Average Emission Rate Research Articles

Real-Time LNG Buses Emissions Prediction Based on a Temporal Fusion Trans-Formers Model

Emissions from transportation are one of the key factors preventing the achievement of carbon peaking and carbon neutrality by 2050, with particular attention to emissions from buses. Specifically, few research has been conducted on the exhaust emissions characteristics of liquified natural gas (LNG) buses under different driving scenarios. This study proposed a framework for predicting exhaust emissions of LNG buses based on the portable emission measurement system and GPS collaborative perception data. Firstly, the emission distribution characteristics of CO2, CO, HC, and NOx from LNG buses in real-world driving were analyzed by visualization methods. Then, the real-time exhaust emissions of LNG buses were predicted based on the temporal fusion transformers model for both urban and suburban sections of Zhenjiang City, and the model validity was verified. The current and past 10 s driving states were used for predicting the emission rate of LNG buses. The results showed that the proposed model outperforms other advanced algorithms in real-time exhaust emissions prediction of LNG buses, with an average R2 value higher than 0.94 and an average MAPE reduction of 14.19%. The error assessment revealed that the emission values and average emission rates are higher when driving in the urban section compared to the suburban section. Among the influencing factors, traffic conditions have the most significant impacts on the exhaust emissions of LNG buses, followed by road conditions and driving states, with relative feature importance of 48.9, 34.8, and 16.3%, respectively. Additionally, the current and past 10 s driving states also significantly influenced real-time predictions. This study provides an essential theoretical reference for reducing exhaust emissions for city buses.

Seasonal ammonia emissions from an intensive beef cattle feedlot in Victoria Australia

Ammonia (NH3) emitted from concentrated animal feeding operations can cause environmental and health problems, and indirectly contribute to greenhouse gas emissions. Cattle feedlots are known to be large sources of NH3, but few studies have documented seasonal emissions from Australian feedlots. We conducted two field campaigns to measure NH3 emissions from an intensive beef cattle feedlot in southeast Australia, and these results were combined with previous measurements at the same feedlot to document seasonal variations in emissions and to derive annual feedlot emission factors (EFs). Emission rates were calculated with an inverse dispersion modelling (IDM) technique, based on NH3 concentrations measured at the feedlot with open-path lasers (OPLs). The average area emission rates in spring, summer, autumn and winter were 90.5, 167.4, 96.2 and 86.8 μg NH3 m−2 s−1 from the cattle pens, and 22.5, 18.1, 7.7 and 20.7 μg NH3 m−2 s−1 from the manure stockpile area, respectively. The total per-animal EFs ranged from 126.0 (autumn) to 190.2 g NH3 animal−1 d−1 (summer), representing a loss of 47.5–64.6% of the fed N. Seasonal variations in emissions were related to air temperature. Slight changes in crude protein content of the cattle diet may also have impacted seasonal variability. Taking seasonal variations into consideration, the average feedlot EF was 160.4 g NH3 animal−1 d−1, with 90% of the emissions coming from the cattle pens. Extrapolating the EF to all feedlot cattle in the country, the direct NH3 emissions from Australian feedlots amount to 65.2 Gg NH3 annually, or 3.7% of the national total. Our study benchmarks seasonal and annual EFs and N losses for Australian commercial feedlots, and provides a baseline for extrapolating the impacts of mitigation efforts.

Non-methane hydrocarbons from integrated semiconductor manufacturing processes: Assessments of chemical footprints, emissions factors, and treatment efficiency

