Emissions from village cookstoves in Haryana, India, and their potential impacts on air quality

Air quality in rural India is impacted by residential cooking and heating with biomass fuels. In this study, emissions of CO, CO2, and 76 volatile organic compounds (VOCs) and fine particulate matter (PM2.5) were quantified to better understand the relationship between cook fire emissions and ambient ozone and secondary organic aerosol (SOA) formation. Cooking was carried out by a local cook, and traditional dishes were prepared on locally built chulha or angithi cookstoves using brushwood or dung fuels. Cook fire emissions were collected throughout the cooking event in a Kynar bag (VOCs) and on polytetrafluoroethylene (PTFE) filters (PM2.5). Gas samples were transferred from a Kynar bag to previously evacuated stainless-steel canisters and analyzed using gas chromatography coupled to flame ionization, electron capture, and mass spectrometry detectors. VOC emission factors were calculated from the measured mixing ratios using the carbon-balance method, which assumes that all carbon in the fuel is converted to CO2, CO, VOCs, and PM2.5 when the fuel is burned. Filter samples were weighed to calculate PM2.5 emission factors. Dung fuels and angithi cookstoves resulted in significantly higher emissions of most VOCs (p < 0.05). Utilizing dung-angithi cook fires resulted in twice as much of the measured VOCs compared to dung-chulha and 4 times as much as brushwood-chulha, with 84.0, 43.2, and 17.2g measured VOCkgg fuel carbon, respectively. This matches expectations, as the use of dung fuels and angithi cookstoves results in lower modified combustion efficiencies compared to brushwood fuels and chulha cookstoves. Alkynes and benzene were exceptions and had significantly higher emissions when cooking using a chulha as opposed to an angithi with dung fuel (for example, benzene emission factors were 3.18gkgg fuel carbon for dung-chulha and 2.38gkgg fuel carbon for dung-angithi). This study estimated that 3 times as much SOA and ozone in the maximum incremental reactivity (MIR) regime may be produced from dung-chulha as opposed to brushwood-chulha cook fires. Aromatic compounds dominated as SOA precursors from all types of cook fires, but benzene was responsible for the majority of SOA formation potential from all chulha cook fire VOCs, while substituted aromatics were more important for dung-angithi. Future studies should investigate benzene exposures from different stove and fuel combinations and model SOA formation from cook fire VOCs to verify public health and air quality impacts from cook fires

Molecular composition of particulate matter emissions from dung and brushwood burning household cookstoves in Haryana, India

Emissions of airborne particles from biomass burning are a significant source of black carbon (BC) and brown carbon (BrC) in rural areas of developing countries where biomass is the predominant energy source for cooking and heating. This study explores the molecular composition of organic aerosols from household cooking emissions with a focus on identifying fuel-specific compounds and BrC chromophores. Traditional meals were prepared by a local cook with dung and brushwood-fueled cookstoves in a village in Palwal district, Haryana, India. Cooking was done in a village kitchen while controlling for variables including stove type, fuel moisture, and meal. Fine particulate matter (PM2.5) emissions were collected on filters, and then analyzed via nanospray desorption electrospray ionization-high-resolution mass spectrometry (nano-DESI-HRMS) and high-performance liquid chromatography-photodiode array-high-resolution mass spectrometry (HPLC-PDA-HRMS) techniques. The nano-DESI-HRMS analysis provided an inventory of numerous compounds present in the particle phase. Although several compounds observed in this study have been previously characterized using gas chromatography methods a majority of the species in the nano-DESI spectra were newly observed biomass burning compounds. Both the stove (chulha or angithi) and the fuel (brushwood or dung) affected the composition of organic aerosols. The geometric mean of the PM2.5 emission factor and the observed molecular complexity increased in the following order: brushwood-chulha (7.3±1.8 g kg-1 dry fuel, 93 compounds), dung-chulha (21.1±4.2 g kg-1 dry fuel, 212 compounds), and dung-angithi (29.8±11.5 g kg-1 dry fuel, 262 compounds). The mass-normalized absorption coefficient (MACbulk) for the organic-solvent extractable material for brushwood PM2.5 was 3.7±1.5 and 1.9±0.8m2 g-1 at 360 and 405 nm, respectively, which was approximately a factor of two higher than that for dung PM2.5. The HPLC-PDA-HRMS analysis showed that, regardless of fuel type, the main chromophores were CxHyOz lignin fragments. The main chromophores accounting for the higher MACbulk values of brushwood PM2.5 were C8H10O3 (tentatively assigned to syringol), nitrophenols C8H9NO4, and C10H10O3 (tentatively assigned to methoxycinnamic acid)

Comparison of next-generation portable pollution monitors to measure exposure to PM2.5 from household air pollution in Puno, Peru.

