Major Emission Sources Research Articles

Investigating the rationale for low-carbon production techniques in agriculture for climate change mitigation and fostering sustainable development via achieving lowcarbon targets.

In China, agricultural activities are a major source of greenhouse gas emissions, ranking second only to another significant source. This presents a significant obstacle to reducing emissions and jeopardizes both the availability of food and the sustainable growth of agriculture. It is primarily the farmers who utilize cultivated land and are thus accountable for the initiation of these emissions. Farmers' role is significant in adopting green and low-carbon (LC) agricultural production practices, and their actions are directly tied to the achievement of the dual goals of carbon reduction. Understanding their motivations for engaging in LC production and the factors that influence their willingness to do so is important for both theory and practice. In this study, data was collected from 260 questionnaires in 13 counties across five major cities in Shaanxi Province. The purpose was to identify factors that impact farmers' motivation and willingness to engage in LC agriculture using linear regression analysis. A structural equation model was constructed to better understand the underlying mechanisms that influence farmers' actions towards LC farming practices. The study's findings indicate that (1) farmers' behavior towards LC production practices is notably impacted by internal motivation based on joy and internal motivation based on responsibility (IMR); (2) IMR has the most pronounced effect on farmers' adoption of LC production practices; (3) the internal motivation based on joy, IMR, behavior attitude, subjective norm, and perceived behavior control are related to each other; and (4) the multi-group analysis of the data indicates that the impact of internal motivation based on joy and IMR on adopting sustainable farming practices may vary among different groups. It is essential to support farmers who have strong intrinsic motivation to engage in sustainable agriculture. Additionally, policymakers must promote positive attitudes towards sustainable farming to achieve the desired environmental (LC) objectives.

A Comparison of Battery and Hydrogen Fuel Cell Electric Vehicles for Clean Transportation

Burning fossil fuels for transportation is a major source of greenhouse gas emissions, which contribute to global warming, air pollution, and health problems. Research estimates that cars alone emit more than 300 million tons of carbon dioxide into the atmosphere each year due to the internal combustion engines that use fossil fuels. In contrast to fossil fuel vehicles, electric vehicles have zero tailpipe emissions. Therefore, countries around the world are making efforts to use electric vehicles instead of fossil fuels. Two of the available alternatives to internal-combustion engines are battery and hydrogen fuel cell electric vehicles. This work investigates the basic functionalities and current advancements of battery and hydrogen fuel cell electric vehicles. A comprehensive comparison of the benefits and drawbacks of each technology is provided, along with future forecasts for transportation.

Data driven analysis of atmospheric methane concentrations as function of geographic, land cover type and season

Over the last decade we have witnessed a rapid, so far unexplained, increase in the emission of methane to the atmosphere and this increase could lead to an acceleration of the ongoing climate changes. The increase is likely to originate from agriculture, but oil and gas production as well as wetlands are also under suspicion. The best way to quantify the emission of methane and other greenhouse gasses to our atmosphere is by using space based remote sensing. Here, we analyse 3 years of measurements of the column-averaged dry-air mole fraction of methane from the Tropospheric Monitoring Instrument on Sentinel-5P obtained with two different retrieval methods in order to evaluate the dependency on geographic, land cover type and season. The land cover types were obtained from the Moderate Resolution Imaging Spectroradiometer aboard the Terra and Aqua satellites and from the World Cover data product using observations from the Copernicus Sentinel-1 and Sentinel-2 missions. The analysis reveals that while the highest methane concentrations are generally found over croplands, the lowest are generally found over shrublands, which is in agreement with expectations. It is more surprising that the analysis also reveals lower than average methane concentrations over wetlands as wetlands are generally thought to be a major source of methane emission. Until this discrepancy is resolved the methane concentration over wetlands from the Tropospheric Monitoring Instrument on Sentinel-5P should be handled with caution. It is also found that the annual methane cycle, as seen in the measured methane concentrations, for croplands, shrublands and savannas is delayed in Africa compared to Asia.

Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector

Managing energy demand and reducing greenhouse gas emissions are among the most significant challenges ahead for many countries. Accurate prediction of energy demand and CO2 emissions is an important method to tackle the challenge. In addition, as a major source of energy consumption and CO2 emissions, the transportation sector is at the center of the problem. This study employs a hybrid approach integrating a multi-objective mathematical model with data-driven machine learning algorithms to predict energy demand and CO2 emissions in the transportation sector with improved accuracy. Sensitivity analyses are conducted to evaluate the impact of individual and joint consumptions of different energy resources on CO2 emissions. Case studies are conducted with countrywide statistics in Canada where the prediction indicates that energy demand and CO2 emissions will increase by 34.72% and 50.02% from 2019 to 2048 in the Canadian transportation sector. Results also indicated the varied impacts of different energy resources such that a 5% increase in energy demand in oil, gas, electricity, and renewable energy will result in + 4.8%, +0.315%, +0.28%, and −0.51% changes in CO2 emissions respectively. The proposed framework with integrated prediction model and the sensitivity analyses can help identify critical factors impacting CO2 emissions in the transportation sector and provide quantitative references for energy demand management and CO2 emissions reduction.

Drivers of behaviors: How do city pilots shape residential energy-related emissions through perceptions?

Residential energy consumption, as a major source of emissions in cities, is also a policy priority for the construction of low-carbon cities. The occurrence of residential energy saving and emissions mitigation behaviors is closely related to low-carbon perceptions. Against this background, cities make efforts to shape residential low-carbon perceptions. In order to investigate residential energy consumption and carbon emissions, this study takes low-carbon city pilots as the policy context and establishes the difference-in-difference model on Chinese prefecture-level cities. Theory of planned behavior is utilized to analyze the influence mechanism of residential low-carbon perceptions. Results indicated that (1) low-carbon city pilots can decrease residential energy-related emissions and pass a variety of robustness tests. Multiple pilot eligibility and policy lag would reinforce policy effects. (2) Mechanism analysis shows that low-carbon city pilots can strengthen residential behavioral attitudes, establish subjective norms, and adjust perceived behavioral control. All three mechanisms together shape residential low-carbon perceptions, which consequently promote energy-related emissions mitigation behaviors. (3) Due to differences in geographic location and city size, there is heterogeneity for the policy effects of low-carbon city pilots. For the future research, it is necessary to expand the scope of residential energy-related emissions, find out the potential influencing factors, and track the policy effects in long-term.

Analyzing the Impact of Critical Barriers on the Stakeholder’s Adoption Behavior toward Green Building Technologies in Pakistan

The construction industry is becoming a major source of carbon emissions in Pakistan due to the country's rapid urbanization. To slow global warming, it is crucial to evaluate and cut construction's carbon emissions. Despite significant international efforts to encourage the use of green construction technology, which have not yet been extensively embraced because of obstacles, these carbon emissions are continuously rising. In this context, the current research looked at how barriers affected how stakeholders adopted green building technologies (GBTs) in Pakistan. Data were collected through a questionnaire from 349 stakeholders in the construction industry. Partial least square structural equation modeling was used for the empirical study. The main findings revealed that, out of the five types of barriers, innovation-related and economic barriers were found the strongest influencing barriers, followed by socio-cultural and governmental barriers, resource-related and regulation-based barriers towards the stakeholders’ adoption behavior of green building technologies in Pakistan. Based on the research's findings, various policy recommendations were made for the government policy-makers and other concerned stakeholders to ensure the effective deployment of green building technologies in Pakistan.

Does self‐face awareness influence green building project performance?—An empirical evidence from China

AbstractIn the circumstances of “Carbon Peak,” it is imminent to implement green construction technology in the construction industry as it is a major source of CO2 emissions. Nevertheless, little study has been done on the impact of self‐face awareness (SA) on green building project performance (GP). On the basis of a large‐scale questionnaire survey in China and an in‐depth analysis of the four categories (cognition, behavior, satisfaction, and performance), this paper has added an SA dimension, and proposed a new impacting path. Then, based on the improved conceptual model for measuring the impact of green construction behavior on GP, this paper has conducted empirical tests by using a large sample of questionnaire data and partial least square‐structural equation modeling method. The results show that: (1) green construction behavior has a positive impact on GP, and the impact of perceived behavioral control of green construction technology is most significant for promoting green construction in the industry, and SA has a significant positive effect on green construction in the industry; (2) environmental perception and perceived behavioral control for green construction technology play a mediating role; and (3) significant differences indeed exist in the multigroup analysis, and the biggest difference is working experiences in the engineering field. Finally, this paper has proposed corresponding management implications based on the above findings. The main contribution of this study is that this study has developed a new dimension (SA) to the model considering Chinese national and cultural differences, in an attempt to reveal the impact of green construction technology on GP in the Chinese context. In addition, the promotion of green construction (PGC) in the construction industry was also added based on the sustainability perspective, which is the first attempt to explore the role of the PGC.

