Implementation Of Mitigation Measures Research Articles

Accessible sampling methodologies to quantify the net methane emission from landfill cells

Landfills are one of the main sources of anthropogenic methane (CH4) emissions in urban areas. The environmental conditions specific to each locality, the waste management and the infrastructure of each landfill will influence the emissions of this gas. By applying different methodologies, this study aims to investigate the total contribution of CH4 to the atmosphere from a landfill, as well as the surface fluxes within the cells and the passive gas vents. According to the results, mean atmospheric CH4 concentrations from landfills were measured up to 6 ppm higher than those recorded in urban areas and locations distant from CH4 fixed sources. Two closed cells were selected to study surface fluxes by static chamber technique. CH4 uptake (1.65E-08 to 2.04E-06 g m−2 d−1) was recorded in between 60% and 87.5% of the chambers, indicating that the surface acted as a sink for the gas, as compared to the mean uptake observed from a control site (1.72E-06 g m-2 d−1). CH4 emissions from gas vents were studied using a tracer technique with permeable capsules, a methodology used for the first time in a landfill. Relatively high emissions were detected, up to 1.20E+04 g d−1. Finally, from the net CH4 emission calculated, the energy produced if the emitted CH4 were captured was estimated to be 3.23 GJ. The results highlight the importance of these studies for decision-makers to plan future landfill infrastructure adequately, implement mitigation measures to reduce emissions of this potent greenhouse gas and contribute to the energy system resources.

Use of Electrical Resistivity Tomography for Joint Geophysical and Geotechnical Landslide Characterization: A Case Study

Abstract Slope movement processes include complex soil and rock failure mechanisms. Their study benefits from a multidisciplinary approach based on a wide range of information including geological and geomorphological mapping, and geotechnical and geophysical investigations. This research paper focuses on the characterization of the Tamentout landslide that occurs in the southeast of Jijel province. The study area belongs to the Tellian domain in which the geological outcrops are dominated by Senonian formations, composed of marl deposits overlain by Numidian flysch of Aquitano-Burdigalian age, with a sloping topography ranging from 20° to 30°. The geophysical approach consists of processing the available geophysical data of resistivity, obtained by the Electrical Resistivity Tomography (ERT). This study aims to characterize the internal structure, and the changes in water saturation within the unstable mass and locate the depth of slip surface associated with this landslide. Through this work, we combined geological and geotechnical investigations with electrical resistivity tomography (ERT). This combination gave a more detailed image of the substrate geology and structure of the landslide zone. The 2D resistivity results show that the basement consists of two main formations. The first one is a highly conductive formation with a resistivity range between 2 and 25 Ωm, a depth of 0-8 m, and is interpreted as a saturated marl-clay overlaying. The second, a hard and compact formation with a resistivity range between 50 and 200 Ωm and a depth range of 8 to 40 m, was interpreted as a substrate of tellian marls. The presence of boulders of Numidian sandstone within the two formations is materialized by a very high resistivity value ranging from 500 to 1000 Ωm. The slip surface was located on the layer with low resistivity (2-25 Ωm). The precise determination of the depth of the shear zone is of vital use in implementing mitigation measures and carrying out the stabilization work of this unstable zone. Through this work, we will highlight the advantage of the combination of available results of the geological, geotechnical investigations and electrical resistivity tomography (ERT) carried out in the study area.

Impact on the German asymptomatic screening strategy based on actual user data from SARS-CoV-2 test centers

Since March 2021, Germany has been providing cost-free severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen tests, and many day-to-day activities following the lockdown have required negative test results. Yet it remains unclear how tests have been used and whether there are patterns connected to mitigation measures. We analyzed over 50,000 anonymized records from eight test centers in a typical medium-sized city, with one of them remaining open continuously from March until December 2021. The centers exhibit distinct patterns of visitor types, with the majority tested only once in the investigated period. Individuals who underwent repeated testing tended to favor the same location. A preference for spontaneous testing grew in proportion to the availability of spare tests. Visitors aged 18 to 30 years were distinctly overrepresented compared to the local demographic. A negative binominal model showed that implemented mitigation measures had an impact on the number of tests conducted. Cost-free testing in private facilities was implemented into the German complementary screening strategy, aiming to achieve weekly population-wide testing. This study demonstrates these facilities were rarely used for regular testing but rather for meeting requirements of certified tests. The results should aid authorities in making future decisions regarding infection control.

