CO2 Pollutants Research Articles

Could environmental regulation effectively boost the synergy level of carbon emission reduction and air pollutants control? Evidence from industrial sector in China

In response to the pressure of combating climate change and enhancing air quality, China has tightened environmental regulations. In China, reducing carbon emissions and air pollutants together has become a critical target for environmental protection. However, no literature explores the impact of environmental regulation on the synergy level of reducing CO2 and air pollutants in the industrial sector. Therefore, this study explores whether environmental regulation could effectively boost the synergy level of carbon emission reduction and air pollutants control in China's industrial sector. We first construct an evaluation indicator system for synergistic emission reduction of CO2 and air pollutants (SO2, NOx, PM), then employs the coupled coordination model to evaluate the synergy level of emission reduction. Furthermore, we explore the impact pathway of environmental regulation on the synergy level of emission reduction. The results indicate that: (1) On the whole, the synergy level shows a slow upward trend, but remains insufficient from 2011 to 2020. (2) Overall, environmental regulation significantly boosts the synergy level of carbon emission reduction and air pollutants control. Regarding the impact pathway, industrial structure has a completely promoting effect, while technical progress exerts a suppressing effect. (3) In regions with higher economic development, environmental regulation has a greater positive influence on synergy level, and the suppressing effect of technical progress is weaker than in less developed regions. These findings could provide helpful references for policy making on synergistic emission reduction of industrial CO2 and air pollutants in China.

Investigation of emissions from passenger flights Denizli Çardak Airport, Türkiye

Due to developing aviation sector, number of aircraft in the world is increasing. Along with this development, problems such as the decrease in air quality in and around the airport also arise. In this study, it is tried to calculate pollutant emissions occurring in 2022 during the LTO cycles of Denizli Çardak Airport in Turkey. These calculations are based on the information obtained from ICAO Engine Emission Data Bank and flight information published by the General Directorate of State Airports Authority (GDSAA). As a result of the data obtained, 74.64 ton/year pollutants (NOx-37.148 t/y, CO-35.398 t/y and HC-2.094 t/y) were calculated for 2022 at Denizli Çardak Airport. Of all emissions, NOx accounted for 50%, CO 47% and HC 3%. In the LTO cycle, the most fuel is burned in taxi cycle and pollutant emissions produced in this cycle are greater. With a 2 min reduction in taxi time, there will be an approximate 6.8% reduction in the total emission rate in the LTO cycle. Similarly, with a 4 min reduction in taxi time, there will be a 13.72% reduction in the whole emission rate in the LTO cycle. Unlike other studies, in this study the emission rates of various engines were compared. It has been calculated that the amount of pollutant emissions produced by the new generation Boeing 737 MAX LEAP-1B powered aircraft in LTO cycle is 25% less than the amount of pollutant emissions produced by the Airbus A320 NEO LEAP-1 A powered aircraft. The biggest factor here is that the emission of CO pollutants is less. Considering the emission rates produced by these four different engines (B737-800 CFM56-7B, A320 V2500-A1, B737 MAX LEAP-1B, A320 NEO LEAP-1 A), the Airbus A320 V2500-A1 engine is a more environmentally friendly engine than the other engines.

Synergistic emission reductions and health effects of energy transitions under carbon neutrality target

China's climate and environmental problems remained prominent. Energy transition was the key to improve climate and pollution, and it was at a critical period. Previous studies mostly focused on the impact of climate policies on the reduction of air pollutants and the environmental health impacts of individual sector or pathway policies. Thus, focusing on the synergistic abatement of CO2 and air pollutants and health effects of energy transition in carbon neutrality was significant for building beautiful and healthy China. LEAP-HEA model was constructed to project energy demand, CO2 and air pollutants, and assess energy transition's environmental health effects from 2020 to 2060. LMDI model was adopted to decompose the abatement contribution. Synergistic assessment index was used to estimate the coordinated abatement effects. Cost-effectiveness of energy transition and regional heterogeneity of PM2.5 toxicity were not considered. The results indicated: (1) energy demand would decrease and structure would be optimized significantly under deep energy transition scenario. (2) Deep energy transition could promote earlier realization of carbon neutrality. It would contribute significantly to CO2 and air pollutants abatement with strong synergistic effects. (3) Energy transition would reduce health risks with large economic benefits. Recommendations were proposed based on the conclusions.

