In 2022, the union territory of Delhi had the highest urban population density of over ** thousand persons per square kilometer. While the rural population density was highest in union territory of Puducherry, followed by the state of Bihar.

Delhi was the largest city in terms of number of inhabitants in India in 2023.The capital city was estimated to house nearly 33 million people, with Mumbai ranking second that year. India's population estimate was 1.4 billion, ahead of China that same year.

BackgroundIn India, acute respiratory infections (ARIs) are a leading cause of mortality in children under 5 years. Mapping the hotspots of ARIs and the associated risk factors can help understand their association at the district level across India.MethodsData on ARIs in children under 5 years and household variables (unclean fuel, improved sanitation, mean maternal BMI, mean household size, mean number of children, median months of breastfeeding the children, percentage of poor households, diarrhea in children, low birth weight, tobacco use, and immunization status of children) were obtained from the National Family Health Survey-4. Surface and ground-monitored PM2.5 and PM10 datasets were collected from the Global Estimates and National Ambient Air Quality Monitoring Programme. Population density and illiteracy data were extracted from the Census of India. The geographic information system was used for mapping, and ARI hotspots were identified using the Getis-Ord Gi* spatial statistic. The quasi-Poisson regression model was used to estimate the association between ARI and household, children, maternal, environmental, and demographic factors.ResultsAcute respiratory infections hotspots were predominantly seen in the north Indian states/UTs of Uttar Pradesh, Bihar, Delhi, Haryana, Punjab, and Chandigarh, and also in the border districts of Uttarakhand, Himachal Pradesh, and Jammu and Kashmir. There is a substantial overlap among PM2.5, PM10, population density, tobacco smoking, and unclean fuel use with hotspots of ARI. The quasi-Poisson regression analysis showed that PM2.5, illiteracy levels, diarrhea in children, and maternal body mass index were associated with ARI.ConclusionTo decrease ARI in children, urgent interventions are required to reduce the levels of PM2.5 and PM10 (major environmental pollutants) in the hotspot districts. Furthermore, improving sanitation, literacy levels, using clean cooking fuel, and curbing indoor smoking may minimize the risk of ARI in children.

As of 2019, the capital Indian territory of Delhi had the highest density of nurses and midwives of about ** per ten thousand people in the country. However, Bihar had the least density of nurses and midwives in the country of about *** per ten thousand people in the state.

