Implementation Of Mitigation Measures Research Articles

Intersecting views of gully erosion in South Africa

AbstractGully erosion is an environmental problem recognized as one of the worst land degradation processes worldwide. Insight into regional gully perturbations is required to combat the serious on‐ and off‐site impacts of gullying on a catchment management scale. In response, we intersect different perspectives on gully erosion‐specific views in South Africa (SA), a country that exhibits various physiographic properties and spans 1.22 million km2. While the debate surrounding gully origin continues, there is consensus that anthropogenic activities are a major contemporary driver. The anthropogenic impact caused gullying to transcend climatic, geomorphic, and land‐use boundaries, although it becomes more prominent in central to eastern SA. Soil erodibility plays a crucial role in what extent of gully erosion severity is attained from human impact, contributing to the east–west imbalance of erosion in SA. Soil erosion rates from gullying and badlands are limited but suggest that it ranges between 30 and 123 t ha−1 yr−1 in the more prominent areas. These soil loss rates are comparable to global rates where gullying is concerned; moreover, they are up to four orders of magnitude higher than the estimated baseline erosion rate. On a national scale, the complexity of gullying is evident from the different temporal timings of (re)activation or stabilizing and different evolution rates. Continued efforts are required to understand the intricate interplay of human activities, climate, and preconditions determining soil erodibility. In SA, more medium‐ to long‐term studies are required to understand better how changing control factors affect gully evolution. More research is needed to implement and appraise mitigation measures, especially using indigenous knowledge. Establishing (semi)‐automated mapping procedures would aid in gully monitoring and assessing the effectiveness of implemented mitigation measures. More urgently, the expected changes in climate and land‐use necessitate further research on how environmental change affects short‐term gully erosion dynamics.

Adapting a Physical Earthquake-Aftershock Model to Simulate the Spread of COVID-19

There exists a need for a simple, deterministic, scalable, and accurate model that captures the dominant physics of pandemic propagation. We propose such a model by adapting a physical earthquake/aftershock model to COVID-19. The aftershock model revealed the physical basis for the statistical Epidemic Type Aftershock Sequence (ETAS) model as a highly non-linear diffusion process, thus permitting a grafting of the underlying physical equations into a formulation for calculating infection pressure propagation in a pandemic-type model. Our model shows that the COVID-19 pandemic propagates through an analogous porous media with hydraulic properties approximating beach sand and water. Model results show good correlations with reported cumulative infections for all cases studied. In alphabetical order, these include Austria, Belgium, Brazil, France, Germany, Italy, New Zealand, Melbourne (AU), Spain, Sweden, Switzerland, the UK, and the USA. Importantly, the model is predominantly controlled by one parameter (α), which modulates the societal recovery from the spread of the virus. The obtained recovery times for the different pandemic waves vary considerably from country to country and are reflected in the temporal evolution of registered infections. These results provide an intuition-based approach to designing and implementing mitigation measures, with predictive capabilities for various mitigation scenarios.

Integrating fishers’ knowledge contributions in Marine Science to tackle bycatch in the Bay of Biscay

The issue of bycatch is raising considerable political, mediatic and scientific attention. Bycatch is one of the main causes of at-sea mortality for small cetacean species and for seabirds. Scientists are raising alerts regarding the potential effects on the structure of the ecosystem, increasingly aiming for research-action. Decision-makers are facing a political trade-off, with increasing pressure from the European Commission and international nongovernmental organizations to implement mitigation measures such as space-time closure of the fisheries, which could present a risk of altering the well-being of the fishing industry in the short-term. The process of co-creation of knowledge on bycatch is key to understand better the fishers-species interactions and to develop regulations that are adapted to local specificities, towards an adaptive and inclusive socio-ecosystem-based management of the fisheries. But the knowledge co-creation process is hindered by tensions between the interests of stakeholders, the climate of mistrust, dense media coverage and power asymmetries between actors. In parallel, the fast rate of biodiversity degradation is calling for the rapid development of regulations. Understanding the complex system dynamics highlighted by these conflicts requires an analysis of the socio-political dimension of the interactions between fisheries and marine biodiversity. Based on a series of ethnographic interviews with the different stakeholders involved in the bycatch mitigation projects in the Bay of Biscay, this paper explores how co-creating knowledge through conflict and collaboration between researchers and fishers can generate collective learning for bycatch mitigation policies. We adopt an epistemological approach, with the objective to promote transparency in the exchange between researchers and fishers and to inform decision-making at various scales of governance. We argue that co-creation of knowledge on bycatch should not aim for consensus. We conclude that acknowledging the presence of conflicts between the stakeholders, and understanding their roots and their impact on the co-design process can allow identifying factors of path-dependency hindering the adaptive capacity of institutions. Moreover, we highlight the key role of the fishers’ representative bodies in knowledge co-creation, and the importance to improve our understanding of fishers’ perception of their political representation.

