Spatiotemporal Emission Research Articles

Spatiotemporal dynamics of energy-related CO2 emissions in China based on nighttime imagery and land use data

It is difficult to accurately estimate the spatial distribution of carbon dioxide (CO2) at the grid scale because of the lack of city-level statistical data. In this paper, CO2 emission regions were devided into urban area, industrial area and rural area in order to accurately calculate CO2 emissions. In addition, a more accurate calculation model for energy-related CO2 emissions was proposed through integrating nighttime light datasets and land use data. The maps of estimated CO2 emissions in different regions were compared and examined through using spatial dependence and grid overlay methods to understand the spatial distribution characteristics and spatiotemporal dynamics of CO2 emissions in China. The results showed that the model proposed in this study was appropriate and reliable for CO2 emissions not only in urban and rural areas but also in industrial areas. CO2 emissions in China were mainly concentrated in the coastal areas and the northern area, and the amount of carbon emissions increased rapidly from 2000 to 2018 in the Middle Yellow River. These results could improve the understanding of regional discrepancies of spatiotemporal CO2 emission dynamics at gride scale, and provide a scientific reference for the formulation of energy conservation and emission reduction policies by the local government.

Characteristics and chemical reactivity of biogenic volatile organic compounds from dominant forest species in the Jing-Jin-Ji area, China

BackgroundBiogenic volatile organic compounds (BVOCs) play an essential role in tropospheric atmospheric chemical reactions. There are few studies conducted on BVOCs emission of dominant forest species in the Jing-Jin-Ji area of China. Based on the field survey, forest resources data and the measured standard emission factors, the Guenther model developed in 1993 (G93) was applied in this paper to estimate the emission of BVOCs from several dominant forest species (Platycladus orientalis, Quercus variabilis, Betula platyphylla, Populus tomentosa, Pinus tabuliformis, Robinia pseudoacacia, Ulmus pumila, Salix babylonica and Larix gmelinii) in the Jing-Jin-Ji area in 2017. Then the spatiotemporal emission characteristics and atmospheric chemical reactivity of these species were extensively evaluated.ResultsThe results showed that the total annual BVOCs emission was estimated to be 70.8 Gg C·year− 1, consisting 40.5 % (28.7 Gg C·year− 1) of isoprene, 36.0 % (25.5 Gg C·year− 1) of monoterpenes and 23.4 % (16.6 Gg C·year− 1) of other VOCs. The emissions from Platycladus orientalis, Quercus variabilis, Populus tomentosa and Pinus tabulaeformis contributed 56.1 %, 41.2 %, 36.0 % and 31.1 %, respectively. The total BVOCs emission from the Jing-Jin-Ji area accounted for 61.9 % and 1.8 % in summer and winter, respectively. Up to 28.8 % of emission was detected from Chengde followed by Beijing with 24.9 %, that mainly distributed in the Taihang Mountains and the Yanshan Mountains. Additionally, the Robinia pseudoacacia, Populus tomentosa, Quercus variabilis, and Pinus tabulaeformis contributed mainly to BVOCs reaction activity.ConclusionsThe BVOCs emission peaked in summer (June, July, and August) and bottomed out in winter (December, January, and February). Chengde contributed the most, followed by Beijing. Platycladus orientalis, Quercus variabilis, Populus tomentosa, Pinus tabulaeformis and Robinia pseudoacacia represent the primary contributors to BVOCs emission and atmospheric reactivity, hence the planting of these species should be reduced.

Spatiotemporal Methane Emission From Global Reservoirs

AbstractInland aquatic systems, such as reservoirs, contribute substantially to global methane (CH4) emissions; yet are among the most uncertain components of the total CH4 budget. Reservoirs have received recent attention as they may generate high CH4 fluxes. Improved quantification of these CH4 fluxes, particularly their spatiotemporal distribution, is key to realistically incorporating them in CH4 modeling and budget studies. Here we report on a new global, gridded (0.25° lat × 0.25° lon) study of reservoir CH4 emissions, accounting for new knowledge regarding reservoir areal extent and distribution, and spatiotemporal emission patterns influenced by diurnal variability, temperature‐dependent seasonality, satellite‐derived freeze‐thaw dynamics, and eco‐climatic zone. The results of this new data set comprise daily CH4 emissions throughout the full annual cycle and show that reservoirs cover 297 × 103 km2 globally and emit 10.1 Tg CH4 yr−1 (1σ uncertainty range of 7.2–12.9 Tg CH4 yr−1) from diffusive (1.2 Tg CH4 yr−1) and ebullitive (8.9 Tg CH4 yr−1) emission pathways. This analysis of reservoir CH4 emission addresses multiple gaps and uncertainties in previous studies and represents an important contribution to studies of the global CH4 budget. The new data sets and methodologies from this study provide a framework to better understand and model the current and future role of reservoirs in the global CH4 budget and to guide efforts to mitigate reservoir‐related CH4 emissions.

