Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, USA.
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.
Sci Total Environ. 2023 Jun 1;875:162582. doi: 10.1016/j.scitotenv.2023.162582. Epub 2023 Mar 3.
Growing cities in sub-Saharan Africa (SSA) experience high levels of ambient air pollution. However, sparse long-term city-wide air pollution exposure data limits policy mitigation efforts and assessment of the health and climate effects. In the first study of its kind in West Africa, we developed high resolution spatiotemporal land use regression (LUR) models to map fine particulate matter (PM) and black carbon (BC) concentrations in the Greater Accra Metropolitan Area (GAMA), one of the fastest sprawling metropolises in SSA. We conducted a one-year measurement campaign covering 146 sites and combined these data with geospatial and meteorological predictors to develop separate Harmattan and non-Harmattan season PM and BC models at 100 m resolution. The final models were selected with a forward stepwise procedure and performance was evaluated with 10-fold cross-validation. Model predictions were overlayed with the most recent census data to estimate the population distribution of exposure and socioeconomic inequalities in exposure at the census enumeration area level. The fixed effects components of the models explained 48-69 % and 63-71 % of the variance in PM and BC concentrations, respectively. Spatial variables related to road traffic and vegetation explained the most variability in the non-Harmattan models, while temporal variables were dominant in the Harmattan models. The entire GAMA population is exposed to PM levels above the World Health Organization guideline, including even the Interim Target 3 (15 μg/m), with the highest exposures in poorer neighborhoods. The models can be used to support air pollution mitigation policies, health, and climate impact assessments. The measurement and modelling approach used in this study can be adapted to other African cities to bridge the air pollution data gap in the region.
撒哈拉以南非洲（SSA）不断发展的城市面临着高水平的环境空气污染。然而，长期缺乏全市范围的空气污染暴露数据限制了政策缓解措施和对健康与气候影响的评估。在西非的第一项此类研究中，我们开发了高分辨率时空土地利用回归（LUR）模型，以绘制西非增长最快的大都市之一——大阿克拉大都市区（GAMA）的细颗粒物（PM）和黑碳（BC）浓度。我们进行了为期一年的测量活动，覆盖了 146 个地点，并将这些数据与地理空间和气象预测因子相结合，以在 100 米的分辨率下分别建立哈马坦和非哈马坦季节 PM 和 BC 模型。最终模型是通过逐步向前选择过程选定的，其性能通过 10 倍交叉验证进行了评估。模型预测与最新的人口普查数据进行了叠加，以估算暴露的人口分布情况和按普查区一级的暴露的社会经济不平等情况。模型的固定效应部分分别解释了 PM 和 BC 浓度的 48-69%和 63-71%的方差。与道路交通和植被有关的空间变量解释了非哈马坦模型中最大的变异性，而时间变量则在哈马坦模型中占主导地位。整个 GAMA 人口都暴露在世界卫生组织指南之上的 PM 水平，甚至包括临时目标 3（15μg/m），贫困社区的暴露水平最高。这些模型可用于支持空气污染缓解政策、健康和气候影响评估。本研究中使用的测量和建模方法可以适应其他非洲城市，以弥合该地区的空气污染数据差距。
