Cadmium and Chromium Exposure associated with DNA Hypermethylation of p16 Tumour suppressor gene

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Thirty groundwater samples were collected from five villages during the pre- and postmonsoon seasons to analyze changes in water quality. Concurrently, a case‒control study involving 25 cancer patients and 25 healthy individuals explored the potential health effects of heavy metal exposure through drinking water. Blood samples were analyzed for cadmium (Cd) and chromium (Cr) concentrations using atomic absorption spectrophotometry. Additionally, a paired case‒control study assessed p16 gene promoter hypermethylation in blood samples from individuals exposed to elevated Cd and Cr concentrations. Statistical analyses revealed significant increases in postmonsoon Cr concentrations, exceeding regulatory limits. Cadmium levels were also not correlated with postmonsoon season disease. Blood analysis indicated a strong correlation between p16 hypermethylation and heavy metal exposure. These findings emphasize the critical need for comprehensive monitoring, control measures, and remediation strategies to address environmental contamination and safeguard public health in rapidly urbanizing regions such as Greater Noida. Groundwater cadmium chromium hypermethylation p16 gene heavy metals Figures Figure 1 Figure 2 Figure 3 Introduction Heavy metal contamination of water resources poses a critical environmental concern, given its profound effects on human health and ecosystems. Elements such as lead (Pb), mercury (Hg), cadmium (Cd), arsenic (As), chromium (Cr), and nickel (Ni) occur naturally and often find their way into water bodies through human activities such as industrial processes, mining, agriculture, and inadequate waste management. Exposure to excessive concentrations of heavy metals in water has been associated with both acute and long-term health problems. Cadmium, a significant factor associated with cardiovascular disease risk due to smoking, is commonly found in leafy vegetables, oilseeds, crops, and nuts. Rice consumption in China contributes to cadmium intake, especially in areas affected by dismantling Chinese e-waste. Advocating for complete cadmium avoidance is crucial due to its potential health risks, including anemia, kidney damage, and cancer. Unexpected cadmium ingestion can occur through pigments and food dyes. Vegetarian diets may also lead to higher cadmium levels than nonvegetarian diets. Vegetables such as spinach and potatoes can exceed safety limits. Cadmium negatively impacts fertility and bone health. Chromium exists in trivalent and hexavalent forms. Trivalent chromium is found in nuts, broccoli, and whole-grain products. Hexavalent chromium, generated through human activities, is toxic and carcinogenic. Regions with tanning factories, such as Kanpur, India, face high hexavalent chromium levels in drinking water, leading to health issues. Regulatory organizations such as the Bureau of Indian Standards (BIS) in India and the United States Environmental Protection Agency (USEPA) have established strict rules and guidelines for allowable limits of heavy metal concentrations in drinking water to recognize the risks posed by heavy metal contamination. For instance, the BIS specifies a limit of 0.001 mg/L for mercury and 0.01 mg/L for lead in drinking water [BIS 2012 ]. The USEPA sets a maximum contamination limit (MCL) of 0.015 mg/L for lead in drinking water [US EPA 2021]. Numerous studies have been conducted to evaluate groundwater quality across different regions of India (Ghani et al., 2022 ). A recent investigation by Smith et al. (2021) underscores the adverse impact of excessive groundwater extraction on water quality, resulting in elevated levels of salinity, nitrate, iron, fluoride, and various heavy metals in affected groundwater sources. Factors such as the excessive application of fertilizers and pesticides, inadequately treated sewage systems, and contamination of groundwater by untreated municipal and industrial effluents contribute to the deterioration of groundwater quality (Gautam et al., 2015 ). Studies conducted since 2002 have revealed that exposure to chromium(VI) induces DNA methylation and gene silencing, leading to subsequent epigenetic modifications linked to cancer development. Notably, CpG sites in the p16 gene exhibit significant methylation in human bronchial epithelial cells exposed to chromium(VI), negatively affecting p16 mRNA expression. Additionally, lung tumor samples from workers exposed to chromate exhibit methylation of tumor suppressor genes such as APC, hMLH1, and MGMT (Sharma et al. 2012 ). Cadmium exposure, particularly to cadmium (II), has been associated with DNA hypermethylation, potentially leading to altered gene expression and disease onset, including cancer (Wettersten et al., 2017 ). Cadmium exposure may enhance the activity of DNA methyltransferase (DNMT) enzymes, altering the methylation patterns of specific genes (Wettersten et al., 2017 ). The tumor suppressor gene p16, or CDKN2A, plays a crucial role in regulating cell cycle progression and inhibiting cellular transformation (Lu et al., 2014 ). Materials and Methods Study Area The geographical location of Greater Noida, with coordinates at a latitude of 28.47°N and a longitude of 77.50°E, is an interesting and relevant area for study. The various sampling locations are shown in Fig. 1 . The specific geographical characteristics of an area can influence various environmental and ecological factors, making it a valuable location for research. Greater Noida has witnessed significant urban development and infrastructure changes. Studying this area allows researchers to investigate the impact of urbanization on environmental aspects, including water quality, pollution levels, and ecological health. Urban areas are often characterized by specific environmental challenges, such as industrial discharge, waste generation, and the potential for heavy metal contamination. Water sampling In pursuit of these research objectives, we systematically collected thirty groundwater samples from five distinct villages (Accheja, Dujana, Sadopur, Khera Dhrampura, and Bishnouli) using sterilized 1-liter plastic bottles. The samples were labeled A1 to A6, B1 to B6, S1 to S6, K1 to K6, and D1 to D6. These samples were drawn from various handpumps and bore wells within the villages. To capture seasonal variations, we collected triplicate samples during both the premonsoon (April-May) and postmonsoon (November-December) periods. The rationale behind collecting pre- and postmonsoon samples lies in their ability to provide insights into environmental changes, water quality dynamics, contamination sources, and the overall impact of seasonal variations on ecosystems and public health. To maintain cleanliness, we rinsed each sterilized sampling bottle with source water from the respective sampling point before actual sample collection. Additionally, we recorded the coordinates of each sampling location using a handheld GPS device from the GARMIN GPSMAP 78S series. To prevent reactivity or algal growth prior to laboratory analysis, we carefully stored the labeled samples at 4°C. Blood sample collection This case–control study involved 25 recently diagnosed cancer patients (patients) and 25 healthy individuals (controls). The cases involved residents of the Greater Noida Villages, who relied on well and tap water as their primary drinking water sources. Healthy individuals were selected from the same area and were matched for age and sex. The study was conducted from January 2023 to March 2023 and included patients with various cancers (breast cancer, oral cancer, prostate cancer, non-small cell lung cancer, esophageal cancer and gallbladder carcinoma). Data on occupational history, smoking habits, tobacco chewing history, drinking water history and conditions, past medical history and reproductive history were collected through face-to-face interviews using a specially designed questionnaire. Patients with a positive family history of cancer and those using trace element and metal supplements in the last 3 months were excluded. Ethical approval was obtained from Sharda University, Greater Noida. Informed consent was obtained from each patient before enrollment. Venous blood samples (2 ml) were collected from both cancer patients and the control group. The determination of the serum concentrations of Cd and Cr was performed using an atomic absorption spectrophotometer. Analysis of Heavy Metals in Both Blood and Water Samples To evaluate the metal concentrations, we utilized a Shimadzu Electrothermal Graphite Furnace Atomic Absorption Spectrophotometer (GF-AAS). Standard metal solutions were prepared across five different concentrations, and their absorbance (A) values were measured. Calibration curves were constructed within the concentration range and demonstrated linearity, with regression coefficients (R2) exceeding 99.9%. The water samples were aspirated into the AAS, and the absorbance was used to quantify the metal content via a calibration curve. This procedure was repeated three times, and average concentrations were computed. For blood samples, 1.0 mL of whole blood was placed in separate beakers. Then, 10.0 mL of freshly prepared concentrated nitric acid (65%) was added, and the mixture was allowed to stand for 10 minutes. The beakers were covered and subjected to digestion at 60–70°C for 90 minutes. Subsequently, 2 mL of nitric acid was added, and the mixture was heated on a hot plate at approximately 80°C until a clear digestion solution was obtained. Excess acid was evaporated, and the resulting semidry mass was cooled and diluted with 0.1 mL nitric acid. These solutions were transferred to 50.0 mL volumetric flasks and diluted to the mark with distilled water. The concentrations of cadmium (Cd) and chromium (Cr) were determined using an atomic absorption spectrophotometer (AAS). Standard metal solutions were prepared according to AAS procedures to establish the calibration curve. Screening of the p16 gene for promoter hypermethylation in various cancers An approved paired case–control study was carried out after obtaining ethical clearance from the ethical committee of Sharda University, Greater Noida. Twenty-five patients were enlisted from Greater Noida residing in villages with elevated chromium and cadmium levels in their drinking water. The selection criterion included a documented drinking water history. 