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

2018 ◽  
Vol 18 (20) ◽  
pp. 15169-15182 ◽  
Author(s):  
Lauren T. Fleming ◽  
Robert Weltman ◽  
Ankit Yadav ◽  
Rufus D. Edwards ◽  
Narendra K. Arora ◽  
...  
Keyword(s):  
Air Quality ◽  
Carbon Balance ◽  
Fine Particulate Matter ◽  
Emission Factors ◽  
Fire Emissions ◽  
Mixing Ratios ◽  
Flame Ionization ◽  
Fine Particulate ◽  
Volatile Organic ◽  
The Relationship

Abstract. 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 17.2 g measured VOC 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 g kg−1 fuel carbon for dung–chulha and 2.38 g 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.

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

2018 ◽  
Author(s):  
Lauren T. Fleming ◽  
Robert Weltman ◽  
Ankit Yadav ◽  
Rufus D. Edwards ◽  
Narendra K. Arora ◽  
...  
Keyword(s):  
Air Quality ◽  
Fine Particulate Matter ◽  
Organic Aerosol ◽  
Rural India ◽  
Aerosol Formation ◽  
Fire Emissions ◽  
Flame Ionization ◽  
Fine Particulate ◽  
Volatile Organic ◽  
The Relationship

Abstract. 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 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 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. Filter samples were weighed to calculate PM2.5 emission factors. Dung fuels and angithi cookstoves resulted in significantly higher emissions of most VOCs (p 

scholarly journals Overview and evaluation of the Community Multiscale Air Quality (CMAQ) model version 5.1

2016 ◽  
Author(s):  
K. Wyat Appel ◽  
Sergey L. Napelenok ◽  
Kristen M. Foley ◽  
Havala O. T. Pye ◽  
Christian Hogrefe ◽  
...  
Keyword(s):  
Air Quality ◽  
Fine Particulate Matter ◽  
Wrf Model ◽  
Organic Aerosol ◽  
Aerosol Chemistry ◽  
The Public ◽  
Mixing Ratios ◽  
Model Version ◽  
Fine Particulate ◽  
Quality Modeling

Abstract. The Community Multiscale Air Quality (CMAQ) model is a comprehensive multi-pollutant air quality modeling system developed and maintained by the U.S. Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public which incorporates a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ; updates to the gas and aerosol chemistry; revisions to the calculations of clouds and photolysis; and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations generally result in greater afternoon and early evening mixing in the model, times when the model historically underestimates mixing. The result is higher ozone (O3) mixing ratios on average due to reduced NO titration and lower fine particulate matter (PM2.5) concentrations due to greater dilution of primary pollutants (e.g. elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced cloudiness and reduced attenuation of photolysis in the model. Updates to the aerosol chemistry results in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing PM2.5 bias, while updates to the gas chemistry result in generally increased O3 in January and July (small) and slightly higher PM2.5 concentrations on average in both January and July. Overall, seasonal variation in simulated PM2.5 generally improves in the new model version, as concentrations decrease in the winter (when PM2.5 is overestimated by CMAQ v5.0.2) and increase in the summer (when PM2.5 is underestimated by CMAQ v5.0.2). Ozone mixing ratios are higher on average with v5.1 versus v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low), however both the error and correlation are largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios showed that v5.1 tends to be slightly more responsive to reductions in NOx (NO + NO2), VOC and SOx (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions. Finally, the computational efficiency of the model was significantly improved in v5.1, which keeps runtimes similar to v5.0.2 despite the added complexity to the model.

scholarly journals Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

2017 ◽  
Vol 10 (4) ◽  
pp. 1703-1732 ◽  
Author(s):  
K. Wyat Appel ◽  
Sergey L. Napelenok ◽  
Kristen M. Foley ◽  
Havala O. T. Pye ◽  
Christian Hogrefe ◽  
...  
Keyword(s):  
Air Quality ◽  
Fine Particulate Matter ◽  
Wrf Model ◽  
Organic Aerosol ◽  
Aerosol Chemistry ◽  
The Public ◽  
Mixing Ratios ◽  
Fine Particulate ◽  
The Us ◽  
Quality Modeling

Abstract. The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O3) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM2. 5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM2. 5 bias (PM2. 5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O3 and PM2. 5 on average in January and July. Overall, the seasonal variation in simulated PM2. 5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM2. 5 concentrations decrease in the winter (when PM2. 5 is generally overestimated by CMAQ) and increase in the summer (when PM2. 5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low); however, O3 correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NOx (NO + NO2), VOC and SOx (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions.

scholarly journals Fine PM emission factors from residential burning of solid fuels using traditional cast-iron coal stoves

2018 ◽  
Author(s):  
Ncobile C Nkosi ◽  
Stuart J Piketh ◽  
Roelof P Burger
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Emission Factor ◽  
Fine Particulate Matter ◽  
Field Measurements ◽  
Emission Factors ◽  
Solid Fuels ◽  
Fine Particulate ◽  
Residential Coal ◽  
Stove Operation

