Continuous growth with fragile stability in China's forest biomass carbon storage since 2002.

Forest biomass carbon (BC) storage is an important means of mitigating climate change. However, the spatiotemporal patterns and stability of BC growth remain unclear in China. Based on the latest BC maps (2002-2021), we calculated the spatiotemporal dynamics of BC and used resilience indicators to reveal the stability of BC.

Inversion of soil organic carbon content based on the two-point machine learning method.

Soil organic carbon (SOC) is vital for the global carbon cycle and environmentally sustainable development. Meanwhile, the fast, convenient remote sensing technology has become one of the notable means to monitor SOC content. Nowadays, limitations are found in the inversion of SOC content with high-precision and complex spatial relationships based on scarce ground sample points.

Integrated assessment of potentially toxic elements in soil of the Kangdian metallogenic province: A two-point machine learning approach.

The accumulation of potentially toxic elements in soil poses significant risks to ecosystems and human well-being due to their inherent toxicity, widespread presence, and persistence. The Kangdian metallogenic province, famous for its iron-copper deposits, faces soil pollution challenges due to various potentially toxic elements. This study explored a comprehensive approach that combinescombines the spatial prediction by the two-point machine learning method and ecological-health risk assessment to quantitatively assess the comprehensive potential ecological risk index (PERI), the total hazard index (THI) and the total carcinogenic risk (TCR).

Crop residue burning in China (2019-2021): Spatiotemporal patterns, environmental impact, and emission dynamics.

Crop residue burning (CRB) is a major contributor to air pollution in China. Current fire detection methods, however, are limited by either temporal resolution or accuracy, hindering the analysis of CRB's diurnal characteristics. Here we explore the diurnal spatiotemporal patterns and environmental impacts of CRB in China from 2019 to 2021 using the recently released NSMC-Himawari-8 hourly fire product.

Land use/cover and land degradation across the Eurasian steppe: Dynamics, patterns and driving factors.

Despite the ecological and socio-economic importance of Eurasian steppe, the land use/cover change, land degradation and the threats facing this precious ecosystem still have not been comprehensively understood. Taking advantages of the land use/cover change monitoring platform (FROM-GLC Plus), this study developed the annual land use/cover maps during 2000-2022, and the land use/cover change, especially the change of grassland, was further analyzed. The grassland area exhibited a net increase, predominantly transformed from cropland, forest, and bareland, accounting for 17.

Causal inference from cross-sectional earth system data with geographical convergent cross mapping.

Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models have serious limitations when time series data are not available or present insignificant variations, which causes a common challenge for earth system science. Meanwhile, there are few spatial causation models for fully exploring the rich spatial cross-sectional data in Earth systems.

The spatiotemporal variation of PM-O association and its influencing factors across China: Dynamic Simil-Hu lines.

In recent years, PM and O composite airborne pollution has become one of the most severe environment issues in China. To get a better understanding and tackle these problems, we employed multi-year data to explore the spatiotemporal variation of the PM-O relationship in China and investigated its major driving factors. Firstly, interesting patterns were found that named dynamic Simil-Hu lines, which presented a combined effect of natural and anthropogenic influences, were closely related to the spatial patterns of PM-O association across seasons.

Causation inference in complicated atmospheric environment.

Reliable attribution is crucial for understanding various climate change issues. However, complicated inner-interactions between various factors make causation inference in atmospheric environment highly challenging. Taking PM-Meteorology causation, which involves a large number of non-Linear and uncertain interactions between many meteorological factors and PM, as a case, we examined the performance of a series of mainstream statistical models, including Correlation Analysis (CA), Partial Correlation Analysis (PCA), Structural Equation Model (SEM), Convergent Cross Mapping (CCM), Partial Cross Mapping (PCM) and Geographical Detector (GD).

Estimating the impact of ground ozone concentrations on crop yields across China from 2014 to 2018: A multi-model comparison.

Ground level ozone exerts a strong impact on crop yields, yet how to properly quantify ozone induced yield losses in China remains challenging. To this end, we employed a series of O-crop models to estimate ozone induced yield losses in China from 2014 to 2018. The outputs from all models suggested that the total Relative Yield Losses (RYL) of wheat in China from 2014 to 2018 was 18.

Influence of meteorological conditions on PM concentrations across China: A review of methodology and mechanism.

Air pollution over China has attracted wide interest from public and academic community. PM is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM concentrations are essential to understand the variability of PM and seek methods to control PM.

A spatiotemporal interpolation method for the assessment of pollutant concentrations in the Yangtze River estuary and adjacent areas from 2004 to 2013.

Nitrogen is one of the most significant pollutants in the Yangtze River estuary (YRE), China. Reliable estimation of nitrogen concentration in the water is crucial for assessment of the water quality of the estuary. Because ocean fronts exist in the YRE, which divide water masses into different regions, it is necessary to account for the heterogeneity of the water surface when predicting nitrogen concentrations.

Understanding the Influence of Crop Residue Burning on PM and PM Concentrations in China from 2013 to 2017 Using MODIS Data.

In recent years, particulate matter (PM) pollution has increasingly affected public life and health. Therefore, crop residue burning, as a significant source of PM pollution in China, should be effectively controlled. This study attempts to understand variations and characteristics of PM and PM concentrations and discuss correlations between the variation of PM concentrations and crop residue burning using ground observation and Moderate Resolution Imaging Spectroradiometer (MODIS) data.

Spatio-temporal variations of PM concentrations and the evaluation of emission reduction measures during two red air pollution alerts in Beijing.

To effectively improve air quality during pollution episodes, Beijing released two red alerts in 2015. Here we examined spatio-temporal variations of PM concentrations during two alerts based on multiple data sources. Results suggested that PM concentrations varied significantly across Beijing.

Detecting the causality influence of individual meteorological factors on local PM concentration in the Jing-Jin-Ji region.

Due to complicated interactions in the atmospheric environment, quantifying the influence of individual meteorological factors on local PM concentration remains challenging. The Beijing-Tianjin-Hebei (short for Jing-Jin-Ji) region is infamous for its serious air pollution. To improve regional air quality, characteristics and meteorological driving forces for PM concentration should be better understood.

State-of-the-Art: DTM Generation Using Airborne LIDAR Data.

Digital terrain model (DTM) generation is the fundamental application of airborne Lidar data. In past decades, a large body of studies has been conducted to present and experiment a variety of DTM generation methods. Although great progress has been made, DTM generation, especially DTM generation in specific terrain situations, remains challenging.

Assessing visual green effects of individual urban trees using airborne Lidar data.

Urban trees benefit people's daily life in terms of air quality, local climate, recreation and aesthetics. Among these functions, a growing number of studies have been conducted to understand the relationship between residents' preference towards local environments and visual green effects of urban greenery. However, except for on-site photography, there are few quantitative methods to calculate green visibility, especially tree green visibility, from viewers' perspectives.

Global mean estimation using a self-organizing dual-zoning method for preferential sampling.

Giving an appropriate weight to each sampling point is essential to global mean estimation. The objective of this paper was to develop a global mean estimation method with preferential samples. The procedure for this estimation method was to first zone the study area based on self-organizing dual-zoning method and then to estimate the mean according to stratified sampling method.