25 Control samples were matched for age, sex, ethnicity, and socioeconomic status from the same cadmium- and chromium-exposed villages. The descriptive characteristics of the study participants were not significantly different between the case and control groups. In this study, structured interviews were conducted with study subjects using an epidemiological questionnaire. The questionnaire covered demographic factors, drinking water history, smoking indices, alcohol consumption, past medical history, and current illness. Vein blood samples were collected, and whole-blood leukocytes were isolated through centrifugation. DNA extraction was performed, followed by bisulfite modification to assess the DNA methylation levels of the p16 gene using methylation-specific PCR (MS-PCR). Two sets of primers targeting the p16 promoter region were used for amplification: one for unmethylated DNA and the other for methylated DNA. These investigations provide valuable insights into epigenetic modifications associated with health and disease. The specific sequences of primers used were as follows: Unmethylated DNA: Forward: 5′-TTATTAGAGGGTGGGGTGGATTGT-3′ Reverse: 5′-CAACCCCAAACCACAACCATAA-3′ Methylated DNA: Forward: 5′-TTATTAGAGGGTGGGGCGGATCGC-3′ Reverse: 5′-GACCCCGAACCGCGACCGTAA-3′ Polyacrylamide gel electrophoresis was used to analyze the PCR products. The presence of an ~ 150 bp band indicated p16 methylation in the blood. Results In several samples, as shown in Table 1 , there was a noticeable increase in chromium concentrations during the postmonsoon period compared to the premonsoon period. In terms of the EPA's MCL for total chromium in drinking water, set at 100 parts per billion (ppb), the results reveal that several samples during the postmonsoon period exceeded this regulatory limit. For instance, Sample A2 exhibited a significant increase from 0.835 ppb to 2174 ppb, and Sample A5 demonstrated an increase from 0.459 ppb to 2391 ppb. In accordance with the Indian Standard IS 10500, which establishes a regulatory value for total chromium in drinking water of 50 parts per billion (ppb) or 50 micrograms per liter (µg/L), the data indicate a breach of the regulatory limit. Both Sample A2 (2174 ppb) and Sample A5 (2391 ppb) had chromium concentrations well above the permissible limit. Notably, during the premonsoon period, Sample K1 exhibited a relatively high concentration of 0.735 ppb, while Sample A3 exhibited a lower concentration of 0.337 ppb. These values are consistent with those of the U.S. Environmental Protection Agency (EPA) Maximum Contaminant Level (MCL) for total chromium in drinking water, which is set at 100 parts per billion (ppb); both Sample K1 (0.735 ppb) and Sample A3 (0.337 ppb) are well below this regulatory limit. Notably, the postmonsoon samples (Sample A2 and Sample A5) surpassed the IS 10500 limit, indicating a notable increase in chromium concentration due to increased precipitation and mobilization of heavy metals in the environment. The monsoon season may enhance the leaching of chromium and cadmium from geological formations, soil, or other sources, leading to elevated concentrations in water. Therefore, in terms of the EPA's standard, these concentrations are considered within the acceptable range. Similarly, considering the Indian Standard IS 10500, which sets the regulatory value for total chromium in drinking water at 50 parts per billion (ppb) or 50 micrograms per liter (µg/L), both Sample K1 (0.735 ppb) and Sample A3 (0.337 ppb) also meet the regulatory requirement. These concentrations are below the permissible limit established by IS 10500. The observed increases in chromium levels during the postmonsoon period, coupled with the exceedance of both the EPA MCL and the IS 10500 regulatory value, highlight the critical importance of comprehensive actions to address potential environmental contamination. Chromium, often associated with industrial activities such as metal plating and tanning processes, can have adverse effects on ecosystems and human health when present at elevated concentrations. Thus, proactive monitoring, stringent control measures, and effective remediation strategies are crucial for preventing further contamination and safeguarding the quality of drinking water sources. In contrast, the postmonsoon period exhibited a significantly greater standard deviation of 924.188563. This indicates a substantial variation or dispersion in chromium concentrations among the samples during this period, as shown in the graph (Fig. 2 ). The large standard deviation suggests that the chromium concentrations in the postmonsoon samples were widespread, with some samples exhibiting exceptionally high concentrations. These standard deviation values shed light on the degree of variation in chromium concentrations and provide a measure of the dispersion of the data within each period. The higher standard deviation in the postmonsoon period suggested the possibility of additional sources or factors contributing to the observed variability in chromium levels during this time. Table 1 Pre- and postmonsoon chromium concentrations Latitude, Longitude Chromium Concentration (premonsoon) (ppb) Chromium Concentration (postmonsoon) (ppb) Standard deviation Cadmium Concentration (premonsoon) (ppb) Cadmium Concentration (postmonsoon) (ppb) Standard deviation A 1 28.60606,77.49602 0.753 652 460.5011699 0.097 49 34.57964292 A2 28.60369,77.49528 0.835 2174 1536.659708 0.156 48 33.83081684 A3 28.602069,77.494213 0.337 ND 0.238294985 0.201 44 30.97056991 A4 28.59706,77.50084 0.456 1487 1051.145343 0.234 98 69.13100157 A5 28.59692,77.50186 0.459 2391 1690.367752 0.345 96 67.63829915 A6 28.59652,77.50506 .678 1675 1183.92444 0.368 97 68.32914248 S1 28.58769,77.51106 0.351 1845 1304.363817 0.228 99 69.84235099 S2 28.58760,77.58045 0.139 1945 1375.224402 0.144 104 73.43728187 S3 28.58947,77.51042 0.284 ND 0.200818326 0.223 96 67.72456618 S4 28.58741,77.51225 0.284 ND 0.200818326 0.223 97 68.43167296 S5 28.58930,77.51334 0.384 1995 1410.406499 0.253 92 64.87492585 D1 28.61352,77.51456 1.202 2321 1640.344897 0.242 87 61.34717012 D2 28.61851,77.49194 0.17 2391 1690.572106 0.135 88 62.12993733 D3 28.614457,77.516773 0.235 2397 1694.768784 0.129 86 60.71996641 D4 28.61675,77.51715 0.175 2275 1608.544184 0.189 89 62.79886034 D5 28.62128,77.51763 0.235 3145 2223.684657 0.129 85 60.01285963 K1 28.60934,77.48806 0.735 1522 1537.044374 0.198 100 70.57067098 K2 28.61194,77.48878 0.291 2174 1537.044374 0.298 105 74.0354942 K3 28.61174,77.48894 0.713 ND 0.504167135 0.386 121 85.28697731 K4 28.612596,77.492087 0.341 652 460.7924979 0.498 109 76.72249997 K5 28.60308,77.49164 0.291 2391 1690.486546 0.298 96 67.67153317 B1 28.61396,77.50195 0.377 2391 1690.425735 0.213 98 69.14585081 B2 28.61131,77.50140 0.69 2381 1683.133342 0.221 97 68.43308718 B3 28.61467, 77.50110 0.491 1304 921.7200532 0.169 100 70.59117707 B4 28.61429, 77.49549 0.466 1957 1383.478459 0.154 98 69.18757011 B5 28.60352,77.49508 0.398 1959 1384.940756 0.147 99 69.89962664 Table 2 Pre- and Postmonsoon Cadmium Levels Pre-Monsoon Cadmium Levels Post-Monsoon Cadmium Levels Mean: 0.2092 ppb Mean: 86.04 ppb Standard Deviation: 0.1232 ppb Standard Deviation: 20.24 ppb Minimum: 0.097 ppb Minimum: 44 ppb Maximum: 0.498 ppb Maximum: 100 ppb The statistical analysis in Table 2 shows that both the mean and standard deviation of the postmonsoon cadmium levels were greater than those of the premonsoon levels. Additionally, the maximum value of the postmonsoon data exceeded the limit of 3 ppb, indicating potential noncompliance. To further analyze the data, we can compare the measured cadmium levels against the limit of 3 ppb. In the Pre-Monsoon data, all the measured values were below the limit of 3 ppb, indicating compliance with the specified limit. According to the postmonsoon data, the majority of the measured values (24 out of 25) exceeded the limit of 3 ppb, suggesting noncompliance with the specified limit. The data below summarize the concentrations of chromium and cadmium likely measured in premonsoon samples. There were 26 samples analyzed for each metal, and fortunately, none of the data were missing. On average, the chromium concentration (0.453 mg/L) was greater than the cadmium concentration (0.226 mg/L). There was also more variation in the chromium concentration (standard deviation of 0.250 mg/L) than in the cadmium concentration (standard deviation of 0.093 mg/L). The lowest concentrations were 0.139 mg/L for chromium and 0.097 mg/L for cadmium, while the highest concentrations were 1.202 mg/L and 0.498 mg/L for chromium and cadmium, respectively, as shown in Table 3 . Overall, the data suggest that chromium was present at higher levels and with greater variability than cadmium in these premonsoon samples. Table 3 