Emission Data Research Articles

Optimizing the early-stage of composting process emissions – artificial intelligence primary tests

Although composting has many advantages in treating organic waste, many problems and challenges are still associated with emissions, like NH3, CO and H2S, as well as greenhouse gases such as CO2. One promising approach to enhancing composting conditions is using novel analytical methods based on artificial intelligence. To predict and optimize the emissions (CO, CO2, H2S, NH3) during the early-stage of composting process machine learning (ML) models were utilized. Data about emissions from laboratory composting with compost’s biochar with different incubation (50, 60, 70 °C) and biochar doses (0, 3, 6, 9, 12, 15% dry mass) were used for ML models selections and training. ML models such as acritical neural network (ANN, Bayesian Regularized Neural Network; R2 accuracy CO:0.71, CO2:0.81, NH3:0.95, H2S:0.72) and decision tree (DT, RPART; R2 accuracy CO:0.69, CO2:0.80, NH3:0.93, H2S:0.65) have demonstrated satisfactory results. The ML models to predict CO and H2S during composting were demonstrated for the first time. Utilizing emission data to predict other noxious gases presents a cost-effective and expeditious alternative to the empirical analysis of compost properties.

Urban Technology Transfer, Spatial Spillover Effects, and Carbon Emissions in China

Technology transfer is essential for addressing technological disparities in urban areas and reducing carbon emissions. This study analyzes inter-city technology patent transfers and carbon emission data from China (2010–2019) using a spatial Durbin model to assess the effects of technology inflow and outflow on carbon emissions and their spatial spillover effects. Key findings include the following: ① a significant spatial correlation exists between technology inflow, outflow, and carbon emissions, with notable spillover effects; ② both technology inflow and outflow reduce intra-regional carbon emissions, but only outflow significantly reduces inter-regional emissions; ③ regional differences are evident, with the eastern and central regions showing significant reductions due to technology transfers, while the western and northeastern regions do not. In the northeastern region, technology transfer significantly aids neighboring cities in reducing emissions. However, the central region lacks spillover effects from outflow, and technology inflow and outflow in the western region hinder reductions. This paper provides policy recommendations to improve urban technology transfer and carbon emissions mitigation.

Augmented pre-training-based carbon emission accounting method using electricity data under small-sample condition

IntroductionAccurate and rapid carbon accounting method for the power industry is crucial to support China’s low-carbon transformation. Currently, carbon emission accounting methods are based on slowly updated fuel statistics or expensive monitoring equipment, resulting in high costs and delays in carbon emission estimation. Power data offers high real-time availability, accuracy, and resolution, and exhibits a strong correlation with carbon emissions. These characteristics provide a pathway for achieving rapid and precise annual carbon emission accountings. However, carbon emission data inherently exhibits small sample characteristics, making these methods less effective in small sample conditions and leading to lower accounting accuracy.MethodsTherefore, this paper proposes an augmented pre-training-based “electricity-to-carbon” method under small sample conditions.ResultsThis approach utilizes the correlation between electricity and carbon data as well as the autocorrelation characteristics of carbon emission data to construct a machine learning-based electricity-carbon fitting model for rapid and accurate carbon emission estimation. To address the challenges of small sample learning, this paper introduces an interpolation pre-training method to optimize the model’s hyperparameters and conserve samples for model training, thereby improving the model’s generalization and robustness.DiscussionCase studies on a real dataset verifies the effectiveness of the proposed method. The findings of this study can promote the development of carbon measurement technology and facilitate the low-carbon transition of developing countries.

Quantification and spatial analysis of gridded (0.1° × 0.1°) emission of indirect GHGs/Air pollutants from anthropogenic sources in India

Indirect greenhouse gases (GHGs) play a key role in modulating both regional and global atmospheric chemistry and act as air pollutants that degrade air quality. The rising number of premature deaths and notable health hazards could be linked to increasing concentration of these air pollutants in the atmosphere. As a developing nation, India is often regarded as one of the most polluting countries due to increasing anthropogenic activities. Therefore, identifying the sources of these indirect GHGs gases such as CO, NOx, VOC and SO2 and quantifying their emissions has become a critical requirement for understanding regional atmospheric chemistry. In this study, we developed a comprehensive, technology-driven gridded emission inventory for India at a high resolution (0.1°×0.1°) for the base year 2020. Using the IPCC bottom-up approach, the study estimated the total emissions of CO (45 Tg/yr), NOx (22.8 Tg/yr), VOC (10.8 Tg/yr) and SO2 (15.1 Tg/yr) from all the dominating sources. Vehicle exhaust contributes the most NOx and VOC whereas the residential and power sectors are the highest emitters of CO and SO2, respectively. This newly reported surface emission data would be an essential tool for policymakers in formulating mitigation strategies and will also serve as a vital input for atmospheric chemistry and climate studies.