Non-methane hydrocarbons (NMHCs) are harmful chemicals produced from organic solvent use in industrial sectors. Wafer fabrication process from semiconductor fabs utilizes many organic solvents that could intensify NMHCs emissions. This study comprehensively assessed the Total NMHCs (TNMHCs) emissions from 141 exhaust pipes of 59 semiconductor fabs in Taiwan with regards to their concentrations, emission factors, treatment efficiency, and total chemical footprint (ChF) for human toxicity. Throughout three semi-processes of wafer fabrication, the raw emitted NMHCs concentrations were in the ranges of 3–131.25 ppm, 8–288 ppm, and 23–567 ppm for wafer cleaning (WC), photoresist stripping (PS), and photolithography (PL). The average untreated TNMHCs emissions rate was 0.2 ± 0.08 kg/h < 1.72 ± 0.09 kg/h < 2.55 ± 0.08 kg/h in the order of WC < PS < PL, whereas the average treated emissions rates were in the ranges of 0.09–0.10 kg/h. The estimated emission factors for wafer fabrication process were 0.30 ± 0.04 kg/kg, 0.07 ± 0.02 kg/piece, and 1.37 ± 0.11 kg/m2. Further assessment has found that zeolite rotary concentrator and regenerative thermal oxidations (ZRC + RTO) possess the highest overall performances for abating TNMHCs emission with efficiency ranges of 92.31–99.56% (n = 66). ZRC + RTO could produce outlet TNMHCs within the ranges of 2.94–5.56 ppm, which were lower than the upcoming revised emission standard of <14 ppm. ChF analysis of the estimated NMHCs species showed that PL had the largest untreated ChF, followed by PS, and WC of which the ChF were 15.72, 7.05, and 6.79 cases. The speciated ChF was dominated by acetone, methanol, and isopropyl alcohol from three semi-processes. The ChF analysis in this study could potentially become a supporting instrument for industrial sectors to determine the major contributing processes and chemicals with adverse impacts to human health.

The Influence of Seasonal Variability of Eutrophication Indicators on Carbon Dioxide and Methane Diffusive Emissions in the Largest Shallow Urban Lake in China

Eutrophication is prevalent in urban lakes; however, a knowledge gap exists regarding eutrophication influences on carbon dynamics in these ecosystems. In the present study, we investigated the carbon dioxide (CO2) and methane (CH4) concentration and diffusion fluxes in Lake Tangxun (the largest shallow Chinese urban lake) in the autumn and winter of 2022 and spring and summer of 2023. We found that Lake Tangxun served as a source of GHGs, with average emission rates of 5.52 ± 12.16 mmol CO2 m−2 d−1 and 0.83 ± 2.81 mmol CH4 m−2 d−1, respectively. The partial pressure of dissolved CO2 (pCO2) (averaging 1321.39 ± 1614.63 μatm) and dissolved CH4 (dCH4) (averaging 4.29 ± 13.71 μmol L−1) exceeded saturation levels. Seasonal variability was observed in the pCO2 and dCH4 as well as CH4 fluxes, while the CO2 flux remained constant. The mean pCO2 and dCH4, as well as carbon emissions, were generally higher in summer and spring. pCO2 and dCH4 levels were significantly related to total nitrogen (TN), total phosphorus (TP), and ammonium-nitrogen (N-NH4+), and N-NH4+ was a main influencing factor of pCO2 and dCH4 in urban eutrophic lakes. The positive relationships of pCO2, dCH4 and trophic state index highlighted that eutrophication could elevate CO2 and CH4 emissions from the lake. This study highlights the fact that eutrophication can significantly increase carbon emissions in shallow urban lakes and that urban lakes are substantial contributors to the global carbon budget.

The ratio but not individual of fragile to refractory DOM affects greenhouse gases release in different trophic level lakes

Inland shallow lakes are recognized as an important source of greenhouse gases (GHGs), and their contribution is expected to increase due to global eutrophication. The generation and release of GHGs involved multiple variables, leading to many uncertain potential factors. This study examined the emission characteristics of GHGs at the water-air interface in 12 shallow lakes categorized into four eutrophic levels in the Yangtze River basin. The average emission rates of CH4, CO2 and N2O were 1.55, 3.43, 18.13 and 30.47 mg m−2 h−1, 4.12, 14.64, 25.11 and 69.84 mg m−2 h−1, and 0.2, 0.25, 0.43 and 0.79 mg m−2 day−1 in the oligotrophic, mesotrophic, eutrophic and hypereutrophic lakes, respectively. There were significant correlations between eutrophic levels and the emission rates of CH4 and CO2 (p < 0.05). Redundancy analysis and Mantel test were conducted to further examine the key factors influencing carbon emissions from eutrophic water. It was found that the presence of algae and nutrients in the overlying water played a crucial role in the release of GHGs, indicating the importance of ecosystem productivity in the carbon budget of the lake. In order to assess the bioavailability of organic matter, a new indicator called R(P/H) was proposed. This indicator represents the ratio of protein and humus-like components, which were obtained through EEMs-PARAFAC modeling. The relationship between R(P/H) and CH4 was found to be exponential (R2 = 0.90). Additionally, R(P/H) showed a linear relationship with CO2 and N2O (R2 = 0.68, R2 = 0.75). Therefore, it is crucial to consider R(P/H) as an important factor in accurately estimating global GHG emission fluxes in the future, especially with advancements in the database.