Assessment of personal exposure to PM2.5 is critical for understanding intervention effectiveness and exposure-response relationships in household air pollution studies. In this pilot study, we compared PM2.5 concentrations obtained from two next-generation personal exposure monitors (the Enhanced Children MicroPEM or ECM; and the Ultrasonic Personal Air Sampler or UPAS) to those obtained with a traditional Triplex Cyclone and SKC Air Pump (a gravimetric cyclone/pump sampler). We co-located cyclone/pumps with an ECM and UPAS to obtain 24-hour kitchen concentrations and personal exposure measurements. We measured Spearmen correlations and evaluated agreement using the Bland-Altman method. We obtained 215 filters from 72 ECM and 71 UPAS co-locations. Overall, the ECM and the UPAS had similar correlation (ECM ρ = 0.91 vs UPAS ρ = 0.88) and agreement (ECM mean difference of 121.7 µg/m3 vs UPAS mean difference of 93.9 µg/m3 ) with overlapping confidence intervals when compared against the cyclone/pump. When adjusted for the limit of detection, agreement between the devices and the cyclone/pump was also similar for all samples (ECM mean difference of 68.8 µg/m3 vs UPAS mean difference of 65.4 µg/m3 ) and personal exposure samples (ECM mean difference of -3.8 µg/m3 vs UPAS mean difference of -12.9 µg/m3 ). Both the ECM and UPAS produced comparable measurements when compared against a cyclone/pump setup

Impacts of household sources on air pollution at village and regional scales in India

Approximately 3 billion people worldwide cook with solid fuels, such as wood, charcoal, and agricultural residues. These fuels, also used for residential heating, are often combusted in inefficient devices, producing carbonaceous emissions. Between 2.6 and 3.8 million premature deaths occur as a result of exposure to fine particulate matter from the resulting household air pollution (Health Effects Institute, 2018a; World Health Organization, 2018). Household air pollution also contributes to ambient air pollution; the magnitude of this contribution is uncertain. Here, we simulate the distribution of the two major health-damaging outdoor air pollutants (PM2:5 and O3) using state-of-thescience emissions databases and atmospheric chemical transport models to estimate the impact of household combustion on ambient air quality in India. The present study focuses on New Delhi and the SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in the Palwal District of Haryana, located about 80 km south of New Delhi. The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al., 2015, 2016), an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al. (2018b), and PM2:5 speciation from cooking fires from Jayarathne et al. (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain. Comparisons between observed and simulated O3 and PM2:5 levels are carried out to assess overall airborne levels and to estimate the contribution of household cooking emissions

Correlation of Serum Uric Acid with Cognition, Severity, and Stage of Disease in Patients with Idiopathic Parkinson’s Disease and Vascular Parkinsonism: A Cross-Sectional Study

Background: Uric acid (UA) being a potent antioxidant may reduce the oxidative stress and progression of Parkinson’s disease. However, the role of UA is not yet established in people with Idiopathic Parkinson’s disease (IPD) and Vascular Parkinsonism (VP). Objectives: We aimed i) to compare the serum UA levels in IPD, VP, and healthy adults and ii) to find a relation between UA levels with disease severity, disease stage, and cognitive function in people with IPD and VP. Methods: A cross-sectional study was conducted among people with IPD (n=70), VP (n=70), and healthy adults (n=70). Demographics details, body mass index, duration of illness, levodopa usage, comorbidities, MDS-UPDRS scores, modified H&Y scale, MMSE, and serum UA levels were collected from participants. Pearson’s correlation coefficient was used to find the correlation between UA levels, MDS-UPDRS, H & Y, and MMSE scores. Results: The age of the participants ranged from 59 to 80 years. Results showed that serum UA level in healthy control (5.41±0.99; p=0.001) and VP groups (5.27 ± 0.99; p=0.001) were significantly higher compared to IPD group (4.34 ±1.03). We found a significant negative correlation between UA and MDS-UPDRS (r=-0.68, p<0.01) and H & Y scores (r =-0.61, p<0.01) and a significant positive correlation of UA with MMSE (r=0.55, p<0.01) in the IPD group. UA levels in the VP group were not correlated with any of the outcome measures. Conclusion: In people with IPD, serum UA level was negatively correlated with severity and progression of the disease but positively correlated with cognitive ability