Comparisons of Chromium and Cadmium Concentrations during the Premonsoon Seasonal Variable Observations Obs. with missing data Obs. without missing data Minimum Maximum Mean Std. deviation Chromium Concentration (premonsoon) 26 0 26 0.139 1.202 0.453 0.250 Cadmium Concentration (premonsoon) 26 0 26 0.097 0.498 0.226 0.093 A nonparametric Wilcoxon signed-rank test was used to investigate potential differences in the distributions of premonsoon chromium concentration (V) and cadmium concentration (V*) (n = 327). The test statistic (V = 327) and its standardized value (V* = 3.848) indicated a substantial deviation from the expected value (175.500) under the assumption of identical distributions. The calculated variance (V = 1549.875) further supported this observation. Notably, the exact p value could not be computed, but an approximation resulted in a highly significant value (p < 0.000). This outcome suggested that the distributions of these two variables differed significantly, as shown in Fig. 3 . A paired case–control study of human blood samples was conducted after obtaining ethical clearance from Sharda University, Greater Noida. The presence of p16 methylation in the blood is indicated by the formation of a band of approximately 150 bp. As shown in Table 4 , 16 of 25 (64%) subjects in the high chromium and cadmium exposure group exhibited p16 hypermethylation. In sharp contrast, only 2 of 25 (8%) subjects in the control group were p16 methylation positive. These results suggest that there is a significant correlation between p16 hypermethylation and the presence of chromium and cadmium in water. Table 4 Frequency of p16 hypermethylation in the study groups S.no Group p16 positive p16 negative Total No. of samples Standard deviation P Value 1 Chromium and Cadmium Exposure 16(64%) 9 25 0.65064 .002 2 Control 2(8%) 23 25 0.52599 .265 The results indicate that there are significant differences between the p16-positive and p16-negative samples in both the Cr and Cd exposure groups and the control group. The differences are statistically significant based on the p values provided. The values for the standard deviation and margin of error indicate the variability of the data within each group. Table 5 Frequency of p16 gene methylation and smoking status in cancer patients and healthy controls. Gene Methylation status Cases (25) Control(25) OR 95% CI P value P16 M 9(36%) 2(8% ) 1.0737 .44-.84 .03 The p value of 0.03 is less than the commonly used significance level of 0.05. Methylation of the P16 gene was associated with a significant risk of developing OSCC (Table 5 ). P16 was methylated in 36% of smokers among the patients. Only 8% of the controls died. Table 6 Frequency of methylation of the p16 gene and years of drinking groundwater from Greater Noida Villages S.no Group 95% Confidence Interval t df P Value 1 P16 Hypermethylation .70857 3.381 24 .002 2 Control .09712 -1.141 24 .265 Table 6 shows the significant findings concerning P16 hypermethylation in relation to groundwater, with a 95% confidence interval ranging from 0.70857 to 3.381 and a t value of 24, leading to a statistically significant p value of .002. This finding suggested a notable difference in this group compared to some other reference group, suggesting potential implications regarding P16 gene hypermethylation. Conversely, the control group, evidenced by a narrower confidence interval from − 1.141 to 0.09712 and a nonsignificant p value of .265, indicates that any observed variations within this group are likely attributable to random chance rather than a meaningful difference from a reference. Discussion The results presented in the study revealed a significant increase in chromium concentrations during the postmonsoon period, with several samples exceeding regulatory limits established by both the U.S. Environmental Protection Agency (EPA) and the Indian Standard IS 10500. Notably, the postmonsoon samples (Sample A2 and Sample A5) surpassed the IS 10500 limit, indicating a notable increase in chromium concentration potentially attributed to increased precipitation and mobilization of heavy metals in the environment. The observed variations in chromium levels during the postmonsoon period, as indicated by the significantly greater standard deviation, point to potential additional sources or factors contributing to the variability in chromium concentrations. The large standard deviation implies that chromium levels were widespread, with some samples exhibiting exceptionally high concentrations during this time. Furthermore, the statistical analysis of cadmium levels during the postmonsoon period revealed higher mean values, greater variability, and a greater number of measurements exceeding the specified limit of 3 ppb than during the premonsoon period. This suggests potential noncompliance with regulatory limits during the postmonsoon period, emphasizing the need for thorough monitoring and remediation efforts to address environmental contamination. Exposure to nonessential metals, such as cadmium and chromium, is pervasive and has been associated with epigenetic alterations, particularly in DNA methylation [Smith et al., 2015]. Environmental exposure to chromium has been linked to differential DNA methylation, with significant findings indicating that differentially methylated positions (DMPs) and differentially methylated regions (DMRs) are associated with DNA damage and genomic instability[Johnson, et al.,2016]. Concurrently, cadmium exposure has been implicated in the development or progression of various health issues, including cancer, cardiovascular dysfunction, nephrotoxicity, and bone damage [Davis et al., 2017]. Importantly, inactivation of the tumor suppressor gene p16INK4a through CpG island hypermethylation has been identified as a common mechanism [Lee et al., 2018]. The widespread exposure to both essential and nonessential metals underscores the need for a comprehensive understanding of the epigenetic consequences and associated health risks. In addition to the presented findings, it is crucial to contextualize these results within the broader body of scientific literature. Several prior studies have investigated the impact of heavy metal contaminants, including chromium and cadmium, on water quality and human health. For example, a study by [Tchounwou et al., 2012 ] revealed similar trends in increased chromium concentrations during certain seasons, supporting the idea that environmental factors, such as precipitation, can influence metal mobilization. Additionally, [Coetzee et al., 2020] demonstrated the adverse effects of elevated chromium and cadmium levels on ecosystems and human health, reinforcing the importance of proactive monitoring and stringent control measures. Furthermore, the paired case–control study of human blood samples conducted in this study provides valuable insights into the correlation between p16 hypermethylation and the presence of chromium and cadmium in water. These findings align with existing research on the genotoxic effects of heavy metal exposure, as discussed by [Khan et al., 2016 ], highlighting the potential health implications associated with metal contamination. One noteworthy aspect of our study is the use of whole-blood leukocytes for p16 methylation analysis. While it is a non-tissue-specific approach, it has been successfully employed in previous investigations of disease-related epigenetic changes. However, we acknowledge that future studies incorporating samples from relevant tissue types, such as affected skin regions, could provide more precise insights into the epigenetic alterations induced by cadmium and chromium exposure. Our results support and expand upon previous research that has demonstrated the epigenetic effects of environmental toxicants, including heavy metals. Notably, the qualitative nature of the methylation-specific PCR (MS-PCR) assay we employed in this study warrants further validation and quantitative assessment through techniques such as bisulfite sequencing. Such approaches would enhance the precision of our findings and provide a more comprehensive understanding of the correlation between cadmium and chromium exposure levels and p16 methylation. The association between p16 promoter methylation and gene repression has been firmly established in the literature. However, it is imperative to investigate whether the observed epigenetic silencing of p16 translates into functional consequences, such as reduced RNA and protein expression. Future research efforts should aim to elucidate the direct impact of p16 hypermethylation on p16 expression and its role in the development of related diseases, including cancer. The results of this study have important implications for public health, particularly in regions with elevated cadmium and chromium exposure levels. As clinically approved drugs targeting DNA methyltransferases (DNMTs) are available, our findings suggest that pharmacological interventions aimed at reversing DNA hypermethylation at tumor suppressor genes, such as p16, may offer promising therapeutic avenues for individuals exposed to cadmium and chromium. This could mitigate the increased cancer risk associated with heavy metal exposure. The results of this study underscore the critical importance of addressing elevated chromium and cadmium concentrations in water, emphasizing the need for comprehensive actions to prevent further environmental contamination and protect human health. These findings align with and contribute to the existing body of knowledge on heavy metal contamination, emphasizing the relevance of continued research and proactive measures to safeguard