Search

Urban quality of life (QoL) is becoming a subject of urban research mainly for western and Asian countries. Such attention is due to an increasing awareness of the contribution of QoL studies in identifying intervention areas and in monitoring urban planning policies. However, most studies are carried out at city or country level that can average out details at small scales. In this paper we present a case study where the urban QoL at small scale is measured and its variability is evaluated for Kirkos sub-city of Addis Ababa, Ethiopia. The study is based on data from a household survey and some secondary data. Geographic information system (GIS) is applied to extract proximity information (e.g., distance to school facilities) and visualize the spatial distribution of QoL. Statistical methods such as factor analysis are applied to establish an index of objective QoL while coefficient of variation is applied to evaluate spatial variability of subjective QoL. The results of this study reveal that the subjective quality of life (QoL) scores show large variation in the sub-city. The mean QoL score also indicates that the respondents in the sub-city, on average, are dissatisfied with the quality of their life. Respondents with higher education level and income are on average, however, more satisfied with their QoL in the sub-city. The results reveal that the lower the QoL in the Kebele, the larger the variability of QoL within the Kebele. Such indicates how aggregation at large scale can average out the variation of QoL at small scales. The results reveal the presence of QoL variability at small scales. The comparison between the subjective and the objective QoL at Kebele level indicated a state of dissonance, adaptation, deprivation or well-being. Such results suggest that the two measures do not always indicate the same level of QoL.

Urban quality of life (QoL) is becoming a subject of urban research mainly for western and Asian countries. Such attention is due to an increasing awareness of the contribution of QoL studies in identifying intervention areas and in monitoring urban planning policies. However, most studies are carried out at city or country level that can average out details at small scales. In this paper we present a case study where the urban QoL at small scale is measured and its variability is evaluated for Kirkos sub-city of Addis Ababa, Ethiopia. The study is based on data from a household survey and some secondary data. Geographic information system (GIS) is applied to extract proximity information (e.g., distance to school facilities) and visualize the spatial distribution of QoL. Statistical methods such as factor analysis are applied to establish an index of objective QoL while coefficient of variation is applied to evaluate spatial variability of subjective QoL. The results of this study reveal that the subjective quality of life (QoL) scores show large variation in the sub-city. The mean QoL score also indicates that the respondents in the sub-city, on average, are dissatisfied with the quality of their life. Respondents with higher education level and income are on average, however, more satisfied with their QoL in the sub-city. The results reveal that the lower the QoL in the Kebele, the larger the variability of QoL within the Kebele. Such indicates how aggregation at large scale can average out the variation of QoL at small scales. The results reveal the presence of QoL variability at small scales. The comparison between the subjective and the objective QoL at Kebele level indicated a state of dissonance, adaptation, deprivation or well-being. Such results suggest that the two measures do not always indicate the same level of QoL. [ABSTRACT FROM AUTHOR]

Poor ventilation and polluting cooking fuels in low-income homes cause high exposure, yet relevant global studies are limited. We assessed exposure to in-kitchen particulate matter (PM 2.5 and PM 10 ) employing similar instrumentation in 60 low-income homes across 12 cities: Dhaka (Bangladesh); Chennai (India); Nanjing (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Akure (Nigeria); Blantyre (Malawi); Dar-es-Salaam (Tanzania) and Nairobi (Kenya). Exposure profiles of kitchen occupants showed that fuel, kitchen volume, cooking type and ventilation were the most prominent factors affecting in-kitchen exposure. Different cuisines resulted in varying cooking durations and disproportional exposures. Occupants in Dhaka, Nanjing, Dar-es-Salaam and Nairobi spent > 40% of their cooking time frying (the highest particle emitting cooking activity) compared with ∼ 68% of time spent boiling/stewing in Cairo, Sulaymaniyah and Akure. The highest average PM 2.5 (PM 10 ) concentrations were in Dhaka 185 ± 48 (220 ± 58) μg m -3 owing to small kitchen volume, extensive frying and prolonged cooking compared with the lowest in Medellín 10 ± 3 (14 ± 2) μg m -3 . Dual ventilation (mechanical and natural) in Chennai, Cairo and Sulaymaniyah reduced average in-kitchen PM 2.5 and PM 10 by 2.3- and 1.8-times compared with natural ventilation (open doors) in Addis Ababa, Dar-es-Salam and Nairobi. Using charcoal during cooking (Addis Ababa, Blantyre and Nairobi) increased PM 2.5 levels by 1.3- and 3.1-times compared with using natural gas (Nanjing, Medellin and Cairo) and LPG (Chennai, Sao Paulo and Sulaymaniyah), respectively. Smaller-volume kitchens (<15 m 3 ; Dhaka and Nanjing) increased cooking exposure compared with their larger-volume counterparts (Medellin, Cairo and Sulaymaniyah). Potential exposure doses were highest for Asian, followed by African, Middle-eastern and South American homes. We recommend increased cooking exhaust extraction, cleaner fuels, awareness on improved cooking practices and minimising passive occupancy in kitchens to mitigate harmful cooking emissions., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)