Development of a Smartwatch with Gas and Environmental Sensors for Air Quality Monitoring.

In recent years, there has been a growing interest in developing portable and personal devices for measuring air quality and surrounding pollutants, partly due to the need for ventilation in the aftermath of COVID-19 situation. Moreover, the monitoring of hazardous chemical agents is a focus for ensuring compliance with safety standards and is an indispensable component in safeguarding human welfare. Air quality measurement is conducted by public institutions with high precision but costly equipment, which requires constant calibration and maintenance by highly qualified personnel for its proper operation. Such devices, used as reference stations, have a low spatial resolution since, due to their high cost, they are usually located in a few fixed places in the city or region to be studied. However, they also have a low temporal resolution, providing few samples per hour. To overcome these drawbacks and to provide people with personalized and up-to-date air quality information, a personal device (smartwatch) based on MEMS gas sensors has been developed. The methodology followed to validate the performance of the prototype was as follows: firstly, the detection capability was tested by measuring carbon dioxide and methane at different concentrations, resulting in low detection limits; secondly, several experiments were performed to test the discrimination capability against gases such as toluene, xylene, and ethylbenzene. principal component analysis of the data showed good separation and discrimination between the gases measured.

Novel Chemoresistive Sensors for Indoor CO2 Monitoring: Validation in an Operational Environment.

Health and safety considerations of indoor occupants in enclosed spaces are crucial for building management which involves the strict control and monitoring of carbon dioxide levels to maintain acceptable air quality standards. For this study, we developed a wireless, noninvasive, and portable platform for the continuous monitoring of carbon dioxide concentration in enclosed environments, i.e., academic rooms. The system aimed to monitor and detect carbon dioxide using novel low-cost metal oxide-based chemoresistive sensors, achieving sensing performance comparable to those of commercially available detectors based on optical working principle, e.g., nondispersive infrared sensors. In particular, a predictive study of carbon dioxide levels was performed by exploiting random forest and curve fitting algorithms on chemoresistive sensor data collected in an academic room, then comparing the results with lab-based measurements. The performance of the models was evaluated with real environment conditions during 7 weeks. The field measurements were conducted to validate and support the development of the system for real-time monitoring and alerting in the presence of relevant concentrations (above 1,000 ppm). Therefore, the study highlighted that the curve fitting model obtained was able to recognize with an F1-score of 0.77 the presence of poor air quality, defined as concentration above 1,000 ppm of carbon dioxide as reported by the Occupational Safety and Health Administration.

Extreme wildfires in Canada and their contribution to global loss in tree cover and carbon emissions in 2023.

Canadian wildfires in 2023 were record breaking with wide-reaching impacts on people, nature, and climate. Extreme heat and low rainfall associated with climate change led to unprecedented forest fires that released enormous amounts of carbon as they burned. This study used data on fire-driven tree cover loss and forest carbon fluxes to estimate the total extent of stand-replacing forest fires and their associated carbon emissions. We found that the 2023 Canadian wildfires burned nearly 7.8 million hectares of forest and accounted for more than a quarter of all tree cover loss globally. Furthermore, forests impacted by wildfires emitted nearly 3 billion tons of CO2 or about 25% more carbon than all primary tropical tree cover loss that year. These results have important implications for global carbon budgets because emissions from these wildfires will largely be excluded from official greenhouse gas reporting.

Estimating the Carbon Footprint of Healthcare in the Canton of Geneva and Reduction Scenarios for 2030 and 2040.

