Suchergebnisse

Low-cost air quality sensors offer significant potential for enhancing urban air quality networks by providing higher-spatiotemporal-resolution data needed, for example, for evaluation of air quality interventions. However, these sensors present methodological and deployment challenges which have historically limited operational ability. These include variability in performance characteristics and sensitivity to environmental conditions. In this work, we investigate field "baselining" and interference correction using random forest regression methods for low-cost sensing of NO 2 , PM 10 (particulate matter) and PM 2.5 . Model performance is explored using data obtained over a 7-month period by real-world field sensor deployment alongside reference method instrumentation. Workflows and processes developed are shown to be effective in normalising variable sensor baseline offsets and reducing uncertainty in sensor response arising from environmental interferences. We demonstrate improvements of between 37 % and 94 % in the mean absolute error term of fully corrected sensor datasets; this is equivalent to performance within ±2.6 ppb of the reference method for NO 2 , ±4.4 µ g m −3 for PM 10 and ±2.7 µ g m −3 for PM 2.5 . Expanded-uncertainty estimates for PM 10 and PM 2.5 correction models are shown to meet performance criteria recommended by European air quality legislation, whilst that of the NO 2 correction model was found to be narrowly ( ∼5 %) outside of its acceptance envelope. Expanded-uncertainty estimates for corrected sensor datasets not used in model training were 29 %, 21 % and 27 % for NO 2 , PM 10 and PM 2.5 respectively.

Low-cost air quality sensors offer significant potential for enhancing urban air quality networks by providing higher-spatiotemporal-resolution data needed, for example, for evaluation of air quality interventions. However, these sensors present methodological and deployment challenges which have historically limited operational ability. These include variability in performance characteristics and sensitivity to environmental conditions. In this work, we investigate field "baselining" and interference correction using random forest regression methods for low-cost sensing of NO2, PM10 (particulate matter) and PM2.5. Model performance is explored using data obtained over a 7-month period by real-world field sensor deployment alongside reference method instrumentation. Workflows and processes developed are shown to be effective in normalising variable sensor baseline offsets and reducing uncertainty in sensor response arising from environmental interferences. We demonstrate improvements of between 37 % and 94 % in the mean absolute error term of fully corrected sensor datasets; this is equivalent to performance within ±2.6 ppb of the reference method for NO2, ±4.4 µg m−3 for PM10 and ±2.7 µg m−3 for PM2.5. Expanded-uncertainty estimates for PM10 and PM2.5 correction models are shown to meet performance criteria recommended by European air quality legislation, whilst that of the NO2 correction model was found to be narrowly (∼5 %) outside of its acceptance envelope. Expanded-uncertainty estimates for corrected sensor datasets not used in model training were 29 %, 21 % and 27 % for NO2, PM10 and PM2.5 respectively.

AbstractWe apply a two-step data driven approach to determine the causal impact of the clean air zone (CAZ) policy on air quality in Birmingham, UK. Levels of NO2, NOx and PM2.5 before and after CAZ implementation were collected from automatic air quality monitoring sites both within and outside the CAZ. We apply a unique combination of two recent methods: (1) a random forest machine learning method to strip out the effects of meteorological conditions on air pollution levels, and then (2) the Augmented Synthetic Control Method (ASCM) on the de-weathered air pollution data to isolate the causal effect of the CAZ. We find that, during the first year following the formal policy implementation, the CAZ led to significant but modest reductions of NO2 and NOX levels measured at the roadside within (up to 3.4% and 5.4% of NO2 and NOX, respectively) and outside (up to 6.6% and 11.9%) the zone, with no detectable changes at the urban background site outside the CAZ. No significant impacts of the CAZ were found on concentrations of fine particulates (PM2.5). Our analysis demonstrates the short-term effectiveness of CAZ in reducing concentrations of NO2 and NOX.

