Rainy Conditions Research Articles

Observed Climatology and Variability of Cattle Heat Stress in Australia

Abstract Exposure to weather extremes, such as heatwaves, can cause discomfort, harm, or death in grazing cattle in pastures. While the Australian Bureau of Meteorology issues sheep graziers alerts when there is an exposure risk to chill for livestock, there is no equivalent alert for heat stress for Australian cattle. Before any such alert system can be developed, a robust assessment and comparison of relevant cattle thermal stress indices is required. This study evaluates and compares the multiyear climatology of three cattle thermal heat stress indices across Australia in the warm season months (October–March). The same indices are then used to assess historical Australian heat events where cattle died from heat exposure. These events are based off official records and survey responses from northern Australian graziers. In the seven historical heat events studied, high relative humidity combined with low wind speeds, or high solar exposure combined with high surface temperatures, exacerbated the impact of heat stress on cattle. In the two historic events where multiple compounding weather factors combined (e.g., high humidity, low winds, and high solar exposure), the cattle mortality levels were significantly high. These events were characterized by rainy conditions followed by a rapid warming, meaning cattle were likely unable to acclimatize to such dramatic temperature changes. This study highlights the need for using more than one thermal stress index when verifying cattle heat stress events and, importantly, calls for further research on standardizing the risk classifications of these thermal indices for cattle in Australia’s variable climate. Significance Statement Cattle across Australia’s northern tropical and semiarid regions often experience extreme hot and humid conditions in the summer months, which increases the risk of heat stress. This is the first study of its kind to evaluate observations of cattle heat stress across Australia using indices that describe the combined effects of solar exposure, wind speed, relative humidity, and surface temperatures. These cattle heat stress indices can be used to evaluate historical cattle mortality events in feedlots and in grazed pastures. This study lays the groundwork for the development of Australian-wide cattle heat stress forecast products on the 7-day to multiweek time scales.

Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles

We introduce an innovative deep learning approach specifically designed for the environment identification of intelligent vehicles under rainy conditions in this paper. In the construction of wireless vehicular communication networks, an innovative approach is proposed that incorporates additional multipath components to simulate the impact of raindrop scattering on the vehicle-to-vehicle (V2V) channel, thereby emulating the channel characteristics of vehicular environments under rainy conditions and an equalization strategy in OFDM-based systems is proposed at the receiver end to counteract channel distortion. Then, a rainy environment identification method for autonomous vehicles is proposed. The core of this method lies in utilizing the Channel State Information (CSI) shared within the vehicular network to accurately identify the diverse rainy environments in which the vehicle operates without relying on traditional sensors. The environmental identification task is considered as a multi-class classification problem and a dedicated Convolutional Neural Network (CNN) model is proposed. This CNN model uses the CSI estimated from CAM exchanged in vehicle-to-vehicle (V2V) communication as training features. Simulation results showed that our method achieved an accuracy rate of 95.7% in recognizing various rainy environments, which significantly surpasses existing classical classification models. Moreover, it only took microseconds to predict with high accuracy, surpassing the performance limitations of traditional sensing systems under adverse weather conditions. This breakthrough ensures that intelligent vehicles can rapidly and accurately adjust driving parameters even in complex weather conditions like rain to autonomous drive safely and reliably.

Intercomparisons of Three Gauge-Based Precipitation Datasets over South America during the 1901–2015 Period

