Study on factors influencing carbon dioxide emissions and carbon peak heterogenous pathways in Chinese provinces.

Implementing a Carbon Peak Action Plan at the regional level requires comprehensive consideration of the developmental heterogeneity among different provinces, which is an effective pathway for China to realize the goal of carbon peak by 2030. However, there is currently no clear provincial roadmap for carbon peak, and existing studies on carbon peak pathways inadequately address provincial heterogeneity. Therefore, this paper employs the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to decompose assess 8 factors influencing carbon emissions of 30 provinces. According to scenario analysis, the paper explores the differentiated pathways for provincial carbon peaks based on policy expectation indicators (including population, economy, and urbanization rate) and comprises policy control indicators (including the energy structure, energy efficiency, industrial structure, transportation structure, and innovation input). The results indicate that population, per capita GDP, urbanization rate, and innovation input are the primary factors for influencing (negatively) the growth of carbon emissions. In contrast, the optimization and upgrading of the industrial structure, energy intensity, energy structure, and transportation structure have mitigating effects on carbon emissions, especially for the first two factors. The forecasting results reveal that robust regulations of the energy and industry can effectively accelerate carbon peak at a reduced magnitude. If developed at BAU, China cannot achieve carbon peak by 2030, continuing an upward trend. However, by maximizing the adjustment strength of energy and industrial transformation within the scope of provincial capabilities, China could achieve carbon peak as early as 2025, with a peak of 12.069 billion tons. In this scenario, 24 provinces could achieve carbon peak before 2030. Overall, this study suggests the feasibility of differentiated pathway to achieve carbon peaks in China, exploring the carbon peak potential and paths of 30 provinces, and identifying provinces where carbon peak is more challenging. It also provides a reference for the design of carbon peak roadmaps at both provincial and national levels and offers targeted recommendations for the implementation of differentiated policy strategies for the government.

Spatiotemporal patterns and deposition of organophosphate esters (OPEs) in air, foliage and litter in a subtropical forest of South China.

Recent studies revealed the un-negligible impact of airborne organophosphate esters (OPEs) on phosphorus (P)-limited ecosystems. Subtropical forests, the global prevalence P-limited ecosystems, contain canopy structures that can effectively sequester OPEs from the atmosphere. However, little is known about the behavior and fate of OPEs in subtropical forest ecosystem, and the impact on the P cycling in this ecosystem. OPE concentrations in the understory air (at two heights), foliage, and litterfall were investigated in a subtropical forest in southern China. The median ∑OPE concentrations were 3149 and 2489 pg/m3 in the upper and bottom air, respectively. Foliage exhibited higher ∑OPE concentrations (median = 386 ng/g dry weight (dw)) compared to litter (median = 267 ng/g dw). The air OPE concentrations were ordered by broadleaved forest > mixed forest > coniferous forest, which corresponds to the results of canopy coverage or leaf area index. The spatial variation of OPEs in foliage and litter was likely caused by the leaf surface functional traits. Higher OPE concentrations were found in the wet season for understory air while in the dry season for foliage and litter, which were attributed to the changes in emission sources and meteorological conditions, respectively. The inverse temporal variation suggests the un-equilibrium partitioning of OPEs between leaf and air. The OPE concentrations during the litter-incubation presented similar temporal trends with those in foliage and litter, indicating the strong interaction of OPEs between the litter layer and the near-soil air, and the efficient buffer of litter layer played in the OPEs partitioning between soil and air. The median OPEs-associated P deposition fluxes through litterfall were 270, 186, and 249 µg P/m2·yr in the broadleaved, mixed, and coniferous forests, respectively. Although the fluxes accounted for approximately 0.2% of the total atmospheric P deposition, their significance to this P-limited ecosystem may not be negligible.

Simulating the land use change effects on non-point source pollution in the Duliujian River Basin.

