Threats to conservation from national security interests.

There is a growing trend of nation states invoking national security and emergency declarations to build state-sponsored infrastructure projects for border defense, energy production, and transportation. Established laws, regulations, and agreements for the protection of nature and cultural heritage within and between countries are becoming secondary to national security, compromising the function of protected areas, such as national parks, wilderness areas, and biosphere reserves that safeguard biodiversity, climate, and human health. We considered cases where decades-long multinational cross-border endangered species recovery programs have been jeopardized by waivers of environmental protection laws to facilitate rapid construction of border barriers that impede the movement and migration of animals, such as at the US-Mexico and Poland-Belarus borders. Renewable energy megaprojects, such as the Pinacate solar plant in Mexico, coupled with power transmission lines and road networks likewise cast a large footprint on the land and are being carried out with minimal to no environmental compliance under the guise of national security. National sovereignty likewise has been used as justification for bypassing laws to proceed with similar projects, such as Mexico's Dos Bocas refinery and Poland's Vistula Spit canal. Emphasis on security is also apparent in increasing military expenditure by the world's largest economies, which has created a mismatch with improvement in environmental policy stringency. Decisions to prioritize security can undermine democratic principles and environmental review protocols, trivialize humanity's dependence on functioning ecosystems, and contradict the United Nation's resolution on the human right to a healthy environment. Framing infrastructure projects as matters of national security also foments civil and political unrest by the labeling and casting of dissenters, including conservation scientists and environmental defenders, as antinational. World leaders must refrain from misusing extraordinary powers, adhere to laws and international agreements, and consult experts and local people before taking unilateral action on projects that affect ecological and human communities.

Amenazas para la conservación a partir de los intereses nacionales de seguridad Resumen En los países existe una tendencia creciente por invocar la seguridad nacional y las declaraciones de emergencia para construir infraestructuras financiadas por el estado para la defensa de las fronteras, producción de energía y transporte. Las leyes, regulaciones y acuerdos establecidos para la protección de la naturaleza y el patrimonio cultural dentro y entre los países se están relegando por la seguridad nacional, lo que compromete la función de las áreas protegidas (parques nacionales, áreas silvestres y reservas de la biósfera) que resguardan la biodiversidad, el clima y la salud humana. Consideramos los casos en donde se han puesto en peligro los programas longevos y multinacionales de recuperación de especies en peligro por las exenciones a las leyes de protección ambiental para facilitar la construcción rápida de barreras fronterizas que impiden el movimiento y la migración de animales, como es el caso de las fronteras entre EU y México y Polonia y Bielorrusia. Los megaproyectos de energía renovable, como la planta solar del Pinacate en México, en conjunto con las líneas de transmisión eléctrica y las redes de carreteras también dejan una gran huella sobre la tierra y se realizan con el mínimo o ningún cumplimiento bajo el aspecto de la seguridad nacional. La soberanía nacional también se ha usado para justificar la omisión de las leyes para proceder con proyectos similares, como la refinería de Dos Bocas en México y el canal Vistula Spit en Polonia. El énfasis sobre la seguridad también es evidente con el incremento del gasto militar de las mayores economías mundiales, lo que ha creado una desigualdad con las mejoras en la exigencia de la política ambiental. Las decisiones para priorizar la seguridad pueden debilitar los principios democráticos y los protocolos de revisión ambiental, banalizar la dependencia de la humanidad por los ecosistemas funcionales y contradecir la resolución de las Naciones Unidas sobre el derecho humano a un ambiente saludable. Cuando se denominan los proyectos de infraestructura como asuntos de seguridad nacional, también se fomenta el malestar civil y político al etiquetar como antinacionales a los disidentes, incluidos los defensores ambientales y los científicos de la conservación. Los líderes mundiales deben abstenerse de usar indebidamente los poderes extraordinarios, adherirse a las leyes y acuerdos internacionales y consultar con expertos y personas locales antes de actuar de forma unilateral en cuanto a proyectos que afectan las comunidades humanas y ecológicas.