Gridded precipitation (PRP) data have been largely used in diagnostic studies on the climate variability in several time scales, as well as to validate model results. The three most used gauge-based PRP datasets are from the Global Precipitation Climatology Centre (GPCC), University of Delaware (UDEL), and Climate Research Unit (CRU). This paper evaluates the performance of these datasets in reproducing spatiotemporal PRP climatological features over the entire South America (SA) for the 1901–2015 period, aiming to identify the differences and similarities among the datasets as well as time intervals and areas with potential uncertainties involved with these datasets. Comparisons of the PRP annual means and variances between the 1901–2015 period and the non-overlapping 30-year subperiods of 1901–1930, 1931–1960, 1961–1990, and the 25-year subperiod of 1991–2015 for each dataset show varying means of the annual PRP over SA depending on the subperiod and dataset. Consistent patterns among datasets are found in most of southeastern SA and southeastern Brazil, where they evolved gradually from less to more rainy conditions from 1901–1930 to the 1991–2015 subperiod. All three datasets present limitations and uncertainties in regions with poor coverage of gauge stations, where the differences among datasets are more pronounced. In particular, the GPCC presents reduced PRP variability in an extensive area west of 50° W and north of 20° S during the 1901–1930 subperiod. In monthly time scale, PRP time series in two areas show differences among the datasets for periods before 1941, which are likely due to spurious or missing data: central Bolivia (CBO), and central Brazil (CBR). The GPCC has less monthly variability before 1940 than the other two datasets in these two areas, and UDEL presents reduced monthly variability before 1940 and spurious monthly values from May to September of the years from 1929 to 1941 in CBO. Thus, studies with these three datasets might lead to different results depending on the study domain and period of analysis, in particular for those including years before 1941. The results here might be relevant for future diagnostic and modelling studies on climate variability from interannual to multidecadal time scales.

Localization-aware logit mimicking for object detection in adverse weather conditions

Adverse weather conditions would decrease the image quality, leading to a sharp decline in detection accuracy. Most of the researches focus on object detection in fine weather conditions, rather than in adverse weather conditions. Recently, some methods attempt to reduce the gap between degraded images and clean images to improve the detection accuracy in adverse weather conditions. Specifically, these methods usually conduct image restoration and object detection in a sequential way or by joint learning. While these methods can improve detection accuracy to a certain extent, image restoration models may introduce noise or artifacts and increase computational burden, limiting the accuracy and efficiency of object detection in adverse weather conditions. In this paper, we propose a knowledge distillation-based method, Localization-aware Logit Mimicking (LaLM), to improve detection accuracy in adverse weather conditions by reducing the gap between degraded images and clean images at the prediction level, rather than the image level. Moreover, the localization quality is designed as the mimicking target to make the knowledge distillation more effective. Experiments conducted on three popular benchmarks (i.e., RTTS, ExDark, and RID) demonstrate that our LaLM can achieve the state-of-the-art detection accuracy and inference speed in foggy, rainy, and low-light conditions. Code is available at: https://github.com/VIPLab-CQU/LaLM.

Impact of Specific Leaf Extracts on Soil pH and Beta-Carotene in Amaranthus in Kiambu County, Kenya

Amaranthus (Amaranthus spp.) leaves are a good source of nutrients including vitamins antioxidants and dietary minerals including calcium, iron, and potassium. Soil pH is a very important chemical property of the soil, as it dictates the availability of plant nutrients. Acidic soils reduce availability of plant nutrients and hinder growth. This is corrected by adding lime which is expensive, does not add nutrients and requires re-application. The objective of this study was to evaluate the effects of leaf extracts from selected tree leaves on soil acidity and beta-carotene levels of amaranthus. Plant leaves that were tested were Turril (Vitex keniensis) Mexican sunflower (Tithonia diversifolia) and Indian nettle (Plectranthus barbatus). The experiment setup was in a 2 x 7 factorial arrangement in a randomized complete block design(RCBDreplicated three timesThe treatments were; the three leaves extracts, lime, farm yard manure, inorganic fertilizer and control.. Leaves were dried,ground and soaked in distilledwater for sixty days as pH was monitored and the resulting extract was added to soil for amanthus grown with plant extracts. Data on plant height, leaf area, number of leaves, fresh weight and dry weight and levels of beta carotene levels were collected. AOAC method was used for analysis of beta carotene. All data was subjected to analysis of variance (ANOVA) at 5% level of significance using SAS and least significant difference (LSD) for means separation. The results showed that leaf extracts significantly reduced soil acidity to above (6) while inorganic fertilizer increased the acidity (pH5.9) to (5.3) on all growing conditions while humus increased acidity during rainy and irrigation conditions. On beta carotene levels, Farmyard Manure had the highest levels(51.1µg) followed by the leaf extracts with extract 3-Plectras barbatus having higher levels. Control had the lowest levels of beta carotene followed by inorganic fertilizer and lime.These results suggest that farmers should be encouraged to grow vegetables using farmyard manure and leaf extracts to increase antioxidant levels and reduce soil acidity.They should also be encouraged to grow more of trees and shrubs studied to increase forest cover.

