Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation.

This paper leverages a data-driven two-step approach to effectively evaluate the effects of COVID-19 lockdown on air pollution in both the short and long-term in China. Using air pollution, meteorological conditions, and air mass clusters from 34 air quality monitoring stations in Beijing from 2015 to 2022, this study first employs a deweathering machine learning technique to decouple the confounding effects of meteorological on the air pollution. Furthermore, a detrending percentage change indictor is applied to remove the influence of seasonal variations on air pollution. The findings reveal that: (1) Human interventions are the primary drivers of changes in air pollution concentrations, whereas meteorological factors have a relatively minor impact. (2) During the COVID-19 lockdown, significant variations in air pollution levels are observed, with the effects of city lockdown ranging from a decrease of 40.11% ± 14.81% to an increase of 20.28% ± 14.36%. Notably, there is a decline in concentrations of NO2, PM2.5, CO, and PM10, while the levels of O3 and SO2 increase even during the strictest lockdown period. (3) In the year following the COVID-19 lockdown, there is a rebound in overall air pollution levels. However, by the second year, a general decline in air pollution is observed, except for O3. Therefore, it is imperative to integrate the confounding effects of meteorological factors into air quality management policies under various future scenarios: adopt high-intensity control measures for sudden air quality deteriorations, advance green recovery initiatives for long-term emission reductions, and coordinate efforts to reduce composite atmospheric pollution.

Doing more with less: How to design a good subgroup governance model for the air pollution transport network in "2+26" cities of China?

Air pollution shares the attributes of significant spatial spillover effects and environmental public goods, leading to the territorial governance model that easily falls into a state of failure. Despite a growing number of studies on the local spatial spillover effect of air pollution, scant evidence currently exists on its global spatial association effect and a good subgroup governance model. Based on a panel data set of the daily prefecture-level city data on air quality measured by the air quality index (AQI) in "2 + 26" cities of China in 2015 and 2018, this study first builds an air pollution transport network (APTN), i.e., the cities as the nodes and the association relationships between the nodes as the edges. Furthermore, this paper reveals the spatial association effect and the temporal lagged attribute of the APTN using the Social network analysis (SNA) and the Generalized impulse response function (GIRF). The results are summarized as follows. (1) Every city has significant spatial association effects of air pollution with at least another city in the APTN, and northern APTN affects most to the air pollution of other cities, while southern APTN is obviously always affected by air pollution in other cities. (2) Transport strength peaks on the second day of an air pollution transport process, and the transport process lasts for 7-12 days. (3) The APTN is divided into four subgroups: Sycophants, Primary, Bidirectional, and Brokers, with Baoding, Zhengzhou, Heze, and Hengshui as the central cities of each group, respectively. Overall, our study provides a networked, modular, and early-warning governance model for policymakers.

Spatiotemporal heterogeneity and influencing factors on urbanization and eco-environment coupling mechanism in China.

High-quality urbanization is the core for realizing human well-beings, for which reason investigating how the relationship evolves between urbanization and eco-environment is of crucial importance. Differing from the rationale of revealing spatial spillover effects using traditional tests, we consider spatial network characteristics to enrich the notion of local coupling and telecoupling from a relational perspective. First, we adopt coupling coordination degree model (CCDM) and decoupling model (DM) to calculate the urbanization and eco-environment coupling coordination degree (UECCD) and the decoupling index (DI) in 30 provinces and municipalities of China from 2008 to 2017. Second, we use gravity model to construct urbanization and eco-environment coupling coordination network (UECCN), in which provinces are nodes and spatial connection relationships of UECCD are edges between nodes. Third, we introduce social network analysis (SNA) to reveal spatial network characteristics of UECCN without using local spatiotemporal heterogeneity. Finally, we employ spatial econometric model to reveal factors that influence urbanization and eco-environment coupling effect. The major findings and conclusions of this study are summarized as follows. (1) The main subclasses of UECCD and DI are basically uncoordinated patterns with eco-environment lagging and weak decoupling, respectively. (2) Only two spatial agglomeration types of UECCD exist, the high-high (H-H) clustering in Shanghai and the low-low (L-L) clustering in western China, whereas no significant spatial agglomeration effect is observed among most provinces. (3) The distribution characteristics of UECCN are sparse in western China and dense in eastern China, and the spatial correlation strength of UECCN improves. (4) Technological innovation plays a critical role in promoting UECCD, while the total population, per capita disposable income, coupling network structure, and environmental regulations exert significant impact on UECCD. Collectively, we propose to prioritize governance provinces with low UECCD in western China as well as adequately utilize the positive externalities of key node provinces in eastern China. Equally importantly, we suggest that it is also critical to fully exert a driving force of technological innovation on improving the UECCD by promoting renewable energy utilization.

Magnetic resonance imaging and photothermal conversion properties of Gd-C nanocomposites for interstitial lymphography.

Dual-functional agents for magnetic resonance imaging (MRI) guided photothermal therapy (PTT) of lymph cancer are highly desired. Signal enhancement, selectivity between lymphatic nodes/vessels and blood vessels, and photothermal conversion property are the criteria for such dual-functional agent. In the current work, we demonstrated the potential of Gd-C nanocomposites as dual-functional agents for the MRI and PTT of lymph node cancer. Gd-C nanocomposites were synthesized via a hydrothermal carbonization approach with gadolinium chloride as Gd source and citric acid (CA) as C source. The particle size of the nanocomposites ranges from 40 to 100 nm which is smaller than the intercellular space of lymphatic vessels but much larger than that of the blood vessels. The nanocomposites were successfully applied to the MRI of cervical lymph nodes of rabbits. The signal enhancement of the lymph nodes reached the maximum value of 434% at 10 min after injection, without displaying any blood vessel. The Gd-C nanocomposites also exhibited strong photothermal conversion effect. Under the illumination of an 808 nm laser, the aqueous suspension containing 1.0 wt % Gd-C nanocomposites gave a maximum temperature rise of 28.2 °C and a light utilization efficiency of 30.4%. The results indicate that Gd-C nanocomposites have significant potential in MRI guided PTT of lymph cancer.

Broadband absorption and enhanced photothermal conversion property of octopod-like Ag@Ag2S core@shell structures with gradually varying shell thickness.

Photothermal conversion materials have promising applications in many fields and therefore they have attracted tremendous attention. However, the multi-functionalization of a single nanostructure to meet the requirements of multiple photothermal applications is still a challenge. The difficulty is that most nanostructures have specific absoprtion band and are not flexible to different demands. In the current work, we reported the synthesis and multi-band photothermal conversion of Ag@Ag2S core@shell structures with gradually varying shell thickness. We synthesized the core@shell structures through the sulfidation of Ag nanocubes by taking the advantage of their spatially different reactivity. The resulting core@shell structures show an octopod-like mopgorlogy with a Ag2S bulge sitting at each corner of the Ag nanocubes. The thickness of the Ag2S shell gradually increases from the central surface towards the corners of the structure. The synthesized core@shell structures show a broad band absorption spectrum from 300 to 1100 nm. Enhanced photothermal conversion effect is observed under the illuminations of 635, 808, and 1064 nm lasers. The results indicate that the octopod-like Ag@Ag2S core@shell structures have characteristics of multi-band photothermal conversion. The current work might provide a guidance for the design and synthesis of multifunctional photothermal conversion materials.