Variational regional inverse modeling of reactive species emissions with PYVAR-CHIMERE-v2019

Abstract. Up-to-date and accurate emission inventories for air pollutants are essential for understanding their role in the formation of tropospheric ozone and particulate matter at various temporal scales, for anticipating pollution peaks and for identifying the key drivers that could help mitigate their concentrations. This paper describes the Bayesian variational inverse system PYVAR-CHIMERE, which is now adapted to the inversion of reactive species. Complementarily with bottom-up inventories, this system aims at updating and improving the knowledge on the high spatiotemporal variability of emissions of air pollutants and their precursors. The system is designed to use any type of observations, such as satellite observations or surface station measurements. The potential of PYVAR-CHIMERE is illustrated with inversions of both carbon monoxide (CO) and nitrogen oxides (NOx) emissions in Europe, using the MOPITT and OMI satellite observations, respectively. In these cases, local increments on CO emissions can reach more than +50 %, with increases located mainly over central and eastern Europe, except in the south of Poland, and decreases located over Spain and Portugal. The illustrative cases for NOx emissions also lead to large local increments (> 50 %), for example over industrial areas (e.g., over the Po Valley) and over the Netherlands. The good behavior of the inversion is shown through statistics on the concentrations: the mean bias, RMSE, standard deviation, and correlation between the simulated and observed concentrations. For CO, the mean bias is reduced by about 27 % when using the posterior emissions, the RMSE and the standard deviation are reduced by about 50 %, and the correlation is strongly improved (0.74 when using the posterior emissions against 0.02); for NOx, the mean bias is reduced by about 24 % and the RMSE and the standard deviation are reduced by about 7 %, but the correlation is not improved. We reported strong non-linear relationships between NOx emissions and satellite NO2 columns, now requiring a fully comprehensive scientific study.

Spatio-temporal structure and emission of a large plasmoid in atmosphere

Atmospheric plasmoids with 20–30 cm diameter are generated via a high-voltage discharge above a water surface. They ascend in the ambient air and exist autonomously for several hundreds of milliseconds. The plasma processes leading to an emission of visible light for more than 350 ms after detachment from the energy supply are still unknown. Visual and spectroscopic high-speed diagnostics with spatial resolution are thus applied. It is shown for the first time that the free-floating body turns to a torus ring to the end of its lifetime, which ascends in air up to more than 1.5 m and radiates longer than 1.5 s in the infrared spectral range, only limited by the structural circumstances in the laboratory. Vortex formation is thus endorsed as being responsible for the structural integrity of the plasma during the autonomous phase. Emission in the optical spectral range (UV-NIR) is limited to the first 500 ms and is governed by radiation from the tap water contents without the influx of ambient air into the plasma. The OH A-X transition is the most intense emission during the entire visible evolution of the plasmoid. Atomic hydrogen emission is observed only during the first 100 ms close to the central electrode (CE) and is highly dynamic, while emission from dissolved salts is detected during the later evolution but is mostly overlaid by a continuum radiation, which is clearly non-thermal. Using the omnipresent OH emission, the optical emission profile of the main plasmoid is shown to be broad in the center and is rotationally symmetric. The radiated energy from the OH radical integrated over the entire plasmoid evolution is less than 100 J, which is about 3% of the total energy dissipated into the plasma. Emission from dissolved sodium is used to track the plasma channel, which connects the main plasmoid to the CE, during its ascension after energy shut-down giving a fourfold ascension velocity compared to the main plasmoid.