Data Generation for Modeling Attacks on UAVs for the Purpose of Testing Intrusion Detection Systems

Today, issues related to ensuring the safety of UAVs are very relevant. Researchers need to develop new protection methods to detect attacks in a timely manner and implement mitigation measures. The authors propose a new concept of attack detection "from inside" the UAV. The idea is to analyze the cyber-physical parameters of the UAV, which may indicate an attack, and its possible consequences. It was determined that to detect an attack and determine the consequences to which it can lead, it is necessary to control not only the initial parameters, but also the internal cyber-physical parameters of the UAV. This will allow predicting the possible consequences of an attack and taking emergency measures. A scheme of the impact of an attack on UAVs and the relationship with security incidents, built using an ontological approach, has been worked out. Two main essences of the UAV are considered - the physical and digital aspects of the UAV. Examples of chains of attacks leading to various consequences are also shown. In the review part, the analysis of methods and algorithms for detecting spoofing attacks using data generators is carried out, based on which conclusions are drawn about their advantages and disadvantages. Further, based on the experiments performed, the authors propose a method for assessing the quality of data and a method for generating anomalous data sets similar to real attack data, which can be used to develop and test methods for detecting and blocking attacks. The architecture of the experimental stand, which was used in the framework of full-scale simulation, is described. At this stand, designed to parse GPS spoofing attacks (GPS spoofing), several scenarios of a normal flight, and then several attack scenarios, were tested. Based on the results of the experiments, a method has been proposed that allows simulating the data corresponding to the attack with the required accuracy. A method for assessing the quality of fake data has also been proposed.

Assessing the spatial-temporal patterns of conflicts between humans and Asiatic black bears (Ursus thibetanus) around the Gaoligongshan Nature Reserve, China

Conflicts between humans and Asiatic black bears (Ursus thibetanus) are widespread in Asia and pose challenges to human-bear coexistence. Identifying effective mitigation measures requires a thorough understanding of human-bear conflicts (HBC). We assessed spatial-temporal patterns of HBC and their impact factors around the Baoshan Section of the Gaoligongshan Nature Reserve (GNNR) between 2012 and 2020. The results suggested that crop raiding by bears occurred most commonly, followed by beehive loss, livestock depredation, and human casualties. HBC hotspots occurred near the protected area where local people frequently encountered bears. The landscapes with lower elevation and human density were at higher risk of HBC. Furthermore, villages with more fragmented forests or less fragmented croplands were more vulnerable to HBC. The differences in agricultural structures contributed to the diverse composition of HBC between the two regions. In addition, crop raiding by bears decreased significantly, probably due to the changing landscape composition and configuration derived from human behaviors, yet livestock depredation and beehive loss increased. Our findings indicated the complex interrelationship between the environment, bears, and humans, which could guide the implementation of mitigation measures. We recommend multiple approaches based on a social-ecological system to mitigate HBC.

Risk Management Activities in a Lung Cancer Multidisciplinary Team at a Comprehensive Cancer Center: Results of a Prospective Analysis.

