainability, and mode substitution of emerging micromobility.

Evaluation of Aging Evolution of Olive Pomace–Modified Asphalt Binders under Natural Weather Aging Conditions

Abstract The natural weather aging test (NWAT) for asphalt binders is an essential benchmark aging test used to investigate the aging evolution of asphalt binders under actual weather conditions. This study assessed the aging rates of olive pomace–modified asphalt binders and the control neat binder under NWAT. Instead of aging days, weather factors were proposed as metrics to correlate with the aging evolution of the rheological properties of asphalt binders. The test results showed that the olive pulp–rich fraction-modified asphalt binders after the rolling thin-film oven (RTFO) test had lower aging susceptibility than the RTFO-aged control binder. The RTFO-aged binders exhibited lower aging rates than the corresponding unaged asphalt binders under NWAT. The sums of weather indexes such as the sum of the daily maximum ultraviolet index are rational field metrics to correlate with the rheological aging properties of natural weather–aged asphalt binders. Caution shall be taken when using aging days to interpret the aging rates of asphalt binders under NWAT.

The influence of the aftereffect of long-term use of mineral fertilizers on sorghum grain productivity and quality on ordinary blackearth of the Central Pre-Caucasus

There have been presented the study results of the experiment of the Geographical Network of Experiments with fertilizers, established in 1975 on ordinary chernozem under conditions of unstable moisture in the Stavropol Territory. The purpose of the current study was to reveal the influence of the aftereffect of the long-term use of mineral fertilizers on the agrochemical properties of soil, productivity, and quality of sorghum grain, depending on the weather conditions of the year. There has been established that, after 30 years of action and 14 years of aftereffect of the use of phosphorus fertilizers in doses of 60–180 kg/ha of a.i. the content of residual phosphorus in the soil layer of 0–20 cm before sowing sorghum varied from 25 to 55 mg/kg. The crop productivity was closely dependent on weather conditions and the agrochemical condition of the soil. In arid conditions in 2020, the largest grain productivity (2.5–3.16 t/ha) was obtained in areas of phosphorus fertilizer’s aftereffect with a high content of P2O5 in soil (more than 30 mg/kg). In the wet year, the largest productivity (5.10–5.64 t/ha) were obtained in nitrogen fertilizers’ aftereffect against the background of low phosphates availability (12–16 mg/kg). In different weather conditions, the maximum level of sorghum productivity was obtained in the aftereffect of complete mineral fertilizer or with a balanced nutrient regime of the soil, in 2020 it was 3.81–4.96 t/ha and in 2022 it was 5.4–6.14 t/ha. Sorghum grain quality was also determined by the weather and soil properties. Grain with the highest crude protein percentage (9.9–13.6 %) was obtained in a dry year after the effect of nitrogen fertilizer. In both years of the study, the products obtained after the application of high doses of one phosphorus fertilizer (120–180 kg/ha a.i.) had the least nutritional value. The crude protein percentage in grain did not exceed 2.2–7.8 %. To stabilize productivity and increase the nutritional value of sorghum, it is necessary, first of all, to optimize the nitrogen nutrition of plants, especially in conditions of good moisture supply.

Bayesian inference of visibility in fog and smoke artificial conditions from 3D-LiDAR point clouds

3D-LiDARs are heavily impacted by a degraded visual environment (DVE) like rain, fog and smoke which limits their use for perception algorithms. The capacity to retrieve information about the environmental conditions from an embedded sensor can be an asset to improve autonomous driving performances. False positive artifacts in the point clouds caused by aerosols and hydrometeors particles tend to cause perception issues and thus need filtered out. However, those artifacts can also be used as valuable information to infer weather properties and maybe improve filters. This article proposes a Bayesian inference model which can classify discrete values of visibility using 3D-LiDAR point clouds. Gamma and Log-normal distributions are used to model the distance distributions of the noise points and the model is extended using the Random Finite Set (RFS) formalism with the Poisson and Binomial RFS models. Experiments in artificial fog and smoke conditions are presented and the classification model is trained and tested independently for each experiment. The used point clouds are extracted from specific parts of the field-of-view that can be used to generalize the proposed method to any outdoor scenario. The model shows good classification results with increased performances when the RFS extension is used.