A COP28 Perspective: Does Chinese Investment and Fintech Help to Achieve the SDGs of African Economies?

Scientific consensus affirms human activity, particularly carbon emissions from market participants, drives global warming. Foreign investment, crucial for sustainability in developing nations, now faces scrutiny regarding its impact on environmental quality in emerging economies. This study examines the influence of Chinese Outward Foreign Direct Investment (OFDI) and fintech on environmental conditions in the top five Chinese-invested African economies, alongside factors such as energy consumption, economic performance, and unemployment affecting CO2 pollution. Quarterly data from 2006–2021 confirm cointegration among variables via panel unit root and cointegration tests. Panel ARDL method estimates coefficients for short and long-run effects. Our findings reveal: (1) A 1% increase in Chinese investment leads to a 0.56% decrease in CO2 emissions, supporting its positive environmental impact. (2) Fintech adoption also demonstrates a beneficial effect, with a 1% increase associated with a 0.18% reduction in CO2 levels. (3) Total energy consumption, as expected, has a detrimental impact, causing a 0.92% increase in CO2 emissions with a 1% rise. (4) Interestingly, economic growth fosters environmental sustainability, while unemployment correlates negatively with it. These findings suggest that targeted Chinese investments and fintech adoption can aid in mitigating CO2 pollution in African economies while balancing economic considerations.

Computational investigation on adsorption and activation of atmospheric pollutants CO, NO and SO on small cobalt clusters

The adsorption of CO, NO and SO on cobalt clusters (Co2-7) were investigated using density functional theory. The adsorption energy supports efficient chemisorption of greenhouse gases on the cobalt clusters, with CO and NO forming one to three CoC and CoN bonds, respectively, the first being the most stable. The SO formed bidentate complexes with CoS and CoO bonds in ConSO structures, displaying notably high adsorption energy. The interactions between Con clusters and gas molecules (G) result in weakened bonds of CO, NO, and SO, evident through increased bond lengths, red-shifted frequencies, and lowered local vibrational force constants in ConG complexes. The results with bond weakening and charge transfer from metal to gas molecules suggest strong catalytic potential for small cobalt clusters in activating gas molecules. The current research findings hold significance in the quest for efficient catalytic processes to capture and recycle gaseous pollutants, contributing to a sustainable future.

Insights into civil aviation emissions in China: Analysis of an emission inventory of air pollutants and the ChinaHighAirPollutants (CHAP) dataset

The emissions from the civil aviation sector are a significant source of CO2 and air pollutants, which represent a serious threat to ambient air quality and public health. To gain a deeper understanding of civil aviation airport emissions, it is imperative to develop a precise and comprehensive emission inventory of China's civil aviation airports. However, there are limited studies dedicated to analyzing and verifying the accuracy and completeness of China's civil aviation emission inventory. Here, this study explored pollution characteristics from temporal trends and spatial distribution perspectives based on a previously developed 2019–2020 high-resolution air pollution and CO2 emission inventory of the landing and take-off (LTO) cycle of civil aviation airports in China and the ChinaHighAirPollutants (CHAP) dataset. Besides, this study established an empirical model to evaluate the relationship between the air pollutant emissions of China's civil aviation sector in 2019–2020 and the pollutant concentration from the CHAP dataset. Compared to those in 2019, the total NOx, CO, PM, and SO2 emissions during the LTO phase in China's civil aviation sector in 2020 decreased by 14.29%–24.32%, and the average concentrations of NO2, CO, PM10, and SO2 in 2020 decreased by 6.33%–9.45%. The eastern, central, and southern regions of China are characterized by high emissions of pollutants, a phenomenon closely related to the economic prosperity and tourism development in these areas. They tend to boast higher route densities, increasing air transport activity and consequently resulting in elevated emissions. In addition, NOx had the highest correlation coefficient in the empirical model, with a correlation coefficient of 0.603 in 2019. Our findings provide new insights into civil aviation emissions in China from the analysis of the emission inventory of air pollutants and the CHAP dataset and provide a new method for verifying the accuracy and completeness of China's civil aviation emission inventory.

