Roadside habitat: Boon or bane for pollinating insects?

Pollinators, which provide vital services to wild ecosystems and agricultural crops, are facing global declines and habitat loss. As undeveloped land becomes increasingly scarce, much focus has been directed recently to roadsides as potential target zones for providing floral resources to pollinators. Roadsides, however, are risky places for pollinators, with threats from vehicle collisions, toxic pollutants, mowing, herbicides, and more. Although these threats have been investigated, most studies have yet to quantify the costs and benefits of roadsides to pollinators and, therefore, do not address whether the costs outweigh the benefits for pollinator populations using roadside habitats. In this article, we address how, when, and under what conditions roadside habitats may benefit or harm pollinators, reviewing existing knowledge and recommending practical questions that managers and policymakers should consider when planning pollinator-focused roadside management.

Geochemical evaluation, ecological and human health risk assessment of potentially toxic elements in urban soil, Southern India.

Roadside soil contamination is mostly caused by human-caused pollutant deposition. PTEs are among the many substances that are harmful for both humans and the environment. PTE concentrations in roadside soil in Chennai, southern India, have been determined in this study. To evaluate the seriousness of the threats, more environmental and geochemical indices have been applied. 83 soil samples have been obtained from the study regions and focusing on important roads. Elemental analysis has been analyzed with ED-XRF and sieve-filtered samples focused on PTEs such as arsenic, barium, cobalt, chromium, copper, iron, potassium, nickel, lead, thorium, titanium, zinc, and uranium. Significant metallic variations have been found in soil samples around roads by the investigation. The elements this study examined section ascending in the following sequence: Fe > Ti > Zn > Cr > Pb > Cu > Ni > Th > As > U > K. In the research area, the CD classification denotes high contamination, whereas the CF indices show mild to significant pollution. PLI indicates moderate to high pollution, whereas EF suggests excessive enrichment. Igeo demonstrates a range from uncontaminated to highly contaminated. PERI showed high levels in the northern study region, whereas GUFI shows several hot spots indicating moderate to severe pollution. The Hazard Index (HI) values for all metals were less than one, demonstrating the absence of non-carcinogenic risks for both adults and children. Multivariate data show natural and anthropogenic PTEs in roadside soil. In addition, a soil quality monitoring system is needed to mitigate continual contamination risks.

Spatial distribution of heavy metal abundance at distance gradients of roadside agricultural soil from the busiest highway in Bangladesh: A multi-index integration approach.

Bangladesh is currently experiencing significant infrastructural development in road networking system through the construction or reconstruction of multiple roads and highways. Consequently, there is a rise in traffic intensity on roads and highways, along with a significant contamination of adjacent agricultural soils with heavy metals. The purpose of this study was to evaluate the ecological risk, health risk and the abundance of seven heavy metals (Cu, Mn, Pb, Cd, Cr, As, and Ni) in three distance gradients (0, 300, and 500 m) of agricultural soil along the Dhaka-Chattogram highway. The concentration of heavy metals was measured with an Atomic Absorption Spectrophotometer (AAS) on a total of 36 soil samples that were taken from 12 different sampling sites. Based on the findings, Cd had a high contamination factor for all distance gradients, whereas Cr had a moderate contamination factor in 67% of the study areas. According to the Pollution Load Index (PLI), Cd, Cr, and Pb were the predominant pollutants. Principal component analysis (PCA) result shows these metals mainly came from anthropogenic sources. The considerable positive correlations between Cu-Pb, Cu-Cd, Pb-Cd, and Cr-Ni all pointed to shared anthropogenic origins. As per Potential Ecological Risk Assessment (PERI) analysis, Pb, Cd, Cr, and Ni each contribute significantly and pose a moderate threat. The Target Hazard Quotient (THQ) values for all pathways of exposure to Pb and Cr in soils were more than 1, which would pose a significant risk to human health in the following order: THQadult female > THQadult male > THQchildren. This study will help to evaluate the human health risk and develop a better understanding of the heavy metal abundance scenario in the agricultural fields adjacent to this highway.

