Varying Weather Conditions Research Articles

How do drought and heat affect the response of soybean seed yield to elevated O3? An analysis of 15 seasons of free-air O3 concentration enrichment studies.

The coincidence of rising ozone concentrations ([O3]), increasing global temperatures, and drought episodes is expected to become more intense and frequent in the future. A better understanding of the responses of crop yield to elevated [O3] under different levels of drought and high temperature stress is, therefore, critical for projecting future food production potential. Using a 15-year open-air field experiment in central Illinois, we assessed the impacts of elevated [O3] coupled with variation in growing season temperature and water availability on soybean seed yield. Thirteen soybean cultivars were exposed to a wide range of season-long elevated [O3] in the field using free-air O3 concentration enrichment. Elevated [O3] treatments reduced soybean seed yield from as little as 5.3% in 2005 to 35.2% in 2010. Although cultivars differed in yield response to elevated [O3] (R), ranging from 17.5% to -76.4%, there was a significant negative correlation between R and O3 dosage. Soybean cultivars showed greater seed yield losses to elevated [O3] when grown at drier or hotter conditions compared to wetter or cooler years, because the hotter and drier conditions were associated with greater O3 treatment. However, year-to-year variation in weather conditions did not influence the sensitivity of soybean seed yield to a given increase in [O3]. Collectively, this study quantitatively demonstrates that, although drought conditions or warmer temperatures led to greater O3 treatment concentrations and O3-induced seed yield reduction, drought and temperature stress did not alter soybean's sensitivity to O3. Our results have important implications for modeling the effects of rising O3 pollution on crops and suggest that altering irrigation practices to mitigate O3 stress may not be effective in reducing crop sensitivity to O3.

A robust photoelectrothermal evaporator with hierarchical NiCo2S4 nanowires grown on nickel foam for a high rate of clean water production

Interfacial solar steam generation (ISSG) stands as a promising technology in addressing global water scarcity, stemming from reasons like global warming and population growth. Despite its potential, ISSG faces challenges due to varying weather conditions and solar intensity fluctuations. To overcome these limitations, a novel approach by integrating photothermal and electrothermal Joule-heating effects is introduced in this study for a high-efficiency and salt-resistant all-weather, all-day seawater desalination. Hierarchical NiCo2S4 nanowires are fabricated on a Ni-foam (NCNNF) using a facile two-step hydrothermal method. Under photothermal evaporation, the NCNNF evaporator produces an evaporation rate of 1.57 ± 0.08 kg m−2 h−1 with an evaporation efficiency of 77 % under 1sun illumination with good anti-fouling ability. In a simple water desalination setup, the surface temperature of the evaporator in a wet state reaches ∼63.5 ± 0.25 °C within 10 min by applying an input voltage of 1 V and 1sun irradiation condition, yielding an outstanding evaporation rate of 21.3 ± 3.6 kg m−2 h−1 and evaporation efficiency of 80 %. The proposed evaporation device can resist salt accumulation on the evaporator surface in long-term operations. The present results would be utilized in developing high-performance Joule-heating assisted solar water evaporation devices, especially for all-day, all-weather desalination to produce clean potable water.

Modeling and Evaluation of a Solar Trigeneration System With Thermal Energy Storage

In the present study, an on-demand solar combined cooling, heating, and power (CCHP) system with parabolic trough collector (PTC) and solid-state thermal energy storage (TES) is investigated, which will be demonstrated on-site in 2024 at the Administration Building of the Lavrio Technological and Cultural Park (LTCP) in Attica, Greece, covering the energy needs of the building in form of heating and electricity in winter and cooling during summer. The system is based on a combined Organic Rankine Cycle and an Ejector Cooling Cycle (ORC-ECC), which is modeled and simulated in a steady-state manner while the PTC and TES systems are modeled dynamically, i.e., they are influenced by the thermal inertia of the components, the variable weather conditions of the location, and the defined operating strategies. The ORC-ECC simulation results show an efficiency of up to 14.20 % for the ORC system and a Coefficient of Performance (COP) of 0.10 for the ECC system under defined conditions. The dynamic simulations for the specified operating strategies indicate that on typical sunny days up to 100 % of the consumers' electricity consumption can be covered by the system.