Beyond Conventional Monitoring: A Semantic Segmentation Approach to Quantifying Traffic-Induced Dust on Unsealed Roads.

Road dust is a mixture of fine and coarse particles released into the air due to an external force, such as tire-ground friction or wind, which is harmful to human health when inhaled. Continuous dust emission from the road surfaces is detrimental to the road itself and the road users. Due to this, multiple dust monitoring and control techniques are currently adopted in the world. The current dust monitoring methods require expensive equipment and expertise. This study introduces a novel pragmatic and robust approach to quantifying traffic-induced road dust using a deep learning method called semantic segmentation. Based on the authors' previous works, the best-performing semantic segmentation machine learning models were selected and used to identify dust in an image pixel-wise. The total number of dust pixels was then correlated with real-world dust measurements obtained from a research-grade dust monitor. Our method shows that semantic segmentation can be adopted to quantify traffic-induced dust reasonably. Over 90% of the predictions from both correlations fall in true positive quadrant, indicating that when dust concentrations are below the threshold, the segmentation can accurately predict them. The results were validated and extended for real-time application. Our code implementation is publicly available.

A Self-Adaptive Automatic Incident Detection System for Road Surveillance Based on Deep Learning.

We present an automatic road incident detector characterised by a low computational complexity for easy implementation in affordable devices, automatic adaptability to changes in scenery and road conditions, and automatic detection of the most common incidents (vehicles with abnormal speed, pedestrians or objects falling on the road, vehicles stopped on the shoulder, and detection of kamikaze vehicles). To achieve these goals, different tasks have been addressed: lane segmentation, identification of traffic directions, and elimination of unnecessary objects in the foreground. The proposed system has been tested on a collection of videos recorded in real scenarios with real traffic, including areas with different lighting. Self-adaptability (plug and play) to different scenarios has been tested using videos with significant scene changes. The achieved system can process a minimum of 80 video frames within the camera's field of view, covering a distance of 400 m, all within a span of 12 s. This capability ensures that vehicles travelling at speeds of 120 km/h are seamlessly detected with more than enough margin. Additionally, our analysis has revealed a substantial improvement in incident detection with respect to previous approaches. Specifically, an increase in accuracy of 2-5% in automatic mode and 2-7% in semi-automatic mode. The proposed classifier module only needs 2.3 MBytes of GPU to carry out the inference, thus allowing implementation in low-cost devices.

Should I stay or move? Quantifying landscape of fear to enhance environmental management of road networks in a highly transformed landscape.

The development and expansion of road networks pose considerable threats to natural habitats and wildlife, fostering a landscape of fear. In addition to direct mortality caused by road collisions, road construction and maintenance often result in habitat fragmentation and loss, impeding animal movement and gene flow between populations. Mountain ungulates are already confined to fragmented habitat patches and roads can cause substantial disturbances to their critical ecological processes, such as dispersal and migration. In this study, we employed two key mountain ungulates, the wild goat (Capra aegagrus) and mouflon (Ovis gmelini), as functional models to examine how road networks impact the quantity and connectivity of natural habitats in southwestern Iran, where extensive road construction has led to significant landscape changes. We used the MaxEnt method to predict species distribution, the circuit theory to evaluate habitat connectivity, and the Spatial Road Disturbance Index (SPROADI) to assess road impacts. During the modeling process, we selected eleven important variables and employed a model parametrization strategy to identify the optimal configuration for the MaxEnt model. For SPROADI index we used three sub-indices, including traffic intensity, vicinity impact, and fragmentation grade. We then integrated the results of these analyses to identify areas with the most significant environmental impacts of roads on the coherency of the natural habitats. The findings indicate that suitable habitats for wild goats are widely distributed across the study area, while suitable habitats for mouflon are primarily concentrated in the northeastern region. Conservation gap analysis revealed that only 8% of wild goat habitats and 7% of mouflon habitats are covered by protected areas (PAs). The SPROADI map highlighted that 23% of the study area is negatively influenced by road networks. Moreover, 30.4% of highest-probability corridors for mouflon, and 25.7% for wild goat, were highly vulnerable to the impacts of roads. Our combined approach enabled us to quantitatively assess species-specific vulnerability to the impacts of heavy road networks. This study emphasizes the urgent need to address the negative effects of road networks on wildlife habitats and connectivity corridors. Our approach effectively identifies sensitive areas, which can help inform mitigation strategies and support more effective conservation planning in significantly transformed landscapes.

