Type of resources
Contact for the resource
This dataset contains weekly volatile organic compounds (VOCs) measurements from the Kirby Misperton site. British Geological Survey (BGS), the universities of Birmingham, Bristol, Liverpool, Manchester and York and partners from Public Health England (PHE) and the Department for Business, Energy and Industrial Strategy (BEIS), are conducting an independent environmental baseline monitoring programme near Kirby Misperton, North Yorkshire and Little Plumpton, Lancashire. These are areas where planning permission has been granted for hydraulic fracturing. The monitoring allows the characterisation of the environmental baseline before any hydraulic fracturing and gas exploration or production takes place in the event that planning permission is granted. The investigations are independent of any monitoring carried out by the industry or the regulators, and information collected from the programme will be made freely available to the public. ----------------------------------------------------------------------------------------------- If you use these data, please note the requirement to acknowledge use. Use of data and information from the project: "Science-based environmental baseline monitoring associated with shale gas development in the Vale of Pickering, Yorkshire (including supplementary air quality monitoring in Lancashire)", led by the British Geological Survey Permission for reproduction of data accessed from the CEDA website is granted subject to inclusion of the following acknowledgement: "These data were produced by the Universities of Manchester and York (National Centre for Atmospheric Science) in a collaboration with the British Geological Survey and partners from the Universities of Birmingham, Bristol and Liverpool and Public Health England, undertaking a project grant-funded by the Department for Energy & Climate Change (DECC), 2015-2016. " ----------------------------------------------------------------------------------------------------------
This dataset contains weekly volatile organic compounds (VOCs) measurements from the Little Plumpton site. British Geological Survey (BGS), the universities of Birmingham, Bristol, Liverpool, Manchester and York and partners from Public Health England (PHE) and the Department for Business, Energy and Industrial Strategy (BEIS), are conducting an independent environmental baseline monitoring programme near Kirby Misperton, North Yorkshire and Little Plumpton, Lancashire. These are areas where planning permission has been granted for hydraulic fracturing. The monitoring allows the characterisation of the environmental baseline before any hydraulic fracturing and gas exploration or production takes place in the event that planning permission is granted. The investigations are independent of any monitoring carried out by the industry or the regulators, and information collected from the programme will be made freely available to the public. ----------------------------------------------------------------------------------------------- If you use these data, please note the requirement to acknowledge use. Use of data and information from the project: "Science-based environmental baseline monitoring associated with shale gas development in the Vale of Pickering, Yorkshire (including supplementary air quality monitoring in Lancashire)", led by the British Geological Survey Permission for reproduction of data accessed from the CEDA website is granted subject to inclusion of the following acknowledgement: "These data were produced by the Universities of Manchester and York (National Centre for Atmospheric Science) in a collaboration with the British Geological Survey and partners from the Universities of Birmingham, Bristol and Liverpool and Public Health England, undertaking a project grant-funded by the Department for Energy & Climate Change (DECC), 2015-2016. " ----------------------------------------------------------------------------------------------------------
This dataset contains hourly VOC concentration measurements from the University of York's two-dimensional gas chromatography with flame ionisation detection instrument. This instrument was located at the Indira Gandi Delhi Technical University for Women (IGDTUW). Mixing ratios are reported in parts per billion by volume (ppbV). The stationary inlet was located on the roof of a single-story building. Calibrations have been performed using a certified NPL 30 component mixture. Certain C4 substituted monoaromatic compounds have been tentatively identified. Monoterpenes have been quantified based on their relative response to liquid injections. The data were collected as part of the DelhiFlux project part of Air Pollution & Human Health in a Developing Indian Megacity (APHH-India) programme.
This dataset contains volatile organic compound concentration measurements made by the University of Lancaster using the UK CEH proton transfer reaction-quadrupole ion guide time of flight-mass spectrometer (PTR-QiTOF-MS). Measurements were made at the Indira Gandhi Delhi Technical University for Women (IGDTUW) field site, Dehli, India during the DelhiFlux campaigns. Measurements from 04/10/2018 to 04/11/2018 were made at ground level (~4 m). Measurements from 05/11/2018 to 23/11/2018 were made at 30 m above ground level. All values are reported in ppbV (parts per billion by volume). The data were collected as part of the DelhiFlux project part of Air Pollution & Human Health in a Developing Indian Megacity (APHH-India) programme.
