A Bayesian Spatiotemporal Model to Estimate Long-Term Exposure to Outdoor Air Pollution at Coarser Administrative Geographies in England and Wales

Mukhopadhyay, Sabyasachi; Sahu, Sujit K.

doi:10.1111/rssa.12299

Cited by 30 publications

(55 citation statements)

References 40 publications

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“…A better approach would be to pool information from specific locations towards the general population through a hierarchy of the city and the Tier to which it belongs [6]. In presence of hierarchical fixed and random effects of temporal or spatial nature, Bayesian hierarchical model have been used to assess impact of human activities on the concentration of PM 2.5 [6,[78][79][80]. Bayesian models use prior knowledge of parameter distribution along with the likelihood function to calculate posterior distribution of the parameters.…”

Section: Hierarchical Bayesian Framework For Lurmentioning

confidence: 99%

Impact of Urban Growth on Air Quality in Indian Cities Using Hierarchical Bayesian Approach

2019

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Several studies have found rising ambient particulate matter (PM 2.5 ) concentrations in urban areas across developing countries. For setting mitigation policies source-contribution is needed, which is calculated mostly through computationally intensive chemical transport models or manpower intensive source apportionment studies. Data based approach that use remote sensing datasets can help reduce this challenge, specially in developing countries which lack spatially and temporally dense air quality monitoring networks. Our objective was identifying relative contribution of urban emission sources to monthly PM 2.5 ambient concentrations and assessing whether urban expansion can explain rise of PM 2.5 ambient concentration from 2001 to 2015 in 15 Indian cities. We adapted the Intergovernmental Panel on Climate Change’s (IPCC) emission framework in a land use regression (LUR) model to estimate concentrations by statistically modeling the impact of urban growth on aerosol concentrations with the help of remote sensing datasets. Contribution to concentration from six key sources (residential, industrial, commercial, crop fires, brick kiln and vehicles) was estimated by inverse distance weighting of their emissions in the land-use regression model. A hierarchical Bayesian approach was used to account for the random effects due to the heterogeneous emitting sources in the 15 cities. Long-term ambient PM 2.5 concentration from 2001 to 2015, was represented by a indicator R (varying from 0 to 100), decomposed from MODIS (Moderate Resolution Imaging Spectroradiometer) derived AOD (aerosol optical depth) and angstrom exponent datasets. The model was trained on annual-level spatial land-use distribution and technological advancement data and the monthly-level emission activity of 2001 and 2011 over each location to predict monthly R. The results suggest that above the central portion of a city, concentration due to primary PM 2.5 emission is contributed mostly by residential areas (35.0 ± 11.9%), brick kilns (11.7 ± 5.2%) and industries (4.2 ± 2.8%). The model performed moderately for most cities (median correlation for out of time validation was 0.52), especially when assumed changes in seasonal emissions for each source reflected actual seasonal changes in emissions. The results suggest the need for policies focusing on emissions from residential regions and brick kilns. The relative order of the contributions estimated by this study is consistent with other recent studies and a contribution of up to 42.8 ± 14.1% is attributed to the formation of secondary aerosol, long-range transport and unaccounted sources in surrounding regions. The strength of this approach is to be able to estimate the contribution of urban growth to primary aerosols statistically with a relatively low computation cost compared to the more accurate but computationally expensive chemical transport based models. This remote sensing based approach is especially useful in locations without emission inventory.

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Section: Hierarchical Bayesian Framework For Lurmentioning

confidence: 99%

Impact of Urban Growth on Air Quality in Indian Cities Using Hierarchical Bayesian Approach

2019

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“…Urban-rural category for each LSOA was based on 2001 Census data (Office for National Statistics, 2004). Modelled ambient air pollution data (Mukhopadhyay and Sahu, 2017) were obtained via the MEDMI projecta (Fleming et al, 2014). Annual averages of background concentrations of NO 2 , PM 2.5 , and ozone were obtained for a 1 km grid for the years 2007–11.Data on long-term average weather parameters for the ten-year period 2006 to 2015 were obtained from the Met Office, also via the MEDMI platform.Income, employment and education deprivation scores for 2011 were obtained from the indices of deprivation for 2015 (Department for Communities and Local Government, 2015).…”

Section: Methodsmentioning

confidence: 99%

“…Modelled ambient air pollution data (Mukhopadhyay and Sahu, 2017) were obtained via the MEDMI projecta (Fleming et al, 2014). Annual averages of background concentrations of NO 2 , PM 2.5 , and ozone were obtained for a 1 km grid for the years 2007–11.…”

Section: Methodsmentioning

confidence: 99%

“…The Medical and Environmental Data Mash-up Infrastructure (MEDMI) project connected diverse climate, environment, and human health databases (Fleming et al, 2014). Ambient air pollution data from MEDMI were high resolution modelled estimates based on data from the UK air pollution monitoring network combined with a UK-specific air quality dispersion model, empirically verified through cross-validation (Mukhopadhyay and Sahu, 2017). Annual averages of background concentrations of NO 2 , PM 2.5 , and ozone were obtained for a 1 km grid (for the years 2007–11); and aggregated to the LSOA boundaries for national analyses and postcode point locations for local analyses.…”

