Interfacing atmospheric variables to web interface using arduino

Forests can play a key role in the mitigation of climate change, although there have been limited regional scale assessments that account for variations in soil type and tree species. Most of the focus has been on their ability to sequester atmospheric CO2, while there is less information on the two other major greenhouse gases (GHGs), N2O and CH4. We examined the GHG budgets of ten forest soils in Poland, considering all three major GHGs, where no previous long-term measurements had been made, which encompassed different tree species, stand age, and contrasting edaphic conditions. In addition to the quantification and assessment of seasonal variability in the major soil GHG fluxes over two years, the aims of the present study were (i) the identification of the main drivers of the soil-based GHG fluxes, (ii) the determination of the contribution of each gas to the Global Warming Potential (GWP), and (iii) to assess the mitigation potential of these fluxes over different forest systems. All the forest soils were sources of CO2 and N2O and sinks for atmospheric CH4 with pronounced seasonal variations in CO2 and CH4 driven by soil moisture and temperature. The soils showed significant differences in annual GHG fluxes, with average values of 16.7 Mg CO2 ha−1, − 3.51 kg CH4 ha−1, and 0.95 kg N2O ha−1. The annual total GWP ranged from 13.1 to 22.0 Mg CO2 eq ha−1 with CO2 making the highest contribution, and forest-specific CH4 uptake resulting in a reduction in GWP, ranging from − 0.08% (in the youngest forest) to -0.97% (in the oldest forest). Mixed forests showed the greatest potential for climate change mitigation, with the highest soil C sequestration, and the lowest GWP values when compared to sites with monocultures. The results suggest that a mixture of tree species could eventually be incorporated into management plans to increase the effectiveness of forests in climate change mitigation.

This paper presents an innovative Internet of Things (IoT)-based smart emergency response system that aims to improve safety and efficiency across vehicles, homes and healthcare settings. The proposed system combines advanced sensors, communication technologies, and intelligent algorithms to provide a comprehensive solution for real-time data monitoring and emergency management. Here, the vehicle device is equipped with temperature, gas, flame, GPS, piezo, and accelerometer sensor and the system can detect accidents, hazardous conditions, precise location automatically notifying emergency services. The home devices incorporate Flame Sensor, humidity sensor, DHT11, MQ-2 gas sensor, MQ-4 Gas Sensor, MQ-135 gas sensor, and intrusion detection to ensure immediate alerts and response to potential security threats. Lastly the health device is equipped with temperature, humidity, stress levels, heart rate, oxygen saturation, and fetal heart rate sensor which can monitor vital signs and detect anomalies, triggering alerts for medical emergencies and facilitating timely intervention. The novelty of the proposed study is it incorporates the three devices, processes the data and automatically notifies the emergency responders for further emergency assistance. Moreover, the system utilizes a cloud-based platform to collect, analyze, and disseminate data, enabling seamless coordination between emergency responders such as ambulance for health and accident emergency, fire brigade for fire emergency and affected individuals. We have also developed a MySQL-PHP-Laravel based live dashboard and emergency response system where both the user and relevant stakeholder will have the access which could mitigate the emergency situation in the quickest amount of time without human intervention. The findings of our study reveals that incorporation of IoT and smart infrastructure offers significant improvement of server response time 3 ms, accuracy > 99% and overall effectiveness of emergency management. This integrated approach not only enhances individual safety but also contributes to broader public safety goals, showcasing the potential of IoT technologies in creating smarter, safer environments.

Effects of nitrogen addition on the combined global warming potential of three major soil greenhouse gases: A global meta-analysis

