PR | IFPRI3; ISI; DCA; 1 Fostering Climate-Resilient and Sustainable Food Supply; 2 Promoting Healthy Diets and Nutrition for all; G Cross-cutting gender theme | DSGD

This study assessed the sources, magnitudes, and chemical compositions of household air pollution (HAP) and personal exposure in traditional Tibetan households. We measured 24-h personal exposures to PM₂.₅ and kitchen area black carbon (BC) concentrations, using MicroPEMs and microAeths, respectively. Particulate polycyclic aromatic hydrocarbon (PAH) and inorganic element concentrations were quantified via post analyses of a subset of MicroPEM sample filters. Household surveys regarding participant demographics, cookstove usage, household fuel, cooking behaviors, and lifestyles were collected. The results reaffirm that burning firewood and yak dung, mainly for cooking, leads to high PM₂.₅ and BC exposures. The geometric mean concentration (95% confidence interval, CI) was 74.3 (53.6, 103) μg/m³ for PM₂.₅ and the arithmetic mean ± standard deviation (SD) concentration was 4.90 ± 5.01 μg/m³ for BC and 292 ± 364 ng/m³ for 15 identified PAHs, respectively. The arithmetic mean ± SD of mass concentrations of 24 detected elements ranged from 0.76 ± 0.91 ng/m³ (Co) to 1.31 ± 1.35 μg/m³ (Si). Our statistical analyses further illustrated that the high concentrations of PM₂.₅, BC, and most PAHs and metals, are significantly associated with nomadic village, poorer stove/chimney conditions and yak dung burning. The results from this study show that substantial HAP exposure is prevalent in Tibetan households and requires immediate actions to mitigate potential negative environmental health impacts. The observational data also revealed the possibility of other important sources (e.g. traffic and garbage burning) that have contributed to personal exposures. These findings improve our understanding of HAP exposure and potential health risks in Tibetan communities and will help inform strategies for reducing HAP in Tibetan households and beyond.

The consequences of indoor and outdoor air pollution on human health are of great concern nowadays. In this study, we firstly evaluated indoor and outdoor air pollution levels (CO, CO₂, NO, NO₂, PM₁₀) at an urban site in Dakar city center and at a rural site. Then, the individual exposure levels to selected pollutants and the variations in the levels of biomarkers of exposure were investigated in different groups of persons (bus drivers, traders working along the main roads and housemaids). Benzene exposure levels were higher for housemaids than for bus drivers and traders. High indoor exposure to benzene is probably due to cooking habits (cooking with charcoal), local practices (burning of incense), the use of cleaning products or solvent products which are important emitters of this compound. These results are confirmed by the values of S-PMA, which were higher in housemaids group compared to the others. Urinary 1-HOP levels were significantly higher for urban site housemaids compared to semirural district ones.Moreover, urinary levels of DNA oxidative stress damage (8-OHdG) and inflammatory (interleukin-6 and -8) biomarkers were higher in urban subjects in comparison to rural ones.The air quality measurement campaign showed that the bus interior was more polluted with PM₁₀, CO, CO₂ and NO than the market and urban or rural households. However, the interior of households showed higher concentration of VOCs than outdoor sites confirming previous observations of higher indoor individual exposure level to specific classes of pollutants.