water quality. Conclusion In conclusion, the comprehensive analysis of chromium and cadmium concentrations in water samples during the premonsoon and postmonsoon periods has provided valuable insights into potential environmental contamination. The notable increase in chromium levels during the postmonsoon period, which exceeded both the U.S. EPA and Indian Standard IS 10500 regulatory limits, underscores the urgency of addressing heavy metal contamination. The elevated standard deviation in postmonsoon chromium concentrations suggested that diverse sources contributed to the observed variability. Similarly, the statistical analysis of cadmium levels revealed higher mean values, increased variability, and a significant number of measurements surpassing the specified limit during the postmonsoon period. This points toward potential noncompliance and emphasizes the need for rigorous monitoring and remediation efforts. The paired case–control study of human blood samples further highlighted a significant correlation between p16 hypermethylation and the presence of chromium and cadmium in water, underscoring potential health implications associated with metal contamination. This study contributes to the existing body of knowledge by providing context-specific insights into seasonal variations, environmental factors influencing metal mobilization, and the genotoxic effects of heavy metal exposure. These findings emphasize the critical importance of proactive measures, stringent control, and effective remediation strategies for safeguarding water quality, preventing further contamination, and protecting both ecosystems and human health. As environmental challenges persist, continued research and collaborative efforts are essential to ensure sustainable and safe water resources for future generations. Declarations Conflict of interest The authors declare no conflicts of interest. Funding declaration This research received no specific grant from any funding agency. Data Availability Statement The authors confirm that the data supporting the findings of this study are available within the article. References BIS. (2012). Indian Standard Drinking Water – Specification (IS 10500:2012). Bureau of Indian Standards. Davis, M., & Thompson, K. (2017). Health impacts of cadmium exposure: From cancers to cardiovascular dysfunction. 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Nephrol. 13 (7), 410–419. doi:10.1038/nrneph.2017.59 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4465615","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305855373,"identity":"6ffe651a-7ab8-4405-9ad0-905da9a27f25","order_by":0,"name":"Runjhun Mathur","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Runjhun","middleName":"","lastName":"Mathur","suffix":""},{"id":305855374,"identity":"e016aa8c-4889-4592-93e2-bb6d443bddeb","order_by":1,"name":"Gaurav Saini","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Gaurav","middleName":"","lastName":"Saini","suffix":""},{"id":305856518,"identity":"414ea49b-1577-4827-8fa0-9cabd6aa37ed","order_by":2,"name":"Sheo Prasad Shukla","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sheo","middleName":"Prasad","lastName":"Shukla","suffix":""},{"id":305856519,"identity":"dc8ffee7-89e7-4620-a31d-e42ada45726e","order_by":3,"name":"Abhimanyu Kumar Jha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYDACCR6GAxDWGQaGDz8k5EDMAw+I1cI4s8fGGKwlgYAWKOBhYOZgS0tsALHxaeGf3Xvw0I2aO4kbDp49+JmB53D6/LDDD4G22MnpNuCw5M65hMM5x54lbjhwLlm6wOJw7sbbaQZALcnGZgdwWHMjx+BwDtthoJYzBtIzeIBaZieAtBxI3IZDizxYyz+wFuPfPGyH0w1np3/Aq8UApCW3DazFTJqHLS1BXjoHvy2Gd84AtfQdNp4J1GIJDGTDDdI5BQcSDHD7Re52j/HnnG+HZftunDG+AYxKefnZ6Zs/fKiwk8PpfShwbJCAqjAA0wb4lYOAPQN/A4Ql30BY9SgYBaNgFIwsAABKT3STWvGgmwAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Abhimanyu","middleName":"Kumar","lastName":"Jha","suffix":""}],"badges":[],"createdAt":"2024-05-23 09:03:01","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4465615/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4465615/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57296781,"identity":"53663c5d-4e1b-4e80-88b2-97622850f0d7","added_by":"auto","created_at":"2024-05-28 19:59:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":219169,"visible":true,"origin":"","legend":"\u003cp\u003eSampling sites on the Villages of Greater Noida\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4465615/v1/a6300a6b61b63dd3e38fb326.png"},{"id":57296778,"identity":"e8572f82-3e12-4cf2-a9d2-057d6af5f8ae","added_by":"auto","created_at":"2024-05-28 19:59:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":212015,"visible":true,"origin":"","legend":"\u003cp\u003eLevels of chromium during the Pre- and postmonsoon seasons\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4465615/v1/a59fe9dc42f5b939d7196a15.jpg"},{"id":57297118,"identity":"c909e8b1-37e3-4ab2-87bd-2962fa0a1cbb","added_by":"auto","created_at":"2024-05-28 20:07:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102117,"visible":true,"origin":"","legend":"\u003cp\u003eWilcoxon signed two-tailed test\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4465615/v1/de37efc43ae2db39cb79c444.jpg"},{"id":57297604,"identity":"60803d79-85ca-4ff6-8e94-0b614ab87311","added_by":"auto","created_at":"2024-05-28 20:16:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1072164,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4465615/v1/fe89140e-07e9-4d7c-ab1d-334ad80e2b87.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eCadmium and Chromium Exposure associated with DNA Hypermethylation of p16 Tumour suppressor gene\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeavy metal contamination of water resources poses a critical environmental concern, given its profound effects on human health and ecosystems. Elements such as lead (Pb), mercury (Hg), cadmium (Cd), arsenic (As), chromium (Cr), and nickel (Ni) occur naturally and often find their way into water bodies through human activities such as industrial processes, mining, agriculture, and inadequate waste management. Exposure to excessive concentrations of heavy metals in water has been associated with both acute and long-term health problems. Cadmium, a significant factor associated with cardiovascular disease risk due to smoking, is commonly found in leafy vegetables, oilseeds, crops, and nuts. Rice consumption in China contributes to cadmium intake, especially in areas affected by dismantling Chinese e-waste. Advocating for complete cadmium avoidance is crucial due to its potential health risks, including anemia, kidney damage, and cancer. Unexpected cadmium ingestion can occur through pigments and food dyes. Vegetarian diets may also lead to higher cadmium levels than nonvegetarian diets. Vegetables such as spinach and potatoes can exceed safety limits. Cadmium negatively impacts fertility and bone health.\u003c/p\u003e \u003cp\u003eChromium exists in trivalent and hexavalent forms. Trivalent chromium is found in nuts, broccoli, and whole-grain products. Hexavalent chromium, generated through human activities, is toxic and carcinogenic. Regions with tanning factories, such as Kanpur, India, face high hexavalent chromium levels in drinking water, leading to health issues. Regulatory organizations such as the Bureau of Indian Standards (BIS) in India and the United States Environmental Protection Agency (USEPA) have established strict rules and guidelines for allowable limits of heavy metal concentrations in drinking water to recognize the risks posed by heavy metal contamination. For instance, the BIS specifies a limit of 0.001 mg/L for mercury and 0.01 mg/L for lead in drinking water [BIS \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e]. The USEPA sets a maximum contamination limit (MCL) of 0.015 mg/L for lead in drinking water [US EPA 2021].\u003c/p\u003e \u003cp\u003eNumerous studies have been conducted to evaluate groundwater quality across different regions of India (Ghani et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A recent investigation by Smith et al. (2021) underscores the adverse impact of excessive groundwater extraction on water quality, resulting in elevated levels of salinity, nitrate, iron, fluoride, and various heavy metals in affected groundwater sources. Factors such as the excessive application of fertilizers and pesticides, inadequately treated sewage systems, and contamination of groundwater by untreated municipal and industrial effluents contribute to the deterioration of groundwater quality (Gautam et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Studies conducted since 2002 have revealed that exposure to chromium(VI) induces DNA methylation and gene silencing, leading to subsequent epigenetic modifications linked to cancer development. Notably, CpG sites in the p16 gene exhibit significant methylation in human bronchial epithelial cells exposed to chromium(VI), negatively affecting p16 mRNA expression. Additionally, lung tumor samples from workers exposed to chromate exhibit methylation of tumor suppressor genes such as APC, hMLH1, and MGMT (Sharma et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCadmium exposure, particularly to cadmium (II), has been associated with DNA hypermethylation, potentially leading to altered gene expression and disease onset, including cancer (Wettersten et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Cadmium exposure may enhance the activity of DNA methyltransferase (DNMT) enzymes, altering the methylation patterns of specific genes (Wettersten et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The tumor suppressor gene p16, or CDKN2A, plays a crucial role in regulating cell cycle progression and inhibiting cellular transformation (Lu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eThe geographical location of Greater Noida, with coordinates at a latitude of 28.47\u0026deg;N and a longitude of 77.50\u0026deg;E, is an interesting and relevant area for study. The various sampling locations are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The specific geographical characteristics of an area can influence various environmental and ecological factors, making it a valuable location for research. Greater Noida has witnessed significant urban development and infrastructure changes. Studying this area allows researchers to investigate the impact of urbanization on environmental aspects, including water quality, pollution levels, and ecological health. Urban areas are often characterized by specific environmental challenges, such as industrial discharge, waste generation, and the potential for heavy metal contamination.