Emissions from village cookstoves in Haryana, India, and their potential impacts on air quality

Air quality in rural India is impacted by residential cooking and heating with biomass fuels. In this study, emissions of CO, CO2, and 76 volatile organic compounds (VOCs) and fine particulate matter (PM2.5) were quantified to better understand the relationship between cook fire emissions and ambient ozone and secondary organic aerosol (SOA) formation. Cooking was carried out by a local cook, and traditional dishes were prepared on locally built chulha or angithi cookstoves using brushwood or dung fuels. Cook fire emissions were collected throughout the cooking event in a Kynar bag (VOCs) and on polytetrafluoroethylene (PTFE) filters (PM2.5). Gas samples were transferred from a Kynar bag to previously evacuated stainless-steel canisters and analyzed using gas chromatography coupled to flame ionization, electron capture, and mass spectrometry detectors. VOC emission factors were calculated from the measured mixing ratios using the carbon-balance method, which assumes that all carbon in the fuel is converted to CO2, CO, VOCs, and PM2.5 when the fuel is burned. Filter samples were weighed to calculate PM2.5 emission factors. Dung fuels and angithi cookstoves resulted in significantly higher emissions of most VOCs (p &lt; 0.05). Utilizing dung–angithi cook fires resulted in twice as much of the measured VOCs compared to dung–chulha and 4 times as much as brushwood–chulha, with 84.0, 43.2, and 17.2&thinsp;g measured VOC&thinsp;kg−1 fuel carbon, respectively. This matches expectations, as the use of dung fuels and angithi cookstoves results in lower modified combustion efficiencies compared to brushwood fuels and chulha cookstoves. Alkynes and benzene were exceptions and had significantly higher emissions when cooking using a chulha as opposed to an angithi with dung fuel (for example, benzene emission factors were 3.18&thinsp;g&thinsp;kg−1 fuel carbon for dung–chulha and 2.38&thinsp;g&thinsp;kg−1 fuel carbon for dung–angithi). This study estimated that 3 times as much SOA and ozone in the maximum incremental reactivity (MIR) regime may be produced from dung–chulha as opposed to brushwood–chulha cook fires. Aromatic compounds dominated as SOA precursors from all types of cook fires, but benzene was responsible for the majority of SOA formation potential from all chulha cook fire VOCs, while substituted aromatics were more important for dung–angithi. Future studies should investigate benzene exposures from different stove and fuel combinations and model SOA formation from cook fire VOCs to verify public health and air quality impacts from cook fires.</p

Implementation Science to Accelerate Clean Cooking for Public Health

Clean cooking has emerged as a major concern for global health and development because of the enormous burden of disease caused by traditional cookstoves and fires. The World Health Organization has developed new indoor air quality guidelines that few homes will be able to achieve without replacing traditional methods with modern clean cooking technologies, including fuels and stoves. However, decades of experience with improved stove programs indicate that the challenge of modernizing cooking in impoverished communities includes a complex, multi-sectoral set of problems that require implementation research. The National Institutes of Health, in partnership with several government agencies and the Global Alliance for Clean Cookstoves, has launched the Clean Cooking Implementation Science Network that aims to address this issue. In this article, our focus is on building a knowledge base to accelerate scale-up and sustained use of the cleanest technologies in low- and middle-income countries. Implementation science provides a variety of analytical and planning tools to enhance effectiveness of clinical and public health interventions. These tools are being integrated with a growing body of knowledge and new research projects to yield new methods, consensus tools, and an evidence base to accelerate improvements in health promised by the renewed agenda of clean cooking.Fil: Rosenthal, Joshua. National Institutes Of Health. Fogarty International Center; Estados UnidosFil: Balakrishnan, Kalpana. Sri Ramachandra University; IndiaFil: Bruce, Nigel. University of Liverpool; Reino UnidoFil: Chambers, David. National Institutes of Health. National Cancer Institute; Estados UnidosFil: Graham, Jay. The George Washington University; Estados UnidosFil: Jack, Darby. Columbia University; Estados UnidosFil: Kline, Lydia. National Institutes Of Health. Fogarty International Center; Estados UnidosFil: Masera, Omar Raul. Universidad Nacional Autónoma de México; MéxicoFil: Mehta, Sumi. Global Alliance for Clean Cookstoves; Estados UnidosFil: Mercado, Ilse Ruiz. Universidad Nacional Autónoma de México; MéxicoFil: Neta, Gila. National Institutes of Health. National Cancer Institute; Estados UnidosFil: Pattanayak, Subhrendu. University of Duke; Estados UnidosFil: Puzzolo, Elisa. Global LPG Partnership; Estados UnidosFil: Petach, Helen. U.S. Agency for International Development; Estados UnidosFil: Punturieri, Antonello. National Heart, Lung, and Blood Institute; Estados UnidosFil: Rubinstein, Adolfo Luis. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sage, Michael. Centers for Disease Control and Prevention; Estados UnidosFil: Sturke, Rachel. National Institutes Of Health. Fogarty International Center; Estados UnidosFil: Shankar, Anita. University Johns Hopkins; Estados UnidosFil: Sherr, Kenny. University of Washington; Estados UnidosFil: Smith, Kirk. University of California at Berkeley; Estados UnidosFil: Yadama, Gautam. Washington University in St. Louis; Estados Unido