Relationships between different types of biochar and N2O emissions during composting based on roles of nosZ-carrying denitrifying bacterial communities enriched on compost and biochar particles

Biochar has demonstrated the potential in mitigating N2O emissions during composting. However, little is known about how microbial communities on biochar particles interact with N2O emissions. This study selected three types of biochar (corn stalk biochar (CSB), rape straw biochar (RSB), and bamboo charcoal (BC)) to investigate the relationship between N2O emissions and denitrifying bacterial communities on compost and biochar particles. The results showed that N2O emissions rate were higher in the thermophilic phase, and the average emissions rate of BC treatment were lower 40% and 26% than CSB and RSB, respectively. The nosZ-carrying denitrifying bacterial community played a key role in reducing N2O emissions, and the network indicated that Rhizobium and Paracoccus on compost particles may have played major roles in reducing N2O emissions, but only Paracoccus on biochar particles. Notably, BC enhanced the efficiency of N2O emission reduction by enhancing the abundance of these key genera.

Modeling Mushrooms’ Carbon Dioxide Emission and Heat Exchange Rates for Synergistic Cultivation with Leafy Greens

The cultivation of mushrooms in controlled environments generates a significant amount of CO2 as a by-product. This presents opportunities for carbon dioxide (CO2) enrichment in leafy green production. This study aimed to develop a model for CO2 emission and heat exchange rates that can be used to support the synergistic cultivation of mushrooms and leafy greens. This was achieved by aggregating data from literature with experimental data gathered in two different testing spaces. The average CO2 emission and heat exchange rates for shiitake incubated at 21 °C were determined and a CO2 emission rate model for mixed substrate in incubation was developed based on indoor temperature variations. The results indicated that oyster mushrooms have a notable CO2 enrichment potential, twice that of shiitake in the incubation stage and five times more in fructification. Additionally, oyster mushrooms released a significant amount of heat during incubation. In contrast, shiitake mushrooms with their minimal heat exchange rate during incubation could offer an energy-efficient option for synergistic cultivation with leafy greens in environments where cooling is required year-round. Moreover, it was observed that the CO2 emission rate of a full-scale incubation chamber is strongly correlated with indoor temperature. These findings offer valuable information for modeling the CO2 emission and heat exchange rates of mushroom and leafy green farms.

Using Ground- and Drone-Based Surface Emission Monitoring (SEM) Data to Locate and Infer Landfill Methane Emissions

Ground- and drone-based surface emission monitoring (SEM) campaigns were performed at two municipal solid waste landfills, during the same week as mobile tracer correlation method (TCM) testing was used to measure the total methane emissions from the same landfills. The G-SEM and the D-SEM data, along with wind data, were used as input into an inverse modeling approach combined with an optimization-based methane emission estimation method (implemented in a tool called SEM2Flux). This approach involves the use of backward dispersion modeling to estimate the whole-site methane emissions from a given landfill and the identification of locations and emission rates of major leaks. SEM2Flux is designed to exploit the measured surface methane concentration concurrently with wind data and tackle two problems: (1) inferring the estimates of methane rates from individual landfills, and (2) identifying the likely locations of the main emission sources. SEM2Flux results were also compared with emission estimates obtained using TCM. In Landfill B, the average TCM-measured methane emissions was 1178 Kg/h, with a standard deviation of 271 Kg/h. In Landfill C, the average TCM-measured emission rate was 601 Kg/h, with a standard deviation of 292 Kg/h. For both landfills, the D-SEM data yielded statistically similar estimates of methane emissions as the TCM-measured emissions. On the other hand, the G-SEM data yielded comparable estimates of emissions to TCM-measured emissions only for Landfill C, where the D-SEM and G-SEM data were statistically not different. The results of this study showcase the ability of this method using surface concentrations to provide a rapid and simple estimation of fugitive methane emissions from landfills. Such an approach can also be used to assess the effectiveness of different remedial actions in reducing fugitive methane emissions from a given landfill.

