Chinese Emissions Research Articles

Life expectancy and emission trading scheme: a case study in China.

Life expectancy can reflect both health benefit and implementation cost of climate policy. Nevertheless, little research has quantified the relation between life expectancy and climate policy in literature. In this paper, we attempt to narrow the research gap by studying how life expectancy is related to the Chinese nationwide emission trading scheme (CNETS). To achieve this research target, a Computable General Equilibrium (CGE) model is employed to simulate the operation of the economic system and the policy shock from emission abatement. The CGE model results show that life expectancy is prolonged by GDP but shortened by emissions, and the GDP impact on life expectancy is larger than the emission impact. Climate policy has dual effects on life expectancy because it relieves both negative emission impacts and positive GDP impacts on lifespan; its net effect on life expectancy is positive. Life expectancy positively impacts GDP, and this impact is moderated by climate policy; specifically, climate policy reinforces the positive impact of life expectancy on GDP. Life expectancy minimally affects carbon emissions during climate policy implementation; in other words, it has minimal impacts on emission abatement. These findings imply that climate policy and life expectancy complement each other; the government could implement climate policy to increase lifespan or prolong life expectancy to facilitate policy implementation.

Literature Review on Carbon Emission Reduction in China's Traditional Manufacturing Industry and Research on The Driving Mechanism of Its Green Transformation

At present, "double carbon" has become one of the most popular social topics. Realizing carbon neutrality and carbon peak is a solemn commitment made by our country. As an extremely important part of China's industrial system, the success of the green transformation of the traditional manufacturing industry also determines whether China's "double-carbon" goal can be successfully implemented. This paper focuses on the Chinese traditional manufacturing carbon emission reduction related literature research, through the relevant literature integration of the modern traditional manufacturing carbon reduction research direction and focus, based on the PSR model to Chinese traditional manufacturing green transformation power analysis, from the perspective of system driven, to establish green transformation power analysis framework, from the system level to explain the dynamic mechanism of traditional manufacturing green transformation.

Assessing influential factors of Chinese industrial aqueous cadmium emissions based on machine learning and shapley additive explanations

As the main contributor of cadmium (Cd) pollution in China, industrial aqueous Cd emissions are influenced by both industrial and socioeconomic characteristics owing to their “environmental service” nature. It is essential to understand the relationship between these characteristics and industrial aqueous Cd emissions during industrialization. In this study, a data-driven framework was proposed to reveal these relationships. The framework comprises two key steps. i) Three state-of-the-art tree-based machine learning models, including LightGBM, Gradient Boosting Decision Tree and Random Forest, were trained to capture the relationships among variables. ii) Shapley additive explanation was utilized to decompose the contribution of the characteristics to the prediction for each sample. The trained LightGBM model demonstrated the most superior performance in predicting industrial aqueous Cd emissions in test datasets (R2=0.881±0.0431， RMSE=58.260±11.839， MAE=27.743±3.274), significantly overperforming traditional linear regression model. Our analysis revealed that during our sample period, the influence of industrial characteristics on regional industrial aqueous Cd emissions was approximately 1.796 times greater than that of socioeconomic characteristics. Shifts in the characteristics of the non-ferrous metal industry contributed to approximately 74% of the average increase in industrial aqueous Cd emissions in China approximately. Regional emissions were found to be positively affected by average size and density but negatively affected by the operation duration of local non-ferrous metal industrial firms. Further driver analysis showed that the growth trajectory of emissions in China can be split into three stages based on the main drivers and growth rate: 2000–2002, 2003–2004, and 2005–2007. The proposed framework overcomes the limitations of the previous methods in terms of application scope, potential factor considerations, and regression structure predefinition. Our analysis implies that policymakers should proactively adjust industrial policies and emissions regulations in response to market shocks and industrial shifts, to a better management of industrial aqueous Cd emissions.