The fate of leaked heavy metals in the urban environment under different persistence and precipitation scenarios

The use of tobacco will lead to the littering of a large number of filters, and the leakage of pollutants from them into the urban environment is a serious concern. The aim of this study was to analyze the leakage of heavy metals from filter and estimate the annual concentration of pollution leakage in different waste routes and different climatic conditions. The results showed that the highest and lowest density of filter in the studied urban environment were 0.51 and 0.01 number/m2, respectively. According to the estimated annual production of 306 million cigarette butts in the studied area, the leakage of the studied metals was estimated to be 401 g. The share of copper, chromium, and cadmium from the total leakage was 67%, 8.3%, and 1.88%, respectively. The leakage of studied metals in rainy conditions was 2.86 times more than sunny conditions. In different scenarios, the minimum and maximum annual leakage of metals were estimated 23,043 and 350,419 mg/year, respectively. Filters are a little but important source of heavy metal emission into the urban environment, the amount of pollution from which is affected by the consumers’ behavior and the efficiency of the urban cleaning system. Education on the correct disposal of filters and increasing the efficiency of the urban cleaning system will lead to a reduction in pollution caused by tobacco consumption.

An algorithm for estimating the ultimate speed of vehicles on wet roads

Real-time estimation of a vehicle’s ultimate hydroplaning speed is crucial for ensuring its safety and stability, especially in rainy weather conditions. This paper proposes an algorithm for estimating the ultimate hydroplaning speed of vehicles on wet and slippery road surfaces based on finite element analysis of tire. Using the ABAQUS simulation software, a finite element model of a 205/55 R16 radial tire is established. Subsequently, a fluid-structure coupling model between the tire, water layer, and road surface is developed. The finite element method is employed to simulate and analyze the effects of tire inflation pressure, water layer thickness, vertical load, and tread wear on the vehicle’s ultimate hydroplaning speed. Simulation results indicate that an increase in tire inflation pressure and vertical load leads to an increase in the ultimate hydroplaning speed, while tread wear and an increase in water layer thickness result in a decrease. Finally, based on the simulation analysis data, a BP neural network-based estimation algorithm for the vehicle’s ultimate hydroplaning speed is proposed by combining the relationship between tire inflation pressure, tire vertical load, tread wear, water layer thickness, and ultimate hydroplaning speed. The results show that the estimated ultimate hydroplaning velocity profile has a high degree of overlap with the actual ultimate hydroplaning velocity profile, with a maximum error of no more than 5 km/h and an average percentage error of 2.31416%.

Optimization strategy for connected automated vehicles to reduce energy consumption on freeway in rainy weather

