Variable Emission Research Articles

Re-estimating China's lake CO2 flux considering spatiotemporal variability

The spatiotemporal variability of lake partial carbon dioxide pressure (pCO2) introduces uncertainty into CO2 flux estimates at the lake water-air interface. Knowing the variation pattern of pCO2 is important for obtaining accurate global estimation. Here we examine seasonal and trophic variations in lake pCO2 based on 13 field campaigns conducted in Chinese lakes from 2017 to 2021. We found significant seasonal fluctuations in pCO2, with decreasing values as trophic states intensify within the same region. Saline lakes exhibit lower pCO2 levels than freshwater lakes. These pCO2 dynamics result in variable areal CO2 emissions, with lakes exhibiting different trophic states (oligotrophication > mesotrophication > eutrophication) and saline lakes differing from freshwater lakes (−23.1 ± 17.4 vs. 19.3 ± 18.3 mmol m−2 d−1). These spatiotemporal pCO2 variations complicate total CO2 emission estimations. Using area proportions of lakes with varying trophic states and salinity in China, we estimate China's lake CO2 flux at 8.07 Tg C yr−1. In future studies, the importance of accounting for lake salinity, seasonal dynamics, and trophic states must be noticed to enhance the accuracy of large-scale carbon emission estimates from lake ecosystems in the context of climate change.

Changes in the socio-economic characteristics of households can decouple carbon emissions and consumption growth in China

The increasing consumption in developing countries poses a significant challenge to global climate change mitigation efforts. Understanding household characteristics is essential in exploring potential strategies to decouple carbon emissions from the increasing consumption. In this study, we utilized the multi-region input-output (MRIO) method to calculate household carbon emissions in China for the years 2012, 2016, and 2018. Using micro-level household survey data, we identified variations in carbon emissions among households with different economic levels and socioeconomic characteristics. Our analysis revealed that changes in household consumption structure and carbon intensity among households with improved economic levels, could result in a reduction of carbon emissions by 14 % to 19 %. Furthermore, households characterized by higher education levels, larger family sizes, and smaller housing units were found to generate lower carbon emissions per capita and per unit of consumption spending. By simulating household carbon emissions up to 2030, we identified three potential pathways to achieve decoupling: household economic upgrading, transitioning to low-carbon household characteristics, and reducing material consumption. Our findings indicate that even with a projected 75 % increase in per capita consumption volume by 2030, per capita household carbon emissions could still be lower than the levels observed in 2016. These results underscore the potential of consumption-focused policy measures in facilitating the decoupling of household consumption from carbon emissions.

Lactation modeling and the effects of rotational crossbreeding on milk production traits and milk-spectra-predicted enteric methane emissions

Rotational crossbreeding has not been widely studied in relation to the enteric methane emissions of dairy cows, nor has the variation in emissions during lactation been modeled. Milk infrared spectra could be used to predict proxies of methane emissions in dairy cows. Therefore, the objective of this work was to study the effects of crossbreeding on the predicted infrared proxies of methane emissions and the variation in the latter during lactation. Milk samples were taken once from 1,059 cows reared in 2 herds, and infrared spectra of the milk were used to predict milk fat (mean ± SD; 3.79 ± 0.81%) and protein (3.68 ± 0.36%) concentrations, yield (21.4 ± 1.5 g/kg dry matter intake), methane intensity (14.2 ± 2.0 g/kg corrected milk), and daily methane production (358 ± 108 g/d). Of these cows, 620 were obtained from a 3-breed (Holstein, Montbéliarde, and Viking Red) rotational mating system, and the rest were purebred Holsteins. Milk production data and methane traits were analyzed using a nonlinear model that included the fixed effects of herd, genetic group, and parity, and the 4 parameters (a, b, c, and k) of a lactation curve modeled using the Wilmink function. Milk infrared spectra were found to be useful for direct prediction of qualitative proxies, such as methane yield and intensity, but not quantitative traits, such as daily methane production, which appears to be better estimated (450 ± 125 g/d) by multiplying a measured daily milk yield by infrared-predicted methane intensity. Lactation modeling of methane traits showed daily methane production to have a zenith curve, similar to that of milk yield but with a delayed peak (53 vs. 37 d in milk), whereas methane intensity is characterized by an upward curve that increases rapidly during the first third of lactation and then slowly till the end of lactation (10.5 g/kg at 1 d in milk to 15.2 g/kg at 300 d in milk). However, lactation modeling was not useful in explaining methane yield, which is almost constant during lactation. Lastly, the methane yield and intensity of cows from 3-breed rotational crossbreeding are not greater, and their methane production is lower than that of purebred Holsteins (452 vs. 477 g/d). Given the greater longevity of crossbred cows, and their lower replacement rate, rotational crossbreeding could be a way of mitigating the environmental impact of milk production.

