Daily CO2 Research Articles

Assessing the Impact of CO2 Emissions from Transport and Power on Health and Environment in Duhok, Kurdistan Region, Iraq

This study investigates the carbon dioxide (CO2) emissions generated by transportation and power generation in Duhok Governorate, Kurdistan Region of Iraq. The research highlights the alarming levels of CO2 emissions resulting from the region's 270,315 vehicles and 1,814 private electric power generators, which consume approximately 1,500,000 liters of gasoline and 650,000 liters of diesel daily. These activities contribute to a total daily CO2 emission of 1,741,188 kg, amounting to over 626,827,680 kg annually. The findings reveal the significant environmental and public health threats posed by these emissions, emphasizing the urgent need for targeted mitigation strategies. Comparisons with other urban regions such as Cairo, Tehran, New Delhi, Lagos, and Nairobi reveal a similar pattern of high vehicular and generator-induced CO2 emissions, underscoring the global nature of this environmental challenge. The study underscores the importance of addressing CO2 emissions through improved regulations, modernized infrastructure, and sustainable energy practices to protect both the environment and public health.

An Enhanced Particle Swarm Optimization Long Short-Term Memory Network Hybrid Model for Predicting Residential Daily CO2 Emissions

This study aims to establish an accurate hybrid model for predicting residential daily carbon dioxide (CO2) emissions, offering essential theoretical insights and data support for decision-makers in the construction industry. A hybrid model named CRLPSO-LSTM was proposed, which integrates an enhanced particle swarm optimization (CRLPSO) algorithm with a long short-term memory (LSTM) network. The CRLPSO algorithm enhances population quality, diversity, and global search efficiency by introducing improved circle chaotic mapping, optimizing worst mutations, and incorporating the Lévy flight strategy. The performance of the CRLPSO algorithm was rigorously evaluated using 23 internationally recognized standard test functions. Subsequently, the CRLPSO algorithm was employed to optimize the parameters of the LSTM model. Experimental validation was performed on three datasets from China, the United States, and Russia, each exhibiting distinct emissions characteristics: China with high emissions and high volatility, the United States with medium emissions and medium volatility, and Russia with low emissions and low volatility. The results indicate that the CRLPSO-LSTM hybrid model outperformed other hybrid models in predicting residential daily CO2 emissions, as demonstrated by superior R2, MAE, and MSE metrics. This study underscores the effectiveness and broad applicability of the CRLPSO-LSTM hybrid model, offering a robust theoretical foundation and data support for advancing the sustainable development goals.

Bitcoin, Fintech, Energy Consumption, and Environmental Pollution Nexus: Chaotic Dynamics with Threshold Effects in Tail Dependence, Contagion, and Causality

The study investigates the nonlinear contagion, tail dependence, and Granger causality relations with TAR-TR-GARCH–copula causality methods for daily Bitcoin, Fintech, energy consumption, and CO2 emissions in addition to examining these series for entropy, long-range dependence, fractionality, complexity, chaos, and nonlinearity with a dataset spanning from 25 June 2012 to 22 June 2024. Empirical results from Shannon, Rényi, and Tsallis entropy measures; Kolmogorov–Sinai complexity; Hurst–Mandelbrot and Lo’s R/S tests; and Phillips’ and Geweke and Porter-Hudak’s fractionality tests confirm the presence of entropy, complexity, fractionality, and long-range dependence. Further, the largest Lyapunov exponents and Hurst exponents confirm chaos across all series. The BDS test confirms nonlinearity, and ARCH-type heteroskedasticity test results support the basis for the use of novel TAR-TR-GARCH–copula causality. The model estimation results indicate moderate to strong levels of positive and asymmetric tail dependence and contagion under distinct regimes. The novel method captures nonlinear causality dynamics from Bitcoin and Fintech to energy consumption and CO2 emissions as well as causality from energy consumption to CO2 emissions and bidirectional feedback between Bitcoin and Fintech. These findings underscore the need to take the chaotic and complex dynamics seriously in policy and decision formulation and the necessity of eco-friendly technologies for Bitcoin and Fintech.

