Nitrogen Dioxide Emissions Research Articles

Implementation of Conventional and Smart Weed Management Strategies in Sustainable Agricultural Production

ABSTRACTConventional weed management is known for the use of selective herbicides, tackling issues of herbicide‐resistant weeds, and endeavors to reduce reliance on herbicides, such as employing biological control of weeds and incorporating allelopathy. These strategies are integrated with traditional and other conventional weed management techniques. New challenges in agricultural production, such as declining population involved in agriculture, global warming, and climate changes make conventional weed management inadequate. Rapid advancements over the past decade in artificial intelligence (AI), unmanned aerial vehicles (UAVs), cameras, and sensors, necessitate the development and implementation of smart weed management (SWM) approaches for the 21st century. Wise weed management practices can control weeds and reduce emissions of methane (CH4), carbon dioxide (CO2), and nitrogen dioxide (NO2). Minimizing the use of herbicides in tandem with the application of smart technologies, like weed mapping and identifying spatial variability of weeds using proximal and remote sensors, multispectral and RGB cameras, can effectively manage weeds while also reducing labor costs. The SWM strategies also encompass the integrated use of organic herbicides, bioherbicidal products, and the judicious application of both organic fertilizers and inorganic fertilizers for weed management. Some approaches, such as alternate wetting and drying (AWD), can contribute to both SWM and carbon credits to farmers. The utilization of computing power, weed control robotics, and big data, combined with the education of the next generation of weed scientists and efforts to shift growers' attitudes and behaviors in weed management, may vary among countries. It requires active collaboration between agricultural extensionists and policymakers.

Do Water Pollutant Reduction Projects Promote or Limit Carbon Reduction? Evidence from Building a Beautiful China

The total control of major water pollutants (TCMWP) is a critical strategy for improving water quality in China, with the added benefit of yielding climate-related advantages. This study uses the emission factor method to quantify the reductions in pollutants and carbon emissions resulting from China’s implementation of TCMWP. A synergy scale for pollution and carbon reduction was constructed to assess the co-benefits of reducing the Chemical Oxygen Demand (COD) and ammonium nitrogen (NH4+-N) and carbon dioxide (CO2) emissions. Furthermore, to account for regional variations in energy structure regulation strategies, whether aggressive or unified, we developed Pollutant Synergistic Carbon Reduction Pathways at the Regional (R.PSCRP) model framework level to evaluate the carbon emission reduction potential of TCMWP during the “14th Five-Year Plan” period. The study revealed that China’s unified TCMWP employs different but highly effective combinations of emission reduction paths across different regions. Notably, new renovations and expansions of wastewater treatment facilities (NRE-WWTFS) and pipeline network construction and renovation (CR-PNK) together accounted for 89.3% of the total emission reduction. The construction of key water pollutant reduction projects plays a significant role in reducing carbon emissions at the district level compared to direct discharge practices. Additionally, indirect emissions resulting from TCMWP implementation account for approximately 50% of the total carbon reduction achieved. By aligning either harmonized or independent energy adjustment targets, regions were able to achieve substantial pollution and carbon reduction benefits, ranging from 7.5 to 8 million tons of CO2-equivalent.

Experimental and density functional theory calculation study on the NO2 adsorption and reduction by KOH activated biochar

Biochar prepared from waste biomass as an adsorption and reduction material can effectively decrease decrease nitrogen dioxide (NO2) emissions. This work aims to select suitable materials to prepare biochar and improve its ability to adsorb and reduce NO2. In this study, the process of adsorption and reduction of NO2 by biochar and the effect of KOH activation on these reactions are studied by using fixed-bed reactor and density functional theory calculation. Experimental results indicate a positive correlation between the adsorption and reduction capabilities of biochar for NO2 and its specific surface area and surface oxygen groups. The biochar sample prepared by pyrolysis at 800 ℃ with the addition of 3 wt% KOH solution exhibits the highest adsorption capacity, likely due to the catalytic role of potassium (K) in the adsorption-reduction reaction. It is worth noting that the theoretical calculation results confirm this point. The K atom primarily facilitates the breaking of the N-O bond in NO2 and the desorption of the NO molecule on biochar through van der Waals forces, thereby lowering the reaction energy barrier. Thermodynamic and kinetic analyses further confirm these findings. This study has a new understanding of the mechanism of NO2 adsorption and reduction of by KOH activated biochar, which provides experimental basis and theoretical guidance for the application of this technology.