Establishment of HFC-134a Emission Inventory in the North China Plain from 1995 to 2020

1,1,1,2-tetrafluoroethane (HFC-134a) is a potent greenhouse gas that can be degraded to produce trifluoroacetic acid (TFA), a degradation product that has an impact on aquatic ecology, so its emission has been a continuous concern worldwide. Existing studies mainly estimate the global- or national-scale emissions of HFC-134a, and there are relatively few studies on regional emissions, all of which used the top-down method. By establishing a regional-scale bottom-up emission inventory and comparing it with the regional-scale top-down estimation results, regional emissions can be verified and their emission characteristics and environmental impacts can be analysed. HFC-134 emissions were estimated for the first time in the North China Plain using the emission factor method, and spatiotemporal characteristics and environmental impacts were analysed for the period of 1995 to 2020. The results showed that the cumulative HFC-134a emissions were 88 (73–103) kt (126 Mt CO2-eq), which have led to an increase in global radiative forcing of 1.1 × 10−3 (0.9 × 10−3–1.3 × 10−3) W m−2, an increase in global surface temperature of 8.9 × 10−4 °C, and a cumulative TFA production of 7.5 (6.2–8.9) kt as of 2020. The major sources of HFC-134a emissions are the refrigeration and air conditioning sector, which involves the automotive air conditioning (MAC), industrial and commercial refrigeration, and air conditioning (ICR) sub-sectors. China joined the Kigali Amendment in 2021 to phase down HFCs and proposed the goal of carbon neutrality by 2060. The North China Plain is a region undergoing rapid economic development, with a relatively high proportion of GDP (29%) and car ownership (23%) in 2020. Additionally, HFC-134a emissions accounted for about 20% of the total emissions in China. Therefore, HFC-134a emissions and their environmental impact on the North China Plain should not be ignored.

CO2 emissions retrieval from coal-fired power plants based on OCO-2/3 satellite observations and a Gaussian plume model

The CO2 emissions from coal-fired power plants are a major source of carbon emissions in China. However, the existing emission inventory does not accurately reflect current emissions because of a lag in the statistical data and the accuracy limit for emissions factor. This study aims to provide a method for estimating CO2 emissions from coal-fired power plants based on an improved Gaussian plume model and the Orbiting Carbon Observatory 2/3 satellite. We quantified CO2 emissions from six coal-fired power plants (>3000 MW) in China using 14 plumes from the OCO-2 (2014.09.06–2021.10.01) and OCO-3 (2019.08.06–2021.10.01) datasets. Four of the plumes are in good agreement with those from co-located the Tropospheric Monitoring Instrument measurements of co-emitted NO2. The estimated CO2 emissions ranged from 31.81 to 99.71 kt/d with correlation coefficient of 0.47–0.95. The uncertainty of individual plumes varied from 8% to 42% (1σ), with wind speed being the greatest source of uncertainty, and the average XCO2 enhancement of all power plants in this study is less than 1 ppm when the wind speed increases to about 10 m/s. After validation, our results show high agreement with the existing emission inventory. However, this study found that the Carbon Monitoring for Action may have underestimated the CO2 emissions from Tuoketuo due to the timing of the update, and carbon brief may have underestimated the emissions from Wujiaqu by ignoring the high emission factor of lignite. This study helps monitor and estimate important point source carbon emissions, which is not only a prerequisite for the power industry to carry out carbon reduction efforts, but also helps develop specific regional carbon reduction policies, which are important for reducing anthropogenic carbon emissions.

