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). The proportions of copper (Cu), cadmium (Cd), manganese (Mn), lead (Pb), zinc (Zn), and arsenic (As) concentrations exceeding the risk screening values (RSVs) were 15.03%, 5.1%, 3.72%, 1.24%, 1.1%, and 0.13%, respectively, across the 725 collected samples. Spatial prediction revealed elevated levels of As, Cd, Cu, Pb, Zn, mercury (Hg), and Mn near the mining sites. Potentially toxic elements exert a slight impact on soil, some regions exhibit moderate to significant ecological risk, particularly in the southwest. Children face higher non-carcinogenic and carcinogenic health risks compared to adults. Mercury poses the highest ecological risk, while chromium (Cr) poses the greatest health hazard for all populations. Oral ingestion represents the highest non-oncogenic and oncogenic risks in all age groups. Adults faced acceptable non-carcinogenic risks. Children in the southwest region confront higher health risks, both non-carcinogenic and carcinogenic, from mining activities. Urgent measures are vital to mitigate Hg and Cr contamination while promoting handwashing practices is essential to minimize health risks.

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. This research introduces the general principles of DTM generation and reviews diverse mainstream DTM generation methods. In accordance with the filtering strategy, these methods are classified into six categories: surface-based adjustment; morphology-based filtering, triangulated irregular network (TIN)-based refinement, segmentation and classification, statistical analysis and multi-scale comparison. Typical methods for each category are briefly introduced and the merits and limitations of each category are discussed accordingly. Despite different categories of filtering strategies, these DTM generation methods present similar difficulties when implemented in sharply changing terrain, areas with dense non-ground features and complicated landscapes. This paper suggests that the fusion of multi-sources and integration of different methods can be effective ways for improving the performance of DTM generation.

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. In this method, spreading of points in both feature and geographical space is considered. The method is tested in a case study on the metal Mn concentrations in Jilin provinces of China. Six sample patterns are selected to estimate the global mean and compared with the global mean calculated by direct arithmetic mean method, polygon method, and cell method. The results show that the proposed method produces more accurate and stable mean estimates under different feature deviation index (FDI) values and sample sizes. The relative errors of the global mean calculated by the proposed method are from 0.14 to 1.47 % and they are the largest (4.83-8.84 %) by direct arithmetic mean method. At the same time, the mean results calculated by the other three methods are sensitive to the FDI values and sample sizes.

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. Our analysis identifies a decreasing directionality in CRB distribution in the Northeast and a notable southward shift of the CRB center, especially in winter, averaging an annual southward movement of 7.5°. Additionally, we observe a pronounced skewed distribution in daily CRB, predominantly between 17:00 and 20:00. Notably, nighttime CRB in China for the years 2019, 2020, and 2021 accounted for 51.9%, 48.5%, and 38.0% respectively, underscoring its significant environmental impact. The study further quantifies the hourly emissions from CRB in China over this period, with total emissions of CO, PM10, and PM2.5 amounting to 12,236, 2,530, and 2,258 Gg, respectively. Our findings also reveal variable lag effects of CRB on regional air quality and pollutants across different seasons, with the strongest impacts in spring and more immediate effects in late autumn. This research provides valuable insights for the regulation and control of diurnal CRB before and after large-scale agricultural activities in China, as well as the associated haze and other pollution weather conditions it causes.

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. It is restrained by the spatial difference in the relationship between SOC content and remote sensing spectra due to the problem of different spectra for the same substance and the influence of topographic and environment (e.g. vegetation and climate). In this regard, the two-point machine learning (TPML) method, which can overcome above problems and deal with complex spatial heterogeneity of relationships between SOC and remote sensing spectra, is used to invert the SOC content in Hailun County, Heilongjiang Province, combined with derived variables from Sentinel-1, Sentinel-2, topography and environment. Based on 10-fold cross-validation and t-test, results indicate that the TPML method boasts the highest inversion accuracy, followed by random forest, gradient boosting regression tree, partial least squares regression and support vector machine. The average r, MAE, RMSE, and RPD of TPML are 0.854, 0.384 %, 0.558 %, and 1.918. Further, the TPML method has been proven to be equal to evaluating the uncertainty of inversion results, by comparing the actual and theoretical error of the inversion result in one subset. The spatial inversion result of SOC content with 10 m resolution by TPML is smoother and has more real details than other models, which are consistent with the distribution of SOC content in different land use types. This study provides both theoretical and technical guidance for using TPML method combined with spectral information of remote sensing to predict soil attributes and offer accurate uncertainty estimation, thereby opening up the opportunity for low-cost, high-precision, and large-scale SOC inversion.

The spatiotemporal variation of PM2.5-O3 association and its influencing factors across China: Dynamic Simil-Hu lines.

