Discipline of Occupational and Environmental Health, University of KwaZulu-Natal, Durban, South Africa.
Institute for Risk Assessment Sciences, Utrecht University, the Netherlands.
Environ Pollut. 2021 Apr 1;274:116513. doi: 10.1016/j.envpol.2021.116513. Epub 2021 Jan 28.
The objective of this paper was to incorporate source-meteorological interaction information from two commonly employed atmospheric dispersion models into the land use regression technique for predicting ambient nitrogen dioxide (NO), sulphur dioxide (SO), and particulate matter (PM). The study was undertaken across two regions in Durban, South Africa, one with a high industrial profile and a nearby harbour, and the other with a primarily commercial and residential profile. Multiple hybrid models were developed by integrating air pollution dispersion modelling predictions for source specific NO, SO, and PM concentrations into LUR models following the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to characterise exposure, in Durban. Industrial point sources, ship emissions, domestic fuel burning, and vehicle emissions were key emission sources. Standard linear regression was used to develop annual, summer and winter hybrid models to predict air pollutant concentrations. Higher levels of NO and SO were predicted in south Durban as compared to north Durban as these are industrial related pollutants. Slightly higher levels of PM were predicted in north Durban as compared to south Durban and can be attributed to either traffic, bush burning or domestic fuel burning. The hybrid NO models for annual, summer and winter explained 60%, 58% and 63%, respectively, of the variance with traffic, population and harbour being identified as important predictors. The SO models were less robust with lower R annual (44%), summer (53%) and winter (46%), in which industrial and traffic variables emerged as important predictors. The R for PM models ranged from 80% to 85% with population and urban land use type emerging as predictor variables.
本文的目的是将两种常用大气扩散模型的源-气象相互作用信息纳入土地利用回归技术中，以预测环境二氧化氮（NO）、二氧化硫（SO）和颗粒物（PM）。该研究在南非德班的两个地区进行，一个地区工业发达，附近有一个港口，另一个地区主要是商业和住宅。通过将特定污染源的空气污染扩散模型预测的 NO、SO 和 PM 浓度整合到 LUR 模型中，根据欧洲空气污染效应研究队列（ESCAPE）方法来描述德班的暴露情况，开发了多个混合模型。工业点源、船舶排放、家用燃料燃烧和车辆排放是关键的排放源。采用标准线性回归方法开发了用于预测空气污染物浓度的年度、夏季和冬季混合模型。与北部德班相比，南部德班的 NO 和 SO 水平预测值更高，因为这些是与工业相关的污染物。与南部德班相比，北部德班的 PM 预测值略高，这可能归因于交通、丛林燃烧或家用燃料燃烧。年度、夏季和冬季的混合 NO 模型分别解释了 60%、58%和 63%的方差，其中交通、人口和港口被确定为重要的预测因子。SO 模型的稳健性较低，其年度（44%）、夏季（53%）和冬季（46%）的 R 值较低，其中工业和交通变量被确定为重要的预测因子。PM 模型的 R 值范围为 80%至 85%，人口和城市土地利用类型成为预测变量。