The uncertainty in the generation and formation of non-point source pollution makes it challenging to monitor and control this type of pollution. The SWAT model is frequently used to simulate non-point source pollution in watersheds and is mainly applied to natural watersheds that are less affected by human activities. This study focuses on the Duliujian River Basin (Xiqing section), which is characterized by a dense population and rapid urbanization. Based on the calibrated SWAT model, this study analyzed the effects of land use change on non-point source pollution both temporally and spatially. It was found that nitrogen and phosphorus non-point source pollution load losses were closely related to land use type, with agricultural land and high-density urban land (including rural settlements) being the main contributors to riverine nitrogen and phosphorus pollution. This indicates the necessity of analyzing the impact of land use changes on non-point source pollution loads by identifying critical source areas and altering the land use types that contribute heavily to pollution in these areas. The simulation results of land use type changes in these critical source areas showed that the reduction effect on non-point source pollution load is in the order of forest land > grassland > low-density residential area. To effectively curb surface source pollution in the study area, strategies such as modifying urban land use types, increasing vegetation cover and ground infiltration rate, and strictly controlling the discharge of domestic waste and sewage from urban areas can be implemented.

[Research on the Evaluation Method and Application of Provincial Differentiated Carbon Peaking in China].

Promoting regions with favorable conditions to take the lead in reaching a carbon peak is an inevitable step towards achieving the dual carbon goals under the "nationwide coordinated action" plan. Considering the differences among Chinese provinces, this study measured the peaking pressure of each province based on the spatial distribution of carbon emissions. We then constructed a provincial peaking capacity evaluation system based on five dimensions, namely, peaking pressure, emission reduction status, economic development, policy support, and resource endowment, to comprehensively evaluate the carbon peaking capacity of 30 provincial administrative regions in China, excluding Hong Kong, Macau, Taiwan, and Tibet, using the entropy value method to determine the index weights. The 30 provinces were divided into five peaking tiers according to the evaluation results. The results showed that:① 18 regions, such as Hainan and Beijing, displayed a surplus in carbon emission space; eight regions, including Hebei and Shandong, showed a deficit in carbon emission space; and the carbon emission spaces allocated to Zhejiang, Anhui, Henan, and Hubei were comparable to their respective actual emissions. ② Developed regions generally had a higher carbon peaking capacity than that of less developed regions, with Beijing and Shanghai showing outstanding carbon peaking capacity, whereas Jiangxi and Guizhou had more room to improve their capacity. Finally, differentiated peaking targets and priority actions were proposed according to the provinces' different peaking tiers and local conditions.

[Evaluation Method and Application for Urban Carbon Peaking & Neutrality Performance].

As the key unit of greenhouse gas emission sources, cities have the most direct and fundamental significance to achieve the national carbon peaking carbon neutrality goal. In order to evaluate the current performance of urban carbon peaking and neutrality, a set of urban peaking and carbon neutrality action index evaluation systems consisting of three criterion layers, seven elements, and fourteen specific index layers were developed based on the analytic hierarchy process considering the preferences of decision makers, through the steps of influencing factor determination, indicator selection, and scoring principle determination, as well the indicator weightings. Thus, a relatively comprehensive scientific evaluation method was formed to fully evaluate the attitude of the government towards the goal of carbon peaking and neutrality, the state of social economy, energy consumption, industrial structure, transportation, and other aspects, as well as the actual effect of emission reduction efficiency and trends. Through the central city evaluation application study, it was found that the first-tier economically developed and low-carbon pilot cities had a more outstanding comprehensive performance in reaching the peak and neutrality. The comprehensive scores of Beijing, Shenzhen, Wuhan, Shanghai, Qingdao, Guangzhou, Chengdu, Xiamen, Kunming, and Lanzhou all exceeded 60 points. Beijing, Xiamen, Ningbo, Shenzhen, and Qingdao had significant climate ambitions, whereas Haikou, Guangzhou, Chengdu, Nanning, and Beijing had a better low-carbon status. Kunming, Lanzhou, Luoyang, Daqing, Jilin, and other cities showed significant emission reduction trends. Most cities still had problems such as insufficient willingness to reach the peak and lack of statistical information disclosure system. The evaluation method could be optimized by improving the index system, updating the empowerment, and forming the annual evaluation mechanism next step. It is suggested to formulate the local carbon reduction work plan by coordinating the whole country at different levels, improve the urban energy and greenhouse gas statistics and information disclosure system, and organize the carbon peaking pilot construction in areas where conditions permit.

