A Comprehensive Review of Auditory and Non-Auditory Effects of Noise on Human Health.

OBJECTIVE: Excessive noise is unpleasant and induces several physiological and psychological effects. Noise pollution is a potential threat to humans, particularly those continuously exposed for extended periods throughout the day over many years. This review aims to examine the various auditory and non-auditory outcomes associated with prolonged exposure to noise pollution. MATERIALS AND METHODS: The review utilized a combination of relevant keywords to search the electronic databases. After screening based on the applied selection criteria for title, abstract, and full text, 44 articles were finally selected for critical review. RESULTS: We identified and analyzed research findings related to noise-induced hearing loss, tinnitus, and sleep disturbances along with non-auditory issues such as annoyance, cognitive impairments, and mental stress associated with cardiovascular disorders. Furthermore, the existing studies were compared and collated to highlight the unique challenges and significance of noise pollution as a distinctive environmental concern and to explore the ongoing efforts in its research and prevention, including the early detection and potential reversal of noise-induced hearing loss. CONCLUSION: The fundamental health consequences of noise pollution underscore the need for extensive research encompassing emerging noise sources and technologies to establish a health management system tailored to address noise-related health concerns and reduce noise exposure risk among populations. Finally, further research is warranted to ensure improved measurement of noise exposure and related health outcomes, especially in the context of occupational noise.

Statistical estimation of noise induced hearing loss among the drivers in one of the most polluted cities of India.

In the present study, an attempt has been made to assess the impact of vehicular noise upon the 3-wheeler tempo drivers and to know whether there is any relationship between hearing loss and cumulative noise exposure. For this purpose, 3-wheeler tempo drivers (Exposed group) and non-commercial light motor vehicle car drivers (Unexposed group) were chosen as study subjects. Three traffic routes were selected to assess the noise level during waiting and running time in the exposed and unexposed groups. Among all three routes, the highest mean noise level (Leq) was observed on the Chowk to Dubagga route for waiting and en-route noise measurement. It was measured as 84.13 dB(A) and 86.36 dB(A) for waiting and en-route periods of 7.68 ± 3.46 and 31.05 ± 6.6 min, respectively. Cumulative noise exposure was found to be significantly different (p < 0.001) in all age groups of exposed and unexposed drivers. Audiometric tests have been performed over both exposed and unexposed groups. The regression analysis has been done keeping hearing loss among tempo drivers as the dependent variable and age (years) and Energy (Pa2 Hrs) as the independent variable using three different criteria of hearing loss definitions, i.e., World Health Organization, National Institute for Occupational Safety and Health (NIOSH), Occupational Safety and Health Administration criteria. Among these three criteria, the NIOSH criterion of hearing loss best explained the independent variables. It could explain the total variation in dependent variable by independent variable quite well, i.e., 68.1%. The finding showed a linear relationship between cumulative noise exposures (Pa2 Hrs) and the exposed group's hearing loss (dB), i.e., hearing loss increases with increasing noise dose. Based on the findings, two model equations were developed to identify the safe and unsafe noise levels with exposure time.

Exploring the association between long-term MODIS aerosol and air pollutants data across the Northern Great Plains through machine learning analysis.

Aerosol optical depth (AOD) and Ångström exponent (AE) are the major environmental indicators to perceive air quality and the impact of aerosol on climate change and health as well as the global atmospheric conditions. In the present study, an average of AOD and AE data from Tera and Aqua satellites of MODIS sensors has been investigated over 7 years i.e., from 2016 to 2022, at four locations over Northern Great Plains. Both temporal and seasonal variations over the study periods have been investigated to understand the behavior of AOD and AE. Over the years, the highest AOD and AE were observed in winter season, varying from 0.75 to 1.17 and 1.30 to 1.63, respectively. During pre-monsoon season, increasing trend of AOD varying from 0.65 to 0.95 was observed from upper (New Delhi) to lower (Kolkata) Gangetic plain, however, during monsoon and post-monsoon a reverse trend varying from 0.85 to 0.65 has been observed. Seasonal and temporal aerosol characteristics have also been analyzed and it has been assessed that biomass burning was found to be the major contributor, followed by desert dust at all the locations except in Lucknow, where the second largest contributor was dust instead of desert dust. During season-wise analysis, biomass burning was also found to be as the major contributor at all the places in all the seasons except New Delhi and Lucknow, where dust was the major contributor during pre-monsoon. A boosting regression algorithm was done using machine learning to explore the relative influence of different atmospheric parameters and pollutants with PM2.5. Water vapor was assessed to have the maximum relative influence i.e., 51.66 % followed by CO (21.81 %). This study aims to help policy makers and decision makers better understand the correlation between different atmospheric components and pollutants and the contribution of different types of aerosols.