Delay-Oriented Roadside Unit Deployment for Highway Intersections in Vehicular Ad Hoc Networks.

Optimizing the deployment of roadside units (RSUs) holds great potential for enhancing the delay performance of vehicular ad hoc networks. However, there has been limited focus on devising RSU deployment strategies tailored specifically for highway intersections. In this study, we introduce a novel probabilistic model to characterize events occurring around highway intersections. By leveraging this model, we analytically determine the expected event reporting delays for both highway segments and intersections. Subsequently, we propose an RSU deployment scheme specifically designed for highway intersections, aimed at minimizing the expected event reporting delay. To implement this scheme, we introduce an innovative algorithm named cooperative walking. Through illustrative examples, we demonstrate that our proposed RSU deployment strategy for highway intersections outperforms the commonly employed uniform RSU deployment scheme and the previously proposed balloon method in terms of delay performance.

Enhanced Perception for Autonomous Vehicles at Obstructed Intersections: An Implementation of Vehicle to Infrastructure (V2I) Collaboration.

In urban intersections, the sensory capabilities of autonomous vehicles (AVs) are often hindered by visual obstructions, posing significant challenges to their robust and safe operation. This paper presents an implementation study focused on enhancing the safety and robustness of Connected Automated Vehicles (CAVs) in scenarios with occluded visibility at urban intersections. A novel LiDAR Infrastructure System is established for roadside sensing, combined with Baidu Apollo's Automated Driving System (ADS) and Cohda Wireless V2X communication hardware, and an integrated platform is established for roadside perception enhancement in autonomous driving. The field tests were conducted at the Singapore CETRAN (Centre of Excellence for Testing & Research of Autonomous Vehicles-NTU) autonomous vehicle test track, with the communication protocol adhering to SAE J2735 V2X communication standards. Communication latency and packet delivery ratio were analyzed as the evaluation metrics. The test results showed that the system can help CAV detect obstacles in advance under urban occluded scenarios.

PDT-YOLO: A Roadside Object-Detection Algorithm for Multiscale and Occluded Targets.

To tackle the challenges of weak sensing capacity for multi-scale objects, high missed detection rates for occluded targets, and difficulties for model deployment in detection tasks of intelligent roadside perception systems, the PDT-YOLO algorithm based on YOLOv7-tiny is proposed. Firstly, we introduce the intra-scale feature interaction module (AIFI) and reconstruct the feature pyramid structure to enhance the detection accuracy of multi-scale targets. Secondly, a lightweight convolution module (GSConv) is introduced to construct a multi-scale efficient layer aggregation network module (ETG), enhancing the network feature extraction ability while maintaining weight. Thirdly, multi-attention mechanisms are integrated to optimize the feature expression ability of occluded targets in complex scenarios, Finally, Wise-IoU with a dynamic non-monotonic focusing mechanism improves the accuracy and generalization ability of model sensing. Compared with YOLOv7-tiny, PDT-YOLO on the DAIR-V2X-C dataset improves mAP50 and mAP50:95 by 4.6% and 12.8%, with a parameter count of 6.1 million; on the IVODC dataset by 15.7% and 11.1%. We deployed the PDT-YOLO in an actual traffic environment based on a robot operating system (ROS), with a detection frame rate of 90 FPS, which can meet the needs of roadside object detection and edge deployment in complex traffic scenes.

Trend-attribute forecasting of hourly PM2.5 trends in fifteen cities of Central England applying optimized machine learning feature selection.