<sec><p indent="0mm">The Paris Agreement has set the goal of limiting the global warming to 2°C above pre-industrial levels and established a process for global carbon stocktaking which will take place every five years starting from 2023. The nationally determined contributions (NDCs) are at the heart of the Paris Agreement, which was adopted at the UN Climate Change Conference (COP21) in 2015. Under the Paris Agreement, each country is required to present its NDCs report using the standard ‘bottom-up’ inventory approach recommended by the Intergovernmental Panel on Climate Change (IPCC). The 49th session of IPCC has suggested a ‘top-down’ inversion approach to assess NDCs using measurements of atmospheric Green House Gases (GHGs). Satellite remote sensing can measure the atmospheric GHG concentration at global scale with fine resolution, high accuracy and high frequency, providing a promising tool to validate each country’s NDCs. The European Union, the United States, Japan, and Canada are vigorously developing their satellite systems for monitoring GHGs’ concentrations and emissions. China is also planning to build a GHGs monitoring satellite system to support the second carbon stocktaking in 2028. Therefore, it is extremely urgent to review and investigate the techniques, methods and cutting-edge applications of remote sensing for GHGs monitoring and stocktaking. </sec><sec> In this paper, we first reviewed the current techniques, networks, and upcoming national or international plans for monitoring atmospheric GHGs based on ground-based, airborne, and satellite platforms. The global carbon stocktaking requires continuous and accurate observations of GHGs’ concentrations and fluxes from ground, tower-based, airborne, and spaceborne platforms. The World Meteorological Congress (WMO) approved an ambitious international project, named the Global Greenhouse Gas Watch (G3W) initiative to address key information gaps in our understanding of anthropic and natural activities on the global carbon cycle, and help parties assess the completion of GST reports. Second, we proposed the key features of the next generation carbon monitoring satellite system, which was characterized by high spatial resolution, high accuracy, high temporal frequency, and multiple gases as well as atmospheric aerosol and co-emitted polluted gases (NO₂, CO). However, it is still challenging to meet the strict accuracy requirements for the atmospheric GHGs’ concentrations, such as <sc>1 ppm</sc> for CO₂, 5 ppb for CH₄, and 1 ppb for N₂O. China’s next-generation carbon satellite is to be launched in 2026, which will greatly enhance observation capabilities, including GHG species, accuracy, time frequency and spatial resolution. Third, taking the Global Carbon Project (GCP) series of assessment reports as the core references, we summarized the current methods, progress and challenges in the scientific assessment of GHGs’ emissions at global or regional scales. We also investigated the roles and current capabilities of satellite remote sensing and assimilation inversion technology in monitoring emissions of the three major greenhouse gases: CO₂, CH₄, and N₂O. At present, there are large uncertainties among the estimated GHGs’ fluxes (both emissions and sinks) due to very limited observations, model accuracy, and coarse resolution of assimilation systems etc. Finally, we investigated China’s demand for GHGs monitoring and emission assessment, and proposed three urgent tasks for global carbon stocktaking using the ‘top-down’ remote sensing method. These tasks include developing a satellite constellation for high-precision GHGs observation, a high-resolution, multi-species global GHG assimilation system, and an operational platform to assess global GHG emissions from terrestrial ecosystems and anthropogenic activities. Therefore, this paper, focusing on the needs of compliance with climate change and the “dual-carbon” targets, provides ideas for the construction of China’s next generation carbon-sensing satellite system. </sec>

For decades, Metal oxide (MOX) gas sensors have been commercially available and used in various applications such as the Smart City, gas monitoring, and safety due to advantages such as high sensitivity, a high detection range, fast reaction time, and cost-effectiveness. However, several factors affect the sensing ability of MOX gas sensors. This article presents the results of a study on the cross-sensitivity of MOX gas sensors toward ambient temperature and humidity. A gas sensor array consisting of temperature and humidity sensors and four different MOX gas sensors (MiCS-5524, GM-402B, GM-502B, and MiCS-6814) was developed. The sensors were subjected to various relative gas concentrations, temperatures (from 16 °C to 30 °C), and humidity levels (from 75% to 45%), representing a typical indoor environment. The results proved that the gas sensor responses were significantly affected by the temperature and humidity. The increased temperature and humidity levels led to a decreased response for all sensors, except for MiCS-6814, which showed the opposite response. Hence, this work proposed regression models for each sensor, which can correct the gas sensor response drift caused by the ambient temperature and humidity variations. The models were validated, and the standard deviations of the corrected sensor response were found to be 1.66 kΩ, 13.17 kΩ, 29.67 kΩ, and 0.12 kΩ, respectively. These values are much smaller compared to the raw sensor response (i.e., 18.22, 24.33 kΩ, 95.18 kΩ, and 2.99 kΩ), indicating that the model provided a more stable output and minimised the drift. Overall, the results also proved that the models can be used for MOX gas sensors employed in the training process, as well as for other sets of gas sensors.

Indoor air quality (IAQ) is a term that describes the air quality of the building occupants. IAQ can be influenced by physical factors like dust particles, chemical or gaseous pollutants (including carbon dioxide, carbon monoxide and volatile organic compounds) and biological factors like molds and bacteria growth which largely depend on temperature and humidity condition of a room. These kinds of pollutants can induce a bad health effects to building occupants. In this study, a web-based system for monitoring indoor air quality is presented. The system has been developed to monitor the environmental parameters of air quality like gaseous pollutants, particulate matter, temperature and humidity that can affect the occupant's comfort, health and indoor work environment if not properly maintained. Generally, the system consists of a desktop computer acting as base station, an array of metal oxide gas sensor, temperature and humidity sensor, particle dust sensor and wireless sensor network (WSN) node as a wireless transmitter. The measured data from sensors are sent to base station via WSN node. The computer will log the collected data using a self-developed server program which can be accessed through the web.