Light-duty gasoline vehicle emission deterioration insights from large-scale inspection/maintenance data: The synergistic impact of usage characteristics

Accurately estimating vehicle emissions is crucial for effective air quality management. As key data for emission inventory construction, emission factors (EFs) are influenced by vehicle usage characteristics and experience deterioration. Current deterioration models often employ single-factor approaches based on vehicle age or accumulated mileage, which fail to capture the effects of varying usage intensities within the same mileage or age intervals. This study addressed this limitation by developing a novel emission deterioration model that incorporates multi-dimensional usage characteristics and that utilizes a large-scale inspection and maintenance (I/M) dataset for light-duty gasoline vehicles (LDGVs). The modeling results reveal distinct deterioration patterns for different pollutants and highlight the synergistic effects of the usage duration and intensity: natural aging significantly impacts HC and NOx emissions, while CO emissions are more strongly affected by intensive use. Specifically, China V LDGVs that were driven 4 × 104 km/yr exhibited HC, CO, and NOx deterioration rates per mile that were approximately 4.1 % lower, 10.3 % higher, and 1.1 % higher, respectively, than those of vehicles driven 2 × 104 km/yr as the mileage increased from 5 × 104 km to 10 × 104 km. By leveraging timely emission data and explicitly accounting for usage intensity, this study corrected biases in local emission estimates by 5–85 % with respect to estimates from commonly used models. This framework enables the development of more effective control strategies and refinements to policy evaluations in megacities with I/M programs.

Ionic Liquid-Based Green Solvents for Extraction and Purification of Natural Plant Products

Introduction: This research paper explores the environmental sustainability of ionic liquid-based green solvents in the extraction and purification of natural plant products, with a focus on their entire life cycle. The objectives of the study were to assess the environmental impact of ionic liquid synthesis, energy consumption, water usage, emissions, recycling rates, policy effects, and stakeholder perceptions. Methods: Methodologically, we conducted a comprehensive Life Cycle Assessment (LCA) that involved primary data collection through field surveys and interviews with key stakeholders in the ionic liquid production and usage industry across various regions in India. The data were analyzed using specialized LCA software tools to quantify environmental impacts. Key findings include the identification of synthesis as a major contributor to environmental impact, emphasizing the need for greener synthesis methods. Results: The study revealed the significant carbon footprint, energy consumption, and water usage during production, highlighting opportunities for improvement. Emissions data underscored the importance of emission control measures, particularly for greenhouse gases and volatile organic compounds. Recycling and reuse were identified as environmentally friendly disposal methods. Policy compliance varied among stakeholders, indicating room for stricter regulations. Stakeholder perceptions varied, with researchers having the most positive outlook. Implications of the findings extend to sustainable chemistry practices, emphasizing interdisciplinary collaboration and the importance of considering the entire life cycle of chemical processes. Conclusion: This research contributes to a deeper understanding of green solvents and provides a foundation for promoting sustainable practices in industrial processes in India and globally.

An Empirical Analysis of Carbon Emissions in Higher Education Institutions: A Case Study of Zhejiang Ocean University Using Emission Factor Methodology

