Estimating Particulate Exposure from Modern Municipal Waste Incinerators in Great Britain (original) (raw)
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Journal of Environmental and Public Health, 2013
Background.Research to date on health effects associated with incineration has found limited evidence of health risks, but many previous studies have been constrained by poor exposure assessment. This paper provides a comparative assessment of atmospheric dispersion modelling and distance from source (a commonly used proxy for exposure) as exposure assessment methods for pollutants released from incinerators.Methods.Distance from source and the atmospheric dispersion model ADMS-Urban were used to characterise ambient exposures to particulates from two municipal solid waste incinerators (MSWIs) in the UK. Additionally an exploration of the sensitivity of the dispersion model simulations to input parameters was performed.Results.The model output indicated extremely low ground level concentrations of PM10, with maximum concentrations of <0.01 μg/m3. Proximity and modelled PM10concentrations for both MSWIs at postcode level were highly correlated when using continuous measures (Spear...
Background. Research to date on the effects of burns on well-being has found limited evidence of the hazards to well-being, but many past reviews have been constrained by poor evaluation of the introduction. This paper provides a similar assessment of environmental dispersion by demonstrating good source tracking (an intermediate commonly used for presentation) as an introduction assessment strategy for contamination released from incinerators. Methods: Our current research was conducted at Mayo Hospital Lahore from June 2018 to May 2019. Good source apportionment methods and the ADMS Urban barometric diffusion model were used to represent exposure to particulate matter from 2 municipal solid waste incinerators (MSWI) in Pakistan. In addition, a study of the affectability of reproductions of the diffusion model to enter limitations remained carried out. Results. The model production showed incredibly little ground-level PM10 clustering, by extreme convergences of <0.02 g/m3. The proximity and concentrations of PM10 displayed for the two MWIPs at the postal code level were strongly related once using incessant measurements (Spearman relationship coefficients ∼ 0.8); however, the understanding of unattenuated measurements (deciles or quintiles, Cohen kappa constants ≤ 0.6) was poor. Conclusion: To give best measure of the overall MWIP presentation, it is fundamental to take into account the qualities of the incinerators, the size of the fumes and the overall meteorological and terrestrial conditions. Reducing misclassification of presentation is particularly important in the ecological study of disease transmission to help identify low-level hazards.
The role of PM10 in air quality and exposure in urban areas
WIT Transactions on Ecology and the Environment, 2008
In recent years, there has been an increase of scientific studies confirming that long-and short-term exposure to particulate matter pollution leads to adverse health effects. The calculation of human exposure in urban areas is the main objective of the current work combining information on pollutant concentration in different microenvironments and personal time-activity patterns. Two examples of PM 10 exposure quantification using population and individual approaches are presented. The results are showing important differences between outdoor and indoor concentrations and stressing the need to include indoor concentrations quantification in the exposure assessment.
Environmental monitoring and assessment, 2018
In this study, PM concentrations and elemental (Al, Fe, Sc, V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Mo, Ag, Cd, Sn, Sb, Ba, Pb, and Bi) contents of particles were determined in Düzce, Turkey. The particulate matter samplings were carried out in the winter and summer seasons simultaneously in both urban and sub-urban sampling sites. The average PM concentration measured in the winter season was 86.4 and 27.3 μg/m, respectively, in the urban and sub-urban sampling sites, while it was measured as 53.2 and 34.7 μg/m in the summer season. According to the results, it was observed that the PM levels and the element concentrations reached higher levels, especially at the urban sampling site, in the winter season. The positive matrix factorization model (PMF) was applied to the data set for source apportionment. Analysis with the PMF model revealed six factors for both the urban (coal combustion, traffic, oil combustion, industry, biomass combustion, and soil) and sub-urban (industry, oil combus...
Atmospheric Dispersion of PM10 in an Urban Area
Present Environment and Sustainable Development, 2014
One way of monitoring the atmospheric pollution is to estimate anthropogenic emissions. This paper presents a study of PM10 emissions in a city SE of Romania (Braila) for the period 2009-2012. PM10 emissions decrease from 304.75 t in 2009 to 78.01 t in 2012. Using data from the Environmental Protection Agency Braila and the METI-LIS dispersion model, four maps were produced in order to estimate the spatial distribution of PM10 emission in each year. Results of dispersion models show that the air quality can change abruptly between points at few meters away. Expectedly, PM10 emissions increase towards the centre of the city centre, are generally higher in the vicinity of busy streets and roads. Unauthenticated Download Date | 2/17/15 12:58 PM Unauthenticated Download Date | 2/17/15 12:58 PM supported by Project SOP HRD -PERFORM /159/1.5/S/138963. References Bravo, M., A, Fuentes, M., Zhang, Y., Burr, M.,J., Bell, M.,L., Comparison of exposure estimation methods for air pollutants: ambient monitoring data and regional air quality simulation, Environ Res. , 2012 Jul;116:1-10.