Real-world tailpipe emissions from autorickshaws (3-wheelers) under heterogeneous traffic conditions.

The current study aimed to measure real-world emissions of three-wheeled autorickshaws powered by CNG and parameters (such as speed, acceleration, air-fuel (A/F) ratio, and rpm) influencing 3-wheeler emission rates. Test vehicles manufactured under Bharat Standards BS-III and BS-IV were monitored for exhaust emissions in Delhi city using a portable exhaust emission measurement system (AVL Ditest Gas 1000). The average emission rates of CO, HC, and NO gases for on-road autorickshaws were found to be 0.015 ± 0.017, 0.003 ± 0.0017, and 0.007 ± 0.005 g/s, respectively. Further, the highest emission factor values of 3.98 g/km and 3.93 g/km were estimated for CO and HC+NO gases, respectively. These values were found to be 1.4-3.2 times higher than the respective BS emission norms (BS III-CO =1.25 g/km, HC+NO = 1.25 g/km; BS-IV-CO = 0.94 g/km and HC+NO = 0.94 g/km). In this study, it was observed that the driving pattern and emissions were affected by traffic characteristics, driver behavior (constant acceleration and deceleration), and vehicle characteristics. The air-fuel ratio (A/F) was found to correlate highly with emission rates, followed by acceleration/deceleration and speed. Further analysis found that more than 70% of the aggregated emissions were due to acceleration and deceleration, which contributed to nearly 70% of the travel time. This was followed by the breakdown of speed and emissions into different bins, which found that 20-30 kmph has a higher emission rate and 40-50 kmph bin has a lower emission rate.

Two years of satellite-based carbon dioxide emission quantification at the world's largest coal-fired power plants

Abstract. Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the globe. Satellites have the ability to detect and quantify emissions from large CO2 point sources, including coal-fired power plants. In this study, we routinely made observations with the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite imaging spectrometer and the Orbiting Carbon Observatory-3 (OCO-3) instrument aboard the International Space Station at over 30 coal-fired power plants between 2021 and 2022. CO2 plumes were detected in 50 % of the acquired PRISMA scenes, which is consistent with the combined influence of viewing parameters on detection (solar illumination and surface reflectance) and unknown factors (e.g., daily operational status). We compare satellite-derived emission rates to in situ stack emission observations and find average agreement to within 27 % for PRISMA and 30 % for OCO-3, although more observations are needed to robustly characterize the error. We highlight two examples of fusing PRISMA with OCO-2 and OCO-3 observations in South Africa and India. For India, we acquired PRISMA and OCO-3 observations on the same day and used the high-spatial-resolution capability of PRISMA (30 m spatial/pixel resolution) to partition relative contributions of two distinct emitting power plants to the net emission. Although an encouraging start, 2 years of observations from these satellites did not produce sufficient observations to estimate annual average emission rates within low (&lt;15 %) uncertainties. However, as the constellation of CO2-observing satellites is poised to significantly improve in the coming decade, this study offers an approach to leverage multiple observation platforms to better quantify and characterize uncertainty for large anthropogenic emission sources.

Spatial attribution of aircraft mass balance experiment CO2 estimations for policy-relevant boundaries: New York City

To effectively address the unprecedented acceleration of climate change, cities across the United States are leading efforts to reduce greenhouse gas emissions. Coherent, aggressive, and lasting mitigation policies in controlling carbon emissions are beginning to be adopted to help strengthen climate resilience across different sectors. However, evaluating the effectiveness of current climate legislation requires careful monitoring of emissions through measurable and verifiable means to inform policy decisions. As a part of this effort, we developed a new method to spatially allocate aircraft-based mass balance carbon dioxide (CO2) emissions. In this work, we conducted 7 aircraft flights, performed downwind of New York City (NYC) to quantify CO2 emissions during the nongrowing seasons between 2018 and 2020. We used an ensemble of emission inventories and transport models to calculate the fraction of enhancements (Φ) produced by sources within the policy-relevant boundaries of the 5 NYC boroughs and then applied that to the bulk emissions calculated using the mass balance approach. We derived a campaign-averaged source-apportioned mass balance CO2 emission rate of (57 ± 24) (1σ) kmol/s for NYC. We evaluated the performance of this approach against other top-down methods for NYC including inventory scaling and inverse modeling, with our mean emissions estimate resulting in a 6.5% difference from the average emission rate reported by the 2 complementary approaches. By combining mass balance and transport model approaches, we improve upon traditional mass balance experiment methods to enable quantification of emissions in complex emission environments. We conducted an assessment using an ensemble of emission inventories and transport models to determine the sources of variability in the final calculated emission rates. Our findings indicate that the choice of inventory accounted for 2.0% of the variability in the emission estimates and that the atmospheric transport model contributed 3.9% at the campaign level. Additionally, on average, at the daily scale, the transport model contributed 7.6% and the inventory accounted for 14.1%. The daily flight-to-flight variability contributed a significant portion, at 42.1%. This approach provides a solution to the difficulty of interpreting aircraft-based mass balance results in complex emission environments.