Effects of vehicle traffic on space use and road crossings of caribou in the Arctic.

Assessing the effects of industrial development on wildlife is a key objective of managers and conservation practitioners. However, wildlife responses are often only investigated with respect to the footprint of infrastructure, even though human activity can strongly mediate development impacts. In Arctic Alaska, there is substantial interest in expanding energy development, raising concerns about the potential effects on barren-ground caribou (Rangifer tarandus granti). While caribou generally avoid industrial infrastructure, little is known about the role of human activity in moderating their responses, and whether managing activity levels could minimize development effects. To address this uncertainty, we examined the influence of traffic volume on caribou summer space use and road crossings in the Central Arctic Herd within the Kuparuk and Milne Point oil fields on the North Slope of Alaska. We first modeled spatiotemporal variation in hourly traffic volumes across the road system from traffic counter data using gradient-boosted regression trees. We then used generalized additive models to estimate nonlinear step selection functions and road-crossing probabilities from collared female caribou during the post-calving and insect harassment seasons, when they primarily interact with roads. Step selection analyses revealed that caribou selected areas further from roads (~1-3 km) during the post-calving and mosquito seasons and selected areas with lower traffic volumes during all seasons, with selection probabilities peaking when traffic was <5 vehicles/h. Using road-crossing models, we found that caribou were less likely to cross roads during the insect seasons as traffic increased, but that response dissipated as insect harassment became more severe. Past studies suggested that caribou exhibit behavioral responses when traffic exceeds 15 vehicles/h, but our results demonstrate behavioral responses at much lower traffic levels. Our results illustrate that vehicle activity mediates caribou responses to road infrastructure, information that can be used in future land-use planning to minimize the behavioral responses of caribou to industrial development in sensitive Arctic landscapes.

Quantifying traffic-related carbon emissions on elevated roads through on-road measurements.

Vehicles generally move smoothly and with high speeds on elevated roads, thereby producing specific traffic-related carbon emissions in contrast to ground roads. Hence, a portable emission measurement system was adopted to determine traffic-related carbon emissions. The on-road measurement results revealed that the instantaneous emissions of CO2 and CO from elevated vehicles were 17.8% and 21.9% higher than those from ground vehicles, respectively. Based on it, the vehicle specific power was confirmed to exhibit a positive exponential relationship with instantaneous CO2 and CO emissions. In addition to carbon emissions, carbon concentrations on roads were simultaneously measured. The average CO2 and CO emissions on elevated roads in urban areas were 1.2% and 6.9% higher than those on ground roads, individually. Finally, a numerical simulation was performed, and the results verified that elevated roads could deteriorate the air quality on ground roads but improve the air quality above them. What ought to be paid attention to is that the elevated roads present varied traffic behaviour and cause particular carbon emissions, indicating that comprehensive consideration and further balance among the traffic-related carbon emissions are necessary when building elevated roads to alleviate the traffic congestion in urban areas.

Neighborhood infrastructure-related risk factors and non-communicable diseases: a systematic meta-review.

