Spatiotemporal Emission Research Articles

Towards sustainable building landscapes: a spatially explicit life-cycle analysis of carbon emissions and mitigation strategies

ContextThe urban landscape plays a crucial role in achieving sustainability goals and aspiring to a zero-emission future. Effective carbon mitigation strategies need a spatially explicit and life-cycle analysis of building emissions, detailed mapping highlights the geographical distribution and specific characteristics of buildings, enabling precise identification and targeted management of emission sources.ObjectivesThis study aims to create a framework that reveals the spatial and temporal dimensions of building carbon emissions at each stage of their life cycle.MethodsWe developed a comprehensive approach for carbon accounting in buildings. This framework captures spatiotemporal carbon emission patterns across various building types, considering material, energy, and waste flows. It also identifies potential mitigation strategies within the urban building landscape.ResultsApplying this framework to a case study in Shenzhen, we observed a fluctuating increase in total carbon emissions, peaking at 201.5 Mt in 2016 and subsequently declining. Embodied emissions dominated before the twenty-first century, while operational emissions became significant afterward. Spatially, emissions hotspots concentrated in central urban districts, expanding outward, with residential buildings contributing the most. Scenario analysis revealed that extending building lifetimes is a key strategy for mitigating embodied carbon, while improving energy efficiency and adopting clean energy work well together to reduce operational carbon.ConclusionsMapping the spatiotemporal patterns of carbon emissions throughout a building’s lifespan can assist urban planners and policymakers in formulating targeted strategies for carbon reduction, thereby enhancing urban sustainability.

Potential for low-emissions oil palm production in Indonesia: insights from spatiotemporal dynamics

Rising global demand for palm oil has created environmental pressures related to deforestation, burning, and peat exploitation, which in turn drives increased greenhouse gas (GHG) emissions. GHG emissions in oil palm (OP) production are known to vary spatially. However, temporal changes across contrasting management and soil types, are less well studied. This paper quantifies spatiotemporal GHG emissions across contrasting regions, management types, and soil types for the period 1990–2019 to assess the potential for reducing emission. The study focusses on Indonesia, as the biggest producer of OP, and in particular on the North Sumatra and Riau provinces, where OP is intensively produced. GHG inventories in 5 year time steps were constructed to investigate the change in drivers of emissions using spatial data, resampled to a 500 m grid. Total GHG emissions were found to have increased in both regions due to expanding OP production. However, results show a reduction in emissions flux from 1.98 to 1.15 Ton Ceq. ha−1yr−1 in North Sumatra and 9.63–2.67 Ton Ceq. ha−1yr−1 in Riau over the study period. This reduced flux was linked to the decreased deforestation and burning activities, together with increased biomass increment from lower carbon stock area conversion to OP. In both provinces, smallholder plantations emitted fewer emissions than industrial ones, and production on organic soils resulted in consistently higher emissions than on mineral soils. In North Sumatra, emissions under all management and soil types were found to decrease. In Riau, however, GHG emissions on organic soils regardless of management types, remained high. Our findings emphasise that potential for low-emissions OP production is attainable by reducing emissions per unit area through an improved understanding of GHG emissions spatiotemporal variability and their drivers. These contribute to reinforcing ongoing government regulations and guiding the industry towards low-emission OP productions.

Spatiotemporal Operational Emissions Associated With Light-, Medium-, and Heavy-Duty Transportation Electrification

The spatiotemporal distribution of pollutants plays a significant role on their impact on ecosystems and human health. This paper presents a strategy for calculating spatiotemporal operational emissions of road transportation and the electric grid to quantify the impact of transportation electrification. Emissions from internal combustion engine vehicles and offset emissions from electric vehicles (EVs) are considered using actual transportation networks and travel data. The spatiotemporal charging demand for light-duty and medium- and heavy-duty EVs are estimated in a behaviorally-informed way and mapped to electrical buses within the power grid and an ac optimal power flow with unit commitment is solved. The methodology is demonstrated with ten scenarios simulated on a grid with 7000 electrical buses geographically sited in Texas, created with actual generator data for the 2020 grid, and a future case including anticipated generator updates by 2030. Results show overall transportation emissions reductions in daily operational emissions of up to 20–30% for all pollutants studied, outweighing the increase in emissions from the electric grid. Considering emissions on an hourly basis, up to approximately 1000% reduction in CO emissions is observed.

