Surface ozone pollution has attracted extensive attention with the decreasing of haze pollution, especially in China. However, it is still difficult to efficiently control the pollution in time despite numbers of reports on mechanism of ozone pollution. Here we report a method for implementing effective control of ozone pollution through power big data. Combining the observation of surface ozone, NO₂, meteorological parameters together with hourly electricity consumption data from volatile organic compounds (VOCs) emitting companies, a generalized additive model (GAM) is established for quantifying the influencing factors on the temporal and spatial distribution of surface ozone pollution from 2020 to 2021 in Anhui province, central China. The average R² value for the modelling results of 16 cities is 0.82, indicating that the GAM model effectively captures the characteristics of ozone. The model quantifies the contribution of input variables to ozone, with both NO₂ and industrial VOCs being the main contributors to ozone, contributing 33.72% and 21.12% to ozone formation respectively. Further analysis suggested the negative correlation between ozone and NO₂, revealing VOCs primarily control the increase in ozone. Under scenarios controlling for a 10% and 20% reduction in electricity use in VOC-electricity sensitive industries that can be identified by power big data, ozone concentrations decreased by 9.7% and 19.1% during the pollution period. This study suggests a huge potential for controlling ozone pollution through power big data and offers specific control pathways.

Terrestrial ecosystem carbon absorption capacity has become an important component of China’s strategy to enhance nature-based climate mitigation. However, existing studies on China’s terrestrial ecosystem carbon dynamics are still insufficient to support source-oriented control and unified supervision by national-level government departments, particularly in terms of temporal consistency, spatial completeness, and cross-ecosystem comparability. Here, we present a systematic assessment framework for NPP-based ecosystem carbon absorption capacity (NPP-based CAC) designed to support unified ecological and environmental supervision. In this study, NPP-based CAC is defined as the annual vegetation production-based carbon fixation capacity derived from net primary productivity. It represents carbon retained by vegetation after plant autotrophic respiration has been accounted for in the NPP estimation, and it should not be interpreted as gross ecosystem carbon uptake, NEP, NBP, net land–atmosphere carbon exchange, or a carbon sink for carbon neutrality accounting. The framework adopts year as the temporal unit and a 500 m × 500 m grid as the spatial unit, and integrates four core components: temporal series analysis, spatial distribution mapping, protection effectiveness evaluation, and enhancement potential estimation. Applying this framework to China from 2000 to 2022, we found that national NPP-based CAC increased from 0.673 Pg C yr⁻¹ in 2000 to 0.868 Pg C yr⁻¹ in 2022, with carbon absorption intensity rising from 98.47 to 121.37 t C km⁻² yr⁻¹. High-value areas covered 3,009,571 km², accounting for 40.90% of the national area with NPP-based CAC, whereas areas showing degradation signals covered 2,075,286 km², accounting for 29.01%. The national historical reference gap, termed enhancement potential in this framework, was estimated at 152.63 Tg C yr⁻¹, equivalent to 17.6% of the 2022 national total; this value represents the difference between current NPP-based CAC and grid-level historical maxima, rather than a directly attainable restoration target. Because no formal attribution analysis was conducted, the observed temporal and spatial patterns are interpreted as diagnostic changes in NPP-based CAC rather than as direct evidence of policy effects or ecosystem degradation. This framework enables integrated assessment across temporal, spatial, and ecosystem-type dimensions and provides an operational diagnostic basis for national-level ecological supervision, spatial prioritization, and follow-up attribution or field verification.

With rapid economic development and urbanization, many cities, particularly in China, face serious PM\(_{2.5}\) pollution issues. In this study, the city of Hefei is selected as the research area to investigate the factors influencing PM\(_{2.5}\) concentrations. Data on electricity consumption of major PM\(_{2.5}\)-emitting industries, meteorological factors (temperature, wind speed, wind direction, relative humidity), and atmospheric pollutant concentrations (NO\(_{2}\),SO\(_{2}\),O\(_{3}\),CO) are utilized to explore PM\(_{2.5}\) concentrations in Hefei from 2020 to 2021 using a generalized additive model (GAM). The aims are to identify the main influencing factors and potential control pathways for particulate matter pollution. Results reveal that CO accounts for 69% of the variation in PM\(_{2.5}\) mass concentration, suggesting it as the dominant factor in Hefei in 2020. Additionally, the major PM\(_{2.5}\)-emitting industries contribute to a 16% change in PM\(_{2.5}\) mass concentration, with a significant impact from smelting industries, which exhibit an increase in electricity consumption associated with an increase in PM\(_{2.5}\) mass concentration. Model fitting indicates that a 50% reduction in electricity consumption within the iron and steel making industries can lead to a 37% decrease in PM\(_{2.5}\) mass concentration compared to pre-reduction levels. Moreover, targeted control measures in winter result in higher reductions in PM\(_{2.5}\) pollution within a 40% reduction compared to consistent emission reductions throughout the year. These findings highlight the effectiveness of more focused control strategies based on localized circumstances. Implementing measures to restrict electricity use by key industries during high pollution seasons and in cities with high pollution levels can effectively address local PM\(_{2.5}\) pollution concerns.

The Yellow River Basin (YRB) is a key ecological barrier and socio-economic region in China, but the spatiotemporal dynamics of its ecosystem carbon sink and the non-linear effects of environmental drivers remain insufficiently understood. This study estimated Net Ecosystem Productivity (NEP) in the YRB from 2001 to 2024 using MODIS Net Primary Productivity (NPP) data and an empirical soil heterotrophic respiration model, analyzed NEP trends with the Theil–Sen estimator and Mann–Kendall test, and quantified non-linear responses to climatic, temporal, and spatial factors using a Generalized Additive Model (GAM). The YRB acted as a persistent and strengthening net carbon sink, with annual total NEP increasing significantly from 39.85 Tg C yr⁻¹ in 2001 to 176.44 Tg C yr⁻¹ in 2024, at a rate of 5.65 Tg C yr⁻¹. NEP showed a clear southeast-to-northwest decreasing gradient, and 85.6% of the basin exhibited increasing trends, particularly on the Loess Plateau. The GAM captured non-linear associations of NEP with temperature, precipitation, solar radiation, relative humidity, year, and spatial location, and achieved a moderate pooled spatial block cross-validated R² of 0.723. NEP displayed a unimodal association with temperature—with a fitted peak near 0 °C reflecting the spatial transition from cold high-altitude to warmer water-limited regions—and a saturation-type response to precipitation, highlighting the joint control of hydrothermal conditions and pervasive water limitation. The fitted spatial smooth further revealed residual spatially structured variation that may be partly associated with irrigation and land management. These findings improve the understanding of carbon-sink dynamics in the YRB and provide scientific support for climate-adaptive ecosystem management and the regional implementation of China’s “dual carbon” goals.