Switzerland, a wealthy country, has a cutting-edge healthcare system, yet per capita, it emits over one ton of CO2, ranking among the world's most polluting healthcare systems. To estimate the carbon footprint of the healthcare system of Geneva's canton, we collected raw data on the activities of its stakeholders. Our analysis shows that when excluding medicines and medical devices, hospitals are the main greenhouse gas emitter by far, accounting for 48% of the healthcare system's emission, followed by nursing homes (20%), private practice (18%), medical analysis laboratories (7%), dispensing pharmacies (4%), the homecare institution (3%), and the ambulance services (<1%). The most prominent emission items globally are medicines and medical devices by far, accounting for 59%, followed by building operation (19%), transport (11%), and catering (4%), among others. To actively reduce Geneva's healthcare carbon emissions, we propose direct and indirect measures, either with an immediate impact or implementing systemic changes concerning medicine prescription, building heating and cooling, low-carbon means of transport, less meaty diets, and health prevention. This study, the first of its kind in Switzerland, deciphers where most of the greenhouse gas emissions arise and proposes action levers to pave the way for ambitious emission reduction policies. We also invite health authorities to engage pharmaceutical and medical suppliers in addressing their own responsibilities, notably through the adaptation of procurement processes and requirements.

How can the digital economy reduce carbon emissions? Empirical evidence from China.

China is transitioning into the digital economy era. The advancement of the digital economy could offer a fresh mechanism to attain carbon peak and carbon neutrality objectives. Applications of the digital economy, such as smart energy management, intelligent transport systems, and digital agricultural technologies, have significantly reduced carbon emissions by optimizing resource use, reducing energy waste, and improving production efficiency. This research does so by devising a theoretical model that looks into the multi-faceted power of the digital economy under a two-sector paradigm. Utilising a panel model, a mediation effect model and a spatial Durbin model to assess the digital economy's power on carbon emissions. This research has determined that the digital economy can significantly diminish carbon emissions, with green tech innovations and industrial transformation being key contributors. The spatial spillover effect was used for the digital economy to aid in lowering carbon emissions in adjacent districts and upgrading better environmental stewardship. The influence of the digital economy has better performance in lowering carbon emissions in mid-western China than in the eastern area. This paper deepens understanding of the drivers of low-carbon growth and the significance, mechanism and regional disparities of the digital economy's effect on reducing carbon emissions. It offers valuable policy insights and guidance for globally achieving digital economy growth, reducing carbon emissions and reaching carbon peak and neutrality goals.

Assessing the impact of climate variability on maize yields in the different regions of Ghana-A machine learning perspective.

Climate variability has become one of the most pressing issues of our time, affecting various aspects of the environment, including the agriculture sector. This study examines the impact of climate variability on Ghana's maize yield for all agro-ecological zones and administrative regions in Ghana using annual data from 1992 to 2019. The study also employs the stacking ensemble learning model (SELM) in predicting the maize yield in the different regions taking random forest (RF), support vector machine (SVM), gradient boosting (GB), decision tree (DT), and linear regression (LR) as base models. The findings of the study reveal that maize production in the regions of Ghana is inconsistent, with some regions having high variability. All the climate variables considered have positive impact on maize yield, with a lesser variability of temperature in the Guinea savanna zones and a higher temperature variability in the Volta Region. Carbon dioxide (CO2) also plays a significant role in predicting maize yield across all regions of Ghana. Among the machine learning models utilized, the stacking ensemble model consistently performed better in many regions such as in the Western, Upper East, Upper West, and Greater Accra regions. These findings are important in understanding the impact of climate variability on the yield of maize in Ghana, highlighting regional disparities in maize yield in the country, and highlighting the need for advanced techniques for forecasting, which are important for further investigation and interventions for agricultural planning and decision-making on food security in Ghana.

Analysis of Photorespiratory Intermediates Under Transient Conditions by Mass Spectrometry.

Photorespiration is an essential process of phototropic organisms caused by the limited ability of rubisco to distinguish between CO2 and O2. To understand the metabolic flux through the photorespiratory pathway, we combined a mass spectrometry-based approach with a shift experiment from elevated CO2 (3000 ppm) to ambient CO2 (390 ppm). Here, we describe a protocol for quantifying photorespiratory intermediates, starting from plant cultivation through extraction and evaluation.

Combining Gas Exchange and Rapid Quenching of Leaf Tissue for Mass Spectrometry Analysis Directly in Gas Exchange Cuvette.