The objective of the study was to highlight sources of harm that could negatively affect the lung cancer multidisciplinary team (MDT) activities to reduce the level of risk of each factor. A modified Delphi approach was used by a board of multi-health care professionals of the lung cancer MDT to identify the main processes, subprocesses, and risk factors of the multidisciplinary pathway of patients with lung cancer. A semiquantitative matrix was built with a five-point scale for probability of harm (likelihood) and severity of harm (consequences) according to the international risk management standards (ISO 31000-2018). The risk level was calculated by multiplying likelihood × consequences. Mitigation strategies have been identified and applied by the MDT to reduce risks to acceptable levels. Three main processes (outpatient specialist visit, MDT discussion, and MDT program implementation), eight related subprocesses, and 16 risk factors were identified. Four risk factors (25%) were related to outpatient specialist visit, seven (43.75%) to case discussion, and five (31.25%) to program implementation. Overall, two risk factors were assigned a low-risk level (12.5%), 11 a moderate-risk level (68.75%), one (6.25%) a high-risk level, and two (12.5%) a very high-risk level. After the implementation of mitigation measures, the new semiquantitative risk analysis showed a reduction in almost all hazardous situations: two risk factors (12.5%) were given a very low level, six (37.5%) a low level, seven (43.75%) a moderate level, and one (6.25%) a very high level. An interdisciplinary risk assessment analysis is applicable to MDT activities by using an ad hoc risk matrix: if the hazard is identified and monitored, the risk could be reduced and managed in a short time.

A reviewof the application of canopy bridges in the conservation of primates and other arboreal animals across Brazil

Abstract Brazil is known as a high biodiversity country, but at the same time, it has an extensive road network that threatens its wildlife and ecosystems. The impacts of roads and railways on vertebrates have been documented extensively, and the discussion concerning the implementation of mitigation measures for terrestrial wildlife has increased in the last decade. Arboreal animals are especially affected by the direct loss of individuals due to animal-vehicle collisions and by the barrier effect, because most arboreal species, especially the strictly arboreal ones, avoid going down to the ground to move across the landscape. Here we summarize and review information on existing canopy bridges across Brazil, considering artificial and natural canopy bridge initiatives implemented mainly on road and railway projects. A total of 151 canopy bridges were identified across the country, 112 of which are human-made structures of different materials, while the remaining 39 are natural canopy bridges. We found canopy bridges in three of the six biomes, with higher numbers in the Atlantic Forest and Amazon, the most forested biomes. Most of the canopy bridges are in protected areas (76%) and primates are the most common target taxa for canopy bridge implementation. Our study is the first biogeographic mapping and review of canopy bridges for arboreal wildlife conservation in a megadiverse country. We synthesize the available knowledge concerning canopy bridges in Brazil and highlight gaps that should be addressed by future research and monitoring projects.