In recent years, with the increase in greenhouse gas emissions, global warming has created a series of complex ecological challenges. Colleges and universities are still in the exploratory stage of constructing low-carbon campuses. This study takes the campus of Zhejiang Ocean University as the research object and obtains carbon emission data from it based on the emission coefficient method by visiting the site and issuing a questionnaire survey. A total of 246 questionnaires were collected in this study, which were quantitatively analyzed to further understand the composition of the carbon emission structure of universities and put forward the corresponding energy-saving and emission-reducing suggestions. This study actively responds to the national call for energy saving and emission reduction and aims to provide a data reference as well as scientific support for the construction of a low-carbon campus. The results show that in 2021, the carbon emissions of Zhejiang Ocean University were 3.39 × 107 kg, of which direct carbon emissions were 3.34 × 105 kg, accounting for 0.99% of the total carbon emissions; indirect carbon emissions were 1.50 × 107 kg, accounting for 44.33% of the total; and other indirect carbon emissions were 1.85 × 107 kg, accounting for the remaining 54.68%. Also, this study found that individuals in colleges and universities pay attention to carbon emissions, but it is difficult for them to maintain a low-carbon approach in daily life. Based on the results of the analysis, this study proposes a series of effective measures to reduce carbon emissions at the campus of Zhejiang Ocean University, unite students in colleges and universities, and fully implement the concepts of energy saving, emission reduction, and sustainable development in college life.

Ultrafast and Ultrasensitive Bacterial Detection in Biofluids: Leveraging Resazurin as a Visible and Fluorescent Spectroscopic Marker.

Here we report the application of chemometric analysis for modeling absorbance spectroscopy and fluorescence emission data from a resazurin-based assay targeting low-level bacterial detection in biofluids. Bacteria spiked samples were incubated with resazurin and absorbance and fluorescence data were collected at 30 min intervals. The absorbance data was subjected to Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) and compared with the univariate fluorescence spectroscopy approach. The analysis demonstrated the multidimensional nature of the absorbance data, highlighting the appearance of the resorufin peak at the 2 h time point with a low bacterial inoculum of 0.01 CFU mL-1 across all the samples tested-water, urine and serum. The PLSR models supported the PCA data and exhibited strong predictive capabilities for water (RC2 = 0.937, RCV2 = 0.934), urine (RC2 = 0.899, RCV2 = 0.880) and serum (RC2 = 0.985, RCV2 = 0.967). Conversely, fluorescence is contingent upon resorufin existence, necessitating a prolonged waiting period postincubation with resazurin to verify the presence of bacteria, especially when contamination levels are low. Given the substantial global impact of bacteria-related infections, this method detects bacteria at low concentrations precisely and rapidly, improving efficiency and adaptability for point-of-care settings, promising swift diagnosis of bacterial infections, environmental monitoring, or food-quality control.

Integration of investor behavioral perspective and climate change in reinforcement learning for portfolio optimization

Addressing environmental impact is increasingly imperative for individual investors and large financial institutions, making it a key objective of socially responsible investing. However, there is a noticeable gap in research on integrating sustainability and low-carbon considerations into machine learning-based portfolio optimization. To meet this challenge, this study introduces a Portfolio Emissions Sentiment Attention Aware Reinforcement Learning (PESAARL) model based on the Proximal Policy Optimization (PPO) algorithm to optimize a portfolio of Dow Jones Industrial Average (DJIA) stocks. PESAARL uniquely integrates environmental impact considerations, specifically carbon footprint using the firm level scope 1 and scope 2 emissions data, alongside firm-level investor sentiment and attention, into the investment decision-making process. Through multiple experiments, PESAARL demonstrates significant advantages, in terms of financial and environmental performance, over the benchmarks.

Value of blockchain for scope 3 carbon disclosure: The moderating role of data processing technologies

Disclosing carbon emissions in Scope 3 is essential for mitigating pollution and the associated environmental damage, and blockchain can enhance the disclosure. However, the effect of blockchain on Scope 3 carbon disclosure remains unclear due to a lack of empirical evidence. This paper investigates the value of blockchain for Scope 3 carbon disclosure and examines whether this value can be strengthened by integrating data processing technologies, including artificial intelligence (AI), cloud computing, and big data analytics (BDA). Drawing upon the coordination theory, we posit that blockchain as a recording and tracing technology can improve the coordination among supply chain members on collecting carbon emissions data, thereby facilitating firms' Scope 3 carbon disclosure. Furthermore, data processing technologies enable efficient utilization and management of the collected data, potentially coordinating with blockchain to enhance Scope 3 carbon disclosure. We test these relationships using regression analysis based on a sample of 422 observations for Chinese listed firms during 2021 and 2022. The results show that blockchain adoption is positively associated with a firm's Scope 3 carbon disclosure. In addition, adopting each of the three data processing technologies—AI, cloud computing, and BDA—further strengthens the positive relationship. This study contributes to academic knowledge and evidence on blockchain and sustainable supply chain management with practical suggestions for managing carbon emissions at the supply chain level through the combined adoption of blockchain and data processing technologies.