Isotopically nonstationary metabolic flux analysis (INST-MFA) is a powerful technique for studying plant central metabolism, which involves introducing a 13CO2 tracer to plant leaves and sampling the labeled metabolic intermediates during the transient period before reaching an isotopic steady state. The metabolic intermediates involved in the C3 cycle have exceptionally fast turnover rates, with some intermediates turning over many times a second. As a result, it is necessary to rapidly introduce the label and then rapidly quench the plant tissue to determine concentrations in the light or capture the labeling kinetics of these intermediates at early labeling time points. Here, we describe a rapid quenching (0.1-0.5 s) system for 13CO2 labeling experiments in plant leaves to minimize metabolic changes during labeling and quenching experiments. This system is integrated into a commercially available gas exchange analyzer to measure initial rates of gas exchange, precisely control ambient conditions, and monitor the conversion from 12CO2 to 13CO2.

Combining Gas Exchange and Rapid Quenching of Leaf Tissue for Mass Spectrometry and NMR Analysis Using an External Chamber.

We describe here a method to study and manipulate photorespiration in intact illuminated leaves. When the CO2/O2 mole fraction ratio changes, instant sampling is critical, to quench leaf metabolism and thus trace rapid metabolic modification due to gaseous conditions. To do so, we combine 13CO2 labeling and gas exchange, using a large custom leaf chamber to facilitate fast sampling by direct liquid nitrogen spraying. Moreover, the use of a high chamber surface area (about 130 cm2) allows one to sample a large amount of leaf material to carry out 13C-nuclear magnetic resonance (NMR) analysis and complementary analyses, such as isotopic analyses by high-resolution mass spectrometry (by both GC and LC-MS). 13C-NMR gives access to absolute 13C amounts at the specific carbon atom position in the labeled molecules and thereby provides an estimate of 13C-flux of photorespiratory intermediates. Since NMR analysis is not very sensitive and can miss minor metabolites, GC or LC-MS analyses are useful to monitor metabolites at low concentrations. Furthermore, 13C-NMR and high-resolution LC-MS allow to estimate isotopologue distribution in response to 13CO2 labeling while modifying photorespiration activity.

Using CO2 and oxidants for in situ regeneration of permeable reactive barriers for leachate-contaminated groundwater.

Groundwater contamination near landfills is commonly caused by leachate leakage, and permeable reactive barriers (PRBs) are widely used for groundwater remediation. However, the deactivation and blockage of the reactive medium in PRBs limit their long-term effectiveness. In the current study, a new methodology was proposed for the in situ regeneration of PRB to remediate leachate-contaminated groundwater. CO2 coupled with oxidants was applied for the dispersion and regeneration of the fillers; by injecting CO2 to disperse the fillers, the permeability of the PRB was increased and the oxidants could flow evenly into the PRB. The results indicate that the optimum filler proportion was zero-valent iron (ZVI)/zeolites/activated carbon (AC) = 3:8:10 and the optimum oxidant proportion was COD/Na2S2O8/H2O2/Fe2+ = 1:5:6:5; the oxidation system of Fe2+/H2O2/S2O82- has a high oxidation efficiency and persistence. The average regeneration rate of zeolites was 72.71%, and the average regeneration rate of AC was 68.40%; the permeability of PRB also increased. This technology is effective for the remediation of landfills in China that have large contaminated areas, an uneven pollutant concentration distribution, and a long pollution duration. The purification mode of long-term adsorption and short-time in situ oxidation can be applied to the remediation of long-term high-concentration organically polluted groundwater, where pollution sources are difficult to cut off.

IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon.