Trajectory-based vehicle emission evaluation for signalized intersection using roadside LiDAR data

Signalized intersections are the bottleneck of urban traffic network and often lead to traffic congestion and increased traffic emissions. Studying and analyzing the spatiotemporal emission patterns at intersections is the prerequisite for traffic emission reduction. This study develops a novel framework for vehicle emission estimation using high-resolution trajectory data based on the roadside light detection and ranging sensor. A meshing method was proposed to divide the area and the trajectory repair model was developed by considering multiple influenced factors to obtain refined LiDAR trajectory data. Then, the Virginia Tech microscopic model was used to perform traffic emission spatiotemporal analysis. In the experiments, trajectory repair results indicate that Root Mean Square Error and Mean Absolute Error are 1.1299m and 0.7816m within 3s, which has excellent prediction accuracy and provides a reliable data basis for accurately estimating emissions. Emission results show that vehicle speed and acceleration are positively correlated with emissions, with the highest emissions generated during acceleration, of which CO could reach 0.0037 g/s. From a temporal perspective, emissions gradually decrease during red light phases and increase when vehicles accelerate during green light phases, suggesting recommendations for signal optimization for areas where emissions exceed standards to reduce vehicle's start and stop. From a spatial perspective, emission rates are highest in the downstream area, with CO reaching more than 0.0043 g/s. It can be suggested that relevant authorities could install speed limit signs or plant trees in this area. Overall, these findings have the potential to alleviate emission pressures at intersections.

Crop residue burning in China (2019–2021): Spatiotemporal patterns, environmental impact, and emission dynamics

Crop residue burning (CRB) is a major contributor to air pollution in China. Current fire detection methods, however, are limited by either temporal resolution or accuracy, hindering the analysis of CRB's diurnal characteristics. Here we explore the diurnal spatiotemporal patterns and environmental impacts of CRB in China from 2019 to 2021 using the recently released NSMC-Himawari-8 hourly fire product. Our analysis identifies a decreasing directionality in CRB distribution in the Northeast and a notable southward shift of the CRB center, especially in winter, averaging an annual southward movement of 7.5°. Additionally, we observe a pronounced skewed distribution in daily CRB, predominantly between 17:00 and 20:00. Notably, nighttime CRB in China for the years 2019, 2020, and 2021 accounted for 51.9%, 48.5%, and 38.0% respectively, underscoring its significant environmental impact. The study further quantifies the hourly emissions from CRB in China over this period, with total emissions of CO, PM10, and PM2.5 amounting to 12,236, 2,530, and 2,258 Gg, respectively. Our findings also reveal variable lag effects of CRB on regional air quality and pollutants across different seasons, with the strongest impacts in spring and more immediate effects in late autumn. This research provides valuable insights for the regulation and control of diurnal CRB before and after large-scale agricultural activities in China, as well as the associated haze and other pollution weather conditions it causes.

Data mining and spatio-temporal characteristics of urban road traffic emissions: A case study in Shijiazhuang, China

Accurate estimation of traffic emissions and analysis of spatio-temporal distribution on urban roads play a crucial role in the development of low-carbon transportation system. Traditionally, a region’s emission characteristics have been studied using numerous emission models with GPS-based spatio-temporal data. Due to the heavy data processing needs of GPS-based data, emission characteristics for a large region have been studied by dividing the region into a limited number of smaller areas or units. Additionally, GPS data are based on a few vehicles in the traffic which does not fully reflect road conditions. This paper proposed an approach that can be used to study and calculate the spatio-temporal emission pattern of a region at a roadway section level by using Baidu’s online traffic data and COPERT model. The proposed method can be used to estimate road-level emission patterns while avoiding the impact of redundant data in large datasets, making the dataset more reliable, applicable, and scalable. The proposed approach has been demonstrated through a study of spatio-temporal emission patterns in the Qiaoxi district within city of Shijiazhuang, China. Online data crawling technology was used to obtain data on urban road traffic speed and driving distance. The linear reference technology was used to construct a two-layer road network model to conduct the coupling and matching of traffic data with the road network data. The COPERT model was implemented to calculate the average traffic emissions on each road in the road network, and a traffic emission intensity index was proposed to quantify the CO, VOC, NOx and CO2 emissions on urban roads in the study area. The analysis results show that the traffic emission intensity of the expressway, trunk road, secondary road, and branch road is high during the morning peak (7 AM-9 AM) and evening peak (5 PM—7 PM). The sections with higher traffic emission intensity are mainly concentrated on the main roads and secondary roads such as Jiefang South Street, Shitong Road and Xinhua Road. Nearly one-third of 2nd Ring and 3rd Ring roads also have relatively high emission intensity. The research results provide new ideas for estimating traffic emissions in urban road networks and analyzing the spatio-temporal distribution of traffic emissions. The research results can also provide a decision-making basis for traffic management departments to formulate energy-saving and emission-reduction measures and promote the development of urban green and low-carbon transportation.