PSIX-22 Maternal methane, carbon dioxide, and oxygen flux compared with progeny gas flux

Abstract Red Angus heifers [n = 19; body weight (BW) = 370 ± 8.1 kg) and their paired offspring (BW = 283 ± 4.2 kg; 18 heifers, 1 steer) were used to compare the respiration gas parameters between dams and their progeny over 2 calving cycles (8 pairs in the 1st cycle, 11 in the 2nd). The methane (CH4), carbon dioxide (CO2) emissions and oxygen (O2) consumption of the dams were measured at 14 mo of age over a 70-d period using an automated head-chamber system (AHCS) that measured the flux of these 3 gases. Dams grazed native mixed-grass prairie and were offered a daily supplement of alfalfa (Medicago sativa L.) pellets (1.0 kg) via the AHCS. Dams were artificially inseminated using sexed semen (female) from a single Black Angus sire. The gas fluxes of the progeny were measured, beginning at 11 mo of age, over a 70-d period. Like the dams, during their measurement period, the progeny grazed native mixed-grass prairie and were offered a daily supplement (1.0 kg) of alfalfa pellets via the AHCS. Pearson correlations were developed between the 70-d means of daily CH4 and CO2 emissions, O2 consumption, CH4:CO2 ratio, respiratory quotient (RQ), and heat of production estimates for the dams and their progeny by regressing maternal parameters on the parameters of the progeny, using beginning dam and progeny BW as covariates, and year as an indicator variable. A dam’s CH4 emission was very strongly correlated with that of their offspring (ρ = 0.86). The CO2 emission and O2 consumption of the dams were both also very strongly (ρ = 0.92 and ρ = 0.97, respectively) correlated, with that of their progeny. The heat production and CH4:CO2 ratio of the dams were very strongly (ρ = 0.96) and fairly (ρ = 0.50) correlated, respectively, with that of their progeny. Lastly, the RQ of the dam was also very strongly correlated (ρ = 0.87) with the RQ of the progeny. These results indicate that CH4 and CO2 emissions, O2 consumption, CH4:CO2 ratio, and heat of production of the dam are fairly to very strongly correlated to the gas flux parameters of their progeny. Hence, even with this small dataset, there is evidence that beef cattle traits related to greenhouse gas emissions are somewhat heritable and could potentially be selected in breeding animals to decrease greenhouse gas emission intensity.

358 The effect of increasing dietary limestone concentration on growth performance, gas flux, and carcass characteristics in finishing beef cattle

Abstract Reducing methane (CH4) emissions from beef cattle is desirable as CH4 is a greenhouse gas that represents an energetic loss from the production system. Limestone is commonly fed to cattle as a source of calcium but may also act as a rumen buffer and decrease CH4 emissions by limiting hydrogen availability for methanogenesis. The objective of this experiment was to determine if diet limestone concentration influences growth performance, gas flux, and carcass characteristics in finishing beef cattle. Yearling steers [n = 16; initial body weight (BW) 619 ± 34 kg] were stratified by BW and randomly assigned to 1 of 2 isocaloric and isonitrogenous dietary treatments in a completely randomized design where limestone was included at 1.3% (1X) or 2.6% (2X) of diet dry matter (DM). Finishing diets were formulated to contain 0.43 or 0.96% DM as Ca and were fed ad libitum using automated cattle feeders (SmartFeed Pro; C-Lock Inc.; Rapid City, SD) in a single pen for the final 37 d of finishing. Body weights were measured on d -1, 0, 36, and 37. An automated head chamber system (GreenFeed; C-Lock Inc.) was used to measure gas flux. Analysis of variance was conducted using JMP Pro 17 to determine the fixed effect of treatment with individual animal as the experimental unit. Statistical significance was defined as P ≤ 0.05 with a tendency defined as 0.05 < P ≤ 0.10. Increasing the dietary limestone concentration from 1.3 to 2.6% of DM did not influence DM intake, but numerically increased final shrunk BW (P = 0.68) and hot carcass weight (P = 0.75) and increased average daily gain (ADG; P = 0.02) and gain to feed ratio (G:F) by approximately 12%. Cattle fed the 2X diet tended to have 6.5% greater ribeye area (P = 0.09), but similar USDA yield grade, backfat thickness, and dressing percentage (P ≥ 0.34) when compared with cattle fed the 1X diet. Daily CO2 and CH4 emissions, daily O2 consumption, CH4 yield, and respiratory quotient were unaffected by treatment (P ≥ 0.47). The improvement in ADG resulted in a tendency for CH4 emission intensity to be decreased by 9.1% (P = 0.09) when the concentration of limestone was 2.6% of dietary DM. While increasing dietary limestone concentration from 1.3 to 2.6% of diet DM did not influence absolute daily CH4 emissions, it increased ADG through an improvement in feed efficiency, which translated to a tendency for a reduction in CH4 emission intensity. These preliminary data suggest that there may be a benefit to feeding limestone or calcium at levels greater than currently recommended by the NASEM (2016). Future research should focus on determining optimum dietary limestone or calcium levels for finishing cattle.