Section: Methodsmentioning

confidence: 99%

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Household energy efficiency and health: Area-level analysis of hospital admissions in England

Sharpe

Machray

Fleming

et al. 2019

Environment International

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IntroductionFuel poverty affects up to 35% of European homes, which represents a significant burden on society and healthcare systems. Draught proofing homes to prevent heat loss, improved glazing, insulation and heating (energy efficiency measures) can make more homes more affordable to heat. This has prompted significant investment in energy efficiency upgrades for around 40% of UK households to reduce the impact of fuel poverty. Despite some inconsistent evidence, household energy efficiency interventions can improve cardiovascular and respiratory health outcomes. However, the health benefits of these interventions have not been fully explored; this is the focus of this study.MethodsIn this cross sectional ecological study, we conducted two sets of analyses at different spatial resolution to explore population data on housing energy efficiency measures and hospital admissions at the area-level (counts grouped over a 3-year period). Housing data were obtained from three data sets covering housing across England (Household Energy Efficiency Database), Energy Performance Certificate (EPC) and, in the South West of England, the Devon Home Analytics Portal. These databases provided data aggregated to Lower Area Super Output Area and postcode level (Home Analytics Portal only). These datasets provided measures of both state (e.g. EPC ratings) and intervention (e.g. number of boiler replacements), aggregated spatially and temporally to enable cross-sectional analyses with health outcome data. Hospital admissions for adult (over 18 years) asthma, chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD) were obtained from the Hospital Episode Statistics database for the national (1st April 2011 to 31st March 2014) and Devon, South West of England (1st April 2014 to 31st March 2017) analyses. Descriptive statistics and regression models were used to describe the associations between small area household energy efficiency measures and hospital admissions. Three main analyses were undertaken to investigate the relationships between; 1) household energy efficiency improvements (i.e. improved glazing, insulation and boiler upgrades); 2) higher levels of energy efficiency ratings (measured by Energy Performance Certificate ratings); 3) energy efficiency improvements and ratings (i.e. physical improvements and rating assessed by the Standard Assessment Procedure) and hospital admissions.ResultsIn the national analyses, household energy performance certificate ratings ranged from 37 to 83 (mean 61.98; Standard Deviation 5.24). There were a total of 312,837 emergency admissions for asthma, 587,770 for COPD and 839,416 for CVD. While analyses for individual energy efficiency metrics (i.e. boiler upgrades, draught proofing, glazing, loft and wall insulation) were mixed; a unit increase in mean energy performance rating was associated with increases of around 0.5% in asthma and CVD admissions, and 1% higher COPD admission rates. Admission rates were also influenced by the type of dwelling, tenure status (e.g. h...

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“…There has been much recent activity in methodological developments in this area. Generic models for analyzing spatiotemporal data were developed by Cressie (), Mardia and Goodall (), Kyriakidis and Journel (), Wikle and Cressie (), Stroud, Müller, and Sansó (), Clark and Gelfand (), Cressie and Wikle (), Christakos (), and Mukhopadhyay and Sahu ().…”

Section: Introductionmentioning

confidence: 99%

Bayesian spatiotemporal modeling for estimating short‐term exposure to air pollution in Santiago de Chile

et al. 2019

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Spatial prediction of exposure to air pollution in a large city such as Santiago de Chile is a challenging problem because of the lack of a dense air‐quality monitoring network. Statistical spatiotemporal models exploit the space–time correlation in the pollution data and other relevant meteorological and land‐use information to generate accurate predictions in both space and time. In this paper, we develop a Bayesian modeling method to accurately predict hourly PM2.5 concentrations in a 1‐km high‐resolution grid covering the city. The modeling method combines a spatiotemporal land‐use regression model for PM2.5 and a Bayesian calibration model for the input meteorological variables used in the land‐use regression model. Using a 3‐month winter‐time pollution data set, the output of sample validation results obtained in this paper shows a substantial increase in accuracy due to the incorporation of the linear calibration model. The proposed Bayesian modeling method is then used to provide short‐term spatiotemporal predictions of PM2.5 concentrations on a fine (1 km2) spatial grid covering the city. Along with the paper, we publish the R code used and the output of sample predictions for future scientific use.

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A Bayesian Spatiotemporal Model to Estimate Long-Term Exposure to Outdoor Air Pollution at Coarser Administrative Geographies in England and Wales

Cited by 30 publications

References 40 publications

Impact of Urban Growth on Air Quality in Indian Cities Using Hierarchical Bayesian Approach

Impact of Urban Growth on Air Quality in Indian Cities Using Hierarchical Bayesian Approach

Household energy efficiency and health: Area-level analysis of hospital admissions in England

Bayesian spatiotemporal modeling for estimating short‐term exposure to air pollution in Santiago de Chile

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