Maritime greenhouse gas emission estimation and forecasting through AIS data analytics: a case study of Tianjin port in the context of sustainable development

The escalating greenhouse gas (GHG) emissions from maritime trade present a serious environmental and biological threat. With increasing emission reduction initiatives, such as the European Union’s incorporation of the maritime sector into the emissions trading system, both challenges and opportunities emerge for maritime transport and associated industries. To address these concerns, this study presents a model specifically designed for estimating and projecting the spatiotemporal GHG emission inventory of ships, particularly when dealing with incomplete automatic identification system datasets. In the computational aspect of the model, various data processing techniques are employed to rectify inaccuracies arising from incomplete or erroneous AIS data, including big data cleaning, ship trajectory aggregation, multi-source spatiotemporal data fusion and missing data complementation. Utilizing a bottom-up ship dynamic approach, the model generates a high-resolution GHG emission inventory. This inventory contains key attributes such as the types of ships emitting GHGs, the locations of these emissions, the time periods during which emissions occur, and emissions. For predictive analytics, the model utilizes temporal fusion transformers equipped with the attention mechanism to accurately forecast the critical emission parameters, including emission locations, time frames, and quantities. Focusing on the sea area around Tianjin port—a region characterized by high shipping activity—this study achieves fine-grained emission source tracking via detailed emission inventory calculations. Moreover, the prediction model achieves a promising loss function of approximately 0.15 under the optimal parameter configuration, obtaining a better result than recurrent neural network (RNN) and long short-term memory network (LSTM) in the comparative experiments. The proposed method allows for a comprehensive understanding of emission patterns across diverse vessel types under various operational conditions. Coupled with the prediction results, the study offers valuable theoretical and data-driven support for formulating emission reduction strategies and optimizing resource allocation, thereby contributing to sustainable maritime transformation.

Identifying emission sources of CH4 in East Asia based on in-situ observations of atmospheric δ13C-CH4 and C2H6

Methane (CH4) is the second most important greenhouse gas influenced by human activity. The increase in atmospheric CH4 concentrations contributed ~23 % to the anthropogenic radiative forcing (Saunois et al., 2020). The current anthropogenic CH4 emissions trajectory implies that large emissions reductions are needed to meet the target of the Paris Agreement (Nisbet et al., 2019). For effective regulation of CH4, it is important to identify spatiotemporal emission sources, in particular those from East Asia – one of the largest CH4 emitters. In this study, we present in-situ observations of atmospheric CH4 concentrations (i.e., dry air mole fractions in part per billion (ppb)) and carbon isotopic compositions of CH4 made during 2017–2020 at the Gosan station (GSN, 33.3°N, 126.2°E, 72 m a.s.l) which is representative of regional background conditions in East Asia. The annual growth rate of the observed CH4 baseline concentrations was 11 ± 1 ppb yr−1. The enhanced pollution concentrations of CH4 showed seasonally distinctive correlations with the corresponding δ13C-CH4. The CH4 source isotopic signature for winter derived based on both the Keeling and Miller-Tans approaches was −40.7 ± 3.4 ‰, suggesting dominant thermogenic sources (e.g., coal and/or gas combustion), whereas the source signature for summer was estimated as −54.1 ± 1.2 ‰, which seemed to represent both microbial sources (e.g., rice paddies) and fossil fuel sources of CH4 emissions. Based on the δ13C-CH4 source signatures, we were able to infer that the proportional contribution of microbial sources to CH4 summer emissions was ranges from 45 to 79 %. The finding indicates that microbial sources account for a substantial portion of CH4 summer emissions, consistent with estimates of 74–80 % derived from the observed correlation between CH4 and C2H6, which serves as a complementary tracer for fossil fuel sources.