Epigenetic factors in breast cancer therapy.

Epigenetic modifications are inherited differences in cellular phenotypes, such as cell gene expression alterations, that occur during somatic cell divisions (also, in rare circumstances, in germ line transmission), but no alterations to the DNA sequence are involved. Histone alterations, polycomb/trithorax associated proteins, short non-coding or short RNAs, long non-coding RNAs (lncRNAs), & DNA methylation are just a few biological processes involved in epigenetic events. These various modifications are intricately linked. The transcriptional potential of genes is closely conditioned by epigenetic control, which is crucial in normal growth and development. Epigenetic mechanisms transmit genomic adaptation to an environment, resulting in a specific phenotype. The purpose of this systematic review is to glance at the roles of Estrogen signalling, polycomb/trithorax associated proteins, DNA methylation in breast cancer progression, as well as epigenetic mechanisms in breast cancer therapy, with an emphasis on functionality, regulatory factors, therapeutic value, and future challenges.

Removal of Disperse Orange and Disperse Blue dyes present in textile mill effluent using zeolite synthesized from cenospheres.

In this research, an efficient, ecofriendly method of using coal fly ash in the form of zeolite to treat wastewater containing dyes was studied. Response surface methodology involving Box-Behnken design was applied to a batch process to evaluate the effect of process parameters such as contact time, dye concentration, agitation speed, pH, and adsorbent dosage onto zeolite. Disperse Orange 25 (DO) dye showed a maximum of 96% removal under optimal conditions of contact time of 119 min, dye concentration of 38.00 mg/L, agitation speed of 158 rpm, pH of 6.10, and adsorbent dosage of 0.67 g/L, whereas 95.23% of Disperse Blue 79:1 (DB) dye removal was observed at adsorbent dose of 1.05 g/L, dye concentration of 26.72 mg/L, agitation speed of 145 rpm, pH of 5.68, and contact time of 122 min. It was concluded that cenosphere-derivatized zeolite adsorbent is efficient, ecofriendly, and economical and has high potential for the removal of DO and DB dyes from aqueous solutions.

Spatio-temporal assessment of ambient air quality, their health effects and improvement during COVID-19 lockdown in one of the most polluted cities of India.

The present work aims to investigate seasonal variations in air pollution levels in Lucknow and assess the ambient air quality of the city together with highlighting the health impacts of major pollutants like PM10, PM2.5, SO2, NO2, Pb, Ni and aerosols from 2010 to 2019. The maximum and minimum values of PM10, PM2.5, SO2, NO2, Pb and Ni were found to be 270.75 and 122.45 µg/m3, 124.95 and 95.52 µg/m3, 25.60 and 8.05 µg/m3, 75.65 and 23.85 µg/m3, 0.66 and 0.03 µg/m3 and 0.07 and 0.01 ng/m3, respectively. Health impact of particulate matter has also been assessed with AirQ+, and it was estimated that long-term exposure of PM10 was attributed to between 37 and 48% for post-neonatal (age 1-12 months) mortality rate due to all causes, whereas long-term attributable proportions in mortality due to exposure of PM2.5 were to about 19 to 28% from all causes. Further, an attempt has also been made to evaluate the impact of lockdown amid COVID-19 on the ambient air quality of Lucknow. During the lockdown, PM2.5 levels reduced by 65% (at Gomti Nagar), 23% (at central school), 79% (at Lalbagh) and 35% (at Talkatora), due to which, air quality index of Gomti Nagar came down to 43, well below 50 which falls in the healthy range. NO2 levels also came down. However, levels of SO2 did not show significant reduction. Correlating the data between aerosol optical depth and Angstrom exponent by Pearson correlation analysis revealed a significant positive correlation (r = 0.65, P < 0.001).