Exposure hazards of particles and volatile organic compounds emitted from material extrusion 3D printing: Consolidation of chamber study data

Ultrafine particles and volatile organic compounds (VOCs) have been detected from material extrusion 3D printing, which is widely used in non-industrial environments. This study consolidates data of 447 particle emission and 58 VOC emission evaluations from a chamber study using a standardized testing method with various 3D printing scenarios. The interquartile ranges of the observed emission rates were 109–1011 #/h for particles and 0.2–1.0 mg/h for total VOC. Print material contributed largely to the variations of particle and total VOC emissions and determined the most abundantly emitted VOCs. Printing conditions and filament specifications, included printer brand, print temperature and speed, build plate heating setup, filament brand, color and composite, also affected emissions and resulted in large variations observed in emission profiles. Multiple regression showed that particle emissions were more impacted by various print conditions than VOC emissions. According to indoor exposure modeling, personal and residential exposure scenarios were more likely to result in high exposure levels, often exceeding recommended exposure limits. Hazardous VOCs commonly emitted from 3D printing included aromatics, aldehydes, alcohols, ketones, esters and siloxanes, among which were various carcinogens, irritants and developmental and reproductive toxins. Therefore, 3D printing emits a complex mixture of ultrafine particles and various hazardous chemicals, exposure to which may exceed recommended exposure limits and potentially induce acute, chronic, or developmental health effects for users depending on exposure scenarios.

Contribution of multi-objective land use optimization to carbon neutrality: A case study of Northwest China

Land use and land cover (LULC) patterns fundamentally influence the carbon cycle of terrestrial ecosystems, and rational optimization of LULC patterns can contribute to achieving carbon neutrality goals. This study constructs a multi-objective LULC optimization coupled model by integrating the objective of carbon neutrality into the LULC optimization process, with Northwest China serving as the study case. This model integrated the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model with the Intergovernmental Panel on Climate Change (IPCC) inventory methodology to assess the spatiotemporal variations in carbon storage and emissions in Northwest China between 2000 and 2020, which aimed to elucidate the mechanisms through which changes in LULC affect carbon neutrality. Additionally, through the amalgamation of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the Patch-Generating Land Use Simulation (PLUS) model, optimized the quantitative structure and spatial layout of four LULC scenarios for Northwest China in 2060: natural development (ND), low carbon emissions (CE), high carbon storage (CS), and carbon neutrality (CN), projected the evolving pathways of carbon storage and emissions within these scenarios while examining the values of different LULC scenarios and their respective contributions to carbon neutrality. The study's findings underscore that the substantial expansion of built-up land development areas and farmland from 2000 to 2020 has resulted in notable carbon storage and emissions increases. Moreover, changes in carbon emissions and carbon storage show significant spatial consistency with changes in LULC patterns. In contrast to the ND scenario, the LULC patterns within the CE, CS, and CN scenarios exhibit higher LULC values and contribute more substantially to carbon neutrality. Remarkably, among these scenarios, the CN scenario stands out for achieving the maximum contribution to carbon neutrality and LULC value, thereby establishing it as the preeminent LULC pattern for the Northwest China region. As the study concludes, it recommended that future developmental strategies in Northwest China prioritize curtailing the expansion of built-up areas while bolstering the conservation of vital ecological zones such as forests and grasslands. This study addresses the inherent tension between regional economic progress and environmental preservation, offering a foundation of scientific insights for effectively managing LULC patterns and attaining carbon neutrality goals.