Rapid implementation of mitigation measures can facilitate decarbonization of the global steel sector in 1.5°C-consistent pathways

Rapid implementation of mitigation measures can facilitate decarbonization of the global steel sector in 1.5°C-consistent pathways

Southern Hemisphere humpback whales (Megaptera novaeangliae) (Artiodactyla: Balaenopteridae) singing activity at Caño Island Biological Reserve, Costa Rica before, during, and after COVID-19 lockdowns

Introduction: Boat traffic is recognized as a major contributor of underwater noise. Increasing presence of boats in coastal habitats is predicted to have important repercussions on the communication of marine mammals. In Costa Rica, the waters of the Caño Island Biological Reserve are an important breeding area for humpback whales from the Breeding-Stock G (BSG). Their predicted and abundant presence has fueled the development of whale watching activities as an important component of the local economy, and while the country has norms of conduct for this activity, whales often interact with multiple boats at the same time. The lockdowns associated with the COVID-19 pandemic provided a unique opportunity to study the potential impacts of noise associated with boat traffic on the singing activity of humpback whales.
 Objective: Determine whether noise levels and boat acoustic presence around Caño Island Biological Reserve changed during the COVID-19 lockdowns, and if it did, what is the impact on song detection of BSG humpback whales.
 Methods: Acoustic recordings were made using a bottom-mounted autonomous underwater recorder for 30 days in September 2019, 2020, and 2021, resulting in a total recording effort of 480 hours.
 Results: Our results show that broadband underwater noise levels (dBRMS) during pre-lockdown were significantly higher, particularly at frequencies below 1kHz, than during and post-lockdown. This is likely due to a decrease in the proportion of boat acoustic presence during the lockdown. Although the proportion of whale songs detected did not vary among years, whale songs were detected similarly throughout the day during the lockdown, compared to pre-and-post lockdown where the proportion of whale song presence decreased during hours when more boats were present.
 Conclusions: This study shows a clear change in underwater noise levels during the COVID-19 lockdown, likely due to a decrease in boat presence. The study also highlights the potential impact of noise associated with boat traffic on humpback whale singing activity. The results of this study can inform the Conservation Areas of Osa (ACOSA) in charge of managing Caño Island Biological Reserve, to develop and implement mitigation measures to regulate underwater anthropogenic noise associated with tour boats.

The Status of the Saudi Construction Industry during the COVID-19 Pandemic

The outbreak of COVID-19 has had a profound impact on the Saudi construction industry as well as the country’s economy. The pandemic jeopardized the positive perspectives and growth in megaproject numbers, as it has amplified the constraints that prevent the construction sector from realizing growth. This research work is intended to evaluate the status of the Saudi construction industry during the COVID-19 pandemic. In order to achieve this objective, a three-phased methodology was developed. In the first part of the research, semi-structured interviews with forty industry experts were conducted. The gathered data from both, the literature review and interviews were synthesized. This process resulted in four domains to be explored: project performance measurement, workforce, supply chain, and financial management. The outcomes from phase one were then utilized to develop a questionnaire survey that was communicated to construction firms all over Saudi Arabia, for which 124 responses were received. Data analysis was carried out, and the obtained results were clarified and triangulated through a focus group discussion in the third phase of the research. The outcomes from the mixed-methods research methodology were aggregated to enrich and interpret findings and draw conclusions and recommendations. The findings indicate that the pandemic has had a total of ten core impacts. The highly impacted areas in the industry were the technical performance of projects, reduction in productivity, risk management practices, downsizing the scope of ongoing projects, reduction in new projects or contracts, material shortage, recruitment of manpower in the construction firms and affiliated projects, and the financial performance of the organization. In the meantime, it was found that the government implemented mitigation measures from which the sector benefited, where 27% of contractors obtained Saned system assistance, 51% received cash compensation and 22% received tax postponement. The scope of this study is limited to exploring the status of the Saudi construction industry (SCI) during the COVID-19 pandemic. The study findings are of added value and represent a significant contribution to the body of knowledge in the field. However, further research on the exit strategies bringing the industry to the new normal, including the use of cutting-edge technologies in the age of multi-faceted disruption would be of great importance.

