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Groundwater variation can cause land-surface movement, which in turn can cause significant and recurrent harm to infrastructure and the water storage capacity of aquifers. The capital cities in the England (London) and India (Delhi) are witnessing an ever-increasing population that has resulted in excess pressure on groundwater resources. Thus, monitoring groundwater-induced land movement in both these cities is very important in terms of understanding the risk posed to assets. Here, Sentinel-1 C-band radar images and the persistent scatterer interferometric synthetic aperture radar (PSInSAR) methodology are used to study land movement for London and National Capital Territory (NCT)-Delhi from October 2016 to December 2020. The land movement velocities were found to vary between −24 and +24 mm/year for London and between −18 and +30 mm/year for NCT-Delhi. This land movement was compared with observed groundwater levels, and spatio-temporal variation of groundwater and land movement was studied in conjunction. It was broadly observed that the extraction of a large quantity of groundwater leads to land subsidence, whereas groundwater recharge leads to uplift. A mathematical model was used to quantify land subsidence/uplift which occurred due to groundwater depletion/rebound. This is the first study that compares C-band PSInSAR-derived land subsidence response to observed groundwater change for London and NCT-Delhi during this time-period. The results of this study could be helpful to examine the potential implications of ground-level movement on the resource management, safety, and economics of both these cities.

Nowadays, monitoring and prediction of air quality parameters are becoming significantly important research topics in the context of increasing urbanization and industrialization. Therefore, efficient modelling of air quality parameters is essential because such an approach would enable to identify the existing and forthcoming implication of air pollution. In recent years, sharp rise in air pollution levels in Indian National Capital Territory of Delhi (NCT-Delhi) has made it the most polluted city of the world. Machine learning approaches are considered as an efficient and cost-effective method to model the air quality parameters and are widely used. However, current methods fail to incorporate long-term dependencies arising due to complex interaction of natural and anthropogenic factors. The present study is mainly aimed at predicting O3, PM2.5, NOx, and CO concentrations at a location in NCT-Delhi using the long short-term memory (LSTM) approach, which is considered as more efficient over other deep learning methods. Factors and parameters such as vehicular emissions, meteorological conditions, traffic data, and pollutant levels are employed in five different combinations. Performance evaluation of LSTM algorithms for hourly concentration prediction is carried out during 2008–2010, and it is found that LSTM models efficiently deal with the complexities and is immensely effective in ambient air quality forecasting. This paper can be considered as a significant motivation for carrying research on urban air pollution using latest LSTMs and helping the government and policymakers a better forecasting methodology for planning measures to curb ill impacts of degrading air quality.

One out of three people in India is urban. In 2011, there were about 53 urban agglomerations larger than 1 million population as against only 35 in 2001. Much of this urban expansion has been occurring in the country’s largest metropolises including the National Capital Territory of Delhi which has expanded horizontally and vertically both. This has also added to overall decline in its already dilapidated housing stock and quality of life. Delhi, a historical hub for regional, national, and international commerce, and a place for the socio-political elites, has failed to provide basic life amenities to its average citizens. This research critiques the (un)sustainable elements of Delhi’s urbanization and concomitant decline in basic amenities pertaining to quality-of-life by examining the growth and expansion of its urban-built-up areas during 2001–2011–2020 and provides nuanced insights into its ‘livability’ by examining select quality-of-life attributes. The LANDSAT imageries for 2010 and 2020 are used to measure NDB-Index that assesses its built-up area and change, which are later corroborated with Census household data to examine change in its ‘livable’ and ‘dilapidated’ housing structures. Significant sub-regional disparity exists in the availability of good and livable homes, with almost 20–30% of several districts still without drinking water source inside premises. However, significant progress is also noted for basic amenities like lighting, latrine and bathing facilities, and majority of Delhi’s built-up area has expanded along newer developments and transportation corridors. This calls for goal-oriented strategic interventions by policymakers to help achieve the SDG-11 on Sustainable Cities.

This study examines the intricate relationship between Delhi's urban water system and environmental factors, with a specific emphasis on the challenges posed by urbanization. It investigates the present conditions of the water resources, infrastructure, and governance in the National Capital Territory (NCT) of Delhi and identifies significant difficulties, such as depletion of groundwater and inadequate access to water. Additionally, it explores innovative approaches to strengthen supply internalization, such as rainfall harvesting, wastewater recycling, and demand management strategies. Therefore, this document establishes a path for future research endeavors and policy suggestions that aim to promote a fair, effective, and environmentally sustainable future for water in Delhi.

A policy of urban decentralisation, limiting or discouraging the growth of population, has been commonly adopted for spatial planning in developing countries over the past decades. Delhi is in a transition stage and sprawling over the years to nearby periphery towns, but NCT Delhi’s urban influence decreases with the increasing distance from the core. Delhi’s urbanization process highly influences the towns within a radius of 50 km from the core. Some Towns located beyond 100 km of radius are also experiencing high growth of urban population because they are the nodal points surrounding the rural hinterland. The later decades of urban development in NCR Delhi led to a high rate of population growth because of the improved employment growth and other economic opportunities that came as a result of inflow of capital and investment to the region. On the other hand, decentralization of employment, urban growth, coupled with the services and housing supply promotes the growth of main and small urban centres in the periphery which is further boosted by a good transport network, connecting and integrating the National Capital Region. Delhi is experiencing declining growth of population and on the other hand, periphery towns are getting boosted by the urban growth.