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Tackling air pollution has become of utmost importance since the last few decades. Different statistical as well as deep learning methods have been proposed till now, but seldom those have been used to forecast future long-term pollution trends. Forecasting long-term pollution trends into the future is highly important for government bodies around the globe as they help in the framing of efficient environmental policies. This paper presents a comparative study of various statistical and deep learning methods to forecast long-term pollution trends for the two most important categories of particulate matter (PM) which are PM2.5 and PM10. The study is based on Kolkata, a major city on the eastern side of India. The historical pollution data collected from government set-up monitoring stations in Kolkata are used to analyse the underlying patterns with the help of various time-series analysis techniques, which is then used to produce a forecast for the next two years using different statistical and deep learning methods. The findings reflect that statistical methods such as auto-regressive (AR), seasonal auto-regressive integrated moving average (SARIMA) and Holt–Winters outperform deep learning methods such as stacked, bi-directional, auto-encoder and convolution long short-term memory networks based on the limited data available. [ABSTRACT FROM AUTHOR]

COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ( R 2 = 0.97 ) with smaller Akaike Information Criterion (AICc = - 66.42 ) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran's I = - 0.0395 and p < 0.01 ) in the residuals. It is found that more than 86% of local R 2 values are larger than 0.60 and almost 68% of R 2 values are within the range 0.80–0.97. Moreover, some interesting local variations in the relationships are also found. [ABSTRACT FROM AUTHOR]

Home Accidents have been identified as the largest single cause of death after the age of one year and are among the most severe health problems facing the world today. Keeping the toddler safe is a major priority for parents. Kid source online categories about toddler's accidents were home safety from poisoning, electrical appliances, environmental safety from fall and drowning and safety play from injurious toys. Injury prevention at home is an important concern of parents of toddlers. situations that can result in injury In both the industrialized world and developing countries, accidents remain one of the major five leading causes of death. The objective of the study was to assess the level of knowledge regarding prevention of domestic accidents A quantitative approach with descriptive research design was selected for the study. 50 number of mothers were selected by convenience sampling techniques from paediatric OPD. After obtaining the permission from institutional authority the consent from the mothers were taken the knowledge was assessed by using a self administered structured questionnaire. The result showed that greater than 54% of mother are having average knowledge, 30% of mother having poor knowledge and 16% of mother are having good knowledge regarding the prevention of domestic accident. [ABSTRACT FROM AUTHOR]

Deep learning methods have become the state of the art for spatio-temporal predictive analysis in a wide range of fields, including environmental management, public health, urban planning, pollution monitoring, and so on. Despite the fact that a variety of powerful deep learning-based models can address various problem-specific issues in different research domain, it has been found that no single optimal model can outperform everywhere. Now, in the last two years, various deep learning-based studies have provided a variety of best-performing techniques for predicting COVID-19 health outcomes. In this context, this study attempts to perform a case study that investigates the spatio-temporal variation in the performance of deep-learning-based methods for predicting COVID-19 health outcomes in India. Various widely applied deep learning models namely CNN (convolutional neural network), RNN (recurrent neural network), Vanilla LSTM (long short-term memory), LSTM Autoencoder, and Bidirectional LSTM are considered to investigate their spatio-temporal performance variation. The effectiveness of the models is assessed using various metrics based on COVID-19 mortality time-series from 36 states and union territories of India. • Deep learning-based COVID-19 health outcome forecasting at the state level in India. • Explore spatio-temporal variability pattern in the performance of learning models. • Finding spatial distribution of the models for predicting COVID-19 mortality data. • Rigorous evaluation of the models based on COVID-19 mortality time-series data. [ABSTRACT FROM AUTHOR]

Global environmental pollution and rapid climate change have become a serious matter of concern. Remarkable spatial and seasonal variations have been observed due to rapid industrialization, urbanization, different festive occasions, etc. Among all the existing pollutants, the fine airborne particles PM 2.5 (with aerodynamic equivalent diameter ≤ 2.5 μ m ) and PM 10 (with aerodynamic equivalent diameter ≤ 10 μ m ) are associated with chronic diseases. This leads to carry out the study regarding the varying relationship between PM 2.5 and other associated factors so that its concentration level might be under control. Existing literature has explored the geographical association between the pollutants and a few other important factors. To address this problem, the present study aims to explore the wide spatio-temporal relationships between the particulate matter ( PM 2.5 ) with the other associated factors (e.g., socio-demographic, meteorological factors, and air pollutants). For this analysis, the geographically weighted regression (GWR) model with different kernels (viz. Gaussian and Bisquare kernels) and the ordinary least squares (OLS) model have been carried out to analyze the same from the perspective of the four major seasons (i.e., autumn, winter, summer, and monsoon) in different districts of India. It may be inferred from the results that the local model (i.e., GWR model with Bisquare kernel) captures the spatial heterogeneity in a better way and their performances have been compared in terms of R 2 values ( > 0.99 in all cases) and corrected Akaike information criterion ( AIC c ) (maximum value - 618.69 and minimum value - 896.88 ). It has been revealed that there is a strong negative impact between forest coverage and PM pollution in northern India during the major seasons. The same has been found in Delhi, Haryana, and a few districts of Rajasthan during the 1-year cycle (October 2022-September 2023). It has also been found that PM concentration levels become high over the specified period with the temperature drop in Delhi, Uttar Pradesh, etc. Moreover, a strong positive association is visible in PM pollution level with the total population., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Mental disorders impose an enormous burden on society. In developing countries like India, there is a lack of adequate number of trained mental health professionals to provide specialized care and 75-85 % of affected individuals do not have access to appropriate mental health services. The National Mental Health Programme (NMHP) is being implemented by the Government of India to support state governments in providing mental health services in the country. The Urban Mental Health Programme (UMHP) is a pilot initiative that has attempted the integration of mental health services in primary health care settings in two municipal wards in Kolkata, West Bengal, India. The overarching aim of this paper is to describe the methodology used for the evaluation of the community based mental health programme and to understand the processes of the programme in terms of barriers and facilitators. The current evaluation is based on a concurrent nested design, where qualitative and quantitative data are both collected at the same time but analysed separately and priority was given to qualitative data. This experience will contribute in helping other researchers to make some evaluations more effective, useful and manageable. Ethics approval was obtained from an institutional ethics committee of an organization (Ekjut) based in Ranchi, Jharkhand, India. The evaluation was undertaken by the George Institute for Global Health, New Delhi from February- June 2016., (Copyright © 2021 Elsevier Ltd. All rights reserved.)