As “kidneys of the earth”, wetlands play an important role in ameliorating weather conditions, flood storage, and the control and reduction of environmental pollution. With the development of local economies, the wetlands in both the Amazon and Yangtze River Basins have been affected and threatened by human activities, such as urban expansion, reclamation of land from lakes, land degradation, and large-scale agricultural development. It is necessary and important to develop a wetland ecosystem health evaluation model and to quantitatively evaluate the wetland ecosystem health in these two basins. In this paper, GlobeLand30 land cover maps and socio-economic and climate data from 2000 and 2010 were adopted to assess the wetland ecosystem health of the Yangtze and Amazon River Basins on the basis of a pressure-state-response (PSR) model. A total of 13 indicators were selected to build the wetland health assessment system. Weights of these indicators and PSR model components, as well as normalized wetland health scores, were assigned and calculated based on the analytic hierarchy process method. The results showed that from 2000 to 2010, the value of the mean wetland ecosystem health index in the Yangtze River Basin decreased from 0.482 to 0.481, while it increased from 0.582 to 0.593 in the Amazon River Basin. This indicated that the average status of wetland ecosystem health in the Amazon River Basin is better than that in the Yangtze River Basin, and that wetland health improved over time in the Amazon River Basin but worsened in the Yangtze River Basin.

Formaldehyde (HCHO) is one of the most important carcinogenic air contaminants in outdoor air. However, the lack of monitoring of the global surface concentration of HCHO is currently hindering research on outdoor HCHO pollution. Traditional methods are either restricted to small areas or, for research on a global scale, too data-demanding. To alleviate this issue, we adopted neural networks to estimate the 2019 global surface HCHO concentration with confidence intervals, utilizing HCHO vertical column density data from TROPOMI, and in-situ data from HAPs (harmful air pollutants) monitoring networks and the ATom mission. Our results show that the global surface HCHO average concentration is 2.30 μg/m³. Furthermore, in terms of regions, the concentrations in the Amazon Basin, Northern China, South-east Asia, the Bay of Bengal, and Central and Western Africa are among the highest. The results from our study provide the first dataset on global surface HCHO concentration. In addition, the derived confidence intervals of surface HCHO concentration add an extra layer of confidence to our results. As a pioneering work in adopting confidence interval estimation to AI-driven atmospheric pollutant research and the first global HCHO surface distribution dataset, our paper paves the way for rigorous study of global ambient HCHO health risk and economic loss, thus providing a basis for pollution control policies worldwide.

Optimization of the locations of air quality monitoring stations has great importance in providing high-quality data for regional air pollution monitoring. To assess the representativeness of the locations of the current air quality monitoring stations, we propose a new method based on satellite observations by applying the stratified sampling approach. Unlike the traditional method, which relies on the simulated spatial distribution of air pollutants from dispersion models, we obtained the sampling population through observations from remote sensing. As a first step, the spatial distribution of aggregated air quality was obtained based on ground concentrations of particulate matter (aerodynamic diameters of less than 10 μm, PM₁₀), fine particulate matter (aerodynamic diameters of less than 2.5 μm, PM_2.5), nitrogen dioxide (NO₂), and sulphur dioxide (SO₂) derived from satellite observations. Second, the representativeness of locations of air quality monitoring stations was assessed using the stratified sampling method. The results demonstrated that air quality monitoring stations in Beijing-Tianjin-Hebei were clustered in areas with heavily polluted air, whereas the number of air quality monitoring stations was insufficient in areas with higher air quality. After optimization, the minimum relative error was only 6.77%. It is indicated that combing remote-sensing data with the stratified sampling approach has great potential in assessing the spatial representativeness of air quality monitoring stations.

The Chinese real estate industry has experienced rapid growth since China’s economic reform. Along with a booming industry, a third of purchased lands were left undeveloped in the last decade. Knowledge of real estate development time lags between land being purchased and property being occupied can enable policymakers to produce more effective policies and regulations to guide the real estate industry and sustain economic development and social welfare. This paper presents an innovative method to estimate provincial land development time lags in China using DMSP/OLS NTL imagery and real estate statistical data. The results showed that real estate development time lag was common in China during 2000–2010. More than half of the study sites showed development time lags of three years or longer. An Increment of Developed Pixels (IDP) index was established to outline yearly land development completions in China between 2000 and 2010. A Comprehensive Real Estate Price Index (CREPI) was created to explore the causes of the time lags. A strong and positive correlation was found between the real estate development time lags and CREPI values (with r = 0.619, n = 31, p < 0.0005). The results indicated that the land development time lag during the study period was positively correlated to the activity of the local real estate market, the price trend of land and housing properties, and the local economic situation. The results also proved that with the support of statistical data the DMSP/OLS NTL image could offer an economically efficient and reliable solution to estimate the time lag of real estate development.

Increasing emissions of ambient pollutants have caused considerable air pollution and negative health impact for human in various regions of China over the past decade. The resulting premature mortality and excessive morbidity caused huge human economic losses to the entire society. To identify the differences of health economic losses in various regions of China and help decision-making on targeting pollutants control, spatial assessment of health economic losses due to ambient pollutants in China is indispensable. In this study, to better represent the spatial variability, the satellite-based retrievals of the concentrations of various pollutants (PM₁₀, PM_2.5, O₃, NO₂, SO₂ and CO) for the time period from 2007 to 2017 in China are used instead of using in-situ data. Population raster data were applied together with exposure-response function to calculate the spatial distribution of health impact and then the health impact is further quantified by using amended human capital (AHC) approach. The results which presented in a spatial resolution of 0.25° × 0.25°, show the signification contribution from the spatial assessment to revealing the spatial distribution and variance of health economic losses in various regions of China. Spatial assessment of overall health economic losses is different from conventional result due to more detail spatial information. This spatial assessment approach also provides an alternative method for losses measurement in other fields.

Formaldehyde (HCHO) is one of the most important carcinogenic air contaminants. However, the lack of global surface concentration of HCHO monitoring is currently hindering research on outdoor HCHO pollution. Traditional methods are either restricted to small areas or data- demanding for a global scale of research. To alleviate this issue, we adopted neural networks to estimate surface HCHO concentration with confidence intervals in 2019, where HCHO vertical column density data from TROPOMI, in-situ data from HAPs (harmful air pollutants) monitoring network and ATom mission are utilized. Our result shows that the global surface HCHO average concentration is 2.30 μg/m3. Furthermore, in terms of regions, the concentration in Amazon Basin, Northern China, South-east Asia, Bay of Bengal, Central and Western Africa are among the highest. The results from our study provides a first dataset of the global surface HCHO concentration. In addition, the derived confidence interval of surface HCHO concentration adds an extra layer for the confidence to our results. As a pioneer work in adopting confidence interval estimation into AI-driven atmospheric pollutant research and the first global HCHO surface distribution dataset, our paper will pave the way for the rigorous study on global ambient HCHO health risk and economic loss, thus providing a basis for pollutant controlling policies worldwide.