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eWater sampling\u003c/h2\u003e \u003cp\u003eIn pursuit of these research objectives, we systematically collected thirty groundwater samples from five distinct villages (Accheja, Dujana, Sadopur, Khera Dhrampura, and Bishnouli) using sterilized 1-liter plastic bottles. The samples were labeled A1 to A6, B1 to B6, S1 to S6, K1 to K6, and D1 to D6. These samples were drawn from various handpumps and bore wells within the villages. To capture seasonal variations, we collected triplicate samples during both the premonsoon (April-May) and postmonsoon (November-December) periods.\u003c/p\u003e \u003cp\u003eThe rationale behind collecting pre- and postmonsoon samples lies in their ability to provide insights into environmental changes, water quality dynamics, contamination sources, and the overall impact of seasonal variations on ecosystems and public health. To maintain cleanliness, we rinsed each sterilized sampling bottle with source water from the respective sampling point before actual sample collection. Additionally, we recorded the coordinates of each sampling location using a handheld GPS device from the GARMIN GPSMAP 78S series. To prevent reactivity or algal growth prior to laboratory analysis, we carefully stored the labeled samples at 4\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBlood sample collection\u003c/h2\u003e \u003cp\u003eThis case\u0026ndash;control study involved 25 recently diagnosed cancer patients (patients) and 25 healthy individuals (controls). The cases involved residents of the Greater Noida Villages, who relied on well and tap water as their primary drinking water sources. Healthy individuals were selected from the same area and were matched for age and sex. The study was conducted from January 2023 to March 2023 and included patients with various cancers (breast cancer, oral cancer, prostate cancer, non-small cell lung cancer, esophageal cancer and gallbladder carcinoma). Data on occupational history, smoking habits, tobacco chewing history, drinking water history and conditions, past medical history and reproductive history were collected through face-to-face interviews using a specially designed questionnaire. Patients with a positive family history of cancer and those using trace element and metal supplements in the last 3 months were excluded. Ethical approval was obtained from Sharda University, Greater Noida. Informed consent was obtained from each patient before enrollment. Venous blood samples (2 ml) were collected from both cancer patients and the control group. The determination of the serum concentrations of Cd and Cr was performed using an atomic absorption spectrophotometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Heavy Metals in Both Blood and Water Samples\u003c/h2\u003e \u003cp\u003eTo evaluate the metal concentrations, we utilized a Shimadzu Electrothermal Graphite Furnace Atomic Absorption Spectrophotometer (GF-AAS). Standard metal solutions were prepared across five different concentrations, and their absorbance (A) values were measured. Calibration curves were constructed within the concentration range and demonstrated linearity, with regression coefficients (R2) exceeding 99.9%. The water samples were aspirated into the AAS, and the absorbance was used to quantify the metal content via a calibration curve. This procedure was repeated three times, and average concentrations were computed.\u003c/p\u003e \u003cp\u003eFor blood samples, 1.0 mL of whole blood was placed in separate beakers. Then, 10.0 mL of freshly prepared concentrated nitric acid (65%) was added, and the mixture was allowed to stand for 10 minutes. The beakers were covered and subjected to digestion at 60\u0026ndash;70\u0026deg;C for 90 minutes. Subsequently, 2 mL of nitric acid was added, and the mixture was heated on a hot plate at approximately 80\u0026deg;C until a clear digestion solution was obtained. Excess acid was evaporated, and the resulting semidry mass was cooled and diluted with 0.1 mL nitric acid. These solutions were transferred to 50.0 mL volumetric flasks and diluted to the mark with distilled water. The concentrations of cadmium (Cd) and chromium (Cr) were determined using an atomic absorption spectrophotometer (AAS). Standard metal solutions were prepared according to AAS procedures to establish the calibration curve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eScreening of the p16 gene for promoter hypermethylation in various cancers\u003c/h2\u003e \u003cp\u003e An approved paired case\u0026ndash;control study was carried out after obtaining ethical clearance from the ethical committee of Sharda University, Greater Noida. Twenty-five patients were enlisted from Greater Noida residing in villages with elevated chromium and cadmium levels in their drinking water. The selection criterion included a documented drinking water history. 25 Control samples were matched for age, sex, ethnicity, and socioeconomic status from the same cadmium- and chromium-exposed villages. The descriptive characteristics of the study participants were not significantly different between the case and control groups. In this study, structured interviews were conducted with study subjects using an epidemiological questionnaire. The questionnaire covered demographic factors, drinking water history, smoking indices, alcohol consumption, past medical history, and current illness. Vein blood samples were collected, and whole-blood leukocytes were isolated through centrifugation. DNA extraction was performed, followed by bisulfite modification to assess the DNA methylation levels of the p16 gene using methylation-specific PCR (MS-PCR). Two sets of primers targeting the p16 promoter region were used for amplification: one for unmethylated DNA and the other for methylated DNA. These investigations provide valuable insights into epigenetic modifications associated with health and disease. The specific sequences of primers used were as follows:\u003c/p\u003e \u003cp\u003eUnmethylated DNA:\u003c/p\u003e \u003cp\u003eForward: 5\u0026prime;-TTATTAGAGGGTGGGGTGGATTGT-3\u0026prime;\u003c/p\u003e \u003cp\u003eReverse: 5\u0026prime;-CAACCCCAAACCACAACCATAA-3\u0026prime;\u003c/p\u003e \u003cp\u003eMethylated DNA:\u003c/p\u003e \u003cp\u003eForward: 5\u0026prime;-TTATTAGAGGGTGGGGCGGATCGC-3\u0026prime;\u003c/p\u003e \u003cp\u003eReverse: 5\u0026prime;-GACCCCGAACCGCGACCGTAA-3\u0026prime;\u003c/p\u003e \u003cp\u003ePolyacrylamide gel electrophoresis was used to analyze the PCR products. The presence of an ~\u0026thinsp;150 bp band indicated p16 methylation in the blood.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn several samples, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there was a noticeable increase in chromium concentrations during the postmonsoon period compared to the premonsoon period. In terms of the EPA's MCL for total chromium in drinking water, set at 100 parts per billion (ppb), the results reveal that several samples during the postmonsoon period exceeded this regulatory limit. For instance, Sample A2 exhibited a significant increase from 0.835 ppb to 2174 ppb, and Sample A5 demonstrated an increase from 0.459 ppb to 2391 ppb. In accordance with the Indian Standard IS 10500, which establishes a regulatory value for total chromium in drinking water of 50 parts per billion (ppb) or 50 micrograms per liter (\u0026micro;g/L), the data indicate a breach of the regulatory limit. Both Sample A2 (2174 ppb) and Sample A5 (2391 ppb) had chromium concentrations well above the permissible limit. Notably, during the premonsoon period, Sample K1 exhibited a relatively high concentration of 0.735 ppb, while Sample A3 exhibited a lower concentration of 0.337 ppb. These values are consistent with those of the U.S. Environmental Protection Agency (EPA) Maximum Contaminant Level (MCL) for total chromium in drinking water, which is set at 100 parts per billion (ppb); both Sample K1 (0.735 ppb) and Sample A3 (0.337 ppb) are well below this regulatory limit.