96 Using greenhouse gas flux to estimate forage dry matter intake in ad libitum fed replacement heifers

Abstract An accurate estimate of feed intake is a critical component in determining individual animal feed efficiency. However, existing feed intake measurement systems are not designed for unprocessed, dry forage diets. Open-circuit gas flux technology measures enteric gas emissions for cattle in a pasture-based setting. If a significant relationship exists between feed intake and gas emissions, this system could be used to rank grazing cattle for forage intake and forage utilization efficiency. Our objective was to develop prediction equations to estimate forage dry matter intake using gas flux and performance data. Angus and Angus x Hereford heifers [n = 71; initial body weight (BW) 329.5 ± 31.1 kg] were used over 3 yr to measure intake, gain, and gas flux. All heifers were subject to a 21-d adaptation and 71-d intake test using individual intake technology (SmartFeed; C-Lock Rapid City, SD) with a stocking rate of 3.3 heifers per feeder. Throughout the study, heifers were fed a long-stem, unprocessed, grass hay diet (≥ 10.8% CP, 2.0 Mcal ME, and 56% TDN) with ad libitum access to a salt/mineral supplement. The GreenFeed system (C-Lock Rapid City, SD) was used to monitor oxygen (O2) consumption (means ± SD; 3364.4 ± 374.8 g/d), methane (CH4; 181.4 ± 24.3 g/d) and carbon dioxide (CO2) production (4,965.3 ± 494.5 g/d). Body weights were collected every 14 d, and average daily gain (ADG) was calculated by regressing BW on day of study. The phenotypic correlations of forage DMI with CO2, CH4, O2, and heat production (HP) were 0.72, 0.66, 0.74, and 0.74, respectively. Average daily gain and midpoint metabolic BW combined with the estimate of daily HP, O2, or CO2 accounted for 60% of the variation in forage DMI. These results suggest that forage intake can be predicted using performance data in conjunction with the GreenFeed gas quantification system.

Influence of extreme 2022 heatwave on megacities' anthropogenic CO2 emissions in lower-middle reaches of the Yangtze River

An unprecedented heatwave hit the Yangtze River Basin (YRB) in August 2022. We analyzed changes of anthropogenic CO2 emissions in 8 megacities over lower-middle reaches of the YRB, using a near-real-time gridded daily CO2 emissions dataset. We suggest that the predominant sources of CO2 emissions in these 8 megacities are from the power and industrial sectors. In comparison to the average emissions for August in 2020 and 2021, the heatwave event led to a total increase in power sector emissions of approximately 2.70 Mt CO2, potentially due to the increase in urban cooling demand. Suzhou experienced the largest increase, with a rise of 1.12 Mt CO2 (12.88 %). Importantly, we observed that changes in daily power emissions exhibited strong linear relationships with temperatures during the heatwave, albeit varying sensitivities across different megacities (with an average of 0.0076 ± 0.0075 Mt d−1 °C−1). Conversely, we find that industrial emissions decreased by a total of 8.45 Mt CO2, with Shanghai seeing the largest decrease of 4.71 Mt CO2, while Hangzhou experienced the largest relative decrease (−21.22 %). It is noteworthy that the majority of megacities rebounded in industrial emissions following the conclusion of the heatwave. We convincingly suggest a tight linkage between the reductions in industrial emissions and China's policy to ensure household power supply. Overall, the reduction in industrial emissions offset the increase in power sector emissions, resulting in weaker emissions for majority of megacities during the heatwave. Despite remaining uncertainties in the emissions data, our study may offer valuable insights into the complexities of anthropogenic CO2 emissions in megacities amidst frequent summer heatwaves intensified by greenhouse warming.