BACKGROUND: With rapid urbanization, the urban environment, especially the neighborhood environment, has received increasing global attention. However, a comprehensive overview of the association between neighborhood risk factors and human health remains unclear due to the large number of neighborhood risk factor-human health outcome pairs. METHOD: On the basis of a whole year of panel discussions, we first obtained a list of 5 neighborhood domains, containing 33 uniformly defined neighborhood risk factors. We only focused on neighborhood infrastructure-related risk factors with the potential for spatial interventions through urban design tools. Subsequently, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic meta-review of 17 infrastructure-related risk factors of the 33 neighborhood risk factors (e.g., green and blue spaces, proximity to major roads, and proximity to landfills) was conducted using four databases, Web of Science, PubMed, OVID, and Cochrane Library, from January 2000 to May 2021, and corresponding evidence for non-communicable diseases (NCDs) was synthesized. The review quality was assessed according to the A MeaSurement Tool to Assess Systematic Reviews (AMSTAR) standard. RESULTS: Thirty-three moderate-and high-quality reviews were included in the analysis. Thirteen major NCD outcomes were found to be associated with neighborhood infrastructure-related risk factors. Green and blue spaces or walkability had protective effects on human health. In contrast, proximity to major roads, industry, and landfills posed serious threats to human health. Inconsistent results were obtained for four neighborhood risk factors: facilities for physical and leisure activities, accessibility to infrastructure providing unhealthy food, proximity to industry, and proximity to major roads. CONCLUSIONS: This meta-review presents a comprehensive overview of the effects of neighborhood infrastructure-related risk factors on NCDs. Findings on the risk factors with strong evidence can help improve healthy city guidelines and promote urban sustainability. In addition, the unknown or uncertain association between many neighborhood risk factors and certain types of NCDs requires further research.

Maternal periconceptional environmental exposure and offspring with congenital heart disease: a case-control study in Guangzhou, China.

BACKGROUND: Congenital heart defects (CHDs) are a major global health problem, yet their crucial environmental risk factors are still unclear. We aimed to explore the associations between maternal periconceptional environmental exposures and all CHDs, isolated and multiple CHDs and CHDs subtypes. METHOD: A case-control study including 675 infants with CHDs and 1545 healthy controls was conducted. Participating mothers who delivered in Guangzhou from October 2019 to November 2021 were recruited. To examine the independent associations between maternal periconceptional environmental exposure and offspring with CHDs, we calculated odds ratios (ORs) and 95% confidence intervals (CIs) using multivariable logistic regression model. RESULTS: Maternal exposure to living near main roads [adjusted OR (aOR) = 1.94, 95% CI = 1.06-3.56] and housing renovation (aOR = 1.94, 95% CI = 1.03-3.67) during the periconceptional period were positively related to a greater risk of all CHDs, similar results were also found in isolated CHDs rather than multiple CHDs. Additionally, living near main roads was positively associated with secundum atrial septal defect/patent foramen ovale (aOR = 2.65, 95% CI = 1.03-6.81) and housing renovation was strongly positively associated with ventricular septal defect (aOR = 5.08, 95% CI = 2.05-12.60). However, no association was observed between incense burning and family relationships and all CHDs, isolated and multiple CHDs and CHDs subtypes. CONCLUSION: Living near main roads and housing renovation during the periconceptional period are significantly associated with the increased risks for all CHDs and isolated CHDs. Further study is needed to extend sample size to explore the effects of time and frequency of burning incense and family relationships on CHDs in offspring.

A better Amazon road network for people and the environment.

The rapidly expanding network of roads into the Amazon is permanently altering the world's largest tropical forest. Most proposed road projects lack rigorous impact assessments or even basic economic justification. This study analyzes the expected environmental, social and economic impacts of 75 road projects, totaling 12 thousand kilometers of planned roads, in the region. We find that all projects, although in different magnitudes, will negatively impact the environment. Forty-five percent will also generate economic losses, even without accounting for social and environmental externalities. Canceling economically unjustified projects would avoid 1.1 million hectares of deforestation and US$ 7.6 billion in wasted funding for development projects. For projects that exceed a basic economic viability threshold, we identify the ones that are comparatively better not only in terms of economic return but also have lower social and environmental impacts. We find that a smaller set of carefully chosen projects could deliver 77% of the economic benefit at 10% of the environmental and social damage, showing that it is possible to have efficient tradeoff decisions informed by legitimately determined national priorities.

