Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. Based on the mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by passenger throughput data of the Shanghai Pudong International Airport, China. The experimental results prove the model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models, outperformed by the MI-SVR model. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on mutual information. The main findings are considered instrumental to the airport operation and air traffic optimization.

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. We cover especially unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

We present a general theory of estimation of analysis error covariances based on cross-validation as well as a geometric interpretation of the method. In particular we use the variance of passive observation–minus-analysis residuals and show that the true analysis error variance can be estimated, without relying on the optimality assumption. This approach is used to obtain near optimal analyses that are then used to evaluate the air quality analysis error using several different methods at active and passive observation sites. We compare the estimates according to the method of Hollingsworth-Lönnberg, Desroziers et al., a new diagnostic we developed, and the perceived analysis error computed from the analysis scheme, to conclude that, as long as the analysis is near optimal, all estimates agree within a certain error margin.

Human exposure to infectious aerosols results a transmission of diseases, such as influenza, tuberculosis, and COVID-19. Most dental procedures generate a significant number of aerosolized particles, increasing transmission risk in dental settings. Since the generation of aerosols in dentistry is unavoidable, many clinics started using intervention strategies such as area-filtration units and extraoral evacuation equipment, especially under the relatively recent constraints of the pandemics. However, the effectiveness of these devices in dental operatories has not been studied. Therefore, the dental personnel's ability to position and operate such instruments efficiently is also limited. To address these challenges, we utilized a real-time sensor network for assessment of aerosol dynamics during dental restoration and cleaning producers with and without intervention. The strategies tested during the procedures were (i) local area high-efficiency particle air (HEPA) filters and (ii) extra-oral suction device (EOSD). The study was conducted at the University of Washington School of Dentistry using a network of thirteen fixed sensors positioned within the operatory and one wearable sensor worn by the dental operator. The sensor network provides time and space-resolved particulate matter (PM) data. Three-dimensional (3D) visualization informs aerosol persistence in the operatory. It was found that area filters did not improve the overall aerosol concentration in dental offices significantly. An average of 16% decrease in PM concentration was observed when EOSD equipment was used during the procedures. The combination of real-time sensors and 3D visualization can provide dental personnel and facility mangers with actionable feedback to effectively assess aerosol transmission in medical settings and develop evidence-based intervention strategies.

Understanding platelet functionality has undergone a sea change in the last decade. No longer are platelets viewed simply as regulators of haemostasis; they are now acknowledged to be pivotal in coordinating the inflammatory and immune responses. This expanded role for platelets brings new opportunities for controlling a range of health conditions, targeting platelet activation and their interactions with other vascular cells. Antiplatelet drugs may be of wider utility than ever expected but often cause too strong a platelet suppression to be used out of clinical settings. Dietary antiplatelets represent a nutritional approach that can be efficacious while safe for general use. Here we review potential new uses for dietary antiplatelets outside the field of cardiovascular health, with specific reference to the water-soluble tomato extract Fruitflow®. Uses in different aspects of inflammation and immune function are discussed, highlighting exercise-induced inflammation, mediating the effects of air pollution, and controlling thrombotic aspects of the immune response. Potential future developments in women’s health, erectile dysfunction, and the allergic response give an indication of how wide-ranging the utility of dietary antiplatelets can possibly be.

To assist firefighters and other first responders to use their existing equipment for respiration during the COVID-19 pandemic without using single-use, low-supply, masks, this study outlines an open source kit to convert a 3M-manufactured Scott Safety self-contained breathing apparatus (SCBA) into a powered air-purifying particulate respirator (PAPR). The open source PAPR can be fabricated with a low-cost 3-D printer and widely available components for less than $150, replacing commercial conversion kits saving 85% or full-fledged proprietary PAPRs saving over 90%. The parametric designs allow for adaptation to other core components and can be custom fit specifically to fire-fighter equipment, including their suspenders. The open source PAPR has controllable air flow and its design enables breathing even if the fan is disconnected or if the battery dies. The open source PAPR was tested for air flow as a function of battery life and was found to meet NIOSH air flow requirements for 4 hours, which is 300% over expected regular use.