Assessing classroom ventilation rates using CO2 data from a nationwide study of UK schools and identifying school-wide correlation factors

Per-person ventilation rates were assessed from data measured in 322 UK schools throughout the Autumn term, 2023. It was found that the overall mean ventilation rate was 5.3 L s−1 p−1; rising to 6.8 L s−1 p−1 during warmer weather and falling to 3.8 L s−1 p−1 during colder weather. Daily mean CO2 levels recorded in the majority of schools consistently fell below the 1500 ppm threshold within government school ventilation guidance but the contribution of relatively few very high CO2 levels in some classrooms shows the need for targeted improvement and highlights the value of large-scale datasets. Exceedance of the 1500 ppm threshold rose to approximately 20% when the daily mean outdoor temperature fell to around 5 °C, indicating a barrier to ventilation. Controlling for outdoor temperature as a covariate, analysis of the CO2 measurements indicated schools in more-deprived regions, state-funded schools (relative to private), and secondary schools (relative to primary) exhibited statistically-significant higher CO2 concentration. Reassuringly, these findings remained unchanged when the analysis was deployed on estimated per-person ventilation rates; except that this enhanced analysis accounted for the greater CO2 production associated with older children and demonstrated that ventilation rates were not significantly different between primary and secondary schools.

Factors driving reduction in CO2 emissions from personal travel: A repeated cross-sectional analysis

This study identifies factors that drive the changes in CO2 emissions from personal travel in urban areas during 2006 and 2016 using repeated cross-sectional household travel surveys conducted in Seoul, Korea. We first estimated the daily travel CO2 emissions of every survey participant for each year based on travel itinerary records and estimated CO2 intensity for the travel mode. The results suggest that total CO2 emissions from personal travel in Seoul declined remarkably between 2006 and 2016, potentially due to reduced vehicle use, transition to non-motorized travel, improved vehicle technology, and eased traffic conditions. Two multilevel mixed-effect Tobit regression models were developed to identify the factors affecting CO2 emissions from personal travel for each year. The results suggest that some cohorts (e.g. males, 30s and 40s, small households, high-income groups, car owners) are likely to produce more CO2 emissions from personal travel consistently in 2006 and 2016. The changes in the estimated coefficients between the two years were also evaluated statistically, suggesting that some population groups (e.g. elderly, large households) are likely to reduce CO2 emissions from personal travel. The reduction in CO2 emissions by these groups may be correlated with continued investment in the public transportation system in Seoul. These findings provide an opportunity to gain a clear understanding of travel behavioral changes related to CO2 emissions from personal travel, with insights toward sustainable development.

Peak patterns and drivers of city-level daily CO2 emissions in China

Cities are the primary carbon emitters in China and play a critical role in achieving national and regional emission-reduction targets. However, because existing CO2 emission datasets are mostly limited to national and provincial levels and have significant time lags, there are few studies on urban emission patterns and drivers below the annual level. In this study, utilizing near-real-time daily CO2 emission data from Carbon Monitor Cities-China, we analyzed the seasonality and peak patterns of city-level emissions and conducted a decomposition of emission drivers by sector based on sociometric and natural factors. Our results indicated that most cities in China exhibited seasonal emissions, with peak emissions occurring during winter. However, heavily industrialized cities tend to produce high year-round emissions. The residential and power sectors are crucial for reducing emissions in winter-peaked cities, whereas the industrial and power sectors are the primary energy consumers in non-winter-peaked cities. The results of the decomposition analysis show that the emission structure of the service industry and the impact of temperature on power generation are the primary drivers of CO2 emissions during winter. The above analysis offers recommendations for developing emission-reduction policies for various types of cities. Service-based cities should concentrate their efforts on reducing emissions from tertiary industries, whereas energy-dependent cities should continue to focus on controlling emissions from secondary industries such as industry and power. In addition, both service-based and energy-dependent cities must address the significant impact of heating demands on emissions.

Analysis of the Relationship between Fuel Prices and Vehicle Numbers in Urban Road Networks

The article presents an analysis of the relationship between the price of fuel and diesel oil and traffic intensity in the city. First, an analysis of fuel prices was prepared over fourteen months and the number of passenger cars and trucks per day was examined at the same time. From the results obtained, the highest, lowest, and average values were obtained. On this basis, it was noticed that when the price of fuel was the highest, there were fewer passenger cars, but when the price dropped, the number of vehicles increased. Another conclusion from the analysis is that when the price of fuel dropped even more than the average, there were no more cars. Based on the analysis, it was noticed that the number of vehicles may vary by up to 8000 passenger vehicles per day within one intersection. Then, a microsimulation model was performed in the PTV Vissim program to check the amount of pollution generated by vehicles in three variants: the highest, lowest, and average traffic intensities. The results show that the average daily CO pollution at the moment of the lowest traffic intensity is 15,000 g lower than the average, so the high price of fuel causes much less pollution for the consumer.