This dataset contains volatile organic compound (VOC) fluxes recorded during two intensive field campaigns in Beijing (winter: 12/11/2016 - 10/12/2016; and summer: 15/05/2017 - 24/06/2017) as part of the Atmospheric Pollution & Human Health in a Chinese Megacity (APHH) programme. VOC concentrations were recorded using the GIG: Proton Transfer Reaction-Time of Flight- Mass Spectrometer (PTR-ToF-MS). Measurements were made at 102 m on the Institute of Atmospheric Physics (IAP) meteorological mast. Fluxes were processed by Lancaster University.
This dataset contains hourly online measurements of VOC mixing ratios using Gas Chromatography with Flame Ionisation Detector (GC-FID) at Indira Gandhi Delhi Technical University for Women (IGDTUW), Dehli, India. Mixing ratios are reported in parts per billion by volume (ppbV). The stationary inlet was located on the roof of a single-story building. This data was collected over two measurements periods (28/05/2018 - 05/06/2018 and 05/10/2018 - 27/10/2018), for the APHH-India DelhiFlux project, by the University of York. Data analysis was completed by Beth Nelson and Jim Hopkins at the University of York. Mixing ratios for the following species are included: ethane, ethene, propane, propane, iso-butane, n-butane, acetylene, trans-2-butene, 1-butene, iso-butene*, cis-2-butene, cyclopentane*, iso-pentane, n-pentane, 1,3-butadiene, trans-2-pentene, 1-pentene, n-octane, n-hexane, isoprene, n-heptane, benzene, toluene, ethylbenzene, combined m,p-xylene, o-xylene, methanol, acetone, ethanol, 1,2-butadiene*, propyne*. Date and time given in Local time as Julian day where 2018 01 01 = 0 Calibrations have been performed using a certified NPL 30 component mixture, and certified NPL 6 component mixture for o-VOC calibration. NOTE: any compound not contained therein has been assumed to have the same response factor as its closest isomer*. The data were collected as part of the DelhiFlux project part of Air Pollution & Human Health in a Developing Indian Megacity (APHH-India) programme.
This dataset contains volatile organic compounds (VOC) and ultrafine particle measurements collected onboard several vehicles for the TRANSITION Clean air Network during May 2021 in the London and surrounding area. The particles measured include VOCs captured in the field using thermal desorption tubes, and then analysed into the component species using highly sensitive Markes International GCxGC-TOF-MS system, and ultrafines data captured using a V2000 sensor from National Air Quality Testing Services by Emissions Analytics. Therefore, a much wider range of pollutants have been tested than in standard air quality monitoring. The harm caused by emissions from vehicles to air quality and the health of humans outside is increasing well understood and It is generally accepted that it is a policy priority to remove high-emitting vehicles from the road and to swap for low-emission vehicles or public transport. What is less well understood is the exposure of the occupants in various transportation modes. Aggregate time spent in vehicles is significant, and can be measured in hours per day for certain commuters and professional drivers. Existing research by Emissions Analytics shows that the worst-performing cars can have particle number concentrations more than three times than in the ambient air. With Net Zero, particles are likely to be the dominant traffic pollutant. This dataset contains interior air quality measurements made on a range of modes of transport including diesel and electric trains, the London Underground, diesel and electric buses, and old and new cars, including a battery electric.
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 state from years where per capita data was available. It was possible to link the Household Consumption of Goods and Services in India and the Energy Sources of Indian Households for Cooking and Lighting surveys for the years 2005, 2010 and 2011. This was done using years where the number of households per 1000 households and kg capita-1 fuel usage statistics were available, as it was possible to calculate the amount of fuel a primary user would use. The fuel use of a primary user here was defined as the amount of fuel a person would burn who was recorded to use a specific fuel type. For example, if the per capita consumption in the Household Consumption of Goods and Services survey in India for fuel wood was 10 kg per capita per 30 days, and the Energy Sources of Indian Households for Cooking and Lighting survey showed 250 households per 1000 households used fuel wood, then the fuel use was estimated to be 40 kg per primary user per 30 days. This was achieved by multiplying the per capita usage for a particular fuel type by the inverse of the ratio of fuel usage in that state in rural or urban environments. The amount of fuel a primary user would use was then used to estimate the amount of fuel consumed per capita in years where only usage per 1000 household statistics were available. Cow dung cake consumption was only reported as number of households per 1000 in these surveys and the amount of cow dung cake burnt per primary user was determined based on