Mortality Trends Among Persons with Hemophilia in the United States, 2000-2021

Introduction: Hemophilia, an inherited bleeding disorder leading to deficiency of clotting factor VIII (hemophilia A) or factor IX (hemophilia B), affects approximately 30,000 people in the United States. Widespread availability of factor replacement products led to reductions in bleeding complications, but contamination of the blood supply prior to implementation of mitigation measures led to increased rates of chronic viral infections among persons with hemophilia (PWH). However, mortality rates among PWH have not been systematically evaluated in a decade. Evaluation of mortality rates helps quantify the burden of hemophilia and helps identify trends, should they exist. Methods: Deaths among PWH in the United States were identified through mortality reporting to bleeding disorder surveillance programs. Deaths occurring 2000-2011 were reported to the Universal Data Collection (UDC) system, a collaboration with the Centers for Disease Control and Prevention (CDC) and the U.S. Hemophilia Treatment Center Network (USHTCN). Deaths occurring during 2012-2021 were reported to the Community Counts (CC) system, a collaboration between CDC, the American Thrombosis and Hemostasis Network (ATHN), and the USHTCN. Deaths were reported using standardized mortality forms. The total population under study was estimated using information from the CC HTC Population Profile, an annual census of the Hemophilia Treatment Center (HTC) patient populations, and UDC Registration records. Data collection included patient demography and disease characteristics. Mortality rates, adjusted for age, severity, and race/ethnicity, were estimated, assuming a Poisson distribution. Trends over time were assessed by including time period in the model. Non-overlapping confidence intervals and p for trend < 0.05 were considered statistically significant. Results: For the time period 2000-2021 1,673 deaths among PWH A and 401 deaths among PWH B were reported from participating HTCs in the United States. Of these, over 97% were male, 1,649 hemophilia A and 349 hemophilia B. Thus, stratum-specific mortality rates were only calculable among males with hemophilia A (MWH-A) or hemophilia B (MWH-B). For both MWH-A and MWH-B mortality rates were highest among the oldest age groups across all time periods. Mortality rates among MWH-A decreased after the 2000-2004 period but remained stable thereafter, with the mortality rate during the 2020-2021 period being 28.1 (95% Confidence Interval [CI]: 23.2 - 34.1) deaths per 10,000 population (Panel A). While confidence intervals overlapped, mortality rate estimates among MWH-A with mild or moderate disease generally rose over each time period and the trend was statistically significant (p < 0.05). In 2020-2021, the mortality rate among MWH-A with mild or moderate disease was 27.7 (95% CI: 18.4 - 41.6) deaths per 10,000 population. In contrast, mortality rate estimates among MWH-A with severe disease declined over each time period; while confidence intervals overlapped, the trend was significant (p < 0.05). In 2020-2021, the mortality rate among MWH-A with severe disease was 35.9 (95% CI: 24.2 - 53.2) deaths per 10,000 population. There were no clear temporal mortality trends among Hispanic, non-Hispanic Black, or non-Hispanic White MWH-A. Mortality rates among MWH-B remained relatively stable across all time periods, with confidence intervals overlapping; the mortality rate among MWH-B was similar to that among MWH-A in the 2020-2021 period (36.5 [95% CI: 25.8 - 51.7] deaths per 10,000 population) (Panel B). Severity-specific and race/ethnicity-specific mortality rate estimates among MWH-B were similar across all time periods, but the analysis was underpowered. Of note, the mortality rate among non-Hispanic Black MWH-B was significantly higher than that among non-Hispanic White MWH-B during the 2020-2021 period (71.4 [95% CI: 42.0 - 121.7] and 22.3 [95% CI: 13.9 - 35.6], respectively). Conclusions: This assessment of mortality rates among PWH using national data indicates there have been improvements in mortality rates among MWH-A, but this improvement may be limited to males with severe disease. Limitations related to estimate precision preclude the assessment of trends in mortality rates among MWH-B and among females with hemophilia. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Land use land cover change in and around Chebera Churchura National Park, Southwestern Ethiopia: implications for management effectiveness

BackgroundLand use-land cover change (LULCC) resulting from anthropogenic activities has been one of the major causes of loss of biodiversity globally. Thus, understanding the dynamics of such LULCC, especially within protected areas, is important to assess their past-present management effectiveness, identify the driving forces and assist future implementation of mitigation measures. The objectives of this study, based on satellite images analysis, were to examine and describe patterns of LULCC within and in the buffer zone area of Chebera Churchura National Park, SW Ethiopia, at five points in time (1985, 1991, 2000, 2010, and 2020); and to examine management effectiveness of the park authority in slowing down the rate of LULCCs after the establishment of the park.ResultsResults showed that different land cover types exhibited varying patterns of change over the last 35 years. However, major differences were detected between the park area boundary and its buffer zone area after establishment of the park. From the year 1991 to 2000 (before the establishment), grassland declined by 52.9% (120.55km2) and 58.9% (151.9km2) within the park boundary and its buffer zone, respectively. After the establishment, grassland continued to decline only in the buffer zone. Similarly, agricultural land declined by 13.2% (2.7 km2) within the park but expanded by 5.4% (3.1 km2) in the buffer zone prior to park establishment. However, after establishment of the park, agricultural land in the buffer zone increased by 99.4% (99.7 km2). Grassland and wooded grassland had the opposite trend within the park area before vs after the establishment of the park.ConclusionsResults of the present study showed that striking differences in the rates of LULCCs within the park area and its buffer zone after establishment of the park, with the implication that the management is effective in slowing down LULCC relative to that in the buffer zone areas. In sum, this study would serve as a showcase on cost-effective means to assess human impacts on biodiversity in protected areas due to policy changes. Findings of the study would serve as a bench mark to evaluate future management effectives of the park authority.