Benign design of intumescent fire protection coatings for steel structures containing biomass humic acid as carbon source

The application of biomass materials in engineering has received a great deal of attention. In this work, a new eco-friendly biomass-based intumescent coating, composed of natural and sustainable biomass humic acid (HA), ammonium polyphosphate (APP), melamine (MEL), and zinc borate (ZB), was developed to endow steel structures with excellent fire resistance and fire retardancy. When exposed to a butane torch, the intumescent coating (1.5 mm thickness) reduced the maximum temperature observed on the backside of a 3 mm thick steel plate by over 350 °C. Moreover, the cone calorimeter results showed that thanks to the formation of an extremely intumescent and dense char layer, the peak heat release rate (pHRR) and total heat release (THR) of intumescent coatings (S5) were reduced by 86 % and 61 %, respectively, as compared to that of the control epoxy coating, meanwhile, significant reductions in fire growth rate (FIGRA, −89 %) and maximum average rate of heat emission (MARHE, −86 %) were also observed, suggesting that 2 wt% ZB loading is required to achieve optimal level of fire resistance and fire retardancy. Furthermore, the mechanism of action was elucidated by SEM-EDX, FTIR, Raman, and XRD analyses. Results revealed that the combination of APP, MEL, HA, and ZB promoted the formation of a continuous and cohesive graphitized intumescent char layer on the surface of the steel plate, reinforced by borophosphate and zinc-containing species, which effectively prevents the transfer of heat and fuel, thereby enhancing the fire resistance and fire retardancy of the intumescent coating. This eco-friendly intumescent coating provides a strategy for developing new green fire protection systems using widely available, low-cost biomass materials without using hazardous ingredients.

Intercomparison of detection and quantification methods for methane emissions from the natural gas distribution network in Hamburg, Germany

Abstract. In August and September 2020, three different measurement methods for quantifying methane (CH4) emissions from leaks in urban gas distribution networks were applied and compared in Hamburg, Germany: the “mobile”, “tracer release”, and “suction” methods. The mobile and tracer release methods determine emission rates to the atmosphere from measurements of CH4 mole fractions in the ambient air, and the tracer release method also includes measurement of a gaseous tracer. The suction method determines emission rates by pumping air out of the ground using soil probes that are placed above the suspected leak location. The quantitative intercomparison of the emission rates from the three methods at a small number of locations is challenging because of limitations of the different methods at different types of leak locations. The mobile method was designed to rapidly quantify the average or total emission rate of many gas leaks in a city, but it yields a large emission rate uncertainty for individual leak locations. Emission rates determined for individual leak locations with the tracer release technique are more precise because the simultaneous measurement of the tracer released at a known rate at the emission source eliminates many of the uncertainties encountered with the mobile method. Nevertheless, care must be taken to properly collocate the tracer release and the leak emission points to avoid biases in emission rate estimates. The suction method could not be completed or applied at locations with widespread subsurface CH4 accumulation or due to safety measures. While the number of gas leak locations in this study is small, we observe a correlation between leak emission rate and subsurface accumulation. Wide accumulation places leaks into a safety category that requires immediate repair so that the suction method cannot be applied to these larger leaks in routine operation. This introduces a sampling bias for the suction method in this study towards the low-emission leaks, which do not require immediate repair measures. Given that this study is based on random sampling, such a sampling bias may also exist for the suction method outside of this study. While an investigation of the causal relationship between safety category and leak size is beyond the scope of this study, on average higher emission rates were observed from all three measurement-based quantification methods for leaks with higher safety priority compared to the leaks with lower safety concern. The leak locations where the suction method could not be applied were the biggest emitters, as confirmed by the emission rate quantifications using mobile and tracer methods and an engineering method based on the leak's diameter, pipeline overpressure, and depth at which the pipeline is buried. The corresponding sampling bias for the suction technique led to a low bias in derived emission rates in this study. It is important that future studies using the suction method account for any leaks not quantifiable with this method in order to avoid biases, especially when used to inform emission inventories.

