Co-drivers of Air Pollutant and CO2 Emissions from On-Road Transportation in China 2010-2020.

Co-controlling the emissions of air pollutants and CO2 from automobiles is crucial for addressing the intertwined challenges of air pollution and climate change in China. Here, we analyze the synergetic characteristics of air pollutant and CO2 emissions from China's on-road transportation and identify the co-drivers influencing these trends. Using detailed emission inventories and employing index decomposition analysis, we found that despite notable progress in pollution control, minimizing on-road CO2 emissions remains a formidable task. Over 2010-2020, the estimated sectoral emissions of VOCs, NOx, PM2.5, and CO declined by 49.9%, 25.9%, 75.2%, and 63.5%, respectively, while CO2 emissions increased by 46.1%. Light-duty passenger vehicles and heavy-duty trucks have been identified as the primary contributors to carbon-pollution co-emissions, highlighting the need for tailored policies. The driver analysis indicates that socioeconomic changes are primary drivers of emission growth, while policy controls, particularly advances in emission efficiency, can facilitate co-reductions. Regional disparities emphasize the need for policy refinement, including reducing dependency on fuel vehicles in the passenger subsector and prioritizing co-reduction strategies in high-emission provinces in the freight subsector. Overall, our study confirms the effectiveness of China's on-road control policies and provides valuable insights for future policy makers in China and other similarly positioned developing countries.

Interaction Patterns between Climate Action and Air Cleaning in China: A Two-Way Evaluation Based on an Ensemble Learning Approach.

China will attempt to achieve its simultaneous goals in 2060, whereby carbon neutrality will be accomplished and the PM2.5 (fine particulate matter) level is expected to remain below 10 µg/m3. Identifying interaction patterns between air cleaning and climate action represents an important step to obtain cobenefits. Here, we used a random sampling strategy through the combination of chemical transport modeling and machine learning approach to capture the interaction effects from two perspectives in which the driving forces of both climate action and air cleaning measures were compared. We revealed that climate action where carbon emissions were decreased to 1.9 Bt (billion tons) could lead to a PM2.5 level of 12.4 µg/m3 (95% CI (confidence interval): 10.2-14.6 µg/m3) in 2060, while air cleaning could force carbon emissions to reach 1.93 Bt (95% CI: 0.79-3.19 Bt) to achieve net carbon neutrality based on the potential carbon sinks in 2060. Additional controls targeting primary PM2.5, ammonia, and volatile organic compounds were required as supplements to overcome the partial lack of climate action. Our study provides novel insights into the cobenefits of air-quality improvement and climate change mitigation, indicating that the effect of air cleaning on the simultaneous goals might have been underestimated before.

Optimizing the development of contaminated land in China: Exploring machine-learning to identify risk markers.

Often available for use, previously developed land, which includes residential and commercial/industrial areas, presents a significant challenge due to the risk to human health. China's 2018 release of health risk assessment standards for land reuse aimed to bridge this gap in soil quality standards. Despite this, the absence of representative indicators strains risk managers economically and operationally. We improved China's land redevelopment approach by leveraging a dataset of 297,275 soil samples from 352 contaminated sites, employing machine learning. Our method incorporating soil quality standards from seven countries to discern patterns for establishing a cost-effective evaluative framework. Our research findings demonstrated that detection costs could be curtailed by 60% while maintaining consistency with international soil standards (prediction accuracy = 90-98%). Our findings deepen insights into soil pollution, proposing a more efficient risk assessment system for land redevelopment, addressing the current dearth of expertise in evaluating land development in China.

EDTA Combined with C-Pilot Files and Microultrasound for Root Canal Calcification: Dredging Effect and Safety Analysis.

Objective: This paper discussed the dredging effect and safety of ethylenediaminetetraacetic acid (EDTA) combined with C-pilot files and microultrasound (mUS) on root canal calcification. Methods: From October 2021 to January 2022, 132 patients with calcified root canals treated in our hospital were selected as the research subjects. Among them, 64 cases who received EDTA combined with C-pilot Files and mUS plus ultrasonic instruments to dredge calcified root canals were regarded as the research group (RG), and another 68 cases given ultrasonic instruments plus C-pilot files were regarded as the control group (CG). The root canal dredging time was recorded, and the dredging success rate and total success rate of different tooth positions and calcification sites were compared between groups. The clinical efficacy was assessed 6 months after treatment. Pain severity was evaluated by the Pain Intensity Numerical Rating Scale (PI-NRS) before (T0), during (T1), and after treatment (T2). Finally, the incidence of adverse reactions within one week after treatment was counted. Results: RG was observed with statistically higher success rates of root canal dredging in different tooth positions and calcified sites than CG (P < 0.05). Besides, the total effective rate of treatment was 92.19% in RG, which was also higher compared with CG, while the root canal dredging time in RG was shorter than that in CG (P < 0.05). Increased NRS scores were found in both groups at T1, and the score in RG was significantly lower compared with that in CG. At T2, both groups showed a lower PI-NRS score than T1 but higher than T0, and the score at T2 was also lower in RG as compared to CG (P < 0.05). Moreover, the reduced incidence of adverse reactions were observed in RG compared with CG (P < 0.05). Conclusions: EDTA combined with C-pilot files and mUS can effectively improve the dredging success rate of root canals obstructed by calcification, shorten the dredging time, and improve patient comfort, which is an effective method for clinical dredging of calcification obstructed root canals.