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.

Health and economic benefits of heavy-duty diesel truck emission control policies in Beijing

PM2.5 emissions from heavy-duty diesel trucks (HDDTs) have a significant impact on air quality, human health, and climate change, and seriously threaten the UN Sustainable Development Goals. Globally, a series of emission control measures have been implemented to reduce pollution emissions from HDDTs. Current studies assessing the impact of these measures on air quality and human health have mainly used coarse-grained emission data as input to dispersion model, resulting in the inability to capture the spatiotemporal variability of pollutant concentrations and tending to increase the uncertainty of health impact assessment results. In this study, we quantified the impact of pollution control policies for HDDTs in Beijing on PM2.5 concentrations, human health, and economic losses by integrating policy scenario analysis, pollution dispersion simulation, public health impact and economic benefit assessment models, supported by high spatiotemporal resolution emission data from HDDTs. The results show that PM2.5 concentrations from HDDTs exhibit significant spatial aggregation characteristics, with the intensity of aggregation at night being about twice as high as that during the day. The emission hotspots are mainly concentrated in the sixth, fifth and fourth rings and major highways. Compared to the “business as usual” scenario in 2018, the current policy of updating the fuel standard to China VI and the emission standard to China 6 can reduce PM2.5 concentrations by 96.72%, thereby avoiding 612 premature deaths, which is equivalent to obtaining economic benefits of 1.65 billion CNY. This study further emphasizes the importance of high spatiotemporal resolution emission data during traffic dispersion modeling. The results can help improve the understanding of the effectiveness of emission reduction measures for HDDTs from a health benefit perspective.

Analysis of the characteristics of major pollutants discharged from wastewater in China's provinces.

In recent years, the discharge of major pollutants in China's wastewater has been decreasing but remains at a high level. Controlling the discharge of pollutants in sewage is of great importance for protecting water quality and maintaining ecological balance. Based on data collected from 31 provinces in China from 2011 to 2020 (except 2018), this study analyzes the spatiotemporal variation emissions of the wastewater pollutants: chemical oxygen demand (COD), ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP). The entropy method was used to evaluate the effectiveness of water pollution control in different provinces. Our results revealed that the total emission per gross domestic product (GDP) for COD, NH3-N, TN and TP in China decreased by 50.7%, 81.9%, 65.4% and 70.8%, respectively. In terms of regional annual emission differences, the Northwest region was the lowest compared with other regions, accounting for 4.87%-6.59% of the national pollutant emissions, and the Central China region was the highest, accounting for 22.4%-26.05% of the national pollutant emissions. The average value of pollutant emissions per unit of GDP decreased year-to-year overall, but Guangxi and Tibet showed a trend of first decreasing and then increasing. The correlation results indicated a significant correlation (0.977) between TN and TP emissions in wastewater in China during 2011-2020. Through clustering and Multidimensional Scaling model (MDS) analysis, Beijing and Shanghai have been performing well in controlling water pollution discharge, while the provinces of Tibet and Guangxi must still increase their efforts in water pollution control. Furthermore, these results demonstrate the experience and achievements of the Chinese government in the treatment of wastewater pollution and provide a useful reference for treatment of wastewater pollution in the world.

Nitrous oxide emissions dynamics in Indian agricultural landscapes: Revised emission rates and new insights

