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Particulate pollutants, particularly PM2.5 and PM10, pose serious threats to human health and environmental quality. Therefore, effectively mitigating and reducing the concentrations of these pollutants is crucial for human survival and development. In this study, we analyzed the distribution characteristics of air particulate pollutants in a typical high-latitude city, extracted urban forest areas from high-resolution remote sensing images, and examined the changing characteristics of PM concentration and the relationship between landscape pattern indexes and PM at different scales. The results showed that the concentrations of PM2.5 and PM10 were highest in winter and lowest in summer. At the small scales of 0.5 km × 0.5 km to 1.5 km × 1.5 km, PM concentration decreased with the decrease in PARA (Perimeter–Area Ratio). At the mesoscales of 2 km × 2 km to 2.5 km × 2.5 km, both PARA and CIRCLE (Related Circumscribing Circle) were highly significant (p < 0.001) correlated with PM concentration. At the large scales of 3 km × 3 km to 4 km × 4 km, PARA and PAFRAC (Perimeter–Area Fractal Dimension) were positively correlated with PM concentration. Our study indicates that reducing the complexity of forest patches in small-scale planning can help mitigate particulate air pollution. In the medium scale of urban forest planning, the more regular the forest patch shape and the more similar the patch shape to the strip, the better PM can be alleviated, while in large-scale planning, increasing the forest area and making the patches more normalized and simplified can reduce PM concentration. Moreover, reducing the complexity of forest patches can significantly mitigate PM pollution at all scales. The results of this research provide theoretical support and guidance for improving air quality in urban forest planning at different scales. [ABSTRACT FROM AUTHOR]

Prussian blue analogues (PBAs) are a class of promising materials for capacitive deionization. However, the kinetic mismatch between their slow ion storage rate and the demand from short‐time desalination severely limits their desalination performance. Here, a group of structure‐tuneable Ni‐Mn PBAs have been developed by a combination strategy of surface‐protected chemical etching and Ostwald ripening to study their ion storage kinetics. Treating them as demos, the characterizations and investigations, e.g., in situ XRD in a three‐electrode system, dynamic impedance, finite element simulation, and DFT calculations etc., reveal that the slow ion diffusion caused by the severe agglomeration of the nanoparticles and the unsuitable lattice parameter controls the final desalination behavior. Therefore, the correspondingly optimized sample (HC‐t) possessing a microscale hollow structure, nanoscale shell thickness, and expanded lattice, displays a fast ion storage kinetics with the ratio of surface‐controlled current as high as 82% at a scan rate of 20 mV s−1. Consequently, it delivers an impressive desalination capacity of 120.8 mg g−1 (2.06 mmol g−1 Na+) with a fast average desalination rate of 0.25 mg g−1 s−1 (0.004 mmol g−1 s−1) at 1.2 V, competitive with those reported in the literature. Moreover, the elucidation of the structure‐performance correlation provides valuable insights for the development and design of next‐generation PBAs for capacitive deionization (CDI). [ABSTRACT FROM AUTHOR]

Forest defoliating pests are significant global forest disturbance agents, posing substantial threats to forest ecosystems. However, previous studies have lacked systematic analyses of the continuous spatiotemporal distribution characteristics over a complete 3–5 year disaster cycle based on remote sensing data. This study focuses on the Dendrolimus superans outbreak in the Changbai Mountain region of northeastern China. Utilizing leaf area index (LAI) data derived from Sentinel-2A satellite images, we analyze the extent and dynamic changes of forest defoliation. We comprehensively examine the spatiotemporal patterns of forest defoliating pest disasters and their development trends across different forest types. Using the geographical detector method, we quantify the main influencing factors and their interactions, revealing the differential impacts of various factors during different growth stages of the pests. The results show that in the early stage of the Dendrolimus superans outbreak, the affected area is extensive but with mild severity, with newly affected areas being 23 times larger than during non-outbreak periods. In the pre-hibernation stage, the affected areas are smaller but more severe, with a cumulative area reaching up to 8213 hectares. The spatial diffusion characteristics of the outbreak follow a sequential pattern across forest types: Larix olgensis, Pinus sylvestris var. mongolica, Picea koraiensis, and Pinus koraiensis. The most significant influencing factor during the pest development phase was the relative humidity of the year preceding the outbreak, with a q-value of 0.27. During the mitigation phase, summer precipitation was the most influential factor, with a q-value of 0.12. The combined effect of humidity and the low temperatures of 2020 had the most significant impact on both the development and mitigation stages of the outbreak. This study's methodology achieves a high-precision quantitative inversion of long-term disaster spatial characteristics, providing new perspectives and tools for real-time monitoring and differentiated control of forest pest infestations. [ABSTRACT FROM AUTHOR]

Urban green spaces (UGSs) provide important support for the health of urban residents and the realization of sustainable urban development. However, the spatiotemporal pattern of urban resident exposure to UGSs in cities is unclear, especially at the national scale in China. Based on the annual 30 m resolution Normalized Difference Vegetation Index (NDVI) data of the Landsat satellite, we quantitatively analyzed the change in UGS coverage from 2000 to 2020 for 320 cities in China and combined it with population data to understand the changing patterns of urban population exposure to different UGS coverage. The results indicated that the average UGS coverage decreased from 63% to 44% from 2000 to 2020 in China, which could be divided into two stages: a rapid decline phase (2000–2014) and a progressive decline phase (2015–2020). Geographically, UGS coverage declined faster in southwestern and eastern cities than in other regions, particularly in medium-sized cities. We also found that urban pixel-based areas in cities with the highest UGS coverage (80–100%) decreased rapidly, and the proportion of the urban population exposed to the highest UGS coverage also declined significantly from 2000 to 2020. Urban pixel-based areas with low UGS coverage (20–40%) continued to expand, and there was a rapid increase in the proportion of the urban population exposed to low UGS coverage, with an increase of 146 million people from 2000 to 2020. The expansion of impervious surfaces had the most significant effect on the change in UGS coverage during different periods (2000–2020, 2000–2014, and 2015–2020). Natural factors such as precipitation, surface maximum temperature, and soil moisture also affected UGS coverage change. These findings provide insights into the impact of urbanization on the natural environment of cities, availability of UGS for residents, and sustainable urban development under rapid urbanization. [ABSTRACT FROM AUTHOR]

Understanding the determinants of the availability and spatial fairness of street greenery is crucial for improving urban green spaces and addressing green justice concerns. While previous studies have mainly examined factors influencing street greenery from an aerial perspective, there has been limited investigation into determinants at eye level, which more closely aligns with people's actual encounters with green spaces. To address this, the Green View Index (GVI) and Gini coefficient were used to assess the availability and spatial fairness of street greenery from a pedestrian's perspective, using Baidu Street View (BSV) images across 49 subdistricts in Changchun City, China. A dataset of 33,786 BSV images from 1877 sites was compiled. Additionally, 21 explanatory factors were collected and divided into three groups: socioeconomic, biogeographic, and landscape patterns. The Boosted Regression Tree (BRT) method was employed to assess the relative influence and marginal effects of these factors on street greenery's availability and spatial fairness. The results showed that street greenery's availability and spatial fairness are predominantly influenced by landscape patterns. Specifically, the percentage of landscape and edge density emerged as the most significant factors, exhibiting a threshold effect on the availability and fairness of street greenery. Increasing the proportion and complexity of urban green spaces can efficiently enhance the availability and spatial fairness of street greenery. These findings lay a new foundation for urban green infrastructure management. [ABSTRACT FROM AUTHOR]