Climate Change Impacts Quantification on the Domestic Side of Electrical Grid and Respective Mitigation Strategy across Medium Horizon 2030

Electrical grids are one of the major sources of emissions of greenhouse gases (GHG), which are harmful to the environment because they contribute to global warming. As the geographical, environmental, political, and policy constraints are different, policies and research frameworks from developed countries cannot be used directly in developing countries. This paper suggests a completely integrated quantification approach (IQA) and sub-methodologies, such as SM1, SM2, and SM3, that consider the limitations, evaluates the effects, and suggest a way to deal with climate change problems on the power grid. From the perspective of renewable energy (RE) integration and GHG emissions (mainly CO2), the proposed approach addresses the limitations in the policy framework extending to 2030. In addition, the effects of the changes in the ambient temperature, from 0.5 °C to 2 °C, have been examined for thermal power generation and transformers. Lastly, the proposed method considers how energy-efficient devices (EEDs) affect the residential load sector. The results show that households used 10.7% less energy and their costs decreased significantly. This work’s quantitative approach gives a specific way to reduce the carbon footprint of the electrical grid.

Quantifications and predictions of sectoral pollutants emissions in Nigeria from 1980 to 2050.

The fast-economic development and population growth in Nigeria have resulted in huge quantities of air pollutants emission which have implications on the environment. Detailed sectoral emission inventory to serve as the basis for policy formation to mitigate the condition is still lacking. This study builds detailed sectoral emission inventory using the emission factor approach to estimates various pollutant emissions from different sources. Five major sources of pollutant emissions were identified which include transportation, energy, municipal solid waste, wood fuel, and agricultural sectors. An increasing trend in emissions from 1980 to 2020 was observed for total emission of CO, NOx, PM2.5, PM10, SO2, NH3 and NMVOC in Nigeria that increased from 1 736-6 210; 143-338; 126-551; 171-717; 19-60; 4-28; and 471-1 587 Gg, respectively. Wood fuel, transportation, and municipal waste sectors are the major sources that contributed to 63%, 16%, and 15% of the total CO emission. Three mitigation scenarios for emission reduction for the future were analyzed. CO emission reductions of 38%, 24%, and 38% will be obtained from the liquefied petroleum gas (LPG) intervention, waste to energy (WTE) technology, and vehicle inspection and maintenance (VIM) policy scenarios, respectively, through to the year 2050.

Monitoring the enterprise carbon emissions using electricity big data: A case study of Beijing

Enterprises are major sources of anthropogenic carbon emissions, and high-quality data on enterprise carbon emissions are prerequisites for climate abatement policies and actions. However, most of the existing data are more than one-year lagging, easily manipulated, and concentrated at the industrial or regional level. To bridge these gaps, this study develops a monitoring approach for enterprise carbon emissions by combining electricity big data, bottom-up emission accounting model, and network model. The proposed approach has then been applied to monitor the real-time carbon emissions of 0.81 million enterprises in Beijing. Our major findings are that: (1) Owing to a large amount of embodied carbon emissions from electricity inflow, Beijing's electricity-related CO2 emissions are 73.57 million tonnes in 2020, constituting 55% of its total. (2) The CO2 emissions per kWh of electricity consumed in Beijing is 645.26 g, whose top three traceable contributors are Hebei (206.71 g), Shanxi (142.21 g), and Beijing itself (133.07 g). (3) The average monitoring error of enterprise carbon emissions is less than 7%, proving the effectiveness of the proposed approach using electricity big data.

Inferred vehicular emissions at a near-road site: Impacts of COVID-19 restrictions, traffic patterns, and ambient air temperature