The Tula Industrial Area Field Experiment: Quantitative Measurements of Formaldehyde, Sulfur Dioxide, and Nitrogen Dioxide Emissions Using Mobile Differential Optical Absorption Spectroscopy Instruments

The Tula industrial area in Central Mexico comprises, among other industries, a refinery and a thermoelectric power plant. It is well known for its constant emissions of gases into the atmosphere and considered an important area where pollutants released into the atmosphere have an influence on local and regional air quality. During March and April 2017, a field campaign was conducted with the objective of quantifying formaldehyde (HCHO), sulfur dioxide (SO2), and nitrogen dioxide (NO2) emissions from this industrial area using mobile differential optical absorption spectroscopy (DOAS) instruments. Calculated average emissions of the Francisco Perez Rios Power Plant and the Miguel Hidalgo Refinery were 3.14 ± 2.13 tons per day of HCHO, 362.08 ± 300.14 tons per day of SO2, and 24.76 ± 12.82 tons per day of NO2. From the measurements conducted, the spatial distribution patterns of SO2, NO2, and HCHO were reconstructed, showing a dispersion pattern of SO2 and NO2 towards the southwest of the industrial complex, impacting agricultural and urban areas. Occasionally, and usually during the morning hours, SO2 and NO2 were dispersed towards the north or northeast of the industrial complex. In the case of HCHO, dispersion was observed towards the south and southeast of the industrial complex. The far-reaching implications of this study are that for the first time, formaldehyde emissions were quantified. In addition, a follow-up study was conducted regarding nitrogen dioxide and sulfur dioxide emissions from the Tula Industrial area.

Modelling and optimization of shea butter biodiesel engine performance evaluation using response surface methodology

In this study, the performance and emission parameters of compression ignition (CI) diesel engine powered by shea butter biodiesel blends with diesel via clay doped ionic liquid catalyst (CD-IL) were modelled, optimized and predicted using Response surface methodology (RSM) technique. Using the (American Society for Testing Materials) ASTM D 6751 criteria, the produced biodiesel's quality was successfully evaluated. The biodiesel blends (B0, B50, B100), load (100, 200, 300)kg and speeds (1400, 1800, 2200) rpm were considered as the factors while Brake thermal efficiency, Brake specific consumption, Brake power, Nitrogen oxide and carbon monoxide emission were considered as the responses of the shea butter biodiesel engine. Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), X-ray diffraction (XRD), X-ray fluorescence (XRF), and Brunaeuer Emmet and Teller (BET) were used to analyze the catalyst before and after treatment to ascertain their suitability for the process. The capability of the models was evaluated using the correlation coefficient (R2) and mean square error (MSE). The second-order polynomial model is shown in the Analysis of Variance (ANOVA) with an (R2 -0.9948, Adjusted R2–0.9880, Predicted R2–0.9163) for brake thermal efficiency, (R2 -0.9908, Adjusted R2–0.9790, Predicted R2–0.8529) for brake specific consumption,(R2 -0.9988, Adjusted R2–0.9972, Predicted R2–0.9807) for brake power, (R2 -0.9995, Adjusted R2–0.9988, Predicted R2–0.9917) for Carbon monoxide emission and (R2 -0.9979, Adjusted R2–0.9951, Predicted R2–0.9659) Nitrogen dioxide emission demonstrating the model's acceptance. The optimal condition for shea butter biodiesel engine performance of brake thermal efficiency (16.3%), brake specific consumption(0.83),brake power (1.5), Carbon monoxide emission (175 ppm) and Nitrogen oxide (150 ppm) was obtained at a biodiesel blend (B50), load of 300 kg and speed of 2200 rpm. The 3D model graphs showed the process parameters impacted on the response significantly with better combustion, cleaner air.fuel ration blend, lesser friction loss and air consumption. Hence the proposed RSM modelling tool for shea butter biodiesel engine performance prediction had the best performance at (B50) (50% biodiesel and 50% diesel).