Analysis of microplastics in spring water

Springs are crucial as primary water sources for various human needs, including in Batu City, East Java, Indonesia. Preserving the integrity of these springs is essential to protect them from contamination, including emerging pollutants such as microplastics, which have been reported in aquatic ecosystems. Therefore, it is vital to analyze the presence of microplastics in springs to mitigate pollution. However, there is a lack of studies reporting the content of microplastics in springs, particularly in Batu City. This study aims to analyze microplastics in multiple springs within Batu City using a descriptive research method. The research materials encompass the abundance and characteristics of microplastics and various water quality parameters such as dissolved oxygen, pH, temperature, turbidity, color, total dissolved solids, taste, odor, and current velocity. Sampling was conducted at six springs in Batu City: Sumber Ngesong, Sumber Banyuning, Sumber Gemulo, Sumber Torong Belok, Sumber Kasinan, and Sumber Dari. The findings of this study indicate that the water quality in the springs of Batu City complies with established standards. However, microplastics were detected in all the sampled springs, with concentrations ranging from 208 to 354 particles/m³. The identified microplastics primarily included fibers, films, and fragments. MP polymers obtained in this study were Nylon, Low-Density Polyethylene (LDPE), Polyethylene terephthalate (PETE), and Polyvinyl chloride (PVC). Based on the study results, while the water in the spring area of Batu City meets water quality standards, it is necessary to implement mitigation measures to address the emerging pollutant pollution, such as microplastics, to safeguard the springs as a vital water source for human life. To the best of our knowledge, this study is the first to report the characteristics of microplastics in the springs of Batu City.

Algorithmic Trading Strategies: Real-Time Data Analytics with Machine Learning

Machine learning algorithms have emerged as potent tools for risk control in algorithmic trading, empowering traders to scrutinize vast volumes of market data, discern patterns, and make well-informed trading decisions. In the contemporary, swiftly evolving, and data-centric financial markets, effective risk management is imperative to navigate market uncertainties and optimize trading performance. Traditional risk control methodologies often falter in grasping complex market dynamics and adapting to swiftly changing conditions, thus propelling the adoption of machine learning algorithms. These algorithms excel in processing large datasets, uncovering concealed patterns, and rendering accurate predictions, thereby enabling traders to devise proactive risk management strategies. Machine learning algorithms confer several advantages in risk control for algorithmic trading. They can analyze an array of data sources such as historical price data, news sentiment, and economic indicators, furnishing valuable insights for risk assessment and decision-making. Additionally, these algorithms can handle time series data, capturing temporal dependencies and adapting to dynamic market conditions. They offer real-time risk monitoring and early warning capabilities, empowering traders to promptly respond to emerging risks and implement risk mitigation measures. Furthermore, machine learning algorithms hold the potential to optimize portfolio management by dynamically adjusting portfolio weights based on risk-return profiles and optimizing asset allocation strategies. Machine learning algorithms have revolutionized risk control in algorithmic trading by furnishing advanced analytics, predictive capabilities, and real-time monitoring. These algorithms enhance risk management strategies, refine decision-making processes, and enable traders to navigate the intricacies of financial markets.

Bibliometric analysis of publications on cyber risks in the services sector

Objective: To explore the progress of scientific production on cyber risks that permeate the service sector, identifying relevant researchers and institutions on this theme, measuring the impact, and identifying trends, contributions, and knowledge gaps. In addition, the study seeks to use bibliometric findings to bring academic and managerial contributions to the subject. Methodology: Bibliometric study, using the method of organization and systematization of information (Chueke & Amatucci, 2015; Guedes & Borschiver, 2015), whose structure followed the premises of the laws of Bradford, Lotka, and Zipf, using the scientific databases of the WoS - Web of Science. The bibliometric study enabled the performance of exploratory and descriptive research without the temporal cut, resulting in the identification of 115 publications (December 1995 to February 2023), which allowed measuring and presenting the characteristics and profile of the publications analyzed. Originality: The study revealed a potential for exploring the theme of Cyber Risks in the Services sector, considering the scarcity of scientific production. It also enabled the identification of emerging trends and clusters in service sector activities and the creation of a conceptual model based on the findings of the analyzed publications. Main results: The analyses revealed which sectors of the service economy are most frequently approached in publications related to the theme of cyber risks. These analyses were organized into ten areas, with the following order of relevance (frequency) of publication: Computer Science, Information Systems, Engineering, Business, Finance and Management, Telecommunications, Computer Science Theory Methods, and Computer Science Artificial Intelligence. The bibliometric findings enabled the creation of the conceptual model of Cyber Risks in Services, which proposes a cyclical and continuous improvement approach to deal with vulnerabilities, cyber threats, and consequences. This includes identifying and assessing existing vulnerabilities, implementing mitigation measures, and constantly monitoring threats and their consequences. Theoretical contributions: The conceptual model of Cyber Risks in Services can be a reference for researchers in various fields of action, considering the breadth of the services sector and the interdisciplinary nature of digital risk mitigation. Managerial contributions: Understanding of cyber risks supports the ability of the organization to respond to them, strengthening its security posture and protecting its critical assets and information from cyber threats.