Vehicles are a major source of anthropogenic emissions of carbon monoxide (CO), nitrogen oxides (NOx), and black carbon (BC). CO and NOx are known to be harmful to human health and contribute to ozone formation, while BC absorbs solar radiation that contributes to global warming and also has negative impacts on human health and visibility. Travel restrictions implemented during the COVID-19 pandemic provide researchers the opportunity to study the impact of large, on-road traffic reductions on local air quality. Traffic counts collected along Interstate-95, a major eight-lane highway in Maryland (US), reveal a 60% decrease in passenger car totals and an 8.6% (combination-unit) and 21% (single-unit) decrease in truck traffic counts in April 2020 relative to prior Aprils. The decrease in total on-road vehicles led to the near-elimination in stop-and-go traffic and a 14% increase in the mean vehicle speed during April 2020. Ambient near-road (NR) BC, CO, NOx, and carbon dioxide (CO2) measurements were used to determine vehicular emission ratios (ΔBC/ΔCO, ΔBC/ΔCO2, ΔNOx/ΔCO, ΔNOx/ΔCO2, and ΔCO/ΔCO2), with each ratio defined as the slope value of a linear regression performed on the concentrations of two pollutants within an hour. A decrease of up to a factor of two in ΔBC/ΔCO, ΔBC/ΔCO2, ΔNOx/ΔCO2, and in the fraction of on-road diesel vehicles from weekdays to weekends shows diesel vehicles to be the dominant source of BC and NOx emissions at this NR site. We estimate up to a 70% reduction in BC emissions in April 2020 compared to earlier years, and attribute much of this to lower diesel BC emissions resulting from improvements in traffic flow and fewer instances of acceleration and braking. Future efforts to reduce vehicular BC emissions should focus on improving traffic flow or turbocharger lag within diesel engines. Inferred BC emissions from the NR site also depend on ambient temperature, with an increase of 54% in ΔBC/ΔCO from −5 to 20 °C during the cold season, similar to previous studies that reported increasing BC emissions with rising temperature. The default setting of MOVES3, the current version of the mobile emission model used by the US EPA, does not adjust hot-running BC emissions for ambient temperature. Future work will focus on improving the accuracy of mobile emissions in air quality modeling by incorporating the effects of temperature and traffic flow in the system used to generate mobile emissions input for commonly used air quality models.

Garbage to Nanocellulose: Quantitative Isolation and Characterization of Steam-Treated Carboxymethyl Holocellulose Nanofibrils from Municipal Solid Waste

Municipal solid waste (MSW) is a major source of greenhouse gas emissions unless its carbons are sequestered into higher-value products. This study focuses on isolating organic solids and cellulose-rich fibers from MSW via high-pressure steam treatment and converting the fibrous, prepulped materials from wastepaper, packaging materials, cardboard, etc., into value-added cellulose nanofibrils. Chemical–mechanical defibrillation of steam-treated municipal solid waste was optimized using heterogeneous and nonregioselective carboxymethyl etherification coupled with shearing by blender, thus transforming a heterogeneous mix of MSW into homogeneous carboxymethyl holocellulose nanofibrils without the use of conventional pretreatments of crude cellulosic feedstock. These carboxymethylated, hemicellulose-coated, cellulose nanofibrils were isolated quantitatively at >95% yield with widths 3–8 nm, thicknesses 1–3 nm, and lengths up to 1000 nm. We posit that this advancement of combining an inexhaustible, global supply of waste cellulose, large-scale steam autoclaving pretreatment, and an industrially relevant carboxymethylation process could unlock the higher potential of sustainable cellulosic nanomaterials for a circular economy.

Methane Emissions from Municipal Wastewater Collection and Treatment Systems.

Municipal wastewater collection and treatment systems are critical infrastructures, and they are also identified as major sources of anthropogenic CH4 emissions that contribute to climate change. The actual CH4 emissions at the plant- or regional level vary greatly due to site-specific conditions as well as high seasonal and diurnal variations. Here, we conducted the first quantitative analysis of CH4 emissions from different types of sewers and water resource recovery facilities (WRRFs). We examined variations in CH4 emissions associated with methods applied in different monitoring campaigns, and identified main CH4 sources and sinks to facilitate carbon emission reduction efforts in the wastewater sector. We found plant-wide CH4 emissions vary by orders of magnitude, from 0.01 to 110 g CH4/m3 with high emissions associated with plants equipped with anaerobic digestion or stabilization ponds. Rising mains show higher dissolved CH4 concentrations than gravity sewers when transporting similar raw sewage under similar environmental conditions, but the latter dominates most collection systems around the world. Using the updated data sets, we estimated annual CH4 emission from the U.S. centralized, municipal wastewater treatment to be approximately 10.9 ± 7.0 MMT CO2-eq/year, which is about twice as the IPCC (2019) Tier 2 estimates (4.3-6.1 MMT CO2-eq/year). Given CH4 emission control will play a crucial role in achieving net zero carbon goals by the midcentury, more studies are needed to profile and mitigate CH4 emissions from the wastewater sector.