Development of the green policy strategies of enterprises: а decent work

This research examines the symbiotic relationship between the creation of green jobs within enterprises, economic growth, and the consequential reduction in CO2 emissions. It delves into the multifaceted advantages derived from integrating sustainable employment practices within businesses, emphasising their substantial contribution to fostering economic prosperity while concurrently mitigating adverse CO2 emissions. The main goals of this article are as follows: to study the experience of developed countries regarding the costs of their sustainable development strategies and the effects that have been achieved; generalise the main tools for ensuring decent work on the example of large companies, evaluate the relationship between reducing nitrogen dioxide emissions and providing green jobs. A comparison of financial instruments for maintaining green workplaces at enterprises in developed countries (USA, Norway, China, Germany, Sweden, and Poland) has been made. CO2 emissions reduction strategies, expenditure, funding, financing, and green jobs by countries have been analyzed. By investing in green initiatives and restructuring operational frameworks to prioritise sustainability, enterprises actively mitigate their carbon footprint, ultimately contributing to a greener and more environmentally conscious business landscape. This comprehensive study explores recent advancements in green job creation and renewable energy development in different countries of the world. The strategies of reducing CO₂ emissions by such companies as IKEA, Google, Unilever, and Tesla show not only positively impact the environment but can also be profitable for their businesses and guarantee decent work for their employees.

High-performance NO2 gas sensor based on reduced graphene oxide/ZrO2 hybrids

The increasing concern over environmental pollution, particularly from nitrogen dioxide (NO2) emissions, necessitates the development of efficient NO2 detection sensors. This study introduces reduced graphene oxide (rGO)/ZrO2 hybrids for enhanced NO2 gas sensing. Utilizing a modified Hummer’s method, graphene oxide (GO) flakes were synthesized and subsequently sputtered with 10 nm ZrO2 film, followed by thermal annealing to produce rGO/ZrO2 hybrids. The hybrids were characterized using various techniques including SEM, TEM, AFM, Raman spectroscopy, and XRD, confirming successful synthesis and reduction of GO, as well as the formation of ZrO2 nanoparticles. Gas sensing tests revealed superior sensitivity to NO2 in the hybrids due to efficient electron transfer between rGO and ZrO2, resulting in increased hole concentration in rGO and enhanced conductivity. The cyclic performance of the hybrids showed stable response and recovery to NO2, while selectivity tests demonstrated high sensitivity to NO2 over other gases including NH3, ethanol, and oxygen. This study highlights the potential of rGO/ZrO2 hybrids as high-performance NO2 gas sensors, offering promising prospects for environmental monitoring and public health protection.

DETERMINATION OF GREENHOUSE GAS CONCENTRATION IN THE ATMOSPHERE BY EARTH REMOTE SENSING MEANS. CARTOGRAPHIC AND ANALYTICAL ASSESSMENT OF THE GEOSPATIAL DISTRIBUTION OF ITS VALUES