[Emission Characteristics and Toxicity Effects of Halogenated Polycyclic Aromatic Hydrocarbons from Coal-Fired and Waste Incineration Power Plants].

Coal-fired power plants (CFPPs) and waste incineration power plants (WIPPs) represent a large portion of polycyclic aromatic hydrocarbons (PAHs) sources in the environment, among which halogenated PAHs (HPAHs) are more toxic to the human body compared with their corresponding parent PAHs. In the current work, we investigated the occurrence, formation mechanism, and toxicity effects of HPAHs in the coal and waste combustion products from three CFPPs and one WIPP. The results indicate that the contents of chlorinated PAHs (Cl-PAHs) in the fly ash from the CFPPs and WIPP were 1.06-1.67 ng·g-1 and 2.76 ng·g-1, respectively, and the contents of brominated PAHs (Br-PAHs) in the fly ash from the CFPPs and WIPP were 26.4-44.2 ng·g-1 and 6.31 ng·g-1, respectively. The HPAH contents in the fly ash from the WIPP were significantly higher than those from the CFPPs primarily due to the abundant plastics in the domestic waste, represented by polyvinyl chloride, resulting in the formation of Cl-PAHs during combustion. The HPAH contents in the fly ash from the pulverized coal-fired (PC) boiler were significantly higher than those from the circulating fluidized bed (CFB) boiler mostly due to the higher combustion temperature operated in the PC boiler. The HPAHs in the fly ash from coal combustion were predominantly 7-BrBaA and 9-ClPhe, and those from domestic combustion were predominantly 9-BrPhe and 2-ClAnt. In addition, the contents of 7-BrBaA and 9,10-Br2 Ant in the coal combustion fly ash were significantly higher than those in domestic waste combustion fly ash, whereas 2-BrFle exhibited a contrasting profile. The content of Br-PAHs in the fly ash treated by semi-dry deacidification was twice that in dust removal fly ash but significantly increased in the chelating agent stabilization fly ash. The Pearson correlation analysis indicated the the formation mechanism of Cl-PAHs and Br-PAHs were the same but a secondary formation of HPAHs during the chelating agent stabilization of the fly ash was deduced. The TEQ values of the HPAHs in the fly ash (8.87×10-3-15.0×10-3 ng·g-1) from the WIPP were similar to those in the fly ash from the CFPPs (10.0×10-3 ng·g-1), which were significantly reduced in the fly ash treated by semi-dry deacidification due to the removal of 7-BrBaA. Moreover, the TEQ values of the HPAHs in the fly ash increased 5.4 times after the chelating agent stabilization. The ecological risk should be considered for the CFPP fly ash due to their massive amount of discharge and high TEQ values.

How to apply O3 and PM2.5 collaborative control to practical management in China: A study based on meta-analysis and machine learning.

Constant increase of atmospheric O3 concentration is a barrier for the further air quality improvement in China. Given that PM2.5 is still controlled as a key pollutant, managements for the collaborative reduction of O3 and PM2.5 are urgently required in China. In the current work, monitoring data of O3 and PM2.5 from 2015 to 2016 in 1464 monitoring sites (MS) was collected and cleaned. Additionally, 7 anthropogenic emission reductions were jointed with the corresponding monitoring data. According to the O3 and PM2.5 variation, a meta-analysis was conducted and divided regions into 4 categories via the effect size, region I: O3 and PM2.5 collaborative reduction, region II: PM2.5 decreased and O3 increased, region III: O3 decreased and PM2.5 increased, regions IV: both O3 and PM2.5 increased. Then, based on the region labels, machine learning was used to identify the pattern between region label and its precursor reductions. The findings were as follows: (1) Principal component analysis showed that NH3 was not focused on. (2) Random forest had a well performance on region classification with the accuracy of 80.40% and the importance of the 7 precursors was in the sequence of VOCs>NH3 > PM2.5 > OC > SO2 > NOX > Coarse PM. (3) Polytomous logistic regression evaluated the critical factors that influenced the region label, which showed that the reductions of VOCs, NH3 and PM2.5 could achieve the collaborative reduction in a short time in most of cities in China. Based on the statistical results above, a kinetic management system including evaluation and policy-making sections was finally established, which filled the gap of the collaborative reduction in environmental management in China.