Publications
4 results found
Wang W, Dack S, Mudway I, et al., 2023, Brownfield land and health: a systematic review of the literature, PLoS One, Vol: 18, Pages: 1-15, ISSN: 1932-6203
BackgroundBrownfield land is vacant or derelict land that was previously used for industrial or commercial purposes. Brownfield land is increasingly being targeted for housing development, however, depending on the previous use and remediation activity, it might pose potential risks to the health of residents on or in the vicinity of redeveloped sites. This systematic review of the literature synthesises the empirical evidence on the associations between brownfield land and health.MethodsWe systematically searched EMBASE, MEDLINE, Global Health, Web of Science, Scopus and GreenFile using a study protocol registered on PROSPERO (CRD42022286826). The search strategy combined the keywords “brownfield” and its interchangeable terms such as “previously developed land”, and any health outcomes such as “respiratory diseases” and “mortality”. Publications identified from the search were screened for eligibility by two authors, and data were extracted from the selected articles. Study quality was assessed based on the Newcastle-Ottawa Scale.ResultsOf the 1,987 records retrieved, 6 studies met the inclusion criteria; 3 ecological studies, 2 cross-sectional studies, and 1 longitudinal study. There was considerable heterogeneity in the exposure metrics and health outcomes assessed. All studies found significant positive associations between brownfield land proximity or density with at least one health relevant outcome, including poorer self-reported general health, increased mortality rates, increased birth defects, increased serum metal levels, and accelerated immune ageing.ConclusionsBrownfield land may negatively affect the health of nearby residents. The epidemiological evidence on health effects associated with brownfield land in local communities, however, remains inconclusive and limited. Further studies are required to build the evidence base to inform future housing policies and urban planning.
de Preux L, Rizmie D, Fecht D, et al., 2023, Does it measure up? A comparison of pollution exposure assessment techniques applied across hospitals in England, International Journal of Environmental Research and Public Health, Vol: 20, Pages: 1-26, ISSN: 1660-4601
Weighted averages of air pollution measurements from monitoring stations are commonly assigned as air pollution exposures to specific locations. However, monitoring networks are spatially sparse and fail to adequately capture the spatial variability. This may introduce bias and exposure misclassification. Advanced methods of exposure assessment are rarely practicable in estimating daily concentrations over large geographical areas. We propose an accessible method using temporally adjusted land use regression models (daily LUR). We applied this to produce daily concentration estimates for nitrogen dioxide, ozone, and particulate matter in a healthcare setting across England and compared them against geographically extrapolated measurements (inverse distance weighting) from air pollution monitors. The daily LUR estimates outperformed IDW. The precision gains varied across air pollutants, suggesting that, for nitrogen dioxide and particulate matter, the health effects may be underestimated. The results emphasised the importance of spatial heterogeneity in investigating the societal impacts of air pollution, illustrating improvements achievable at a lower computational cost.
Chen K, Klompmaker JO, Roscoe CJ, et al., 2023, Associations between greenness and predicted COVID-19-like illness incidence in the United States and the United Kingdom, Environmental Epidemiology, Vol: 7, ISSN: 2474-7882
UNLABELLED: Green spaces may be protective against COVID-19 incidence. They may provide outdoor, ventilated, settings for physical and social activities and therefore decrease transmission risk. We examined the association between neighborhood greenness and COVID-19-like illness incidence using individual-level data. METHODS: The study population includes participants enrolled in the COVID Symptom Study smartphone application in the United Kingdom and the United States (March-November 2020). All participants were encouraged to report their current health condition and suspected risk factors for COVID-19. We used a validated symptom-based classifier that predicts COVID-19-like illness. We estimated the Normalized Difference Vegetation Index (NDVI), for each participant's reported neighborhood of residence for each month, using images from Landsat 8 (30 m2). We used time-varying Cox proportional hazards models stratified by age, country, and calendar month at study entry and adjusted for the individual- and neighborhood-level risk factors. RESULTS: We observed 143,340 cases of predicted COVID-19-like illness among 2,794,029 participants. Neighborhood NDVI was associated with a decreased risk of predicted COVID-19-like illness incidence in the fully adjusted model (hazard ratio = 0.965, 95% confidence interval = 0.960, 0.970, per 0.1 NDVI increase). Stratified analyses showed protective associations among U.K. participants but not among U.S. participants. Associations were slightly stronger for White individuals, for individuals living in rural neighborhoods, and for individuals living in high-income neighborhoods compared to individuals living in low-income neighborhoods. CONCLUSIONS: Higher levels of greenness may reduce the risk of predicted COVID-19-like illness incidence, but these associations were not observed in all populations.
Wang W, Fecht D, Beevers S, et al., 2022, Predicting daily concentrations of nitrogen dioxide, particulate matter and ozone at fine spatial scale in Great Britain, Atmospheric Pollution Research, Vol: 13, Pages: 101506-101506, ISSN: 1309-1042
Short-term exposure studies have often relied on time-series of air pollution measurements from monitoring sites. However, this approach does not capture short-term changes in spatial contrasts in air pollution. To address this, models representing both the spatial and temporal variability in air pollution have emerged in recent years. Here, we modelled daily average concentrations of nitrogen dioxide (NO2), particulate matter (PM2.5 and PM10) and ozone (O3) on a 25 m grid for Great Britain from 2011 to 2015 using a generalised additive mixed model, with penalised spline smooth functions for covariates. The models included local-scale predictors derived using a Geographic Information System (GIS), daily estimates from a chemical transport model, and daily meteorological characteristics. The models performed well in explaining the variability in daily averaged measured concentrations at 48–85 sites: 63% for NO2, 77% for PM2.5, 80% for PM10 and 85% for O3. Outputs of the study include daily air pollution maps that can be applied in epidemiological studies across Great Britain. Daily concentration values can also be predicted for specific locations, such as residential addresses or schools, and aggregated to other exposure time periods (including weeks, months, or pregnancy trimesters) to facilitate the needs of different health analyses.
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