Effects of the vehicle fleet on air quality in an urban university environment of Ecuador

Introduction: Air quality in university environments is critical for the health and well-being of students and staff. This study aims to identify the permissible pollutants emitted in the parking lots of the State Technical University of Quevedo (UTEQ) in the canton of Quevedo, province of Los Ríos, Ecuador. Methods: An abductive (inductive-deductive) approach was employed, utilizing statistical methods to analyze primary emission data. In-situ measurements of pollutants, specifically carbon monoxide (CO) and particulate matter (PM2.5 and PM10), were conducted monthly across three parking lots at the university. Each parking lot contained three strategically established monitoring points, where measurements were taken during peak entry and exit hours and on days of highest traffic, using the Temtop M2000C-2º sensor. Results: The findings revealed significant statistical differences in pollutant concentrations among the parking lots. CO levels were notably higher in the main parking lot during midday, while PM2.5 and PM10 exhibited greater dispersion in the rear parking lot. The maximum measured values were 456.71 ppm (CO) in the main parking lot, 22.29 µg/m³ (PM2.5) in the Produteq lot, and 32.54 µg/m³ (PM10) in the rear parking lot, all of which remain within the permissible limits established by Ecuadorian legislation. Conclusion: Although air pollution levels at UTEQ are classified as moderate, the results underscore the necessity for implementing short-term mitigation measures to enhance air quality. It is recommended that university authorities develop projects and actions aimed at reducing vehicular emissions and improving overall environmental conditions on campus.

The decline in tropical land carbon sink drives high atmospheric CO2 growth rate in 2023

Abstract Atmospheric CO2 growth rate (CGR), reflecting the carbon balance between anthropogenic emissions and net uptake from land and ocean, largely determines the magnitude and speed of global warming. CGR at Mauna Loa Baseline Observatory reached record high in 2023. Here we quantified major components of global carbon balance for 2023, by developing a framework which integrated fossil fuel CO2 emissions data, an atmospheric inversion from Global ObservatioN-based system for monitoring Greenhouse GAses (GONGGA) with two artificial intelligence (AI) models derived from dynamic global vegetation models. We attributed the record high CGR increase in 2023 compared to 2022 primarily to the large decline in land carbon sink (1803 ± 197 TgC year−1), with minor contributions from a small reduction in ocean carbon sink (184 TgC year−1) and a slight increase in fossil fuel emissions (24 TgC year−1). At least 78% of the global decline in land carbon sink was contributed by the decline in tropical sink, with GONGGA inversion (1354 TgC year−1) and AI simulations (1578 ± 666 TgC year−1) showing similar declines in the tropics. We further linked this tropical decline to the detrimental impact of El Niño-induced anomalous warming and drying on vegetation productivity in water-limited Sahel and southern Africa. Our successful attribution of CGR increase within the framework combining atmospheric inversion with AI simulations enabled near-real-time tracking of the global carbon budget which had a one-year reporting lag.

NOx reduction scenarios under real-world driving conditions for light-duty diesel vehicles

This study analyze analyzes the impact of stringent emissions regulations on future NOx reductions. Using scenario analysis, NOx emissions of diesel light-duty vehicles from 2019 to 2040 are estimated using emission factors from real driving emissions data. The baseline scenario assumes current regulations will be maintained, while other scenarios assess the impact of implementing specific Euro standards as final regulations. If Euro 5 regulations are implemented by 2040, cumulative emissions will increase by 229.1 % compared to the baseline scenario. Implementing Euro 6 a/b/c regulations will increase by 156.2 %, while Euro 6 d-temp and Euro 6 d regulations will increase by 8.4 % and 0.7 %, respectively. These findings highlight the significant contribution of real driving emission test introduced with Euro 6 d-temp to NOx reduction. Furthermore, they indicate that Euro 6 d regulation has already achieved emission levels similar to those of the upcoming Euro 7 regulation.

