The rapid response to the COVID-19 pandemic led to unprecedented decreases in economic activities, thereby reducing the pollutant emissions. A random forest (RF) model was applied to determine the respective contributions of meteorology and anthropogenic emissions to the changes in air quality. The result suggested that the strict lockdown measures significantly decreased primary components such as Cr (−67 %) and Fe (−61 %) in PM_2.5 (p<0.01), whereas the higher relative humidity (RH) and NH₃ level and the lower air temperature (T) remarkably enhanced the production of secondary aerosol, including ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ (29 %), ${\mathrm{NO}}_{\mathrm{3}}^{-}$ (29 %), and ${\mathrm{NH}}_{\mathrm{4}}^{+}$ (21 %) (p<0.05). The positive matrix factorization (PMF) result suggested that the contribution ratios of secondary formation (SF), industrial process (IP), biomass burning (BB), coal combustion (CC), and road dust (RD) changed from 36 %, 27 %, 21 %, 12 %, and 4 % before the COVID-19 outbreak to 44 %, 20 %, 20 %, 9 %, and 7 %, respectively. The rapid increase in the contribution ratio derived from SF to PM_2.5 implied that the intermittent haze events during the COVID-19 period were characterized by secondary aerosol pollution, which was mainly contributed by the unfavorable meteorological conditions and high NH₃ level.

Atmospheric trace metals entail significant damages in human health and ecosystem safety, and thus a series of clean air actions have been implemented to decrease the ambient element concentrations. Unfortunately, the impact of these emission control measures on element concentrations in fine particles remains poorly understood. In our study, the random forest (RF) model was applied to distinguish the effects of emission and meteorology to trace elements in PM_2.5 in a typical industrial city named Tangshan based on a 3-year (2017–2020) hourly field observation. The result suggested that the clean air actions have facilitated the dramatic decreases of the deweathered concentrations of Ga, Co, Pb, Zn, and As by 72 %, 67 %, 62 %, 59 %, and 54 %, respectively. It is attributable to the strict implementation of “coal to gas” strategies and optimisation of industrial structure and layout. However, the deweathered levels of Ca (8.3 %), Cr (18.5 %), and Fe (23 %) only displayed minor decreases, indicating that the emission control measures for ferrous metal smelting and vehicle emission were not very effective. The positive matrix factorisation (PMF) results suggested that the contribution ratios of biomass burning, non-ferrous metal smelting, coal combustion, ferrous metal smelting, heavy oil combustion, and traffic-related dust changed from 33 %, 11 %, 15 %, 13 %, 3 %, and 25 % to 33 %, 8 %, 8 %, 13 %, 4 %, and 33 %, respectively. To date, no significant non-carcinogenic and carcinogenic risks were observed for all of the elements, while both of As and Pb still showed relatively high health damages. It was proposed to further cut down the combustion-related emissions (e.g. As and Pb) because it showed the highest marginal health benefits. Besides this, the control of traffic-related emissions might be a key abatement strategy to facilitate the reduction of elements in fine particles.

Building landscape resilience inspires the cultivation of the landscape’s capacity to recover from disruption and live with changes and uncertainties. However, integrating ecosystem and society within such a unified lens—that is, socio–ecological system (SES) resilience—clashes with many cornerstone concepts in social science, such as power, democracy, rights, and culture. In short, a landscape cannot provide the same values to everyone. However, can building landscape resilience be an effective and just environmental management strategy? Research on this question is limited. A scoping literature review was conducted first to synthesise and map landscape management change based on 111,653 records. Then, we used the Nuozhadu (NZD) catchment as a case study to validate our findings from the literature. We summarised current critiques and created a framework including seven normative categories, or common difficulties, namely resilience for “whom”, “what”, “when”, “where”, “why”, as well as “can” and “how” we apply resilience normatively. We found that these difficulties are overlooked and avoided despite their instructive roles to achieve just landscape management more transparently. Without clear targets and boundaries in building resilience, we found that some groups consume resources and services at the expense of others. The NZD case demonstrates that a strategy of building the NZD’s resilience has improved the conservation of the NZD’s forest ecosystems but overlooked trade-offs between sustaining people and the environment, and between sustainable development for people at different scales. Future researchers, managers, and decision-makers are thereby needed to think resilience more normatively and address the questions in the “seven difficulties” framework before intervening to build landscape resilience.

Yunnan is rich in renewable energy resources. An understanding of its energy structure and developmental trajectories would assist in enabling the design of suitable decarbonizing pathways and how to fit into the national 30–60 agenda. Drawing from endogenous growth theory and time series analysis, our study employs comparative functions and scenario assessments to predict the changes in the key economic indicators, such as GDP, industrial structure shifts, population, and urbanization rates, during the low-carbon transition. We further show energy structure patterns and intensity trends using regression-modeling and data-fitting methods. Based on our analyses, we project that by 2035, Yunnan’s GDP will grow to CNY 5.4761 trillion, with secondary and tertiary industries contributing 88.8%. The population is estimated to grow to 52.08 million with an urbanization rate of 70%. Moreover, fossil fuel energy consumption is forecasted to diminish to 38.7%, and energy consumption intensity is projected to be reduced to 0.38 tons of standard coal per CNY 10,000. If these metrics follow the inherent endogenous growth trend, Yunnan’s emissions are forecasted to peak at around 220 million tons of CO₂ by 2030. These findings not only provide a data foundation for Yunnan’s low-carbon development goals but also illuminate pathways for regions rich in renewable resources to transition towards sustainable growth, emphasizing the harmony between advancement and environmental stewardship.