In the past five decades, nitrous oxide (N2O) emissions from Indian agricultural landscapes have significantly increased, making India the fifth-largest emitter of N2O globally (∼4.8%). The few studies that evaluated N2O at the India scale using a bottom-up approach were constrained to a brief period and employed country-level emission factors (EFs), despite the availability of long-term inventory data and spatially varying EFs. To fill this gap and comprehend the spatio-temporal N2O emissions dynamics at the district-level across India, this study created district- and crop-specific N2O EFs from 82 field plot studies with 861 observations across 13 locations. Through these improved EFs, district-level emissions were calculated from 1966 to 2017 using a modified IPCC Tier I-Tier II approach (specific EFs for synthetic fertilizer, animal manure, and crop residue return) and highlighted significant spatio-temporal drivers. Total N2O emissions in Indian agricultural soils increased from 73.7 ± 3.3 Gg in 1966–279.3 ± 15.8 Gg in 2017. The increase in total N2O emissions (3.9 Gg yr−1) from 1966 to 2017 is associated with the increase in crop production (4.1 Tg yr−1) driven by the increase in fertilization (0.3 Tg yr−1). N2O emissions from synthetic fertilizer, animal manure, crop residue return, soil organic matter, volatilization, leaching, and runoff increased from 1966 to 2017. On the national scale, N2O emissions from synthetic fertilization accounted for 57% in 2017, followed by animal manure (15%) and soil organic matter (9%). While the national emission totals are dominated by synthetic fertilizer application, district-level analyses revealed the possibility of the highest contributions from animal manure, crop residue return, and soil organic matter in 18% of the districts. The overall uncertainty from this modified method and refined EFs was 5.1%. This study is the first to capture long-term N2O dynamics at the district scale from Indian agricultural soils.

Reduction potentials for particulate emissions from household energy in India

Household access to clean energy is a priority for public health and the environment in low- and middle-income countries. However, past illustrative studies have explored benefits of replacing all polluting energy sources, a transition that is only theoretically possible. Factors that limit achievement of the entire theoretical reduction potential should be explored to inform programmatic decision making. We propose a hierarchy of reduction potentials for emissions from household energy, representing different implementation barriers. Following similar work in renewable energy, we propose four categories of reduction potentials beyond the theoretical maximum: distributional, technical, economic, and market. We apply this framework to household energy emissions using a high-resolution spatiotemporal emission inventory of India, a country chosen for its data availability and level of interest in mitigation. We explore distributional potential using distance from urban areas, technical potential by attributing emissions to energy services, and economic potential with a village- level proxy for likelihood of program success. For distributional potential (spatial accessibility), we find that applying reduction programs within 5 km of urban centers would achieve 36%–78% of the theoretical potential across seven regions in India; extension to 10 km yields reductions of 63%–90%. Technical and economic reduction potentials differ most greatly from theoretical potential in regions that contribute the most to national emissions. Even if some of the relationships underlying emission causes are not completely known, reflecting the factors that affect transitions can inform practitioners and programs seeking to scale and deliver clean energy solutions. We assert that including these important influences should be a goal of emission inventory development, beyond the simple quantification of baseline emissions.

Collective photon emission of two correlated atoms in free space

By preparing two distant emitters in entangled Dicke states via detection of a single photon, the subsequent spatiotemporal photon emission is investigated. Depending on the parity of the established quantum state, emission patterns for the second scattered photon are observed featuring spatial superradiance as well as subradiance. We employ ultrafast single-photon resolving cameras with high spatial resolution disclosing the spatiotemporal emission characteristics. By recording the first photon in one direction and the second photon in another, revealing the spatial two-photon cross-correlation function ${g}^{(2)}({\mathbf{r}}_{1},{\mathbf{r}}_{2})$, we characterize the collective spontaneous emission behavior of two ions in free space. We explain the observed contrast of ${g}^{(2)}({\mathbf{r}}_{1},{\mathbf{r}}_{2})$ considering independently derived experimental parameters. Our results show how the detection of a single photon can profoundly modify the collective spontaneous emission dynamics of an atomic ensemble.

Analysis of spatiotemporal distribution of methane column concentration and emission source in permafrost area of Northeast China based on AIRS data

Northeast China has the largest high-latitude permafrost distribution zone in China. With the intensification of global warming, the carbon stored in the permafrost will gradually thaw and release in the form of methane gas to air, thus increasing the methane column concentration in the near-surface troposphere. However, at present, the spatiotemporal distribution and emission source of methane column concentration in the near-surface troposphere are not clear. In this paper, using the AIRS (Atmospheric Infrared Sounder) remote sensing data from Aqua satellite, we analyzed the distribution and change trend of the methane column concentration in the near-surface troposphere in Northeast China from 2003 to 2021, and combined with the national meteorological and field monitoring data, we studied the emission mechanism and emission source of surface methane in the permafrost area of Northeast China. Study results show that: the permafrost zone in Northeast China has a high methane emission capacity in four seasons, and the methane column concentration shows a significant double-peak seasonal variation. The first peak appears in summer (June to August) and the maximum appears in August, the second peak appears in winter (December to February) and the maximum appears in December. However, the maximum average growth rate of near-surface methane column concentration in Northeast China appeared in spring (5.378ppbv/a), the reason is that the carbon stored under the permafrost is gradually exposed and released in the form of methane. The emission sources include microbial action, methane transported by wetland groundwater, geological methane (metastable methane hydrate, steady-state methane hydrate and thermogenic methane produced in the deep underground or coal seams) stored in frozen layer. The study provide data and technical support for the estimation of carbon emissions in permafrost areas in Northeast China.