Superior room-temperature efficacy of selective catalytic reduction of NOx by CO utilizing metal-modified Fe2O3 catalysts

Fe2O3-based catalysts demonstrate significant potential for the selective catalytic reduction (SCR) of nitrogen oxides (NOx) by CO. However, their effectiveness is limited at lower temperatures, impeding widespread industrial adoption. To overcome this, we optimize the Fe2O3-based catalyst component through 3 wt% cobalt (Co) loading, which improves the synergistic removal of CO and NOx pollutants. Our developed catalyst, Co-Fe2O3 on an amorphous SiO2 substrate, exhibits superior room-temperature activity in CO-SCR, achieving CO and NOx conversion ratios as well as N2 selectivity above 80 % at 25 °C, and over 93 % at 100–500 °C. The enhancement in catalyst performance is elucidated through density functional theory calculations and experimental investigations, revealing the creation of heteronuclear active sites via metal modification, and consequent improvements in redox characteristics and acidity. Our findings offer a robust pathway towards the industrial implementation of ammonia-free-SCR technology with synergistic removal of CO and NOx and provide insights for designing superior heterogeneous catalysts.

Investigating the impact of air pollution and temperature changes on emergency admissions (cardiovascular + respiratory) disease in the city of Bojnord, northeastern Iran

Cardiovascular (CVD) + Respiratory diseases are recognized as the main cause of death worldwide. Fluctuations in temperature and air pollution have been reported as one of the most important causes of cardiovascular & respiratory diseases. Therefore, in the current study, we assessed the relationship between ambient air temperature and pollution on the number of total emergency hospital admission due to cardiovascular and respiratory conditions in the City of Bojnord, northeastern Iran. The meteorological data, including daily temperature, relative humidity and concentrations of five air pollutants CO, NO2, NOX SO2, and PM10 were obtained from online electronic sensors at the Bojnurd meteorological station from 21th March 2018 to 20th March 2020. Statistical analysis, penalized distributed lag non-linear method was applied using R Software. Also, sensitivity analysis test was calculated by using appropriate application.The results of the study revealed that the effect of higher and lower temperatures was observed immediately from the first day and the second week, respectively. Also result showed with increase and decrease temperature, significantly increased the risk of hospitalization by 36% (RR, 1.36; 95% CI (1), 0.95 to 1.95) and 17% (RR, 1.17; 95% CI (1), 0.88 to 1.55) until the lag 25th day, respectively. Based on the results, increasing temperature significantly increased the hospitalization rate of cardiopulmonary patients, but the effect of cold was not significant on the population as well as age and gender subgroups. Study have also proved that there is no significance correlation between air pollutant and Cardiovascular & respiratory diseases.

The role of renewable energy in the energy–growth–emission nexus in the ASEAN region

BackgroundThe Association of Southeast Asian Nations (ASEAN) relies mainly on fossil fuels in their energy supply, leading to higher CO2 emissions, pollution, and further environmental degradation. This paper uses the panel vector autoregressive and the Granger non-causality test in the heterogeneous panels, together with long-run estimation techniques, to examine the dynamic link among energy consumption, economic growth, and carbon emissions with the focus on renewable energy for the ASEAN countries in the past three decades.ResultsThe findings from this paper indicate that carbon emissions are associated with energy consumption. In contrast, renewable energy usage reduces CO2 emissions, improving environmental quality. Economic growth is associated with increased energy consumption and carbon emissions in the ASEAN countries. The findings also indicate that the effects of energy consumption on economic growth are more significant than those of renewable energy in ASEAN. When considered together, these findings form a vicious circle regarding the energy–growth–emission nexus for the ASEAN economies. In addition, a bidirectional Granger causality among energy consumption, economic growth, CO2 emissions and renewable energy usage is confirmed.ConclusionsRenewable energy has emerged as an important viable option for the ASEAN nations to achieve their dual objectives of enhanced economic growth, reduced CO2 emission, leading to improved environmental quality.