Study on the spatio-temporal evolution characteristics and driving mechanism of China’s carbon emissions

The present study aims to explore the spatial and temporal changes and drivers of carbon emission patterns in China, with the aim of encouraging county-level carbon reduction policies in pursuit of sustainable development. To this end, we have studied the spatial disparities, spatio-temporal patterns, and evolution characteristics of carbon emissions using county-level carbon emissions data from China between 2002 and 2017. Additionally, we have comprehensively considered the dynamic impacts of both county-level and city-level environmental factors on carbon emissions based on an optimized hierarchical random forest model. The results show that the carbon emissions of China’s counties have generally followed an upward trend before stabilizing. Notable characteristics include elevated carbon emissions in the northern regions and reduced carbon emissions in the southern areas. Additionally, there are higher carbon emissions in the eastern regions compared to lower emissions in the western and inland areas, with discernible local clustering patterns. These findings underscore the importance of tailoring the government’s emission reduction strategy to address the phased variations in carbon emissions across different districts and counties. It is essential to emphasize the key role of major urban agglomerations and metropolitan areas in carbon emission reduction, while also addressing potential emission sources in the resource-rich, yet technologically disadvantaged, northwest region. Furthermore, improving energy efficiency through technological innovation should be the primary means of carbon emission reduction at the county level.

129 The Relationship Between Pulmonary Arterial Pressure Expected Progeny Differences and Methane Emissions

Abstract The objective of this study was to determine the relationship between pulmonary arterial pressure (PAP) expected progeny differences (EPD) and methane emissions. Pulmonary arterial pressure is an indicator of pulmonary arterial hypertension in cattle and is especially relevant at high altitudes, with increasing importance in moderate elevation feedlot and finishing systems. Enteric methane emissions from cattle are increasingly emphasized due to their contribution of greenhouse gases in the agricultural sector. As pulmonary arterial hypertension continues to be an issue in high altitudes and feedlots, understanding relationships with methane emission rates will be useful for both concomitant selection with the goal of improving environmental sustainability. The PAP EPD data were collected on 123 total heifers (n = 37) and steers (n = 86) located on a ranch in southern Wyoming at an elevation of 2,194.56 meters. Gas emissions were collected using a GreenFeed system (C-Lock, Rapid City, South Dakota), which measures the gas fluxes of individual animals at the CSU ARDEC facility at an elevation of 1,557.53 meters. Individual animal methane emissions were measured as grams/day. A generalized linear regression model was used to regress average daily methane emissions (mean = 163.08, SD = 38.74, min = 87.72, max = 247.03) on PAP EPD (mean = -0.464, SD = 1.565, min = -6.382, max = 4.286), pen, and age in days (mean = 258.2, SD = 20.009, min = 200, max = 298). The PAP EPD (P = 0.484) did not account for a significant amount of variation in methane emissions. Additionally, the effect of pen (P < 0.05) and age (P < 0.05) were significant sources of variation for methane production. The effects of pen had a range of 60.916 units of methane emission/day (P < 0.05). Age of the individual was evaluated as a linear covariate and had a solution of 0.678 units of methane emission/day (P < 0.05), suggesting that older individuals had increased methane emissions. These results show no statistical relationship between PAP EPD and methane in these data. While preliminary results suggest that selection for low PAP EPD individuals will have no adverse effects on sustainability metrics, future research is needed to fully understand the relationship between beef cattle production traits and sustainability metrics.

Towards sustainable transport: quantifying and mitigating pollutant emissions from heavy-duty diesel trucks in Northeast China.

Heavy-duty diesel trucks (HDDTs) have caused serious environmental pollution in China. Accurate estimation of their pollutant emission characteristics is essential to reduce emissions and associated environmental and public health impacts. To achieve sustainable development for transport emissions in Northeast China, we developed localized emission factors and a high-resolution emission inventory of HDDTs, based on on-board test, Guidebook and international vehicle emission (IVE) model. The results show that the total emissions of CO, NO, NO2, and PM from HDDTs in Northeast China in 2020 were 172.2 kt, 531.5 kt, 11.2 kt, and 921.4 t, respectively. In terms of spatial distribution, emissions decreased from the city center to the city fringe. Temporally, the NOx emission variation curves of different types of roads presented a "single-peak" emission characteristic, which was different from the peak of traffic flow. Three emission reduction scenarios are further developed in the paper. Scenario analysis shows that elimination of HDDTs that follow the old China III emission standard and installing tailpipe treatment devices are the most effective pollutant reduction measure. The reduction percentages for CO, NO, NO2, and PM ranged from 62.9 to 83.89%. The results of our study could inform policymakers to devise feasible strategies to reduce vehicle pollution in Northeast China.

