Consistency of Urban Background Black Carbon Concentration Measurements by Portable AE51 and Reference AE22 Aethalometers: Effect of Corrections for Filter Loading

Monitoring exposure to black carbon with portable devices is an important part of researching the health impacts of combustion-related air pollutants. We collected 786 hourly averaged equivalent black carbon (eBC) measurements from co-located duplicate portable AE51 Aethalometers and a UK Government reference AE22 Aethalometer (the data for the latter were corrected for filter darkening effects using a standard procedure), at an urban background site in Glasgow, UK. The AE51 and the reference concentrations were highly correlated (R 2 ≥ 0.87) for the combined deployment periods. The application of a previously reported method for correcting the AE51’s underestimation of concentrations, associated with filter loading, generally led to an overestimation of values (specifically, the normalised mean bias values for the two AE51s increased from –2% and +3% to +14% and +25% across the full range of measurements after correction). We found only limited and inconsistent evidence that the AE51 Aethalometers (attenuation [AE51_ATN] ≤ ~52) underestimated the eBC concentrations compared to the reference measurements. Thus, our observations indicate that the AE51 can achieve close agreement with the reference AE22 monitor without applying corrections for filter loading at relatively low AE51_ATN values in environments with low eBC concentrations

Measurement error in a multi-level analysis of air pollution and health: a simulation study.

BACKGROUND: Spatio-temporal models are increasingly being used to predict exposure to ambient outdoor air pollution at high spatial resolution for inclusion in epidemiological analyses of air pollution and health. Measurement error in these predictions can nevertheless have impacts on health effect estimation. Using statistical simulation we aim to investigate the effects of such error within a multi-level model analysis of long and short-term pollutant exposure and health. METHODS: Our study was based on a theoretical sample of 1000 geographical sites within Greater London. Simulations of "true" site-specific daily mean and 5-year mean NO2 and PM10 concentrations, incorporating both temporal variation and spatial covariance, were informed by an analysis of daily measurements over the period 2009-2013 from fixed location urban background monitors in the London area. In the context of a multi-level single-pollutant Poisson regression analysis of mortality, we investigated scenarios in which we specified: the Pearson correlation between modelled and "true" data and the ratio of their variances (model versus "true") and assumed these parameters were the same spatially and temporally. RESULTS: In general, health effect estimates associated with both long and short-term exposure were biased towards the null with the level of bias increasing to over 60% as the correlation coefficient decreased from 0.9 to 0.5 and the variance ratio increased from 0.5 to 2. However, for a combination of high correlation (0.9) and small variance ratio (0.5) non-trivial bias (> 25%) away from the null was observed. Standard errors of health effect estimates, though unaffected by changes in the correlation coefficient, appeared to be attenuated for variance ratios > 1 but inflated for variance ratios < 1. CONCLUSION: While our findings suggest that in most cases modelling errors result in attenuation of the effect estimate towards the null, in some situations a non-trivial bias away from the null may occur. The magnitude and direction of bias appears to depend on the relationship between modelled and "true" data in terms of their correlation and the ratio of their variances. These factors should be taken into account when assessing the validity of modelled air pollution predictions for use in complex epidemiological models

What drives the viability of waste-to-energy? Modelling techno-economic scenarios of anaerobic digestion and energy generation for the Scottish islands

Anaerobic digestion, a technology which converts biowaste into biogas, can address issues of waste utilisation, energy security and reducing emissions. Co-digestion of waste could improve biogas yields and synergies between sectors but requires transport of waste. To improve on existing biowaste-to-energy models which consider simple transport costs, this work combines a techno-economic model with a capacitated vehicle routing problem (CVRP) solver to consider detailed waste transport costs with actual Open Street Map (OSM) road networks. This addresses whether biowaste-to-energy techno-economic modelling is improved with more specific transport costs and more broadly how factors of resource availability, generation technology and transport costs influence the viability of anaerobic digestion and generation plants. The levelised cost of energy (LCOE) is used to compare scenarios of these aspects. The Scottish islands have been modelled as a case study due to high biowaste potential and varied topographies, which both influence transport costs. Number of waste vehicles required is improved by 42.8% and the unit cost of collection varies from £0.1–1670.0/tonne. Local topographies and waste availability significantly affects the viability of individual facilities, which might not be considered by simpler collection cost metrics. Between 14.0 and 20.6% of the regions electricity demand could be met by biogas. While industrial facilities co-located with demand have the cheapest LCOE, this can in some cases be improved with other waste streams, highlighting the need for further research on and policies supporting co-digestion, as well as improving household and business participation rates. Incentives and avoided costs are crucial to supporting biowaste-to-energy if more isolated regions are to benefit from improved waste utilisation

Data to support small area health impact modelling of air pollution in the United Kingdom.