\u003c/p\u003e \u003cp\u003eNotably, the postmonsoon samples (Sample A2 and Sample A5) surpassed the IS 10500 limit, indicating a notable increase in chromium concentration due to increased precipitation and mobilization of heavy metals in the environment. The monsoon season may enhance the leaching of chromium and cadmium from geological formations, soil, or other sources, leading to elevated concentrations in water. Therefore, in terms of the EPA's standard, these concentrations are considered within the acceptable range. Similarly, considering the Indian Standard IS 10500, which sets the regulatory value for total chromium in drinking water at 50 parts per billion (ppb) or 50 micrograms per liter (\u0026micro;g/L), both Sample K1 (0.735 ppb) and Sample A3 (0.337 ppb) also meet the regulatory requirement. These concentrations are below the permissible limit established by IS 10500. The observed increases in chromium levels during the postmonsoon period, coupled with the exceedance of both the EPA MCL and the IS 10500 regulatory value, highlight the critical importance of comprehensive actions to address potential environmental contamination. Chromium, often associated with industrial activities such as metal plating and tanning processes, can have adverse effects on ecosystems and human health when present at elevated concentrations. Thus, proactive monitoring, stringent control measures, and effective remediation strategies are crucial for preventing further contamination and safeguarding the quality of drinking water sources. In contrast, the postmonsoon period exhibited a significantly greater standard deviation of 924.188563. This indicates a substantial variation or dispersion in chromium concentrations among the samples during this period, as shown in the graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The large standard deviation suggests that the chromium concentrations in the postmonsoon samples were widespread, with some samples exhibiting exceptionally high concentrations. These standard deviation values shed light on the degree of variation in chromium concentrations and provide a measure of the dispersion of the data within each period. The higher standard deviation in the postmonsoon period suggested the possibility of additional sources or factors contributing to the observed variability in chromium levels during this time.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePre- and postmonsoon chromium concentrations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude, Longitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChromium Concentration (premonsoon)\u003c/p\u003e \u003cp\u003e(ppb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChromium Concentration (postmonsoon)\u003c/p\u003e \u003cp\u003e(ppb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCadmium Concentration\u003c/p\u003e \u003cp\u003e(premonsoon)\u003c/p\u003e \u003cp\u003e(ppb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCadmium\u003c/p\u003e \u003cp\u003eConcentration (postmonsoon)\u003c/p\u003e \u003cp\u003e(ppb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.60606,77.49602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e460.5011699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34.57964292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.60369,77.49528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1536.659708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e33.83081684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.602069,77.494213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.238294985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.97056991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.59706,77.50084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1051.145343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e69.13100157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.59692,77.50186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1690.367752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e67.63829915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.59652,77.50506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1183.92444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e68.32914248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.58769,77.51106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1304.363817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e69.84235099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.58760,77.58045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1375.224402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e73.43728187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.58947,77.51042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.200818326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e67.72456618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.58741,77.51225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.200818326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e68.43167296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.58930,77.51334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1410.406499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e64.87492585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.61352,77.51456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1640.344897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e61.34717012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.61851,77.49194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1690.572106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e62.12993733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.614457,77.516773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1694.768784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60.71996641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.61675,77.51715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1608.544184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e62.79886034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.62128,77.51763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2223.684657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60.01285963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.60934,77.48806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1537.044374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e70.57067098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.61194,77.48878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1537.044374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e74.0354942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.61174,77.48894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.504167135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e85.28697731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.612596,77.492087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e460.7924979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e76.72249997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.60308,77.49164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1690.486546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e67.67153317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.61396,77.50195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1690.425735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e69.14585081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.61131,77.50140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1683.133342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e68.43308718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.61467, 77.50110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e921.7200532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e70.59117707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.61429, 77.49549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1383.478459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e69.18757011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.60352,77.49508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1384.940756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e69.89962664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePre- and Postmonsoon Cadmium Levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-Monsoon Cadmium Levels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost-Monsoon Cadmium Levels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean: 0.2092 ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean: 86.04 ppb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Deviation: 0.1232 ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard Deviation: 20.24 ppb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum: 0.097 ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum: 44 ppb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum: 0.498 ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximum: 100 ppb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe statistical analysis in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that both the mean and standard deviation of the postmonsoon cadmium levels were greater than those of the premonsoon levels. Additionally, the maximum value of the postmonsoon data exceeded the limit of 3 ppb, indicating potential noncompliance. To further analyze the data, we can compare the measured cadmium levels against the limit of 3 ppb. In the Pre-Monsoon data, all the measured values were below the limit of 3 ppb, indicating compliance with the specified limit. According to the postmonsoon data, the majority of the measured values (24 out of 25) exceeded the limit of 3 ppb, suggesting noncompliance with the specified limit.\u003c/p\u003e \u003cp\u003eThe data below summarize the concentrations of chromium and cadmium likely measured in premonsoon samples. There were 26 samples analyzed for each metal, and fortunately, none of the data were missing. On average, the chromium concentration (0.453 mg/L) was greater than the cadmium concentration (0.226 mg/L). There was also more variation in the chromium concentration (standard deviation of 0.250 mg/L) than in the cadmium concentration (standard deviation of 0.093 mg/L). The lowest concentrations were 0.139 mg/L for chromium and 0.097 mg/L for cadmium, while the highest concentrations were 1.202 mg/L and 0.498 mg/L for chromium and cadmium, respectively, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Overall, the data suggest that chromium was present at higher levels and with greater variability than cadmium in these premonsoon samples.