Recorded particulate matter (PM2.5) hourly trends are compared for fifteen urban recording sites distributed across central England for the period 2018 to 2022. They include 10 urban-background and five urban-traffic (roadside) sites with some located within the same urban area. The sites all show consistent background and peak distributions with mean annual values and standard deviations higher for 2018 and 2019 than for 2020 to 2022. The objective of this study is to demonstrate that trend attributes extracted from hourly recorded univariate PM2.5 trends at these sites can be used to provide reliable short-term hourly predictions and provide valuable insight into the regional variations in the recorded trends. Fifteen trend attributes extracted from the prior 12 h (t-1 to t-12) of recorded PM2.5 data were compiled and used as input to four supervised machine learning models (SML) to forecast PM2.5 concentrations up to 13 h ahead (t0 to t+12). All recording sites delivered forecasts with similar ranges of error levels for specific hours ahead which are consistent with their PM2.5 recorded ranges. Forecasting results for four representative sites are presented in detail using models trained and cross-validated with 2020 and 2021 hourly data to forecast 2021 and 2022 hourly data, respectively. A novel optimized feature selection procedure using a suite of five optimizers is used to improve the efficiency of the forecasting models. The LASSO and support vector regression models generate the best and most generalizable hourly PM2.5 forecasts from trained and validated SML models with mean average error (MAE) of between ∼1 and ∼3 µg/m3 for t0 to t+3 h ahead. A novel overfitting indicator, exploiting the cross-validation mean values, demonstrates that these two models are not affected by overfitting. Forecasts for t+6 to t+12 h forward generate higher MAE values between ∼3 and ∼4 µg/m3 due to their tendency to underestimate some of the extreme PM2.5 peaks. These findings indicate that further model refinements are required to generate more reliable short-term predictions for the t+6 to t+24 h ahead.

Total Mineral Content in Raw Materials of Ten Medicinal Plants from Roadside Biotopes of Voronezh Oblast.

The total mineral content was studied in medicinal plants from roadside and railside biotopes of the Voronezh region. Pharmacopoeial plant raw materials of 10 species were evaluated: roots of Taraxacum officinale F.H. Wigg and Arctium lappa L.; herb of Polygonum aviculare L., Artemisia absinthium L., Leonurus quinquelobatus Gilib., and Achillea millefolium L.; leaves of Urtica dioica L. and Plantago major L.; and flowers of Tanacetum vulgare L. and Tilia cordata Mill. Plant raw materials were collected near roads and railways of various types in the periods specified in regulatory documents. The total ash content in plant material was used to determine the minimum allowable distances from various roads and railways for collecting plant material. The minimum allowable distance from heavy-traffic motorways was recommended to be 210 m in the forest zone, 240 m in the forest-steppe zone, and 380 m in the steppe zone. A distance of at least 80 m was recommended for secondary low-speed roads and railways.

Carbon Sequestration Potential of Biomass Production along Highways in China.

In response to climate change, China is making great efforts to increase the green area for carbon sequestration. Road verges, as marginal land with favorable conditions for plant growth and ease of transportation, can be used for biomass production, but the biomass production and carbon sequestration potential have not been assessed. Here, we mapped the biomass production potential of road verges in China by combining a biomass model and Geographic Information System and then evaluated the effect of road runoff and CO2 fertilization on the production according to the runoff coefficient and vehicle emission inventory. Nationwide, road verges can produce 15.86 Mt C yr-1 of biomass. Road runoff contributes to a biomass production of 1.26 Mt C yr-1 through increasing soil water availability, which mainly occurs in arid regions. The CO2 fertilization effect by vehicle emission is considerable in Eastern and Southern China, contributing to a production of 0.09 Mt C yr-1. Life cycle assessment shows that major road verges in China have a carbon sequestration potential of 6.87 Mt C yr-1 currently. Our results revealed that road verges can make a significant contribution to carbon neutrality under proper management.

A Numerical Investigation of Rider Injury Risks During Falls Caused by E-Scooter-Stopper Impacts.