Development of a Smart Signalization for Emergency Vehicles.

As the population increases, the number of motorized vehicles on the roads also increases. As the number of vehicles increases, traffic congestion occurs. Traffic lights are used at road junctions, intersections, pedestrian crossings, and other places where traffic needs to be controlled to avoid traffic chaos. Due to traffic lights installed in the city, queues of vehicles are formed on the streets for most of the day, and many problems arise because of this. One of the most important problems is that emergency vehicles, such as ambulances, fire engines, police cars, etc., cannot arrive on time despite traffic priorities. Emergency vehicles such as hospitals and police departments need to reach the scene in a very short time. Time loss is a problem that needs to be addressed, especially for emergency vehicles traveling in traffic. In this study, ambulances, fire brigades, police, etc., respond to emergencies. A solution and a related application have been developed so privileged vehicles can reach their target destination as soon as possible. In this study, a route is determined between the current location of an emergency vehicle and its target location in an emergency. Communication between traffic lights is provided with a mobile application developed specifically for the vehicle driver. In this process, the person controlling the lights can turn on the traffic lights during the passage of vehicles. After the vehicles with priority to pass passed, traffic signaling was normalized via the mobile application. This process was repeated until the vehicle reached its destination.

A Model for Infrastructure Detection along Highways Based on Remote Sensing Images from UAVs.

Infrastructure along the highway refers to various facilities and equipment: bridges, culverts, traffic signs, guardrails, etc. New technologies such as artificial intelligence, big data, and the Internet of Things are driving the digital transformation of highway infrastructure towards the future goal of intelligent roads. Drones have emerged as a promising application area of intelligent technology in this field. They can help achieve fast and precise detection, classification, and localization of infrastructure along highways, which can significantly enhance efficiency and ease the burden on road management staff. As the infrastructure along the road is exposed to the outdoors for a long time, it is easily damaged and obscured by objects such as sand and rocks; on the other hand, based on the high resolution of the images taken by Unmanned Aerial Vehicles (UAVs), the variable shooting angles, complex backgrounds, and high percentage of small targets mean the direct use of existing target detection models cannot meet the requirements of practical applications in industry. In addition, there is a lack of large and comprehensive image datasets of infrastructure along highways from UAVs. Based on this, a multi-classification infrastructure detection model combining multi-scale feature fusion and an attention mechanism is proposed. In this paper, the backbone network of the CenterNet model is replaced with ResNet50, and the improved feature fusion part enables the model to generate fine-grained features to improve the detection of small targets; furthermore, the attention mechanism is added to make the network focus more on valuable regions with higher attention weights. As there is no publicly available dataset of infrastructure along highways captured by UAVs, we filter and manually annotate the laboratory-captured highway dataset to generate a highway infrastructure dataset. The experimental results show that the model has a mean Average Precision (mAP) of 86.7%, an improvement of 3.1 percentage points over the baseline model, and the new model performs significantly better than other detection models overall.

Traffic Stream Analysis by Radar Sensors on Two-Lane Roads for Free-Moving and Constrained Vehicles Identification.

This paper focuses on the analysis of traffic streams on two-lane highways, which are crucial components of transportation networks. Traffic flow measurement technologies, such as detection stations, radar guns, or video cameras, have been used over the years to detect the level of traffic and the operating conditions. This type of sensor can record a large amount of data which is useful to evaluate and monitor road traffic conditions, and it is possible to identify free-moving and constrained vehicles by processing the collected data. This study introduces an exponential headway model to identify the headway threshold above which vehicles can be considered as unconditioned. However, this value could identify vehicles which still retain some autonomy in their speed and maneuvering. Therefore, an additional criterion to distinguish between apparently and actually conditioned vehicles has been introduced, analyzing the speed differences between a vehicle and the preceding one. Three-month sequences of traffic monitored through radar devices placed on some Italian two-lane roads have been analyzed and an exponential headway model has been introduced, as an illustrative example. The results show that introducing the criterion of maneuvering freedom can significantly improve traffic flow analysis, modifying the starting critical values of 4 and 8 s per each studied section, to 2.5 and 3 s, approaching the values suggested by international manuals for traffic flow quality analysis.