This contains gridded non-methane volatile organic compound (NMVOC) emission inventories for India derived as part of burning studies performed during the APHH-INDIA campaign. For data files with more than 1 million rows, NASA AMES metadata headers have been provided as a separate document, which has the identical name of the data it applies to but also includes _metadata. For years 1993, 1994, 1999, 2002, 2005, 2006, 2007, 2010, 2011 and 2016 inventories have been produced in terms of total NMVOC emission from each source sector (kg/km2). There are also two upper limit scenarios of emissions from cow dung cake combustion based on data from PPAC and PPAC supplemented with additional cow dung cake consumption for states now covered by this survey. The speciation factors of NMVOCs released from particular sources are also provided so that these years can be speciated by source simply by multiplying the total emission from each source by the ratio of species released from the source. This allows future users to produce speciated emission inventories for years other than 2011 if they need. Gridded inventories are also provided for emissions of 21 polycyclic aromatic hydrocarbons for the year 2011 from fuelwood, cow dung cake, charcoal, liquefied petroleum gas and municipal solid waste. These are provided as total PAH emissions from a source with speciation factors also provided to allow speciation should it be required by multiplying the total NMVOC emission from a source by the speciation factors from that source. Gridded inventories are provided for crop residue burning at 1km2 and 10km2. These were calculated with total agricultural area identified in a state from either NASA MODIS (1 km2) or Ramankutty et al. (2008) (10 km2). A second inventory was produced at 10km2 as it was felt that the NASA data offered little variation within respective states. These have been split into total emissions from each of the 5 emission factors applied, RiceEFyearlyVOCKG (for rice), WheatEFyearlyVOCKG (for wheat, coarse cereal and maize), JowarEFyearlyVOCKG (for Jowar and Bajra), MeanEFyearlyVOCKG (for 9 oilseeds, groundnut, rapeseed, mustard, sunflower, cotton, jute and mesta) and SugarcaneEFyearlyVOCKG (for sugarcane). The inventories were produced using emission factors developed as part of the APHH-INDIA project as well as from a different publication focussed on the burning of crops. The inventories have been developed in the following manner. The emission factors used in this study come from a variety of recently published sources. All emission factors applied in this study included measurement by PTR-ToF-MS, a technique well suited to species released in significant quantities from solid fuel combustion such as small oxygenated species, phenolics and furanics. These species are often missed by GC measurement alone. Preference has been given to emission factors from studies which: (1) have many measurements (n), (2) use samples collected from India or (3) use samples collected from similar countries. Fully speciated emission factors are available from the references given. For residential fuel combustion, the emission factors measured by Stewart et al. (2021a) were used and were developed from 76 combustion experiments of fuel wood, cow dung cake, LPG and MSW samples collected from around Delhi. This study was extremely detailed and measured online, gas-phase, speciated NMVOC emission factors for up to 192 chemical species using dual-channel gas chromatography with flame ionisation detection (DC-GC-FID, n = 51), two-dimensional gas chromatography (GC×GC-FID, n = 74), proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS, n = 75) and solid-phase extraction two-dimensional gas chromatography with time-of-flight mass spectrometry (SPE-GC×GC-ToF-MS, n = 28). Comparison of these emission factors to those obtained in similar studies is provided in Stewart et al. (2021a). The emission factors used as part of this study are larger than those measured by Stockwell et al. (2016), Fleming et al. (2018) and several other studies which were based on gas chromatography techniques alone. The emission factors here measure many more NMVOC species, use techniques which target a range of species which more traditional GC analyses do not detect and make online measurements which minimise loss of intermediate-volatility and semi-volatile organic species, which may be lost through the collection of whole air samples, but have been shown to represent a large proportion of total emissions from biomass burning (Stockwell et al., 2015). Emission factors for combustion of crop residues on fields were taken from measurements by Stockwell et al. (2015) made using PTR-ToF-MS of 115 NMVOCs (Stockwell et al., 2015) for wheat straw (n = 6), sugarcane (n=2), rice straw (n=7) and millet (n=2). This study also included the mean crop residue emission factor for 19 food crops, for use when no current emission factor had been comprehensively measured using PTR-ToF-MS. The emission factor applied (38.8 g kg-1) was evaluated against that for crop residues used for domestic combustion in Delhi (37.9 g kg-1). Whilst the values measured by Stockwell et al. (2015) and Stewart et al. (2021a) were comparable, the value from Stockwell et al. (2015) was used as the crop types were more reflective of the crop residues burnt on fields after harvest, compared to those burnt to meet residential energy requirements. The mean emission factor for crop residue combustion on fields was used for specific crop types with smaller levels of cultivation. Emissions from coal burning were estimated using a mean emission factor from the combustion of bituminous coal from China (n = 14), a