A previous study observed high blood lead levels (BLL) in preschool children attending 50 day care centers (DCC) in São Paulo, Brazil.To identify whether lead levels found in both homes and DCC environments are associated with high blood lead levels.Children attending 4 DCCs, quoted here as NR, VA, PS and PF, were divided into two groups according to BLL: high exposure (HE: ≥13.9 μg/dL; 97.5 percentile of the 2013 year sample) and low exposure (LE: <5 μg/dL). For in situ lead measurements (lead paint mode: mg/cm² and ROHS mode: μg/g) in the children's households and in the DCC environments, a field portable X-ray-fluorescence analyzer was used. Multiple logistic regressions were performed to control for confounding factors. Odds ratios were adjusted for age, sex, day care center's measured lead, and tobacco.In an NR DCC building, 33.8% of the measurements had lead levels >600 μg/g, whereas such levels were observed in 77.1% of NR playground measurements. In VA DCC, 22% and 23% of the measurements in the building and in the playgrounds had levels higher than 600 μg/g, respectively. The percentage of high lead levels in the children's houses of the LE group was 5.9% (95% CI: 4.3–7.6%) and 13.2 (95% CI: 8.3–18.0%) in the HE group. Moreover, a significant association was found between high BLLs and lead levels found both in households and DCCs (p < 0.001). Most of the high lead measurements were found in tiles and playground equipment.Lead exposure estimated from the DCCs, where children spend about 10 h/day, can be as relevant as their household exposure. Therefore, public authorities should render efforts to provide a rigorous surveillance for lead-free painting supplies and for all objects offered to children.

Because people spend most of their time indoors, the characterization of indoor air quality is important for exposure assessment. Unfortunately, indoor air data are scarce, leading to a major data gap in risk assessment. In this study, PM2.5 concentrations in both indoor and outdoor air were simultaneously measured using on-line particulate counters in 13 households in Haidian, Beijing for both heating and non-heating seasons. A bimodal distribution of PM2.5 concentrations suggests rapid transitions between polluted and non-polluted situations. The PM2.5 concentrations in indoor and outdoor air varied synchronously, with the indoor variation lagging. The lag time in the heating season was longer than that in the non-heating season. The particle sizes in indoor air were smaller than those in ambient air in the heating season and vice versa in the non-heating season. PM2.5 concentrations in indoor air were generally lower than those in ambient air except when ambient concentrations dropped sharply to very low levels or there were internal emissions from cooking or other activities. The effectiveness of an air cleaner to reduce indoor PM2.5 concentrations was demonstrated. Non-linear regression models were developed to predict indoor air PM2.5 concentrations based on ambient data with lag time incorporated. The models were applied to estimate the overall population exposure to PM2.5 and the health consequences in Haidian. The health impacts would be significantly overestimated without the indoor exposure being taken into consideration, and this bias would increase as the ambient air quality improved in the future.

Nano silver and nano zinc-oxide monthly concentrations in surface waters across Europe were modeled at ∼6 × 9 km spatial resolution. Nano-particle loadings from households to rivers were simulated considering household connectivity to sewerage, sewage treatment efficiency, the spatial distribution of sewage treatment plants, and their associated populations. These loadings were used to model temporally varying nano-particle concentrations in rivers, lakes and wetlands by considering dilution, downstream transport, water evaporation, water abstraction, and nano-particle sedimentation. Temporal variability in concentrations caused by weather variation was simulated using monthly weather data for a representative 31-year period. Modeled concentrations represent current levels of nano-particle production. Two scenarios were modeled. In the most likely scenario, half the river stretches had long-term average concentrations exceeding 0.002 ng L−1 nano silver and 1.5 ng L−1 nano zinc oxide. In 10% of the river stretches, these concentrations exceeded 0.18 ng L−1 and 150 ng L−1, respectively. Predicted concentrations were usually highest in July.

Atmospheric PM10 were measured for 12 months at 18 sites along a 2500 km profile across northern China. Annual mean PM10 concentrations in urban, rural village, and rural field sites were 180 ± 171, 182 ± 154, and 128 ± 89 μg/m3, respectively. The similarities in PM10 concentrations between urban and rural village sites suggest that strong localized emissions and severe contamination in rural residential areas are derived from solid fuels combustion in households. High PM10 concentrations in Wuwei and Taiyuan were caused by either sandstorms or industrial activities. Relatively low PM10 concentrations were observed in coastal areas of Dalian and Yantai. Particulate air pollution was much higher in winter and spring than in summer and fall. Multiple regression analysis indicates that 35% of the total variance can be attributed to sandstorms, precipitation and residential energy consumption. Over 40% of the measurements in both urban and rural village areas exceeded the national ambient air quality standard.