Field demonstration of a 10 MW CO2 capture pilot plant in extended operation using KoSol absorbents for treating off-gas in a coal fired power plant

Efficient CO2 absorbents are crucial for reducing energy use in CO2 capture absorption process. This study evaluates the performance of amine-based CO2 absorbents, KoSol-5 and KoSol-6, developed by the Korea Electric Power Corporation. In a 10 MW pilot plant, KoSol-6 was found to capture more than 90% of the daily CO2 emissions, amounting to over 180 tons per day, from the exhaust gasses of a coal-fired power plant. Its regeneration energy (Qregen) was 2.42 GJ/ton CO2 in the standard absorption process, which is 39.2% less than the monoethanolamine (MEA) system. Implementing advanced techniques like intercooling, lean vapor compression, and thermal vapor recompression further optimized this process. Among these, the thermal vapor recompression mode turned out to be the most efficient and stable. In addition, KoSol-6 showcased excellent thermal and chemical stability, with a consistent Qregen of 2.34 GJ/ton CO2 over a 10,000-hour test. The study provides insights into the potential of KoSol-5 and KoSol-6 for large-scale applications and further developments in CO2 capture technology.

Exploring Biochar with N-Fertilizer Effects on Soil CO2 Emissions and Physical-Chemical Properties as a Climate Change Mitigation Tool

Abstract The global agriculture industry is facing never before faced issues in the form of soil degradation, water scarcity, rising greenhouse gas emissions, and climate change. Among the possible remedies, applying biochar to the soil has drawn interest as a viable strategy. Although a great deal of literature has been written about the benefits and drawbacks of applying biochar initially, there is still a significant amount of research on the effects of using biochar repeatedly. This study seeks to address this gap by examining the varied effects of both the initial application (at rates of 0 t.ha−1, 10 t.ha−1, and 20 t.ha−1) and the reapplication of biochar (at rates of 0, 10, and 20 t.ha−1), especially when combined with different levels of nitrogen fertilizer (0, 108, and 162 kg.ha−1). The investigation focuses on soil properties and CO2 emissions from Haplic Luvisol in the temperate climate zone (Slovakia). The results showed that biochar generally improved soil properties, such as soil pH (KCl) (p <0.05), shifting it from acidic towards moderately acidic, and generally led to a decrease in ammonium (NH4 +) and nitrate (NO3 -) content. The second level of fertilization, combined with different biochar treatments, yielded the most efficient results in physical properties such as soil temperature, bulk density (BD), and soil water content (SWC) compared to control treatments without biochar. Biochar application contributed to the reduction of both average daily CO2 emissions and cumulative CO2 emissions during the study period (April – October) in 2022 compared to the control without biochar application.

Spatial and temporal patterns of fuelwood consumption and its associated CO2 emissions in Muzaffarabad division, a western Himalayan region

BackgroundIn the Himalayan region, fuelwood serves as a critical energy source for rural communities. Being vital for meeting energy needs, fuelwood combustion is a source of carbon dioxide (CO2) emission and, consequently, global warming, as well as deforestation and public health damage. Therefore, quantifying fuelwood consumption patterns and its associated CO2 emissions is essential to understand the environmental impact and promote sustainable resource management.MethodsThis research conducts an evaluation of fuelwood burning patterns and the associated CO2 emissions in Azad Jammu and Kashmir (AJK), situated within the western Himalayan region. The study entails an extensive survey of 24 villages representing 240 households, equally distributed between the subtropical and temperate regions, each comprising 120 households. Data collection was executed through a combination of direct queries and the weight survey method, following standard protocols.ResultsIn the study area, the mean annual fuelwood comsumption per household amounts to 24.28 ± 3.1 Mg (or 3.195 ± 1 Mg capita−1). A variance was observed between subtropical and temperate zones, with the latter exhibiting higher consumption rates. The consequential CO2 emissions were assessed as 41.88 ± 4.5 Mg per household (5.51 ± 0.6 Mg capita−1). On a daily basis, households consumed an average of 66.52 ± 6.4 kg of fuelwood (8.75 ± 1.5 kg capita−1), resulting in a daily CO2 release rate of 114.745 ± 8.6 kg (15.095 ± 2 kg capita−1). The findings unveiled seasonal variations, indicating increased fuelwood consumption and emissions during the winter season. Statistical analysis shed light on the significance of altitude and family size in shaping the patterns of fuelwood use.ConclusionsThe results revealed the importance of prioritizing forest conservation and strategically implementing sustainable practices, including reforestation, afforestation, responsible harvesting, and actively promoting sustainable fuel sources. This research highlights the vital role of well-designed policies focused on preserving ecosystems and improving energy management. Policy intervention can ensure the sustainable stewardship of local and regional forest resources.