Trends of Full-Volatility Organic Emissions in China from 2005 to 2019 and Their Organic Aerosol Formation Potentials

Emissions of organic compounds have strong influences on the environment. Most previous emission inventories only cover the emissions of particulate organic carbon and nonmethane volatile organic compounds (NMVOC) but neglect the semivolatile and intermediate volatile organic compounds (S/IVOC), which considerably contribute to the organic aerosol (OA) burden. Herein, we developed a full-volatility emission inventory of organic compounds in China from 2005 to 2019 and analyzed the OA formation potential (OAFP) of each volatility bin and source using a two-dimensional volatility basis set (2D-VBS) box model. The emissions of low/extremely low/ultralow VOC (xLVOC) decreased substantially during 2005–2019, while the emissions of SVOC showed significant decline after 2014, mainly because of reduced residential biomass consumption. IVOC and VOC emission amounts in 2019 were similar to those in 2005; however, the major sources of emissions changed substantially. Emissions from volatile chemical products (VCP) increased significantly and became the dominant source of IVOC and VOC emissions. The S/IVOC from VCP contributed 1322 kt of OAFP in 2019, higher than the total anthropogenic xLVOC emissions. Considering the high of S/IVOC, future air pollution control policies should prioritize VCP, residential biomass burning, and diesel vehicles.

Exploring the contributions of major emission sources to PM2.5 and attributable health burdens in China

Ambient fine particulate matter (PM2.5) pollution is the principal environmental risk factor for health burdens in China. Identifying the sectoral contributions of pollutant emissions sources on multiple spatiotemporal scales can help in the formulation of specific strategies. In this study, we used sensitivity analysis to explore the specific contributions of seven major emission sources to ambient PM2.5 and attributable premature mortality across mainland China. In 2016, about 60% of China's population lived in areas with PM2.5 concentrations above the Chinese Ambient Air Quality Standard of 35 μg/m3. This percentage was expected to decrease to 35% and 39% if industrial and residential emissions were fully eliminated. In densely populated and highly polluted regions, residential sources contributed about 50% of the PM2.5 exposure in winter, while industrial sources contributed the most (29–51%) in the remaining seasons. The three major sectoral contributors to PM2.5-related deaths were industry (247,000 cases, 35%), residential sources (219,000 cases, 31%), and natural sources (87,000, 12%). The relative contributions of the different sectors varied in the different provinces, with industrial sources making the largest contribution in Shanghai (65%), while residential sources predominated in Heilongjiang (63%), and natural sources dominated in Xinjiang (82%). The contributions of the agricultural (11%), transportation (6%), and power (3%) sources were relatively low in China, but emissions mitigation was still effective in densely populated areas. In conclusion, to effectively alleviate health burdens across China, priority should be given to controlling residential emissions in winter and industrial emissions all year round, taking additional measures to curb emissions from other sources in urban hotspots, and formulating air pollution control strategies tailored to local conditions.

Towards a Conceptual Framework of Using Technology to Support Smart Construction: The Case of Modular Integrated Construction (MiC)

Construction is a major source of carbon emissions. Moreover, it faces various other sustainability challenges, such as construction waste, construction noise, vehicular traffic near construction sites, dust and other air and water pollutants, and safety and well-being of construction workers. Poorly designed and constructed buildings will continue to affect the well-being of their occupants and overall energy efficiency throughout the building lifecycle. Hence, accelerating the transformation of the construction industry towards smart construction or Construction 4.0 is an important topic. The ways that technology can help to achieve smart construction, especially with the adoption of construction methods with increasing construction modularity, should be further explored. Focusing on modular integrated construction (MiC), this paper examines the following questions: (1) How has technology been applied to support MiC development and smart construction in Hong Kong? (2) What are the lessons learned? A case study approach of a building information model (BIM)-enabled multifunctional blockchain-based digital platform is adopted to allow us to systematically consider (1) the main objectives and scope, (2) the stakeholders involved, (3) the key outcomes and processes, (4) the applications of blockchain technology, and (5) the integration with other digital software and management platforms in practice. Drawing upon the experience, we propose a generic four-stage approach in understanding and facilitating the adoption of relevant technology towards smart construction. At Stage One, the technologies of BIM, RFID, and blockchain are applied to support the core elements of MiC production: just-in-time transportation and on-site installation. At Stage Two, the digital platform is extended to serve as an interface for third parties, notably government; monitoring, authentication, and certifications for information sharing; visualization; and real-time monitoring and updating of MiC projects. At Stage Three, the system focuses on people in the construction process, aiming to enhance the safety and well-being of workers and drivers throughout the construction process. Different Internet-of-Thing devices and sensors, construction robotics, closed-circuit television, dashboards, and cloud-based monitoring are deployed. At Stage Four, the full construction lifecycle is the focus, whereby a centralized smart command theatre is set up with multiple sources of data in a city information model.