Ozone and fine particles (PM_2.5) are the two main air pollutants of concern in the New South Wales Greater Metropolitan Region (NSW GMR) region due to their contribution to poor air quality days in the region. This paper focuses on source contributions to ambient ozone concentrations for different parts of the NSW GMR, based on source emissions across the greater Sydney region. The observation-based Integrated Empirical Rate Model (IER) was applied to delineate the different regions within the GMR based on the photochemical smog profile of each region. Ozone source contribution is then modelled using the CCAM-CTM (Cubic Conformal Atmospheric Model-Chemical Transport Model) modelling system and the latest air emission inventory for the greater Sydney region. Source contributions to ozone varied between regions, and also varied depending on the air quality metric applied (e.g., average or maximum ozone). Biogenic volatile organic compound (VOC) emissions were found to contribute significantly to median and maximum ozone concentration in North West Sydney during summer. After commercial domestic, power station was found to be the next largest anthropogenic source of maximum ozone concentrations in North West Sydney. However, in South West Sydney, beside commercial and domestic sources, on-road vehicles were predicted to be the most significant contributor to maximum ozone levels, followed by biogenic sources and power stations. The results provide information which policy makers can devise various options to control ozone levels in different parts of the NSW Greater Metropolitan Region.

Air pollution is a critical issue impacting urban environments, leading to severe health problems and environmental degradation. This comprehensive review examines the potential of green systems—specifically green walls, active green walls, and urban greenery systems—to mitigate atmospheric pollutants such as particulate matter (PM), volatile organic compounds (VOCs), and carbon dioxide (CO2). By systematically analyzing 44 recent studies, the review highlights the pollutant capture efficiency of various green technologies and plant species in both indoor and outdoor settings. Active green walls, particularly those utilizing plant species such as Chlorophytum comosum and Sansevieria trifasciata, were found to be highly effective, with VOC reduction efficiencies up to 96.34%, PM reductions by 65.42%, and CO2 reduction rates reaching 4.8% under optimal conditions. The review identifies key strengths in current research, including diverse experimental setups and the use of sophisticated measurement techniques, but also notes significant limitations such as variability in experimental conditions and a lack of long-term performance data. The study underscores the importance of proper maintenance to sustain green systems' efficacy and highlights the potential issue of pollutant resuspension, which remains under-researched. Practical implications for urban planning are discussed, advocating for the integration of effective green systems into urban infrastructure to enhance air quality and public health. Recommendations for future research include the need for standardized metrics, long-term studies, economic feasibility analyses, and real-world validation of simulation models to better understand and optimize green systems for urban air pollution mitigation.

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The early part of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns have been quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology-air pollution relationships have been visualised/interpretated on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs elevated PM10 pollution in the valley. Two local meteorological patterns, the hot-dry-northwesterly wind conditions and the hot-dry-calm conditions, were found most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of eastward migrating continental high pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere.

On April 17, 2019, a coach with tourists from Germany crashed in Madeira requiring repatriation by the German Air Force. The Advance Trauma Life Support (ATLS) concept was the central component of patient care. Data in Madeira were collected through a structured interview. The analysis of the Aeromedical Evacuation was based on intensive care transport records. In Germany all available were reviewed for data collection. Quality of life (HRQoL) was evaluated by the 12-item Short-Form Health Survey (SF-12). Twenty-eight prehospital patients were transported to the Level III Trauma Center in Funchal (Madeira). Five operative procedures were performed. Fifteen patients were eligible for Aeromedical Evacuation (AE). In the second hospital phase in Germany in total 82 radiological images and nine operations were performed. Hospital stay lasted 11 days (median, IQR 10–18). Median Follow-up (14 of 15 patients) was 16 months (IQR 16–21). 80% (8 out of 10) showed an increased risk for post-traumatic stress disorder (PTSD). Six key findings were identified in this study: divergent injury classification, impact of AE mission on health status, lack of communication, need of PTSD prophylaxis, patient identification, media coverage. Those findings may improve AE missions in the future e.g. required after armed conflicts.

Recently, the Integrated Air Defense System (IADS) has become vital for the defense system as the military defense system is vital for national security. Placing Integrated Air Defense System batteries among locations to protect locations assets is a crucial problem because optimal solutions are needed for interceptor missiles to intercept attacker missiles for maximizing protection of assets across locations or places. In this research, the procedures of using network algorithms along with developing several network algorithms are going to be demonstrated to develop a model for sequential development of seven network securing strategies of placing Surface to Air Missile (SAM) batteries to maximize the protection of assets across locations (based on given asset values) by generating optimal solutions through computation to destroy maximum attacker missiles by using minimum interceptor missiles with given intercept probability. This network securing strategies can be implemented not only for Integrated Air Defense System (IADS) planning but also Counter Air (CA) planning as Integrated Air Defense System (IADS) is conducted with defensive counter air supported by attack operations in offensive counter air.

