Studying heavy metal adsorption on soil is important for understanding the fate of heavy metals and properly assessing the related environmental risks. Existing experimental methods and traditional models for quantifying adsorption, however, are time-consuming and ineffective. In this study, we developed machine learning models for the soil adsorption of six heavy metals (Cd(II), Cr(VI), Cu(II), Pb(II), Ni(II), and Zn(II)) using 4420 data points (1105 soils) extracted from 150 journal articles. After a comprehensive comparison, our results showed that the gradient boosting decision tree had the best performance for a combined model based on all the data. The Shapley additive explanation method was used to identify the feature importance and the effects of these features on the adsorption, based on which six independent models were developed for the six metals to achieve better model performance than the combined model. Using these independent models, the global distribution of heavy metal adsorption capacities on soils was predicted with known soil properties. Reversed models, including one combined model for all the six metals and six independent models, were also built using the same data sets to predict the heavy metal concentration in water when the adsorbed amount is known for a soil/sediment.

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Objectives COVID-19 pandemic has already had a tremendous impact on the process of human society; the survival of mankind and the healthy living environment deterioration with the influence will last for many years. This meta-analysis aims to assess the risk of COVID-19 in patients with rheumatic diseases. Methods PubMed, Web of Science, Embase, China National Knowledge Infrastructure (CNKI), and Chinese Biomedical Database (CBM) were systematically searched with no language restriction up to July 5, 2021. The pooled rates were synthesized by fixed effect model or random effect model depending on heterogeneity. Results A total of 83 articles were included in this meta-analysis. The incidence of COVID-19 in patient with rheumatic diseases was 0.0190 (95% CI : 0.0136-0.0252), and the hospitalization rate, intensive care unit admission rate, mechanical ventilation rate, and case fatality rate of patients with rheumatic diseases infected with COVID-19 were 0.4396 (95% CI : 0.3899-0.4898), 0.0635 (95% CI : 0.0453-0.0836), 0.0461 (95% CI : 0.0330-0.0609), and 0.0346 (95% CI : 0.0218-0.0493), respectively. Conclusions Our research shows that patients with rheumatic diseases have great risk of COVID-19. Differences in COVID-19 incidence, hospitalization rates, and mortality rates in regions were statistically significant. We still need to pay attention to the risk of COVID-19 in patients with rheumatic diseases. Key Points • Although the risk of COVID-19 in patients with rheumatic diseases has been discussed in previous meta-analysis, their research directions were inconsistent, and few studies focus on prevalence or serious outcomes of COVID-19 in patient with rheumatic diseases, while the quality of these articles was variable. • The incidence of COVID-19 and serious clinical outcomes in patients with rheumatic diseases were still high along with differential risks in most regions. • The use of glucocorticoids and conventional synthetic disease-modifying antirheumatic drugs did not affect the hospitalization rate and mortality in rheumatism patients with COVID-19. Supplementary Information The online version contains supplementary material available at 10.1007/s10067-022-06087-1.