Low-carbon transition paths and benefits for the power sector at city level: A case study in Zhengzhou, China

The power sector significantly contributes to global greenhouse gas emissions, making its transition to low-carbon energy crucial for achieving carbon neutrality. Zhengzhou, recognized as one of the China's "National Central Cities", has a high energy demand. Its power sector, which is primarily reliant on coal, is responsible for half of the city's CO2 emissions. The low emissions analysis platform (LEAP) model serves as an effective tool for exploring the power sector's transition to low-carbon sources. However, studies on the combined reduction in air pollutants and CO2 at the city level are scarce. This study focuses on Zhengzhou's power sector, examining the power composition, coal usage, CO2 and air pollutant emissions, social costs, and employment effects across five scenarios using LEAP. The five scenarios are the business-as-usual scenario, the reference scenario, the carbon neutrality scenario, and two optimization scenarios with different carbon peak and carbon neutrality time. CO2 emissions will peak in 2030 at 34.6 Mt in the reference scenario, and in 2022 at 32.4 Mt in the carbon neutrality scenario. Emissions in the reference and carbon neutrality scenarios will decrease by 64.8% and 75.2%, respectively, from those in the business-as-usual scenario due to emission reduction strategies. In the carbon neutrality scenario, coal power reformation is the most significant measure, accounting for 45.7% of the total CO2 reduction compared to that in the business-as-usual scenario, followed by the integration of external electricity (17.8%) and solar energy (13.6%). This scenario also demonstrated a strong synergistic effect on reducing air pollutants and CO2 emissions. Although the carbon neutrality scenario incurs costs 60.4 billion yuan higher than those of the reference scenario, its CO2 mitigation cost is 66.1 yuan/t lower. This indicated that more rapid CO2 reduction, despite higher economic inputs, is justified by ambitious CO2 reduction goals and environmental benefits. Enhancing the use of renewable energy technologies for research and development to lower costs could increase the economic feasibility of the carbon neutrality scenario. In the two optimization scenarios, complete elimination of CO2 and air pollutant emissions is achieved by 2060, with all the electricity generated from renewable sources, although issues of uncertainty and stability need to be addressed. The study also highlights that renewable energy development will significantly boost employment, with an estimated 1.19 million jobs in 2060 in the carbon neutrality scenario. Finally, the paper concludes with a sensitivity analysis and offers policy recommendations for the power sector's low-carbon transition.

CO2 synergistic emission reduction and health benefits of PM2.5 reaching WHO-III level in Pearl River Delta

Protecting human health is one of the fundamental goals of continuous air quality improvement. Considering the same sources of CO2 and air pollutants emissions, their emission control measures often have a certain degree of synergistic emission reduction effects To balance the costs and policy efficiency, policy makers should assess the synergistic benefits of CO2 emission reduction when formulating air quality improvement strategies. Aimed at the air quality improvement strategy targeting WHO-III level (i.e. 15 μg/m3) for PM2.5 in the Pearl River Delta (PRD) region, this study evaluates the synergistic CO2 reduction effects and the associated health benefits from this strategy which includes a range of air pollution prevention and control measures. The results of the study show that promoting the attainment of WHO-III for PM2.5 in the PRD region can bring about 74 Mt of synergistic CO2 emission reduction, resulting in an 18% reduction of CO2 emissions in the PRD compared to 2017. Among various measures considered, industrial restructuring, power supply transition and industrial energy consumption transformation exhibit the most pronounced synergistic effects. Therefore, these measures are recommended to be prioritized and promoted in the next stage of air pollution prevention and CO2 emission reduction. Furthermore, when the PM2.5 concentration in the PRD region reaches the WHO-III level, the number of PM2.5-related deaths will be estimated to reduce approximately 5.5 thousand compared to that in the current policy scenario. Through continuous structural transformation and emission reduction efforts, it not only facilitates the decline in regional PM2.5 concentration but also helps more regional residents to live in an environment with a relatively low PM2.5 concentration. In addition, in order to reduce the health impacts of PM2.5, it is recommended that the government should guide people to change their production and living styles in order to reduce pollutant emissions from anthropogenic activities.