Multiwavelength analysis of Fermi-LAT blazars with high-significance periodicity: detection of a long-term rising emission in PG 1553+113

ABSTRACT Blazars display variable emission across the entire electromagnetic spectrum, with time-scales that can range from a few minutes to several years. Our recent work has shown that a sample of five blazars exhibit hints of periodicity with a global significance ${\gtrsim}2\, \sigma$ at γ-ray energies, in the range of 0.1 GeV < E < 800 GeV. In this work, we study their multiwavelength emission, covering the X-ray, ultraviolet, optical, and radio bands. We show that three of these blazars present similar periodic patterns in the optical and radio bands. Additionally, fluxes in the different bands of the five blazars are correlated, suggesting a co-spatial origin. Moreover, we detect a long-term (≈10 yr) rising trend in the light curves of PG 1553+113, and we use it to infer possible constraints on the binary black hole hypothesis.

Responses of soil greenhouse gas emissions to soil mesofauna invasions and its driving mechanisms in the alpine tundra: A microcosm study

Climate change is resulting in significant modifications of the altitudinal patterns of soil fauna in mountains, leading to their upward invasion and alteration of soil ecological processes. However, the effects of soil greenhouse gas (GHG) emissions from soil mesofauna invasion and their driving mechanisms have not been clearly understood. To address this knowledge gap, we simulated a soil mesofauna invasion from an Erman's birch forest (EB) to the alpine tundra (AT) of the Changbai Mountain in Northeast China. Four treatments were established: no soil mesofauna (S0), native species (SN), invasive species (SI), and invasive species superposed native species (SS). We conducted a 79-day microcosm experiment, utilizing gas chromatography and high-throughput sequencing, to explore the variations in soil greenhouse gas emissions and their driving factors. Results showed that the cumulative CO2 emissions under SN, SI, and SS, compared with S0, increased by 34.13 %, 73.93 %, and 107.64 % and cumulative N2O emissions increased by 59.05 %, 101.18 %, and 183.88 %, respectively. Compared to SN, the cumulative emissions of CO2 and N2O increased by 29.89 % and 26.31 % under SI and by 54.91 % and 78.59 % under SS, respectively. The impacts of invasive species and native species on greenhouse gases were not a simple additive effect. Abiotic (soil variables) and biotic (soil mesofauna and microbial diversity) factors explained 37.76 % and 44.41 % of the total variations in CO2 and N2O emissions, respectively, in which NH4+-N and C: N ratios contributed the largest variations. The contribution of soil mesofauna diversity to the variations in CO2 and N2O emissions was higher than that of microbial diversity. The bacterial network graph density was correlated with soil CO2 and N2O emissions. Our findings highlight that soil mesofauna invasions increased GHG emissions, and these variations were predominantly explained by biotic rather than abiotic factors.

Carbon emissions from various natural gas end-use sectors for 31 Chinese provinces between 2017 and 2021

Natural gas (NG) is a low-carbon fuel that is becoming a crucial transitional energy in China for reducing carbon emissions. In this study, a life-cycle assessment was performed to correlate carbon emissions and NG consumption for different end uses in China. A bottom-up life-cycle assessment framework was combined with carbon emission coefficients to quantify NG consumption in 31 Chinese provinces between 2017 and 2021, as well as the carbon emissions (in carbon dioxide (CO2) equivalents, including CO2 and methane) released during NG production, transportation, and consumption. The carbon emission factors for different types of end-use consumption were considered. The assessment results indicate that both NG consumption and life-cycle carbon emissions from NG use have increased since 2017. Between 2017 and 2021, NG consumption in China increased from 260 to 370 billion cubic meters and life-cycle carbon emissions from NG increased by 39% (from 930 to 1292 Mt CO2). The carbon emissions released during NG production and transportation accounted for approximately 31% of NG life-cycle emissions. Considerable variations in NG life-cycle carbon emissions were identified across different provinces and sectors, highlighting the need for targeted efforts to reduce carbon emissions. The objective of this study was to provide useful insights into sustainability development of the NG industry in China for optimizing NG allocations to different end uses and maximizing the environmental and economic benefits of NG.