SIMULATION OF THE IMPACT OF HYDROFLUOROCARBON (HFC) EMISSIONS IN TOGO ON GLOBAL AVERAGE TEMPERATURE

Background:Several IPCC scenarios have predicted a global temperature increase of 1.5°C by 2030 attributable to greenhouse gases (GHGs) including hydrofluorocarbons (HFCs) controlled by the Kigali Amendment. Objectives:Thus, this study aims to simulate the increase in global temperature by 2030 attributed only to HFC emissions in Togo, given that the country is a party to the Kigali amendment and the Paris agreement. Methods:The methodology applied in this study is based on the projection to 2030 of HFC emissions in 2018 in order to simulate their impact on global average temperature using the LEAP-IBC tool. Results:HFCs emissions in Togo will reach 2598.13 GgCO2-e and will contribute to the increase in global average temperature by representing 18/100000 of the 0.5°C over the period 2018 to 2030 predicted by the IPCC scenarios. This increase attributed to the HFCs sector alone in Togo once again shows the impact of GHG on global average temperature.These results should therefore alert political decision-makers to redouble their efforts in the implementation of mitigation measures.

Multilocus sequence typing of diverse phytoplasmas using hybridization probe-based sequence capture provides high resolution strain differentiation.

Phytoplasmas are insect-vectored, difficult-to-culture bacterial pathogens that infect a wide variety of crop and non-crop plants, and are associated with diseases that can lead to significant yield losses in agricultural production worldwide. Phytoplasmas are currently grouped in the provisional genus ‘Candidatus Phytoplasma’, which includes 49 ‘Candidatus’ species. Further differentiation of phytoplasmas into ribosomal groups is based on the restriction fragment length polymorphism (RFLP) pattern of the 16S rRNA-encoding operon, with more than 36 ribosomal groups (16Sr) and over 100 subgroups reported. Since disease symptoms on plants are not associated with phytoplasma identity, accurate diagnostics is of critical importance to manage disease associated with these microorganisms. Phytoplasmas are typically detected from plant and insect tissue using PCR-based methods targeting universal taxonomic markers. Although these methods are relatively sensitive, specific and are widely used, they have limitations, since they provide limited resolution of phytoplasma strains, thus necessitating further assessment of biological properties and delaying implementation of mitigation measures. Moreover, the design of PCR primers that can target multiple loci from phytoplasmas that differ at the sequence level can be a significant challenge. To overcome these limitations, a PCR-independent, multilocus sequence typing (MLST) assay to characterize an array of phytoplasmas was developed. Hybridization probe s targeting cpn60, tuf, secA, secY, and nusA genes, as well as 16S and rp operons, were designed and used to enrich DNA extracts from phytoplasma-infected samples for DNA fragments corresponding to these markers prior to Illumina sequencing. This method was tested using different phytoplasmas including ‘Ca. P. asteris’ (16SrI-B), ‘Ca. P. pruni’ (16SrIII-A),‘Ca. P. prunorum’ (16SrX-B), ‘Ca. P. pyri’ (16SrX-C), ‘Ca. P. mali’ (16SrX-A), and ‘Ca. P. solani’ (16SrXII-A). Thousands of reads were obtained for each gene with multiple overlapping fragments, which were assembled to generate full-length (typically >2 kb), high-quality sequences. Phytoplasma groups and subgroups were accurately determined based on 16S ribosomal RNA and cpn60 gene sequences. Hybridization-based MLST facilitates the enrichment of target genes of phytoplasmas and allows the simultaneous determination of sequences corresponding to seven different markers. In this proof-of-concept study, hybridization-based MLST was demonstrated to be an efficient way to generate data regarding ‘Ca. Phytoplasma’ species/strain differentiation.