We investigated total daily intake of As by residents in Prey Veng province in the Mekong River basin of Cambodia. Groundwater (n = 11), rice (n = 11) and fingernail (n = 23) samples were randomly collected from the households and analyzed for total As by inductively coupled plasma mass spectrometry. Calculation indicated that daily dose of inorganic As was greater than the lower limits on the benchmark dose for a 0.5% increased incidence of lung cancer (BMDL0.5 equals to 3.0 μg d−1 kg−1body wt.). Moreover, positive correlation between As in fingernail and daily dose of As from groundwater and rice and total daily dose of As were found. These results suggest that the Prey Veng residents are exposed to As in groundwater. As in rice is an additional source which is attributable to high As accumulation in human bodies in the Mekong River basin of Cambodia.

To identify different NO₃ ⁻ sources in surface water and to estimate their proportional contribution to the nitrate mixture in surface water, a dual isotope and a Bayesian isotope mixing model have been applied for six different surface waters affected by agriculture, greenhouses in an agricultural area, and households. Annual mean δ¹⁵N–NO₃ ⁻ were between 8.0 and 19.4‰, while annual mean δ¹⁸O–NO₃ ⁻ were given by 4.5–30.7‰. SIAR was used to estimate the proportional contribution of five potential NO₃ ⁻ sources (NO₃ ⁻ in precipitation, NO₃ ⁻ fertilizer, NH₄ ⁺ in fertilizer and rain, soil N, and manure and sewage). SIAR showed that “manure and sewage” contributed highest, “soil N”, “NO₃ ⁻ fertilizer” and “NH₄ ⁺ in fertilizer and rain” contributed middle, and “NO₃ ⁻ in precipitation” contributed least. The SIAR output can be considered as a “fingerprint” for the NO₃ ⁻ source contributions. However, the wide range of isotope values observed in surface water and of the NO₃ ⁻ sources limit its applicability.

Cooking and heating with solid fuels results in high levels of household air pollutants, including particulate matter (PM); however, limited data exist for size fractions smaller than PM₂.₅ (diameter less than 2.5 μm). We collected 24-h time-resolved measurements of PM₂.₅ (n = 27) and particle number concentrations (PNC, average diameter 10–700 nm) (n = 44; 24 with paired PM₂.₅ and PNC) in homes with wood-burning traditional and Justa (i.e., with an engineered combustion chamber and chimney) cookstoves in rural Honduras.The median 24-h PM₂.₅ concentration (n = 27) was 79 μg/m³ (interquartile range [IQR]: 44–174 μg/m³); traditional (n = 15): 130 μg/m³ (IQR: 48–250 μg/m³); Justa (n = 12): 66 μg/m³ (IQR: 44–97 μg/m³). The median 24-h PNC (n = 44) was 8.5 × 10⁴ particles (pt)/cm³ (IQR: 3.8 × 10⁴–1.8 × 10⁵ pt/cm³); traditional (n = 27): 1.3 × 10⁵ pt/cm³ (IQR: 3.3 × 10⁴–2.0 × 10⁵ pt/cm³); Justa (n = 17): 6.3 × 10⁴ pt/cm³ (IQR: 4.0 × 10⁴–1.2 × 10⁵ pt/cm³). The 24-h average PM₂.₅ and particle number concentrations were correlated for the full sample of cookstoves (n = 24, Spearman ρ: 0.83); correlations between PM₂.₅ and PNC were higher in traditional stove kitchens (n = 12, ρ: 0.93) than in Justa stove kitchens (n = 12, ρ: 0.67). The 24-h average concentrations of PM₂.₅ and PNC were also correlated with the maximum average concentrations during shorter-term averaging windows of one-, five-, 15-, and 60-min, respectively (Spearman ρ: PM₂.₅ [0.65, 0.85, 0.82, 0.71], PNC [0.74, 0.86, 0.88, 0.86]).Given the moderate correlations observed between 24-h PM₂.₅ and PNC and between 24-h and the shorter-term averaging windows within size fractions, investigators may need to consider cost-effectiveness and information gained by measuring both size fractions for the study objective. Further evaluations of other stove and fuel combinations are needed.