Impact of COVID-19 mitigations on anxiety and depression amongst university students: A systematic review and meta-analysis.

While much research has addressed mental health concerns related to the coronavirus disease 2019 (COVID-19) pandemic, there remains a scarcity of studies specifically exploring the changes in anxiety and depression among university students before and after the implementation of COVID-19 mitigation measures. In this systematic review and meta-analysis, we searched databases including MEDLINE (Ovid), EMBASE (Ovid), PsycINFO (Ovid), CINAHL (EBSCO), ERIC (EBSCO), the WHO COVID-19 database, Scopus, and Science Citation Index (Web of Science) as of 15 February 2023. We included studies that used a validated tool to measure changes in anxiety or depression at two distinct time points - before (T1) and during (T2); during (T2) and after (T3); or before (T1) and after (T3) COVID-19 mitigation. The quality of studies was assessed using an adapted Joanna Briggs Institute Checklist for longitudinal studies. Utilising random-effects models, we synthesised changes in continuous outcomes as standardised mean difference (SMD) with 95% confidence interval (CI) and binary outcomes as risk difference (RD) with 95% CI. In total, 15 studies were included in this review, with eight of moderate and seven of high quality. In most of the included studies (n = 13), the majority of participants were women. Eleven studies analysed mental health outcomes between T1 and T2 of COVID-19 mitigations. Continuous symptom changes were a minimal or small improvement for anxiety (SMD = -0.03, 95% CI = -0.24 to 0.19, I2 = 90%); but worsened for depression (SMD = 0.26, 95% CI = -0.01 to 0.62). However, the proportions of students reporting moderate-to-severe symptoms, defined by specific cut-offs, increased during COVID-19 mitigation measures for both anxiety (RD = 0.17, 95% CI = -0.04 to 0.38, I2 = 95%) and depression (RD = 0.12, 95% CI = 0.03 to 0.22, I2 = 72%). Sensitivity analyses, which distinguished between baseline periods based on awareness of COVID-19, demonstrated an exacerbation of both symptoms when comparing the period before the global awareness of the COVID-19 outbreak (before December 2019) with the period during the implementation of mitigation measures. Mental health outcomes, especially depressive symptoms, were observed to worsen in university students during COVID-19 mitigations. Despite considerable heterogeneity requiring careful interpretation of results, the impact of COVID-19 mitigations on mental health in university students is evident. PROSPERO (CRD42021266889).

Assessment of Land Surface Temperature Variations and Implications of Land Use/Land Cover Changes: A Case of Malappuram Urban Agglomeration Region, Kerala, India

Urbanization is taking place faster, and urban air temperatures are gradually rising in all cities across the world. Uncontrolled and unplanned urbanization leads to constant environmental threats and can alter local and regional climates. According to the survey published by Economist Intelligence Unit, in India, Kerala's Malappuram district ranks first among the fastest-growing urban areas globally, with a 44.05% growth rate. Hence, the present study aims to identify the hotspot regions of extreme heat within the Malappuram urban agglomeration region and suggest strategies for its improvement. The split-window algorithm retrieved land surface temperature (LST) for 1991, 1998, 2014, and 2020 using Landsat 5 ETM and Landsat 8 satellite imageries. A rising trend in LST has been detected in the last 30 years, and the mean value has increased by 1.70°C within the region. Among the selected hotspots, an LST increase of 1.84°C was observed for those areas with the highest increase in urban density with decreased vegetation. The increasing impact of urbanization and the subsequent change in land use patterns at the cost of greenery have caused a substantial effect on the local climate. Accordingly, planning and policy directions are proposed for the local government that can help provide awareness to the people through the effective implementation of mitigation measures.