Engineered nanomaterials (ENMs) are of significant relevance due to their unique properties, which have been exploited for widespread applications. Cerium oxide nanoparticles (CeO2-NPs) are one of most exploited ENM in the industry due to their excellent catalytic and multi-enzyme mimetic properties. Thus, toxicological effects of these ENMs should be further studied. Acute and subchronic toxicity of CeO2-NPs were assessed. First an in vitro multi-dose short-term (24h) toxicological assessment was performed in three different cell lines: A549 and Calu3, representing the lung tissue, and 3T3 as an interstitial tissue model. After that, a sub-chronic toxicity assessment (90 days) of these NPs was carried out on a realistic and well stablished reconstituted primary human airway epithelial model (MucilAir™), cultured at the Air-Liquid Interface (ALI), to study long-term effects of these particles. Results showed minor toxicity of CeO2-NPs in acute exposures. However, in subchronic exposures, cytotoxic and inflammatory responses were observed in the human airway epithelial model after 60 days of exposure to CeO2-NPs. These results suggest that acute toxicity approaches may underestimate the toxicological effect of some ENM, highlighting the need of subchronic toxicological studies in order to accurately assess the toxicity of ENM and their cumulative effects in the organism.

The growing market penetration of heat pumps indicates the need for a performance test method which better reflects the dynamic behavior of heat pumps. In this contribution, we developed and implemented a dynamic test method for the evaluation of the seasonal performance of heat pumps by means of laboratory testing. Current standards force the heat pump control inactive by fixing the compressor speed. In contrast, during dynamic testing, the compressor runs unfixed while the heat pump is subjected to a temperature profile. The profile consists of the different outdoor temperatures of a typical heating season based on the average European climate and also includes temperature changes to reflect the dynamic behavior of the heat pump. The seasonal performance can be directly obtained from the measured heating energy and electricity consumption making subsequent data interpolation and recalculation with correction factors obsolete. The method delivers results with high precision and high reproducibility and could be an appropriate method for a fair rating of heat pumps.

The burgeoning field of Urban Air Mobility (UAM) promises transformative solutions to urban transportation challenges, with the potential to revolutionize cargo logistics in metropolitan areas. This research investigates the role of tiltrotor technology as a pivotal enabler of seamless UAM integration for efficient cargo transportation within urban environments. The study delves into the existing challenges within UAM, such as infrastructure limitations and the need for adaptable aerial vehicles that can navigate confined spaces while maintaining payload capacity. Tiltrotor Unmanned Aerial Vehicles (UAVs) are examined as a solution to these challenges due to their unique capabilities, combining vertical takeoff and landing (VTOL) with efficient forward flight. This research evaluates the potential of tiltrotor UAVs for urban cargo transportation, focusing on their operational efficiency, safety, and economic viability. Furthermore, the study explores the potential impact of tiltrotor technology on urban cargo logistics, considering factors such as reduced congestion, shorter delivery times, and environmentally sustainable operations. Real-world case studies and practical implementation strategies are discussed, shedding light on the importance of integration of tiltrotor UAVs into existing cargo transportation networks.The findings of this research underscore the transformative potential of tiltrotor technology in enhancing UAM for cargo transportation within urban areas. The study contributes valuable insights to the ongoing discourse on UAM integration and lays the groundwork for future developments in autonomous aerial cargo delivery systems.

The public health risk associated with the autopsy unit is high and information on indoor microbial air contamination is limited. Airborne pathogenic fungi thrive in moist indoor environments similar to autopsy room conditions. They spread through spores, cause infections in immunocompromised patients, and exhibit resistance to antimicrobials, making them a public health threat. This study profiled fungi circulating in the indoor air of a referral autopsy unit in Accra, Ghana. The different sections of the unit were sampled using the standard open plate technique. Fungal isolates obtained were phenotypically and molecularly characterized. The resistance to fluconazole and itraconazole, as well as a germicide used in routine cleaning of the unit, was tested using broth microdilution assays. Fungi isolated include species of clinically relevant strains of Candida and Aspergillus; also, Penicillium spp. and Mucor spp. They exhibited high levels of resistance to fluconazole and itraconazole, as well as Denzal® germicide used in routine cleaning of the autopsy unit. This study is the first to profile the fungal diversity of an autopsy unit in Ghana, and the findings suggest that workers might be exposed to fungi with high pathogenic potential that could cause infections.

