Cookstove Smoke Impact on Ambient Air Quality and Probable Consequences for Human Health in Rural Locations of Southern Nepal

Geospatial determinants of maternal and child exposure to fine particulate matter in Kintampo, Ghana: Levels within the household and community, by surrounding building density and near roadways

BACKGROUND

Personal exposure to fine particulate matter (PM2.5) from household air pollution is well-documented in sub-Saharan Africa, but spatiotemporal patterns of exposure are poorly characterized.

OBJECTIVE

We used paired GPS and personal PM2.5 data to evaluate changes in exposure across location-time environments (e.g., household and community, during cooking and non-cooking hours), building density and proximity to roadways.

METHODS

Our study included 259 sessions of geolocated, gravimetrically-calibrated one-minute personal PM2.5 measurements from participants in the GRAPHS Child Lung Function Study. The household vicinity was defined using a 50-meter buffer around participants' homes. Community boundaries were developed using a spatial clustering algorithm applied to an open-source dataset of building footprints in Africa. For each GPS location, we estimated building density (500 m buffer) and proximity to roadways (100 m buffer). We estimated changes in PM2.5 exposure by location (household, community), time of day (morning/evening cooking hours, night), building density, and proximity to roadways using linear mixed effect models.

RESULTS

Relative to nighttime household exposure, PM2.5 exposure during evening cooking hours was 2.84 (95%CI = 2.70-2.98) and 1.80 (95%CI = 1.54-2.10) times higher in the household and community, respectively. Exposures were elevated in areas with the highest versus lowest quartile of building density (FactorQ1vsQ4 = 1.60, 95%CI = 1.42-1.80). The effect of building density was strongest during evening cooking hours, and influenced levels in both the household and community (31% and 65% relative increase from Q1 to Q4, respectively). Being proximal to a trunk, tertiary or track roadway increased exposure by a factor of 1.16 (95%CI = 1.07-1.25), 1.68 (95%CI = 1.45-1.95) and 1.27 (95%CI = 1.06-1.53), respectively.

IMPACT

Household air pollution from cooking with solid fuels in sub-Saharan Africa is a major environmental concern for maternal and child health. Our study advances previous knowledge by quantifying the impact of household cooking activities on air pollution levels in the community, and identifying two geographic features, building density and roadways, that contribute to maternal and child daily exposure. Household cooking contributes to higher air pollution levels in the community especially in areas with greater building density. Findings underscore the need for equitable clean household energy transitions that reach entire communities to reduce health risks from household and outdoor air pollution.

Kitchen Characteristics and Practices Associated with Increased PM2.5 Concentration Levels in Zimbabwean Rural Households

Household air pollution (HAP) from biomass fuels significantly contributes to cardio-respiratory morbidity and premature mortality globally. Particulate matter (PM), one of the pollutants generated, remains the most accurate indicator of household air pollution. Determining indoor air concentration levels and factors influencing these levels at the household level is of prime importance, as it objectively guides efforts to reduce household air pollution. This paper describes household factors associated with increased PM_2.5 levels in Zimbabwean rural household kitchens. Our HAP and lung health in women study enrolled 790 women in rural and urban households in Zimbabwe between March 2018 and December 2019. Here, we report data from 148 rural households using solid fuel as the primary source of fuel for cooking and heating and where indoor air samples were collected. Data on kitchen characteristics and practices were collected cross-sectionally using an indoor walk-through survey and a modified interviewer-administered questionnaire. An Air metrics miniVol Sampler was utilized to collect PM_2.5 samples from the 148 kitchens over a 24 h period. To identify the kitchen features and practices that would likely influence PM_2.5 concentration levels, we applied a multiple linear regression model. The measured PM_2.5 ranged from 1.35 μg/m³ to 1940 μg/m³ (IQR: 52.1-472). The PM_2.5 concentration levels in traditional kitchens significantly varied from the townhouse type kitchens, with the median for each kitchen being 291.7 μg/m³ (IQR: 97.2-472.2) and 1.35 μg/m³ (IQR: 1.3-97.2), respectively. The use of wood mixed with other forms of biomass was found to have a statistically significant association (p < 0.001) with increased levels of PM_2.5 concentration. In addition, cooking indoors was strongly associated with higher PM_2.5 concentrations (p = 0.012). Presence of smoke deposits on walls and roofs of the kitchens was significantly associated with increased PM_2.5 concentration levels (p = 0.044). The study found that kitchen type, energy type, cooking place, and smoke deposits were significant predictors of increased PM₂.₅ concentrations in the rural households. Concentrations of PM_2.5 were high as compared to WHO recommended exposure limits for PM_2.5. Our findings highlight the importance of addressing kitchen characteristics and practices associated with elevated PM_2.5 concentrations in settings where resources are limited and switching to cleaner fuels may not be an immediate feasible option.

Ranking the environmental factors of indoor air quality of metropolitan independent coffee shops by Random Forests model

Independent coffee shops are the alternative workplaces for people working remotely from traditional offices but are not concerned about their indoor air quality (IAQ). This study aimed to rank the environmental factors in affecting the IAQ by Random Forests (RFs) models. The indoor environments and human activities of participated independent coffee shops were observed and recorded for 3 consecutive days including weekdays and weekend during the business hours. The multi-sized particulate matter (PM), particle-bound polycyclic aromatic hydrocarbons (p-PAHs), total volatile organic compounds (TVOCs), CO, CO₂, temperature and relative humidity were monitored. RFs models ranked the environmental factors. More than 20% of the 15-min average concentrations of PM₁₀, PM_2.5, and CO₂ exceeded the World Health Organization guidelines. Occupant density affected TVOCs, p-PAHs and CO₂ concentrations directly. Tobacco smoking dominated PM₁₀, PM_2.5, TVOCs and p-PAHs concentrations mostly. CO concentration was affected by roasting bean first and tobacco smoking secondly. The non-linear relationships between temperature and these pollutants illustrated the relative low concentrations happened at temperature between 22 and 24 °C. Tobacco smoking, roasting beans and occupant density are the observable activities to alert the IAQ change. Decreasing CO₂ and optimizing the room temperature could also be the surrogate parameters to assure the IAQ.