Refined spatio-temporal emission assessment of Hg, As, Cd, Cr and Pb from Chinese coal-fired industrial boilers

Coal-fired industrial boilers (CFIBs) are a significant source of trace metals (TMs) emissions, due to its large number and widespread industry application. In this study, a highly resolved of county-based atmospheric emission inventory of Hg, As, Cd, Cr and Pb for Chinese CFIBs in 2017 is established firstly based on integrated source-specific information of both activity level and air pollution control devices (APCDs) from ~61,000 CFIBs in mainland China. Our results estimated that the total emissions of Hg, As, Cd, Cr and Pb from national CFIBs in 2017 were about 25.46, 115.33, 7.04, 371.40, and 589.76 t, respectively. Hg0 was the majority species of atmospheric Hg, accounting for 71.5% of the total Hg emission. The peak emission intensities of five TMs were mainly concentrated in northern and eastern region of China due to its large demand of coal consumption for winter heating, broad economic activity, as well as high population density. Monthly emission characteristics of TMs exhibited seasonal peak. The overall uncertainties of the newly updated emission inventory were estimated at the ranges of −42.0%–45.5%. Generally, TMs emissions from Chinese CFIBs were effectively controlled through implementing energy substitution, APCDs upgrading, and ultra-low emission (ULE) retrofitting in different regions of China, which contributed to 18.8%–29.4% of five TMs emission reductions. We believe that our highly resolved county-level emission inventory will be comprehensively explored the current tempo-spatial emission characteristics of atmospheric TMs from Chinese CFIBs and the forecast results will be useful for developing effective emission control programs for policy makers in the county levels and improving the regional air quality in the future.

Mining urban sustainable performance: Spatio-temporal emission potential changes of urban transit buses in post-COVID-19 future

Emission benefits of transit buses depend on ridership. Declines in ridership caused by COVID-19 leads uncertainty about the emission reduction capacity of buses. This paper provides a method framework for analyzing spatio-temporal emission patterns of buses in combination with real-time ridership and potential emission changes in the post-COVID-19 future. Based on GPS trajectory and Smart Card data of 2056 buses from 278 routes covering 1.5 million ridership in Qingdao, China, spatio-temporal emissions characteristics of buses are studied. 7589 taxis with 0.2 million passengers’ trips are used for acquiring private cars’ emissions to evaluate the emissions difference between buses and cars. Empirical results show that the average difference between buses and cars with 2 persons can reach up to 117 g/km-person during 7:00–8:59 and 115 g/km-person during 17:00–18:59. However, buses have various emission benefits around the city at different periods. A double increase in emissions during non-rush hours can be observed compared with rush hours. 224 online survey data are used to study the potential ridership reduction trend in post-COVID-19. Results show that 56.3% of respondents would decrease the usage of buses in the post-COVID-19 future. Based on this figure, our analysis shows that per kilometer-person emissions of buses are higher than cars during non-rush hours, however, still lower than cars during rush hours. We conclude that when ridership reduces by more than 40%, buses cannot be “greener” travel modal than cars as before. Finally, several feasible policies are suggested for this potential challenge. Our study provides convincing evidence for understanding the emission patterns of buses, to support better buses investment decisions and promotion on eco-friendly public transport service in the post-COVID-19 future.

Carbon dioxide emission in a single-lane cellular automaton model with a series of traffic lights

In this paper, we propose a cellular automaton model to simulate traffic flow controlled by a series of traffic lights. The synchronized traffic light and the green wave light strategies were investigated. The spatiotemporal diagrams, energy dissipation, and CO2 emission of the system were presented. Our simulations are conducted to clarify the difference between both strategies and their effects on the traffic flow and the CO2 emission. We found that the traffic flow depends mainly on the strategy used for managing the traffic lights as well as on the parameters of the traffic lights, namely the cycle length, the number of traffic lights and the length of the system. The fundamental diagram has barely the same characteristics for both methods and it depends on the combination of the parameters of the system. We find that the green wave is more convenient for the management of a series of traffic lights than the synchronized control strategy in terms of throughput, especially for large-sized systems. Unlike in terms of CO2 emission and energy dissipation, both control strategies outperform each other depending on the density regions and the parameters of the system. Finally, we investigate the effect of both cycles (i.e. red and green) for the synchronized control method on the CO2 emission. It is found that the green cycle generates often a series of acceleration events that increase CO2 emission.

