Delhi Research Articles

Decadal growth in emission load of major air pollutants in Delhi

Abstract. The Indian capital megacity of Delhi is reeling under deteriorating air quality, and control measures are not yielding any significant changes, mainly due to a poor understanding of sources of emissions; hence, priority option in mitigation planning is lacking. In this paper, we have made an attempt to develop a spatially resolved technological high-resolution gridded (∼0.4km×0.4km) emission inventory for eight major pollutants of the Delhi region where high-resolution activity data of all possible major and unattended minor sources are generated by organizing a mega-campaign involving hundreds of volunteers. It is for the first time that we are able to estimate the decadal growth in emissions of various pollutants by comparing newly developed 2020 emissions with SAFAR (System of Air Quality and Weather Forecasting and Research) emissions of 2010 using the identical methodology and quantum of activity data. The estimated annual emissions for PM2.5, PM10, CO, NOx, VOC, SO2, BC, and OC over the Delhi National Capital Region (NCR) are estimated to be 123.8, 243.6, 799.0, 488.9, 730.0, 425.8, 33.6, and 20.3 Gg yr−1, respectively, for the year 2020. The decadal growth (2010–2020) in PM2.5 and PM10 is found to be marginal at 31 % and 3 %, respectively. The maximum growth is found to be in the transport sector followed by the industrial and other sectors. Maximum decadal growth found for the pollutants BC, OC, and NOx is 57 %, 34 %, and 91 %, respectively. The decadal shift of sectorial emissions with changing policies is examined. The complete dataset is available on Zenodo at https://doi.org/10.5281/zenodo.7715595 (Sahu et al., 2023).

Impact of cross border reverse migration in Delhi–UP region of India during COVID-19 lockdown

Abstract The declaration of a nationwide lockdown in India led to millions of migrant workers, particularly from Uttar Pradesh (UP) and Bihar, returning to their home states without proper transportation and social distancing from cities such as Delhi, Mumbai, and Hyderabad. This unforeseen migration and social mixing accelerated the transmission of diseases across the country. To analyze the impact of reverse migration on disease progression, we have developed a disease transmission model for the neighboring Indian states of Delhi and UP. The model’s essential mathematical properties, including positivity, boundedness, equilibrium points (EPs), and their linear stability, as well as computation of the basic reproduction number ( R 0 ) \left({R}_{0}) , are studied. The mathematical analysis reveals that the model with active reverse migration cannot reach a disease-free equilibrium, indicating that the failure of restrictive mobility intervention caused by reverse migration kept the disease propagation alive. Further, PRCC analysis highlights the need for effective home isolation, better disease detection techniques, and medical interventions to curb the spread. The study estimates a significantly shorter doubling time for exponential growth of the disease in both regions. In addition, the occurrence of synchronous patterns between epidemic trajectories of the Delhi and UP regions accentuates the severe implications of migrant plight on UP’s already fragile rural health infrastructure. By using COVID-19 incidence data, we quantify key epidemiological parameters, and our scenario analyses demonstrate how different lockdown plans might have impacted disease prevalence. Based on our observations, the transmission rate has the most significant impact on COVID-19 cases. This case study exemplifies the importance of carefully considering these issues before implementing lockdowns and social isolation throughout the country to combat future outbreaks.

Investigating the Impact of Flood on Low Lying Settlements of Delhi, India: A Planning Perspective