Within the past decade, injuries caused by electric scooter (e-scooter) crashes have significantly increased. A primary cause is front wheel collisions with a vertical surface such as a curb or object, generically referred to as a "stopper." In this study, various e-scooter-stopper crashes were simulated numerically across different impact speeds, approach angles, and stopper heights to characterize the influence of crash type on rider injury risk during falls. A finite element (FE) model of a standing Hybrid III anthropomorphic test device was used as the rider model after being calibrated against certification test data. Additionally, an FE model of an e-scooter was developed based on reconstructed scooter geometry. Forty-five FE simulations were run to investigate various e-scooter crash scenarios. Test parameters included impact speed (from 3.2 m/s to 11.16 m/s), approach angle (30 deg to 90 deg), and stopper height (52 mm, 101 mm, and 152 mm). Additionally, the perpendicular (90 deg) impact scenarios were run twice: once with Hybrid-III arm activation to mimic a rider attempting to break a fall with their hands and once without this condition. Overall, the risks of serious injury to the rider varied greatly; however, roughly half the impact scenarios indicated serious risk to the rider. This was expected, as the speeds tested were in the upper 25th percentile of reported scooter speeds. The angle of approach was found to have the greatest effect on injury risk to the rider, and was shown to be positively correlated with injury risk. Smaller approach angles were shown to cause the rider to land on their side, while larger approach angles caused the rider to land on their head and chest. Additionally, arm bracing was shown to reduce the risk of serious injury in two thirds of the impact scenarios.

Evaluation of Roadside LiDAR-Based and Vision-Based Multi-Model All-Traffic Trajectory Data.

Trajectory data has gained increasing attention in the transportation industry due to its capability of providing valuable spatiotemporal information. Recent advancements have introduced a new type of multi-model all-traffic trajectory data which provides high-frequency trajectories of various road users, including vehicles, pedestrians, and bicyclists. This data offers enhanced accuracy, higher frequency, and full detection penetration, making it ideal for microscopic traffic analysis. In this study, we compare and evaluate trajectory data collected from two prevalent roadside sensors: LiDAR and camera (computer vision). The comparison is conducted at the same intersection and over the same time period. Our findings reveal that current LiDAR-based trajectory data exhibits a broader detection range and is less affected by poor lighting conditions compared to computer vision-based data. Both sensors demonstrate acceptable performance for volume counting during daylight hours, but LiDAR-based data maintains more consistent accuracy at night, particularly in pedestrian counting. Furthermore, our analysis demonstrates that, after applying smoothing techniques, both LiDAR and computer vision systems accurately measure vehicle speeds, while vision-based data show greater fluctuations in pedestrian speed measurements. Overall, this study provides insights into the advantages and disadvantages of LiDAR-based and computer vision-based trajectory data, serving as a valuable reference for researchers, engineers, and other trajectory data users in selecting the most appropriate sensor for their specific needs.

Secure Data Transfer Based on a Multi-Level Blockchain for Internet of Vehicles.

Because of the decentralized trait of the blockchain and the Internet of vehicles, both are very suitable for the architecture of the other. This study proposes a multi-level blockchain framework to secure information security on the Internet of vehicles. The main motivation of this study is to propose a new transaction block and ensure the identity of traders and the non-repudiation of transactions through the elliptic curve digital signature algorithm ECDSA. The designed multi-level blockchain architecture distributes the operations within the intra_cluster blockchain and the inter_cluster blockchain to improve the efficiency of the entire block. On the cloud computing platform, we exploit the threshold key management protocol, and the system can recover the system key as long as the threshold partial key is collected. This avoids the occurrence of PKI single-point failure. Thus, the proposed architecture ensures the security of OBU-RSU-BS-VM. The proposed multi-level blockchain framework consists of a block, intra-cluster blockchain and inter-cluster blockchain. The roadside unit RSU is responsible for the communication of vehicles in the vicinity, similar to a cluster head on the Internet of vehicles. This study exploits RSU to manage the block, and the base station is responsible for managing the intra-cluster blockchain named intra_clusterBC, and the cloud server at the back end is responsible for the entire system blockchain named inter_clusterBC. Finally, RSU, base stations and cloud servers cooperatively construct the multi-level blockchain framework and improve the security and the efficiency of the operation of the blockchain. Overall, in order to protect the security of the transaction data of the blockchain, we propose a new transaction block structure and adopt the elliptic curve cryptographic signature ECDSA to ensure that the Merkle tree root value is not changed and also make sure the transaction identity and non-repudiation of transaction data. Finally, this study considers information security in a cloud environment, and therefore we propose a secret-sharing and secure-map-reducing architecture based on the identity confirmation scheme. The proposed scheme with decentralization is very suitable for distributed connected vehicles and can also improve the execution efficiency of the blockchain.