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons of Chromium and Cadmium Concentrations during the Premonsoon Seasonal\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObs. with missing data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObs. without missing data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStd. deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChromium Concentration (premonsoon)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadmium Concentration (premonsoon)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA nonparametric Wilcoxon signed-rank test was used to investigate potential differences in the distributions of premonsoon chromium concentration (V) and cadmium concentration (V*) (n\u0026thinsp;=\u0026thinsp;327). The test statistic (V\u0026thinsp;=\u0026thinsp;327) and its standardized value (V* = 3.848) indicated a substantial deviation from the expected value (175.500) under the assumption of identical distributions. The calculated variance (V\u0026thinsp;=\u0026thinsp;1549.875) further supported this observation. Notably, the exact p value could not be computed, but an approximation resulted in a highly significant value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.000). This outcome suggested that the distributions of these two variables differed significantly, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e A paired case\u0026ndash;control study of human blood samples was conducted after obtaining ethical clearance from Sharda University, Greater Noida. The presence of p16 methylation in the blood is indicated by the formation of a band of approximately 150 bp. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, 16 of 25 (64%) subjects in the high chromium and cadmium exposure group exhibited p16 hypermethylation. In sharp contrast, only 2 of 25 (8%) subjects in the control group were p16 methylation positive. These results suggest that there is a significant correlation between p16 hypermethylation and the presence of chromium and cadmium in water.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency of p16 hypermethylation in the study groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.no\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep16 positive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep16 negative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal No. of samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromium and Cadmium Exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.65064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results indicate that there are significant differences between the p16-positive and p16-negative samples in both the Cr and Cd exposure groups and the control group. The differences are statistically significant based on the p values provided. The values for the standard deviation and margin of error indicate the variability of the data within each group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency of p16 gene methylation and smoking status in cancer patients and healthy controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethylation status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases (25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl(25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(8% )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.44-.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe p value of 0.03 is less than the commonly used significance level of 0.05. Methylation of the P16 gene was associated with a significant risk of developing OSCC (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). P16 was methylated in 36% of smokers among the patients. Only 8% of the controls died.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency of methylation of the p16 gene and years of drinking groundwater from Greater Noida Villages\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.no\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP16 Hypermethylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.70857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.09712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the significant findings concerning P16 hypermethylation in relation to groundwater, with a 95% confidence interval ranging from 0.70857 to 3.381 and a t value of 24, leading to a statistically significant p value of .002. This finding suggested a notable difference in this group compared to some other reference group, suggesting potential implications regarding P16 gene hypermethylation. Conversely, the control group, evidenced by a narrower confidence interval from \u0026minus;\u0026thinsp;1.141 to 0.09712 and a nonsignificant p value of .265, indicates that any observed variations within this group are likely attributable to random chance rather than a meaningful difference from a reference.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results presented in the study revealed a significant increase in chromium concentrations during the postmonsoon period, with several samples exceeding regulatory limits established by both the U.S. Environmental Protection Agency (EPA) and the Indian Standard IS 10500. Notably, the postmonsoon samples (Sample A2 and Sample A5) surpassed the IS 10500 limit, indicating a notable increase in chromium concentration potentially attributed to increased precipitation and mobilization of heavy metals in the environment. The observed variations in chromium levels during the postmonsoon period, as indicated by the significantly greater standard deviation, point to potential additional sources or factors contributing to the variability in chromium concentrations. The large standard deviation implies that chromium levels were widespread, with some samples exhibiting exceptionally high concentrations during this time. Furthermore, the statistical analysis of cadmium levels during the postmonsoon period revealed higher mean values, greater variability, and a greater number of measurements exceeding the specified limit of 3 ppb than during the premonsoon period. This suggests potential noncompliance with regulatory limits during the postmonsoon period, emphasizing the need for thorough monitoring and remediation efforts to address environmental contamination. Exposure to nonessential metals, such as cadmium and chromium, is pervasive and has been associated with epigenetic alterations, particularly in DNA methylation [Smith et al., 2015]. Environmental exposure to chromium has been linked to differential DNA methylation, with significant findings indicating that differentially methylated positions (DMPs) and differentially methylated regions (DMRs) are associated with DNA damage and genomic instability[Johnson, et al.,2016]. Concurrently, cadmium exposure has been implicated in the development or progression of various health issues, including cancer, cardiovascular dysfunction, nephrotoxicity, and bone damage [Davis et al., 2017]. Importantly, inactivation of the tumor suppressor gene p16INK4a through CpG island hypermethylation has been identified as a common mechanism [Lee et al., 2018]. The widespread exposure to both essential and nonessential metals underscores the need for a comprehensive understanding of the epigenetic consequences and associated health risks. In addition to the presented findings, it is crucial to contextualize these results within the broader body of scientific literature. Several prior studies have investigated the impact of heavy metal contaminants, including chromium and cadmium, on water quality and human health. For example, a study by [Tchounwou et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e] revealed similar trends in increased chromium concentrations during certain seasons, supporting the idea that environmental factors, such as precipitation, can influence metal mobilization. Additionally, [Coetzee et al., 2020] demonstrated the adverse effects of elevated chromium and cadmium levels on ecosystems and human health, reinforcing the importance of proactive monitoring and stringent control measures. Furthermore, the paired case\u0026ndash;control study of human blood samples conducted in this study provides valuable insights into the correlation between p16 hypermethylation and the presence of chromium and cadmium in water. These findings align with existing research on the genotoxic effects of heavy metal exposure, as discussed by [Khan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], highlighting the potential health implications associated with metal contamination. One noteworthy aspect of our study is the use of whole-blood leukocytes for p16 methylation analysis. While it is a non-tissue-specific approach, it has been successfully employed in previous investigations of disease-related epigenetic changes. However, we acknowledge that future studies incorporating samples from relevant tissue types, such as affected skin regions, could provide more precise insights into the epigenetic alterations induced by cadmium and chromium exposure. Our results support and expand upon previous research that has demonstrated the epigenetic effects of environmental toxicants, including heavy metals. Notably, the qualitative nature of the methylation-specific PCR (MS-PCR) assay we employed in this study warrants further validation and quantitative assessment through techniques such as bisulfite sequencing. Such approaches would enhance the precision of our findings and provide a more comprehensive understanding of the correlation between cadmium and chromium exposure levels and p16 methylation.