In the past, the Yamuna River and the Najafgarh Drainage System have flooded the city of Delhi severely. In the past 33 years, Yamuna has 25 times exceeded its risk threshold, which is set at 204.83m. Since 1900, Delhi has suffered six significant floods when the Yamuna River has reached a height of one metre or more above the accidental 204.49m on the old railway bridge (2.66m above the accident level). These floods happened in 1924, 1947, 1976, 1978, 1988, and 1995. The Yamuna recently climbed more than two metres beyond the danger threshold in Delhi, breaking the milestone 207 metres for the first time in 32 years. The second highest point, at 206.92 metres, was on September 27, 1988. There is a direct impact of floods on the Yamuna River and the city drainage network. People who live along the Yamuna River Belt, on the river's banks, and in the drain are impacted by this. The amount of the solid regions causing local floods has increased landslides as a result of deforestation and local floods. Low-lying communities, particularly haphazard colonies that fall into the water, will be most impacted by this. Hathinikund erupts high in Haryana because of the persistent, intense rainfall on the high cliffs, draining water upstream, continuing to flood Delhi and harming the lives of those who live in low-lying areas. Therefore, torrential rains caused severe damage to the city, and even more so to those families built on the wrong side, which were the first and most frequent sightings of their homes and property being flooded. Families had already begun to lose livestock and livestock during this time, before being evacuated and their situation remains so dire that one has to leave the rest of the house to relocate and relocate when the water level drops. The purpose of the study was to explore about the kind of impacts generally inhabitants face during and post flooding and to find out the implementation of strategies. The following methods were used: Site survey of the case study area in which structured questionnaire and observations were conducted through primary survey, analysis and assessment, study of issues and concerns. As a result, flood prevention, preparedness and mitigation strategies with reference to low lying area have been analyzed and planning recommendations have been proposed.

Impact of Assistive Listening Devices with Hearing AIDS

Background: Hearing aids plays a significant role in management of hearing loss. Communication technologies when integrated with hearing aids leads to a better understanding of listening and a better outcome in their quality of life.
 Objective: To measure the quality of life with the use of Assistive Listening Devices (ALD) when paired with hearing aid.
 Methods: Cross sectional study conducted in the Delhi and NCR region across hospitals and clinics. Total 109 participants were included in this study over a period of one year.
 Results: Assistive listening devices when used with the hearing aids, participants reported a significant improvement in quality of life.
 Conclusion: It is recommended to use ALDs with hearing aids for a satisfactory listening quality which leads to a better quality of life.

A meso-level exploration of citizen’s mGov adoption from SD logic perspective: the case of COVID-19 tracking app of India

PurposeThis study aims to explore the factors affecting the adoption of mGov apps using the theoretical foundations of service-dominant (SD) logic. The authors specifically explore the impact of resource distribution (infrastructure and knowledge) and environmental variables (health infrastructure) among the Indian states on the adoption of Aarogya Setu (the COVID-19 tracking app) by their citizens.Design/methodology/approachFor the meso-level study, the states of India are the unit of analysis. The authors have used secondary data published by the government and other reliable organizations. The study is based on 29 states of India and Delhi, a union territory and the capital of India. The authors conducted a regression and moderation analysis using SPSS and PROCESS macros on the collected secondary data.FindingsThe findings reveal that operand resources (state domestic productivity per capita and internet penetration rate) positively impact the adoption of the mGov app. The operant resource (literacy) and the environmental variable (health index) are, however, negatively affecting the adoption of the mGov app. On the other hand, another operant resource, digital literacy, was found to have no significant effect on the adoption of the mGov app. Further, the moderating variable, health index, is found to be moderating all the relationships except internet penetration and adoption of the mGov app.Originality/valueThe study is novel in two aspects. First, in using the theoretical foundation of SD logic to examine the factors impacting the adoption of mGov app. Second, it is a meso-level study, which is not a widely explored avenue in mGov research.