Modelling road transport emissions in Germany – Current day situation and scenarios for 2040

In the German project Traffic Development and the Environment an advanced model chain was built up that includes traffic models, fleet composition developments, new driving technologies, and emission factors in order to produce spatio-temporal emission distributions for use in atmospheric chemistry transport models. This novel model chain was first used to calculate current day traffic emissions in Germany and then to develop consistent future scenarios for 2040. In all scenarios, NOx emissions from traffic decrease by approximately 80% while PM emissions show a lower reduction. The scenarios Free Play, which is based on a free market economics logic, and Regulated Shift, which considers stricter environmental regulations, represent large differences in traffic emissions. NOx emissions will be 32% lower and PM emissions 13% lower in the Regulated Shift scenario compared to the Free Play. The data can be combined with other anthropogenic emissions for investigating air quality with chemistry transport models.

Advances in the estimation of high Spatio-temporal resolution pan-African top-down biomass burning emissions made using geostationary fire radiative power (FRP) and MAIAC aerosol optical depth (AOD) data

We provide major updates to the ‘top down’ Fire Radiative Energy Emissions (FREM) approach to biomass burning emissions calculations, bypassing the estimation of fuel consumption that is a major source of uncertainty in widely used ‘bottom up’ approaches. The FREM approach links satellite observations of fire radiative power (FRP) to emission rates of total particulate matter (TPM) via spatially varying smoke emissions coefficients (g.MJ−1) – each derived from matchups of FRP and smoke plume aerosol optical depth (AOD). In the original FREMv1 approach, FRP data came from the geostationary Meteosat satellite and AOD data from the 10 km spatial resolution MODIS MOD04 aerosol product. However, the latter often performs quite poorly close to biomass burning sources due to its large 10 km pixels, bias at high MODIS view zenith angles, and saturation and/or removal of areas of high AOD - limitations introducing bias and uncertainty into the final FREM-derived smoke emissions estimates. We address each of these issues through a series of significant methodological and input data improvements, including exploitation of the 1 km MODIS MAIAC AOD product that performs far better close to fire sources. We use our FREMv2 methodology to generate a new pan-African fire emissions inventory for TPM and the carbonaceous gases CO2, CO and CH4, and our annual mean TPM emissions are within 11% of those of the MODIS-based FEER top-down approach, but significantly higher than those of GFASv1.2 and GFEDv4.1s (by 114% and 69% respectively) - agreeing with independent assessments that aerosol emissions of GFASv1.2 require upscaling by a factor of 2 to 3.4 to deliver matching magnitudes between modelled and observed AODs. From our carbonaceous emissions totals we map dry matter consumed (DMC) across Africa, and dividing this by the FireCCISFD11 20 m burned area product we provide one of the first data-driven pan-African maps of fuel consumption per unit area (kg.m−2) which in many areas is higher than in GFEDv4.1s. Our estimates represent the highest spatio-temporal resolution biomass burning emissions data yet available over Africa, and significantly advance the aim of a pan-tropical and mid-latitude inventory based on FRP from the global geostationary satellite network (Meteosat, Meteosat IOD, GOES and Himawari).

Spatio-temporal distribution of ammonia (NH3) emissions in agricultural fields across North China.

Ammonia (NH3) is one of the main polluted gases in the atmosphere, and its emission has markedly increased in recent years. In China, NH3 is mainly emitted from agricultural fields. Using city-wide data on NH3 emissions in agricultural fields, the spatio-temporal emission of NH3 was estimated for North China. This included emissions from nitrogen fertilizers, field straws, background soil, nitrogen-fixing plants, human feces, and livestock/poultry manure. Based on the results, the range of NH3 emission in agricultural lands was 1623.0-1801.5Gg/year. The rate of increase in NH3 emission in the period 2003-2015 was 0.74% per year, which was relatively stable. The leading sources of NH3 emission included the excessive use of chemical fertilizers in agriculture and the continuous expansion of livestock and poultry industries scale, accounting respectively for 44.9% and 43.9% NH3 emission in the study area, respectively. Hebei and Shandong provinces contributed the highest NH3 emission in North China. The contribution rate of NH3 emission in each province varied with sources, agricultural development, and population density. Based on the 1km × 1km grid resolution map for NH3 emission, the range and average of emission were 9.72-10.13kg/ha and 9.95kg/ha, respectively. High emissions were in the southeast of Hebei province and most of Shandong province. For these regions, there is a need for changes in policies relating to the use of chemical fertilizers in agriculture and the management methods of livestock production in the region.