Pricing of Chinese Certified Emissions Reduction Scheme in a Supply Chain Under the Cap-and-Trade Policy

Abstract Against the backdrop of carbon trading, the Chinese Certified Emissions Reduction (CCER) scheme is one of the most important measures of China to reduce carbon emissions. We study a supply chain consisting of a CCER supplier and a manufacturer that buys CCER quotas from the supplier. The supplier is a non-emissions-control enterprise engaged in clean energy projects, while the manufacturer is classified into three types: a clean, a relatively clean, and an unclean firm. We analyse the impacts of the price and offset rate of CCER quotas on firms’ emissions reductions. We find that when the price of CCER quotas is lower than the price of Chinese Emissions Allowances (CEAs): (1) for both clean and unclean manufacturers, with increasing CCER offset rate (quota price), technical emissions reduction decreases (increases). (2) The incentive for the relatively clean manufacturer to reduce emissions is not affected by other extraneous factors. When the prices of CCER quotas and CEAs are uncertain, we find that (1) the technology abatement decisions of the three types of manufacturer are consistent and the corresponding CCER suppliers price their CCER quotas in the same way. (2) When the proportion of CCER quotas purchased by the manufacturer increases, the unit technical emissions reduction increases. Confirming that introduction of the CCER scheme can reduce carbon emissions, our findings provide theoretical guidance for suppliers to make pricing decisions on CCER quotas and for manufacturers to make technical emissions reduction decisions.

Trading our way to a greener future: how environmental rights trading reduces urban carbon emissions in China-empirical analysis based on China's emission rights trading.

Environmental rights trading (ERT) is considered one of the crucial instruments for environmental governance in China, accelerating urban low-carbon sustainable development. This study utilized the Chinese Emissions Trading System (ETS), which has been implemented most widely, longest, and most typical, to represent ERT and examine its contribution to urban carbon emission reduction. Through a quasi-natural experiment perspective, urban panel data were collected from 2003 to 2019, and a time-varying difference-in-differences model and moderating models were developed. The study indicates that ERT can significantly reduce carbon emissions in cities, with robust results. In addition, the carbon reduction effect of ERT varies by city region, type, and size. This phenomenon is more obvious in central and western cities and resource-based cities. Finally, a framework to strengthen the carbon reduction effect of ERT was creatively constructed. The strengthening effects of green innovation, industrial structure, and clean energy are evident. This helps provide evidence for ERT layout and extension, especially in countries with high carbon reduction needs such as China. ERT affects not only policymakers but also a greener future for every organization and individual in the city.

Tracing the regulated emissions of field-aged gasoline/natural gas bi-fuel taxis from new to 160,000 km: Deterioration and environmental implications

Natural gas (NG) is considered an economically viable transport decarbonization option, especially for developing countries. However, as per the ongoing European and Chinese vehicle emission regulations, the durability and in-service compliance tests of the exhaust emissions from gasoline/NG bi-fuel vehicles are only mandated in gasoline mode, leaving the real-world emission performance of NG fueling at risk. To bridge this gap, the exhaust emissions from five gasoline/NG bi-fuel taxis were traced from new (less than 10,000 km) to 160,000 km after every 10,000 km of field driving. Both room-temperature cold-start and warm-start chassis dynamometer tests were performed over the certification drive cycle. This tracing study lasted for 3.5 years and covered the variations of seasons, routine services, and minor repairs. The results manifested that compared to gasoline fueling, NG had statistically lower carbon monoxide before 100,000 km of driving but higher total hydrocarbon and nitrogen oxides emissions throughout the useful life in both cold- and warm-start tests. By applying linear regression to the data points obtained across the 160,000 km tracing, NG fueling showed a noticeably quicker deterioration with mileage than gasoline. However, given that gasoline and NG modes shared the same catalysts, this is attributed to the less careful calibration of NG mode predominantly because of the void of regulatory requirements. On average, NG fueling produced about 20 % less carbon dioxide emissions than gasoline, but the observed fuel leakage in NG mode partially offset the global warming potential advantage. Overall, NG sees benefits and challenges in mitigating vehicle-related environmental issues. To better control this risk, it is strongly recommended that in future regulations, the durability and in-service compliance requirements of NG fueling shall be complemented, and fuel leakage shall be included in the control of evaporative emissions.