Spatio-temporal Variation Analysis of Thermal Climate in Response to Urban Cover Changes: A Case Study of Delhi Using Landsat Data

ABSTRACT Urbanization and changes in Land Use Land Cover (LULC) influence the formation of urban microclimate (UMC). The current study analysed the urbanization and microclimatic changes in the National Capital Territory (NCT) of Delhi utilizing the Landsat-derived LULC map, Land Surface Temperature (LST), and a variety of spectral indices viz., Heat Effect Contribution Index (HECI), Surface Urban Heat Island (SUHI), Thermal Index (TI) and Environment Criticality Index (ECI). The results indicate that Delhi has undergone a substantial increase in urbanization and concomitant urban warming (1999–2021). The mean LST had risen across all LULC classes, with vegetation exhibiting the most significant variance in mean LST. Largely, water bodies and vegetation lands converted to other LULC classes yielded the highest rise in LST. Notably, the contribution of the build-up to the heat effect rose by more than 40%. A rise in LST and its association with different LULC classes led to the widespread development and spatiotemporal shifting of SUHI and a subsequent decline in thermal comfort. This research uniquely quantifies LST changes associated with shifts in LULC categories and utilizes multiple heat indices to study the UMC of Delhi, offering crucial insights for future planning and policy formulation.

Prevalence and Correlates of Vitamin D Deficiency among Adult Population in Urban and Rural Areas of the National Capital Region of Delhi, India

Abstract High prevalence of Vitamin D deficiency has been reported among selective population, but its population prevalence from representative adult population is lacking in India. The aim of this study was to estimate the prevalence and identify the correlates of Vitamin D deficiency among urban and rural areas of the National Capital Region (NCR) of Delhi, India. Serum Vitamin D levels of 1403 adults (aged 30 years above), 702 from urban and 701 from rural NCR of Delhi, who participated in a representative cross-sectional survey were measured using the quantitative chemiluminescent immunoassay method. The prevalence of Vitamin D deficiency was classified as severe deficient, and insufficient at three serum levels of 25-hydroxyvitamin D–<10, 10–<20, and 20–<30 ng/mL, respectively. The median (interquartile range) 25-hydroxyvitamin D levels in urban and rural areas were 7.7 (5.2, 10.8) ng/mL and 16.2 (10.9, 22.3) ng/mL, respectively. The prevalence of Vitamin D severe deficiency, deficiency, and insufficiency in urban areas were 71%, 27%, and 2%, respectively. The corresponding prevalence in rural areas was 20%, 47%, and 25%. Urban location (odds ratio [OR] [95% confidence interval [CI]: 11.7 [8.6, 15.9]), female gender (OR [95% CI]: 1.5 [1.1, 2.2]), and abdominal obesity (OR [95% CI]:1.5 [1.1, 2.0]) were independently associated with severe deficiency. This study revealed a high prevalence of severe vitamin deficiency among the adult living in NCR, more so among urban areas, women, and obese.

Assessment of soil liquefaction beneath the National Capital Region of Delhi: implications for earthquake-resilient structures

Assessment of soil liquefaction beneath the National Capital Region of Delhi: implications for earthquake-resilient structures

Social marketing framework for anti-littering behavior: an integrated serial mediation model

PurposeLittering has been identified as a significant social issue in India that has negative impact on the environment as well as public health. Social marketing is a powerful tool for changing and sustaining pro-environmental behaviors, but so far no research has been found on the application of social marketing on changing littering behavior in India. This paper aims to integrate the two widely used social marketing models, health belief model (HBM) and theory of planned behavior (TPB), with an extra factor, environmental factors, in a holistic model to understand and predict anti-littering behavior in India.Design/methodology/approachIn total, 750 individuals were interviewed at 34 locations across the states of New Delhi and Punjab (India) in a field survey. For data analysis, confirmatory factor analysis and structural equation model techniques in AMOS 21.0 have been used.FindingsThe results of the study suggest that the perceived benefits and perceived barriers have a significant direct effect on the attitude toward anti-littering behavior. No direct impact of perceived threat has been found on attitude toward anti-littering behavior. The results also confirm the role of attitude, subjective norms and self-efficacy in influencing the anti-littering intention with attitude and subjective norms having a greater impact on intention as compared to self-efficacy. Furthermore, the results also highlight that the environmental factors and intention carry more weights than perceived behavioral control in influencing anti-littering behavior. Recognizing the importance of sequential mediators in the present study, the results of the serial mediation analysis revealed that attitude and intention sequentially mediate the path linking perceived benefits, perceived barriers and anti-littering behavior.Originality/valueTo the best of the authors’ knowledge, the current study in terms of theory building appears to be the first to integrate social marketing models HBM and TPB to explain and understand not only anti-littering behavior but also for setting a platform of behavior change in a larger context.