Spatially resolved hourly traffic emission over megacity Delhi using advanced traffic flow data

Abstract. This paper presents a bottom-up methodology to estimate multi-pollutant hourly gridded on-road traffic emission using advanced traffic flow and speed data for Delhi. We have used the globally adopted COPERT (Computer Programme to Calculate Emissions from Road Transport) emission functions to calculate the emission as a function of speed for 127 vehicle categories. At first, the traffic volume and congestion (travel time delay) relation is applied to model the 24 h traffic speed and flow for all the major road links of Delhi. The modelled traffic flow and speed shows an anti-correlation behaviour having peak traffic and emissions in morning–evening rush hours. We estimated an annual emission of 1.82 Gg for PM (particulate matter), 0.94 Gg for BC (black carbon), 0.75 Gg for OM (organic matter), 221 Gg for CO (carbon monoxide), 56 Gg for NOx (oxides of nitrogen), 64 Gg for VOC (volatile organic compound), 0.28 Gg for NH3 (ammonia), 0.26 Gg for N2O (nitrous oxide) and 11.38 Gg for CH4 (methane) for 2018 with an uncertainty of 60 %–68 %. The hourly emission variation shows bimodal peaks corresponding to morning and evening rush hours and congestion. The minimum emission rates are estimated in the early morning hours whereas the maximum emissions occurred during the evening hours. Inner Delhi is found to have higher emission flux because of higher road density and relatively lower average speed. Petrol vehicles dominate emission share (>50 %) across all pollutants except PM, BC and NOx, and within them the 2W (two-wheeler motorcycles) are the major contributors. Diesel-fuelled vehicles contribute most of the PM emission. Diesel and CNG (compressed natural gas) vehicles have a substantial contribution in NOx emission. This study provides very detailed spatiotemporal emission maps for megacity Delhi, which can be used in air quality models for developing suitable strategies to reduce the traffic-related pollution. Moreover, the developed methodology is a step forward in developing real-time emission with the growing availability of real-time traffic data. The complete dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.6553770 (Singh et al., 2022).

Spatiotemporal pattern of greenhouse gas emissions in China’s wastewater sector and pathways towards carbon neutrality

Greenhouse gas (GHG) emissions from the wastewater sector are a major contributor to the overall GHG emissions of all countries, however, there is currently a lack of global, national-scale, detailed spatiotemporal emission data. In this study we elaborated dynamic plant-resolved emission factor values based on the case-specific operating parameters of each municipal wastewater treatment plant (WWTP) and the associated sewers and sludge disposal utilities in China, contrasting with previous estimations that typically focused on WWTP operation without differentiating their spatiotemporal discrepancies. We demonstrate here that China’s municipal wastewater industry generated 53.0 MtCO2e in total GHG emissions in 2019, with the northern and southern areas exhibiting noticeably higher GHG intensities. Due to improved wastewater treatment, the national average wastewater GHG intensity grew by 17.2% between 2009 and 2019, although it is anticipated that the intensity will begin to fall starting in 2020 at rates dependent on the chosen treatment methods. A net-zero emission by the entire sector may be achieved as early as 2044 with continually increasing decarbonized energy and a progressive shift to resource-oriented operations, compared with just a 23.7% decrease by 2050 under the baseline scenario. Joint efforts at the scientific, economic and policy level will be required to make this happen. This study may serve as a roadmap for developing carbon-neutral wastewater management policies and technologies in China and the rest of the globe. Based on case-specific operating parameters of each municipal wastewater treatment plant and the associated sewers and sludge disposal utilities, this study presents a detailed analysis of the current carbon footprint of the wastewater sector in China and scenarios for reducing future emissions.