Adsorption and desorption of heavy metals at water sediment interface based on bayesian model.

Estuarine areas are not only the main gathering point for human sewage but also the place where one-way and two-way fluids interact, thus forming a complex and changeable geochemical physical field. Here, heavy metals (HMs) are adsorbed and desorbed due to physical, chemical, and biochemical processes. However, the adsorption and desorption behavior of HMs in the aquatic environment is complex, and physicochemical processes occurring in the estuarine sediment-water interface control the direction and boundaries of the system. This study analyzed the migration and distribution of HMs in rivers and lakes, and established a Bayesian network model to quantitatively understand the impact of nutrients and key environmental factors on the adsorption-desorption behavior of HMs in lake and estuaries, as well as the competitive relationship between environmental factors. The influence of environmental factors and the occurrence of HMs are both important model inputs. Our findings indicated that the migration risk of Cd in Qinghai Lake was high. Environmental factors such as Cation exchange capacity (CEC), Organic matter (OM), Soluble fluoride (SFL), and pH play the most important role in the adsorption and desorption of HMs. Our findings also indicated that the exchange and activity of HMs in sediments were much higher than in the overlying water. The organic matter content was the most complex environmental factor affecting HMS adsorption and desorption at the water-sediment interface. Additionally, the mass concentration of dissolved oxygen (DO) has a linear relationship with bioavailable HMs in river and lake sediments, but has no linear relationship with the concentration of water-soluble HMs. Interestingly, there are synergistic effects between environmental factors, which directly or indirectly affect the release of bioavailable HMs. However, it is important to determine whether the effects of different environmental factors on the exchange of bioavailable HMs are negative or positive. Our findings suggested that Bayesian network (BN) signals (positive or negative) could provide insights into the transfer direction of metals in the water-sediment interface.

Levels, origins and probabilistic health risk appraisal for trace elements in drinking water from Lhasa, Tibet.

Due to the lack of monitoring systems and water purification facilities, residents in western China may face the risk of drinking water pollution. Therefore, 673 samples were collected from Lhasa's agricultural and pastoral areas to reveal the status quo of drinking water. We used inductively coupled plasma-mass spectrometry to determine trace elements concentrations for water quality appraisal, source apportionment, and health risk assessment. The results indicate that concentrations of V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd, Ba, and Pb are below the guidelines, while As concentrations in a few samples exceed the standard. All samples were classified into "excellent water" for drinking purpose based on Entropy-weighted water quality index. Thereafter by principal component analysis, three potential sources of trace elements were extracted, including natural, anthropogenic, and mining activities. It is worth noting that geotherm and mining exploitation does not threaten drinking water safety. Finally, health risks were assessed using Monte Carlo technique. We found that the 95th percentiles of hazard index are 1.80, 0.80, and 0.79 for children, teenagers, and adults, indicating a non-carcinogenic risk for children, but no risks for the latter two age groups. In contrast, the probabilities of unacceptable cautionary risk are 7.15, 2.95 and 0.69% through exposure to Cr, Ni, As, and Cd for adults, children, and teenagers. Sensitivity analyses reveal As concentration and ingestion rate are most influential factors to health risk. Hence, local governments should pay more attention to monitoring and removal of As in the drinking water.

Spatial distribution, ecological risk, and human health assessment of heavy metals in lake surface sections - a case study of Qinghai Lake, China.

Qinghai Lake is the largest inland saltwater lake in China, with a drainage area of 29,661 km2. This study sought to conduct an ecological and human health risk assessment of metals and heavy metals, including copper, as well as investigate their concentration, distribution, and source distribution. In terms of seasonal variation, the increases in Fe, Cr, As, Pb, and Hg were relatively large, and the spatial distribution of metals presented a three-level stepped distribution trend, gradually increasing from east to west. By further exploring the source and migration path of pollutants, our study found that the source of metals in the sediments of Qinghai Lake is mainly controlled by five rivers entering the lake. Enrichment factor (EF) calculations indicated that the metal accumulation or enrichment capacity of the three central points in Qinghai Lake Basin was strong. Interestingly, the enrichment capacity of Cu and Zn was the strongest among all metals but occurred at low and medium concentration levels, respectively. The Igeo and [Formula: see text] ecological risk assessment results indicated that the individual metals posed little to no ecological risks to the Qinghai Lake Basin. However, the multi-element environmental risk comprehensive index (RI) indicated that Hg (RI = 147.97) represented a slight ecological hazard, Mn (RI = 181.13) posed moderate ecological hazards, and Zn (RI = 386.66) posed strong ecological hazards. The human health risk assessment results showed that the heavy metals in the surface sediments of Qinghai Lake currently do not pose a threat to human health. This information may facilitate the implementation of more stringent monitoring programs in the aquatic ecosystem by the relevant regulatory authorities.