A full computational framework for the temperature analysis of a flax-reinforced composite footbridge with field monitoring validation

This research presents a computational framework for predicting temperatures in flax-reinforced composite sandwich footbridges. The case study is a 15m-span footbridge situated in Almere, the Netherlands, instrumented with 82 fiber Bragg grating (FBG) sensors and 8 thermocouples within an IoT-based structural health monitoring (SHM) system, enabling real-time evaluation. The main focus of this article is to enhance the understanding of structural temperature distributions in FRP sandwich cross-sections for bridge engineering applications. Furthermore, providing a comprehensive and accurate framework for temperature normalization of FBG strain measurements. The methodology involves environmental data, point-by-point solar radiation analysis with sun sheltering, and thermal properties characterization, culminating in a numerical analysis using Abaqus. A k-d tree binary search is performed previous to the numerical analysis to solve compatibility between Abaqus and Rhino's meshing algorithms. The non-uniform heat flux is used as input for the numerical analysis and handled through UEXTERNALDB and DFLUX subroutines. The use of a sub-modeling technique, allows the isolation, in correspondence of the sensor locations, of one critical section of the bridge for detailed analysis. Verification against in-situ measurements demonstrates the framework's efficacy. It was concluded that the framework restitutes errors in the range of ±1 ÷ 3 °C across varying weather conditions, during different seasons, and consistently for multiple sensor locations. Finally, the results are used to compensate strain measurements of nearby selected FBG sensors from the temperature counterparts.

Airborne pollen and spore monitoring for seasonal trends and dynamic interconnection with meteorological parameters in Rawalpindi city, Pakistan

Aerobiological studies are essential to understand the pollen and spore diversity and allergy hazards. Airborne pollen/spore assemblages seasonally and regionally are affected by weather variables. Local vegetation plays an integral role in atmospheric pollen loads. This study identified the pollen taxa families and spore types in conjunction with influencing meteorological parameters e.g. temperature, rainfall, wind speed, relative humidity. Daily airborne pollen and spore were monitored to estimate seasonal diversity and intensity (measured in grains/m3) by using volumetric method throughout the year 2023. Daily weather updates and monthly reports of Pakistan Meteorological Department were assessed to collect meteorological data. Results revealed that pollen and spore concentration were low in the start and end months of the year. First pollen and spore peak were observed on 14 February and 23 April respectively. Maximum pollen concentration was observed in February–April, August–September. Spore concentration was high during March–May, August–October. Total 34 pollen families and 10 spore types were identified on morphological basis with Pinaceae (18%) found abundantly followed by Poaceae (11%) and Betulaceae (8%). Fall season prominent pollen families were Asteraceae and Cannabaceae. Some families with very low concentration were also identified categorized as “others”. Among spores, Alternaria and Aspergillus conidia were found maximum in April and September respectively. Statistical and qualitative investigation of dynamic interrelation between airborne pollen/spore and meteorological parameter explored the significant association with temperature and not-significant with rainfall and relative humidity. Wind speed was highly significant parameter for pollen loads but did not majorly affect the spore counts. It was observed from p-values obtained by single-factor ANOVA test. This study concluded that airborne pollen and spore loads influenced by variations in weather conditions and surrounding flora. Healthcare professionals can utilize findings of such researches for implementation of preventive measures to vulnerable population.

Educational Station for the Generation and Use of Green Hydrogen

Globally, electrical energy predominantly derives from fossil fuels such as coal, oil, and natural gas, which significantly emit greenhouse gases, thereby accelerating ecosystem degradation and climate change. In response, recent decades have witnessed a surge in the development of clean, sustainable electricity generation technologies such as wind and solar photovoltaic systems. These renewable sources are expected to replace fossil fuel-based methods but are limited by their dependency on variable weather conditions, which cannot align with electricity demand. Periods when energy production exceeds demand highlight the critical role of innovative storage solutions. Notably, surplus energy can be utilized for hydrogen production via water electrolysis, producing “Green Hydrogen”, which generates no polluting emissions. This shift towards sustainable technologies necessitates novel educational approaches. It is essential to revise curricula to include these technologies, ensuring students are well-prepared for the evolving energy sector. This article discusses the design and construction of a green hydrogen educational station built with commercial materials for laboratory use, embodying the integration of renewable energy technologies into academic programs and promoting hands-on learning experiences in energy management and sustainability.