Temporal and spatial evolution characteristics and decoupling trend of Chinese agricultural carbon emission efficiency.

This study investigates the spatio-temporal evolution of agricultural carbon emission efficiency (ACEE) in China and its relationship with agricultural economic growth (AEG). The results indicate several findings: Firstly, between 2012 and 2021, China's agricultural carbon emission efficiency exhibited an upward trend, with the mean value increased from 0.349 to 0.807. Furthermore, the distribution pattern shifted from a dispersed, point-like distribution to an aggregated and continuous distribution. Secondly, the average agricultural carbon emission efficiency in China following a decreasing order: South China, Northwest China, Southwest China, East China, North China, Central China and Northeast China. Thirdly, the relationship between agricultural carbon emission efficiency and the agricultural economy in China has transitioned from weak decoupling to negative decoupling. Based on these findings, this study proposes some recommendations to enhance agricultural carbon emission efficiency and promote its decoupling from agricultural economic growth. These recommendations aim to achieve low-carbon and high-efficiency development of agriculture.

Unveiling the driving patterns of carbon prices through an explainable machine learning framework: Evidence from Chinese emission trading schemes

Effectively modeling carbon prices while maintaining interpretability is essential, given the potential risks associated with unexpected price fluctuations. To this end, this study proposes an explainable machine learning (XML) framework to predict and explain carbon prices in China’s three representative carbon markets: Shenzhen, Hubei, and Beijing. Leveraging the strengths of tree-based machine learning models and Tree SHAP algorithms, we unveil global and local explanations in the driving patterns of carbon prices. Our findings indicate that the distribution of local explanatory effects exhibits asymmetric and long-tailed characteristics. Notably, the top global drivers in Shenzhen, Hubei, and Beijing are the photovoltaic price index, coal prices, and the industrial added value of electricity sectors, respectively. Furthermore, we uncover the nonlinear impacts of key drivers on individual carbon price predictions, and identify three key interaction patterns through the calculation of SHAP interaction values. Lastly, we evaluate the explainable performance of various XML benchmarks to validate the superiority of our XML framework, as well as demonstrate its economic significance via a switching trading strategy. Our research provides a reference for mitigating large price fluctuations in local carbon price samples, thereby promoting the stability of carbon markets.

The Impact of Financial Geographical Structure on Corporate Pollution Emissions

This chapter takes a macro perspective on the financial geographical structure, starting from the perspective of financial agglomeration and financial center spillover. Based on the Chinese industrial enterprise database, Chinese pollution emission database, and the China City Statistical Yearbook, it explores the impact of financial geographical structure on corporate pollution reduction. The study finds that local financial agglomeration and financial center spillover can significantly reduce the intensity of corporate pollution emissions, which remains robust after a series of rigorous tests. Mechanism analysis indicates that local financial agglomeration and financial center spillover can promote corporate pollution reduction through the effect of technological innovation. The research conclusions of this chapter can provide new insights for deepening the structural reform of financial supply side.

Equilibrating provincial carbon increments for residential buildings in China under carbon peaking constraints

