Based on the meteorological observation data of air temperature, surface temperature and albedo data retrieved from remote sensing images inside and outside the photovoltaic station, as well as the measured soil moisture content and bulk density at different locations of the photovoltaic power station in 2019, the impact of large-scale desert photovoltaic power plants on climate and environment was studied. The results show that air temperature, surface temperature and albedo inside the photovoltaic power station are lower than those outside the station, which are obvious in winter and not obvious in summer. Therefore, the photovoltaic power station is a cold source and an energy sink. The soil moisture content under and between the photovoltaic arrays is larger than other sampling points, and the soil bulk density gradually decreases with the distance from the center of the photovoltaic power station. Therefore, future plans for desert photovoltaic power station construction should take into account the impacts on local climate and environment.

Solar photovoltaics is a direct use of solar resources to generate electricity, which is one of the most important renewable energy application approaches. Regional PV output could be affected by the regional patterns of temperature and irradiance, which are impacted by climate change. This study examines the impact of climate change on the energy yields from solar PV across China in the future under the medium-emission scenario (SSP245) and high-emission scenario (SSP585) by calculating PV potential using the data of solar radiation on a tilted surface and temperature. Generally, under the SSP245 scenario, solar radiation increased by 0.8% and 2.15%, and PV energy yields increased by 0.28% and 1.21% in 2020–2060 and 2061–2099, respectively; under the SSP585 scenario, solar radiation increased by 0.73% and 1.35%, and PV energy yields increased by 0.04% and −1.21% in 2020–2060 and 2061–2099, respectively. Under both scenarios, PV energy potential showed an obvious increase in southeast and central China and a significant decrease in northwest China, Tibet, and Inner Mongolia. Therefore, it is suggested that under the medium-emission scenario, climate change could increase the PV energy potential, while under the high-emission scenario, it could inhibit the PV energy potential in China.

When vegetable waste (VW) is used as a sole substrate for anaerobic digestion (AD), the rapid accumulation of volatile fatty acids (VFAs) can impede interspecies electron transfer (IET), resulting in a relatively low biogas production rate. In this study, Chinese cabbage and cabbage were selected as the VW substrates, and four continuous stirred tank reactors (CSTRs) were employed. Different concentrations of biochar-loaded nano-Fe₃O₄(Fe₃O₄@BC) (100 mg/L, 200 mg/L, 300 mg/L) were added, and the organic loading rate (OLR) was gradually increased during the AD process. The changes in biogas production rate, VFAs, and microbial community structure in the fermentation tanks were analyzed to identify the optimal dosage of Fe₃O₄@BC and the maximum OLR. The results indicated that at the maximum OLR of 3.715 g (VS)/L·d, the addition of 200 mg/L of Fe₃O₄@BC most effectively promoted an increase in the biogas production rate and reduced the accumulation of VFAs compared to the other treatments. Under these conditions, the biogas production rate reached 0.658 L/g (VS). Furthermore, the addition of Fe₃O₄@BC enhanced both the diversity and abundance of bacteria and archaea. At the genus level, the abundance of Christensenellaceae_R-7_group, Sphaerochaeta, and the archaeal genus Thermovirga was notably increased.

The spatial and temporal variation analysis of land cover classification is important for studying the distribution and transformation of regional land cover changes. The Qilian Mountain National Park (QMNP), an important ecological barrier in northwestern China, has lacked land cover products for long time series. The Landsat images available on the Google Earth Engine (GEE) make it possible to analyze the land cover changes over the past three decades. The purpose of this study was to generate a long time series of datasets of land cover classification based on the method of sample migration in the QMNP in Northwest China. The Landsat 5, 7, and 8 images and field sample data were combined with multiple image features and the random forest algorithm to complete the land cover classification of the QMNP from 1990 to 2020. The results indicate that (1) the method of Jeffries–Matusita (J-M) distance can reduce image feature redundancy and show that elevation and phenological features have good differentiability among land cover types that were easy to mix with feature classes; (2) the spatial distribution of land cover every 10 years between 1990 and 2020 was consistent in the QMNP, and there were obvious differences in land cover from the east to the west part of the QMNP, with a large area of vegetation distribution in Sunan county in the central part and Tianzhu county in the east part of the QMNP; (3) over the past 30 years, forests and grasslands decreased by 62.2 km² and 794.7 km², respectively, while shrubs increased by 442.9 km² in the QMNP. The conversion of bare land to grassland and the interconversion between different vegetation types were the main patterns of land cover changes, and the land cover changes were mainly concentrated in pastoral areas, meaning that human activity was the main factor of land cover changes; and (4) when the samples of 2020 were migrated to 2010, 2000, and 1990, the overall classification accuracies were 89.7%, 88.0%, 86.0%, and 83.9%, respectively. The results show that the vegetation conservation process in the QMNP was closely related to human activities.