Global inequalities in non-carcinogenic health risks associated with drinking arsenic-contaminated groundwater - Ruohan Wu; Qinchuan Xin; Yin Yang; Ying Sun; Xuewen Zhou; Yongjian Ruan; Kun Xiao; Taiyuan Chen; Jianxiu Qiu (Journal of Hazardous Materials, 2026)

MnO₂ polymorphs direct the selective degradation of fluoroquinolone antibiotics: Insights from adsorption, oxidation, and O₂ activation mechanisms - Ruohan Zhao; Enhui Wu; Xiangyu Zhu; Xinfei Ge; Xin Xiao; Baoliang Chen (Chemical Engineering Journal, 2026)

Targeting programmed cell death: a novel therapeutic paradigm for cancer based on mode-of-death classification - Xi Qiao; Ting Ye; Tinggai Wu; Xu Han; Tiantian Sun; Haiqiang Wang; Junru Jia; Yuanyuan Dai; Ruohan Yun; Shuo Jiang; Yuming Qin; Zhenzhen Liu; et al. (Apoptosis, 2026)

High temperature favors sedimentary organic carbon fixation potential via chemoautotrophy and iron fraction transformation in urban artificial lakes - Ruohan Huang; Junru Guo; Kaiming Peng; Chen Cai; Xiangfeng Huang; Qiaofeng Wu; Jia Liu (Water Research, 2026)

Host genotype-enriched rhizosphere microbes enhance pea (Pisum sativum L.) yield - Qinwen Yang; Qiong Liu; Ke Dong; Kaile Zhang; Chuanfa Wu; Ruohan Yin; Linying Xu; Zhijuan Feng; Tao Yang; Tida Ge (Plant and Soil, 2026)

Quantifying key design factors for thermal comfort in underground space through global sensitivity analysis and machine learning - Shisheng Chen; Nyuk Hien Wong; Chao Cen; Ruohan Xu; Lei Xu; Zhenjiang Shen; Zhigang Wu; Jiayan Fu; Zhongqi Yu (Tunnelling and Underground Space Technology, 2026)

Rambutan-inspired tri-layer architecture with regulated strain and lithium transport for high-capacity and stable lithium storage - Huan Du; Ganggang Ma; Ziqing Yin; Shaoqing Rao; Sheng Lin; Tianyi Zhang; Jingke Ren; Yucai Wu; Wei Zhang; Ruohan Yu; Dongyuan Zhao; Wei Li; et al. (Science Bulletin, 2026)

One-step molding to prepare durable dual superlyophobic composites for non-loss mass transfer, immiscible solvent separation, and CO2 bubble removal - Chang-Lian Xu; Xingyue Wu; Yuqing Song; Jialin Liu; Ruohan Xiong; Xiaoxun Xu; Guiyin Wang; Zhanbiao Yang; Zhang Cheng; Shirong Zhang; Xin Qi; Guochun Lv; et al. (Journal of Environmental Chemical Engineering, 2025)

Development of system analysis code and its application in transient simulation of supercritical CO2 Brayton cycle direct cooled reactors - Haijie Wu; Minyun Liu; Shenghui Liu; Jiatao Meng; Shuangqing Chen; Ruohan Zheng; Yanping Huang; Houjun Gong; Xiaoliang Zhu; TianYang Xing; Mudi Jiang (Annals of Nuclear Energy, 2025)

The combined effects of microplastics and their additives on mangrove system: From the sinks to the sources of carbon - Junru Guo; Mingqing Yang; Ruohan Huang; Junyi Yu; Kaiming Peng; Chen Cai; Xiangfeng Huang; Qiaofeng Wu; Jia Liu (Science of The Total Environment, 2025)