Air pollution and online lender behavior: Evidence from Chinese peer-to-peer lending

This paper examines the effect of air pollution on individual lenders' decisions. Using unique data from the leading peer-to-peer lending platform, we find evidence that for every 10 units increase in the air quality index (AQI), the lender would lend RMB 5.47 yuan more in one single bid and 6% earlier in the fund-raising period. Moreover, the impacts from PM2.5 and CO pollutants are serious. The older people and people that are not well-educated would significantly suffer from air pollution. We further explore three potential mechanisms: physical channel, physiological channel as well as psychological channel, and show that the first two are valid. Overall, our findings shed light on the influence of environmental factors on online lending participants.

Influence of Calcined Diatoms on the Properties of Conventional Concrete

Today, the construction industry has expressed its preference for the use of sustainable materials due to the significant impact they have on the environment. Although concrete is widely used in construction due to its diverse properties, one of its main components, known as clinker, is a major contributor to CO2 pollution. Therefore, the feasibility of partially replacing this material with alternatives is being investigated. Among them, calcined diatoms are presented as a promising option. These are an extremely fine and highly porous material, composed mainly of silica, which has a large specific surface area. The abundant presence of active silica improves the properties of concrete, such as its strength, while the ultrafine particles of diatoms densify the concrete structure, thus achieving a concrete with greater durability. The objective of this study was to evaluate the properties of conventional concrete, both fresh and hardened, by adding calcined diatomite in proportions of 5%, 10% and 15%, with the purpose of determining the optimum percentage of calcined diatomiteto achieve adequate concrete development. Test specimens were elaborated without the addition of calcined diatoms and with the addition of calcined diatoms, then immersed in water for 7d, 14d and 28d. Tests were carried out in the fresh state to obtain workability, bleeding, setting time, air content, temperature, and unit weight. In the hardened state, tests were carried out to obtain compressive strength, static modulus of elasticity and permeability. It was found that with a lower addition of calcined diatomaceous earth, the concrete can develop as a normal concrete, and improvements in compression were obtained, increasing with respect to the control concrete. However, by increasing the percentage of calcined diatomite, the workability is reduced and in general, the properties do not show an improvement. Concrete with the addition of calcined diatomite can be used as a thermal insulator, since it presents very fine particles that perform a filling effect, densifying the concrete matrix, and it can also be used for lightweight concrete since it reduces 17kg/m3. On the other hand, the compressive strength with 5% calcined diatomite increased by 9% with respect to the standard, due to its high pozzolanic activity.In addition, a lower permeability was found with the addition of 5% calcined diatomite. In general, calcined diatomite in lower proportions can generate resistant and durable concrete.

Vibration analysis using wavelet transformation technique and performance characteristics of a diesel engine fueled with tamarind biodiesel-diesel blends and diverse additives

This study investigates the combustion, performance characteristics, and emission levels of diesel and tamarind biodiesel mixtures (B20, B30, B40, and B100) in a single-cylinder, four-stroke, direct injection (DI) diesel engine. Further, two fuel additives, namely Butylated Hydroxytoluene (BHT) (an antioxidant) and Diethyl ether (DEE) (a cetane improver), were separately added to B20 to enhance the engine characteristics. In addition, Wavelet Transform and Wavelet Packet algorithms were used to investigate the vibration behaviour of the tested fuels. The results demonstrate that the B20 blend improved the performance and generated less emission than the other blends. Moreover, the brake thermal efficiency (BTE) and brake-specific fuel consumption (BSFC) for B20+BHT were 3.8% higher and 4.1% less than B20, respectively. B20+DEE combustion showed lower CO and smoke pollutants by 51% and 19% compared to B20, respectively. The NOx emissions for the blends containing BHT and DEE were decreased by 16% and 9%, respectively. The engine vibration analysis showed almost the same frequency with fuel blends B20 and B30, which were similar to the diesel fuel values. The results concluded the tamarind biodiesel/diesel mixtures with BHT and DEE improved the performance and emissions behaviors of DI diesel engine and could be considered a potential alternative biofuel for such engines. Wavelet analysis of the engine was accurately employed to extract the prominent features of the vibrations.