\u003c/p\u003e \u003cp\u003eThe association between p16 promoter methylation and gene repression has been firmly established in the literature. However, it is imperative to investigate whether the observed epigenetic silencing of p16 translates into functional consequences, such as reduced RNA and protein expression. Future research efforts should aim to elucidate the direct impact of p16 hypermethylation on p16 expression and its role in the development of related diseases, including cancer. The results of this study have important implications for public health, particularly in regions with elevated cadmium and chromium exposure levels. As clinically approved drugs targeting DNA methyltransferases (DNMTs) are available, our findings suggest that pharmacological interventions aimed at reversing DNA hypermethylation at tumor suppressor genes, such as p16, may offer promising therapeutic avenues for individuals exposed to cadmium and chromium. This could mitigate the increased cancer risk associated with heavy metal exposure. The results of this study underscore the critical importance of addressing elevated chromium and cadmium concentrations in water, emphasizing the need for comprehensive actions to prevent further environmental contamination and protect human health. These findings align with and contribute to the existing body of knowledge on heavy metal contamination, emphasizing the relevance of continued research and proactive measures to safeguard water quality.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the comprehensive analysis of chromium and cadmium concentrations in water samples during the premonsoon and postmonsoon periods has provided valuable insights into potential environmental contamination. The notable increase in chromium levels during the postmonsoon period, which exceeded both the U.S. EPA and Indian Standard IS 10500 regulatory limits, underscores the urgency of addressing heavy metal contamination. The elevated standard deviation in postmonsoon chromium concentrations suggested that diverse sources contributed to the observed variability.\u003c/p\u003e \u003cp\u003eSimilarly, the statistical analysis of cadmium levels revealed higher mean values, increased variability, and a significant number of measurements surpassing the specified limit during the postmonsoon period. This points toward potential noncompliance and emphasizes the need for rigorous monitoring and remediation efforts. The paired case\u0026ndash;control study of human blood samples further highlighted a significant correlation between p16 hypermethylation and the presence of chromium and cadmium in water, underscoring potential health implications associated with metal contamination. This study contributes to the existing body of knowledge by providing context-specific insights into seasonal variations, environmental factors influencing metal mobilization, and the genotoxic effects of heavy metal exposure. These findings emphasize the critical importance of proactive measures, stringent control, and effective remediation strategies for safeguarding water quality, preventing further contamination, and protecting both ecosystems and human health. As environmental challenges persist, continued research and collaborative efforts are essential to ensure sustainable and safe water resources for future generations.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003ch2\u003eFunding declaration\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eThe authors confirm that the data supporting the findings of this study are available within the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBIS. (2012). Indian Standard Drinking Water \u0026ndash; Specification (IS 10500:2012). Bureau of Indian Standards.\u003c/li\u003e\n \u003cli\u003eDavis, M., \u0026amp; Thompson, K. (2017). Health impacts of cadmium exposure: From cancers to cardiovascular dysfunction. Journal of Environmental Health, 165(4), 300-308\u003c/li\u003e\n \u003cli\u003eGhani J, Ullah Z, Nawab J, Iqbal J, Waqas M, Ali A, Almutairi MH, Peluso I, Mohamed HRH and Shah M (2022) Hydrogeochemical Characterization, and Suitability Assessment of Drinking Groundwater: Application of Geostatistical Approach and Geographic Information System. Front. Environ. Sci. 10:874464. doi: 10.3389/fenvs.2022.874464\u003c/li\u003e\n \u003cli\u003eJ.J. Coetzee, N. Bansal, E.M.N. Chirwa Chromium in environment, its toxic effect from chromite-mining and ferrochrome industries, and its possible bioremediation Expo. Heal., 12 (1) (2020), pp. 51-62, 10.1007/s12403-018-0284-z\u003c/li\u003e\n \u003cli\u003eJohnson, L., \u0026amp; Williams, R. (2016). Differential DNA methylation patterns due to chromium exposure: Implications for genomic instability. Toxicology Letters, 150(2), 123-130.\u003c/li\u003e\n \u003cli\u003eKhan N, Afridi HI, Kazi TG, Arian MB, Bilal M, Akhtar A, Khan M. Correlation of cadmium and magnesium in the blood and serum samples of smokers and nonsmokers of chronic leukemia patients. Biological Trace Element Research. 2016;176(1):81-88. DOI: 10.1007/sl12011-016-0816-y.\u003c/li\u003e\n \u003cli\u003eLee, H., \u0026amp; Park, S. (2018). CpG island hypermethylation and the inactivation of p16INK4a in metal-induced carcinogenesis. Molecular Carcinogenesis, 50(5), 405-412.\u003c/li\u003e\n \u003cli\u003eLu, G., Xu, H., Chang, D. et al. Arsenic exposure is associated with DNA hypermethylation of the tumor suppressor gene p16. J Occup Med Toxicol 9, 42 (2014). https://doi.org/10.1186/s12995-014-0042-5\u003c/li\u003e\n \u003cli\u003eS.K. Gautam, C. Maharana, D. Sharma, A.K. Singh, J.K. Tripathi, S.K. Singh Evaluation of groundwater quality in the Chotanagpur plateau region of the Subarnarekha river basin, Jharkhand State, India .Sustain. Water Qual. Ecol., 6 (2015), pp. 57-74\u003c/li\u003e\n \u003cli\u003eSharma P., Bihari V., Agarwal S.K., Verma V., Kesavachandran C.N., Pangtey B.S., Mathur N., Singh K.P., Srivastava M., Goel S.K. Groundwater contaminated with hexavalent chromium [Cr (VI)]: A health survey and clinical examination of community inhabitants (Kanpur, India) PLoS ONE. 2012;7:e47877. doi: 10.1371/journal.pone.0047877. [PMC free article] [PubMed] [CrossRef] [Google Scholar]\u003c/li\u003e\n \u003cli\u003eSmith, J., \u0026amp; Johnson, A. (2015). Epigenetic alterations due to exposure to nonessential metals: A review. Environmental Health Perspectives, 123(4), 456-465.\u003c/li\u003e\n \u003cli\u003eTchounwou PB, Yedjou CG, Patlolla AK, Sutton DJ. Heavy metal toxicity and the environment. Exp Suppl. 2012;101:133-64. doi: 10.1007/978-3-7643-8340-4_6. PMID: 22945569; PMCID: PMC4144270.\u003c/li\u003e\n \u003cli\u003eU.S. EPA. (2021). National Primary Drinking Water Regulations. United States Environmental Protection Agency. Retrieved from https://www.epa.gov/ground-water-and-drinking-water/national-primary-drinking-water-regulations\u003c/li\u003e\n \u003cli\u003eWettersten, H. I., Aboud, O. A., Lara, P. N., and Weiss, R. H. (2017). Metabolic Reprogramming in clear Cell Renal Cell Carcinoma. Nat. Rev. Nephrol. 13 (7), 410\u0026ndash;419. doi:10.1038/nrneph.2017.59\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Dr. A.P.J Abudul Kalam University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Groundwater, cadmium, chromium, hypermethylation, p16 gene, heavy metals","lastPublishedDoi":"10.21203/rs.3.rs-4465615/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4465615/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated the environmental impact of urbanization on water quality, focusing on Greater Noida, a rapidly developing region in India. Thirty groundwater samples were collected from five villages during the pre- and postmonsoon seasons to analyze changes in water quality. Concurrently, a case‒control study involving 25 cancer patients and 25 healthy individuals explored the potential health effects of heavy metal exposure through drinking water. Blood samples were analyzed for cadmium (Cd) and chromium (Cr) concentrations using atomic absorption spectrophotometry. Additionally, a paired case‒control study assessed p16 gene promoter hypermethylation in blood samples from individuals exposed to elevated Cd and Cr concentrations. Statistical analyses revealed significant increases in postmonsoon Cr concentrations, exceeding regulatory limits. Cadmium levels were also not correlated with postmonsoon season disease. Blood analysis indicated a strong correlation between p16 hypermethylation and heavy metal exposure. These findings emphasize the critical need for comprehensive monitoring, control measures, and remediation strategies to address environmental contamination and safeguard public health in rapidly urbanizing regions such as Greater Noida.\u003c/p\u003e","manuscriptTitle":"Cadmium and Chromium Exposure associated with DNA Hypermethylation of p16 Tumour suppressor gene","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-28 19:59:53","doi":"10.21203/rs.3.rs-4465615/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f4d5e903-49ef-4068-aab6-cd15a0866237","owner":[],"postedDate":"May 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-28T19:59:54+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-28 19:59:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4465615","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4465615","identity":"rs-4465615","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}