Correlative Assessment of Water Quality and Qualitative and Quantitative Fish Production from River Yamuna, In the State NCT Delhi

River Yamuna originates from the Yamunotri Glacier at a height 6,387 Metres. Earlier, the waters of the Yamuna were distinguishable as "clear blue", as compared to the silt-laden yellow of the Ganges. However, due to high density population growth and rapid industrialization, today Yamuna is one of the most polluted rivers in India, especially around New Delhi, the capital of India, which dumps about 58% of its treated or partially treated waste into the river. Hence, water quality of river Yamuna has widely been studied with regard to physico-chemical characteristics. The paper presents their impact of pollution on the production of fishes in the state of NCT Delhi. The river Yamuna has been reduced to a small stream due to sewage and industrial effluents draining into it. Even though, the Govt. of India is taking stringent measures to assuage these pollution loads to save an ailing river to flourish the aquatic life, revival is not so eminent. Due to that the production and quality of fish is affected badly. Economical aspects of the state NCT Delhi has also impacted.

Construction and Validation of Inventory for Usage of Social Networking Sites

Communication is the best way to enhance our knowledge. If we talk about education then, knowledge is the key to broaden its horizons. Hence, education and communication have a compact relation with each other. Communication in the past was built by various mediums, i.e. telephone, letters etc. but at present it is through mobiles, email, WhatsApp, Twitter, Facebook, LinkedIn among many. Basically, the communication system is more dependent on Social Networking Sites. The gap of personal communication paved by COVID-19 crisis has been bridged by the usage of Social Networking Sites (SNS). SNS has also emerged as a fast mean of spreading information. This preliminary research records the response of senior secondary students to questions that stimulate the need of usage of SNS to construct and validate a tool for usage of Social Networking Sites among the senior secondary students. For that purpose, 45 statements were prepared related to the various aspects of social networking sites such as social awareness, interpersonal relationship and global knowledge gaining ability among them with the dimension of (i) Social Relationship (ii) Academic oriented (iii) Informativeness (iv) Entertainment oriented (v) Climatic change oriented. The gathered 45 statements were undergone pilot study with 100 senior secondary students in the East Delhi region and on the basis of statistical ‘t’ value statements have been chosen. The reliability and validity have also been found for this inventory. Hence, a complete tool for usage of Social Networking Sites has been constructed and validated through this study and it will be a useful tool to measure the usage of Social Networking Sites among the senior secondary students.

Metabolic non-communicable disease health report of India: the ICMR-INDIAB national cross-sectional study (ICMR-INDIAB-17)