Residential burning of solid fuels is a major source of fine particulate matter (PM2.5), which degrades indoor and ambient air quality in low-income settlements. The adverse impact of fine particulate emissions on the environment and human health is well-documented in other countries such as China and India; however, there is need for local studies to report on emission factors from residential burning of solid fuels. An emission factor quantifies the total mass of a pollutant emitted per amount of fuel burned. Emission factor is an input parameter in air quality modelling to forecast a pollutant concentrations over time and when calculating total emissions from a specific source. Local emission factors are central to managing air quality for they give results that are representative of the source compared with international emission factors. Quantifying emissions, understanding household fuel use patterns and interaction with the stove (stove operation behaviour) during a burning event is fundamental when designing emission control strategies. The aim of the study is to quantify fine particulate matter emissions from residential coal burning using systematic field measurements. The objectives of the study are (i) to characterize stove operation behavior effect on the emissions and (ii) to quantify PM2.5 emission factors using field measurements. Isokinetic (2015) and direct (2014) stack sampling tests were done to observe how PM emissions profiles change with stove operation behavior and to quantify PM2.5 emitted per kilogram of fuel burned. Fine PM emission profiles change with stove operation behavior with an emission factor ranging 6.8 g.kg-1 and 13.5 g.kg-1. The study results implies that residential coal burning is a major source of fine particulate matter in the residential area. As demonstrated that stove operation behaviour affect stove to fuel combination emissions; it is therefore suggested that those factors leading to increase emissions should be kept minimum.

scholarly journals Description and Evaluation of the Fine Particulate Matter Forecasts in the NCAR Regional Air Quality Forecasting System

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 302
Author(s):  
Rajesh Kumar ◽  
Piyush Bhardwaj ◽  
Gabriele Pfister ◽  
Carl Drews ◽  
Shawn Honomichl ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Information Dissemination ◽  
Fine Particulate Matter ◽  
Model Errors ◽  
Air Quality Forecast ◽  
Fine Particulate ◽  
Regional Air Quality ◽  
Forecasting System ◽  
Air Quality Forecasting

This paper describes a quasi-operational regional air quality forecasting system for the contiguous United States (CONUS) developed at the National Center for Atmospheric Research (NCAR) to support air quality decision-making, field campaign planning, early identification of model errors and biases, and support the atmospheric science community in their research. This system aims to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA), not to replace them. A publicly available information dissemination system has been established that displays various air quality products, including a near-real-time evaluation of the model forecasts. Here, we report the performance of our air quality forecasting system in simulating meteorology and fine particulate matter (PM2.5) for the first year after our system started, i.e., 1 June 2019 to 31 May 2020. Our system shows excellent skill in capturing hourly to daily variations in temperature, surface pressure, relative humidity, water vapor mixing ratios, and wind direction but shows relatively larger errors in wind speed. The model also captures the seasonal cycle of surface PM2.5 very well in different regions and for different types of sites (urban, suburban, and rural) in the CONUS with a mean bias smaller than 1 µg m−3. The skill of the air quality forecasts remains fairly stable between the first and second days of the forecasts. Our air quality forecast products are publicly available at a NCAR webpage. We invite the community to use our forecasting products for their research, as input for urban scale (<4 km), air quality forecasts, or the co-development of customized products, just to name a few applications.

scholarly journals Anthropogenic enhancements to production of highly oxygenated molecules from autoxidation

2019 ◽  
Vol 116 (14) ◽  
pp. 6641-6646 ◽  
Author(s):  
Havala O. T. Pye ◽  
Emma L. D’Ambro ◽  
Ben H. Lee ◽  
Siegfried Schobesberger ◽  
Masayuki Takeuchi ◽  
...  
Keyword(s):  
Fine Particulate Matter ◽  
Peroxy Radicals ◽  
Dominant Component ◽  
Fine Particulate ◽  
Biogenic Voc ◽  
Aircraft Observations ◽  
Volatile Organic ◽  
Climate Interactions ◽  
Urban Plume

Atmospheric oxidation of natural and anthropogenic volatile organic compounds (VOCs) leads to secondary organic aerosol (SOA), which constitutes a major and often dominant component of atmospheric fine particulate matter (PM2.5). Recent work demonstrates that rapid autoxidation of organic peroxy radicals (RO2) formed during VOC oxidation results in highly oxygenated organic molecules (HOM) that efficiently form SOA. As NOxemissions decrease, the chemical regime of the atmosphere changes to one in which RO2autoxidation becomes increasingly important, potentially increasing PM2.5, while oxidant availability driving RO2formation rates simultaneously declines, possibly slowing regional PM2.5formation. Using a suite of in situ aircraft observations and laboratory studies of HOM, together with a detailed molecular mechanism, we show that although autoxidation in an archetypal biogenic VOC system becomes more competitive as NOxdecreases, absolute HOM production rates decrease due to oxidant reductions, leading to an overall positive coupling between anthropogenic NOxand localized biogenic SOA from autoxidation. This effect is observed in the Atlanta, Georgia, urban plume where HOM is enhanced in the presence of elevated NO, and predictions for Guangzhou, China, where increasing HOM-RO2production coincides with increases in NO from 1990 to 2010. These results suggest added benefits to PM2.5abatement strategies come with NOxemission reductions and have implications for aerosol–climate interactions due to changes in global SOA resulting from NOxinteractions since the preindustrial era.

scholarly journals Meteorological controls on atmospheric particulate pollution during hazard reduction burns

2017 ◽  
Author(s):  
Giovanni Di Virgilio ◽  
Melissa Anne Hart ◽  
Ningbo Jiang
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Fine Particulate Matter ◽  
Meteorological Conditions ◽  
Atmospheric Conditions ◽  
Hazard Reduction ◽  
Particulate Pollution ◽  
Fine Particulate ◽  
Projected Changes ◽  
Sydney Australia

Abstract. Internationally, severe wildfires are an escalating problem likely to worsen given projected changes to climate. Hazard reduction burns (HRB) are used to suppress wildfire occurrences, but they generate considerable emissions of atmospheric fine particulate matter, which depending upon prevailing atmospheric conditions, can degrade air quality. Our objectives are to improve understanding of the relationships between meteorological conditions and air quality during HRBs in Sydney, Australia. We identify the primary meteorological covariates linked to high PM2.5 pollution (particulates

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