The effects of environmental regulation on spatio-temporal carbon emissions patterns: Empirical analysis of prefecture-level cities in Northeast China

The effects of environmental regulation on spatio-temporal carbon emissions patterns: Empirical analysis of prefecture-level cities in Northeast China

A high-resolution emissions inventory and its spatiotemporal pattern variations for heavy-duty diesel trucks in Beijing, China

Heavy-duty diesel trucks (HDDTs) cause serious pollution, and a high spatiotemporal resolution emissions inventory is a valuable assessment tool for use in quantitatively understanding the emissions mechanisms of HDDTs and scientifically developing associated emissions reduction measures. This study aims to comprehensively utilize multi-source spatiotemporal data on transportation—including fine-scale trajectories of HDDTs, road traffic conditions, and attribute data for road networks and HDDTs—supplemented by relatively mature vehicle pollution emissions models to establish a high spatiotemporal resolution emissions inventory for HDDTs in Beijing using a bottom-up approach. Spatial statistical techniques, including spatial autocorrelation, high/low clustering, and outlier analysis, are also used to explore the spatiotemporal distribution pattern of pollution emissions in the city. The results showed the following: (1) spatially, nitrogen oxide (NOx) and particulate matter (PM) emission hotspots spread from the Beijing sixth-ring roads to the fourth-ring roads from daytime to nighttime. The road segments with high emissions intensities have pronounced spatial agglomeration effects at night, but these are scattered during daytime. (2) Temporally, total HDDT NOx emissions are consistent with the traffic volume trends and are lower during major festivals. The highest NOx emissions occur at intercity highways, and this reflects the severe impact that intercity freight traffic has on air quality. The dominant HDDT NOx emissions are from vehicles belonging to the China 4 emissions standard. (3) NOx and PM emissions have a significant spatial autocorrelation and exhibit high-value clustering as a whole. (4) At different time intervals, the distribution of High-High/Low-Low clustering and outliers of NOx and PM in the road network is consistent with the spatial distribution of the pollutant emission intensity. The High-Low outlier is mainly distributed within the fourth-ring roads, and the number gradually reduces between night and day. The Low-High outlier is affected by the heterogeneous distribution of HDDTs and exhibits discontinuous distribution characteristics. Our results effectively evaluate Beijing’s emissions control measures for HDDTs and provide a scientific decision-making basis for developing targeted emission reduction strategies for HDDTs.

Inventory of Spatio-Temporal Methane Emissions from Livestock and Poultry Farming in Beijing

Livestock and poultry farming sectors are among the largest anthropogenic methane (CH4) emission sources, mainly from enteric fermentation and manure management. Previous inventories of CH4 emission were generally based on constant emission factor (EF) per head, which had some weaknesses mainly due to the succession of breeding and feeding systems over decades. Here, more reliable long-term changes of CH4 emissions from livestock and poultry farming in Beijing are estimated using the dynamic EFs based on the Intergovernmental Panel on Climate Change (IPCC) Tier 2 method, and high-resolution spatial patterns of CH4 emissions are also estimated with intensive field survey. The results showed that the estimated CH4 emissions derived by dynamic EFs were approximately 13–19% lower than those based on the constant EF before 2010. After 2011, however, the dynamic EFs-derived CH4 emissions were a little higher (3%) than the constant EF method. Temporal CH4 emissions in Beijing had experienced four developing stages (1978–1988: stable; 1989–1998: slow growth; 1999–2004: rapid growth and reached hot moments; 2005–2014: decline) during 1978–2014. Over the first two decades, the contributions of pigs (45%) and cattle (46%) to annual CH4 emission were similar; subsequently, the cattle emitted more CH4 compared to the pigs. At a spatial scale, Shunyi, Daxing, and Tongzhou districts with more cattle and pigs are the hotspots of CH4 emission. In conclusion, the dynamic EFs method obviously improved the spatio-temporal estimates of CH4 emissions compared to the constant EF approach, and the improvements depended on the period and aquaculture structure. Therefore, the dynamic EFs method should be recommended for estimating CH4 emissions from livestock and poultry farming in the future.