On-farm evaluation of a crop forecast-based approach for season-specific nitrogen application in winter wheat

Inadequate nitrogen (N)-fertilisation practices, that fail to consider seasonally variable weather conditions and their impacts on crop yield potential and N-requirements, cause reduced crop N-use efficiency. As a result, both the ecological and economic sustainability of crop production systems are put at risk. The aim of this study was to develop a season-specific crop forecasting approach that allows for a targeted application of N in winter wheat while maintaining farm revenue compared to empirical N-fertilisation practices. The crop forecasts of this study were generated using the process-based crop model SSM in combination with state-of-the-art seasonal ensemble weather forecasts (SEAS5) for the case study region of Eastern Austria. Results from three winter wheat on-farm experiments showed a significant reduction in applied N when implementing a crop forecast-based N-application approach (-43.33 kgN ha-1, -23.42%) compared to empirical N-application approaches, without compromising revenue from high-quality grain sales. The benefit of this reduced N-application approach was quantified through the economic return to applied N (ERAN). While maintaining revenue, the lower amounts of applied N led to significant benefits of + 30.22% (+ 2.20 € kgN-1) in ERAN.

Water stress combining weather condition shapes wheat yield and inter-annual yield variability: Field observations from a six-year study

Irrigation has played a pivotal role in Chinese wheat production and is becoming increasingly crucial in adapting to the changing climate. However, the benefits of water-saving wheat production in the long-term period and its response to climate change have received limited attention. In this study, a 6-year field experiment was conducted to investigate the grain yield and water use with three treatments such as Irrigation three times (I3), Irrigation two times (I2), and disposable pre-sowing irrigation (I0), and their sensitivity to weather conditions. The average yield over six years for the I2 treatment is 8.3 Mg ha−1, similar to I3 treatment while using 10.2 % less irrigation water and improving 8.0 % water use efficiency. In contrast, the grain yield in I0 treatment is 28.4 % lower than I3 treatment while consuming 36.9 % less irrigation water. Furthermore, 90.2 % of the yield decrease in I0 treatment results from the lower ear number. Water stress from jointing to flowering accounts for 58.8 % of ear number decrease. Although interannual yield variation is similar among the three treatments, the source of the variation is very different. Kernel weight explains the yield variation by 92.3 % in I3 treatment and 66.1 % in I2 treatment, while ear number accounts for 60.7 % of the variation in I0 treatment. Minimum temperature for kernel weight in both I3 and I2 treatments and rainfall for ear number in I0 treatment is the most important weather factor, respectively. In summary, this study provides valuable insights into the delicate balance between water conservation and food security while adapting to varying weather conditions.

Prediction of global ionospheric TEC using attention based bidirectional long short-term memory and gated recurrent unit

An accurate prediction of ionospheric total electron content (TEC) at the primary stage is essential for applications related to global navigation satellite systems (GNSS) under varying weather conditions. The previous TEC prediction schemes contribute for each time step that increases the prediction time. The eye contact phenomenon establishes a metaphorical connection which intends to capture and emphasize the attention worthy elements in a sequence. This research introduces a deep learning approach which is a combination of attention-based bidirectional long short-term memory and gated recurrent unit (Bi-LSTM GRU) to predict TEC in the ionosphere. Bidirectional LSTM is the better option for achieving durability when combined with a gated recurrent unit (GRU) to predict TEC in the ionosphere. The proposed approach is evaluated with the existing LSTM approach for root mean square error (RMSE) during training and validation. The RMSE while predicting the global ionospheric delay using the existing LSTM for 20 epochs is seen to be 0.004, whereas the existing approach achieves a training error of 0.003.

COTTON-YOLO: Enhancing Cotton Boll Detection and Counting in Complex Environmental Conditions Using an Advanced YOLO Model

This study aims to enhance the detection accuracy and efficiency of cotton bolls in complex natural environments. Addressing the limitations of traditional methods, we developed an automated detection system based on computer vision, designed to optimize performance under variable lighting and weather conditions. We introduced COTTON-YOLO, an improved model based on YOLOv8n, incorporating specific algorithmic optimizations and data augmentation techniques. Key innovations include the C2F-CBAM module to boost feature recognition capabilities, the Gold-YOLO neck structure for enhanced information flow and feature integration, and the WIoU loss function to improve bounding box precision. These advancements significantly enhance the model’s environmental adaptability and detection precision. Comparative experiments with the baseline YOLOv8 model demonstrated substantial performance improvements with COTTON-YOLO, particularly a 10.3% increase in the AP50 metric, validating its superiority in accuracy. Additionally, COTTON-YOLO showed efficient real-time processing capabilities and a low false detection rate in field tests. The model’s performance in static and dynamic counting scenarios was assessed, showing high accuracy in static cotton boll counting and effective tracking of cotton bolls in video sequences using the ByteTrack algorithm, maintaining low false detections and ID switch rates even in complex backgrounds.