BACKGROUND The World Health Organisation reported that 45% of global acute respiratory infection (ARI) deaths in children under five years are attributable to household air pollution, which has been recognised to be strongly associated with solid biomass fuel usage in domestic settings. The introduction of legislative restrictions for charcoal production or purchase can result in unintended consequences, such as reversion to more polluting biomass fuels such as wood; which may increase health and environmental harms. However, there remains a paucity of evidence concerning the relative health risks between wood and charcoal. This study compares the risk of respiratory symptoms, ARI, and severe ARI among children aged under five years living in wood and charcoal fuel households across 30 low- and middle-income countries. METHODS Data from children ( = 475,089) residing in wood or charcoal cooking households were extracted from multiple population-based Demographic and Health Survey databases (DHS) ( = 30 countries). Outcome measures were obtained from a maternal report of respiratory symptoms (cough, shortness of breath and fever) occurring in the two weeks prior to the survey date, generating a composite measure of ARI (cough and shortness of breath) and severe ARI (cough, shortness of breath and fever). Multivariable logistic regression analyses were implemented, with adjustment at individual, household, regional and country level for relevant demographic, social, and health-related confounding factors. RESULTS Increased odds ratios of fever (AOR: 1.07; 95% CI: 1.02-1.12) were observed among children living in wood cooking households compared to the use of charcoal. However, no association was observed with shortness of breath (AOR: 1.03; 95% CI: 0.96-1.10), cough (AOR: 0.99; 95% CI: 0.95-1.04), ARI (AOR: 1.03; 95% CI: 0.96-1.11) or severe ARI (AOR: 1.07; 95% CI: 0.99-1.17). Within rural areas, only shortness of breath was observed to be associated with wood cooking (AOR: 1.08; 95% CI: 1.01-1.15). However, an increased odds ratio of ARI was observed in Asian (AOR: 1.25; 95% CI: 1.04-1.51) and East African countries (AOR: 1.11; 95% CI: 1.01-1.22) only. CONCLUSION Our population-based observational data indicates that in Asia and East Africa there is a greater risk of ARI among children aged under 5 years living in wood compared to charcoal cooking households. These findings have major implications for understanding the existing health impacts of wood-based biomass fuel usage and may be of relevance to settings where charcoal fuel restrictions are under consideration.

Obesity is a global epidemic affecting all age groups, populations and income levels across continents. The causes of obesity are complex and are routed in health behaviours, environmental factors, government policy and the cultural and built environment. Consequently, a Whole System Approach (WSA) which considers the many causes of obesity and shifts the focus away from individuals as points of intervention and puts an emphasis on understanding and improving the system in which people live in is required. This protocol describes a programme of research that will: critically evaluate the evidence for WSAs; assess longitudinally the implementation of a WSA to diet and healthy weight to explore the range of levers (drivers) and opportunities to influence relevant partnerships and interventions to target obesity in East Scotland. The programme consists of four workstreams within a mixed methods framework: 1) Systematic review of reviews of WSAs to diet and healthy weight; 2) Longitudinal qualitative process evaluation of implementing two WSAs in Scotland; 3) Quantitative and Qualitative momentary analysis evaluation of a WSA; and 4) the application of System Dynamics Modelling (SDM) methodology to two council areas in Scotland. A Public Involvement in Research group (PIRg) have informed each stage of the research process. The research programme's breadth and its novel nature, mean that it will provide valuable findings for the increasing numbers who commission, deliver, support and evaluate WSAs to diet and healthy weight nationally and internationally.

This book documents the early lives of almost 19,000 children born in the UK at the start of the 21st century, and their families. It is the first time that analysis of data from the hugely important Millennium Cohort Study, a longitudinal study following the progress of the children and their families, has been drawn together in a single volume. The unrivalled data is examined here to address important policy and scientific issues. The book is also the first in a series of publications that will report on the children's lives at different stages of their development. The fascinating range of findings presented here is strengthened by comparison with data on earlier generations. This has enabled the authors to assess the impact of a wide range of policies on the life courses of a new generation, including policies on child health, parenting, childcare and social exclusion. Babies of the new millennium (title tbc) is the product of an exciting collaboration from experts across a wide range of health and social science fields. The result is a unique and authoritative analysis of family life and early childhood in the UK that cuts across old disciplinary boundaries. It is essential reading for academics, students and researchers in the health and social sciences. It will also be a useful resource for policy makers and practitioners who are interested in childhood, child development, child poverty, child health, childcare and family policy