Spatial and Temporal Variation in Soil Nitrous Oxide Emissions from a Rehabilitated and Undisturbed Riparian Forest.

Riparian zones enhance water quality and provide wildlife habitat, but high nutrient input in agricultural landscapes causes nitrous oxide (NO) emissions, potentially negating their benefits of C sequestration. The objectives of this study were to quantify spatiotemporal NO emissions in a rehabilitated and undisturbed natural riparian forest. We also determined soil and vegetation characteristics, and their role in driving spatiotemporal NO emissions. Mean NO-N emissions were not significantly ( < 0.05) different between rehabilitated (7.62 μg m h) and undisturbed (5.93 μg m h) riparian forests. The greatest ( < 0.05) NO-N emissions in both riparian forests were observed during the summer. Soil moisture, temperature, and N were significantly correlated to NO-N emissions. Our results show that soil and vegetation characteristics varied between the two riparian forests, but differences in NO-N emissions were negligible. We also found that NO emissions were influenced by soil characteristics and seasonality, rather than vegetation characteristics or spatial position.

Mapping spatiotemporal variations of CO2 (carbon dioxide) emissions using nighttime light data in Guangdong Province

Industrialization and urbanization in China have resulted in a substantial increase in carbon emissions. Spatial and temporal analyses of the carbon emission inventory have become very necessary to policymaking and management. This study corrected inconsistency and uncontinuity of nighttime light (NTL) data based on stable light value pixels, and performed a distribute model of CO2 emissions in Guangdong province during the period of 2005–2013. The model results clearly showed the spatial and temporal variations of CO2 emissions during 2005–2013 in Guangdong, with the Pearl River Delta region contributing the highest CO2 emissions. These results are relevant for understanding the spatiotemporal CO2 emission dynamics at a county level and establishing policies for carbon emission mitigation. This research highlights the importance of spatial spillovers, suggesting that future policies need to encourage inter-regional resource sharing, promote cooperation to ensure energy conservation and emission reductions, and optimize the distribution of urban areas.

Exploring China's 21-year PM10 emissions spatiotemporal variations by DMSP-OLS nighttime stable light data

With the rapid growth of economy and urbanization rate, haze pollution is becoming more and more serious in China, and it is harmful to human health and China's sustainable development. For exploring spatiotemporal variations of PM10 pollution problem from 1992 to 2012 in China, we first proved that there existed a positive correlation between the intercalibrated Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) sensor nighttime stable light (NSL) data and PM10 emissions in this study. Then linear regression analysis was utilized to model spatiotemporal PM10 emission variations in China by the intercalibrated DMSP-OLS NSL data. PM10 emissions at prefectural level were selected as validation data for verifying the accuracy of spatiotemporal PM10 emissions variations and the results demonstrated that the DMSP-OLS NSL data can simulate PM10 emissions at 1 km resolution well. At last, spatiotemporal PM10 emission dynamics were analyzed from national scale down to regional and urban agglomeration scales. The results clearly noticed that the variations of PM10 emissions in different regions and urban agglomerations were different. The high-growth type of PM10 emissions was mainly located in the Eastern region, Central region, Western region, Shandong Peninsula, Wuhan Metropolitan Area and Beijing-Tianjin-Tangshan, with significant lower concentrations in the Northeastern region, Middle south of Liaoning and Pearl River Delta. The high grade of PM10 emissions was mainly distributed in the Eastern region, Central region and Shandong Peninsula, with significant lower concentrations in the Western region, Northeastern region, Sichuan-Chongqing. Considering the difference of temporal and spatial variations of PM10 emissions in the four regions, the mitigation strategy in the Eastern and Central China should mainly focus on the adjustment of industrial structure, while in Western and Northern China, we should pay attention to the improvement and optimization of regional energy structure and energy efficiency.