Surface structure on abandoned upland blanket peatland tracks.

Temporary permissions are often granted for track use on peatlands. However, even when peatland track designs attempt to minimise environmental impacts via use of mesh systems, such linear disturbances may have persistent impacts. We evaluated the surface peatland structure of five abandoned tracks (four with a mesh surface, one unsurfaced) with varying past usage frequencies, at an upland site in northern England. Simplification of the surface nanotopography was found on all tracks compared to surrounding control areas, with increased micro-erosion patterns in rutted areas, and invasive species on some treatments. The frequency of previous usage was not found to be a significant factor controlling nano-topographic loss. Edge effects and hillslope position were influential in places, but these effects were not consistent across treatments. Nano-topographic recovery was found to be inhibited when track usage commenced within a short time frame after track construction. Mesh tracks appear to create a spatial constraint leading to poor development of plants and a reduced ability to form characteristic structures which are integral to mire function.

The overlooked threat of land take from wind energy infrastructures: Quantification, drivers and policy gaps.

Wind harnessing is a fast-developing and cost-effective Renewable Energy Source, but the land impacts of wind power stations are often overlooked or underestimated. We digitized land take, i.e., the generation of artificial land, derived from 90 wind power stations in Greece constructed between 2002 and 2020 (1.2 GW). We found substantial land take impacts of 7729 m2/MW (3.5 m2/MWh) of new artificial land, 148 m/MW of new roads and 174 m/MW of widened roads on average. Models showed that the number and size of wind turbines, the absence of other existing infrastructures and the elevational difference across new access roads increased artificial land generation. The elevational difference across new and widened access roads also increased their length. New wind power stations in Greece are planned to be installed at higher elevations and in terrains facing higher risks for soil erosion and soil biodiversity. The general tendency in the European Union is to sit fewer wind power stations in mountainous and forested land. Still, this pattern is inversed in several countries, particularly in Southern Europe. After screening 29 policy and legal documents, we found that land take is indirectly inferred in the global policy but more directly in the European policy through five non-legally binding documents and three Directives. However, the current European energy policies seem to conflict with nature conservation policies, risking land take acceleration. The study provides insights for reducing land take when planning and constructing wind power stations. We underline the need for better quantification of land take and its integration in the complex process of sustainable spatial planning of investments.

Removal of mesh track on an upland blanket peatland leads to changes in vegetation composition and structure.

Mesh tracks on peatlands are often granted permits on a temporary basis under the presumption that the tracks are either removed at the end of their permitted use or remain unused in situ. However, the fragility of peatland habitats and poor resilience of the specialist plant communities within them, mean that these linear disturbances may persist post-abandonment or post-removal. We removed sections of mesh track, abandoned five years earlier, from a blanket peatland using two different removal treatment methods (mown and unprepared) and studied a third treatment with sections left in place over a period of 19 months. On abandoned tracks, invasive species including Campylopus introflexus and Deschampsia flexulosa had established, while track removal led to extensive loss of Sphagnum species. Loss of surficial nanotopographic vegetation structures during track removal was extensive, and micro-erosion features were prevalent in both removal treatments. Abandoned sections of track performed comparably better across all metrics than removed sections. However, similarity between the vegetation assemblage of the abandoned track and the controls was <40% at the study outset, with NMDS (Non-metric Multidimensional Scaling) highlighting divergences. There was a mean species loss of 5 per quadrat for the removed sections. Bare peat was present in 52% of all track quadrats by the finish of the study. Our findings suggest that mesh tracks left in situ and track removal both present significant barriers to recovery and additional conservation interventions may be required after peatland tracks are abandoned.

Factors contributing to rill erosion of forest roads in a mountainous watershed.

