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首页> 外文期刊>Environmental Health: A Global Access Science Source >Prediction and analysis of near-road concentrations using a reduced-form emission/dispersion model
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Prediction and analysis of near-road concentrations using a reduced-form emission/dispersion model

机译：使用简化形式的排放/弥散模型预测和分析近路浓度

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Background Near-road exposures of traffic-related air pollutants have been receiving increased attention due to evidence linking emissions from high-traffic roadways to adverse health outcomes. To date, most epidemiological and risk analyses have utilized simple but crude exposure indicators, most typically proximity measures, such as the distance between freeways and residences, to represent air quality impacts from traffic. This paper derives and analyzes a simplified microscale simulation model designed to predict short- (hourly) to long-term (annual average) pollutant concentrations near roads. Sensitivity analyses and case studies are used to highlight issues in predicting near-road exposures. Methods Process-based simulation models using a computationally efficient reduced-form response surface structure and a minimum number of inputs integrate the major determinants of air pollution exposures: traffic volume and vehicle emissions, meteorology, and receptor location. We identify the most influential variables and then derive a set of multiplicative submodels that match predictions from "parent" models MOBILE6.2 and CALINE4. The assembled model is applied to two case studies in the Detroit, Michigan area. The first predicts carbon monoxide (CO) concentrations at a monitoring site near a freeway. The second predicts CO and PM2.5 concentrations in a dense receptor grid over a 1 km2 area around the intersection of two major roads. We analyze the spatial and temporal patterns of pollutant concentration predictions. Results Predicted CO concentrations showed reasonable agreement with annual average and 24-hour measurements, e.g., 59% of the 24-hr predictions were within a factor of two of observations in the warmer months when CO emissions are more consistent. The highest concentrations of both CO and PM2.5 were predicted to occur near intersections and downwind of major roads during periods of unfavorable meteorology (e.g., low wind speeds) and high emissions (e.g., weekday rush hour). The spatial and temporal variation among predicted concentrations was significant, and resulted in unusual distributional and correlation characteristics, including strong negative correlation for receptors on opposite sides of a road and the highest short-term concentrations on the "upwind" side of the road. Conclusions The case study findings can likely be generalized to many other locations, and they have important implications for epidemiological and other studies. The reduced-form model is intended for exposure assessment, risk assessment, epidemiological, geographical information systems, and other applications.
机译：背景技术与交通相关的空气污染物在近道路上的暴露已受到越来越多的关注，这是因为有证据表明，高通行道路的排放与不良健康后果相关。迄今为止，大多数流行病学和风险分析都利用简单但粗略的暴露指标，最典型的是接近性度量，例如高速公路和住宅之间的距离，来表示交通对空气质量的影响。本文推导并分析了简化的微观模拟模型，该模型旨在预测道路附近短期（每小时）到长期（每年平均）的污染物浓度。敏感性分析和案例研究用于突出预测近距离道路暴露的问题。方法基于过程的模拟模型使用计算效率高的简化形式的响应面结构和最少的输入量，综合了空气污染暴露的主要决定因素：交通量和车辆排放，气象学和受体位置。我们确定最有影响力的变量，然后派生一组与“父”模型MOBILE6.2和CALINE4的预测匹配的乘法子模型。组装模型应用于密歇根州底特律的两个案例研究。第一个预测高速公路附近监测点的一氧化碳（CO）浓度。第二种方法预测了两条主要道路交叉点周围1 km2区域内密集的受体网格中的CO和PM2.5浓度。我们分析了污染物浓度预测的时空格局。结果预测的CO浓度与年平均和24小时的测量值显示出合理的一致性，例如，在较温暖的月份中，CO排放量更一致时，24小时的预测值中有59％处于观察值的两倍之内。据预测，在不利的气象（例如低风速）和高排放（例如工作日繁忙时间）期间，CO和PM2.5的最高浓度将发生在主要道路的交叉口和顺风附近。预测浓度之间的时空变化很明显，并导致异常的分布和相关特征，包括道路相对两侧的受体强烈负相关，以及道路“上风”一侧的短期浓度最高。结论案例研究的发现可能可以推广到其他许多地方，并且对流行病学和其他研究具有重要意义。简化形式的模型旨在用于暴露评估，风险评估，流行病学，地理信息系统以及其他应用。

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