Why energy access is not enough for choosing clean cooking fuels? Evidence from the multinomial logit model.

The transition to sustainable energy requires an assessment of drivers of the use of clean and dirty fuels for cooking. Literature highlights the importance of access to clean fuel for switching from dirty fuels to clean fuels. Though access to cleaner fuels, such as electricity promotes clean fuel use, it does not necessarily lead to a complete transition to the use of clean fuels. Households continue using traditional fuels in addition to the clean fuels. The main objective of this paper is to explain the choice of dirty cooking fuels even when access to electricity is provided. We use nationally representative household survey data to study the household energy use decisions in three middle-income countries, namely, India, Kazakhstan, and the Kyrgyz Republic. The study discusses the role of access to natural gas, free fuel, convenience or multi-use of fuels featured by the heating system installed, built-in environment, and other socio-economic factors in household fuel choice for cooking. The results show that access to natural gas increases the likelihood of opting for clean fuel, while the availability of free fuel in rural areas and the coal-based heating system promote the use of solid fuels.

Trends and health impacts of major urban air pollutants in Kazakhstan.

The air quality in cities in Kazakhstan has been poorly investigated despite the worsening conditions. This study evaluates national air pollution monitoring network data (Total Suspended Particle-TSP, NO2, SO2, and O3) from Kazakhstan cities and provides estimates of excess mortality rates associated with PM2.5 exposure using the Global Exposure Mortality Model (GEMM) concentration-response function. Morbidity rates associated with PM10 exposure were also estimated. Annual average (2015-2017) population-weighted concentrations were Kazakhstan cities was 157, 51, 29, and 41 µg m-3 for TSP, NO2, SO2, and O3 respectively. We estimated a total of 8134 adult deaths per year attributable to PM2.5 (average over 2015-2017) in the selected 21 cities of Kazakhstan. The leading causes of death were ischemic heart disease (4080), stroke (1613), lower respiratory infections (662), chronic obstructive pulmonary disease (434), lung cancer (332). The per capita mortality rate attributable to ambient air pollution (per 105 adults per year) was less than 150 in nine cities, between 150 and 204 in nine cities, and between 276 and 373 in three industrial cities (Zhezkazgan, Temirtau, and Balkhash). Implications: Quantitative information on the health impacts of air pollution can be useful for decision-makers in Kazakhstan to justify environmental policies and identify policy and funding priorities for addressing air pollution issues. This information can also be useful for policymakers by improving the quality of government-funded environmental reports and strategic documents, as they have many shortcomings in terms of the selection of air quality indicators, identification of priority pollutants, and identification of sources of pollution. This study has high significance due to the lack of data and knowledge in Central Asia, especially Kazakhstan.

Assessing air quality changes in large cities during COVID-19 lockdowns: The impacts of traffic-free urban conditions in Almaty, Kazakhstan.

Number of cities worlwide experienced air quality improvements during COVID-19 lockdowns; however, such changes may have been different in places with major contributions from nontraffic related sources. In Almaty, a city-scale quarantine came into force on March 19, 2020, which was a week after the first COVID-19 case was registered in Kazakhstan. This study aims to analyze the effect of the lockdown from March 19 to April 14, 2020 (27 days), on the concentrations of air pollutants in Almaty. Daily concentrations of PM2.5, NO2, SO2, CO, O3, and BTEX were compared between the periods before and during the lockdown. During the lockdown, the PM2.5 concentration was reduced by 21% with spatial variations of 6-34% compared to the average on the same days in 2018-2019, and still, it exceeded WHO daily limit values for 18 days. There were also substantial reductions in CO and NO2 concentrations by 49% and 35%, respectively, but an increase in O3 levels by 15% compared to the prior 17 days before the lockdown. The concentrations of benzene and toluene were 2-3 times higher than those during in the same seasons of 2015-2019. The temporal reductions may not be directly attributed to the lockdown due to favorable meteorological variations during the period, but the spatial effects of the quarantine on the pollution levels are evidenced. The results demonstrate the impact of traffic on the complex nature of air pollution in Almaty, which is substantially contributed by various nontraffic related sources, mainly coal-fired combined heat and power plants and household heating systems, as well as possible small irregular sources such as garbage burning and bathhouses.

Trends and health impacts of major urban air pollutants in Kazakhstan.

Air quality in Kazakhstan cities has been poorly investigated despite their deteriorating situations. This study evaluates the national air pollution monitoring network data (PM10, NO2, SO2, and O3) in Kazakhstan cities, and provides the estimates of excess mortality rates associated with PM2.5 exposure using the Global Exposure Mortality Model (GEMM) concentration-response function. Morbidity rates associated with PM10 exposure were also estimated. Firstly, an air quality-based priority ranking of the cities was suggested based on the annual concentrations of the pollutants during the period of 3 years between 2015 and 2017. Nur-Sultan, Almaty, Ust-Kamenogorsk, and Aktobe were identified as the most polluted cities by PM10, NO2, SO2, and O3 respectively. Then, the exposure-response assessment was conducted for 21 cities. In major cities of Kazakhstan attributable to ambient air pollution per capita, mortality rate is 1 193 per 100 000 population per year, which is 8.97 times higher than in Europe. It is estimated that fine particulate matter exposure in the major cities of Kazakhstan causes 101 139 premature deaths annually. This study provides quantitative information on potential public health risks and impacts of air pollution, which is valuable for decision-makers to justify national environmental policies. Implications: Quantitative information on health impacts from air pollution can be useful for decision makers in Kazakhstan to justify environmental policies and identify policy and funding priorities for tackling air pollution issues. It can be also useful for policy makers in improving the quality of government funded environmental reports and strategic documents as they have many shortcomings in terms of selection of air quality indicators, identification of priority pollutants, identification of sources of pollution. This study has high significance due to the lack of data and knowledge in the region of Central Asia, in particular, of Kazakhstan.

Quantifying trace elements in the emitted particulate matter during cooking and health risk assessment.

Particulate matter (PM) measurements were conducted during heating corn oil, heating corn oil mixed with the table salt and heating low fat ground beef meat using a PTFE-coated aluminum pan on an electric stove with low ventilation. The main objectives of this study were to measure the size segregated mass concentrations, emission rates, and fluxes of 24 trace elements emitted during heating cooking oil or oil with salt and cooking meat. Health risk assessments were performed based on the resulting exposure to trace elements from such cooking activities. The most abundant elements (significantly different from zero) were Ba (24.4 ug m-3) during grilling meat and Ti during heating oil with salt (24.4 ug m-3). The health assessment indicates that the cooking with an electric stove with poor ventilation leading to chronic exposures may pose the risk of significant adverse health effects. Carcinogenic risk exceeded the acceptable level (target cancer risk 1 × 10-6, US EPA 2015) by four orders of magnitude, while non-carcinogenic risk exceeded the safe level (target HQ = 1, US EPA 2015) by a factor of 5-20. Cr and Co were the primary contributors to the highest carcinogenic and non-carcinogenic risks, respectively.