Hlatshwayo Sphamandla N, Tesfamichael Solomon G, Kganyago Mahlatse
Department of Geography, Environmental Management and Energy Studies, University of Johannesburg, Johannesburg, South Africa.
PLoS One. 2024 Aug 8;19(8):e0308484. doi: 10.1371/journal.pone.0308484. eCollection 2024.
Atmospheric nitrogen dioxide (NO2) pollution is a major health and social challenge in South African induced mainly by fossil fuel combustions for power generation, transportation and domestic biomass burning for indoor activities. The pollution level is moderated by various environmental and social factors, yet previous studies made use of limited factors or focussed on only industrialised regions ignoring the contributions in large parts of the country. There is a need to assess how socio-environmenral factors, which inherently exhibit variations across space, influence the pollution levels in South Africa. This study therefore aimed to predict annual tropospheric NO2 column density using socio-environmental variables that are widely proven in the literature as sources and sinks of pollution. The environmental variables used to predict NO2 included remotely sensed Enhanced Vegetation Index (EVI), Land Surface Temperature and Aerosol Optical Depth (AOD) while the social data, which were obtained from national household surveys, included energy sources data, settlement patterns, gender and age statistics aggregated at municipality scale. The prediction was accomplished by applying the Multiscale Geographically Weighted Regression that fine-tunes the spatial scale of each variable when building geographically localised relationships. The model returned an overall R2 of 0.92, indicating good predicting performance and the significance of the socio-environmental variables in estimating NO2 in South Africa. From the environmental variables, AOD had the most influence in increasing NO2 pollution while vegetation represented by EVI had the opposite effect of reducing the pollution level. Among the social variables, household electricity and wood usage had the most significant contributions to pollution. Communal residential arrangements significantly reduced NO2, while informal settlements showed the opposite effect. The female proportion was the most important demographic variable in reducing NO2. Age groups had mixed effects on NO2 pollution, with the mid-age group (20-29) being the most important contributor to NO2 emission. The findings of the current study provide evidence that NO2 pollution is explained by socio-economic variables that vary widely across space. This can be achieved reliably using the MGWR approach that produces strong models suited to each locality.
大气二氧化氮（NO₂）污染是南非面临的一项重大健康和社会挑战，主要由用于发电、交通的化石燃料燃烧以及用于室内活动的家庭生物质燃烧所致。污染水平受到各种环境和社会因素的调节，但以往的研究使用的因素有限，或仅关注工业化地区，而忽略了该国大部分地区的贡献。有必要评估社会环境因素如何影响南非的污染水平，这些因素在空间上存在固有差异。因此，本研究旨在利用文献中广泛证明的作为污染源和汇的社会环境变量，预测对流层年度NO₂柱密度。用于预测NO₂的环境变量包括遥感增强植被指数（EVI）、地表温度和气溶胶光学厚度（AOD），而从全国家庭调查中获得的社会数据包括能源数据、居住模式、按市政规模汇总的性别和年龄统计数据。预测是通过应用多尺度地理加权回归来完成的，该回归在建立地理局部关系时会微调每个变量的空间尺度。该模型的总体R²为0.92，表明预测性能良好，且社会环境变量在估算南非NO₂方面具有重要意义。在环境变量中，AOD对增加NO₂污染的影响最大，而以EVI表示的植被则具有降低污染水平的相反作用。在社会变量中，家庭用电和木材使用对污染的贡献最为显著。社区居住安排显著降低了NO₂，而非正式定居点则显示出相反的效果。女性比例是降低NO₂的最重要人口变量。年龄组对NO₂污染有混合影响，中年组（20 - 29岁）是NO₂排放的最重要贡献者。本研究结果表明，NO₂污染可由空间上差异很大的社会经济变量来解释。使用MGWR方法可以可靠地实现这一点，该方法能产生适用于每个地点的强大模型。
