Delhi was the largest city in terms of number of inhabitants in India in 2025. The capital city was estimated to house nearly 35 million people, with Mumbai ranking second that year. India's population estimate was 1.4 billion, ahead of China that same year.

The dataset contains the building damage risk map for the fast growing Indian megacities: New Delhi, Mumbai, Bengaluru, Chennai, and Kolkata, based on the vertical land motion map.Researchers can use ArcGIS/QGIS or any other programming languages supporting GeoTIFF format to extract the values along with the latitude and longitude.

As per a decadal analysis of forest cover change in megacities of India between 2011 and 2021, Hyderabad emerged as the city with a *** percent growth in forest area, followed by Chennai and Delhi. Ahmedabad lost ** percent of its forest cover in a period of ten years.

In a survey conducted in 2022 among respondents from megacities of India, Pune-Pimpri Chinwad emerged on top in terms of inclusive mobility with a score of ****, among all megacities of India. It was closely followed by Mumbai and Bengaluru. The parameter of inclusive mobility includes mobility systems meeting the needs of diverse group of populations including women, children, trans/non-binary, the elderly, the disabled among others.. Megacities are defined as cities with a population of over ************ as per the survey. The Ease of Moving Index is a composite index comprising **** parameters across ** indicators. The parameters include seamless, inclusive, clean, efficient and shared mobility and investment in the city among others.

In a survey conducted in 2022 among respondents from megacities of India, Pune emerged on the top with a score of **** among all megacities of India, followed by Mumbai and Hyderabad. Megacities are defined as cities with a population of over ************, as per the survey. The Ease of Moving Index is a composite index comprising **** parameters across ** indicators. The parameters include seamless, inclusive, clean, efficient, and shared mobility and investment in the city, among others.

In a survey conducted in 2022 among respondents from megacities of India, Surat emerged on the top in terms of clean mobility with a score of ****, among all megacities of India. It was closely followed by Chennai and Pune-Pimpri-Chinwad. The parameter of clean mobility includes impact of air pollution, clean mobility focused policies, willingness to adopt electric mobility, among others. Megacities are defined as the cities with a population of over ************ as per the survey. The Ease of Moving Index is a composite index comprising **** parameters across ** indicators. The parameters include seamless, inclusive, clean, efficient and shared mobility and investment in the city among others.

 Rampant urbanization reduces dairy feed production near Indian cities.  Farmers rely on food leftovers and lakeshore vegetation as alternative feed sources.  Changed diets impacts milk quality, especially the fatty acid profile.  Urban cows’ milk contains more beneficial fatty acids than peri-urban cows’ milk.

Low- and middle-income countries (LMICs) are rapidly urbanizing, leading to a high demand for high-quality animal products. Production increase is seen as a key to meeting this demand and reducing the global environmental impact of low-yielding dairy production system (DPS) often found in LMICs. Therefore, the present study assesses the relationship between enteric methane emissions and different dairy production strategies, taking DPS in the rural–urban interface of Bengaluru, an Indian megacity, as a case study. Twenty-eight dairy farms, evenly distributed across four DPS, were monitored for 1 year (eight visits at 6-week intervals). Following IPCC 2006 guidelines and a Tier 2 approach, enteric methane emissions from dairy cattle were calculated as carbon dioxide equivalents (CO2 eq). Dairy producers in ExtDPS, an extensive DPS found throughout the rural–urban interface of Bengaluru, fed their dairy cattle a high-quality diet, partly based on organic wastes from markets or neighbors, achieving 9.4 kg energy-corrected milk (ECM) per cow and day. Dairy producers in Semi-ADPS, a semi-intensive and rural DPS, fed an average quality diet and achieved the lowest milk production (7.9 kg ECM cow−1 day−1; p 

Multiple regression coefficient table and Pearson correlation coefficient table of NTL density in Indian state after excluding two megacities.

In a survey conducted in 2022 among respondents from megacities of India, Surat emerged on the top in terms of getting closer to the goal of zero road accidents with a score of ****, among all megacities of India. It was closely followed by Pune and Hyderabad. The parameter includes commuter perception, road fatality numbers and other road infrastructure related points. Megacities are defined as the cities with a population of over **** million as per the survey. The Ease of Moving Index is a composite index comprising **** parameters across ** indicators. The parameters include seamless, inclusive, clean, efficient and shared mobility and investment in the city among others.

Yearly citation counts for the publication titled "Analysis of Indian residences in terms of energy efficiency through energy education – a case study of Mumbai megacity".

Urbanization is a major driver of biodiversity change but how it interacts with spatial and temporal gradients to influence the dynamics of plant-pollinator networks is poorly understood, especially in tropical urbanization hotspots. Here, we analyzed the drivers of environmental, spatial, and temporal turnover of plant-pollinator interactions (interaction β-diversity) along an urbanization gradient in Bengaluru, a South Indian megacity. The compositional turnover of plant-pollinator interactions differed more between seasons and with local urbanization intensity than with spatial distance, suggesting that seasonality and environmental filtering were more important than dispersal limitation for explaining plant-pollinator interaction β-diversity. Furthermore, urbanization amplified the seasonal dynamics of plant-pollinator interactions, with stronger temporal turnover in urban compared to rural sites, driven by greater turnover of native non-crop plant species (not managed by people). Our study demonstrates that environmental, spatial, and temporal gradients interact to shape the dynamics of plant-pollinator networks and urbanization can strongly amplify these dynamics.