the energy density compared to fuel wood. This was done using calorimetry data which showed that cow dung cake was 1.3-1.9 times less efficient than fuel wood (EPA, 2000; Gadi et al., 2012). For this reason, the amount of fuel per primary user for fuel wood in a state has been multiplied by 1.6 to give the equivalent amount of cow dung cake a user would need to burn for their cooking needs. Upper and lower estimates for cow dung cake consumption were based on the range 1.3-1.9. This was then converted to fuel use per capita in kg per user per 30 days by rearranging E2. This has been evaluated to validate this approach, which estimated Indian cow dung cake consumption to be in the range 25.7-79.7 Tg yr-1 from 1993-2016. This was generally towards the lower end of consumption values previously reported of 35-128 Tg yr-1 (Habib et al., 2004). For this reason, emission inventory estimates were also compared to those produced using cow dung cake consumption based on the TERI Energy Data Directory and Yearbook (TEDDY) 2012/2013 data and a study from the Petroleum Planning & Analysis Cell (PPAC) from 2016 with population indicated at the 2011 level (TEDDY, 2012; PPAC, 2016). The amount of MSW burnt was estimated using an established approach (IPCC, 2006; Wiedinmyer et al., 2014) with revised inputs for India based on per capita MSW generation from over 300 Indian cities (Annepu et al., 2012), state wise MSW collection figures (CPCB, 2013) as well as estimates of the amount of urban (NEERI, 2010) and rural MSW burnt (World Bank, 2012). This estimate does not include incineration for electrical power generation. Wiedinmyer et al. (2014) assessed worldwide emissions from MSW burning based on IPCC guidelines (IPCC, 2006). The approach used here was similar, with modifications to the input data which made them more specific to India. The approach split the amount of MSW burnt into the MSW burnt by rural and urban populations in the country. For rural populations this was given by per capita rural MSW generation multiplied by the population of rural grid cell multiplied by the fraction of MSW burnt residentially. Per capita rural MSW generation was set at the lower limit indicated by the World Bank for South Asia of 0.12 kg capita-1 day-1 and evaluated in the range 0.08 kg capita-1 day-1 (Parmar and Pamnani, 2018) to 0.12 kg capita-1 day-1 (World Bank, 2012). The fraction of MSW burnt rurally was set to 0.6 which was the IPCC estimate (IPCC, 2006) and was further supported by a recent study which showed that only around 40% of rural MSW was collected in South Asia (Kaza et al., 2018). The fraction of MSW burnt for an urban population was estimated by the sum of two calculations. The first was for street MSW burning which was calculated by per capita urban MSW generation multiplied by the population of urban grid cell multiplied by the fraction of MSW which was not collected multiplied by the fraction burnt. The weighted per capita urban MSW generation was calculated by averaging per capita MSW generation statistics from 366 Indian cities by state (Annepu et al., 2012). The fraction of MSW which was uncollected was calculated from the Central Pollution Control Board (CPCB), as the difference in the amount of MSW generated and collected (CPCB, 2013). Urban per capita MSW generation was scaled to its estimated change for different years of interest. The second calculation was for the MSW burnt on landfill sites, which was calculated by the MSW per capita produced in urban environments, multiplied by the urban population, multiplied by the fraction collected in an urban environment multiplied by the fraction burnt at the landfill site. The fraction of MSW collected came from CPCB statistics, but was reduced by 17-50% due to the informal recycling sector, based on very limited data from studies focussed on MSW recovery by the informal sector which showed 17% recovery in Delhi (Talyan et al., 2008), 20% recovery at a landfill site in Pune (Annepu et al., 2012), 4% in Pondicherry (Rajamanikam et al., 2014) and up to 40-50% in Mohali (Nandy et al., 2015). This was due to the large contribution of the informal recycling sector to recycling in India, where waste was collected by waste merchants, garbage collectors and waste pickers from highways, waste depots and landfill sites. This was an important consideration in India as studies have shown recovery of between 8.5-80 kg of material per picker per day and large cities such as Delhi having 80,000-100,000 pickers (Nandy et al., 2015). The fraction of waste burnt in a dump (Bfrac,dump) was given by NEERI who estimated that 10% of landfill MSW in Mumbai was burnt (NEERI, 2010). This was reinforced by a further study which examined the amount of waste burnt based on satellite studies of a landfill site in India which showed that approximately 10% of the waste that entered the site each day ended up being burnt (Sharma et al., 2019). Bfrac,dump was notably lower here (0.1) than in Wiedinmyer et al. (2014) (0.6) which was based on the 2006 IPCC Guidelines for National GHG Inventories. The estimate used in this study represented a conservative estimate of NMVOC emissions from landfill