Battery-Powered RSU Running Time Monitoring and Prediction Using ML Model Based on Received Signal Strength and Data Transmission Frequency in V2I Applications.

The application of the Internet of Things (IoT), vehicles to infrastructure (V2I) communication and intelligent roadside units (RSU) are promising paradigms to improve road traffic safety. However, for the RSUs to communicate with the vehicles and transmit the data to the remote location, RSUs require enough power and good network quality. Recent advances in technology have improved lithium-ion battery capabilities. However, other complementary methodologies including battery management systems (BMS) have to be developed to provide an early warning sign of the battery's state of health. In this paper, we have evaluated the impact of the received signal strength indication (RSSI) and the current consumption at different transmission frequencies on a static battery-based RSU that depends on the global system for mobile communications (GSM)/general packet radio services (GPRS). Machine learning (ML) models, for instance, Random Forest (RF) and Support Vector Machine (SVM), were employed and tested on the collected data and later compared using the coefficient of determination (R2). The models were used to predict the battery current consumption based on the RSSI of the location where the RSUs were imposed and the frequency at which the RSU transmits the data to the remote database. The RF was preferable to SVM for predicting current consumption with an R2 of 98% and 94%, respectively. It is essential to accurately forecast the battery health of RSUs to assess their dependability and running time. The primary duty of the BMS is to estimate the status of the battery and its dynamic operating limits. However, achieving an accurate and robust battery state of charge remains a significant challenge. Referring to that can help road managers make alternative decisions, such as replacing the battery before the RSU power source gets drained. The proposed method can be deployed in other remote WSN and IoT-based applications.

Vehicle Detection and Tracking with Roadside LiDAR Using Improved ResNet18 and the Hungarian Algorithm.

By the end of the 2020s, full autonomy in autonomous driving may become commercially viable in certain regions. However, achieving Level 5 autonomy requires crucial collaborations between vehicles and infrastructure, necessitating high-speed data processing and low-latency capabilities. This paper introduces a vehicle tracking algorithm based on roadside LiDAR (light detection and ranging) infrastructure to reduce the latency to 100 ms without compromising the detection accuracy. We first develop a vehicle detection architecture based on ResNet18 that can more effectively detect vehicles at a full frame rate by improving the BEV mapping and the loss function of the optimizer. Then, we propose a new three-stage vehicle tracking algorithm. This algorithm enhances the Hungarian algorithm to better match objects detected in consecutive frames, while time-space logicality and trajectory similarity are proposed to address the short-term occlusion problem. Finally, the system is tested on static scenes in the KITTI dataset and the MATLAB/Simulink simulation dataset. The results show that the proposed framework outperforms other methods, with F1-scores of 96.97% and 98.58% for vehicle detection for the KITTI and MATLAB/Simulink datasets, respectively. For vehicle tracking, the MOTA are 88.12% and 90.56%, and the ID-F1 are 95.16% and 96.43%, which are better optimized than the traditional Hungarian algorithm. In particular, it has a significant improvement in calculation speed, which is important for real-time transportation applications.

Sensor Fusion-Based Vehicle Detection and Tracking Using a Single Camera and Radar at a Traffic Intersection.

Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to achieve cost-effective and efficient vehicle detection and tracking. Initially, vehicles are independently detected and classified using the camera and radar. Then, the constant-velocity model within a Kalman filter is employed to predict vehicle locations, while the Hungarian algorithm is used to associate these predictions with sensor measurements. Finally, vehicle tracking is accomplished by merging kinematic information from predictions and measurements through the Kalman filter. A case study conducted at an intersection demonstrates the effectiveness of the proposed sensor fusion method for traffic detection and tracking, including performance comparisons with individual sensors.

Obstacle Detection Method Based on RSU and Vehicle Camera Fusion.