Deciphering the detection and electrochemical sensing of the environmental pollutant CO gas with Ga12As12 and Al12As12 nanostructured materials: an insight from first-principle calculations

Deciphering the detection and electrochemical sensing of the environmental pollutant CO gas with Ga12As12 and Al12As12 nanostructured materials: an insight from first-principle calculations

Potential of wind turbines on the alteration of carbon dioxide concentration

Anthropogenic carbondioxide (CO2) emissions are a major factor in global warming, requiring significant cuts to combat climate change. A crucial technology to reduce global CO2 concentration is direct air capture (DAC) of CO2. However, existing DAC techniques are expensive because of low CO2 concentrations, and they frequently rely on fossil fuel-based energy. In this article, we investigate how wind turbines can influence local CO2 levels and potentially collaborate with DAC and other technologies. To explore this idea, we performed large-eddy simulations using two 5 MW commercial-scale wind turbines. We incorporated realistic CO2 profiles collected from 13 different global locations across different seasons. The simulations were performed under neutral atmospheric boundary layer conditions. The results demonstrate that the wake recovery mechanism of a wind turbine promotes rapid mixing of CO2 both above and below the turbine blade tips in the wind turbine wake. In cases where the initial concentrations of CO2 were elevated above the turbine, downward entrainment of CO2 occurred. Conversely, when high concentrations of CO2 were present in the lower atmosphere, wind turbines facilitated a decrease in concentration at that layer by up to 138 kg/m within the intermediate wake (within 7 diameters) of the second turbine, T2. These discoveries inspire further investigation into the potential synergies between wind turbines and DAC devices or local CO2 pollutant diverters, depending on the prevailing CO2 profile. Consequently, this article marks the initial showcase of wind turbines' capability to influence CO2 levels by creating an entrainment and removal effect.

Statistical Analysis and Modeling of the CO2 Series Emitted by Thirty European Countries

In recent decades, an increase in the earth’s atmospheric temperature has been noticed due to the augmentation of the volume of gases with the greenhouse effect (GHG) released into the atmosphere. To reduce this effect, the European Union’s directives indicate the action directions for reducing these emissions, among which carbon dioxide (CO2) recorded the highest amount. In this context, the article analyzes the CO2 series reported in 1990–2021 by 30 European countries. The Kruskal-Wallis test rejected the hypothesis that the series comes from the same underlying distribution. The Anderson-Darling test rejected the normality hypothesis for seven series out of thirty, and Sen’s procedure found a decreasing trend slope only for 17 series. ARIMA models have been built for all individual series. Grouping the series (by the k-means and hierarchical clustering) provided the base for building the Regional series (RegS), which describes the CO2 pollution evolution over Europe. The advantage of this approach is to provide the synthetic image of the regional evolution of the CO2 emission volume (mt), incorporating information from 30 series (one for each country) in only one—RegS. It is also shown that selecting the number of clusters involved in building RegS and assessing their stability is essential for the model’s goodness of fit.