The speedy raise of residential buildings' carbon emissions is a hindrance to achieving China's 2030 carbon peak goal. This study constructs an assessment framework for comprehensive consideration of 30 Chinese provinces' socioeconomic circumstances, energy demand, and emissions reduction technology to meet the consistent coupling degree of equity and efficiency (CDEE). This study is the first to propose an allocation scheme for equilibrating provincial carbon increments for rural and urban residential buildings in 2030 under carbon peaking constraints. The relevant results are fourfold. (1) Residential building's floor area per capita and energy carbon emissions coefficients are the soliddest drivers to facilitate and inhibit the raise of carbon emissions during 2010–2020. (2) Through dynamic Monte Carlo simulation from 2021 to 2030, we demonstrate that provinces with the most gamey carbon emissions in urban and rural areas include Shandong, at 121.52 (± 5.50) Mt. and Hebei, at 61.34 (± 3.08) Mt. in 2030, respectively. (3) A CDEE of 52.3% (biased equity) in urban areas and 34.5% (biased efficiency) in rural areas indicates equilibrated allocation of provincial carbon increment. (4) In the final 2030 allocation scheme, the greatest carbon mitigation pressures are in Beijing (11.34 Mt) and Heilongjiang (3.23 Mt), and the provinces with the largest carbon increment in urban areas include Hebei, Henan, and Guangdong, while the largest carbon increments in rural areas are in Hebei, Henan, and Guangdong. Overall, this study furnishes a targeted and valuable decision making reference for the government to determine provincial carbon peak goals for Chinese residential buildings.

Evaluation and quantitative characterization for the ecological environment impact of open pit mining on vegetation destruction from landsat time series: A case study of Wulishan limestone mine

Quantitatively evaluating the ecological environment impacts of vegetation destruction due to open-pit mining activities is vital for enhancing the green mining standard and cost management capabilities of mining enterprises. Based on the Landsat time series, this study proposes an ecological environment impact assessment and quantitative characterization method for vegetation destruction in mining areas resulting from open-pit mining activities. First, the modified normalized difference water index and the normalized difference vegetation index time series data were calculated. The water body thresholds and the fraction of vegetation coverage were ascertained using the K-means clustering algorithm and the dimidiate pixel model, respectively, to determine the area of direct vegetation destruction in mining areas. Second, utilizing the Theil-Sen Median trend analysis and the Mann-Kendall test, the indirect impact area of vegetation in the mining region was identified. Lastly, by integrating vegetation's net primary productivity with the Chinese Emission Allowance price index, the total carbon emission cost of vegetation destruction due to mining activities over 20 years was calculated to be about 2.122 million yuan. The findings indicated that the ecological environmental impact of open-pit mining activities on vegetation destruction cannot be ignored. From 2000 to 2020, open-pit mining at the Wulishan limestone mine in Anhui Province, China, increased the area of direct vegetation destruction by 9.072 × 105 m2, and the indirect impact area on vegetation was 7.371 × 105 m2. The carbon emission cost of vegetation destruction in the direct destruction area was about 104,000 yuan per year, and the carbon emission cost of vegetation damage in the indirect impact area was approximately 2,082.53 yuan per year. This research provides a scientific foundation for ecological environmental protection, regulations, green mining, and cost management for mining enterprises, promoting the harmonious progress of both the economy and environmental protection.

Update of the year 2019 modeling emission inventory in China

Using updated emission inventories can enhance the accuracy of air quality forecast models. Given China’s rapid economic growth and Korea’s geographical and meteorological position on the windward side of China, updating China’s emission inventory has become particularly crucial for Korea’s air quality modeling. This study aimed to develop an updated version of China’s Emission Inventory in Comprehensive Regional Emissions for Atmospheric Transport Experiments version 3 for the base year of 2019 (CREATEv3 (YR 2019)). To achieve this goal, we utilized the Chinese emission inventory of CREATEv3 for the base year of 2015 (CREATEv3 (YR 2015)) as a framework to incorporate the latest Chinese emission data from the Multi-resolution Emission Inventory Model for Climate and Air Pollution Research for the base year of 2019 (MEIC COVID-19 (YR 2019)) and update the inventory. The updated China’s annual emissions are now reflected in CREATEv3 (YR 2019), and the amounts are as follows: 132 Tg for CO, 21 Tg for NOx, 8 Tg for SO2, 7 Tg for PM2.5, 9 Tg for NH3, and 28 Tg for volatile organic compound (VOC). By comparing previous Chinese emission inventories with the updated inventory developed in this study, it was found that SO2, NOx, VOC, and NH3 emissions were decreased. Therefore, using the updated inventory seemingly reduces the impact of China’s fine dust on Korea. By comparing emissions by pollutant and region in China using CREATEv3 (YR 2019), it was found that regions with high emissions of targeted pollutants strongly correlated with major industries operating in those areas. This study is expected to provide insights into China’s emission changes in 2019 and support air quality forecasting.