The data presented in this article were used to estimate the impacts of air pollution policies on population health and health inequalities within a spatial microsimulation model, MicroEnv [1]. They provide a basis for comparison with similar models and allow researchers to integrate additional model components without duplication of effort. Relative risk estimates for the association between air pollution and rates of ischaemic heart disease (IHD) incidence, IHD case fatality and all-cause mortality were taken from a review of the epidemiological literature and meta-analyses [2]. Modelled small area air pollution data (PM2.5) for Greater London, UK were obtained from an environmental consultancy. All other data were collected from open source Governmental or Non-Government Organisation (NGO) data repositories. These include all-cause mortality rates; IHD incidence, prevalence and mortality rates; general fertility rates; small area socio-economic deprivation data; and relative risk estimates for the association between deprivation and all-cause mortality

An increasing role for solvent emissions and implications for future measurements of volatile organic compounds : Solvent emissions of VOCs

Volatile organic compounds (VOCs) are a broad class of air pollutants which act as precursors to tropospheric ozone and secondary organic aerosols. Total UK emissions of anthropogenic VOCs peaked in 1990 at 2,840 kt yr -1 and then declined to approximately 810 kt yr -1 in 2017 with large reductions in road transport and fugitive fuel emissions. The atmospheric concentrations of many non-methane hydrocarbons (NMHC) in the UK have been observed to fall over this period in broadly similar proportions. The relative contribution to emissions from solvents and industrial processes is estimated to have increased from approximately 35% in 1990 to approximately 63% in 2017. In 1992, UK national monitoring quantified 19 of the 20 most abundant individual anthropogenic VOCs emitted (all were NMHCs), but by 2017 monitoring captured only 13 of the top 20 emitted VOCs. Ethanol is now estimated to be the most important VOC emitted by mass (in 2017 approx. 136 kt yr -1 and approx. 16.8% of total emissions) followed by n-butane (52.4 kt yr -1) and methanol (33.2 kt yr -1). Alcohols have grown in significance representing approximately 10% of emissions in 1990 rising to approximately 30% in 2017. The increased role of solvent emissions should now be reflected in European monitoring strategies to verify total VOC emission reduction obligations in the National Emissions Ceiling Directive. Adding ethanol, methanol, formaldehyde, acetone, 2-butanone and 2-propanol to the existing NMHC measurements would provide full coverage of the 20 most significant VOCs emitted on an annual mass basis. This article is part of a discussion meeting issue 'Air quality, past present and future'

Exploiting crowdsourced geographic information and GIS for assessment of air pollution exposure during active travel

Improvement on assessment of air pollution exposure will enhance assessment of health risk-benefit when active travel (cycling and walking). Earlier studies assessed air pollution exposure according to travel time and city-level air pollution. The lack of spatially fine-grained travel data is a barrier to an accurate assessment of air pollution exposure. Due to a high-level spatial granularity, Strava Metro provides an opportunity to assessing air pollution exposure in combination with spatially varying air pollution concentrations. Strava Metro anonymized and aggregated a large volume of users’ traces to streets for each city. In this study, to explore the potential of crowdsourced geographic information in research of active travel and health, we used Strava Metro data and GIS technologies to assess air pollution exposure in Glasgow, UK. Particularly, we incorporated time of the trip to assess average inhaled dose of pollutant during a single cycling or pedestrian trip. Empirical results demonstrate that Strava Metro data provides an opportunity to an assessment of average air pollution exposure during active travel. Additionally, to demonstrate the potential of Strava Metro data in policy-making, we explored the spatial association of air pollution concentration and active travel. As a result, we identified areas that require investment priority, and finally offered implications for policies

Influence of wind-speed on short-duration NO2 measurements using Palmes and Ogawa passive diffusion samplers

We assessed the precision and accuracy of nitrogen dioxide (NO2) concentrations over 2-day, 3-day and 7-day exposure periods measured with the following types of passive diffusion samplers: standard (open) Palmes tubes; standard Ogawa samplers with commercially-prepared Ogawa absorbent pads (Ogawa[S]); and modified Ogawa samplers with absorbent-impregnated stainless steel meshes normally used in Palmes tubes (Ogawa[P]). We deployed these passive samplers close to the inlet of a chemiluminescence NO2 analyser at an urban background site in Glasgow, UK over 32 discrete measurement periods. Duplicate relative standard deviation was < 7% for all passive samplers. The Ogawa[P], Ogawa[S] and Palmes samplers explained 93%, 87% and 58% of temporal variation in analyser concentrations respectively. Uptake rates for Palmes and Ogawa[S] samplers were positively and linearly associated with wind-speed (P < 0.01 and P < 0.05 respectively). Computation of adjusted uptake rates using average wind-speed observed during each sampling period increased the variation in analyser concentrations explained by Palmes and Ogawa[S] estimates to 90% and 92% respectively, suggesting that measurements can be corrected for shortening of diffusion path lengths due to wind-speed to improve the accuracy of estimates of short-term NO2 exposure. Monitoring situations where it is difficult to reliably estimate wind-speed variations, e.g. across multiple sites with different unknown exposures to local winds, and personal exposure monitoring, are likely to benefit from protection of these sampling devices from the effects of wind, for example by use of a mesh or membrane across the open end. The uptake rate of Ogawa[P] samplers was not associated with wind-speed resulting in a high correlation between estimated concentrations and observed analyser concentrations. The use of Palmes meshes in Ogawa[P] samplers reduced the cost of sampler preparation and removed uncertainty associated with the unknown manufacturing process for the commercially-prepared collection pads