Household PM2.5 pollution in rural Chinese homes: Levels, dynamic characteristics and seasonal variations

Humans generally spend most of their time indoors, and fine particulate matter (PM_2.5) in indoor air can have seriously adverse effects on human health due to the long exposure time. This study conducted field measurements to explore seasonal variations of PM_2.5 concentrations in household air by revisiting the same rural homes in southern China and factors influencing indoor PM_2.5 concentrations were explored mainly by one-way ANOVA. The PM_2.5 concentrations of outdoor, kitchen and living room air were 38.9 ± 12.2, 47.1 ± 20.3 and 50.8 ± 24.1 μg/m³ in summer, respectively, which were 2.3 to 2.9 times lower than those in winter (p < 0.05). The lower indoor PM_2.5 pollution in summer was attributed to the transition to clean household energy and better ventilation. Fuel type can significantly affect PM_2.5 concentrations in the kitchen, with greater PM_2.5 pollution associated with wood combustion than electricity. Our study firstly found mosquito coil emission was an important contributor to PM_2.5 in the living room of rural households, which should be investigated further. Dynamic variations of PM_2.5 suggested that cooking, heating and mosquito coil emission can rapidly increase indoor PM_2.5 concentrations (up to one order of magnitude higher than baseline values), as well as the indoor/outdoor PM_2.5 ratios. This study had the first insight of seasonal differences of household PM_2.5 in the same rural homes using real-time monitors, confirming the different patterns and characteristics of household PM_2.5 pollution in different seasons.

Impact of the Restaurant Chimney Emissions on the Outdoor Air Quality

The emission of cooking fumes becomes a serious concern due to the fast development of the restaurant business because it harms the health of restaurant workers and customers and damages the outdoor air quality. This study was conducted to evaluate the impact of restaurant emissions on ambient air quality. Twenty restaurants with four different types of food cooking were selected in Dammam City, which represents a densely populated urban city in Saudi Arabia. Levels of five air pollutants were simultaneously measured in the restaurants’ chimneys and in the surrounding ambient air. The highest mean levels of CO (64.8 ± 44.3 ppm), CO2 (916.7 ± 463.4 ppm), VOCs (105.1 ± 61.3 ppm), NO2 (4.2 ± 2.4 ppm), and SO2 (8.0 ± 7.4 ppm) were recorded in chimneys of the grilling restaurants. Similarly, the highest levels of all pollutants were recorded in the areas adjacent to the grilling restaurants rather than other types.

Association of cooking water with the health of middle-aged and elderly Chinese individuals: evidence from a national household longitudinal survey

Little evidence has demonstrated the association between health conditions and cooking water. The purpose of this study was to explore the relationship, using a representative sample of 10,531 subjects selected from the China Family Panel Study (CFPS). The usage rate of surface-exposed water showed a slight upward trend from 2010 to 2018. The adjusted odds ratio (OR) of chronic diseases with surface-exposed and well water was 1.140 (95% CI: 0.989-1.315) and 0.902 (95% CI: 0.839-0.969), respectively, with reference to tap/purified water. Surface-exposed water increased the likelihood of a worsening health change by 25.5% (OR: 1.255; 95% CI: 1.123-1.411), while well water was associated with poor self-rated health (OR: 1.169; 95% CI: 1.094-1.249). As such, surface-exposed water was associated with chronic diseases and worsening changes in health, and well water was negatively associated with chronic diseases. Although efforts to improve quality of drinking water have been made in China for decades, our conclusions reveal that water quality still remains a critical public livelihood issue among middle-aged and elderly populations. More in-depth research is required on whether the disinfection ingredients of tap water may increase the risk for chronic disease.

The Development of a CO2 Emission Coefficient for Medium- and Heavy-Duty Vehicles with Different Road Slope Conditions Using Multiple Linear Regression, and Considering the Health Effects

The current studies on carbon dioxide (CO2) emissions and the impacts on public health focus mainly on evaluating CO2 emissions from two types of emission sources. The first is a fixed source type from industrial plants, which can be controlled or easily evaluated. The second is a mobile source type from the transport sector, especially from medium- and heavy-duty vehicles (MHDVs), which produce high emissions. However, the common methods of evaluation of the average value of CO2 emissions per kilometer of a vehicle use a general Intergovernmental Panel on Climate Change (IPCC) model that does not consider the topography or road conditions. This affects the accuracy of CO2 emission assessments and, in turn, affects the accuracy of any analysis needed to establish health policies and the management of public health within the affected area. In this paper, therefore, we present the development of emission coefficient calculations with varying topography conditions for MHDVs with consideration of the health effects on the populace. The study area was the Asian highway network in Thailand that passes through all regions and is geographically diverse. Data were collected from the Department of Highway’s records, global positioning system (GPS) and electronic control unit (ECU) with data consisting of road data, slope, distance, traffic level and vehicle weight, as well as fuel consumption along the transportation route. To analyze and map out the correlation of the CO2 emission coefficients for each road slope, we determined the coefficient of the CO2 emissions using multiple linear regression analysis and validated this using Huber–White robust standard errors for heteroscedasticity. This method was more cost-efficient and time-efficient compared to the conventional approaches. We also created CO2 emission maps with risk points for health effects, and we propose policy designs and plans to manage the traffic level in each of these areas prone to higher levels of such emissions.