Impact Factors of Industrial Pollution and Carbon Reduction under the “Dual Carbon” Target: A Case Study of Urban Aggregation in the Pearl River Delta and Yangtze River Delta

China is facing pressure to reduce carbon emissions and control pollution. Promoting the synergy between pollution reduction and carbon reduction has become an inevitable choice to achieve the construction of a beautiful China and meet the dual carbon target. This study examines the main factors influencing industrial pollution and carbon reduction in the Pearl River Delta (PRD) and Yangtze River Delta (YRD) urban agglomerations based on data on industrial CO2 and local air pollutants (LAP) from 2002 to 2021, using the random forest regression model. The results indicate that (1) industrial CO2 emissions have increased overall, while intensity has decreased. Additionally, both industrial LAP emissions and intensity have decreased. (2) The main factor influencing industrial CO2 and LAP emissions is the proportion of industrial value added above the scale. Additionally, the proportion of R&D internal expenditure in GDP and total trade imports and exports are the main influencing factors of industrial CO2 emissions. The industrial fume and dust removal rate mainly affects industrial LAP emissions. (3) There is a clear non-linear relationship between industrial CO2- and LAP-influencing factors and emissions, which can be attributed to the scale effect factor, the lagging effect of R&D expenditure, and the inappropriate treatment of the “three wastes” by relevant departments that is non-linear. The urban agglomerations of PRD and YRD should prioritize the reduction of carbon emissions, upgrading and transforming their industrial structures, promoting the impact of foreign trade on pollution and carbon reduction, and achieving a balance between sustainable economic development and environmental protection.

The Coordinated Effects of CO2 and Air Pollutant Emission Changes Induced by Inter-Provincial Trade in China

Inter-provincial trade leads to changes in CO2 and air pollutant emissions. However, there is a research gap regarding the coordinated effects (co-effects) between embodied CO2 and air pollutant emissions in trade. Understanding co-effects in inter-provincial trade is a prerequisite for driving the green transformation of trade and achieving coordination between pollution and carbon reduction. Here, we calculated provincial-level CO2 and air pollutant emission leakage in 2012 and 2017 based on a modified input–output model and, for the first time, investigated the co-effects between CO2 and air pollutant emission leakage caused by emissions transfers in China. Three types of co-effects, categorized as co-benefits, trade-offs, and co-damage, were discovered and defined to reveal the provincial differences. Furthermore, combined with structural decomposition analysis (SDA), we calculated the interannual variation in trade-induced emissions and identified the key driving factors of provincial-level co-effects from 2012 to 2017. Optimizing the energy structure has led to the greatest co-benefits, while changes in the industrial structure and emission coefficients have led to limited co-benefits in specific provinces. Variations in trade volume have led to co-damages across all provinces, and changes in emission coefficients have led to trade-offs in the majority of provinces. The case analysis confirmed that identifying and adjusting the key driving factors of co-effects can promote the transformation from co-damage and trade-offs to co-benefits. The findings implied a new approach for the reduction in pollution and carbon through inter-provincial trade.

Estimation of the main air pollutants from different biomasses under combustion atmospheres by artificial neural networks

The production of biofuels to be used as bioenergy under combustion processes generates some gaseous emissions (CO, CO2, NOx, SOx, and other pollutants), affecting living organisms and requiring careful assessments. However, obtaining such information experimentally for data evaluation is costly and time-consuming and its in situ obtaining for regional biomasses (e.g., those from Northeast Brazil (NEB) is still a major challenge. This paper reports on the application of artificial neural networks (ANNs) for the prediction of the main air pollutants (CO, CO2, NO, and SO2) produced during the direct biomass combustion (N2/O2:80/20%) with the use of ultimate analysis (carbon, hydrogen, nitrogen, sulfur, and oxygen). 116 worldwide biomasses were used as input data, which is a relevant alternative to overcome the lack of experimental resources in NEB and obtain such information. Cross-validation was conducted with k-fold to optimize the ANNs and performance was analyzed with the use of statistical errors for accuracy assessments. The results showed an acceptable statistical performance for all architectures of ANNs, with 0.001–12.41% MAPE, 0.001–5.82 mg Nm−3 MAE, and 0.03–52.30 mg Nm−3 RMSE, highlighting the high precision of the emissions studied. On average, the differences between predicted and real values for CO, CO2, NO, and SO2 emissions from NEB biomasses were approximately 0.01%, 10−6%, 0.14%, and 0.05%, respectively. Pearson coefficient provided consistent results of concentration of the ultimate analysis in relation to the emissions studied and effectiveness of the test set in the developed models.