An innovative virtual sensing system for the vehicle-centric evaluation of emissions in the sustainable mobility transition

The objective of the research is to develop a new methodology capable of quantifying the emissions of internal combustion engine cars based not only on their technology but also on the dynamic behavior of the individual vehicle. No complicated exhaust measurement apparatus is needed. Employing just a telematic device equipped with GNSS and an inertial measurement unit, which can be easily attached to the vehicle, we demonstrate how individualised, accurate climate-altering emissions result by using figures extracted from European test databases, appropriately combined with real travelled distances, speeds, and driving style of each individual car. Pollutant emissions are, in this paper, also assessed though in a more general manner, and this represents a starting point for a future complete vehicle-centric emissions evaluation. This research demonstrates that circulating car emissions have to be assessed considering not only the Euro class and type of fuel, but also how the vehicle is driven. In this work a new, implementation oriented methodology is proposed in order to properly merge average European standard emission data with car-based telematic measures to highlight a new paradigm for regulating in a more conscious way the transition of the internal combustion engine car park towards a large scale, sustainable mobility ecosystem. This approach would place greater responsibility on the driver, without leaving anyone behind and without compelling the car scrapping solely based on the Euro class of its engine. The key contribution is ultimately to establish an easy to handle methodology enabling targeted and behavior-based traffic management policies. In fact, through a massive telematics recent dataset, the proposed virtual emission sensing system reveals that an extensive and inefficient use of eco-friendly vehicles produces much more greenhouse gas emissions than older vehicles driven in an environmentally responsible way.

Study on prediction model of nitrogen emission in the production stage of residential building materials—A case study of Guangdong province

This study aims to reduce nitrogen emissions from residential buildings and establishes a prediction model for nitrogen emissions during the production of building materials. The calculation boundary, content, and method of nitrogen emission in the production stage of residential building materials are accurately analyzed. Based on the nitrogen emission data of 20 residential buildings in Guangdong, the composition and distribution characteristics of nitrogen emission in the production stage of building materials are analyzed. The coupling relationship between building design parameters and building materials’ nitrogen emissions is established using linear regression and ridge regression. 10 kinds of nitrogen emission prediction models based on the design parameters of residential buildings in the production stage of building materials were established and verified. The results show that the linear model M3, based on the number of floors above and below ground, the area width and depth, and the ridge regression model M5, based on the number of floors above and below ground, the area width and depth, and the total number of main functional rooms, have good fitting and prediction performance, respectively. The linear regression model M6, based on the number of floors above and below ground, the area width and depth, and the total number of rooms, has the best fitting and prediction performance. M3, M5, and M6 can accurately predict the nitrogen emission composition and distribution characteristics of building materials in residential building design and lay a foundation for future research on nitrogen emission evaluation and calculation methods and nitrogen emission reduction technology strategies.

How data factor flow affects corporate pollution reduction: The environmental effects of digital empowerment

This study the theoretical hypotheses by building a relatively exogenous data factor flow indicator and a corporate pollution emission intensity indicator system using China's data flow restriction policy and corporate pollution emission data. Data factor is emerging as an essential new factor of production in today's digital economy, and data cannot function valuablely without an efficient flow. This study constructs a theoretical model of the influence of data factor flow on the pollution emissions of downstream manufacturing enterprises based on the vertical correlation of inputs and outputs. Additionally, we employ additional contaminants as a robustness test and sulfur dioxide, a common representation of air pollution, as the baseline. The empirical analysis's findings indicate that: (1) Data factor flow significantly reduces pollution in manufacturing enterprises. (2) The main way that data factor flow helps organizations minimize pollution is by increasing their productivity and ability to innovate in technology. (3) Domestic data flow is more important than cross-border data flow in helping industrial companies reduce pollution. (4) In general, foreign-owned enterprises have a greater need for data factor flow to reduce pollution than do state-owned and private businesses.

The Share of Carbon Emissions by the Non-Rich and Support for Higher Environmental Taxes in Cross-National and Longitudinal Perspective

This comparative study covering 41 countries explores whether the distribution of national carbon emissions shapes the public willingness to pay higher environmental taxes and if this relationship is moderated by the quality of government. Using multilevel regression models, it draws on data from all four waves of the International Social Survey Programme’s Environment modules (1993–2020), combined with carbon emissions data from the World Inequality Database. Cross-sectional and longitudinal results suggest that the public willingness to pay additional taxes is lower in contexts where the non-rich are responsible for a larger or increasing share of national carbon emissions, particularly in contexts with low or declining quality of government. It is concluded that the distribution of emissions may impact public support for environmental taxes, but that well-functioning government institutions can mitigate negative public backlash linked to the often-perceived regressive character of these taxes.