This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is an air quality assessor and therefore a means of achieving the Sustainable Development Goals (SDGs), in particular, SDG 3.9 (substantial reduction of the health impacts of hazardous substances) and SDG 11.6 (reduction of negative impacts on cities and populations). AQI quantifies and informs the public about air pollutants and their adverse effects on public health. The proposed air quality monitoring device is low-cost and operates in real-time. It consists of a hardware unit that detects various pollutants to assess air quality as well as other airborne particles such as carbon dioxide (CO2), methane (CH4), volatile organic compounds (VOCs), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter with an aerodynamic diameter of 2.5 microns or less (PM2.5). To predict air quality, the device was deployed from November 1, 2022, to February 4, 2023, in certain bauxite-rich areas of Adamawa and certain volcanic sites in western Cameroon. Therefore, machine learning algorithm models, namely, multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), XGBoost (XGB), and K-nearest neighbors (KNN) were applied to analyze the collected concentrations and predict the future state of air quality. The performance of these models was evaluated using mean absolute error (MAE), coefficient of determination (R-square), and root mean square error (RMSE). The obtained data in this study show that these pollutants are present in selected localities albeit to different extents. Moreover, the AQI values obtained range from 10 to 530, with a mean of 132.380 ± 63.705, corresponding to moderate air quality state but may induce an adverse effect on sensitive members of the population. This study revealed that XGB regression performed better in air quality forecasting with the highest R-squared (test score of 0.9991 and train score of 0.9999) and lowest RMSE (test score of 1.5748 and train score of 0. 0073) and MAE (test score of 0.0872 and train score of 0.0020), while the KNN model had the worst prediction (lowest R-squared and highest RMSE and MAE). This embryonic work is a prototype for projects in Cameroon as measurements are underway for a national spread over a longer period of time.

Microbial competition for phosphorus limits the CO2 response of a mature forest.

The capacity for terrestrial ecosystems to sequester additional carbon (C) with rising CO2 concentrations depends on soil nutrient availability1,2. Previous evidence suggested that mature forests growing on phosphorus (P)-deprived soils had limited capacity to sequester extra biomass under elevated CO2 (refs. 3-6), but uncertainty about ecosystem P cycling and its CO2 response represents a crucial bottleneck for mechanistic prediction of the land C sink under climate change7. Here, by compiling the first comprehensive P budget for a P-limited mature forest exposed to elevated CO2, we show a high likelihood that P captured by soil microorganisms constrains ecosystem P recycling and availability for plant uptake. Trees used P efficiently, but microbial pre-emption of mineralized soil P seemed to limit the capacity of trees for increased P uptake and assimilation under elevated CO2 and, therefore, their capacity to sequester extra C. Plant strategies to stimulate microbial P cycling and plant P uptake, such as increasing rhizosphere C release to soil, will probably be necessary for P-limited forests to increase C capture into new biomass. Our results identify the key mechanisms by which P availability limits CO2 fertilization of tree growth and will guide the development of Earth system models to predict future long-term C storage.

Capturing carbon dioxide from air with charged-sorbents.

Emissions reduction and greenhouse gas removal from the atmosphere are both necessary to achieve net-zero emissions and limit climate change1. There is thus a need for improved sorbents for the capture of carbon dioxide from the atmosphere, a process known as direct air capture. In particular, low-cost materials that can be regenerated at low temperatures would overcome the limitations of current technologies. In this work, we introduce a new class of designer sorbent materials known as 'charged-sorbents'. These materials are prepared through a battery-like charging process that accumulates ions in the pores of low-cost activated carbons, with the inserted ions then serving as sites for carbon dioxide adsorption. We use our charging process to accumulate reactive hydroxide ions in the pores of a carbon electrode, and find that the resulting sorbent material can rapidly capture carbon dioxide from ambient air by means of (bi)carbonate formation. Unlike traditional bulk carbonates, charged-sorbent regeneration can be achieved at low temperatures (90-100 °C) and the sorbent's conductive nature permits direct Joule heating regeneration2,3 using renewable electricity. Given their highly tailorable pore environments and low cost, we anticipate that charged-sorbents will find numerous potential applications in chemical separations, catalysis and beyond.

Techno-economic evaluation of GHG emissions mitigation of biomethane upgrading technologies.