Forest roads are a major source of and transport pathway for eroded sediments in mountainous watersheds. When rills develop on these roads' surfaces, they amplify sediment erosion. Best management practices can decrease sediment erosion, but in order to efficiently implement these practices it is necessary to determine which factors have the most influence on rill development on forest roads. Despite this need, there is scarce literature on rill development on forest roads. To fill this gap in knowledge, based on field survey and multivariate statistical methods including redundancy analysis (RDA) and variation partitioning analysis (VPA), we investigated unpaved forest roads in the Xiangchagou watershed in China and quantified the extent to which various factors influenced rill formation. Specifically, we studied how rill erosion intensity (REI) and rill morphological characteristics (like rill length, mean width and depth, density, and severity of fragmentation) varied along the slope of a forest road. We also introduced the concept of a road's hydrological constituents (its upslope catchment, surface, and cutslopes), and determined how much each constituent contributed to REI. We found that REI and morphological characteristics decreased moving from the upper portion of road segment downward, implying that rills developed more intensely uphill. Additionally, REI increased exponentially with rill width, density, and severity of fragmentation, and increase linearly with length and depth. Conversely, REI decreased exponentially with rill width-depth ratio. These relationships suggest that the morphological characteristics of rills could be used to predict the REI of a given road segment. Finally, we found that the road characteristics that best predicted rill formation included catchment area, cutslope area, and gravel bareness. Correspondingly, the upslope catchment, cutslopes, and road surface contributed 11.56%, 30.83%, and 8.23% of the variation in REI and morphological characteristics. The interaction between upslope catchment and road surface explained 19.89% of the variation. These results suggest that when best management practices are implemented to decrease erosion caused by forest roads in mountainous watersheds, they should integrate these hydrological constituents of a road.

Evaluating the real-world emissions of diesel passenger Car in Indian heterogeneous traffic.

A 30 portable emission measurement system (PEMS) test was conducted in this study to examine the effect of driving modes (aggressive and normal) and road type (urban and rural) on tailpipe emissions. Driving modes were assessed using relative positive acceleration and velocity × positive acceleration factors. The findings revealed that aggressive and normal driving modes differed significantly on urban and rural roads, as evident from paired sample t-test (p < 0.05). Furthermore, aggressive driving exhibited more prominent speed and acceleration on rural roads, while normal driving modes showed consistent acceleration or speed patterns regardless of road conditions as observed from kernel density estimation and box plot analysis. Emission rates (CO, CO2, HC, and NOx) significantly varied between aggressive and normal driving modes on urban and rural roads, as indicated by paired sample t-test analysis (p < 0.05). Aggressive driving increased CO2, CO, and HC emission rates for acceleration and deceleration modes by 18% to 40% compared to normal driving. Aggressive driving modes increased the emission factors (CO, HC, and CO2) by 5% to 25% compared to the normal driving mode on both urban and rural roads. Moreover, the NOx emission factors were also found significant during normal driving conditions on urban roads. This study provides real-world emission factors of diesel cars considering the impact of route, vehicle familiarity, and driving behavior induced by varying traffic conditions, which will contribute to improve the current emissions inventory on both a local and global level.

Temporal-spatial dynamic characteristics of vehicle emissions on intercity roads in Guangdong Province based on vehicle identity detection data.

Estimating intercity vehicle emissions precisely would benefit collaborative control in multiple cities. Considering the variability of emissions caused by vehicles, roads, and traffic, the 24-hour change characteristics of air pollutants (CO, HC, NOX, PM2.5) on the intercity road network of Guangdong Province by vehicle categories and road links were revealed based on vehicle identity detection data in real-life traffic for each hour in July 2018. The results showed that the spatial diversity of emissions caused by the unbalanced economy was obvious. The vehicle emissions in the Pearl River Delta region (PRD) with a higher economic level were approximately 1-2 times those in the non-Pearl River Delta region (non-PRD). Provincial roads with high loads became potential sources of high emissions. Therefore, emission control policies must emphasize the PRD and key roads by travel guidance to achieve greater reduction. Gasoline passenger cars with a large proportion of traffic dominated morning and evening peaks in the 24-hour period and were the dominant contributors to CO and HC emissions, contributing more than 50% in the daytime (7:00-23:00) and higher than 26% at night (0:00-6:00). Diesel trucks made up 10% of traffic, but were the dominant player at night, contributed 50%-90% to NOX and PM2.5 emissions, with a marked 24-hour change rule of more than 80% at night (23:00-5:00) and less than 60% during daytime. Therefore, targeted control measures by time-section should be set up on collaborative control. These findings provide time-varying decision support for variable vehicle emission control on a large scale.