A Spatially Explicit Hydro-Economic Modeling Procedure to Design Cost-Effective Agri-Environment Schemes for Mitigating Water Pollution from Cropland

Agricultural intensification is a key driver of water pollution in many parts of the world. A frequent policy response is to implement mitigation measures in the context of agri-environmental schemes (AESs). We develop a hydro-economic modeling procedure to design cost-effective AESs to mitigate water pollution from nitrogen and phosphorus from cropland. Our procedure considers spatial heterogeneity of mitigation impacts and of costs of cropland management measures. It further takes into account in the optimization that AES participation is voluntary and farmers who may participate in an AES will select between different cropland management measures. We demonstrate how the procedure works by applying it to the Baishahe watershed in Shanxi Province, China. As a result, we obtain a cost-effective AES with a set of cropland management measures and the associated payment levels that are able to achieve weighted minimized nitrogen and phosphorus loads in the watershed for different budgets. We find, for example, that a cost-effective AES for a budget of 100,000[Formula: see text]RMB consists of the two measures of implementing a filter strip of 5[Formula: see text]m and reducing chemical fertilizers by 25% for which the farmers receive payments of 134[Formula: see text]RMB and 88[Formula: see text]RMB, respectively, which induces the reduction of nitrogen by 6.9% and phosphorus by 7.8%.

Strengthening Coastal and Maritime Security of India in the Indian Ocean Region: An In-depth Analysis of the Challenges and Opportunities presented by Seabed Mining in India

This research paper aims to provide a comprehensive analysis of India's coastal and maritime security in the Indian Ocean Region, specifically focusing on explicitly the challenges and opportunities associated with seabed mining in a kind of significant way. The legal framework governing seabed mining in India is primarily established by the “Mines and Minerals (Development and Regulation) Act, 1957 (MMDR Act)” in a considerable way. This act grants the central government the authority to regulate and control mining operations within India's territorial waters, exclusive economic zone, and continental shelf, which generally is reasonably significant. The paper highlights the challenges posed by seabed mining, including potential environmental impact, adherence to basically international agreements and conventions, and technological and operational challenges. It explores the importance of conducting environmental impact assessments (EIAs) and implementing mitigation measures to ensure sustainable mining practices in a big way. The steps taken to abide by the rules and regulations set by international organisations.

Simulating the environmental and economic effects of a carbon tax in Vietnam: a static computable general equilibrium analysis

PurposeA carbon tax has been widely discussed and implemented in developed countries to mitigate carbon emissions, but this measure is still quite new in developing countries. Recently, the ambition of Vietnam's government in mitigating emissions has been mentioned in international commitments. To achieve these targets, the government is making efforts to seek and implement mitigation measures in the country. While carbon pricing was introduced in Vietnam, there is no study simulating the effects of a carbon tax in the country. This study simulates the environmental and economic effects of a carbon tax and then proposes appropriate policies in Vietnam.Design/methodology/approachThis study investigates the impact on the Vietnamese economy within the static computable general equilibrium (CGE) framework. Compared with previous models, the proposed model in this paper is a fairly standard CGE approach that tries to picture the economic system of Vietnam. In addition, a carbon tax on output will be modeled in this framework. This carbon tax mechanism is more flexible and direct when a carbon tax is based on direct emissions by industry level and the industry's carbon intensity. The paper decomposes the Vietnamese economy into 18 different production sectors, based on the different emission levels of CO2. The CGE model makes possible to examine the impact of a carbon tax on the whole economy through all possible channels and to differentiate a separate carbon tax among different production sectors. The impact of a differentiated carbon tax is explored not only at the macroeconomic level but also at each different industrial level. Another feature of this paper is to investigate the impact of reallocation revenue from the carbon tax.FindingsThis paper has found that by designing carbon tax scenarios at different carbon prices ($1/tCO2, $5/tCO2, $10/tCO2) with different targeted industries, this study shows that higher carbon prices cause greater damage to GDP and welfare, but also better reductions in emissions. In addition, a carbon tax on the energy sectors results in milder economic and welfare damage but less emission reduction than when levying on all sectors. At the sectoral level, a carbon tax might cause sectoral restruction. Interestingly, the electricity sector is the most affected and also is the main contributor to reducing emissions in Vietnam. Finally, the study also shows that reallocation policies of new revenue from the carbon tax would reduce the economic damage caused by carbon taxes, and in many cases promote GDP and welfare. However, these policies reduce the environmentally positive impact of the carbon tax and even induce an increase in emissions in some cases.Originality/valueThis paper studies the pure impacts of a carbon tax, it also simulates the impact of several recycling policies where the increased tax revenue is incorporated. Thereby, this research supports to design and implement carbon tax policies in Vietnam. This paper also would contribute to the literature an example of the adoption of the carbon tax in a developing country, and it could be a lesson for others with similar conditions. Compared with previous models, the proposed model in this paper is a fairly standard CGE approach that tries to picture the economic system of Vietnam. In addition, a more flexible carbon tax mechanism is proposed to improve adequate coverage of emission resources.