The adoption of new technologies for sustainable risk management in logistics planning: A sequential dynamic approach

Nowadays, organizations seek to effectively manage their logistics processes to increase customer satisfaction and achieve a competitive advantage. To improve the performance and sustainability of logistics processes, managers should analyze the risks in such processes and implement risk mitigation measures to reduce their negative impact on system integrity. The adoption of new technologies in risk mitigation planning can help managers improve the performance of logistics processes. This study proposes a sequential dynamic approach for analyzing the risks and assessing the effectiveness of risk mitigation measures in an uncertain environment. The causal relationships between risks of logistics processes are extracted after the risk identification based on the process-oriented perspective. Afterward, a multi-expertise team employs the Z-number theory-based linguistic variables to determine the value of triple risk parameters for each risk (i.e., probabilities of destructiveness, occurrence, and undetectability). In this phase, the multi-expertise team’s opinions are aggregated using an extended version of Dempster-Shafer’s evidence theory in the Z-number environment to consider uncertainty and reliability simultaneously. Then, the sequential dynamic model of the identified risks is developed using the Bayesian belief network. This model enables decision-makers to identify the critical risks in each logistics sub-process and assess risk mitigation measures, defined by focusing on new technologies. The proposed model is used to estimate the effectiveness of risk mitigation measures after determining the impressionable risks in their implementation. The results of this study imply that new technology-based solutions can improve critical sub-processes (i.e., resource planning, procurement, and production planning) significantly.

Seismic surveys near gray whale feeding areas off Sakhalin Island, Russia: assessing impact and mitigation effectiveness

In 2015, two oil and gas companies conducted seismic surveys along the northeast coast of Sakhalin Island, Russia, near western gray whale (Eschrichtius robustus) feeding areas. This population of whales was listed as Critically Endangered at the time of the operations described here but has been reclassified as Endangered since 2018. The number and duration of the 2015 seismic surveys surpassed the level of previous seismic survey activity in this area, elevating concerns regarding disturbance of feeding gray whales and the potential for auditory injury. Exxon Neftegas Limited (ENL) developed a mitigation approach to address these concerns and, more importantly, implemented a comprehensive data collection strategy to assess the effectiveness of this approach. The mitigation approach prioritized completion of the seismic surveys closest to the nearshore feeding area as early in the season as possible, when fewer gray whales would be present. This was accomplished by increasing operational efficiency through the use of multiple seismic vessels and by establishing zones with specific seasonal criteria determining when air gun shutdowns would be implemented. These zones and seasonal criteria were based on pre-season modeled acoustic footprints of the air gun array and on gray whale distribution data collected over the previous 10 years. Real-time acoustic and whale sighting data were instrumental in the implementation of air gun shutdowns. The mitigation effectiveness of these shutdowns was assessed through analyzing short-term behavioral responses and shifts in gray whale distribution due to sound exposure. The overall mitigation strategy of an early survey completion was assessed through bioenergetics models that predict how reduced foraging activity might affect gray whale reproduction and maternal survival. This assessment relied on a total of 17 shore-based and 5 vessel-based teams collecting behavior, distribution, photo-identification, prey, and acoustic data. This paper describes the mitigation approach, the implementation of mitigation measures using real-time acoustic and gray whale location data, and the strategy to assess impacts and mitigation effectiveness.

Real-time acoustic monitoring with telemetry to mitigate potential effects of seismic survey sounds on marine mammals: a case study offshore Sakhalin Island

