Freeman Brian, Gharabaghi Bahram, Thé Jesse, Munshed Mohammad, Faisal Shah, Abdullah Meshal, Al Aseed Athari
a School of Engineering , University of Guelph , Guelph , Ontario , Canada.
b Lakes Environmental , Waterloo , Ontario , Canada.
J Air Waste Manag Assoc. 2017 May;67(5):565-581. doi: 10.1080/10962247.2016.1265025. Epub 2016 Dec 20.
This study presents a new method that incorporates modern air dispersion models allowing local terrain and land-sea breeze effects to be considered along with political and natural boundaries for more accurate mapping of air quality zones (AQZs) for coastal urban centers. This method uses local coastal wind patterns and key urban air pollution sources in each zone to more accurately calculate air pollutant concentration statistics. The new approach distributes virtual air pollution sources within each small grid cell of an area of interest and analyzes a puff dispersion model for a full year's worth of 1-hr prognostic weather data. The difference of wind patterns in coastal and inland areas creates significantly different skewness (S) and kurtosis (K) statistics for the annually averaged pollutant concentrations at ground level receptor points for each grid cell. Plotting the S-K data highlights grouping of sources predominantly impacted by coastal winds versus inland winds. The application of the new method is demonstrated through a case study for the nation of Kuwait by developing new AQZs to support local air management programs. The zone boundaries established by the S-K method were validated by comparing MM5 and WRF prognostic meteorological weather data used in the air dispersion modeling, a support vector machine classifier was trained to compare results with the graphical classification method, and final zones were compared with data collected from Earth observation satellites to confirm locations of high-exposure-risk areas. The resulting AQZs are more accurate and support efficient management strategies for air quality compliance targets effected by local coastal microclimates.
A novel method to determine air quality zones in coastal urban areas is introduced using skewness (S) and kurtosis (K) statistics calculated from grid concentrations results of air dispersion models. The method identifies land-sea breeze effects that can be used to manage local air quality in areas of similar microclimates.
本研究提出了一种新方法，该方法结合了现代空气扩散模型，能够在考虑政治和自然边界的同时，兼顾当地地形和海陆风效应，从而更准确地绘制沿海城市中心的空气质量区（AQZ）地图。此方法利用每个区域的当地沿海风型和关键城市空气污染源，更精确地计算空气污染物浓度统计数据。新方法在感兴趣区域的每个小网格单元内分布虚拟空气污染源，并针对一整年的1小时预测气象数据分析烟团扩散模型。沿海和内陆地区风型的差异，使得每个网格单元地面受体点的年平均污染物浓度产生显著不同的偏度（S）和峰度（K）统计数据。绘制S-K数据突出显示了受沿海风与内陆风影响的主要污染源分组情况。通过为科威特国开展案例研究以开发新的空气质量区来支持当地空气管理计划，展示了新方法的应用。通过比较空气扩散模型中使用的MM5和WRF预测气象数据，验证了由S-K方法建立的区域边界；训练支持向量机分类器以将结果与图形分类方法进行比较；并将最终区域与从地球观测卫星收集的数据进行比较，以确认高暴露风险区域的位置。所得的空气质量区更为准确，并支持针对受当地沿海微气候影响的空气质量合规目标的有效管理策略。
引入了一种利用空气扩散模型网格浓度结果计算的偏度（S）和峰度（K）统计数据来确定沿海城市地区空气质量区的新方法。该方法识别出海陆风效应，可用于管理类似微气候区域的当地空气质量。