Predicting extreme floods and droughts in East Africa using a deep learning approach

The East African region is highly susceptible to severe floods and persistent droughts, which greatly impact the livelihood of millions of people. Early warnings, at least a few seasons in advance, would help implement mitigation measures. However, most prediction systems using dynamical models perform poorly at long lead times. In this study, we propose a statistical deep learning approach based on a convolutional neural network (CNN) to predict extreme floods and droughts during the short rains season (October–December). The proposed CNN model captures the phase of extreme floods and droughts two to three seasons ahead, except for a few cases. By diagnosing the model’s skills using heatmaps, we find that predicted extreme floods and droughts are linked with the sea surface temperature anomalies of the Indian Ocean Dipole at shorter leads and with western and southern Indian Ocean, equatorial Pacific, and southern Atlantic Ocean at longer leads. Although there were a few poorly predicted exceptions, the superior skill of our CNN-based predictions at longer leads provides a significant advantage in developing mitigation measures.

Exploring the Impact of El Niño–Southern Oscillation (ENSO) on Temperature Distribution Using Remote Sensing: A Case Study in Kuching City

Malaysia’s location in Southeast Asia exposes it to various weather patterns influenced by El Niño–Southern Oscillation (ENSO), monsoons, the Madden–Julian Oscillation (MJO), and the Indian Ocean Dipole (IOD). To overcome the limitations of previous studies due to insufficient spatial information, this study utilizes remote sensing (RS) data from Landsat and MODIS satellites, along with the Oceanic Niño Index (ONI), to analyze the spatial distribution of temperature affected by El Niño–Southern Oscillation (ENSO). This study employs radiometric and atmospheric corrections on remote sensing (RS) data, converting them to surface temperature data. Our analysis reveals a correlation coefficient of 0.73 (MODIS) and 0.71 (Landsat) between the ONI and RS temperature data. During El Niño events, Landsat recorded temperature increases of 0–1.6 °C, while MODIS showed increases of 2.2–2.8 °C. The spatial information obtained assists in identifying affected areas and facilitating the implementation of mitigation measures by the government. By utilizing RS data, this research enhances our understanding of the ENSO–temperature relationship, surpassing previous limitations and providing valuable insights into climate dynamics.

EPA Proposal

EPA Proposes New Ag Herbicide Rules ‐ Q&A Six-Pack: EPA Draft Herbicide Strategy The EPA’s Draft Herbicide Strategy Framework outlines how the agency intends to provide protection for threatened and endangered species while ensuring herbicide access to users. The herbicide toolbox is about to increase for US farmers. For nearly two years, the Environmental Protection Agency (EPA) has said it will no longer turn a blind eye toward the Endangered Species Act (ESA) and its legal obligation to ensure that pesticides do not jeopardize the continued existence of nearly 1,700 federally threatened or endangered species. In July 2023, EPA took what it believes is another step toward ESA compliance, releasing the “Draft Herbicide Strategy Framework.” The 96-page proposal outlines how the agency intends to protect more than 900 listed species and their designated critical habitats (CH) from agricultural uses of conventional herbicides in the lower 48 states. The document is available for public comment until 22 October. The draft herbicide strategy presents substantial change, requiring herbicide users to implement mitigation measures for potential impacts much earlier ‐ even before EPA or the US Fish and Wildlife Service (USFWS) determines definitely that a herbicide poses a risk. Here are answers to six questions about EPA’s Draft Herbicide Strategy Framework. For a quick explanation of some of the terms that EPA uses, read this post on the DTN Production Blog.