In recent years, PM2.5 and O3 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 PM2.5-O3 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 PM2.5-O3 association across seasons. Furthermore, regions with lower altitudes, higher humidity, higher atmospheric pressure, higher temperature, fewer sunshine hours, more accumulated precipitation, denser population and higher GDP often show positive PM2.5-O3 associations, regardless of seasonal variations. Amongst these factors, humidity, temperature and precipitation were dominant factors. This research suggests that the collaborative governance of composite atmospheric pollution should be implemented dynamically, in consideration of geographical locations, meteorological conditions and socioeconomic conditions.

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 generalized embedding theorem proves that observations can be combined together to construct the state space of the dynamic system, and if two variables are from the same dynamic system, they are causally linked. Inspired by this, here we show a Geographical Convergent Cross Mapping (GCCM) model for spatial causal inference with spatial cross-sectional data-based cross-mapping prediction in reconstructed state space. Three typical cases, where clearly existing causations cannot be measured through temporal models, demonstrate that GCCM could detect weak-moderate causations when the correlation is not significant. When the coupling between two variables is significant and strong, GCCM is advantageous in identifying the primary causation direction and better revealing the bidirectional asymmetric causation, overcoming the mirroring effect.

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 PM2.5-Meteorology causation, which involves a large number of non-Linear and uncertain interactions between many meteorological factors and PM2.5, 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). From a coarse perspective, the Top 3 major meteorological factors for PM2.5 in 190 cities across China extracted using different models were generally consistent. From a strict perspective, the extracted dominant meteorological factor for PM2.5 demonstrated large model variations and shared a limited consistence. Such models as SEM and PCM, which are capable of further separating direct and indirect causation in simple systems, performed poorly to identify the direct and indirect PM2.5-Meteorology causation. The notable inconsistence denied the feasibility of employing multiple models for better causation inference in atmospheric environment. Instead, the sole use of CCM, which is advantageous in dealing with non-linear causation and removing disturbing factors, is a preferable strategy for causation inference in complicated ecosystems. Meanwhile, given the multi-direction, uncertain interactions between many variables, we should be more cautious and less ambitious on the separation of direct and indirect causation. For better causation inference in the complicated atmospheric environment, the combination of statistical models and atmospheric models, and the further exploration of Deep Neural Network can be promising strategies.

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 O3-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 18.4%-49.3% and the total RYL of rice was 6.2%-52.9%. Consequently, the total Crop Production Losses (CPL) of wheat and rice could reach 63.9-130.4 and 28.3-35.4 million tons, and the corresponding Total Economic Losses (TEL) could reach 20.5-44.7 and 11.0-15.3 billion dollars, stressing the great importance and urgency of national ozone management. Meanwhile, the estimation outputs highlighted the large variations between different regional O3-crop models when applying to large scales. Instead of applying one unified O3-crop models to all regions across China, we also explored the strategy of employing specific O3-crop models in corresponding (and neighboring) regions to estimate ozone induced yield loss in China. The comparison of two strategies suggested that the mean value from multiple models may still present an inconsistent over/underestimation trend for different crops. Therefore, it is a preferable strategy to employ corresponding O3-crop models in different regions for estimating the national crop losses caused by ozone pollution. However, the severe lack of regional O3-crop models in most regions across China makes a robust estimation of national yield losses highly challenging. Given the large variations between O3-crop interactions across regions, a systematic framework with massive regional O3-crop models should be properly designed and implemented.

Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism.

Air pollution over China has attracted wide interest from public and academic community. PM2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM2.5 concentrations are essential to understand the variability of PM2.5 and seek methods to control PM2.5. Since 2013, the measurement of PM2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM2.5 concentrations. We start with an introduction of general meteorological conditions and PM2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM2.5 concentrations. Next, major methods used to quantify meteorological influences on PM2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM2.5. The feedback effects of PM2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM2.5 pollution are made finally.

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. A new geostatistical method, called spatiotemporal point mean of surface with non-homogeneity (ST-PMSN), is proposed to model the non-stationary spatiotemporal random process of nitrogen concentrations between 2004 and 2013 in the YRE. The method considers the spatiotemporal correlation of surface water nitrogen and uses information from both sides of a boundary for heterogeneous water masses. Comparing with several other interpolating methods, including spatial ordinary kriging (OK), stratified ordinary kriging (SOK), point mean of surface with non-homogeneity (P-MSN), spatiotemporal ordinary kriging (STK), and stratified spatiotemporal ordinary kriging (SSTK), the cross-validation results show that ST-PMSN has the highest accuracy, followed by SSTK, STK, P-MSN, SOK, and OK in descending order. ST-PMSN is therefore demonstrated to be effective in estimating the nitrogen pollutant concentrations in a stratified estuary. According to interpolated nitrogen concentrations in the YRE, water quality has generally deteriorated-with fluctuations-from 2004 to 2013. The average annual reduction in area of water quality of Grades I and II from 2004 to 2013 was 1.10%. At the same time, the average annual increase in area of water quality of Grades III and IV was 0.89% and that of Grade V was 0.21%. The results of this study provide a new and more accurate interpolating method for assessing the pollutant concentration in the marine and offers guidance for more precise classification of water quality in the YRE.