Machine Learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the PLSR model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to be strongly impacting on Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between predicted and measured Tair from the test set. It was concluded the remote sensing is an effective tool to estimate Tair distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations such as precipitation and wind speed, and the use of non-parametric ML techniques.

The hospital indoor environment plays a significant role in shaping the microbial exposures that humans encounter. Resistance to antibiotics is one of the mechanisms employed by bacteria to develop resilience against stress in indoor environments. The extensive usage of antibiotics has been a key factor in driving changes in the ecological function of antibiotic resistance genes (ARGs) and their acquisition by pathogenic bacteria. By integrating the 16S rRNA Illumina sequencing and high throughput-qPCR approaches with water and air dust samples across seven departments at Peking University Shenzhen Hospital, China, this study yielded intriguing findings regarding the department-specific variation, transmission, and correlation of bacteria, ARGs and MGEs within the hospital indoor environment. One notable observation was that the seasonal variation played a vital role in shaping the bacterial composition in the overall hospital indoor environment across seven studied departments. Nevertheless, a similar significant seasonal impact was only observed in the blood test room and emergency departments, rather than in the other five departments. Another intriguing finding was the correlation between ARGs and MGEs abundance. This correlation was evident in the overall hospital indoor environment, which was conversely not found in the emergency, pneumology and gynecology departments. Additionally, while there was no significant relationship that occurred between bacterial communities and ARGs/MGEs within the hospital, the gynecology department demonstrated a close link between bacterial communities and the presence of ARGs/MGEs. Notably, the gynecology department also stood out as an important source of bacteria and ARGs/MGEs for other departments. This study underscores the need for careful surveillance and monitoring of antibiotic resistance, particularly in Legionella spp. in man-made water systems. The findings also show the importance of monitoring and understanding the genetic elements such as Tp614 involved in gene transfer and recombination, as well as their impact on antimicrobial treatment efficacy.

Autonomous aircraft are the key enablers of future urban services, such as postal and transportation systems. Digital Twins (DT) are promising cutting-edge technologies that can transform the future transport ecosystem into an autonomous and resilient system. However, since DT is a data-driven solution based on AI, proper data management is essential in implementing DT as a Service (DTaaS). One of the challenges in DT development is the availability of real-life data, particularly for training algorithms and verifying the functionality of DT. The current article focuses on data augmentation through synthetic data generation. This approach can facilitate the development of DT in case the developers do not have enough data to train the Machine Learning (ML) algorithm. The current twinning approach provides a prospective ideal state of the engine used for proactive monitoring of the engine’s health as an anomaly detection service. In line with the track of Unmanned Aircraft Vehicles (UAV) for Urban Air Mobility in Smart Cities applications, this paper focuses specifically on the common Hybrid turbo-shaft in drones/helicopters. However, there is a significant gap in real-life similar synthetic data generation in the UAV domain literature. Therefore, Rolling Linear Regression and Kalman Filter algorithms were implemented on noise-added data, which simulate the data measured from the engine in a real-life operational lifecycle. For both thermal and hybrid models, the corresponding DT model has shown high efficiency in noise filtration and a certain amount of predictions with a lower error rate on all engine parameters, except the engine torque.

The verticals of 5G, such as the automotive, smart grid and smart cities sectors, will bring lots of new sensors and IoT devices requiring Internet connectivity. Most of these machine-type terminals will be sparsely distributed, covering a very large geographical area and, from time to time, will have to update their software, firmware and/or other relevant data. Given this situation, one viable solution to implement the “Over-the-Air” update of these IoT terminals can be with the aid of GEO satellite systems. However, due to the ultra-dense radio frequency reuse that contemporary High-Throughput Satellite (HTS) systems implement in the access link to serve the IoT terminals, the use of a time-packed Free Space Optical (FSO) link represents a practical solution to avoid the bottleneck that the satellite gateway experiences in the feeder link. The performance of both Detect-and-Forward and Decode-and-Forward relaying strategies are studied, assuming that the single-carrier M-PAM symbols that are transmitted on the optical feeder link are mapped into M-QAM symbols that modulate the multiple subcarriers of the OFDM-based radio access link. In addition, the benefits of encapsulating the NB-IoT frames into DVB-S2(X) satellite frames is also analyzed in detail. The effects of the impairments introduced in both optical feeder and radio access links are characterized in detail, and the end-to-end error correction capabilities of the Modulation and Coding Schemes (MCS) defined in the contemporary releases of the NB-IoT and DVB-S2(X) standards are studied for different working regimes.