Challenges to Accurate Estimation of Methane Emission from Septic Tanks with Long Emptying Intervals.

Septic tanks in low- and middle-income countries are often not emptied for a long time, potentially resulting in poor pollutant removal efficiency and increased greenhouse gas emissions, including methane (CH4). We examined the impact of long emptying intervals (4.0-23 years) on the biochemical oxygen demand (BOD) removal efficiency of 15 blackwater septic tanks and the CH4 emission rates of 23 blackwater septic tanks in Hanoi. The average BOD removal efficiency was 37% (-2-65%), and the average CH4 emission rate was 10.9 (2.2-26.8) g/(cap·d). The emptying intervals were strongly negatively correlated with BOD removal efficiency (R = -0.676, p = 0.006) and positively correlated with CH4 emission rates (R = 0.614, p = 0.001). CH4 emission rates were positively correlated with sludge depth (R = 0.596, p = 0.002), but against expectation, negatively correlated with BOD removal efficiency (R = -0.219, p = 0.451). These results suggest that shortening the emptying interval improves the BOD removal efficiency and reduces the CH4 emission rate. Moreover, the CH4 emission estimation of the Intergovernmental Panel on Climate Change, which is a positive conversion of BOD removal, might be inaccurate for septic tanks with long emptying intervals. Our findings suggest that emptying intervals, sludge depth, and per-capita emission factors reflecting long emptying intervals are potential parameters for accurately estimating CH4 emissions from septic tanks.

Climatic zone effects of non-native plant invasion on CH4 and N2O emissions from natural wetland ecosystems

Plant invasion can significantly alter the carbon and nitrogen cycles of wetlands, which potentially affects the emission of greenhouse gases (GHGs). The extent of these effects can vary depending on several factors, including the species of invasive plants, their growth patterns, and the climatic conditions prevailing in the wetland. Understanding the global effects of plant invasion on the emission of methane (CH4) and nitrous oxide (N2O) is crucial for the climate-smart management of wetlands. Here, we performed a global meta-analysis of 207 paired case studies that quantified the effect of non-native plant invasion on CH4 and N2O emissions in tropical/sub-tropical (TS) and temperate (TE) wetlands. The average emission rate of CH4 from the TS wetlands increased significantly from 337 to 577 kg CH4 ha−1 yr−1 in areas where native plants had been displaced by invasive plants. Similarly, in TE wetlands, the emission rates increased from 211 to 299 kg CH4 ha−1 yr−1 following the invasion of alien plant species. The increase in CH4 emissions at invaded sites was attributed to the increase in plant biomass, soil organic carbon (SOC), and soil moisture (SM). The effects of plant invasion on N2O emissions differed between TS and TE wetlands in that there was no significant effect in TS wetlands, whereas the N2O emissions reduced in TE wetlands. This difference in N2O emissions between climate zones was attributed to the depletion of NH4+ and NO3− in soils and the lower soil temperature in temperate regions. Overall, plant invasion increased the global net CH4 emissions from natural wetlands by 10.54 Tg CH4 yr−1. However, there were variations in CH4 emissions across different climatic zones, indicated by a net increase in CH4 emissions, of 9.97 and 0.57 Tg CH4 yr−1 in TS and TE wetlands, respectively. These findings highlight that plant invasion not only strongly stimulates the emission of CH4 from TS wetlands, but also suppresses N2O emissions from TE wetlands. These novel insights immensely improve our current understanding of the effects of climatic zones on biogeochemical controlling factors that influence the production of greenhouse gases (GHGs) from wetlands following plant invasion. By analyzing the specific mechanisms by which invasive plants affect GHG emissions in different climatic zones, effective strategies can be devised to reduce GHG emissions and preserve wetland ecosystems.