Impacts of Ship Emissions on Air Quality in Southern China: Opportunistic Insights from the Abrupt Emission Changes in Early 2020.

In early 2020, two unique events perturbed ship emissions of pollutants around Southern China, proffering insights into the impacts of ship emissions on regional air quality: the decline of ship activities due to COVID-19 and the global enforcement of low-sulfur (<0.5%) fuel oil for ships. In January and February 2020, estimated ship emissions of NOx, SO2, and primary PM2.5 over Southern China dropped by 19, 71, and 58%, respectively, relative to the same period in 2019. The decline of ship NOx emissions was mostly over the coastal waters and inland waterways of Southern China due to reduced ship activities. The decline of ship SO2 and primary PM2.5 emissions was most pronounced outside the Chinese Domestic Emission Control Area due to the switch to low-sulfur fuel oil there. Ship emission reductions in early 2020 drove 16 to 18% decreases in surface NO2 levels but 3.8 to 4.9% increases in surface ozone over Southern China. We estimated that ship emissions contributed 40% of surface NO2 concentrations over Guangdong in winter. Our results indicated that future abatements of ship emissions should be implemented synergistically with reductions of land-borne anthropogenic emissions of nonmethane volatile organic compounds to effectively alleviate regional ozone pollution.

The impact of digital inputs on pollution reduction in Chinese manufacturing enterprises

Facilitating pollution reduction in manufacturing enterprises is crucial for implementing the new development concept and achieving sustainable, environmentally friendly growth. This research delves into the influence of digital inputs on pollution emission reduction in Chinese manufacturing enterprises. The study utilizes a combination of the World Input-Output Database, the Chinese Industrial Enterprises Database, and the Chinese Industrial Enterprises Pollution Emission Database covering the period from 2000 to 2012. The findings reveal that digital inputs make a significant contribution to pollution reduction in Chinese manufacturing enterprises. Specifically, digital software inputs and foreign digital factor inputs display more pronounced effects on pollution reduction. The analysis of the mechanism reveals that the pollution reduction effects of digital inputs in manufacturing enterprises can be attributed to several effective mechanisms: the enhancement of energy efficiency, technological innovation, and servitization, as well as the crowding-out effect of pollution control investment. The former mechanisms demonstrate a promoting effect, while the latter exhibits an inhibiting effect. The impact of digital inputs on pollution reduction in manufacturing enterprises displays heterogeneity, with a more significant emission reduction effect observed in industries facing higher environmental regulatory intensity, digitization-unrelated industries, non-state-owned firms, and firms with higher levels of financing constraints. These findings offer empirical evidence and serve as a policy foundation for promoting digital transformation and achieving green economic development in China.

Evaluating the synergistic effect of digitalization and industrialization on total factor carbon emission performance

Combating climate change and reducing carbon dioxide emissions are serious challenges shared by countries around the world. In the current era, digitalization has a significant impact on CO2 emissions. However, prior studies have not assessed the synergy between digitalization and industrialization on carbon emission performance. The principal component analysis and non-radial directional distance function (NDDF) are used to measure the digitalization and total factor carbon emission performance of Chinese 245 prefecture-level cities from 2003 to 2019. This study establishes a fixed effects model to study the panel data. The findings are as follows: (1) Digitalization can significantly promote Chinese cities' CO2 emission reduction. This result still holds after several robustness checks. (2) The heterogeneity results indicate that digitalization mainly improves central cities’ carbon emission performance. Meanwhile, the impact of digitalization is more obvious after 2011. (3) Digitalization improves urban carbon emission performance through energy efficiency, industrial transformation, and technological innovation. (4) It is worth noting that digitalization synergizes with industrialization to improve carbon emission performance in Chinese cities. This study provides empirical evidence and some constructive policy recommendations for the government to push the collaborative development of the digitalization and low-carbon economy.