Longitudinal modeling of residual carbon dioxide and residual feed intake in the Nordic Red dairy cattle

Feed utilization efficiency is an important trait in dairy production playing a significant role in reducing feed costs and lowering methane emission. One of the metrics used to measure feed efficiency in dairy cows is residual feed intake (RFI). This metric requires routine measurement of feed intake. Since there is a positive high correlation between heat production and carbon dioxide (CO2) production on the one hand and heat production and efficiency on the other hand, residual carbon dioxide (RCO2) might be a useful metric to improve feed efficiency. The objectives of this study were to model the trajectories of RCO2 and RFI as well as to estimate their repeatabilities and correlations at different stages of lactation. Daily CO2 output and feed intake were recorded from 46 primiparous Nordic Red dairy cows using two Greenfeed Emissions Monitoring™ systems from 2 to 305 days in milk (DIM). Edited data comprised 5 995 daily averages. To calculate predicted values of CO2 and DM intake (DMI), prediction models were developed by fitting multiple regression models to observations. Subsequently, RCO2 and RFI were calculated by subtracting predicted values of CO2 and DMI from their corresponding actual observations. A random regression bivariate model was fitted to estimate repeatabilities and animal correlations within lactation at different DIMs between RCO2 and RFI traits. The model fitted included fixed effects of year-month of recording, lactation month, fixed regressions as well as random regressions for the animal effect. The residual variance was considered to be heterogeneous. Repeatabilities and animal correlations of RCO2 and RFI between selected DIM (for every 30 DIM i.e., 6, 36,…, 246 and 276) were calculated. Repeatability of RCO2 was high at the beginning of lactation (0.72 at DIM 6) and decreased around the peak of milk production (0.27 at DIM 96) and again increased gradually toward the end of lactation. Similarly, RFI also had high repeatability at the beginning (0.86 at DIM 6); however, it decreased in mid-lactation (0.37 at DIM 156) and then increased toward the end of lactation. Animal correlations between RCO2 and RFI were moderate to high on the same DIM and ranged from 0.37 to 0.88. Overall, we found that animals with higher CO2 production than expected also consume more DMI than expected, but the moderate correlation between RCO2 and RFI found in this study calls for more research to assess the potential of RCO2 to become a new feed efficiency metric.

Ondokuz Mayıs University drinking water treatment plant carbon footprint: emission sources and strategies for sustainability

In this study, it was aimed to calculate the carbon footprint of the drinking water treatment facility within Samsun Ondokuz Mayıs University and to determine the greenhouse gas emission sources within the facility. The total daily CO2 emission of the drinking water treatment plant is calculated as 85.05 kg CO2e/d. The analyses show that off-site CO2 emissions are higher than on-site CO2 emission values. It has been determined that the primary source of off-site emissions arises from the electrical energy consumption used in the units, constituting approximately 86.4% of the total CO2 emissions. The second important contribution parameter arises from the reaction of coagulants in mechanical mixing processes. This study emphasizes the importance of taking measures that support greener and sustainable production to reduce the current greenhouse gas emissions of the facility. In addition, calculating the carbon emissions of the drinking water treatment plant is important in informing the relevant institutions in the European Union’s efforts to achieve the goal of zeroing carbon emissions by 2050.