Spatiotemporal analysis of CO2 emissions and emission reduction potential of Beijing buses using smart card data

Human activities, primarily carbon dioxide emissions, have undeniably caused global warming. The transportation sector contributes about a quarter of global CO2 emissions. While replacing traditional buses with electric ones has reduced emissions, it is crucial to consider the indirect emissions resulting from electricity consumption. This study proposes a framework for modeling bus emissions using smart card data, integrating spatiotemporal distribution characteristics and emission reduction potentials. Our analysis reveals that routes spanning 10–30 km contribute to 81% of total bus emissions, with an average emission rate of 56.2 gCO2/per-km for residents traveling by bus. Bus emissions also exhibit cyclical variations during holidays, weekdays, and weekends, indicating spatial clustering and trends. Although the area within Beijing's 4th Ring Road constitutes only 13% of the total area within the 6th Ring Road, it generates almost half of the CO2 emissions. With urban expansion, total bus emissions increase gradually, but emission intensity decreases. This study emphasizes the potential for reducing emissions through improved public transportation operations. It recommends fully electrifying the bus fleet and employing low grid emission factors, which could reduce emissions by up to 71% compared to diesel options. Electrification of buses and optimizing power generation on the grid are essential priorities for emission reduction.

Hydrologic and Organic Carbon Quality Controls on Nitrous Oxide Dynamics Across a Variably Confined Karst Aquifer

AbstractAtmospheric concentrations of the potent greenhouse gas nitrous oxide (N2O) have increased approximately 20% since the pre‐industrial era. Riverine systems represent an important atmospheric source of N2O however, the influence of varying hydrogeological conditions and dissolved organic carbon (DOC) quantity and quality on N2O dynamics remains poorly understood. We investigate effects of these processes on N2O dynamics in the Santa Fe River watershed in north‐central Florida, USA where a gradient in DOC quantity and quality is caused by varying surface water‐groundwater exchange. The watershed is underlain by the karstic Floridan aquifer, which is confined in the headwaters and contains terrigenous recalcitrant DOC, while the unconfined lower reaches contain protein‐like labile DOC substrates. Where extensive surface water‐groundwater exchange occurs at the boundary between the confined and unconfined aquifer, incomplete denitrification raises N2O concentration to 6.1 μg N‐N2O L−1, approximately 20 times greater than the concentration expected from equilibration with atmospheric N2O. These elevated concentrations create average local fluxes reaching ∼950 μg N‐N2O m−2 hr−1 within the mixing zone. Summation of spatially explicit emission estimates for upstream, mixing zone, and downstream river sections yield a total average emission rate of ∼3,758 kg N‐N2O yr−1. Despite having the smallest surface area across the river transect (∼8%), emissions from the mixing zone account for 26% of the total river emission rate. These findings highlight the potential contributions from karst landscapes to N2O cycling caused by varying redox conditions during surface water‐groundwater exchange, which creates hotspots of N2O production and emissions.

CO2, CH4, and N2O emissions from dredged material exposed to drying and zeolite addition under field and laboratory conditions

Dredging, the removal of sediment from water courses, is generally conducted to maintain their navigability and to improve water quality. Recent studies indicate that dredging can significantly reduce aquatic greenhouse gas (GHG) emissions. These studies, however, do not consider the potential emission from the dredged material (sludge) in the depot. In addition, it is unknown if and how GHG emissions from sludge depots can be reduced. Here we present spatiotemporal variations of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) fluxes, as well as environmental variables from a sludge depot located in the Netherlands. Measurements were conducted monthly from the time the depot was filled until the sludge was dry and the depot was abolished. We also experimentally assessed the GHG mitigation potential of 1) keeping the sludge permanently inundated, and 2) the addition of different amounts of zeolite to increase sludge nitrogen binding capacity to reduce N2O emissions. In the depot and in the laboratory, a decrease in moisture content coincided with increased CO2 and N2O emissions while CH4 emissions decreased. We observed that permanent inundation reduced emissions (∼4 times less CO2-eq than in drying sludge). Adding zeolite lowered N2O fluxes from permanently inundated sludge but did not reduce total GHG emissions. During the depot's operational period, average CO2, CH4, and N2O fluxes were 5078, 27, and 5 mg m−2 d−1, respectively. GHG emissions from drying sludge occurred mainly in the form of CO2 (73% of the total CO2-eq emissions), with average GHG emission rates comparable to those reported for ditches and ponds. We estimate that approximately 14 tons of CO2-eq were emitted from the 0.011 km2 depot, which contained ∼20,000 m3 of sludge, during its entire operational period, and we argue that more studies are needed, considering different sludge origins, to expand our understanding of sludge depots.