fires. Due to lack of reliable data in establishing Bfrac,dump, and the associated uncertainty, the sensitivity of urban landfill burning emissions over the range 0.1-0.6 was evaluated as part of the uncertainty range given in this study. This provided the upper limit to the uncertainty range of the potential amount of landfill waste burnt. This depicts scenarios before the new MSW management rules in 2016. NMVOC emissions from crop residue burning on fields in India were estimated to evaluate the relative importance of different burning sources using the most up-to-date input data currently available. A calculation was carried out for 2011, as NMVOC emissions from crop-residue burning on fields showed little year-on-year variation from 1995-2009 (Jain et al., 2014). The residue generated from the cultivation of four main categories of crops was estimated. The amount of crop types produced in each state (Ministry of Agriculture, 2012) was collated for cereals (rice, wheat, coarse cereals, maize, jowar, bajra), oilseeds (groundnut, rapeseed, mustard, sunflower and 9 oilseeds), fibres (cotton, jute and mesta) and sugarcane. The amount burnt was calculated using India specific estimates of the residue to crop ratio, dry matter fraction and fraction burnt (Jain et al., 2014). Emissions were estimated using factors from recent studies of crop residues routinely burnt on fields using PTR-ToF-MS (Stockwell et al., 2015). When the exact residue was measured (e.g., rice straw, wheat straw, sugarcane and millet) the correct emission factor was used. For cases where the exact residue was not measured, the mean reported crop residue emission factor was used. The spatial distribution of croplands was then either indicated using agricultural land identified by the high-resolution 500 m NASA MODIS land use product reduced to 1 km2 resolution or through croplands identified at 10 km2 through evaluation of the distribution of agricultural lands (Ramankutty et al., 2008). The total amount of crop residue burnt in a state was calculated using the approach given in Jain et al. (2014) but with the up-to-date inputs discussed. The inventories were produced by Gareth Stewart at the University of York. Full details of the methodology are provided in the publication associated with these inventories. The inventories provided here cover most of the land mass of India, but may vary slightly compared to those presented in the publication. This is associated with the North of India, particularly around the Pakistan and Chinese borders. This is due to how the boundaries of India were defined in the base data used for this study (WorldPop and GADM) and changes to states in the North of India after the period of interest (i.e. formation of Ladakh in 2019). An inventory for coal has not been included due to low total emissions. Acronyms PAH = Polycyclic aromatic hydrocarbon NMVOCs = non-methane volatile organic compounds MSW = Municipal solid waste Crop = agricultural crop residue Wood = Fuel wood Dung = Cow dung cakes Charcoal = Charcoal fuel Coal = Coal fuel APHH-INDIA = Atmospheric Pollution and Human Health in an Indian Megacity project RiceEFyearlyVOCKG = Total NMVOC emission in 2011 from agricultural on field burning of agricultural rice residues WheatEFyearlyVOCKG = Total NMVOC emission in 2011 from agricultural on field burning of agricultural wheat, coarse cereal and maize residues JowarEFyearlyVOCKG = Total NMVOC emission in 2011 from agricultural on field burning of agricultural jowar and bajra residues MeanEFyearlyVOCKG = Total NMVOC emission in 2011 from agricultural on field burning of agricultural 9 oilseeds, groundnut, rapeseed, mustard, sunflower, cotton, jute and mesta residues SugarcaneEFyearlyVOCKG (for sugarcane) = Total NMVOC emission in 2011 from agricultural on field burning of agricultural sugarcane residues Bfracdump = fraction of waste burnt in the dump
This dataset contains Volatile Organic Compound (VOCs) measurements made at the Institute of Atmospheric Physics land station, IAP-Beijing, site using the York Gas Chromatograph with Flame Ionisation Detectors (GC-FID) System, during the summer and winter APHH-Beijing campaign for the Atmospheric Pollution & Human Health in a Chinese Megacity (APHH) programme.
This dataset contains Volatile Organic Compounds (VOC) concentrations taken from a large, population-scale study, which was conducted for a total of 19 weeks during the winter and summer of 2019. VOC concentration data were collected for 39 VOC species across 60 houses in Ashford, United Kingdom. Samples were collected in evacuated stainless-steel canisters over 72 hours using restricted flow inlets. A number of houses were randomly selected to also collect an outdoor sample. Each household, per campaign, was associated with at least three canister IDs and some with an additional outdoor sample. This dataset contains information on all VOCs collected, listing in which season each sample was taken, the associated canister ID and the analytical instrument with which each VOC was measured. Household, demographic, and product use information is available, as is a logbook outlining further sample information.