How are electricity generation effective on carbon neutrality in the global south? Evidence from sectoral CO2 emissions by daily data

In light of the efforts to ensure carbon neutrality by combating climate-related problems, the study investigates the effectiveness of electricity generation (EG) from the main renewable sources (hydro-HEG, solar-SEG, and wind-WEG). In this context, the study examines the countries of the Global South (i.e., Brazil, China, and India), considers EG at a disaggregated level and sectoral CO2 emissions, applies nonlinear methods, and uses daily data between January 2, 2019 and December 31, 2022. The results demonstrate that (i) disaggregated EG sources have a stronger (weaker) time and frequency dependency on sectoral CO2 emissions in China (Brazil and India); (ii) HEG has a stimulating impact on sectoral CO2 emissions in all countries; (iii) SEG has an increasing impact on sectoral CO2 emissions in Brazil and China, while it provides a decrease in sectoral CO2 emissions in India; (iv) WEG upsurges sectoral CO2 emissions in China, while it achieves a CO2 reduction in Brazil and India; (v) disaggregated level EG has a causal impact on sectoral CO2 emissions across all quantiles except some lower, middle, and higher quantiles. The study adds scientific value to existing knowledge by analyzing for the first time which EG sources are effective in reducing daily CO2 emissions in the Global South. Based on the outcomes, the study demonstrates that WEG is the best EG source for Brazil, that SEG and WEG are optimal EG sources for India, and that China cannot benefit from the EG sources considered. In this way, the study provides fresh insights for the countries of the Global South and underlines the crucial role of renewable EG in ensuring carbon neutrality.

An empirical study of indoor air quality in badminton stadiums in hot summer and cold winter regions of China during spring and fall seasons

The indoor air quality has a direct impact on human health. In order to obtain the current status of indoor air quality in typical sports buildings in hot summer and cold winter climate zones in China, indoor badminton courts in 10 cities in Hubei Province in this climate zone were selected as research objects for field testing of indoor environmental parameters in spring and autumn, and predict air quality parameters for non-testing times. All the tested stadiums are naturally ventilated in non-event conditions, and the average daily indoor CO2 concentration was 526.78 ppm in spring and 527.63 ppm in autumn, and the average daily PM2.5 concentration was 0.035 mg/m3 in spring and 0.024 mg/m3 in autumn, all of which met the requirements of GB/T 18883-2022, the average concentration of CO2 ≤ 1000 ppm and PM2.5 ≤ 0.05 mg/m3. The indoor CO2 concentration and PM2.5 concentration of the tested badminton halls under natural ventilation gradually increased with the accumulation of exercise time, making the indoor air quality of the badminton halls decrease, which would negatively affect the health of the people exercising in this environment.

The influence of the hydrometeorological factors on the CO2 fluxes from the oligotrophic bog surface.

The influence of the hydrometeorological factors on the CO2 fluxes from the oligotrophic bog surface.

Ambient Air Pollution Monitoring and Health Studies Using Low-Cost Internet of Things Monitor within KNUST Community

Urban environments with high industrialization are infested with hazardous chemicals and airborne pollutants. These pollutants CO, O3, SO2, NO2, and PM can have devastating effects on human health, causing both acute and chronic diseases such as respiratory infections, lung cancer, and heart disease. Air pollution monitoring is vital to warn citizens of the health risks associated with exposure to high concentrations of these criteria pollutants. This study designed a low-cost IoT monitor to measure concentration levels of criteria pollutants emitted from transportation sources within Kwame Nkrumah University of Science and Technology environs. Three monitoring sites, KNUST Tech junction, Ayeduase gate junction and KNUST campus junction, were identified as the locations within the proximity of the University for the Deployment of the monitor. Hourly and mean daily CO, NO2, O3 and SO2 concentrations at each of the three sites were measured for a week using the IoT monitor, when students were in school and when students were on vacation. The average daily CO, NO2 and O3 concentrations measured at the selected locations when school was in session and during vacation were presented on histogram. The mean weekly concentrations of CO, NO2 and O3 were also estimated as 13.2ppm, 0.277ppm and 0.106ppb respectively at KNUST Tech junction; 10.1ppm, 0.254ppm and 0.110ppb respectively at Ayeduase gate junction; and 8.0ppm, 0.415ppm and 0.100ppb respectively at the KNUST campus junction when school was in session. The results show that the concentrations of all the pollutants were higher and exceeded the EPA standards except for CO at KNUST Campus junction monitoring site. These high levels of emissions is an indication of a health concern for the students at the university and university authorities can device means of curbing it.