Using a price floor on carbon allowances to achieve emission reductions under uncertainty

We build a real options investment model for carbon-allowance trading markets with a price-floor mechanism. A power plant faces a choice between undertaking an irreversible investment in carbon emission reduction or holding the option to invest later. The government encourages firms to invest immediately by guaranteeing a minimum value of the allowances. We use the Least Squares Monte Carlo method to find the value of the option and derive the firm’s threshold condition of whether to invest or to wait. The model allows us to compare the government’s costs of alternative policies for encouraging the investment. Using the newly established Chinese carbon emission trading scheme as an example to calibrate the model, simulations show that the expected cost to the government of the price-floor policy is lower compared with using a lump-sum subsidy or a preferential tax-rate policy designed to achieve the same level of reduction in emissions.

A novel fractional-order grey prediction model: a case study of Chinese carbon emissions.

Objective and accurate prediction of carbon emissions can provide a basis for the country to achieve carbon emission reduction targets and can also comprehensively and scientifically predict the peak of carbon emissions effectively, providing valuable reference information for the implementation of specific emission reduction policies and measures at each stage. In this paper, a novel fractional-order grey multivariate forecasting model is established to analyze and forecast China's carbon emissions, reflecting the principle of new information priority. The model adds fractional-order cumulative sequences to the traditional integer-order cumulative sequences, uses the Gamma function to represent the fractional-order sequences and the time-response equation, and uses the particle swarm algorithm to find the optimal order of the cumulative sequence. Finally, the modeling steps of the model are given. Then, the new model is analyzed for its effectiveness from three different perspectives using 21years of Chinese carbon emission data. The results of the first and second cases show that the newly established particle swarm optimization fractional-order model is superior to the grey multivariate comparison model. The results of the third case show that the new model is superior to the three classical grey prediction comparison models. It has stable characteristics for both simulation and prediction and also shows high accuracy, and all three cases fully illustrate the effectiveness of the new model. Finally, this new model is applied to forecast China's carbon emissions from 2022-2026, analyze the forecast results, and make relevant recommendations.

Study of microscopic properties and heavy metal solidification mechanism of electrolytic manganese residue-based cementitious materials.

Electrolytic manganese residue (EMR) is a solid waste that contains a significant amount of soluble manganese and ammonia nitrogen, which can pose risks to human health if improperly disposed of. This study aimed to prepare cementitious materials containing abundant ettringite crystals by mixing EMR with various proportions of granulated blast furnace slag (GBFS) and alkaline activators (CaO, Ca(OH)2, clinker, NaOH). The resulting cementitious material not only utilized a substantial amount of EMR but also exhibited comparable strength to ordinary Portland cement. The optimal ratios were determined through mechanical testing. Additionally, the leaching toxicity of cementitious materials was assessed using ICP-MS (inductively coupled plasma mass spectrometer) tests. The microscopic properties, hydration, and mechanism of heavy metal solidification in the cementitious materials were evaluated using XRD (X-ray diffraction), SEM (scanning electron microscope), EDS (energy-dispersive spectrometer), FTIR (Fourier transform infrared spectroscopy), and TG (thermogravimetric) techniques. The results showed that the optimal ratio for the cementitious materials was 60% EMR, 36% GBFS, and 4% Ca(OH)2. The hardened mortar exhibited compressive strengths of 34.43 MPa, 41.3 MPa, and 50.89 MPa at 3 days, 7 days, and 28 days, respectively, with an EMR utilization rate of 60%. The hydration products of EMR-based cementitious materials were C-(A)-S-H, AFt, and ferromanganese compounds, which contribute to the mechanical strength. The Mn2+ and NH4+-N contents of raw EMR were 1220 and 149 mg/L, respectively. Nonetheless, the leaching of Mn2+ and NH4+-N in the alkali-EMR-GBFS system was significantly below the limits set by the Chinese emission standard GB8978-1996. Within this system, C-(A)-S-H and AFt could physically adsorb and displace heavy metals, Ca6Mn2(SO4)2(SO3)2(OH)12·24H2O could replace Al ions with Mn ions, and ferromanganese compounds Fe2Mn(PO4)2(OH)2·(H2O)8 and MnFe2O4 could chemically precipitate Mn2+.