Heat transfer processes in 'Shine Muscat' grapevine leaves in solar greenhouses under different irrigation treatments

The use of solar greenhouses in China is increasing because they permit environmental conditions to be controlled. Studies of the heat transfer processes in the leaves of plants cultivated within solar greenhouses are needed. Here, we studied heat transfer processes in 'Shine Muscat' grapevine leaves under moderate deficit irrigation (MDI), severe deficit irrigation (SDI), and full irrigation (FI) treatments under varying weather conditions. The stomatal conductance, leaf temperature, and transpiration rate of both shade and sun grapevine leaves were measured, and the effects of ambient temperature and relative humidity on these variables were determined. A thermal physics model of the leaves was established to explore the heat dissipation process. On sunny days, the transpiration heat transfer of sun leaves in the MDI, SDI, and FI treatments was 2.62 MJ m−2·day−1, 2.44 MJ m−2·day−1, and 3.86 MJ m−2·day−1and 0.818 MJ m−2·day−1, 0.782 MJ m−2·day−1, and 1.185 MJ m−2·day−1 on rainy days, respectively. There was a significant difference in transpiration heat transfer under fully irrigated and deficit irrigation conditions under different weather conditions. Furthermore, transpiration heat transfer accounted for 41.49 % and 25.03 % of the total heat transfer of sun leaves in the FI treatment and 33.94 % and 29.43 % of the total heat transfer of shade leaves on rainy days, respectively, indicating that relative humidity plays a key role in determining transpiration heat transfer and leaf temperature and that its effect was greater on sun leaves than on shade leaves.

Comprehensive bottom-up methodology for generating high-resolution yearly building load profiles: A case study in temperate oceanic climate

To facilitate the transition of residential buildings towards decarbonized energy sources, various energy systems are currently being investigated within the scientific community. The accurate sizing and performance evaluation of these systems heavily rely on the quality of input profiles. Addressing this necessity, a method for generating diverse, high-resolution, continuous, consistent demand and production profiles for a whole year is proposed. This method is structured in a modular fashion and draws upon a widely recognized demand model from literature. Each module of the method is systematically presented, and the parametrization process is detailed through a case study focusing on single-family houses in temperate climate. This comprehensive description facilitates the replication of the method in different geographical regions. Subsequently, a Monte Carlo simulation is employed, incorporating variations in weather conditions, building properties, and occupant behaviors. This simulation generates an openly accessible dataset comprising thermal, electrical and photovoltaic profiles for 3500 configurations. The generated weather and electricity demand profiles exhibit trends and variations that closely match the measured data. Photovoltaic production profiles were validated against PVGIS data, showing similar monthly variations and diversity. The generated dataset includes houses with energy consumption profiles that correspond to Energy Performance Certificates ranging from A to E.

Identifying decadal trends in deweathered concentrations of criteria air pollutants in Canadian urban atmospheres with machine learning approaches

Abstract. This study investigates long-term trends of criteria air pollutants, including NO2, CO, SO2, O3 and PM2.5, and Ox (meaning NO2+O3) measured in 10 Canadian cities during the last 2 to 3 decades. We also investigated associated driving forces in terms of emission reductions, perturbations due to varying weather conditions and large-scale wildfires, as well as changes in O3 sources and sinks. Two machine learning methods, the random forest algorithm and boosted regression trees, were used to extract deweathered mixing ratios (or mass concentrations) of the pollutants. The Mann–Kendall trend test of the deweathered and original annual average concentrations of the pollutants showed that, on the timescale of 20 years or longer, perturbation due to varying weather conditions on the decadal trends of the pollutants are minimal (within ±2 %) in about 70 % of the studied cases, although it might be larger (but at most 16 %) in the remaining cases. NO2, CO and SO2 showed decreasing trends in the last 2 to 3 decades in all the cities except CO in Montréal. O3 showed increasing trends in all the cities except Halifax, mainly due to weakened titration reaction between O3 and NO. Ox, however, showed decreasing trends in all the cities except Victoria, because the increase in O3 is much less than the decrease in NO2. In three of the five eastern Canadian cities, emission reductions dominated the decreasing trends in PM2.5, but no significant trends in PM2.5 were observed in the other two cites. In the five western Canadian cities, increasing or no significant trends in PM2.5 were observed, likely due to unpredictable large-scale wildfires overwhelming or balancing the impacts of emission reductions on PM2.5. In addition, despite improving air quality during the last 2 decades in most cities, an air quality health index of above 10 (representing a very high risk condition) still occasionally occurred after 2010 in western Canadian cities because of the increased large-scale wildfires.

