The No. 8 Block of Xinjiang represents a typical ultra-low-permeability fractured oilfield in which conventional water-quality evaluation techniques fail to reproduce the water-quality evolution and associated reservoir responses that occur within fractured reservoirs. To address this limitation, a graded water-quality evaluation method and an associated apparatus tailored for water injection in ultra-low-permeability fractured reservoirs were independently developed. Using this method and apparatus, fracture–matrix core water-injection displacement experiments were conducted to quantify water–reservoir compatibility and to characterize permeability evolution in fracture–matrix cores during displacement. An economic evaluation of fracture–matrix core water-injection schemes was subsequently performed. The results indicate that, for fracture–matrix cores with a permeability of 8 mD, suspended particles with a median diameter ≤ 1.5 μm and a concentration ≤ 8 mg/L cause a total permeability impairment of < 20%, demonstrating favorable compatibility and unobstructed migration, with particle retention concentrated primarily within fracture zones. For fractures with a permeability of 20 mD, suspended particles with a median diameter ≤ 3 μm and a concentration ≤ 6 mg/L similarly result in permeability impairment < 20%, indicating good compatibility and successful passage. By integrating reservoir-permeability variations and historical water-injection data, an economic assessment model that accounts for reservoir evolution, oil prices, and injected-water quality was established. The model enables the identification of water-quality standards that both ensure effective reservoir development and maintain economic viability.

Sustainable forest planning in disaster-prone landscapes requires the integration of ecological risk considerations into spatial decision-making frameworks. In this study, a spatial modeling framework was developed to integrate landslide risk into forest land suitability evaluation in Sukabumi, Indonesia, a region that has experienced the highest recorded landslide frequency in West Java over the past decade. Foundational spatial data were derived from forest site typologies and subsequently restructured to isolate environmental variables associated with landslide susceptibility. A spatial multi-criteria evaluation approach was implemented to integrate both qualitative and quantitative criteria within a unified spatial decision-support framework. Criterion prioritization and weighting were determined through the analytical hierarchy process, enabling structured expert judgment to be incorporated into the evaluation process. The resulting spatial suitability map delineated seven optimal forest land-use categories across the study area: pine resin production (19,485.81 ha; 35.03%), teak timber production (8,083.95 ha; 14.45%), agroforestry systems (7,130.91 ha; 12.82%), protection forest zones (2,258.62 ha; 4.06%), biomass cultivation areas (2,117.12 ha; 3.81%), ecotourism development zones (480.28 ha; 0.86%), and biomass processing facility sites (16.90 ha; 0.03%). Spatial prioritization emphasized areas where landslide susceptibility is minimized while maintaining high economic potential for forest-based production systems. The results demonstrate that the systematic incorporation of landslide hazard information can significantly improve the reliability of forest land suitability assessments, providing a more balanced decision-support tool for forest management planning. The approach offers practical value for policymakers and land managers seeking to enhance sustainable forest land-use planning in mountainous and disaster-sensitive regions.

Surface ozone is a major secondary air pollutant carrying significant implications for the stability of the ecosystem, public health, and climate forcing. In Nigeria, rapid urbanization, emission growth, and climatic variability interact to shape ozone dynamics; however, there is little understanding of the specific contribution of meteorological factors. This study investigated the influence of key meteorological variables on surface ozone variability in Nigeria based on a combined Principal Component Analysis (PCA) and Linear Regression (LR) framework. Meteorological parameters include temperature, wind speed, relative humidity, rainfall, cloud cover, short-wave radiation, and atmospheric pressure. PCA results revealed that the first three principal components (PCs) explained approximately 76% of the total variance, suggesting that a limited number of climatic modes, primarily associated with temperature, wind speed, and humidity, dominated ozone variability. LR further quantified the individual contributions of these variables. Cloud cover (-1.015), temperature (-0.975), and wind speed (-0.665) exerted the strongest negative influence on surface ozone concentrations. In contrast, rainfall (0.306) demonstrated a positive association, potentially linked to enhanced post-precipitation soil NO$_x$ emissions. Other variables, including short-wave radiation, atmospheric pressure, and relative humidity, exhibited relatively minor effects. While the model reflected robust predictive performance (Mean Squared Error (MSE) = 0.044), the findings emphasized the significant role of meteorological processes in shaping ozone variability. In drastically urbanizing tropical regions, meteorological dynamics should be incorporated into the forecast of air quality and planning of environmental policies.