Analysis of spatial and temporal characteristics of carbon emission efficiency of pig farming and the influencing factors in China.

Pig farming has been a crucial contribution to China's food security although intestinal fermentation and its excrement during pig breeding are major sources of greenhouse gas emissions. In this paper, we measured the carbon emission efficiency of pig farming in 30 provinces (autonomous regions and municipalities) from 2010 to 2020 by using the non-expected output Slack-Based Measure (SBM) model and analyzed the spatial characteristics of the carbon emission efficiency of pig farming in China. We also examined and analyzed the factors influencing the carbon emission efficiency of pig farming by using the limited dependent variable model (Tobit). The results show that: the carbon emission efficiency of pig farming in China shows an M-shaped upward trend over time by comparing the carbon emission efficiency longitudinally during the study period and the carbon emission efficiency of pig farming shows a decreasing trend in the east, central and west regions of China by comparing the carbon emission efficiency of different regions horizontally. It's also shown that regions with low- and extremely-low-efficiency transfer from the east to the central and west regions and the central and regions with high-efficiency transfer to the east. The regression analysis of the factors influencing the carbon emission efficiency of pig breeding shows that the comparative advantage of the pig industry and transportation accessibility is positively correlated with the carbon emission efficiency of pig breeding, whereas the proportion of food resources and market scale is negatively correlated with the carbon emission efficiency of pig breeding. At the same time, the production layout index has no significant influence on the carbon emission efficiency of pig breeding. The research results provide a theoretical basis for regional differentiation of carbon emission management from pig farming, optimizing the layout of the pig industry and reducing environmental pollution.

Air Quality and Atmospheric Emissions from the Operation of the Main Mexican Port in the Gulf of Mexico from 2019 to 2020

Pollutant emissions into the atmosphere derived from port activities can be transported to surrounding regions and cities depending on wind speed and direction, having an impact on air quality. In this research, emissions of atmospheric pollutants (NOx, CO, NMHCs, CO2, SO2, TSP, PM2.5 and PM10) were estimated for: tanks, container, roll-on/roll-off (RO-RO), bulk carriers and general cargo ships, using emission factors in the hoteling and maneuvering stage in the port area of Veracruz, Mexico, during 2019 and 2020 despite the suspension period of activities due to the SARS-CoV-2/COVID-19 pandemic. Among the total estimated emissions, CO2 presented the highest values for 2019 (31,177 kg/year) and 2020 (29,003 kg/year), whereas CH4 presented the lowest values with 0.294 kg/year for 2019 and 0.273 kg/year for 2020. The highest estimated emissions for CO2, NOx and SO2 occurred in the maneuvering stage in 2019 for bulk carriers, tanks and container ships. Likewise, the highest estimated emissions were during the hoteling stage of the container ships in 2020. This study will provide an updated ship emissions inventory for the Gulf of Mexico region where the Port of Veracruz is located. In addition, SO2 and PM2.5 measurements were performed from October 2019 to December 2020. PM2.5 concentrations exceeded the Mexican Ambient Air Quality Standard (MAAQS) value of 45 µg m−3 for the 24-h average concentration several times, on the opposite, SO2 exhibited concentrations up to 20 times lower than the 24-h MAAQS value of 40 ppb. Results showed that pollutant emissions in the port of Veracruz exhibited a seasonal variability, modifying their dispersion and the possible effects. Our main conclusion is that current port area is the major source of pollutant emissions (SO2 and PM2.5) throughout the year, whereas the expansion area of the port of Veracruz does not represent still a significant rise of pollutant emissions, but it is expected that the growth of port activity will directly increase the concentrations of pollutants emitted.