Energy consumption on freeway significantly contributes to environmental pollution. Rainy weather, as a common adverse condition, will exert a negative impact on car-following behavior of vehicles on freeway and further affect their energy consumption. The emergence of connected automated vehicles (CAVs) has created an opportunity to mitigate these impacts. This paper aims to propose an optimization strategy for CAVs that can reduce energy consumption during car-following behavior on freeway under different rainy weather conditions. To begin with, a calibrated car-following model for regular vehicles (RVs) on freeway in rainy weather was used to derive an optimization strategy for CAVs that have vehicle-to-vehicle communication capability to stabilize traffic flow with smoothed speed fluctuations. The proposed optimization strategy for CAVs was then subjected to simulation experiments to validate its effectiveness. Results indicate that energy consumption on freeway in rainy weather is closely linked to speed fluctuations. Frequent speed fluctuations during car-following behavior could cause more energy consumption. The proposed optimization strategy for CAVs is capable of reducing energy consumption in rainy weather by smoothing speed fluctuations. CAVs equipped with this optimization strategy shows an energy-saving of 34.69%–61.11% compared to RVs under various rainy weather conditions.

Fully polarized radar target detection and waveform optimization under rainy condition

Fully polarized radar target detection and waveform optimization under rainy condition

Hydrolysis reactivity reveals significant seasonal variation in the composition of organic peroxides in ambient PM2.5

Atmospheric organic peroxides (POs) play a key role in the formation of O3 and secondary organic aerosol (SOA), impacting both air quality and human health. However, there still remain technical challenges in investigating the reactivity of POs in ambient aerosols due to the instability and lack of standards for POs, impeding accurate evaluation of their environmental impacts. In the present study, we conducted the first attempt to categorize and quantify POs in ambient PM2.5 through hydrolysis, which is an important transformation pathway for POs, thus revealing the reactivities of various POs. POs were generally categorized into hydrolyzable POs (HPO) and unhydrolyzable POs (UPO). HPO were further categorized into three groups: short-lifetime HPO (S-HPO), intermediate-lifetime HPO (I-HPO), and long-lifetime HPO (L-HPO). S-HPO and L-HPO are typically formed from Criegee intermediate (CI) and RO2 radical reactions, respectively. Results show that L-HPO are the most abundant HPO, indicating the dominant role of RO2 pathway in HPO formation. Despite their lower concentration compared to L-HPO, S-HPO make a major contribution to the HPO hydrolysis rate due to their faster rate constants. The hydrolysis of PM2.5 POs accounts for 19 % of the nighttime gas-phase H2O2 growth during the summer observation, constituting a noteworthy source of gas-phase H2O2 and contributing to the atmospheric oxidation capacity. Seasonal and weather conditions significantly impact the composition of POs, with HPO concentrations in summer being significantly higher than those in winter and elevated under rainy and nighttime conditions. POs are mainly composed of HPO in summer, while in winter, POs are dominated by UPO.

The rooftop cooling potentials of evaporative cooling porous tiles and high reflective coatings in hot and humid regions

Altering the reflectivity and permeability of surfaces in high-density cities is a key approach to mitigate rising urban heat islands. High Reflectance Coatings (HRC) and Evaporative Cooling Porous Tiles (ECPT) offer potential solutions for urban cooling. However, previous studies have mainly focused on roof surface temperatures, neglecting changes in near-roof air temperature. ECPT, a new permeable material, is mostly used in urban pavements and ignoring the underutilized rooftops, with limited research on its cooling effects in these settings. Therefore, this study investigates the impact of HRC and ECPT on near-roof air temperatures in the Pearl River Delta through observational experiments under various weather conditions. The results show that both HRC and ECPT significantly reduce near-roof air temperatures, but their effectiveness decreases above 1.8 m. In rainy conditions, these strategies are less effective due to their dependence on solar radiation. HRC works by reflecting short-wave radiation, while ECPT cools by evaporation. A key advantage of ECPT is its ability to be actively watered, quickly reducing temperatures by 2.14 °C at 0.3 m. This research is valuable for developing urban cooling strategies in high-density areas during extreme temperature conditions.