Non-communicable disease (NCD) rates are rapidly increasing in India with wide regional variations. We aimed to quantify the prevalence of metabolic NCDs in India and analyse interstate and inter-regional variations. The Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) study, a cross-sectional population-based survey, assessed a representative sample of individuals aged 20 years and older drawn from urban and rural areas of 31 states, union territories, and the National Capital Territory of India. We conducted the survey in multiple phases with a stratified multistage sampling design, using three-level stratification based on geography, population size, and socioeconomic status of each state. Diabetes and prediabetes were diagnosed using the WHO criteria, hypertension using the Eighth Joint National Committee guidelines, obesity (generalised and abdominal) using the WHO Asia Pacific guidelines, and dyslipidaemia using the National Cholesterol Education Program-Adult Treatment Panel III guidelines. A total of 113 043 individuals (79 506 from rural areas and 33 537 from urban areas) participated in the ICMR-INDIAB study between Oct 18, 2008 and Dec 17, 2020. The overall weighted prevalence of diabetes was 11·4% (95% CI 10·2-12·5; 10 151 of 107 119 individuals), prediabetes 15·3% (13·9-16·6; 15 496 of 107 119 individuals), hypertension 35·5% (33·8-37·3; 35 172 of 111 439 individuals), generalised obesity 28·6% (26·9-30·3; 29 861 of 110 368 individuals), abdominal obesity 39·5% (37·7-41·4; 40 121 of 108 665 individuals), and dyslipidaemia 81·2% (77·9-84·5; 14 895 of 18 492 of 25 647). All metabolic NCDs except prediabetes were more frequent in urban than rural areas. In many states with a lower human development index, the ratio of diabetes to prediabetes was less than 1. The prevalence of diabetes and other metabolic NCDs in India is considerably higher than previously estimated. While the diabetes epidemic is stabilising in the more developed states of the country, it is still increasing in most other states. Thus, there are serious implications for the nation, warranting urgent state-specific policies and interventions to arrest the rapidly rising epidemic of metabolic NCDs in India. Indian Council of Medical Research and Department of Health Research, Ministry of Health and Family Welfare, Government of India.

Seasonal variation of biochemical parameters and air pollution tolerance index (APTI) of selected plant species in Delhi city, and detailed meta-analysis from Indian metropolitan cities

Plants act as the primary barrier to atmospheric pollution. Thus, while considering the role of plants in restricting air pollutants, one of the environmental tools used to assess tolerant plant species is the Air Pollution Tolerance Index (APTI). We conducted a study in Delhi during the pre-and post-monsoon seasons at polluted (Nehru Nagar) and non-polluted (Pusa Road) sites to assess the site-specific and seasonal variation of biochemical parameters (ascorbic, pH, relative water content, and chlorophyll) and APTI. Two-way ANOVA, Pearson's test, and Principal Component Analysis (PCA) were performed for the statistical analysis. Also, a detailed meta-analysis of biochemical parameter and their influence on APTI based on published literature in metropolitan cities of India has been performed. The study conducted in Delhi at a non-polluted site found higher APTI in the pre-monsoon than post-monsoon season. In addition, the maximum ascorbic acid content of 2.47 mg/g (Azadirachta indica A.Juss) and 1.37 mg/g (Ficus benjamina L) was reported at polluted and non-polluted sites, respectively, during pre-monsoon. The detailed meta-analysis of published literature revealed that plants' chlorophyll and ascorbic acid content were more at polluted sites than at non-polluted sites and substantially impacted APTI calculation. Linear regression showed a significant correlation of ascorbic acid (Mangifera indica L, R2 = 0.9670) and chlorophyll content (Polyalthia longifolia (Sonn.), R2 = 0.958) with APTI. Also, in the PCA test, measured characteristics of different species were strongly correlated, with 72.55% of overall variability. Furthermore, the meta-analysis revealed the average APTI of the plants in the following order Ficus benghalensis L > Polyalthia longifolia > M.indica > A. indica > Delonix regia (Boj. ex Hook) > Ficus religiosa L. A substantial seasonal, site-specific variation and statistically significant differences in the biochemical parameters of plants were observed. These results could be used to classify plant species as air pollution sinks and bioindicators.

Assessment of Type 2 Diabetes Risk in General Population using Bitter Taste Sensitivity Status to Phenylthiocarbamide - A Pilot Study

