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Abstract Geospatial methods, such as GIS and remote sensing, map radon levels, pinpoint high-risk areas and connect geological traits to radon presence. These findings direct health planning, focusing tests, mitigation, and policies where radon levels are high. Overall, geospatial analyses offer vital insights, shaping interventions and policies to reduce health risks from radon exposure. There is a formidable threat to human well-being posed by the naturally occurring carcinogenic radon (222Rn) gas due to high solubility in water. Under the current scenario, it is crucial to assess the extent of 222Rn pollution in our drinking water sources across various regions and thoroughly investigate the potential health hazards it poses. In this regard, the present study was conducted to investigate the concentration of 222Rn in groundwater samples collected from handpumps and wells and to estimate health risks associated with the consumption of 222Rn-contaminated water. For this purpose, groundwater samples (n = 30) were collected from handpumps, and wells located in the Mulazai area, District Peshawar. The RAD7 radon detector was used as per international standards to assess the concentration of 222Rn in the collected water samples. The results unveiled that the levels of 222Rn in the collected samples exceeded the acceptable thresholds set by the US Environmental Protection Agency (US-EPA) of 11.1 Bq L−1. Nevertheless, it was determined that the average annual dose was below the recommended limit of 0.1 mSv per year, as advised by both the European Union Council and the World Health Organization. In order to avoid the harmful effects of such excessive 222Rn concentrations on human health, proper ventilation and storage of water in storage reservoirs for a long time before use is recommended to lower the 222Rn concentration.

Stemming is an essential step in various Natural Language Processing (NLP) applications and is used to reduce different variants of the query words to a standard form to avoid the vocabulary mismatch issue in Information Retrieval (IR) systems. Due to specific grammatical rules and complex morphological structures, finding an effective stemming algorithm in Urdu is a challenging task. Although, several stemming algorithms have been proposed for the Urdu text stemming; however, none of them extract the stem from multilevel inflected forms. In this context, according to the best of our knowledge, this is a first effort towards the proposition and evaluation of a novel Urdu Text Stemmer (UTS) that can deal with multi-level inflection forms in Urdu text. The experimental evaluation of the proposed scheme has been conducted on the text-based and word-based custom-developed corpus. The proposed stemming technique is rigorously evaluated and compared with state-of-the-art stemming algorithms. Experimental results demonstrate that UTS outperforms existing Urdu stemmers and achieves an accuracy of 94.92% and 91.8% on word corpus and text corpus, respectively. We also evaluated our proposed system in an Information Retrieval application for Urdu, using the Collection for Urdu Retrieval Evaluation (CURE) dataset. Our approach for information retrieval outperformed and improved both recall and precision metrics.

The exponential increase in textual unstructured digital data creates significant demand for advanced and smart stemming systems. As a preprocessing stage, stemming is applied in various research fields such as information retrieval (IR), domain vocabulary analysis, and feature reduction in many natural language processing (NLP). Text stemming (TS), an important step, can significantly improve performance in such systems. Text-stemming methods developed till now could be better in their results and can produce errors of different types leading to degraded performance of the applications in which these are used. This work presents a systematic study with an in-depth review of selected stemming works published from 1968 to 2023. The work presents a multidimensional review of studied stemming algorithms i.e., methodology, data source, performance, and evaluation methods. For this study, we have chosen different stemmers, which can be categorized as 1) linguistic knowledge-based, 2) statistical, 3) corpus-based, 4) context-sensitive, and 5) hybrid stemmers. The study shows that linguistic knowledge-based stemming techniques were widely used for highly inflected languages (such as Arabic, Hindi, and Urdu) and have reported higher accuracy than other techniques. We compare and analyze the performance of various state-of-the-art TS approaches, including their issues and challenges, which are summarized as research gaps. This work also analyzes different NLP applications utilizing stemming methods. At the end, we list the future work directions for interested researchers.