Environment International, 2005
Recent studies have pointed to evidence that fine particles in the air could be significant contributors to respiratory and cardiovascular diseases and mortality. Epidemiologists looking at the health effects of particulate pollution need more information from various receptor locations to improve the understanding of this problem. Detailed information on temporal, spatial and size distributions of particulate pollution in urban areas is also important for air quality modellers as well as being an aid to decision and policy makers of local authorities. This paper presents a detailed analysis of temporal and seasonal variation of PM 10 and PM 2.5 levels at one urban roadside, one urban background and one rural monitoring location. Levels of PM 10 , PM 2.5 and coarse fraction of particulates are compared. In addition, particulate levels are compared with NO 2 and CO concentrations. The study concludes that PM 10 and PM 2.5 are closely related at urban locations. Diurnal variation in PM 2.5 /PM 10 ratio shows the influence of vehicular emission and movement on size distribution. This ratio is higher in winter than in summer, indicating a build-up or longer residence time of finer particulates or washout due to wet weather in winter. In the second part of this study, a disease burden analysis is carried out based on the dose-response relationships recommended by the UK Committee on the Medical Effects of Air Pollution. The disease burden analysis indicates that if Marylebone Road (MR) levels of PM 10 were prevalent all over London, it will result in around 2.5% increase in death rates due to all causes. Whereas, if Bloomsbury (BB) levels were prevalent in London, which is more likely to occur as this is more representative of the urban background environment to which people in London are likely to be exposed, the corresponding increase would be around 1.7%. Considering this, in London, at Bloomsbury levels, 973 deaths and 1515 respiratory hospital admissions (RHA) are attributable to PM 10 while 2140 RHA are attributable to NO 2 . After deducting the disease burden due to background levels at Rochester (RC), PM 10 emission caused by anthropogenic activities in London equates to 273 additional deaths and 410 additional RHA, while NO 2 account for additional 1205 incidences of RHA. D
Estimating Personal Exposure to Airborne Pollutants: PM 10 in London
This paper describes a conceptual framework within which models can be de-veloped for predicting the exposure of randomly selected individuals to a specified pollutant (e.g. for health impact analysis). They can help answer questions like: (i) What fraction of the population sustained 'high' levels of exposure? (ii) How many sustained such exposures for say 10 days in a row? (iii) What benefit will a mitigation strategy have on those under the age of 4? The framework is developed by abstracting the 'building blocks' of existing exposure models. One implementation of the above framework is a WWW computing platform, ac-cessible to remote online users, referred to as 'pCNEM'. They can construct a model of the type referred to above, for any specified: pollutant; study area; study period. To so so, they would through their chosen web-browser: upload local pollution and meteorological data; create relevant microenvironments; designate population sub-groups of inte...
X-Ray Spectrometry, 2007
Energy dispersive x-ray fluorescence (EDXRF) analysis of airborne particles has previously been shown to be a powerful technique for identifying key elements or elemental ratios for identification of important sources of air pollution. In the present work the technique was used for assignment of major sources of aerosol particles (PM2.5) in a medium sized Swedish city in southwestern Sweden, in which a new incinerator of household and industrial waste had recently been installed. Data on particle mass and black carbon contents in PM2.5 were also recorded together with SO 2 and NO 2 during the same study period. In spite of the small data set it was possible to identify five major sources for collected PM2.5, namely, waste incineration together with other local sources, oil incineration, biomass burning, long-distance transport and traffic emissions. Major characteristic elements for the respective sources were identified from regression analysis of the data and from information obtained in previous studies. A receptor model based on the use of trace observations was used for quantitative calculation of the source contribution to PM2.5. The relative strength of the identified sources was seen to change when the variables included in the analysis were varied in number and character, although the same sources remained. It must be noted that the quantitative contribution from the different sources may be treated only as informative at present, since the number of observations are small compared to the number of variables.
Atmospheric Environment, 2001
We have developed a model for evaluating the mass-based concentrations of urban particulate matter. The basic model assumption is that local vehicular traffic is responsible for a substantial fraction of the street-level concentrations of both PM 10 and NO x , either due to primary emissions or resuspension from street surfaces. The modelling system utilises the data from an air quality monitoring network in the Helsinki Metropolitan Area. We have determined linear relationships between the measured urban PM 10 data against those of NO x in various urban surroundings, based on continuously measured hourly concentration values. The data was obtained from two stations in central Helsinki and one suburban station in the Helsinki Metropolitan Area during a period of 3 yr, from 1996 to 1998. The model also includes a treatment of the regional background concentrations, and resuspended particulate matter. The model performance was evaluated against the measured PM 10 data from the above-mentioned three stations and from two other stations, using data that was measured in 1999. We used two alternative model versions, one based on separate correlation parameters (PM 10 vs. NO x ) for each station, and another based on parameters averaged over the stations considered. We analysed the agreement between the measured and predicted hourly concentration time series, utilising the values of the fractional bias (FB) and the so-called index of agreement (IA). As expected, the model predicts relatively well the yearly mean concentrations of PM 10 : the FB values range from À0.05 to +0.09. Model performance is also relatively good when predicting the yearly mean values that are classified separately for each hour of the day: the corresponding IA values range from 0.85 to 0.96. However, model performance is substantially worse in predicting the hourly time series of the year: the IA values using the station-specific parameters range from 0.46 to 0.65. The model was applied in evaluating the yearly average spatial concentration distribution of PM 10 in central Helsinki, based on the corresponding modelled NO x concentrations. With re-evaluation of a few parameters that can be determined empirically, the model could be evaluated, and most probably applied, in other urban areas as well. r