Discipline of Occupational and Environmental Health, University of KwaZulu-Natal, Durban 4041, South Africa.
Institute for Risk Assessment Sciences, Utrecht University, 3508TD Utrecht, The Netherlands.
Int J Environ Res Public Health. 2020 Jul 27;17(15):5406. doi: 10.3390/ijerph17155406.
Multiple land use regression models (LUR) were developed for different air pollutants to characterize exposure, in the Durban metropolitan area, South Africa. Based on the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology, concentrations of particulate matter (PM and PM), sulphur dioxide (SO), and nitrogen dioxide (NO) were measured over a 1-year period, at 41 sites, with Ogawa Badges and 21 sites with PM Monitors. Sampling was undertaken in two regions of the city of Durban, South Africa, one with high levels of heavy industry as well as a harbor, and the other small-scale business activity. Air pollution concentrations showed a clear seasonal trend with higher concentrations being measured during winter (25.8, 4.2, 50.4, and 20.9 µg/m for NO, SO, PM, and PM, respectively) as compared to summer (10.5, 2.8, 20.5, and 8.5 µg/m for NO, SO, PM, and PM, respectively). Furthermore, higher levels of NO and SO were measured in south Durban as compared to north Durban as these are industrial related pollutants, while higher levels of PM were measured in north Durban as compared to south Durban and can be attributed to either traffic or domestic fuel burning. The LUR NO models for annual, summer, and winter explained 56%, 41%, and 63% of the variance with elevation, traffic, population, and Harbor being identified as important predictors. The SO models were less robust with lower R annual (37%), summer (46%), and winter (46%) with industrial and traffic variables being important predictors. The R for PM models ranged from 52% to 80% while for PM models this range was 61-76% with traffic, elevation, population, and urban land use type emerging as predictor variables. While these results demonstrate the influence of industrial and traffic emissions on air pollution concentrations, our study highlighted the importance of a Harbor variable, which may serve as a proxy for NO concentrations suggesting the presence of not only ship emissions, but also other sources such as heavy duty motor vehicles associated with the port activities.
针对不同的空气污染物，我们在南非德班大都市区开发了多个多区域回归模型（LUR），以对暴露情况进行特征描述。该研究基于欧洲空气污染效应队列研究（ESCAPE）方法，在德班市的两个区域开展了为期一年的大气采样工作，在 41 个 Ogawa 徽章监测点和 21 个 PM 监测点测量了颗粒物（PM 和 PM）、二氧化硫（SO）和二氧化氮（NO）的浓度。采样工作在南非德班市的两个区域进行，一个区域重工业和港口高度集中，另一个区域则以小规模商业活动为主。大气污染浓度呈现出明显的季节性趋势，冬季的浓度更高（NO、SO、PM 和 PM 的浓度分别为 25.8、4.2、50.4 和 20.9 µg/m），而夏季的浓度更低（NO、SO、PM 和 PM 的浓度分别为 10.5、2.8、20.5 和 8.5 µg/m）。此外，与德班北部相比，德班南部的 NO 和 SO 浓度更高，因为这些污染物与工业有关，而德班北部的 PM 浓度则高于德班南部，这可能是由于交通或家用燃料燃烧造成的。NO 的年度、夏季和冬季 LUR 模型分别解释了 56%、41%和 63%的方差，海拔、交通、人口和港口被确定为重要的预测因子。SO 模型的稳健性较差，年度（37%）、夏季（46%）和冬季（46%）的 R 值较低，工业和交通变量是重要的预测因子。PM 模型的 R 值范围在 52%到 80%之间，而 PM 模型的 R 值范围在 61%到 76%之间，交通、海拔、人口和城市土地利用类型成为预测变量。虽然这些结果表明工业和交通排放对大气污染浓度有影响，但我们的研究还强调了港口变量的重要性，它可能是 NO 浓度的一个替代指标，这表明不仅有船舶排放，还有其他与港口活动相关的重型机动车等来源。