Air pollution occurs due to presence of harmful gases like dust and smoke. Inhaling these gases leads to health problem. The inhaling of dust leads to breathing problems and lung issues which are of major concern in human life. The green house gases like synthetic chemicals present due to emission of human activities. The major green house gases are carbon dioxide, chlorofluorocarbons, water vapor, ozone, methane, and nitrous oxide. Greenhouse gases absorb infrared radiation. Air pollution is monitored by Governments and various local agencies. The prime responsibility of the proposed system is to detect the concentrations of major air pollutant gases that are present in the air which cause harm to humans. Air pollution detection is developed using IoT for detecting pollutant gases using MQ2 and MQ135 sensors. The gases like carbon dioxide (CO2), carbon monoxide (CO), ethyl alcohol, nitric oxide, nitrogen dioxide (NO2), and sulfur dioxide (SO2) will be detected using these sensors. The detected parameters are analyzed using machine learning (ML) algorithms to estimate air quality. The ecosystem developed helps in learning correlation among gases. This helps in estimating impact and level of air pollutants to measure air quality.KeywordsAir pollutionInternet of thingsMQ2MQ135 and correlation

Non-carbon dioxide greenhouse gases are considered in "Canada's National Report on Climate Change: Actions to Meet Commitments Under the United Nations Framework Convention on Climate Change". By including all major greenhouse gases and their anthropogenic sources and sinks using best available science, the Report provides a practical illustration of the "comprehensive approach" policy to implementing the Convention's requirements. In addition to carbon dioxide emissions from fossil fuel combustion, the Report includes information on other sources and sinks for carbon dioxide, and for methane and nitrous oxide. Other gases considered include polyflourocarbons, hydroflourocarbons, and the primary tropospheric ozone precursors, nitrogen oxides and volatile organic compounds. Current Global Warming Potential indices are used to compare and integrate the best estimates of climate change impacts of the major greenhouse gases. The presentation of emission data is intended to be transparent and comparable. The relative quality of the data for various gases and sources is indicated. The existence of environmental, economic, and other benefits to limiting emissions of all greenhouse gases, in addition to carbon dioxide, should be recognized. Continuing assessments and actions on non-carbon dioxide greenhouse gas emissions, both nationally and internationally, are suggested.

Carbon dioxide (CO2) a major Green House Gas (GHG) in the atmosphere, is believed to be largely responsible for global climate change through industrial emissions. The level of CO₂ concentration has exponentially increased from about 280 ppm at the start of the industrial revolution to about 380 ppm to date. Although Kyoto protocol has bound industrialized nations to reduce green house gas emissions by 5.2% below 1990 levels around year 2008-2012, but violation continues. The cement industry is one of the major emitter of green house gases, particularly CO₂ due to its energy intensive production process. It is estimated that approximately 1 tone of CO₂ is released during the manufacturing of each tone of Portland cement. Most of CO₂ emissions originate from burning fossil fuels and de-carbonization of limestone in a cement plant. During past several decades, the use of by-product materials in concrete, either as components of blended cements or as admixtures, has increased significantly. In this study, another alternate Supplementary Cementing Material (SCM), <em>Laterite</em> has been used with the objectives: to evaluate the performance of cement containing different percentages of <em>laterite</em> (5, 10, 15, 20, 25, and 30 %); to identify the optimum replacement percentage; and to investigate the effects of different concentrations of <em>laterite</em> on various properties of cement. For that purpose, <em>laterite</em> was tested: before blending (for elemental and mineralogical composition by using XRF, SEM and XRD): after blending (Elemental analysis using XRF, fineness test by using Blaine’s air permeability test and for particle size % on 45, 90 and 200 μ sieve, respectively); and after hydration (for mineralogical analysis using SEM). Furthermore, physical tests of manufactured cement, i.e., water consistency, setting time, <em>Le-Chatlier</em>-expansion and compressive strength were also evaluated and compared with limestone and fly-ash cement blends. The results show that with the replacement level of up to 15%, the quality of cement is not disturbed. In addition, the requirement of intake of energy has also decreased (~30%). Current findings indicate that by using <em>Laterite</em>, as SCM, cement production can be increased without consuming more energy and hence reduce GHG emissions.