This article covers the issue of improving the methodology of remote determination of the concentration of greenhouse gases in the atmosphere using the Copernicus Program — Sentinel-5P and MOD11A2.061 Terra satellite systems, as well as the cartographic-analytical assessment of its geospatial distribution. The specified methodology provided for remote determination of the concentration of greenhouse gases CH4, CO, and NO2, development of maps of the distribution of the determined concentration on the territory of Ukraine, localization of areas of formation of the intensity of emission and sequestration of greenhouse gases taking into account data on the soil cover, abiotic conditions of the territory and anthropogenic influences, in particular, military activities. A set of maps of the geospatial distribution of CH4, CO, and NO2, greenhouse gas concentrations, the temperature of the Earth’s surface within the warm period (01.05—30.10) during 2019—2022, as well as a map of the geospatial localization of the maximum concentrations of greenhouse gases within the warm period of the research time interval developed according to the results of emissions from soils, landscapes, production facilities, and combat zones. It was determined that for the warm period of the year, on average for 2019—2022, the optimal temperature range within which the intensity of nitrogen dioxide emission on the territory of Ukraine reaches maximum values is from 13 to 19 °C. If the temperature of the Earth’s surface exceeds the value of 20 °C, the volume of emission emissions is significantly reduced. It is shown that soils with different emission and assessment status are widespread in Ukraine during the warm period. In the South and Southwest of the state, soils with the highest emission capacity of methane to the atmosphere prevail, in particular Jc49-1/3a — Calcaric Fluvisols, Lg54-1a — Gleyic Luvisols, Kh31-2a — Haplic Kastanozems, Gh23-3a — Humic Gleysols. In large cities, as well as in the territories of the South-East of Ukraine, where normal chernozems (Ch22-2a) / (normal chernozems) are common, the maximum concentration of NO2 in the atmosphere is formed, which is caused by the influence of the high temperature of the earth’s surface and the localized consequences of Russia’s military aggression. It was revealed that over the past 4 years, there has been a trend of decreasing CO concentration, which indirectly indicates the negative impact of Russia’s military aggression, a decrease in industrial production, and the relocation of production facilities to the west of the country.

Low-fare public transport and modal shift – Lessons from Bonn, Germany

The air quality in many German cities does not comply with EU-wide standards. Vehicle emissions, in particular, have been identified as an important cause of air pollution. As a result, driving bans for diesel vehicles with critical pollutant groups have been imposed by courts in many places in recent history. Against the backdrop of the growth of major German cities over the last few years, the question has become whether and how a change in the modal split in favor of more environmentally and climate-friendly public transport sector can be achieved. The Federal City of Bonn is one of five model cities that is testing measures to reduce traffic-related nitrogen dioxide emissions through a Climate Ticket as a mobility flat rate for one year for 365 €, which is part of the two-year "Lead City" project funded by the federal government. A quantitative survey (n = 1,315) of Climate Ticket users as well as the logistic regression carried out confirm that a change in individual mobility behavior in favor of public transport is possible by subsidizing the ticket price. The results show that individual traffic could be saved on the city's main congestion axes. In order to achieve a sustainable improvement in air quality, such a Climate Ticket must be granted on a permanent basis, with a well-designed mobility offer and must be generous in terms of the group of authorized persons and the area of validity.

Human activities and increased anthropogenic emissions: A remote sensing study in Cyprus

BackgroundThe effects of human activity on the environment are widespread and widely known. Human activities on most continents can be generalized as shelters and food chains due to the basic requirements of human life. Most of these activities require Land-use Land-cover (LULC) transformations, and their effects can be seen as changes such as increases in the global Land Surface Temperature (LST) and air pollutant concentrations. The present research aims to use remote sensing to monitor LULC transformations in Cyprus. MethodThis research uses Sentinel-3 data, Python programming, geographical information systems, and remote sensing to develop a moving average research method for a case study of Cyprus. Importantly, this work eliminates all political and ethnic boundaries to produce a unified analysis. ResultBased on the research outcomes, the highest mean LST and sulfur dioxide, nitrogen dioxide, and particulate matter (with a diameter of 10 μm or less) emissions occur in the Limassol, Famagusta, Nicosia, and Larnaca districts. These emissions are mainly attributable to artificial surfaces, agricultural areas, and forested and semi-natural areas. These trends may relate to electric power plants, a cement factory, an airport, and intensified agricultural activities in the research area.