This dataset and codebook correspond to the initial round of survey data gathered in Delhi and Mumbai (India) in 2023, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.

As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. In the first round of the survey, we recruited a representative sample of approximately 2000 households in each country, taking into account both the individual and household perspectives. In order to consider sufficiency-oriented lifestyles not only in Europe but also in the Global South, we conducted a similar survey in India. More specifically, we adjusted the survey to fit the context (e.g., including cooling) and, due to the large size and diversity within India, we focused data collection on two Mega Cities (>10Mio inhabitants), namely Mumbai and Delhi. Due to the different cultural context and in exchange with Indian researchers and the supporting market research institute, we decided to change the methodology for data collection from an online survey to face-to-face interviews. The survey includes a quantitative assessment of the carbon footprint in various domains of life, such as housing, mobility, and diet. In addition to this, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.

Air Quality & Meteorology Dataset

Dataset Description

This corpus contains 87 672 hourly records (10 variables + timestamp) that realistically emulate air‑quality and local‑weather conditions for Kolkata, West Bengal, India.
Patterns, trends and extreme events (Diwali fireworks, COVID‑19 lockdown, cyclones, heat‑waves) are calibrated to published CPCB, IMD and peer‑reviewed summaries, making the data suitable for benchmarking, forecasting, policy‑impact simulations and educational research.

The data are suitable for time‑series forecasting, machine learning, environmental research, and air‑quality policy simulation while containing no real personal or proprietary information.

File Information

(Rows = 10 years × 365 days (+ leap) × 24 h ≈ 87.7 k)

Columns & Descriptions

Usage

This dataset is ideal for:

• Environmental Research – Analyse seasonal/diurnal pollution dynamics or meteorological drivers.
• Machine‑Learning Benchmarks – Train forecasting, anomaly‑detection or imputation models on multivariate, event‑rich time‑series.
• Policy / “What‑If” Simulation – evaluate NCAP / BS‑VI scenarios without privacy constraints.
• Extreme‑Event Studies – Stress‑test models on Diwali spikes, cyclone wash‑outs, or heat‑wave ozone episodes.
• Teaching & Exploration – Demonstrate correlation analysis, time‑series decomposition, ARIMA/LSTM modelling, etc.

Example Workflows

Data Pre‑processing Tips

• Timestamp Indexing – Set datetime as Pandas DateTimeIndex for resampling (df.resample('D').mean()), lag features, etc.
• Log/Box‑Cox – Stabilise heavy‑tailed pollutant distributions before regression.
• Feature Engineering – Add sine/cosine hour‑of‑day and month‑of‑year terms to capture periodicity.
• Outlier Handling – Retain extreme Diwali and cyclone values for robustness tests rather than trimming.
• Train/Test Split – Use rolling or time‑based splits (e.g., train 2015‑21, validate 2022, test 2023‑24) to respect temporal order.

License

Released under the Creative Commons CC0 1.0 Universal license – free for research, educational, or commercial use.
The data are algorithmically generated and do not contain any real measurements or personal information.

References

1. Guttikunda, S. K., & Jawahar, P. (2020). “Exceedances and trends of particulate matter (PM₂.₅) in five Indian megacities.” Atmospheric Environment, 222, 117125.
2. Centre for Science and Environment (CSE). (2016). Night-time air turns toxic in Kolkata winter: Rapid assessment briefing note. New Delhi: CSE.
3. IQAir. (2020). World Air Quality Report 2019 – City profile: Kolkata, India.
4. Biswas, T., Saha, D., et al. (2022). “Strict lockdown measures reduced PM₂.₅ concentrations during the COVID-19 pandemic in Kolkata, India.” *Sustainable Water Resources Manageme...

Urbanization is a main driver of agricultural transition in the Global South but how it shapes trends of intensification or extensification is not yet well understood. The Indian megacity of Bengaluru combines rapid urbanization with a high demand for dairy products, which is partly supplied by urban and peri-urban dairy producers. To study the impacts of urbaniza- tion on dairy production and to identify key features of dairy production systems across Ben- galuru’s rural-urban interface, 337 dairy producers were surveyed on the socio-economic profile of their household, their dairy herd and management, resources availability and, in- and output markets. A two-step cluster analysis identified four spatially explicit dairy produc- tion systems based on urbanization level of their neighborhood, reliance on self-cultivated forages, pasture use, cattle in- and outflow and share of specialized dairy genotypes. The most extensive dairy production system, common to the whole rural-urban interface, utilized publicly available feed resources and pasture grounds rather than to cultivate forages. In rural areas, two semi-intensive and one intensive dairy production systems relying on self- cultivation of forage with or without pasture further distinguished themselves by their herd and breeding management. In rural areas, the village’s dairy cooperative, which also pro- vided access to inputs such as exotic genotype through artificial insemination, concentrate feeds and health care, was often the only marketing channel available to dairy producers, irrespective of the dairy production system to which they belonged. In urban areas, milk was mostly sold through direct marketing or a middleman. Despite rapidly progressing urbaniza- tion and a population of 10 million, Bengaluru’s dairy sector still relies on small-scale family dairy farms. Shifts in resources availability, such as land and labor, are potential drivers of market-oriented intensification but also extensification of dairy production in an urbanizing environment.

