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Formaldehyde (HCHO) is a toxic and hazardous air pollutant that widely exists in atmosphere. Insufficient spatial and temporal coverage of surface HCHO measurements is limiting studies on surface HCHO-related air quality management and health risk assessment. This study develops a method to derive global ground-level HCHO concentrations from satellite-based tropospheric HCHO columns using TM5-simulated surface-to-column conversion factor with coarse spatial resolution. The method improves the factor more representative in finer grids by constraining TM5-simulated vertical profile shapes with satellite HCHO columns. The surface HCHO concentrations derived by the Ozone Mapping and Profiler Suite (OMPS) show good correlation with in situ HCHO measurements (R = 0.59) from the U.S. Environmental Protection Agency surface network. We investigated how surface HCHO relates to urbanization and population aggregation over seven regions with high HCHO pollution. The results show urban HCHO increases as a power function with population size in China, India, and West Asia. HCHO concentrations in rural aeras also present strong log–log relationship with population aggregation in China, India, the United States, and Europe. Moreover, OMPS-derived ground-level HCHO concentrations were used to estimate global cancer burden caused by long-term outdoor HCHO exposure. The results show that up to 418188 more people worldwide will develop this cancer during the human life cycle. The global cancer burden is mainly from the South-East Asia region (33.11 %) and the Western Pacific region (22.95 %). This cancer occurrence in India and China is ranked 1st and 2nd in the world due to the large population size and serious HCHO pollution. Besides, global surface HCHO concentrations and cancer burden derived from the Environmental Trace Gases Monitoring Instrument which is China's first hyperspectral space-based spectrometer are found similar patterns with that from OMPS. Our results provide new insight into the impact of population urbanization on HCHO pollution and global outdoor HCHO-caused health risks. [ABSTRACT FROM AUTHOR]

[Display omitted] • Calculate ecological quality using the Ensemble Learning method. • Gridding the regional space and proposing an Ecological Information State Layer (EISL). • Semi-supervised learning has better portability in ecological computing. • Analyze the weight of ecological factor characteristics to provide recommendations for ecological conservation. Habitat quality is an indicator of the ecological evolution of a region, and evaluating habitat quality utilizing Machine Learning approaches can reflect the ecological status of a region more objectively. Gridded statistical ecological factors in the study region were utilized to build the Ecological Information State Layer (EISL) in the grid space. Calculate the Environmental Quality Index (EQI) of the sampled grid to evaluate the performance index of the models on the sampled grid. The model with the optimal performance index is selected for the task of habitat quality classification in the research region, and then especially decision-making advice is offered by inverting the degree of influence of ecological elements on habitat quality. The results show that: (1) The Semi-supervised Ensemble Learning model (Tri-training) Accuracy, Kappa coefficient, and F1-score are 0.93, 0.89, and 0.92, respectively, as the optimal model for the habitat quality classification task. (2) Based on the results of the ecological categories calculation, the current ecosystem quality of the southwestern section of the Daxia River Basin is maintained as a positive indicator. the ecosystem quality inside the living space centered on the cities of Linxia and Hezuo shows negative indicator changes. (3) The calculation results of the inverse importance of ecological factors show that vegetation index and human population density are the key factors impacting the habitat quality of the Daxia River Basin. In the ecological management of Daxia River Basin, the degree of spatial aggregation of people's settlements should be reduced while preserving and expanding vegetation cover. Using Tri-training's comprehensive analysis of multiple ecological factors, regional habitat quality can be accurately assessed. [ABSTRACT FROM AUTHOR]