Road obstacle detection is an important component of intelligent assisted driving technology. Existing obstacle detection methods ignore the important direction of generalized obstacle detection. This paper proposes an obstacle detection method based on the fusion of roadside units and vehicle mounted cameras and illustrates the feasibility of a combined monocular camera inertial measurement unit (IMU) and roadside unit (RSU) detection method. A generalized obstacle detection method based on vision IMU is combined with a roadside unit obstacle detection method based on a background difference method to achieve generalized obstacle classification while reducing the spatial complexity of the detection area. In the generalized obstacle recognition stage, a VIDAR (Vision-IMU based identification and ranging) -based generalized obstacle recognition method is proposed. The problem of the low accuracy of obstacle information acquisition in the driving environment where generalized obstacles exist is solved. For generalized obstacles that cannot be detected by the roadside unit, VIDAR obstacle detection is performed on the target generalized obstacles through the vehicle terminal camera, and the detection result information is transmitted to the roadside device terminal through the UDP (User Data Protocol) protocol to achieve obstacle recognition and pseudo-obstacle removal, thereby reducing the error recognition rate of generalized obstacles. In this paper, pseudo-obstacles, obstacles with a certain height less than the maximum passing height of the vehicle, and obstacles with a height greater than the maximum passing height of the vehicle are defined as generalized obstacles. Pseudo-obstacles refer to non-height objects that appear to be "patches" on the imaging interface obtained by visual sensors and obstacles with a height less than the maximum passing height of the vehicle. VIDAR is a vision-IMU-based detection and ranging method. IMU is used to obtain the distance and pose of the camera movement, and through the inverse perspective transformation, it can calculate the height of the object in the image. The VIDAR-based obstacle detection method, the roadside unit-based obstacle detection method, YOLOv5 (You Only Look Once version 5), and the method proposed in this paper were applied to outdoor comparison experiments. The results show that the accuracy of the method is improved by 2.3%, 17.4%, and 1.8%, respectively, compared with the other four methods. Compared with the roadside unit obstacle detection method, the speed of obstacle detection is improved by 1.1%. The experimental results show that the method can expand the detection range of road vehicles based on the vehicle obstacle detection method and can quickly and effectively eliminate false obstacle information on the road.

Infrastructure-Based Vehicle Localization through Camera Calibration for I2V Communication Warning.

In recent years, the research on object detection and tracking is becoming important for the development of advanced driving assistance systems (ADASs) and connected autonomous vehicles (CAVs) aiming to improve safety for all road users involved. Intersections, especially in urban scenarios, represent the portion of the road where the most relevant accidents take place; therefore, this work proposes an I2V warning system able to detect and track vehicles occupying the intersection and representing an obstacle for other incoming vehicles. This work presents a localization algorithm based on image detection and tracking by a single camera installed on a roadside unit (RSU). The vehicle position in the global reference frame is obtained thanks to a sequence of linear transformations utilizing intrinsic camera parameters, camera height, and pitch angle to obtain the vehicle's distance from the camera and, thus, its global latitude and longitude. The study brings an experimental analysis of both the localization accuracy, with an average error of 0.62 m, and detection reliability in terms of false positive (1.9%) and missed detection (3.6%) rates.

A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network.

The roadside unit (RSU) is one of the fundamental components in a vehicular ad hoc network (VANET), where a vehicle communicates in infrastructure mode. The RSU has multiple functions, including the sharing of emergency messages and the updating of vehicles about the traffic situation. Deploying and managing a static RSU (sRSU) requires considerable capital and operating expenditures (CAPEX and OPEX), leading to RSUs that are sparsely distributed, continuous handovers amongst RSUs, and, more importantly, frequent RSU interruptions. At present, researchers remain focused on multiple parameters in the sRSU to improve the vehicle-to-infrastructure (V2I) communication; however, in this research, the mobile RSU (mRSU), an emerging concept for sixth-generation (6G) edge computing vehicular ad hoc networks (VANETs), is proposed to improve the connectivity and efficiency of communication among V2I. In addition to this, the mRSU can serve as a computing resource for edge computing applications. This paper proposes a novel energy-efficient reservation technique for edge computing in 6G VANETs that provides an energy-efficient, reservation-based, cost-effective solution by introducing the concept of the mRSU. The simulation outcomes demonstrate that the mRSU exhibits superior performance compared to the sRSU in multiple aspects. The mRSU surpasses the sRSU with a packet delivery ratio improvement of 7.7%, a throughput increase of 5.1%, a reduction in end-to-end delay by 4.4%, and a decrease in hop count by 8.7%. The results are generated across diverse propagation models, employing realistic urban scenarios with varying packet sizes and numbers of vehicles. However, it is important to note that the enhanced performance parameters and improved connectivity with more nodes lead to a significant increase in energy consumption by 2%.