Novel insights into the NOx emissions characteristics in PEMS tests of a heavy-duty vehicle under different payloads

Real Drive Emission (RDE) test with Portable Emission Measurement System (PEMS) is a widely adopted way to assess vehicle emission compliance. However, the current NOx emissions calculation method stipulated in the China VI emission standard easily ignores the NOx emissions during cold start and low-power operation. To study the effect of cold start and low-power operation on the calculation of NOx emissions in the PEMS test, in this study, a China VI Heavy-Duty Vehicle (HDV) for urban use was used to conduct PEMS tests under various vehicle payload conditions. The data analysis results show that the increase in vehicle payload is beneficial to reducing the specific NOx emissions and passing the NOx emission compliance test because the increased payload improves the NOx conversion efficiency of the SCR system. Cold start duration has no obvious relationship with vehicle payload, accounting for only about 4∼6% in each test, but contributing more than 30% of NOx emissions. Due to the effect of the power threshold and the 90th cumulative percentile, the cold start data has little influence on the result of the NOx emissions assessment and the maximum variation of the NOx emissions result in this study is an 8% rise. For the HDV for urban use, the variation of the power threshold resulting from vehicle payload is small, no more than 2% in this study. The presence of the power threshold makes almost only the low-power operation in the second half of urban driving have an impact on the NOx emissions calculation, which may make more than 50% of NOx emissions in the PEMS test be neglected. The impact of the low-power operation on NOx emissions calculation result will be significantly enhanced if all windows are considered in the Moving Average Window (MAW) method. In the meantime, the degree of variation is closely related to the NOx emissions level during the first half of urban driving. The maximum deterioration of NOx emission assessment result can be more than 90% in this study.

Defect mediated magnetism in Pr/TiO2- reduced graphene oxide nanocomposites

Synthesis and characterization of Pr/TiO2 nanoparticles with its reduced graphene oxide nanocomposites by sol-gel and hydrothermal reaction and its magnetic properties is presented. X ray Diffraction and Raman analysis confirms the anatase phase structure in both nanoparticles and composites with Pr doping hindering the crystallite growth. Field Emission Scanning Electron Microscope and High Resolution Transmission Electron Microscope analysis confirm unambiguously the attachment of TiO2 spherical nanoparticles onto the graphene sheets forming an intimate interfacial contact between the particle and sheets enabling electron charge transfers. Difference in surface areas and surface energies give rise to variable emission intensity of the samples. Pr/TiO2 nanocomposites exhibit an increased bandgap values than their nanoparticles. Both Photoluminescence and Electron Paramagnetic Resonance spectroscopy confirm an increase in defect structure with increase of Pr doping. Magnetization studies show weak ferromagnetic ordering for pure TiO2 nanoparticles. The soft magnetic behaviour of the synthesised materials is useful in fabricating memory based devices.

Effect of different types of biochar on soil properties and functional microbial communities in rhizosphere and bulk soils and their relationship with CH4 and N2O emissions.

Biochar as an agricultural soil amendment plays vital roles in mediating methane (CH4) and nitrous oxide (N2O) emissions in soils. The link between different types of biochar, bulk soil, and rhizosphere microbial communities in relation to CH4 and N2O emissions is being investigated in this study. The rice pot experiment was conducted using biochar at two temperatures (300°C and 500°C) in combination with three biochar levels (0, 2, 10% w/w). Soil properties and the abundance of genes associated with CH4 and N2O emissions from both rhizosphere and bulk soils were investigated. The study also aimed to examine the structure of microbial communities (pmoA, nosZ) in rhizosphere and bulk soils whereas CH4 and N2O emissions were monitored while growing rice. Results showed that biochar at 300°C and 10% incorporation significantly increased the CH4 emissions by up to 59% rise compared to the control group. Random Forest analysis revealed that the ratio of mcrA/pmoA along with the abundance of mcrA from both rhizosphere and bulk soils, the abundance of AOA, TN, DOC, and the community composition of pmoA-harboring microorganisms from both bulk and rhizosphere soils were important predictors of CH4 emissions. Therefore, the ratio of mcrA/pmoA in rhizosphere soil and the abundance of AOA in bulk soil were the main factors influencing CH4 emissions. Variation Partitioning Analysis (VPA) results indicated that the effects of these factors on bulk soil were 9% of CH4 emissions variations in different treatments, which contributed more than rhizosphere soils' factors. Moreover, random forest analysis results indicated that the abundance of AOB in bulk soil was the most important predictor influencing N2O emissions. The VPA result revealed that the factors in rhizosphere soil could explain more than 28% of the variations in N2O emissions. Our study highlights that rhizosphere soil has a more significant effect than bulk soil on N2O production. Our findings further the understanding of the link between bulk and rhizosphere attributes, and their impact on CH4 and N2O emissions in paddy soils. In summary, we recommend the application of biochar at 500°C and 2% incorporation rate for agricultural production in the area.