Bismaleimide resin co‐modified by allyl ether of resveratrol and allyl ether of eugenol‐grafted polysiloxane with enhanced toughness, heat resistance, and flame retardancy

AbstractModifying bismaleimide (BMI) resin to improve its toughness and flame retardancy without sacrificing the glass transition temperature (Tg) and thermal decomposition stability is still a challenge. In this study, we employed the allyl ether of resveratrol (AER) and eugenol allyl ether grafted polysiloxane (PMES‐Allyl) to co‐modify BMI resin. For the BMI/AER/PMES‐Allyl (BAPA) resin, the content of PMES‐Allyl was 15 wt%, and the molar ratio of maleimide and allyl groups was controlled as 1:0.8. Compared to commercial 2,2′‐diallyl bisphenol A modified BMI (BD) resin, the BAPA resin exhibited superior thermal properties after curing, due to the role of AER in improving the crosslinking density and chain rigidity of the network. The cured BAPA resin had a Tg more than 380°C, and its initial thermal decomposition temperature (Td5%) and char yield (Y800°C) reached 424.8°C and 46.4%, respectively. Meanwhile, PMES‐Allyl as a rubber phase could effectively improve the toughness of the cured resin. The impact strength of the cured BAPA resin was 15.2 kJ/m2. Moreover, the two modifiers both helped to improve the flame retardancy of the material. Compared with the cured BD resin, the cured BAPA resin showed a decrease of peak heat release rate (PHRR), total heat release (THR), and maximum average rate of heat emission (MARHE) by 25.5%, 35.5%, and 31.5%, respectively. Its total smoke production (TSP) was only 35.6% of that of the cured BD resin. In addition, the cured BAPA resin also displayed a lowered water absorption and improved thermal aging resistance. Therefore, it is suggested to be a promising and high‐performance thermosetting resin for cutting‐edge applications.

Multi-objective task scheduling method for cyber–physical–social systems in fog computing

Cyber–physical–social systems refer to new systems that involve a large number of Internet of Things (IoT) devices connected through an interconnected network. These systems gather data for various services such as transportation, energy, healthcare, etc. This data is then transmitted to the cloud layer for storage, processing, and access. To handle the significant workload offloaded from resource-constrained IoT devices without latency or bandwidth issues, a technology called fog computing has emerged as an intermediary layer between the cloud and IoT devices. The quality of service (QoS) provided by fog computing relies on efficient task scheduling to optimize energy consumption and maximum execution time. This task scheduling problem is known to be NP-hard and cannot be solved with high precision in a reasonable time using traditional techniques or existing metaheuristic-based task schedulers. Therefore, this study proposes a new multi-objective task scheduling approach based on a modified marine predators algorithm (MMPA) to simultaneously minimize energy consumption and make-span under the Pareto optimality theory. This variant is named the multi-objective MMPA (M2MPA). M2MPA enhances the performance of the MPA by incorporating the polynomial crossover operator to improve its exploration operator and the adaptive CF parameter to enhance its exploitation operator. The effectiveness of M2MPA is evaluated using eighteen tasks with different scales (small, large, and medium) and heterogeneous workloads assigned to two hundred fog devices with varying processing speeds. Finally, M2MPA is compared with six well-established techniques using various performance indicators, including carbon dioxide emission rate, flowtime, make-span, and energy consumption. The experimental findings demonstrate the substantial superiority of M2MPA over all competing algorithms across different performance indicators and the Wilcoxon rank-sum test. Quantitatively, M2MPA achieves an average make-span of 10.095, average energy consumption of 3.77E+06, average flowtime of 8.39E+03, and average carbon dioxide emission rate of 8.88E+06.