Methane is a major greenhouse gas, second only to carbon dioxide in its contribution to future global warming. Methane concentrations have more than doubled over the last two centuries and continue to rise annually. These increases are largely correlated with increasing human populations. Methane emissions from human related activities currently account for about 70 percent of annual emissions. Because methane has a shorter lifetime than other major greenhouse gases, efforts to reduce methane emissions may fairly quickly be translated into lower atmospheric concentrations of methane and lower levels of radiative forcing. This fairly quick response would have the benefit of slowing the rate of climate change and hence allow natural ecosystems more time to adapt.

Oil provides 33% of the world’s energy (Wilcox, 2014) and transportation engines account for over 60% of the 70 million barrels of crude used each day. Engines power the world’s roughly one billion passenger vehicles, as well as trucks and heavy-duty vehicles. In theUSA, engines consume 14million barrels of oil per day or 2.5 gal/person. Since there are insufficient reserves, 62% is imported, and at current prices (e.g., $80 a barrel), the USA spends about 1 billion dollars a day on imported oil. is also impacts national security. It is unreasonable to think that this vast consumption of fuel is sustainable. But prospects for replacing the IC engine with more fuel efficient and cleaner power plants are not hopeful. Indeed, a recent report (NRC, 2011) concluded that “. . .the internal combustion engine (ICE) will be the dominant prime mover for light-duty vehicles for many years, probably decades. us, it is clearly important to perform R&D to provide a better understanding of the fundamental processes affecting engine efficiency and the production of undesirable emissions.” Also, there is no obvious alternative to the IC engine for mediumand heavy-duty commercial vehicles, which account for a quarter of all fuel used (mostly diesel). e fuel used by IC engines also has a major impact on our global environment. Burning one 1 kg of fuel consumes about 15 kg of air, and signi cant energy is required to pump it into and out of the engine. In addition, about 3 kg of CO2 is generated, which contributes to the world’s annual production of 37 billion tons of CO2, a major green house gas (GHG). Some fear that GHGs can cause climate change with unpredictable consequences. To address this problem, the International Energy Agency’s roadmap is to reduce fuel use by 30–50% in new road vehicles worldwide by 2030, and in all vehicles by 2050 (IEA, 2012). Although 2050 appears distant, the time required to bring new engines to production, together with the years needed for new technology to permeate the vehicle eet, means that major effort (and investment) will be required. us, the grand challenge faced by engine and automotive engineering researchers over the next decades will be to devise technological advances thatmaximize engine efficiency, minimize pollutant emissions, and optimize tolerance to a wider variety of fuels in power generation and transportation systems.

This study presents the environmental sensing hub with vertical integration of humidity, pressure, and temperature sensors on a single chip (Fig. 1). The presented device is fabricated through TSMC 0.18 <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">µ</inf> m 1P6M CMOS platform, and in-house post-CMOS processes. The proposed environmental sensing hub has following features: (1) monolithic integration of humidity (H), pressure (P), and temperature (T) sensors to form the sensing hub, (2) vertical integration of H/P/T sensors to reduce the footprint of chip (as compare with the existing side by side designs). Measurement results of presented environmental sensing hub are: humidity sensor with sensitivity of 2.025fF/%RH, pressure sensor with sensitivity of 0.38fF/kPa, and diode temperature detector with sensitivity of 1.6mV/ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> C.

Greenhouse-gas budgets for irrigated dairy pasture and a winter-forage kale crop

Facilitating emission reduction using the dynamic line switching and rating

As a major greenhouse gas, carbon dioxide emissions (CO2E) are the main cause of global warming and environmental pollution. Thus, the reduction in carbon emissions has become a global concern. The widespread use of renewable energy and the improvement of energy efficiency seem to provide a feasible path for countries to achieve carbon emissions reduction, and have received the attention of governments and academics. However, very little literature has focused on the spatial and temporal heterogeneity of the effects of renewable energy use and energy efficiency on carbon emission intensity. This study applies the geographically and temporally weighted regression (GTWR) model and complies with a balanced panel of data from member countries of the Regional Comprehensive Economic Partnership (RCEP) between 2000 and 2019 to investigate the spatiotemporal heterogeneity of the effects of renewable energy and energy efficiency on CO2E. The results show that CO2E per capita (CO2EP) increases over time, but its distribution across RCEP member countries remains largely stable. Overall, the usage of renewable energy tends to decrease CO2EP, while energy efficiency tends to increase it. However, with the passage of time and changes in geographical location, renewable energy consumption and energy efficiency show significant temporal and spatial heterogeneity in their effects on CO2EP of RCEP member countries. Additionally, the effects of industrial structure, merchandise trade, urbanization rate, and energy poverty on CO2EP of RCEP member countries are also characterized by spatial and temporal heterogeneity.