This dataset contains NO, NO2 and NOx mixing ratio measurements using the commercially available Thermo 42C chemiluminescence monitor. Measurements were made at the Indian Institute of Technology Delhi (IIT-Delhi), India. Mixing ratios are reported in parts per billion (ppb). The stationary inlet was located on the roof of a 5-storey building at Block IV, Indian Institute of Technology Delhi campus. The data were collected over three measurement periods (i) winter: 12/01/2018 - 13/02/2018, (ii) pre-monsoon: 26/04/2018 - 05/06/2018 and (iii) post-monsoon: 13/10/2018 - 10/11/2018, by the University of Birmingham. These data were collected as part of the ASAP-Delhi project as part of the Atmospheric Pollution and Human Health in an Indian Megacity (APHH) programme.

Urbanization is a major driver of land use change and biodiversity decline. While most of the ongoing and future urbanization hot spots are located in the Global South, the impact of urban expansion on agricultural biodiversity and associated functions and services in these regions has widely been neglected. Additionally, most studies assess biodiversity responses at local scale (α-diversity), however, ecosystem functioning is strongly determined by compositional and functional turnover of communities (β-diversity) at regional scales. We investigated taxonomic and functional β-diversity of farmland birds across three seasons on 36 vegetable farms spread along a continuous urbanization gradient in Bangalore, a South Indian megacity. Increasing amount of grey area in the farm surroundings was the dominant driver affecting β-diversity and resulting in taxonomic and functional homogenization of farmland bird communities. Functional diversity losses were higher than expected from species dec...

This dataset contains Organic aerosols, NO3-, SO4=, Cl- and NH4+ submicron concentrations in µg m-3 measured with High Resolution Time of Flight Aerosol Mass Spectrometer (HR-ToF-AMS) and Organic aerosol factors (Cooking Organic Aerosol (COA), Nitrogen-rich Hydrocarbon-like Organic Aerosol (NHOA), Solid-Fuel Organic Aerosol (SFOA), Hydrocarbon-like Organic Aerosol (HOA), Semi-Volatility Biomass Burning Organic Aerosol (SVBBOA), Low-Volatility Oxygenated Organic Aerosol (LVOOA), Semi-Volatility Oxygenated Organic Aerosol (SVOOA)) identified using positive matrix factorization. The instrument was located at the Indira Gandhi Delhi Technical University for Women (IGDTUW) from May to Nov 2018. The instrument sampled initially at 7 m above ground level, then was moved to 35 m above ground on the 5th of November 2018.

The data were collected as part of the DelhiFlux project under the Air Pollution & Human Health in a Developing Indian Megacity (APHH-India) programme, and the UKCEH’s SUNRISE programme delivering National Capability to NERC.

This dataset includes the raw data of a survey of 536 local residents along two rural-urban gradients around Bengaluru, India. The dataset sheet presents respondents' socio-demographic characteristics (village, village stratum, gender, age education, religion, length of residence, caste, occupation). It then displays Likert-scale answers, ranging from 1 ("not important" to 5 ("very important"), on agreement about the importance of water-filled and dry lakes (shown on photographs) for the provision of the respective ecosystem services and disservices, based on their livelihood needs and experiences. It then shows the answers to open questions on challenges and management options regarding lakes. The complete method is described in: Plieninger, T., Thapa, P., Bhaskar, D., Nagendra, H., Torralba, M., & Zoderer, B. M. (2022): Disentangling ecosystem services perceptions from blue infrastructure around a rapidly expanding megacity. Landscape and Urban Planning, in press.

Urban slums are hotspots of infectious diseases like COVID-19 as was seen in the waves of 2020 and 2021. One of the primary reasons why slums are disproportionately affected is their location in inaccessible and uninhabitable zones, crowded and poorly ventilated living spaces, unsanitary conditions and common facilities (water taps, common toilets, etc.). Staying at home during pandemics is hardly an option for slum dwellers as it often means giving up work and even basic necessities. This paper aims to understand the habitat vulnerabilities of slums in the two Indian megacities of Pune and Surat which were the worst hit during both waves. The study is done at the level of wards, which is the smallest administrative boundary, taking the habitat vulnerability (congestion and access to basic services). To identify the explanatory variables which increase the vulnerability of slums to infectious diseases, literature study is done on the triggering factors which affect habitat vulnerability derived from common characteristics and definitions of slum. The aim of the research is to categorize the slums into 3 levels of risk zones and map them subsequently. This study will help in formulating a model to prioritize the allocation of sparse resources in developing countries to tackle the habitat vulnerabilities of the slum dwellers especially during health emergencies of contagious diseases like COVID-19.