Cloud point extraction (CPE) combined with single particle -inductively coupled plasma-mass spectrometry (SP-ICP-MS) to analyze and characterize nano-silver sulfide in water environment.

Silver Nanoparticles (Ag-NPs), an emerging type of pollutant, might occur various physical and chemical transformations, which would affect its environmental fate, transformation and biological effects. Sulfurization is the most common conversion of Ag-NPs, accompanied by the formation of nano-silver sulfide (Ag2S-NPs). The method of Ag2S-NPs analysis and characterization is of great significance for assessing the environmental risks of Ag. In this study, cloud point extraction (CPE) and Single Particle-Inductively Coupled Plasma-Mass Spectrometry (SP-ICP-MS) were used in combination to establish a simple and reliable analysis method to quantify Ag2S-NPs in water, with the morphology unchanged. Non-Ag2S-NPs were dissociated into Ag+ firstly, and Ag2S-NPs and Ag+ were separated by CPE, followed by SP-ICP-MS analysis. The extraction rate based on particle number concentration was between (76.19 ± 0.56) % to (106.35 ± 0.00) % in environmental waters. Compared with the (76.96 ± 2.18) nm Ag2S-NPs spiked, the particle size extracted increased slightly with (94.19 ± 2.72) nm- (97.25 ± 0.22) nm as the large-size Ag2S-NPs originally presented in waters, instead of agglomeration. This method could be generally applicable to the analysis of Ag2S-NPs in waters, and provide ideas for other metal sulfide nanoparticles (MS-NPs), which has certain significance.

Assessing the environmental occurrence and risk of nano-silver in Hunan, China using probabilistic material flow modeling.

Using a probabilistic material flow analysis model, we investigate the mass balance and occurrence of nano-silver in the environment of Hunan, renowned as "the home to non-ferrous metals" in China. The model builds on China-specific production and environmental information and incorporates sulfidation of nano-silver in urban wastewater treatment systems. We predict that the bulk of nano-silver (>90%), primarily originating from production and consumption of nano-silver products, ends up in sewage treatment plants (STPs, 5.3t/a), followed by the landfill (4.7t/a). More than 99% of nano-silver in STPs reacts with sulfide and thus does not appear in effluents. Direct release of nano-silver from production and consumption is identified as the dominant source of nano-silver in the environment, most of which enters surface water (0.71t/a). As such, regulation of direct emissions from production and consumption of nano-silver products can be of priority for local environmental management. The modeled regional concentrations (modes) of nano-silver are 0.9ng/L in surface water, 20ng/kg in soil, 6.9µg/kg in sediment, and negligible in the air, which agree well with measurements in the modeled region. Based on the modeled concentrations, we calculate that the risk characterization ratio is <1 in the air, surface water and soil, which means that nano-silver currently poses no risk to organisms living in these environmental compartments.

Characterisation of silver release from nanoparticle-treated baby products.

We tested six kinds of commercially available baby products containing nano-silver and divided them into two categories: baby food containers and baby oral care products, according to usage. We measured the total mass of silver, particle size, particle number concentration and the mobility under different simulated conditions. The total mass of silver in baby products and migration solutions was quantified by ICP-MS analysis, and the size of the migrated particles was investigated by single particle ICP-MS and transmission electron microscopy-energy dispersive spectrometer. Nano-silver has different migration behaviour in different materials, and the total mass of silver released in 3% acetic acid was generally highest for plastic material, while highest in 10% ethanol for silicone material. For baby food containers and baby oral care products, the total amount of silver in the migration solutions varied from 2.98 to 126.46 ng/dm2 and from 2.01 to 2.83 ng/g, respectively. The research shows that the use of baby products containing silver may expose babies to silver nanoparticles.