In light of the critical and emerging global challenge posed by the increasing frequency and severity of heatwaves in recent decades, this study demonstrates a practical and robust workflow that bridges the gap in understanding how the effective integration of remote sensing data with ground-based observations can facilitate a comprehensive assessment of spatiotemporal heatwave patterns and trends in the central region of Thailand. This research transcends traditional methods, utilizing air temperature (Tair) and satellite-derived land surface temperature (LST) data from 1981 to 2019. Results exhibit that an analysis of Tair indicates increasing daytime heatwaves in peri-urban areas, with significant trends in heatwave number (HWN), heatwave frequency (HWF), and heatwave duration (HWD), heatwave amplitude (HWA) rises in urban settings while nighttime heatwaves significantly intensify in rural locales. Notably, LST trends reveal varied patterns, with peri-urban areas showing marked daytime increases in heatwave magnitude (HWM), HWA, and HWF. Correlation analysis (p=0.05) highlights strong daytime associations between Tair and LST in rural (HWN, HWF, HWD, r>0.90) and peri-urban (HWM, HWA, r>0.65) regions. Overall, this study advances approaches to the adaptable measurement and spatial-temporal pattern of heatwave-related risk areas, providing insights for decision-makers to develop sustainable practices and strategies for climate change adaptation.

Distracted driving is the leading cause of accidents, the cause of these distractions may include drowsiness, using cell phone, smoking a cigarette, etc. The proposed paper aims at monitoring the attentiveness of the driver and alerting the driver when his/her alertness goes below a certain threshold. It uses image processing using OpenCV (Computer Vision) and You Look Only Once (YOLO) algorithm to detect the eyes and object respectively and sounds an alarm when the driver is either drowsy or using his cell phone or smoking a cigarette.

The article discusses determinants of living environment in Central and Eastern Europe. As an example, the city of Radzionkow was chosen, with 16 thousand inhabitants, located in the Silesia agglomeration in southern Poland, in the area where hard coal has been mined for almost two hundred years, which largely serves as a source of heating for houses and flats. 360 buildings in 6 groups of 60 buildings were examined in the selected city, which allowed to distinguish 3 different areas in terms of the quality of the living environment depending on the technical condition of buildings, the method of heating and location. To a large extent, determinants are the existing spatial and geophysical conditions of a given location. Many research and reports on the living environment do not take into account the factors mentioned, focusing only on the statistical data of pollution, excluding spatial factors. In the research presented, the author, taking into account location variables, shows differences in the measurements of air pollution in relation to the designated location zones depending on the morphological structure of the building, the degree of its modernization and the types of heating used in buildings.

Following the coronavirus disease 2019 (COVID-19) pandemic, several studies have examined the possibility of correlating the virulence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, to the climatic conditions of the involved sites; however, inconclusive results have been generally obtained. Although either air temperature or humidity cannot be independently correlated with virus viability, a strong relationship between SARS-CoV-2 virulence and the specific enthalpy of moist air appears to exist, as confirmed by extensive data analysis. Given this framework, the present study involves a detailed investigation based on the first 20–30 days of the epidemic before public health interventions in 30 selected Italian provinces with rather different climates, here assumed as being representative of what happened in the country from North to South, of the relationship between COVID-19 distributions and the climatic conditions recorded at each site before the pandemic outbreak. Accordingly, a correlating equation between the incidence rate of the pandemic and the average specific enthalpy of atmospheric air was developed, and an enthalpy-based seasonal virulence risk scale was proposed as a tool to predict the potential danger of COVID-19 spread due to the persistence of weather conditions favorable to SARS-CoV-2 viability. For practical applications, a conclusive risk chart expressed in terms of coupled temperatures and relative humidity (RH) values was provided, showing that safer conditions occur in case of higher RH at the highest temperatures, and of lower RH at the lowest temperatures. The proposed risk scale was in agreement with the available infectivity data in the literature for a number of cities around the world.