Objective Cost-Informed Cutoff Criteria Improve the Utility of Machine Learning Models of Environmental Hazards: A Case Study of Groundwater Arsenic Distribution in India - Ruohan Wu; David A. Polya (Exposure and Health, 2024)
Although there are an increasing number of artificial intelligence/machine learning models of various hazardous chemicals (e.g. As, F, U, NO 3 − , radon) in environmental media (e.g. groundwater, soil), these most commonly use arbitrarily selected cutoff criteria to balance model specificity and sensitivity. This results in models of hazard distribution that, whilst often of considerable interest and utility, are not designed to optimize cost benefits of the mitigation of those hazards. In this case study, building upon recent machine learning modelling of the geographical distribution of groundwater arsenic in India, we show that the use of objective cost-informed criteria not only results in (i) different cutoff values for the classification of areas as of high or low groundwater arsenic hazard but also, more importantly, (ii) a reduction of overall potential (mitigation + testing + health impacts) costs. Further, we show that the change in optimal cutoff values and the reduction in overall costs vary from state to state depending upon locally specific classification-dependent costs, the prevalence of high arsenic groundwaters, the heterogeneity of the distribution of those high arsenic groundwaters, and the extent to which inhabitants are exposed to the hazard. It follows more generally that using cost-optimized criteria will result in different, more objective, and more cost-relevant appropriate balances being made between specificity and sensitivity in modelling environmental hazard distribution in different regions. This indicates also the utility of developing machine learning models at an appropriate local (e.g. country, state, district) scale rather than more global scales in order to better inform local-scale mitigation strategies.

Icaritin activates p53 and inhibits aerobic glycolysis in liver cancer cells - Xiangyang Zhou; Di Wu; Tian Mi; Ruohan Li; Tao Guo; Wenjuan Li (Chemico-Biological Interactions, 2024)

Characterization of carbonaceous particles by single particle aerosol mass spectrometer in the urban area of Chengdu, China - Junke Zhang; Rui Wang; Chunying Chen; Yunfei Su; Luyao Chen; Wei Zhang; Yingwei Xi; Yangchun Yu; Ruiyan Pu; Minghui Lu; Ruohan Wu; Xuhui Shen (Environmental Science and Pollution Research, 2024)

Calcium alginate-nZVI-biochar for removal of Pb/Zn/Cd in water: Insights into governing mechanisms and performance - Ruohan Zhao; Bing Wang; Pan Wu; Qianwei Feng; Miao Chen; Xueyang Zhang; Shengsen Wang (Science of The Total Environment, 2023)

Geostatistical model of the spatial distribution of arsenic in groundwaters in Gujarat State, India - Ruohan Wu; Joel Podgorski; Michael Berg; David A. Polya (Environmental Geochemistry and Health, 2021)
Geogenic arsenic contamination in groundwaters poses a severe health risk to hundreds of millions of people globally. Notwithstanding the particular risks to exposed populations in the Indian sub-continent, at the time of writing, there was a paucity of geostatistically based models of the spatial distribution of groundwater hazard in India. In this study, we used logistic regression models of secondary groundwater arsenic data with research-informed secondary soil, climate and topographic variables as principal predictors generate hazard and risk maps of groundwater arsenic at a resolution of 1 km across Gujarat State. By combining models based on different arsenic concentrations, we have generated a pseudo-contour map of groundwater arsenic concentrations, which indicates greater arsenic hazard (> 10 μg/L) in the northwest, northeast and south-east parts of Kachchh District as well as northwest and southwest Banas Kantha District. The total number of people living in areas in Gujarat with groundwater arsenic concentration exceeding 10 μg/L is estimated to be around 122,000, of which we estimate approximately 49,000 people consume groundwater exceeding 10 µg/L. Using simple previously published dose–response relationships, this is estimated to have given rise to 700 (prevalence) cases of skin cancer and around 10 cases of premature avoidable mortality/annum from internal (lung, liver, bladder) cancers—that latter value is on the order of just 0.001% of internal cancers in Gujarat, reflecting the relative low groundwater arsenic hazard in Gujarat State.

Reservoir characteristics and controlling factors of Silurian Lower Kepingtage Formation in Tahe area, Tarim Basin, NW China - Ruohan Liu; Zaixing Jiang; Ming Wang; Weili Yang; Jingxiang Guo; Minghao Wu; Yi Gao; Shanyazi Wei; Zhihong Nie; Hong He (Journal of Earth Science, 2017)