Seasonal and Elevational Variability in Dwarf Birch VOC Emissions in Greenlandic Tundra

AbstractRising temperatures amplify biogenic volatile organic compound (VOC) emissions from Arctic vegetation, causing feedbacks to the climate system. Changes in climate also alter plant physiology and vegetation composition, all of which can influence VOC emissions. Moreover, leaf development and biotic stresses cause highly variable emissions during the growing season. Therefore, linking VOC emissions with plant traits and tracking responses to climate change might provide better understanding of VOC emission regulation under future conditions. We measured VOC emissions and other plant traits in dwarf birch (Betula glandulosa) at two elevations in Narsarsuaq, South Greenland. The measurements were performed in warming experiments that have run since 2016. We collected VOCs using the branch enclosure method from early June until late July 2019 (n = 200). Emissions of green leaf volatiles (GLVs), oxygenated monoterpenes (oMTs), and homoterpenes followed a seasonal trend. VOC emission rates and the diversity of the VOC blend decreased at the end of the measurement period. Differences in VOC emission rates between elevations were pronounced early in the season. Majority of the traits did not explain the variation in VOC emissions. We show strong seasonal variability in VOC emissions within the growing season, which is likely driven by leaf phenology. While the diversity of VOCs was greater at the milder low‐elevation site, VOC emission rates were higher or similar at the harsher high‐elevation site, showing stronger VOC emission potentials than previously assumed. Seasonal variations in the emissions of VOCs are crucial for accurate predictions of current and future VOC emissions from arctic ecosystems.

Is Democracy the Answer to Intractable Climate Change?

Abstract Climate change is the greatest governance challenge humanity has ever faced. Understanding why some governments successfully reduce greenhouse gas emissions and others fail is thus imperative. While regime type is often hypothesized to be a source of variation in greenhouse gas emissions, empirical findings about the effects of democracy and autocracy on climate action are contradictory. This research note reconciles these inconsistencies and adopts a quasi-experimental approach to investigate the relationship between democratization and greenhouse gas emissions. A fixed effects model with a synthetic control estimator is used to construct appropriate counterfactuals and evaluate the effect of regime type on emissions with data from the World Bank and Varieties of Democracy Project. The analysis shows that movement toward democracy does not have a significant effect on emissions, suggesting that research on the politics of emissions reduction should focus on factors other than regime type.

Investigating the EKC hypothesis with disaggregated energy use and multi-sector production.

The Middle East and North African (MENA) economies experienced substantial economic fluctuations due to variations in carbon emissions and energy use. For this purpose, the present study examines the factors influencing carbon emissions in MENA economies, particularly economic growth and energy use. To this end, this study disaggregates economic growth into three sectors (agriculture, industry, and services) and energy use into renewable and non-renewable, and examines their environmental impacts by including the roles of urbanization and trade openness in the environment Kuznets curve (EKC) framework. This study uses panel data from 16 MENA countries over the period 1990-2018 to estimate the short-run and long-run coefficients and the Granger causality between the variables. The empirical results using the Mean Augmented Group (AMG) and the Common Correlated Effects Mean Group (CCE-MG) revealed that (i) the signs of GDP per capita and its squared scores validate the EKC hypothesis only at the aggregate level; (ii) the coefficients of sectoral GDP show that the industry and services sectors have the highest contributions to carbon emissions in the MENA region; (iii) non-renewable energy increases emissions, whereas renewable energy lessens them. The outcomes of the Granger causality confirm (i) a bidirectional relationship between emissions and per capita GDP as well as sectoral GDP, between CO2 emissions and renewable energy, and between per capita GDP and renewable and non-renewable energy however; (ii) a unidirectional causal impact running from non-renewable energy to CO2 emissions is found in the short term. The study calls for effective policies to focus on curbing emissions in secondary and tertiary economic sectors by growing the part of renewable energy in the total energy mix.