A concise review towards a novel target specific multi-source unsupervised transfer learning technique for GDP estimation using CO2 emission data

Though economic growths of most of the nations have seen exponential rise due to industrialization, it has also caused proportional increase in their carbon emissions. This paper exploits this proportionate relationship of carbon emission with GDP to predict the per-capita GDP of those nations whose GDP values are missing in the world bank database. The reason behind the same was, those countries were either war-torn or politically isolated/unstable. To achieve the objective of predicting the missing GDP values of those countries from their carbon emissions, this paper exploits the non-linear relationship among the carbon emissions from solid fuels, liquid fuels, and gaseous fuels. It is so because even the differential utilization of these fuels impact economy differently. Use of traditional solid fuel for cooking points toward energy poverty, and access to clean cooking gas indicates higher living standard. However, the available data from the war-torn or isolated countries are very little, and hence insufficient for building a robust predictive machine learning model. So, this paper employs multi-source unsupervised transfer learning to precisely estimate the missing per-capita GDP of those nations. It suitably enlarges the training domains for the prediction models to be more robust. We empirically evaluate the proposed methodology for different regression techniques to estimate the missing GDP values of eleven different nations belonging to diverse strata of economies viz. developed economies, developing, and/or least developing economies. Proposed methodology profoundly improves the prediction preciseness of these regression techniques in estimating the missing per-capita GDP of the considered nations.

Identification and Distance Measurement of Dust Clouds at High Latitude by a Clustering Hierarchical Algorithm

We present a catalog of dust clouds at high Galactic latitude based on the Planck 857 GHz dust emission data. Using a clustering hierarchical algorithm, 315 dust clouds at high Galactic latitudes are identified. Additionally, using the optical and ultraviolet extinction of 4 million and 1 million stars, respectively, provided by Sun et al., we derive the distances and physical properties for 190 high Galactic latitude dust clouds and the ultraviolet excess ratios for 165 of them. Through the study of color excess ratios, this work confirms that molecular clouds with large Galactic distances and low extinction likely have a higher proportion of small-sized dust grains. In addition, clouds with well-defined distances in the catalog are used to trace the Local Bubble, showing good consistency with the boundary of the Local Bubble from the literature.

Impacts of On‐Road Vehicular Emissions on U.S. Air Quality: A Comparison of Two Mobile Emission Models (MOVES and FIVE)

AbstractOn‐road vehicles are significant contributors to air pollution globally, particularly to nitrogen oxides (NOx) and ozone (O3). Quantifying their contribution to air quality is crucial to understanding the trends of vehicle emissions as low‐ and “zero” emission vehicles join the fleet. Modeling on‐road emissions is complex due to various factors like fleet activities, traffic patterns, and meteorological conditions. We compare on‐road emissions from two mobile models: the National Oceanic and Atmospheric Administration Fuel‐based Inventory of Vehicle Emission (FIVE) and the US Environmental Protection Agency Motor Vehicle Emission Simulator (MOVES), finding they contribute 4%–33% to volatile organic compounds (VOCs), NOx, and fine particulate matter (PM2.5) in the contiguous United States (CONUS). Using a regional chemical transport model, we assess air quality effects under different emission scenarios. Both emission data sets yield satisfactory performance, with MOVES showing lower biases in ozone (O3) and PM2.5 over CONUS, while FIVE performs better at city scales due to higher urban NOx emissions. In January, on‐road emissions increased surface O3 over western and southern US by up to 9.1%–13.1% but decreased by 2.5% over the northeastern US, while PM2.5 predictions vary across the US (−85% to 24%). In July, on‐road emissions elevate O3 and PM2.5 concentrations by 15%–20% across CONUS, except in some west coast cities. They also greatly contribute to nitrogen dioxide (NO2) by more than 80% near roads and in urban areas. This study highlights the significant impact of on‐road emissions on urban air quality and provides insights for improving air quality forecasting and management.