Technology of estimating nitrogen dioxide and carbon dioxide emissions by large industrial centers of Western Siberia

Technology of estimating nitrogen dioxide and carbon dioxide emissions by large industrial centers of Western Siberia

Investigating the Nexus Between GHG Emissions and AFOLU Activities: New Insights from C-Vine Copula Approach

The greenhouse gas (GHG) emissions from agriculture, forestry, and other land use (AFOLU) account for more than 10% of the total GHG emissions in Iran. To reduce the environmental impact, assessments of Iran’s GHG emissions status are critical for identifying the national policies to achieve Sustainable Development Goals (SDGs) in the bio-based industry. However, there is no study exploring the dependency between AFOLU and GHG emissions in Iran by using the Vine Copula approach. Hence, the study aims to examine the causality direction and correlation structure among selected horticulture, farming crops, livestock, and poultry products and carbon dioxide (CO2), nitrogen dioxide (N2O), and methane emissions (CH4) in the Iranian agriculture sector over the period 1961–2019, to determine which crops or products are more responsible to deteriorate the environment. The empirical strategy used a C-Vine Copula model to measure the correlations together with the Granger causality (GC) test to analyze the causality links. According to the empirical findings, several crops and products are the sources of emissions. Rice and vegetable cultivations, as well as meat and milk products (Kendall’s τ values of 0.37, 0.33, 0.31, and 0.31, respectively), are the leading sources of CH4 emissions. Legumes, eggs, maize, rice, and milk enhance N2O emissions, while CO2 emissions are caused by apple, potato, and apricot crops (Kendall’s τ values of 0.22, 0.18, and 0.16, respectively). Finally, based on the findings, policy implications are offered.

Defect Engineering for SnO2 Improves NO2 Gas Sensitivity by Plasma Spraying.

Large emissions of nitrogen dioxide (NO2) pose a significant threat to human health, Monitoring its content and implementing timely measures are crucial. Utilizing oxide semiconductors, such as tin dioxide (SnO2), has proven to be an effective way to detect and analyze NO2. The design and preparation of sensing materials with high sensitivity and excellent selectivity is the key to improve the detection efficiency. SnO2 nanopowders with small and uniform particle size, large specific surface area, adjustable defect content, and no impurities were prepared by a new plasma spraying method. The SnO2 nanopowders exhibit outstanding performance in detecting NO2 at a low temperature of 100 °C, the response to 5 ppm of NO2 reaches 48, and the material demonstrates rapid response and recovery times, coupled with excellent selectivity. The exceptional gas-sensitive properties can be attributed to the superior morphology and structure of SnO2. It provides more reaction sites for gas sensitive reactions, fast electron transport, a large number of charge carriers, and improved adsorption of the material to the target gas. This study provides valuable insights into nanomaterial preparation and the enhancement of gas-sensitive properties for SnO2.

The impact of household wealth on soil organic carbon and nitrogen stocks in enset (Ensete ventricosum (welw.) cheesman) based farming systems in southern Ethiopia

Enset [Ensete ventricosum (welw.) cheesman] is a multi-purpose perennial crop that is an important keystone species for home garden agroforestry systems in Ethiopia and has great potential to increase resilience to climate change, sequester carbon and reduce net greenhouse gas emissions. Therefore, it could be an important crop to be grown more widely in the future. Carbon sequestration is in part due to application of large amounts of organic matter to enset. However, availability of organic matter for use in enset-based farming systems is likely to be related to wealth status of farmers. Because evidence on the influence of wealth on soil carbon is limited, this study assessed influence of household wealth on soil organic carbon, nitrogen and carbon dioxide emissions from enset-based farming systems in southern Ethiopia. Farmlands managed by resource rich farmers had significantly higher organic carbon in 0–20 cm soil depth (4.6 % and 5.8 %) than those managed by resource-poor farmers (3.7 % and 4.3 %). Total nitrogen followed similar trends. Results suggest that wealth status influences soil properties by determining organic inputs, highlighting the importance of livestock. Resource-poor farmers who do not own livestock should therefore take measures to obtain inputs from other sources, such as compost or vermi-compost.