The current trend in the European biogas industry is to shift away from electricity production towards the production of biomethane for the need to replace natural gas. The upgrading of biogas to biomethane is normally performed by separating the biogas in a stream containing natural gas grid quality methane and a stream containing mostly CO2. The CO2 stream is normally released into the atmosphere; however, part of the methane may still remain in it, and, if not oxidized, even a small fraction of methane released may jeopardise all the GHG emissions savings from producing the biomethane, being methane a powerful climate forcer. Scope of this work is to assess the opportunity cost of installing an Off Gas Combustion (OGC) device in biomethane upgrading plants. The currently available technologies for biogas upgrading to biomethane and the most common technology of OGC (the Regenerative Thermal Oxidisers, RTO) are described according to their performances and cost. Then the cost per tonne of CO2eq avoided associated to the adoption of RTO systems in relation to the upgrading performance is calculated to identify a potential threshold for an effective and efficient application of the RTO systems. It is found that, in case of upgrading technologies which can capture almost all biomethane in the upgrading off-gas (i.e. 99.9%), currently the adoption of an RTO to oxidise the methane left in the off-gas would add costs and need additional fuel to be operated, but would generate limited GHG emission savings, therefore the cost per tonne of CO2eq emissions avoided would result not competitive with other GHG emissions mitigation investments. While the installation of RTOs on upgrading systems with a methane slip of 0.3%, or higher, normally results cost competitive in reducing GHG emissions. The installation of an RTO on systems with a methane slip of 0.2% results in a cost per tonne of CO2eq emissions avoided of 50-100 euro, which is comparable to the current cost of CO2 emissions allowances in the EU ETS carbon market, representing therefore a reasonable choice for a threshold on methane slip regulation for biogas upgrading systems.

Cross-efficiency analysis of energy sector using stochastic DEA: Considering pollutant emissions.

Undesirable outputs can be challenging to avoid in the production of goods and services, often overlooked. Pollution is generally regarded as a negative externality and is taken into account during the production process. The novelty of this study lies in introducing CO2 as an economic "bad" in the energy sector's efficiency measure through a stochastic data envelopment analysis (DEA) cross-efficiency model. Unlike pollution and economic goods, where increased production leads to more pollution, CO2 is weakly disposable, meaning that higher CO2 values lead to a decrease in the number of good outputs produced. The study proposes a new stochastic model based on an extension of the cross-efficiency model and applies it to measure the energy efficiency of 32 thermal power plants in Angola in the presence of undesirable outputs. This will help promote better environmental management. The study's findings offer vital policy insights for the energy sector. The introduction of new stochastic models enables more accurate efficiency measurement under uncertain conditions, aiding policymakers in resource allocation decisions. Additionally, the adoption of stochastic cross-efficiency methods enhances performance assessments, facilitating targeted interventions for underperforming units. These findings contribute to evidence-based policymaking, promoting sustainability and competitiveness within the energy sector.

Achieving zero emission targets: The influence of green bonds on clean energy investment and environmental quality.

The effectiveness of green finance in driving clean energy and environmental sustainability in the current era is receiving attention. Therefore, this study proposes an empirical framework highlighting the effects of green bonds (GB) on clean energy investment (CEI), clean energy investment efficiency (CEE) and environmental sustainability of 29 green bond issuing countries between 2014 and 2022. Using system and difference GMM approaches, this study finds that (i) green bond issuance drives clean energy investment. (ii) Green bonds sufficiently enhance the selected countries' environmental quality. These results supplement the promotion of green bonds in increasing the transfer of funds towards renewable energy projects by reducing reliance on fossil fuels. (iii) Using Driscoll & Kraay, Fully Modified-OLS, and changing the dependent variable, this study further supported the idea that green bonds effectively promote the CEE and environmental sustainability of the chosen countries. (iv) Similarly, this study conducted income heterogeneity, showing that green bonds improve high- and middle-income countries' CEI and environmental quality. (v) Finally, the results indicate that resource consumption escalates CO2 emissions by declining the CEI. Technological innovations increase CEI, whereas they do not mitigate CO2 emissions directly, hinting at the requirement for a comprehensive approach. Therefore, inclusive policies on green bond frameworks, robust incentives, and rigorous environmental criteria should be implemented to attract investment in clean energy development and ensure the environmental sustainability of the selected countries.