Exxon Neftegas Ltd. (ENL) carried out three 4D seismic surveys during the summer of 2015. Seismic operations in two of these fields (Odoptu and Chayvo) ensonified the nearshore feeding area of Korean-Okhotsk (western) gray whales (Eschrichtius robustus), potentially disturbing feeding activities. Following model-based optimization of the source design to minimize its lateral acoustic footprint, pre-season modeling was used to compute the acoustic exposure along each survey line. Real-time acoustic data facilitated implementation of mitigation measures aimed to minimize disturbance of whales. Acoustic data originated from underwater recorders deployed on the seafloor. Two complementary approaches were used to transmit recorded sound data to a computer housed at the Central Post (CP), where decisions regarding mitigation shut downs were made. In the first approach, a limited bandwidth (2–2000 Hz) sampling of the data was transmitted via cable to a surface buoy, which relayed these data to a shore station up to 15 km away via digital VHF telemetry. At the shore station, acoustic impulses from the seismic surveys were processed to compute impulse characteristics in the form of estimates of sound exposure level and peak sound pressure level, as well as one-minute-average 1/3-octave power spectral density coefficients, which were then transmitted to the CP via the internet. In the second, the pulse characteristics were computed through algorithms running on an onboard processor in each recorder’s surface buoy and sent directly to the CP computer via an Iridium satellite uplink. Both methods of data transfer proved viable, but Iridium transmission achieved the goal without the need for any shore based relay stations and is therefore more operationally efficient than VHF transmission. At the CP, analysts used the real-time acoustic data to calibrate and adjust the output of pre-season acoustical model runs. The acoustic footprint for the active seismic source, advancing synchronously with the motion of the seismic vessel and changing as the sound propagation environment changed, was computed from the calibrated and adjusted model output and integrated through the software Pythagoras with locations of gray whales provided by shore-based observers. This enabled analysts to require air gun array shutdowns before whales were exposed to mean square sound pressure levels greater than the behavioral response threshold of 163 dB re 1 μPa2. The method described here provides a realistic means of mitigating the possible effects of air guns at a behavioral response level, whereas most seismic surveys rely on pre-established mitigation radii to manage the risk of injury to a whale.

Using Machine Learning to estimate the impact of different modes of transport and traffic restriction strategies on urban air quality

Classical pollutant dispersion models, based on the numerical resolution of some approximate form of the momentum, energy and chemical species conservation equations, are usually limited by incomplete descriptions of the atmospheric boundary layer hydrodynamics, partial characterizations of the emission inventories and, often, high computational costs. Using the metropolitan area of Barcelona as benchmark, the Machine Learning aproach presented here alleviates these limitations providing very accurate local predictions of key pollutant concentrations. Originating mostly from Open Data sources, time-series data on road, maritime and air traffic along with meteorological records from October 2017 to June 2021, have allowed, by means of Machine Learning techniques, to create a model capable of estimating the individual contributions of each mode of transport to worsened Air Quality. Also, when used to investigate the impact of recently implemented mitigation measures, model results predict a reduction of approximately 8 μg·m−3 for CO and NOx. In contrast, O3, PM10 and SO2 are found to be unaffected. The COVID-19 lockdown provided an accidental opportunity to improve the model's robustness and predictive capability through unusually low emission rates from transportation.

Unpacking the challenges of wildlife governance in community‐based conservation programs to promote human–wildlife coexistence

AbstractOne of the numerous global challenges today is how to promote biodiversity persistence and at the same time reduce poverty and improve livelihoods, especially in multiple‐use landscapes shared by people and wildlife. In such landscapes, where the cost of sharing the landscape with large wildlife can be high, Community Based Natural Resource Management (CBNRM) program is a model promoted to offset negative wildlife impacts. However, such programs, that promote sustainable utilization of wildlife, are not always successful. To better understand how CBNRM governance systems enable or hinder human–wildlife coexistence, I implemented an 11‐weeks participatory co‐learning program in conservancies in the Zambezi region of Namibia. Framing human–wildlife relationships as occurring within complex social–ecological systems, participatory dialogues were used to unpack the system to better understand why human–wildlife conflict remains a challenge after 30 years of CBNRM in Namibia. From listening to communities I identified five key areas requiring improvement: (1) inefficient communication and transfer of information between management and community; (2) lack of policy and process clarity; (3) lack of support, knowledge, resources, and implementation of mitigation measures; (4) lack of accountability by both management and conservancy members; and (5) lack of skills and capacity by both management and conservancy members. To address these problem areas, I propose three key priority actions: (1) harmonize conservancy policies and management practices; (2) improve information flow; and (3) implement an adaptive management mitigation measure program. While this study focused on a small part of Namibia, I expect this methodology and many of the key findings and proposed interventions will be relevant for other areas applying collaborative approaches to improve human–wildlife coexistence.