Performance prediction and multi-objective optimization for the Atkinson cycle engine using eXtreme Gradient Boosting

In this study, substantial efforts have been done to enhance both the economy and nitrogen dioxide (NOx) emission characteristics of an Atkinson cycle engine (ACE). The integrated simulation model was meticulously formulated and calibrated utilizing GT-Power software based on test data, and four machine learning (ML) models were developed to predict the performance of the ACE. On this basic, the improved multi-objective hybrid particle swarm optimization (IMHPSO) algorithm was proposed to conduct comprehensive optimization of ACE performance. The results highlight the superior prediction performance and generalization ability of the eXtreme Gradient Boosting (XGBoost) model, attaining an average coefficient of determination (R2) of 0.9978 and 0.9966 on the train and test sets, respectively. The IMHPSO algorithm exhibited efficient convergence, uniform distribution and rich diversity across five benchmark test problems, effectively mitigating issues related to local optima and premature convergence. The Optimal Brake Specific Fuel Consumption (BSFC) solution is chosen as the preferred optimized scheme and realizes significant reductions of 8.5 % for BSFC and 96.1 % for NOx emissions, which experiences a remarkable increase of over 1300 times in computational efficiency compared to the GT-Power model. These findings provide theoretical basis, data support and novel insights for multi-objective optimization of ACE performance.

Microwave-assisted efficient synthesis of ZnO nanospheres for low temperature NO2 gas sensor

The development of high-performance nitrogen dioxide gas sensors is indispensable and critical for environmental monitoring and medical fields. Different nanostructural ZnO were efficiently synthesized via a microwave-assisted gas-liquid interface method and the gas sensing performance of ZnO-based gas sensors with different morphologies was comparatively investigated. The results show that the ZnO-3 sensor (microwave reaction temperature is 180 ℃) has the best sensing performance to NO2 at lower operating temperature (120 ℃). It exhibits a high response (Rg/Ra = 397), short response/recovery time (21 s/30 s), and good selectivity to 100 ppm NO2. Moreover, the ZnO-3 sensor exhibits remarkably high response (185 to 100 ppm NO2) even at 25 ℃. In addition, the ZnO sensor can maintain good stability and repeatability in long-term cycling tests to 5 ppm NO2. Evidently, this research offers a reference value for the efficient synthesis of ZnO nanomaterials and real-time detection of nitrogen dioxide emissions.

Impact of urban spatial factors on NO2 concentration based on different socio-economic restriction scenarios in U.S. cities

The impact of socio-economic restriction measures in the United States showed a significant reduction in nitrogen dioxide (NO2) emissions due to the changes in commuting patterns, travel restrictions, and reduced social and economic activities. The data from 60 observation stations across different states representing of different urban settings were used to assess the impacts of dynamic changes in human mobility and static emission sources on NO2 concentrations. Two predictive models, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX) and Long Short-Term Memory (LSTM), were used to forecast NO2 concentrations under a counterfactual scenario in the assumption of no lockdown was implemented. The predicted concentrations were then compared to the observed concentrations to evaluate the difference between the two scenarios, during lockdown and after lockdown by using the last five-years (2015–2019) average as baseline. Results showed that reduction in NO2 varied across stations due to a combination of factors such as human mobility, population density, income, local climate, and stationary sources of NO2 emissions in the neighbourhood. The stations located in high-income regions with higher population density experienced a greater reduction in mobility, resulting in a greater variation in NO2 levels during both periods. In contrast, stations in densely populated low-income regions experienced lower variation in mobility and NO2 levels during the lockdown period but showed recovery during the post-lockdown period. Stations located outside the city with very low population density experienced no significant reduction in NO2 emissions. The study highlights the disparities in NO2 reduction and income inequality exposed by the pandemic, which may have scientific impacts on air quality and health disparities.

Mitigation potential of local traffic interventions on paediatric asthma incidence in EU cities