[An object-oriented intelligent engineering design approach for lake pollution control].

Regarding the shortage and deficiency of traditional lake pollution control engineering techniques, a new lake pollution control engineering approach was proposed in this study, based on object-oriented intelligent design (OOID) from the perspective of intelligence. It can provide a new methodology and framework for effectively controlling lake pollution and improving water quality. The differences between the traditional engineering techniques and the OOID approach were compared. The key points for OOID were described as object perspective, cause and effect foundation, set points into surface, and temporal and spatial optimization. The blue algae control in lake was taken as an example in this study. The effect of algae control and water quality improvement were analyzed in details from the perspective of object-oriented intelligent design based on two engineering techniques (vertical hydrodynamic mixer and pumping algaecide recharge). The modeling results showed that the traditional engineering design paradigm cannot provide scientific and effective guidance for engineering design and decision-making regarding lake pollution. Intelligent design approach is based on the object perspective and quantitative causal analysis in this case. This approach identified that the efficiency of mixers was much higher than pumps in achieving the goal of low to moderate water quality improvement. However, when the objective of water quality exceeded a certain value (such as the control objective of peak Chla concentration exceeded 100 microg x L(-1) in this experimental water), the mixer cannot achieve this goal. The pump technique can achieve the goal but with higher cost. The efficiency of combining the two techniques was higher than using one of the two techniques alone. Moreover, the quantitative scale control of the two engineering techniques has a significant impact on the actual project benefits and costs.

Guided adaptive optimal decision making approach for uncertainty based watershed scale load reduction.

Previous optimization-based watershed decision making approaches suffer two major limitations. First of all, these approaches generally do not provide a systematic way to prioritize the implementation schemes with consideration of uncertainties in the watershed systems and the optimization models. Furthermore, with adaptive management, both the decision environment and the uncertainty space evolve (1) during the implementation processes and (2) as new data become available. No efficient method exists to guide optimal adaptive decision making, particularly at a watershed scale. This paper presents a guided adaptive optimal (GAO) decision making approach to overcome the limitations of the previous methods for more efficient and reliable decision making at the watershed scale. The GAO approach is built upon a modeling framework that explicitly addresses system optimality and uncertainty in a time variable manner, hence mimicking the real-world decision environment where information availability and uncertainty evolve with time. The GAO approach consists of multiple components, including the risk explicit interval linear programming (REILP) modeling framework, the systematic method for prioritizing implementation schemes, and an iterative process for adapting the core optimization model for updated optimal solutions. The proposed approach was illustrated through a case study dealing with the uncertainty based optimal adaptive environmental management of the Lake Qionghai Watershed in China. The results demonstrated that the proposed GAO approach is able to (1) efficiently incorporate uncertainty into the formulation and solution of the optimization model, and (2) prioritize implementation schemes based on the risk and return tradeoff. As a result the GAO produces more reliable and efficient management outcomes than traditional non-adaptive optimization approaches.

Water quality modeling for load reduction under uncertainty: a Bayesian approach.

A Bayesian approach was applied to river water quality modeling (WQM) for load and parameter estimation. A distributed-source model (DSM) was used as the basic model to support load reduction and effective water quality management in the Hun-Taizi River system, northeastern China. Water quality was surveyed at 18 sites weekly from 1995 to 2004; biological oxygen demand (BOD) and ammonia (NH(4)(+)) were selected as WQM variables. The first-order decay rate (k(i)) and load (L(i)) of the 16 river segments were estimated using the Bayesian approach. The maximum pollutant loading (L(m)) of NH(4)(+) and BOD for each river segment was determined based on DSM and the estimated parameters of k(i). The results showed that for most river segments, the historical loading was beyond the L(m) threshold; thus, reduction for organic matter and nitrogen is necessary to meet water quality goals. Then the effects of inflow pollutant concentration (C(i-1)) and water velocity (v(i)) on water quality standard compliance were used to demonstrate how the proposed model can be applied to water quality management. The results enable decision makers to decide load reductions and allocations among river segments under different C(i-1) and v(i) scenarios.