The Location Choice and Survival of Polluting Firms under Environmental Regulation in Urban Agglomerations of China

There are few studies discussing the relationship between the increasing intensity of environmental regulation in the central cities of urban agglomerations, which is the main method used to solve the problem of regional environmental pollution, with the survival status of polluting firms in different spatial locations. In order to more clearly describe the evolution trend of the spatial distribution of polluting firms and coordinate inter-city environmental regulation policies in a more targeted manner, we examine the impact of polluting firms’ location choice on their survival under the central–periphery spatial structure in national-level urban agglomerations from 1998 to 2013 with the accelerated failure time (AFT) model and Cox proportional hazards (Cox PH) model and using matching data of the Chinese Industrial Enterprise Database and the Chinese Industrial Enterprise Pollution Emission Database. The results find that (1) under the influence of central cities’ environmental regulations, there is likely to be an inverted U-shaped relationship between the survival of polluting firms and their spatial distance from the central cities; (2) the inverted U-shaped relationship is the motivation of the interaction between environmental regulation and market potential, which means increases in the environmental regulation intensity in the central cities of the urban agglomeration will have a negative effect on the survival of polluting firms, and the central cities’ market potential will partially offset the negative impact; (3) the inverted U-shaped relationship is more significant for private firms, and it does not necessarily exist for new entrants. The above results help to provide important policy inspiration for promoting the joint prevention and control mechanism for pollution in urban agglomerations and coordinate the effects of environmental regulation and economic transformation policies between central and peripheral cities.

Facile synthesis of platinum-loaded carbon nanosheets with high specific surface area for efficient CWAO of ammonia nitrogen: A low-cost alternative route for treating ammonia containing wastewater

Deep treatment is an important tool to solve the environmental pollution of ammonia-containing wastewater. Considering the stability and economy of industrial application, we propose a simple and economical synthesis strategy. The procedure is based on the carbonization of citrate at elevated temperatures to synthesize high specific surface area and pore-rich carbon nanoflowers, and carbon nanoflower-loaded Pt catalysts were constructed for the catalytic wet air oxidation (CWAO) of ammonia-containing wastewater. The results showed that the synergistic effect of Pt, edge carbon and carboxyl groups led to the increase of CWAO activity. During the reaction, Pt activated NH3 to put it in a high-energy state, and there was a positive synergistic effect between the oxygen-containing functional groups (carboxyl groups) on the surface of the porous carbon nanoflowers and the edge carbon exposed at the adjacent defect sites, which promoted the activation of O2 and the generation of reactive oxygen species. At 200 °C, the Pt1/PC catalysts showed high activity and hydrothermal stability for CWAO of NH3. The conversion of NH3 was 98.24 % within 4 h, and the selectivity of N2 was 100 %. The total nitrogen (TN) was reduced to below 15 mg‧L−1, which meets the Chinese emission standard for TN in petrochemical industry. Therefore, the novel catalyst proposed in this study effectively solved the problems of complicated catalyst synthesis steps and poor hydrothermal stability in ammonia CWAO studies. The catalyst has potential industrial applications for CWAO of ammonia-containing wastewater.