Abstract Issue/Problem Motorised road traffic is a major contributor to Nitrogen Dioxide (NO2) emissions, contributing to the development and worsening of asthma. With a substantial and escalating prevalence of asthma in urban areas worldwide, this is an issue of public health significance. Description of the problem Deploying methods such as Random Forests and Boosted Regression Trees, this study aims to determine the potential reduction in paediatric asthma incidence in European cities that could be achieved through policy interventions targeting local traffic reductions, such as car-free days. Results In trafficked areas in European cities such as Brussels and Paris, up to 40% of asthma incidence can be attributed to NO2 exposure. Preliminary results show a potential reduction of 29% [95% CI: 20-34%], 21% [95% CI: 13-25%], and 12% [95% CI: 7-15 %] in paediatric asthma incidence in trafficked areas, central urban areas, and residential areas in Brussels, respectively. In Trafficked and Urban Background Areas in Paris, we found a potential reduction of 17% [95% CI: 11 -19%] and 13% [95% CI: 8-16%] in paediatric asthma incidence respectively. Trends and findings are consistent with several methodologies applied. Lessons Using real-world interventions such as car-free days is an innovative way to assess the mitigation potential of diseases associated with traffic. Our study highlights the public health significance of local traffic reduction policies in addressing asthma incidence in urban areas. The recommended public health actions include implementing policy interventions to reduce traffic levels and emissions in urban areas. These findings can be useful for other countries/settings facing similar issues with traffic-related air pollution and asthma incidence. The policy intervention can also positively impact other traffic-related health effects such as physical activity, noise, and availability of green spaces. Key messages • Local traffic reduction policies can significantly reduce (paediatric) asthma incidence in urban areas in large European Cities. • Reducing traffic levels reduces traffic-related air pollution and improves public health via pathways such as promoting physical activity, reducing noise, and increasing green space availability.

Assessment of a deep learning model for monitoring atmospheric pollution: Case study in Antwerp, Belgium

Urban areas face a significant health risk due to atmospheric pollution necessitating continuous monitoring to assess and mitigate its impact and achieve a sustainable city. In this study, a deep learning model was developed and trained using Computational Fluid Dynamics (CFD) simulations to predict the dispersion of nitrogen dioxide (NO2) emissions from traffic. The model’s performance was evaluated by comparing it to real-world field measurements conducted in Antwerp, Belgium, throughout 2016. Temporal comparisons with a traffic-influenced air quality station over the entire year yielded an average correlation coefficient (R) of 0.83 and a mean relative error (MRE) of 0.21. Additionally, a spatial evaluation was conducted by comparing the model’s predictions to a measurement campaign involving 73 samplers. The spatial evaluation resulted in an R of 0.72 and a MRE of 0.18 for samplers located near known emission sources. Notably, the deep learning model demonstrated computational efficiency, outperforming CFD simulations by an order of magnitude of 100 to 1000, enabling real-time pollution monitoring and large-scale scenario studies without compromising the consideration of micro-scale effects that are typically overlooked in larger-scale models.

Effects of integrated aftertreatment system on regulated and unregulated emission characteristics of non-road methanol/diesel dual-fuel engine

In order to ensure that output power, fuel economy and harmful emissions of non-road diesel engines meet the limit requirements, and reduce the dependence on fossil fuels, methanol/diesel dual-fuel combustion mode was conducted on an agricultural four-cylinder common-rail engine, and the effects of DOC + DPF + SCR aftertreatment system on overall performance and emission characteristics of dual-fuel mode were systematically investigated. Results show that dual-fuel mode can reduce both nitrogen oxide (NOx) and soot emissions, but increase nitrogen dioxide (NO2), methanol and formaldehyde (HCHO) emissions. With the increase of load, the conversion efficiency of diesel oxidation catalyst (DOC) to carbon hydrogen (HC), carbon monoxide (CO), methanol and HCHO emissions is continuously improved. DOC can increase NO2/NOx ratio for diesel mode, but reduce NO2/NOx ratio for dual-fuel mode. At full load, the average soot capture efficiency of diesel particulate filter (DPF) for dual-fuel engine is 98.84%, basically realizing zero soot emission, while the conversion efficiency of selective catalytic reduction (SCR) for NOx is 95.22%. The increase of methanol ratio can reduce NOx emissions and nitrous oxide (N2O) generation in SCR for dual-fuel mode. Under non-road steady cycle (NRSC), the maximum brake thermal efficiency (BTE) of diesel mode and dual-fuel mode are 39.4% and 41.0%, respectively. The regulation emissions of dual-fuel engine coupling with integrated DOC + DPF + SCR aftertreatment system can meet non-road Euro-V and US-Tier 4 emission standards.