Standardisation facilitates reliable interpretation of ETCO2 during manual cardiopulmonary resuscitation.

BACKGROUND: Interpretation of end-tidal CO2 (ETCO2) during manual cardiopulmonary resuscitation (CPR) is affected by variations in ventilation and chest compressions. This study investigates the impact of standardising ETCO2 to constant ventilation rate (VR) and compression depth (CD) on absolute values and trends. METHODS: Retrospective study of out-of-hospital cardiac arrest cases with manual CPR, including defibrillator and clinical data. ETCO2, VR and CD values were averaged by minute. ETCO2 was standardised to 10 vpm and 50 mm. We compared standardised (ETs) and measured (ETm) values and trends during resuscitation. RESULTS: Of 1,036 cases, 287 met the inclusion criteria. VR was mostly lower than recommended, 8.8 vpm, and highly variable within and among patients. CD was mostly within guidelines, 49.8 mm, and less varied. ETs was lower than ETm by 7.3 mmHg. ETs emphasized differences by sex (22.4 females vs. 25.6 mmHg males), initial rhythm (29.1 shockable vs. 22.7 mmHg not), intubation type (25.6 supraglottic vs. 22.4 mmHg endotracheal) and return of spontaneous circulation (ROSC) achieved (34.5 mmHg) vs. not (20.1 mmHg). Trends were different between non-ROSC and ROSC patients before ROSC (-0.3 vs. + 0.2 mmHg/min), and between sustained and rearrest after ROSC (-0.7 vs. -2.1 mmHg/min). Peak ETs was higher for sustained than for rearrest (53.0 vs. 42.5 mmHg). CONCLUSION: Standardising ETCO2 eliminates effects of VR and CD variations during manual CPR and facilitates comparison of values and trends among and within patients. Its clinical application for guidance of resuscitation warrants further investigation.

Can China's carbon generalized system of preferences reduce urban residents' carbon emissions? Evidence from a quasi-natural experiment.

The carbon generalized system of preferences (CGSP) is an innovative incentive mechanism implemented by the Chinese government, which has also become an important part of carbon emission reduction at the living end, and it is of great significance to study whether the Pilot Policy can reduce the carbon emissions of residents. This study firstly accounts for the total carbon emissions and per capita carbon emissions of the residents of 284 cities in China, and on this basis, adopts the SCM method to quantitatively study and analyze the overall and local implementation effects of CGSP in China by taking the first batch of CGSP pilots in China as an example, and further applies the mediation effect model to test the pathways of the role of CGSP. The main findings of the study are as follows: (1) During the period of 2010-2020, the total carbon emissions from urban residents' living in China showed a yearly growth trend, from 36,623.98 ×10-2Mt in 2010-85,241.20 ×10-2Mt in 2020, an increase of 8.83%. Total carbon emissions present a structural difference of "electricity consumption > central heating > private transport > gas (oil, natural gas) consumption". (2) Overall, the implementation of the CGSP had a robust positive impact on the overall carbon emission reduction in the pilot cities, with an average annual emission reduction effect value of 36.53 ×10-2Mt. Locally, the annual net policy effect values of Dongguan, Zhongshan, Heyuan, and Guangzhou are 6169.79 ×10-2, 26,600.17 ×10-2, 17,081.34 ×10-2 and 9393.36 ×10-2Mt respectively. (3) CGSP has a good carbon emission reduction effect by suppressing the impact on residents' carbon emissions through enhancing the city's innovation capacity and promoting electricity saving and consumption reduction, while the mediating effect played by the promotion of green and low-carbon travel in the pilot policy is not significant. Finally, based on the research findings, relevant suggestions are targeted.