neighbouring Asian country, made using PTR-ToF-MS. Whilst the chemical composition of the coal may be more important than the development status of the country, there was overall a low level of reported residential coal use and this estimate was included for completeness. A total of 89 NMVOCs were identified, which represented 90-96% of the total mass spectra (Cai et al., 2019). Indian specific PAH emission factors were recently measured in gas- and particle-phases using PTR-ToF-MS and GC×GC-ToF-MS (Stewart et al., 2021). This dataset provided PAH emission factors collected from combustion of fuel wood (n = 16), cow dung cake (n = 3), crop residue from domestic combustion (n = 3), MSW (n = 3), LPG (n = 1) and charcoal (n = 1) samples. High resolution, gridded population data for India (WorldPop, 2017) was used at a resolution of 1 km2. Officially, urban populations in India are defined as having a population density > 400 people km-2, 75% of men employed in non-agricultural industries and a population of town > 5000 people. Rural populations in India cannot be identified simply by having a population density of < 400 people km-2, as some states such as Uttar Pradesh have an average population density of around 800 people km-2. Rural grid squares were therefore identified by calculating the population density threshold in each state in which the sum of the 1km2 grid squares below this threshold correctly reproduced the rural populations in these states from the 2001 and 2011 censuses (Government of India, 2014). A small uncertainty existed over the exact population of India and we used population statics indicated by the 2011 census. NMVOC and PAH emissions from domestic solid fuel combustion were plotted against this high-resolution population data in the R statistical programming language at 1 km2 for 2001 and 2011, with the population datasets scaled to the percentage changes in Indian population indicated by the World Bank for additional years of interest. Preference was given to large fuel usage surveys which included tens to hundreds of thousands of respondents. The Household Consumption of Goods and Services in India survey by the National Sample Survey Office (NSSO, 2007a, 2012a, 2014) gave state-wise kg capita-1 fuel wood, LPG, charcoal and coal burning statistics for rural and urban environments and was used for the years 2004-2005, 2009-2010 and 2011-2012. NMVOC emissions for these years were calculated by multiplying the NMVOC emission factor for the fuel, by the yearly fuel consumption per capita by the population of the grid cell. Data were collected from additional large surveys previously conducted. These surveys collected data in terms of the number of households using specific fuels per 1000 households in different Indian states in rural and urban environments. The Fifth Quinquennial Survey on Consumer Expenditure provided data for 1993-1994 (NSSO, 1997), the Energy Sources of Indian Households for Cooking and Lighting provided data for years 2004-2005, 2009-2010 and 2010-2011 (NSSO, 2007b, 2012b, 2015) and the Household Consumer Expenditure and Employment-Unemployment Situation in India for 2002 and 2006-2007 (NSSO, 2003, 2008). The National Family Health Survey presented India-wide fuel use as a percentage of the population. To reflect spatial variation in fuel use, the raw data from these surveys were accessed (from the DHS Programme, U.S. Agency for International Development), extracted through the SPSS statistics software package and processed in the R programming language. This increased fuel usage data availability as the number of households per 1000 households using specific fuels in Indian states and covered the years 1992-1993, 1998-1999, 2005-2006 and 2015-2016 (International Institute for Population Sciences, 1995, 2000, 2007, 2017). These were extensive datasets with 1992-1993, 1998-1999 and 2005-2006 surveying just under 100,000 households and 2015-2016 around 600,000 households. To allow the incorporation of data from years which were based on the number of households using a particular fuel per 1000 households (1993, 1994, 1999, 2002, 2006, 2007 and 2016), a scaling factor was developed. The scaling factor was based on the ratio of fuel use in the

In 1800, the population of the region of present-day India was approximately 169 million. The population would grow gradually throughout the 19th century, rising to over 240 million by 1900. Population growth would begin to increase in the 1920s, as a result of falling mortality rates, due to improvements in health, sanitation and infrastructure. However, the population of India would see it’s largest rate of growth in the years following the country’s independence from the British Empire in 1948, where the population would rise from 358 million to over one billion by the turn of the century, making India the second country to pass the billion person milestone. While the rate of growth has slowed somewhat as India begins a demographics shift, the country’s population has continued to grow dramatically throughout the 21st century, and in 2020, India is estimated to have a population of just under 1.4 billion, well over a billion more people than one century previously. Today, approximately 18% of the Earth’s population lives in India, and it is estimated that India will overtake China to become the most populous country in the world within the next five years.

In the financial year 2023, the average number of vehicles per one thousand inhabitants in Delhi, India, was ***. Vehicles to population ratio in Delhi experienced steady and subsequent increases from financial year 2006 until 2021, when it reached a peak of *** vehicles per thousand inhabitants.