Understanding the Influence of Crop Residue Burning on PM2.5 and PM10 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 PM10 and PM2.5 concentrations and discuss correlations between the variation of PM concentrations and crop residue burning using ground observation and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The results revealed that the overall PM concentration in China from 2013 to 2017 was in a downward tendency with regional variations. Correlation analysis demonstrated that the PM10 concentration was more closely related to crop residue burning than the PM2.5 concentration. From a spatial perspective, the strongest correlation between PM concentration and crop residue burning existed in Northeast China (NEC). From a temporal perspective, the strongest correlation usually appeared in autumn for most regions. The total amount of crop residue burning spots in autumn was relatively large, and NEC was the region with the most intense crop residue burning in China. We compared the correlation between PM concentrations and crop residue burning at inter-annual and seasonal scales, and during burning-concentrated periods. We found that correlations between PM concentrations and crop residue burning increased significantly with the narrowing temporal scales and was the strongest during burning-concentrated periods, indicating that intense crop residue burning leads to instant deterioration of PM concentrations. The methodology and findings from this study provide meaningful reference for better understanding the influence of crop residue burning on PM pollution across China.

Detecting the causality influence of individual meteorological factors on local PM2.5 concentration in the Jing-Jin-Ji region.

Due to complicated interactions in the atmospheric environment, quantifying the influence of individual meteorological factors on local PM2.5 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 PM2.5 concentration should be better understood. This research examined seasonal variations of PM2.5 concentration within the Jing-Jin-Ji region and extracted meteorological factors strongly correlated with local PM2.5 concentration. Following this, a convergent cross mapping (CCM) method was employed to quantify the causality influence of individual meteorological factors on PM2.5 concentration. The results proved that the CCM method was more likely to detect mirage correlations and reveal quantitative influences of individual meteorological factors on PM2.5 concentration. For the Jing-Jin-Ji region, the higher PM2.5 concentration, the stronger influences meteorological factors exert on PM2.5 concentration. Furthermore, this research suggests that individual meteorological factors can influence local PM2.5 concentration indirectly by interacting with other meteorological factors. Due to the significant influence of local meteorology on PM2.5 concentration, more emphasis should be given on employing meteorological means for improving local air quality.

Spatio-temporal variations of PM2.5 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 PM2.5 concentrations during two alerts based on multiple data sources. Results suggested that PM2.5 concentrations varied significantly across Beijing. PM2.5 concentrations in southern parts of Beijing were higher than those in northern areas during both alerts. In addition to unfavorable meteorological conditions, coal combustion, especially incomplete coal combustion contributed significantly to the high PM2.5 concentrations. Through the CAMx model, we evaluated the effects of emission-reduction measures on PM2.5 concentrations. Through simulation, emergency measures cut down 10% - 30% of the total emissions and decreased the peaks of PM2.5 concentrations by about 10-20% during two alerts. We further examined the scenario if emergency measures were implemented several days earlier than the start of red alerts. The results proved that the implementation of emission reduction measures 1-2 days before red alerts could lower the peak of PM2.5 concentrations significantly. Given the difficulty of precisely predicting the duration of heavy pollution episodes and the fact that successive heavy pollution episodes may return after red alerts, emergency measures should also be implemented one or two days after the red alerts.

Assessment of pollutant mean concentrations in the Yangtze estuary based on MSN theory.

Reliable assessment of water quality is a critical issue for estuaries. Nutrient concentrations show significant spatial distinctions between areas under the influence of fresh-sea water interaction and anthropogenic effects. For this situation, given the limitations of general mean estimation approaches, a new method for surfaces with non-homogeneity (MSN) was applied to obtain optimized linear unbiased estimations of the mean nutrient concentrations in the study area in the Yangtze estuary from 2011 to 2013. Other mean estimation methods, including block Kriging (BK), simple random sampling (SS) and stratified sampling (ST) inference, were applied simultaneously for comparison. Their performance was evaluated by estimation error. The results show that MSN had the highest accuracy, while SS had the highest estimation error. ST and BK were intermediate in terms of their performance. Thus, MSN is an appropriate method that can be adopted to reduce the uncertainty of mean pollutant estimation in estuaries.

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. To fill this research gap, a case study was conducted in the city of Cambridge, which has a diversity of tree species, sizes and shapes. Firstly, a photograph-based survey was conducted to approximate the actual value of visual green effects of individual urban trees. In addition, small footprint airborne Lidar (Light detection and ranging) data was employed to measure the size and shape of individual trees. Next, correlations between visual tree green effects and tree structural parameters were examined. Through experiments and gradual refinement, a regression model with satisfactory R2 and limited large errors is proposed. Considering the diversity of sample trees and the result of cross-validation, this model has the potential to be applied to other study sites. This research provides urban planners and decision makers with an innovative method to analyse and evaluate landscape patterns in terms of tree greenness.