Spatial and temporal distribution of nutrients in the Ciliwung River, DKI Jakarta

The Ciliwung River, one of the largest rivers to run through Jakarta, is particularly important for its water quality because it travels through the city’s center and several settlements, highly populated housing areas, and slums. The growth of algae and macrophytes, or aquatic plants, is dependent on the nutrients in the streams. These plants serve as major food sources for fish and other small invertebrates. This study aimed to describe the distribution of nutrients (ammonia, nitrite, nitrate, total nitrogen, and total phosphate) in the Ciliwung River, DKI Jakarta, in terms of both space and time. Water quality was monitored under rainy (February and June 2021) and dry (August and September 2021) conditions. The research area, which included 18 observation stations, was situated in the DKI Jakarta sector along the Ciliwung River. Geographically, all river regions’ concentrations of total nitrogen, nitrate, and total phosphate exceeded the quality criteria; however, ammonia and nitrite tended to be high and did not meet norms across the Ciliwung River area, DKI Jakarta. While ammonia concentrations were greater during the dry season and nitrate concentrations were higher during the rainy season, there was a temporary difference in the concentrations of nitrite, total nitrogen, and total phosphate between the seasons.

Sunshine to Rainstorm: Cross-Weather Knowledge Distillation for Robust 3D Object Detection

LiDAR-based 3D object detection models inevitably struggle under rainy conditions due to the degraded and noisy scanning signals. Previous research has attempted to address this by simulating the noise from rain to improve the robustness of detection models. However, significant disparities exist between simulated and actual rain-impacted data points. In this work, we propose a novel rain simulation method, termed DRET, that unifies Dynamics and Rainy Environment Theory to provide a cost-effective means of expanding the available realistic rain data for 3D detection training. Furthermore, we present a Sunny-to-Rainy Knowledge Distillation (SRKD) approach to enhance 3D detection under rainy conditions. Extensive experiments on the Waymo-Open-Dataset show that, when combined with the state-of-the-art DSVT model and other classical 3D detectors, our proposed framework demonstrates significant detection accuracy improvements, without losing efficiency. Remarkably, our framework also improves detection capabilities under sunny conditions, therefore offering a robust solution for 3D detection regardless of whether the weather is rainy or sunny.

Short‐term power forecasting method for 5G photovoltaic base stations on non‐sunny days based on SDN‐integrated INGO‐BP and RGAN

AbstractIn response to the suboptimal efficiency observed in the network configuration and administration of 5G photovoltaic base stations (PVBSs), as well as the inherent limitations in accurately forecasting photovoltaic power during inclement weather conditions, this research article introduces a concise and effective method for short‐term power prediction of PVBSs when subjected to non‐sunny weather conditions, leveraging the paradigm of software defined networking. This method ingeniously amalgamates the improved Northern Goshawk optimized back propagation neural network (INGO‐BP) and recurrent generative adversarial networks (RGAN). The investigation commences by delineating a comprehensive PVBS system framework underpinned by SDN principles. Subsequently, it employs the Pearson correlation coefficient technique and principal component analysis for the judicious selection of pertinent features from the PVBS photovoltaic dataset, thereby ameliorating the issue of multicollinearity within the predictive model. To culminate the research endeavour, the INGO algorithm is enlisted to optimize the weightings and bias parameters of the BP neural network, while RGAN is harnessed for the acquisition of distinctive distribution patterns in photovoltaic data across diverse meteorological conditions. This augmentation enhances the model's capacity to discern the intricate long‐term mappings between photovoltaic power and meteorological data. The empirical findings substantiate the elevated accuracy of the INGO‐BP prediction model across sunny, cloudy, and rainy weather conditions, with a discernible enhancement in predictive accuracy for PVBS performance under non‐sunny weather conditions facilitated by data augmentation via RGAN.

