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Comparative Assessment of Particulate Air Pollution Exposure from Municipal Solid Waste Incinerator Emissions

机译:城市固体废物焚化炉排放的颗粒空气污染暴露的比较评估

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摘要

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 PM10 concentrations for both MSWIs at postcode level were highly correlated when using continuous measures (Spearman correlation coefficients ~ 0.7) but showed poor agreement for categorical measures (deciles or quintiles, Cohen's kappa coefficients ≤ 0.5). Conclusion. To provide the most appropriate estimate of ambient exposure from MSWIs, it is essential that incinerator characteristics, magnitude of emissions, and surrounding meteorological and topographical conditions are considered. Reducing exposure misclassification is particularly important in environmental epidemiology to aid detection of low-level risks.
机译:背景。迄今为止,有关与焚烧有关的健康影响的研究发现,健康风险的证据有限,但是以前的许多研究都受到不良接触评估的限制。本文提供了对大气弥散模型和到源的距离(通常是暴露的代表)的比较评估,作为焚烧炉释放污染物的暴露评估方法。方法。利用距源的距离和大气扩散模型ADMS-Urban来表征英国两个市政固体废物焚化炉(MSWI)暴露于颗粒的环境暴露。另外,对色散模型仿真对输入参数的敏感性进行了探索。结果。该模型输出显示了PM10的极低水平浓度,最大浓度为<0.01μg/ m 3 。当使用连续测量时(Spearman相关系数〜0.7),两个MSWI在邮政编码级别的接近度和模型PM10浓度高度相关(Spearman相关系数〜0.7),但对于分类测量(十分位数或五分位数,Cohen卡帕系数≤0.5)显示出较差的一致性。结论。为了提供最适当的MSWI环境暴露估计,必须考虑焚化炉的特性,排放量以及周围的气象和地形条件。减少接触不良分类在环境流行病学中尤其重要,以帮助发现低水平风险。

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