A new framework to estimate spatio-temporal ammonia emissions due to nitrogen fertilization in France

In France, agriculture is responsible for 98% of ammonia (NH3) emissions with over 50% caused by nitrogen (N) fertilization. The current French national inventory is based on default emission factors (EF) and does not account for the main variables influencing NH3 emissions. To model the spatio-temporal variability of NH3 emissions due to mineral and organic N fertilization, we implemented a new method named CADASTRE_NH3. The novelty lies in the combined use of two types of resources: the process-based Volt'Air model and geo-referenced and temporally explicit databases for soil properties, meteorological conditions and N fertilization. Simulation units are the Small Agricultural Regions. Several sources of information were combined to obtain N fertilization management: census and surveys of the French Ministry of Agriculture, statistics on commercial fertilizer deliveries, and French expertise on physicochemical properties of organic manure. The practical interest of this new framework was illustrated for France during the crop year 2005/06. Aggregation at crop year level showed a reasonable agreement between estimated values derived from CADASTRE_NH3 and those from the French inventory method, for N and ammoniacal-N (TAN) application rates, total NH3 emissions and NH3 EF. Discrepancies were large for organic manure only; national TAN application rates and NH3 emissions were 62–63% lower with CADASTRE_NH3. This was due to divergences in the representation of cattle farm yard manure and in the TAN:N ratio of solid manure. Annual emissions for fertilization in France were estimated to be 270 Gg NH3, 29% lower than the French national inventory estimate. At the regional level, organic manure contributed to 73% of field NH3 emissions in intensive livestock husbandry areas and to 41% in the other areas. The CADASTRE_NH3 framework can be seen as a Tier 3 approach able to estimate specific regional EF for different mineral fertilizers and organic manure.

The Modeling of Time-Dependent Deformation and Fracturing of Brittle Rocks Under Varying Confining and Pore Pressures

A numerical hydro-mechanical model for brittle creep is proposed to describe the time-dependent deformation of heterogeneous brittle rock under constant confining and pore pressures. Material heterogeneity and a local material degradation law are incorporated into the model at the mesoscale which affects the mechanical behavior of rocks to capture the co-operative interaction between microcracks in the transition from distributed to localized damage. The model also describes the spatiotemporal acoustic emissions in the rock during the progressive damage process. The approach presented in this contribution differs from macroscopic approaches based on constitutive laws and microscopic approaches focused on fracture propagation. The model is first validated using experimental data for porous sandstone and is then used to simulate brittle creep tests under varying constant confining and pore pressures and applied differential stresses. We further explore the influence of sample homogeneity on brittle creep. The model accurately replicates the classic creep behavior observed in laboratory brittle creep experiments. In agreement with experimental observations, our model shows that decreasing effective pressure, increasing the applied differential stress, and decreasing sample homogeneity increase the creep strain rate and decrease the time-to-failure, respectively. The model shows that complex macroscopic time-dependent behavior can be explained by the microscale interaction of elements. The fact that the simulations are able to capture a similar hydro-mechanical time-dependent response to that of laboratory experiments implies that the model is an appropriate tool to investigate the complex time-dependent behavior of heterogeneous brittle rocks under coupled hydro-mechanical loading.

Spatiotemporal Patterns of Carbon Emissions and Taxi Travel Using GPS Data in Beijing

Taxis are significant contributors to carbon dioxide emissions due to their frequent usage, yet current research into taxi carbon emissions is insufficient. Emerging data sources and big data–mining techniques enable analysis of carbon emissions, which contributes to their reduction and the promotion of low-carbon societies. This study uses taxi GPS data to reconstruct taxi trajectories in Beijing. We then use the carbon emission calculation model based on a taxi fuel consumption algorithm and the carbon dioxide emission factor to calculate emissions and apply a visualization method called kernel density analysis to obtain the dynamic spatiotemporal distribution of carbon emissions. Total carbon emissions show substantial temporal variations during the day, with maximum values from 10:00–11:00 (57.53 t), which is seven times the minimum value of 7.43 t (from 03:00–04:00). Carbon emissions per kilometer at the network level are steady throughout the day (0.2 kg/km). The Airport Expressway, Ring Roads, and large intersections within the 5th Ring Road maintain higher carbon emissions than other areas. Spatiotemporal carbon emissions and travel patterns differ between weekdays and weekends, especially during morning rush hours. This research provides critical insights for taxi companies, authorities, and future studies.