Advanced Systems for Detecting and Recognizing Traffic Objects

Traffic object detection and recognition systems have become critical components in the advancement of intelligent transportation systems (ITS). These systems leverage various technologies such as computer vision, machine learning, and sensor fusion to accurately identify and classify objects on the road, including vehicles, pedestrians, traffic signs, and obstacles. The integration of these technologies enhances traffic management, improves road safety, and facilitates the development of autonomous vehicles. This paper provides an overview of the state-of-the-art methods and technologies used in traffic object detection and recognition. It also discusses the challenges faced in real-world implementations, such as varying weather conditions, lighting changes, and occlusions, and explores potential solutions and future research directions to address these issues.

The Role of Physical Literacy in the Association Between Weather and Physical Activity: A Longitudinal Multilevel Analysis With 951 Children.

Numerous studies showed an effect of weather on physical activity (PA) levels in children. However, no study has yet examined the relevance of personal factors in this relationship. Therefore, this study analyzes (1) whether there are systematic interindividual differences in the extent to which weather affects the PA behavior and (2) whether physical literacy (PL) moderates the weather-PA association in children. A total of 951 children in 12 Danish schools (age 9.76 [1.59]y; 54.3% girls) completed objective PA assessments via accelerometry (moderate to vigorous PA, light PA, and sedentary behavior). Local weather data (precipitation, wind speed, temperature, and sunshine duration) were provided by the Danish Meteorological Institute. Participants' PL was measured employing the Danish version of the Canadian Assessment of Physical Literacy-2. The 4116 accelerometer days underwent longitudinal multilevel analyses while considering their nesting into pupils and school classes (n = 51). Fluctuations in all PA indicators were significantly explained by variations in weather conditions, especially precipitation (P ≤ .035). Significant interindividual differences were found for 9 of 12 analytical dimensions, suggesting that weather changes influence PA behavior differently across individuals (especially moderate to vigorous PA, χ2[4] ≥ 11.5, P ≤ .021). However, PL moderated the relationship between weather and PA in only 2 of the 48 analytical constellations. Despite the varying impact of weather on PA across individuals, the present study favors a main effect model in which weather and PL exert independent effects on children's PA. The insufficient support for PL as a moderating factor calls for future studies to test alternative mechanisms in the weather-PA association.

TARGET IDENTIFICATION AND TRACKING IN COMPLEX ENVIRONMENT

This study focuses on developing advanced methods for the identification and classification of objects in complex environments. Over the past two years, there has been an increase in the use of advanced technologies in various challenging scenarios. This research is centered on accurately identifying targets and tracking them. The study addresses challenges related to object detection in multi-dimensional and intricate settings, taking into account natural conditions like rain and fog, as well as technical limitations such as camera capabilities. Special emphasis is placed on data collection for training the identification model, followed by extensive data preprocessing, including cleaning, labeling, and augmentation. The research employs YOLO and Deep Sport machine learning algorithms, focusing on improving the accuracy and reliability of target recognition and increasing data processing speed to minimize misidentification risks. The integration of YOLO, known for its quick real-time object detection, with Deep Sport, which excels in detailed feature extraction and classification, forms the basis of our methodology. This fusion is a complex combination of both models' strengths, with YOLO quickly identifying relevant objects and Deep Sport performing an in-depth analysis. The experimental phase involves extensive testing of the models in varied weather conditions and settings to evaluate performance under challenging circumstances. This work aims to enhance object identification techniques in complex environments, a critical aspect for the effectiveness of various advanced operations. The findings are expected to significantly contribute to the field by enabling quicker and more accurate target identification.