The effect of Penning ionization reactions on the evolution of He with O2 admixtures plasma jets

In this study, the effect of the O2 and N2 Penning ionization reactions on the evolution a capillary helium atmospheric pressure plasma jet (APPJ) with and without the presence of oxygen admixtures is investigated numerically using a two dimensional axi-symmetric model. In order to better understand potential applications of plasma surface interaction, the He and He + O2 (1000 ppm) APPJ is launched into a dielectric target. Moreover, the numerical model is compared with experimental measurements showing that the model captures the right physics and can elucidate the discharge dynamics. The comparison is realized for axial and radial measurements in order to have a complete picture of the plasma jet evolution. In this paper, the velocity and the shape of the plasma guided streamer were chosen as indicators of plasma jet evolution. In particular, the investigation is accomplished experimentally by tracking the spatio-temporal resolved emission of 706.5 nm (3s3S → 2p3P) line and numerically by calculating the reaction rate for the same transition. The O2 and N2 Penning reactions are then independently considered in the numerical model in order to isolate their contribution to the plasma evolution. It is shown that for the pure helium plasma, the N2 Penning reactions are more important to the plasma evolution. However, when oxygen admixtures are added to the plasma gas, the O2 Penning reactions become more important than the N2 ones.

Spatiotemporal Dynamic Correlation Characteristics and Driving Factors of Major Air Pollutant Emissions in China

Air pollution is closely associated with human health and the economy. Therefore, it is important to understand variations in the spatiotemporal and sectoral emission distributions of major air pollutants and their drivers. The policies (APAPPC) promulgated by China in 2013 have also achieved remarkable results. Rate of change, trend analysis, and a geographically and temporally weighted regression model were used to study the effects of socioeconomic factors on NOx, SO2, and dust emissions in China during 2011–2017. During the study period, annual average emissions of NOx, SO2, and dust decreased by 11.45, 13.42, and 4.82 Mt (−47.64, −60.53, and −39.05%), respectively. Pollutant emissions were concentrated in North China, with Shandong and Hebei provinces exhibiting the highest NOx and SO2 and dust emissions, respectively. Pollutant emissions from the power and industrial sectors were mainly distributed in East (27.08 and 28.00%, respectively) and North China (23.57 and 20.04%, respectively), whereas emissions from the residential sector were mainly concentrated in North (22.48%) and Southwest China (20.07%). Pollutant emissions were positively correlated with electricity generation, urban population density, urban green spaces, private car ownership, the secondary industry as a share of regional GDP, and steel production and negatively correlated with disposable income and gross construction output. Per capita disposable income was the dominant driving factor.

Determining the Concentration Level of Air Pollutants Related to Road Traffic in Kano Metropolis

The problem of road traffic-related air pollutants is universal. Traffic air pollution usually occurs on roads and mainly at intersections which significantly affects the air quality of such areas. The research aims to provide empirical evidence on the relationship between road transport air pollutants and temperature conditions on climate modification in the study area, which might perfect environmental sustainability. The study was best designed on field Experimentation to assess the spatiotemporal emission levels and analyses the implications of road Vehicular traffic-related air pollutants Concentration (such as, Carbon monoxide, Nitrogen dioxide, Sulphur dioxide, Hydrogen Sulphide and Methane gas) on Kano weather conditions (precisely Temperature) in 15 selected sample sites or locations, the measurements of pollutants Concentration, temperature level were carried out using the instruments called a Crowcorn gas detector, field thermometer were used together with the help of 5 research assistants. presents the periodic concentration levels of CH4 (methane) in relation to road traffic in different time periods: afternoon, evening, and morning, categorized into three concentration levels: high, medium, and low. The table provides various statistical measures to describe the distribution of methane concentrations within each category. High Concentrations in the afternoon reveal an average mean methane concentration is 0.20. The coefficient of variability, which measures the dispersion of the data points around the mean, is 46.06%. The skewness of 1.19 implies a moderately positively skewed distribution. The kurtosis of 1.38 indicates a distribution with a peak slightly higher than a normal distribution and a moderate presence of outliers. These findings could inform air quality management and monitoring efforts in Kano Metropolis. If temperature is consistently correlated with higher CO and CH4 levels, authorities may need to consider seasonal variations and temperature-related interventions when addressing air quality issues.