Metals stress on soil enzyme activities and herbs defense in the vicinity of high traffic roadways.

This study has investigated the impact of vehicle sourced heavy metals (HMs) on soil enzyme activities and plants in and around high traffic roadways near the metropolitan area. In detail, the defense response against HM pollution was studied by considering the commonly available herbs around the roadside area namely Alternanthera paronychioides, Ageratum conyzoides, Spilanthes acmella, and Parthenium hysterophorus. The study reported that the HM concentrations such as Cu, Ni, Zn, Mn, and Cr were observed in the range of 6.05 ± 0.1 to 309 ± 0.5 mg/kg in roadside soil and 5.2 ± 0.1to 451 ± 4.2 mg/kg in the herbs collected from roadside area. The soil enzyme (urease, dehydrogenase, amylase, catalase, peroxidase, and polyphenol oxidase) activities decreased by 22.56 to 77.84% in roadside soil and lower IC50 values were observed for DPPH (2.32-4.67) and H2O2 (1.59-2.15) free radical scavenging activities in plants collected from roadside area. The flavonoid and phenolic content in plants collected from the roadside area ranges from 12.65 ± 0.2 to 15.75 ± 0.3 mg quercitin/g and 0.61 ± 0.04 to 1.16 ± 0.1 mg gallic acid/g respectively while in plant collected from the control areas ranges from 7.96 ± 0.1 to 11.24 ± 0.05 and 0.47 ± 0.01 to 0.61 ± 0.1. In addition, the contamination factor (CF) (1.53-11.92) and geo-accumulation index (Igeo) (0.031-2.99) in soil and bioaccumulation factor (BAF) (0.72-2.73) of Cu, Ni, Zn, Mn, and Crin plants indicated that the soil and plants growing along the highway were heavily contaminated with HM. Finally, Pearson correlation matrix confirmed the inhibition effect of HM on soil enzymatic activities and enzymatic defense of plants in response to the HM stress.

Assessment of air pollution tolerance and anticipated performance index of roadside trees in urban and semi-urban regions.

Rapid urbanization and rising vehicular population are the main precursors in increasing air pollutants concentration which negatively influences the surrounding ecosystem. Roadside plants are frequently used as the barrier against traffic emissions to minimize the effects of air pollution. They are, however, vulnerable to various contaminants, and their tolerance capacity varies. This necessitates a scientific inquiry into the role of roadside plantations in improved urban sprawl planning and management, where chosen trees could be cultivated to reduce air pollution. The present study assesses biochemical and physiological characteristics to evaluate the air pollution tolerance index (APTI) in Ranchi, Jharkhand. The anticipated performance index (API) was assessed based on calculated APTI and socioeconomic characteristics of a selected common tree species along the roadside at different sites. According to APTI, Mangifera indica and Eugenia jambolana were the most tolerant species throughout all the sites, while Ficus benghalensis and Ficus religiosa were intermediately tolerant towards air pollution. The one-way ANOVA shows no significant variation in APTI throughout all the sites. The regression plot shows the positive correlation of APTI with ascorbic acid among all the parameters. According to API, the Mangifera indica, Eugenia jambolana Ficus religiosa and Ficus benghalensis were excellent and best performers among all the sites. So, the air pollution-resistant tree species can be recommended for roadside plantations for the development of green belt areas in urban regions.