Long-term variations of global dust emissions and climate control

Dust discharged from the surface into the air has an important impact on global climate change, the ecological environment, and human health. However, the spatiotemporal variations of global dust emissions and the climate control of dust emissions from different dust sources in recent decades are still unclear. This study explores the spatiotemporal variations of global dust emissions from 1980 to 2020 based on the MERRA-2 dust emissions dataset and provides a detailed investigation of the interannual variations of dust emissions from major dust sources in the world and their contribution to the global dust cycle. On this basis, the association between global dust emissions and average wind speed (AWS), surface air temperature (SAT), precipitation (Ppt), relative humidity (RH), soil evaporation (SE), soil moisture (SM), and solar radiation (SR) were explored. In particular, the comparative importance of these climatic factors and their combined structures on dust emissions from different dust sources. The results show that North Africa contributed the most to global dust emissions, contributing 58% of the total global emissions, while South Africa and North America contributed the least to global dust emissions, at less than 1%, respectively. Classification and Regression Tree (CART) analysis shows that SR was the major factor affecting the dust emissions of Australia, East Asia, South America, and Central Asia. AWS was the major factor influencing dust emissions in North Africa and South Asia. SAT, RH, and SM were the major factors affecting dust emissions in West Asia, North America, and South Africa, respectively. There were great differences in the climatic factors combinations on dust emissions intensity in different dust sources. These findings assist us in better understanding the control of climatic factors on dust emissions from global dust sources and have important scientific significance for accurately predicting dust events and reducing disaster risks.

The spatial and temporal disaggregation models of high-accuracy vehicle emission inventory

A high-accuracy gridding vehicle emission inventory is not only the foundation for developing refined emission control strategies but a necessary input to air quality model as well. An accurate approach to the spatiotemporal disaggregation is the key step to improving the accuracy of gridding emission inventories. The existing spatial disaggregation method considers relatively fewer impact factors, lacking adequate correlation analysis among impact factors. Additionally, the existing temporal disaggregation method does not correspond with the actual travel behavior of residents. This paper proposes a multi-factor spatial disaggregation model by principal component analysis (PCAM), based on a correlation analysis of the main impact factors. Further, a new temporal disaggregation model is proposed based on the congestion delay index combined with the traffic flow fundamental model (CDITF). The results from a case study in Jinan show that the square of correlation coefficients (RSQ) between the model- disaggregated NO2 emissions based on PCAM and the monitored NO2 concentration increased by 34.4% compared to the traditional disaggregation model based on the standard road length, and the RSQ for CO increased by 13%; the NMD and NME of the simulation results based on CMAQ model compared to standard road length model decrease by approximately 33.7% and 35.5%, respectively. The trend of the monthly, daily, and hourly variations of NO2 and CO emissions disaggregated by the proposed temporal disaggregation model is quite consistent with that of the monitored concentration data. The PCAM method and the CDITF proposed in this paper are more in line with the actual situation using the cumulative emissions on road sections. The vehicle emissions in Jinan are found to be concentrated in the center of each district and county and near high-grade roads. The disaggregation results in areas with large road slopes are more realistic for considering road slope factors. The trend of the monthly, daily, and hourly variations of NO2 and CO emissions disaggregated by the proposed temporal disaggregation model is quite consistent with that of the monitored concentration data, however, the monitored concentration data presents a certain degree of time lag. The proposed spatiotemporal disaggregation model in this paper improves the accuracy of gridding vehicle emission inventory, which is of a great significance for developing precise control strategies of vehicle emissions and improving the urban air quality in general.

Hotspot Detection and Estimation of Methane Emissions from Landfill Final Cover

The main objectives of this study were to identify methane hotspots through spatial distribution tests of the surface methane concentration above a landfill final cover and to investigate the effects of rainfall, atmospheric pressure, ground temperature, and ambient methane concentration on methane emissions. A portable laser methane detector was used to measure the spatial distribution of methane concentrations. The methane concentration distribution showed a distinct spatial variability. The maximum methane concentration reached 3225 ppm, while 73.0% of the methane concentration values were below 10.0 ppm. Several meteorological factors were found to be associated with the variation in methane emissions. Rainfall limited gas transport in the cover, resulting in more significant methane hotspots. Atmospheric pressure was negatively correlated with methane emission. The ambient methane concentration and methane flux had a significant positive linear correlation. Based on a linear correlation equation, the spatial distribution of methane concentrations in the landfill could be converted into a methane emission distribution. The estimated average value for methane emissions in the test area was approximately 4.3 g m−2 d−1. This study provides an experimental basis for locating methane hotspots and assessing methane emissions in landfill final covers, and proposes supplementary means for detecting geomembrane damage in landfill covers.