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.

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.

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.

Construction and performance of a simple and efficient g-C3N4 photocatalytic hydrogen production system.

Surface and bulk structure modification is an effective strategy to improve the photocatalytic performance of g-C3N4 (CN). In this work, dilute NaOH solution was used in situ to regulate the CN structure for enhanced photocatalytic hydrogen evolution reaction (HER). Characterization results indicate that after treatment with dilute NaOH solution, the surface of CN was hydroxylated, resulting in the change of CN structure and the increase of BET specific surface area. Furthermore, some Na+ ions can be intercalated into the framework of CN, and form the Na-N bond. These modifications boost the HER activity of CN. The test carried out in 7.5 mM NaOH solution shows the highest activity and it is almost 3.7 times higher than that performed in water. Control tests indicate that hydroxides of other alkali and alkali earth metals such as LiOH, KOH, Ca(OH)2, and Ba(OH)2 have similar promotion effects. This work demonstrates a valid and simple way to enhance the HER activity of CN through performing the reaction in a weakly alkaline solution.