Validation of a questionnaire to identify noise-induced hearing loss among drivers.

OBJECTIVE: To assess the validity of a questionnaire (consisting of 10 items/questions) to identify hearing loss (HL) among three-wheeler tempo and noncommercial car drivers. MATERIALS AND METHODS: This was a cross-sectional study conducted in an urban area of Lucknow city. Three-wheeler tempo drivers and noncommercial car drivers were assessed for HL by audiometry. A total of 300 subjects, who fulfilled the study criteria, were selected for the interview and health assessment. The pure tone audiometry was conducted after >12 hours of the last noise exposure to avoid temporary threshold shift. RESULTS: The percentage of respondents aged between 31 and 40 years was 36%. The highest affirmative response item was "Do you have trouble hearing in noisy background?" constituting 68% and the lowest affirmative response item was "Do you have trouble understanding the speech of women and children?" constituting 33.7%. Kappa values showed that there was significantly (<0.05) mild agreement between most of the items and the gold standard for mid and high-frequency HL. The area under the curve for low, mid, and high frequency HL was 0.76% (95% CI = 0.68-0.84), 0.69 (95% CI = 0.73-0.75), and 0.67 (95% CI = 0.62-0.73), respectively. The sensitivity and specificity were reasonable for all the definition of HL at different cutoff scores. CONCLUSION: A self-reported questionnaire-based approach may be used for the assessment of HL especially when audiometry is not feasible.

Multi-stakeholder policy modeling for collection and recycling of spent portable battery waste.

Policies have been structured for collection and recycling of spent portable battery waste within a framework of stakeholders (recycling council body, producer, recycler and consumer) especially for those battery units that are discarded worldwide because of their expensive cost of recycling. Applicability of stakeholders' policies in their coalition framework have been reviewed and critically analyzed using the Shapley value of cooperative game theory models. Coalition models for 'manufacturer and recycler' indicated the dominating role of manufacturers over the recyclers, and waste management is highly influenced by producer responsibility. But, the take-back policy enables recyclers' dominance role in the management and yields maximum benefit to both recyclers and consumers. The polluter pays principle has been implemented in formulating policies to key stakeholders, 'manufacturers' as well as 'consumers', of battery products by the introduction of penalties to encourage their willingness to join the Environment, Health and Safety program. Results indicated that the policies of the framework have the potential to be implemented within a marginal rise in battery price by 12% to 14.3% in the range of recycling cost per tonne of US$2000 to US$5000. The policy of the stakeholders' framework presented in the study could be an important aid to achieve high collection and recycling rates of spent portable batteries.

Linear and nonlinear modeling approaches for urban air quality prediction.

In this study, linear and nonlinear modeling was performed to predict the urban air quality of the Lucknow city (India). Partial least squares regression (PLSR), multivariate polynomial regression (MPR), and artificial neural network (ANN) approach-based models were constructed to predict the respirable suspended particulate matter (RSPM), SO(2), and NO(2) in the air using the meteorological (air temperature, relative humidity, wind speed) and air quality monitoring data (SPM, NO(2), SO(2)) of five years (2005-2009). Three different ANN models, viz. multilayer perceptron network (MLPN), radial-basis function network (RBFN), and generalized regression neural network (GRNN) were developed. All the five different models were compared for their generalization and prediction abilities using statistical criteria parameters, viz. correlation coefficient (R), standard error of prediction (SEP), mean absolute error (MAE), root mean squared error (RMSE), bias, accuracy factor (A(f)), and Nash-Sutcliffe coefficient of efficiency (E(f)). Nonlinear models (MPR, ANNs) performed relatively better than the linear PLSR models, whereas, performance of the ANN models was better than the low-order nonlinear MPR models. Although, performance of all the three ANN models were comparable, the GRNN over performed the other two variants. The optimal GRNN models for RSPM, NO(2), and SO(2) yielded high correlation (between measured and model predicted values) of 0.933, 0.893, and 0.885; 0.833, 0.602, and 0.596; and 0.932, 0.768 and 0.729, respectively for the training, validation and test sets. The sensitivity analysis performed to evaluate the importance of the input variables in optimal GRNN revealed that SO(2) was the most influencing parameter in RSPM model, whereas, SPM was the most important input variable in other two models. The ANN models may be useful tools in the air quality predictions.