The integration of smart city planning policy with climate change adaptation and mitigation actions: a review of kathmandu

Many urban systems worldwide are projected to be affected by the consequences of climate change, such as increased rainfall intensity, storm surge, flooding, and the urban heat island effect. This will significantly impact the management of cities and their populations, and the services they provide. To overcome this effect, adaptation will likely be required. This article examines how climate change moderation and variation are combined with intelligent urban planning policies (strategies, legislation, and laws) in the Kathmandu district. The three governance sciences look at the two levels of governance (national and local) (intelligent urban planning, pollution management, and climate change). The assessment process is one of the few instruments to review the adequacy of climate change and strategic policies. The findings show that intelligent city planning documents should present climate change adaptation and mitigation actions. In addition, adaptation and mitigation measures still need to be well integrated. Significant prospects for greater policy coordination through guidelines and levels of government under the Paris Agreement priorities have been identified, ensuring implementation in land use and planning decisions. Innovative city development policies must be better able to design and implement climate resilience and mitigation measures.

Developing a hospital disaster preparedness evaluation tool for Sri Lanka - A modified Delphi study

The 2005 World Conference on Disaster Reduction promoted the “Safe Hospitals” concept, which ensures that all new hospitals are built with a level of resilience that strengthens their capacity to remain operational in disaster situations. Also, the model endorsed the implementation of mitigation measures to strengthen existing health facilities. So far, there is no widely accepted and validated toolkit for conducting hospital disaster preparedness assessments in Sri Lanka. To address this shortcoming, this study aimed to create a comprehensive hospital disaster preparedness evaluation tool for Sri Lanka. A modified Delphi technique was used to develop this toolkit. The Delphi panel consisted of 18 local experts in health disaster management. A draft toolkit was prepared by the research team using the knowledge gained through an extensive literature review. The panel of experts rated the importance of each indicator in the draft toolkit on a five-point Likert Scale, ranging from extremely important to not at all important. Also, they were requested to suggest new indicators to improve the toolkit. Three rounds of Delphi process were conducted to obtain the consensus. To be included in the final toolkit ≥80% agreement was required for any indicator. All the indicators which failed to obtain 80% agreement were rejected at the third round. A comprehensive toolkit for hospital disaster preparedness was developed through the consensus of local experts based on an all-hazards approach. The finalized toolkit was comprised of 142 indicators under 15 themes designed on the 4S’s domains of preparedness; space, stuff, staff and systems.

Investigating the implementation of the mitigation hierarchy approach in environmental impact assessment in relation to biodiversity impacts

Global loss of biodiversity has directly and indirectly been caused by human activities. Environmental Impact Assessment (EIA) attempts to address the loss of biodiversity caused by development projects, by avoiding, reducing or compensating the loss (in that order following the mitigation hierarchy approach). Evidence suggests that in practice the mitigation hierarchy is not always applied correctly, and that monitoring is frequently absent, or flawed, meaning that the success of the mitigation measures, and their associated biodiversity outcomes, remain unknown. However, there is no literature that has systematically examined the application of the mitigation hierarchy and assessed the effectiveness of associated monitoring in an EIA system. This study fills that gap using Chile as an example because of its high biodiversity setting, and ease of access to EIA-related data. The results indicate that the use of compensation measures exceeded what would be expected from correct implementation of the mitigation hierarchy, and that there was also some misclassification of the measures. Monitoring studies focused on inspecting implementation of mitigation measures rather than measuring biodiversity outcomes (meaning that mitigation effectiveness cannot be fully evaluated). Further, there was a focus on specific elements of ecosystems and lack of consideration for broader biodiversity implications. Thus, the findings raise some concerns over the ability of EIA to achieve its goals of zero net loss of biodiversity. We make suggestions to improve the mitigation and monitoring aspects of the EIA process in Chile and would suggest that the recommendations are likely to have wider relevance to other jurisdictions.