Intensified exploitation of animal products in the Mediterranean Copper Age: isotopic evidence from Scaba ’e Arriu (Sardinia)

In this paper, dietary patterns are reconstructed across two phases represented at the Copper Age of Scaba ’e Arriu (Sardinia, Italy) via isotopic analysis of human and animal skeletal remains. Collagen carbon, nitrogen, and bioapatite carbon, and oxygen isotopes were used to infer diet, economic practices, and possibly different climatic conditions. Differential management of cattle, sheep/goats, and pigs was detected, with scarce animal products in the human diet in the Early Copper Age, followed by an increase in animal products identifiable in herbivore livestock, coincident with more rainy conditions in the Late Copper Age. Dietary information was then considered in light of the climatic periods already known for the period and compatible with the data presented. The study’s outcome indicates generally sedentary and endogamous groups with only a few outliers in both phases.

A class of photosensitizer highly effective to control bacterial infection in plants even on rainy days with dim light

Bacterial infections of plants are widespread and highly virulent, causing serious economic losses in agricultural production worldwide. These infections become particularly prevalent during rainy weather, characterized by high humidity and low light intensity (about 2–10% of a sunny day). Here we identified a class of photosensitizers that were effective in inhibiting plant bacterial infections under rainy weather with dim light. One of these compounds, compound 1, demonstrated exceptional efficacy by eliminating more than 4 logs (99.99%) of either Xanthomonas perforans or Xanthomonas citri subsp. citri (Xcc) strain at a concentration of 6.25 μM under rainy weather conditions with a light exposure of 28.51 J/cm2. For Clavibacter michiganensis (Cm), compound 1 demonstrated a comparable bactericidal effect at a lower concentration of 3.13 μM. Furthermore, compound 1 was effective in controlling plant leaf infections during rainy weather. Moreover, compound 1 exhibited a potent inhibitory effect on the biofilms pertinent to tomato spot disease and citrus canker under natural illumination on rainy days. Additionally, compound 1 displayed remarkable resilience to rain-wash on plant leaves, retaining 41.20% of its initial concentration following exposure to three simulated rain events. These findings provide a new strategy for plant pathogen disease management highlight its feasibility even during rainy weather.

Luminescent Solar Concentrator with Advanced Structure for Reabsorption Loss Suppression and Synergistic Energy Harvesting

AbstractAs large‐area and optically transparent photon harvesting devices, luminescent solar concentrators (LSCs) are promising candidates for building‐integrated photovoltaics owing to their high transmittance and resistance to shadowing effects existing in solar cells. Up to now, there are still many challenges in the practical application of LSCs: 1) Reabsorption loss is inevitable during photoluminescence transmission due to indirect illumination of solar cells in LSC system. 2) Satisfactory energy harvesting cannot be achieved in rainy conditions due to the substantial attenuation of the incident light intensity. 3) Evaporation residue on the surface of LSCs leads to device performance degradation. Pioneering researches and feasible strategies for reabsorption loss suppression, energy harvesting in rainy days as well as self‐cleaning property are still lacking demonstration. In this work, reabsorption loss is suppressed based on advanced structural LSCs with universally applicable optical spacer layer. Then the integrated LSCs with droplet‐based electricity generator (DEG) are proposed for the first time. Such DEG‐LSCs not only realize synergistic harvesting of solar and raindrop energy, but also possess self‐cleaning properties. Finally, a self‐powered temperature and humidity sensing system is designed and demonstrated to provide feasible ideas for the practical application of DEG‐LSCs in self‐powered intelligent buildings.

Explainable machine learning methods for predicting water treatment plant features under varying weather conditions

Accurately predicting key features in WWTPs is essential for optimizing plant performance and minimizing operational costs. This study assesses the potential of various machine learning models for predicting the inflow to anoxic sludge reactors. Firstly, it conducts a comprehensive evaluation of diverse machine learning models, including k-Nearest Neighbors (kNN), Random Forest (RF), XGBoost, CatBoost, LightGBM, and Decision Tree Regression (DTR), for predicting the flow into the Anoxic section under various weather conditions (dry, rainy, and stormy). Secondly, the study introduces parsimonious models guided by variable importance from the XGBoost algorithm. Furthermore, the study employs SHAP (SHapley Additive exPlanations) to elucidate model predictions, providing insights into the contribution of each feature. Data from the COST Benchmark Simulation Model (BSM1) is used to verify the investigated models' effectiveness. Each dataset consists of 14 days of influent data at 15-minute intervals, with 80% of the data used for model training. Results show that ensemble learning methods, particularly CatBoost and XGBoost, demonstrate satisfactory predictive results for Anoxic section flow in the presence of increased variability under rainy and stormy conditions. Notably, the CatBoost and XGBoost models achieve average Mean Absolute Percentage Error values of 1.33% and 1.59%, outperforming the other methods.

