ABSTRACT

Chemical transport models (CTM) can have large biases and errors when simulating pollutant concentrations. To improve the characterization of fine particulate matter (PM_2.5) over complex terrain for exposure assessments, three mathematical formulae that utilized the relationship between modeled and observed quantile concentrations at a monitor location were developed. These were then applied to 1 year of CMAQ model output of PM_2.5 over the Terrace–Kitimat Valley of northwestern British Columbia, Canada. The final products enhanced the representation of ambient levels at existing monitoring stations when evaluated with conventional statistical measures. Better agreement of corrected outputs with observed compliance metrics was also found. On average, the absolute errors of amended outputs were 11% and 10% for the annual mean PM_2.5 and 98th percentiles of daily concentrations, respectively, compared to 45% and 61%, respectively, in the original outputs. These improvements provided greater confidence to use the amended outputs to estimate concentrations at locations without monitors. The predominance of pristine conditions in the modeling domain was exploited to derive annual background PM_2.5 concentrations over the valley, which was estimated to be 2.0–2.3 μg m⁻³. To our knowledge, this is the first study to calculate background PM_2.5 concentrations over northern BC coastlands using bias-corrected outputs from an air quality model.

Implications: Bias correction of CMAQ model output was necessary for assessing regulatory compliance for ambient PM_2.5. The implications are notable. First, for low to moderate spatial heterogeneity in monitoring data, the use of regression equations that relates quantile mean concentrations of model outputs to those of observational data enhances the estimation of PM_2.5 at unmonitored locations. Second, by providing spatial pollutant distribution ahead of planned industrial development in Terrace–Kitimat Valley (TKV), corrected model output offers a baseline for tracking progress in airshed management. Third, correction improved pollutant exposure classification, for which the risk was predominantly low. Finally, 2.0–2.3 μg m⁻³ should be considered as PM_2.5 concentrations that are irreducible when setting voluntary targets for ambient levels in the area.

摘要

在模拟污染物浓度时，化学传输模型 (CTM) 可能有很大的偏差和错误。为了改进复杂地形上细颗粒物 (PM _2.5 )的表征以进行暴露评估，开发了三个数学公式，这些公式利用了在监测位置建模和观察到的分位数浓度之间的关系。然后将这些应用于 PM _{2.5 的}1 年 CMAQ 模型输出在加拿大不列颠哥伦比亚省西北部的 Terrace-Kitimat 山谷上空。当使用传统统计措施进行评估时，最终产品增强了现有监测站环境水平的表示。还发现更正后的输出与观察到的合规性指标之间的一致性更好。平均而言，年平均 PM _2.5和日浓度 98 % 的修正输出的绝对误差分别为 11% 和 10% ，而原始输出中分别为 45% 和 61%。这些改进为使用修正后的输出来估计没有监测器的位置的浓度提供了更大的信心。利用建模域中原始条件的优势来推导年度背景 PM _2.5谷内的浓度，估计为 2.0–2.3 μg m ^-3。据我们所知，这是第一项使用空气质量模型的偏差校正输出计算不列颠哥伦比亚省北部沿海地区背景 PM _2.5浓度的研究。

含义： CMAQ 模型输出的偏差校正对于评估环境 PM _{2.5 的}法规遵从性是必要的。其影响是显着的。首先，对于监测数据的低到中等空间异质性，使用回归方程将模型输出的分位数平均浓度与观测数据的分位数平均浓度相关联，增强了对未监测位置PM _2.5的估计。其次，通过在 Terrace-Kitimat Valley (TKV) 的计划工业开发之前提供空间污染物分布，修正后的模型输出为跟踪气域管理进展提供了基线。第三，修正改进了污染物暴露分类，其风险主要是低。最后，2.0–2.3 μg m ^-3在为该地区的环境水平设定自愿目标时，应将其视为不可降低的PM _2.5浓度。