Eating habits and genetic factors contribute to diseases such as obesity and Type 2 Diabetes Mellitus (T2DM). Variation in bitter taste perception has been linked with intake of alcohol, coffee, vegetable, and smoking habit as well as with adiposity, a risk factor for diabetes development. Therefore, it was hypothesized that bitter taste perception could lead to differences in eating/drinking behavior among individuals, which may lead T2DM development later in the life. Bitter taste sensitivity was assessed using paper strips having supra-threshold concentration of Phenyl Thio Carbamide (PTC). Lifestyle variables were assessed using standard anthropometry measurements and a questionnaire. T2DM risk was assessed using a point based system developed by Finnish Diabetes Association (FINDRISC score). SPSS software was used for statistical analysis. A total of 498 volunteers from New Delhi region participated in the present study, where the mean age of PTC tasters was 24 ± 12 years and for non-tasters was 29 ± 16 years. PTC taster status was significantly correlated with age (p ≤ 0.01), weight (p ≤ 0.05), BMI (p ≤ 0.05) and waste circumference (p ≤ 0.05). A positive correlation was observed for type of chocolate liking (r = 0.113, p ≤ 0.001) and for T2DM risk (p ≤ 0.012) with PTC non-taster status. Logistic regression analysis showed that PTC non-taster individuals are at a higher risk (OR: 1.558, 95% CI: 1.037-2.342, p=0.033) for developing T2DM in the next ten years. Present results have shown that bitter taste sensitivity modulates liking towards certain food and non-tasters for PTC have a higher BMI, weight and are at a higher risk for T2DM development. PTC tasting could be employed as a method for assessing risk of diabetes in healthy individuals. We recommend large scale screening among young adults to promote awareness and early prevention measures.

Indraprastha School: A Rendezvous of Colonial Modernity in Delhi City

Indraprastha School: A Rendezvous of Colonial Modernity in Delhi City

Effect of CSR activities on customers’ purchase intention: The mediating role of trust

Corporate social responsibility (CSR) is the belief that businesses must consider ethical, social, and environmental issues when making decisions. Several studies have found that CSR improves corporate performance. Moreover, CSR is thought to increase consumer trust and purchase intention. This study investigates the effect of CSR activities on customers’ purchase intentions and the mediating role of trust. It draws linkages and establishes that businesses can improve their bottom lines while positively affecting society by acting responsibly.The present study selected two independent variables (ethical corporate social responsibility and legal corporate social responsibility) alongside one dependent variable (purchase intention) and one intervening variable (trust). The sample comprises two hundred and thirty respondents from India’s National Capital Region (NCR), Delhi, and nearby regions. The data were examined using structural equation modeling (SEM). The results showed that CSR activities significantly positively affected customers’ purchase intention. Furthermore, trust mediated the linkages between CSR activities and customers’ purchase intention. The findings suggest that CSR activities can increase customers’ purchase intention by fostering trust. In other words, when customers trust a firm, they are more likely to intend to purchase its products or services. CSR managers should therefore focus on creating and maintaining trust with their customers. In addition, findings suggest that firms should be conscious of their social image. This study offers significant insights because they show that any business can be successful and socially responsible. Acknowledgment This study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).

Distributed mathematical model for simulating temperature profile in landfill

Elevated Landfill temperatures have an undesirable effect on landfill cover, stability, slope and leachate migration pattern. Thus, to predict the temperature profile in the landfill a distributed numerical model using MacCormack finite difference method is developed. The developed model considers stratification of the upper and lower layers of the waste as new and old waste by assigning different values of heat generation for aerobic and anaerobic processes. Further, as the new layers of the waste get accumulated over the older layers, the density, moisture content and hydraulic conductivity of the underlying waste layers get modified. The mathematical model utilizes a predictor–corrector approach with a Dirichlet boundary at the surface and no flow condition at the bottom. The developed model is applied to the Gazipur site located in Delhi in India. A correlation coefficient of 0.8 and 0.73 is obtained between the simulated and observed temperatures in calibration and validation respectively. The result shows that the temperature at all the depths and in all the seasons was found to be higher than the atmospheric temperature. The maximum difference of 333 °C was observed in December, and the minimum difference of 22 °Cs was observed in June. The temperature rise is higher in the upper waste layers as it undergoes aerobic degradation. The locus of the maximum temperature gets modified with moisture movement. Since the developed model shows a good agreement with the field observation, it can be used to predict the temperature variation within the landfill under different climatic conditions.

