In 2020, lockdown control measures were implemented to prevent a novel coronavirus disease 19 (COVID-19) pandemic in many places of the world, which largely reduced human activities. Here, we detect changes in weekly cycles of PM_2.5, NO₂, SO₂, CO and O₃concentrations in 2020 compared to 2018 and 2019 using the observed data at 32 stations in Beijing. Distinct weekly cycles of annual average PM_2.5, NO₂, SO₂and CO concentrations existed in 2018, while the weekend effects changed in 2020. In addition, the weekly cycle magnitudes of PM_2.5, NO₂, SO₂, and O₃concentrations in 2020 decreased by 29.60–69.26% compared to 2018, and 4.49–47.21% compared to 2019. We propose that the changing weekend effects and diminishing weekly cycle magnitudes may be tied to the COVID-19 lockdown controls, which changed human working and lifestyle cycles and reduced anthropogenic emissions of air pollutants on weekends more than weekdays.

The spatial pattern of color patches plays a crucial role in affecting the visual quality of peri-urban forests dominated by Cotinus coggygria var. cinerea Engl. in autumn. The impact mechanism has been studied to facilitate algorithm-based automatic visual quality estimation. The color patterns of 120 photographs were calculated after color quantization and automatic color substitution. The scenic beauty of the forest was estimated by 698 respondents. Multiple correlations between visual quality and color pattern metrics were explored with stepwise regression. Principal component analysis (PCA) was also employed to investigate the impact mechanism of color patterns on visual quality. Number of patches (NP), largest patch index (LPI), mean patch area (AREA_MN), patch size standard deviation (AREA_SD), and Shannon’s evenness index (SHEI) were the main factors affecting the visual quality of the Cotinus coggygria forest. AREA_MN correlated positively with visual quality, while NP, LPI, AREA_SD, and SHEI correlated negatively. Moreover, AREA_SD had the most significant impact on the visual quality of the landscape, while SHEI, LPI, and AREA_MN had the second-highest impact. The evenness and the size of color patches significantly affected the visual quality of the forest landscapes. Balancing the diversity and evenness of color patches plays a decisive role in creating a forest landscape with high visual quality.