Pollutant Emissions and Energy Efficiency of Chinese Gasifier Cooking Stoves and Implications for Future Intervention Studies

Carter, Ellison M.; Shan, Ming; Yang, Xudong; Li, Jiarong; Baumgartner, Jill

doi:10.1021/es405723w

Cited by 99 publications

(69 citation statements)

References 17 publications

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“…Furthermore, the PM EF with forced air (fuel mass-based average 0.016 g/kg) was lower than other stoves without it (fuel mass-based average 0.075 g/kg). It was suggested that Chinese stove designers should pay more attention to CO emissions during their research [16].…”

Section: Resultsmentioning

confidence: 99%

The Influences of Various Testing Conditions on the Evaluation of Household Biomass Pellet Fuel Combustion

Zhang

Zhou

et al. 2018

Energies

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Direct combustion of solid biomass fuel is one of the most common energy sources in developing countries. Evaluation of technology for household biomass pellet fuel combustion is critical, since promoting poorly designed devices may have risks due to exposure to high levels of emissions. This study evaluated the effects of various testing conditions on a top-lit forced-up-draft semi-gasifier cooking stove. An orthogonal test was designed with different fuel masses, chamber heights, air supply rates, and ending points. The investigation showed that using forced secondary air and more fuel tended to improve both thermal and gas emissions performance. The ending points did not have significant effects on thermal efficiency or the carbon dioxide emission factor, but did affect particulate matter emission. A relatively lower chamber height demonstrated better performance on thermal metrics. However, a taller flame had better performance on particulate matter emission factors. The results of the indicators reported by different bases, such as fuel mass-based or useful energy-based were also quite different. The study showed that different testing conditions had significant effects on combustion performances. Testing sequences and emission factors should be reviewed and defined clearly when forming testing methods and standards for biomass pellet fuel combustion.

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Section: Resultsmentioning

confidence: 99%

The Influences of Various Testing Conditions on the Evaluation of Household Biomass Pellet Fuel Combustion

Zhang

Zhou

et al. 2018

Energies

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“…Hot and wet conditions occur during the summer, and cold and dry conditions persist throughout the winter. Air pollution is more serious in Beijing during the winter because pollution is emitted from traffic, industries, coal combustion, and biomass combustion for cooking and winter heating [43]. In'addition, activities such as lighting fireworks for spring festivals contribute to air pollution in Beijing.…”

Section: Study Areamentioning

confidence: 99%

Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China

Ji-ping

Zongyi

2016

Sustainability

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Heavy air pollution, especially fine particulate matter (PM 2.5 ), poses serious challenges to environmental sustainability in Beijing. Epidemiological studies and the identification of measures for preventing serious air pollution both require accurate PM 2.5 spatial distribution data. Land use regression (LUR) models are promising for estimating the spatial distribution of PM 2.5 at a high spatial resolution. However, typical LUR models have a limited sampling point explanation rate (SPER, i.e., the rate of the sampling points with reasonable predicted concentrations to the total number of sampling points) and accuracy. Hence, self-adaptive revised LUR models are proposed in this paper for improving the SPER and accuracy of typical LUR models. The self-adaptive revised LUR model combines a typical LUR model with self-adaptive LUR model groups. The typical LUR model was used to estimate the PM 2.5 concentrations, and the self-adaptive LUR model groups were constructed for all of the sampling points removed from the typical LUR model because they were beyond the prediction data range, which was from 60% of the minimum observation to 120% of the maximum observation. The final results were analyzed using three methods, including an accuracy analysis, and were compared with typical LUR model results and the spatial variations in Beijing. The accuracy satisfied the demands of the analysis, and the accuracies at the different monitoring sites indicated spatial variations in the accuracy of the self-adaptive revised LUR model. The accuracy was high in the central area and low in suburban areas. The comparison analysis showed that the self-adaptive LUR model increased the SPER from 75% to 90% and increased the accuracy (based on the root-mean-square error) from 20.643 µg/m 3 to 17.443 µg/m 3 for the PM 2.5 concentrations during the winter of 2014 in Beijing. The spatial variation analysis for Beijing showed that the PM 2.5 concentrations were low in the north, especially in the northwest region, and high in the southern and central portions of Beijing. This spatial variation was consistent with the fact that the northern region is mountainous and has fewer people and less traffic, which results in lower air pollution, than in the central region, which has a high population density and heavy traffic. Moreover, the southern region is adjacent to Hebei province, which contains many polluting enterprises; thus, this area exhibits higher air pollution levels than Beijing. Therefore, the self-adaptive revised LUR model is effective and reliable.

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“…The heavy air pollution in China is likely to have more diverse sources than that in European and North American cities: not only traditional sources such as traffic and industry emission but also coal burning and biomass combustion for cooking and winter heating (Carter et al 2014;Yu et al 2013;Zhao et al 2013b). Moreover, other specific activities like setting off fireworks during the Spring Festival (Wang et al 2007;Zhang et al 2010) and open straw burning over the harvest season (Cheng et al 2013b;Li et al 2014;Qu et al 2012) may also aggravate pollution concentrations in some periods, especially in winter.…”

Section: Responsible Editor: Gerhard Lammelmentioning

confidence: 99%

“…The winter model with only two variables produced a lower R 2 , which was mainly on account of a larger spatial variation in the measured PM 2.5 concentrations. This might be explained by specific PM 2.5 sources that are distributed in a small scale, such as fossil fuel combustion, biomass burning for cooking and winter heating (Carter et al 2014;Cheng et al 2013b;Yu et al 2013;Zhao et al 2013b), and setting off fireworks in the winter (Wang et al 2007;Zhang et al 2010). Road length variables were no longer significant in winter compared with these in other three seasons, as the p values were 0.54 and 0.59 for the most predictive road-length variables in categories of major and common roads in the winter LUR model.…”

Section: Seasonal Variationsmentioning

confidence: 99%

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Peng

et al. 2014

Environ Sci Pollut Res

128

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Fine particulate matter (PM 2.5 ) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM 2.5 by building separate LUR models in Beijing. Hourly routine PM 2.5 measurements were collected at 35 sites from 4th March 2013 to 5th March 2014. Seventy-seven predictor variables were generated in GIS, including street network, land cover, population density, catering services distribution, bus stop density, intersection density, and others. Eight LUR models were developed on annual, seasonal, peak/non-peak, and incremental concentration subsets. The annual mean concentration across all sites is 90.7 μg/m 3 (SD=13.7). PM 2.5 shows more temporal variation than spatial variation, indicating the necessity of building different models to capture spatiotemporal trends. The adjusted R 2 of these models range between 0.43 and 0.65. Most LUR models are driven by significant predictors including major road length, vegetation, and water land use. Annual outdoor exposure in Beijing is as high as 96.5 μg/m 3 . This is among the first LUR studies implemented in a seriously air-polluted Chinese context, which generally produce acceptable results and reliable spatial air-pollution maps. Apart from the models for winter and incremental concentration, LUR models are driven by similar variables, suggesting that the spatial variations of PM 2.5 remain steady for most of the time. Temporal variations are explained by the intercepts, and spatial variations in the measurements determine the strength of variable coefficients in our models.

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Pollutant Emissions and Energy Efficiency of Chinese Gasifier Cooking Stoves and Implications for Future Intervention Studies

Cited by 99 publications

References 17 publications

The Influences of Various Testing Conditions on the Evaluation of Household Biomass Pellet Fuel Combustion

The Influences of Various Testing Conditions on the Evaluation of Household Biomass Pellet Fuel Combustion

Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

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