Active faults studies in Delhi and national capital region (NCR): Inferences from satellite data and field investigations

In recent years, the National Capital Region (NCR) of Delhi has experienced several earthquakes ranging in magnitude from 1.0 to 6.7. According to the last 50 years of earthquake data, the majority of earthquakes in the NCR have occurred near the Mahendragarh Dehradun Fault (MDF) and the Sohna Fault (SF). The region is bounded by a number of subsurface Ridges, Faults, and Lineaments, which are also influenced by the active plate boundary of the Indian and Eurasian plates. Active fault mapping is critical for the precise identification and marking of active fault traces in the NCR area for a precise seismic hazard assessment. We used high resolution Cartosat-1 stereopair data obtained from NRSC, Hyderabad, and Anaglyph (A 3D representation of the surface) and DEM prepared with ENVI software to map the active faults. We identified 12 sites in the NCR region based on satellite data interpretation, primarily along the MDF and Sohna Fault and their extensions. The presence of tectono-geomorphic markers along the MDF and Sohna Fault, such as warped surfaces indicative of fault scarps, stream offsets, gully erosion, and sag ponds, suggests active tectonic movement along these faults, most likely in the recent geological past. We believe the MDF is a right-lateral strike-slip fault with a compressional component on the western side and an extensional component on the eastern side. It acts as a segment boundary between compressional and extensional boundaries. We also identified the right lateral Nuh-Jhirka fault (NJF), which can be the Sohna Fault’s southern extension from Nuh to Jhirka. The western limb of the Delhi Mega fold has also seen a few right-lateral strike-slip movements that have extended up to the eastern bank of the Yamuna River, where the river reflects the base-level change and tight meandering on its upward side and a straight pattern on its downward side. This fault is known as the Delhi Fault (DF). The findings are preliminary, and further research would be required to create a detailed active fault map of the Delhi-NCR region to conduct a precise Seismic Hazard Assessment (SHA) of the region.

The homeless of urban India under COVID‐19 lockdown: Rethinking their rights and the role of public policy

AbstractHomelessness comes with a slew of challenges and a never‐ending struggle for survival. This study examines the impact of the pandemic and lockdown on the homeless population of urban India. The study reflects on the experiences of the homeless in the major cities of India during the first wave of the pandemic as well as explores the aspect of their rights and the role of government policy in this regard. The methodology of the study is qualitative in nature, where secondary data collected from various sources such as news articles, blogs, books, journals, and reports are put to analysis for deriving the findings. The study reveals the sufferings of the homeless in the cities of Delhi, Mumbai, Kolkata, Bengaluru, and Chennai. Their fight is not merely with the SARS‐CoV‐2 virus but also with hunger, unemployment, and neglect faced by them. The study highlights the fact that their rights are being compromised due to drawbacks relating to policy implementation. The study concludes with certain suggestions such as, addressing the loopholes in policy implementations and creating sustainable livelihood opportunities.

Traffic congestion forecasting using multilayered deep neural network

ABSTRACT This study proposes a multilayered deep neural network (MLDNN) and a congestion index (CI) based on traffic density factor to forecast traffic congestion directly. Data were collected in Delhi city from a selected location using video cameras during peak hours of weekdays from Monday to Sunday to test the proposed model. Collected data were categorized in a matrix format in the intervals of five-minutes. The input matrix was divided into a number of intervals to train, validate, and test the MLDNN and baseline models, including support vector regression, multi-layer perceptron neural network, gated recurrent unit (GRU) neural network, long short-term memory (LSTM) neural network, convolutional neural network (CNN), CNN-GRU neural network, and CNN-LSTM neural network. Results of the study show that the MLDNN and proposed CI can be applied to predict traffic congestion successfully in heterogeneous traffic.