Comparative Analysis of LiDAR and CCTV Sensor Accuracy at Signalized Intersections Under Varied Weather Conditions

Comparative Analysis of LiDAR and CCTV Sensor Accuracy at Signalized Intersections Under Varied Weather Conditions

Coupling of photovoltaic thermal with hybrid forward osmosis-membrane distillation: Energy and water production dynamic analysis

This study proposes a novel solar photovoltaic thermal (PVT)-driven forward osmosis (FO) and membrane distillation (MD) system for desalinating brackish water, addressing water scarcity issues by combining the advantages of FO's low operating cost with MD's ability to treat hypersaline water. A physics-based dynamic model was developed for the system, and the model equations were discretized and solved using an open-source algebraic modeling language. Model validation involved verifying the consistency of outputs between successive unit operations and conducting sensitivity analysis by varying one operating condition at a time. The analysis considered varying weather conditions while maintaining constant operating conditions. Increasing the flow rate of brackish water (cooling fluid) from 19.79 to 59.38 kg/h improved thermal efficiency by 28.7 %; however, FO and MD water fluxes decreased by 1.12 LMH and 7.77 kg/m2/h, respectively. Peak thermal efficiency (81.2 %) was achieved at the highest flow rate. Incorporating heat transfer analysis into FO models goes beyond the common focus on mass transfer, allowing for a more comprehensive understanding of energy dynamics within the system. Both FO and MD water fluxes improved with irradiance and ambient temperature but decreased with cooling fluid flow rate and wind velocity. Increasing feed concentration to 10,000 mg/l reduced water flux by 0.66 LMH, and for each 0.5 M increase in draw solution concentration, FO water flux increased by >10 LMH. Compared to MD water flux, FO water flux is more sensitive to changes in concentration than to changes in weather conditions.

Modelowanie hydrologiczne jako wsparcie dla projektowania i utrzymania infrastruktury

One of the many concerns related to climate change is its impact on infrastructure, for example in the case of structures such as bridges or culverts which are more frequently exposed to conditions like significant differences in water table levels or floods. Varying weather conditions can also cause deterioration of properties of the materials which they are made of, which coupled with higher loads (water, wind or temperature variation, depending on the structure) decreases their durability and hinders their operation. Existing infrastructure facilities were designed with the use of historical data, and those design assumptions can be out of date at the moment, moreover their validity decreases with further climate changes. In order to support the design of new infrastructure facilities and the maintenance of already existing ones, climate change should be taken into account. One of the means that can be used to assess the impact of climate change on bridges and other river infrastructure is hydrological modelling. In this paper, the authors present a hydrological model of the flow in the Ślęza River, a 78.6 km long, left- bank tributary of the Odra River, as well as in its tributaries, with a particular focus on the points where bridges are located. The model was performed with QSWAT software, taking into account two scenarios of climate change: SSP2-4.5 and SSP5-8.5 (obtained with the use of NorESM2-LM model) and calibrated with the use of historical meteorological data. The results of the model include daily flows for years 2023-2050, which allows to compare characteristic (statistical) flows and observe trends; also a change in the dynamics of the river caused by thaws can also be observed. A greater number of extreme events can be seen in the results for the SSP2-4.5 scenario, the values of flood flows are also higher for this scenario, whereas the average flows are higher for the SSP5-8.5 scenario, which is due to higher rainfall in this scenario – although the threat of short-term extreme events is lower, but nevertheless, due to increased flows, scouring development can occur in this scenario, which when left uncontrolled can pose great risks to bridges. The obtained results would be helpful for engineers who plan the maintenance of infrastructure facilities, as this analysis would provide additional data in order to choose optimal solutions for the costs of exploitation and for the environment.

A reinforcement learning based autonomous vehicle control in diverse daytime and weather scenarios

Autonomous driving holds significant promise for substantially reducing road fatalities. Unlike traditional machine learning methods that have conventionally been applied to enhance the motion control of Autonomous Vehicles (AVs), recent attention has shifted toward the utilization of Deep Learning (DL) and Deep Reinforcement Learning (DRL) techniques. These advanced approaches have the potential to greatly improve AV vehicle control and empower vehicles to learn from their surroundings. However, the majority of existing research has concentrated on straightforward scenarios, often neglecting the intricate challenges posed by vulnerable road users such as pedestrians, cyclists, and motorcyclists, as well as the influence of varying weather conditions. In this study, we propose a novel model founded on DRL, specifically leveraging Deep-Q Networks (DQN), to effectively manage AVs in complex scenarios characterized by heavy traffic, diverse road users, and diverse weather conditions. Our approach involves training the model in diverse weather conditions, encompassing clear daytime and nighttime as well as challenging weather conditions like heavy rainfall during both the day and sunset. Through this comprehensive training, the AV becomes proficient in navigating safely through intersections and reaching its destination without any accidents. To rigorously evaluate and validate our proposed approach, extensive testing was conducted employing the CARLA simulator. The simulation results unequivocally demonstrate that our model not only reduces travel delays but also minimizes the occurrence of collisions, marking a significant step forward in achieving safer and more efficient autonomous driving.