Shading and waterlogging interactions exacerbate summer maize yield losses by reducing assimilate accumulation and remobilization processes

Persistent overcast rain was an essential limiting factor for summer maize production, of which immediate impact was the dual pressure of waterlogging and shading. However, the mechanism of independent and combined effects of waterlogging and shading induced maize yield losses are rarely studied, especially at different growth stages. Denghai 605 (DH605) was selected to be subjected shading, waterlogging, and their combined stress at the 3rd leaf stage (V3), the 6th leaf stage (V6), and tasseling stage (VT). Results showed that shading, waterlogging and their combination significantly limited the expansion of leaf area, and decreased leaf net photosynthetic rate (Pn) and net assimilation rate (NAR), thus reducing the crop growth rate (CGR) and biomass accumulation. At the same time, compared to control, the process of lignin synthesis was inhibited under stressed treatment, resulting in reduced stem mechanical strength and a poor development of the vascular system, of which change significantly reduced efficiency of assimilate remobilization to the ear and ultimately grain yield. The most significant effects of waterlogging and combined stresses on yield were occurred at V3 stage, followed by the V6 and VT stages. The most significant effects of shading were occurred at VT stage, followed by the V6 and V3 stages. Moreover, the compound stress exacerbated the damage brought about by a single stress. It is predicted that climate change will increase the frequency of abiotic stress assemblages, and the results of these findings provide some direction for further research on maize breeding in summer maize under continuous rainy conditions in the future.

Car-Following Strategy Involving Stabilizing Traffic Flow with Connected Automated Vehicles to Reduce Particulate Matter (PM) Emissions in Rainy Weather

On highways, it is commonplace to observe car-following behavior among vehicles. Unfortunately, this behavior results in significant particulate matter (PM) emissions, which greatly contribute to environmental pollution. Additionally, adverse weather conditions such as rain can negatively affect vehicles’ car-following behavior and have further influences on their PM emissions. The technology of connected automated vehicles (CAVs) offers a promising solution for mitigating these negative influences. This paper investigates the effect of various rainy weather conditions on PM emissions during car-following behavior on highways and proposes a CAV car-following strategy to reduce these emissions. Firstly, we employed a calibrated car-following model of traditional vehicles to perform simulation experiments, examining characteristics of PM emissions under four levels of rain and two simulation scenarios. Secondly, based on the relationship between PM emissions and speed fluctuations, we proposed a CAV car-following strategy by stabilizing traffic flow to smooth speed fluctuations. The proposed CAV car-following strategy was then validated through simulation experiments, and its effectiveness in reducing PM emissions under rainy conditions was assessed. The results indicate that higher speed fluctuations during car-following behavior lead to more PM emissions in rainy weather. By utilizing the proposed car-following strategy, CAVs can significantly reduce PM emissions in rain conditions, with average reductions of 41.07%, 59.46%, 49.60%, and 71.66% under very light rain, light rain, moderate rain, and heavy rain conditions, respectively. The findings of this paper facilitate the assessment of PM emissions fluctuations in different rainy weather conditions, which in turn can contribute to the development of more effective PM emissions control strategies. The proposed CAV car-following strategy can smooth speed fluctuations, and improve traffic flow stability, thus reducing PM emissions in rainy weather. It has the potential to mitigate environmental pollution from the transportation sector.