Air pollution, climate change and global warming show direct linkage. Introduction - Background: Research in the field has proposed and demonstrated their link to increased allergenicity in plants, leading to increased allergic diseases. Allergen patterns are changing in response to climate change. Across Europe, up to 12% of the population suffers from allergic diseases (bronchial asthma and allergic rhinitis) caused by the pollen grains. The medical costs of these types of allergies are enormous and are increasing with the spread of common ragweed - Ambrosia artemisiifolia* in Europe. In practice, more and more patients are turning to allergists. Methods: Based on these data, the research aims to identify safety measures and specific legislation to control and eradicate common ragweed in the countries most affected by the spread of this plant in Europe, increase in population exposure in countries affected by the presence of common ragweed and the socio-economic impact of allergic rhinitis and bronchial asthma at European level.[1][2] Our database search did not reveal any other research that has addressed this topic to date. Results: This research identifies specific legislation to the countries most affected by the spread of this plant pollen grains at European level, changes in allergen patterns and monitoring/safety eradication measures decided by local authorities, and data on the spread of common ragweed over the last two decades, costs per treatment, increased allergic rhinitis and bronchial asthma morbidity in the most affected areas. Conclusions: At European level, there are no universally valid models for eradicating this weed. Various methods can be used to limit the spread of common ragweed and its pollen production (e.g. mowing once or twice a year of overgrown land, ploughing, chemical control, public information, etc.). Local and regional cooperation should be considered essential to promote phytosanitary & safety measures and exchange of information on methods to identify and manage the spread of common ragweed. Unified legislation at European level would help all affected countries; reduce morbidity from respiratory allergic diseases and the costs of treating these diseases.

Forest carbon sequestration is a widely accepted natural climate solution, however, methods to determine net carbon offsets are limited to commercial carbon proxies and CO2 eddy covariance research. Non-CO2 greenhouse gases (GHG) (e.g., CH4, N2O) receive less attention in the context of forests, in part, due to emphasis on CO2 and the operational requirements and cost for three-gas eddy covariance platforms. In this study, Howland forest flux tower (CO2, CH4) and soil flux data (CO2, CH4, N2O), representing net emission reductions, are linked to their respective social costs to estimate commercial revenue if sold as a GHG social cost forest offset product (GHG-SCF). Estimated annual revenue for GHG-SCF products, applicable to realization of a Green New Deal, range from 120,000 covering the site area of 557 acres in 2021, to 12,000,000 for extrapolation to 40,000 acres in 2040, assuming a 3% discount rate. The Howland Forest CO2 flux record for two adjacent towers is compared to California Air Resources Board forest carbon proxy data for compliance sequestration offsets, the only project site where these approaches overlap. Overcrediting, incomplete carbon accounting with annual errors of up to 2,256%, inadequate third-party verification, and limited application to non-CO2 GHG’s are established. In contrast, direct measurement of one or more GHG’s offers new forest products and revenue incentives to restore and conserve forests worldwide.

Air pollution has been a major environmental issue worldwide from the last few decades. It is a man-made issue that has a serious harmful impact on the environment and the health of humans. There are different sources of emission, but industrial processes and motor vehicles contribute to a chief part of air pollution. It has acute as well as chronic effects on the health of humans and affects different organs. It is thought the main environmental risk factor in the incidence and progression of several diseases such as lung cancer, asthma, Alzheimer's and Parkinson's diseases. Long-term exposure to air pollution has different toxicological impacts on humans such as respiratory and cardiovascular diseases, neuropsychiatric complications, eye irritation and skin diseases. As per the study by the World Health Organization (WHO), nitrogen oxides (NOx), carbon monoxide (CO), ozone (O₃), sulfur dioxide (SO₂), volatile organic compounds (VOCs) and heavy metals are the major air contaminants. These pollutants are different in their properties, composition and time of disintegration. The aim of this study is to review the different sources, types, impacts, challenges and mitigation of air pollution in detail.

Background: This study examines the relationship between energy usage, health issues, and pro-environmental behavior (PEB) in Kyrgyzstan, amidst the country's commitment to transition from coal-based energy to renewable sources in line with the Paris Agreement. The purpose is to investigate citizens' attitudes towards PEB and their intentions to engage in environmentally friendly actions, focusing on gas, electricity, and coal. Methods: Drawing upon the Theory of Planned Behavior (TPB) framework, a survey was conducted among 1,455 respondents to explore attitudes towards PEB and energy sources' impact on health issues. Results: Decarbonization efforts in Kyrgyzstan and Central Asia are in their early stages, with coal remaining a primary energy source. The study emphasizes the importance of governmental policies and citizen action in achieving decarbonization goals. Rising electricity costs outweigh the increase in indirect energy costs for food, posing challenges for households in adapting to changing energy dynamics. Conclusions: Targeted interventions and communication strategies are crucial to promote pro-environmental behavior and facilitate the transition to sustainable energy sources. Understanding the relationships between health concerns, air pollution awareness, PEB, and energy source choices can inform policymakers and organizations in their efforts to ensure a sustainable and healthy future for Kyrgyzstan and other Central Asian countries.