A dataset of road-killed vertebrates collected via citizen science from 2014–2020

Data on road-killed animals is essential for assessing the impact of roads on biodiversity. In most European countries data on road-killed huntable wildlife exists, but data on other vertebrate species (e.g. amphibians, reptiles, small mammals) is scarce. Therefore, we conducted a citizen science project on road-killed vertebrates as a useful supplement to data on huntable wildlife collected by public authorities. The dataset contains 15198 reports with 17163 individual road-killed vertebrates collected by 912 participants. The reports were made in 44 countries, but the majority of data was reported in Austria. We implemented a data validation routine which led to three quality levels. Reports in quality level 1 are published via GBIF, reports in quality level 2 via Zenodo and reports in quality level 3 were deleted. The dataset is relevant for the scientific community studying impacts of roads on fauna as well as for those who are responsible for road planning and implementing mitigation measures.

Influence of stratospheric aerosol geoengineering on temperature mean and precipitation extremes indices in Africa

PurposeThree Coupled Model Intercomparison Project Phase 5 models involved in the G4 experiment of the Geoengineering Model Inter-comparison Project (GeoMIP) project were used to investigate the impact of stratospheric aerosol injection (SAI) on the mean surface air temperature and precipitation extremes in Africa.Design/methodology/approachThis impact was examined under G4 and Representative Concentration Pathway (RCP) 4.5 scenarios on the total precipitation, the number of rainy days (RR1) and of days with heavy rainfall (R20 mm), the rainfall intensity (SDII), the maximum length of consecutive wet (CWD) and dry (CDD) days and on the maximum rainfall in five consecutive days (Rx5day) across four regions: Western Africa (WAF), Eastern Africa (EAF), Northern Africa and Southern Africa (SAF).FindingsDuring the 50 years (2020–2069) of SAI, mean continental warming is −0.40°C lower in G4 than under RCP4.5. During the post-injection period (2070–2090), the temperature continues to increase, but at a lower rate (−0.19°C) than in RCP4.5. During SAI, annual rainfall in G4 is significantly greater than in RCP4.5 over the high latitudes (especially over SAF) and lower over the tropics. The termination of SAI leads to a significant increase of rainfall over Sahel and EAF and a decrease over SAF and Guinea Coast (WAF).Practical implicationsCompared to RCP4.5, SAI will contribute to reducing significantly regional warming but with a significant decrease of rainfall in the tropics where rainfed agriculture account for a large part of the economies. After the SAI period, the risk of drought over the extratropical regions (especially in SAF) will be mitigated, while the risk of floods will be exacerbated in the Central Sahel.Originality/valueTo meet the Paris Agreement, African countries will implement mitigation measures to contribute to keep the surface air temperature below 2°C. Geoengineering with SAI is suggested as an option to meet this challenge, but its implication on the African climate system needs a deep investigation in the aim to understand the impacts on temperature and precipitation extremes. To the best of the authors’ knowledge, this study is the first to investigate the potential impact of SAI using the G4 experiment of GeoMIP on temperature and precipitation extremes of the African continent.

Early Response to COVID-19

Early Response to COVID-19

Soil Risk Assessment in the Surrounding Area of Hulene-B Waste Dump, Maputo (Mozambique)

Soil contamination in areas close to unplanned dumpsites represents an increasing risk to the ecosystems and human health. This study aimed to evaluate soil quality in the area surrounding the Hulene-B waste dump, Maputo, Mozambique, and to estimate potential ecological and human health risks. A total of 71 surface soil samples were collected in the surrounding area of the dump, along with 10 samples in areas considered not impacted by the dump. Chemical and mineralogical analyses were performed using XRF and XRD. Quartz was the most abundant mineral phase, followed by feldspars, carbonates, clay minerals, and Fe oxides/hydroxides. Results showed a significant contribution to ecological degradation by PTE enrichment, ranked as Zn &gt;&gt; Cu &gt; Cr &gt; Zr &gt; Pb &gt; Ni &gt; Mn. Carcinogenic risk for both children and adults was significant due to Pb soil content. Soil sample concentrations of Cr, Cu, Mn, Ni, Pb, Zn, and Zr, posing a risk especially in children, suggested the need for continuous monitoring, as well as the definition and implementation of mitigation measures.