Impacts of household sources on air pollution at village and regional scales in India

Approximately 3 billion people worldwide cook with solid fuels, such as wood, charcoal, and agricultural residues. These fuels, also used for residential heating, are often combusted in inefficient devices, producing carbonaceous emissions. Between 2.6 and 3.8 million premature deaths occur as a result of exposure to fine particulate matter from the resulting household air pollution (Health Effects Institute, 2018a; World Health Organization, 2018). Household air pollution also contributes to ambient air pollution; the magnitude of this contribution is uncertain. Here, we simulate the distribution of the two major health-damaging outdoor air pollutants (PM_(2.5) and O₃) using state-of-the-science emissions databases and atmospheric chemical transport models to estimate the impact of household combustion on ambient air quality in India. The present study focuses on New Delhi and the SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in the Palwal District of Haryana, located about 80 km south of New Delhi. The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al., 2015, 2016), an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al. (2018b), and PM_(2.5) speciation from cooking fires from Jayarathne et al. (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain. Comparisons between observed and simulated O₃ and PM_(2.5) levels are carried out to assess overall airborne levels and to estimate the contribution of household cooking emissions. Observed and predicted ozone levels over New Delhi during September 2015, December 2015, and September 2016 routinely exceeded the 8 h Indian standard of 100 µg m⁻³, and, on occasion, exceeded 180 µg m⁻³. PM_(2.5) levels are predicted over the SOMAARTH headquarters (September 2015 and September 2016), Bajada Pahari (a village in the surveillance site; September 2015, December 2015, and September 2016), and New Delhi (September 2015, December 2015, and September 2016). The predicted fractional impact of residential emissions on anthropogenic PM_(2.5) levels varies from about 0.27 in SOMAARTH HQ and Bajada Pahari to about 0.10 in New Delhi. The predicted secondary organic portion of PM_(2.5) produced by household emissions ranges from 16 % to 80 %. Predicted levels of secondary organic PM_(2.5) during the periods studied at the four locations averaged about 30 µg m⁻³, representing approximately 30 % and 20 % of total PM_(2.5) levels in the rural and urban stations, respectively

Myocardial production and release of MCP-1 and SDF-1 following myocardial infarction: differences between mice and man

<p>Abstract</p> <p>Background</p> <p>Stem cell homing to the heart is mediated by the release of chemo-attractant cytokines. Stromal derived factor -1 alpha (SDF-1a) and monocyte chemotactic factor 1(MCP-1) are detectable in peripheral blood after myocardial infarction (MI). It remains unknown if they are produced by, and released from, the heart in order to attract stem cells to repair the damaged myocardium.</p> <p>Methods</p> <p>Murine hearts were studied for expression of MCP-1 and SDF-1a at day 3 and day 28 following myocardial infarction to determine whether production is increased following MI. In addition, we studied the coronary artery and coronary sinus (venous) blood from patients with normal coronary arteries, stable coronary artery disease (CAD), unstable angina and MI to determine whether these cytokines are released from the heart into the systemic circulation following MI.</p> <p>Results</p> <p>Both MCP-1 and SDF-1a are constitutively produced and released by the heart. MCP-1 mRNA is upregulated following murine experimental MI, but SDF-1a is suppressed. There is less release of SDF-1a into the systemic circulation in patients with all stages of CAD including MI, mimicking the animal model. However MCP-1 release from the human heart following MI is also suppressed, which is the exact opposite of the animal model.</p> <p>Conclusions</p> <p>SDF-1a and MCP-1 release from the human heart are suppressed following MI. In the case of SDF-1a, the animal model appropriately reflects the human situation. However, for MCP-1 the animal model is the exact opposite of the human condition. Human observational studies like this one are paramount in guiding translation from experimental studies to clinical trials.</p