Environmentally induced functional shifts in phytoplankton and their potential consequences for ecosystem functioning.

Phytoplanktonic organisms are particularly sensitive to environmental change, and, as they represent a direct link between abiotic and biotic compartments within the marine food web, changes in the functional structure of phytoplankton communities can result in profound impacts on ecosystem functioning. Using a trait-based approach, we examined changes in the functional structure of the southern North Sea phytoplankton over the past five decades in relation to environmental conditions. We identified a shift in functional structure between 1998 and 2004 which coincides with a pronounced increase in diatom and decrease in dinoflagellate abundances, and we provide a mechanistic explanation for this taxonomic change. Early in the 2000s, the phytoplankton functional structure shifted from slow growing, autumn blooming, mixotrophic organisms, towards earlier blooming and faster-growing microalgae. Warming and decreasing dissolved phosphorus concentrations were linked to this rapid reorganization of the functional structure. We identified a potential link between this shift and dissolved nutrient concentrations, and we hypothesise that organisms blooming early and displaying high growth rates efficiently take up nutrients which then are no longer available to late bloomers. Moreover, we identified that the above-mentioned functional change may have bottom-up consequences, through a food quality-driven negative influence on copepod abundances. Overall, our study highlights that, by altering the phytoplankton functional composition, global and regional changes may have profound long-term impacts on coastal ecosystems, impacting both food-web structure and biogeochemical cycles.

Longitudinal comparison of the self-entry Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-RSE) and Rasch-Built Overall Amyotrophic Lateral Sclerosis Disability Scale (ROADS) as outcome measures in people with amyotrophic lateral sclerosis.

INTRODUCTION/AIMS: Improved functional outcome measures in amyotrophic lateral sclerosis (ALS) would aid ALS trial design and help hasten drug discovery. We evaluate the longitudinal performance of the Rasch-Built Overall Amyotrophic Lateral Sclerosis Disability Scale (ROADS) compared to the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised for Self-Entry (ALSFRS-RSE) as patient reported outcomes of functional status in people with ALS. METHODS: Participants completed the ROADS and the ALSFRS-RSE questionnaires at baseline, 3-, 6-, and 12- mo using Research Electronic Data Capture as part of a prospective, longitudinal, remote, online survey study of fatigue in ALS from 9/2020 to 12/2021. The scales were compared cross-sectionally (at baseline) and longitudinally. Correlation coefficients, coefficients of variation, and descriptive statistics were assessed. RESULTS: A total of 182 adults with ALS consented to the study. This volunteer sample was comprised of predominantly White, non-Hispanic, non-smoking participants. Consented participant survey completion was approximately 90% at baseline and greater than 40% at 12 mo. The ALSFRS-RSE and the ROADS had high, significant agreement at 3 and 6 mo by Cohen's kappa ≥71% (p < 0.001); the number of functional increases or plateaus on the two scales were not significantly different; and the coefficient of variation of functional decline was similar at the 6-month mark, though higher for the ROADS at 3 mo and lower at 12 mo. DISCUSSION: Although the ROADS performed similarly to the ALSFRS-RSE in an observational cohort, it has psychometric advantages, such as Rasch-modeling and unidimensionality. It merits further investigation as a patient reported outcome of overall disability and efficacy outcome measure in ALS trials.